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Referee #2
Evidence, reproducibility and clarity
Lineages of neural stem cells are of great interest to understand how many neural types are generated. They produce very diverse neurons, often in a highly stereotyped series. However, they must terminate their life when the animal becomes functional or if neurons need time to become mature before birth.
In the Drosophila optic lobes, neural stem cells are produced over a period of several days by a wave of neurogenesis that transforms a neuroepithelium into neural stem cells that undergo a series of temporal patterning steps. It has been reported that they finish their life when a symmetric division generates glial cells. The authors however analyze the end of a particular lineage, that of the latest born neural stem cells of the medulla. The paper shows that neural stem cells stop being produced when the neuroepithelium is consumed. But how do the latest born neural stem cells stop their lineage?
The results show that they do so by several means, which is quite unexpected: they may die from apoptosis, or autophagy, by becoming glioblasts or by a terminal symmetric division.
There are no major issues affecting the conclusions
- The paper shows that the end of production of neural stem cells occurs the neuroepithelium is completely transformed. The experiments performed by the authors are fine and show that, if the transition is delayed, neural stem cells terminate their life later, and vice versa. However, the lifespan of the neural stem cells is not affected by the timing of the transition. Therefore, these experiments do not tell us how neural stem cells terminate their life, which is the central question of the study. The discussion should be written accordingly and the title and the model in Fig 6 modified to reflect the importance of the end of life of the stem cells, the main theme of the paper.
- The authors talk about Pros-dependent symmetric division and gliogenic switch as two separate processes, but these may be two sides of the same phenomenon. Tll+ gcm+ neural stem cells undergo Pros-dependent cell cycle exit, generating glial progeny. If the authors agree with this, could they update their model (and discussion) to reflect the fact that gliogenic switch occurs via a Pros-dependent symmetric division, and these are not two separate processes independently contributing to the depletion of the neural stem cell pool? Ideally, a triple staining between Dpn, Pros, and gcm would show that the symmetrically dividing cells seen by the authors are committed to the glial fate.
- Why were Notch RNAi experiments assessed for the presence of neural stem cells at P12 and gcm RNAi experiments at P24? Given that most optic lobe neural stem cells disappear between P12-18, a subtle effect of gcm RNAi may have been missed. Do the authors have data for gcm RNAi at P12?
- The authors should acknowledge that the inhibition of either apoptosis or autophagy alone may not be fully sufficient to prevent the death of NBs. In mushroom body neural stem cells, both processes must be inhibited simultaneously to produce a strong effect on their survival (Pahl et al. 2019, PMID 30773368).
- There is an important missing point that should be addressed: is there a specific point in time when all neural stem cells must stop their lineage wherever they are in the temporal series and either die or divide symmetrically? One possibility that is not discussed is that most neural stem cells end their life through a gliogenic symmetric division while those that were generated late must stop en route and die by apoptosis and/or autophagy. This would solve the strange diversity of end-of-life, which could be easily addressed by identifying the temporal stage of the neural stem cells that undergo apoptosis
Minor suggestions:
- Line 46: Specify that there are 8 type II neural stem cells in each hemisphere*.
- The statement in lines 181-182 that "cell death, and not autophagy, makes a minor contribution to..." should be replaced with "apoptosis, and not autophagy," as autophagy is also a type of cell death.
- The authors should adjust the logic of the section "Medulla neuroblasts terminate during early pupal development": Describe the wild-type pattern first (the decrease in the number of neural stem cells and their size with age) and then describe the perturbations aimed at disrupting the number and the size of neural stem cells
- Line 151 should refer to Fig. 2I-K, not Fig. 2J-K.
Referees cross-commenting
How can NBs die by different mechanisms?? This might only happen is they are in a different states, an issue that is not addressed. it has been shown that optic lobe NBs end their life by a symmetric, gliogenic last division at the end of the last temporal window, and not by PCD. It is likely, and the authors do hint at it, that NBs only die by PCD when they prematurely interrupt the temporal series in early pupation when neurons synchronously start undergoing maturation. I believe that the authors should explain this, if this is indeed their model, and show that NBs die while still in early temporal windows.
Significance
Lineages of neural stem cells are of great interest to understand how many neural types are generated. They produce very diverse neurons, often in a highly stereotyped series. However, they must terminate their life when the animal becomes functional or if neurons need time to become mature before birth.
In the Drosophila optic lobes, neural stem cells are produced over a period of several days by a wave of neurogenesis that transforms a neuroepithelium into neural stem cells that undergo a series of temporal patterning steps. It has been reported that they finish their life when a symmetric division generates glial cells. The authors however analyze the end of a particular lineage, that of the latest born neural stem cells of the medulla.
The paper shows that neural stem cells stop being produced when the neuroepithelium is consumed. But how do the latest born neural stem cells stop their lineage?
The results show that they do so by several means, which is quite unexpected: they may die from apoptosis, or autophagy, by becoming glioblasts or by a terminal symmetric division.
There are no major issues affecting the conclusions
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Referee #1
Evidence, reproducibility and clarity
The manuscript by Nguyen and Cheng is investigating the timing and mechanism of cessation of neuroblasts in the pupal optic lobe. Previous studies by several groups have determined the spatial and temporal factors required for the neuroepithelial to neuroblast transition and neuroblast to neural/glycogenesis in third instar larvae such that neuroblasts are eliminated. The mechanism of elimination of neuroblasts in the VNC or mushroom bodies have been investigated, but the mechanism(s) and the timing of elimination of medulla neuroblasts has not been investigated. The authors suggest that medulla neuroblasts are eliminated via a combination of mechanisms including apoptosis, prospero induced size symmetric terminal differentiation and a switch to gliogenesis by gcm expression. Expression of Tailless also was found to affect the timing of medulla neuroblast termination. They also ruled out several mechanisms such as ecdysone pulses.
Major comments
Clearly written and logical flow to experiments and results not over interpreted. Clearly show that the neuroblast number and size decrease (12 to 18 hrs) and are eliminated by 30 hours
Figure 2a Marking of the Neuroepithelium. Would be more convincing if shown by PatJ expression and is clonal analysis. While the following panels use PatJ in clones suggesting are NE and NBs present it is more difficult to put into the context in the higher magnification images (Figure 2 D- M) and the Miranda expression in F' seems to be the entire lobe and it is not clear if would be any NE which does not agree with what is shown in panel A.<br /> Is difficult to see the neuroblasts in Figure 2 D D" and E. The figure does not match what is stated in the results in that the neuroblasts are difficult to observe. If the point is that there is fewer NE cells and more neuroblasts then this is hard to see. It has been previously shown that with Notch RNAi clones prematurely extrude form the NE (Egger 20210; Keegan 2023) and could be expressing more Neuroblast markers but this is not visible in the panels as shown. Are the images single focal plane or maximum projections? Imaging more deeply in the brain or viewing in cross section would account for these possibilities. The possibility that are more neuroblasts but not all at the surface of the OL should be addressed as this could also alter the overall results. Figure 2 is key to first point of the paper so needs to be addressed.
Minor comments
Why express volume of DPN in clone volume. Would make the point more clear and more strong be to express as number of NB in the 3-D volume of the clone. This measurement occurs in several figures. Use of Miranda to mark NBs is unclear in Figure 2. Perhaps more clear in B&W. Make clear in figures (or figure legend) if single focal plane or projections. It is unclear what percentage of NB the Gal4 line eyR16F10 are expressed in. Veen 2023 state that the GAL4 is also expressed in neurons and at different levels whether deeper within the brain or superficially on the surface of the brain. At 16 APF it is expressed but it is not clear whether it is in all cells at a low level or only within a few cells Some RNAi lines referenced as previously validated and other are not. For example: EcR, Oxphos, Med27, Notch need references or confirmation of specificity to the intended target (qRT) At least 2 animals per genotype were used. While I appreciate the technical difficulty of working in pupae this seems a bit low in terms of number of samples and data would be more robust with more numbers.
Significance
This provides mechanism and timing for the elimination of neuroblasts (NE to NB) that arise from the medulla. As these are most similar to mammalian brain development (Radial glial to NSC) this information provides more context to interpret the formation of glial and neurons in the adult optic lobe given the effect on timing and mechanisms of elimination.
This paper would be of interest to developmental biologist who work with Drosophila or mice who are looking at neural development. An understanding of how neural diversity is achieved and the mechanisms behind this that can be dysfunctional in terms of etiology of neural diseases. Is a well done study for the most part that would be improved by clarifying some data and provided more replicates for robustness of the data. I am a developmental biologist working with Drosophila in larval and adult neural development.
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Reply to the reviewers
Rebuttal for Review Commons to:
“A specific innate immune response silences the virulence of Pseudomonas aeruginosa in a latent infection model in the Drosophila melanogaster host”
We thank the reviewers for their careful scrutiny of our manuscript. We believe that we have addressed satisfactorily the points raised by the reviewers and that our revised manuscript is definitely improved. Our replies below are in blue and use a distinctive font.
Reviewer #1
__Evidence, reproducibility and clarity __
This works describes a latent Drosophila intestinal infection, which spreads systemically, with a direct systemic Drosophila infection using a common laboratory strain of Pseudomonas aeruginosa. The major observation of this study is that P. aeruginosa can cause a latent infection via its passage through the gut (as opposed to being injected). In doing so it exhibits cell rounding (instead of elongation), reduced cell motility, loss of O5-antigen, antibiotic resistance, ability to cross the intestinal barrier and circulate in the hemolymph and infiltrate the host tissue underneath the cuticle. In addition, latent infection bacteria induce all brunches of the systemic response: the Imd pathway, phagocytosis, and the melanization cascade. Moreover, the melanization pathway protects the host from a secondary systemic infection with various types of bacterial and fungal microbes.
An issue that needs to be clarified is the sensitivity of P. aeruginosa virulence to its biochemical environment. The authors note that. For example, liquid bacterial culture in BHI induces the latent form of bacteria. So the growth conditions and the infection media play a major role in the infection process. They authors need to clarify further the effect of media and infection vehicles, sucrose (high/low), LB, and BHI (as well as temperature) on the latent phenotype.
Temperature is definitely an important parameter and bacteria appear to be somewhat more virulent at 25°C. This point is now addressed in the Material and Methods section (lines 675-679) and in Fig. S6I.
As regards the influence of the composition of the infection solution, it does not seem to be a critical parameter that we have described in the context of continuous feeding on the bacterial solution (Limmer et al., PNAS, 2011). In preliminary experiments, we had tried LB or BHB medium to grow the bacteria and this did not make any difference (see Panel A below for LB [BHB used in our experiments]). As regards the sucrose concentration to the infection solution, we have tried two concentrations and did not observe any difference as regards the establishment of the latent infection. (see panel B below for 50 mM sucrose [100mM used in our experiments]). Of note, P. aeruginosa does not grow on sucrose solution alone. However, a latent infection was still established upon feeding the flies with PAO1 in sucrose alone, albeit likely with a mildly increased virulence, in the absence of any BHB medium (see panels C-D) below.
A) Comparison of LB vs. BHB B) Establishment of latent infection with 50mM sucrose
C) Establishment of a latent infection with a sucrose-only bacterial solution D) Colonization of host tissues by PAO1 ingested in a sucrose-only bacterial solution Minor issues: -Lines 579-581> How were the PAO1GFP/RFP constructed (details are needed)
Done; please, see lines 641-643.
-Figure 1D and other figures > CFUs given as Log2 are unconventional. One cannot easily deduce the burden unless e.g. translate 2e10 to ~1000 and 2e30 to ~10e9 CFUs.
True, but bacterial titer increases by a factor of two at each division cycle. Even though we have previously used a Log10 representation, we now prefer using a Log2 representation. This representation has also been used by other authors in the field, e.g., Duneau et al., eLife, 2019.
-Figire S1DB (now S1C)> "but from the outside of the gut". The given experiment does not prove that statement.
This issue has been already dealt with in the Nehme et al. PLoS Pathogens 2007 article, as cited in the manuscript. We further provide in Fig. S1B pictures documenting the presence of bacteria associated with visceral muscles. Finally, we also show that the gut lumen is essentially cleared of bacteria after a period of feeding on a sucrose solution or gentamicin. Hence, most bacterial colonies originate from the outer layer of the gut. We clarify the issue in the text (lines 154-158).
-Lines 146-7 > data are missing in support to the statement.
We have now added Fig. S1B to document that the gentamicin treatment does work, as actually does feeding on sucrose solution alone, as previously documented in Limmer et al., 2011 (Fig. S2B). Of note, we cannot exclude that a few bacteria remain, especially in the crop, but those would be at very low titer. Please, see also reply to Reviewer 2.
-Figure S1C > The effect of injury seems to be huge, and may account for much/most of the differences observed (including those between latent and active infection). This is further supported by Figure4A, injury may account for gut collapse and/or systemic stress.
It is well known that injury alone induces the systemic IMD pathway response 6 hours after injury but largely subsides by 24 hours. The point of Fig. S1C is that the level of induction reached during latent infection is very low as compared to that observed during a systemic infection, here obtained for reference with an Escherichia coli injection and to a lesser extent with a PBS injection. In our latent infection model, we do not perform any injury, except as noted by the reviewer in Fig. 4: the effects of an experimental injury are observed only while the bacteria are crossing the intestinal barrier and hardly any effect is observed when the injury is performed on day 10 (Fig. S4B).
-Figure S1D > How was "fated to die" assessed?
The fluorescent flies were sorted out and their subsequent survival was monitored. As compared to nonfluorescent flies from the same batch, they died within two days of sorting them.
-Figure 3B/10th day > Average line is misplaced.
We thank the reviewer for pointing out this problem. The line is not the average but the median. We have now added a precise description of the bars to all the figure legends.
-Lines 382-5 > what is the evidence of gut damage (or the absence of it)? How do the bacteria escape the gut?
The absence of major gut damages has been documented in Limmer et al, PNAS, 2011. How the bacteria escape the gut remains an open question (intracellular and/or paracellular route).
-Lines437-442 > The distinction between dormant P. aeruginosa in the fly tissues and persister cells (upon antibiotic treatment) cannot be justifies on the basis of relative bacterial numbers in the two systems. The extent of resistance to antibiotics though my serve that purpose.
In our latent infection model, most of the bacteria that have crossed the gut barrier become dormant and are associated with tissues, except at the beginning of the infection. In contrast, when a bacterial culture is treated with antibiotics, most of the bacteria are killed by the treatments and only a few ones persist, likely because of an inactive metabolism. Thus, the induction of dormancy in our latent infection model does not rely on the selection of a few metabolically-inactive bacteria able to withstand an immune response or an antibiotic treatment.
Significance
The study is a significant advance to our knowledge. Notwithstanding further explanations, it provides a solid basis of understanding active versus dormant bacteria. It further establishes a mode of intestinal to systemic infection as a tool for further explorations.
Reviewer #2
Evidence, reproducibility and clarity
Summary: In this study, Chen and colleagues investigated a new latent infection model for Pseudomonas aeruginosa using Drosophila melanogaster as a host. First, the authors established a new model for latent Pseudomonas infection. The key feature of this model is the translocation of P. aeruginosa from the gut to the hemolymph and the colonization of fly tissues by the dormant bacteria. Bacteria that translocated from the gut appeared strikingly different in morphology and resistance to antibiotics compared to bacteria that were directly injected. Next, the authors suggest that melanization but not the Imd pathway or hemocytes are necessary to promote dormancy and colonization of fly tissues. Finally, flies with latent P. aeruginosa infection exhibit improved survival after secondary infections in a melanisation-dependent manner. The study reports an interesting model for latent infection, provides insights into the host factors promoting latency and describes some of the consequences of such latent infection for the host. However, some of the conclusions are not fully supported by the data and need further experimental evidence.
Major comments: 1. The latent infection model requires some clarifications. First, temperature. Could the authors explain why they used 18 {degree sign}C and could low temperature contribute to the establishment of dormancy?
As shown in Fig.S6I, the latent infection model is less compelling at 25°C in terms of survival curves, which may reflect an increased rate of spontaneous reactivation of the virulence, a phenomenon we have not studied at 25°C. In another manuscript in preparation (Lin et al.), we actually show that a small heat shock does contribute to reactivation of the bacteria, an issue that is outside of the scope of the present study. Please, see also reply to reviewer 1.
Second, the use of gentamycin. How does gentamicin affect PAO1 outside the gut? From Fig.1C It looks like the cfus in the hemolymph diminished rapidly after gentamicin treatment (around day 3), suggesting the potential effect of the antibiotic. Once the bacteria have crossed the gut and entered the hemolymph, they could still be affected by feeding flies the antibiotic. Is there a possibility that gentamicin treatment is a stress factor that could trigger or facilitate the transition to dormancy? The authors could test this experimentally either by omitting the antibiotic and assessing dormancy or by feeding injected flies with gentamycin and scoring dormancy.
We had actually tested the issue about the potential role of gentamicin outside of the gut compartment. We have thus fed flies on different concentrations of gentamicin and monitored the survival of those flies to the injection of PAO1 bacteria (please, see Figure below). When flies were feeding on the highest concentration of gentamicin tested, 32 mg/mL, they were succumbing fast to the PAO1 challenge, but not as fast as nontreated positive control PAO1 injected flies. The use of lower concentrations (16, 8, and 4 mg/mL) led to a progressively stronger protection from PAO1 injection that inversely correlated with the gentamicin dose. We interpret the data with high gentamicin concentrations as an indication that gentamicin at such concentrations is likely directly toxic to the flies, an issue that could be experimentally tested but is not relevant to this study. Interestingly, lower doses led to a much-decreased protection from PAO1 (2mg/mL) to no protection at the dose we use to establish latent infection (100 µg/mL). Thus, these data show that gentamicin can pass the gut barrier when provided at high concentrations, down to 2 mg/mL. However, there is no proof of such a passage at the dose we use. In keeping with this latter possibility, we made a control experiment in which the gentamicin treatment step was replaced by simply feeding on the sucrose solution: the bacterial titer decreased in the hemolymph at the same rate as for gentamicin-treated flies (new Fig. S1F), demonstrating that ingested gentamicin does not contribute to the decreased titer. Rather, the likely depletion of the “source”, that is PAO1 in the gut lumen, best accounts for this phenomenon.
We have now cited references which document a lack of permeability of the gut barrier to ingested gentamicin in vertebrate animals (lines 130-133).
As regards the possibility that gentamicin acts as a stress factor on bacteria, our data do not support this possibility, as a latent infection is established in the absence of gentamicin by just feeding the flies on a sucrose solution. We had previously reported that flies fed with P. aeruginosa for up to three days do not succumb within the next two weeks when they are fed on a sterile sucrose solution after having ingested the bacterial solution (Limmer et al., PNAS, 2011; Fig. 1C). Under the conditions of two days of PAO1 ingestion, we document in novel Fig. S1G that the carcass is equally well colonized under these conditions.
Figure: impact of gentamicin ingestion at diverse concentrations on the survival of injected PAO1 bacteria. The ingested antibiotics can act on bacteria present in the hemocoel at concentrations over 2 mg/mL and not at that used in our experiments (100 µg/mL).
Does melanization really induce the dormant state of the bacteria? I am not sure the provided data fully support this claim. Addressing these questions might provide a stronger evidence: Fig. 2 A-F: What causes the morphological changes of the bacteria? Melanization or the passage through the gut? Do authors see the same changes in bacteria retrieved from PO-deficient mutant flies? Fig. 2G: Do the authors see the same resistance of PAO1 that colonized PO mutant flies to antibiotics?
In a novel Fig. S4, we now document comprehensively the physiological state of PAO1 bacteria fed to PO-deficient flies. We find that these bacteria are susceptible to antibiotics treatment as they can be rescued from death by the injection of antibiotics on day 3 (Fig. S4A-B). Second, they show a mixed phenotype in terms of colony morphology (Fig. S4C) and bacterial morphology and cell wall properties: even though most bacteria appeared to have kept a rounded morphology, they predominantly (about 75%) expressed the O5-LPS antigen. We interpret these data in terms of a slower transition to virulence than in a septic injury model. Melanization thus strongly contributes to the establishment of latency, even though it is likely that other factors contribute to the establishment of dormancy, but at best provide a minor contribution.
How do PO mutants behave after PAO1 injection? Are they similarly more susceptible?
PPO1-PPO2 mutants are not more susceptible to PAO1 injection than wt controls (new Fig. S3C).
Fig. 3F: PPO1 is believed to be the fast-acting PPO, whereas PPO2 is deployed later in infection.
This statement is based on experimental data gained on larvae, not adults. It is not really clear whether the about 10% adult hemocytes that express PPO2 actually contain crystals, in as much as the adult may be better oxygenated than larvae that grow in a hypoxic environment (description by the laboratory of Prof. Jiwon Shim of a role for PO in respiration at the latest EDRC meeting).
How does the Western blot look for PPO1? Will it show an early induction of melanization that could drive the change into the dormant state?
We provide below a characterization of the PPO antibody we use by Western blot analysis. This antibody had originally been raised by the late Dr. Hans-Michael Müller against a PPO from mosquito cell lines, hence explaining its cross-reaction to both * Drosophila PPO1 and PPO2 (Muller, H.M., Dimopoulos, G., Blass, C., and Kafatos, F.C. (1999)). A hemocyte-like cell line established from the malaria vector Anopheles gambiae expresses six prophenoloxidase genes. J Biol Chem 274*, 11727-11735.). It follows that at least one PO is partially cleaved at day 2 and that both are fully cleaved by day6 of the establishment of the latent infection (Fig. 3F, Fig. S3F).
Figure: characterization of the antibody raised against A. gambiae PPO
Alternatively, the induction of melanization could also be measured with an L-DOPA test.
This experiment is not needed given the explanation provided above.
Fig. 3E: Melanization prevents the growth of PAO1 adhering to tissues, as shown in Fig. 3E. One can see higher levels of cfus in the carcass in PO deficient flies compared to wt flies. However, after, 5 days, there is no difference in the cfus of wt and mutant flies anymore. If the growth inhibition was melanization mediated, would we not expect a consistent growth of bacteria in PO mutants? How to explain the drop in cfus in PO deficient mutants?
This observation is difficult to account for and the explanations we can put forward at this stage are somewhat speculative. It appears that bacteria found in the tissues in PO-deficient flies have a morphology found in in vitro culture and within the gut, which does not correlate with virulence but also not with the avirulence state since they are LPS O5 positive. Given the shallow survival curves, we envision that there is a progressive release of bacteria from the tissue and then quick proliferation in the hemolymph in a few flies that would then die, but at a frequency too low to reliably ascertain in our hemolymph titer data, with a few flies displaying a high titer (Fig. 3D). By day5, the decreased titer in the carcass may reflect the progressive depletion of tissue-associated bacteria as they progressively become planktonic.
Fig. 5D: How do PAO1 bacteria react to Levofloxacin treatment? Do they still go into the dormant state? Do they still attach to tissues? The authors should show that Levofloxacin treatment leads to the same dormant state as gentamycin before interpreting the results of this experiment.
Taken together, our data yield a mixed result. When levofloxacin was fed for two days to latently-infected flies, we found that colonization was not altered (Fig. S2D’), in contrast to a septic injury model in which injected bacteria were susceptible to the ingested antibiotics (Fig. S2D”). Following the reviewer’s query, we have further monitored survival and bacterial colonization in the levofloxacin ingestion model. Fig. S2D had already demonstrated that ingested levofloxacin protects the flies from injected PAO1. Fig. S6F shows that the double mutant PO bacteria are protected from ingested PAO1 by the ingestion of this antibiotics. When we monitored the bacterial burden, we found for both wild-type and double PO mutant flies that the bacteria had been cleared in some 50% of the flies. The exact interpretation of the wild-type data is not straightforward. On the one hand, the colonizing bacteria may have become susceptible to the antibiotics even though they remained dormant. On the other hand, they might have been reactivated in their virulence and thus become secondarily susceptible to the antibiotics. For the double PO mutants, the 40% bacteria remaining may witness the mixed bacterial state of PAO1 in these mutants, as documented in Fig. S4. Nevertheless, the important point is that bacteria are unlikely to contribute to the demise of secondarily infected flies since they have been cleared in at least 50% of the flies, yet the secondarily challenged flies become susceptible only when the relevant melanization genes are affected. The nonPAO1-infected controls succumb faster to the infection than infected ones: the protection against secondary infections is provided by the activation of the melanization cascade by colonizing PAO1 bacteria, even if the colonization is transient in the levofloxacin treatment.
We have altered the main text to reflect these novel data: lines 387-403.
Minor comments:
Lines 68-72. Mechanisms that are listed are not specific against Gram-negative bacteria but rather general. Please correct.
We are of course aware of this. If it is general, it also applies to Gram-negative bacteria that are the focus of this article. Actually, an earlier version of the manuscript just mentioned the IMD pathway, hence the reference to Gram-negative bacteria. However, the Toll pathway is also required in the host defense against some Gram-negative pathogens such as P. aeruginosa. We have now deleted “Gram-negative” in this corrected version.
Line 95. In - as?
We are not sure we understand this comment. We have now added a reference documenting that P. aeruginosa can be found in rotting fruits (line 97).
Lines 182-187. Some background information is needed. What is O5 LPS antigen? What motivated the authors to look at it specifically?
The O-antigen is a long-chain polysaccharide motif that constitutes the outermost part of the cell wall. It varies according to the strain. We have added a couple of references that refer to O-antigen (line 198). We had actually already found out this result (unpublished) with the Serratia marcescens Db11 O-antigen (O18) that was not found in bacteria that had crossed the gut. The loss of the O5 antigen changes the surface of the bacterium and likely its interactions with tissues and/or the immune system. In the case of Serratia, we suspect that the loss of its O-antigen allows the bacterium to be phagocytosed in an eater-dependent manner.
Fig. 3C: Why PPO1 and Hayan and PPO1,2 and Sp7 are compared but not mutant vs wild type?
The reason is that it was obviously significant. We have now added the comparisons to wild-type in the revised figure.
How precise is estimation of bacteria in the carcass?
Even though it is not possible to measure how precise these measures are, they are nevertheless reproducible making us confident that they provide an estimate of the rough number of these bacteria found associated to tissues.
How do the authors prevent dissemination of the bacteria during dissection? I wonder if some bacteria might by lost during the dissection (when removing the gut and ovaries) or if you carry over some bacteria from the hemolymph into the carcass measurement? How to make sure, that the bacteria you recover were really adherent and were not leftover from the hemolymph?
It is not possible to prevent dissemination as we cannot fix the tissues and bacteria if we make cfu counts. However, the finding that bacteria are found in the hemolymph only for the first three days, with a distinct morphology from tissue-associated bacteria, and not at later time points make us confident that this is not an issue, which suggests that the bacteria are rather tightly attached to the tissues. As regards contamination of tissues by hemolymph, it is also not an issue since the hemolymph titers are so low. However, when the bacteria are actively proliferating to high levels, this is a legitimate concern.
I am also curious how the differences in the cfu levels between whole fly and carcass can be explained (Fig. 1D). After day 5 there are almost no bacteria left in the hemolymph, however, if you compare cfus in the whole fly vs. the carcass, one can see that the whole fly cfus are rising from day 4 onwards. Where do these bacteria come from if not from the hemolymph?
To assess the statement of the reviewer, we now have included the numerical values of the medians of the bacterial burdens displayed in Fig. 1D. There is no increased bacterial burden in whole flies between days 5 to 12; however, the titer is increased at days 15 and 22. Whether this slight increase is biologically relevant is questionable given the spread of the data (see also reply to previous point on the precision of measures). We cannot rigorously exclude that there might be a low-level proliferation of colonizing bacteria late in the latent infection, which has been observed in specific conditions of reactivation of dormant bacteria (Lin et al., in preparation).
Fig. S4D: If the protection to secondary PAO1 infection is not mediated via Imd or phagocytosis, is it mediated via melanization? How do melanization mutants (increased or decreased) respond to PAO1 secondary infection?
We have performed the experiment (Fig. S6A-B) and found that the protection was abrogated. As noted in the main text, the interpretation is however difficult since the bacteria are no longer in a dormancy state in the PPO mutants.
Significance
This study suggests that host factors, particularly specific immune responses, could drive the latent infections. Hence, besides bacterial mechanisms that received significant attention, we should not underestimate the host's contribution to promoting the latent state in bacteria.
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Referee #2
Evidence, reproducibility and clarity
Summary:
In this study, Chen and colleagues investigated a new latent infection model for Pseudomonas aeruginosa using Drosophila melanogaster as a host. First, the authors established a new model for latent Pseudomonas infection. The key feature of this model is the translocation of P. aeruginosa from the gut to the hemolymph and the colonization of fly tissues by the dormant bacteria. Bacteria that translocated from the gut appeared strikingly different in morphology and resistance to antibiotics compared to bacteria that were directly injected. Next, the authors suggest that melanization but not the Imd pathway or hemocytes are necessary to promote dormancy and colonization of fly tissues. Finally, flies with latent P. aeruginosa infection exhibit improved survival after secondary infections in a melanisation-dependent manner. The study reports an interesting model for latent infection, provides insights into the host factors promoting latency and describes some of the consequences of such latent infection for the host. However, some of the conclusions are not fully supported by the data and need further experimental evidence.
Major comments:
- The latent infection model requires some clarifications. First, temperature. Could the authors explain why they used 18 {degree sign}C and could low temperature contribute to the establishment of dormancy? Second, the use of gentamycin. How does gentamicin affect PAO1 outside the gut? From Fig.1C It looks like the cfus in the hemolymph diminished rapidly after gentamicin treatment (around day 3), suggesting the potential effect of the antibiotic. Once the bacteria have crossed the gut and entered the hemolymph, they could still be affected by feeding flies the antibiotic. Is there a possibility that gentamicin treatment is a stress factor that could trigger or facilitate the transition to dormancy? The authors could test this experimentally either by omitting the antibiotic and assessing dormancy or by feeding injected flies with gentamycin and scoring dormancy.
- Does melanization really induce the dormant state of the bacteria? I am not sure the provided data fully support this claim. Addressing these questions might provide a stronger evidence: Fig. 2 A-F: What causes the morphological changes of the bacteria? Melanization or the passage through the gut? Do authors see the same changes in bacteria retrieved from PO-deficient mutant flies? Fig. 2G: Do the authors see the same resistance of PAO1 that colonized PO mutant flies to antibiotics? How do PO mutants behave after PAO1 injection? Are they similarly more susceptible?
- Fig. 3F: PPO1 is believed to be the fast-acting PPO, whereas PPO2 is deployed later in infection. How does the Western blot look for PPO1? Will it show an early induction of melanization that could drive the change into the dormant state? Alternatively, the induction of melanization could also be measured with an L-DOPA test.
- Fig. 3E: Melanization prevents the growth of PAO1 adhering to tissues, as shown in Fig. 3E. One can see higher levels of cfus in the carcass in PO deficient flies compared to wt flies. However, after, 5 days, there is no difference in the cfus of wt and mutant flies anymore. If the growth inhibition was melanization mediated, would we not expect a consistent growth of bacteria in PO mutants? How to explain the drop in cfus in PO deficient mutants?
- Fig. 5D: How do PAO1 bacteria react to Levofloxacin treatment? Do they still go into the dormant state? Do they still attach to tissues? The authors should show that Levofloxacin treatment leads to the same dormant state as gentamycin before interpreting the results of this experiment.
Minor comments:
Lines 68-72. Mechanisms that are listed are not specific against Gram-negative bacteria but rather general. Please correct.
Line 95. In - as?
Lines 182-187. Some background information is needed. What is O5 LPS antigen? What motivated the authors to look at it specifically?
Fig. 3C: Why PPO1 and Hayan and PPO1,2 and Sp7 are compared but not mutant vs wild type? How precise is estimation of bacteria in the carcass? How do the authors prevent dissemination of the bacteria during dissection? I wonder if some bacteria might by lost during the dissection (when removing the gut and ovaries) or if you carry over some bacteria from the hemolymph into the carcass measurement? How to make sure, that the bacteria you recover were really adherent and were not leftover from the hemolymph? I am also curious how the differences in the cfu levels between whole fly and carcass can be explained (Fig. 1D). After day 5 there are almost no bacteria left in the hemolymph, however, if you compare cfus in the whole fly vs. the carcass, one can see that the whole fly cfus are rising from day 4 onwards. Where do these bacteria come from if not from the hemolymph?
Fig. S4D: If the protection to secondary PAO1 infection is not mediated via Imd or phagocytosis, is it mediated via melanization? How do melanization mutants (increased or decreased) respond to PAO1 secondary infection?
Significance
This study suggests that host factors, particularly specific immune responses, could drive the latent infections. Hence, besides bacterial mechanisms that received significant attention, we should not underestimate the host's contribution to promoting the latent state in bacteria.
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Referee #1
Evidence, reproducibility and clarity
This works describes a latent Drosophila intestinal infection, which spreads systemically, with a direct systemic Drosophila infection using a common laboratory strain of Pseudomonas aeruginosa. The major observation of this study is that P. aeruginosa can cause a latent infection via its passage through the gut (as opposed to being injected). In doing so it exhibits cell rounding (instead of elongation), reduced cell motility, loss of O5-antigen, antibiotic resistance, ability to cross the intestinal barrier and circulate in the hemolymph and infiltrate the host tissue underneath the cuticle.
In addition, latent infection bacteria induce all brunches of the systemic response: the Imd pathway, phagocytosis, and the melanization cascade. Moreover, the melanization pathway protects the host from a secondary systemic infection with various types of bacterial and fungal microbes.
An issue that needs to be clarified is the sensitivity of P. aeruginosa virulence to its biochemical environment. The authors note that. For example, liquid bacterial culture in BHI induces the latent form of bacteria. So the growth conditions and the infection media play a major role in the infection process. They authors need to clarify further the effect of media and infection vehicles, sucrose (high/low), LB, and BHI (as well as temperature) on the latent phenotype.
Minor issues:
- Lines 579-581> How were the PAO1GFP/RFP constructed (details are needed)
- Figure 1D and other figures > CFUs given as Log2 are unconventional. One cannot easily deduce the burden unless e.g. translate 2e10 to ~1000 and 2e30 to ~10e9 CFUs.
- Figire S1D > "but from the outside of the gut". The given experiment does not prove that statement.
- Lines 146-7 > data are missing in support to the statement.
- Figure S1C > The effect of injury seems to be huge, and may account for much/most of the differences observed (including those between latent and active infection). This is further supported by Figure4A, injury may account for gut collapse and/or systemic stress.
- Figure S1D > How was "fated to die" assessed?
- Figure 3B/10th day > Average line is misplaced.
- Lines 382-5 > what is the evidence of gut damage (or the absence of it)? How do the bacteria escape the gut?
- Lines437-442 > The distinction between dormant P. aeruginosa in the fly tissues and persister cells (upon antibiotic treatment) cannot be justifies on the basis of relative bacterial numbers in the two systems. The extent of resistance to antibiotics though my serve that purpose.
Significance
The study is a significant advance to our knowledge.
Notwithstanding further explanations, it provides a solid basis of understanding active versus dormant bacteria.
It further establishes a mode of intestinal to systemic infection as a tool for further explorations.
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- Jan 2024
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Reply to the reviewers
1. General Statements [optional]
All four reviewers have positive comments on the paper. We totally agree with their comments, and proposed controls and experiments. Most of them are already introduced in the present text and several new figures added, as we had the controls/experiments proposed. Few others are now being done and we hope to have the complete set of experiments ready in 2-3 months.
2. Description of the planned revisions
Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.
Reviewer #1
Most comments of this reviewer have already been done and included in the transferred manuscript, except for part of the first comment:
1.1 b. Is it possible that the loss of function of Wengen on its own has a phenotype? If so, that would suggest that Wgn in addition to its role in regeneration might be implicated in pro-survival processes in homeostatic conditions?
This issue is very important to understand the differential role of Wgn and Grnd. First of all, Wengen knock out (wgnKO; Andersen et al., 2015) is viable in homozygosis. However, in this paper we have focused on inducible mutants. Therefore, we have now crossed the flies to get the genotype hh-Gal UAS RNAi wgn and we will check for apoptotic phenotype, as suggested. This will take us few weeks of work.
Reviewer #2 Most comments have been already carried out and included in the transferred manuscript, except these ones:
*2.3. Aside from wgn, other RNAi experiments are not validated through independent RNAi lines. I suggest expanding the Supplemental Figures to reproduce a few key findings with independent RNAi lines. *
We have recently received a set of independent RNAi line to repeat the experiments for Traf1, Traf2, Ask1 from Bloomington Stock Center. And We did not do it before mainly because we wanted to focus on wgn and grnd. However, we agree with the Reviewer 2 and we will do the experiments. Another RNAi from VDRC for grnd and Tak1 have been ordered. These experiments will take about 2 months from the crosses to the analysis of results (some flies still to arrive, and many crosses will be done at 17ºC).
*2 4. In Figure 1E, the authors show that wgn RNAi enhances cell death caused by hh>egr. What is missing here is a wgn RNAi control without hh>egr. Is there any cell death caused by the loss of wgn alone (without hh>egr)? *
This control is now in progress. Expected to have it complete in 2 weeks.
*2.5. If wgn RNAi causes some degree of cell death, is the observed effect with hh>egr a significant genetic interaction, or merely additive? *
The result from the previous comment will help us to respond this point.
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- Is the wgn-p38 pathway sufficient to block egr induced cell death? The authors could test this by having hh>egr in the licT1.1 background. The authors have a more complex experiment in Figure 3, where licT1.1 is introduced into the hh>egr, wgn RNAi background. However, testing the effect of licT1.1 without wgn would establish a more direct relationship between egr and wgn-p38. *
We have set the crosses for the experiment hh>egr and licT1.1 as suggested. The results will be included in the new version of the manuscript. 1 month.
Reviewer #3
All comments already carried out and included in the transferred manuscript. See next sections.
__Reviewer #4 __
Major comments:
*4.3 In Figure 5, the cells expressing Rpr appeared to be pulled/extruded basally as expected. It would be beneficial to quantify Wgn and Grnd signals along cross-sections and provide higher magnification images of domain boundaries to illustrate differences in TNFR localization and levels. ** The micrographs for Grnd Figure 5B,D, F capture substantial signal from the peripodial epithelium where the salE/Pv> driver is likely not active? *
We will do a thorough quantification of high-resolution stacks of images and include higher magnification of the analyzed stacks. To this aim, we need some more weeks to collect the images of each genotype, processed and quantify them. We propose to do have this work done in two months.
*4.4 The non-autonomous induction of Wgn seems stronger when facing dying Rpr overexpressing cells simultaneously depleted of Eiger compared to Rpr OE alone. Should this be a reproducible, could the authors discuss potential reason for this observation? *
It is difficult to respond this question, without quantification. The quantification suggested in the previous point, will allow us to state if Wgn is more accumulated in rpr +egr than rpr alone. Therefore, the previous point will tell us if there are significant differences and if, so it will help us to discuss it.
Timing: The entire plan can be executed in 2-3 month.
3. Description of the revisions that have already been incorporated in the transferred manuscript
Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.
__Reviewer #1 __
1.1 a- *The result in Fig1.H is somehow surprising. How does the overexpression of Egr induce caspase activation in the absence of its receptor Grnd? *
The results of Fig. 1H, in which egr+grndRNAi+wgnRNAi results in high apoptosis indicates that wgn down regulation compromises survival even in the absence of grnd. The reviewer correctly points that “How does the overexpression of Egr induce caspase activation in the absence of its receptor Grnd?”.
There is evidence that Eiger is involved in the regulation of the pro-apoptotic gene head involution defective (hid) in primordial germ cells (Maezawa 2009 Dev. Growth Differ., 51 (4) (2009), pp. 453-461) and in the elimination of damaged neurons during development (Shklover et al., 2015). Moreover, Eiger is necessary for HID stabilization and regulates HID-induced apoptosis independently of JNK signaling (Shklover et al., 2015). Therefore, in our discs egr activation in the absence of grnd and wgn can still result in apoptosis because of the absence of wgn’s survival signal and, presumably, activation of hid.
We have introduced this issue in the text as:
“To check for epistasis between grnd and wgn, we activated hh> egrweak and knocked down both TNFRs. We found high levels of cell death compared to wgn RNAi alone (Fig. 1H and 1I), which demonstrates that wgn down-regulation is dominant over grnd. This is surprising as it is generally assumed that Egr interacts with Grnd to induce apoptosis via JNK, which in turn activates the proapoptotic gene hid (Andersen et al., 2015; Diwanji & Bergmann, 2020; Fogarty et al., 2016; Igaki et al., 2002; Moreno, Yan, et al., 2002; Sanchez et al., 2019; Shlevkov & Morata, 2012). Interestingly, Egr is necessary for HID stabilization and can regulate HID-induced apoptosis independently of JNK (Shklover et al., 2015). Therefore, cells egrweak that downregulate grnd and wgn can still be eliminated because the lack of both Wgn-survival signal and the pro-apoptotic Grnd/JNK signal could result in an alternative pathway of apoptosis.”
*1.2- In Fig.6, it would be relevant to include wengen inactivation within the domain where rpr is expressed to show that wengen is not required autonomously for regeneration (sal>rpr + wgn RNAi). What is the phenotype of the adult wing of sal-lexA>rpr + nub-gal4 >wgn RNAi animals.? *
We have already added a new figure (Fig. S4C) containing this data. As shown, both wgnRNAi alone and wgn RNAi + rpr do not show relevant anomalies and regenerate normally. Therefore, we conclude that wgn is not autonomously required for regeneration.
The adult wings sal-lexA>rpr + nub-gal4 >wgn RNA result in a strong aberration, as regeneration is inhibited. This experiment has been also added in another figure (Fig. S4B) it is done.
*1.4 Minor- In fig.1I, it is surprising that knockdown of neither Grnd nor dTRAF2 significantly affects Egr-induced apoptosis *
After applying a One-Way ANOVA test to compare all the groups against all the groups in fig. 1B no significative differences were detected between Control and RNAi grnd or RNAi dTRAF2 (p>0,05). But if we apply a Student’s T test, which is less restrictive, we obtain, indeed, significative differences:
Control vs. RNAigrnd p=9,48x10-7
Control vs. RNAi dTRAF2 p=2,47x10-7
We have now added in the text:
“Note that when egrweak cells downregulated dTRAF2 or grnd the cell death area ratio is slightly lower than egrweak alone (Fig. 1I), comfirming that dTRAF2 and Grnd contribute to apoptosis in egrweak cells.”
*1.5 Minor The ability of the wing disc to regenerate has been associated with the induction of a developmental delay mediated by Dilp8. Are the authors observing this developmental delay is the case of sal-lexA>rpr + Ap-gal4 >wgn RNAi or sal-lexA>rpr + Ci-Gal4>wgn RNAi *
The developmental delay due to Dilp8 has been observed by many laboratories, indeed. The question of the reviewer is relevant because if there is no delay in pupariation, regeneration could be compromised not because regeneration has been affected but because after pupariation regeneration is impeded.
However, delay in pupariation has been found in our experiments. Usually for 11hrs of heat shock (to induce apoptosis) we found 1-2 days of delay.
We have added the following text:
“The ability of the wing disc to regenerate after genetic ablation has been associated with the induction of a developmental delay (Colombani et al., 2012; Garelli et al., 2012; Jaszczak et al., 2015; Katsuyama et al., 2015; Smith-Bolton et al., 2009). All genotypes analyzed in figure 6 showed a similar developmental delay of 1-2 days (at 17ºC) after genetic ablation in comparison to the animals of the same genotype in which no genetic ablation was induced, i.e. developed continuously at 17ºC (Fig. S4A). After the adults emerged, the wings were dissected, and regeneration was analyzed.”
*1.7 Minor - The investigation of the evolutionary origin of TNFR in drosophila included in Fig.2 is cutting a bit the flow of the results. *
The evolutionary origin starts now with a sentence that can smoothen the flow and few changes in that paragraph have been made:
“Opposing roles between proteins of the TNFR superfamily suggests that they have an ancient origin and have followed divergent evolutionary paths. To track the differences observed between grnd and wgn, we decided to investigate the evolutionary origin of these two Drosophila genes.”
*1.8 Minor The authors could explain in more details the double transactivation system for non-fly specialists. *
The entire section has been re-written in Material and Methods.
*1.9 Minor - It can be interesting to include and/or discuss these few references: *
*PLoS Genet. 2019 Aug; 15(8): e1008133. ** PLoS Genet. 2022 Dec 5;18(12):e1010533. FEBS Lett. 2023 Oct;597(19):2416-2432. *
*Curr Biol. 2016 Mar 7;26(5):575-84. *
*Nat Commun. 2020 Jul 20;11(1):3631. **
*
All these references, and few others, have been introduced in the text.
__Reviewer #2 __ *2. 1. The authors find that wgn RNAi enhances hh>egr-induced apoptosis. They validate the results with two independent RNAi lines in Figure S1. However, Figure S1 is missing a control without wgn RNAi, and therefore, the results are difficult to assess. *
Fig S1A now contains this control.
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- Are the two independent wgn RNAi lines targeting different regions of the coding sequence? *
As the regions targeted by the 2 RNAi’s are different, see below, we have included in the text:
“This observation was corroborated with an independent RNAi-wgn strain targeting a different region in the coding sequence (Fig. S1A and S1B). “
Bloomington BL55275 (dsRNA-HMCO3962)
VDRC GD9152 (dsRNA-GD3427)
*2.7. In Figure 4, the authors show that egr expression induces ROS and performs anti-oxidant experiments. This part could be strengthened if they show that the ROS sensor signal disappears after Sod::Cat expression. *
We had done this experiment and there is a definitively drop in Mitosox in discs in which the weak allele of egr is active. We have included this new image in Figure 4G and in the text.
*2.8. How effective is egr RNAi? In Figure 5E, F, the authors knock down egr and obtain negative results. Based on this, the authors argue that Wgn localization occurs through an egr-independent mechanism. Drawing strong conclusions based on a negative result with egr RNAi is not a good practice since one cannot rule out residual egr activity that mediates the effect (of course , because there is cell death as well, death cells express egr). I suggest either finding ways to completely abolish egr function, or tone down the conclusion. *
We have used ‘after knocking down eiger’ instead of in the ‘absence’ or ‘abolish’ eiger.
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- Figure 6 shows that wgn RNAi aggravates the reaper phenotype. What's missing is a control that expresses wgn RNAi but not reaper. *
Control experiments using the UAS-wgnRNAi in the absence of rpr are now shown in figure S4C.
Reviewer #3 ____ 3.1.Minor Fig 6C-E would need a control disc without induced apoptosis (ie wildtype disc) stained for phospho-p38 as a baseline comparison. This is important to judge the significance of the remaining phospho-p38 in panel E where wgn is knocked down. The authors write ** " However, after knocking down wgn, phosphorylated p38 in the wing pouch ** surrounding the apoptotic cells was abolished (Fig. 6E)." *Depending on the amount of phospho-p38 in control discs, this may need to be rephrased to "strongly reduced" instead of "abolished". *
A control disc stained with P-p38 has been added in Figure 6.
We have changes ‘abolished’ by ‘strongly reduced’.
* 3.2. This sentence in the Intro needs fixing because TNFa doesn't transduce the signal from TNFR to Ask1 since it's upstream of TNFR: "TNFα can transduce the TRAF-mediated signal from TNFR to Ask1 to modulate its activity (Hoeflich et al., 1999; Nishitoh et al., 1998, p. 0; Obsil & Obsilova, 2017; Shiizaki et al., 2013)." *
We have rephrased this sentence by:
“TNFα binds to TNFRs which in turn interact with TRAFs to transduce the signal to Ask1 to modulate its activity”.
*3.3a In the results section, the authors start by ectopically overexpressing Eiger. Are there conditions where Eiger expression is induced in the wing? If yes, it would be helpful for the reader to mention that this system with the genetically GAL4-induced expression of Eiger aims to phenocopy one of these conditions. *
Eiger ectopic expression has been induced in the wing to generate apoptosis. This is a common technique in Drosophila, and the Reviewer3 is right that a sentence should be useful for the reader.
A sentence has been introduced at the beginning of the results section:
“Ectopic expression of egr in Drosophila imaginal discs results in JNK-dependent apoptosis (Brodsky et al., 2004; Igaki et al., 2002; Moreno, Yan, et al., 2002).”
*3.3b Fig 2C is not very self-explanatory: it is worth writing out what Hsa (H. sapiens), Bla and Sco stand for (there is plenty of space). *
We have re-designed figure 2 to make it more self-explanatory.
*3.4. This sentence is confusing: ** " ...Wgn localization were due to ROS or to the expression of egr, we used RNAi to knock down egr in the apoptotic cells and found that reduced Egr/TNFα had no effect on Wgn localization (Fig. 5E, 5F)." The authors may want to specify that Wgn is still accumulated even without Egr. ("No effect" is unclear). *
This sentence has been modifies as:
“Wgn localization were due to ROS or to the expression of egr, we used RNAi to knock down egr in the apoptotic cells and found that Wgn accumulation was not altered by the knocking down Egr/TNFα (Fig. 5E, 5F). “
*3.5 Comment. It discovers that Wengen is activated by ROS. In fact, since Wengen binds TNF with an affinity that is several orders of magnitude lower than Grindelwald, and since Wengen is not even located at the cell membrane, these data call into question whether Wengen is a TNF receptor, or a ROS receptor? Could the authors comment on this ? Could it be that the results obtained in the past showing that Wengen is activated by TNF were actually due to TNF inducing apoptosis, leading to production of ROS, leading to activation of Wengen?
*
We totally agree with Reviewer#3. We have added a final paragraph in the discussion section.
“We speculate that the subcellular location of Wgn and Grnd may contribute to the different functions of both receptors. Grnd is more exposed at the apical side of the plasma membrane, which makes this receptor more accessible for ligand interactions (Palmerini et al., 2021). Wgn, embedded in cytoplasmic vesicles, is less accessible to the ligand and could be more restricted to being activated by local sources of signaling molecules, such as ROS. In contrast to initial reports (Kanda et al., 2002; Kauppila et al., 2003), los-of-function of wgn does not rescue Egr-induced apoptosis in the Drosophila eye (Andersen et al., 2015), which supports our observation in the wing that Wgn is not required for Egr-induced apoptosis. Instead, Egr-induced apoptosis generates ROS which target intracellular Wgn to foster a cell survival program of cells close to the apoptotic zone.”
__Reviewer #4 __
*4.1 b Are phospho-p38 levels increased in cells expressing Egr[weak]? *
We have the results of these experiments. To respond to this point, a new figure has been added (Fig. S4) in which we show the P-p38 levels are increased (non-autonomously) in egrw, as previously found for reaper. In addition, we show that egrw + activation of p35 and egrw + activation of Sod1::Cat results in strong reduction of P-p38. This indicates that P-p38 is stimulated by the ROS produced by apoptotic cells.
The text now:
“It is worth noting that cells egrw induce phosphorylation of p38 in neighboring cells (Fig. S4A) and that, as previously found for rpr (REF), depends on ROS generated by egrw apoptotic cells (Fig. S4B, C).”
*4.2 In Figure 4C it appears that the Dcp-1 positive cells move apically rather than basally. Including nuclear staining would be very informative allowing assessment of tissue morphology. ** The authors focus on the pouch region of the wing imaginal disc, where phenotypes are strong and obvious. However, the hh-Gal4 driver also affects posterior cells in the hinge and notum, where the effects of Eiger[weak] overexpression seem weaker (e.g., minimal to no MitoSox signal in hinge and notum posterior cells). Could the authors explain this observation? *
Point 1: Actually, cells move more basally, though some move more apical as well. Depending on the section cells the image could be confusing. To solve that, we show now a plane on these discs at apical and a plane basal. Both high magnifications. There one can see that there is more concentration of pyknotic nuclei basally. We have added this observation in a new supplementary figure (Fig. S3) and the corresponding text in page 5: “Apoptotic cells in egrweak are characterized by pyknotic nuclei and are positive for Dcp1. These cells tend to concentrate in the basal side of the epithelium, although some are scattered apically (Fig. S3). Accumulation of Wgn was observed in healthy anterior cells adjacent to both apical and basal egrweak cells (Fig. 4, Fig. S3A, B).”
Point 2 Comment on MitoSOX in notum: At the stages of the imaginal discs used in this study, almost all notum cells are anterior compartment. The hh positive cells in notum much less abundant, therefore most of the staining was found in the posterior compartment of the wing pouch.
*4.5 Figure 6 C-E. Does WgnRNAi potentiates and GrndRNAi suppress Rpr-induced apoptosis similarly to their effects when knocked down in Eiger[weak]OE cells? *
The areas controlled by salE/Pv >rpr (dotted lines) are full of pycnotic nuclei, which indicates concentration of apoptotic cells in all genotypes shown.
Thus, in the conditions generated here, apoptosis is not inhibited and grnd RNAi does not interfere with the activation of P-p38. In wgn knock down, phospho-p38 is strongly inhibited, indicating the importance of wgn in phosphorylation of p38.
To clarify this point, we have added in the text: “Note that rpr-induced apoptosis is not suppressed after knocking down grnd or wgn.” Also in the figure legend we added: “White lines in the confocal images outline the salE/Pv-LHG,LexO-rpr dark area full of pyknotic nuclei of apoptotic cells.”
4.6 The activation of p38 following salE/Pv>rpr-mediated ablation as shown by immunostaining is noteworthy. While loss Grnd knockdown leads to phospho-p38 signal enrichment around the rpr-expressing cells, WgnRNAi results in reduced phospho-p38 signal in the wing pouch but also beyond the nub-expression domain. Do salE/Pv>rpr nub>WgnRNAi cells still generate ROS?
So far there is no evidence of Wengen as a ROS scavenger. We have evidence that ROS (using MitoSox probe) are produced in egrweak + Wgn RNAi cells. Thus, the inhibition of wgn expression does not block ROS production. A new figure shows this observation (Figure S4A).
4.7 Are ROS responsible for the long-range signaling and p38 activation, referring to authors' previous work Santabarbara-Ruiz et al., 2019, PLoS Genet 15(1): e1007926. https://doi.org/10.1371/journal. pgen.1007926, Figure 5G?
ROS are responsible for p38 activation as shown in a new figure (Fig. S4). In this new figure egrweak is activated in hh, and p38 is most of cells in the posterior compartment, and also anterior. However, after blocking apoptosis or ROS production, this P-p38 is reduced.
4.8 Minor I propose rephrasing the description of "UAS-Egr[weak] transgene, a strain that produces a reduced Egr/TNFα activity". It could imply a loss of function strain rather than a transgene that causes mild/moderate Egr overexpression.
The sentence has been rephrases as suggested (End of the first paragragraph in results section).
*4.9 Minor. I recommend the authors to revise the charts for improved clarity in genotype representation. For example, in Figure 1I, the label "control-GFP" might be misleading. It would be beneficial to specify that "control" refers to Eiger[weak] alone with other manipulations being done simultaneously with Eiger[weak] overexpression. *
All charts have been revised.
4.10 Minor. Additionally, considering that individuals with color blindness may struggle to differentiate between red and green colors, I strongly suggest using a color-blind-friendly palette, especially in Figure 4A, C, G, and Figure 4A, C, E." ** All images have been revised for color blind code.
- 11 Minor. Providing detailed information regarding the reagents used in the study, such as Catalogue Numbers or RRIDs, is beneficial for enhancing reproducibility. *
We have added the RRID and Cat #. If no ID was available, we added the reference or contact.
4.12 This reviewer points two limitations that we are now trying to solve:
*Limitations: *
*Quality of the imaging – higher magnification images and quantification would enhance the study. ** The summarizing model may contain excessive speculations that lack support from the data or references to the existing literature. *
Quality of imaging. We have now an extra supplemental figure with higher magnifications. Extra higher magnifications will be included in the next version as well as quantification, as exposed for the Revision Plan points 4.3 and 4.4.
Model: We have re-written the paragraph on the model, introduced references and drop some speculations. We hope the current version will be more convincing for the reader.
4. Description of analyses that authors prefer not to carry out
Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.
Reviewer 1
*1.3. Is the overexpression of Wengen sufficient to increase tissue regeneration? *
The suggestion of the reviewer is a key point in regeneration biology: how to accelerate regeneration?
We have demonstrated that Wengen is upstream the Ask1-p38 axis that drives regeneration. The reviewer wonders if Wengen overexpression can result in increase in regeneration. In a previous work we have demonstrated that p38 activation is key for regeneration but its overexpression can be deleterious (Esteban-Collado et al., 2021). Only in discs that sensitized for low p38 (starvation, low Akt, Ask1S83A mutant), the overexpression rescues regeneration. Therefore, the levels of the Wgn-Ask1-p38 have to be very tightly controlled. An excess will be deleterious. We are aware of the importance of the question, but at this point we do not have the technology to finely control Wgn-Ask1-p38 levels to do this experiment.
1.6 Minor - It possible to test the induction of apoptosis in a wgn null mutant background to see if the phenotype is even stronger than the one observed with RNAi (the wgn RNAi is induced at the same time than egr or rpr overexpression).
Flies wgnKO survive, but they gave us problems when carrying transgenes for our design of genetic ablation. Indeed, we tried to generate wgnKO carrying Gal4+tubGal80+eigerweak without success.
In addition, the reason we have used an inducible mutant is because it allows us to work in time and space without altering expression in other cell types beyond wing discs. Wgn is required in other organs during development like gut, trachea and axon growth, etc.., and thus, we ensure the affected cells belong to the tissue analyzed.
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Referee #4
Evidence, reproducibility and clarity
The study by Florenci Serras and colleagues presents compelling evidence highlighting distinct functions of the two Drosophila TNFRs, Wengen (Wgn) and Grindewald (Grnd) in developing imaginal epithelia. The study shows that while Grnd-dTraf2-Tak1 module controls apoptosis in Eiger (Egr)-dependent manner, Wgn-dTraf1-Ask1 promotes survival likely via p38 signaling independent of Egr. Their phylogenetic analysis underscores the ancient origin of both receptors while revealing their divergent evolutionary path, manifested by markedly different CRD sequences. Moreover, Wgn shows higher degree of similarity to mammalian TNFRs. Using functional genetics and confocal imaging of immunostained wing imaginal discs, the authors confirm the differential localization of Wgn and Grnd in imaginal cells, consistent with several recent studies. Interestingly, they demonstrate distinct responses between Grnd that is internalized upon Eiger[weak] overexpression from the plasma membrane, and Wgn, which cytoplasmic levels decrease in Eiger[weak] OE cells but become enriched in neighboring wild type cells. The non-autonomous accumulation of Wgn required ROS but not Eiger production by dying cells. Finally, employing an elegant double-driver lexA/lexO and Gal4/UAS system, enabling independent gene manipulation in specific domains of the wing imaginal discs, the authors established the essential role of Wgn, but not Grnd, in the regenerative response to apoptosis, including the occurrence of phosphorylated p38.
Major comments:
The conclusion regarding the protective role of p38 in response to Egr[weak] should be supported by a p38 knockdown experiment. Are phospho-p38 levels increased in cells expressing Egr[weak]?
In Figure 4C it appears that the Dcp-1 positive cells move apically rather than basally. Including nuclear staining would be very informative allowing assessment of tissue morphology. The authors focus on the pouch region of the wing imaginal disc, where phenotypes are strong and obvious. However, the hh-Gal4 driver also affects posterior cells in the hinge and notum, where the effects of Eiger[weak] overexpression seem weaker (e.g., minimal to no MitoSox signal in hinge and notum posterior cells). Could the authors explain this observation?
In Figure 5, the cells expressing Rpr appeared to be pulled/extruded basally as expected. It would be beneficial to quantify Wgn and Grnd signals along cross-sections and provide higher magnification images of domain boundaries to illustrate differences in TNFR localization and levels. The micrographs for Grnd Figure 5B,D, F capture substantial signal from the peripodial epithelium where the salE/Pv> driver is likely not active?
The non-autonomous induction of Wgn seems stronger when facing dying Rpr overexpressing cells simultaneously depleted of Eiger compared to RprOE alone. Should this be a reproducible, could the authors discuss potential reason for this observation?
Figure 6 C-E. Does WgnRNAi potentiates and GrndRNAi suppress Rpr-induced apoptosis similarly to their effects when knocked down in Eiger[weak]OE cells? The activation of p38 following salE/Pv>rpr-mediated ablation as shown by immunostaining is noteworthy. While loss Grnd knockdown leads to phospho-p38 signal enrichment around the rpr-expressing cells, WgnRNAi results in reduced phospho-p38 signal in the wing pouch but also beyond the nub-expression domain. Do salE/Pv>rpr nub>WgnRNAi cells still generate ROS? Are ROS responsible for the long-range signaling and p38 activation, referring to authors' previous work Santaba ́rbara-Ruiz et al., 2019, PLoS Genet 15(1): e1007926. https://doi.org/10.1371/journal. pgen.1007926, Figure 5G?
Minor comments:
I propose rephrasing the description of "UAS-Egr[weak] transgene, a strain that produces a reduced Egr/TNFα activity". It could imply a loss of function strain rather than a transgene that causes mild/moderate Egr overexpression.
I recommend the authors to revise the charts for improved clarity in genotype representation. For example, in Figure 1I, the label "control-GFP" might be misleading. It would be beneficial to specify that "control" refers to Eiger[weak] alone with other manipulations being done simultaneously with Eiger[weak] overexpression. Additionally, considering that individuals with color blindness may struggle to differentiate between red and green colors, I strongly suggest using a color-blind-friendly palette, especially in Figure 4A, C, G, and Figure 4A, C, E."
Providing detailed information regarding the reagents used in the study, such as Catalogue Numbers or RRIDs, is beneficial for enhancing reproducibility.
Significance
This is a very solid study that uncovers unique roles of Drosophila TNFRs in regulating imaginal cell behaviors crucial for tissue regeneration. It expands our knowledge on processes controlled by TNFR-mediated signaling, highlighting the potential for ligand-independent regulation. The study nicely complements recent findings by several laboratories (Letizia et al., 2023; Loudhaief et al., 2023; Palmerini et al., 2021). Beyond its contribution to fundamental biology, the study has biomedical implication for regenerative medicine. It emphasizes the necessity of balancing TNFR activities, downstream signaling and their dependence on ligands, providing important insights for the development of receptor agonists or antagonists. The findings are relevant to audience interested in developmental and regenerative biology, gene evolution.
Strengths: functional genetics revealing distinctive roles for the two TNFRs in Drosophila and their dependency on ligand in the paradigm of tissue regeneration.
Limitations: quality of the imaging - higher magnification images and quantification would enhance the study. The summarizing model may contain excessive speculations that lack support from the data or references to the existing literature.
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Referee #3
Evidence, reproducibility and clarity
Summary:
TNF receptors have a broad range of possible function, from inducing apoptosis to promoting cell survival and proliferation. How this works is not completely understood. Drosophila has two TNF receptors, Wengen and Grindelwald. This manuscript nicely shows that Grindelwald is pro-apoptotic while Wengen promotes cell survival and proliferation. Strikingly, if TNF is expressed in Drosophila tissue, knockdown of the receptor Wengen leads to elevated levels of apoptosis, clearly showing its cell-protective function. Interestingly, the authors find that Wengen is activated by ROS produced by neighboring dying cells - regardless of whether they are dying due to TNF signaling or not - and that Wengen then activates p38 downstream to mediate a regenerative response.
Major comments:
Overall the conclusions are interesting, clear and convincing. The data are of very good quality. I only have a few minor comments below.
Minor comments:
- Fig 6C-E would need a control disc without induced apoptosis (ie wildtype disc) stained for phospho-p38 as a baseline comparison. This is important to judge the significance of the remaining phospho-p38 in panel E where wgn is knocked down. The authors write " However, after knocking down wgn, phosphorylated p38 in the wing pouch surrounding the apoptotic cells was abolished (Fig. 6E)." Depending on the amount of phospho-p38 in control discs, this may need to be rephrased to "strongly reduced" instead of "abolished".
- This sentence in the Intro needs fixing because TNFa doesn't transduce the signal from TNFR to Ask1 since it's upstream of TNFR: "TNFα can transduce the TRAF-mediated signal from TNFR to Ask1 to modulate its activity (Hoeflich et al., 1999; Nishitoh et al., 1998, p. 0; Obsil & Obsilova, 2017; Shiizaki et al., 2013)."
- In the results section, the authors start by ectopically overexpressing Eiger. Are there conditions where Eiger expression is induced in the wing? If yes, it would be helpful for the reader to mention that this system with the genetically GAL4-induced expression of Eiger aims to phenocopy one of these conditions.
- Fig 2C is not very self-explanatory: it is worth writing out what Hsa (H. sapiens), Bla and Sco stand for (there is plenty of space).
- This sentence is confusing: " ...Wgn localization were due to ROS or to the expression of egr, we used RNAi to knock down egr in the apoptotic cells and found that reduced Egr/TNFα had no effect on Wgn localization (Fig. 5E, 5F)." The authors may want to specify that Wgn is still accumulated even without Egr. ("No effect" is unclear).
Significance
This manuscript makes several important discoveries:
- it clearly shows that one TNF receptor, Grindelwald, is mainly pro-apoptotic, while the other, Wengen, is mainly pro-survival. This provides a mechanistic explanation for the dual role of the TNF, Eiger.
- It discovers that Wengen is activated by ROS. In fact, since Wengen binds TNF with an affinity that is several orders of magnitude lower than Grindelwald, and since Wengen is not even located at the cell membrane, these data call into question whether Wengen is a TNF receptor, or a ROS receptor? Could the authors comment on this ? Could it be that the results obtained in the past showing that Wengen is activated by TNF were actually due to TNF inducing apoptosis, leading to production of ROS, leading to activation of Wengen?
- It was previously shown that damage, for instance in the fly intestine, induces production of ROS, which then activates p38, leading to a proliferative/regenerative response. This manuscript provides a missing mechanistic link, showing that the ROS activates Wengen, which in turn activates p38. This thereby completes the mechanistic chain of events from damage to the regenerative response.
Hence, overall, this is a very interesting study. It will be of interest for a broad audience of people studying TNF signaling, stress signaling and stress response, tissue damage and repair, and regeneration.
My expertise: Drosophila, growth, signaling
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Referee #2
Evidence, reproducibility and clarity
The Drosophila genome encodes a single TNFa ortholog, eiger (egr), and two TNF Receptros (TNFR), wengen (wgn) and grindelwald (grnd). While egr overexpression can cause apoptosis, the authors here report that wgn and grnd have opposing roles in cell death and survival. Specifically, the authors show evidence that grnd promotes cell death in response to egr expression, while wgn promotes cell survival through the p38 MAP Kinase pathway. They further show that apoptotic cells have high levels of ROS, which activates the wgn-p38 axis for tissue regeneration, independent of egr.
Overall, the manuscript is well written. At the same time, there are some technical concerns and missing controls that need to be addressed. Below are a few specific comments for the authors' consideration:
Major Comments
- The authors find that wgn RNAi enhances hh>egr-induced apoptosis. They validate the results with two independent RNAi lines in Figure S1. However, Figure S1 is missing a control without wgn RNAi, and therefore, the results are difficult to assess.
- Are the two independent wgn RNAi lines targeting different regions of the coding sequence?
- Aside from wgn, other RNAi experiments are not validated through independent RNAi lines. I suggest expanding the Supplemental Figures to reproduce a few key findings with independent RNAi lines.
- In Figure 1E, the authors show that wgn RNAi enhances cell death caused by hh>egr. What is missing here is a wgn RNAi control without hh>egr. Is there any cell death caused by the loss of wgn alone (without hh>egr)?
- If wgn RNAi causes some degree of cell death, is the observed effect with hh>egr a significant genetic interaction, or merely additive?
- Is the wgn-p38 pathway sufficient to block egr induced cell death? The authors could test this by having hh>egr in the licT1.1 background. The authors have a more complex experiment in Figure 3, where licT1.1 is introduced into the hh>egr, wgn RNAi background. However, testing the effect of licT1.1 without wgn would establish a more direct relationship between egr and wgn-p38.
- In Figure 4, the authors show that egr expression induces ROS and performs anti-oxidant experiments. This part could be strengthened if they show that the ROS sensor signal disappears after Sod::Cat expression.
- How effective is egr RNAi? In Figure 5E, F, the authors knock down egr and obtain negative results. Based on this, the authors argue that Wgn localization occurs through an egr-independent mechanism. Drawing strong conclusions based on a negative result with egr RNAi is not a good practice since one cannot rule out residual egr activity that mediates the effect. I suggest either finding ways to completely abolish egr function, or tone down the conclusion.
- Figure 6 shows that wgn RNAi aggravates the reaper phenotype. What's missing is a control that expresses wgn RNAi but not reaper.
Significance
There is now a detailed understanding of mammalian TNFRs, which play pro-apoptotic and non-apoptotic roles depending upon the context. Previous studies had also reported that TNFR1 respond to ROS. By comparison, our understandings of the two TNFRs in Drosophila remain rudimentary. The two receptors have different loss-of-function phenotypes, some of which may be independent of egr signaling. The major significance of this work is in delineating the distinct behaviors of the two Drosophila TNFRs, centering around their pro-apoptotic, or pro-survival properties.
Audience: This study will draw the interest of Drosophila geneticists, those interested in Reactive Oxygen Species and cell death, and evolutionary biologists.
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Referee #1
Evidence, reproducibility and clarity
Tumor necrosis factor (TNF)-α stands out as a remarkably conserved pro-inflammatory cytokine that plays crucial roles in immunity, tissue repair, and cellular homeostasis. The Drosophila TNF-TNF receptor (TNFR) system, known for its simplicity, combined with a versatile genetic toolkit, has been instrumental in unraveling the intricate mechanisms governing both the physiological and pathological functions mediated by TNF. Recently, the fly TNFR Wengen has been described to have ligand independent functions in maintaining tissue homeostasis and tracheal remodeling. The current manuscript describes a novel TNF/Egr-independent function of Wengen in regulating tissue regeneration in imaginal discs. The authors identify both the upstream regulator (ROS) and the downstream signaling pathway through Ask1/p38 MAPK. The data presented are solid and support an interesting model where ROS emanating from damaged tissue triggers Wgn-dependent signaling in adjacent cells to promote regeneration. Few points could be addressed:
Major points:
- The result in Fig1.H is somehow surprising. How does the overexpression of Egr induce caspase activation in the absence of its receptor Grnd? Is it possible that the loss of function of Wengen on its own has a phenotype? If so, that would suggest that Wgn in addition to its role in regeneration might be implicated in pro-survival processes in homeostatic conditions?
- In Fig.6, it would be relevant to include wengen inactivation within the domain where rpr is expressed to show that wengen is not required autonomously for regeneration (sal>rpr + wgn RNAi). What is the phenotype of the adult wing of sal-lexA>rpr + nub-gal4 >wgn RNAi animals?
- Is the overexpression of Wengen sufficient to increase tissue regeneration?
Minor points:
- In fig.1I, it is surprising that knockdown of neither Grnd nor dTRAF2 significantly affects Egr-induced apoptosis
- The ability of the wing disc to regenerate has been associated with the induction of a developmental delay mediated by Dilp8. Are the authors observing this developmental delay is the case of sal-lexA>rpr + Ap-gal4 >wgn RNAi or sal-lexA>rpr + Ci-Gal4>wgn RNAi
- It possible to test the induction of apoptosis in a wgn null mutant background to see if the phenotype is even stronger than the one observed with RNAi (the wgn RNai is induced at the same time than egr or rpr overexpression).
- The investigation of the evolutionary origin of TNFR in drosophila included in Fig.2 is cutting a bit the flow of the results.
- The authors could explain in more details the double transactivation system for non-fly specialists.
- It can be interesting to include and/or discuss these few references:
PLoS Genet. 2019 Aug; 15(8): e1008133.
PLoS Genet. 2022 Dec 5;18(12):e1010533.
FEBS Lett. 2023 Oct;597(19):2416-2432.
Nat Commun. 2020 Jul 20;11(1):3631.
Curr Biol. 2016 Mar 7;26(5):575-84.
Significance
The understanding of the mechanistic interplay between TNFR in integrating TNF-dependent and independent signals to stimulate distinct downstream responses lays the foundation for investigating whether these insights can be generalized to other members within the TNFR superfamily in all organisms. This work is relevant for a large audience of researchers working in the field of inflammation and TNFR.
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Reply to the reviewers
We thank the reviewers for their reading of our manuscript, which we believe has led to substantial improvements.
To aid clarity, we have split Fig. 1 into three separate figures.
For convenience, we have put all major changes in the text in blue.
Reviewer #1
Evidence, reproducibility and clarity
Summary: Hui et al. tackle a crucial question in biology: what factors influence the preference for carbon sources in yeasts?
They reveal that the growth rate on palatinose exceeds that on glucose,
The above statement is incorrect --- we think the reviewer may have confused sugars.
despite palatinose utilization being repressed in the presence of glucose. Consequently, the favored carbon source does not necessarily align with the one supporting the fastest growth rate. The study also delves into potential regulatory mechanisms governing carbon source preference and dismisses certain existing theories, such as the general carbon flux sensing mechanism proposed by Okano et al. [25].
Major comments: None
Minor comments:
The authors suggest that a higher growth rate implies a higher glycolytic flux (l63), a crucial assumption underpinning their interpretation of the absence of a ``general carbon flux sensing mechanism' (l65). To substantiate this significant conclusion, they could calculate the extracellular uptake fluxes (based on the time-course concentrations of biomass and substrates).
This suggestion is a good one, but unfortunately the number of data points in the new Fig. 3 are insufficient to estimate the uptake flux reliably.
To address whether glycolytic flux increases, we have added a new paragraph to the introduction explaining how all the sugars we consider feed upper glycolysis, providing either its first or second metabolite. We therefore think it highly likely that any differences in growth rate are generated by differences in glycolytic flux. Indeed, Hackett et al., 2016, showed that the glycolytic flux increases with growth rate when they changed extracellular glucose concentrations. We now include this reference in the Discussion.
The accumulation of certain by-products is known to be toxic, reducing cellular growth rate (e.g., acetate DOI: 10.1038/srep42135, ethanol DOI: 10.1016/B978-0-12-040308-0.50006-9, etc.), while they can also enhance growth under specific conditions (e.g., acetate DOI: 10.15252/embj.2022113079). Considering this is crucial to rule out certain hypotheses, such as the possibility that a by-product produced during growth on the first carbon source would not modulate growth on the second carbon source, potentially influencing the growth rate differentially in each phase. Although the authors use mutant strains to eliminate the role of some C2 compounds (acetate and ethanol), alternative pathways could be implicated in the (co-)utilization of these by-products. This aspect should be discussed, and ideally, the authors could quantify the time-course concentrations of by-products to assess their potential role.
We agree with the reviewer that extracellular acetate and ethanol may inhibit growth, although budding yeast might be less sensitive than E. coli, the subject of most of the studies provided.
Nevertheless, we think it unlikely that these chemicals modify the decision-making we see. First, the icl1Δ mutant we tested is unable to consume ethanol (Fernandez et al., 1992) or acetate (Lee et al., 2011) --- we now include these references in the SI --- and yet has wild-type behaviour (Fig. S2D). Second, we observe that isomaltase expression strongly decreases in the presence of galactose when we grow cells in a microfluidic device (Fig. S4), just like it does in batch culture (Fig. 3A), even though the constant flow of medium through the device removes any chemicals the cells excrete.
The general flux-sensing regulatory mechanism proposed by Okano et al. [25], which has been dismissed by this study, has recently been questioned, as discussed in DOI: 10.15252/embj.2022113079. This aspect should be included in the discussion.
Okano et al. studied E. coli while we study budding yeast. We therefore have shown that the understanding for that organism does not transfer to our eukaryotic example. We suspect that control in budding yeast combines both flux-sensing and specific regulation, as we say in the discussion, and so we consider our results to build on those of Okano et al.
Significance
Strengths & limitations: The work is robust, and the experiments in the study have been appropriately designed and conducted. The primary question of this study has been tackled using a combination of experimental and computational methods to thoroughly assess various regulatory and functional aspects. However, there are gaps in the data that could enhance key conclusions, notably the absence of glycolytic flux measurements. Moreover, further evidence is needed to substantiate the assertion that by-products do not play a role in carbon source preference.
Advance: This study represents a significant step forward in comprehending the nutritional strategy of microbes. The authors demonstrate that the preferred carbon source may not necessarily be the one supporting the fastest growth rate. Furthermore, they dismiss certain theories that have been proposed to explain the growth strategy of microbes on mixed carbon sources.
Audience: By addressing a fundamental question in life science, this work is important in the field of biology in general and of particular interest in systems biology, biotechnology, synthetic biology, and health. Consequently, it will be of interest to a broad audience.
Reviewer #2
Evidence, reproducibility and clarity
Summary: The authors have used microtiter plates to produce growth profiles on combinations of different sugars. From this data they have evaluated whether the sugars are co-consumed or if there is a preference for either sugar, seen as a diauxic shift. They found diauxie between galactose and the disaccharide palatinose, but co-consumption between palatinose and fructose. They further used strains with perturbations in their GAL regulon to attempt to explain this discrepency.
Major comments:
I unfortunately found a large portion of the present manuscript unintelligable.
Firstly, figures were incorrect to the point I could not dechiffre them: Figure 2A-C have black solid and dashed lines in the legend that are not found in the graph, instead there are orange and blue dashed lines in the graph with no legends. Figure 4C has no description of the y-axis. The growth rates in Figure 1C are very hard to follow, and there are definitely local maxima in both the blue and green profiles that are not being discussed (at 15-20 h). I cannot evaluate the conclusions drawn from the data until these issues have been resolved.
We apologise for the difficulties experienced by this reviewer.
The black lines in the old Fig. 2's legend, now Fig. 4, explain the different styles used: dashed lines are for single sugars regardless of their concentration and full lines are for mixtures regardless of their concentration. We now explicitly say this in the caption.
We have fixed the missing label in what is now Fig. 6C and have moved the statement that we are showing two biological replicates for each set of concentrations earlier in Fig. 2's caption.
We now explore the meaning of the shoulder for the fructose-palatinose mixture in Fig. 2B in the Discussion. This point is not a local maximum, unlike the case for diauxie, because the growth rate always decreases. The shoulder for the glucose-palatinose mixture was likely an artefact generated by measurement noise at low ODs because it was not present when we repeated the experiment. We now use that data for Fig. 2A & B. We also include a new Fig. S5 showing that there are sucrose-palatinose concentrations too that have a similar shoulder.
Secondly, the language in the Results and Discussion sections is confusing. Alternating between present and imperfect tense as well as active and passive form makes it hard to distinguish the authors own results from literature findings (Results are usually written in passive, imperfect tense). Examples are found on lines 24, 29, 37-38, 59, 84, 131, and 165.
We have made both sections flow more smoothly with substantial re-writing. As before, we cite all results that are not our own.
The authors also do not consider the differences and similarities in catabolic pathways for assimilation of galactose, fructose and palatinose. Even if they do not see a reason to continue that as a possible explanation for the co-consumption between fructose and palatinose a discussion of why it is disregarded would not be out of place here.
A good point, and we now state in the Introduction that all the sugars we study feed upper glycolysis.
Significance
There is some novelty to the authors findings, but I would argue it is being overstated in the present manuscript. Some examples of studies looking at catabolite repression, the main cause of diauxie, of sugars other than glucose can be found in: Simpson-Lavy and Kupiec (2019), Gancedo (1998), Prasad and Venkatesh (2008) and Borgstrom et al (2022).
We strongly disagree with this statement. The papers cited do not address, as we do, the co-consumption between two sugars neither of which is glucose. Where they study two sugars, they always study glucose.
Simpson-Lavy and Kupiec, 2019, investigate the interaction between acetate and ethanol, neither of which are sugars. Further, they are not independent carbon sources because cells convert ethanol into acetate when catabolising ethanol.
Gancedo, 1998, is a review of glucose repression and describes how glucose represses the expression of genes for other sugars. Although Gancedo mentions ``galactose repression', this repression is of genes encoding enzymes for gluconeogenesis and the TCA and glyoxylate cycles, not of other sugar regulons, our subject.
Prasad and Venkatesh, 2008, also focus on glucose and the well studied diauxie between glucose and galactose.
Borgstrom et al., 2022, focus too on glucose and growth on glucose and xylose in recombinant strains. The standard laboratory strains we study have not be artificially engineered to consume xylose. They do mention that galactose causes repression of TPS1, which encodes an enzyme that synthesises the storage carbohydrate trehalose. This repression is again not of a sugar catabolic regulon, our subject.
I would not say that the field would be significantly advanced by the publication of this manuscript, and the authors have themselves not explained the application of futhering the understanding palatinose metabolism in yeast. As mentioned above, the catabolite repression potential of galactose is already known, it just hasn't been shown for palatinose specifically before.
We again strongly disagree. Our findings are novel. The reviewer did not provide any evidence for galactose repression of other sugar regulons, which is not widely recognised as we emphasised in the Discussion. We believe that the reviewer has confused the known "galactose repression' of gluconeogenic or TCA-cycle genes with our new report of repression of other sugar regulons in the presence of the sugar catabolised by the regulon.
I would recommend a complete rewrite of the manuscript as presented, with a lower stated novelty, clearer language and comprehensible figures.
Reviewer #3
Evidence, reproducibility and clarity
Summary: Microbes grow at different growth rates in different carbon sources. When more than one carbon sources are present in the media microbes often show a preference over certain carbon sources, and 'non-preferred' carbons sources are used only when the preferred carbon source is exhausted in the media, this process called diauxic shift.
Why microbes exhibit such utilization preference over certain carbon sources, is an interesting question in microbiology and evolutionary biology, and the molecular mechanisms that enable microbes to preferentially use one carbon over another is worth investigating. It is intuitive to think that microbes will prefer to use a carbon source that confers maximum growth rate, but when tested experimentally it has been often observed that a carbon source in which microbes grow at sub optimal growth rate is actually preferentially used.
Although the reviewer states that "it has been often observed that a carbon source in which microbes grow at sub optimal growth rate is actually preferentially used“, we are unaware of this work and would appreciate references, particularly for budding yeast. The most systematic study we know, in E. coli by Aidelberg et al., 2014 --- reference 13, concludes that "the faster the growth rate, the higher the sugar on the hierarchy“, the opposite behaviour.
In this study authors demonstrate that budding yeast prefer to use galactose over palatinose, but not over sucrose or fructose where all three sugars can support faster growth rate compared to palatinose. Authors presented data where preferential galactose use and diauxic shift is observed in the growth curve when galactose and palatinose or glucose and palatinose combinations were used.
No diauxic shift was observed in the growth curve when fructose-palatinose, or sucrose-palatinose combination were used. In fructose-palatinose and sucrose-palatinose combinations growth curves agree more with co-utilization strategies. Authors used transcriptomics and genetic perturbations to decipher the molecular mechanism of such preferential carbon use, and reports preference of galactose over palatinose is achieved by preventing positive feedback of MAL regulon, which encodes the genes for palatinose catabolism. We found this observation is interesting and the molecular mechanism of such preferential carbon use is nicely described in this paper. We also find claims authors made are well supported by experiments. Although catabolite repression and diauxic transitions are known in yeast, and authors also pointed out such previous references, but preferential use of a slower carbon source, i.e. galactose over at least one other fast-growing carbon is interesting enough for publication. We would like to support the publication of this article, but we have major concerns about the data analysis and data presentation. Authors must address our concerns which are mentioned below.
Major comments:
- This study mainly hinges on growth rate measurements, but we found growth rates are not properly represented in the figures. Growth curves are always shown in linear scale, which makes it almost impossible to compare fast and slow growth when presented in same plot. All growth curves must be shown on log scale.
We have changed all growth curves to log2 scale, following New et al., 2014, rather than Monod's choice of linear scale that we had originally.
Our conclusions are unaffected.
- Growth rates of the Yeast strain growing individual single carbon sources (galactose, palatinose, sucrose and fructose) should be shown as a figure panel and t-test should be performed to conclude if the individual growth rates are significantly different or not.
We already showed these growth rates in their own panel in Fig. 1B. Following the reviewer's suggestion, we have now added their statistical significance to the caption.
- Growth phase, lag phase, diauxic shift and post shift growth should be clearly shown in figure 2 and 4, each phase should be clearly marked, carbons used in each phase should be mentioned on the plot. Also, the growth curve must be plotted using log scale.
Although we have changed all growth curves to log scale, we decided against include this additional labelling for two reasons. First, we are presenting evidence that some of the growth we observe is diauxic and labelling the curves as diauxic before we discuss this evidence undermines that discussion. Second, any further labels would clutter the figures, and we believe would hinder rather than help the reader.
Instead we changed the colour scheme and the boldness of the diauxic growth curves in Fig. 2, which we hope the reviewer agrees adds the clarity they felt was missing.
- Authors has taken in account that MAL12 gene overexpression causes long lag when cells need to switch to maltose from glucose, and shown deletion of IMA1 decreases the lag with subsequent 2% growth rate increase in palatinose. How significant is this increase?
We have confirmed the statistical significance through a t-test and added the results to the caption of Fig. 6C.
- Authors have an interesting observation that in sucrose-palatinose and fructose palatinose combinations, most probably co utilization of the carbons is taking place. Authors should discuss this in more details. In galactose-palatinose scenario intracellular galactose-based repression of gal80 and subsequent lack of feed forward of the Mal regulon is expected to stop co-utilization of palatinose. As authors have RNA seq data, can they make predictions for other carbon pairs, where sequential utilization can occur based on their model?
We agree and have added more discussion of the fructose- and sucrose-palatinose mixtures to the Discussion and a new figure, Fig. S5.
Our RNAseq data reveals the difference in gene expression caused by an active versus an inactive GAL regulon. In Fig. S11, we show that the hexose transporters HXT2 and HXT7 are down regulated in 0.1% fructose when the GAL regulon is active, perhaps implying that cells are able to prioritise galactose over other hexoses. Nevertheless, to predict if particular carbon sources are therefore favoured, we would need to know whether cells use specific hexose transporters to drive growth on different carbon sources, which has been little investigated.
Minor comments:
- In figure 5, authors attempted to summarize the model, which is informative, but it will be more useful for non-specific reader if a cell-based cartoon, with transports on surface and catabolic enzymes inside is also added.
We have re-designed Fig. 5, now Fig. 7, following this suggestion and agree it improves clarity.
In this schematic diagram, switch from galactose (blue line) to red line (palatinose) shows a mixed color zone, it's a bit confusing, as this represents a bi-stable state. Authors should clearly comment on possibility of biostability while discussing their proposed mechanism.
In the new figure, this part has been removed.
- The author may want to put their work in the context of other recent observations that bacteria do not try to maximize their growth rates in many conditions. Fast growth is often associated with expansive tradeoffs, and a carbon source which confers fast growth rate may confer selective disadvantage. Thus, there are evolutionary benefits of sub-optimal growth, which could be discussed in the manuscript. In this regard a recent study (bioRxiv (2023) doi:10.1101/2023.08.22.554312.) has established the link between resource allocation strategies, growth rates and tradeoffs, which may be taken in account while discussing. Are there any known tradeoffs, when galactose is used over palatinose and which is not the case sucrose or fructose?
This is an interesting reference looking at growth on a single carbon source. We are unaware of similar tradeoffs relevant to our study. For example, we see little evidence for a constraint on the proteome because in a strain with a constitutively active GAL regulon there is no change in phenotype if we delete the genes for the three highly expressed GAL enzymes (Fig. S6B). Nevertheless and as we state in the penultimate paragraph of the Discussion, we agree that such a constraint must exist, although perhaps this constraint is ecological.
Referees cross-commenting
As other reviewers pointed out, this study has merit and addressed interesting questions, but needed to be written well in a more understandable form, we agree with this assessment. Also figures must be made much clearer, as all of the reviewers pointed out. In summary, this is an interesting study, but needs some work before publication.
Significance
General assessment: Strength and limitations:
This study addressed an interesting question regarding resource preference and growth rate optimization in microbes. This is an important question in the field. Study is well designed and claims are backed up with experimental results. One of the limitations of the study is lack of predictability. Authors explained the mechanism for one pair of carbon sources, but how applicable that will be in general is not clear.
We would argue that one of our important findings is to demonstrate that the scientific community is missing the information needed to make such predictions. We provide a counter example to the generally accepted belief that accurate predictions can be made using growth rates. Our work poses the question: what then are the physiological variables required to predict how a cell will consume a pair of carbon sources?
Advance: This study helps to advance our knowledge. Their observation regarding preferential utilization of a carbon source which supports slower growth over a carbon source which can support faster growth, and the molecular mechanism provided will help researchers to understand resource allocation strategies better.
Audience: Microbiology, systems biology, evolutionary biology, fermentation and bio process engineering research.
Reviewer expertise: Biochemistry, systems biology, metabolic strategies and tradeoffs in microbes, microbial ecology.
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Referee #3
Evidence, reproducibility and clarity
Review of the paper by Yu Huo et al.
Summary:
Microbes grow at different growth rates in different carbon sources. When more than one carbon sources are present in the media microbes often show a preference over certain carbon sources, and 'non-preferred' carbons sources are used only when the preferred carbon source is exhausted in the media, this process called diauxic shift. Why microbes exhibit such utilization preference over certain carbon sources, is an interesting question in microbiology and evolutionary biology, and the molecular mechanisms that enable microbes to preferentially use one carbon over another is worth investigating. It is intuitive to think that microbes will prefer to use a carbon source that confers maximum growth rate, but when tested experimentally it has been often observed that a carbon source in which microbes grow at sub optimal growth rate is actually preferentially used. In this study authors demonstrate that budding yeast prefer to use galactose over palatinose, but not over sucrose or fructose where all three sugars can support faster growth rate compared to palatinose. Authors presented data where preferential galactose use and diauxic shift is observed in the growth curve when galactose and palatinose or glucose and palatinose combinations were used.
No diauxic shift was observed in the growth curve when fructose-palatinose, or sucrose-palatinose combination were used. In fructose-palatinose and sucrose-palatinose combinations growth curves agree more with co-utilization strategies. Authors used transcriptomics and genetic perturbations to decipher the molecular mechanism of such preferential carbon use, and reports preference of galactose over palatinose is achieved by preventing positive feedback of MAL regulon, which encodes the genes for palatinose catabolism. We found this observation is interesting and the molecular mechanism of such preferential carbon use is nicely described in this paper. We also find claims authors made are well supported by experiments. Although catabolite repression and diauxic transitions are known in yeast, and authors also pointed out such previous references, but preferential use of a slower carbon source, i.e. galactose over at least one other fast-growing carbon is interesting enough for publication. We would like to support the publication of this article, but we have major concerns about the data analysis and data presentation. Authors must address our concerns which are mentioned below.
Major comments:
- This study mainly hinges on growth rate measurements, but we found growth rates are not properly represented in the figures. Growth curves are always shown in linear scale, which makes it almost impossible to compare fast and slow growth when presented in same plot. All growth curves must be shown on log scale.
- Growth rates of the Yeast strain growing individual single carbon sources (galactose, palatinose, sucrose and fructose) should be shown as a figure panel and t-test should be performed to conclude if the individual growth rates are significantly different or not.
- Growth phase, lag phase, diauxic shift and post shift growth should be clearly shown in figure 2 and 4, each phase should be clearly marked, carbons used in each phase should be mentioned on the plot. Also, the growth curve must be plotted using log scale.
- Authors has taken in account that MAL12 gene overexpression causes long lag when cells need to switch to maltose from glucose, and shown deletion of IMA1 decreases the lag with subsequent 2% growth rate increase in palatinose. How significant is this increase?
- Authors have an interesting observation that in sucrose-palatinose and fructose palatinose combinations, most probably co utilization of the carbons is taking place. Authors should discuss this in more details. In galactose-palatinose scenario intracellular galactose-based repression of gal80 and subsequent lack of feed forward of the Mal regulon is expected to stop co-utilization of palatinose. As authors have RNA seq data, can they make predictions for other carbon pairs, where sequential utilization can occur based on their model?
Minor comments
- In figure 5, authors attempted to summarize the model, which is informative, but it will be more useful for non-specific reader if a cell-based cartoon, with transports on surface and catabolic enzymes inside is also added.
In this schematic diagram, switch from galactose (blue line) to red line (palatinose) shows a mixed color zone, it's a bit confusing, as this represents a bi-stable state. Authors should clearly comment on possibility of biostability while discussing their proposed mechanism. 2. The author may want to put their work in the context of other recent observations that bacteria do not try to maximize their growth rates in many conditions. Fast growth is often associated with expansive tradeoffs, and a carbon source which confers fast growth rate may confer selective disadvantage. Thus, there are evolutionary benefits of sub-optimal growth, which could be discussed in the manuscript. In this regard a recent study (bioRxiv (2023) doi:10.1101/2023.08.22.554312.) has established the link between resource allocation strategies, growth rates and tradeoffs, which may be taken in account while discussing. Are there any known tradeoffs, when galactose is used over palatinose and which is not the case sucrose or fructose?
Referees cross-commenting
As other reviewers pointed out, this study has merit and addressed interesting questions, but needed to be written well in a more understandable form, we agree with this assessment. Also figures must be made much clearer, as all of the reviewers pointed out. In summary, this is an interesting study, but needs some work before publication.
Significance
General assessment: Strength and limitations: This study addressed an interesting question regarding resource preference and growth rate optimization in microbes. This is an important question in the field. Study is well designed and claims are backed up with experimental results. One of the limitations of the study is lack of predictability. Authors explained the mechanism for one pair of carbon sources, but how applicable that will be in general is not clear.
Advance: This study helps to advance our knowledge. Their observation regarding preferential utilization of a carbon source which supports slower growth over a carbon source which can support faster growth, and the molecular mechanism provided will help researchers to understand resource allocation strategies better.
Audience: Microbiology, systems biology, evolutionary biology, fermentation and bio process engineering research.
Reviewer expertise: Biochemistry, systems biology, metabolic strategies and tradeoffs in microbes, microbial ecology.
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Referee #2
Evidence, reproducibility and clarity
Summary: The authors have used microtiter plates to produce growth profiles on combinations of different sugars. From this data they have evaluated whether the sugars are co-consumed or if there is a preference for either sugar, seen as a diauxic shift. They found diauxie between galactose and the disaccharide palatinose, but co-consumption between palatinose and fructose. They further used strains with perturbations in their GAL regulon to attempt to explain this discrepency.
Major comments: I unfortunately found a large portion of the present manuscript unintelligable.
Firstly, figures were incorrect to the point I could not dechiffre them: Figure 2A-C have black solid and dashed lines in the legend that are not found in the graph, instead there are orange and blue dashed lines in the graph with no legends. Figure 4C has no description of the y-axis. The growth rates in Figure 1C are very hard to follow, and there are definitely local maxima in both the blue and green profiles that are not being discussed (at 15-20 h). I cannot evaluate the conclusions drawn from the data until these issues have been resolved.
Secondly, the language in the Results and Discussion sections is confusing. Alternating between present and imperfect tense as well as active and passive form makes it hard to distinguish the authors own results from literature findings (Results are usually written in passive, imperfect tense). Examples are found on lines 24, 29, 37-38, 59, 84, 131, and 165.
The authors also do not consider the differences and similarities in catabolic pathways for assimilation of galactose, fructose and palatinose. Even if they do not see a reason to continue that as a possible explanation for the co-consumption between fructose and palatinose a discussion of why it is disregarded would not be out of place here.
Significance
There is some novelty to the authors findings, but I would argue it is being overstated in the present manuscript. Some examples of studies looking at catabolite repression, the main cause of diauxie, of sugars other than glucose can be found in: Simpson-Lavy and Kupiec (2019), Gancedo (1998), Prasad and Venkatesh (2008) and Borgstrom et al (2022).
I would not say that the field would be significantly advanced by the publication of this manuscript, and the authors have themselves not explained the application of futhering the understanding palatinose metabolism in yeast. As mentioned above, the catabolite repression potential of galactose is already known, it just hasn't been shown for palatinose specifically before.
I would recommend a complete rewrite of the manuscript as presented, with a lower stated novelty, clearer language and comprehensible figures.
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Referee #1
Evidence, reproducibility and clarity
Summary: Hui et al. tackle a crucial question in biology: what factors influence the preference for carbon sources in yeasts? They reveal that the growth rate on palatinose exceeds that on glucose, despite palatinose utilization being repressed in the presence of glucose. Consequently, the favored carbon source does not necessarily align with the one supporting the fastest growth rate. The study also delves into potential regulatory mechanisms governing carbon source preference and dismisses certain existing theories, such as the general carbon flux sensing mechanism proposed by Okano et al. [25].
Major comments: None
Minor comments:
- The authors suggest that a higher growth rate implies a higher glycolytic flux (l63), a crucial assumption underpinning their interpretation of the absence of a "general carbon flux sensing mechanism" (l65). To substantiate this significant conclusion, they could calculate the extracellular uptake fluxes (based on the time-course concentrations of biomass and substrates).
- The accumulation of certain by-products is known to be toxic, reducing cellular growth rate (e.g., acetate DOI: 10.1038/srep42135, ethanol DOI: 10.1016/B978-0-12-040308-0.50006-9, etc.), while they can also enhance growth under specific conditions (e.g., acetate DOI: 10.15252/embj.2022113079). Considering this is crucial to rule out certain hypotheses, such as the possibility that a by-product produced during growth on the first carbon source would not modulate growth on the second carbon source, potentially influencing the growth rate differentially in each phase. Although the authors use mutant strains to eliminate the role of some C2 compounds (acetate and ethanol), alternative pathways could be implicated in the (co-)utilization of these by-products. This aspect should be discussed, and ideally, the authors could quantify the time-course concentrations of by-products to assess their potential role.
- The general flux-sensing regulatory mechanism proposed by Okano et al. [25], which has been dismissed by this study, has recently been questioned, as discussed in DOI: 10.15252/embj.2022113079. This aspect should be included in the discussion.
Significance
Strengths & limitations: The work is robust, and the experiments in the study have been appropriately designed and conducted. The primary question of this study has been tackled using a combination of experimental and computational methods to thoroughly assess various regulatory and functional aspects. However, there are gaps in the data that could enhance key conclusions, notably the absence of glycolytic flux measurements. Moreover, further evidence is needed to substantiate the assertion that by-products do not play a role in carbon source preference.
Advance: This study represents a significant step forward in comprehending the nutritional strategy of microbes. The authors demonstrate that the preferred carbon source may not necessarily be the one supporting the fastest growth rate. Furthermore, they dismiss certain theories that have been proposed to explain the growth strategy of microbes on mixed carbon sources.
Audience: By addressing a fundamental question in life science, this work is important in the field of biology in general and of particular interest in systems biology, biotechnology, synthetic biology, and health. Consequently, it will be of interest to a broad audience.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
This well done and interesting paper examining the connection between TXNIP and GDF15. The main thrust is that TXNIP upregulation chemotherapies, such as Oxa, results in an a down regulation of GDF15 early in tumorigenesis. Later in tumorigenesis, TXNIP upregulation is less pronounced, elevating GFP15 resulting in a blockage of tumor suppressive immune responses. Generally the work is convincing. For example, it's clear that TXNIP is up regulated by Oxa in an ROS and MondoA-dependent manner. Likewise its quite clear TXNIP loss reads to an upregulation of GDF15. However, it's also quite clear that Oxa suppresses GDF15 in a manner that appears to be completely independent of TXNIP. The writing in the paper implies strongly that there is a mechanistic connection between TXNIP and GDF15, but no experiments investigate this possibility.
We feel this is very fair and is reflective of a) perhaps an overemphasis of the TXNIP knockout observation and supportive tissue data, which suggests a relationship but not a mechanistic understanding b) an underemphasis of the data in Figure 3 that shows a decrease in GDF15 after oxaliplatin treatment in TXNIP knockout lines.
We have addressed these concerns in several ways:
- We have carried out knockdown experiments using siRNA for ARRDC4, which we felt, given its regulation by MondoA and ROS, and homology to TXNIP, may also regulate GDF15. This was found to be the case and may explain the data in Figure 3. At the very least it shows that other factors involved in oxidative stress management may have similar impacts – a form of functional redundancy. Lines 553-559 “Finally, given our previous data (Figure S4) we looked to assess the role of ARRDC4 on GDF15 expression. In the absence of oxaliplatin, knocking down ARRDC4 in DLD1 and HCT15 cells drove an increase in GDF15. When challenged with oxaliplatin, both ARRDC4 and TXNIP expression increased and GDF15 decreased. When the ARRDC4 knockdown was challenged TXNIP increased further and GDF15 decreased further (Figure S6G-J). Given the common regulatory pathways and homology between TXNIP and ARRDC4, and their similar functional roles, we suggest these data are evidence of redundancy within this system. “
We have included some context in the discussion:
Lines 930-933: “Further support for both TXNIP and ARRDC4’s role in regulating GDF15 after the induction of ROS comes from a pan cancer meta-analysis assessing the impact of metformin (which has been reported to inhibit ROS) on gene expression. Here the top two downregulated genes were TXNIP and ARRDC4 and the top four upregulated genes were DDIT4, CHD2, ERN1 and GDF1572
We have tempered the text:
Lines 522-524 “It is important to note however that here we saw clear evidence that TXNIP was not solely responsible for the downregulation of GDF15 post oxaliplatin treatment, with decreased levels seen in knockout lines (Figure 3C-G, S5E).”
Lines 926-929 “It must be stressed that these data do not place TXNIP as the sole regulator of GDF15, for example ARRDC4 can also be seen to regulate GDF15. We envisage TXNIP as one of a number of ROS-dependent GDF15 regulators, with this redundancy potential evidence of the importance of this regulatory framework.”
We have carried out additional analysis detailed in the discussion and in Figure S12 which suggests TXNIP impacts MYC function, as reported elsewhere (detailed below). For ease, the key paper can be accessed through this link https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001778
Lines 934-956: “The main shortcoming of this paper is the lack of mechanistic understanding linking TXNIP to GDF15. There are 650 transcription factors that have been shown, or are predicted, to bind to GDF15 promoter and/or enhancer regions. By assessing our list of differentially expressed genes (Suppl. Table 1-2) for the presence of these factors we identified 6 GDF15 binding TFs that show significantly decreased expression after oxaliplatin treatment in both cell lines (ATF4, MYC, SREBF1, PHB2, HBP1, KLF9). There was only one, MYC, that was downregulated by oxaliplatin treatment (validated; Figure S12A), and with this downregulation partially being rescued in a matched TXNIP knockout line (Figure S12B). We then observed that c-myc has been shown or is predicted to bind to promoter/enhancer regions of the top five transcriptomic and proteomic differentials in TXNIP knockout lines, including TXNIP itself (apart from C16orf90). Even with c-myc’s promiscuity (binds to 10-20% of all promoters/enhancers) this may be suggestive of a specific relationship. Finally, when looking at the correlations between these 6 TFs and TXNIP and GDF15 in the TCGA COAD dataset, MYC has the greatest and most significant negative correlation to TXNIP (r=-0.4631 p=1.42e-28) and the greatest and most significant positive correlation to GDF15 (r=0.4653 p=7.32e-29). ATF4 and PHB2 are the other TFs in the list, that show the same significant trends (Figure S12C), and therefore may play a role in the TXNIP-independent oxaliplatin-dependent regulation of GDF15. Further exploration of these additional TFs is outside the scope of the current manuscript.
MYC’s role in bridging from TXNIP to GDF15 is further supported by a recent paper which shows that TXNIP is “a broad repressor of MYC genomic binding” and that “TXNIP loss mimics MYC overexpression”73. Furthermore, the inter-dependent regulatory relationship between MondoA, TXNIP, and MYC has been seen in a variety of models74, whilst the impact of NAC on MYC-dependent pathways has been seen in lymphoma75. These studies lend credence to the idea that MYC is the most likely TXNIP-regulated TF that regulates GDF15 in our systems.”
It seems equally likely that TXNIP and GDF15 represent independent parallel pathways. Even if TXNIP is a direct regulator of GDF15, it's also clear that other "factors" up or down-regulated by Oxa also contribute to the regulation of GDF15. These are not explored and even though TXNIP is highly regulated genes shown Figure 2 that are not identified or discussed that may also be contributing to GDF15 regulation.
As mentioned above, the new data suggests that at least one other factor, ARRDC4, can regulate GDF15 (changes upon oxaliplatin treatment) and that MYC is a potential mechanistic bridge between TXNIP and GDF15. Whilst assessing for the transcription factor that may link TXNIP and GDF15 we found an additional 5 TXNIP-independent factors (ATF4, PHB2, SREBF1, HBP1, KLF9) that bind to GDF15 promoter/enhancer regions and are downregulated post-oxaliplatin treatment. When looking at correlations between these factors and GDF15 in the TCGA COAD dataset, ATF4 and PHB2 correlate most closely with GDF15 (when removing MYC) and so we would cautiously suggest that these may be the most pertinent. This data is now included.
Further, the experiments treating PBMCs with conditioned media contain other cytokines/factors, in addition to GDF15, that likely also contribute the observed effects on the different immune cells understudy. The conditioned media from GDF15 knock out cells are a good experiment, but the media is not rigorously tested to see what other cytokines/factors might have also been depleted.
The TXNIP knockout media is the same as that analysed by mass spec and the protein array, however as the reviewer states there is no analysis (excluding assessing for the presence or absence of GDF15) on the double knockout supernatant or over-expression supernatant. The text has been corrected as follows:
Lines 675-679. “In light of other secreted factors being seen to be regulated by TXNIP (Figure 3A-B), we included double knockouts (TXNIP and GDF15 knockout; GTKO) as well as an overexpression system (GDF15a) to test for GDF15 specific effects. However, we do not know the impact of knocking out or overexpressing GDF15 on the broader secretome.”
Perhaps a GDF15 complementation experiment would help here.
We felt that the association between GDF15 and Treg induction is reasonably well established in the literature, and so once we saw that the supernatant from our GDF15 overexpression system (+/- CD48 blockade) complemented what has already been demonstrated, we were encouraged. However we needed more – hence the TCGA data and IHC staining.
Finally, even if completely independent, a TXNIP/GDF15 ratio does seem to have utility in determining chemo-therapeutic response.
We agree – we feel that conceptually this may be the most interesting part of the project and is an example of what can be done with these tools.
Other major points: 1. Please label the other highly regulated genes shown in Fig 2A and B. Might they also explain some of the underlying biology. This could be on the current figures or in a supplement, though the former is preferred.
Many thanks – we have done this.
Please address why the TXNIP induction is so much less in patient-derived organoids vs. cell line spheroids (Fig S2). By the western blots, TXNIP inductions in the organoids looks quite modest. Further, the text is quite cryptic and implies that the "upregulation" is similar in both organoids and spheroids.
You are absolutely correct. Many apologies, the wording has changed:
Lines 320-323 “In both models we observed the upregulation of TXNIP mRNA (Figure S2E-H) and TXNIP protein (Figure S2I-L) after oxaliplatin treatment, with spheroids showing greater responsiveness. This difference is most likely due to culturing conditions or differences in the number and location of cycling cells.”
We have two possible explanations. Firstly the media in which the organoids are cultured contains a lower glucose concentration than that used for the spheroids. As per some of our new data (Figure S3 – later in the rebuttal), the upregulation of TXNIP after oxaliplatin is glucose dependant, with lower concentrations resulting in less of a differential. Secondly, while restricted to the periphery, the Ki67 signal in DLD1 spheroids is quite pronounced indicating that, within the outer zone, many cells (probably the majority) are in the S/G1/G2 phase of the cell cycle at any given point in time (figure below this text).
This is not the case for the organoids, where the Ki67 (and pCDK1) signal is quite weak, and only sporadic in the outer layer. So we believe that there are many more rapidly cycling cells in the most drug-exposed layer of spheroids when compared to the comparable region in organoids. As the spheroid cells are likely cycling more rapidly, they would also be expected to be more adversely affected by the drug within the finite drug treatment window. Indeed, these spheroids grow large, and quite quickly. If the organoid cells are cycling more slowly and if, within the cell layer most exposed to drug, these cycling cells are less abundant, then the TXNIP response may well be subdued in organoids when compared with spheroids.
We have decided to not include the above (full) explanation and figure within the new draft, as we feel it may distract from the central message. However do let ourselves and the editor know if you disagree.
What was the rationale of performing the MS experiment on control and TXNIP KO DLD1 cells in the absence of oxaliplatin? The other experiments in Fig 3 clearly show that Oxa can repress GDF15 even in the absence of TXNIP, which implicates other pathways. ARRDC4? Or something else? This needs to be addressed.
We adopted this approach because of the order in which the assays occurred and technical issues surrounding the use of post-oxaliplatin treated supernatant. By the time we moved to the proteomics we had already identified, and validated, GDF15 as our number one candidate (initially from the protein array), in terms of response to oxaliplatin and dependence on TXNIP. This led us to the next stage of the project – to assess the environmental impacts of this factor in vitro before validation in situ. To do this, aware of the issue of contaminated recombinant GDF15, we decided early on to use cell line supernatant. We carried out some pilot studies on immune cells using supernatant from oxaliplatin treated cell lines and we had several technical issues (difficulty in determining the correct controls, immune cell death…). This changed the emphasis to using supernatant from knockout models rather than knockout and treated models. Before we began these assays in earnest we wanted to assess exactly what was enriched in TXNIP knockout supernatant and so we turned to proteomics. When this further validated GDF15, we then generated GDF15 and TXNIP/GDF15 knockouts to further elucidate GDF15’s role specifically.
With regards the other pathways, as you correctly predicted, ARRDC4 also appears to regulate GDF15 – many thanks for helping with this line of enquiry. Please see earlier in the rebuttal for more details and the data.
The data in 3J with the MondoA knockdown is not convincing. The knockdown is weak and TXNIP goes down a smidge. Agree that GDF15 goes up
We agree. We have re-run this and pooled the densitometry data – see new figure below (Panel 3J).
Minor points 1. Line 79. The "loss" of TXNIP/GDF15 axis is confusing. It's really loss of TXNIP and upregulation of GDF15, right?
Absolutely - corrected to responsiveness.
Lines 144-147: “Intriguingly, multiple models including patient-derived tumor organoids demonstrate that the loss of TXNIP and GDF15 responsiveness to oxaliplatin is associated with advanced disease or chemotherapeutic resistance, with transcriptomic or proteomic GDF15/TXNIP ratios showing potential as a prognostic biomarker.”
Please provide an explanation for the different stages in tables 1 and 2. This will likely not be clear to non-clinicians.
Many thanks. The following has been added at the bottom of the second table.
Lines 304-309: “The TNM staging system stands for Tumor, Node, Metastasis. T describes the size of the primary tumor (T1-2; 5cm). N describes the presence of tumor cells in the lymph nodes (N0; no lymph nodes. N1-3 >0). M describes whether there are any observable metastases (M0; no metastases. M1; metastases). The clinical stage system is as follows: I/II; the tumor has remained stable or grown, but hasn’t spread. III/IV; the tumor has spread, either locally (III) or systemically (IV).”
Line 231 should probably read ...cysteine (NAC), a reactive oxygen species inhibitor,
Many thanks - corrected
Line 247, should be RT-qPCR I think.
Many thanks - corrected
Lines 343-345. I don't quite understand the wording. Does this mean to say that 675 soluble proteins were not changed between the condition media from both cell populations?
Yes, exactly this. We have removed as this is superfluous and confusing.
The data in FigS1 B and C don't seem to reach the standard p value of > 0.05
Very true – we have rewritten the text to make sure the reader knows there is no significance.
Lines 269-271. “High levels of both the protein (significantly) and the transcript (not significantly) were seen to be associated with favourable prognosis (Figure 1G,H and S1B,C).”
**Referee Cross-Commenting**
cross comment regarding referees 2 and 3 above. I'm am convinced that TXNIP is at least contemporaneously upregulated with GDF15 downregulation. However, the strong implication from the writing is that TXNIP regulates GDF15 directly. I agree with the comment above that exploring mechanisms may be open-ended especially as TXNIP has been implicated in gene regulation by several different mechanism. I'd be satisfied with a more open-minded discussion of potential mechanisms by which TXNIP may repress GDF15 and the possibility of other parallel pathways that likely contribute to GDF15 repression.
Many thanks, this is a generous and understanding approach. As described above we have carried out extra analysis and have found 6 differentially regulated transcription factors which have been shown to bind GDF15 promoter or enhancer regions with 1 of these, MYC, being significantly affected in the TXNIP knockout cell lines, which in combination with supportive literature suggests a degree of TXNIP dependence. We have also identified ARRDC4 as an additional regulator of GDF15 – again please see above.
Reviewer #1 (Significance (Required)):
This is an interesting contribution but the mechanistic connection between GDF15 and TXNIP is relatively weak. That said, even as independent variables they do seem to have utility in predicting therapeutic response.
Many thanks for the comment – we concur. We have reanalysed our data looking for relevant transcription factors (those that bind GDF15 promoter / enhancer regions) finding MYC as the most likely bridge. Please see above.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
The manuscript by Deng et al. investigates a mechanistic link between TXNIP and GDF15 expression and oxaliplatin treatment and acquired resistance. They observe an upregulation in TXNIP expression in the tumors of patients who have previously received chemotherapy. They demonstrate oxaliplatin-driven MondoA transcriptional activity is what underlies the induction of TXNIP. They further demonstrate that TXNIP is a negative regulator of GDF15 expression. Together, oxaliplatin induces MondoA activity and TXNIP expression, resulting in a downregulation of GDF15 expression and consequently decreased Treg differentiation.
Major Comments
- The authors suggest that TXNIP induction and GDF15 downregulation are a common effect of chemotherapies; however, the mechanistic studies were limited to oxaliplatin. The authors should clarify this point through further investigation using other commonly used CRC chemotherapies (5-FU, irinotecan, etc.),or through textual changes. To be clear, I think that the oxaliplatin results could potentially stand on their own but would require additional clarification. For example, regarding the patient samples analyzed in 1D and 4F, which patients received oxaliplatin? Could the analysis of publicly available molecular data be drilled down to just the patients who received oxaliplatin?
Many thanks – this is an excellent point. Firstly, all the patients in 1D and 4F received oxaliplatin. Secondly, we have included new data looking at the impact of other chemotherapies (FOLRIRI, FU-5 and SN-38) on aspects of the study, ultimately finding that these processes (especially an anti-correlation between GDF15 and TXNIP changes upon chemo treatment) appear to be specific to oxaliplatin. These data have been added (Figure S11) and throughout the emphasis has been switched from chemotherapeutic treatment to oxaliplatin treatment.
Lines 796-799: “To check if the pre-treatment GDF15/TXNIP ratio could be used for patients treated with FOLFIRI we performed the same analyses finding no significance (S11A-D). This oxaliplatin specificity was then confirmed by western blot analysis in DLD1 and HCT15 cells treated with 5-FU or SN38 (Figure S11E-F).
Example of change of emphasis from ‘chemotherapy’ to ‘oxaliplatin’ – lines 139-142: “Here, in colorectal adenocarcinoma (CRC) we identify oxaliplatin-induced Thioredoxin Interacting Protein (TXNIP), a MondoA-dependent tumor suppressor gene, as a negative regulator of Growth/Differentiation Factor 15 (GDF15).”
The data demonstrating the induction of MondoA transcriptional activity and TXNIP expression in response to oxaliplatin treatment is quite convincing. The data regarding ROS induction of TXNIP is interesting, especially in light of other studies arguing that ROS limits MondoA activity (PMID: 25332233). Given this apparent disparity, I think that this study could really be strengthened by also investigating other potential mechanisms of oxaliplatin induction of MondoA. In particular, given many studies arguing for direct nutrient-regulation of MondoA, the authors should address the potential for oxaliplatin regulation of glucose availability and a potential glucose dependence of oxaliplatin-induced TXNIP. 2
In line with the previous point, since MondoA activity and TXNIP expression are sensitive to glucose levels, the authors should investigate oxaliplatin-regulation of TXNIP under physiological glucose levels. No need to replicate everything, just key experiments.
We feel these are excellent point and really help the piece – many thanks. We have carried out assays around these points suggested and have included the findings in the new draft – see below.
Lines 332-339: “As such, we went back to first principles and assessed the impact of different concentrations of glucose on TXNIP induction +/- oxaliplatin treatment, finding a concentration dependent effect (Figure S3A). Intriguingly, high glucose alone was able to induce increased TXNIP expression. We then assessed if oxaliplatin treatment drove an increase in glucose uptake, with this seen at concentrations >10mM (Figure S3B). Next, to investigate the impact of glucose metabolism, and consequent ROS generation, on TXNIP induction we treated cells with Antimycin A, an inhibitor of oxidative phosphorylation, finding a complete block in oxaliplatin-induced TXNIP (Figure S3C).”
The authors did a good job of linking TXNIP and GDF15 in untreated conditions; however, the data arguing for oxaliplatin regulation of GDF15 through TXNIP is less clear. For example, in 3B-H, oxaliplatin treatment reduces GDF15 approximately to the same extent in the NTC and TKO cells, potentially in line with a mechanism of downregulation that doesn't involve TXNIP.
A very salient point and completely in line with the other reviewers. We have carried out a few additional analyses mentioned previously in this letter. The most pertinent for this specific point are the experiments around ARRDC4, where we found evidence to suggest that, like TXNIP, it regulates GDF15.
Minor Comments
- The presentation of data in Figure 5 is confusing. A-B include raw cell numbers, whereas C-F show "normalized proliferation." What does this mean? And how was the normalization done?
Apologies for this. Legend test has been corrected to “Normalised proliferation (normalised to MFI from control: i.e. cells treated with supernatant from NTC cells) on gated CD3+CD8+ or CD3+CD4+ cells is shown. n=6. (G-H) Normalised IFNg concentrations (normalised to MFI from control: i.e. cells treated with supernatant from NTC cells) in the supernatant of cells from C-F.” (lines 727-729).
**Referee Cross-Commenting**
cross-comment regarding reviewer #1
I agree with the referee that the link between TXNIP and GDF15 is weak, though as I mentioned before, this is particularly true in the context of oxaliplatin-regulation of TXNIP. I agree that given all the presented data, it is likely that oxaliplatin-regulation of TXNIP and GDF15 are independent. In my opinion, the referee brought up all valid concerns, but this is by far the biggest concern that I share.
We agree that this is the weakest aspect of the paper, however our new analyses plus supportive literature, suggests that the relationship between TXNIP and GDF15 may be mediated by MYC (please see above)
cross-comment regarding reviewer #3
The major concern that this referee addresses is whether another transcription factor supersedes the proposed MondoA/TXNIP induction in regulating GDF15 expression in later stage CRC. In my opinion, this another other concerns of the referee are all valid, but still I remain unconvinced that TXNIP induction underlies the oxaliplatin-regulation of GDF15. I think fleshing out that aspect of the study would potentially help the authors tease apart how this potential MondoA-TXNIP-GDF15 axis is dysregulated later in CRC progression.
This is a great discussion. Interestingly enough, c-myc is seen at higher levels in late stage CRC (Hu X, Fatima S, Chen M, Huang T, Chen YW, Gong R, Wong HLX, Yu R, Song L, Kwan HY, Bian Z. Dihydroartemisinin is potential therapeutics for treating late-stage CRC by targeting the elevated c-Myc level. Cell Death Dis. 2021 Nov 5;12(11):1053. Doi: 10.1038/s41419-021-04247-w. PMID: 34741022; PMCID: PMC8571272.), is seen as an important factor in resistance, and as this review argues, is driven by stress (Saeed H, Leibowitz BJ, Zhang L, Yu J. Targeting Myc-driven stress addiction in colorectal cancer. Drug Resist Updat. 2023 Jul;69:100963. Doi: 10.1016/j.drup.2023.100963. Epub 2023 Apr 20. PMID: 37119690; PMCID: PMC10330748.). So it is very plausible that the partial TXNIP-mediated regulation of myc in early / sensitive CRCs that we may be observing, and has been reported recently (TXNIP loss expands Myc-dependent transcriptional programs by increasing Myc genomic binding Lim TY, Wilde BR, Thomas ML, Murphy KE, Vahrenkamp JM, et al. (2023) TXNIP loss expands Myc-dependent transcriptional programs by increasing Myc genomic binding. PLOS Biology 21(3): e3001778. https://doi.org/10.1371/journal.pbio.3001778) is lost in late stage / resistant CRCs. If this is the case, in effect what we would have observed is the loss of a stress-associated method (TXNIP) of controlling c-myc activity. What makes our collective lives difficult is that, as reported “this expansion of Myc-dependent transcription following TXNIP loss occurs without an apparent increase in Myc’s intrinsic capacity to activate transcription and without increasing Myc levels.” (TXNIP loss expands Myc-dependent transcriptional programs by increasing Myc genomic binding Lim TY, Wilde BR, Thomas ML, Murphy KE, Vahrenkamp JM, et al. (2023) TXNIP loss expands Myc-dependent transcriptional programs by increasing Myc genomic binding. PLOS Biology 21(3): e3001778. https://doi.org/10.1371/journal.pbio.3001778)
Reviewer #2 (Significance (Required)):
Generally speaking the experiments are well controlled and the findings are significant and novel. Though the link between MondoA activity and ROS could be strengthened, and the data could be validated under more physiological settings. Further, the authors should clarify their interpretations so as to not overstate the findings.
Many thanks for the comments. We have taken onboard the need for more physiological settings and have included varying levels of glucose to reflect concentrations in different environments. We have repeated the siMondoA work in 3J strengthening the conclusions wrt its impact on TXNIP and GDF15 expression (see above).
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In this well-written manuscript, the authors show that chemotherapy increases a MondoA-dependent oxidative stress-associated protein, TXNIP, in chemotherapy-responsive colorectal cancer cells. They show that TXNIP negatively regulates GDF-15 expression. GDF-15, in turn, correlates with the presence of T cells (Treg), and inhibits CD4 and CD8 T cell stimulation. In advanced disease and chemo-resistant cancers, upregulation of TXNIP and downregulation of GDF-15 appear to get lost. Based on a somewhat smallish data set, the authors suggest that the pre-treatment GDF-15/TXNIP ratio can predict responses to oxaliplatin treatment. This is a very interesting, novel finding. In general, the quality of the experiments and the data are high and the conclusions appear sound. Still, there are a number of aspects that should still be improved:
The observed loss of the ROS - MondoA - TXNIP - GDF15 axis in chemoresistant and/or metastatic tumors implies that another transcription factor or pathway becomes dominant upon tumor progression. As this switch would be key to better understanding the mechanism underlying the prognostic role of the TXNIP/GDF15 ratio, the authors should at least do data mining followed by ChEA or Encode (or other) analysis to identify transcription factors or pathways that become activated in late-stage/metastatic CRC cells. There is a high likelihood that a transcription factor or pathway involved in GDF-15 upregulation in cancer (e.g. p53, HIF1alpha, Nrf2, NF-kB, MITF, C/EBPß, BRAF, PI3K/AKT, MAPK p38, EGR1) supersedes the inhibitory effect of the MondoA-TXNIP axis. As it stands, the proposed loss of function of the ROS - MondoA - TXNIP - GDF-15 axis is far less convincing than almost all other aspects of the study.
An extremely fair point. We adopted a similar approach to that suggested – as mentioned above, we looked at TFs that bind to GDF15 promoter/enhancer regions and then looked at the presence of these in our transcriptomic data – specifically any evidence of change post oxaliplatin treatment. We found 6 such TFs that were decreased post-oxaliplatin treatment. We then looked for any evidence of TXNIP dependence in these TFs by comparing post-oxaliplatin treatment across NTC and TXNIP knockout lines, when we did this we found only one GDF15 promoter/enhancer binding TF was significantly changed: MYC. We then looked at the relationship between MYC,TXNIP, and GDF15 against the other 5 ‘control’ TFs in the TCGA COAD dataset, we found that MYC showed the strongest correlations, in the ‘correct’ directions. This finding was further backed up in the literature where a TXNIP knockout in a breast cancer model drove c-myc-dependent transcription, whilst c-myc has been observed to increase in later stage CRC patients, is associated with cellular stress and resistance. The collective evidence therefore suggests that MYC is the factor that is initially at least partially regulated by TXNIP, before this regulation is lost in advanced / resistant disease. Continuing on this line, it is likely that the predictive GDF15/TXNIP ratio is at least in part, a measure of c-myc responsiveness to oxaliplatin. All the while we must bear in mind TXNIP-independent oxaliplatin-dependent regulation of GDF15, most likely ARRDC4, as described earlier in this document.
Using pathway analysis software to compare our transcriptomic data from cell lines treated with/without oxaliplatin, the most likely pathways upstream of MYC/c-myc that are negatively affected by chemotherapy are BAG2, Endothelin-1, telomerase, ErbB2-ErbB3 and Wnt/B-catenin. When looking at the comparison of UTC and resistant lines’ transcripts there is only one key component of these pathways which is upregulated in both lines - ERBB3 – which has already been shown to be important in CRC metastasis and resistance (Desai O, Wang R. HER3- A key survival pathway and an emerging therapeutic target in metastatic colorectal cancer and pancreatic ductal adenocarcinoma. Oncotarget. 2023 May 10;14:439-443. doi: 10.18632/oncotarget.28421. PMID: 37163206; PMCID: PMC10171365.). It is highly speculative, but our data suggests the most likely pathway to supersede TXNIP in its (partial) regulation of MYC is the ErbB2-ErbB3 pathway.
My further criticisms are mostly more technical:
Figure 2 I-L: What was the extent of MondoA downregulation achieved by siRNA treatment? Could the effects also be seen with the small molecule mondoA inhibitor SBI-477 (or a related substance)?
This experiment has been repeated. The pooled densiometric data is also now given (please see above).
How do you explain the different GDF-15 levels between untreated non-target control cells (NTC) and TXNIP knock-down cells (TKO) in Figures 3C-F?
The only way to interpret this is that there is a TXNIP-independent pathway regulating GDF15 expression after oxaliplatin treatment, as described this is most likely to be ARRDC4 - the text has been updated to:
Lines 522-524: “It is important to note, however, that we saw clear evidence that TXNIP was not solely responsible for the downregulation of GDF15 post oxaliplatin treatment (Figure 3C-G, S6E).”
In figures 3 E-G the dots for the individual measurements should be indicated. This would be more informative than just the bar graphs.
Completed.
Figure 4C,D and Table 3: Data on the role of GDF-15 in CRC are largely valedictory of previous work (e.g. Brown et al. Clin Cancer Res 2003, 9(7):2642-2650, Wallin et al., Br J Cancer. 2011 May, 10;104(10):1619-27). Therefore, the previous studies should be cited.
Apologies for the oversight and many thanks – this is an excellent addition.
Figure 5C-F: Please indicate in the figure legend how proliferation was assessed.
Many thanks. This was noticed by another reviewer also. We have changed the text to include how the data was normalised: “(C-F) Labelled PBMCs were stimulated with anti-CD3 and anti-CD28 for 4 days in the presence of fresh supernatant from indicated cell lines, before being stained with anti-CD3 and anti-CD8 (C-D) or anti-CD4 (E-F) antibodies and measured by flow cytometry. Normalised proliferation (normalised to MFI from control: i.e. cells treated with supernatant from NTC cells) on gated CD3+CD8+ or CD3+CD4+ cells is shown. n=6. (G-H) Normalised IFNg concentrations (normalised to MFI from control: i.e. cells treated with supernatant from NTC cells) in the supernatant of cells from C-F.” (lines 724-730)
Figure S8E-G: Please indicate the analysed parameters in the graphs. In Figure S8G, the legend just indicates that "aggression of tumour" is dichotomized and plotted. This clearly requires a better definition.
Many thanks, this has been changed as per the below.
Lines 862-868: “(E-G) Receiver operating characteristic (ROC) curves showing area under the curve and p values for the use of GDF15/TXNIP ratio in predicting origin of cell line (E; primary; DLD1, HCT15, HT29, SW48 [n=4] or secondary; DiFi, LIM1215 [n=2]), sensitivity to oxaliplatin (F; parental DLD1 (plus biological repeat), HCT15 [n=3] or resistant DLD1 (plus biological repeat), HCT15 [n=3]), aggression of tumor (G; non-aggressive; The authors propose a novel ROS - MondoA - TXNIP - GDF15 - Treg axis, where MondoA activation, TXNIP up- and GDF-15 downregulation enhance tumor immunogenicity. While this axis has been analyzed in some detail, GDF-15 is not only linked to induction of regulatory T cells. There has been a report showing that GDF-15/MIC-1 expression in colorectal cancer correlates with the absence of immune cell infiltration (Brown et al. Clin Cancer Res 2003, 9(7):2642-2650). The link between GDF-15 and immune cell exclusion has also been confirmed in other conditions, including different cancers (Kempf et al. Nat Med 2011, 17(5):581-588, Roth P et al. Clin Cancer Res 2010, 16(15):3851-3859, Haake et al. Nat Commun 2023, 14(1):4253). A key mechanism is the GDF-15 mediated inhibition of LFA-1 activation on immune cells. As the authors argue that the described pathways turns cold tumors hot in response to oxaliplatin-based chemotherapy, this GDF-15 dependent immune cell exclusion mechanism might be at least as relevant than induction of Treg. Likewise, inhibition of dendritic cell maturation by GDF-15 (Zhou et al. PLoS One 2013, 8(11):e78618) could explain why GDF-15high tumors are immunologically cold. Reviewed in 3
The authors propose that the pathways discovered by them contributed to the "heating up" of the tumor microenvironment after oxaliplatin-based chemotherapy. The authors should thus look in their data sets for the presence of cytotoxic T cells and their possible correlation with TXNIP and GDF-15 levels.
This is a wonderful explanation – many thanks. We have taken the opportunity to assess the impact of GDF15 expression on a variety of T cell markers (Figure S9). In this data a negative association between GDF15 and CD8 CTLs can clearly be seen, as predicted by the reviewer.
Lines 712-717: “To assess if the GDF15-dependent presence of Tregs may be associated with a decrease in activated cytotoxic CD8 T cells, we interrogated the TCGA COAD dataset. We found that low GDF15 tumors carried significantly higher levels of CD8, CD69, IL2RA, CD28, PRF1, GZMA, GZMK, TBX21, EOMES and IRF4 (Figure S9); transcripts indicative of activated cytotoxic CD8 T cells. High GDF15 tumors were enrichment for FOXP3 and, interestingly, RORC (Figure S9). These data support the hypothesis that GDF15 induces Foxp3+ve Tregs which inhibit CD8 T cell proliferation and activation in the TME.”
The paragraph on GDF-15 receptors needs to be corrected: The purported role of a type 2 transforming growth factor (TGF)-beta receptor in GDF-15 signalling had been due to a frequent contamination of recombinant GDF-15 with TGF-beta (Olsen et al. PLoS One 2017, 12(11):e0187349). There have been a number of screenings for GDF-15 receptors that have all failed to show an interaction between GDF-15 and TGF-beta receptors. Instead, only GFRAL was found in these large-scale screenings (Emmerson et al. Nat Med 2017, 23(10):1215-1219, Hsu et al. Nature 2017, 550(7675):255-259, Mullican et al. Nat Med 2017, 23(10):1150-1157, Yang et al. Nat Med 2017, 23(10):1158-1166). The one subsequent report that shows a link between GDF-15, engagement of CD48 on T cells and induction of a regulatory phenotype (Wang et al. J Immunother Cancer 2021, 9(9)) still awaits independent validation. Considering that CD48 lacks an intracellular signaling domain that would be critical for a classical receptor function, I recommend to be more cautious regarding the role of CD48 as GDF-15 receptor. Given the mechanism outlined by Wang et al. the word interaction partner might be more apt. Moreover, an anti-GDF-15 antibody would be a good control for the experiments involving an anti-CD48 antibody in Figure 5.
Thank you so much for this concise and highly informative paragraph. We have changed the text to read:
202-204: “As a soluble protein, GDF15 exerts its effects by binding to its cognate receptor, GDNF-family receptor a-like (GFRAL)44,45,46,47 or interaction partner, CD48 receptor (SLAMF2)43, with the latter still requiring additional verification.”
We would have ideally included an anti-GDF15 antibody in the CD48 assay at the time but didn’t have the foresight. We have included the additional text to temper any conclusions.
Lines 701-711: “Furthermore, when stimulating naïve CD4 T cells in the presence of GDF15 enriched supernatant we were able to both differentiate these cells into functional Tregs and also block the generation of this functionality using an anti-CD48 antibody (Figure 5M-N). However, it must be stressed that the binding and functional impacts of GDF15’s interaction with CD48 still require further verification.”
Cell surface externalization of annexin A1 has been described as a failsafe mechanism to prevent inflammatory responses during secondary necrosis (PMID: 20007579). Thus, I am surprised that the authors list annexin A1 among the immune-stimulatory molecules exposed or released in response to chemotherapy-induced cell death (line 103). Please clarify!
We agree – it shouldn’t be there!! Removed. Many thanks.
**Referee Cross-Commenting**
Regarding the cross-comment by referee 2: In my opinion, the data shown in Figure 3C-H clearly demonstrates that TXNIP can repress GDF-15 expression. I agree that there will likely be further regulators. The GDF-15 promoter is constantly regulated by a multitude of factors (which mostly induce transcription). As downregulation of GDF-15 in response to oxaliplatin is the opposite of the frequently described induction of GDF-15 upon chemotherapy, net effects may always be "smudged" by contributions from different pathways (e.g. by cell stress due to siRNA transfection). Therefore, I believe that the data are as good as it will get. Accordingly, I would not force the authors to further amplify the observed effect.
Many thanks for your understanding – yes, GDF15 has >650 TFs that bind its promoter/enhancer regions – a number we found rather daunting. Happily your comments and those of the other reviewers inspired us to dig and we now have data that is supportive of MYC’s and ARRDC4’s involvement – detailed throughout this reply.
cross comment regarding referee #1: I share the general assessment of the referee and recognize the very detailed mechanistic analysis. To further support the moderate effects of the MondoA knockdown, a small molecule inhibitor like SBI-477 might be useful. (I had already suggested using this inhibitor to support these data.)
Many thanks for the suggestion. We opted to increase the number of siRNA repeats instead – with the data included in Figure 3J (above).
Still, my view on the potential relevance of oxaliplatin-induced, TXNIP-independent downregulation of GDF-15 differs from that of referee 1. In the clinics, platinum-based chemotherapy is one of the strongest inducers of GDF-15 (compare Breen et al. GDF-15 Neutralization Alleviates Platinum-Based Chemotherapy-Induced Emesis, Anorexia, and Weight Loss in Mice and Nonhuman Primates. Cell Metabolism 32(6), P938-950, 2020.DOI:https://doi.org/10.1016/j.cmet.2020.10.023). I was thus surprised that the authors found a pathway, which leads to an outcome that an exactly opposite effect.
This is fascinating that oxaliplatin drives this increase in GDF15 – we were unaware of this paper. Looking at figure 2(H-K), GDF15 is being produced from multiple non-diseased tissues after systemic chemotherapy – even at day 19 post-treatment – this suggests that wrt this study, systemic GDF15 could not be used as a readout of success or otherwise – which is extremely helpful! Thank you.
Thus far, the only obvious reason for reduced GDF-15 secretion upon treatment with cytotoxic drugs was a reduction in tumor cell number due to cytotoxicity.
Please do not discount this. This study was focused on the cells which survived oxaliplatin treatment – the cells which did not were discarded. Our view, given your input, would be a complex picture where in early stages systemic GDF15 goes up, due to off-target effects, but locally levels drop owing to cell death and this, and other, stress-related pathways in the remaining tumor cells.
Still, the authors managed to convince me that the described pathway (ROS - MondoA - TXNIP - GDF-15) exists. (Here, I still largely concur with referee 1.) Moreover, as we have identified some factors required for GDF-15 biosynthesis that could easily interact with TXNIP, I find the proposed mechanism plausible.
Extremely encouraging for us to hear!
Nevertheless, as a downregulation of GDF-15 in response to chemotherapy is hardly ever observed in late-stage cancers, I believe that the observed switch in pathway activation between early- and late-stage cancers might be highly relevant - in particular, as there is so much evidence for platinum-based induction of GDF-15 in late-stage cancer patients. Emphasizing the divergent clinical observations (e.g. by Breen et al.) could thus help to put the finding into perspective.
Very much agree. We did see this phenomenon in LIM1215 cells (Figure 6B) and the resistant lines we generated continually produced higher levels.
Analysing TXNIP-independent mechanisms involved in the oxaliplatin-dependent repression of GDF-15, as suggested by referee #1, will require enormous efforts and resources, and may still turn out to be fruitless. Personally, I would thus be content if the authors just mentioned possible contributions from other pathways upon cancer progression. To me, the described pathway seems to be limited to early-stage cancers, and the actual finding that GDF-15 is downregulated is an interesting observation, irrespective of further involved pathways.
Many thanks – this is extremely fair. Happily we have managed to make some tentative steps forward in highlighting the potential role of MYC, and the suggestion of redundancy wrt ARRDC4, but as you say, much more work needs to be done to fully understand these processes.
cross comment regarding referee #2: I fully agree with the referee that activation of the pathway by further chemotherapeutic drugs could be a valuable addition. As Guido Kroemer´s lab has described oxaliplatin to induce a more immunogenic cell death compared to other platinum-based chemotherapies, even a rather limited comparison between oxaliplatin and cisplatin could be very interesting.
Absolutely agree – extra data on this has been included in Figure S11, which is included earlier in this letter. We also uncovered a meta-analysis using metformin, which has been seen to inhibit ROS, where TXNIP and ARRDC4 are the top two downregulated transcripts whilst GDF15 appears in the top four upregulated. This may suggest that chemotherapeutic immunogenicity, at least through the presence or absence of GDF15, may in part be driven by ROS.
Lines 930-933: “Further support for both TXNIP and ARRDC4’s role in regulating GDF15 after the induction of ROS comes from a pan cancer meta-analysis assessing the impact of metformin (which has been reported to inhibit ROS) on gene expression. Here the top two downregulated genes were TXNIP and ARRDC4 and the top four upregulated genes were DDIT4, CHD2, ERN1 and GDF1572 “
Reviewer #3 (Significance (Required)):
In general, this is a very interesting manuscript describing a cascade of events that may contribute to successful chemotherapy (which likely requires induction of an immune response against dying tumor cells.) The observation that this pathway is only active in early/non-metastatic cancer cells is striking. Unfortunately, the authors cannot explain inactivation of this pathway in later stage/ metastatic/ highly aggressive cancers. Understanding this switch could easily be the most important finding triggered by this report. Therefore, I highly recommend to make some effort in this direction. Strikingly, the authors find that disruption of TXNIP-mediated GDF-15 downregulation is strongly associated with worse prognosis. They also suggest that this ratio could indicate whether a patient will respond to oxaliplatin-based chemotherapy.
This is again very fair – we have posited a potential mechanism for the loss of this switch elsewhere in this reply– one which involves a change in TXNIP-mediated MYC regulation and/or increased HER2-HER3 signalling – but although reasonable for a rebuttal (and publication in that context) we do not feel we have the evidence to include this within the full manuscript.
Altogether, the findings described in manuscript are very novel and may have prognostic (or, in case of the presumed loss of the MondoA - TXNIP - GDF-15 pathway) therapeutic implications. Thus, the manuscript certainly fills various gaps and should be of major interest for cell biologists working on immunogenic cell death, or colorectal cancer, or MondoA, TXNIP or GDF-15. Still, due to its translational implications, it would also be worthwhile reading for a large number of researchers in the oncology field.
We are very grateful for your kind comments.
1 Sinclair, L. V., Barthelemy, C. & Cantrell, D. A. Single Cell Glucose Uptake Assays: A Cautionary Tale. Immunometabolism 2, e200029, doi:10.20900/immunometab20200029 (2020).
2 Yu, F. X., Chai, T. F., He, H., Hagen, T. & Luo, Y. Thioredoxin-interacting protein (Txnip) gene expression: sensing oxidative phosphorylation status and glycolytic rate. J Biol Chem 285, 25822-25830, doi:10.1074/jbc.M110.108290 (2010).
3 Wischhusen, J., Melero, I. & Fridman, W. H. Growth/Differentiation Factor-15 (GDF-15): From Biomarker to Novel Targetable Immune Checkpoint. Front Immunol 11, 951, doi:10.3389/fimmu.2020.00951 (2020).
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Referee #3
Evidence, reproducibility and clarity
In this well-written manuscript, the authors show that chemotherapy increases a MondoA-dependent oxidative stress-associated protein, TXNIP, in chemotherapy-responsive colorectal cancer cells. They show that TXNIP negatively regulates GDF-15 expression. GDF-15, in turn, correlates with the presence of T cells (Treg), and inhibits CD4 and CD8 T cell stimulation. In advanced disease and chemo-resistant cancers, upregulation of TXNIP and downregulation of GDF-15 appear to get lost. Based on a somewhat smallish data set, the authors suggest that the pre-treatment GDF-15/TXNIP ratio can predict responses to oxaliplatin treatment. This is a very interesting, novel finding. In general, the quality of the experiments and the data are high and the conclusions appear sound. Still, there are a number of aspects that should still be improved:
The observed loss of the ROS - MondoA - TXNIP - GDF15 axis in chemoresistant and/or metastatic tumors implies that another transcription factor or pathway becomes dominant upon tumor progression. As this switch would be key to better understanding the mechanism underlying the prognostic role of the TXNIP/GDF15 ratio, the authors should at least do data mining followed by ChEA or Encode (or other) analysis to identify transcription factors or pathways that become activated in late-stage/metastatic CRC cells. There is a high likelihood that a transcription factor or pathway involved in GDF-15 upregulation in cancer (e.g. p53, HIF1alpha, Nrf2, NF-kB, MITF, C/EBPß, BRAF, PI3K/AKT, MAPK p38, EGR1) supersedes the inhibitory effect of the MondoA-TXNIP axis. As it stands, the proposed loss of function of the ROS - MondoA - TXNIP - GDF-15 axis is far less convincing than almost all other aspects of the study.
My further criticisms are mostly more technical:
Figure 2 I-L: What was the extent of MondoA downregulation achieved by siRNA treatment? Could the effects also be seen with the small molecule mondoA inhibitor SBI-477 (or a related substance)?
How do you explain the different GDF-15 levels between untreated non-target control cells (NTC) and TXNIP knock-down cells (TKO) in Figures 3C-F?
In figures 3 E-G the dots for the individual measurements should be indicated. This would be more informative than just the bar graphs.
Figure 4C,D and Table 3: Data on the role of GDF-15 in CRC are largely valedictory of previous work (e.g. Brown et al. Clin Cancer Res 2003, 9(7):2642-2650, Wallin et al., Br J Cancer. 2011 May, 10;104(10):1619-27). Therefore, the previous studies should be cited.
Figure 5C-F: Please indicate in the figure legend how proliferation was assessed.
Figure S8E-G: Please indicate the analysed parameters in the graphs. In Figure S8G, the legend just indicates that "aggression of tumour" is dichotomized and plotted. This clearly requires a better definition.
The authors propose a novel ROS - MondoA - TXNIP - GDF15 - Treg axis, where MondoA activation, TXNIP up- and GDF-15 downregulation enhance tumor immunogenicity. While this axis has been analyzed in some detail, GDF-15 is not only linked to induction of regulatory T cells. There has been a report showing that GDF-15/MIC-1 expression in colorectal cancer correlates with the absence of immune cell infiltration (Brown et al. Clin Cancer Res 2003, 9(7):2642-2650). The link between GDF-15 and immune cell exclusion has also been confirmed in other conditions, including different cancers (Kempf et al. Nat Med 2011, 17(5):581-588, Roth P et al. Clin Cancer Res 2010, 16(15):3851-3859, Haake et al. Nat Commun 2023, 14(1):4253). A key mechanism is the GDF-15 mediated inhibition of LFA-1 activation on immune cells. As the authors argue that the described pathways turns cold tumors hot in response to oxaliplatin-based chemotherapy, this GDF-15 dependent immune cell exclusion mechanism might be at least as relevant than induction of Treg. Likewise, inhibition of dendritic cell maturation by GDF-15 (Zhou et al. PLoS One 2013, 8(11):e78618) could explain why GDF-15high tumors are immunologically cold.
The authors propose that the pathways discovered by them contributed to the "heating up" of the tumor microenvironment after oxalilatin-based chemotherapy. The authors should thus look in their data sets for the presence of cytotoxic T cells and their possible correlation with TXNIP and GDF-15 levels.
The paragraph on GDF-15 receptors needs to be corrected: The purported role of a type 2 transforming growth factor (TGF)-beta receptor in GDF-15 signalling had been due to a frequent contamination of recombinant GDF-15 with TGF-beta (Olsen et al. PLoS One 2017, 12(11):e0187349). There have been a number of screenings for GDF-15 receptors that have all failed to show an interaction between GDF-15 and TGF-beta receptors. Instead, only GFRAL was found in these large-scale screenings (Emmerson et al. Nat Med 2017, 23(10):1215-1219, Hsu et al. Nature 2017, 550(7675):255-259, Mullican et al. Nat Med 2017, 23(10):1150-1157, Yang et al. Nat Med 2017, 23(10):1158-1166). The one subsequent report that shows a link between GDF-15, engagement of CD48 on T cells and induction of a regulatory phenotype (Wang et al. J Immunother Cancer 2021, 9(9)) still awaits independent validation. Considering that CD48 lacks an intracellular signaling domain that would be critical for a classical receptor function, I recommend to be more cautious regarding the role of CD48 as GDF-15 receptor. Given the mechanism outlined by Wang et al. the word interaction partner might be more apt. Moreover, an anti-GDF-15 antibody would be a good control for the experiments involving an anti-CD48 antibody in Figure 5.
Cell surface externalization of annexin A1 has been described as a failsafe mechanism to prevent inflammatory responses during secondary necrosis (PMID: 20007579). Thus, I am surprised that the authors list annexin A1 among the immune-stimulatory molecules exposed or released in response to chemotherapy-induced cell death (line 103). Please clarify!
Referee Cross-Commenting
Regarding the cross-comment by referee 2: In my opinion, the data shown in Figure 3C-H clearly demonstrates that TXNIP can repress GDF-15 expression. I agree that there will likely be further regulators. The GDF-15 promoter is constantly regulated by a multitude of factors (which mostly induce transcription). As downregulation of GDF-15 in response to oxaliplatin is the opposite of the frequently described induction of GDF-15 upon chemotherapy, net effects may always be "smudged" by contributions from different pathways (e.g. by cell stress due to siRNA transfection). Therefore, I believe that the data are as good as it will get. Accordingly, I would not force the authors to further amplify the observed effect.
cross comment regarding referee #1: I share the general assessment of the referee and recognize the very detailed mechanistic analysis. To further support the moderate effects of the MondoA knockdown, a small molecule inhibitor like SBI-477 might be useful. (I had already suggested using this inhibitor to support these data.) Still, my view on the potential relevance of oxaliplatin-induced, TXNIP-independent downregulation of GDF-15 differs from that of referee 1. In the clinics, platinum-based chemotherapy is one of the strongest inducers of GDF-15 (compare Breen et al. GDF-15 Neutralization Alleviates Platinum-Based Chemotherapy-Induced Emesis, Anorexia, and Weight Loss in Mice and Nonhuman Primates. Cell Metabolism 32(6), P938-950, 2020.DOI:https://doi.org/10.1016/j.cmet.2020.10.023). I was thus surprised that the authors found a pathway, which leads to an outcome that an exactly opposite effect. Thus far, the only obvious reason for reduced GDF-15 secretion upon treatment with cytotoxic drugs was a reduction in tumor cell number due to cytotoxicity. Still, the authors managed to convince me that the described pathway (ROS - MondoA - TXNIP - GDF-15) exists. (Here, I still largely concur with referee 1.) Moreover, as we have identified some factors required for GDF-15 biosynthesis that could easily interact with TXNIP, I find the proposed mechanism plausible. Nevertheless, as a downregulation of GDF-15 in response to chemotherapy is hardly ever observed in late-stage cancers, I believe that the observed switch in pathway activation between early- and late-stage cancers might be highly relevant - in particular, as there is so much evidence for platinum-based induction of GDF-15 in late-stage cancer patients. Emphasizing the divergent clinical observations (e.g. by Breen et al.) could thus help to put the finding into perspective. Analysing TXNIP-independent mechanisms involved in the oxaliplatin-dependent repression of GDF-15, as suggested by referee #1, will require enormous efforts and resources, and may still turn out to be fruitless. Personally, I would thus be content if the authors just mentioned possible contributions from other pathways upon cancer progression. To me, the described pathway seems to be limited to early-stage cancers, and the actual finding that GDF-15 is downregulated is an interesting observation, irrespective of further involved pathways.
cross comment regarding referee #2: I fully agree with the referee that activation of the pathway by further chemotherapeutic drugs could be a valuable addition. As Guido Kroemer´s lab has described oxaliplatin to induce a more immunogenic cell death compared to other platinum-based chemotherapies, even a rather limited comparison between oxaliplatin and cisplatin could be very interesting.
Significance
In general, this is a very interesting manuscript describing a cascade of events that may contribute to successful chemotherapy (which likely requires induction of an immune response against dying tumor cells.) The observation that this pathway is only active in early/non-metastatic cancer cells is striking. Unfortunately, the authors cannot explain inactivation of this pathway in later stage/ metastatic/ highly aggressive cancers. Understanding this switch could easily be the most important finding triggered by this report. Therefore, I highly recommend to make some effort in this direction. Strikingly, the authors find that disruption of TXNIP-mediated GDF-15 downregulation is strongly associated with worse prognosis. They also suggest that this ratio could indicate whether a patient will respond to oxaliplatin-based chemotherapy.
Altogether, the findings described in manuscript are very novel and may have prognostic (or, in case of the presumed loss of the MondoA - TXNIP - GDF-15 pathway) therapeutic implications. Thus, the manuscript certainly fills various gaps and should be of major interest for cell biologists working on immunogenic cell death, or colorectal cancer, or MondoA, TXNIP or GDF-15. Still, due to its translational implications, it would also be worthwhile reading for a large number of researchers in the oncology field.
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Referee #2
Evidence, reproducibility and clarity
The manuscript by Deng et al. investigates a mechanistic link between TXNIP and GDF15 expression and oxaliplatin treatment and acquired resistance. They observe an upregulation in TXNIP expression in the tumors of patients who have previously received chemotherapy. They demonstrate oxaliplatin-driven MondoA transcriptional activity is what underlies the induction of TXNIP. They further demonstrate that TXNIP is a negative regulator of GDF15 expression. Together, oxaliplatin induces MondoA activity and TXNIP expression, resulting in a downregulation of GDF15 expression and consequently decreased Treg differentiation.
Major Comments
- The authors suggest that TXNIP induction and GDF15 downregulation are a common effect of chemotherapies; however, the mechanistic studies were limited to oxaliplatin. The authors should clarify this point through further investigation using other commonly used CRC chemotherapies (5-FU, irinotecan, etc.), or through textual changes. To be clear, I think that the oxaliplatin results could potentially stand on their own but would require additional clarification. For example, regarding the patient samples analyzed in 1D and 4F, which patients received oxaliplatin? Could the analysis of publicly available molecular data be drilled down to just the patients who received oxaliplatin?
- The data demonstrating the induction of MondoA transcriptional activity and TXNIP expression in response to oxaliplatin treatment is quite convincing. The data regarding ROS induction of TXNIP is interesting, especially in light of other studies arguing that ROS limits MondoA activity (PMID: 25332233). Given this apparent disparity, I think that this study could really be strengthened by also investigating other potential mechanisms of oxaliplatin induction of MondoA. In particular, given many studies arguing for direct nutrient-regulation of MondoA, the authors should address the potential for oxaliplatin regulation of glucose availability and a potential glucose dependence of oxaliplatin-induced TXNIP.
- In line with the previous point, since MondoA activity and TXNIP expression are sensitive to glucose levels, the authors should investigate oxaliplatin-regulation of TXNIP under physiological glucose levels. No need to replicate everything, just key experiments.
- The authors did a good job of linking TXNIP and GDF15 in untreated conditions; however, the data arguing for oxaliplatin regulation of GDF15 through TXNIP is less clear. For example, in 3B-H, oxaliplatin treatment reduces GDF15 approximately to the same extent in the NTC and TKO cells, potentially in line with a mechanism of downregulation that doesn't involve TXNIP.
Minor Comments
- The presentation of data in Figure 5 is confusing. A-B include raw cell numbers, whereas C-F show "normalized proliferation." What does this mean? And how was the normalization done?
Referee Cross-Commenting
cross-comment regarding reviewer #1
I agree with the referee that the link between TXNIP and GDF15 is weak, though as I mentioned before, this is particularly true in the context of oxaliplatin-regulation of TXNIP. I agree that given all the presented data, it is likely that oxaliplatin-regulation of TXNIP and GDF15 are independent. In my opinion, the referee brought up all valid concerns, but this is by far the biggest concern that I share.
cross-comment regarding reviewer #3
The major concern that this referee addresses is whether another transcription factor supersedes the proposed MondoA/TXNIP induction in regulating GDF15 expression in later stage CRC. In my opinion, this another other concerns of the referee are all valid, but still I remain unconvinced that TXNIP induction underlies the oxaliplatin-regulation of GDF15. I think fleshing out that aspect of the study would potentially help the authors tease apart how this potential MondoA-TXNIP-GDF15 axis is dysregulated later in CRC progression.
Significance
Generally speaking the experiments are well controlled and the findings are significant and novel. Though the link between MondoA activity and ROS could be strengthened, and the data could be validated under more physiological settings. Further, the authors should clarify their interpretations so as to not overstate the findings.
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Referee #1
Evidence, reproducibility and clarity
This is well done and interesting paper examining the connection between TXNIP and GDF15. The main thrust is that TXNIP upregulation chemotherapies, such as Oxa, results in an a down regulation of GDF15 early in tumorigenesis. Later in tumorigenesis, TXNIP upregulation is less pronounced, elevating GFP15 resulting in a blockage of tumor suppressive immune responses. Generally the work is convincing. For example, it's clear that TXNIP is up regulated by Oxa in an ROS and MondoA-dependent manner. Likewise its quite clear TXNIP loss reads to an upregulation of GDF15. However, it's also quite clear that Oxa suppresses GDF15 in a manner that appears to be completely independent of TXNIP. The writing in the paper implies strongly that there is a mechanistic connection between TXNIP and GDF15, but no experiments investigate this possibility. It seems equally likely that TXNIP and GDF15 represent independent parallel pathways. Even if TXNIP is a direct regulator of GDF15, it's also clear that other "factors" up or down-regulated by Oxa also contribute to the regulation of GDF15. These are not explored and even though TXNIP is highly regulated genes shown Figure 2 that are not identified or discussed that may also be contributing to GDF15 regulation. Further, the experiments treating PBMCs with conditioned media contain other cytokines/factors, in addition to GDF15, that likely also contribute the observed effects on the different immune cells understudy. The conditioned media from GDF15 knock out cells are a good experiment, but the media is not rigorously tested to see what other cytokines/factors might have also been depleted. Perhaps a GDF15 complementation experiment would help here. Finally, even if completely independent, a TXNIP/GDF15 ratio does seem to have utility in determining chemo-therapeutic response.
Other major points:
- Please label the other highly regulated genes shown in Fig 2A and B. Might they also explain some of the underlying biology. This could be on the current figures or in a supplement, though the former is preferred.
- Please address why the TXNIP induction is so much less in patient-derived organoids vs. cell line spheroids (Fig S2). By the western blots, TXNIP inductions in the organoids looks quite modest. Further, the text is quite cryptic and implies that the "upregulation" is similar in both organoids and spheroids.
- What was the rationale of performing the MS experiment on control and TXNIP KO DLD1 cells in the absence of oxaliplatin? The other experiments in Fig 3 clearly show that Oxa can repress GDF15 even in the absence of TXNIP, which implicates other pathways. ARRDC4? Or something else? This needs to be addressed.
- The data in 3J with the MondoA knockdown is not convincing. The knockdown is weak and TXNIP goes down a smidge. Agree that GDF15 goes up
Minor points
- Line 79. The "loss" of TXNIP/GDF15 axis is confusing. It's really loss of TXNIP and upregulation of GDF15, right?
- Please provide an explanation for the different stages in tables 1 and 2. This will likely not be clear to non-clinicians.
- Line 231 should probably read ...cysteine (NAC), a reactive oxygen species inhibitor,
- Line 247, should be RT-qPCR I think.
- Lines 343-345. I don't quite understand the wording. Does this mean to say that 675 soluble proteins were not changed between the condition media from both cell populations?
- The data in FigS1 B and C don't seem to reach the standard p value of > 0.05
Referee Cross-Commenting
cross comment regarding referees 2 and 3 above. I'm am convinced that TXNIP is at least contemporaneously upregulated with GDF15 dowregulation. However, the strong implication from the writing is that TXNIP regulates GDF15 directly. I agree with the comment above that exploring mechanisms may be open-ended especially as TXNIP has been implicated in gene regulation by several different mechanism. I'd be satisfied with a more open-minded discussion of potential mechanisms by which TXNIP may repress GDF15 and the possibility of other parallel pathways that likely contribute to GDF15 repression.
Significance
This is an interesting contribution but the mechanistic connection between GDF15 and TXNIP is relatively weak. That said, even as independent variables they do seem to have utility in predicting therapeutic response.
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Reply to the reviewers
We are sincerely thankful to all reviewers for their work and constructive comments that allowed us to improve the quality of the present manuscript. We are very pleased to announce that we were able to tackle all the raised concerns (except the reporter assays which is a focus of future research for our laboratory, see below), and would like to briefly mention here three major improvements:
1) We have crossed our data in frog with available human ATAC-Seq datasets. We have followed a similar approach to the one we employed in Xenopus, by “subtracting” human osteoblastic ATAC-Seq peaks with human liver, heart and lung. This cross-species validation strategy led to the identification of osteoblast-specific NFRs in human that compare very well to the Xenopus osteoblastic regulatory landscape (new Fig 6). 2) We have included ChIP-Seq data that was performed by Patricia Hanna, a former PhD student from our laboratory, in collaboration with Laurent Sachs and Nicolas Buisine (these three researchers were incorporated as new co-authors). We were planning to publish this ChIP-Seq separately but find that it contributes very well to this manuscript (modified Fig 4, new Fig 7). 3) We have included in situ hybridization analyses on frog and shark performed by David Muñoz, a former PhD student from our laboratory, in collaboration with Melanie Debiais-Thibaud and Catherine Boisvert (these three researchers were incorporated as new co-authors). This data ends nicely the manuscript by providing a biological dimension and by strengthening our evolutionary model (See new Fig 7).
We hope that our responses match the quality criteria of Review Commons and of its affiliated journals, thank you very much once again and kind regards, Sylvain Marcellini
Point-by-point description of the revisions:
Reviewer #1 (Evidence, reproducibility and clarity (Required)): Summary: The paper investigates the genetic mechanisms driving osteoblast differentiation in Xenopus tropicalis, shedding light on bone diseases and early skeletal evolution. Through ATAC-seq analysis, the study identifies osteoblast-specific regulatory regions, confirming their role as osteogenic transcriptional enhancers. A substantial number of these enhancers are conserved in humans, potentially offering insights into skeletal disorders. Additionally, the research highlights an evolutionary perspective by revealing shared regulatory elements between Xenopus tropicalis and the elephant shark, suggesting an ancient origin for mineralized tissues in vertebrates.
Major comments:
Methodology of this paper is kinda vague and the paper seems to be fragmented and not logically organized in a linear fashion.
Reply: We have improved the methodology section. We provide the accession numbers for all raw sequencing datasets generated for this study have been submitted and linked to the NCBI BioProject database (page 27). The paper has been almost completely rewritten and the figures substantially modified. There are now less figure which contain more information presented in a friendly fashion. The logic of the paper is as follows: -Identification of enhancers and promoters (Figs 1 and 2) -Characterization of their nucleotide sequence and TFBSs (Fig 3) -Validation with RNA-Seq and ChIP-Seq (Fig 4) -Global sequence conservation (Fig 5) -Cross validation with ATAC-Seq in human (Fig 6) -Evolutionary model (Fig 7).
Authors could provide evidentiary support that the control tissues are non-mineralized (and exp tissues are) by simple calcein staining. Mineralization occurs during tadpole stage, and calcification of heart and lung tissue in amphibians is not well understood. This will strengthen the attestation of these tissues as controls and provide a useful diagram for exactly what tissues were used.
Reply: We have performed Alizarin reg staining on larval skull, liver, heart and lung and show that, like in mammals, only the calvaria is mineralized (see page 6 and new Supporting Information 1).
There appears to be no mention of osteocytes or other cell types. What measures were taken to ensure that osteoblasts are the principal cell type being described? The reference for bone tissue extraction refers to a cell culture technique in which it is likely no osteocytes would prevail.
Reply: This is an important point to clarify because osteoblasts and their osteocytic progeny harbour a completely different function, physiology and gene expression profile. Our laboratory has studied frog osteocytes in details (Fritz et al, 2018), and we have added the following sentence “Of note, this extraction procedure does not harvest osteocytes that lie embedded within the bone matrix, allowing us to exclusively study osteoblasts. As controls, we also included larval liver, heart and lung following the criteria that they are nonmineralized (Supporting information 1) and unrelated to skeletal tissues”. See page 6.
Minor comments:
Data on conservation of mentioned transcription factors could be easily added (NFAT, etc.)
Reply: We have performed extensive protein alignments showing broad conservation of the osteogenic transcription factors for which we detected binding site enrichment in osteoblast-specific enhancers (see page 10 and new Supporting Information 7).
The data presentation is poor, especially figure 2 and figure 4.
Reply: Following the reviewer’s advice these figures have been eliminated and replaced by Figures 2B and 2C, which, we believe, present the same information in a much clearer and friendly fashion.
Line 115-117: "By focusing on annotated Xt transcription start sites (TSSs), we found that the ATAC-Seq NFR and mononucleosome signals form two distinct clusters," it would be helpful to briefly explain the significance of these two clusters. What does it indicate about the regulatory regions associated with TSSs?
Reply: We have clarified this point by being more explicit: “The first cluster is composed of 5,949 promoters harbouring a robust NFR located immediately upstream of the TSS and flanked by two well-positioned nucleosomes (Fig 1B, left panel), likely corresponding to expressed genes. By contrast, the second cluster contains 16,947 promoters showing weak NFR and diffuse mononucleosome signals (Fig 1B, right panel), and is probably enriched in transcriptionally repressed genes or genes expressed at low levels”. See Page 6.
Line 133-139: When discussing hierarchical clustering and the similarity of NFR landscapes between different tissues, you could provide a sentence or two to speculate on the potential biological implications. For instance, why might heart and lung tissues exhibit more similarity in NFR landscapes compared to osteoblasts and liver?
Reply: This is an interesting point to raise because there is data in the literature supporting our findings. We have modified the following sentence on page 7: “Hierarchical clustering showed that the landscape of the NFRs from heart and lung are more similar to each other than to osteoblasts or liver, which is true both for TSS and non-TSS regions (Fig 1D) and which parallels data obtained in mouse [10]”. Our novel analysis with human ATAC-Seq data also leads to the same finding (Page 13): “Available human liver, heart and lung ATAC-Seq datasets were retrieved, and hierarchical clustering confirmed a higher similarity for heart and lung, and that the osteoblast sample substantially differs from the three other tissues (Supporting information 11), similarly to the situation in frog (Fig 1D) and mouse [10]”.
Line 134: To enhance clarity, you might consider using phrases like "Figure 3A" and "Figure 3B" instead of "Compare Fig 3A and B" to directly refer to the figures in the text.
Reply: This has been corrected has we have deeply improved the figures. See “Globally, the Pearson correlation coefficient was much higher for TSS than non-TSS peaks (Fig 1D), a finding consistent with previous studies showing that, between distinct cell types, histone marks are largely invariable at promoters while they display highly context-dependent patterns at enhancers [6, 7].” on page 7.
Line 142-144: Please consider briefly explaining why you chose liver, heart, and lung tissues as controls. What specific characteristics or functions of these tissues make them suitable for this comparative analysis?
Reply: We now mention “As controls, we also included larval liver, heart and lung following the criteria that they are nonmineralized (Supporting information 1) and unrelated to skeletal tissues.” on page 6.
When discussing the potential function of osteoblastic enhancers in cartilaginous fish, you might briefly mention the role of cartilage in these organisms and how these enhancers may have evolved to regulate cartilage-related processes.
Reply: We agree with the reviewer that this is an exciting point which is of high interest for our laboratory (see for instance our review, Cervantes et al, 2017). However, as we feel that the manuscript is already quite long and has many references, we preferred not to discuss this point and to simply focus on the osteoblast/odontoblast aspect of skeletal evolution.
Ensure that the formatting of your methods section is consistent. For example, consistently use italics for software/tool names (e.g., "SAMtools") and follow a standard format for listing parameters or options used in software/tools.
Reply: We have corrected these points.
Reviewer #1 (Significance (Required)): The paper's significance lies in its elucidation of osteoblast-specific regulatory regions in Xenopus tropicalis. By characterizing these regions and connecting them to specific genes and pathways, the study advances our understanding of osteogenesis. Additionally, the identification of conserved elements across vertebrates provides insights into the deep evolutionary origins of skeletal features, offering a unique perspective on vertebrate evolution. However, one of the main limitations of the study is the lack of extensive experimental validation for the identified regulatory regions, leaving a gap in confirming their functionality.
Reply: Thank you very much again for your helpful and constructive comments. As a functional validation, at least from the chromatin perspective, we have incorporated ChIP-Seq data (Fig 4) with four key histone marks present at active promoters (H3K4me3), active enhancers (H3K4me1), and at active chromatin (H3K27Ac) and repressed chromatin (H3K27me3). This ChIP-Seq was already available in our laboratory (thereby explaining the incorporation of three new co-authors, Dr Hanna, Dr Sachs and Dr Buisine), but we were planning to incorporate it in a different manuscript. However, we feel that it is important to include it in the present paper. Another functional validation lies in the identification of 138 conserved osteogenic enhancers harbouring a NFR both in frog and human (Fig 6). We do not intend to incorporate reporter assays at this stage, as this is a future direction of research for our laboratory, together with CRISPR mutagenesis.
Reviewer #2 (Evidence, reproducibility and clarity (Required)): In this study, Hector Castillo and the coauthors conducted ATAC-seq and RNA-seq analyses across several cell types in Xenopus tropicalis (Xt) to identify regulatory elements specific to osteoblasts. They explored the evolutionary conservation of the osteoblast regulatory elements across species. Their research encompassed the identification of osteoblast-specific regulatory elements through cross-tissue analysis, offering comprehensive insights into tissue-specific regulatory elements. These insights included cell-type-specific chromatin accessibility, biological functions predicted by gene ontology analysis, and potential transcriptional regulators associated with these regions. The cross-species analysis unveiled partial conservation of osteoblast-specific regulatory regions between the Xt and the human genome, with the shared genomic regions being linked to osteoblast-related genes. Additionally, the enriched transcription factors were identified in these regions. The study further explored comparative analyses involving multiple species, providing evolutionary insights into the gene regulatory mechanisms underlying osteoblast identity and pathology.
Major comment All the cross-species analyses in this study were primarily based on sequence conservation. However, since human osteoblast ATAC-seq data, as well as ChIP-seq and Hi-C data, are publicly available (PMID: 35906483), conducting a direct comparative analysis between Xenopus tropicalis (Xt) osteoblast ATAC-seq and human osteoblast ATAC-seq could provide more concrete evidence regarding the conservation of chromatin-accessible regions between these two species. This additional analysis has the potential to significantly strengthen the conclusions drawn in the study.
Reply: We are thankful to the reviewer for this insightful comment that dramatically improved the scope of our work. We have indeed incorporated available ATAC-Seq experiments performed on human osteoblasts (SRR12933513 and SRR12933514), liver (SRR21927033 and SRR21927032), heart (SRR21927531 and SRR21927534) and lung (SRR21927095 and SRR21927098). This is explained on pages 13-14 (results), pages 19-20 (discussion) and pages 22-24 (methods). Hence, we have uncovered 138 conserved enhancers that display an osteoblast-specific NFR both in frog and human (see new Fig 6). As the reviewer states, we believe that our conclusions have been significantly strengthened, allowing us to reformulate the manuscript title which now vehiculates a more functional message. Also, thanks to this comment, we were able to propose a more attractive title for our work: “Cross-validation of conserved osteoblast-specific enhancers illuminates bone diseases and early skeletal evolution”.
Minor comment 1 The authors made use of "annotated human enhancers" in their study; however, the specific definition or source of this annotation was not provided in the manuscript. It is crucial that the authors clarify the criteria or source used for annotating human enhancers to ensure transparency and allow readers to better understand the basis of their analyses and conclusions.
Reply: The reviewer is correct. These “annotated human enhancers” have now completely been eliminated for the study and replaced by the analysis shown in Fig 6 (and see our reply to the previous comment).
Minor comment 2 In relation to the association studies conducted between Xenopus tropicalis (Xt) osteoblast enhancers and genes related to human bone diseases, it's important for the authors to express their statements with caution. While the putative target genes may be potentially regulated by shared regulatory elements between Xt and humans, there exists no direct evidence demonstrating that these regulatory regions are the causative factors behind these diseases. It's worth noting that there are several other open chromatin regions in proximity to these putative target genes. As a result, the shared genomic regions may or may not have a direct relationship with human diseases. To establish a substantial linkage, more in-depth analyses would be required to provide evidence of a pathological connection.
Reply: This is an important point, on page 14 we now state “While the osteoblast-specific regulatory regions reported here might not be directly involved in the aetiology of the aforementioned diseases, their identification considerably improves our understating of the transcriptional control of these genes”.
Minor comment 3 In lines 394 to 397, the authors assert that the enrichment of TWIST1/2 transcription factor binding sites (TFBS) at Xenopus tropicalis (Xt) osteogenic enhancers is a novel finding. However, this claim lacks clarity regarding the novelty of this discovery, given that they reference previous literature (reference 42) that has already demonstrated the involvement of TWIST1/2 in osteoblast differentiation. The authors should provide a more precise explanation of how their specific findings related to TWIST1/2 TFBS enrichment contribute to existing knowledge or differ from previous studies to clarify the novelty of their results.
Reply: We now provide a clearer explanation by mentioning “In this respect, the reported enrichment in TWIST1/2 TFBS (Fig 3 and Supporting information 5) represents the first evidence that TWIST proteins might control the timing of osteoblastic differentiation through binding to hundreds of osteogenic enhancers, a possibility that could be confirmed by ChIP-Seq” on page 19.
Minor comment 4 Depositing the NGS data, including ATAC-seq and RNA-seq datasets, in a public database would be a valuable contribution to the research community.
Reply: Yes, this data has now been made available, see pages 26-27: “Data Availability. The raw sequencing datasets generated for this study have been submitted and linked to the NCBI BioProject database with the following accession numbers: PRJNA1011469 (ATAC-seq), PRJNA1021677 (RNA-seq), and PRJNA1056467 (ChIP-seq)”.
Reviewer #2 (Significance (Required)): The comparative analysis of ATAC-seq among different cell types in Xenopus tropicalis (Xt) provides a broad perspective on cell-type-specific chromatin accessible regions, which is a notable strength of the study. It's worth highlighting that, as far as known, this study represents the first report of ATAC-seq in Xt osteoblasts. However, it's important to acknowledge that the overall message of the study is consistent with previous findings in mammals. For example, the observation that non-transcription start site (TSS) regions were more cell-type-specific, correlating with cell-type distinct gene expressions, aligns with findings in mammalian systems. Additionally, many of the osteoblast regulators predicted from the data are already known osteogenic factors in mammals. The cross-species analysis provides valuable insights into the evolutionary aspects of putative enhancers in osteoblasts. The study identifies conserved gene regulatory regions and putative transcription factors associated with these genomic regions, shedding light on their potential roles in gene regulation. Moreover, the identification of conserved regions possibly linked to human skeletal diseases is a noteworthy aspect of the research, showcasing its strengths. However, it's essential to acknowledge a potential limitation related to this aspect of the study: the analyses conducted so far have been descriptive, primarily focusing on DNA sequence conservation. Given that several osteoblast ATAC-seq datasets from different species are publicly available, a more direct comparison between the Xt dataset and these other datasets could provide a deeper understanding of enhancer conservation and evolution. This study offers valuable resources for researchers in the field of skeletal biology and evolution. The comprehensive analysis of osteoblast-specific regulatory elements in Xenopus tropicalis, along with insights into their conservation and potential roles in human skeletal diseases, provides a foundation for further investigations in this area. Additionally, the evolutionary insights offered by the cross-species analysis contribute to the growing body of knowledge in evo-devo studies, shedding light on the evolution of gene regulatory mechanisms related to osteoblast identity. These resources and insights can serve as a valuable reference and guide for future research endeavors in both bone biology and evolutionary developmental biology. This reviewer specializes in the study of gene regulatory mechanisms in skeletal development and metabolism, primarily utilizing mouse and human tissues.
Reply: Thank you very much again for your helpful and constructive comments.
Reviewer #3 (Evidence, reproducibility and clarity (Required)): Summary Starting with ATAC-seq on the Xenopus tropicalis (Xt) genome, the authors tried to identify regulatory regions, which were evolutionally conserved and critical for osteoblasts, through computational approaches. They obtained profiles of the nucleosome-free regions (NFRs), i.e., open chromatin regions, in bone, liver, heart, and lung of Xt. The NFRs contain TSS-associated regions (TSS regions) and non-TSS regions. They then identified tissue-specific NFRs. Tissue-specific NFRs were predominantly located in introns and intergenic regions, and the trend was more highlighted in non-TSS regions. Regarding osteoblast-specific NFRs, non-TSS regions were associated with genes related to osteoblasts. Osteoblast-specific TSS- and non-TSS regions were enriched with motifs of osteoblast-related transcription factors (TFs), including Smad, AP-1, TEAD, Runx2, Nfic, Twist, and Nfat. By integrating ATAC-seq data with RNA-seq data, they found that osteoblast-specific NFRs were associated with transcriptionally active genes. When inter-species conservation of the Xt tissue-specific NFRs was analyzed, osteoblast-specific ones were well conserved in human, chick, and Callorhinchus milii (elephant shark). The authors further identified human homologous regions to Xt osteoblast-specific NFRs, which were enriched with binding motifs of osteoblast-related TFs, proposing putative osteogenic enhancers associated with skeletal diseases. Lastly, they identified a set of Xt osteoblast-specific NFRs that were conserved with the human, chick, and elephant shark genomes. The putative target genes of NFRs are enriched with osteogenesis-related TFs. Based on these data, they propose that evolutionary origins of osteoblast and odontoblasts are common, given that elephant shark is a cartilaginous fish, where bone is absent but odontoblast is present.
Major comments A major critical concern on this work is that their findings and claim fully rely on bioinformatic analyses. Bioinformatic prediction should be verified by wet-type experiments. Otherwise, it is quite difficult to draw definitive conclusions. In particular, it remains to be verified if the "putative enhancers" that they computationally identified have actual enhancer activities in in-vivo contexts. ATAC-seq alone identifies open chromatin regions on the genome and is not enough to define the location of enhancers and their activities. The authors need to perform ChIP-seq for enhancer marks and reporter assays for enhancer activities, in order to verify their prediction on at least several key regions they propose.
Reply: We have taken very seriously the reviewer´s comments and have incorporated three major experimental validations that go beyond bioinformatic analyses: -ChIP-Seq data on 4 key histone marks, previously performed in our laboratory, performed on Xenopus primary osteoblasts (see Fig 4). -Available human ATAC-Seq data for osteoblasts and control tissues (see new Fig 6). -In situ hybridization on elephant shark dental plates (see new Fig 7). We therefore have deeply modified the whole manuscript and now propose a more attractive title for our work: “Cross-validation of conserved osteoblast-specific enhancers illuminates bone diseases and early skeletal evolution”. We were not able to incorporate Reporter assays because (i) these experiments are lengthy, (ii) the current manuscript is already quite extensive and (iii) this is a major future research focus of our laboratory.
Minor comments Line 146: Fig S2 is unlikely to be provided.
Reply: We would like to keep this data available for readers, former Fig S2 is now “Supporting Information 3”.
Lines 158 to 163 and Fig. 4: GO analysis was performed only on non-TSS peaks. What about TSS peaks?
Reply: We now state on page 8 “Due to the low number of regions, no significant results were obtained with lung-specific non-TSS ATAC-Seq peaks, or with any category of TSS”.
Line 269: In the text, the authors describe that 48 osteoblast-specific TSS peaks are aligned to corresponding regions on the human genome. However, Fig. S7 shows 46 peaks are aligned. Please double-check.
Reply: This discrepancy has now been corrected.
Lines 289 to 296, Figs. 8, and S11: Although TRPS1 appears in Fig. S11, the authors did not mention it in the main text and Fig. 8. Why is the gene specifically excluded from the explanation?
Reply: This omission has now been corrected and now trps1 appears in Fig 6C, in Supporting Information 12, and is mentioned in the abstract and at pages 13-14 “Some cross-validated osteoblastic promoters and enhancers are located at loci of genes involved in skeletal diseases (See Supporting information 12 and Ref. [49]), such as osteoarthritis (adam12), osteoporosis (etv1), geroderma osteodysplasticum (gorab), keipert syndrome (gpc4), buschke-Ollendorff syndrome (lemd3), cleidocranial dysplasia (runx2) and trichorhinophalangeal syndrome type I (trps1).”.
Reviewer #3 (Significance (Required)): - This work is potentially interesting, not just leading to identification of regulatory regions critical for osteoblast biology, but also providing evolutionary insight into bone development. However, as mentioned, lack of validation of bioinformatic prediction is a major weakness of this work. This work's concept would engage the interest in the field of bone development and skeletal transcriptional programs. However, the reviewer is not sure how much this work engages general interest. - Expertise of the reviewer is mammalian skeletal development, particularly focusing on gene regulatory networks and epigenome during the process.
Reply: Thank you very much again for your helpful and constructive comments.
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Referee #3
Evidence, reproducibility and clarity
Summary
Starting with ATAC-seq on the Xenopus tropicalis (Xt) genome, the authors tried to identify regulatory regions, which were evolutionally conserved and critical for osteoblasts, through computational approaches. They obtained profiles of the nucleosome-free regions (NFRs), i.e., open chromatin regions, in bone, liver, heart, and lung of Xt. The NFRs contain TSS-associated regions (TSS regions) and non-TSS regions. They then identified tissue-specific NFRs. Tissue-specific NFRs were predominantly located in introns and intergenic regions, and the trend was more highlighted in non-TSS regions. Regarding osteoblast-specific NFRs, non-TSS regions were associated with genes related to osteoblasts. Osteoblast-specific TSS- and non-TSS regions were enriched with motifs of osteoblast-related transcription factors (TFs), including Smad, AP-1, TEAD, Runx2, Nfic, Twist, and Nfat. By integrating ATAC-seq data with RNA-seq data, they found that osteoblast-specific NFRs were associated with transcriptionally active genes. When inter-species conservation of the Xt tissue-specific NFRs was analyzed, osteoblast-specific ones were well conserved in human, chick, and Callorhinchus milii (elephant shark). The authors further identified human homologous regions to Xt osteoblast-specific NFRs, which were enriched with binding motifs of osteoblast-related TFs, proposing putative osteogenic enhancers associated with skeletal diseases. Lastly, they identified a set of Xt osteoblast-specific NFRs that were conserved with the human, chick, and elephant shark genomes. The putative target genes of NFRs are enriched with osteogenesis-related TFs. Based on these data, they propose that evolutionary origins of osteoblast and odontoblasts are common, given that elephant shark is a cartilaginous fish, where bone is absent but odontoblast is present.
Major comments
A major critical concern on this work is that their findings and claim fully rely on bioinformatic analyses. Bioinformatic prediction should be verified by wet-type experiments. Otherwise, it is quite difficult to draw definitive conclusions. In particular, it remains to be verified if the "putative enhancers" that they computationally identified have actual enhancer activities in in-vivo contexts. ATAC-seq alone identifies open chromatin regions on the genome and is not enough to define the location of enhancers and their activities. The authors need to perform ChIP-seq for enhancer marks and reporter assays for enhancer activities, in order to verify their prediction on at least several key regions they propose.
Minor comments
Line 146: Fig S2 is unlikely to be provided. Lines 158 to 163 and Fig. 4: GO analysis was performed only on non-TSS peaks. What about TSS peaks? Line 269: In the text, the authors describe that 48 osteoblast-specific TSS peaks are aligned to corresponding regions on the human genome. However, Fig. S7 shows 46 peaks are aligned. Please double-check. Lines 289 to 296, Figs. 8, and S11: Although TRPS1 appears in Fig. S11, the authors did not mention it in the main text and Fig. 8. Why is the gene specifically excluded from the explanation?
Significance
- This work is potentially interesting, not just leading to identification of regulatory regions critical for osteoblast biology, but also providing evolutionary insight into bone development. However, as mentioned, lack of validation of bioinformatic prediction is a major weakness of this work. This work's concept would engage the interest in the field of bone development and skeletal transcriptional programs. However, the reviewer is not sure how much this work engages general interest.
- Expertise of the reviewer is mammalian skeletal development, particularly focusing on gene regulatory networks and epigenome during the process.
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Referee #2
Evidence, reproducibility and clarity
In this study, Hector Castillo and the coauthors conducted ATAC-seq and RNA-seq analyses across several cell types in Xenopus tropicalis (Xt) to identify regulatory elements specific to osteoblasts. They explored the evolutionary conservation of the osteoblast regulatory elements across species. Their research encompassed the identification of osteoblast-specific regulatory elements through cross-tissue analysis, offering comprehensive insights into tissue-specific regulatory elements. These insights included cell-type-specific chromatin accessibility, biological functions predicted by gene ontology analysis, and potential transcriptional regulators associated with these regions.
The cross-species analysis unveiled partial conservation of osteoblast-specific regulatory regions between the Xt and the human genome, with the shared genomic regions being linked to osteoblast-related genes. Additionally, the enriched transcription factors were identified in these regions. The study further explored comparative analyses involving multiple species, providing evolutionary insights into the gene regulatory mechanisms underlying osteoblast identity and pathology.
Major comment
All the cross-species analyses in this study were primarily based on sequence conservation. However, since human osteoblast ATAC-seq data, as well as ChIP-seq and Hi-C data, are publicly available (PMID: 35906483), conducting a direct comparative analysis between Xenopus tropicalis (Xt) osteoblast ATAC-seq and human osteoblast ATAC-seq could provide more concrete evidence regarding the conservation of chromatin-accessible regions between these two species. This additional analysis has the potential to significantly strengthen the conclusions drawn in the study.
Minor comment 1
The authors made use of "annotated human enhancers" in their study; however, the specific definition or source of this annotation was not provided in the manuscript. It is crucial that the authors clarify the criteria or source used for annotating human enhancers to ensure transparency and allow readers to better understand the basis of their analyses and conclusions.
Minor comment 2
In relation to the association studies conducted between Xenopus tropicalis (Xt) osteoblast enhancers and genes related to human bone diseases, it's important for the authors to express their statements with caution. While the putative target genes may be potentially regulated by shared regulatory elements between Xt and humans, there exists no direct evidence demonstrating that these regulatory regions are the causative factors behind these diseases. It's worth noting that there are several other open chromatin regions in proximity to these putative target genes. As a result, the shared genomic regions may or may not have a direct relationship with human diseases. To establish a substantial linkage, more in-depth analyses would be required to provide evidence of a pathological connection.
Minor comment 3
In lines 394 to 397, the authors assert that the enrichment of TWIST1/2 transcription factor binding sites (TFBS) at Xenopus tropicalis (Xt) osteogenic enhancers is a novel finding. However, this claim lacks clarity regarding the novelty of this discovery, given that they reference previous literature (reference 42) that has already demonstrated the involvement of TWIST1/2 in osteoblast differentiation. The authors should provide a more precise explanation of how their specific findings related to TWIST1/2 TFBS enrichment contribute to existing knowledge or differ from previous studies to clarify the novelty of their results.
Minor comment 4
Depositing the NGS data, including ATAC-seq and RNA-seq datasets, in a public database would be a valuable contribution to the research community.
Significance
The comparative analysis of ATAC-seq among different cell types in Xenopus tropicalis (Xt) provides a broad perspective on cell-type-specific chromatin accessible regions, which is a notable strength of the study. It's worth highlighting that, as far as known, this study represents the first report of ATAC-seq in Xt osteoblasts. However, it's important to acknowledge that the overall message of the study is consistent with previous findings in mammals. For example, the observation that non-transcription start site (TSS) regions were more cell-type-specific, correlating with cell-type distinct gene expressions, aligns with findings in mammalian systems. Additionally, many of the osteoblast regulators predicted from the data are already known osteogenic factors in mammals.
The cross-species analysis provides valuable insights into the evolutionary aspects of putative enhancers in osteoblasts. The study identifies conserved gene regulatory regions and putative transcription factors associated with these genomic regions, shedding light on their potential roles in gene regulation. Moreover, the identification of conserved regions possibly linked to human skeletal diseases is a noteworthy aspect of the research, showcasing its strengths. However, it's essential to acknowledge a potential limitation related to this aspect of the study: the analyses conducted so far have been descriptive, primarily focusing on DNA sequence conservation. Given that several osteoblast ATAC-seq datasets from different species are publicly available, a more direct comparison between the Xt dataset and these other datasets could provide a deeper understanding of enhancer conservation and evolution.
This study offers valuable resources for researchers in the field of skeletal biology and evolution. The comprehensive analysis of osteoblast-specific regulatory elements in Xenopus tropicalis, along with insights into their conservation and potential roles in human skeletal diseases, provides a foundation for further investigations in this area. Additionally, the evolutionary insights offered by the cross-species analysis contribute to the growing body of knowledge in evo-devo studies, shedding light on the evolution of gene regulatory mechanisms related to osteoblast identity. These resources and insights can serve as a valuable reference and guide for future research endeavors in both bone biology and evolutionary developmental biology.
This reviewer specializes in the study of gene regulatory mechanisms in skeletal development and metabolism, primarily utilizing mouse and human tissues.
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Referee #1
Evidence, reproducibility and clarity
Summary:
The paper investigates the genetic mechanisms driving osteoblast differentiation in Xenopus tropicalis, shedding light on bone diseases and early skeletal evolution. Through ATAC-seq analysis, the study identifies osteoblast-specific regulatory regions, confirming their role as osteogenic transcriptional enhancers. A substantial number of these enhancers are conserved in humans, potentially offering insights into skeletal disorders. Additionally, the research highlights an evolutionary perspective by revealing shared regulatory elements between Xenopus tropicalis and the elephant shark, suggesting an ancient origin for mineralized tissues in vertebrates.
Major comments:
Methodology of this paper is kinda vague and the paper seems to be fragmented and not logically organized in a linear fashion. Authors could provide evidentiary support that the control tissues are non-mineralized (and exp tissues are) by simple calcein staining. Mineralization occurs during tadpole stage, and calcification of heart and lung tissue in amphibians is not well understood. This will strengthen the attestation of these tissues as controls and provide a useful diagram for exactly what tissues were used. There appears to be no mention of osteocytes or other cell types. What measures were taken to ensure that osteoblasts are the principal cell type being described? The reference for bone tissue extraction refers to a cell culture technique in which it is likely no osteocytes would prevail.
Minor comments:
Data on conservation of mentioned transcription factors could be easily added (NFAT, etc.) The data presentation is poor, especially figure 2 and figure 4. Line 115-117: "By focusing on annotated Xt transcription start sites (TSSs), we found that the ATAC-Seq NFR and mononucleosome signals form two distinct clusters," it would be helpful to briefly explain the significance of these two clusters. What does it indicate about the regulatory regions associated with TSSs? Line 133-139: When discussing hierarchical clustering and the similarity of NFR landscapes between different tissues, you could provide a sentence or two to speculate on the potential biological implications. For instance, why might heart and lung tissues exhibit more similarity in NFR landscapes compared to osteoblasts and liver? Line 134: To enhance clarity, you might consider using phrases like "Figure 3A" and "Figure 3B" instead of "Compare Fig 3A and B" to directly refer to the figures in the text. Line 142-144 :Please consider briefly explaining why you chose liver, heart, and lung tissues as controls. What specific characteristics or functions of these tissues make them suitable for this comparative analysis? When discussing the potential function of osteoblastic enhancers in cartilaginous fish, you might briefly mention the role of cartilage in these organisms and how these enhancers may have evolved to regulate cartilage-related processes. Ensure that the formatting of your methods section is consistent. For example, consistently use italics for software/tool names (e.g., "SAMtools") and follow a standard format for listing parameters or options used in software/tools.
Significance
The paper's significance lies in its elucidation of osteoblast-specific regulatory regions in Xenopus tropicalis. By characterizing these regions and connecting them to specific genes and pathways, the study advances our understanding of osteogenesis. Additionally, the identification of conserved elements across vertebrates provides insights into the deep evolutionary origins of skeletal features, offering a unique perspective on vertebrate evolution. However, one of the main limitations of the study is the lack of extensive experimental validation for the identified regulatory regions, leaving a gap in confirming their functionality.
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Reply to the reviewers
1. General Statements [optional]
We would like to extend our warmest thanks to the reviewers for their constructive comments and strong support for our study.
2. Point-by-point description of the revisions
Reviewer #1:
Table
- It would be nice to have a table of isoform, dose, promoter, enhancer and other conditions tested and the brief summary of phenotype as Table.
We thank the reviewer for this valuable suggestion and have now included a summary Table (Table 1) cited in the last result section.
Discussion
- This experiment was done on knockout condition but in real patient different form of mutant protein will exist in retinal tissue. Authors indicated that co‐expression of short and long form of FAM161A worked better to rescue function. How would authors cope with interfering endogenous mutant protein in real patients?
We thank the reviewer for raising this interesting point. Most mutations described so far are nonsense or frameshift mutations common to both long and short isoforms which, consequently, are not present at the protein level (Beryozkin et al 2020, doi.org/10.1038/s41598-020-72028-0, Matsevich et al 2022, doi.org/10.1016/j.xops.2022.100229). Thus, we don’t expect to have an imbalance between the remaining functional alleles and the therapeutic ones. However, we cannot exclude the discovery of missense mutations and the effect of such allele would have to be molecularly evaluated to determine if gene replacement is limited for this specific condition. This question could be assessed in cellular models by co-expression of both mutated and WT-tagged proteins or in organoid models.
- Related to the first question, the expression of these retinal structural proteins will be different in mice and human. How would authors optimize the vector for human patient gene therapy?
Aware that the 60% homology between the human and mouse protein could cause important limitations for the evaluation of the vector in the mouse model, we are continuing the validation of our vectors in human retina organoïds. We plan to test both the reliable localization of the human isoforms in WT organoid and the rescue of structural photoreceptor defects of FAM161A-deficient human organoids. In parallel, vector-derived expression will also be validated in non-human primates.
Reviewer #2:
Scotopic and photopic ERG were performed to study retinal function. However, mouse behavior tests such as optomotor response should be employed to confirm vision restoration.
In our hand, we didn’t notice a significant modification of the optomotor response between 4 and 16 weeks (for figure on visual acuity changes with age in Fam161atmb/tmb mice (n=6-9), see uploaded word document), and consequently of the estimated visual acuity, in Fam161atmb/tmb mice at 3.5 months corresponding to the endpoint of our study (see figure below). In a separate study to this work, we are thus conducting a follow-up long term gene therapy study to be able to complete the functional analysis of the gene therapy rescue with the optomotor response at age with significant decreased visual acuity in untreated mice compared to WT. We will have to wait at least 6 months to expect to see a difference between groups.
The immunostaining in Figure 3 has some noise. Filtering the blocking solution before use could improve the quality of the staining.
We thank the reviewer for this suggestion. The blocking solution was already filtered and the limited success of the mouse FAM161A staining is due to the imperfect recognition of anti-human FAM161A antibodies to the mouse protein.
In Figure 5f, the data of wildtype mice should be included for comparison.
As noted by reviewer 3, in Fig5 F, the plain gray horizontal line surrounded by the 2 dotted ones are referring to the mean +/- SEM of the WT value respectively. We added “WT” on the right of the graph to highlight the plain line.
The cited paper, such as 'Garafalo AV, Cideciyan AV, Heon E, Sheplock R, Pearson A, WeiYang Yu C, Sumaroka A, Aguirre GD, and Jacobson SG. Progress in treating inherited retinal diseases: Early subretinal gene therapy clinical trials and candidates for future initiatives. Prog Retin Eye Res. 2020;77(100827),' should be an original research paper, not a review article.
As noted by reviewer 3, we think appropriate to cite this review which is a complete reference to the different gene therapy approaches developed for inherited retinal diseases.
Major:
Fig 1A‐B. Do hTERT‐RPE1 cells endogenously express FAM161A? This set of images lacks a negative control (i.e., no transfected RPE1 cells). Western blot of FAM161A is recommended, similar to Fig 1C.
We previously showed that hTERT-RPE1 cells express FAM161A in the basal body of the centriole (Di Gioia 2015), but we recognized that it is not apparent in Figure 1A and B, probably due to a limitation of the antibody reactivity which labeled only overexpressed proteins. We thus performed additional experiments using the human ARPE19 cell line to demonstrate endogenous FAM161A expression in untransfected cells and to perform a Western blot from human transfected cells. We observed that in untransfected cells FAM161A labeling is weak and is only revealed in the centriole labeled by centrin after a long exposure time (Figure 1A). When FAM161A HS or HL is overexpressed the FAM161A labeling is present in the cell body, very strong, and is observed with short exposure time (Figure 1A). We also extracted protein from untransfected and HS- or HL-transfected ARPE-19 cells to identify the FAM161A protein by Western blot (Figure 1B). Thus, we added the negative control and a western blot from human cells to answer reviewer comments.
Fig 1C. The authors noted in the discussion that HS isoform is more abundant than HL isoform from human retinal extract. Although this is from 661W, a mouse photoreceptor cell line, it seems this is aligned with the notion. To echo with the last comment, I am curious to see if under the same transfection, the HS isoform is preferentially expressed in hTERT‐RPE1 cells.
We do not think that transfection experiment is sufficient to prove that HS is preferentially express than HL. Even if we transfect the same amount of DNA, we would need an internal control for transfection to allow relative quantification of the protein expression after transfection. However, we performed an additional experiment in human RPE cells using the ARPE-19 cell line which is more efficiently transfected than hTERT-RPE1 in our hands. As shown in Figure 1B, we observed again more abundant expression of HS in these human transfected cells. However, we cannot exclude difference in transfection efficiency between HL and HS conditions that could explain the difference in the final amount of FAM161A protein.
Fig 3 and Fig 5: low mag WT images of FAM161A are the same. But higher mag images (presumably selected from ROIs in low mag) are not the same. Please make sure of no duplication images.
We are facing technical limits with the labeling of the mouse Fam161A. The antibodies available have limited affinity for the mouse Fam161A protein. While we were able to perform Uex-M from mouse tissue samples (flatmount retina) to study Fam161A expression in the connecting cilium (Mercey et al PLoS Biol 2022), it was more challenging to obtained low magnification picture from mouse retina sections. We propose to show in Figure 3 mouse Fam161A expression obtained from retina section and keep the low magnification from a flatmount for the figure 5. Thus, there will be no duplication of images as recommended by the reviewer.
Fig 4H. HS+HL combo, and HL alone, showed almost a polarized quantification, quite variable. Can the authors speculate the reason?
Despite the fact that injections are targeting similar retinal region in treated animals, there is still variation in the localization and extend of the gene transfer due to the surgical success. Indeed, the area of retinal detachment is hard to control in the mouse as of the quality of re-attachment. Moreover, the effective dose may lightly vary when some viral particles might be loss due to reflux. One would need to treat a larger number of eyes to really conclude that HS alone would be less variable than HL alone or HS+HL. However, we could also speculate that HS+HL and HL treatments being more efficient to rescue connecting cilium length compared to HS alone (Fig 5F) could, in the best injected eyes, have a better ONL thickness rescue than the limited ONL rescue induced by HS treatment.
Also can the authors comment on if there is any associated notable inflammation especially in high tier dosage (10^11 GC)?
We didn’t follow inflammation directly by fundus examination or OCT imaging following injection. However, despite the high dose used in our successful conditions (10E11 GC/eye), we didn’t notice any differences in the general mouse welfare after injection compare to lower doses. Systemic administration of Rimadyl (carprofen) was however adapted to each mouse during the 24 hrs post-surgery. In comparison to other groups with lower vector doses, no particular emergence of inflammatory cells or damages were observed by histology.
Can the authors comment on the difference in the injection time, PN14‐15 in this study vs. PN24‐29 in their previous study? Have the authors attempted to treat the older mice with the optimized vector?
The gene therapy study using the mouse cDNA was performed before establishing the time course of connecting cilia disruption in the Fam161atmb/tmb mouse (Mercey et al. 2022). Following the observation that CC develop similarly to healthy animal up to postnatal day 10, we decided to treat the mouse earlier for the second gene therapy study using human proteins. Nonetheless, the action of the vector occurred when the cilium is already disorganized as we expect expression of the WT Fam161A from 2 weeks post-injection. We are now testing treatments at different ages, including PN28, to determine the therapeutic window and if the optimal conditions (dose, ratio) may vary with the age at treatment.
Can the authors speculate on why IRBP‐GRK1 human FAM161A did not realize functional rescue (Fig 2) as it did with mouse FAM161A (previous work)?
Our hypothesis to explain the absence of functional rescue following IRBP-GRK1 vector injection is that the difference in human protein distribution compared to the mouse protein in the mouse retina could impact the function of the photoreceptor by interfering with physiological process such as transport. As mentioned in our discussion: “overexpression of these proteins could saturate the transport system impacting the cellular processes”.
As mentioned in the discussion, there is only 60% of homology between human and mouse proteins which could induce a major impact on protein localization and function. Post-translational modification which are also known to be crucial for modulating connecting cilium addressing (Rao et al. 2016) could also differ and impact both human protein distribution and function (for example 3 cysteines in the human protein sequence could be palmytoylated (C359, C366, C367) and are absent in the mouse sequence). Moreover, the exact role of the human long and short isoforms are unknown and their adaptability to the mouse system not yet identified. Further studies should be performed to understand the consequence of such differences on the function and to unravel the function of both long and short human isoforms in the retina.
Minor:
While the manuscript is overall well communicated, there are areas requiring further proofread. For example, in the Abstract section, "In 15 years" should be "For 15 years", "14‐days FAM161atm1b/tm1b mice" should be "14‐day old". In the Introduction, "... suggesting that protein miss‐localization" should be "mis‐localization". In the last paragraph before Discussion, "(iii) the restauration of CC..." should be "restoration", etc.
We corrected these errors and carefully proofread the whole manuscript to avoid typing mistakes.
I recommend the authors to use a table to summarize different promoters, titers and key findings (e.g., expression level, localization) used and refer back to each figure.
We thank the reviewer for this valuable suggestion and have now included a summary Table (Table 1) cited in the last result section.
Scale bars on all figures, or every set of images.
We added scale bars on figures containing microscopic images.
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Referee #3
Evidence, reproducibility and clarity
This manuscript led by Arsenijevic and Chang is an important technical development to the ocular gene therapy space, and touches on the important aspect of structural protein restoration by gene therapy, that is, the precise control of localization and subsequent functional realization. Overall the manuscript is well written, and the experiments are technically sound, with limitations acknowledged.
To briefly summarize, the authors wanted to understand precise control of FAM161A expression and connecting cilium (CC) restoration. They built on, and extended their previous work that showed limited structural and functional rescue by photoreceptor expression of the longer isoform of mouse FAM161A in Fam161a KO driven by IRBP-GRK1 promoter. In the current work, the authors experimented with delivering human ortholog of FAM161A cDNA, short, or long, or both isoforms using newly devised, relatively weak promoters. The main readouts include retinal morphology (e.g., ONL thickness), ERG, and protein localization by IHC (e.g., correct location, no ectopic expression). It is worth noting that the authors highlighted the use of expansion microscopy technology to examine the connecting cilium (CC) organization and protein expression, which may minimize the use of TEM for CC structure determination and enable acceleration.
My enthusiasm for recommending it for publication is high. Nonetheless, I have the following comments, hoping the authors could address to further improve the manuscript.
Major:
Fig 1A-B. Do hTERT-RPE1 cells endogenously express FAM161A? This set of images lacks a negative control (i.e., no transfected RPE1 cells). Western blot of FAM161A is recommended, similar to Fig 1C.
Fig 1C. The authors noted in the discussion that HS isoform is more abundant than HL isoform from human retinal extract. Although this is from 661W, a mouse photoreceptor cell line, it seems this is aligned with the notion. To echo with the last comment, I am curious to see if under the same transfection, the HS isoform is preferentially expressed in hTERT-RPE1 cells..
Fig 3 and Fig 5: low mag WT images of FAM161A are the same. But higher mag images (presumably selected from ROIs in low mag) are not the same. Please make sure of no duplication images.
Fig 4H. HS+HL combo, and HL alone, showed almost a polarized quantification, quite variable. Can the authors speculate the reason? Also can the authors comment on if there is any associated notable inflammation especially in high tier dosage (10^11 GC)?
Can the authors comment on the difference in the injection time, PN14-15 in this study vs. PN24-29 in their previous study? Have the authors attempted to treat the older mice with the optimized vector?
Can the authors speculate on why IRBP-GRK1 human FAM161A did not realize functional rescue (Fig 2) as it did with mouse FAM161A (previous work)?
Minor:
While the manuscript is overall well communicated, there are areas requiring further proofread. For example, in the Abstract section, "In 15 years" should be "For 15 years", "14-days FAM161atm1b/tm1b mice" should be "14-day old". In the Introduction, "... suggesting that protein miss-localization" should be "mis-localization". In the last paragraph before Discussion, "(iii) the restauration of CC..." should be "restoration", etc.
I recommend the authors to use a table to summarize different promoters, titers and key findings (e.g., expression level, localization) used and refer back to each figure.<br /> Scale bars on all figures, or every set of images.
Referees cross-commenting
To reviewer #2, Fig5f - WT data was shown as the gray horizontal line. I had the same question but then saw they noted in the legends. I think it is fine to cite the PRER review article to make their point.
I agree with the comments addressed by Reviewer #1 and am glad we both raise the point of using table for summarization.
Significance
This well-drafted paper represents a technical development that could supplement current gene therapy strategies to certain ciliopathies. In this particular case, the authors chose FAM161A, a disease causal gene to retinitis pigmentosa-28 and encodes for a microtubule-associated ciliary protein involved in organizing the connecting cilium in photoreceptors. Of importance, the authors devised novel promoters to drive gene expression and took advantage of expansion microscopy for quickly examining cilia proteins and structures. Conceptually, the techniques developed in this manuscript could be applicable to several other inherited retinal dystrophies that share similar disease mechanisms.
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Referee #2
Evidence, reproducibility and clarity
Arsenijevic et al. investigated the therapeutic function of the FCBR1-F0.4 promoter-driven expression of both the short and long isoforms of human FAM161A. The results showed that this method not only repaired the disorganized connecting cilium but also restored the appropriate expression and localization of other proteins in the connecting cilium, thus restoring retinal function. Additionally, the study systematically evaluated the AAV dose, different promoters, and FAM161A isoforms' effects on retinal survival and function. Overall, the study is novel and robust. Here are some suggestions that may help improve the manuscript:
Scotopic and photopic ERG were performed to study retinal function. However, mouse behavior tests such as optomotor response should be employed to confirm vision restoration.
The immunostaining in Figure 3 has some noise. Filtering the blocking solution before use could improve the quality of the staining.
In Figure 5f, the data of wildtype mice should be included for comparison.
The cited paper, such as 'Garafalo AV, Cideciyan AV, Heon E, Sheplock R, Pearson A, WeiYang Yu C, Sumaroka A, Aguirre GD, and Jacobson SG. Progress in treating inherited retinal diseases: Early subretinal gene therapy clinical trials and candidates for future initiatives. Prog Retin Eye Res. 2020;77(100827),' should be an original research paper, not a review article.
Referees cross-commenting
Agree with the comments addressed by Reviewer #1 and #3
Significance
Overall, the manuscript is clear and interesting. I suggest a major resion for the manuscript.
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Referee #1
Evidence, reproducibility and clarity
The manuscript "Fine-tuning FAM161A gene augmentation therapy to restore retinal function" submitted by Arsenijevic et al., describes gene therapy for RP28 caused by mutation in FAM161A in human. The authors worked on Fam161a-dficient mice by testing different isoforms, dose, promoter and enhancers to control the expression level and localization of the protein to functionally rescue the mice to prevent blindness. The tight control of protein expression is required for mutation in genes coding structural proteins in the retina.
The authors have clearly showed the optimized combination of conditions to restore function of Fam161atm1b/tm1b mice and also area of improvement to make.
Comments
Table
- It would be nice to have a table of isoform, dose, promoter, enhancer and other conditions tested and the brief summary of phenotype as Table.
Discussion
- This experiment was done on knockout condition but in real patient different form of mutant protein will exist in retinal tissue. Authors indicated that co-expression of short and long form of FAM161A worked better to rescue function. How would authors cope with interfering endogenous mutant protein in real patients?
- Related to the first question, the expression of these retinal structural proteins will be different in mice and human. How would authors optimize the vector for human patient gene therapy?
Significance
This is an important and excellent work showing tight control of expression is required for future retinal gene therapy.
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
1. General Statements [optional]
We are thankful to the reviewers for the time and effort invested in assessing our manuscript and for their suggestions to improve it. We have now considered the points raised by them, carried out additional experiments, and modified the text and figures to address them. We feel that the new experiments and modifications have been able to solve all concerns raised by the reviewers and have improved the manuscript substantially, strengthening and extending our conclusions.
The main modifications include:
- We have extended the analysis of the overexpression strains to highly stringent conditions, which revealed a mild acidification defect for the strain overexpressing Oxr1. In addition, we have included in our analysis a strain in which both proteins are overexpressed, which resulted in a further growth defect.
- We have analyzed the recruitment of Rtc5 to the vacuole under additional conditions: deletion of the main subunit of the RAVE complex RAV1, medium containing galactose as the sole carbon source and pharmacological inhibition of the V-ATPase. These experiments allowed us to strengthen and extend our conclusions regarding the requirements for Rtc5 targeting to the vacuole.
- We have analyzed V-ATPase disassembly in intact cells, by addressing the localization to the vacuole of subunit C (Vma5) in glucose and galactose-containing medium. The results strengthen our conclusion that both Rtc5 and Oxr1 promote an in vivo state of lower V-ATPase assembly.
- We have extended our analyses of V-ATPase function to medium containing galactose as a carbon source, since glucose availability is one of the main regulators of V-ATPase function in vivo. The results are consistent with what we observed in glucose-containing medium.
- We have included a diagram of the structure of the V-ATPase for reference.
- We have included a diagram and a paragraph describing Oxr1 and Rtc5 regarding protein length and domain architecture and comparing them to other TLDc domain-containing proteins.
- We have made changes to the text and figures to improve clarity and accuracy, including a methods section that was missing. We include below a point-by-point response to the reviewers´ comments.
2. Point-by-point description of the revisions
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
__ __Suggestions:
- The authors observed that knockout of Rtc5p or Oxr1p does not affect vacuolar pH. If Rtc5p and Oxr1p both cooperate to dissociate V-ATPase, the authors may wish to characterize the effect of a ∆Rtc5p∆Oxr1p double knockout on vacuolar pH. The double mutant ∆rtc5∆oxr1 was already included in the original manuscript (the growth test is shown in Figure 5 B and the BCECF staining is shown in Figure 5C). This strain behaved like wt in both of these assays. Of note, what we observe for the deletion strains is increased assembly (Figure 5 D - G), so we expect that it would be hard to observe a difference in vacuole acidity or growth in the presence of metals.
Therefore, we have now also included a strain with the double overexpression of Oxr1 and Rtc5. Since overexpression of the proteins results in decreased assembly, it is more likely that this strain will show impaired growth under conditions that strongly rely on V-ATPase activity. Indeed, we observed that the overexpression of Oxr1 alone resulted in a slight growth defect in media containing high concentrations of ZnCl2 and the double overexpression strain showed an even further defect (Figure 6 A and C).
The manuscript would benefit from a well-labelled diagram showing the subunits of V-ATPase (e.g. in Figure 2D).
We agree with the reviewer and we have now added a diagram of the structure of the V-ATPase labeling the different subunits in Figure 2B.
The images of structures, especially in Figure 1-Supplement 1B, are not particularly clear and could be improved (e.g. by removing shadows or using transparency).
We are thankful to the reviewer for this suggestion. To improve the clarity of the structures in Figure 1 C and Figure 1 – Supplement 1A, we are now presenting the different subunits in the structures with different shades of blue and grey.
The authors should clearly describe the differences between Rtc5p and Oxr1p in terms of protein length, sequence identity, domain structure, etc.
We are thankful for this suggestion and we have now included a diagram of the domain architecture and protein length of Rtc5 and Oxr1, comparing with two human proteins containing a TLDc domain in Figure 5A. In addition, we have added the following paragraph describing the features of the proteins.
“Rtc5 is a 567 residue-long protein. Analysis of the protein using HHPred (Zimmermann et al., 2018), finds homology to the structure of porcine Meak7 (PDB ID: 7U8O, (Zi Tan et al., 2022)) over the whole protein sequence (residues 37-559). For both yeast Rtc5 and human Meak7 (Uniprot ID: Q6P9B6), HHPred detects homology of the C-terminal region to other TLDc domain containing proteins like yeast Oxr1 (PDBID: 7FDE), Drosophila melanogaster Skywalker (PDB ID: 6R82), and human NCOA7 (PDB ID: 7OBP), while the N-terminus has similarity to EF-hand domain calcium-binding proteins (PDB IDs: 1EG3, 2CT9, 1S6C6, Figure 5A). HHPred analysis of the 273 residue long Saccharomyces cerevisiae Oxr1, on the other hand, only detects similarity to TLDc domain containing proteins (PDB IDs: 7U80, 6R82, 7OBP), which spans the majority of the sequence of the protein (residues 71-273). The overall sequence identity between Oxr1 and Rtc5 is 24% according to a ClustalOmega alignment within Uniprot. The Alphafold model that we generated for Rtc5 is in good agreement with the available partial structure of Oxr1 (7FDE) (root mean square deviation (RMSD) of 3.509Å) (Figure 5 - S1 A), indicating they are structurally very similar, in the region of the TLDc domain. Taken together, these analyses suggest that Oxr1 belongs to a group of TLDc domain-containing proteins consisting mainly of just this domain like the splice variants Oxr1-C or NCOA7-B in humans (NP_001185464 and NP_001186551, respectively), while Rtc5 belongs to a group containing an additional N-terminal EF-hand-like domain and a N-myristoylation sequence, like human Meak7 (Finelli & Oliver, 2017) (Figure 5 A).”
Minor:
- The "O" in VO should be capitalized. This has been corrected.
In Figure 4 supplement 1, the labels "I", "S", and "P" should be defined.
This has been clarified in the figure legend.
Please clarify what is meant by "switched labelling"
This refers to the SILAC vacuole proteomics experiments, for which yeast strains are grown in medium containing either L-Lysine or 13C6;15N2- L-Lysine to produce normal (‘light’) or heavy isotope-labeled (‘heavy’) proteins. This allows comparing two conditions. To increase the robustness of the comparisons, the experiments are done twice with both possible labeling schemes (condition A – light, condition B – heavy + condition A – heavy + condition B – light), which is commonly described as switched labeling or label switching.
We have exchanged the original sentence in the manuscript for:
“Performing the same experiments but switching which strain was labeled with heavy and light amino acids gave consistent results.”
The meaning of the sentence "Indeed, this was the case for both of them" is ambiguous.
We have now replaced this sentence with the following:
“Indeed, overexpression of either Rtc5 or Oxr1 resulted in increased growth defects in the context of Stv1 deletion (Figure 7 H and I).”
For Figure 1-Supplement 1B it is hard to see the crosslink distances.
We have updated this figure to improve the visibility of the cross-links. In addition, we now include a supplemental table (supplemental table 5) with a list of the Cα- Cα distances measured for all the crosslinks we mapped onto high-resolution structures.
The statement "The effects of Oxr1 are greater than those caused by Rtc5" requires more context. Is there a way of quantifying this effect for the reader?
We agree that this sentence was too general and vague. The effects caused by one or the other protein depend on the condition and the assay. We have thus deleted this sentence, and we think it is better to refer to the description of the individual assays performed.
The phrase "negative genetic interaction" should be clarified.
We have included in the text the following explanation of genetic interactions:
“A genetic interaction occurs when the combination of two mutations results in a different phenotype from that expected from the addition of the phenotypes of the individual mutations. For example, deletion of OXR1 or RTC5 has no impact on growth in neutral pH media containing zinc in a control background but improves the growth of RAV1 deletion strains (Figure 7 E and F), so this is a positive genetic interaction. On the other hand, overexpression of either Rtc5 or Oxr1 results in a growth defect in a background lacking Rav1 in neutral media containing zinc, a negative genetic interaction.”
* * In the sentence "Isogenic strains with the indicated modifications in the genome where spotted as serial dilutions in media with pH=5.5, pH=7.5 or pH=7.5 and containing 3 mM ZnCl2", "where" should be "were".
This has been corrected.
Figure 2D: the authors should consider re-coloring these models, as it is challenging to distinguish Rtc5p from the V-ATPase.
We have changed the coloring of this structure and added a diagram of the V-ATPase structure with the same coloring scheme to improve clarity.
Reviewer #1 (Significance (Required)):
The vacuolar protein interaction map alone from this manuscript is a nice contribution to the literature. Experiments establishing colocalization of Rtc5p to the vacuole are convincing, as is dependence of this association on the presence of assembled V-ATPase. Similarly, experiments related to myristoylation are convincing. The observed mislocalization of V-ATPases that contain Stv1p to the vacuole (which is also known to occur when Vph1p has been knocked out) upon knockout of Oxr1p is also extremely interesting. Overall, this is an interesting manuscript that contributes to our understand of TLDc proteins.
We are thankful to the reviewer for their appreciation of the significance of our work, including the interactome map of the vacuole as a resource and the advances on the understanding of the regulation of the V-ATPase by TLDc domain-containing proteins.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Major points:
- The evidence of Oxr1 and Rtc5 as V-ATPase disassembly factors is circumstantial. The authors base their interpretation primarily on increased V1 (but not Vo) at purified vacuoles from Oxr1- or Rtc5-deleted strains, which does not directly address disassembly. Of course, the results regarding Oxr1 confirm detailed disassembly experiments with the purified protein complex (PMID 34918374), but on their own are open to other interpretations, e.g. suppression of V-ATPase assembly. Of note, the authors emphasize that they provide first evidence of the in vivo role of Oxr1, but monitor V1 recruitment with purified vacuoles and do not follow V-ATPase assembly in intact cells. We are thankful to the reviewer for pointing this out. We did not want to express that the molecular activity of the proteins is the disassembly of the complex, as our analyses include in vivo and ex vivo experiments and do not directly address this. We rather meant that both proteins promote an in vivo state of lower assembly of the V-ATPase. We have modified the wording throughout the manuscript to be clearer about this.
In addition, we have added new experiments to monitor V-ATPase assembly in intact cells, as suggested by the reviewer. Previous work has shown that in yeast, only subunit C leaves the vacuole membrane under conditions that promote disassembly, while the other subunits remain at the vacuole membrane (Tabke et al 2014). Our own experiments agree with what was published (Figure 3 D). We have thus monitored Vma5 localization to the vacuole under glucose or after shift to galactose containing media in cells lacking or overexpressing Rtc5 or Oxr1. We observed that cells overexpressing either TLDc domain protein show lower levels of Vma5 recruitment to the vacuole in glucose (Figure 6 D and E). Additionally cells lacking either Rtc5 or Oxr1 contain higher levels of Vma5 at the vacuole after 20 minutes in galactose medium (Figure 5 F and G). Thus, these results re-inforce our conclusions that Rtc5 and Oxr1 promote states of lower assembly.
Oxr1 and Rtc5 have very low sequence similarity. It would be helpful if the authors provided more detail on the predicted structure of the putative TLDc domain of Rtc5 and its relationship to the V-ATPase - Oxr1 structure. Is Rtc5 more closely related to established TLDc domain proteins in other organisms?
We have now included a diagram of the domain architecture of Rtc5 and Oxr1, and comparison to the features of other TLDc domain containing proteins in Figure 5 A, as well as a paragraph describing them:
“Rtc5 is a 567 residue-long protein. Analysis of the protein using HHPred (Zimmermann et al., 2018), finds homology to the structure of porcine Meak7 (PDB ID: 7U8O, (Zi Tan et al., 2022)) over the whole protein sequence (residues 37-559). For both yeast Rtc5 and human Meak7 (Uniprot ID: Q6P9B6), HHPred detects homology of the C-terminal region to other TLDc domain containing proteins like yeast Oxr1 (PDBID: 7FDE), Drosophila melanogaster Skywalker (PDB ID: 6R82), and human NCOA7 (PDB ID: 7OBP), while the N-terminus has similarity to EF-hand domain calcium-binding proteins (PDB IDs: 1EG3, 2CT9, 1S6C6, Figure 5A). HHPred analysis of the 273 residue long Saccharomyces cerevisiae Oxr1, on the other hand, only detects similarity to TLDc domain containing proteins (PDB IDs: 7U80, 6R82, 7OBP), which spans the majority of the sequence of the protein (residues 71-273). The overall sequence identity between Oxr1 and Rtc5 is 24% according to a ClustalOmega alignment within Uniprot. The Alphafold model that we generated for Rtc5 is in good agreement with the available partial structure of Oxr1 (7FDE) (root mean square deviation (RMSD) of 3.509Å) (Figure 5 - S1 A), indicating they are structurally very similar, in the region of the TLDc domain. Taken together, these analyses suggest that Oxr1 belongs to a subfamily of TLDc domain-containing proteins consisting mainly of just this domain like the splice variants Oxr1-C or NCOA7-B in humans (NP_001185464 and NP_001186551, respectively) , while Rtc5 belongs to a subfamily containing an additional N-terminal EF-hand-like domain and a N-myristoylation sequence, like human Meak7 (Finelli & Oliver, 2017) (Figure 5 A).”
The authors conclude vacuolar recruitment of Rtc5 depends on the assembled V-ATPase, based on deletion of different V1 and Vo domain subunits. However, these genetic manipulations likely cause a strong perturbation of vacuolar acidification; indeed, the images show drastically altered vacuolar morphology. To strengthen their conclusion, it would be helpful to show that Rtc5 recruitment is not blocked by inhibition of vacuolar acidification, and that conversely it is blocked by deletion of rav1.
We are thankful to the reviewer for this insightful suggestion and we have now performed both experiments suggested. The experiment regarding rav1Δ is now Figure 3C, and we observed that this mutation also disrupts Rtc5 localization to the vacuole. In addition, we decided to include an experiment showing the subcellular localization of Rtc5 after shifting the cells to galactose containing medium for 20 minutes, as a physiologically relevant condition that results in disassembly of the complex (Figure 3D). We observed that under these conditions Rtc5 re-localizes to the cytosol. This result is particularly interesting given that in yeast only subunit C (but not other V1 subunits) re-localizes to the cytosol under these conditions. In addition, the experiment using Bafilomycin A to inhibit the V-ATPase shows that Rtc5 is still localized at the vacuole membrane under conditions of V-ATPase inhibition (Figure 3 F). Taken together these results allowed us to strengthen our original interpretation that Rtc5 requires an assembled V-ATPase for its localization and extend it to the fact that the V-ATPase does not need to be active.
Reviewer #2 (Significance (Required)):
This is an interesting paper that confirms and extends previous findings on TLDc domain proteins as a novel class of proteins that interact with and regulate the V-ATPase in eukaryotes. The title seems to exaggerate the findings a bit, as the authors do not investigate V-ATPase (dis)assembly directly and only phenotypically describe altered subcellular localization of the Golgi V-ATPase in Oxr1-deleted cells. A recent structural and biochemical characterization of Oxr1 as a V-ATPase disassembly factor (PMID 34918374) somewhat limits the novelty of the results, but the function of Oxr1 in regulating subcellular V-ATPase localization and the identification of a second potential TLDc domain protein in yeast provide relevant insights into V-ATPase regulation. This paper will be of interest to cell biologists and biochemists working on lysosomal biology, organelle proteomics and V-ATPase regulation.
We thank the reviewer for the assessment of our work, and for recognizing the novel insights that we provide. Regarding the previous biochemical work on Oxr1 and the V-ATPase, we have clearly cited this work in the manuscript. In our opinion, our results complement and extend this article, showing that the function in disassembly is relevant in vivo. Additionally, this is only one of five major points of the article, the other four being
- The interactome map of the vacuole as a resource
- The identification of Rtc5 as a second yeast TLDc domain containing protein and interactor of the V-ATPase.
- The identification of the role of Rtc5 in V-ATPase assembly.
- The identification of the role of Oxr1 in Stv1 subcellular localization. We believe these additional points add important insights to researchers interested in lysosomes, the V-ATPase, intracellular trafficking and TLDc-domain containing proteins.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Major comments
__1) Re: A cross-linking mass spectrometry map of vacuolar protein interactions (results) __ While XL-MS is a very powerful method, it is a high-throughput approach and there should be some kind of negative control in these experiments. In cross-linking experiments, non-cross-linked samples are usually used as negative controls. What was the negative control in cross-linking mass-spectrometry experiments here? If there was no negative control, how the specificity of interactions was evaluated? Maybe the authors analyzed the dataset for highly improbable interactions and found very few of them?
We fully agree that it is crucial to ensure the specificity of the interactions detected by XL-MS. To achieve this, one needs to control (1) the specificity of the data analysis (i.e. that the recorded mass spectrometry data are correctly matched to cross-linked peptides from the sequence database) and (2) the biological specificity (i.e. that the cross-linking captured natively occurring interactions).
To ascertain that criterion (1) is met, cross-link identifications are filtered to a pre-defined false-discovery rate (FDR) – an approach that the XL-MS field adopted from mass spectrometry-based proteomics. As a result, low-confidence identifications (e.g. cross-linked peptides that are only supported by a few signals in a given mass spectrum) are removed from the dataset. FDR filtering in XL-MS is a rather complex matter as it can be done at different points during data analysis and the optimal FDR cut-off depends on the specific scientific question at hand (for more details see for example Fischer and Rappsilber, Anal Chem, 2017). Generally speaking, an overly restrictive FDR cut-off would remove a lot of correct identifications, thereby greatly limiting the sensitivity of the analysis. On the other hand, a too relaxed FDR cut-off would dilute the correct identifications with a high number of false-positives, which would impair the robustness and specificity of the dataset. While many XL-MS study control the FDR on the level of individual spectrum matches, we opted for a 2% FDR cut-off on the level of unique residue pairs, which is more stringent (see Fischer and Rappsilber, Anal Chem, 2017). Our FDR parameters are described in the Methods section (Cross-linking mass spectrometry of isolated vacuoles - Data analysis). Of note, we have made all raw mass spectrometry data publicly available through the PRIDE repository (https://www.ebi.ac.uk/pride/ ; accession code PXD046792; login details during peer review: Username = reviewer_pxd046792@ebi.ac.uk, Password = q1645lTP). This will allow other researchers to re-analyze our data with the data analysis settings of their choice in the future.
To ascertain that criterion (2) is met, we mapped the identified cross-links onto existing high-resolution structures of vacuolar protein complexes. Taking into account the length of our cross-linking reagent, the side-chain length of the cross-linkable amino acids (i.e. lysines), and a certain degree of in-solution flexibility, cross-links can reasonably occur between lysines with a mutual Cα-Cα distance of up to 35 Å. Using this cut-off, the lysine-lysine pairs in the high-resolution structures we studied can be split into possible cross-linking partners (Cα-Cα distance 35 Å). Of all cross-links we could map onto high-resolution structures, 95.2% occurred between possible cross-linking partners. In addition, our cross-links reflect numerous known vacuolar protein interactions that have not yet been structurally characterized. These lines of evidence increase our confidence that our XL-MS approach captured genuine, natively occurring interactions. These analyses are described in more detail in the first Results sub-section (“A cross-linking mass spectrometry map of vacuolar protein interactions”).
In addition, the high purity of vacuole preparation is critical. How was it assessed by the authors?
We disagree that the purity of the vacuole preparation is critical for this analysis to be valid. The accuracy of the protein-protein interactions detected will depend on their preservation during sample preparation until the sample encounters the cross-linker, and the data analysis, as described above. The experiment would have been equally valid if performed on whole cell lysates without any enrichment of vacuoles, but the coverage of vacuolar proteins would have likely been very low. For this reason, we decided to use the vacuole isolation procedure to obtain better coverage of the proteins of this particular organelle. The use of the Ficoll gradient protocol (Haas, 1995) was based on that it is a protocol that yields strong enrichment of proteins annotated with the GO Term “vacuole” (Eising et al, 2019) and that it preserves the functionality of the organelle, as evidenced by its use for multiple functional assays (vacuole-vacuole fusion (Haas, 1995), autophagosome-vacuole fusion (Gao et al, 2018), polyphosphate synthesis by the VTC complex (Desfougéres et al, 2016), among others).
2) Re: Rtc5 and Oxr1 counteract the function of the RAVE complex (results)
Taken together, data, presented in this section of the manuscript, provide strong evidence that Rtc5 and Oxr1 negatively regulate V-ATPase activity, counteracting the V-ATPase assembly, facilitated by the activity of the RAVE complex. However, the complete deletion of the major RAVE subunit Rav1p was required to observe this effect in vivo in yeast. The other way to induce V-ATPase disassembly in yeast is glucose deprivation. It will be interesting to study if there is a synergistic effect between glucose deprivation and RTC5/OXR1 deletion on V-ATPase assembly, vacuolar pH, and growth of single oxr1Δ, rtc5Δ or double oxr1Δrtc5Δ mutants (OPTIONAL). Glucose deprivation is a more physiologically relevant condition than a deletion of an entire gene.
We would like to point out that an effect on assembly is observed without deleting the RAVE complex: deletions of Oxr1 or Rtc5 resulted in increased V-ATPase assembly in vivo in the presence of glucose and of the RAVE complex (Figures 5 D and E). We have now also added the experiments showing that the overexpression strains have a mild growth defect under conditions that force cells to strongly rely on V-ATPase activity (Figures 6 A and C).
Nevertheless, we agree that addressing the effect of changing the levels of Oxr1 and Rtc5 under low-glucose conditions is an interesting physiologically relevant question. We have now included growth assays and BCECF staining in medium containing galactose as the carbon source (Figures 5 – Supplement 1 B and C, and Figure 6 C and Figure 6- Supplement 1A). In addition, we have addressed the vacuolar localization of Vma5 in medium containing glucose or after shifting to medium containing galactose for 20 minutes, as a proxy for V-ATPase disassembly in intact cells (Figure 5 F and G, Figure 6 D and E). Taken together, these analyses reinforce our conclusions that both Rtc5 and Oxr1 promote an in vivo state of lower V-ATPase assembly, based on the following observations:
- Higher localization of Vma5 to the vacuole after 20 mins in galactose in cells lacking Oxr1 or Rtc5 (Figure 5 F and G).
- Lower localization of Vma5 to the vacuole in medium containing glucose in cells overexpressing Oxr1 or Rtc5 (Figure 6 D and E).
- Growth defect of the strain overexpressing Oxr1 in medium containing galactose with pH = 7.5 and zinc chloride, with a further growth defect caused by additional overexpression of Rtc5 (Figure 6 C). 3) Re: Figure 6 - supplement 1. The title is relevant to panel D only, it should be renamed to reflect the results of the disassembly of V-ATPase in rav1Δ mutant strains, while results about the stv1Δ-based strains (Panel D) should be shown together with similar experiments in Figure 7 - supplement 2 for clarity.
We have shifted the Panel D from the original Figure 6 – Supplement 1 to the main Figure (now Figure 7 – H and I). Regarding the title of the Figure, whether Supplemental Figures have titles or not will depend on the journal where the manuscript is published. For now, we have removed all titles from supplemental figures, as they are conceived to complement the main Figures.
4) Re: Figure 7 - supplement 1, Panel A. The proper assay to show that Stv1-mNeonGreen is functional is to express it in double mutant vph1Δstv1Δ to see if the growth defect is reversed. In addition, the vph1Δ growth defect is not changed (improved or worsened) in the presence of Stv1-mNeonGreen, so it means that the expression of Stv1-mNeonGreen does not further compromise the V-ATPase function, but it does not mean that it improves its function.
It is clear from the experiment suggested by the reviewer that they think that we have expressed Stv1-mNeonGreen from a plasmid. This was not the case, Stv1 was C-terminally tagged with mNeonGreen in the genome. It is thus the only expressed version in the strain. The experiment we have performed is thus equivalent to the one suggested by the reviewer, but for genomically expressed variants. For reference, the genotypes of all the strains used can be found in Supplemental Table 1.
5) Re: Figure 7 - supplement 2. This figure should be combined with Fig. 6- suppl 1, panel D as also mentioned above. The figure seems to lack some labels, and conclusions are not accurate as discussed below. However, this data provides important additional information about relationships between isoform-specific subunits of V-ATPase Vph1 and Stv1 and both Rtc5 and Oxr1 and should be repeated if it is not done yet to have a better idea about these relationships.
Panel B: Based on this picture, deletion of RTC5 has a negative genetic interaction with the deletion of VPH1, since double deletion mutant vph1Δ rtc5Δ grows worse than each individual mutant. Although it also means that there is no positive interaction, it is not the same.
Indeed, there is a negative genetic interaction between the deletion of RTC5 and VPH1. We have replaced the growth tests in this figure (Figure 8 – Supplement 2 A in the new manuscript) to show this negative genetic interaction better. This effect is reproducible, as shown in the repetitions of the experiments.
Panel C: Same as for panel B. Based on this picture, the deletion of OXR1 has a weak negative genetic interaction with the deletion of STV1, since double deletion mutant stv1Δ oxr1Δ grows worse than each individual mutant at 6 mM ZnCl2.
Panel D: Same as for panels B and C. Based on this picture, deletion of RTC5 has a negative genetic interaction with the deletion of STV1, since double deletion mutant stv1Δ rtc5Δ grows worse than each individual mutant at 6 mM ZnCl2. There is no label in the middle panel (growth conditions) and no growth assay data in the presence of CaCl2.
However, these results will be then in contradiction with the results from Figure 6 - Supplement 1, panel D, showing negative genetic interaction between the overexpression of Rtc5 or Oxr1 and deletion of Stv1, since both deletion and overexpression of Rtc5 or Oxr1 would have negative genetic interactions with Stv1.
For both Panels C and D (Now Figure 8 - Supplement 2 B and C). The effect pointed out by the reviewer (slightly stronger growth defect for the double mutants than for the single mutants) is very mild. We have attempted to make it more evident by assessing growth in medium with higher and lower concentrations of zinc and this was not possible. This is in contrast with the very clear positive genetic interaction that we observe between the deletion of OXR1 and VPH1 (Now Figure 8 H). This is the reason that we decided to report the lack of a positive genetic interaction instead of the presence of a negative one, as we do not want to draw conclusions based on results that are borderline detectable.
In addition, there is no label for the media in the middle panel, is it just YPAD pH=7.5, without the addition of any metals?
Indeed, the media is YPAD pH=7.5, without the addition of any metals. The line drawn above several images based on this media indicated this. Since this form of labeling appears to be confusing, we have now replaced it and placed the label directly above the image.
Why there is no growth assay in the presence of CaCl2, like in panels A and B?
Every growth test shown in the manuscript was performed including growth in YPD pH=5,5 as a control of a permissive condition for lack of V-ATPase activity, and then in YPD pH=7,5 including a broad range of Zinc Chloride and Calcium chloride concentrations. From all these pictures, the conditions where the differences among strains were clearly visible were chosen to assemble the figures. Conditions that did not provide any information for that particular experiment were not included in the figure to avoid making them unnecessarily large and crowded.
Re: Figure 7 - supplement 2, continued. How many times all these experiments were repeated? These experiments should be repeated at least 3 times, which is especially necessary for the experiments in panel C, because the effects are borderline. If results are reproducible and statistically significant, although small, the conclusion should be changed from "no positive genetic interactions" to "negative genetic interactions", which is more precise and informative.
All growth tests shown in the manuscript were repeated at least three times for the conditions shown. We are thankful to the reviewer for pointing out that this was not mentioned, and we have added this to the methods section. We have assembled a file with all repetitions of the shown growth tests and added it at the end of this file. In doing so, these are already available for the public. These repetitions show that all effects reported are reproducible. We will then discuss with the editors of the journal where this manuscript is published about the necessity of including it with the final article.
Regarding reporting the lack of a positive genetic interaction vs. a negative one, we have discussed this above. Shortly, for Panel B (Figure 8 – Supplement 2 A in the new manuscript) we have changed the conclusion to “negative genetic interaction” as adjusting the zinc chloride concentration allowed us to show this clearly and reproducibly, as shown by the repetitions of the experiments. For panels C and D (Now Figure 8 - Supplement 2 B and C), the effect is really mild and barely detectable, even when we tried a wide range of zinc chloride concentrations. For this reason, we would prefer to maintain the “no positive genetic interaction” conclusion.
Re: Methods. There is no description of yeast serial dilution growth assay at all. In addition, why the specific media (neutral pH, in the presence of high concentrations of calcium or zinc) was used is not explained either in the results or methods. Appropriate references should be included, for example, PMID: 2139726, PMID: 1491236.
We apologize for the oversight of the missing methods section, which we have now included.
Regarding the explanation of the media used, the following section was already a part of the results section, before the description of the first growth test:
“The V-ATPase is not essential for viability in yeast cells, and mutants lacking subunits of this complex grow similarly to a wt strain in acidic media. However, when cells grow at near-neutral pH or in the presence of divalent cations such as calcium and zinc, the mutants lacking V-ATPase function show a strong growth impairment (Kane et al, 2006).”
We have now replaced this with the following, more complete version:
“As a first approach for addressing the role of these proteins, we tested growth phenotypes related to V-ATPase function in strains lacking or overexpressing them. The V-ATPase is not essential for viability in yeast cells, and mutants lacking subunits of this complex grow similarly to a wt strain in acidic media, but display a growth defect at near-neutral pH the mutants (Nelson & Nelson, 1990). In addition, the proton gradient across the vacuole membrane generated by the V-ATPase energizes the pumping of metals into the vacuole, as a mechanism of detoxification. Thus, increasing concentrations of divalent cations such as calcium and zinc, generate conditions in which growth is increasingly reliant on V-ATPase activity (Förster & Kane, 2000; MacDiarmid et al, 2002; Kane, 2006).”
MINOR COMMENTS
Yeast proteins are named with "p" at the end, such as "Rtc5p".
This nomenclature rule is falling into disuse during the last decades, as the use of capitals vs lowercase and italics allows to distinguish between genes proteins and strains (OXR1 = gene, Oxr1 = protein, oxr1Δ = strain). As an example, I include a list of the latest papers by some of the major yeast labs around the world, all of which use the same nomenclature as we do (in alphabetical order). This list even includes some work in the field of the V-ATPase.
- Alexey Merz, USA. PMID: 33225520
- Benoit Kornmann, UK. PMID: 35654841
- Christian Ungermann, Germany. PMID: 37463208
- Claudio de Virgilio, Switzerland. PMID: 36749016
- Daniel E. Gottschling, USA. PMID: 37640943
- David Teis, Austria. PMID: 32744498
- Elizabeth Conibear, Canada. PMID: 35938928
- Fulvio Reggiori, Denmark. PMID: 37060997
- J Christopher Fromme, USA. PMID: 37672345
- Maya Schuldiner, Israel. PMID: 37073826
- Patricia Kane, USA. PMID: 36598799
- Scott Emr, USA. PMID: 35770973
- W Mike Henne, USA. PMID: 37889293
- Yoshinori Ohsumi, Japan. PMID: 37917025 In addition, we would prefer to keep the nomenclature that we already use, to keep consistency with other published articles from our lab.
Re: Introduction. In the introduction it should be indicated that Rtc5 was originally discovered as a "restriction of telomere capping 5", using screening of temperature-sensitive cdc13-1 mutants combined with the yeast gene deletion collection [PMID: 18845848]. A couple of sentences should be written about the RAVE complex and its role in V-ATPase assembly.
We are thankful for this suggestion and we have now included both pieces of information in the introduction.
*“The re-assembly of the V1 onto the VO complex when glucose becomes again available, is aided by a dedicated chaperone complex known as the RAVE complex, which also likely has a general role in V-ATPase assembly (Seol et al, 2001; Smardon et al, 2002, 2014).” *
“In our cross-linking mass spectrometry interactome map of isolated vacuoles we found that the only other TLDc-domain containing protein of yeast, Rtc5, is a novel interactor of the V-ATPase. Rtc5 is a protein of unknown function, originally described in a genetic screen for genes related to telomere capping (Addinall et al, 2008)”
Re: The TLDc domain-containing protein of unknown function Rtc5 is a novel interactor of the vacuolar V-ATPase (results)
1) It is important to understand, that Oxr1 was co-purified before with the V1 domain of V-ATPase from a certain mutant strain, not wild-type yeast [PMID: 34918374]. It may explain why the authors did not identify it in their original protein-protein interactions screen here.
The structural work on the V1 domain bound to Oxr1 (Khan et al, 2022) showed that the binding of Oxr1 prevented V1 from assembling onto the Vo. Since our experiments rely on the purification of vacuoles, they should contain mainly only V1 assembled onto the VO, and not the free soluble V1. This is likely the reason that we do not detect Oxr1, in addition to it being less abundant. We have clarified this now in the manuscript and added the fact that Oxr1 was co-purified with a V1 containing a mutant version of the H subunit.
“In a previous study, Oxr1 was co-purified with a V1 domain containing a mutant version of the H subunit, and its presence prevented the in vitro assembly of this V1 domain onto the VO domain and promoted disassembly of the holocomplex (Khan et al., 2022). This is likely the reason why we do not detect Oxr1 in our experiments, which rely on isolated vacuoles and thus would only include V1 domains that are assembled onto the membrane. In addition, Oxr1 is less abundant in yeast cells than Rtc5 according to the protein abundance database PaxDb (Wang et al, 2015).”
2) It is a wrong conclusion that because Rtc5 was co-purified with both V1 and V0 domain subunits it interacts with the assembled V-ATPase, this does not exclude a possibility that Rtc5 also interacts with separate V1 sector or separate V0 sector of V-ATPase.
We agree with the reviewer that the co-purification of Rtc5 with both V1 and VO domain subunits does not necessarily mean that it interacts with the assembled V-ATPase. Thus, we have modified the text in this part to:
“The fact that we can co-enrich Rtc5 both with Vma2 and with Vph1 indicates that it can interact either with both the VO and V1 domains or with the assembled V-ATPase.”
However, other results throughout the manuscript can be taken into account to strengthen this idea:
- Rtc5 requires an assembled V-ATPase to localize to the vacuole membrane, and thus seems not to interact with free VO domains, which would be available when we delete V1 subunits or in medium containing galactose.
- Rtc5 becomes cytosolic in galactose-containing media. This would indicate that it also does not interact with free V1 domains, which are still localized to the vacuole membrane under these conditions. Taken together with the pull-downs, these results suggest that Rtc5 interacts with the assembled V1-VO V-ATPase. Thus, we have included the following sentence after Figure 3, which shows the subcellular localization experiments.
*“Taking into account that Rtc5 is co-enriched with subunits of both the VO and V1 domain, and that it localizes at the vacuole membrane dependent on an assembled V-ATPase, we suggest that Rtc5 interacts with the assembled V-ATPase complex.” *
Re: Figure 1, Panel C. Is it possible to show individual proteins in different colors for clarity?
Panel D. How were cross-link distances measured? It is not obvious if you are not an expert in the field and it is not described in the methods.
We have modified Figure 1 C and Figure 1 – Supplement 1B (now Figure 1 – Supplement 1 A) to present the different subunits in the structures with different shades of blue and grey.
Furthermore, we have clarified the distance measurement approach in the methods section and in the legend of Fig 1D: “Ca-Ca distances were determined using the measuring function in Pymol v.2.5.2 (Schrodinger LLC).”
__Re: Figure 1 - Supplement 1, __
Panel A. What scientific information are we getting from this picture?
This panel was just a visual representation of the complexity of the protein network we obtained. Indeed, there was no specific scientific message, so we have decided to remove this panel from the revised manuscript.
Panel B. Why are these complexes shown separately from the complexes in Figure 1, panel C? Also, can individual proteins be colored differently here as well?
We did not want to overload Fig 1C, so we decided to show some of the protein complexes in Fig 1 – Supplement 1B. The most important information is the histogram showing that 95% of the mapped cross-links fall within the expected length range, and this is shown in the main Figure (Figure 1D). As stated above, we have adjusted the subunit coloring in Figure 1 C to improve clarity.
Re: Figure 3. It will be nice to show the localization of the untagged protein as well if antibodies are available (OPTIONAL).
Unfortunately, there are no available antibodies for either Rtc5 or Oxr1. This hinders us from detecting the endogenous untagged proteins. We would like to point out that we have been very careful in showing which tagged proteins are functional (C-terminally tagged Rtc5) and which are not (C-terminally tagged Oxr1), so that the reader can know how to interpret the localization data.
Re: Figure 4. Why different tags were used in panels A (GFP), C (msGFP2) and D
(mNeonGreen)?
In general, we prefer to use mNeonGreen as a tag for microscopy experiments because it is brighter and more stable, and msGFP2 as a tag for experiments involving Western blots because we have better antibodies available. There was a mistake in the labeling, and actually, all constructs labeled as GFP were msGFP2. We have now corrected this. Of note, we have tested the functionality of both tagged version (mNeonGreen and msGFP2).
Panels B and C. Were Rtc5 fusions detected using anti-GFP antibodies?
Indeed, Rtc5-msGFP2 was detected with an anti-GFP antibody. We have now indicated next to each Western blot membrane the primary antibody used. In addition, all antibodies are detailed in Supplemental Figure 3.
The authors should have full-size Western blots available, not just cut-out bands, as some journals and reviewers require them for publication.
For all western blots, we always showed a good portion of the membrane and not cut-out bands. The cropping was performed to avoid making figures unnecessarily large. The whole membranes are of course available and will be included in an “extended data file” if required by the journal.
Re: Figure 4 - Supplement 1, Panel A. Does "-" and "+" mean -/+ Azido-Myr?
Indeed. We have now added this label to the figure.
Panel B. There is no blot with a membrane protein marker (Vam3 or Vac8), it should be included.
We have replaced this western blot for a different repetition of this experiment in which a membrane protein marker was included. Of note, the two other repetitions of the experiment shown (Figure 4 – Supplement 1 panel C and Figure 4 panel C) also include both a membrane protein marker and a soluble protein marker.
Re: Figure 5. The title does not describe all results in this figure and should be modified accordingly.
The original data from Figure 5 is now separated into Figures 5 and 6 because of the additional experiments included during revisions. We have modified the Figure titles to be descriptive of the overall message of the Figures.
Panel C. Statistical significance value for *** should be indicated in the legend.
This has been indicated in the Figure legend.
It is not clear how many times yeast growth assays were repeated. Usually, all experiments should be done in triplicates or more.
All shown growth tests were performed at least three times for the conditions shown. We have now indicated this in the materials and methods section. In addition, we now provide in this response a file with all repetitions of growth tests, which will be appended to the article if deemed necessary by the editors.
Re: Figure 5 - supplement 1. No title
Re: Figure 5 - supplement 2. No title
Whether the supplemental Figures should have a title or not will depend on the style of the journal where the manuscript is finally published. The current idea of the supplemental Figures is that they complement the corresponding main Figure. For this reason, we have removed all titles from supplemental Figures.
Re: Figure 6. There is a typo on the second lane in the legend: "...the genome were", not "...the genome where".
This has been corrected.
Panel C. Why the analysis of BCECF vacuole staining of double mutants oxr1Δrav1Δ and rtc5Δrav1Δ is not shown? Was it done at all?
We had not included this piece of data, as we thought that the genetic interaction of RTC5 and OXR1 and rav1Δ was sufficiently well supported with the included data (growth tests in combination with the deletion, growth tests in combination with the overexpression, vacuole proteomics in combination with overexpression, and BCECF staining in combination with the overexpression). Because of the request of the reviewer, we have now included this experiment as Figure 7 G.
Re: Figure 6 - Supplement 2. Why were two different tags (2xmNG and msGFP2) used?
We tried both tags to see if one of them would be functional. Unfortunately, they both resulted in non-functional proteins, as shown by the corresponding growth tests.
Did the authors study N-terminally tagged Oxr1? Was it functional?
We have tagged Oxr1 N-terminally, and this unfortunately resulted in a protein that was not completely functional. We show below the localization of N-terminally mNeon-tagged Oxr1, under the control of the TEF1 promoter. The protein appears cytosolic (Panel A) but is not completely functional (Panel B). The localization of Oxr1 had already been misreported by using a tagged version that we now show to be non-functional. For this reason, we preferred not to include this data in the manuscript, to avoid again including in the literature subcellular localizations that correspond to non-functional or partially functional proteins.
Panel B. Results for the untagged TEF1pr-Oxr1 overexpression are not shown, thus tagged and untagged proteins can't be compared. Are they available? What is the promoter for the expression of 2xmNG fusion constructs?
Oxr1-2xmNG was C-terminally tagged in the genome, which means that the promoter is the endogenous one, it was not modified. For this reason, the correct controls are a strain expressing Oxr1 at endogenous levels (the wt strain) and a strain lacking Oxr1. Both controls were included in the Figure, and in all repetitions made of this experiment. For reference, all the genotypes of the strains used are found in Supplemental Table 1.
Re: Methods. Were vacuoles prepared differently for XL-MS and SILAC-based vacuole proteomics (there are different references) and why? Methods for XL-MS and quantitative SILAC-based proteomics can be placed together for clarity.
The basis for the method of vacuole purification is the same, from (Haas, 1995). This reference was included in both protocols that include vacuole purifications. However, modifications of this method were performed to fit the crosslinking method (higher pH, no primary amines) or to fit the SILAC labeling (combination of two differentially labeled samples in one purification). The reference for the vacuole proteomics (Eising et al 2022) corresponds to a paper in which the SILAC-based comparison of vacuoles from different mutant strains was optimized, and includes not only the vacuole purification but the growth conditions and downstream processing of the vacuoles.
Since both the SILAC-based vacuole proteomics and the XL-MS are multi-step methods, containing numerous parameters including the sample preparation, processing for MS, MS run and data analysis, we would prefer to keep them separate. We think this would allow a person attempting to reproduce these methods to go through them step by step.
What is CMAC dye? Why was it used to stain the vacuolar lumen?
We apologize for this oversight, we have included the definition of CMAC as 7-Amino-4-Chlormethylcumarin. It is a standard-used organelle marker for the lumen of the vacuole.
Some abbreviations (TEAB, ACN) are not explained.
We apologize for this oversight. We have now replaced these abbreviations with the full names of the compounds in the article.
What is 0% Ficoll?
We used the term 0% Ficoll, because this is the name given to the buffer in the original Haas 1995 paper on vacuole purifications. However, we agree that the term is misleading and we have now added the composition of the buffer (10 mM PIPES/KOH pH=6.8, 0.2 M Sorbitol).
Reviewer #3 (Significance (Required)):
The vacuolar-type proton ATPase, V-ATPase, is the key proton pump, that hydrolases ATP and uses this energy to pump protons across membranes. Amazingly, this proton pump and its function are conserved in eukaryotes from yeast to mammals. While V-ATPase structure and function have been studied for more than 30 years in various organisms, its regulation is not completely understood. The very recent discoveries of two new V-ATPase interacting proteins in yeast, first Oxr1 (OXidative Resistance 1), and now Rtc5 (Restriction of Telomere Capping 5), both the only two members of TLDc (The Tre2/Bub2/Cdc16 (TBC), lysin motif (LysM), domain catalytic) proteins in yeast, provide new insights in V-ATPase regulation in yeast, and because the interaction is conserved in mammals its relevance to mammalian V-ATPases regulation as well.
TLDc proteins are best known for their role in protection from oxidative stress, in particular in yeast and in the nervous system in mammals. The discovery of the novel Rtc5-V-ATPase interaction points to the role of V-ATPase not only in protection from oxidative stress but also in restriction of telomere capping in yeast and most likely higher species. The studies of other species also highlight the possible conserved role of V-ATPase in lifespan determination and Torc1 signaling, mediated through these interactions. Thus, the discovery of this new functionally important interaction between the second TLDc family member in yeast, Rtc5, and V-ATPase will shed light on the molecular mechanisms of all these essential biological processes and pathways.
In addition, because the authors performed a comprehensive proteomics protein-protein interaction study of the purified yeast vacuole it provides a valuable resource for all researchers who study vacuoles and/or related to them lysosomes.
The follow-up functional studies using the rav1Δ strain clearly demonstrated that Rtc5 and Oxr1 disassemble V-ATPase and counteract the function of V-ATPase assembly RAVE complex in vivo in yeast. Thus, they are essentially the first discovered endogenous eukaryotic protein inhibitors of V-ATPase. Moreover, because the authors obtained the evidence that Oxr1 is the regulator of the specific subunit isoform of V-ATPase Stv1p in vivo in yeast, it suggests that different TLDc proteins may regulate different specific V-ATPase subunit isoforms in cell- and tissue-specific manner in higher eukaryotes. The mechanism of this isoform-specific regulation in yeast and other species needs further investigation in the future.
Because of the conservation of the TLDc-V-ATPase interactions, all this information can be extrapolated to higher species, all the way to humans, in whom genetic mutations in various TLDc proteins are known to cause devastating diseases and syndromes.
We are thankful to the reviewer for their positive comments about the significance of our work.
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Referee #3
Evidence, reproducibility and clarity
Summary
In this manuscript, the authors used a proteomics approach to comprehensively study yeast vacuole protein-protein interactions using cross-linking mass-spectrometry (XL-MS). They identified 16694 interactions between 2051 proteins. Many known vacuolar protein complexes were found and used as positive controls, confirming the high quality of the dataset, however, no negative controls were reported, and this issue is raised in the 'Major comments' section. The authors then focused on one particular previously unknown protein-protein interaction between the TLDc-domain containing protein of unknown function Rtc5 and the vacuolar-type proton ATPase, V-ATPase, which acidifies yeast vacuoles. The methods and results regarding Rtc5 discovery as a novel interactor of the V-ATPase, Rtc5 myristoylation, and its V-ATPase-dependent localization to the vacuole membrane are convincing. The authors then moved on to study the in vivo function of Rtc5 as well as Oxr1, the only other TLDc-domain-containing protein in yeast. Interestingly, they did not originally detect Oxr1 in their protein-protein interaction studies, apparently due to its very low abundance in yeast. However, they found that deletion of either RTC5 or OXR1 in vivo resulted in more assembled V-ATPase at the yeast vacuole and this effect was stronger in oxr1Δ cells. However, RTC5/OXR1 deletion or overexpression in parental yeast strains did not affect either vacuolar pH (a readout of functional V-ATPase) or yeast growth, including growth under specific conditions (neutral pH, in the presence of high concentrations of calcium or zinc), which is used to reveal a conditional lethal phenotype of unfunctional V-ATPase (the Vma− phenotype). Since they did not observe any in vivo phenotype in parental yeast strains, they subsequently studied the effects of RTC5/OXR1 deletion and overexpression in the 'sensitized' rav1Δ strain, lacking a specific assembly factor of V-ATPase, Rav1, one of the subunits of RAVE complex. In this strain, RTC5/OXR1 overexpression resulted in less acidic vacuolar pH and reduced growth of double mutant cells, compared to the single rav1Δ mutant. In addition, overexpression of Oxr1, but not Rtc5, caused disassembly of the V-ATPase in rav1Δ cells, noteworthy this effect was not detectable in the parent strain with intact Rav1p. Finally, they found that in oxr1Δ cells there is more Stv1 in the vacuole and concluded that Oxr1 is necessary for the retention of Stv1 containing V-ATPase at the vacuole. However, the mechanism seems to be complicated and remains to be elucidated. In summary, an impressive variety of methods from a technologically advanced XL-MS to classical yeast growth assays were used to identify Rtc5 interaction with V-ATPase and analyze its functional role in vivo in yeast, making the conclusions well justified overall.
Major comments
Re: A cross-linking mass spectrometry map of vacuolar protein interactions (results)
While XL-MS is a very powerful method, it is a high-throughput approach and there should be some kind of negative control in these experiments. In cross-linking experiments, non-cross-linked samples are usually used as negative controls. What was the negative control in cross-linking mass-spectrometry experiments here? If there was no negative control, how the specificity of interactions was evaluated? Maybe the authors analyzed the dataset for highly improbable interactions and found very few of them? In addition, the high purity of vacuole preparation is critical. How was it assessed by the authors? All this is important to know to use this dataset as a reliable resource in the future.
Re: Rtc5 and Oxr1 counteract the function of the RAVE complex (results)
Taken together, data, presented in this section of the manuscript, provide strong evidence that Rtc5 and Oxr1 negatively regulate V-ATPase activity, counteracting the V-ATPase assembly, facilitated by the activity of the RAVE complex. However, the complete deletion of the major RAVE subunit Rav1p was required to observe this effect in vivo in yeast. The other way to induce V-ATPase disassembly in yeast is glucose deprivation. It will be interesting to study if there is a synergistic effect between glucose deprivation and RTC5/OXR1 deletion on V-ATPase assembly, vacuolar pH, and growth of single oxr1Δ, rtc5Δ or double oxr1Δrtc5Δ mutants (OPTIONAL). Glucose deprivation is a more physiologically relevant condition than a deletion of an entire gene.
Re: Figure 6 - supplement 1. The title is relevant to panel D only, it should be renamed to reflect the results of the disassembly of V-ATPase in rav1Δ mutant strains, while results about the stv1Δ-based strains (Panel D) should be shown together with similar experiments in Figure 7 - supplement 2 for clarity.
Re: Figure 7 - supplement 1, Panel A. The proper assay to show that Stv1-mNeonGreen is functional is to express it in double mutant vph1Δstv1Δ to see if the growth defect is reversed. In addition, the vph1Δ growth defect is not changed (improved or worsened) in the presence of Stv1-mNeonGreen, so it means that the expression of Stv1-mNeonGreen does not further compromise the V-ATPase function, but it does not mean that it improves its function.
Re: Figure 7 - supplement 2. This figure should be combined with Fig. 6- suppl 1, panel D as also mentioned above. The figure seems to lack some labels, and conclusions are not accurate as discussed below. However, this data provides important additional information about relationships between isoform-specific subunits of V-ATPase Vph1 and Stv1 and both Rtc5 and Oxr1 and should be repeated if it is not done yet to have a better idea about these relationships. Panel B: Based on this picture, deletion of RTC5 has a negative genetic interaction with the deletion of VPH1, since double deletion mutant vph1Δ rtc5Δ grows worse than each individual mutant. Although it also means that there is no positive interaction, it is not the same. Panel C: Same as for panel B. Based on this picture, the deletion of OXR1 has a weak negative genetic interaction with the deletion of STV1, since double deletion mutant stv1Δ oxr1Δ grows worse than each individual mutant at 6 mM ZnCl2. In addition, there is no label for the media in the middle panel, is it just YPAD pH=7.5, without the addition of any metals? Why there is no growth assay in the presence of CaCl2, like in panels A and B? Panel D: Same as for panels B and C. Based on this picture, deletion of RTC5 has a negative genetic interaction with the deletion of STV1, since double deletion mutant stv1Δ rtc5Δ grows worse than each individual mutant at 6 mM ZnCl2. There is no label in the middle panel (growth conditions) and no growth assay data in the presence of CaCl2.
Re: Figure 7 - supplement 2, continued. How many times all these experiments were repeated? These experiments should be repeated at least 3 times, which is especially necessary for the experiments in panel C, because the effects are borderline. If results are reproducible and statistically significant, although small, the conclusion should be changed from "no positive genetic interactions" to "negative genetic interactions", which is more precise and informative. However, these results will be then in contradiction with the results from Figure 6 - Supplement 1, panel D, showing negative genetic interaction between the overexpression of Rtc5 or Oxr1 and deletion of Stv1, since both deletion and overexpression of Rtc5 or Oxr1 would have negative genetic interactions with Stv1. In addition, apparently, there is no data about genetic interaction between the overexpression of Rtc5 or Oxr1 and the deletion of Vph1. All this needs clarification, therefore repeating these experiments is essential. In conclusion, while genetic interactions between RTC5/OXR1 and RAV1 are straightforward, they seem to be more complex with STV1/VPH1.
Re: Methods. There is no description of yeast serial dilution growth assay at all. In addition, why the specific media (neutral pH, in the presence of high concentrations of calcium or zinc) was used is not explained either in the results or methods. Appropriate references should be included, for example, PMID: 2139726, PMID: 1491236.
Minor comments
Yeast proteins are named with "p" at the end, such as "Rtc5p".
Re: Introduction. In the introduction it should be indicated that Rtc5 was originally discovered as a "restriction of telomere capping 5", using screening of temperature-sensitive cdc13-1 mutants combined with the yeast gene deletion collection [PMID: 18845848]. A couple of sentences should be written about the RAVE complex and its role in V-ATPase assembly.
Re: The TLDc domain-containing protein of unknown function Rtc5 is a novel interactor of the vacuolar V-ATPase (results) 1) It is important to understand, that Oxr1 was co-purified before with the V1 domain of V-ATPase from a certain mutant strain, not wild-type yeast [PMID: 34918374]. It may explain why the authors did not identify it in their original protein-protein interactions screen here. 2) It is a wrong conclusion that because Rtc5 was co-purified with both V1 and V0 domain subunits it interacts with the assembled V-ATPase, this does not exclude a possibility that Rtc5 also interacts with separate V1 sector or separate V0 sector of V-ATPase.
Re: Figure 1, Panel C. Is it possible to show individual proteins in different colors for clarity? Panel D. How were cross-link distances measured? It is not obvious if you are not an expert in the field and it is not described in the methods.
Re: Figure 1 - Supplement 1, Panel A. What scientific information are we getting from this picture? Panel B. Why are these complexes shown separately from the complexes in Figure 1, panel C? Also, can individual proteins be colored differently here as well?
Re: Figure 3. It will be nice to show the localization of the untagged protein as well if antibodies are available (OPTIONAL).
Re: Figure 4. Why different tags were used in panels A (GFP), C (msGFP2) and D (mNeonGreen)? Panels B and C. Were Rtc5 fusions detected using anti-GFP antibodies? The authors should have full-size Western blots available, not just cut-out bands, as some journals and reviewers require them for publication.
Re: Figure 4 - Supplement 1, Panel A. Does "-" and "+" mean -/+ Azido-Myr? Panel B. There is no blot with a membrane protein marker (Vam3 or Vac8), it should be included.
Re: Figure 5. The title does not describe all results in this figure and should be modified accordingly. Panel C. Statistical significance value for *** should be indicated in the legend. It is not clear how many times yeast growth assays were repeated. Usually, all experiments should be done in triplicates or more.
Re: Figure 5 - supplement 1. No title
Re: Figure 5 - supplement 2. No title
Re: Figure 6. There is a typo on the second lane in the legend: "...the genome were", not "...the genome where". Panel C. Why the analysis of BCECF vacuole staining of double mutants oxr1Δrav1Δ and rtc5Δrav1Δ is not shown? Was it done at all?
Re: Figure 6 - Supplement 2. Why were two different tags (2xmNG and msGFP2) used? Did the authors study N-terminally tagged Oxr1? Was it functional? Panel B. Results for the untagged TEF1pr-Oxr1 overexpression are not shown, thus tagged and untagged proteins can't be compared. Are they available? What is the promoter for the expression of 2xmNG fusion constructs?
Re: Methods. Were vacuoles prepared differently for XL-MS and SILAC-based vacuole proteomics (there are different references) and why? Methods for XL-MS and quantitative SILAC-based proteomics can be placed together for clarity. What is CMAC dye? Why was it used to stain the vacuolar lumen? Some abbreviations (TEAB, ACN) are not explained. What is 0% Ficoll?
Referees cross-commenting
I agree with both reviewers, although I think that it is a pretty novel finding because while I was familiar with Oxr1 data I did not realize until now that there is a second protein in yeast. I think it is because homology between Oxr1 and Rtc5 is really low. I also agree that they should study more about what happens with V0 subunits.
Significance
Field of expertise keywords:
Protein-protein interactions, V-ATPase, TLDc
The vacuolar-type proton ATPase, V-ATPase, is the key proton pump, that hydrolases ATP and uses this energy to pump protons across membranes. Amazingly, this proton pump and its function are conserved in eukaryotes from yeast to mammals. While V-ATPase structure and function have been studied for more than 30 years in various organisms, its regulation is not completely understood. The very recent discoveries of two new V-ATPase interacting proteins in yeast, first Oxr1 (OXidative Resistance 1), and now Rtc5 (Restriction of Telomere Capping 5), both the only two members of TLDc (The Tre2/Bub2/Cdc16 (TBC), lysin motif (LysM), domain catalytic) proteins in yeast, provide new insights in V-ATPase regulation in yeast, and because the interaction is conserved in mammals its relevance to mammalian V-ATPases regulation as well.
TLDc proteins are best known for their role in protection from oxidative stress, in particular in yeast and in the nervous system in mammals. The discovery of the novel Rtc5-V-ATPase interaction points to the role of V-ATPase not only in protection from oxidative stress but also in restriction of telomere capping in yeast and most likely higher species. The studies of other species also highlight the possible conserved role of V-ATPase in lifespan determination and Torc1 signaling, mediated through these interactions. Thus, the discovery of this new functionally important interaction between the second TLDc family member in yeast, Rtc5, and V-ATPase will shed light on the molecular mechanisms of all these essential biological processes and pathways.
In addition, because the authors performed a comprehensive proteomics protein-protein interaction study of the purified yeast vacuole it provides a valuable resource for all researchers who study vacuoles and/or related to them lysosomes.
The follow-up functional studies using the rav1Δ strain clearly demonstrated that Rtc5 and Oxr1 disassemble V-ATPase and counteract the function of V-ATPase assembly RAVE complex in vivo in yeast. Thus, they are essentially the first discovered endogenous eukaryotic protein inhibitors of V-ATPase. Moreover, because the authors obtained the evidence that Oxr1 is the regulator of the specific subunit isoform of V-ATPase Stv1p in vivo in yeast, it suggests that different TLDc proteins may regulate different specific V-ATPase subunit isoforms in cell- and tissue-specific manner in higher eukaryotes. The mechanism of this isoform-specific regulation in yeast and other species needs further investigation in the future.
Because of the conservation of the TLDc-V-ATPase interactions, all this information can be extrapolated to higher species, all the way to humans, in whom genetic mutations in various TLDc proteins are known to cause devastating diseases and syndromes.
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Referee #2
Evidence, reproducibility and clarity
Using cross-linking proteomics, Klössel et al. identify the yeast TLDc domain protein Rtc5 as a novel interactor of yeast V-ATPase and characterize functions for Rtc5 and the TLDc domain protein Oxr1 in V-ATPase assembly and localization.
Major points:
- The evidence of Oxr1 and Rtc5 as V-ATPase disassembly factors is circumstantial. The authors base their interpretation primarily on increased V1 (but not Vo) at purified vacuoles from Oxr1- or Rtc5-deleted strains, which does not directly address disassembly. Of course, the results regarding Oxr1 confirm detailed disassembly experiments with the purified protein complex (PMID 34918374), but on their own are open to other interpretations, e.g. suppression of V-ATPase assembly. Of note, the authors emphasize that they provide first evidence of the in vivo role of Oxr1, but monitor V1 recruitment with purified vacuoles and do not follow V-ATPase assembly in intact cells.
- Oxr1 and Rtc5 have very low sequence similarity. It would be helpful if the authors provided more detail on the predicted structure of the putative TLDc domain of Rtc5 and its relationship to the V-ATPase - Oxr1 structure. Is Rtc5 more closely related to established TLDc domain proteins in other organisms?
- The authors conclude vacuolar recruitment of Rtc5 depends on the assembled V-ATPase, based on deletion of different V1 and Vo domain subunits. However, these genetic manipulations likely cause a strong perturbation of vacuolar acidification; indeed, the images show drastically altered vacuolar morphology. To strengthen their conclusion, it would be helpful to show that Rtc5 recruitment is not blocked by inhibition of vacuolar acidification, and that conversely it is blocked by deletion of rav1.
Significance
This is an interesting paper that confirms and extends previous findings on TLDc domain proteins as a novel class of proteins that interact with and regulate the V-ATPase in eukaryotes. The title seems to exaggerate the findings a bit, as the authors do not investigate V-ATPase (dis)assembly directly and only phenotypically describe altered subcellular localization of the Golgi V-ATPase in Oxr1-deleted cells. A recent structural and biochemical characterization of Oxr1 as a V-ATPase disassembly factor (PMID 34918374) somewhat limits the novelty of the results, but the function of Oxr1 in regulating subcellular V-ATPase localization and the identification of a second potential TLDc domain protein in yeast provide relevant insights into V-ATPase regulation. This paper will be of interest to cell biologists and biochemists working on lysosomal biology, organelle proteomics and V-ATPase regulation.
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Referee #1
Evidence, reproducibility and clarity
Klössel et al. explore the role of the TLDc domain-containing proteins Oxr1p and Rtc5p in Saccharomyces cerevisiae. They performed cross-linking mass spectrometry and detected the interaction of Rtc5p with V-ATPase. TLDc domains have previously been found to serve as V-ATPase interacting domains. The authors find that both Oxr1p and Rtc5p induce dissociation of V-ATPase in vivo, an activity that was previously established for Oxr1p in vitro. They propose that this activity counteracts the activity of the V-ATPase assembling RAVE complex. They also find that Oxr1p is necessary for late Golgi retention of the Golgi form of the V-ATPase (i.e. containing the Stv1p isoform of subunit a). It is a little surprising that Oxr1p binding to V-ATPase was not detected by the cross-linking mass spectrometry, although the authors argue that this absence may be owing to the abundance of the proteins, which sounds reasonable.
Suggestions:
- The authors observed that knockout of Rtc5p or Oxr1p does not affect vacuolar pH. If Rtc5p and Oxr1p both cooperate to dissociate V-ATPase, the authors may wish to characterize the effect of a ∆Rtc5p∆Oxr1p double knockout on vacuolar pH.
- The manusript would benefit from a well-labelled diagram showing the subunits of V-ATPase (e.g. in Figure 2D).
- The images of structures, especially in Figure 1-Supplement 1B, are not particularly clear and could be improved (e.g. by removing shadows or using transparency).
- The authors should clearly describe the differences between Rtc5p and Oxr1p in terms of protein length, sequence identity, domain structure, etc.
Minor:
- The "O" in VO should be capitalized.
- In Figure 4 supplement 1, the labels "I", "S", and "P" should be defined.
- Please clarify what is meant by "switched labelling"
- The meaning of the sentence "Indeed, this was the case for both of them" is ambiguous.
- For Figure 1-Supplement 1B it is hard to see the crosslink distances.
- The statement "The effects of Oxr1 are greater than those caused by Rtc5" requires more context. Is there a way of quantifying this effect for the reader?
- The phrase "negative genetic interaction" should be clarified.
- In the sentence "Isogenic strains with the indicated modifications in the genome where spotted as serial dilutions in media with pH=5.5, pH=7.5 or pH=7.5 and containing 3 mM ZnCl2", "where" should be "were".
- Figure 2D: the authors should consider re-coloring these models, as it is challenging to distinguish Rtc5p from the V-ATPase.
Significance
The vacuolar protein interaction map alone from this manuscript is a nice contribution to the literature. Experiments establishing colocalization of Rtc5p to the vacuole are convincing, as is dependence of this association on the presence of assembled V-ATPase. Similarly, experiments related to myristoylation are convincing. The observed mislocalization of V-ATPases that contain Stv1p to the vacuole (which is also known to occur when Vph1p has been knocked out) upon knockout of Oxr1p is also extremely interesting.
Overall, this is an interesting manuscript that contributes to our understand of TLDc proteins.
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Reply to the reviewers
'The authors do not wish to provide a response at this time.'
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Referee #3
Evidence, reproducibility and clarity
In this manuscript, Grossmann et al. present a new potential pathway that regulates PLK4 levels in cells mediated by the CRL4^DCAF1 E3 ubiquitin ligase complex (CUL4A/B-DDB1-DCAF1). PLK4 plays a crucial role in centriole assembly acting as a master regulator of the centriole biogenesis and thus contributes to centriole number control. Centriole numbers need to be tightly regulated as deviations could lead to aneuploidy and potentially cancer. At the onset of centriole assembly in G1/S, PLK4 is focusing into a single point on each parental centriole together with STIL and SAS6 defining the site of procentriole formation. For this process to happen, PLK4 trans-phosphorylates itself creating a binding site for SCF^β-TrCP E3 ubiquitin ligase that targets PLK4 for ubiquitination and degradation by the proteasome. The authors identified by co-IP and mass spectrometry the CRL4DCAF1 E3 ubiquitin ligase complex as a potential regulator of PLK4. They show that CRL4^DCAF1 E3 ubiquitin ligase complex binds to PLK4 and targets it for degradation. Furthermore, the authors present data where knockdown of DCAF1 leads to increased levels of PLK4 and centriole amplification. Using AlphaFold and followed by IPs with PLK4 point-mutants, they propose that DCAF1 binds to the dimer of PLK4 at PB1-PB2 at a similar site where Cep192/Cep152 bind. Then, they move on to show that CRL4^DCAF1 E3 ubiquitin ligase complex ubiquitylates PLK4 predominantly in G2 phase. Lastly, they propose that DCAF1 regulates the interaction of PLK4 with STIL and that it is required to prevent premature centriole disengagement in G2 phase. The manuscript is written in a clear and concise manner while the experimental approaches are sound and well described. The experimental data are well presented with a good number of replicates in most cases. However, some of the conclusions are drawn from marginal differences in the data and without statistical tests (cases indicated in detail below). I believe that this work is of interest to the scientific community, but it would require revisions to address the following major and minor comments.
Major comments:
- The key finding of the paper is that PLK4 co-IPs with DCAF1 and DDB1 that are core components of CUL4A/B E3 ubiquitin ligase. However, the only evidence that this interaction between PLK4 and DCAF1 is direct relies on the ubiquitylation assay performed in E. coli. This experiment was performed only once, and no quantitation is performed (Fig 4A). Given that the components are overexpressed in this heterologous system, it is very plausible to have a non-specific interaction between the DCAF1-Acidic domain and PLK4-PB1-PB2 considering that native binders to this region (Cep192/Cep152) are absent. The PLK4-DCAF1 model that was generated with AlphaFold suggests that this interaction is plausible but stronger verification that the interaction is direct are necessary in this reviewer's point of view. This could be performed for instance by purifying proteins (or fragments of them) to test binding in vitro, or through IPs of full-length proteins from bacterial extracts. If the interaction between PLK4 and DCAF1 is indeed direct, then the authors would need to provide an explanation of the feasibility of this interaction given that the binding site is occupied by Cep192 or Cep152 at the centrioles. Based on the current knowledge, PLK4 is loaded to the centriole through the interaction with Cep192 which is then switched to interaction with Cep152. For the DCAF1 to be able to bind to PLK4 it would need to outcompete Cep152. Thus, in order to prove that DCAF1 can control PLK4 at the centrosome, evidence would need to be provided that this interaction is possible. If that is not the case, then the most likely alternative is that DCAF1 interactions with cytoplasmic pool of PLK4, thus only indirectly controlling the PLK4 levels at centrioles. A plausible alternative interpretation of the data provided would be that DCAF1-Acidic domain could bind weakly and perhaps non-specifically to PLK4 but in human cells the interaction is mediated through another component such as Cep152 or Cep192 (which are also present in the MS data). Based on the AlphaFold model, the authors introduced point mutations that abolish the PLK4-DCAF1 interaction, but this effect could just as easily be an indirect effect due to abolishing of the PLK4-Cep152/Cep192 interaction.
- The authors state that DCAF1 depletion with siRNA or shRNA leads to increased level of PLK4 which triggers centriole overduplication. However, this statement is not entirely supported by the data provided. Firstly, in the western blots shown (Fig2A, 2D) the increase in PLK4 levels is hardly visible. Given that this is a key finding, stronger evidence would need to be provided. Furthermore, the quantification of the PLK4 levels upon siRNA mediated DCAF1 depletion are confusing as siDCAF1#2 leads to higher PLK4 levels than siDCAF1#1 despite being less effective in DCAF1 depletion (Fig 2A). More importantly, the quantification on the HeLa tet-on shDCAF1, that are used in many experiments, is missing an important statistical test (Fig 2D). Similarly, no statistical test is performed on the quantification of centriole numbers (Fig 2F) which puts to question the conclusion that "CRL4DCAF1 might function to keep PLK4 protein levels low, thus preventing centriole overduplication". Moreover, GFP-PLK4 levels shown in Fig 6A seem unaltered (if not lowered) upon DCAF1 depletion. Lastly, DCAF1 overexpression does not seem to decrease PLK4 levels as shown in Fig 6B. In that experiment, though, PLK4 is also overexpressed. In order to support the proposed function that CRL4DCAF1 keeps PLK4 levels low, it would be useful to also investigate whether overexpression of DCAF1 would lead to further decrease of PLK4 levels.
- In page 7, the authors mention: "A premature onset of centriole duplication in the absence of DCAF1 should also result in increased numbers of already disengaged centrioles in G2 phase." This premise is not correct as it is inverted to the current knowledge. It is the premature disengagement that licences for premature centriole duplication (or as often stated as re-duplication) rather than the premature onset of centriole duplication that causes disengagement. This is also what the authors correctly state in the discussion. In the data presented (Fig 6C) the authors observe centriole disengagement upon DCAF1 depletion using expansion microscopy, but no re-duplication is visible in the images provided. This is contrary to the overduplication claim made earlier on (Fig 2F). As such, the data presented do not fully support the drawn conclusion that DCAF1 controls PLK4 levels in G2 to prevent unscheduled centriole duplication. The authors would also need to investigate whether the prolonged use of Cdk1 inhibitor RO-3306 to synchronise the cells in G2 in addition to DCAF1 depletion contributes to the centriole disengagement that is observed, considering that Cdk1-Cyclin B acts also on PLK4-STIL complex.
- The mechanism proposed by the authors is that DCAF1 maintains PLK4 at low levels throughout G2 which prevents premature disengagement. Subsequently, low PLK4 levels prevent binding and activation of STIL impeding premature initiation of centriole duplication. However, this would not happen since centrioles remain engaged at this stage. Overall, some of the aspects of the proposed mechanism are not fully supported by the data presented. In addition, the proposed mechanism does not offer a suitable mechanistic explanation of how lower PLK4 levels by CRL4^DCAF1 mediated ubiquitylation and degradation prevent centriole disengagement.
Minor comments
- In Fig S2A authors need to indicate the expected size of the expressed protein. In its current form blot is difficult to be assessed. More specifically, it is unclear what is the result on the IP with the PLK4 fragment (1-879) since the more intense band in the input in not the same as in in the IP with Flag.
- In Fig 1C, S2B, S3B, it would be helpful to have a summary of the interactions observed next to each construct. This is commonly represented with (-, +, ++, +++) depending on the amounts present in the IP.
- In Fig 1D, even though not statistically significant, there seems to be a reduction in the IP of AA and PEST. Do the authors have some suggestion why that might be?
- Authors used two different cell lines in the experiments presented in Fig2A and Fig 2B. Given that depletion of siDCAF5 is provided as a control of having no effect in the PLK4 levels I would expect to have the experiment performed on same cell line.
- No statistical test is provided in the comparison on PLK4 levels upon siRNA treatment coupled with CHX (Fig 2C).
- In the quantification of the PLK4 levels at the centrosomes (Fig 2E), it is not specified whether a background subtraction step was performed prior to the normalisation to the untreated control.
- In the blot shown in Fig S3, no input is visible in the lane with expression of the Acidic domain.
- Authors claim that both WD40 and acidic domain contribute to binding of PLK4 because WD40-Acidic is more efficient in binding PLK4 that Acidic domain alone. However, in the blot provided, WD40 alone does not interact with PLK4. Thus, the most likely explanation would be that Acidic domain is the major interactor and WD40 has only minor contribution or it offers a stabilisation role to the acidic domain.
- Regarding the AlphaFold model provided, and in addition to the comments above, some further clarifications and controls would need to be provided. AlphaFold is a powerful tool but not without its caveats and needs to be used with caution. The authors need to provide a description on how they used AlphaFold to generate the model presented. Typically, AlphaFold produces 5 output models. At which site was DCAF1-Acidic domain positioned in the other output models? Based on what criteria the model shown was selected? Also, a confidence score for the model should be provided.
- The authors compare their PLK4-DCAF1 AlphaFold model with the structure of PLK4-CEP192 complex but not with the PLK4-Cep152. What is the explanation for this? Given that Cep152 is reported to have higher affinity than Cep192 (Park SY et al., 2014) it would be important to be included in the comparisons performed.
- The phrase "An overlay between the two structures revealed that ..." is not accurate as one is a merely a model. There are also other instances in the text that the model is referred to as 'structure' which is not correct.
- Please provide a citation for "Poisson-Boltzmann solver (APBS)".
- In Fig 3A ribbon representations are too small to see DCAF1 in the printout.
- The mutations designed might affect the folding of the PBs and thus no interaction is observed. Authors could test how the mutations would affect PB1-PB2 and also design one or two mutants that are in the vicinity but not in the interaction interface to serve as true negative controls in addition to the PLK4-WT. Do these mutants localise to centrioles or also the interaction with Cep192/Cep152 is affected?
- There is no statistical test in the quantification in Fig 3D, but it is not critical as the difference is very clear and certainly statistically significant.
- Authors state that DCAF1 strongly interacts with PLK4 during interphase but only weakly in mitosis with quantification in Fig 5A but there is no statistical test.
- Based on the data shown in Fig 5B, authors state that PLK4 is predominantly ubiquitylated by CRL4DCAF1 in G2 phase. However, in the blot shown, PLK4 seems to be in more abundance in G2 that might explain the apparent higher ubiquitylation. Furthermore, the experiment was performed once and no quantification of the ubiquitylation is performed. Lastly, there are no evidence that this apparent higher ubiquitylation in G2 is mediated by CRL4DCAF1.
- In Fig 6A, STIL levels upon DCAF1 depletion seem to be lower, is there any potential explanation for that? No statistical test is performed for the STIL/GFP-PLK4 levels difference in siGL2 versus siDCAF1. The authors should provide a justification for over-expressing PLK4 in this experiment. Similarly, in Fig 6B, the authors use overexpression of both PLK4 and DCAF1 and no statistical test is performed.
- Authors report in Fig 6C disengaged centrioles. How are disengaged centriole defined, is it based on a distance cut-off or loss of orthogonality? In the images provided, this reviewer's impression is that in the (+) Dox condition, there are two parental centrioles that have separated rather disengaged procentriole. Do the images come from the same cell?
- Based on the data presented, would overexpression of PLK4 in G2 would result in centriole disengagement? This is something that the authors would optionally check.
- The quantification of rootletin as an additional confirmation of centriole disengagement is puzzling to me as I would expect an increase rather than decrease of its levels. As centrioles disengage, a new link would need to form and thus the expected increase in its levels. However, new rootlet might form only later in mitosis. Also, given that the cells are synchronised in G2 the quantification is more complex. In late G2, centrioles separate in order to move to opposite poles to form the mitotic spindle. This would result in removal of the rootlet that might reflect the reduction the authors report. Ideally the quantification should be limited to cells in late G2 (that centrioles have separated) stained with Centrin 2 to allow for a quantification per centriole pair.
- In the discussion, authors state "It is conceivable that increasing amounts of PLK4 during mitosis, when the interaction between CRL4DCAF1 and PLK4 is weak, might capture STIL from binding to CDK1 initiating the interaction between PLK4 and STIL". In mitosis CDK1-Cyclin B binds to STIL and prevents formation of the PLK4-STIL complex, thus inhibiting untimely onset of centriole biogenesis (Zitouni et al., 2016). In addition, the authors show that total PLK4 levels are low in mitosis (Fig 5A). The conclusion drawn are not in line with the current literature.
- The addition of a graphical representation of the proposed mechanism would be beneficial to the readers.
- A reference for the Ac.Tubulin antibody used is missing.
- Please provide a citation for FiJi.
Referees cross-commenting
I find the comments by the other two reviewers to be valid, clear, insightful, and complementary to those made by this reviewer. There is a good convergence between the reviewers on the critical aspects in this manuscript that require attention. Following revisions this study will contribute to the understanding of regulatory mechanisms acting at the centrioles.
Significance
Centriole number control is an important aspect that is relevant not only to the centrosome research field but is also related to cilia, cells signaling, and cancer research. This work presents a novel pathway involved in the regulation of PLK4 levels in cells mediated by the CRL4^DCAF1 E3 ubiquitin ligase complex (CUL4A/B-DDB1-DCAF1). The authors present extensive data to characterise when and how DCAF1 interactions with PLK4 to lowers its levels through ubiquitination and subsequent degradation by the proteasome. However, the effects from various treatments are often minor. The study from Grossmann et al. comes to complement already known pathways of controlling centriole numbers, at G1/S through SCFβ-TrCP E3 ubiquitin ligase mediated PLK4 degradation, and in mitosis by CDK1-Cyclin B through STIL 'capturing' to block centriole reduplication. Given that certain aspects of the manuscript are revised, and an updated and more thorough mechanism is proposed and supported, it will contribute to the conceptual advancement or our understanding of centriole number control across the cell cycle. It could potentially also contribute to the ubiquitin research field of research, but it is hard for me to assess this as it is not my field of expertise.
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Referee #2
Evidence, reproducibility and clarity
Polo-like kinase 4 (Plk4) is the master regulator of centriole assembly and previous studies have shown that its level must be tightly regulated to ensure the precise duplication of centrioles during each cell cycle. It is well documented that the abundance of Plk4 is regulated by E3 ubiquitin ligases and in particular the SCF-TrCp ubiquitin ligase. However, in the absence of SCF-TrCp mediated regulation, PLk4 is still ubiquitylated suggesting that other ubiquitin ligases function to regulate Plk4 levels. Here Grossman and colleagues identify the CUL4-DDB1-DCAF1 (CRL4DCAF1) E3 ubiquitin ligase as a regulator of Plk4 levels and show that it functions predominantly in G2 phase to prevent centriole assembly in M phase. They propose a model whereby SCF-TrCp and CRL4DCAF1 cooperate to control the levels of PLK4 at different points in the cell cycle.
This study has the potential to yield some important and novel insights into the regulation of centriole assembly. However, in its current form the relationship between the increased Plk4 levels and the other effects described by the authors remain unclear. In particular, it is not clear to me that the small increase in Plk4 levels upon CRL4DCAF1 inhibition is responsible for the multipolar spindle phenotype. Nor is it clear how this increase in Plk4 is related to the premature disengagement defect. Finally, some of the experimental results could be made more convincing by including quantitation and/or additional controls. Major and minor issues are listed point-by-point below.
Major issues
Figure 2A and E. The authors report that depletion of DCAF-1 results in an increase in Plk4 levels. However, the actual increase is pretty small, about 1.5 fold for total levels (2A) and approximately 1.2 fold at centrosomes. How can the authors be sure this small increase in Plk4 levels is responsible for the multipolar spindle phenotype reported in figure 2F? It seems to me that CRL4DCAF1 could have other relevant substrates that are responsible for this defect. Related to this, can the authors show that the multipolar spindle phenotype is due to an overproduction of centrioles versus some other defect such as cytokinesis failure? Did the authors examine DCAF-1-depleted cells to if there are cell division defects that could explain the multipolar spindle defect?
Do the authors know if DCAF1 is operating within the context of the CRL4DCAF1 complex to control Plk4 levels? I know they showed that the entire complex is bound to Plk4 in pull down experiments, but have they tried to deplete other components of CRL4DCAF1 to see if they have the same effect on Plk4 levels?
Page 5 and Figure 3A. Th authors provide a model where the acidic domain of DCAF1 binds to a groove within the PB1-2 domain of Plk4. This is the same groove that binds CEP192, a protein that cooperates with Cep152 to recruit Plk4 to centrioles. Could it be that DCAF-1, at least in part, is competing with Cep192 and possibly cep152 for binding to Plk4? Thus, in the absence of DCAF1, Cep192 (and possibly Cep152) could recruit more Plk4. Can such a model be ruled out?
Figure 4. I don't find the results of the in vitro ubiquitin assays all that compelling. Here the authors are fusing DCAF1 to the E2 enzyme and show that this synthetic construct can ubiquitylate Plk4. I wonder in such a system if any protein could be ubiquitylated simply by tethering a binding domain for that protein to an E2 enzyme. So, I guess this is a question of specificity. Is there a control the authors can do to demonstrate specificity in this system?
Figure 5A and S5A. In figure 5A the authors use a flag-tagged Plk4 pulldown to show that DCAF1 strongly interacts with Plk4 during interphase and weakly during mitosis. In figure S5A, they perform the reverse experiment by pulling down endogenous DCAF1 and state that they obtained similar results. Looking at Figure S5A, this doesn't appear to be true. There is not much difference in the amount of Plk4 pulled down from interphase cells versus mitotic cells. The authors also do not indicate if any of the differences are significant.
Figure 5B. The authors investigate the cell-cycle-dependent pattern of Plk4 ubiquitination by co-expressing Flag-Plk4, HA-ubiquitin, and Myc-DCAF1 in HEk293 cells followed by a series of Flag IPs from cells arrested at different points in the cell cycle. They claim based on the retarded migration of Plk4, that CRL4DCAF1 ubiquitylates Plk4 specifically during G2 phase. It's hard to make any firm conclusions without quantitation. Furthermore, it's impossible to know how much of the ubiquitylation at any given cell cycle stage is dependent on DCAF1. The correct experiment would have been to have a no DCAF1 control for each cell cycle stage and to quantitate the differences. Since ubiquitin is tagged with HA, would it not be possible to probe the immunoprecipitate with an anti-HA antibody followed by quantitation.
Figures 6A and 6B. Why do the levels of Plk4 not respond to decreased or increased levels of DCAF1? In 6A for instance strong depletion of DCAF1 does not appear to affect the level of Plk4. Also, given that there is no change in Plk4 levels, the amount of STIL that is pulled down with PLK4 still increases upon DCAF1 knockdown. Does this mean that DCAF1 might function by directly inhibiting the Plk4-STIL interaction.
Figure 6C The authors find that upon DCAF1 knockdown, centrioles prematurely disengage during G2. They attribute this effect to the increased levels of Plk4. Is there any evidence that increased Plk4 levels lead to premature disengagement? Isn't it possible that this defect is independent of the increase in Plk4 protein? Either the authors should provide evidence of this or offer the possibility that the premature disengagement defect arises independently of the effect on Plk4 levels.
The authors should also consider exploring the possibility that CRL4DCAF1 functions semi-redundantly with the SCF. It would be interesting to see if there is a synergistic effect of knocking out both E3 ligases on Plk4 levels and centriole number. Such a finding would highlight the importance of the cooperative model the authors propose in this paper.
Minor issues
Page 5 typo: "in addition DCAF1 strongly binds to a WD40-acidic motif" I think you meant to say Plk4.
Figure 4. The terms l.e. and s.e. should be explicitly defined.
Many figures: Error bars are not defined. Do these represent SD or SE?
SCF-TrCp is not the only known E3 ligase that controls Plk4 levels. For instance Erich Nigg's group showed some time ago that the E3 ubiquitin ligase Mindbomb (Mib1) also regulates Plk4. (CAjanek et al 2015 J. Cell Sci. 128: 1674-82). This should also be mentioned in the introduction in order to paint a more complete picture of what is known about E3-based regulation of Plk4.
Significance
If my criticisms can be successfully addressed, this study has the potential to provide significant new insight into how centriole number is controlled. At least two E3 ligases have already been described that regulate Plk4 levels. This manuscript would provide a third. In an of itself, the discovery of a third E3 involved in the regulation of PLK4 levels would not have a major effect on the field. However if the authors can demonstrate how these two E3s are coordinated to control centriole assembly during the cell cycle that would be a great interest to those studying centriole assembly.
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Referee #1
Evidence, reproducibility and clarity
The CUL4-DDB1-DCAF1 E3 ubiquitin ligase complex regulates PLK4 protein levels to prevent premature centriole duplication
Previously, it was thought that PLK4 is mainly regulated by autophosphorylation and degradation by the E3 ligase SCFSlimb/beta-TrCP in a phosphorylation dependent manner. In this manuscript by the Hoffmann group, the authors add an additional layer to the regulation of PLK4 as they identify the CRL4DCAF1 E3 ubiquitin ligase as a regulator of PLK4 that prevents PLK4 accumulation in G2 when beta-TrCP is low and therefore helps to restrict centrosome duplication to one event per cell cycle. More specifically, Grossmann et al. identified CRL4DCAF1 E3 ubiquitin ligase subunits in an immunoprecipitation mass spec approach. Using PLK4 kinase dead and phospho-mutants, they first show that CRL4DCAF1 binding is distinct from the SCFSlimb/beta-TrCP binding site. Depletion of DCAF1 leads to a modest increase in cellular PLK4 levels, PLK4 at centrosomes and cells with supernumerary centrosomes. Based on an IP experiments, they convincingly show that the acid C-terminus of DCAF1 interacts with PLK4 and they provide a model based on AlphaFold and analysis of mutations in the putative interaction interface how PLK4 and DCAF1 interact. They further provide evidences that DCAF1 directly ubiquitinates PLK4 in vitro. The interaction between DCAF1 and PLK4 is cell cycle dependent (peak in G2; Fig. 5) following the ubiquitination of PLK4. In Fig. 6 the authors analyze whether PLK4-STIL interaction is regulated by DCAF1. This is indeed the case and Fig. 6B likely indicates that DCAF1 functions as a competitive inhibitor for PLK4 and in this way blocks PLK4 binding to STIL. Finally, in Fig. 6C the authors analyze centrioles by expansion microscopy. The authors show mother-daughter centriole pair disengagement upon depletion of DCAF1 (on p. 7, bottom: "knockdown of DCAF1 leads to a significant higher number of disengaged centrioles"). This is similar to CEP57 depletion as shown by Kitagawa: JCB 2021 220: e202005153. Instead of analyzing centriole disengagement in further depth, the authors analyze in Fig. 6D centrosome separation, which is mechanistically quite distinct from centriole disengagement. Centrosome separation (mother-daughter pairs) in G2 is triggered by resolution of the rootletin linker through the action of the kinase Nek2A. Thus, Fig. 6 refers to two different events/mechanisms and it will be important to clarify whether DCFA1 depletion causes centriole disengagement or centrosome separation (e.g. by analyzing the centrin pattern and whether daughter centrioles mature). To my knowledge, there is no connection between PLK4/STIL and the centrosome linker. Thus, if DCFA1 regulates centrosome separation, Fig. 6 would be disconnected from the rest of the paper.
Main points
- Fig. 2E: it would make sense to quantify the PLK4 signal at centrioles according to the cell cycle phase of the cell. G2 is probably the cell cycle phase when PLK4 is regulated by the CRL4DCAF1 E3 ubiquitin ligase.
- It is known that PLK4 has a function in cytokinesis (i.e.: https://doi.org/10.1073/pnas.181882011). Thus, there is the possibility that the supernumerary centrosomes observed in Fig. 2F result from a cytokinesis defect and not from centriole over-duplication. To address this, the authors can use procentriole marker Sas6, and show that a newly disengaged centriole should still posseses Sas6. When the premature onset of centriole duplication happens to those newly disengaed centrioles, both mother and daughter centriole in the pair should posses Sas6 since Sas6 removal only happens in upcoming mitosis.
- The authors suggest a competitive interaction between proteins DCAF1, PLK4 and STIL in Fig. 6A and Fig. 6B. However, they have not excluded direct binding of DCAF1 to STIL as an alternative explanation. Additionally, is the enhanced PLK4/STIL interaction in Fig. 6A G2 dependent?
- The quality of the expansion microscopy in Fig. 6C could be improved.
- The authors have to resolve whether Fig. 6C and D relate to centriole disengagement or centrosome separation and how this is connected to DCAF1, STIL and PLK4.
Minor points
- P. 5: WD40-Acidic motif. This fragment needs to be described in the text and not just in Fig. S3.
- Fig. 2E: The authors analyze the phenotype by combining all data points from three experiments. It would be better to show the average of the three independent experiments and do the statistics on the three data points.
- Is the difference (> 4) in Fig. 2F significant?
- Fig. 3A-C is difficult to follow. It is too small and DCAF1 and CEP192 are very difficult to see. I am sure that there are simple ways to improve this figure.
- Define BP1 and BP2 in Fig. 3A. Does BP1 = PB1?
- P. 5. "the first helix (D1420-E1436) of DCAF1 positioned .... (add DCAF1).
- P. 20 Fig. 4B: 200 nm should be 200 nM.
- The authors may want to test additional PLK4 mutations that are not localized in the predicted interaction interface with DCAF1 to show that these mutations do not affect binding.
- In Fig. 4A the authors could IP GFP-PLK4 and show that a fraction of this protein carries His-Ubi conjugation using His antibodies.
- Difference in quantification of Fig. 6D is not significant,
- Fig. 4B (also Fig. 5B): Explain "l.e." and "s.e." in figure. Both blots are not the same (at least in case of Fig. 4B comparing the kDa numbers), thus l.e. = low exposure and s.e. = short exposure does not work. How was PLK4 detected in Fig. 4B?
Referees cross-commenting
I believe that all three reviewers have very similar concerns. I guess, everybody agrees that this manuscript, although potentialy very intresting, needs a substrainail amout of revision
Significance
The manuscript convincingly identifies CRL4DCAF1 E3 ubiquitin ligase as an PLK4 regulator and therefore is a very important contribution to the field. However, the impact of DCAF1 depletion is not too high. I therefore recommend double depletion of DCAF1 and SCFSlimb/beta-TrCP (not absolutely necessary but could increase impact). The interaction analysis of DCAF1 with PLK4 and the ubiquitination of PLK4 by the DCAF1 E3 ligase is convincing. I see a problems with the data in Fig. 6 that need to be revised.
Thus, key experiments that should be done are Main points 2), 3) and 5). The revision of the manuscript will take 3-6 months.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The manuscript develops the authors' previous work on the structure of the YeeE protein by presenting a co-structure with YeeD and investigating the role of certain key cysteine residues, especially C17 of YeeD. To this reviewer an entirely plausible mechanism for YeeD/E co-ordinated transport of thiosufate through the membrane and cleavage to sulfide and sulfite which are released into the cytoplasm is proposed on the basis of functional studies. The work is clearly described, the crystallography stats look good.
Thank you very much for your highly positive comments. We sincerely appreciate them.
Major comment: The 'cysteine relay' followed by a key role for C17 of YeeD in releasing a sulfide looks very plausible and makes the work of more general interest. An aspect that is not addressed is that of energetics. Moving thiosulfate into the cytoplasm as sulfide and sulfite means apparently that two negative charges net are generated in the cytoplasm for each thiosulfate taken up. This seems too simplistic (protons released as the bound sulfite is released b hydrolysis) but if thiosulfate were to be moved the whole way across there would be a divalent anion uniport which would work against the membrane potential negative inside (ie the main component of the protonmotive force). There is no mention in the paper of any pmf dependence and presumably the structure of YeeE shows no evidence of putative proton pathways? Some discussion of this and any wider implications could enhance the paper. In some ways the proposed transport scheme has some resemblance to Mitchells's old group translocation proposal for transport.
Thank you for highlighting the significance of the 'cysteine relay.' We also believe that this aspect is likely to interest a broad readership. Regarding protons, YeeE does not have apparent proton pathways inside, and we currently do not have data on its dependence on the pmf. Investigating pmf dependence falls beyond the scope of this study, hence we plan to explore this in future research. We appreciate you for pointing out that the YeeE-YeeD is a reminiscence of Mitchell’s original proposal of group translocation. This is a very intriguing point, and we have now included a discussion of this, along with a relevant citation, in the Discussion section (lines 356-357).
Reviewer #1 (Significance (Required)):
The subject of thiosulfate transport (movement) into bacteria is arguably of interest only to a narrow group of bacterial biochemists. However, the contents of this manuscript ought to be of wider interest because the YeeD/E system described is unusual in doing more that catalysing transport alone. Whether the authors' description in their title of 'sophisticated' is an appropriate adjective I am not sure. The term 'cofactor' applied to YeeD seems 'odd' to this reviewer. It is not a cofactor in the usual sense eg NADH.
We appreciate your comments. We have modified the title and avoided the unsuitable word 'cofactor' to describe YeeD.
reviewer's expertise: bactrial energetics but little knowledge of sulfur metabolism
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary:
The publication "Structure and function of a YeeE-YeeD complex for sophisticated thiosulfate uptake" by Ikei et al. shows the protein-protein interaction of a thiosulfate transporter YeeE and a sulfur transferase YeeD, a TusA-family protein. The transporter YeeE has been structurally characterized previously, without showing its functional activity in a purified reconstituted system. This experiment complementing the previous publication is provided here, furthermore proving the functionality of the transporter. These experiments were further extended by the characterization of the cytoplasmic acceptor protein. This acceptor was proven to be YeeD, by structural characterization and biolayer interferometry. The binding kinetics between YeeD and YeeE were measured, quantifying the binding affinity between the two proteins. Furthermore, the surface residues of YeeD were specified by amino acid exchange mutants. Thus, the structure and essential residues were characterized protein. The interaction of sulfur transferase YeeD with the thiosulfate transporter YeeE is a novelty to the field. This illuminates the first time a specific function of YeeD in thiosulfate assimilation.
We appreciate your positive review and for recognizing the significance of our work in uncovering the functions of the YeeE and YeeD complex. We have addressed the following major and minor comments, thereby improving our manuscript. We appreciate the your constructive feedback.
Major comments:
I see the following major problem: The YeeD protein preparations used in the experiments contained several different protein species. Mass spectrometry showed the existence of the monomeric reduced protein, a TusA sulfinate and a TusA thiosulfonate. There is obviously an oxidation of cysteine to cysteine sulfinate, possibly due to the presence of oxygen as shown in Fig. 2D and stated in the text. The formation of sulfinates has to be avoided. This can be achieved by the use of stronger reducing agents or by purification under strict exlusion of oxygen. The formation of sulfenic, sulfinic and sulfonic acid on cysteines by oxidation has been reviewed by Ezraty et al 2017 Nat Rev Microbiol.
To answer these points, we have extensively several experiments and analyses, and modified the text. In the mass spectrometry analysis of purified StYeeD, three major peaks are observed (Fig. 2D), but they do not necessarily reflect actual relative abundances due to the nature of mass spectrometry analysis. Therefore, we also analyzed the purified StYeeD by non-reducing SDS-PAGE, which showed very few molecular species with S-S bonds, with over 90% existing as YeeD-SH (Fig. S2D). We considered this level of purity sufficient for conducting biochemical analyses. Furthermore, although a small amount of YeeD-SO2- was observed, this would be inactive and thus not impact the activity of StYeeD because a similar irreversible modification product, NEM-modified StYeeD(WT), was inactive (Fig. S2G).
We have also provided non-reducing SDS-PAGE results for each mutant StYeeD in Fig. S2F. All StYeeD mutants except for L45A showed a similar pattern to StYeeD(WT). Conducting experiments under anaerobic conditions is quite challenging in our laboratory facility, so we have displayed non-reducing SDS-PAGE profiles of all proteins used in order to avoid misunderstanding. We have also tried the purification in the presence of DTT, a stronger reducing agent, but the fraction of YeeD-SO2- was not significantly changed.
In the revised version, mass spectrometry analyses were reperformed using DTT-reduced YeeD, resulting in more precise data (Fig. 2D–H). Based on these results and your valuable comments, we have rewritten the paragraph entitled 'T____hiosulfate decomposition activity of YeeD and its catalytic center residue' to represent the reduction/oxidation forms accurately. We have also cited the Nat. Rev. Microbiol. review in the text (line 185).
In their in vitro assays, the authors use exceptionally high thiosulfate concentrations of 300 mM. This is so far from any physiologically relevant concentrations that strong doubt is shed the validity of any conclusions transferred from the in vitro to the in vivo situation.
In the revised version, the mass spectrometry analysis was reperformed with a thiosulfate concentration of 500 µM, which is the same concentration of thiosulfate used in the thiosulfate decomposition experiments. To clarify this, we have included the thiosulfate ion concentrations in the legend of Fig 2.
L247 and Fig5: The proposed mechanism cannot be true. Binding of thiosulfate to a reduced TusA protein is not possible without release of electrons. Where do these electrons go? In the proposed scheme, the number of electrons before and after the reaction steps is not equal (Fig. 5). A release of the sulfur atom between the cysteine sulfur atom and the oxidized sulfur atom is impossible.
Thank you for your insightful comments. We have revised Fig. 5B to represent a better model. However, elucidating the electron pathway falls outside the scope of this study, and we cannot offer a definitive explanation. We have addressed this limitation in the Discussion section and highlighted it as a topic for future research.
Have the authors checked whether TusA dimers are formed via disulfide bridges? If so, thiosulfate could resolve these disulfides leading to reduced TusA and thiosulfonated TusA (YeeD-S-S-YeeD + S2O32- → YeeD-S-S-SO3- + YeeD-S-).
It cannot be excluded that the YeeD-S-SO3- species is a result of removal of sulfite from the YeeD-S-S2O3- species (possibly by transfer to another YeeD molecule) resulting in YeeD-S-S- oxidized by molecular oxygen to YeeD-S-SO3-.
Upon answering to this comment, we have re-examined the gel filtration result using gel filtration markers. We found that a fraction of YeeD exists as dimers in solution, as shown in Fig. S2C. By performing non-reducing SDS-PAGE, it was shown that these YeeD dimers were not due to intermolecular disulfide bond (Fig. S2D). Following your valuable suggestion, we have introduced the possibility that YeeD can function as a dimer into our model, as presented in a box in Fig. 5B.
Sulfide may be formed by a reaction of YeeD-S- with S2O32- to YeeD-S-SO3- and S2- or reaction of YeeD-S-S- with S2O32- to YeeD-S-S2O3- and S2-. As there is the formation of sulfinic acid that prevents clear conclusions, I suggest repeating the experiments on thiosulfate decomposition under anaerobic conditions to clarify the reaction mechanism. Anoxic buffers and strong reducing agents may prevent chemical oxidation.
As described above, based on the non-reducing SDS-PAGE results (Fig. S2D), we believe that the low presence of oxidized species does not significantly affect our analysis. Moreover, the mass spectrometry analysis after DTT treatment yielded more precise results (Fig. 2D–H). As noted above, conducting experiments under anaerobic conditions is challenging in our facility, so we kindly request your understanding and consideration of the revisions made in this manuscript.
Minor comments:
In response to the minor comments, we have revised the manuscript.
L58 What is the nature of the binding of the thiosulfate ion during the transport via YeeE. Is it covalently bound? Please comment in the text.
In our previous study (Tanaka et al., Sci. Adv., 2020), we proposed that thiosulfate ions were transported via hydrogen bonds. Responding to your comment, we have included the explanation in the text and cited Tanaka et al., 2020 (lines 66-67).
L76-L77 Is there a publication on the functionality of the Corynebacterium YeeD-YeeE fusion? The term "cofactor" does not apply to YeeD, which is a 9-kDa protein.
Since the function of Corynebacterium YeeD-YeeE has not been reported, we have changed the sentence to "In some bacteria, such as Gram-positive Corynebacterium species, YeeE and YeeD are encoded as one polypeptide." We have also avoided the word "cofactor" in the revised text (lines 89-91).
L114 YeeD was probably accidentally lowercased here as Yeed
We have corrected this error (line 134).
L119 Please specify what the negative control consisted of.
We have elaborated on the conditions (lines 140-141).
L120-122 In Fig 2c, the mutations E19A, K21A, E26A, D31A, E32A and D38A are still shown, but an explanation or description of the results is missing. The reason for investigation of these mutations should be stated in the text.
We have added the requested mutation information (line 146).
L137 If thiosulfate was not added before the MALDI-TOF, where did the sulfonate S-SO3 originate from? Is this an artifact formed during the heterologous production or purification? Please comment on this possibility in the text.
We think that the -S-SO3- form arose during purification (Fig. 2D). The -S-SO3- form disappeared upon reduction by DTT (Fig. 2F). It is possible to consider it as an intermediate state in the catalytic cycle of YeeD. We commented on this in the section entitled "Thiosulfate decomposition activity of YeeD and its catalytic center residue."
L144 Please state in the text whether these experiments were performed under aerobic or anaerobic conditions. The sulfinic acid is likely a product of a spontaneous chemical reaction with molecular oxygen.
Thank you for your feedback. We have now included information about the aerobic conditions in the main text (line 166-167) and added comments regarding the mass spectrometry results at the end of the paragraph (lines 191-201).
L148 It should be stated in the text whether YeeD in Fig2G was reduced with DTT as in Fig 2F or non-reduced as in Fig. 2D before thiosulfate was added. Only the reduced YeeD can yield conclusive results on the loading with sulfur, as there is already a thiosulfonate bound to the protein after purification.
Thank you for pointing this out. For mass spectrometry analysis, data were re-obtained, and DTT-treated sample was used for the thiosulfate condition in this revised version. Furthermore, we performed mass spectrometry analysis for the hydrogen peroxide condition using DTT-treated sample. Figures were replaced with revised ones (Fig. 2D–H). The text in the section "Thiosulfat____e decomposition activity of YeeD and its catalytic center residue" was appropriately re-written. Detailed sample preparation is also described in MATERIALS AND METHODS section.
L154 The YeeD used for measurement of sulfide formation must be reduced before the experiments. It is not stated in the text if this is the case. Also, the release of sulfide requires electrons. It should be commented where these electrons originate from.
The sample in the purification process contains β-ME until just before the final column (gel filtration). As shown in Fig. S2D, more than 90% of the purified product is in a reduced state after gel filtration. For mass spectrometry analysis, data were re-obtained using DTT-treated samples, and the figures were replaced with new ones (Fig. 2D–H). Binding and activity measurements were conducted in the presence of β-ME. To avoid the confusion of the readers, the buffer conditions were included in the legends of both Fig. 2 and Fig. 4, along with the details in the MATERIALS AND METHODS section. Regarding electron origin, since the electron route remains unknown at this stage, we have added the explanation as a sentence in the Discussion section (lines 370-372).
L159-160 If the mutation of the non-conserved YeeD cysteine inhibits growth, can anything be said about its function?
Regarding the non-conserved Cys in EcYeeD, we added some sentences in the Discussion section (lines 393-397)
L214 Is it possible to provide the Kd and KD values for the mutant proteins?
The ka, kd and KD values the interactions between YeeE and YeeD proteins have been provided in Table 2. To provide these values for all the YeeD derivatives, the data was re-analyzed, and therefore, the value of the WT YeeD is slightly different from the original manuscript.
L229 Stating a need of YeeD for thiosulfate uptake by YeeE is somewhat misleading as thiosulfate was also imported into liposomes by YeeE alone. Maybe state that YeeD is a required component for growth when thiosulfate is imported via YeeE.
We have addressed the incorrect wording (lines 317-318).
Reviewer #2 (Significance (Required)):
The work of Ikei and colleagues significantly advances our understanding of thiosulfate import in Escherichia coli (E. coli) and prokaryotes in general. Sulfur metabolism as a field is generally considered to be underexplored, with a notable lack of biochemical and structural information on membrane transporters responsible for the movement of both inorganic and organic sulfur compounds. The mechanisms involved in sulfur transport are also relatively poorly understood.
The proteins of the TusA family in E. coli exhibit distinct functions, although the precise function has only been determined for the canonical and namesake protein TusA. The discovered genetic evidence and the interaction of YeeE and YeeD adds significantly to our understanding of sulfur transfer reactions.
The novelty of this reaction is of particular interest to researchers studying prokaryotic physiology, especially the synthesis of sulfur-containing cofactors such as coenzyme A (CoA), biotin, lipoate, thiamine, and iron-sulfur (FeS) clusters, as well as the biosynthesis of cysteine and methionine. In addition, recent findings related to the TusA family protein YeeD elucidate a novel mechanism for sulfur mobilization and transfer that will be of interest to researchers involved in the regulation of sulfur metabolism, sulfur dissimilation, and ecological studies focused on sulfur utilization. Thus, a wide range of studies could be influenced by this review.
Areas of expertise include dissimilatory sulfur oxidation, sulfur transfer reactions, and protein-protein interactions.
Thank you again for emphasizing the importance of our work. We also believe this study significantly advances the understanding of thiosulfate import in prokaryotes, shedding light on the underexplored field of sulfur metabolism. This has implications for various areas of study.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
The manuscript "Structure and function of a YeeE-YeeD complex for sophisticated thiosulfate uptake" by Ikei et al., reports the enzymatic characterization, transport capability and concerted function of YeeE and YeeD. Moreover, the authors report the crystal structures of two mutant variants of the complex.
The present work fills an important gap in understanding thiosulfate uptake and the individual roles of the YeeE and YeeD proteins in this process. This Reviewer believes that the paper has the potential of becoming an important reference in the field. However, this Reviewer has two or three major comments, besides a couple of minor ones, that would like the authors to address.
We appreciate your valuable comments. We have addressed both major and minor comments in our revisions, improving our manuscript.
This Reviewer hypothesizes that some of the comments might derive from a poor understanding of the text, derived from the way the manuscript is written. So, this Reviewer urges the Authors to take these comments as positive feedback, and build on these to improve the manuscript (namely on English and grammar).
We have diligently revised the manuscript, addressing your major concerns related to sulfide terminology and explanations in crystal structure analysis as below. These revisions have enhanced clarity, and a native English speaker has reviewed and refined our text for language and grammar.
MAJOR CONCERNS
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There is no clue on the title and, more importantly, on the Abstract, to which microorganism the Authors are reporting this work. Only later one we are introduced to Spirochaeta thermophila, but this information should be front and center (at least in the Abstract);
We recognize the importance of clearly indicating the microorganism in our work. In accordance with the comments, we have revised both the title and Abstract, ensuring that the species is clearly identified in the Abstract.
Also, in the Abstract, the Authors only mention the 2.6 A resolution structure, leaving behind the 3.34 A one. This becomes very confusing, especially once one gets to the Results section (more comments below);
We apologize for any confusion arising from the omission of the 3.34 A resolution structure in the Abstract. In the revised Abstract, we have included both the 2.60 A and 3.34 A resolution structures. As per your suggestion, we have also provided detailed information about the determination of these structures in the Results, minimizing potential confusion for readers (lines 217-233).
The Authors mention in line 137 and Fig. 2D that a "sulfonate" moiety is formed at C17. However, cysteine sulfonation is an irreversible process, so how would the enzyme recover from this modification to allow turnover of the mechanism?;
We apologize for the poorly written passage that led to confusion. This paragraph has been revised with the appropriate wording and a proper mention of the reduction and oxidation of the -SH group. We now use the appropriate terms, such as sulfinic acid (-S-O2-), sulfonic acid (-S-O3-), and perthiosulfonic acid (-S-SO3-) to describe the sulfur-related modification states. In contrast to sulfonic acid (-SO3-) formed by the oxidization of the cysteine residue that is an irreversible process, perthiosulfonic oxidization of cysteine residue (-S-SO3-) is a reversible process, as shown in (E. Doka et al., Sci Adv 6, eaax8358 (2020)). Therefore, the modified YeeD molecules should be able to recover to the original state.
If the "sulfonylation" reported in line 137 and Fig. 2D is not a sulfonylation of the cysteine (because the peak disappears upon reduction with DDT as visible in Fig. 2F), but rather a sulfonylation of the cysteine-persulfide version of C17, this was already reported previously and should be referenced [PDB ID 5LO9, Brito et al. (2016) J Biol Chem 291: 24804-24818];
Because there was a misleading statement, as replied above, we have rewritten this paragraph.
The perthiosulfonic acid (-S-SO3-) in Fig.2D is different from this -S-S2O3- in Brito et al., (2016), but consistent with Fig. 2G. This point is included in the text and the suggested paper has been cited, as requested. (lines 191-193)
Section "Crystal structure of the YeeE-YeeD complex" should be re-written. Not only it is confusing, but also undermines the tremendous amount of work done by the Authors. Please state clearle what was crystallized, how and why. Specify clearly the mutation introduced and complement Table 1 with this information;
Thank you for these comments. The determination of the structures was certainly challenging. We have restructured the first part of the section entitled "Crystal structure of the YeeE-YeeD complex". We have included a comprehensive explanation of the crystallization process and the construction of YeeE-YeeD. Additionally, we have updated Table 1 to provide more detailed information on the two structures.
Lines 403-407: are the crystallization conditions already cryo-protected or no cryo-protection was added before flash freezing? Please state clearly;
In response to your feedback, we have added the missing information in MATERIALS AND METHODS section.
Table 1:
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Is the multiplicity of PDB ID 8K1R correct? Is it really 321?? If so, is there any radiation damage to the crystal? If not, how?? Fine-fine-slicing during data collection, big crystals with elliptical data collection?? Pleas elaborate;
The multiplicity for PDB ID 8K1R is correct. We have provided detailed information on data collection in MATERIALS AND METHODS section.
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There are water molecules in the structure so please report number of atoms and B-factors for waters ("Solvent"), and ligands (e.g., thiosulfate, or others, if any), separately;
We have updated Table 1 to include the requested information.
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Please provide validation statistics for the structures, namely, rotamer outliers, clashscore and MolProbity score.
We have added the validation statistics to Table 1.
MINOR CONCERNS
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Always reference paper and PDB ID for all structures. E.g., at line 181, only the paper is referenced;
We have ensured that all structures are properly referenced with both the paper and the corresponding PDB ID (lines 246, 250).
Remove "alpha" in line 199;
We have removed the "alpha" (line 268).
Add units to all concentrations. E.g., at lines 326 and 327, (w/V) and (V/V) are missing.
We have incorporated concentration units, (w/v) or (v/v), for percentages in the appropriate locations.
Reviewer #3 (Significance (Required)):
The scientific rationale is robust and the experimental approach is adequate and provide support to the conclusion drawn. However, there are some questions this Reviewer would like to see clarified, namely on the data collection and processing of PDB ID 8K1R.
We appreciate your feedback. These revisions enhance the clarity and accuracy of this manuscript.
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Referee #3
Evidence, reproducibility and clarity
The manuscript "Structure and function of a YeeE-YeeD complex for sophisticated thiosulfate uptake" by Ikei et al., reports the enzymatic characterization, transport capability and concerted function of YeeE and YeeD. Moreover, the authors report the crystal structures of two mutant variants of the complex.
The present work fills an important gap in understanding thiosulfate uptake and the individual roles of the YeeE and YeeD proteins in this process. This Reviewer believes that the paper has the potential of becoming an important reference in the field. However, this Reviewer has two or three major comments, besides a couple of minor ones, that would like the authors to address. This Reviewer hypothesizes that some of the comments might derive from a poor understanding of the text, derived from the way the manuscript is written. So, this Reviewer urges the Authors to take these comments as positive feedback, and build on these to improve the manuscript (namely on English and grammar).
Major concerns
- There is no clue on the title and, more importantly, on the Abstract, to which microorganism the Authors are reporting this work. Only later one we are introduced to Spirochaeta thermophila, but this information should be front and center (at least in the Abstract);
- Also, in the Abstract, the Authors only mention the 2.6 A resolution structure, leaving behind the 3.34 A one. This becomes very confusing, especially once one gets to the Results section (more comments below);
- The Authors mention in line 137 and Fig. 2D that a "sulfonate" moiety is formed at C17. However, cysteine sulfonation is an irreversible process, so how would the enzyme recover from this modification to allow turnover of the mechanism?;
- If the "sulfonylation" reported in line 137 and Fig. 2D is not a sulfonylation of the cysteine (because the peak disappears upon reduction with DDT as visible in Fig. 2F), but rather a sulfonylation of the cysteine-persulfide version of C17, this was already reported previously and should be referenced [PDB ID 5LO9, Brito et al. (2016) J Biol Chem 291: 24804-24818];
- Section "Crystal structure of the YeeE-YeeD complex" should be re-written. Not only it is confusing, but also undermines the tremendous amount of work done by the Authors. Please state clearle what was crystallized, how and why. Specify clearly the mutation introduced and complement Table 1 with this information;
- Lines 403-407: are the crystallization conditions already cryo-protected or no cryo-protection was added before flash freezing? Please state clearly;
- Table 1:
a. Is the multiplicity of PDB ID 8K1R correct? Is it really 321?? If so, is there any radiation damage to the crystal? If not, how?? Fine-fine-slicing during data collection, big crystals with elliptical data collection?? Pleas elaborate;
b. There are water molecules in the structure so please report number of atoms and B-factors for waters ("Solvent"), and ligands (e.g., thiosulfate, or others, if any), separately;
c. Please provide validation statistics for the structures, namely, rotamer outliers, clashscore and MolProbity score.
Minor concerns
- Always reference paper and PDB ID for all structures. E.g., at line 181, only the paper is referenced;
- Remove "alpha" in line 199;
- Add units to all concentrations. E.g., at lines 326 and 327, (w/V) and (V/V) are missing.
Significance
The scientific rationale is robust and the experimental approach is adequate and provide support to the conclusion drawn. However, there are some questions this Reviewer would like to see clarified, namely on the data collection and processing of PDB ID 8K1R.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The publication "Structure and function of a YeeE-YeeD complex for sophisticated thiosulfate uptake" by Ikei et al. shows the protein-protein interaction of a thiosulfate transporter YeeE and a sulfur transferase YeeD, a TusA-family protein. The transporter YeeE has been structurally characterized previously, without showing its functional activity in a purified reconstituted system. This experiment complementing the previous publication is provided here, furthermore proving the functionality of the transporter. These experiments were further extended by the characterization of the cytoplasmic acceptor protein. This acceptor was proven to be YeeD, by structural characterization and biolayer interferometry. The binding kinetics between YeeD and YeeE were measured, quantifying the binding affinity between the two proteins. Furthermore, the surface residues of YeeD were specified by amino acid exchange mutants. Thus, the structure and essential residues were characterized protein. The interaction of sulfur transferase YeeD with the thiosulfate transporter YeeE is a novelty to the field. This illuminates the first time a specific function of YeeD in thiosulfate assimilation.
Major comments:
I see the following major problem: The YeeD protein preparations used in the experiments contained several different protein species. Mass spectrometry showed the existence of the monomeric reduced protein, a TusA sulfinate and a TusA thiosulfonate. There is obviously an oxidation of cysteine to cysteine sulfinate, possibly due to the presence of oxygen as shown in Fig. 2D and stated in the text. The formation of sulfinates has to be avoided. This can be achieved by the use of stronger reducing agents or by purification under strict exlusion of oxygen. The formation of sulfenic, sulfinic and sulfonic acid on cysteines by oxidation has been reviewed by Ezraty et al 2017 Nat Rev Microbiol. In their in vitro assays, the authors use exceptionally high thiosulfate concentrations of 300 mM. This is so far from any physiologically relevant concentrations that strong doubt is shed the validity of any conclusions transferred from the in vitro to the in vivo situation. L247 and Fig5: The proposed mechanism cannot be true. Binding of thiosulfate to a reduced TusA protein is not possible without release of electrons. Where do these electrons go? In the proposed scheme, the number of electrons before and after the reaction steps is not equal (Fig. 5). A release of the sulfur atom between the cysteine sulfur atom and the oxidized sulfur atom is impossible. Have the authors checked whether TusA dimers are formed via disulfide bridges? If so, thiosulfate could resolve these disulfides leading to reduced TusA and thiosulfonated TusA (YeeD-S-S-YeeD + S2O32- → YeeD-S-S-SO3- + YeeD-S-). It cannot be excluded that the YeeD-S-SO3- species is a result of removal of sulfite from the YeeD-S-S2O3- species (possibly by transfer to another YeeD molecule) resulting in YeeD-S-S- oxidized by molecular oxygen to YeeD-S-SO3-. Sulfide may be formed by a reaction of YeeD-S- with S2O32- to YeeD-S-SO3- and S2- or reaction of YeeD-S-S- with S2O32- to YeeD-S-S2O3- and S2-. As there is the formation of sulfinic acid that prevents clear conclusions, I suggest repeating the experiments on thiosulfate decomposition under anaerobic conditions to clarify the reaction mechanism. Anoxic buffers and strong reducing agents may prevent chemical oxidation.
Minor comments:
L58 What is the nature of the binding of the thiosulfate ion during the transport via YeeE. Is it covalently bound? Please comment in the text.
L76-L77 Is there a publication on the functionality of the Corynebacterium YeeD-YeeE fusion? The term "cofactor" does not apply to YeeD, which is a 9-kDa protein.
L114 YeeD was probably accidentally lowercased here as Yeed
L119 Please specify what the negative control consisted of.
L120-122 In Fig 2c, the mutations E19A, K21A, E26A, D31A, E32A and D38A are still shown, but an explanation or description of the results is missing. The reason for investigation of these mutations should be stated in the text.
L137 If thiosulfate was not added before the MALDI-TOF, where did the sulfonate S-SO3 originate from? Is this an artifact formed during the heterologous production or purification? Please comment on this possibility in the text.
L144 Please state in the text whether these experiments were performed under aerobic or anaerobic conditions. The sulfinic acid is likely a product of a spontaneous chemical reaction with molecular oxygen.
L148 It should be stated in the text whether YeeD in Fig2G was reduced with DTT as in Fig 2F or non-reduced as in Fig. 2D before thiosulfate was added. Only the reduced YeeD can yield conclusive results on the loading with sulfur, as there is already a thiosulfonate bound to the protein after purification.
L154 The YeeD used for measurement of sulfide formation must be reduced before the experiments. It is not stated in the text if this is the case. Also, the release of sulfide requires electrons. It should be commented where these electrons originate from.
L159-160 If the mutation of the non-conserved YeeD cysteine inhibits growth, can anything be said about its function?
L214 Is it possible to provide the Kd and KD values for the mutant proteins?
L229 Stating a need of YeeD for thiosulfate uptake by YeeE is somewhat misleading as thiosulfate was also imported into liposomes by YeeE alone. Maybe state that YeeD is a required component for growth when thiosulfate is imported via YeeE.
Significance
The work of Ikei and colleagues significantly advances our understanding of thiosulfate import in Escherichia coli (E. coli) and prokaryotes in general. Sulfur metabolism as a field is generally considered to be underexplored, with a notable lack of biochemical and structural information on membrane transporters responsible for the movement of both inorganic and organic sulfur compounds. The mechanisms involved in sulfur transport are also relatively poorly understood.
The proteins of the TusA family in E. coli exhibit distinct functions, although the precise function has only been determined for the canonical and namesake protein TusA. The discovered genetic evidence and the interaction of YeeE and YeeD adds significantly to our understanding of sulfur transfer reactions. The novelty of this reaction is of particular interest to researchers studying prokaryotic physiology, especially the synthesis of sulfur-containing cofactors such as coenzyme A (CoA), biotin, lipoate, thiamine, and iron-sulfur (FeS) clusters, as well as the biosynthesis of cysteine and methionine. In addition, recent findings related to the TusA family protein YeeD elucidate a novel mechanism for sulfur mobilization and transfer that will be of interest to researchers involved in the regulation of sulfur metabolism, sulfur dissimilation, and ecological studies focused on sulfur utilization. Thus, a wide range of studies could be influenced by this review.
Areas of expertise include dissimilatory sulfur oxidation, sulfur transfer reactions, and protein-protein interactions.
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Referee #1
Evidence, reproducibility and clarity
The manuscript develops the authors' previous work on the structure of the YeeE protein by presenting a co-structure with YeeD and investigating the role of certain key cysteine residues, especially C17 of YeeD. To this reviewer an entirely plausible mechanism for YeeD/E co-ordinated transport of thiosufate through the membrane and cleavage to sulfide and sulfite which are released into the cytoplasm is proposed on the basis of functional studies. The work is clearly described, the crystallography stats look good.
Major comment: The 'cysteine relay' followed by a key role for C17 of YeeD in releasing a sulfide looks very plausible and makes the work of more general interest. An aspect that is not addressed is that of energetics. Moving thiosulfate into the cytoplasm as sulfide and sulfite means apparently that two negative charges net are generated in the cytoplasm for each thiosulfate taken up. This seems too simplistic (protons released as the bound sulfite is released b hydrolysis) but if thiosulfate were to be moved the whole way across there would be a divalent anion uniport which would work against the membrane potential negative inside (ie the main component of the protonmotive force). There is no mention in the paper of any pmf dependence and presumably the structure of YeeE shows no evidence of putative proton pathways? Some discussion of this and any wider implications could enhance the paper. In some ways the proposed transport scheme has some resemblance to Mitchells's old group translocation proposal for transport.
Significance
The subject of thiosulfate transport (movement) into bacteria is arguably of interest only to a narrow group of bacterial biochemists. However, the contents of this manuscript ought to be of wider interest because the YeeD/E system described is unusual in doing more that catalysing transport alone. Whether the authors' description in their title of 'sophisticated' is an appropriate adjective I am not sure. The term 'cofactor' applied to YeeD seems 'odd' to this reviewer. It is not a cofactor in the usual sense eg NADH.
reviewer's expertise: bactrial energetics but little knowledge of sulfur metabolism
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Reply to the reviewers
- General Statements We thank the Editors and the Reviewers for their time and constructive criticism, which has allowed us to improve our manuscript. All of our responses are indicated in blue font. Revision Figures for the Reviewers are included just below the response. The line numbers given here refer to those in the revised manuscript, where we have marked the changes in red.
- Description of the planned revisions If granted a full revision, we will experimentally address the following major points, which were raised by more than one Reviewer: ● Repeat experiment in Figure 4 C to assess statistical significance (Reviewer 1 and 3) ● Western blot analysis of HDV infected HLCs showing small and large delta antigens. We have already performed such an analysis on HLCs (see Revision Figure 2). In addition, we will perform a comparative analysis with common HDV infection models dHepaRG and Huh7-NTCP cells over time (Reviewers 2 and 3). ● Additional characterisation of the two HLC subpopulations at transcript and protein level (Reviewer 1 and 3). In addition, we planned to conduct the following experiments in response to the individual Reviewers: In response to Reviewer 1: We thank the Reviewer for their encouraging feedback on our model and for their helpful comments, allowing us to improve our manuscript. Figure 1: The observation of a denser subpopulation of hepatocytes more susceptible to HDV is interesting. Do you have more characterization of this cell subpopulation, by IFA, in term of hepatic maturation marker, known HDV host factors and particularly NTCP expression? We agree with the Reviewer that this is an interesting observation. We separated the two hepatocyte subpopulations to analyse the gene expression of the liver maturation markers NTCP and ALB by RT-qPCR (see Revision Figure 1A). Surprisingly, we found that the low-density population expressed higher levels of both ALB and NTCP, suggesting that they are more mature than the high-density population. In addition, we stained both markers by immunofluorescence and observed no apparent differences (see Revision Figures 1B & C). In contrast, the new host factor identified in our study, CD63, appeared to be more highly expressed in the high-density population compared to the low-density population (Fig. 6G). However, we cannot exclude the Revision Plan possibility that other factors play an additional role. As outlined in our response to Reviewer 3, we will separate the two populations and analyse the gene expression of other known HBV and HDV co-host factors to assess whether they play a role in addition to CD63 in conferring the higher susceptibility to HDV infection to the highly dense HLC population. Revision Figure 1: High-density HLCs population is not more mature than the low-density HLC population. (A) The low-density HLCs population was separated from the high-density HLC population by gentle dissociation. Total RNAs were isolated from both populations and Albumin and NTCP expression was analysed by RT-qPCR. (B & C) High-density HLCs (upper image) and low-density HLCs (bottom image) were stained with Albumin specific antibody. Shown are either images taken on an epifluorescence microscope (B) or single slices of confocal images acquired on a Airyscan confocal microscope (C). Fig 1B and C: Can a BLV control be included in the figure? Thank you for this suggestion, we will repeat the experiment for these panels and add BLV as control. Fig 1A-F: What is the overall level of NTCP between HLC, HepaRG, Huh7NTCP and HLCAAV- NTCP? Can NTCP and HDAg be stained simultaneously in your cells? This is an excellent question and we will compare the total NTCP levels between differentiated HepaRG, Huh7 NTCP, HLCs +/- AAV NTCP by Western blot analysis and immunofluorescence (IF) staining. Comparing NTCP expression in HLCs +/- AAV NTCP, we observed a strong upregulation of surface NTCP upon AAV transduction by IF staining (Figure 1D). Unfortunately, our initial attempts to simultaneously detect NTCP and HDAg were technically hampered. Since HDAg is mainly localised in the nuclei, we have to permeabilize the cells in a harsh manner, which interferes with the detection of membrane NTCP. The latter is further hampered by the availability of suitable anti-NCTP antibodies for IF staining. In our study, we used high doses of fluorescence-conjugated MyrB peptide to stain NTCP, but unfortunately it is very sensitive to the harsh permeabilization detergents mentioned above. However, since we have meanwhile optimised HDV infection, we will likewise try again to optimise the staining Revision Plan protocol. If we succeed, we will repeat the co-staining of NTCP and HDAg and include it in a revised manuscript. Figure 4: While the strategy is interesting, based on what has been previously shown for HCV in Wu et al., 2012, the lack of statistical data prevents the reader to really understand and see drastic difference in term of susceptibility to infection and level of expression of host genes. In panel C, is the difference between day 13 and 15 statistically significant? Same for panel D, day 17 vs 19?As a remark, day 19, the peak of susceptibility to HDV, seems to be also the peak of maturation, based on ALB RTqPCR (panel B). Thank you for this comment, and will perform another set of experiments allowing us to calculate statistical significance. The Reviewer correctly points out the correlation between HDV infection and hepatocyte maturity, which we find very intriguing. To identify potential host co- or restriction factors expressed in highly mature HLCs, we then performed the differential gene expression analysis (Figure 5). As shown in the new Figure 5A, GO analysis revealed that genes involved in pathways regulating viral entry into host cells were most significantly upregulated in mature HLCs and, as a probable consequence, they were more permissive to HDV infection. Indeed, among these factors, we identified CD63 as a novel host cofactor that renders mature HLCs susceptible to HDV infection (Figure 6). In response to Reviewer 2: We thank the Reviewer for their assessment of our study and for critically pointing out the increments over the previous study by Lange et al. We also appreciate their helpful suggestions, which allow us to improve the manuscript. The manuscript would benefit from a more detailed virological analysis, such as: •Determination of HDV genome and antigenome sequences and analysis of HDV editing. We thank the Reviewer for this comment. Accordingly, we will determine HDV genomes and antigenomes by Northern blot analysis and study HDV editing rates by sequencing in HDVinfected HLCs. •Analysis of HDV short and large antigens by western blot. We have already detected small and large HDAg in HDV-infected HLCs (see Revision Figure 2). To also satisfy Reviewer 3, we will additionally compare the S/L-HDAg ratios over time in HLCs, dHepaRGs, and Huh7-NTCP cells and include the results in a revised manuscript. Revision Figure 2: Detection of small and large delta antigen in HDV-infected HLCs. Mature HLCs were infected with HDV (MOI= 5 Int. Units/cell) and harvested 1 or 3 days post-infection. Cell lysates were analysed by Western blotting using antibodies against HDAg and b-actin. Revision Plan •Analysis of HBV-related virological parameters in monoinfected and co-infected cells. We agree with the Reviewer and we will include the characterisation of more HBV-related virological parameters in our mono- and co-infected HLCs. Accordingly, we will assess HBV cccDNA, RNA, and DNA by RT-qPCR, as well as released HBsAg and HBeAg via ELISA and add the results to the revised manuscript. In response to Reviewer 3: We thank the Reviewer for their positive evaluation, and we acknowledge their helpful comments, which will help us to improve our manuscript. Line 143: the authors describe two forms of HLCs (less and more confluent with differences regarding the susceptibility to HDV infection). The characteristics of the less and more confluent HLCs should be described in more detail-what is causative for the differences in susceptibility for HDV infection of these two forms? We thank the Reviewer for this comment. We likewise find this observation intriguing. As stated in our response to Reviewer 1, we have ruled out that NTCP and/or other mature markers such as ALB are differentially expressed between the two subpopulations. As one factor that could make a difference, we have identified CD63, which is highly expressed in the high-density HLC population and less so in the low-density HLC population (Figure 6G). Nevertheless, we will separate the two populations and analyse by RT-qPCR the expression of other known HBV and HDV host co-factors that may be additional factors governing the increased susceptibility of the highly dense HLC population. The statistical analyses should be improved: There are no p-values provided for the data presented in the supplement and a variety of figures lacks p-values We have added p-values to the Supplementary Figures (see revised Supplementary Fig. S2) and will repeat the experiments for Fig. 4 and Supplementary Fig. S1B and Fig S3 so that we can calculate the corresponding p-values. Kinetic of the infection: Here it would be interesting to see a comparative analysis by western blot investigating the ratio HBsAg/HDAg over the time in HLCs, HepaRGs and NTCP oe cells We thank the Reviewer for his comments. As stated in our response to Reviewer 2, we will perform this WB analysis to detect S/L-HDAg over time in infected HLCs, dHepaRG, and Huh7- NTCP cells. Line 157: What is the experimental evidence for the proper localization and functionality of the ectopically expressed NTCP in HLCs. Did the authors study the taurocholate transport after overexpression of NTCP? We thank the Reviewer for this comment. We analysed endogenous and ectopic NTCP expression by microscopy using a fluorescently conjugated peptide Atto-MyrB-565, which specifically binds to the ectodomain of human NTCP (Figure 2D) and found that both Revision Plan endogenously and ectopically expressed NTCP are located on the cell surface. To further confirm the correct localisation, we will perform NTCP co-staining with a cell membrane marker. We will also test the proper function of the ectopically expressed NTCP using a specific taurocholate transport assay as shown in our previous study (Ni et al, 2014, Gastroenterology). Line 169: The authors should include data comparing the number of double positive cells in HLCs, HepaRGs and Huh7NTCP o.e. expressing cells under the chosen experimental conditions We thank the Reviewer for this suggestion. We have already performed HBV/HDV co-infection of dHepaRG cells (Revision Figure 3) and we will perform the same experiment with Huh7-NTCP cells. Revision Figure 3: HBV/HDV co-infection of dHepaRG cells. Differentiated HepaRG were infected with HBV (MOI = 450 genome copies/cell) and HDV (MOI = 5). Cells were stained against HBV core (HBc), HDAg, and nuclei (DAPI) ten days p.i.. HBc- and HDAg-positive cells were counted using Cellprofiler imaging software to quantify HBV (pink) and HDV (green) single and co-infection (white) events. Images are representative of three independent differentiations. Line 291: expression analysis by RT-PCR is not sufficient. It will be important to study by CLSM if the identified factors are really present as proteins and properly localized. To satisfy this Reviewer, we will be happy to perform WB analysis of lysates from cells obtained at different stages of HLC differentiation to detect LDLR, LAMP1 and SR-B1 to further confirm our transcriptome analysis. As protein expression is easier to compare by WB analysis, we prefer this method to microscopic analysis. Regarding the role of CD63: what is the evidence for a direct role of CD63 for HDV entrycan the authors exclude that CD63 is relevant for targeting other factors to the surface? What is the impact of loss of CD63 on the functionality of the autophagosomal-MVB-EV system in HLCs? Since downregulation of CD63 before but not after impairs HDV infection, we conclude that CD63 is likely to be important for the early steps of the HDV life cycle, namely cell entry. Indeed, we speculate that CD63 may be critical for HDV trafficking to the vesicle, where fusion of the HBV glycoproteins is induced to allow capsid entry, based on the following observations: Although neither the precise site of HBV viral fusion nor the cues that induce fusion are currently fully understood, studies suggest that HBV can be co-transported with EGFR and NTCP to late endosomes for trafficking (Herrscher et al; 2020, Cells). We speculate that similar to what has been described for Lujo virus, CD63 may be involved in either HDV trafficking and/or virus fusion in the endosomal system (late endosome or lysosome) (Tominaga et al, 2014 Molecular cancer). Revision Plan CD63 is a ubiquitously expressed protein that localises to the endosomal system and, in its glycosylated form, to the cell surface. Non-glycosylated CD63 is not properly trafficked and aggregates at the nuclear periphery instead of the cell membrane (Tominaga et al., 2014, Molecular Cancer). According to the Western blot analysis in Figure 6, immature HLCs appear to express less glycosylated CD63 than mature HLCs. We will confirm the glycosylation by treating the cell lysates with PNGase F. Although AAV transduction enhanced CD63 expression of all three HLC stages tested (see new Supplementary Figure S6 in the revised manuscript), it only enhanced HDV infection of immature HLCs, in which the non-glycosylated form of CD63 appears to be the predominant form. To demonstrate that the glycosylated form of CD63 is involved in HDV entry, we will rescue WT CD63 in parallel with a glycosylation-deficient CD63 mutant (Yoshida et al., 2009, Microbiology and Immunology) in immature HLCs. We will also stain CD63 in both immature and mature HLCs to compare the subcellular localisation (plasma membrane/endosomes vs. nuclear membrane) of CD63 between the two stages.
- Description of the revisions that have already been incorporated in the transferred manuscript Based on the constructive comments by the Reviewers we already made the following changes, which are highlighted in red in the revised manuscript. In response to Reviewer 1: Fig 1B-C: the comparison with dHepaRG is very interesting, and confirms the validity of SC derived hepatocytes as a model for HDV infection. dHepaRG can be heterogeneous. Do you also see the same phenotype of enriched HDV infection within a denser subpopulations of dHepaRG We thank the Reviewer for their comment. Undifferentiated bipotent HepaRG cells are not permissive for HDV infection due to the lack of surface NTCP expression. Due to their bipotent nature and upon differentiation, two morphologically distinct populations become apparent: hepatocyte-like cells and biliary epithelial-like cells (McGill et al., 2010, Hepatology). As shown in the Figure 1 of the study by Mesnage et al. (2018, Molecular Toxicology), dense hepatocyte-like colonies are surrounded by clear epithelial cells corresponding to primitive biliary cells. In agreement with other studies, we only observe that the ALB-positive hepatocyte-like cells are permissive to HBV and HDV infection (Hantz et al., 2009, Journal of General Virology), highlighting their specific hepatic tropism and the cellular determinants required. Fig 1I is confusing. Was BLV assay also performed on the HLC infection (Day 0), or only during the titration assay in Huh7NTCP? We apologise for the confusion in this panel. BLV was only added during the titration assay on Huh7NTCP cells to confirm new and productive infections and to rule out carry-over. We have changed the order of Figures 1I - 1K to make this clearer and explain this better in the new results section (line 171-179) and figure legend (line 797-806). Revision Plan Fig 1K: x-axis is confusing... is it number of HBV, HDV and HBV/HDV positive cells? Or number of infected cells upon inoculation with HBV, HDV, or both? Please clarify. We apologise for this additional confusion caused in this panel. We infected HLCs with both HBV and HDV simultaneously and then counted the number of positive cells that were either single infected with HBV (pink cells/column), single infected with HDV (green cells/column) or double infected with both viruses (white cells/column). We have clarified this in the revised Results section (line 172-176) and in the revised Figure Legend (line 798-803). Figure 2: The AAV based vector to over express HBsAg is a very interesting tool, and the figure convincingly show production of HDV progeny viruses in HLC-AAV-HBsAg. Results shown are in agreement with previous studies based on hepatoma cell lines. We thank the Reviewer for this positive comment and we agree that AAVs represent interesting tools to genetically manipulate HLCs and other hepatocyte culture systems. Figure 2B: What is IU/ml? Infectious Unit? International Unit? Are units in Fig 1B, 2B and 2C the same? We apologise for the lack of clarity. In Figures 1B and 2C, IU corresponds to infectious units of HDV, whereas in Figure 2B, IU corresponds to international units for the assessment of secreted HBSAg levels in the supernatant. To make the difference clearer, we have changed the unit on the y-axis in Figure 2B and explicitly stated the abbreviations in the corresponding revised Figure Legends (lines 785, 786, 794, 795, 816, and 819). Figure 3: What is the overall number of transmission events observed in the co-culture setup? Can you visually observed viral spreading? Panel A shows only 1 event, making it hard to assess its efficiency. Titration assay in Fig 2C show production of up to 4-5 log of infectious HDV. But HLCs susceptibility to HDV infection may change during time... Thank you for your comment and for raising this important issue. Panel A clearly and visually demonstrates that extracellular spread of HDV had occurred in the HLCs system, as initially only WT and non-GFP positive HLCs were infected with HDV. After co-culture, the progeny of WT HLCs were able to infect GFP-HLCs (Figure 3A). The overall efficiency of HDV spread/transmission in HLC efficiency is shown in Figure 3C. If we allow spread to occur (DMSOtreated condition), the total number of HDV-positive HLCs grown in a 24-well plate is approximately 1000. When we block secondary infection of progeny with BLV and thus spread, we count only about 500 HDV-positive HLCs in a well. In general, spreading in HLCs (Figure 3C) is not as efficient as retitration to Huh7-NTCP (Figure 2C) for the following reasons: In Figure 2C, we wanted to have an estimation of the maximum amount of secreted infectious progeny from HDV-producing HLCs. To this end, we did not want the re-infection itself to be a major bottleneck and used the most susceptible model Huh7-NTCP and infected them under the best conditions, which includes the addition of 4% PEG and 2% DMSO in the culture medium. For our spread assay in HLCs, we cannot add PEG to the cells over the course of the experiment and we also wanted to be as physiological as possible. PEG significantly enhances HDV infection Revision Plan of HLCs (Supplementary Fig. S2) and Huh7-NTCP cells (Revision Figure 4), which is in agreement with previous studies (Michailidis et al., 2017, Scientific reports). In addition, as the Reviewer correctly points out, similar to other primary hepatocyte culture models, the HLC system deteriorates over time. However, we have found that HLCs can be cultured for up to 3 weeks. Nevertheless, we believe that the efficiency of HDV spread in HLCs is sufficient for drug testing (Fig. 3C & D). Revision Figure 4: PEG enhances HDV infection of Huh7-NTCP cells. Huh7- NTCP cells were infected with HDV (MOI= 5 Int. Units/cell) in the absence or presence of PEG. Cells were harvested on D5 pi and HDV genome copies were quantified by RT-qPCR. Figure 5: In panel A, GO pathways should be sorted based on significance, not Number of genes. In panel B-D, what is the scale of the heatmap on figure 5: change in CPM values, however log2, log10? Thank you for this comment, we have sorted the GO pathways based on significance (new Figure 5A). For panels B-D, we did not calculate the fold change in CPM values and they were not log transformed. Instead, we calculated the z-scores of the genes shown by comparing the expression level of a given gene (in CPM) in a given sample with the expression level of that gene across all samples. To avoid further confusion, we have added "z-score" to the new Figure 5. Figure 6: Do you have info about CD63 in other mature model, like dHepaRG and PHHs? Is CD63 also limiting in these models? Our data in Figure 6 suggest that CD63 may be a limiting factor for HDV infection of immature HLCs but not mature HLCs. Both dHepaRG cells and PHHs are mature hepatocyte models and therefore we speculate that CD63 is not rate limiting. However, we will investigate whether CD63 is rate-limiting in undifferentiated HepaRG cells. In response to Reviewer 2: Additional information that needs to be added, better explained, or corrected: The authors should explain why they used different MOIs depending on the genotype. In our previous study by Wang et al. 2021 J Hepatol, we found that the different HDV genotypes are heterogeneous in their ability to infect Huh7 NCTP cells. For example, as shown in Figure 4B of Wang et al. 2021 J Hepatol, GT 4 and 5 are less infectious than other genotypes. Based on the different infectious titres of the genotypes obtained on Huh7 NTCP cells, we then decided to use different MOIs for infection of our HLCs. The aim of the present study by Chi et al. was not to Revision Plan compare the different HDV genotypes, but to analyse whether they can all infect HLCs. In order to obtain similar infection efficiencies of our HLCs with the different genotypes, we used higher MOIs for those genotypes that were less infectious in Huh7-NTCP cells compared to those genotypes that were more infectious in Huh7-NCTP cells. We apologise for not making this sufficiently clear and have added this information to the results section (line 167-170) and the corresponding figure legend (line 796) of the revised manuscript. In Figure 1, it is unclear on which day the HCLs were infected by HDV and on which day they were transduced with AAV-NTCP. We apologise for the lack of clarity in the experimental design. We transduced HLCs with AAV two days before HDV infection to ensure sufficient ectopic NTCP expression on the day of HDV infection to study its effect on HDV entry. We have clarified this in the results section (line 153, 156) and in the figure legend (line 788) in the revised manuscript. It is not very clear if the authors used AAV serotype 6 consistently to transduce the cells. It would be valuable to show the transduction efficiency of AAV at different time points of HLC maturation, as it might also be affected and could explain some results. For example, in Figure 6H, why does AAV-CD63 transduction increase HDV infectivity at day15 but not at day 10? It would be interesting to repeat the anti-CD63 western blot after AAV-CD63 transduction. Thank you for this comment. Yes, we have consistently used AAV 6 due to its relatively broad tissue tropism (Verdera et al., 2020, Molecular Therapy) and we have clarified this information in the revised manuscript (see line 331). We agree with the Reviewer's concerns regarding the variable transduction efficiency. We have previously tested different AAV capsids and found that AAV6 transduced mature HLCs at high levels (Zhang et al., 2022, Hepatol Commun). In this study, we also performed Western blot analysis to confirm successful CD63 overexpression by AAV transduction at different stages of hepatocyte differentiation. As shown in new Supplementare Figure 6, although there were some differences in transduction efficiency, the majority of all cells at each stage of differentiation were successfully transduced to ectopically express CD63. The authors claim that by using AAV to express HBsAg, they are mimicking the expression of HBsAg from the integrated sequence rather than cccDNA. However, it is the opposite, as AAV genomes, like cccDNA, remain as episomes in the cells. Yes, the Reviewer is conceptually correct and we apologise for the incorrect wording. In principle, we aim to trans-complement HBsAg in a setting outside of HBV infection and thus mimic the expression of antigen from integrated cells, although AAVs of course remain mostly episomal. We have clarified this in the revised manuscript (see lines 188 & 378). In response to Reviewer 3: Line 217: the complete inhibition of cell to cell spread by myrcludex suggests that there is no spread by cell-cell contact. This should be discussed. Revision Plan Yes, there is no evidence of HDV spread by cell-cell contact because, as the Reviewer correctly points out, BLV treatment almost completely blocked HDV de novo infection (Figure 2D & E). To our knowledge, cell-to-cell spread has not been demonstrated for HDV. According to our own studies by Zhang et al, 2021/2022, Journal of Hepatology, HDV spreads either extracellularly (which can be blocked by BLV) or by cell division (discussed in lines 362). Since HLC are similar to primary human hepatocytes and do not divide in vitro, we believe that extracellular spread is the predominant mode of spread in HLC (line 365). Line 210ff:Is there any evidence for syncytia formation in this system? No, we have not observed syncytia formation. Since HDV has no glycoproteins, we would not expect syncytia to form. Line 42: secrete should be replaced by release We thank the Reviewer for pointing out the inaccuracy in our terminology. We have replaced "secrete" with "release" (line 42). Line 241: proteins are not expressed, genes are expressed Thank you, we agree and changed the wording accordingly (line 246).
- Description of analyses that authors prefer not to carry out In response to Reviewer 1: Fig 1B: Unit is confusing, using terms usually used for titration of infectivity, from the virus input point of view, not from the cellular point of view. Can you use % infected cells instead, or "HDV infection rate" like in Supp Fig 1B? We apologise for this confusion. For other viruses, such as but not limited to HCV or HEV, the most common method is to report focus forming units per ml (FFU/ml). HLCs do not divide and, in the absence of HBV S antigen, no cell-division mediated HDV spread can occur and only single infection events can be observed (hence infectious unit = IU/ml). Since differentiated, authentic hepatocyte culture models such as PHHs, HLCs or HepaRG cells are always characterised by strong cell heterogeneity, it is difficult to directly compare the overall percentage of infection with a homogeneous cell population such as Huh7-NTCP cells. Therefore, if the Reviewer allows us, we prefer to keep this unit in our main figures. However, and hopefully to the satisfaction of this Reviewer, we have also calculated the percentage of infected cells of this exact dataset and show it in the Supplementary figures (Suppl. Fig. S1 C). The proportion of infection efficiency comparing HLCs, dHepaRGs, and Huh7-NTCP cells does not differ when presented either as IU/ml or as percentage of infected cells.
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Referee #3
Evidence, reproducibility and clarity
In their manuscript entitled " An HBV/HDV Infection Model Using Human Pluripotent Stem 1 Cell-Derived Hepatocyte- Like Cells for Virus Host Interactions and Antiviral Evaluation" describe the use of HLCs derived from hPSCs as infection model for analysis of HDV life cycle. The ms is well written and clearly structured. It is easy to follow the concept of the study. The ms addresses a relevant topic and could help to overcome limitations in the analysis of HDV life cycle. The authors perform in many points a detailed characterization of this experimental system but there are still a variety of open points which must be addressed:
Specific points:
Line 143: the authors describe two forms of HLCs (less and more confluent with differences regarding the susceptibility to HDV infection). The characteristics of the less and more confluent HLCs should be described in more detail-what is causative fir the differences in susceptibitly for HDV infection of these two forms? The statistical analyses should be improved: There are no p-values provided for the data presented in the supplement and a variety of figures lacks p-values Kinetic of the infection: Here it would be interesting to see a comparative analysis by western blot investigating the ratio HBsAg/HDAg over the time in HLCs, HepaRGs and NTCP oe cells
Line 157: What is the experimental evidence for the proper localization and functionality of the ectopically expressed NTCP in HLCs. Did the authors study the taurocholate transport after overexpression of NTCP?
Line 169: The authors should include data comparing the number of double positive cells in HLs, HepaRGs and NTCP o.e. expressing cells under the chosen experimental conditions
Line 217: the complete inhibition of cell to cell spread by myrcludex suggests that there is no spread by cell-cell contact. This should be discussed.
Line 210ff:Is there any evidence for syncytia formation in this system?
Line 291: expression analysis by RT-PCR is not sufficient. It will be important to study by CLSM if the identified factors are really present as proteins and properly localized. Regarding the role of CD63: what is the evidence for a direct role of CD63 for HDV entry-can the authors exclude that CD63 is relevant for targeting other factors to the surface? What is the impact of loss of CD63 on the functionality of the autophagosomal-MVB-EV system in HLCs?
Minor points:
Line 42: secrete should be replaced by release Line 241: proteins are not expressed, genes are expressed
Significance
The manuscript describes the use of HLCs derived from hPSCs as infection model for analysis of HDV life cycle. The ms is well written and clearly structured. It is easy to follow the concept of the study.
The ms addresses a relevant topic and could help to overcome limitations in the analysis of HDV life cycle. The authors perform in many points a detailed characterization of this experimental system but there are still a variety of open points which must be addressed.
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Referee #2
Evidence, reproducibility and clarity
In the present work, Chi et al. demonstrated that Hepatocyte-like cells (HCLs) derived from human pluripotent cells (hPCs) can be infected by HBV. The development of new HDV cellular models is of great value for understanding HDV biology and developing new treatments. However, the relevance of the present work is limited by a recent publication by Lange et al., in which they also showed that HCLs derived from hPCs can be infected by HDV, inducing the activation of the innate immune response, as previously demonstrated in cells and mice.
The authors added new information to the work of Lange et al, including:
- HLCs derived from human pluripotent cells can be infected by different HDV genotypes.
- They proved that infectious HDV particles are formed.
- They identified CD63 as a potential HDV coreceptor.
The manuscript would benefit from a more detailed virological analysis, such as:
- Determination of HDV genome and antigenome sequences and analysis of HDV editing.
- Analysis of HDV short and large antigens by western blot.
- Analysis of HBV-related virological parameters in monoinfected and co-infected cells.
Additional information that needs to be added, better explained, or corrected:
The authors should explain why they used different MOIs depending on the genotype.
In Figure 1, it is unclear on which day the HCLs were infected by HDV and on which day they were transduced with AAV-NTCP.
It is not very clear if the authors used AAV serotype 6 consistently to transduce the cells. It would be valuable to show the transduction efficiency of AAV at different time points of HLC maturation, as it might also be affected and could explain some results.
For example, in Figure 6H, why does AAV-CD63 transduction increase HDV infectivity at day 15 but not at day 10? It would be interesting to repeat the anti-CD63 western blot after AAV-CD63 transduction.
The authors claim that by using AAV to express HBsAg, they are mimicking the expression of HBsAg from the integrated sequence rather than cccDNA. However, it is the opposite, as AAV genomes, like cccDNA, remain as episomes in the cells.
Significance
In the present work, Chi et al. demonstrated that Hepatocyte-like cells (HCLs) derived from human pluripotent cells (hPCs) can be infected by HBV. The development of new HDV cellular models is of great value for understanding HDV biology and developing new treatments. However, the relevance of the present work is limited by a recent publication by Lange et al., in which they also showed that HCLs derived from hPCs can be infected by HDV. The authors added new information to the work of Lange et al, including:
- HLCs derived from human pluripotent cells can be infected by different HDV genotypes.
- They proved that infectious HDV particles are formed.
- They identified CD63 as a potential HDV coreceptor.
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Referee #1
Evidence, reproducibility and clarity
Summary: In the present manuscript, H Chi et al describe the infection of stem cell derived hepatocytes with HBV and HDV. They suggest that it could be used to validate antiviral treatment in a mature hepatocyte model. Moreover, they take advantage of the differentiation process of the cells to identify time points correlating with significant change in viral permissivity, and focus on one of these time points in an attempt to identify new host factors of HDV.
Overall, the manuscript is well written and brings interesting information toward the establishement of an efficient HBV HDV coinfection model in stem cell derived hepatocytes. Particularly, comparison to dHepaRG, another model relying on in vitro differentiation and commonly used to study HBV and HDV infection, reveals the potential of stem cell derived hepatocytes. While the efficiency of co infection in the stem cell derived hepatocytes may seem low, the manuscript goes in the direction of helping establishing a new mature in vitro model of infection.
Figure 1: The observation of a denser subpopulation of hepatocytes more susceptible to HDV is interesting. Do you have more characterization of this cell subpopulation, by IFA, in term of hepatic maturation marker, known HDV host factors and particularly NTCP expression?
Fig 1B-C: the comparison with dHepaRG is very interesting, and confirms the validity of SC derived hepatocytes as a model for HDV infection. dHepaRG can be heterogeneous. Do you also see the same phenotype of enriched HDV infection within a denser subpopulations of dHepaRG?
Fig 1B: Unit is confusing, using terms usually used for titration of infectivity, from the virus input point of view, not from the cellular point of view. Can you use % infected cells instead, or "HDV infection rate" like in Supp Fig 1B?
Fig 1B and C: Can a BLV control be included in the figure?
Fig 1A-F: What is the overall level of NTCP between HLC, HepaRG, Huh7NTCP and HLC-AAV-NTCP? Can NTCP and HDAg be stained simultaneously in your cells?
Fig 1I is confusing. Was BLV assay also performed on the HLC infection (Day 0), or only during the titration assay in Huh7NTCP?
Fig 1K: x-axis is confusing... is it number of HBV, HDV and HBV/HDV positive cells? Or number of infected cells upon inoculation with HBV, HDV, or both? Please clarify.
Figure 2: The AAV based vector to over express HBsAg is a very interesting tool, and the figure convincingly show production of HDV progeny viruses in HLC-AAV-HBsAg. Results shown are in agreement with previous studies based on hepatoma cell lines.
Figure 2B: What is IU/ml? Infectious Unit? International Unit? Are units in Fig 1B, 2B and 2C the same?
Figure 3: What is the overall number of transmission events observed in the co-culture setup? Can you visually observed viral spreading? Panel A shows only 1 event, making it hard to assess its efficiency. Titration assay in Fig 2C show production of up to 4-5 log of infectious HDV. But HLCs susceptibility to HDV infection may change during time...
Figure 4: While the strategy is interesting, based on what has been previously shown for HCV in Wu et al., 2012, the lack of statistical data prevents the reader to really understand and see drastic difference in term of susceptibility to infection and level of expression of host genes. In panel C, is the difference between day 13 and 15 statistically significant? Same for panel D, day 17 vs 19? As a remark, day 19, the peak of susceptibility to HDV, seems to be also the peak of maturation, based on ALB RTqPCR (panel B).
Figure 5: In panel A, GO pathways should be sorted based on significance, not Number of genes. In panel B-D, what is the scale of the heatmap on figure 5: change in CPM values, however log2, log10?
Figure 6: Do you have info about CD63 in other mature model, like dHepaRG and PHHs? Is CD63 also limiting in these models?
Significance
Overall, the manuscript brings interesting information toward the establishement of an efficient HBV HDV coinfection model in stem cell derived hepatocytes. Particularly, comparison to dHepaRG, another model relying on in vitro differentiation and commonly used to study HBV and HDV infection, reveals the potential of stem cell derived hepatocytes. While the efficiency of co infection in the stem cell derived hepatocytes may seem low, the manuscript goes in the direction of helping establishing a critical needed and long awaited mature in vitro model of HDV HBV infection.
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Reply to the reviewers
REVIEWER #1:
The authors identify ZMYND8 as a bromodomain protein: is there evidence the actions described in this paper involve interaction of ZMYND8 with acetylated lysines? Does this mechanism play a role in ZMYND8's transcriptional regulatory activities?
ZMYND8 is recruited to chromatin via its Bromo, PHD, and PWWP domains which recognize H3K4me1 and/or H3K14ac marks. Methyl marks on H3K4 are regulated by several lysine methyltransferases (e.g., MLL family and SETD1A/B) and demethylases (e.g., KDM5A-D) while H3K14ac is regulated by GCN5/PCAF, p300/CBP and/or Myst3. ZMYND8 also recruits histone deacetylases to chromatin including members of the highly conserved Nucleosome Remodeling and Deacetylase (NuRD) complex, HDAC1 and HDAC2. NuRD primarily deacetylates H3K27ac marks, however it is possible other acetyl moieties are affected by this complex.
Using ChIP-seq, we now show that Zmynd8-cKO cardiomyocytes retain H3K27ac marks at misexpressed genes. Interestingly, while some of these genes have altered H3K27ac at their promoters (and therefore have full-length misexpressed transcripts; i.e., Casq1, Cdh16) other genes (e.g., Lamb3, Chst3) show changes in H3K27ac in the middle of the gene and this tracks with gene expression changes. We interpret this unusual transcript and H3K27ac pattern as evidence of potential ZMYND8-regulated intragenic enhancer elements. We include the following in our resubmission:
- Figure 5 which shows changes in H3K27ac levels at different genes, showing examples of genome browser tracks at the following genes Casq1, Cdh16, Camk1g, and Chst3.
- Supplemental Figure S5 showing H3K27ac and H3K27me3 marks at the cardiac myosin locus (i.e., Myh6 and Myh7) and surrounding genes in control and Zmynd8-cKO * We also show retention of H3K27ac at the Zmynd8 gene in Zmynd8-cKO cardiomyocytes, again supporting an autoregulatory mechanism of Zmynd8 *expression.
- An additional section in Results titled “H3K27 acetylation marks are retained at specific loci in Zmynd8-cKO cardiomyocytes”
- New “ChIP-seq and Analysis” section in Materials and Methods
- An updated model in Figure 6 that includes ZMYND8’s activities in modulating H3K27ac levels This first analysis on H3K27ac and H3K27me3 deposition in Zmynd8-cKO cardiomyocytes is not comprehensive and genome-wide analysis on these datasets will ultimately be performed in combination with additional datasets including ZMYND8 ChIP-seq from isolated cardiomyocytes. However, given the pertinence to ZMYND8’s transcriptional activities and in response to this reviewer’s critique, we include this pertinent H3K27ac and H3K27me3 ChIP-seq data.
Given the newness of this model and multiple isoform issues, the authors should show the entire gel for the westerns in SFigure 1C.
We now show the entire blots for all western blots in Supplementary Figure 1.
Nuclear staining is in SFigure 1E (typo in text): most of the staining in the control is non-myocyte and non-nuclear, making the statement about IHC showing depletion less convincing for Nkx lines.
We have fixed the typo in the text on page 5 line 128 and now correctly refer to this figure as Supplemental Figure S2. To better visualize nuclear ZMYND8 staining in this figure, we now show an adjusted image with increased contrast and brightness settings on both control and Zmynd8-cKO images and added arrowheads to indicate nuclei in the isolated cardiomyocytes. We also note that the flox sites only span the nuclear localization sequence for the protein so cytoplasmic ZMYND8 may still be present in Zmynd8-cKO cells.
Regarding perinuclear ZMYND8 staining: am I accurate in observing the perinuclear staining is still present in the KO? What do the authors make of this?
We do not observe perinuclear staining of ZMYND8 in KO cells. In Figure 1C, we believe the reviewer is observing potential staining in the cytoplasm, not perinuclear staining of ZMYND8 that we see in the control Myh6-CreTg/0 cardiomyocytes. We have added yellow arrowheads in Figure 1C to delineate perinuclear ZMYND8 staining we describe in the text.
What is the protein amount in the Zmynd8fl/+ mice? Do the hearts upregulate the protein to compensate?
We have added a gel in Supplemental Figure 1 that now shows protein isolated from Myh6-CreTg/0; Zmynd8fl/+ hearts and Myh6-CreTg/0 controls (Supplemental Figure 1C, right gel). It does not appear that Myh6-CreTg/0; Zmynd8fl/+ cardiomyocytes upregulate ZMYND8 to compensate for loss of one allele, as determined by Western blotting. However, our analysis shows differing ratios of the detected bands between conditional heterozygous mice, underscoring the need to further study the different ZMYND8 species present in cardiomyocytes. We state this in the results section (page 5, lines 123-124).
Do the individual cardiomyocytes hypertrophy in the Zymnd8 cKO mice? Do they proliferate?
Our analysis of cardiomyocyte morphology does reveal hypertrophy. The results we report include a new observation of variation in cell shape and are likely at least as sensitive as WGA staining which we find to be confounded by sectioning artifacts, cell identity, and position of the sections in the heart. We do not observe changes in H3S10ph staining between wild type and knockout hearts (data not shown) however we acknowledge further analysis of this may be warranted via other cell proliferation markers.
Regarding this statement: "These results show that ZMYND8 is necessary to prevent the onset of contractile dysfunction that leads to heart failure and death." I think what the authors showed is that loss of ZMYND8 causes contractile dysfunction, heart failure and death.
We acknowledge the difference in these statements and have now changed the text on page 7, lines 160-162 to “…these results show that loss of ZMYND8 from cardiomyocytes leads to contractile dysfunction, heart failure, and death.”
The switch like up regulation of skeletal muscle genes is an interesting observation. Do the authors have any evidence how this works? Other studies with EZH2 are mentioned, and if ZYMND8 is in fact acting as a bromodomain, the mechanism might involve regulation of enhancer methylation/acetylation at K27. This is testable, certainly at the target genes the investigators have identified (Casq1 and Tnni2), by ChIP-PCR.
As described above, we now include ChIP-seq data of H3K27ac and H3K27me3 marks in control and Zmynd8-cKO cardiomyocytes. As the reviewer suggests, there is retention of H3K27Ac marks in cKO cardiomyocytes, suggesting that ZMYND8 is necessary to recruit histone deacetylases to specific loci to remove acetyl moieties from H3K27. Regarding specific skeletal muscle genes, we do find a difference in histone acetylation at the promoter of the Casq1 gene and show this in Figure 5.
The model in Figure 4C makes sense, but the authors do not present any data to support this molecular mechanism. If the authors ChIP for localization of TFs in KO vs control and/or examine histone marks, they could build support for this model, particularly since they have already identified target genes.
We have now updated our model in Figure 6 to include ZMYND8’s role in modulating H3K27ac levels at target loci, leading to upregulation of mRNA transcripts. We add consideration of the implications of this in the Discussion.
Reviewer #1 (Significance (Required)):
The authors identify ZMYND8 as a bromodomain protein: is there evidence the actions described in this paper involve interaction of ZMYND8 with acetylated lysines? Does this mechanism play a role in ZMYND8's transcriptional regulatory activities? We include new data to demonstrate this. Please see above.
REVIEWER #2:
The study is reporting the role of ZMYND8 chromatin factor in the mouse heart. Mutations have been previously identified in genetic studies of atrioventricular septal defects and syndromic congenital cardiac abnormalities. Therefore the authors perform cardiomyocyte specific knockout of exon 4 (with the nuclear localisation signal) using Myh6 and Nkx2.5 cre. Full length protein seems to be removed from the nucleus. The knockout doesn't seem to affect embryonic development, but leads to hypertrophy and premature death. The authors perform transcriptome analysis and find 55 upregulated and 4 downregulated genes that are mainly related to contraction and ion transport. especially they find skeletal muscle proteins including fast-twitch troponin I upregulated. Tnni2 seems to be integrated into the sarcomeres, albeit the antibody staining is not in the expected location (see below). Shape of cardiomyocytes was apparently different, although this is seemingly not related to Tnni2 expression.
Specific points: - ZMYND8 has been previously linked to atrioventricular septal defects, but the authors do not explore if this is the case also in their model; could the authors please expand
We have not seen obvious septal defects in any Zmynd8-cKO mice. We now state this explicitly in the Results section on page 7, lines 159-160 and discuss this discrepancy from the observations in humans in our Discussion on page 12. The human study analyzing families carrying Zmynd8 mutations reported a variety of heart malformations in 7 of the 11 individuals. The septal defects observed in these individuals were not consistent and may be incidental to the molecular function of ZMYND8 within cardiomyocytes. One possibility is that these malformations are caused by stress during development, with Zmynd8 mutations sensitizing the heart to these defects. We acknowledge in the discussion that further analyses of septal defects in this knockout model could be useful in the future with more stringent stereoscopic techniques.
- the initial section is difficult to follow. Especially, the authors seem surprised regarding the size of the bands. They should make clear what the expected band size should be after removal of exon 4 and if this doesn't fit, explore the reasons experimentally if possible.
Rigorous analyses of the different Zmynd8 isoforms in cardiomyocytes will be a focus of future work as this may explain the mosaicism seen in cKO cardiomyocytes and the discrepancy between TNNI2 expression and cell shape (see below). We have reorganized the section and discuss potential explanations for our observed band sizes.
- the authors explore the shape of the cardiomyocytes and find cells that are shorter and thicker. It would be meaningful to include other metrics including, sarcomere length, contractility measurements and calcium transients (especially in light of the change ion transporters).
We agree that an investigation of the effects of the mutation and the skeletal muscle proteins on cellular contractility could be very interesting. Here we have contented ourselves with evaluating the effects at a physiological level through assessment of cardiac function.
- it is unclear why Tnni2 stains for the M-band (where in fact should be no actin and troponin) and not a typical double band with the H zone excluded (see here for good staining example: https://www.biorxiv.org/content/10.1101/2020.09.09.288977v1.full.pdf). also the staining looks very fuzzy. can the authors provide evidence that the antibody is staining troponin I in skeletal muscle at the correct localisation to demonstrate the specificity of the antibody?
We thank the reviewer for raising this point and do agree that there are instances where we observe TNNI2 staining colocalizing with MYOM1 staining. After closer examination of our images, we believe we do also see TNNI2 staining between M-lines and attribute this discrepancy to our antibody staining and/or biological differences between cells however, further analysis with better microscopy and immunostaining techniques is warranted. We have added an additional image to Figure 4A and have modified this results section on page 9, lines 217-222.
- it is interesting why Tnni2 is detectable only in a subfraction of cells, but this remains unexplored. Could this e.g. be right vs left ventricular cardiomyocytes? or is this related to the remaining isoforms of ZMYND8? The authors should try to identify the source of this variability
We agree that the TNNI2 mosaicism is an interesting phenotype and thank the reviewer for possible explanations. We favor the model of mosaicism being an effect of compensatory mechanisms by other ZMYND8 isoforms and discuss this in the discussion on page 8, line 228-229. This will be a focus of future work.
- if Tnni2 is unrelated to the changes in hypertrophic phenotype of the cardiomyocytes, then the authors should aim to identify if one of the other differentially regulated proteins might be related (e.g. ion transporter). The experiments above might help to identify this
We agree that identifying the causal agents of hypertrophy in this model would be interesting. It is however possible that we are simply seeing the expected effect of reduced contractility leading to hypertrophic compensation. Sorting this out will require additional mutant analyses and/or siRNA experiments all of which come with their own caveats and are outside of the scope of this initial analysis. Our aim for this manuscript was to report on the effects of ZMYND8 removal from cardiomyocytes. Additionally, it is certainly possible that phenotypes we report in this article are independent of the gene expression changes we have detected in the mutant and could be caused by other roles for ZMYND8 such as the DNA damage response. We include this possibility in our discussion.
Reviewer #2 (Significance (Required)):
Overall the manuscript is interesting in principle - it documents the role of a disease linked protein that hasn't been explored in the heart in detail, however at this point it seems premature and doesn't follow through on a solid detailed analysis.
The change in transcription profiles and especially the upregulation of skeletal muscle isoforms is intriguing, but should be further explored. There seems a lack of hypothesis and instead the authors analyse Tnni2 and cell shape, but while the cell shape is different they don't find a correlation with Tnni2. so if the authors suggest that cell shape is important (as indeed might be), how is this regulated?
Our goal for this initial paper is to describe the physiological and molecular phenotypes of the Zmynd8-cKO mouse model. It would be interesting to pursue a study directed at this question, perhaps of cell sorted "fat" and "thin" myocytes, but that would be beyond the scope of this report.
The study could be of interest to cardiovascular researchers, but needs to be expanded on the points above.
My expertise is in cardiovascular research
REVIEWER #3:
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Summary:
Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.
The authors found that Zmynd8-cKO mice develop dilated hearts, decreased cardiac function, and illegitimate expression of skeletal muscle genes. They concluded that ZMYND8 is necessary to maintain appropriate cardiomyocyte gene expression and cardiac function.
Major comments:
- Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them? The claim that "Zmynd8 is dispensable for cardiac development" is not supported by the lethality of Zmynd8 D/D mice.
We interpret our observation that viable Nkx2.5-CreTg/0; Zmynd8fl/fl mice are born and grow to adulthood as evidence that Zmynd8 is not necessary for establishment of the cardiac lineage. However, we do agree that labeling Zmynd8 as dispensable is not supported by the experiments using Zmynd8D/D mice. We hypothesize that the lethality of the Zmynd8D/D mice is due to early embryonic events since empty egg sacs were observed at E8.0, however we do agree that ZMYND8’s role in cardiac development cannot be assessed using this line. We state that empty yolk sacs are found in mother uteri 8 days after mating on page 4, lines 94-96.
- Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether. The claim should be changed into "function of Zmynd8 in cardiac development can not be fully assessed in Zmynd8 D/D mice".
We agree that the lethality of Zmynd8D/D * mice prevents any analysis of early embryonic roles for the establishment of the cardiac lineage. This is additionally confounded by the fact that other partial-length isoforms of Zmynd8* may still be present in our knockout model. We have modified our interpretation and have further discussed the potential role of ZMYND8 in early cardiac development on page 4, line 96.
- If you have constructive further reaching suggestions that could significantly improve the study but would open new lines of investigations, please label them as "OPTIONAL". OPTIONAL: What about the phenotype of Nkx2-5 Cre mediated knockout of Zmynd8? Is it more severe than Myh6 Cre mediated knockout? At more earlier embryonic stage when cardiomyocytes are differentiated, are the skeletal muscle developmental genes ectopically upregulated in heart tube?
This is an interesting observation and deserves further investigation. Our initial analysis of Nkx2.5-CreTg/0; Zmynd8fl/fl mice reveals that these mice do not die earlier than Myh6-CreTg/0; Zmynd8fl/fl mice or have a more severe phenotype. In fact, mice with Nkx2.5-Cre mediated cKO mice live longer than Myh6-Cre mediated cKO mice. We show that these mice do have ZMYND8 depleted from their cardiomyocyte nuclei and ectopically express TNNI2.
This discrepancy in phenotype has been observed recently in mice lacking Kdm8 (Ahmed et al, 2023) and has been attributed to a lower efficiency of the Nkx2.5-Cre recombinase compared to Myh6-driven Cre.
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Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated time investment for substantial experiments. Yes.
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Are the data and the methods presented in such a way that they can be reproduced? Yes.
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Are the experiments adequately replicated and statistical analysis adequate? Yes.
Minor comments:
- Specific experimental issues that are easily addressable. Have the female Zmynd8-cKO mice always died before their male siblings been pregnant with heart overload?
All lifespan data are of non-pregnant females. All mice (i.e., both males and females) used in these analyses were not used for mating. We now explicitly say this in the mouse husbandry section of our Materials and Methods section.
- Are prior studies referenced appropriately?
This paper "De Novo ZMYND8 variants result in an autosomal dominant neurodevelopmental disorder with cardiac malformations" should be referenced.
Thank you. We have referenced this paper (Dias et al. 2022) on page 3, line 61 as well as in the Discussion on page 9, line 211.
- Are the text and figures clear and accurate? Description of "cardiomegaly, preventing a compact myocardium phenotype, heart enlargement and thinning of the ventricular" should be more accurate and professional. We have changed the following in the text:
Page 6, line 150 “preventing a compact myocardium phenotype” to “during later stages of cardiac development” on
Page 6, line 153 “heart enlargement” to “The heart weight of Zmynd8-cKO mice”
Page 7, line 158 “thinning of the ventricular” to “dilated cardiomyopathy”
- Do you have suggestions that would help the authors improve the presentation of their data and conclusions? GSEA analysis of RNA-seq can be used to show the enrichment of cardiac and skeletal genes.
Because GSEA analysis requires at least three replicates per group to have the appropriate statistical power, we opted to show Gene Ontology analysis using DAVID software.
Reviewer #3 (Significance (Required)):
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General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed? This study show that Zmynd8-cKO mice develop dilated hearts, decreased cardiac function, and illegitimate expression of skeletal muscle genes. However, the genes regulated by Zmynd8 during early developmental stage have not been identified and the functional mechanism of Zmynd8 during heart development remains unclear.
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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,...). Genetic mutations of Zmynd8 have been identified in congenital heart diseases with cardiac structural defects. And this study further shows that dysfunction/weaker mutations of Zmynd8 as a reason for dilated cardiomyopathy with decreased function.
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Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field? This study shows that dysfunction of Zmynd8 as a reason for dilated cardiomyopathy with decreased function. Researchers of "basic research" and "clinical" may be interested in this study.
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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. heart development, dilated cardiomyopathy, epigenetics
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Referee #3
Evidence, reproducibility and clarity
Summary:
Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.
The authors found that Zmynd8-cKO mice develop dilated hearts, decreased cardiac function, and illegitimate expression of skeletal muscle genes. They concluded that ZMYND8 is necessary to maintain appropriate cardiomyocyte gene expression and cardiac function.
Major comments:
- Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?
The claim that "Zmynd8 is dispensable for cardiac development" is not supported by the lethality of Zmynd8 / mice. - Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether.
The claim should be changed into "function of Zmynd8 in cardiac development can not be fully assessed in Zmynd8 / mice". - If you have constructive further reaching suggestions that could significantly improve the study but would open new lines of investigations, please label them as "OPTIONAL".
OPTIONAL: What about the phenotype of Nkx2-5 Cre mediated knockout of Zmynd8? Is it more severe than Myh6 Cre mediated knockout? At more earlier embryonic stage when cardiomyocytes are differentiated, are the skeletal muscle developmental genes ectopically upregulated in heart tube? - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated time investment for substantial experiments.
Yes. - Are the data and the methods presented in such a way that they can be reproduced?
Yes. - Are the experiments adequately replicated and statistical analysis adequate?
Yes.
Minor comments:
- Specific experimental issues that are easily addressable.
Have the female Zmynd8-cKO mice always died before their male siblings been pregnant with heart overload? - Are prior studies referenced appropriately?
This paper "De Novo ZMYND8 variants result in an autosomal dominant neurodevelopmental disorder with cardiac malformations" should be referenced. - Are the text and figures clear and accurate?
Description of "cardiomegaly, preventing a compact myocardium phenotype, heart enlargement and thinning of the ventricular" should be more accurate and professional. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
GSEA analysis of RNA-seq can be used to show the enrichment of cardiac and skeletal genes.
Significance
- General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed? This study show that Zmynd8-cKO mice develop dilated hearts, decreased cardiac function, and illegitimate expression of skeletal muscle genes. However, the genes regulated by Zmynd8 during early developmental stage have not been identified and the functional mechanism of Zmynd8 during heart development remains unclear.
- 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,...). Genetic mutations of Zmynd8 have been identified in congenital heart diseases with cardiac structural defects. And this study further shows that dysfunction/weaker mutaions of Zmynd8 as a reason for dilated cardiomyopathy with decreased 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? This study shows that dysfunction of Zmynd8 as a reason for dilated cardiomyopathy with decreased function. Researchers of "basic research" and "clinical" may be interested in this study.
- 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. heart development, dilated cardiomyopathy, epigenetics
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Referee #2
Evidence, reproducibility and clarity
The study is reporting the role of ZMYND8 chromatin factor in the mouse heart. Mutations have been previously identified in genetic studies of atrioventricular septal defects and syndromic congenital cardiac abnormalities. Therefore the authors perform cardiomyocyte specific knockout of exon 4 (with the nuclear localisation signal) using Myh6 and Nkx2.5 cre. Full length protein seems to be removed from the nucleus. The knockout doesn't seem to affect embryonic development, but leads to hypertrophy and premature death. The authors perform transcriptome analysis and find 55 upregulated and 4 downregulated genes that are mainly related to contraction and ion transport. especially they find skeletal muscle proteins including fast-twitch troponin I upregulated. Tnni2 seems to be integrated into the sarcomeres, albeit the antibody staining is not in the expected location (see below). Shape of cardiomyocytes was apparently different, although this is seemingly not related to Tnni2 expression.
Specific points:
- ZMYND8 has been previously linked to atrioventricular septal defects, but the authors do not explore if this is the case also in their model; could the authors please expand
- the initial section is difficult to follow. Especially, the authors seem surprised regarding the size of the bands. They should make clear what the expected band size should be after removal of exon 4 and if this doesn't fit, explore the reasons experimentally if possible.
- the authors explore the shape of the cardiomyocytes and find cells that are shorter and thicker. It would be meaningful to include other metrics including, sarcomere length, contractility measurements and calcium transients (especially in light of the change ion transporters)
- it is unclear why Tnni2 stains for the M-band (where in fact should be no actin and troponin) and not a typical double band with the H zone excluded (see here for good staining example: https://www.biorxiv.org/content/10.1101/2020.09.09.288977v1.full.pdf). also the staining looks very fuzzy. can the authors provide evidence that the antibody is staining troponin I in skeletal muscle at the correct localisation to demonstrate the specificity of the antibody?
- it is interesting why Tnni2 is detectable only in a subfraction of cells, but this remains unexplored. Could this e.g. be right vs left ventricular cardiomyocytes? or is this related to the remaining isoforms of ZMYND8? The authors should try to identify the source of this variability
- if Tnni2 is unrelated to the changes in hypertrophic phenotype of the cardiomyocytes, then the authors should aim to identify if one of the other differentially regulated proteins might be related (e.g. ion transporter). The experiments above might help to identify this
Significance
Overall the manuscript is interesting in principle - it documents the role of a disease linked protein that hasn't been explored in the heart in detail, however at this point it seems premature and doesn't follow through on a solid detailed analysis.
The change in transcription profiles and especially the upregulation of skeletal muscle isoforms is intriguing, but should be further explored. There seems a lack of hypothesis and instead the authors analyse Tnni2 and cell shape, but while the cell shape is different they don't find a correlation with Tnni2. so if the authors suggest that cell shape is important (as indeed might be), how is this regulated?
The study could be of interest to cardiovascular researchers, but needs to be expanded on the points above.
My expertise is in cardiovascular research
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Referee #1
Evidence, reproducibility and clarity
The authors identify ZMYND8 as a bromodomain protein: is there evidence the actions described in this paper involve interaction of ZMYND8 with acetylated lysines? Does this mechanism play a role in ZMYND8's transcriptional regulatory activities?
Given the newness of this model and multiple isoform issues, the authors should show the entire gel for the westerns in SFigure 1C. Nuclear staining is in SFigure 1E (typo in text): most of the staining in the control is non-myocyte and non-nuclear, making the statement about IHC showing depletion less convincing for Nkx lines.
Regarding perinuclear ZMYND8 staining: am I accurate in observing the perinuclear staining is still present in the KO? What do the authors make of this?
What is the protein amount in the Zmynd8fl/+ mice? Do the hearts upregulate the protein to compensate?
Do the individual cardiomyocytes hypertrophy in the Zymnd8 cKO mice? Do they proliferate?
Regarding this statement: "These results show that ZMYND8 is necessary to prevent the onset of contractile dysfunction that leads to heart failure and death." I think what the authors showed is that loss of ZMYND8 causes contractile dysfunction, heart failure and death.
The switch like up regulation of skeletal muscle genes is an interesting observation. Do the authors have any evidence how this works? Other studies with EZH2 are mentioned, and if ZYMND8 is in fact acting as a bromodomain, the mechanism might involve regulation of enhancer methylation/acetylation at K27. This is testable, certainly at the target genes the investigators have identified (Casq1 and Tnni2), by ChIP-PCR.
The model in Figure 4C makes sense, but the authors do not present any data to support this molecular mechanism. If the authors ChIP for localization of TFs in KO vs control and/or examine histone marks, they could build support for this model, particularly since they have already identified target genes.
Significance
The authors identify ZMYND8 as a bromodomain protein: is there evidence the actions described in this paper involve interaction of ZMYND8 with acetylated lysines? Does this mechanism play a role in ZMYND8's transcriptional regulatory activities?
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
In this manuscript, the authors report a novel and simple method to analyze the heterogeneity of various organelles. After imaging a large set of fluorescent-marker-labeled organelles, cluster analysis is adapted for illuminating the dynamics of organelles. Through this novel method, the authors are able to report organelle contact, which previously can only be observed by super-resolution imaging. This is method could significantly accelerate future discoveries at the cellular level. The manuscript is well written and has the potential be published in high-ranking journals, after a minor revision.
To further demonstrate the unique power of this new method, the authors should test cells under known stimulation altering the dynamics of organelles. For instance, wortmannin can blocks the conversion from early endosomes to late endosomes. By doing that, the potential of this new method will be endorsed.
Response:
We thank Reviewer #1 for the positive comments. We will add an experiment using wortmannin to block the process of endocytosis at a specific stage, as part of the experiments analyzing the process of endocytosis.
**Minor issue:** The authors should include more details about how to avoid signal crosstalk between adjacent fluorescent channels.
Response:
In the Methods section, we have added the following sentences to Lines 398-405.
“In order to avoid signal crosstalk between adjacent fluorescence channels, eight fluorophores with distinct spectral distances were selected, and the samples were irradiated sequentially with lasers in the order from the longest wavelength, i.e., fluorescence from 646 to 731 nm was excited by a 640 nm laser, fluorescence from 569 to 634 nm was excited by a 561 nm laser, fluorescence from 494 to 554 nm was excited by a 488 nm laser, and fluorescence from 411 to 481 nm was excited by a 405 nm laser, as shown in Extended Data Fig. 1b.”
Reviewer #1 (Significance (Required)):
The comprehensive monitoring of organelle dynamics through the integration of multi-dimensional parameters can proficiently evaluate the condition and prognosticate the destiny of living cells in response to external stimulations. This new multi-dimensional assay reported in this manuscript represents a huge step towards this goal. Since this new method is simple and powerful, cell biologists will quickly start to use this new method for the study of subcellular dynamics.
My lab is also developing a similar approach for organelles based on super-resolution imaging. I would like to congratulate the authors for this beautiful work.
Response:
We thank Reviewer #1 for the positive comment.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
The manuscript reports a multi-parametric particle-based method for analysis of organelles. The method aims to resolve heterogeneous populations of organelles involved in various cellular processes. They propose to isolate organelles labelled with multiple markers, after homogenization and sonification of the cells, and analyse the resulting particles by fluorescence microscopy using spectral imaging. Afterwards, the authors visualize and analyse the obtained data with dimension reduction techniques.
Even though an interesting approach, the method and presented applications needs major improvisations before it can prove to be impactful for the field
I note some possible improvement points below:
- Initially, I think the current set of cell lines and labels should be extended also to include a wider set. The current limited set raises the question if the method authors report is also applicable to other cell lines, or if it only feasible with overexpressed markers. Including different cell lines with different labels would make the study more convincing and comprehensive.
Response:
We thank Reviewer #2 for this constructive comment. Regarding cell types, we will conduct experiments with HEK293T cells in addition to HeLa cells, labeling at least five different types of typical organelles. In our method, as shown in Figure 1a and 5a, we have already used not only overexpressed markers but also fluorescently labeled ligands (EGF-Alexa, transferrin-Alexa) and antibodies against endogenous proteins (anti-PMP70, anti-LAMP1), as well as direct labeling of cell membrane proteins (Alexa-NHS). Therefore, there are no significant limitations with respect to organelle labeling methods.
- It is surprising that the authors explicitly list already the limitations of fluorescence microscopy and super-resolution microscopy in the second paragraph of their introduction, however present a method fully dependent on fluorescence labelling and imaging methods. Actually their approach takes away the spatial information of FM approaches, and further makes the approach prone to the limitations they state.
They are also not fully fair about the limitation they state for Electron microscopy, as newly developed approaches (e.g. doi:10.1093/micmic/ozad067.1091; doi:10.1126/science.aay3134) widely extend the limited field of view and sampling capacity of EM. I recommend the authors to state the potential advantage/superiority of the reported method rather than stating the unclear limitations of the existing powerful methods.
Response:
Regarding fluorescence microscopy, it appears that our description was inadequate and misled the reviewers. There is no problem with fluorescence microscopy itself. What we intended to convey was that “when attempting to detect individual organelles ‘in cells’, there are limitations in the resolution of fluorescence microscopy because organelles are densely packed”. We have added this to the text on Line 49. Also, we thank Reviewer #2 for informing us about the high-speed 3D electron microscopy. We have cited the indicated papers in the text at Lines 54-55 and mention that “except for the recently developed high-throughput electron microscopy”.
- Most organelle markers the isolation of organelles are based on are overexpressed in the cells: endoplasmic reticulum (ER, mTagBFP2 (BFP)-SEC61B), mitochondria (GFP-OMP25 and SNAP-OMP25), and the Golgi (Venus-GS27). This raises significant questions about the native state relevance of the reported results, and how well they represent the endogenous processes.
Response:
We will add experiments analyzing the behavior of both endogenous and exogenous markers for the same organelles, for example, anti-LAMP1 antibody and VAMP7-GFP for lysosomes, and anti-PMP70 antibody and PEX16-GFP for peroxisomes.
- For the application on endosomes, can the authors state what is the new information enabled by their method? They study the very trafficking of EGF and Transferrin, 2 widely used endosomal cargoes with very well characterized trafficking steps, and show they are trafficked through Rab5/7 and Rab11 positive endosomes, respectively. This recapitulates the existing information, however falls short in delivering new insight. The authors can use these cargoes for proof-of-concept, but I would recommend to extend their study with less exploited cargoes to represent the potential of the reported method to deliver new information.
Response:
We thank Reviewer #2 for the positive suggestion about the potential of our method to provide new information. However, to demonstrate new biological insights, it would take a lot of time and delay the provision of our methodology, so we would like to submit this manuscript as a Methods paper with the proof-of-concept data.
Reviewer #2 (Significance (Required)):
The significance of biochemical and cellular processes being spatially regulated cellular organelles, and the roles of specific organelles in diseases from cancer to neurodegeneration are continuously being discovered and appreciated. Therefore development of methods reporting on the structure and function of organelles is important to accelerate these studies. In the reported method, however, the ultrastructure (as in Fib 1b) and the spatial information of the cellular organelles are inherently lost. The method falls in between a biochemical and a microscopic approach, however the advantages are not clearly portrayed. I recommend the authors to carefully and explicitly state where their method would be the method of choice rather than a biochemistry, mass spectroscopy, or microscopy approach. The authors should critically consider such an experiment as a proof-of-concept case.
Response:
We thank Reviewer #2 for the valuable suggestion. We added the following to the Discussion (Lines 267-277).
“A further potential application of our method would be to measure how the levels of key molecules in an organelle change during its differentiation or maturation. For example, the levels of PI4P and syntaxin 17 change during autophagosome maturation (Shinoda et al. eLife Preprint Review doi.org/10.7554/eLife.92189.1), which can be better demonstrated by this method using multiple markers for each stage of autophagosome formation and maturation, PI4P, and syntaxin17 because autophagosomes at different stages coexist in cells. In such cases, our single-particle analysis method, which examines the state of individual autophagosomes, would be more appropriate than biochemical methods that examine averages. In addition, it is difficult to quantitatively analyze many organelle structures in cells using fluorescence microscopy. Our particle-based analysis method can overcome this problem.”
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
**Comments, suggestions, and questions**
- I would like to start with a positive suggestion. The authors completely miss out on the opportunity to promote their approach by not relying on any type of fixation. In most multiplexing experiments, the first major challenge is to find antibodies that work well for imaging. The second challenge is then to find antibodies that work well under the same fixation conditions. The authors present a multiplexing approach that is completely independent of fixation. I suggest discussing this in the manuscript and promoting the approach in that regard.
Response:
We thank Reviewer #3 for pointing out the advantages of our method. We have added “Our method that is independent of fixation is advantageous for the optimization of the staining condition (Lines 298-299).
- I am wondering what defines the ‘resolution’ of this approach. I assume it is a combination of the sonication time -the longer the cell is sonicated, the smaller the fragments are - and the density of particles on the coverslip. What are the limits here? How does this affect the UMAP analysis? I would encourage the authors to discuss this in the manuscript.
Response:
The particle density on a coverslip can be easily reduced by simply diluting the particles in a buffer solution. Therefore, there is no density limit, which is an advantage of a cell-free system. To improve the resolution within a single organelle, for example, to separate distinct subdomains, as the reviewer mentioned, we can prolong the sonication time to make the particles smaller. However, since this will reduce the signal-to-background ratio and destroy organelle contacts, we used the sonication conditions as mild as possible. To investigate organelle subdomains and fragile contacts, the sonication conditions need to be optimized carefully, which should affect the UMAP analysis, but we think that these will be future work.
We do not think that prolonged sonication will affect the UMAP analysis because relative fluorescent signals of each particle would not change. However, as mentioned above, too strong sonication would worsen the signal-to-noise ratio, resulting in poor clustering.
We have added the above discussion to the Discussion (Lines 288-293).
“Also, to improve the resolution within a single organelle, for example, to separate distinct subdomains, we can prolong the sonication time to make the particles smaller. However, since this will reduce the signal-to-background ratio and may destroy organelle contacts, we used the sonication conditions as mild as possible. To investigate organelle subdomains and fragile contacts, the sonication conditions need to be optimized carefully.”
- The only real control the authors present are the correlative light and electron microscopy (CLEM) three images in Figure 1b, which seems very minimalistic for a very central and essential control experiment. How many of these control images did the authors take? Is there possibly a second method for a control experiment to link the fluorescence readout to an organelle fragment (e.g., purification or pulldown)?
Response:
Since all the markers we used are well-established, we believe that there is no concern about the fluorescence readouts to the organelle fragments. We have cited the following papers in Lines 84-85.
SEC61B: Rapoport, T. A., Jungnickel, B. & Kutay, U. Protein transport across the eukaryotic endoplasmic reticulum and bacterial inner membranes. Annu Rev Biochem 65, 271–303 (1996).
OMP25: Horie, C., Suzuki, H., Sakaguchi, M. & Mihara, K. Characterization of signal that directs C-tail-anchored proteins to mammalian mitochondrial outer membrane. Mol Biol Cell 13, 1615–1625 (2002).
GS27: Hay, J. C. et al. Localization, Dynamics, and Protein Interactions Reveal Distinct Roles for ER and Golgi SNAREs. J Cell Biol 141, 1489–1502 (1998).
Fusella, A., Micaroni, M., Di Giandomenico, D., Mironov, A. A. & Beznoussenko, G. V. Segregation of the Qb-SNAREs GS27 and GS28 into Golgi Vesicles Regulates Intra-Golgi Transport. Traffic 14, 568–584 (2013).
Although it is relatively easy to identify mitochondria-derived particles by EM based on their size and the presence of cristae-like structures (indeed we see many examples), it is more challenging for other organelles (because they appear simple vesicles). This is why we showed only mitochondria in Fig. 1b. Furthermore, the main purpose of this EM image is to show membrane contacts between the ER and mitochondria (related to Fig. 3).
- Line 37-41: Could the authors please strengthen these statements with an appropriate citation (e.g., a review)?
Response:
We have cited the textbook Molecular Biology of THE CELL (the 6th edition, Chapter 12 and Chapter 13) in Lines 37 and 41.
- Line 51: The statement, “Super-resolution microscopy could partially solve the resolution problem, but it is currently limited to four-color imaging,” is incorrect. Agasti et al. demonstrated up to nine target multiplexed super-resolved imaging with DNA-PAINT in 2017 (https://pubs.rsc.org/en/content/articlehtml/2017/sc/c6sc05420j). Additionally, two papers currently on Biorxiv demonstrate 12 target and 30 target multiplexed super-resolution imaging with FLASH-PAINT (https://www.biorxiv.org/content/10.1101/2023.05.17.541061v1.abstract) and SUM-PAINT(https://www.biorxiv.org/content/10.1101/2023.05.17.541210v1.abstract). Please cite these papers accordingly.
Response:
We thank Reviewer #3 for notifying us of these important studies. We have rewritten the sentence on Lines 51-52 to read “Although multicolor imaging has been attempted with super-resolution microscopy (references of the indicated papers), it only partially solves the issue of resolution.”
- The authors use spectral unmixing to overcome the limit of spectral multiplexing. While this has been demonstrated to work well for less than ten targets, it does not scale to multiplexing experiments with more than ten target species. I suggest that the authors discuss in the discussion part of the manuscript the potential of DNA-based multiplexed imaging, such as CODEX or DNA-PAINT, in combination with the presented approach.
Response:
In the Discussion (Lines 295-298), we have added the sentence “Current fluorescent particle detection uses spectral multiplexing, but this method has only been able to detect up to eight colors. Methods such as CODEX or DNA-PAINT with wide-field type illumination could significantly increase the number of targets”.
- Regarding the spectral unmixing approach, please cite previous work described in the literature (e.g.,https://www.nature.com/articles/nature22369, or earlier work).
Response:
We thank Reviewer #3 for informing us. We have cited it in Line 72.
- By using spectral unmixing for multiplexing, this method is limited to confocal due to spectral detection needs and therefore limited in throughput. It would be beneficial if it could work with wide-field type illumination. This could substantially increase the throughput, which is another reason why I think it would be important to discuss sequential multiplexing.
Response:
We agree with the Reviewer’s comment. We have added the discussion to Lines 295-298 as described in our response to Reviewer #3, Comment (6).
- To image contact sites, the authors use split GFP. There have been discussions that split GFP might, in some cases, facilitate the process that is supposed to be measured, in this case, the formation of contact sites. I suggest using at transient version of split GFP, called split fast, for follow-up experiments in the authors’ next papers (https://www.nature.com/articles/s41467-019-10855-0).
Response:
We thank Reviewer #3 for providing this information. We will do it as suggested in the next paper.
- Line 27 & 253: Please drop the term ‘intuitive’ or explain better what you mean by intuitive. For me, UMAP is certainly a very useful tool, but it is not at all what I would describe as intuitive.
Response:
We have deleted ‘intuitive’ in all seven places and rewritten them (Lines 27, 43, 58, 72, 180, 231, and 253).
- Lastly, I want to mention that the authors state they used ChatGPT, DeepL, and DeepL Write for translation from Japanese to English. I appreciate their honesty.
Response:
We thank Reviewer #3 for the comment.
Reviewer #3 (Significance (Required)):
In the manuscript titled “Organelle Landscape Analysis Using a Multi-parametric-Based Method,” Kurikawa et al.present a method for multi-parametric, particle-based analysis of cellular organelles. After lysing cells, the fractions of the organelles are partially labeled with fluorescently tagged antibodies, while others are already tagged with fluorescent proteins, using six to eight spectrally different fluorescent dyes/proteins. These fractions are subsequently immobilized on a poly-L-lysine-coated coverslip. The authors use spectral unmixing to distinguish these markers. The6-8 multiplexed imaging data is then presented in two-dimensional UMAP space. The authors then use this approach to visualize seven major organelles, transitional sites of endocytic organelles, and contact sites between the endoplasmic reticulum and mitochondria using split GFP.
The authors present, in my opinion, a conceptually new and interesting approach by combining spectral unmixing for imaging up to eight targets, with organelle fragment imaging, and presenting multidimensional data in two-dimensional Uniform Manifold Approximation and Projection (UMAP) space in this manuscript. They further validated this approach by linking the results of the experiments to results established or at least reported in the literature.
In general, the manuscript is, in my opinion, a good fit for publication as it presents a conceptionally new approach and an interesting example of applying the UMAP approach, which might be of interest to a broader readership. Therefore, after an appropriate response to my comments, suggestions, and questions (see below), I would recommend this manuscript for publication.
Response:
We thank Reviewer #3 for the positive comment.
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Referee #3
Evidence, reproducibility and clarity
Comments, suggestions, and questions
- I would like to start with a positive suggestion. The authors completely miss out on the opportunity to promote their approach by not relying on any type of fixation. In most multiplexing experiments, the first major challenge is to find antibodies that work well for imaging. The second challenge is then to find antibodies that work well under the same fixation conditions. The authors present a multiplexing approach that is completely independent of fixation. I suggest discussing this in the manuscript and promoting the approach in that regard.
- I am wondering what defines the 'resolution' of this approach. I assume it is a combination of the sonication time - the longer the cell is sonicated, the smaller the fragments are - and the density of particles on the coverslip. What are the limits here? How does this affect the UMAP analysis? I would encourage the authors to discuss this in the manuscript.
- The only real control the authors present are the correlative light and electron microscopy (CLEM) three images in Figure 1b, which seems very minimalistic for a very central and essential control experiment. How many of these control images did the authors take? Is there possibly a second method for a control experiment to link the fluorescence readout to an organelle fragment (e.g., purification or pulldown)?
- Line 37-41: Could the authors please strengthen these statements with an appropriate citation (e.g., a review)?
- Line 51: The statement, "Super-resolution microscopy could partially solve the resolution problem, but it is currently limited to four-color imaging," is incorrect. Agasti et al. demonstrated up to nine target multiplexed super-resolved imaging with DNA-PAINT in 2017 (https://pubs.rsc.org/en/content/articlehtml/2017/sc/c6sc05420j). Additionally, two papers currently on Biorxiv demonstrate 12 target and 30 target multiplexed super-resolution imaging with FLASH-PAINT (https://www.biorxiv.org/content/10.1101/2023.05.17.541061v1.abstract) and SUM-PAINT (https://www.biorxiv.org/content/10.1101/2023.05.17.541210v1.abstract). Please cite these papers accordingly.
- The authors use spectral unmixing to overcome the limit of spectral multiplexing. While this has been demonstrated to work well for less than ten targets, it does not scale to multiplexing experiments with more than ten target species. I suggest that the authors discuss in the discussion part of the manuscript the potential of DNA-based multiplexed imaging, such as CODEX or DNA-PAINT, in combination with the presented approach.
- Regarding the spectral unmixing approach, please cite previous work described in the literature (e.g., https://www.nature.com/articles/nature22369, or earlier work).
- By using spectral unmixing for multiplexing, this method is limited to confocal due to spectral detection needs and therefore limited in throughput. It would be beneficial if it could work with wide-field type illumination. This could substantially increase the throughput, which is another reason why I think it would be important to discuss sequential multiplexing.
- To image contact sites, the authors use split GFP. There have been discussions that split GFP might, in some cases, facilitate the process that is supposed to be measured, in this case, the formation of contact sites. I suggest using a transient version of split GFP, called split fast, for follow-up experiments in the authors' next papers (https://www.nature.com/articles/s41467-019-10855-0 ).
- Line 27 & 253: Please drop the term 'intuitive' or explain better what you mean by intuitive. For me, UMAP is certainly a very useful tool, but it is not at all what I would describe as intuitive.
- Lastly, I want to mention that the authors state they used ChatGPT, DeepL, and DeepL Write for translation from Japanese to English. I appreciate their honesty.
Significance
In the manuscript titled "Organelle Landscape Analysis Using a Multi-parametric-Based Method," Kurikawa et al. present a method for multi-parametric, particle-based analysis of cellular organelles. After lysing cells, the fractions of the organelles are partially labeled with fluorescently tagged antibodies, while others are already tagged with fluorescent proteins, using six to eight spectrally different fluorescent dyes/proteins. These fractions are subsequently immobilized on a poly-L-lysine-coated coverslip. The authors use spectral unmixing to distinguish these markers. The 6-8 multiplexed imaging data is then presented in two-dimensional UMAP space. The authors then use this approach to visualize seven major organelles, transitional sites of endocytic organelles, and contact sites between the endoplasmic reticulum and mitochondria using split GFP.
The authors present, in my opinion, a conceptually new and interesting approach by combining spectral unmixing for imaging up to eight targets, with organelle fragment imaging, and presenting multidimensional data in two-dimensional Uniform Manifold Approximation and Projection (UMAP) space in this manuscript. They further validated this approach by linking the results of the experiments to results established or at least reported in the literature.
In general, the manuscript is, in my opinion, a good fit for publication as it presents a conceptionally new approach and an interesting example of applying the UMAP approach, which might be of interest to a broader readership. Therefore, after an appropriate response to my comments, suggestions, and questions (see below), I would recommend this manuscript for publication.
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Referee #2
Evidence, reproducibility and clarity
The manuscript reports a multi-parametric particle-based method for analysis of organelles. The method aims to resolve heterogeneous populations of organelles involved in various cellular processes. They propose to isolate organelles labelled with multiple markers, after homogenization and sonification of the cells, and analyse the resulting particles by fluorescence microscopy using spectral imaging. Afterwards, the authors visualize and analyse the obtained data with dimension reduction techniques.
Even though an interesting approach, the method and presented applications needs major improvisations before it can prove to be impactful for the field
I note some possible improvement points below:
- Initially, I think the current set of cell lines and labels should be extended also to include a wider set. The current limited set raises the question if the method authors report is also applicable to other cell lines, or if it only feasible with overexpressed markers. Including different cell lines with different labels would make the study more convincing and comprehensive.
- It is surprising that the authors explicitly list already the limitations of fluorescence microscopy and super-resolution microscopy in the second paragraph of their introduction, however present a method fully dependent on fluorescence labelling and imaging methods. Actuallt their approach takes away the spatial information of FM approaches, and further makes the approach prone to the limitations they state. They are also not fully fair about the limitation they state for Electron microscopy, as newly developed approaches (e.g. doi:10.1093/micmic/ozad067.1091; doi: 10.1126/science.aay3134) widely extend the limited field of view and sampling capacity of EM. I recommend the authors to state the potential advantage/superiority of the reported method rather than stating the unclear limitations of the existing powerful methods.
- Most organelle markers the isolation of organelles are based on are overexpressed in the cells: endoplasmic reticulum (ER, mTagBFP2 (BFP)-SEC61B), mitochondria (GFP-OMP25 and SNAP-OMP25), and the Golgi (Venus-GS27). This raises significant questions about the native state relevance of the reported results, and how well they represent the endogenous processes.
- For the application on endosomes, can the authors state what is the new information enabled by their method? They study the very trafficking of EGF and Transferrin, 2 widely used endosomal cargoes with very well characterized trafficking steps, and show they are trafficked through Rab5/7 and Rab11 positive endosomes, respectively. This recapitulates the existing information, however falls short in delivering new insight. The authors can use these cargoes for proof-of-concept, but I would recommend to extend their study with less exploited cargoes to represent the potential of the reported method to deliver new information.
Significance
The significance of biochemical and cellular processes being spatially regulated cellular organelles, and the roles of specific organelles in diseases from cancer to neurodegeneration are continuously being discovered and appreciated. Therefore development of methods reporting on the structure and function of organelles is important to accelerate these studies. In the reported method, however, the ultrastructure (as in Fib 1b) and the spatial information of the cellular organelles are inherently lost. The method falls in between a biochemical and a microscopic approach, however the advantages are not clearly portrayed. I recommend the authors to carefully and explicitly state where their method would be the method of choice rather than a biochemistry, mass spectroscopy, or microscopy approach. The authors should critically consider such an experiment as a proof-of-concept case.
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Referee #1
Evidence, reproducibility and clarity
In this manuscript, the authors report a novel and simple method to analyze the heterogeneity of various organelles. After imaging a large set of fluorescent-marker-labeled organelles, cluster analysis is adapted for illuminating the dynamics of organelles. Through this novel method, the authors are able to report organelle contact, which previously can only be observed by super-resolution imaging. This is method could significantly accelerate future discoveries at the cellular level. The manuscript is well written and has the potential be published in high-ranking journals, after a minor revision.
To further demonstrate the unique power of this new method, the authors should test cells under known stimulation altering the dynamics of organelles. For instance, wortmannin can blocks the conversion from early endosomes to late endosomes. By doing that, the potential of this new method will be endorsed.
Minor issue: The authors should include more details about how to avoid signal crosstalk between adjacent fluorescent channels.
Significance
The comprehensive monitoring of organelle dynamics through the integration of multi-dimensional parameters can proficiently evaluate the condition and prognosticate the destiny of living cells in response to external stimulations. This new multi-dimensional assay reported in this manuscript represents a huge step towards this goal. Since this new method is simple and powerful, cell biologists will quickly start to use this new method for the study of sub-cellular dynamics.
My lab is also developing a similar approach for organelles based on super-resolution imaging. I would like to congratulate the authors for this beautiful work.
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Reply to the reviewers
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General Statements We appreciate the insightful reviewer comments. Both reviewers alluded to the logical lack of connection between two themes in the original paper. Specifically, we showed that N-cad differentially regulates migration in different environments, and that leader and follower cells differ phenotypically, but did not connect the two themes. In this revised version, we've performed additional experiments and undertaken a comprehensive reorganization of both the manuscript and figures. The major changes are outlined below:
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Figure 4 A-C has been moved to Figure 6 F-H.
- Figure 5 has been moved to Figure S3 F-H.
- Figure 6 F has been moved to Figure 7 A.
- Figure 6 G-H have been moved to Figure 7 D-E.
- Figure 6 I-J have been moved to Figure S5 A-B.
- Figure 7 C-F have been moved to Figure S5 C-F.
- Added transcriptome profiling of control and N-cad-depleted cells and of leader and follower cells (Figures 6 E, S1 H and S4 C-D, Tables S2 and S3). We have incorporated additional figures (Figure 4 and 5 in the revised manuscript) to support the idea that the amount of N-cad at the cell surface is regulated by endocytic recycling, thereby stimulating glioma migration in the different local environments. Furthermore, our new findings showed that YAP1/TAZ regulates the surface level of N-cad during glioma migration (Figure 8). We trust that these additions contribute to the clarity and robust justification of our paper.
Similar to other types of tumors, our findings revealed that pediatric high-grade gliomas migrate collectively, possibly contributing to a more aggressive invasion than single cells. In this study, we found that N-cad mediates this collective glioma migration. Interestingly, within these migrating groups, leader and follower cells dynamically interchange positions during migration, accompanied by changes their phenotypic characteristics. This suggests that differences in phenotypes, including N-cad recycling, proliferation and YAP activation, may be predominantly regulated by cell-extrinsic factors rather than being predetermined by genetic or epigenetic factors. Moreover, our new RNA-sequencing results indicate minimal difference between leader and follower cells, except for the upregulation of YAP response and wound healing migration genes in leader cells. Although genomic alterations still possibly encode the leader-follower exchange, our findings strongly suggest that the activation of YAP1 and glioma migration are regulated by the cellular context, specifically where cells are located within the group.
Contrary to our initial findings suggesting a positive feedback loop between N-cad endocytosis and nuclear YAP1, our revised data indicates that nuclear YAP appears to be independent of N-cad. We observed that homotypic interactions with N-cad present in the surrounding environment, such as neurons (Figure 6 C-D) or N-cad extracellular domain-coated surface (Figure 7 B-C), did not affect nuclear YAP1. However, YAP1/TAZ depletion decreased N-cad expression and altered its localization at the surface (Figure 8). This leads us to propose a revised model where nuclear YAP1 stimulates surface N-cad, thereby facilitating the distinct modes of migration on ECM and neurons (Figure 8 I).
- Point-by-point description of the revisions
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
In this manuscript, Kim and colleagues describe the role of N-Cadherin during pediatric glioma migration. They compare cell lines that have similar transcripts but different levels of N-Cadherin protein and find that N-Cadherin levels influence the route of migration - whether it be on ECM or other tissues. They also describe molecular feedback between N-Cadherin and YAP in leader vs follow cells of their systems. The data are clear, well presented, and convincing; and the conclusions described by the manuscript are mostly justified. My major criticism of the manuscript is that the line of questioning undertaken does not appear well justified. At many points, I was left asking "but why are they doing this?" and I could not understand the rationale for some of the experiments that were performed (even if they were performed well). The manuscript opens by validly describing how gliomas are highly invasive, poorly understood and that N-Cadherin was highly expressed in comparison to other adhesion proteins. This opened the path for the questions and experiments performed that contributed to Figures 1-3, which I thought were interesting. From there on, I found the logic of the story unclear and poorly justified. For example, I do not know why leader and follower cells were justified - when it had nothing to do with N-Cadherin which was the focus of the work prior. And then, having rightly concluded in Figure 4 that the data suggested that leader and follower cells dynamically exchange positions rather than being pre-determined, they went onto further figures focusing on differences between leader and follower cells, which left my quite confused. I am likewise confused by the model proposed in that, they authors describe that the difference between leader and follower cells contributes to a nuclear YAP/N-Cad endocytosis feedback loop that feeds into the speed of migration. Yet, the authors describe earlier that leader and follower cells frequently exchange positions, with no evidence that they are pre-determined. How do the authors square these seemingly conflicting points? And further, what is the relevance of this to understanding the differing modes of migration (on ECM or other tissues)? On this issue, I suggest authors re-consider whether the order of figures or logic of the story is appropriate (perhaps consider moving some figures to supplement?), and to clearly justify in the text the elements that are being addressed. Overall, I think the messaging, logic and justification could be use significant improvement; the experiments however are well performed, and the figures are very clear and nicely presented, and I don't have any qualms about them.
We appreciate your insightful comments, recognizing the need for logical and justifiable improvements in certain sections of our previous manuscript. Please see Section 1, General Statements, for an explanation of changes made.
Minor Comments
- Not required, but the authors may wish to consider putting t=0 pictures of the experiments in the supplement as supportive evidence for the circles of the initial seeding location they show in Fig 1.
We provide the t=0 images in Figure S1 N and O.
- I assume the title of the second results section should say "migration speed" rather than "speed migration"
The new title of the second results section is “N-cad stimulates and inhibits migration through intercellular homotypic interaction”.
- Fig. 4D - Are both example cell pictures leaders? If so, I'm not sure why two have been provided; I'm guessing the bottom set are supposed to be follower cells. If so, please label as appropriate. (And if not, a representative set of pictures from a follower cell should be provided).
We have enhanced the clarity of the labels. We provide representative high magnification images of leader and follower cells. The updated figure can be found in Figure 5 A.
- Figure 5 Legend - the title of this figure is too definitive, and exaggerates further than the main text does, which was correct in saying that the experiments only suggest that N-Cadherin endocytosis might regulate the localisation of b-catenin and p120-catenin. Probably I would go further and say that there is no experimental evidence provided that even suggests that in the first place, and that this is a hypothesis that remains to be tested. The authors should inhibit endocytosis specifically (rather than just depleting N-Cad) and see the effect, to justify their conclusion.
We appreciated your points and concerns. Following your earlier suggestion, we have moved the figure to the supplementary section (Figure S3 F-H). Moreover, we have addressed the reciprocal regulation of N-cad and catenins by knocking down p120-, β- or α-catenin. Our new findings showed that p120-, β- or α-catenin depletion decrease the amount of N-cad at the cell surface, not steady-state protein level, resulting in decreased migration on astrocytes but increased migration on ECM (see Figure 4). These findings support the idea that catenins play a role in glioma migration according to the environment by altering surface N-cad level. With that, we updated the figure title to “Catenins regulate N-cad surface levels to stimulate or inhibit migration.”
Reviewer #1 (Significance (Required)):
The manuscript provides a characterised of invasive glioma migration that was previously lacking. It also provides interesting observations related to the role of N-Cadherin for different modes of migration (on ECM or on tissues) that will be of interest for further exploration. It makes a good advance in terms of addressing a highly invasive cell type that has poor prognosis. I anticipate that now this initial characterisation has been performed, authors and others will be interested in gaining a deeper understanding as to how these two modes of migration are controlled, how there might be interplay between them and how such findings contribute to its highly invasive nature. I have expertise in collective cell migration and directed cell migration.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary In the submitted manuscript, Kim et al. describe various aspects of N-cadherin function in the collective migration of PBT-05 cells, a pediatric high-grade glioma line, on laminin, 3D-matrigel, neurons or astrocytes. N-cadherin promotes the collective migration on neurons or astrocytes, whereas it suppresses the migration on laminin or 3D-matrigel. The authors also show that N-cadherin is actively internalized and recycled in the leader, but not follower, cells of the collective, which induce the nuclear accumulation of YAP/TAZ proteins. YAP/TAZ proteins are shown to regulate the collective migration.
Thank you for the comments. Please see Section 1, General Statements, for a summary of changes made. Please also note that our new experiments failed to show that N-cad levels or traffic regulate YAP/TAZ nuclear accumulation. Rather, YAP/TAZ are regulated by cell density independent of N-cad, and YAP/TAZ regulate N-cad protein levels and traffic independent of changes in N-cad RNA levels
Major comments
- In Fig. 1G, N-cadherin knockdown seems to affect the distribution of astrocytes. The authors should stain a marker for astrocytes, instead of actin, and the red alone images should be provided.
Astrocytes were cultured for 4 days to generate 3D scaffolds before adding the glioma spheroid, essentially as described (Gritsenko et al., Histochem Cell Biol, 2017). Co-cultures were stained for human-specific vimentin (glioma) or actin (glioma and astrocytes) (see Figure 1 G and separate channels in new Figure S1 P). There do not appear to be major changes in astrocyte organization outside the migration front, but we lack a way to stain for astrocytes specifically and cannot visualize astrocytes under the glioma cells. It remains possible that astrocytes may be affected differently by contact with control and N-cad-deficient glioma cells. However, we added a new experiment, assaying migration on decellularized astrocyte ECM. While N-cad stimulated migration on astrocytes it inhibited migration on astrocyte ECM (Figures 1 I and J and S1 Q). Thus N-cad stimulates glioma migration on astrocyte cells and not their ECM.
- The colocalization between N-cadherin and Rab11 may not be high in Figs. 4F and S2B. It is unclear whether the majority of the internalized N-cadherin is recycled to the plasma membrane. In Fig. S2B, the internalized N-cadherin may be located mainly at the early endosomes before transported to the recycling endosomes (Is it 20 min after the N-cadherin antibody internalization?). First, the authors should analyze the colocalization between the N-cadherin and Rab11 at 30-40 min after the internalization. If the colocalization with Rab11 would be still low at that time point, some of the internalized N-cadherin might be degraded in the lysosomes. To test this possibility, the authors should analyze the colocalization between N-cadherin and LAMP1 under the treatment with a lysosome inhibitor.
At steady state, N-cad co-localized better with Rab5 than with Rab11 or LAMP1 (Figure 5 C-D). In kinetics experiments, N-cad antibodies were internalized for 40 min. They colocalized better with Rab5 or EEA1 than with Rab11 or LAMP1. When we allowed recycling for an additional 20 min, the surface level of N-cad antibodies partially recovered in leader cells more than follower cells (see Figures 5 G and S3 D). We tested whether treatment with lysosomal inhibitors would increase co-localization of N-cad with Rab11 in recycling endosomes. Surprisingly, however, Chloroquine or Bafilomycin A1 decreased the amount of internalized N-cad antibody in leader and follower cells, and long-term treatment did not increase total N-cad levels. Therefore, the fate of internalized N-cad in follower cells remains unclear.
- When N-cadherin is depleted, dissociated single cells are increased, but these cells are not well characterized. A high magnification image of the dissociated single cells is required. In addition, the migration speed of the dissociated single cells should be measured.
We didn’t quantify single cell migration because only a minority of cells separate from the collective even when N-cad is depleted. Therefore, we quantified migration directionality and speed for cells at or near the front of collective migration (Figure 2 D-I). We have updated the image of single cells, providing representative high-magnification images in Figure S1 N and O.
- In Fig. S2D, treatment with Pitstop-2 alone or Dyngo-4a alone is required. Dynamin is also involved in clathrin-independent endocytosis and N-cadherin is reported to be internalized via caveolin-1-mediated endocytosis as well as clathrin-mediated during neuronal migration. It would be better to clarify which type of endocytosis occurs in the leader cells.
We have removed results showing inhibition of cell migration and N-cad endocytosis by Pitstop-2 and Dyngo-4a from the paper. Treatment with either chemical alone had much less effect on internalization or migration than adding both together (see figure below). This is hard to explain. Pitstop-2 should inhibit clathrin-coated pit formation and Dyngo-4a should inhibit clathrin and caveolin-mediated endocytosis. Caveolin-1 and 2 transcripts were not detected in our cells (Table S2). There may be some other form of clathin-independent endocytosis. Interpretation is also challenging since these inhibitors will inhibit endocytosis of many receptors, not just N-cad. Accordingly, we have removed these results in the revised manuscript.
- In Fig. 2, N-cadherin depletion disturbs the migration directionality. Is this a result from disruption of cell polarity? To test this, the position of centrosome or Golgi or lamellipodia in the leader cells should be analyzed. (OPTIONAL)
We elected not to perform this analysis.
- I cannot understand the significance of Fig. 5F and 5G. If the authors would speculate that alpha- and beta-Catenins may transduce the intracellular signaling from the internalized N-cadherin, the authors should perform the knockdown experiments of the Catenins and analyze whether it may affect the nuclear accumulation of YAP/TAZ. (OPTIONAL)
We agree. In the initial manuscript, we showed that N-cad depletion altered the localization of p120-, β-, and α-catenin (previously shown in Figure 5 F-G). For better clarity and logic, these figures have been moved to Figure S2 H in the revised manuscript. Additionally, to test whether catenins regulate N-cad and YAP1, we depleted p120-, β-, or α-catenin using shRNA. We found that downregulation of p120-, β-, or α-catenin decreased N-cad surface levels, consequently slowing migration on astrocytes and stimulating migration on laminin (Figure 4). In other words, depleting catenins altered migration in parallel with the changes in N-cad surface level. Catenin depletion also increased single-cell dissociation, reduced the crowding of leader and follower cells, and increased nuclear YAP1 (see figure below). These findings suggest that the main role of p120-, β-, or α-catenin is to regulate surface N-cad. Since this result does not support a role for catenins transducing an N-cad signal to YAP1, we have not included it in the paper.
Minor comments
- The quantitative data is required in Fig. 5E.
Quantitative data from three independent experiment are now presented in Figure S2 G.
- Vinculin is associated with the cadherin-catenin complex and it may not be a good loading control (Fig. 3C and 3L).
The Western blot data has been updated and is now presented in new Figure 3 B and 3 F, with β-tubulin as a loading control.
**Referees cross-commenting**
I totally agree with the other Reviewers' comments and evaluation. As the reviewer-1 pointed out, I also think the experiments are well performed, but it would lack logic at least in part (see my comment-6). In addition, as the reviewer-3 pointed out, the linking mechanism of N-cadherin homophilic interaction with YAP/TAZ signaling is important to improve this manuscript
We hope the revisions have improved the logical flow. We have also added new results showing that YAP/TAZ regulate N-cad protein levels and localization but not N-cad RNA. N-cad is not needed for cell density-dependent regulation of YAP1 localization. The model is shown in Figure 8 I.
Reviewer #2 (Significance (Required)):
Strength N-cadherin has multiple function in cancer and neuronal migration, and both positive and negative effects of N-cadherin on cancer cell migration have been reported. In this regard, different behaviors of N-cadherin in the leader and follower cells of the collective are interesting and may explain the controversial previous results.
Limitation This study reveals various aspects of N-cadherin function in the collective migration of the glioma cell line, but it is unclear whether these findings are applied to pediatric high-grade gliomas in vivo.
Thus, this study is a potentially important and informative to cell biologists and researchers in cancer biology, although this reviewer also found several weak points that should be improved.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In this manuscript, the authors explore the role of N-cadherin in the migratory/infiltrative behavior of human pediatric brain tumor cells, in light of their surrounding microenvironment. Their in-depth phenotype analysis allows to document the behavior of migrating cells and revisit the concept of leading/follower migratory cells (somehow more commonly applied to endothelial cells). They suspected that the YAP/TAZ pathway might modulate N-cadherin endocytosis and vice versa, using imagery-based cell tracking.
Major comments
- To control for co-culture models, migration should be evaluated on decellularized matrices from astrocyte and neuron cultures.
We thank for your suggestion. We tested glioma migration on astrocyte-derived decellularized matrices. The mouse astrocytes we used are known to produce various extracellular matrices with a composition similar to Matrigel, except for laminin α5. (Gritsenko et al., J Cell Sci, 2018). N-cad shRNA cells migrated faster on decellularized ECM than control (Figure 1 I-J and S1 Q). This result agrees with N-cad depletion increasing migration on ECM but is opposite to migration on astrocytes.
- N-cadherin was stably knocked down with shRNA, which raises the question of adaptative/compensatory mechanisms. First, one could ask what happen in knockout conditions. Similarly, transient siRNA-mediated silencing might help to strengthen the findings. Second, is there any impact of Ncad knock down on alternate adhesive receptors (either cell-cell or cell-ECM). This should be verified with bulk RNAseq.
Transient knockdown with N-cad siRNA also increased migration on laminin-coated surface (Figure S1 L-M). Unfortunately, N-cad depletion with siRNA was short-lived, precluding its use for long-term assays, like coculture with neurons or astrocytes. To test whether there is any impact of N-cad knockdown on alternative adhesion receptors, we performed RNA-Seq (Figure S1 H, Table S2). We found N-cad depletion did not alter expression of other cell-cell and cell-ECM adhesive receptors except CDH3 (2.8-fold increase compared with 7-fold decrease in CDH2). Integrin expression was unchanged.
- It would be interesting to evaluate the impact of N-cadherin/N-cadherin homotypic interactions on YAP/TAZ signaling, using for instance N-cad-coated surface.
We observed that the homotypic interaction of N-cad with surrounding neurons and astrocytes did not hinder the accumulation of nuclear YAP1 in leader cells (Figure 6 C-D). To further support the idea that N-cad does not directly regulate YAP1 signaling, we have now measured YAP1 localization in cells migrating over N-cad ECD. The new data confirms that N-cad does not directly regulate YAP1 localization (Figure 7 B-C).
- along this line, the impact of mechanical cues (stiffness, 2D vs 3D) is not explored.
We appreciate your suggestion. It is possible that different mechanical and cytoskeletal cues between leader and follower cells affect YAP1 signaling. In this study, we would like to focus more on the role of N-cad-mediated cell adhesions in YAP signaling.
Minor comments
- Levels of N-cadherin expression in normal Astro and Neurons to compare with pediatric brain cancer cells (S1C)
A new western blot analysis to show N-cad levels in DMG, PHGG and mouse cerebellar neurons and astrocytes has been added to Figure S1 F.
- Low versus high density culture conditions should be controlled and its further impact on the YAP/Ncad endocytosis route should be supported experimentally, or to be omitted from their proposed model.
We previously used different size of micropattern discs to control low or high cell density. Smaller cell clusters, with more edge cells and hence fewer cell-cell interactions, had higher nuclear YAP1 (Figure 7 D-E). We have repeated this experiment, including N-cad ECD antibodies to measure N-cad endocytosis. Smaller cell clusters had higher N-cad antibody internalization (Figure 7 F). Together with our evidence that leader cells have higher YAP1 and more N-cad internalization than followers, and that YAP/TAZ knockdown inhibits N-cad internalization, these results high YAP/TAZ in leader cells regulates N-cad internalization.
Reviewer #3 (Significance (Required)):
This paper presents robust image analysis of human pediatric brain tumor migration in the context of the different microenvironment that they might encounter (matrices, neurons, astrocytes). This study brings new concepts on the way N-cadherin might contribute to tumor cell migratory behavior based on the nature of the interactions in which N-cadherin is involved. As a limitation, it remains unclear the mechanism by which N-cadherin endocytosis is driven.
We now discuss the limitations of the study as follows:
“The mechanisms by which YAP1 regulates N-cad levels and trafficking remain to be explored. YAP1 is widely expressed in human brain tumors and strongly associated poor survival. Leader cells expressed higher levels of YAP1-response and wound-healing gene transcripts, but transcript levels of N-cad and proteins known to regulate cadherin traffic, such as p120-catenin, Rab5/11 and Rac1, were similar. Therefore, N-cad is likely regulated at the level of protein synthesis or turnover. More endosomal N-cad recycled to the surface of leader than follower cells, implying that follower cells might divert more N-cad for lysosomal degradation, but our attempts to interfere with N-cad endocytosis or degradation specifically were unsuccessful. Further understanding of the mechanism and function of N-cad recycling for glioma cell migration will require cargo-specific ways to selectively regulate endocytosis and recycling”.
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Referee #3
Evidence, reproducibility and clarity
In this manuscript, the authors explore the role of N-cadherin in the migratory/infiltrative behavior of human pediatric brain tumor cells, in light of their surrounding microenvironment. Their in-depth phenotype analysis allows to document the behavior of migrating cells and revisit the concept of leading/follower migratory cells (somehow more commonly applied to endothelial cells). They suspected that the YAP/TAZ pathway might modulate N-cadherin endocytosis and vice versa, using imagery-based cell tracking.
Major comments:
- To control for co-culture models, migration should be evaluated on decellularized matrices from astrocyte and neuron cultures.
- N-cadherin was stably knocked down with shRNA, which raises the question of adaptative/compensatory mechanisms. First, one could ask what happen in knockout conditions. Similarly, transient siRNA-mediated silencing might help to strengthen the findings. Second, is there any impact of Ncad knock down on alternate adhesive receptors (either cell-cell or cell-ECM). This should be verified with bulk RNAseq.
- It would be interesting to evaluate the impact of N-cadherin/N-cadherin homotypic interactions on YAP/TAZ signaling, using for instance N-cad-coated surface.
- along this line, the impact of mechanical cues (stiffness, 2D vs 3D) is not explored.
Minor comments:
- Levels of N-cadherin expression in normal Astro and Neurons to compare with pediatric brain cancer cells (S1C)
- Low versus high density culture conditions should be controlled and its further impact on the YAP/Ncad endocytosis route should be supported experimentally, or to be omitted from their proposed model.
Significance
This paper presents robust image analysis of human pediatric brain tumor migration in the context of the different microenvironment that they might encounter (matrices, neurons, astrocytes).
This study brings new concepts on the way N-cadherin might contribute to tumor cell migratory behavior based on the nature of the interactions in which N-cadherin is involved.
As a limitation, it remains unclear the mechanism by which N-cadherin endocytosis is driven.
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Referee #2
Evidence, reproducibility and clarity
Summary
In the submitted manuscript, Kim et al. describe various aspects of N-cadherin function in the collective migration of PBT-05 cells, a pediatric high-grade glioma line, on laminin, 3D-matrigel, neurons or astrocytes. N-cadherin promotes the collective migration on neurons or astrocytes, whereas it suppresses the migration on laminin or 3D-matrigel. The authors also show that N-cadherin is actively internalized and recycled in the leader, but not follower, cells of the collective, which induce the nuclear accumulation of YAP/TAZ proteins. YAP/TAZ proteins are shown to regulate the collective migration.
Major comments
- In Fig. 1G, N-cadherin knockdown seems to affect the distribution of astrocytes. The authors should stain a marker for astrocytes, instead of actin, and the red alone images should be provided.
- The colocalization between N-cadherin and Rab11 may not be high in Figs. 4F and S2B. It is unclear whether the majority of the internalized N-cadherin is recycled to the plasma membrane. In Fig. S2B, the internalized N-cadherin may be located mainly at the early endosomes before transported to the recycling endosomes (Is it 20 min after the N-cadherin antibody internalization?). First, the authors should analyze the colocalization between the N-cadherin and Rab11 at 30-40 min after the internalization. If the colocalization with Rab11 would be still low at that time point, some of the internalized N-cadherin might be degraded in the lysosomes. To test this possibility, the authors should analyze the colocalization between N-cadherin and LAMP1 under the treatment with a lysosome inhibitor.
- When N-cadherin is depleted, dissociated single cells are increased, but these cells are not well characterized. A high magnification image of the dissociated single cells is required. In addition, the migration speed of the dissociated single cells should be measured.
- In Fig. S2D, treatment with Pitstop-2 alone or Dyngo-4a alone is required. Dynamin is also involved in clathrin-independent endocytosis and N-cadherin is reported to be internalized via caveolin-1-mediated endocytosis as well as clathrin-mediated during neuronal migration. It would be better to clarify which type of endocytosis occurs in the leader cells.
- In Fig. 2, N-cadherin depletion disturbs the migration directionality. Is this a result from disruption of cell polarity? To test this, the position of centrosome or Golgi or lamellipodia in the leader cells should be analyzed. (OPTIONAL)
- I cannot understand the significance of Fig. 5F and 5G. If the authors would speculate that alpha- and beta-Catenins may transduce the intracellular signaling from the internalized N-cadherin, the authors should perform the knockdown experiments of the Catenins and analyze whether it may affect the nuclear accumulation of YAP/TAZ. (OPTIONAL)
Minor comments
- The quantitative data is required in Fig. 5E.
- Vinculin is associated with the cadherin-catenin complex and it may not be a good loading control (Fig. 3C and 3L).
Referees cross-commenting
I totally agree with the other Reviewers' comments and evaluation. As the reviewer-1 pointed out, I also think the experiments are well performed, but it would lack logic at least in part (see my comment-6). In addition, as the reviewer-3 pointed out, the linking mechanism of N-cadherin homophilic interaction with YAP/TAZ signaling is important to improve this manuscript
Significance
Strength
N-cadherin has multiple function in cancer and neuronal migration, and both positive and negative effects of N-cadherin on cancer cell migration have been reported. In this regard, different behaviors of N-cadherin in the leader and follower cells of the collective are interesting and may explain the controversial previous results.
Limitation
This study reveals various aspects of N-cadherin function in the collective migration of the glioma cell line, but it is unclear whether these findings are applied to pediatric high-grade gliomas in vivo.
Thus, this study is a potentially important and informative to cell biologists and researchers in cancer biology, although this reviewer also found several weak points that should be improved.
-
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Referee #1
Evidence, reproducibility and clarity
Kim et al review
In this manuscript, Kim and colleagues describe the role of N-Cadherin during pediatric glioma migration. They compare cell lines that have similar transcripts but different levels of N-Cadherin protein and find that N-Cadherin levels influence the route of migration - whether it be on ECM or other tissues. They also describe molecular feedback between N-Cadherin and YAP in leader vs follow cells of their systems. The data are clear, well presented, and convincing; and the conclusions described by the manuscript are mostly justified. My major criticism of the manuscript is that the line of questioning undertaken does not appear well justified. At many points, I was left asking "but why are they doing this?" and I could not understand the rationale for some of the experiments that were performed (even if they were performed well). The manuscript opens by validly describing how gliomas are highly invasive, poorly understood and that N-Cadherin was highly expressed in comparison to other adhesion proteins. This opened the path for the questions and experiments performed that contributed to Figures 1-3, which I thought were interesting. From there on, I found the logic of the story unclear and poorly justified. For example, I do not know why leader and follower cells were justified - when it had nothing to do with N-Cadherin which was the focus of the work prior. And then, having rightly concluded in Figure 4 that the data suggested that leader and follower cells dynamically exchange positions rather than being pre-determined, they went onto further figures focusing on differences between leader and follower cells, which left my quite confused.
I am likewise confused by the model proposed in that, they authors describe that the difference between leader and follower cells contributes to a nuclear YAP/N-Cad endocytosis feedback loop that feeds into the speed of migration. Yet, the authors describe earlier that leader and follower cells frequently exchange positions, with no evidence that they are pre-determined. How do the authors square these seemingly conflicting points? And further, what is the relevance of this to understanding the differing modes of migration (on ECM or other tissues)? On this issue, I suggest authors re-consider whether the order of figures or logic of the story is appropriate (perhaps consider moving some figures to supplement?), and to clearly justify in the text the elements that are being addressed. Overall, I think the messaging, logic and justification could be use significant improvement; the experiments however are well performed, and the figures are very clear and nicely presented, and I don't have any qualms about them.
Minor Comments
- Not required, but the authors may wish to consider putting t=0 pictures of the experiments in the supplement as supportive evidence for the circles of the initial seeding location they show in Fig 1.
- I assume the title of the second results section should say "migration speed" rather than "speed migration"
- Fig. 4D - Are both example cell pictures leaders? If so, I'm not sure why two have been provided; I'm guessing the bottom set are supposed to be follower cells. If so, please label as appropriate. (And if not, a representative set of pictures from a follower cell should be provided).
- Figure 5 Legend - the title of this figure is too definitive, and exaggerates further than the main text does, which was correct in saying that the experiments only suggest that N-Cadherin endocytosis might regulate the localisation of b-catenin and p120-catenin. Probably I would go further and say that there is no experimental evidence provided that even suggests that in the first place, and that this is a hypothesis that remains to be tested. The authors should inhibit endocytosis specifically (rather than just depleting N-Cad) and see the effect, to justify their conclusion.
Significance
The manuscript provides a characterised of invasive glioma migration that was previously lacking. It also provides interesting observations related to the role of N-Cadherin for different modes of migration (on ECM or on tissues) that will be of interest for further exploration. It makes a good advance in terms of addressing a highly invasive cell type that has poor prognosis. I anticipate that now this initial characterisation has been performed, authors and others will be interested in gaining a deeper understanding as to how these two modes of migration are controlled, how there might be interplay between them and how such findings contribute to its highly invasive nature.
I have expertise in collective cell migration and directed cell migration.
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Reply to the reviewers
1. General Statements
We appreciate the reviewers’ thoughtful feedback and thank them for their valuable suggestions to improve the manuscript. We have endeavored to respond to all their comments, with many of their concerns already incorporated in the manuscript. Validations for the additional experiments to be incorporated into the manuscript have been performed and show that all the plans outlined in Section 2 are highly feasible and will be added for the full revision. We believe that the incorporated and planned revisions contribute to a significant improvement on the original manuscript.
2. Description of the planned revisions
Reviewer 1
Major comments:
Point 3. p. 5. The authors do not describe any relationship to notch signaling. But notch signaling is the mechanism by which a sprout is selected. The CA phenotype shows no selection, and every sprout can continue migration. Did the authors check for any relationship between notch signaling c-Src activation? Does upregulation of C-Src downregulate notch?
In previous unpublished results examining the impact of the loss of endothelial c-Src on notch signaling, we observed no alteration in DLL4 expression in the sprouting retina on postnatal day 5. Furthermore, no change in tip cell number was observed in mice with a loss of endothelial c-Src, suggesting c-Src depletion does not impact notch activity (Schimmel et al., Development, 2020, Figure 1M). We have started additional preliminary experiments performing immunostaining with a DLL4 antibody in migrating c-Src-CA cells to assess activation of notch signaling upon c-Src activation. We will continue these experiments for the full revision and will confirm the results via further analysis of notch activation by assessing DLL4 expression in the c-Src mutant cells using Western blot.
Reviewer 2
Major comments:
Point 1. The authors have only used one type of vein endothelial cells from one single donor but they conclude that is effect is general for all endothelial cells. Endothelial cells are very heterogeneous, not only depending on their function and localization, vein, artery or capillary, but also between different organs and in disease (PMID: 22315715, PMID: 28775214, PMID: 31944177, PMID: 33514719). The authors, should either repeat some of the key experiments in other type of endothelial cells, maybe arterial or microvasculature cells which are commercially available or at least state that the observations presented in this manuscript apply to HUVECs and discuss whether this would also apply for other cell types.
We agree it would be highly beneficial to assess whether c-Src-CA induces vascular expansion in other endothelial cell types. We have successfully transduced human arterial endothelial cells (HAEC) with empty vector and c-Src-CA lentivirus and are able to grow HAECs in 3D vessels. This demonstrates that introducing the c-Src constructs into other endothelial cells and putting them in 3D assays is highly feasible. We have also used human microvascular endothelial cells (HMVEC) in 3D vessels in previous studies (Schimmel et al., Clin Trans Immunol, 2021). Therefore, we will perform experiments introducing the full set of c-Src mutations in HAEC and/or HMVEC in 3D vessels for the revision to strengthen our findings.
Reviewer 3
Major comments:
Point 1. "This was further supported by our observation that there were no changes in proliferation in c-Src mutant cells grown in a 2D monolayer". Figure 1A appears to have increased number of cells in the c-Src-CA condition compared to the control condition. Could the authors quantify the number of cells/area as they did for their 3D vessel model? This would reinforce the idea that the ballooning phenotype they observe is not due to differences in proliferation.
We have started quantification on the number of cells per bead for the 3D bead sprouting experiments shown in Figure 1. We will complete this quantification for 3 independent experiments and the results will be added for the full revision.
Point 2. Would be strengthened with analysis of another proliferation marker, such as EdU label, which is incorporated only during S phase of the cell cycle. Comparing ki67 staining and EdU staining would provide more insights. Also, using their 3D vessel model for this analysis would increase its relevance.
We agree that showing proliferation in a 3D setting would be highly beneficial. We tested proliferation marker Ki67 in 3D vessels to ensure this analysis will be possible. We will perform full analysis of proliferation across c-Src mutations in 3D for the revision. We have started with BrdU labelling in 2D, and we will perform full analysis of proliferation with BrdU across c-Src mutations for the revision.
Point 3. In Figure 1E', cells expressing the constitutively active form of cSrc appear to detach, giving the impression of cell death. Have the authors tested the viability/apoptosis of c-Src-CA cells, particularly in their 3D model?
We agree that showing cell death in our model, especially in a 3D setting, would be highly beneficial. We have tested cell death marker Cleaved Caspase 3 (CC-3) in 3D vessels to ensure this analysis is feasible. We will perform full analysis of cell death across c-Src mutations in 3D for the revision.
Point 4. "Therefore, reduction of endothelial cell-cell contacts in c-Src-CA cells may be due to elevated VE-cadherin phosphorylation and subsequent internalisation", "As reduction in cell-cell junction integrity has been shown to increase migratory capacity and sprouting angiogenesis [38], our data suggest that a balanced control of both cell-matrix and cell-cell junctions is essential for mediating migration." In general, it's not clear how constitutively active cSrc affects focal adhesions and cell-cell adhesion and how this is responsible for their ballooning phenotype. The role of the phosphorylation of the VE-Cadherin and cell-cell junctions in this process is not clear either. Further analysis of cell-cell junctions and focal adhesions (co-staining of phosphorylated paxillin and VE-Cadherin) and focal adhesions/fibronectin (like in figure 4C) in the context of cell migration (scratch wound assay) would provide important information to strengthen this notion of balanced control of both cell-matrix and cell-cell junctions.
We will perform experiments on migrating cells in 2D, co-staining for p-paxillin and VE-cadherin, and p-paxillin and Fibronectin, to address the role of balanced cell-matrix and cell-cell junction adhesion, and how they influence Fibronectin deposition in migrating cells.
Point 6. "Taken together, these results reveal that proteases produced by c-Src-CA cells are locally secreted at FAs but are membrane bound." The claim that proteases are membrane-bound is not convincingly demonstrated. Could the authors assess whether the constitutive form of cSrc activates the expression of specific genes encoding MMPs by qPCR? Or is it more a matter of the effect of c-Src on the transport of MMPs by microtubules?
We would like to clarify the content of Figure 5, which presents two distinct sets of experiments supporting the assertion that the proteases under investigation are membrane-bound. Firstly, the transfer of conditioned medium from c-Src mutant cells demonstrated no degradation of fibronectin fibrils. Secondly, in the bead sprouting assay, a mixed culture of untransduced and c-Src-CA expressing cells was utilised. The results revealed that only c-Src-CA cells formed balloons, while untransduced cells sprouted normally right next to or sometimes even through a balloon.
Recognising the need for a more in-depth understanding, we acknowledge the importance of analysing specific MMP gene expression. To this end, we have ordered qPCR primers for distinct MMPs, namely MMP2, MMP7, MMP9, and MT1-MMP. These forthcoming experiments are not only highly feasible but will also contribute valuable insights. The results of this gene expression analysis will be incorporated into the revision, shedding light on whether constitutively active c-Src induces MMP gene expression or influences MMP transport.
Minor comments:
Point 2. The lab already showed in a previous study that mice lacking c-Src specifically in endothelial cells have reduced blood vessel sprouting, leading to the expectation that the constitutively active form of cSrc would increase sprout number in the sprouting assay. Could the authors explain why the constitutively active form of cSrc induces this vascular ballooning and not an increase in the number of sprouts?
In line with analysis to be performed on notch activity and DLL4 expression (Reviewer 1 point 3), we will provide additional discussion on the role of notch signalling and tip cell identity with the full revision.
3. Description of the revisions that have already been incorporated in the transferred manuscript
R____eviewer 1
Major comments:
Point 1. p5. Fig 1: The sentence that the dominant negative completely abrogated 'this' phenotype implies that the dominant negative was put into the same cells as the constitutively active mutation. 'Abrogated' means it stops the phenotype, and the phenotype in the sentence prior was constitutively active. It is more accurate to say that the dominant negative was not distinguishable from wild type, which is what the statistics show. No double transfection (DN-CA) was performed.
We have changed the wording in the manuscript accordingly to ‘The c-Src-DN mutation showed no phenotype distinguishable from Ctrl (Fig 1A-D).’ on page 5.
Point 2. p.5. Fig 1: the phenotype of the CA cells is fascinating. They expand far beyond their normal territory, but they are held together in a lacy bubble. To me, this looks like a different phenotype from the ballooning that might occur in an arteriovenous malformation in vivo, as in vivo malformations are continuously covered by cells. I understand why the authors might use the term ballooning but given that the cells expand without continuously touching each other, I do not think this is the correct term. Would blebbing, or radial migration in a lace-like discontinuous pattern describe it better?
We have changed the phrasing from ‘ballooning morphology’ to ‘radial migration in a lace-like discontinuous pattern’ on page 5. For brevity, this has been referred to as ‘ballooning’ for the remainder of the manuscript, as noted on page 5.
Point 4. The statistical methods are not described in the methods (GraphPad?). These need to be added. Are only significant comparisons plotted? In Fig 6 and 7 only pairwise statistics are shown. If all significant comparisons are plotted, then this means that the comparison between the rescued CA and the treated or untreated control is not significant. This can be thought of as a partial rescue towards a wild type, but it is definitely not a full rescue. None of the statistical comparisons in Figure 6 or 7 show significant comparisons to wildtype. This needs more discussion.
We have now added additional clarification on statistical methods. Details on the statistical tests for each figure are mentioned in the figure legends. A general section on the statistical methods is now added to the methods section on page 18. Only significant comparisons are displayed in the graphs, but as mentioned by reviewer 2 (minor point 2), we have added additional information for transparency. Each of the different comparisons that were made, and their precise p value, have been compiled a table which has been added as Supplementary Table 1 to the manuscript.
In Figures 6 and 7, we exclusively plotted pairwise comparisons to assess the impact of Marimastat treatment. As outlined in Supplementary Table 1, there is still a statistical significance when comparing Marimastat-treated c-Src-CA with either Marimastat-treated Ctrl or Marimastat-treated c-Src-WT. This suggests a partial rescue. For clarity, we kept only pairwise comparisons in the graphs, but discussed the partial rescue due to remaining significant difference between Marimastat-treated c-Src-CA and Ctrl or c-Src-WT cells in the results, referring to Supplementary Table 1 for p values. An important sidenote: c-Src-CA treated cells cannot exhibit complete rescue since they are initially seeded without Marimastat, and have already initiated ballooning by the time treatment commences.
Point 5. Mmp activity is inferred, but not measured. This is a limitaion as the assumption is that marimostat acting through the expected pathway.
Marimastat is one of the most commonly used broad spectrum MMP inhibitors, with potent activity against major MMPs, including MMP1, MMP3, MMP2, MMP9, MMP7 and MMP14. This is outlined in the existing reference (Rasmussen and McCann, 1997). We have adjusted phrasing to clarify the potency of Marimastat and have emphasised this is an MMP targeting drug which has been widely utilised in oncology clinical trials (page 8).
Minor comments:
Point 1. Fig 5D. The presentation of the data in this graph is difficult to understand. It is trying to show the proportion of mScarlet in sprouts or balloons a percentage of all the scarlet cells. It would be better to have all cells represented in one bar, distributed between sprout and balloon in that one bar. i.e., for the control and dominant negative, the bars would be all black and then for the CA it would be all white. The zero data points are confusing. A proportions graph should be investigated here.
We have changed the graph in Figure 5D, which now represents the % of the outgrowth area, sprouts for Ctrl, c-Src-WT and c-Src-DN and balloon for c-Src-CA, that are mScarlet positive. Resulting in all black bars for Ctrl, c-Src-WT and c-Src-DN and all white bar for c-Src-CA, as the reviewer predicted.
Point 2. The methods for vessel coverage for quantification in figs 1 and 7 are missing.
We have added details of how quantification of vessel coverage in Figure 1 and 7 was performed to the methods section on page 17/18 as follow: ‘Microfluidic vessel coverage was measured by tracing any holes in the vessel wall (inverse of cell area marked by phalloidin) and dividing this by the total cell area per image.’
Reviewer 2
Minor comments:
Point 1. Although the methods are well written and can be understood. To improve transparency, the authors should reduce the referring to other papers to describe the methods they perform and at least some kind of brief description should be included.
We have added a brief description of the methods that included references to other papers; lentiviral transduction and microfluidic devices. More details about the lentivirus transduction were added on page 15 and a short description about the fabrication of the microfluidic devices was added on page 15/16.
Point 2. The authors should report the real p value for their tests. Also, when the test is not significant.
To provide more transparency about all of the different comparisons that were made and their precise p value, we have compiled a table listing all the p values and which is added as Supplementary Table 1 to the manuscript.
Reviewer 3
Minor comments:
Point 3. In Figure 1A, it would be beneficial to include images from orthogonal views. Indeed, in the c-Src-CA condition, it's not clear whether the vascular ballooning observed represents a cluster of cells or an empty space between the bead and the endothelial cells. (Supp movie 1 helps, but it would be useful to add orthogonal views to the figure)
For clarity, we have added single Z plane image for cross sectional views of the bead sprouts in Figure 1A to show that the c-Src-CA cells have an empty space inside the balloon, rather than being a big cluster of cells.
Point 4. In Figure 1D, the method used to analyze sprout shape is not clear, especially for the c-Src-CA condition where the number of sprouts is close to 0. The figure legend indicates that this measurement corresponds to the shape of the sprouting area. Could the authors clarify and explain their quantification method?
The shape of the sprouting area refers to the circularity index of the vascular area, measured by tracing the perimeter of the cell area in a minimum Z-projection of brightfield images and subtracting the area of the bead. For better clarity, we have adjusted the title of Figure 1D and Figure 6D to ‘Vascular area shape’ and added details of the quantification method in the methods section on page 17.
Point 5. "however cells within the vessel still maintained some connections (Fig 1E')": The connections between cells are difficult to see in the images in Figure 1E'. Could the authors provide higher magnification images of the VE-cadherin staining to illustrate these connections between cells?
For improved clarity, we have added high magnification images of the VE-cadherin channel only in black and white (Figure 1E’’) and indicated some of the maintained cell-cell connections in the c-Src-CA cells with black arrowheads.
Point 6. "The reduction in migration correlated with an increase in FA size c-Src-CA expressing cells.": Could the authors give more explanation?
We have adjusted phrasing to provide additional information (page 6/7) as follows: ‘The reduction in migration velocity in c-Src-CA cells coincides with an increase in FA size, number and density (Fig 2A-D). This suggests that the reduction of migration velocity is due to increased cellular adhesion via FAs.’
Point 7. Could the authors widen the cell trajectory trace in Supplementary Figure 3A?
We have adjusted the trajectory traces in Supplementary Figure 3A with wider lines for improved visibility.
Point 8. it is very difficult to distinguish fibronectin fibrils on the images shown in figure 4C. it would be beneficial to change the images.
We have enlarged the zoomed areas for better visibility of the focal adhesions and fibronectin degradation underneath those areas in the c-Src-CA cells. Additionally, arrows are added to indicate fibronectin fibrils.
Point 9. "Treatment of ECs with Marimastat in a fibrin bead sprouting assay resulted in a rescue of the ballooning morphology observed in the c-Src-CA cells" Based on the images displayed in the figure and the associated quantifications, it still appears that c-Src-CA+Marimastat induces a vascular ballooning even if it is less pronounced than in the DMSO condition. Hence, it would be more accurate to describe the observed effect as a "partial rescue". In the microfabricated 3D vessel, in the figure 7A, cell-cell junctions still appear altered by c-Src-CA after the treatment with Marimastat, compared to the c-Src-WT-Marimastat, it would be more appropriate to talk about "partial rescue".
We have changed ‘rescue’ to ‘partial rescue’ when referring to results in Figure 6 and 7 (page 8).
Point 10. In Figure 6A, it seems that there is a decrease in the number of sprouts in the c-Src-DN condition compared to the control condition after the DMSO treatment, which is not observed in Figure 1, could the authors explain why?
In Figure 1C, the number of sprouts is also reduced in the c-Src-DN condition compared to c-Src-WT, but this is not significant when compared to control (see Supplementary Table 1 for p values of all comparisons). However, it is true that the number of sprouts in the c-Src-DN condition is significantly reduced compared to both control and c-Src-WT upon DMSO treatment (Fig 6C). Reduction of sprouts in c-Src-DN cells was expected due to the dysfunctional kinase domain, as mentioned on page 5 and shown in reference 30 (Shvartsman, D.E., et al., J Cell Biol, 2007. 178(4): p. 675-86.). Why DMSO treatment seems to enhance the effects of dominant negative c-Src expression on sprouting behaviour remains unclear. However, DMSO has adverse effects on sprouting shown by reduction of sprouts in both control and c-Src-WT cells (comparing untreated condition in Fig 1C with DMSO treated condition in Fig 6C). We believe that DMSO treatment is an extra challenge for cells on top of c-Src-DN expression, which therefore display reduced sprouting compared to control and c-Src-WT.
Point 11. There is no statistical paragraph in the method section.
As pointed out by reviewer 1 and 2, we have now added a general section on the statistical methods to the method section on page 18. Additional details on the tests used for each specific graph can be found in the figure legends and Supplementary Table 1.
4. Description of analyses that authors prefer not to carry out
Reviewer 3
Major comments:
Point 5. It is not clear how the constitutive activation of c-Src affects both cell-cell junction and focal adhesion morphology. Did the authors study signaling pathways downstream of c-Src such as the PI3K-AKT pathway?
c-Src is well known to regulate a multitude of signalling pathways, which was definitively shown in analysis by Ferrando et al. using phosphoproteomics (Ferrando, I.M., et al., Mol Cell Proteomics, 2012. 11(8): p. 355-69.) In this manuscript, our primary emphasis is on elucidating the role of c-Src in governing cell-matrix adhesions and the degradation of the extracellular matrix. We delve into the nuanced connection between focal adhesions (FAs) and VE-cadherin through the actin framework in the discussion (see page 10). Additionally, we highlight that beyond its recognised direct targets in FAs and adherens junctions (AJs), c-Src exerts regulatory influence on these structures through its effects on the actin cytoskeleton.
The PI3K/AKT pathway is implicated in the progression of vascular malformations in Hereditary Hemorrhagic Telangiectasia (HHT), where patients exhibit rapid vasculature expansion akin to the observed effects upon introducing the c-Src-CA mutation. In HHT, PTEN inhibition triggers heightened activity of VEGFA/VEGFR2 and subsequent AKT kinase activation. Although we have conducted preliminary analysis revealing elevated phospho-AKT, we contend that an in-depth examination of each signaling pathway perturbed downstream of c-Src-CA is beyond the current scope of this manuscript. Our future studies will specifically address this, providing a meticulous exploration of c-Src activity in HHT and its intricate interaction with the AKT pathway.
Minor comments:
Point 1: General comment: The authors have predominantly presented composite images with overlapping staining, making it challenging to differentiate between different labels. It would be beneficial if the authors could provide individual channel images along with a merge.
Given the large numbers of multi-channel composite images, we believe it is not feasible to show each individual channel of every merged image in the manuscript. We have included individual channel images where we believe is appropriate. For example, p-paxillin Y118 (Figure 2), Fibronectin (Figure 4). We are happy to provide individual channel images for any image, where specifically requested, such as in Figure 1E’’ where VE-cadherin channel was added.
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Referee #3
Evidence, reproducibility and clarity
Summary:
In this study, Essebier et al., investigated the impact of constitutive activation of cSrc on endothelial cell behavior during vascular sprouting and homeostasis. The authors generated various mutant versions of cSrc to enable the expression of wild type cSrc, constitutively active cSrc, or cSrc with a dysfunctional kinase domain in HUVEC. They used a range of in vitro methods, including traditional 2D culture techniques and cutting-edge approaches like microfabricated vessels for 3D cell culture. They showed that the constitutive activation of c-Src resulted in a vascular ballooning phenotype both in a 3D angiogenic sprouting assay and in microfabricated blood vessels subjected to shear stress. The expression of this mutant form of c-Src was associated with an increase of focal adhesion size and number and an increase of extracellular matrix degradation. The vascular ballooning phenotype induced by constitutive activation of c-Src was partially rescued by the pharmacological inhibition of the matrix metalloproteinase (MMPs).
Major:
- "This was further supported by our observation that there were no changes in proliferation in c-Src mutant cells grown in a 2D monolayer".
- Figure 1A appears to have increased number of cells in the c-Src-CA condition compared to the control condition. Could the authors quantify the number of cells/area as they did for their 3D vessel model? This would reinforce the idea that the ballooning phenotype they observe is not due to differences in proliferation.
- Would be strengthened with analysis of another proliferation marker, such as EdU label, which is incorporated only during S phase of the cell cycle. Comparing ki67 staining and EdU staining would provide more insights. Also, using their 3D vessel model for this analysis would increase its relevance.
- In Figure 1E', cells expressing the constitutively active form of cSrc appear to detach, giving the impression of cell death. Have the authors tested the viability/apoptosis of c-Src-CA cells, particularly in their 3D model?
- "Therefore, reduction of endothelial cell-cell contacts in c-Src-CA cells may be due to elevated VE-cadherin phosphorylation and subsequent internalisation", "As reduction in cell-cell junction integrity has been shown to increase migratory capacity and sprouting angiogenesis [38], our data suggest that a balanced control of both cell-matrix and cell-cell junctions is essential for mediating migration."
In general, it's not clear how constitutively active cSrc affects focal adhesions and cell-cell adhesion and how this is responsible for their ballooning phenotype. The role of the phosphorylation of the VE-Cadherin and cell-cell junctions in this process is not clear either.
- Further analysis of cell-cell junctions and focal adhesions (co-staining of phosphorylated paxillin and VE-Cadherin) and focal adhesions/fibronectin (like in figure 4C) in the context of cell migration (scratch wound assay) would provide important information to strengthen this notion of balanced control of both cell-matrix and cell-cell junctions.
- It is not clear how the constitutive activation of c-Src affects both cell-cell junction and focal adhesion morphology. Did the authors study signaling pathways downstream of c-Src such as the PI3K-AKT pathway?
- "Taken together, these results reveal that proteases produced by c-Src-CA cells are locally secreted at FAs but are membrane bound." The claim that proteases are membrane-bound is not convincingly demonstrated. Could the authors assess whether the constitutive form of cSrc activates the expression of specific genes encoding MMPs by qPCR? Or is it more a matter of the effect of c-Src on the transport of MMPs by microtubules?
Minor:
- General comment: The authors have predominantly presented composite images with overlapping staining, making it challenging to differentiate between different labels. It would be beneficial if the authors could provide individual channel images along with a merge.
- The lab already showed in a previous study that mice lacking c-Src specifically in endothelial cells have reduced blood vessel sprouting, leading to the expectation that the constitutively active form of cSrc would increase sprout number in the sprouting assay. Could the authors explain why the constitutively active form of cSrc induces this vascular ballooning and not an increase in the number of sprouts?
- In Figure 1A, it would be beneficial to include images from orthogonal views. Indeed, in the c-Src-CA condition, it's not clear whether the vascular ballooning observed represents a cluster of cells or an empty space between the bead and the endothelial cells. (Supp movie 1 helps, but it would be useful to add orthogonal views to the figure)
- In Figure 1D, the method used to analyze sprout shape is not clear, especially for the c-Src-CA condition where the number of sprouts is close to 0. The figure legend indicates that this measurement corresponds to the shape of the sprouting area. Could the authors clarify and explain their quantification method?
- "however cells within the vessel still maintained come connections (Fig 1E')": The connections between cells are difficult to see in the images in Figure 1E'. Could the authors provide higher magnification images of the VE-cadherin staining to illustrate these connections between cells?
- "The reduction in migration correlated with an increase in FA size c-Src-CA expressing cells.": Could the authors give more explanation?
- Could the authors widen the cell trajectory trace in Supplementary Figure 3A?
- it is very difficult to distinguish fibronectin fibrils on the images shown in figure 4C. it would be beneficial to change the images.
- "Treatment of ECs with Marimastat in a fibrin bead sprouting assay resulted in a rescue of the ballooning morphology observed in the c-Src-CA cells" Based on the images displayed in the figure and the associated quantifications, it still appears that c-Src-CA+Marimastat induces a vascular ballooning even if it is less pronounced than in the DMSO condition. Hence, it would be more accurate to describe the observed effect as a "partial rescue". In the microfabricated 3D vessel, in the figure 7A, cell-cell junctions still appear altered by c-Src-CA after the treatment with Marimastat, compared to the c-Src-WT-Marimastat, it would be more appropriate to talk about "partial rescue".
- In Figure 6A, it seems that there is a decrease in the number of sprouts in the c-Src-DN condition compared to the control condition after the DMSO treatment, which is not observed in Figure 1, could the authors explain why?
- There is no statistical paragraph in the method section.
Referees cross-commenting
Agree that the comments of the reviews all seem reasonable. Since cultured EC do not retain very specialized characteristics, perhaps repeating experiments with many other ECs would not be helpful, but suggest some key experiments be performed with one other type of EC.
Significance
General assessment:
The authors generated different mutant forms of c-Src and used them in innovative 3D endothelial cell culture models. The vascular ballooning phenotype induced by constitutive activation of c-Src is particularly interesting and impressive, especially as it can be reproduced in 2 different culture models. The model of cSrc inducing extracellular matrix degradation specifically at the level of focal adhesions is compelling, although it lacks rigorous support in the 3D model. Further analysis of signaling pathways downstream of c-Src would strengthen the work. The link and the necessity of a balance between cell adhesion and cell-cell junctions are mentioned and have started to be explored, particularly through the phosphorylation of Ve-Cadherin, and more in-depth analysis would strengthen this aspect of the work.
Advance:
This study provides new insight on the role of c-Src in vascular homeostasis and during sprouting angiogenesis and starts to explore cross-talk between EC cell junctions and focal adhesions. This study also provides new elements crucial for our understanding of vascular malformations and the implication of cell-adhesion to the extracellular matrix in this process. This study may lead to further investigations into the role of c-Src in tumor angiogenesis.
Audience:
Basic research / Specialized
- "This was further supported by our observation that there were no changes in proliferation in c-Src mutant cells grown in a 2D monolayer".
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Referee #2
Evidence, reproducibility and clarity
In this work, Essebier and colleagues have shown that the upregulation of c-Src in endothelial cells results in vascular dilation independently of growth factors or shear stress. The authors have shown that this effect is driven by alteration in the number of focal adhesion and the secretion of matrix metalloproteinases responsible for extracellular matrix remodeling as the inhibition of the MMPs rescues the observed effects.
This is an elegant work, with well-designed experiments and nice images to illustrate them. Congratulations. Nevertheless, the results not really support the conclusions drawn by the authors. The authors have only used one type of vein endothelial cells from one single donor but they conclude that is effect is general for all endothelial cells. Endothelial cells are very heterogeneous, not only depending on their function and localization, vein, artery or capillary, but also between different organs and in disease (PMID: 22315715, PMID: 28775214, PMID: 31944177, PMID: 33514719).
The authors, should either repeat some of the key experiments in other type of endothelial cells, maybe arterial or microvasculature cells which are commercially available or at least state that the observations presented in this manuscript apply to HUVECs and discuss whether this would also apply for other cell types. Minor. Although the methods are well written and can be understood. To improve transparency, the authors should reduce the referring to other papers to describe the methods they perform and at least some kind of brief description should be included.
The authors should report the real p value for their tests. Also when the test is not significant.
Referees cross-commenting
I agree with reviewer #1. Description of the statistical methods should be described in the methods. I have nothing else to add to the comments from the other reviewers.
Significance
The work presented here by Essebier and colleagues is very well designed and performed. The main strength of the manuscript is the study of the molecular mechanism that regulate the relationship between cells and the extracellular matrix. This is not very well studied in the context of disease. Although all the assays have been performed elegantly, the main limitation of this study is that it has been performed in only one type of endothelial cell. For this reason, it is not possible to extrapolate the conclusions drawn to all endothelial cells like the authors do.
This work advances our knowledge of endothelial cell biology and it will be of special interest for the vascular biology and development communities.
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Referee #1
Evidence, reproducibility and clarity
The manuscript demonstrates the effects of overexpression of c-Src variants in HUVEC endothelial cells. The c-Src kinase interacts with cell adhesion machinery, and the manuscript dissects relationships downstream of c-Src with respect to cellular behavior. Transduced wild type, constituent active, dominant negative c-Src is assayed by sprouting in 3D using a bead system, growth of microfabricated vessels under oscillatory flow, focal adhesion analysis, migration analysis, ECM analysis, and by rescue with a matrix Metalloprotease inhibitor.
Major comments:
- p5. Fig 1: The sentence that the dominant negative completely abrogated 'this' phenotype implies that the dominant negative was put into the same cells as the constitutively active mutation. 'Abrogated' means it stops the phenotype, and the phenotype in the sentence prior was constitutively active. It is more accurate to say that the dominant negative was not distinguishable from wild type, which is what the statistics show. No double transfection (DN-CA) was performed.
- p.5. Fig 1: the phenotype of the CA cells is fascinating. They expand far beyond their normal territory, but they are held together in a lacy bubble. To me, this looks like a different phenotype from the ballooning that might occur in an arteriovenous malformation in vivo, as in vivo malformations are continuously covered by cells. I understand why the authors might use the term ballooning but given that the cells expand without continuously touching each other, I do not think this is the correct term. Would blebbing, or radial migration in a lace-like discontinuous pattern describe it better?
- p. 5. The authors do not describe any relationship to notch signaling. But notch signaling is the mechanism by which a sprout is selected. The CA phenotype shows no selection, and every sprout can continue migration. Did the authors check for any relationship between notch signaling c-Src activation? Does upregulation of C-Src downregulate notch?
- The statistical methods are not described in the methods (GraphPad?). These need to be added. Are only significant comparisons plotted? In Fig 6 and 7 only pairwise statistics are shown. If all significant comparisons are plotted, then this means that the comparison between the rescued CA and the treated or untreated control is not significant. This can be thought of as a partial rescue towards a wild type, but it is definitely not a full rescue. None of the statistical comparisons in Figure 6 or 7 show significant comparisons to wildtype. This needs more discussion.
- Mmp activity is inferred, but not measured. This is a limitaion as the assumption is that marimostat acting through the expected pathway.
Minor concerns:
- Fig 5D. The presentation of the data in this graph is difficult to understand. It is trying to show the proportion of mScarlet in sprouts or balloons a percentage of all the scarlet cells. It would be better to have all cells represented in one bar, distributed between sprout and balloon in that one bar. i.e., for the control and dominant negative, the bars would be all black and then for the CA it would be all white. The zero data points are confusing. A proportions graph should be investigated here.
- The methods for vessel coverage for quantification in figs 1 and 7 are missing.
Referees cross-commenting
The comments from the other reviewers seem reasonable.
Significance
The work is well executed and takes a mechanistic approach. The images are well put together and the movies significantly add to the manuscript. The phenotype describes highly unusual endothelial behavior, which is of interest, and an advance in the field for its novelty. Linking cSrc to downstream signalling including mmps and demonstrating a rescue is also novel and a strength. This is a conceptual advance in the relationship between a kinase and cell behaviour in 3D.
Understanding this mechanism may be useful in understanding enlarged vessels in vascular malformations, although the direct relevance is not clear due to limitations of using cultured cells in artificial environments, lacking, for instance, support by secondary cells and ECM that might be contributed by support cells and perhaps modulate the phenotype.
The audience would be specialized in the basic research community.
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- Dec 2023
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Reply to the reviewers
The authors will submit a complete point-by-point response to the reviewer's comments when submitting a fully revised version of the manuscript
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Referee #4
Evidence, reproducibility and clarity
This is a short report featuring an innovative proximity labeling approach to the identification of proteins enriched in distinct types of chromatin domains. The domains compared are centromeric heterochromatin and X-linked hyperactive chromatin in Drosophila cells. These are relatively well-described domains, thus serving as an excellent test for the targeting of biotinylation in the permeabilized nucleus via interaction of specific antibodies with ProteinA-Apex2 provided exogenously. In parallel with the signature chromatin proteins CID or MSL2 as baits, the authors also target proteins in proximity to specific histone tail PTMs. Taking the work one step further, they compare the recovery of proteins +/- pretreatment of nuclei with RNase. They conclude that in each case selective interactions are specifically lost with pre-treatment of RNase.
Major comment:
As mentioned above, the approach is innovative and raises the possibility of a simpler MS method to identify protein-protein interactions. The RNase result is also provocative. However, in each case the specificity of potentially novel results are not explored further. Thus, the work is of interest but clearly still preliminary.
Significance
Did the authors dig deeper into novel interactions without obtaining convincing validation? Did they conclude that the MS approach is worth pursuing further or not? Admittedly the RNase result is difficult to follow up, but additional discussion of prior related work as well as consideration of future experiments would help improve the manuscript.
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Referee #3
Evidence, reproducibility and clarity
The manuscript entitled 'The role of RNA in the maintenance of chromatin domains as revealed by antibody mediated proximity labelling coupled to mass spectrometry' by Choudhury et al. describe a new method, which they termed AMPL-MS (Antibody mediated proximity labelling mass spectrometry). The technique is based on proximity labelling but uses antibodies instead of fusion proteins. They use this method to characterize chromatin domains containing specific signature proteins or histone modifications and focus on the composition of chromocenter as well as the chromosome territory containing the hyperactive X-chromosome in Drosophila. Last but not least they include data that show that RNA is involved in maintaining the integrity of chromatin domains by RNAse treatment and mass spec analysis. The technique works well and the results are very clear. I therefore expect that, in the right hands, it is very reproducible.
There are a few points that the authors may want to address:
- Title 'The' role of RNA in the maintenance of chromatin domains as..., seems too much of a statement. The title is therefore an overstatement that needs to be fixed.
- Figure 1 In Figure 1 the authors show very convincingly that the methods works well in their hands. They report on 172 proteins that localized in proximity to CID containing centromeric chromatin but do not provide the list of proteins as far as I can tell. Especially the RNA binders should be named.
- Figure 2 Using the hyperactive X is very clever when addressing RNA function but it should be stated in the discussion that there may be certain aspects that are specific to the male x and that is impossible to discriminate general and specific effects uncovered by this method.
- Figure 3 The authors should state more clearly the new findings of this figure since it is not fully obvious from its current representation.
- Figure 4 These are certainly interesting data but the authors remain in the very descriptive state. This is fine for a methods paper but then, the authors should hypothesize more on what the results mean. Are certain RNA dependent factors specific or general and they then recruit a specific set of factors that fall off upon RNAse treatment as a secondary effect or because they bind RNA directly. I feel like there may be more information that they authors got get out of there data than what they currently provide.
- Discussion The authors state: 'While we have not identified the RNAs responsible for the formation of theses domains, we clearly observe that they do confer specificity for the domains as we observe very little overlap in the factors lost from the corresponding domains (Fig 4h). the 'specificity' is hard to determine since factors bound to these regions are different, and therefore different factors will fall off, regardless of whether the RBP are specific unless the RNA is involved in recruiting the factors specifically, which the authors have not shown. Therefore, this result is suggestive and interesting but the statement is too strong and not backed by their results.
Significance
Overall, this is an interesting method that has been used in the past to identify protein modifications with high quality antibodies available. The authors show here that the method can also be used to different nuclear proteins and detect changes in protein complex composition. As it is it is primarily a methods paper, and for that the results are very clear. Gain of new info is not large but it is a useful technique to continue research on this subjectand is a nice start of many new avenues into how RNA effects chromatin,
My expertises are in epigenetics, chromatin biology, RNA and Drosophila genetics
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Referee #2
Evidence, reproducibility and clarity
This manuscript by Choudhury et al. describes a new method for antibody-mediated proximity labeling and applies it in the cell nucleus. In short, nuclei are isolated, fix and permeabilized, proteins are labeled with primary antibodies, a bacterially expressed/purified protein-A-APEX2 fusion protein is added, conventional H2O2/biotin phenol labeling of proximate protein is performed, proteins are un-crosslinked and biotin-affinity captured for MS analysis. The application to nuclear proteins and results seems appropriate. The method is highly similar to and more complicated than prior methods as described in more detail below. I would focus the impact of this paper towards its biological results and not the novelty of the methods used.
Prior methods that effectively accomplish the same outcome (fixed cells/tissues, antibodies and proximity labeling for AP-MS) have been published before. Perhaps most recently it was reinvented as the so called BAR method in PMID 29256494. That paper was cited here but incorrectly as BirA-related, which it is not. Of course that prior manuscript itself ignored prior methods from years back (2008, 2012, 2014, 2015, PMID 18495923, 22936677, 24706754, 25829300) using the same approaches of antibody targeted peroxidase for the same purposes of proximity labeling.
This method seems a somewhat Rube Golderbergian approach to antibody-mediated proximity labeling, which has been performed previously in multiple reports. APEX/2was developed to function inside of living cells since HRP does not. The value of doing the proximity labeling in living cells was either to capture protein associations over time, as with BioID/TurboID, or to get snapshots of protein associations in living cells with APEX/2. HRP does however function quite well for proximity labeling outside of cells, or in fixed/permeabilized cells, as has been demonstrated in the prior methods/papers that are referenced above. Replacing commercially available secondary antibodies fused to HRP with homemade protein-A-fused to APEX2 seems counterintuitive and/or unnecessary.
Could the authors explain the mechanisms that underly the reported enhanced sensitivity of AMPL-MS compared to conventional APEX2 in living cells. Is there something about the nuclear isolation that reduces interfering background, the loss of small soluble molecules in the nucleus after isolation and/or permeabilization that enhance the proximity labeling, penetration issues with the biotin-phenol in living cells, and/or something else?
There seems to be the use of various controls based on the figures and legends, but they are not clearly described in the results or methods.
All MS results should be provided, preferably in an Excel file format.
Significance
This manuscript by Choudhury et al. describes a new method for antibody-mediated proximity labeling and applies it in the cell nucleus. In short, nuclei are isolated, fix and permeabilized, proteins are labeled with primary antibodies, a bacterially expressed/purified protein-A-APEX2 fusion protein is added, conventional H2O2/biotin phenol labeling of proximate protein is performed, proteins are un-crosslinked and biotin-affinity captured for MS analysis. The application to nuclear proteins and results seems appropriate. The method is highly similar to and more complicated than prior methods as described in more detail below. I would focus the impact of this paper towards its biological results and not the novelty of the methods used.
Prior methods that effectively accomplish the same outcome (fixed cells/tissues, antibodies and proximity labeling for AP-MS) have been published before. Perhaps most recently it was reinvented as the so called BAR method in PMID 29256494. That paper was cited here but incorrectly as BirA-related, which it is not. Of course that prior manuscript itself ignored prior methods from years back (2008, 2012, 2014, 2015, PMID 18495923, 22936677, 24706754, 25829300) using the same approaches of antibody targeted peroxidase for the same purposes of proximity labeling.
This work may be of interest to investigators studying the nuclear proteins/structures to which the APML-MS was applied.
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Referee #1
Evidence, reproducibility and clarity
Summary:
This manuscript describes the application of a new variation of proximity (biotin) labelling (antibody mediated proximity labelling coupled to mass spectrometry, AMPL-MS). Combining protein- or histone variant-specific antibodies with a APEX2-proteinA fusion protein, they characterise the proteome of nuclear subdomains and demonstrate that RNA is important for the integrity of two tested domains, the Drosophila chromocenter and the chromosomal territory of the hyperactive X chromosome.
Major comments:
The vast majority the experimental results, statistical analysis, and conclusions drawn by the authors appear sound and are described in way that should allow reproduction (however, see my comments below for some suggestions for minor improvements). The authors rigorously test their method, using the Drosophila chromodomain as 'playground', before applying it to other chromosomal areas and histone variants/modifications. Besides providing proteomes of the targeted nuclear subcompartments, they show that RNase treatment of the cells radically changes the proteome(s) and conclude a role for RNA in the integrity of the corresponding compartments. This is shown by immunofluorescence staining as well as proteomic analysis of the biotinylated proteins. The images in figure 4b (and to lesser extent 4c) show an increased intensity and more diffuse labelling. Can the authors exclude that RNase treatment simply leads to an increase in accessibility for the biotin-phenol, hence a visibly higher biotinylation? Along these lines, have the authors maybe observed an increase in overall labelling/pulldown efficiency or for biotinylated proteins in their proteomic data?
Minor comments:
- In figures 1a and 4a (as well as in the Methods section), the authors use the term 'biotin-tyramide' as labelling agent, but in the main text and figure legends 'biotin-phenol' is used. For clarity, only one term should be used.
- Figure 2a shows a magnified cell/nucleus in the last column. To what cells do the magnifications in this last column refer to? Maybe these cells could be boxed in the second last column?
- In figures 4b + c:, the figure legend mentions the individual rows as '(I)' and '(II)' but no such label seen in the corresponding panel(s).
- The Quantification method for co-localization (e.g. 1c and 2b) is insufficiently described to the reader (reference simply relates to Fiji package). What module/script within the Fiji package has been used?
- The RNase treatment is not described at all in the methods section or the supplementary information and should be added.
- The sentence on page 6 ('As expected, neither the targeted signature factor or proteins that mainly interact with them protein-protein interactions such as MSL1,3 and MOF for MSL2 or Cenp-C for Cid are not affected by RNAase treatment') should be rephrased as it is not comprehensible in the current form.
Significance
The findings in this manuscript advance our portfolio of proximity labelling techniques although this advancement is not a major step forward. As the authors state themselves, antibody-based proximity labelling has already been introduced, even in the context of chromosomal proteomes (e.g. Gan et al., 2022; https://doi.org/10.1016/j.gpb.2021.09.003). One major technical advance is the finding that modifications or protein variants can now reliably be targeted for proximity labelling, using their method. Furthermore, the number of cells that are required for proximity labelling and detection of biotinylated proteins could be significantly reduced compared to previous approaches (although this might simply be due to the use of a more advanced proximity labelling enzyme). I should state her that as I am not an expert in the field of chromatin domains, I cannot be certain if the proteomes and changes of proteomes the authors report are providing a significant increase in our knowledge on these domains, especially related to their individual functions.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
In the paper entitled GOT1 primes the cellular response to hypoxia by supporting glycolysis and HIF1α stabilization, Grimm and co-authors investigate the metabolic adaptations of cancer cells upon acute hypoxia. By measuring metabolite levels at early time points upon hypoxia, they observe the accumulation of lactate and depletion of aspartate, along with other TCA cycle metabolites. Importantly, they demonstrate that these metabolic changes are independent of the HIF alpha-dependent transcriptional response. The authors investigate the role of aspartate during these initial phases of hypoxia. To this aim, they characterize cells devoid of glutamate oxaloacetate transaminase (GOT1), in which aspartate accumulates and can no longer be used for replenishing NAD+ via the downstream conversion of oxaloacetate to malate, via malate dehydrogenase. These cells have lower cytosolic NAD+ which affects glycolytic flux through the rate-limiting, NAD+-dependent enzyme GAPDH. GOT1 KO cells have a decrease in glucose consumption, lactate secretion and metabolite levels downstream of GAPDH upon early hypoxia, however ATP levels and viability are only affected with additional lactate dehydrogenase (LDH) impairment. Finally, the authors demonstrate that GOT1 KO cells have higher alpha-ketoglutarate (aKG) levels during early hypoxia, which could contribute to higher prolyl-hydroxylation and subsequent degradation of HIF, regulating the transcriptional response mediated by transcription factor.
* Major comments *
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The authors claim that they were unable to supplement cells with aspartate (Figure S3), (even though an increase of aspartate is instead observed in cells treated with sodium aspartate) and had to resort to the GOT1 knock-out model to "prevent aspartate from decreasing in hypoxia". This approach implicitly assumes that Got1 is the main driver of aspartate depletion upon hypoxia. However, although steady-state levels of aspartate are indeed higher in these cells, there is still a strong decrease upon hypoxia, which the authors acknowledge but merely ascribe to "attenuated production from glutamine". This seems an insufficient explanation, considering the very fast depletion upon hypoxia originally observed. The authors should provide further information regarding why aspartate is depleted in these conditions and consider other aspartate-consuming enzymes such as GOT2, ASNS, or even nucleotide biosynthesis and urea cycle enzymes. These observations could be made using the labeling experiments already acquired. In addition, to corroborate their hypothesis, the authors could supplement 13 C-aspartate at a supraphysiological concentration (i.e. 5-10 mM) to determine to what extent it is consumed by GOT1 or other pathways. > We thank the reviewer for this comment that helped us to recognise, in retrospect, that by focusing on GOT1ko as a means to rescue aspartate levels detracted from our main finding and extensive mechanistic insights into the role of GOT1 in sustaining the increase in glycolysis in early hypoxia. As we detail in our response to the Reviewer’s point 2, we have now re-written our results section to better clarify why we focused on GOT1 (lines 175-223 of the revised manuscript – please note that line numbering corresponds to the word document with the track changes off). However, we also agree that, because the motivation that led us to GOT1 was the counter-correlation between aspartate and lactate, expanding on the pathways that determine aspartate levels in hypoxia would be useful to the reader.
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To address the reviewer’s point, in revised Fig. S3E, we present new data where we incubated cells in normoxia or hypoxia for 3h in the presence of 1.5 mM 13C-aspartate. We opted for an intermediate aspartate concentration which was enough to observe intracellular labelling while minimising significant perturbation to cells. We found that the amount of labelled aspartate that accumulates intracellularly is not significantly different between normoxia and hypoxia. At the same time, we observe a vast depletion of unlabelled aspartate. We accept that aspartate labelling may not have reached isotopic steady state within the 3h time point we are confined to for our experiments. However, if increased consumption contributed significantly to aspartate depletion within this timeframe, the amount of labelled aspartate that accumulated would be lower in hypoxia compared to normoxia. Therefore, the data in Fig. S3E indicate that, at least within the timeframe of our experiments, the magnitude of aspartate consumption is not likely to increase to such an extent that could significantly contribute to the depletion in aspartate.
We had, indeed considered other aspartate-consuming pathways, however, in light of the above results and our subsequent finding that GOT1 is needed for increased glycolysis, we did not pursue these investigations any further and focused on the role of GOT1 instead.
- In revised Figure S3, and also in response to one of the Reviewer’s other comments below, we have now replotted the data from the experiment in the original manuscript to show both absolute and fractional isotopologue abundances of TCA intermediates from cells labelled with 13C-glucose or 13C-glutamine. Based on these re-plotted data, we find that the amounts of labelled intermediates from both labels decreases; the apparent decrease from glutamine appears greater than that from glucose, likely because glutamine labels more rapidly a greater fraction of TCA intermediates. Moreover, glutamate fractional labelling from glutamine decreases, but modestly increases from glucose over time in hypoxia compared to normoxia. These data raise the possibility that TCA intermediates are diverted to glutamate synthesis. However, as we point out in the revised text, the fact that only glutamine has reached an isotopic steady state by the end of the time course precludes us from making a more accurate quantitative statement and therefore we have refrained from further elaborating on these observations.
Taking the above observations together, in the revised text we do not dismiss increased consumption as a factor in decreased aspartate levels and rather state that “within the timeframe tested, decreased production is a significant contributor to the low aspartate levels in early hypoxia.” (lines 187-188).
In line with the previous comment, the conclusion that "GOT1 activity, rather than a decrease in aspartate concentration itself, is required to sustain the increase in glycolysis in early hypoxia." seems questionable, especially considering the failed aspartate supplementation. The authors suspect low expression of plasma membrane aspartate transporters as the reason and quote Garcia-Bermudez et al.2018 (PMID: 29941933). This paper contains ranked SLC1A2 mRNA expression data from the Cancer Cell Line Encyclopedia (CCLE). The authors may apply aspartate supplementation and "early hypoxia" to a cancer cell line expressing SLC1A2 or other aspartate transporters. Alternatively, they could try introducing the transporter by overexpression.
> We concede that the way we phrased this statement was not ideal and has rightly led to the reviewer’s criticism. In particular, referring to a “decrease in aspartate concentration”, could mislead the reader into thinking that we were referring to the process of aspartate consumption, rather than the low aspartate levels themselves, which is what we aimed to explore. In the revised text, we now carefully make this distinction; we show new data (Figure S3G) supporting the idea that low aspartate levels are not necessary for increased lactate; we explain that, given the known role of the malate-aspartate shuttle in coordinating redox balance and potentially affecting glycolytic flux, the fact that aspartate didn’t appear to be limiting was surprising and we therefore asked whether GOT1, which depends on aspartate, had a role in the increased glycolysis in early hypoxia. Given that GOT1ko attenuated the increase in glycolysis we subsequently focused on the mechanism underlying this observation. In more detail:
As Reviewer 2 noted in point 1 of their review, the increase in lactate became more apparent after 2 h, when aspartate levels had almost reached their minimum. This successive timing of abundance changes raised the possibility that low aspartate levels precede, and possibly drive, the increased lactate. Therefore, we sought to test whether this was the case by preventing depletion of aspartate in hypoxia with exogenous aspartate. We agree that, to address the comment of Reviewer 1 here, overexpression of an aspartate transporter would have been a good way to overcome poor aspartate uptake by MCF7 cells, however, at the time we initiated this study, SLC1A2 was not known as an aspartate transporter. We, therefore, cultured MCF7 cells for several weeks in media containing 0.5 mM aspartate (which is normally absent in our standard media formulation) because we expected that cells would adapt to take up more aspartate. We, thereby, obtained a derivative cell line that we called MCF7Asp. In new Figure S3G, we show that addition of 0.5 mM aspartate in the media of MCF7Asp cells largely prevented the decrease in intracellular aspartate seen in parental MCF7 cells after 3h in 1% O2; however, the increase in lactate was similar between MCF7 and MCF7Asp cells. These data are consistent with the idea that the low aspartate levels in hypoxia are not the likely cause for the increase in lactate.
As the Reviewer notes in point 3 below, production of malate m+1 from 2H-glucose does not decrease below the levels found in normoxia (Fig. 4H), even though aspartate levels are depleted (Fig. 1C). Together with the fact that maintaining aspartate levels to near-normoxic levels does not further boost lactate levels (Figure S3G), these findings speak against the notion that the lack of increased GOT1-MDH1 flux is due to insufficient aspartate and are aligned with the idea that the malate-aspartate shuttle is saturated (PMID: 35973426, 21982705).
- The observation that labelled m+1 malate produced from [4-2H]-glucose is similar in normoxia and hypoxia (Figure 4G), does not support the notion that GOT1-MDH axis is increased at low oxygen and seems to suggest that the depletion of aspartate observed in early hypoxia is unrelated to this axis. The authors should resolve this discrepancy.*
> In our manuscript, we do not claim that the flux through the GOT1-MDH1 axis is increased but, instead, we emphasise the fact that, as the reviewer observed, malate labelling from 2H-glucose is unchanged (e.g. see text in our original manuscript - lines 519-522 of the revised version: “Importantly, a model where increased upper glycolysis due to the Pasteur effect overwhelms GAPDH capacity also elucidates the apparent increase in the reliance of glycolysis on GOT1-MDH1 in hypoxia, even though flux through this pathway is not elevated.”). As we also detail in our responses to comments 1 and 2, above, in the revised manuscript, we have re-written the discussion to better explain that the reliance on GOT1 in hypoxia is not driven by increased flux through this pathway (which is likely saturated as outline in our response to point 2, above), but rather from the increased demand imposed by the elevation in incoming glucose carbons due to the Pasteur effect (lines 504-531). This is akin to a situation where increased demand for a product drives its price up if the manufacturer does not boost production to increase supply. We hope that the reviewed discussion makes this clearer and addresses the reviewer’s comment.
- The alpha-KG level regulation by Got1 and the subsequent HIF1alpha "priming" seem quite promising and likely the most novel part of the manuscript. However, further proof should be added to support this strong claim. First, aKG to succinate ratio, rather than aKG alone, is a better indicator of aKG-dependent dioxygenases activity. So. the authors should provide this measurement. *
In line with the reviewer’s excellent suggestion, in the revised manuscript, we added new panel in Figure 6F (discussed in lines 457-458) that shows αKG levels alongside the corresponding αKG/succinate ratios. These data agree with our original interpretation that cofactor levels in GOT1ko cells favour increased dioxygenase activity.
*Second, the authors should rule out the possibility that the differential hydroxylation of HIF is due to the redistribution of intracellular oxygen due to alterations in mitochondrial function. To do this, they could determine whether cytosolic oxygen levels differ in the two conditions. *
The reviewer raises the interesting hypothesis that, given the decreased respiration in hypoxic GOT1ko cells, one could expect increased availability of oxygen that could contribute to the destabilisation of HIF1α. To the best of our knowledge, measuring absolute cytosolic O2 concentration, particularly in hypoxia, would require specialised equipment [e.g. phosphorescence lifetime imaging (PMID: 26065366), or phosphorescence quenching oxymetry (PMID: 21912692); unfortunately, we do not have access to such equipment. In the revised manuscript, we acknowledge the reviewer’s point with added new text in the discussion (lines 576-577).
Finally, the authors could test whether α-ketoglutarate or 2-hydroxyglutarate supplementation affects HIF stability in their experimental conditions.
> We thank the reviewer for this suggestion. In the revised manuscript (new Figure S6H and lines 453-455) we show that addition of DM-αKG, a cell-permeable form of αKG, to the media of MCF7 cells incubated at 1% O2, decreases HIF1α protein levels in a dose-dependent manner and, at the highest dose, to a degree comparable to that of GOT1ko cells.
Minor comments:
- The glycerol-3-phosphate shuttle is another means of re-oxidizing NADH and α-GP is indeed higher in GOT1 KO. According to this, in Fig 5C a clear increase in a-GP is observed in LDH KO cells. Would the phenotype be stronger upon additional GPD1 knockout or inhibition?
> The main phenotype of combined LDHA/GOT1 inhibition is a deficit in ATP and decreased cell survival. While increased flux through GPD1 could, indeed, provide more NAD+, this would come at the expense of glucose carbons that would otherwise need to flow into lower glycolysis to produce ATP. Consistent with this idea, our data show that, even if GPD1 or other dehydrogenases reoxidise NADH, as would be the case in both the LDHAko and GOT1ko cells where α-GP is elevated, they are not sufficient to compensate for the decrease in LDH and GOT1 activity. Therefore, we did not pursue this hypothesis further.
* - Aspartate and lactate levels appear unchanged in MDA-MB231 upon hypoxia. Can these changes be ascribed to a pseudohypoxic state? The authors should comment on this observation.*
> In Figure S2A, we show that MDA-MB-231 cells have increased basal levels of HIF1α compared to the almost undetectable HIF1α seen in BT474 (same figure, adjacent panel) or MCF7 cells (Figure 2A). We, therefore, agree with the reviewer’s hypothesis that the attenuated changes in aspartate or lactate levels in MDA-MB-231 cells are likely due to a pseudohypoxic state. As this is speculative, we have refrained from elaborating on this point further in the manuscript.
* - Figure S3B: The authors do not provide information on the length of hypoxia for these experiments. *> The data shown in original Figure S3B (new Fig. S3A-B) are a time course. Cells were incubated at 21% or 1% O2 with the respective isotope label for increasing lengths of time, with the longest time point shown (6h) being the longest time we incubated cells in hypoxia. If the reviewer meant another panel, the length of hypoxia would be 3h unless otherwise stated.
- Glucose and glutamine isotopic labelling should be accompanied by graphs showing the total pool levels of these metabolites, and also the uptake of glucose and glutamine (and their specific isotopologue distribution). It would be important to show the isotopologue distribution of aKG in all the conditions tested, in particular, because of its proposed regulation by Got1.
> In the revised manuscript, new Fig. S3 panels A-D, we now show absolute and fractional isotopologue distributions for TCA intermediates for both glucose and glutamine labelling. We have omitted showing αKG in this figure as we could not reliably quantify it in the glutamine-labelling experiment. Also, unfortunately, quantification of glutamine in our GC-MS datasets is not reliable due to conversion to 5-oxoproline.
- Malate generated by MDH1 can be converted by ME1 into Pyruvate, which could be further processed by LDH. Have the authors measured this conversion in their dataset.
> In the figure below we labelled cells with [U-13C]-glutamine for 3 h at 21% or 1% O2 and plotted the fractional labelling for all observable isotopologues in malate, pyruvate and lactate. These data show that there is minimal labelling in pyruvate and lactate (- Aspartate absolute levels across cell lines appear different. Is this due to differences in cell volume? Can the authors comment on this observation?
> To address the reviewer’s hypothesis, we focused on MCF7 and MDA-MB-231, the two cell lines with the highest and lowest aspartate levels, respectively. The volume of MCF7 is approx. 19% higher than that of MDA-MB-231 (calculated based on cell size data from PMID: 31015463). Based on this calculation, and bearing in mind that cell volume is a good predictor of biomass content (PMID: 18595067), cell volume differences may contribute to, but cannot fully account for the one order of magnitude difference in aspartate abundance we see between these cell lines (Figures 1C and S1A).
The cell lines we used in this manuscript (MCF7, BT474, MDA-MB-231, MCF10A) represent different breast cancer (or untransformed, in the case of MCF10A) cell types, with different oncogenic mutation content (PMID: 17157791, 22460905) and proliferation rates (PMID: 22628656); all these factors can be related to steady-state cellular metabolite levels (PMID: 31015463). In the figure below, we have plotted aspartate abundance data (from PMID: 31068703) in 928 cell lines of various origins. These data show that aspartate levels can differ as much as 2 orders of magnitude between cancer cell lines and about half an order of magnitude between MCF7 and MDA-MB-231 or BT474 (MCF10A was not present in this dataset); they also show that aspartate levels in the three cell lines rank in the same order as in our manuscript (MCF7>BT474>MDA-MB-231), although, it is unclear if cells in this dataset were also cultured in dialysed serum as in ours, so we cannot confidently compare the absolute aspartate measurements between our studies.
In conclusion, we suspect that cell volume differences together with other factors, such as proliferation rates and metabolic network differences may account for the differences in intracellular aspartate levels.
- Under hypoxia the contribution of glutamine (labelled fraction, Fig. S3) to TCA cycle intermediates decreases. However, this is not paralleled by an increase in the contribution of glucose, as also supported by an increase in the m+0 in the glutamine labeling but not in the glucose one. How do the authors explain this apparent inconsistency? Are there sources of unlabelled TCA cycle during the hypoxic experiment?
> While glucose and glutamine are the major carbon sources in many cultured cancer cell lines, incl. MCF7 as indicated by the data in Figure S3A-D, other nutrients (such as amino acids, other than glutamine, and fatty acids) can also provide carbons at various points of the TCA cycle. The fact that fractional labelling of glutamate from glutamine is decreased in hypoxia would suggest that the source of decreased contribution of glutamine into the TCA is unlabelled glutamate. We can exclude uptake of exogenous glutamate, because all our metabolic measurements are performed with cells incubated in media without glutamate and supplemented with dialysed serum. However, we observe a modest increase in the fractional labelling from glucose into glutamate (Figure S3A). As glucose labelling into the TCA cycle is not at steady-state even after 5h, it is hard to assess whether, increased labelling from glucose suffices to explain the dilution of glutamine-derived labelling into glutamate a quantitative conclusion but it points to efflux of intermediates out of the TCA cycle (discussed in lines 181-183 of the revised manuscript).
We thank the reviewer for their time and thoughtful comments that helped us improve the presentation of our work.
**Referees cross-commenting**
Referee 2 raises important questions that are in part aligned with referee 1 and are reasonable and doable is the time frame proposed. These are all important questions and comments to consolidate the central hypothesis of the work and I believe are required for publication.
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Reviewer #1 (Significance (Required)):*
Overall, this is an exciting and well-executed piece of work focusing on the early biochemical consequences of hypoxia that the wide metabolism/biochemistry audience will appreciate. While most of these observations are not entirely unexpected, the work brings a sufficiently novel perspective and insights to the field and deserves publication. However, some conclusions are not fully supported by the data and some additional experiments are suggested to bring clarification and strengthen the authors' conclusions.
We are a lab expert in cancer metabolism.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary ** This manuscript represents an interesting and novel description of the role of a cytosolic transaminase, glutamic-oxaloacetate transaminase 1 (GOT1) on both cytosolic redox (and therefore glycolysis through its functional linkage with malate dehydrogenase 1) and the availability of alpha-ketoglutarate for stabilisation of HIF1a in hypoxia. Some of the most interesting data are the evidence for increased cytosolic NAD+ regeneration through the combined action of LDHA (known) and GPD1 (less well-described increase in activity in hypoxia). The manuscript as a whole describes the multiple systems required for the early response to hypoxia, but the focus of the title and way the article is written do not entirely reflect this. For example, the title focuses on GOT1 as the enzymes whose activity is responsible for the early response to hypoxia. However, this is not reflected in some of the data - the deuteron labelling in particular - which shows that LDH and GPD1 are responsible for the biggest redox activity (i.e. support of glycolysis). A degree of reframing of the article may therefore be of benefit.
We thank the reviewer for their constructive suggestions. In the revised manuscript, we have re-written the title and the relevant parts of the results section, and we have significantly re-structured the discussion section to reflect the fact that multiple enzyme systems, one of which is GOT1, converge to support the glycolytic increase and cell survival in early hypoxia. Furthermore, in our point-by-point responses, below, we highlight in detail how we have streamlined the way we present our results.
*Major comments. *
In Figure 1 C and D, the data suggest significant changes in the decrease in cellular aspartate between 1-2 hours, which then slow. This is followed by a change in lactate concentrations from 2 hours onwards, which is observed in the cells (D) and media (F). The rapid decrease in aspartate concentration suggests a relatively large change, which does not correspond to the later lack of alteration in deuteron labelling from d4-glucose (Figure 4H-J) in m+1 malate. This therefore suggests that the biggest determinant of decreased aspartate is not coupled to MDH1 activity directly. If the manuscript is focused on the relevance of GOT1 activity to the early hypoxic response, this should be better resolved. Given that this could undermine the strength of the case being made for GOT1 activity playing a significant role (through MDH1), could the authors perform the same experiments but in the GOT1KO cells to show how NADH is handled under these conditions by LDHA and GPD1? If the focus of the manuscript is shifted, these experiments would likely not be necessary.
> We thank the reviewer for these comments, which, together with those by Reviewer 1, highlighted that the way we presented our results warranted improvement. First, we would like to clarify that by referring to a “decrease in aspartate concentration”, we may have misled the reader into thinking that we were referring to the process of aspartate consumption; rather we wanted to explore whether the low aspartate level itself could be causing the increase in lactate. This is because, as the Reviewer points out, the rate of lactate accumulation picked up after aspartate had almost reached its minimum. Furthermore, by not elaborating on the cause of decreased aspartate and by focusing on GOT1ko as a means to rescue aspartate levels implied a hypothesis whereby GOT1 was the main aspartate consumer, thereby detracting from our main finding and extensive mechanistic insights into the role of GOT1 in sustaining the increase in glycolysis in early hypoxia (regardless the contribution of GOT1 activity in the observed depletion of aspartate).
In the revised text, we have re-written parts of the results section to better clarify these points (e.g. lines 175-223 - please note that line numbering corresponds to the word document with the track changes off). In summary, and as detailed below, we explore the glucose and glutamine data further and present new data with 13C-Asp, which, together support the idea that decreased aspartate in early hypoxia is largely attributable to decreased synthesis and, to a lesser extent, if at all, to increased degradation. We then explain that, given the known role of the malate-aspartate shuttle in coordinating redox balance and potentially affecting glycolytic flux, we asked whether GOT1, which depends on aspartate, still had a role in the increased glycolysis vis-à-vis the low aspartate levels in early hypoxia. Given that GOT1ko did attenuate the increase in glycolysis we subsequently focused on the mechanism underlying this observation. We have re-structured the discussion, to highlight that GOT1 is one the multiple systems required for survival in early hypoxia. We also explain that the reliance on GOT1 in hypoxia is not driven by increased flux through the GOT1-MDH1 axis (which is likely saturated), but rather from the increased demand imposed by the elevation in incoming glucose carbons due to the Pasteur effect (lines 504-531). A relatable situation is when increased demand for a product drives its price up if the manufacturer does not boost production to increase supply. We hope that the revised text better clarifies these points.
Below, we detail the new experimental evidence/analyses we referred to above:
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In revised Figure S3A-D, we have now replotted the data from the experiments in the original manuscript to show both absolute and fractional isotopologue abundances of TCA intermediates from cells labelled with 13C-glucose or 13C-glutamine. Based on these re-plotted data, we find that the amounts of labelled intermediates from both labels decreases; the apparent decrease from glutamine appears greater than that from glucose, likely because glutamine labels more rapidly a greater fraction of TCA intermediates. Moreover, glutamate fractional labelling from glutamine decreases, but modestly increases from glucose over time in hypoxia compared to normoxia. These data raise the possibility that TCA intermediates are diverted to glutamate synthesis. However, as we point out in the revised text, the fact that only glutamine has reached an isotopic steady state by 5h precludes us from making a more accurate quantitative statement and therefore we have refrained from further elaborating on these observations.
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In revised Fig. S3E, we present new data where we incubated cells in normoxia or hypoxia for 3h in the presence of 1.5 mM 13C-aspartate. We found that the amount of labelled aspartate that accumulates intracellularly is not significantly different between normoxia and hypoxia. At the same time, we observe a vast depletion of unlabelled aspartate. We accept that aspartate labelling may not have reached isotopic steady state within the 3h time point we are confined to for our experiments. However, if increased consumption contributed significantly to aspartate depletion within this timeframe, the amount of labelled aspartate that accumulated would be lower in hypoxia compared to normoxia. Therefore, the data in Fig. S3E indicate that, at least within the timeframe of our experiments, the magnitude of aspartate consumption is not likely to increase to such an extent that could significantly contribute to the depletion in aspartate.
Together with the data in Fig. S3A-D, these findings suggest that decreased aspartate in early hypoxia is to a great degree driven by decreased production.
- The authors present data in Figure 1 and 3 using 2DG as a surrogate for glucose uptake. 2DG has been previously shown not to always be a surrogate for glucose uptake (Sinclair et al. Immunometabolism 2020). Given that this paper highlighted warns in particular about assuming SLC2A1 and SLC2A3 activities based on 2DG uptake, and that these two transporters are the major glucose transporters regulated by hypoxia, a cautious approach to these data is recommended. Assuming that 2DG uptake is a surrogate for glucose in this system (panel C), the effect of GOT1 appears to be at the level of glucose uptake even at 3 hours - it has been marked as being significant by the authors. This suggests that loss of GOT1 has an effect on glucose uptake prior to any transcriptional response is observed. Is the plasma membrane occupancy by the SLC2A1 or SLC2A3 been reduced after GOT1 KO? The same is true for Figure 1 - as intracellular aspartate and lactate and extracellular lactate is shown, could change in extracellular glucose not be presented as a direct measure?*
The reviewer raises two points: (a) that using 2DG may not faithfully report transporter-mediated glucose uptake and (b) that, if our observations with 2DG are valid, they could point to the possibility that attenuation of glycolysis in GOT1ko cells may be attributable to effects in glucose uptake. In brief, we cannot use glucose measurements in media as an indicator of glucose uptake rates because we do not observe measurable glucose depletion from media within the relevant timeframe (3h) of our experiments.
(a) Given that we did not have access to a set up for using radionuclides, we explored both 2DG-based and glucose depletion from media as potential means to assess glucose uptake. We found that, over 24h, MCF7 cells deplete glucose faster than cells incubated in normoxia for the same amount of time (figure below, A). The magnitude of this increase is similar to that we report using 2-DG (~3-fold, Fig. 1E and 3C). However, we observed only minimal depletion of glucose in the first 3-5 h of culturing cells with fresh media (figure below, B). This is perhaps not surprising given that studies that look at metabolite exchange rates (incl. glucose) typically sample over a period of one to several days rather than hours (e.g. PMID: 31015463, 22628656). In conclusion, we reasoned that detecting a positive change in signal (intracellular 2DG) would provide a more sensitive means than a decrease in extracellular glucose to enable assessment of glucose use within the early time-points that our manuscript is mainly concerned with.
(b) Indeed, we were initially intrigued by the decrease in glucose uptake by GOT1ko cells as it could explain decreased lactate production. However, the upregulation of upstream glycolytic intermediates in GOT1ko cells in both normoxia and hypoxia (Figure 4A) together with the evidence of increased α-GP production from glucose (Figure 4K-L) suggested that, even if less glucose is taken up by GOT1ko cells, there is still a bottleneck at the GAPDH step that prevents maximal flow of glycolytic intermediates to lower glycolysis. We therefore did not pursue further the cause of decreased glucose uptake by GOT1ko cells at this stage.
- The data shown in Figure 2D suggests that there is little change in overall contribution to citrate from glucose in hypoxia compared to normoxia, and that HIF1 is does not play a role in the hypoxic response at this point. However, the data presented are overall fractional labelling, and therefore do not focus on the main hypoxia-dependent point of control highlighted before this by the authors - pyruvate oxidation through PDH. Could the authors consider plotting m+2 isotopomer of citrate either alongside or instead of the total fractional label (which includes hypoxia-independent PC activity and cycling carbons). *
We agree with the reviewer’s suggestion. In the revised manuscript, we added a new panel in Fig. 2D that shows the m+2 citrate isotopologue alongside the original fractional labelling data. This new panel is shown as a bar graph to enable the presentation of individual datapoints and statistical test results.
Additionally, the experimental set-up means that average incorporation over the time shown is represented - i.e. the 3h timepoint is incorporation over the first two hours, while the 24 hour timepoint is averaged over the whole period. It is therefore likely under-representing the decrease in glucose contribution to citrate at 24 hours - the authors could point this out, or OPTIONALLY perform a more time-resolved experiment where flux over shorter periods is assessed for each of the timepoints (i.e. 0-1, 2-3, 5-6, 23-24).
Indeed, we did consider a more time-resolved labelling experiment as the reviewer suggests, however, we decided against this approach as we were concerned that even if we pre-equilibrated the labelling media in hypoxia, it would be challenging to avoid perturbations associated with handling of the cells during addition of the isotopically labelled compound. The new panel in Fig. 2D that shows absolute citrate m+2 abundances should address this point, however, in the revised text (lines 162-164) we added new text that points out this issue.
- Figure 3 data are key for the GOT1 theme of the manuscript, as the authors show that loss of GOT1 increases cellular aspartate in both normoxia and hypoxia - suggesting that GOT1 is an aspartate-consuming enzyme in both conditions. Indeed the magnitude of the change in aspartate after GOT1 knockdown appears similar in both conditions (Panel B). These are interesting data, as they contrast with a recently published study (Altea-Manzano et al. Molecular Cell 2022) suggesting that in respiration-deficient cells (a condition with parallels with hypoxia), GOT1 activity may be aspartate producing to supply aspartate to the mitochondria for GOT2. It would be important for the authors to discuss the differences between studies.*
Following the reviewer’s suggestion, in the revised manuscript (lines 547-556), we have now expanded our previous discussion on the functions of GOT1 in cells with respiration defects.
- Panel E shows data at 5 hours, while the rest of the panels here are a mix of 1 and 3h timepoints. Equally panel E also presents concentration, while D presents relative abundance of lactate - could a consistent approach to presenting the results be taken?*
We agree. Taking into consideration that the data in this panel show one time point of the full time-course in Figure S3F, and to streamline the presentation of these data, in the revised manuscript, we have moved the time-course graph to the main figure.
- In Figure S3, the authors show the lack of direct aspartate uptake, or supplementation through the use of an esterified form. OPTIONAL: they could consider using the expression of SLC1A3 (Tajan et al. Cell Metabolism 2018; Hart et al eLife 2023) to increase aspartate uptake in order to test their hypothesis. *
We agree that, to address this point, overexpression of an aspartate transporter would have been a good way to overcome poor aspartate uptake by MCF7 cells, however, at the time we initiated this study, SLC1A2 was not known as an aspartate transporter. We, therefore, cultured MCF7 cells for several weeks in media containing 0.5 mM aspartate (which is normally absent in our standard media formulation) because we expected that cells would adapt to take up more aspartate. We, thereby, obtained a derivative cell line that we called MCF7Asp. In new Figure S3G, we show that addition of 0.5 mM aspartate in the media of MCF7Asp cells largely prevented the decrease in intracellular aspartate seen in parental MCF7 cells after 3h in 1% O2. However, the increase in lactate was similar between MCF7 and MCF7Asp cells. These data are consistent with the idea that the low aspartate levels in hypoxia are not the likely cause for the increase in lactate.
*Figure S3B-E - the authors suggest based on these data that aspartate decrease in hypoxia is through decreased glutamine contribution. Indeed they could also interrogate the data further, as the defect is observed in glutamate, perhaps suggesting that glutamine metabolism through glutaminase is altered. *
To address the Reviewer’s point, in revised Figure S3, we have now replotted the data from the experiment in the original manuscript to show both absolute and fractional isotopologue abundances of TCA intermediates from cells labelled with 13C-glucose or 13C-glutamine. We have elaborated on these results in our response to point 1, and we re-iterate our conclusions here for the Reviewer’s convenience: Based on these re-plotted data, we find that the amounts of labelled intermediates from both labels decreases; the apparent decrease from glutamine appears greater than that from glucose, likely because glutamine labels more rapidly a greater fraction of TCA intermediates. Moreover, glutamate fractional labelling from glutamine decreases but modestly increases from glucose over time in hypoxia compared to normoxia. These data raise the possibility that TCA intermediates are diverted to glutamate synthesis. However, as we point out in the revised text, the fact that only glutamine has reached an isotopic steady state by 5h precludes us from making a more accurate quantitative statement and therefore we have refrained from further elaborating on these observations.
*Figure S3D and E - the authors show data from 3 hours of labelling, which is not at steady-state (observable from the timecourse also shown in B and C). To be able to compare the glucose and glutamine labelling, a timepoint in which (pseudo)steady-state is achieved would be better chose. *
In the revised manuscript, this concern is now addressed by showing both absolute and relative isotopologue abundances for all available time points. We agree that quantitative comparison of labelling must be done at steady-state conditions, however, as we also point out in the revised text (lines 180-181), only glutamine reaches isotopic steady state by 5h whereas glucose hasn’t.
Additionally, within the aspartate isotopomers arising from glutamine, there is an odd m+1 for aspartate not observed in the other proximal metabolites. Is this a technical defect or is there a biological reason for the significant fractional amount in normoxia?
We thank the reviewer for pointing this irregularity, which we should have clearly identified as such during proofreading of the manuscript. Probed by the reviewer’s comment, we reviewed the corresponding data tables used to plot these data and found that M+1 had exactly the same values as M+0. We then inspected the original data and confirmed that this resulted from an error during the copying of the data from the R-script output data table to GraphPad Prism for plotting (the line containing the replicates for the m+0 isotopologue was pasted again in the line of the M+1 isotopologues). This issue is now obsolete, as, in the revised manuscript Fig S3 new panels A-D, we have replaced the fractional data with detailed absolute and fractional labelling showing all isotopologues. We apologise for this error.
- Figure S6F - all samples from GOT1 KO cells have less actin - could an appropriately loaded western blot be presented?*
In the revised manuscript, we added a new panel with the Ponceau (27/02/2018) staining of the same membrane used for immunoblotting. This staining shows equal loading between all lanes. It is unclear why despite equal loading, the actin signal differs between the two lines.
- In Figure S5B, the authors present ATP data in wild-type control cells, and LDHA-KO with LDHA re-expression. These should be phenotypically similar, but clearly are not. It suggests that there is something not correct with the system being used.*
As shown in the western blot of this figure, expression of exogenous LDH only reaches a fraction of endogenous levels, which likely explains the partial, albeit significant, rescue of the ATP depletion observed in the LDHAko cells. We have not been able to achieve higher LDH expression in our cell preparations that would enable us to address this point further.
*
*
Minor comments
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- PHDs need iron, alpha-ketoglutarate, oxygen and critically ascorbate (Introduction page 2)*
We thank the reviewer for highlighting this critical omission. In the revised manuscript, we have now added this information (line 58).
- PHDs need iron, alpha-ketoglutarate, oxygen and critically ascorbate (Introduction page 2)*
* PDK1 phosphorylation of PDH leads to a reduction in pyruvate oxidation, rather than entry of glucose carbons to the TCA cycle (Introduction page 3)*
We agree with the reviewer that our wording was not accurate, and, in the revised text, we have re-written this part (lines 72-74): “…[PDK1] catalyses the inhibitory phosphorylation of pyruvate dehydrogenase (PDH), leading to attenuated pyruvate oxidation and, consequently, decreased contribution of glucose-derived carbons into the tricarboxylic acid (TCA) cycle.”
* SLC25A51 has been identified as being required for NAD transport into the mitochondria (Kori et al. Science Advances 2020), so it is incorrect to say that the inner mitochondrial membrane is impermeable to this metabolite (page 7)*
We agree that, in light of the Kori et al. study, the phrasing in our text presented an outdated view of pyridine nucleotide compartmentalisation. The data in Kory et al. support SLC25A51 as a mitochondrial NAD+ transporter, however, it is not clear if NADH is also a substrate. Furthermore, as the authors also point out, SLC25A51 has a relatively low affinity for NAD+ and therefore unlikely to interfere with the functions of the malate-aspartate shuttle. Taking all this into consideration, in the revised text (line 249), we acknowledge the existence of a low-affinity mitochondrial NAD+ transporter and retained the statement about impermeability specifically for NADH.
* Figure S6D - authors shows a highly significant increase in the mRNA for EGLN3, which is a HIF1 target gene, as well as encoding PHD3, which acts to hydroxylate HIF1a alongside PHD2. This should be commented on in the text.*
In the revised discussion (lines 577-578), we acknowledge that increased PHD3 (together with increased oxygen availability, related to Reviewer 1’s comment), may additionally contribute to HIF1α destabilisation. Please note that we have also added new data (Figure S6H) in response to Reviewer 1, where we show that exogenous αKG causes HIF1α destabilisation in hypoxia, further supporting the notion that boosting intracellular αKG, alone, can destabilise HIF1α.
* Figure S5G - could it be made clear on the graph whether this is at 21% or 1% O2?*
We thank the reviewer for pointing out this omission. We now state clearly both in the revised corresponding legend (line 937) and revised figure that these data are at 1% O2.
- Figure 5I shows ATP level against % labelling of alpha-GP. It isn't clear whether this is abundance or fractional label, but if the latter this it potentially misleading, as if the concentration of alpha-GP increases as fractional label decreases, there is effectively no change. Could the authors extract the steady-state data from the analysis and use this to calculate amount of m+3 label instead of fraction? Similarly for Figure S1H showing fractional labelling of lactate from glucose. It is likely that the title of this graph is a typo, and that m+3 instead was meant. Additionally, measurement of fractional labelling does not demonstrate increased concentrations of the metabolite, but the glucose carbons making up this isotopomer in the pool.*
For Figure 5I, we confirm that what we show is based on abundance of α-GP m+3 labelling from glucose and, in the revised manuscript (line 895), we amended the legend to clarify this important point.
We concede that the way we had originally written this sentence, suggested that we derived our conclusion that increased lactate in media was due to increased glycolysis based solely on the fractional data in Fig. S1H. In the revised manuscript, we have re-phrased the relevant sentence (lines 136-137) to indicate that our conclusion is based on the fractional data, together with the total lactate data that we show in Fig. 1F.
For all our GC-MS experiments we used ions that we detected reliably in all our experiments – in the case of lactate this is m/z 117. This is a 2-carbon fragment as indicated in the original legend; the molecular formula of the derivatised fragment is shown in Table S2. In the revised manuscript (line 671) we clarify that this fragment contains carbons 2 and 3 of lactate (which we concluded from experiments where labelling with 3,4-13C-glucose (which labels lactate at C1) led to partial decrease in this isotopologue); therefore changes in 117 m+2 indicate changes in glycolysis rather glycolysis and the PPP.
* Figure S2G - the purpose of the measurement of cysteine is unclear; measurement of NAC directly within cells would be a clearer demonstration of its uptake, and to demonstrate direct contribution to antioxidant response would instead require measurement of cellular antioxidants rather than cysteine itself.*
We agree with the reviewer’s comment that, ideally, we would have measured antioxidants, however, unfortunately our GC-MS experiments do not detect glutathione; we, therefore, opted to show cysteine as the best available proof that NAC was added to these cells from the same experiments where we measured aspartate and lactate.
* There is no Figure S3F (page 6 of text)*
In the original version of our manuscript we had awkwardly placed Figure S3F at the top right side of the figure due to space limitations, so, understandably, the reviewer may have missed it. In the revised manuscript, we have now moved this panel to the main Figure 3E, to also address the reviewer’s point 5, above (presentation of lactate data).
* Figure 2E, lactate excretion into the media is presenting an odd profile, suggesting that between 3 and 6 hour there is uptake by cells. Equally, the 24 hour timepoint is being presented as p
The overlap of the error bars arises from error propagation as we report the values at each time point relative to t=0h. The statistical difference we reported was calculated on the original values at 24 h alone, so to avoid this discrepancy we have opted for removing the results of this statistical test altogether.
*
Reviewer #2 (Significance (Required)):*
* The data throughout this paper provide some strong evidence for an early and likely HIF-independent metabolic response - while this is understood, detailed studies have not been performed into the various redox balancing cytosolic pathways, which are presented here. The focus on GOT1 is also interesting and novel, but represents part of a larger overall picture presented, which is not reflected in the title.*
* This is suitable for a relatively broad audience, as the phenotype is likely not cancer specific.
*__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __
* Here, Grimm and colleagues investigate the immediate cellular response to hypoxia, prior to onset of HIF1a stabilization/activity. Consistent with established findings they describe that glycolysis is rapidly upregulated under hypoxia, in a HIF1 alpha independent manner, this correlates with an decreased aspartate levels. From this basis, they describe a key role for GOT1 activity in regulating the early hypoxic response, demonstrating its requirement for glycolysis, maintaining the NAD/NADH balance and - in combination with LDHA - maintaining ATP homeostasis in hypoxia. Finally they describe a role for GOT1 (though alpha KG depletion) in contributing to HIF1 alpha stabilization.*
* In sum, the authors present a compelling study investigating the mechanistic basis of early response to hypoxia, placing GOT1 as a key metabolic regulator of this response. The question of how cell metabolically adapt in the short term to hypoxia is, in my view, an often overlooked area of investigation but clearly has importance across biology, not least in cancer biology - thus the area of investigation is topical. The authors conclusions are supported by their data, often in multiple cell lines and/or through orthologous methods. I would support publication of this study as is.*
* Reviewer #3 (Significance (Required)):*
* Significance is stated in my review above, an understudied area of investigation (early hypoxic responses) but clearly important since without a transient response, the long-term impact of HIF1 stress responses would not be possible*
We thank the reviewer for their time assessing our manuscript and for their positive feedback.
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Referee #3
Evidence, reproducibility and clarity
Here, Grimm and colleagues investigate the immediate cellular response to hypoxia, prior to onset of HIF1 stabilization/activity. Consistent with established findings they describe that glycolysis is rapidly upregulated under hypoxia, in a HIF1 alpha independent manner, this correlates with an decreased aspartate levels. From this basis, they describe a key role for GOT1 activity in regulating the early hypoxic response, demonstrating its requirement for glycolysis, maintaining the NAD/NADH balance and - in combination with LDHA - maintaining ATP homeostasis in hypoxia. Finally they describe a role for GOT1 (though alpha KG depletion) in contributing to HIF1 alpha stabilization.
In sum, the authors present a compelling study investigating the mechanistic basis of early response to hypoxia, placing GOT1 as a key metabolic regulator of this response. The question of how cell metabolically adapt in the short term to hypoxia is, in my view, an often overlooked area of investigation but clearly has importance across biology, not least in cancer biology - thus the area of investigation is topical. The authors conclusions are supported by their data, often in multiple cell lines and/or through orthologous methods. I would support publication of this study as is.
Significance
Significance is stated in my review above, an understudied area of investigation (early hypoxic responses) but clearly important since without a transient response, the long-term impact of HIF1 stress responses would not be possible
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Referee #2
Evidence, reproducibility and clarity
Summary
This manuscript represents an interesting and novel description of the role of a cytosolic transaminase, glutamic-oxaloacetate transaminase 1 (GOT1) on both cytosolic redox (and therefore glycolysis through its functional linkage with malate dehydrogenase 1) and the availability of alpha-ketoglutarate for stabilisation of HIF1a in hypoxia. Some of the most interesting data are the evidence for increased cytosolic NAD+ regeneration through the combined action of LDHA (known) and GPD1 (less well-described increase in activity in hypoxia). The manuscript as a whole describes the multiple systems required for the early response to hypoxia, but the focus of the title and way the article is written do not entirely reflect this. For example, the title focuses on GOT1 as the enzymes whose activity is responsible for the early response to hypoxia. However, this is not reflected in some of the data - the deuteron labelling in particular - which shows that LDH and GPD1 are responsible for the biggest redox activity (i.e. support of glycolysis). A degree of reframing of the article may therefore be of benefit.
Major comments
- In Figure 1 C and D, the data suggest significant changes in the decrease in cellular aspartate between 1-2 hours, which then slow. This is followed by a change in lactate concentrations from 2 hours onwards, which is observed in the cells (D) and media (F). The rapid decrease in aspartate concentration suggests a relatively large change, which does not correspond to the later lack of alteration in deuteron labelling from d4-glucose (Figure 4H-J) in m+1 malate. This therefore suggests that the biggest determinant of decreased aspartate is not coupled to MDH1 activity directly. If the manuscript is focused on the relevance of GOT1 activity to the early hypoxic response, this should be better resolved. Given that this could undermine the strength of the case being made for GOT1 activity playing a significant role (through MDH1), could the authors perform the same experiments but in the GOT1KO cells to show how NADH is handled under these conditions by LDHA and GPD1? If the focus of the manuscript is shifted, these experiments would likely not be necessary.
- The authors present data in Figure 1 and 3 using 2DG as a surrogate for glucose uptake. 2DG has been previously shown not to always be a surrogate for glucose uptake (Sinclair et al. Immunometabolism 2020). Given that this paper highlighted warns in particular about assuming SLC2A1 and SLC2A3 activities based on 2DG uptake, and that these two transporters are the major glucose transporters regulated by hypoxia, a cautious approach to these data is recommended. Assuming that 2DG uptake is a surrogate for glucose in this system (panel C), the effect of GOT1 appears to be at the level of glucose uptake even at 3 hours - it has been marked as being significant by the authors. This suggests that loss of GOT1 has an effect on glucose uptake prior to any transcriptional response is observed. Is the plasma membrane occupancy by the SLC2A1 or SLC2A3 been reduced after GOT1 KO? The same is true for Figure 1 - as intracellular aspartate and lactate and extracellular lactate is shown, could change in extracellular glucose not be presented as a direct measure?
- The data shown in Figure 2D suggests that there is little change in overall contribution to citrate from glucose in hypoxia compared to normoxia, and that HIF1 is does not play a role in the hypoxic response at this point. However, the data presented are overall fractional labelling, and therefore do not focus on the main hypoxia-dependent point of control highlighted before this by the authors - pyruvate oxidation through PDH. Could the authors consider plotting m+2 isotopomer of citrate either alongside or instead of the total fractional label (which includes hypoxia-independent PC activity and cycling carbons). Additionally, the experimental set-up means that average incorporation over the time shown is represented - i.e. the 3h timepoint is incorporation over the first two hours, while the 24 hour timepoint is averaged over the whole period. It is therefore likely under-representing the decrease in glucose contribution to citrate at 24 hours - the authors could point this out, or OPTIONALLY perform a more time-resolved experiment where flux over shorter periods is assessed for each of the timepoints (i.e. 0-1, 2-3, 5-6, 23-24).
- Figure 3 data are key for the GOT1 theme of the manuscript, as the authors show that loss of GOT1 increases cellular aspartate in both normoxia and hypoxia - suggesting that GOT1 is an aspartate-consuming enzyme in both conditions. Indeed the magnitude of the change in aspartate after GOT1 knockdown appears similar in both conditions (Panel B). These are interesting data, as they contrast with a recently published study (Altea-Manzano et al. Molecular Cell 2022) suggesting that in respiration-deficient cells (a condition with parallels with hypoxia), GOT1 activity may be aspartate producing to supply aspartate to the mitochondria for GOT2. It would be important for the authors to discuss the differences between studies.
- Panel E shows data at 5 hours, while the rest of the panels here are a mix of 1 and 3h timepoints. Equally panel E also presents concentration, while D presents relative abundance of lactate - could a consistent approach to presenting the results be taken?
- In Figure S3, the authors show the lack of direct aspartate uptake, or supplementation through the use of an esterified form. OPTIONAL: they could consider using the expression of SLC1A3 (Tajan et al. Cell Metabolism 2018; Hart et al eLife 2023) to increase aspartate uptake in order to test their hypothesis. Figure S3B-E - the authors suggest based on these data that aspartate decrease in hypoxia is through decreased glutamine contribution. Indeed they could also interrogate the data further, as the defect is observed in glutamate, perhaps suggesting that glutamine metabolism through glutaminase is altered. Figure S3D and E - the authors show data from 3 hours of labelling, which is not at steady-state (observable from the timecourse also shown in B and C). To be able to compare the glucose and glutamine labelling, a timepoint in which (pseudo)steady-state is achieved would be better chose. Additionally, within the aspartate isotopomers arising from glutamine, there is an odd m+1 for aspartate not observed in the other proximal metabolites. Is this a technical defect or is there a biological reason for the significant fractional amount in normoxia?
- Figure S6F - all samples from GOT1 KO cells have less actin - could an appropriately loaded western blot be presented?
- In Figure S5B, the authors present ATP data in wild-type control cells, and LDHA-KO with LDHA re-expression. These should be phenotypically similar, but clearly are not. It suggests that there is something not correct with the system being used.
Minor comments
- PHDs need iron, alpha-ketoglutarate, oxygen and critically ascorbate (Introduction page 2)
- PDK1 phosphorylation of PDH leads to a reduction in pyruvate oxidation, rather than entry of glucose carbons to the TCA cycle (Introduction page 3)
- SLC25A51 has been identified as being required for NAD transport into the mitochondria (Kori et al. Science Advances 2020), so it is incorrect to say that the inner mitochondrial membrane is impermeable to this metabolite (page 7)
- Figure S6D - authors shows a highly significant increase in the mRNA for EGLN3, which is a HIF1 target gene, as well as encoding PHD3, which acts to hydroxylate HIF1a alongside PHD2. This should be commented on in the text.
- Figure S5G - could it be made clear on the graph whether this is at 21% or 1% O2?
- Figure 5I shows ATP level against % labelling of alpha-GP. It isn't clear whether this is abundance or fractional label, but if the latter this it potentially misleading, as if the concentration of alpha-GP increases as fractional label decreases, there is effectively no change. Could the authors extract the steady-state data from the analysis and use this to calculate amount of m+3 label instead of fraction? Similarly for Figure S1H showing fractional labelling of lactate from glucose. It is likely that the title of this graph is a typo, and that m+3 instead was meant. Additionally, measurement of fractional labelling does not demonstrate increased concentrations of the metabolite, but the glucose carbons making up this isotopomer in the pool.
- Figure S2G - the purpose of the measurement of cysteine is unclear; measurement of NAC directly within cells would be a clearer demonstration of its uptake, and to demonstrate direct contribution to antioxidant response would instead require measurement of cellular antioxidants rather than cysteine itself.
- There is no Figure S3F (page 6 of text)
- Figure 2E, lactate excretion into the media is presenting an odd profile, suggesting that between 3 and 6 hour there is uptake by cells. Equally, the 24 hour timepoint is being presented as p<0.01 for 4 replicates with error bars that cross the mean of one of the values. Could the authors possibly check that this is indeed the case?
Significance
The data throughout this paper provide some strong evidence for an early and likely HIF-independent metabolic response - while this is understood, detailed studies have not been performed into the various redox balancing cytosolic pathways, which are presented here. The focus on GOT1 is also interesting and novel, but represents part of a larger overall picture presented, which is not reflected in the title.
This is suitable for a relatively broad audience, as the phenotype is likely not cancer specific.
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Referee #1
Evidence, reproducibility and clarity
In the paper entitled GOT1 primes the cellular response to hypoxia by supporting glycolysis and HIF1α stabilization, Grimm and co-authors investigate the metabolic adaptations of cancer cells upon acute hypoxia. By measuring metabolite levels at early time points upon hypoxia, they observe the accumulation of lactate and depletion of aspartate, along with other TCA cycle metabolites. Importantly, they demonstrate that these metabolic changes are independent of the HIF alpha-dependent transcriptional response. The authors investigate the role of aspartate during these initial phases of hypoxia. To this aim, they characterize cells devoid of glutamate oxaloacetate transaminase (GOT1), in which aspartate accumulates and can no longer be used for replenishing NAD+ via the downstream conversion of oxaloacetate to malate, via malate dehydrogenase. These cells have lower cytosolic NAD+ which affects glycolytic flux through the rate-limiting, NAD+-dependent enzyme GAPDH. GOT1 KO cells have a decrease in glucose consumption, lactate secretion and metabolite levels downstream of GAPDH upon early hypoxia, however ATP levels and viability are only affected with additional lactate dehydrogenase (LDH) impairment. Finally, the authors demonstrate that GOT1 KO cells have higher alpha-ketoglutarate (aKG) levels during early hypoxia, which could contribute to higher prolyl-hydroxylation and subsequent degradation of HIF, regulating the transcriptional response mediated by transcription factor.
Major comments
- The authors claim that they were unable to supplement cells with aspartate (Figure S3), (even though an increase of aspartate is instead observed in cells treated with sodium aspartate) and had to resort to the GOT1 knock-out model to "prevent aspartate from decreasing in hypoxia". This approach implicitly assumes that Got1 is the main driver of aspartate depletion upon hypoxia. However, although steady-state levels of aspartate are indeed higher in these cells, there is still a strong decrease upon hypoxia, which the authors acknowledge but merely ascribe to "attenuated production from glutamine". This seems an insufficient explanation, considering the very fast depletion upon hypoxia originally observed. The authors should provide further information regarding why aspartate is depleted in these conditions and consider other aspartate-consuming enzymes such as GOT2, ASNS, or even nucleotide biosynthesis and urea cycle enzymes. These observations could be made using the labeling experiments already acquired. In addition, to corroborate their hypothesis, the authors could supplement 13 C-aspartate at a supraphysiological concentration (i.e. 5-10 mM) to determine to what extent it is consumed by GOT1 or other pathways.
- In line with the previous comment, the conclusion that "GOT1 activity, rather than a decrease in aspartate concentration itself, is required to sustain the increase in glycolysis in early hypoxia." seems questionable, especially considering the failed aspartate supplementation. The authors suspect low expression of plasma membrane aspartate transporters as the reason and quote Garcia-Bermudez et al.2018 (PMID: 29941933). This paper contains ranked SLC1A2 mRNA expression data from the Cancer Cell Line Encyclopedia (CCLE). The authors may apply aspartate supplementation and "early hypoxia" to a cancer cell line expressing SLC1A2 or other aspartate transporters. Alternatively, they could try introducing the transporter by overexpression.
- The observation that labelled m+1 malate produced from [4-2H]-glucose is similar in normoxia and hypoxia (Figure 4G), does not support the notion that GOT1-MDH axis is increased at low oxygen and seems to suggest that the depletion of aspartate observed in early hypoxia is unrelated to this axis. The authors should resolve this discrepancy.
- The alpha-KG level regulation by Got1 and the subsequent HIF1alpha "priming" seem quite promising and likely the most novel part of the manuscript. However, further proof should be added to support this strong claim. First, aKG to succinate ratio, rather than aKG alone, is a better indicator of aKG-dependent dioxygenases activity. So. the authors should provide this measurement. Second, the authors should rule out the possibility that the differential hydroxylation of HIF is due to the redistribution of intracellular oxygen due to alterations in mitochondrial function. To do this, they could determine whether cytosolic oxygen levels differ in the two conditions. Finally, the authors could test whether α-ketoglutarate or 2-hydroxyglutarate supplementation affects HIF stability in their experimental conditions.
Minor comments:
- The glycerol-3-phosphate shuttle is another means of re-oxidizing NADH and α-GP is indeed higher in GOT1 KO. According to this, in Fig 5C a clear increase in a-GP is observed in LDH KO cells. Would the phenotype be stronger upon additional GPD1 knockout or inhibition?
- Aspartate and lactate levels appear unchanged in MDA-MB231 upon hypoxia. Can these changes be ascribed to a pseudohypoxic state? The authors should comment on this observation.
- Figure S3B: The authors do not provide information on the length of hypoxia for these experiments.
- Glucose and glutamine isotopic labelling should be accompanied by graphs showing the total pool levels of these metabolites, and also the uptake of glucose and glutamine (and their specific isotopologue distribution). It would be important to show the isotopologue distribution of aKG in all the conditions tested, in particular, because of its proposed regulation by Got1.
- Malate generated by MDH1 can be converted by ME1 into Pyruvate, which could be further processed by LDH. Have the authors measured this conversion in their dataset.
- Aspartate absolute levels across cell lines appear different. Is this due to differences in cell volume? Can the authors comment on this observation?
- Under hypoxia the contribution of glutamine (labelled fraction, Fig. S3) to TCA cycle intermediates decreases. However, this is not paralleled by an increase in the contribution of glucose, as also supported by an increase in the m+0 in the glutamine labeling but not in the glucose one. How do the authors explain this apparent inconsistency? Are there sources of unlabelled TCA cycle during the hypoxic experiment?
Referees cross-commenting
Referee 2 raises important questions that are in part aligned with referee 1 and are reasonable and doable is the time frame proposed. These are all important questions and comments to consolidate the central hypothesis of the work and I believe are required for publication.
Significance
Overall, this is an exciting and well-executed piece of work focusing on the early biochemical consequences of hypoxia that the wide metabolism/biochemistry audience will appreciate. While most of these observations are not entirely unexpected, the work brings a sufficiently novel perspective and insights to the field and deserves publication. However, some conclusions are not fully supported by the data and some additional experiments are suggested to bring clarification and strengthen the authors' conclusions.
We are a lab expert in cancer metabolism.
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Reply to the reviewers
Manuscript number: RC-2023-01935
Corresponding author(s): Vincent Mirouse
1. General Statements [optional]
First of all, we would like to thank the three reviewers for the interest they expressed in our work. Moreover, we believe that, aided by their suggestions, we managed to significantly improve our manuscript.
2. Point-by-point description of the revisions
From here, Reviewers’ comments are in black and our reply in italic dark blue
__Reviewer #1 (Evidence, reproducibility and clarity): ______
This manuscript by Dennis et al. reports a study of the polarized secretion of basement membrane Collagen IV in the Drosophila (fruit fly) follicular epithelium. Using genetic manipulations and confocal imaging, the authors show that Rab-GTPases Rab8 and Rab10, both known to be required for proper basal secretion of Collagen IV (work by the labs of Sally Horne-Badovinac and Trudi Schupbach, respectively), mediate two alternative secretion routes: Rab8 mediates basal-most secretion of soluble Collagen IV that is incorporated homogenously into the basement membrane, whereas Rab10 mediates basal-lateral secretion of Collagen IV that produces insoluble fibers. The authors additionally study the relation between Rab10 and Dystroglycan/Dystrophin (Dystrophin-associated protein complex, DAPC), which they previously showed to be essential for fibril formation (Cerqueira-Campos et al., 2020). They show here that Dystrophin and Rab10 colocalize at the basal trailing side of follicle cells and that overexpressed Dystroglycan can recruit Rab10 to the plasma membrane; however, they also show that Dystrophin mutants fail to display an effect on Rab10 localization, leaving the significance of the proposed Rab10-DAPC interaction unresolved. Finally, the authors present convincing evidence that the exocyst complex opposes fibril formation, and suggestive but comparatively weaker results pointing that this opposition is due to two independent separate exocyst roles: an inhibitory interaction exocyst-Dystrophin (Dystrophin being required for fibril formation), and a positive role in the alternative Rab8 non-fibril route.
Major comment:
- There are several instances throughout the study in which the authors seem to have problems quantifying results. This affects some assertions central to the message of the paper that are not supported by the quantifications presented. It also casts doubts on accessory points deduced from quantitative differences (or lack of difference) that do not seem fully reliable. I would urge the authors to reevaluate their quantification methods.
a) Rab8 KD does not significantly increase apical fraction of Collagen IV with respect to control (Fig. 1H). The image in 1C clearly shows that Col IV is present apically, something that has been shown by others and that never occurs in the wild type. Failure of the quantifying method to detect a difference can only mean the quantifying method is not adequate. A 10% average in the control when it's clear that no Col IV at all is found apically in the wild type suggests that the authors are quantifying background signal that they should not be acquiring, and, if acquired, they should be subtracting. Rab8/Rab10 double knock down is said to show a synergistic effect, when an additive effect would be more consistent with alternative routes. Other problematic deductions drawn from apical fraction quantifications are found in Fig. 5J (Dys- enhancing Rab8 KD but not Rab10 KD) and Fig. 7D (Exo70- enhancing Rab10 KD but not Rab8 KD).
We agree that this quantification was not optimal. We improved it by quantifying a narrower and more precise region for each domain. The new results are shown in Figure 1H. This improvement reduces the apical signal in the control from 10% to 6% and allows us to detect a significant increase between the control and Rab8 KD, thus resolving the problem raised. After verification, we did not subtract the background because there was no electronic background in our images (i.e. black is really black and equal to zero). Thus, the remaining signal is the true cytoplasmic GFP signal and it may not be appropriate to subtract it. Other data (fig 5J and 7D, now named fig 5G and 7H) were also re-analyzed with no major change.
- b) Similar to apical fraction, measurements of planar polarization (trailing/lateral ratio) show average ratios near 1 for Dg, Rab10 and Dys, which is striking given that the localization of these proteins is so clearly polarized. Ratios lower than 1, which are reported for many individual cells in these graphs, should mean reversed polarity. In light of this, I would not be too confident on the effects reported in 5O-Q. In fact, on two occasions, the authors obtain significant differences in these planar polarization measurements that they themselves disregard: Fig. 6J (Rab10 in Exo70-) and Fig. 7I (Dys in Rab8 KD). *We agree that this quantification could be improved. Our initial quantification of the planar polarized proteins, Rab10 and Dys, found at the trailing edge, was confounded by their lateral spread. We have now reported with only the front half of the lateral side. By doing this for instance on Figure 5, we increased the ratio in the control conditions, with almost no points below the value of 1, while the conditions in which polarity is visually affected are unchanged and still close to 1. Thus, this new quantitative approach reinforced our conclusions on this figure. *
*For the figure 6, this new analysis confirm our previous observations : we observed a significant effect of Exo70 mutant, but not of Exo70 overexpression, on Rab10 localization (Figure 6J) while both impact Dys localization (Figure 6F). Main text mentions these two results. *
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Regarding the effect of Rab8 on Dys localization, we indeed observed a slight decrease of its polarization that we currently cannot explain (Figure 7). The important point here is that this effect is opposite to the one observed in Exo70 mutants. Thus, Exo70 effect on Dys cannot be explained by the fact that Rab8 route is blocked in this context. Text has been modified: “Conversely, Rab8 KD slightly affected Dys localization, but, importantly, this effect is opposite to the one observed in the Exo70 null mutant (Figure 7I).”*
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c) Quantifications of lateral fraction Col IV in mosaic experiments do not support decreased lateral secretion in Rab8 OE (3G) or Dys- (S5C), which are central tenets of the study. *We have endeavoured to detect such differences in Dys mutants and Rab8 OE and do not see any possible improvement in the quantification method and we therefore attempted, instead, additional experiments. *
*With respect to Rab8 OE, we suspect that this gain of function is not sufficiently effective under the specific conditions of the experimental setup described in Figure 3, as its effect appears to be more subtle than that of Rab10 OE in Figure 2. We therefore tried to repeat this experiment in a sensitized background in which Rab10 function was partially affected. Unfortunately, we did not see an improvement. However, since downregulation of Rab10 is not sufficient on its own to induce significant differences in this experimental setup, such an experiment is unconclusive and was not added the article. Nonetheless, we modified the results and the discussion to underly the data we got that strongly support that Rab8 route is targeted towards the basal domain with for instance the fact that Exo70 is required for Rab8 route and for basal secretion of collagen. *
*Regarding Dystrophin, we attempted to see whether its effect could be specific on its canonical ECM ligand that is Laminin A. Though we did not have the proper construct (UAS:LanA-GFP) to reproduce the same experiment set-up as with collagen, we tried to see whether Dg overexpressing clones, in presence or absence of Dys, were able to target LanA-GFP( under its own promoter) to the lateral domain of the cells. However, the result was negative and the experiment has not been included in the article. Thus, potential explanations of our results involving Dystrophin and Dystroglycan are detailed in the discussion. *
Minor comments:
- It is stated that Rab10 and Dys associate with tubular endosomes, but no data here support identification as endosomes of these tubular structures, to my understanding.
*We agree with this comment and we modified the text accordingly, mentioning a “tubular compartment” or” a subcellular compartment, with structures reminiscent of tubular endosomes.” *
- The authors call sup-basal the cell region immediately apical to the most basal. Is there sufficient reason to not call this lateral? If a new term is needed, shouldn't it be supra-basal?
*It was changed everywhere for supra-basal. *
- In Fig. S1A and B, Col IV is labeled as green but represented in cyan.
*Sorry for this mistake that has been corrected. *
- Fig. S1A should present a wild type control.
A control has been added.
- It is not clear where Y2H results in Fig 6A come from.
*The Legend has been modified to make it clearer : “ scheme of Dys domains and the fragments identified in a yeast two-hybrid screen with Exo70 as prey (Formstecher et al, 2005).” *
- Fig. 3C'-E' label suggests a gradient made from multiple images, but it looks like just two images and two colors.
*It is actually a true color gradient depending on z axis but the signal is indeed mainly found at the extremities of the z-stack. *
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Graphs in Fig. 3H-J, S5D and 7B are not legible.
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Fig. S1B does not seem to make a significant point in the context of this study.
*Although we understand this comment, we followed suggestion of R#2 who asked in its major comments for more details with other cell polarity markers. *
- I suggest drawing a summary scheme to aid readers better assess interpretations alternative to the ones given in the text.
*Such a summary scheme is now shown in the last figure. *
__Reviewer #1 (Significance (Required)): __
This study reports important new information on the secretion of Collagen IV by polarized cells of the Drosophila follicular epithelium. It complements previous studies on the roles of Rab8, Rab10 and Dystroglycan/Dystrophin, additionally uncovering a role for the exocyst complex. Addressing some issues with quantitative imaging should increase confidence in its most critical conclusions.
__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __
Summary: In this manuscript, Dennis et al. identify different secretory routes and cell exit sites involved in basement membrane secretion and diversification in epithelial cells. Using the follicular epithelium of the Drosophila ovary as their model system coupled with genetics, imaging, and image analysis approaches, they show that two previously identified RabGTPases, Rab8 and Rab10, work in parallel routes for basement membrane secretion. These two small GTPases work in a partially redundant manner, where Rab8 promotes basal secretion leading to a homogenous basement membrane, while Rab10 promotes lateral and planer-polarized secretion, leading to the formation of fibrils. The authors also show that Rab10 and the dystrophin-associated protein act together to regulate lateral secretion, and dystrophin (Dys) is necessary for dystroglycan (Dg) to recruit Rab10. Furthermore, DAPC is shown to be essential for fibril formation and is sufficient to reorient Collagen IV to the Rab10-dependent secretory route. Dys was also shown to interact directly with exocyst subunit Exo70. Using overexpression and loss of function approaches the authors claim that Exo70 limits the planer polarization of Dys, and as a result, Rab10, hence limiting basement membrane fibril formation. Finally, the authors state that the Exocyst (Exo70) is also required for the Rab8-dependent basement membrane route. Overall, the data described in this manuscript are convincing and the authors' claims are supported by the presented data. We have mainly minor comments and only a few major comments that need to be addressed.
Major Comments:
• In the text for Figure 1G-H (page 4), the authors stated that the basal secretion was not restored in Rab8, 10, and 11 triple KD, in our opinion, it is unclear how the authors came to this strong conclusion from the presented data. It would be good if the authors explicitly explain how they come to this conclusion. Is it only based on the weak Coll-IV-GFP signal in the Rab8, 10, and 11 triple KD data compare to the control? If so, the authors should statistically quantify the difference with the control. In Figure 1H, no statistical analysis is provided between the control and triple KD conditions.
We agree that it was not entirely appropriate to give such conclusions on the basis of the quantifications available. A new graph showing basal fluorescence intensity (new Figure 1H) (and not just the ratio of apical to apical plus basal as in Figure 1I) has been added to better support the text. A relevant statistical comparison has been added to Figure 1H (old Figure 1I). We apologize for this oversight.
- From the data presented in Figure S1B, the authors state that the basement membrane mislocalization observed in Rab8/10KD has no major impact on polarity maintenance. They based this statement only on the localization of the apical marker aPKC. Although the aPKC data are convincing, it would be more compelling if the authors observe the distribution of other polarity proteins such as Dlg, E-Cadherin, and armadillo to better assess if the overall epithelial polarity is maintained in this condition. *Dlg and Ecad staining in these different genotypes were added to figure S1 with no major impact on the conclusions. *
Minor Comments: General comments: • In the text describing their data, we recommend that the authors clearly indicate which panel(s) they are referring to.
*We paid attention to this point in the revised manuscript. *
- The authors should also be consistent with the diction throughout the manuscript when referring to the cortical domain or region of the cell (back/rear/trailing edge/leading edge). *We tried to be more consistent. We now only speak about the “front” for one side and for the other of “trailing edge” or “rear half of the cell”, the latter corresponding to a more extended part of the cell than the previous. A scheme on figure 2A illustrates these terms. *
The following specific comments are in order of appearance in the manuscript. Introduction Section: The following statements in the introduction should be supported by specific references: • "BM is critical for tissue development, homeostasis and regeneration, as exemplified in humans by its implication in many congenital and chronic disorders."
We added the following reference: (Sekiguchi and Yamada, 2018)
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"BM is assembled from core components conserved throughout evolution: type IV collagen (Col IV), the heparan sulfate proteoglycan perlecan, and the glycoproteins laminin and nidogen." We added the following reference: (Mouw et al, 2014)
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"During development, the dynamic interplay between cells and BM participates in sculpting organs and maintaining their shape." We added the following references: (Sherwood, 2021; Jayadev and Sherwood, 2017; Walma and Yamada, 2020; Pastor-Pareja, 2020).
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"BM protein secretion shows some specificities, mainly because of the large size of the protein complexes (e.g., procollagen) that must transit from the endoplasmic reticulum to the cell surface". This statement could be supported with references including specific Drosophila references. Additionally, the authors need to clarify what they mean by "some specifies". We added the following references: (Ke et al ,2018; Feng et al, 2021).
Results section: • In the text describing Fig. 2 (page 5), the authors describe two different basement membrane types: fibrils and homogenous. Moreover, the manuscript focuses on the role of Rab8 and Rab10 in the formation of these two structures. Thus, the authors must better describe the two different types of basement membrane structures and their known roles. This will be helpful for the readers to analyze the presented data, especially for those that are not familiar with the system.
We rewrite the beginning of this paragraph : “ *Follicle BM is composed of an homogenous matrix from the very first stages while BM fibrils are added during the collective cell migration (Figure 2A, top) (Haigo and Bilder, 2011; Isabella 2016). Although the exact contribution of each of these BM types is not yet fully understood, genetic manipulation indicated that they are both required for the proper morphogenesis of the future egg (Haigo and Bilder, 2011; Isabella et al, 2016; Cerqueira Campos et al, 2020). Findings mainly based on gain of function experiments suggest that Rab10 participates in the follicle cell BM diversification by contributing to the formation of BM fibrils that are deposited as the cells migrate (Isabella et al, 2016). On the other hand, the route to generate homogenous BM remains unknown.” *
In Figure 2A, the authors describe stage 3 basement membrane as uniform BM, do they mean homogenous?
*Figure 2A has been corrected. *
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In the text describing the data for Fig. 3 (page 6), the authors should clearly explain the reason to use anti-GFP antibodies in a non-permeabilized condition (i.e., to detect specifically the extracellular secretion of BM proteins). This will help the readers to interpret the data presented. It is now explained as following “ Thus, detection of Col IV with an anti-GFP antibody and a Cy3- or Cy5-conjugated secondary antibody without permeabilization allowed discriminating secreted collagen from the total protein.*“ *
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On page 9, the authors stated that the precise localization of Dg in follicle cells is unknown. This statement is incorrect. It has been shown, using a Dg antibody, that Dg localizes at a high level at the basal side of the follicle cells and at a lower level at the apical side (Deng et al, 2003 and Denef et al. 2008). It has been corrected : “Endogenous Dg was described by immunostaining to be mainly enriched on the basal side of the cells (Denef et al, 2008; Deng et al, 2003).*“ *
Discussion Section: • The following statement is not clear: "Thus, three different Rab proteins are targeted towards the three distinct domains of epithelial cells defined by apical basal polarity, and at least of them is also planar polarized". The authors should rephrase and describe specifically which Rabs they are talking about.
*Text has been changed as following “Thus, these three different Rab proteins, Rab11, Rab10 and Rab8, are targeted towards the three distinct domains of epithelial cells defined by apical basal polarity, apical, lateral and basal, respectively. “ *
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This statement is vague: "These three Rab GTPases have been jointly involved in different processes (Knödler et al, 2010; Sato et al, 2014; Vogel et al, 2015; Eguchi et al, 2018; Häsler et al, 2020)". The authors could also mention the processes in which Rab8, 10, and 11 are involved. We tried to be more precise : “The same three Rab GTPases have been jointly involved in different processes such as ciliogenesis, targeted exocytosis or lysosome homeostasis where they have been proposed to act in a redundant manner”
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The following statements need to be supported by references. "Therefore, more investigations are required to define exactly how the DAPC allows the formation of BM fibrils. Nonetheless, given the importance of the DAPC and BM proteins in muscular dystrophies, our results will pave the way to determine whether a similar function is present also in muscle cells. Interestingly, the extracellular matrix is different between the myotendinous junction and the interjunctional sarcolemmal basement membrane and may provide another developmental context where several routes targeted to different subcellular domains may be implicated". *The following reference has been added : (Jacobson et al, 2020). *
Experimental Procedure Section: • In the dissection and immunostaining section (p14), there is a typo: it should be for "20 min" instead of "2for 0 min"
*It has been corrected. * • For the GST pulldown experiments, the authors mention that they use a standard protocol to produce S35 Exo 70 and the GST pulldown experiments. The authors should provide references.
A reference has been added.
Figure and Figure Legend: • General comment: The orientation of the images showing the rotation and leading and trailing edges need to be consistent in the different figures (e.g., In Figures 3 and 7, the leading edge is oriented to the top while in Figures 4, S4, 5, 6, the leading edge is oriented to the bottom). This will help the readers to analyze the data.
*We apologize for this, and we carefully checked image orientation throughout the figures. *
- In Figure 1 C-G the scale bars are missing and should be added as Fig. 1B.• In Figure 4, some scale bars are missing.• In Figure 6, some scale bars are missing. *Scale bars have been added. *
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Figure S1A: The data presented in Figure S1A is convincing. However, a control panel should be added showing the absence of apical Coll IV for comparison. This information will help with the interpretation of the data. A control has been added.
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In Figure 3 legend: it should be "immunostained" for GFP instead of stain for f-actin and GFP.• In Figure 4 legend: it should be "(A, E)" after (i.e 0.8 µm above the basal surface) instead of "(C, G)". In Figure 5 legend (p23), it should be "plane" and not "plan". *Legends of figure 3, 4, 5 have been corrected. *
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In Figure 5A-E, the authors show quantification of the fibril fraction for Dys-, Rab10 OE, and Rab10OE+Dys, Rab8KD, and Rab8KD+Dys-, and images of the collagen fibril for all the conditions except Dys-, it will be informative that the authors present a representative image of the Coll IV fibril in Dys- condition for comparison. The above comment also applies to Figure 5F-J, and it will be also informative to have a representative image of Dys- condition. The requested panels have been added.
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Overall, the legend for Fig. S5 is not clear and we recommend the authors to clearly described the different panels. (e.g., it should be "(D)" instead of "(H-J)") *Legend is now detailed as requested. *
__Reviewer #2 (Significance): __
Despite the important roles of the basement membrane for mechanical support, tissue and organ development, and function, the mechanisms that control the polarized deposition of basement membrane proteins are largely unknown. The contribution of Rab 8 and Rab 10 in the polarized deposition of the basement membrane was previously shown. However, by identifying two competitive secretory routes for the basal secretion of the basement membrane proteins that required these two different RabGTPases, controlled by the DAPC and the exocyst complexes, the authors make a novel contribution to our understanding of the mechanism that leads to the polarized secretion of basement membrane proteins (in that case Collagen IV). Since the basement membrane has critical roles in tissue and organ morphogenesis and functions, and its misregulation has been associated with developmental defects and pathological conditions, this research sheds light on the mechanisms important in these morphogenetic processes and will give insights into their deregulations in pathological conditions.
__Reviewer #3 (Evidence, reproducibility and clarity ): __
In the present work the authors have elucidated a novel mechanistic model of basement membrane morphogenesis using Drosophila ovarian follicle cells as a model. The authors have employed extensive quantification approaches to justify the spatio-temporal expression of the molecules under study such as Collagen IV, Rab8, Rab10, DAPC, etc. The authors suggest distinct exit domains of BM protein Collagen IV facilitated by Rab8 and Rab10 via distinct routes as they interact with each other. The authors further show that DAPC plays an essential role in Rab10 mediated baso-lateral fibrillar BM synthesis whereas Rab8 functions are more Exocyst (Exo-70 dependent).
Major comments: Result 1: The authors use RNAi lines to arrive at their conclusions, however, the extent of inhibition of gene expression achieved by the RNAi, has not been justified. Also observations from only one RNAi stock may not be completely conclusive:
i) Efficiency of RNAi has not been tested or shown. No supporting data. Rab10-RNAi stock is 26289 BDSC which is in Valium10, which is a weak RNAi line and needs a Dicer. ii) Can same observations be made using classic alleles or generate somatic clones on follicular epithelial cells?
*R#3 raised several questions regarding the efficiency of RNAi, the use of different lines and/or the use of classical mutants as an alternative method. *
For Rab10, we tested three different lines with similar results as shown now in Figure S1A-B. These data are also consistent with those obtained by overexpression of a dominant-negative form of Rab10 (Lerner et al, 2013). Unfortunately, Rab10 is located extremely close to the X chromosome centromere and is even more proximal than the FRT transgenes. It is therefore impossible to generate somatic mutant clones.
*Regarding Rab8, it is already published that Rab8 RNAi, expression of a dominant-negative form of Rab8 and Rab8 mutant cells obtained by somatic clones give similar defects (Devergne et al, 2017). The text has been modified to better illustrate the available data validating our approach. *
In addition, mutant clones would not allow analysis of genetic interactions in complex genetic contexts such as double and triple KDs. Similarly, the choice of the Rab10 line was motivated by the ease of obtaining the appropriate genetic combination according to their genomic location.
iii) Intensity of Collagen IV in the basement membrane in Rab11 knock-down mutants seems to be significantly low as compared to the Rab8 and Rab10 knock downs in supplementary Fig 1B. Are the authors very sure that Rab11 has no functions in basement membrane basal organization?
Good catch! Indeed, Rab11 RNAi significantly reduces basal secretion as now shown on fig 1H. Rab11 has pleiotropic functions in epithelial cells notably for their polarity (Choubey and Roy, 2017, Fletcher et al, 2012… and Fig S1). Thus, the reason for such a decrease is unclear and could be an indirect consequence of an overall abnormal epithelial structure. Thus, we now report this observation but have not taken its interpretation too far.
- iv) Authors need to show where and how fluorescence intensities have been measured. *Magenta rectangles with dashed lines on Figure 1A illustrate the ROIs used for this analysis and more details have been added in the ‘experimental procedures’ section. *
Result 2: Confusing diagram. The authors should clarify whether the BM fibrils indicate lateral or planar BM components which they show to be more prominently expressed in Rab10 over-expression mutants.
A short note or an accompanying explanatory diagram on the source of the BM fibrils in the cellular context should make things less confusing.
*Schemes on figure 2A have been improved to make it clearer. *
FF calculation is an ingenious way of trying to look into functions.
The term Opposite effects/functions may be reconsidered as Rab8 and Rab10 compete with each other to deposit Collagen at spatially distinct domains. Opposite functions may give an impression that Rab8 actually represses Rab10 activity or vice versa, which may not be the case here.
*Text has been modified as suggested, speaking about “contrary effects” and “distinct functions”. *
Result 3: Why was anti-GFP Ab detected with Cy3-Cy5 secondary Ab. GFP itself is green so why detect it with a Red secondary? Logic? How clone Collagen GFP and ECM collagen GFP was differentiated? Please justify
It is now explained as following “ Thus, detection of Col IV with an anti-GFP antibody and a Cy3- or Cy5-conjugated secondary antibody ithout permeabilization allowed discriminating secreted collagen from the total protein.*“ *
A panel with a dotted line joining the peripheral or lateral Collagen as shown in panels D' E' of Fig 3 would support the cartoon provided and link the cartoon to the actual microscopic images.
*Figure has been modified as suggested. *
Result4:
The authors suggest a UAS-Rab10-RFP transgene show same results as endogenous Rab10-YFP as compared to spatial expression pattern. This is worrisome as expression of full length functional gene tagged with a fluorophore may be an overexpression. A control experiment would be helpful in suggesting/comparing with the Rab10 OE phenotype and that will be more convincing.
We are not sure that we fully understand the reviewer's comment. However, we initially compared endogenous Rab10 and UAS-RAB10 at 25°C, a temperature at which the latter has no visible impact on BM structure (Cerqueira-Campos et al, 2020). Furthermore, even when higher expression was induced (by increasing the temperature and therefore Gal4 activity) and this had an impact on BM structure, this did not change the subcellular localization of Rab10, i.e. it was still planarly polarized, as shown in Fig 5S. The text has been modified to emphasize this point.
When the authors mention back of cells, where do the authors exactly mean? A cartoon of "the back of follicle cells", wrt the entire ovarian follicle would be helpful.
*As asked by R#2, we are now more consistent throughout the paper, and a scheme illustrates these terms on Fig 2A. *
The authors suggest basal Rab10 expression domain near the Golgi exit point. Can the authors use a Trans-Golgi marker in order to confirm this statement other than the references stated?
*Trans golgi marker and Rab10 are now shown on figure S4. *
Result 5
The authors may provide a Rab10 expression profile in DAPC null or KD mutants which would make their claims more comprehensive.
*Data showing Rab10 localization in Dys mutant cells are shown on Figure S5A-B. *
Result 6:
Exo 70 is a versatile molecule and Rho kinases such as Cdc42 can direct Polarised exocytosis through interaction of Rab effectors with Exo 70. Have the authors considered this?
*We agree that it is an interesting prospect, but we consider it as beyond the scope of this article. *
In general some immunostainings should be carried out if not in all at least in some experiments with some cell domain specific markers, more specifically PCP markers such as Flamingo/Vangl and basolateral markers such as Lgl/Dlg. This makes the positions specific claims of the authors more valid in the eyes of the reader.
We agree that this may help the reader but the mentioned pcp markers are not expressed in this tissue. However, the tissue planar orientation is now systematically indicated and consistent in all figures. We did not generally perform immunostaining for lateral markers but routinely included F-actin staining to detect cellular cortex. Our quantifications or cortical segmentations were based on the cell outline provided by this stain. On the basis of this staining, the outline of the cells was added on certain figures to facilitate understanding of the images.
Reviewer #3 (Significance):
The findings impinge on a critical cellular process of Rab protein interactions in the genesis of the basement membrane which is of potential interest. This falls under basic research. Since Rab molecules have emerged as molecules governing membrane morphogenesis, Cell and Molecular Biologists as well as a wide audience including clinicians will be interested on this.
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Reply to the reviewers
Reviewer #1 Evidence, reproducibility and clarity: Bompierre et al have presented a set of interesting findings that demonstrate the interconnected roles of multiple PDEs in striatal cholinergic interneurons. They show that the regulation of neurons by PDEs differs between ChiNs and MSNs. Disentangling the complex and interconnected biology of PDE subtypes and calcium signalling is notoriously difficult and I commend the authors for not shying away from it. The data are interesting and compelling however I have a number of readily addressable concerns regarding their statistical analysis.
We are very grateful to the reviewer for the careful reading of our manuscript, positive comments and useful suggestions to enhance its readability. We also thank the reviewer for highlighting the difficulties resulting from the scarcity of these neurons. As described in the manuscript, during the course of several other projects, we noticed this sparse neuronal population that clearly departed from the vast majority of striatal neurons, the medium-sized spiny neurons. This particular neuronal population was identified as ChINs only later with immunohistochemistry. We were quite surprised that our visual identification was confirmed by immunohistochemistry in all cases, as described, now with more details, in the manuscript. We then performed a number of new experiments focused on ChINs while we could also re-analyze older experiments performed for other projects and in which ChINs were visually identified - all our experiments are terminated with the acquisition of a Z stack which allows a precise observation of each neuron in the imaging field. This explains some changes in the drugs presented in this manuscript.
Major concerns: • Statistics, general comments: o When performing multiple comparisons (as in figures 2-6) the authors should be using a one-way ANOVA or Friedman's test (for non-parametric).
Since data normality of a few samples was rejected by the Shapiro-Wilk test, and in line with our previous publication, we used non-parametric statistics for all of this study. The Friedman’s test is now applied to all situations comparing more than two conditions.
o When comparing an effect size between experimental conditions e.g. cAMP following NMDA with or without Lu AF64193, the conditions need to be compared directly, not just inferred from being 0.05. [ see Makin and Orban de Xivry 2019 PMID: 31596231 and Nieuwnhuis et al 2011 PMID: 31596231 for more details].
We agree with this comment and the recommended tests have been performed.
o Please include P values and degrees of freedom for each analysis, rather than just * indicating PP values are now indicated in the text - although a smaller P value does not imply that the difference is “more” significant. Degrees of freedom do not apply with Friedman and Wilcoxon rank tests.
• Introduction: o Page 3 para, describing the various PDEs reported to operate in ChiNs vs MSNs, is very convoluted and hard to follow. I recommend replacing this paragraph with a table: column 1: PDE subtype, 2: neuron type, 3: effect reported 4: ref.
We agree that this paragraph was difficult to understand. We tried to build a table as suggested, but such table could not convey all the aspects that we think should be made clear (mRNA or protein data, for example). Instead, we propose a revised paragraph that we hope will be easier to read.
• Methods: o Please include explanation for estimated cAMP concentration (right axis on figs 2c, 3b,5c-f
This was described in “Methods / Estimates of biosensor activation level”. We also added a similar estimate for cGMP concentration based on our previous work with the cGMP biosensor cyGNAL in Figures 4 and 6.
o Please specify slice thickness and age of mice
Slice thickness was missing and now added in methods (300 µm). The age was indicated in the first paragraph of Methods: “7 to 12 days”.
• Figure 1: o describe if 13 uM fsk is supposed to be a maximal concentration, what is the justification for this concentration. Is there a previously published dose-response curve?
A biochemical study of adenylyl cyclase intracellular domains (VC1 and IIC2 heteromer) reports a Kd for forskolin of 0.1 µM {Dessauer et al., 1997, J Biol Chem, 272, 22272-7}, well below the dose we used. To our knowledge, there is no published dose-response analysis of forskolin effect in ChINs and we did not perform this measurement. We routinely use 13 µM for practical reasons, our stock being 25 mM diluted 2,000-fold. ChINs display a much lower cAMP response than MSNs at this dose: we think that this interesting observation deserves to be reported (but we would like to point out that this is only a marginal aspect of our study). We totally agree that this point deserves more explanation and a paragraph has been added in the discussion: “The low responsiveness of ChINs to cAMP-activating signals such as forskolin is striking but the underlying cellular mechanisms remain to be determined. All adenylyl cyclases except AC9 are activated by forskolin {Defer et al., 2000, Am J Physiol Renal Physiol, 279, F400-16}. AC1, AC2 and AC5 are widely expressed in the brain, including the striatum {Matsuoka et al., 1997, J Neurochem, 68, 498-506}. Cluster analysis of mRNA transcript suggest that AC1 and AC2 predominate in ChINs whereas AC5 is mainly expressed in MSNs {Saunders et al., 2018, Cell, 174, 1015-1030.e16}. This indicates that the reduced responsiveness to forskolin in ChINs compared to MSNs does not result from ChINs lacking adenylyl cyclases sensitive to forskolin.”.
• Figure 2c: o Clarify which replicates are shown on the graph (I assume it's the n=11 i.e. number of slices)
Yes, the plot shows the 11 replicates of the same protocol, with each set of 3 points of a same color linked with a line showing the measurements for one experiment. This is now stated more clearly in the figure legend.
o You say "PDE3 thus contributes importantly to the regulation of cAMP level, and when this phosphodiesterase is inactivated pharmacologically, cAMP level becomes controlled by PDE4." But in fig 2c panel 2 inhibition of PDE4 with Picla increases cAMP without PDE3 being already inhibited. This doesn't agree with your statement, which implies that PDE4 only controls cAMP once PDE3 has been inhibited.
This is unfortunate since we did not want to suggest that PDE4 has no effect unless PDE3 is inhibited. Indeed, this experiment together with the next actually demonstrate that PDE3 and 4 are simultaneously engaged in cAMP regulation. This ambiguity was also noted by reviewer 3. We thus rephrased the link between between these two paragraphs.
o Data described but not shown: "In the absence of forskolin, application of PDE3 inhibitor (cilostamide, 1 µM, N=3; n=3; A=3) or PDE4 inhibitor (rolipram, 1 µM, N=2; n=2; A=2) or their combination (N=6; n=8; A=3) induced no significant ratio change in ChINs (as well as in MSNs)".
We removed the mention to the experiments with cilostamide and rolipram alone since N was less than 5. The effect of rolipram and cilostamide together are displayed in Figure 2, with proper statistics indicated in the text.
Also please explain why you have changed PDE inhibitor?
This project developed over several years and the phosphodiesterase inhibitors used in the team changed depending on their availability. In addition, some data such as the lack of effects on baseline cAMP level were extracted from experiments performed for other purposes, hence some differences in the inhibitors we used. Please note that given the scarcity of this neuronal population, many more trials and mice would be required to reproduce these experiments with a single inhibitor. We further consider that our approach contributes to the desired reduction in the use of animals in research (the 3-R).
• Figure 4: o Please clarify in text that data in 4b is from ChiNs and not pooled with data from MSNs? i.e. is the summary data from MSNs not shown?
Figure 4B and C only show average calcium responses in ChINs. This is now indicated in the results section “Figure 4B shows the calcium response to NMDA in ChINs with the average trace (left), and baseline and peak amplitude (right) for individual ChINs.” The calcium response to NMDA uncaging in MSNs has already been published (Betolngar 2019) and is not shown in this study.
o Response to DHPG is not formally compared between MSNs and ChiNs
In MSNs, the calcium response to DHPG showed variability, with a lack of response in most experiments but a clear calcium signal in a few MSNs. The cause of this variability was not the focus of this study but, nevertheless, we wanted to mention this qualitative observation to stimulate future studies on this subject. However, if a quantitative measurement of this variability is required, the description of the DHPG effect on MSNs will be removed.
• Figure 5: o Explain reasoning for switching PDE4 inhibitor (roflumilast).
At the time these experiments were performed, we were using roflumilast to inhibit PDE4.
Explain change in fsk concentration (0.5 uM in figure 5 vs 13 uM in earlier figs)
We wanted to study PDE1 in isolation, i.e. in conditions in which both PDE3 and PDE4 were inhibited. Simultaneous inhibition of PDE3 and 4 leads to biosensor saturation (Figure 2), a situation in which a PDE1-mediated decrease in cAMP level might be difficult to resolve. The forskolin concentration was therefore reduced to decrease adenylyl cyclase activation. This is now explained in the manuscript: ”In order to stimulate a moderate cAMP production and thus maintain the visibility of PDE1 action, a lower concentration of forskolin (0.5 µM) was employed in these experiments.“
o Text states: "These effects were blocked with Lu AF64196 (1-10 µM), a potent and selective PDE1 inhibitor" but this was not formally tested and in the figure a response is still visible (smaller than before Lu, but not blocked)
Indeed, a small change in the average ratio trace is still visible, so we changed “blocked” by “largely reduced”. The effect of Lu AF64196 in the NMDA condition was tested as follows: the change in ratio level induced by NMDA uncaging was calculated in control and Lu AF64196 conditions. This ratio change was compared between control and Lu condition with a Wilcoxon rank test. The same test was applied to compare control and DHPG condition. This is now indicated in the manuscript.
o Please explain the reason for the different time courses in figure 5ab vs 5c-f)
Figure 5A,B are illustrative experiments. Figure 5C-F are average traces from several experiments. This is indicated in the figure legend.
o Data not shown: "Of note, the addition of the PDE1 inhibitor did not increase the steady-state cAMP level, demonstrating that PDE1 did not exhibit a tonic activity before its activation by the calcium signal"
In our experimental conditions, the cAMP level is too high to faithfully report an increase in cAMP upon Lu AF64196 application, so we removed this sentence. However, the cGMP level in the presence of DEANO is farther from saturation, allowing to perform this control: the application of Lu AF64196 produced no significant increase in cGMP level, indicating that PDE1 is not active in our experimental conditions. This has been added in the results.
• Figure 6: o Comment on why using quisqualate (mixed AMPA and mGLuR) rather than DHPG as used previously?
These early experiments were performed with these drugs until we made sure that group 1 mGlu was responsible for this effect and the more specific agonist DHPG was used. The drug combination, however, is specific of group I mGlu activation.
o Again, effect reported as being blocked, but not formally tested and a response is still evident on the graph. If the authors believe that response is an artefact from the stim then please show data with NMDAR antagonists.
We agree that a small change in the average ratio trace is still visible, so we changed “blocked” by “largely reduced”. The effect of Lu AF64196 was tested as described above for cAMP, which is now indicated in the manuscript. We agree that NMDA as well as mGlu stimulation, by increasing calcium, can affect cyclic nucleotides by other mechanisms than PDE1 activation. This was already reported in our previous work, in particular in the hippocampus and cortex (Betolngar 2019). We are not sure that NMDAR antagonists would clarify the situation since NMDA receptor blockade would probably suppress the cAMP change that is still visible in the presence of the PDE1 inhibitor. Nonetheless our manuscript reports experimental conditions in which the vast majority of the effect that we focus on is blocked by the PDE1 selective inhibitor.
• Discussion: o Add references in para 2 describing the PDEs expressed by ChiNs
Done
o Figure 7: cartoon indicates that calcium will exclusively activate PDE1A, is this for simplicity or is their evidence to support this?
Single-cell RT-PCR data {Saunders et al., 2018, #42891} indicate that ChINs express PDE1A but not PDE1B. This is now indicated in the introduction. The cartoon has been changed accordingly.
o Please comment on the specificity of the pharmacological tools used throughout the study.
Phosphodiesterases bear a cleft-shaped catalytic site that is particularly amenable to chemical inhibition, and phosphodiesterase thus constitute a therapeutic target of great interest: a large number of highly specific phosphodiesterase inhibitors have been developed and tested by pharmaceutical companies. It nonetheless remains that our demonstration relies on the specificity of the phosphodiesterase inhibitors. We added a sentence in the discussion “We used highly specific phosphodiesterase inhibitors to acutely test the functional contribution of these phosphodiesterases.” to acknowledge this.
o In the context of regulation of striatal cholinergic and dopaminergic signalling by NO (via cGMP), I believe the authors should cite Hartung et al PMID: 21508928
This very valuable citation has been added in the discussion.
Minor comments: • Page 3 paragraph 1 and 3 the authors have used ellipsis instead of finishing their sentences.
Done.
• In figure 1a please indicate you are recording in dorsomedial region of the striatum (you state in methods this is your recording location, but it would be helpful to show it here)
We thought of improving the cartoon in Figure 1 by drawing a rectangle over the dorso-medial striatum on the image of the brain slice, but that would suggest that the slices had been cut at this stage of the preparation, which was not the case. Adding another image of a brain slice would clutter the figure. We leave it to the Editor to decide how this should be handled.
• The authors use a lot of different drugs, I think a table listing the drugs, concentrations and their targets would help limit confusion.
This will certainly make it easier for the reader. This table has been added in Methods / Chemicals and drugs.
• I found the graphs with each replicate in a different colour quite difficult to read. My personal preference would be for graphs to show each replicate as a transparent line/small symbol, with the mean and SEM shown larger and in bold.
We tried to enhance the visibility as suggested. SEM should not be shown since our statistics are non-parametric.
Significance: General strengths: The question of how PDEs interact to regulate striatal output is extremely interesting and notoriously difficult to tackle. The authors have relied upon pharmacological manipulation of PDEs. A pharmacological approach has both strengths (intact system with little compensation occurring between PDEs, which would occur with a genetic strategy) and weaknesses (relying on each of the drugs to act selectively and specifically). By investigating multiple PDEs in the same system in two neuron types, I believe the authors are illustrating interesting findings. For these findings to be more concrete I believe they need a more robust statistical approach, but the experiments and questions are valid. PDEs are of interest to most neuroscientists and understanding cholinergic function is of interest to anyone studying the striatum of basal ganglia more broadly. My expertise is investigating the interactions between striatal cholinergic and dopaminergic signalling.
Reviewer #2 Evidence, reproducibility and clarity : The work characterizes in depth the dynamics of the cyclic nucleotide signaling in cholinergic interneurons (ChINs) in the striatum and the interconnection with calcium signaling. The study is ambitious and risky since it targets a minority of neurons representing only 1% of the total population of striatal neurons. For that they used genetically encoded biosensors, at a very low infection rate, and highly specific phosphodiesterase inhibitors. With these tools they defined PDE1, PDE3 and PDE4 as the key regulators of cAMP levels in ChINs and the interplay with incoming signals raising cGMP and free-calcium levels after nitric oxide or glutamate activation of NMDA or mGlu1/5 receptors, respectively. The conclusions of the study are solid and well supported by the experimental results.
We would like to thank the reviewer for his/her positive comments about our work.
The team has the necessary technical and conceptual background in the field. This is very important to trust the criteria they used to identify ChINs, a fundamental hallmark in this study.
Again, we are very grateful to the reviewer for this very positive comment.
Still, confirmation by immunohistochemical labeling with ChAT antibodies sounds important and perhaps it should had been performed in more experiments.
We agree that our qualitative immunostaining validation of ChAT expression was too terse. We re-analyzed our archived data to provide a more precise account of our observations. We first identified ChINs from their morphology in the biosensor image stack, then checked whether these neurons were positive for ChAT. This is now explained in detail in “Identification of Cholinergic Interneurons in a brain slice”: “11 brain slices were fixed after the biosensor experiment and later processed for ChAT immunoreactivity. In these slices, 15 neurons were visually identified as ChINs during the biosensor recording session. All of these neurons showed a positive ChAT labelling.”
Significance: The results represent an important conceptual advance in the field. To understand better the signaling that regulates firing of cholinergic neurons in the striatum might be relevant to explain pathological responses and they could be useful to define better strategies for the treatment of Parkinson's patients, for instance. In this regard, this study fills an existing gap since this elusive neuronal population was not functionally characterized before. The basic aspects of the study could be of interest to a broad audience.
Reviewer #3 Evidence, reproducibility and clarity: Summary: The author's present very elegant findings regarding how NO regulates cAMP in striatal cholinergic interneurons (ChINs). The major strength of the manuscript lies in the approach, which enables single-cell imaging of neuronal signaling in acute brain slices. A clever combination of pharmacological tools were then utilized to dissect PDE contribution toward cAMP alterations in ChINs. While the manuscript is high-quality, there are a few controls and points of discussion that need to be considered.
We would like to thank the reviewer for his/her careful reading of our manuscript and very positive comments about our study.
Major: There are numerous references to results seemingly missing from the figures. - Figure 2 TP-10 - Figure 2 PF-05 traces - Figure 2 data in absence of FSK (rolipram, cilo) - Figure 3 ODQ
We thought that these experiments showing a lack of effect were of little interest to the reader and therefore omitted raw traces and statistics from the manuscript. However, we fully agree to display more of our data, as long as it does not clutter the main points of our manuscript. We now illustrate the lack of effect of PDE2A inhibition with a typical experiment in Figure 2C. The ratio level is now shown in Figure 2D for roli-cilo and TP-10, with matching statistics in the text. Figure 3 now shows the lack of effect of ODQ.
Have the author's considered an alternative perspective that slow cAMP detection in ChIN, relative to MSN, could be due to the size of neuron? The significantly greater volume of ChIN soma could conceivably require more cAMP to reach the detection threshold of the biosensor. Therefore, how do the author's reconcile such technical caveats?
This is a very interesting hypothesis that is supported by many theoretical and experimental data: it takes longer for membrane adenylyl cyclases to fill up a void volume in which both phosphodiesterases and the biosensor reside. We could rule-out the buffering effect of the biosensor by the experiment described in Figure 1B, but a lower surface to volume ratio such as that observed for ChINs vs MSNs could indeed explain a biologically slower onset in cAMP level. However, a lower surface to volume ratio should not affect the steady-state level that will be eventually reached upon continuous forskolin application: it takes longer to fill up the volume but, if waiting long enough, the final level will be only determined by the equilibrium between cyclase and phosphodiesterase. Forskolin applications of more than 10 min (Figure 2) led to steady-state levels that were far below biosensor saturation, while it did reach saturation in MSNs. Therefore, while we certainly acknowledge the importance of the peculiar neuronal morphology of ChINs, there must be additional specific differences between ChINs and MSNs. In any case, we agree that this important point was missing in our manuscript and we added a paragraph in the discussion to discuss differences in adenylyl cyclases and cell geometry.
In the traces from Figure 2, it is unclear why PDE3 and PDE4 have differential contributions toward cAMP elevation depending on the order of inhibition.
Thank you for pointing out our poor wording, which has also been noted by the first reviewer. Indeed, the data in Figure 2 shows that PDE3 and PDE4 are simultaneously engaged in cAMP regulation. This part has been rewritten.
Moreover, we cannot conclude that PDE3 and PDE4 the major PDEs in ChIN from such experiment based on the result that IBMX did not further raise cAMP. Likely, the biosensor has reached the detection ceiling. This should be discussed as a possibility.
It is an interesting possibility that cAMP levels higher than biosensor saturation level ([cAMP] above 100 µM) could be modulated after PDE3 and PDE4 inhibition, in concentration ranges that go beyond biosensor saturation level. However, our experiments clearly demonstrate that the combined action of PDE3 and PDE4 constitutes the first line of cAMP control since the concomitant inhibition of PDE3 and PDE4 raises [cAMP] beyond physiological relevance. When both PDE3 and PDE4 were inhibited, cAMP indeed reached the level of biosensor saturation which led us to state “The ratio was not further raised by the non-specific phosphodiesterase inhibitor IBMX (200 µM), indicating the saturation level of the biosensor by cAMP (Rmax)”, a conclusion that remains valid.
The author's intepretation ignores the influence of calcium on the activity of various types of ACs, which seems to be a critical feature given the experimental design that first broadly stimulates ACs with forskolin.
We agree with the Reviewer that calcium will certainly activate AC1 (possibly present in ChINs) and inhibit AC5 (expressed in MSNs and possibly also in ChINs). Our experimental design relies on the highly specific PDE1 inhibitor to isolate the selective contribution of PDE1, but minor changes in cAMP and cGMP remained even after PDE1 inhibition, as also pointed out by the first Reviewer. We found experimental conditions in which the contribution of PDE1 could be largely visible, which was the point of this study. However, we certainly agree with the Reviewer that a calcium signal will affect cyclic nucleotide levels through a number of other mechanisms. Therefore, we added the sentence “It should also be noted that, in the presence of the PDE1 inhibitor Lu AF64196, some changes in cAMP level still remained, which can result either from incomplete PDE1A inhibition and/or from NMDA effects on other targets, such as calcium-modulated adenylyl cyclases.”
A missing control in Figure 5 is the effect of Lu AF64196 by itself on cAMP (and in the presence of FSK pre stimulation).
We agree that this is an important control. However, this could not be tested on this protocol with cAMP since the ratio was too close to Rmax, and an increase in cAMP level following PDE1 inhibition would be undetectable. However, with cGMP imaging, the ratio reached with DEANO was farther from saturation such that an increase in cGMP resulting from the inhibition of PDE1 should be detectable. However, Lu AF64196 showed no significant effect. This measurement was added in the manuscript: “As a control, we verified that PDE1inhibition had no effect on the steady-state cGMP level elicited by 10 µM DEANO (Figure 7G: in DEANO: 0.78 of Rmax; in DEANO and Lu AF64196: 0.81; N=5, n=6, a=5; Wilcoxon P=0.094).”
The mechanism would be signicantly bolstered by measuring cAMP from PDE4 inhibition following forskolin and DEANO (Figure 3).
It is true that, in the presence of forskolin and DEANO, the only PDE that remains is PDE4: its inhibition should increase cAMP to the maximal level. Unfortunately, this experimental scheme was not tested. However, Figure 3 is focusing on the regulation of PDE3 by cGMP and at this stage of the reasoning, it might be confusing to get back to the question of which PDE remains after PDE3 has been blocked. In this manuscript, we want to highlight that PDE3 is blocked via the NO-cGMP signaling pathway, and we think that the data in Figure 3 demonstrates this point clearly.
Minor: The introduction should discuss the critical role of ChIN in striatum rather than simply stating "critical role in striatal functions"
A paragraph has been added in the Introduction to highlight the importance of ChINs in striatal function.
PDE should be included as abbreviation in introduction after first mention of phosphodiesterases.
We agree that this was inducing confusion. We keep PDE1-11 as it is the official names of proteins, but we replaced all occurrences of “PDE” by “phosphodiesterases” when alluding to the general concept of this class of enzymes.
Light sources (e.g. laser) for excitation during imaging are missing from the methods.
This was indicated in Methods / Biosensor imaging: “LED light sources (420 nm with a 436 nm excitation filter and 360 nm) were purchased from Mightex (Toronto, Canada).”
The study certainly provides implication for diseases associated with striatal dysfunction such as Parkinson's disease. However, it may be important to note that experiments were performed on slice preparations from very young animals, which could have inherent differences in functionality relative to an aged or diseased context.
We agree that our preparation of brain slices from mice pups is not representative of what could be found in adults, and even less in pathological conditions. Nonetheless, we believe that we identified a crosstalk mechanism that has never been reported in neurons, and that is not taken into account in theories of striatal functions. We hope that this novel understanding will lead to the development of experiments on adult mice that might confirm the functional importance of this effect, in particular pharmacological studies in adult animals with PDE3 inhibitors.
Significance: General assessment: The study utilizes pharmacological tools to selectively target enzymes and receptors in the cAMP cascade to mechanistically dissect how cAMP is handled in ChINs. The major strength is ability to perform such experiments in acute brain slices, i.e. a "native" neuronal context. An exciting aspect is stimulation of NMDAR by agonist-uncaging, thereby revealing an endogenous signaling route that modulates cAMP. A major physiological limitation is reliance on fsk to induce AC activity, however the approach is suitable to obtain mechanistic information. Moreover, conducting experiments in a Parkinsonian disease model would provide tremendous value, although such pursuits are beyond the scope of the work here. Advance: The study builds off robust studies previously published by the author's. The work is also similar to AC-cAMP investigations on acute brain slices performed by the Sabatini (24765076, 29154125) and Martemyanov (29298426, 31644915) labs, which unfortunately were not discussed/cited here. This is perhaps a missed opportunity to highlight the significance of the author's study. For instance, the Sabatini lab investigated calcium influence on cAMP but in the hippocampus. The Martemyanov lab investigated striatal cAMP, but not in ChINs or through calcium or cGMP mechanisms. Therefore there is a gap toward understanding striatal ChINs, which is clearly demonstrated in the author's work here.
These very important references have been left out of our manuscript intentionally since not pertaining directly to the topic of the manuscript. We would like to point out that we also omitted citations to our own work which had also described dopamine, adenosine and acetylcholine responses measured with various biosensors in striatal neurons (Castro 2013 23551948; Yapo 2017 28782235; Nair 2019 24560149). Indeed, this research field is flourishing and we hope that people interested in the general question of cAMP signal integration in neurons will easily find many such relevant publications.
Audience: I find this basic research to have a relatively broad appeal. For example, my lab is working on behavioral aspects of motor dysfunction. Therefore, the pharmacological insight here is very intriguingly. The mechanistic nature of the work may also appeal to those working on the signaling aspect of such diseases.
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Referee #3
Evidence, reproducibility and clarity
Summary:
The author's present very elegant findings regarding how NO regulates cAMP in striatal cholinergic interneurons (ChINs). The major strength of the manuscript lies in the approach, which enables single-cell imaging of neuronal signaling in acute brain slices. A clever combination of pharmacological tools were then utilized to dissect PDE contribution toward cAMP alterations in ChINs. While the manuscript is high-quality, there are a few controls and points of discussion that need to be considered.
Major:
There are numerous references to results seemingly missing from the figures. - Figure 2 TP-10 - Figure 2 PF-05 traces - Figure 2 data in absence of FSK (rolipram, cilo) - Figure 3 ODQ
Have the author's considered an alternative perspective that slow cAMP detection in ChIN, relative to MSN, could be due to the size of neuron? The significantly greater volume of ChIN soma could conceivably require more cAMP to reach the detection threshold of the biosensor. Therefore, how do the author's reconcile such technical caveats?
In the traces from Figure 2, it is unclear why PDE3 and PDE4 have differential contributions toward cAMP elevation depending on the order of inhibition. Moreover, we cannot conclude that PDE3 and PDE4 the major PDEs in ChIN from such experiment based on the result that IBMX did not further raise cAMP. Likely, the biosensor has reached the detection ceiling. This should be discussed as a possibility.
The author's intepretation ignores the influence of calcium on the activity of various types of ACs, which seems to be a critical feature given the experimental design that first broadly stimulates ACs with forskolin.
A missing control in Figure 5 is the effect of Lu AF64196 by itself on cAMP (and in the presence of FSK pre stimulation).
The mechanism would be signicantly bolstered by measuring cAMP from PDE4 inhibition following forskolin and DEANO (Figure 3).
Minor:
The introduction should discuss the critical role of ChIN in striatum rather than simply stating "critical role in striatal functions"
PDE should be included as abbreviation in introduction after first mention of phosphodiesterases.
Light sources (e.g. laser) for excitation during imaging are missing from the methods.
The study certainly provides implication for diseases associated with striatal dysfunction such as Parkinson's disease. However, it may be important to note that experiments were performed on slice preparations from very young animals, which could have inherent differences in functionality relative to an aged or diseased context.
Significance
General assessment:
The study utilizes pharmacological tools to selectively target enzymes and receptors in the cAMP cascade to mechanistically dissect how cAMP is handled in ChINs. The major strength is ability to perform such experiments in acute brain slices, i.e. a "native" neuronal context. An exciting aspect is stimulation of NMDAR by agonist-uncaging, thereby revealing an endogenous signaling route that modulates cAMP. A major physiological limitation is reliance on fsk to induce AC activity, however the approach is suitable to obtain mechanistic information. Moreover, conducting experiments in a Parkinsonian disease model would provide tremendous value, although such pursuits are beyond the scope of the work here.
Advance:
The study builds off robust studies previously published by the author's. The work is also similar to AC-cAMP investigations on acute brain slices performed by the Sabatini (24765076, 29154125) and Martemyanov (29298426, 31644915) labs, which unfortunately were not discussed/cited here. This is perhaps a missed opportunity to highlight the significance of the author's study. For instance, the Sabatini lab investigated calcium influence on cAMP but in the hippocampus. The Martemyanov lab investigated striatal cAMP, but not in ChINs or through calcium or cGMP mechanisms. Therefore there is a gap toward understanding striatal ChINs, which is clearly demonstrated in the author's work here.
Audience:
I find this basic research to have a relatively broad appeal. For example, my lab is working on behavioral aspects of motor dysfunction. Therefore, the pharmacological insight here is very intriguingly. The mechanistic nature of the work may also appeal to those working on the signaling aspect of such diseases. 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.
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Referee #2
Evidence, reproducibility and clarity
The work characterizes in depth the dynamics of the cyclic nucleotide signaling in cholinergic interneurons (ChINs) in the striatum and the interconnection with calcium signaling. The study is ambitious and risky since it targets a minority of neurons representing only 1% of the total population of striatal neurons. For that they used genetically encoded biosensors, at a very low infection rate, and highly specific phosphodiesterase inhibitors. With these tools they defined PDE1, PDE3 and PDE4 as the key regulators of cAMP levels in ChINs and the interplay with incoming signals raising cGMP and free-calcium levels after nitric oxide or glutamate activation of NMDA or mGlu1/5 receptors, respectively. The conclusions of the study are solid and well supported by the experimental results. The team has the necessary technical and conceptual background in the field. This is very important to trust the criteria they used to identify ChINs, a fundamental hallmark in this study. Still, confirmation by immunohistochemical labeling with ChAT antibodies sounds important and perhaps it should had been performed in more experiments.
Significance
The results represent an important conceptual advance in the field. To understand better the signaling that regulates firing of cholinergic neurons in the striatum might be relevant to explain pathological responses and they could be useful to define better strategies for the treatment of Parkinson's patients, for instance. In this regard, this study fills an existing gap since this elusive neuronal population was not functionally characterized before. The basic aspects of the study could be of interest to a broad audience.
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Referee #1
Evidence, reproducibility and clarity
Bompierre et al have presented a set of interesting findings that demonstrate the interconnected roles of multiple PDEs in striatal cholinergic interneurons. They show that the regulation of neurons by PDEs differs between ChiNs and MSNs. Disentangling the complex and interconnected biology of PDE subtypes and calcium signalling is notoriously difficult and I commend the authors for not shying away from it. The data are interesting and compelling however I have a number of readily addressable concerns regarding their statistical analysis.
Major concerns:
- Statistics, general comments:
- When performing multiple comparisons (as in figures 2-6) the authors should be using a one-way ANOVA or Friedman's test (for non-parametric).
- When comparing an effect size between experimental conditions e.g. cAMP following NMDA with or without Lu AF64193, the conditions need to be compared directly, not just inferred from being < or > 0.05. [ see Makin and Orban de Xivry 2019 PMID: 31596231 and Nieuwnhuis et al 2011 PMID: 31596231 for more details].
- Please include P values and degrees of freedom for each analysis, rather than just * indicating P<0.05
- Introduction:
- Page 3 para, describing the various PDEs reported to operate in ChiNs vs MSNs, is very convoluted and hard to follow. I recommend replacing this paragraph with a table: column 1: PDE subtype, 2: neuron type, 3: effect reported 4: ref.
- Methods:
- Please include explanation for estimated cAMP concentration (right axis on figs 2c, 3b,5c-f
- Please specify slice thickness and age of mice
- Figure 1:
- describe if 13 uM fsk is supposed to be a maximal concentration, what is the justification for this concentration. Is there a previously published dose-response curve?
- Figure 2c:
- Clarify which replicates are shown on the graph (I assume it's the n=11 i.e. number of slices)
- You say "PDE3 thus contributes importantly to the regulation of cAMP level, and when this phosphodiesterase is inactivated pharmacologically, cAMP level becomes controlled by PDE4." But in fig 2c panel 2 inhibition of PDE4 with Picla increases cAMP without PDE3 being already inhibited. This doesn't agree with your statement, which implies that PDE4 only controls cAMP once PDE3 has been inhibited.
- Data described but not shown: "In the absence of forskolin, application of PDE3 inhibitor (cilostamide, 1 µM, N=3; n=3; A=3) or PDE4 inhibitor (rolipram, 1 µM, N=2; n=2; A=2) or their combination (N=6; n=8; A=3) induced no significant ratio change in ChINs (as well as in MSNs)". Also please explain why you have changed PDE inhibitor?
- Figure 4:
- Please clarify in text that data in 4b is from ChiNs and not pooled with data from MSNs? i.e. is the summary data from MSNs not shown?
- Response to DHPG is not formally compared between MSNs and ChiNs
- Figure 5:
- Explain reasoning for switching PDE4 inhibitor (roflumilast). Explain change in fsk concentration (0.5 uM in figure 5 vs 13 uM in earlier figs)
- Text states: "These effects were blocked with Lu AF64196 (1-10 µM), a potent and selective PDE1 inhibitor" but this was not formally tested and in the figure a response is still visible (smaller than before Lu, but not blocked)
- Please explain the reason for the different time courses in figure 5ab vs 5c-f)
- Data not shown: "Of note, the addition of the PDE1 inhibitor did not increase the steady-state cAMP level, demonstrating that PDE1 did not exhibit a tonic activity before its activation by the calcium signal"
- Figure 6:
- Comment on why using quisqualate (mixed AMPA and mGLuR) rather than DHPG as used previously?
- Again, effect reported as being blocked, but not formally tested and a response is still evident on the graph. If the authors believe that response is an artefact from the stim then please show data with NMDAR antagonists.
- Discussion:
- Add references in para 2 describing the PDEs expressed by ChiNs
- Figure 7: cartoon indicates that calcium will exclusively activate PDE1A, is this for simplicity or is their evidence to support this?
- Please comment on the specificity of the pharmacological tools used throughout the study.
- In the context of regulation of striatal cholinergic and dopaminergic signalling by NO (via cGMP), I believe the authors should cite Hartung et al PMID: 21508928
Minor comments:
- Page 3 paragraph 1 and 3 the authors have used ellipsis instead of finishing their sentences.
- In figure 1a please indicate you are recording in dorsomedial region of the striatum (you state in methods this is your recording location, but it would be helpful to show it here)
- The authors use a lot of different drugs, I think a table listing the drugs, concentrations and their targets would help limit confusion.
- I found the graphs with each replicate in a different colour quite difficult to read. My personal preference would be for graphs to show each replicate as a transparent line/small symbol, with the mean and SEM shown larger and in bold.
Significance
General strengths: The question of how PDEs interact to regulate striatal output is extremely interesting and notoriously difficult to tackle. The authors have relied upon pharmacological manipulation of PDEs. A pharmacological approach has both strengths (intact system with little compensation occurring between PDEs, which would occur with a genetic strategy) and weaknesses (relying on each of the drugs to act selectively and specifically).
By investigating multiple PDEs in the same system in two neuron types, I believe the authors are illustrating interesting findings. For these findings to be more concrete I believe they need a more robust statistical approach, but the experiments and questions are valid. PDEs are of interest to most neuroscientists and understanding cholinergic function is of interest to anyone studying the striatum of basal ganglia more broadly. My expertise is investigating the interactions between striatal cholinergic and dopaminergic signalling.
- Statistics, general comments:
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary
In this manuscript, the authors characterized the molecular function of the brain-enriched kinase KIS by combining transcriptome-wide approaches with molecular and functional studies. They uncover that KIS regulates isoform selection of genes involved in neuronal differentiation and inhibits through phosphorylation the capacity of the splicing regulator PTB2 to interact with both target RNAs and protein partners.
Major comments
- This is a very clear and well-written manuscript presenting high-quality and carefully controlled experimental results. The authors used an impressive range of approaches (transcriptome-wide exon usage, phospho-proteomic, imaging, biochemical assays..) to profile exon usage alterations upon KIS knock down and provide a mechanistic understanding of how KIS regulate the splicing activity of PTBP2. Specifically, they convincingly demonstrate that the phosphorylation of PTBP2 by KIS leads to both dismantling of PTBP2 protein complexes and impaired RNA binding.
My only main concerns relate to the understanding of the biological context in which the mechanism studied may be at play. That KIS can counteract PTB2 activity through direct phosphorylation has been very clearly shown by the authors using overexpression of KIS and /or PTB constructs in different contexts (HEK293T cells, N2A cell line, hippocampal neurons). Whether this occurs endogenously in the context of neuronal differentiation, and how much this contributes to the overall phenotypes induced by KIS inactivation, is less clear. While fully investigating the interplay between KIS and PTB2 in the context of neuronal differentiation is beyond the scope of this study, the three following points could be addressed to provide some evidence in this direction.
1- Building on the experiments they perform in a KIS knock-down context (e.g. Fig. 3B, or previously described spine phenotype), the authors should investigate whether inhibiting PTBP2 in this context (through shRNA or expression of a phospho-mimetic construct) might suppress the phenotypes observed when inactivating KIS.
As suggested by the reviewer, we have added a new results section showing the effects on spine maturation in hippocampal neurons expressing PTBP2 phosphomutants and in a PTBP2-KIS double knockdown scenario (Fig4 and S4 Fig; Results section: P6 L12-P7 L21). First, PTBP2-overexpression effects on post-synaptic protrusion density are exacerbated by the phosphoablated mutant. Intriguingly, the phosphomimetic mutant still has a negative impact in spine formation, suggesting either a residual ability of this protein to interact with its normal partners or the existence of additional roles of PTBP2 in spine development that are Matrin3 and hnRNPM independent. Second, KIS knockdown partially suppresses the defects in mature spine formation produced by the loss of PTBP2. In all, these data support the notion of KIS being a phosphorylation-mediated inhibitor of PTBP2 activity during neuronal differentiation.
2- Based on Figures 1E and 3A, it seems that KIS downregulation affects both exon inclusion and exon skipping, and that its function in exon usage is only partly explained by modulation of PTBP2-dependent exons. Have the authors analyzed the populations of PTBP2-dependent exons that are regulated by KIS in an opposite manner? This may point to specific classes of transcripts (in terms of expression pattern, function, molecular signature) important in the context of endogenous neuronal differentiation.
We have analyzed the GO terms of genes with KIS-upregulated exons by that are either downregulated or upregulated by PTBP2. In both groups we found an enrichment of genes in terms associated with calcium ion activity but with different specific functions. Genes with exons downregulated by PTBP2 are more involved in transmembrane transfer of calcium ions from intracellular stores whereas genes with exons upregulated by PTBP2 facilitate the diffusion of calcium ions through transmembrane postsynaptic. Interestingly, with respect to the cytoskeleton, the two groups show a clearly different term enrichment. Genes with exons downregulated by PTBP2 are significantly associated with the tubulin cytoskeleton, whereas genes with exons upregulated by PTBP2 are associated with the actin cytoskeleton. We have added a paragraph in the results section (P8 L6-15) and a new panel in S4C Fig.
3- The authors should better discuss when and where they think PTBP2 phosphorylation by KIS might be relevant. Is there evidence that this process (or PTBP2 complex assembly) might be regulated upon differentiation or plasticity?
We have modified the Discussion section (P11 L35-P12 L14) as follows:
Regarding neuronal differentiation, it is worth noting that KIS expression increases during brain development (Bièche et al, 2003) and in vitro differentiation of hippocampal cultures (Fig. 1B), coinciding with postnatal decrease of PTBP2 levels (Zheng et al, 2012). Therefore, the phosphorylation-dependent inhibition of PTPB2 by KIS and the concerted relative inversion in their levels would generate a molecular switch linking transcriptional and alternative exon usage programs in neuronal development (Fig 6D). In mature neurons, alternative splicing has a well-established role in expanding proteome diversity (Mauger & Scheiffele, 2017). Although the connection between synaptic activity and the control of KIS expression and/or kinase activity is not yet established, the contraposition of PTBP2 and KIS in splicing may constitute a fine-tuning mechanism to modulate proteome diversity as a function of plasticity-inducing signals. In this regard, single-cell transcriptomic data from hippocampal neurons show that expression variability of KIS and PTBP2 is much higher compared to actin (Perez et al, 2021) (S6B Fig). Thus, differences in the expression of these two splicing regulators at a single neuron level would increase protein isoform variability and expand diversity in neural circuits, a crucial property in information processing (Miller et al, 2019).
Minor comments
1- Figures and associated legends are overall very clear and well-organized. Addressing the following points would however help improving the clarity of some Figures:
- In Figure 2EV2C legend, the characteristics of the 3SA constructs are not described
We have modified the legend of Fig 2EV2C (S2C Fig in revised version) to clarify this point.
- the difference between Figure EV1A and Figure 1H classifications is unclear, nor the interpretation regarding the different GO classes identified
The gene lists used for the two GO term analyses are different. In Fig EV1A (now S1A Fig) the gene list is more restrictive than in Fig 1H as we choose genes with more than one exon upregulated by KIS. In contrast, the analysis in Fig 1H includes all genes with one exon upregulated by KIS.
2- Whether PTBP2 is endogenously the major target of KIS explaining transcriptome-wide changes in exon selection is a possibility that remains to be demonstrated. Thus, the authors should correct and tune down the following sentences:
"KIS phosphorylation counteracts PTBP2 activity and thus alters isoform expression patterns ..." (end of introduction)
"PTBP2 being one of the most relevant phosphotargets" (results, end of the second section)
We agree with the reviewer and we have modified the two sentences (P4 L6-8) and (P6 L10) in the revised version of the paper.
Reviewer #1 (Significance (Required)):
- The splicing regulator PTBP2 is a known master regulator of neuronal fate whose tightly controlled expression drives the progenitor-to-neuron transition as well as the establishment of neuronal differentiation programs. How this protein is regulated at the post-translational level has so far remains poorly investigated. In this manuscript, the authors provide a thorough mechanistic understanding of how KIS-mediated phosphorylation of PTB2 impacts on its regulation of exon usage. They also provide a transcriptome-wide view on the function of the brain-enriched KIS kinase in exon usage, uncovering its broad functions in alternative splicing.
If the physiological context in which KIS-mediated phosphorylation of PTB2 is induced remains to be precisely defined, this work opens interesting new perspectives on regulatory mechanisms at play during neuronal differentiation. Providing extra lines of evidence indicating that KIS acts on neuronal functions through PTBP2 phosphorylation will help further strengthen this aspect.
- This manuscript will be of interest to different large communities interested on one hand on the regulation of gene expression programs underlying neuronal differentiation and on the other hand on the molecular regulation of major complexes involved in alternative splicing and isoform selection. It opens new perspectives related to the spatiotemporal regulation of neuronal isoform selection.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary
The manuscript by Moreno-Aguilera et al. shows that the brain enriched protein kinase KIS targets the well known neuronal splicing regulator PTBP2 and several of its interaction partners. As a consequence, PTBP2 activity is down-regulated. Using cultured primary immature neurons they show that KIS expression increases during differentiation and that shRNA knockdown of KIS alters the splicing of many alternative exons. Phosphoproteomic analysis of HEK293 cells transfected with KIS or a kinase dead mutant (K545A) show that it phosphorylates both PTBP2 as well as a cluster of proteins that are known to interact with PTBP2 or its paralog PTBP1. By comparing the new data on KIS-dependent splicing with previous data-sets on PTBP2-dependent splicing targets they show that KIS appears to act antagonistically with PTBP2 when it acts as a repressive regulator, but not when it is an activator. Using combinations of wt and kinase-dead KIS with PTBP2 mutants in the 3 main phosphorylation sites (3SA - non-phosphorylatable, S3D - phosphomimetic) to look at the effects on a known PTBP2 functional target, PSD95, they show that the likely effect of KIS is to antagonise PTBP2 function by phosphorylation at one or more of three residues (S178, S308, S434). Finally, they show that transfected KIS (but not K54A) reduces known protein-protein interactions of PTBP2 and that the triple phosphomimetic PTBP2 mutant shows reduced binding to RNA. Alphafold2 predictions show that the S178 phosphomimetic mutant might alter the conformation of the RRM2 domain, in particular altering the environment of Y244, which has been shown in PTBP1 to be critical for interaction with MATR3 and other coregulators.
Major points
In general, the conclusions drawn are consistent with the data. I have a few suggestions where the authors could either extend their findings with a few straightforward additional experiments, or clarify some of the existing data.
FigEV4 (also introductory text on p3): RRMs 3 and 4 of PTBP1/2/3 fold as a single back to back packed didomain - with the so-called linker contributing to the didomain fold (e.g. PMID: 24688880, PMID: 16179478) and also extending the RNA binding surface by creating a positive patch (e.g. PMID:20160105 PMID: 24957602). AlphaFold successfully predicts the didomain in full length PTBP2 (https://alphafold.ebi.ac.uk/entry/A0A7I2RVZ4). The authors should therefore use AlphaFold2 to predict the RRM3-4 di-domain structure of wt and phosphomimetic mutant PTBP2s. Phosphorylation of S434 or S434D, which is on the C-terminal end of RRM3 may have no predicted effect on RRM3 alone (FigEV4), but it could conceivably disrupt didomain packing, which could itself have important knock-on consequences for RNA binding. In addition, the inrtoduction of negative charges at S434 might affect the ability of R438, K440 & K441 to interact with RNA. An image of the didomain charge density of WT and mutant PTBP2 would be useful to address this.
As suggested by the reviewer, we have considered the di-domain structure of RRM3 and RRM4, and AlphaFold2 predicted no effects by the phosphomimetic residues. We have added the di-domain predictions to S6B Fig.
S434 lies at the very end of RRM3 and limits with a basic region that would not bind RNA in a canonical RRM-dependent manner. In addition, as predicted by AlphaFold2, this basic region is not structured and the effects of a nearby negative charge may be difficult to predict.
Figure 4 could also easily go further in experimentally testing the effects of individual phosphomimetic mutations upon protein-protein interactions (Alphafold predicts that S178D, but not S308 or S434D, should affect Y244 mediated interactions, such as MATR3). The co-IP approach in Fig 4A could readily be used with FLAG-PTBP2 mutants. Likewise, consequences of individual mutations upon RNA binding (Fig 4D) could be tested. The use of a Y244N mutant here would test whether the loss of RNA binding is a consequence of the loss of protein-protein interactions. Such experiments are not essential, but they are readily carried out and have the potential to unravel the consequences of the individual phosphorylation events (more correctly of phosphomimetic mutants).
After building the Y244N mutant we tested PTBP2 interactions with protein partners and observed no significant changes in the levels of Matrin3 and hnRNPM proteins in FLAG-PTBP2 immunoprecipitates nor in the RNA binding ability of PTBP2. These data suggest that, although Y244 is involved in the interaction between PTBP1 and PRI-containing proteins such as Raver1, the interaction between Matrin3 and PTBP2 would involve structural determinants other than the Matrin3 PRI and the PTPB2 Y244 residue. Compared to PTBP1, the nearby flexible loop between RRM2 and RRM3 in PTBP2 is very different and could accommodate specific interaction determinants with Matrin3.
Minor points
Do KIS regulated exons show enrichment of motifs associated with PTBP2, consistent with the proposed model - particularly CU-rich motifs upstream of exons that are more repressed upon KIS shRNA treatment.
We have not observed a significant enrichment of CU-rich sequences upstream of the top 100 exons upregulated by KIS. Indeed, our data suggest that only a fraction of exons upregulated by KIS are inhibited by PTBP2.
For the splicing analysis pipeline, how were exon-exon junction reads treated? If "only exons with more than 5 reads in all samples" were considered, will this not exclude highly regulated exons that are completely skipped under one condition?
This sentence has been corrected as "only exons with more than 5 reads in all samples of one condition..." (P17 L9)
The Introduction mentions U2AF homology (UHM) domains, but neglects to discuss their known binding partners - ULMs (UHM ligand motifs), which contain an essential tryptophan. It would be useful for the discussion to highlight whether any direct KIS interactors possess ULMs and how this relates to the phospho-targets identified here. The authors may wish to draw the parallel with the structurally analogous way that PTBP1 (and presumably PTBP2) interact with their short peptide ligand motifs.
As suggested by the reviewer, we have searched for ULM sequences in the identified KIS phosphotargets, but we only found clear ULMs in SUGP1, which contains KRKRKSR__W__385 and KMG__W__573K. The absence of ULM motifs in most of the proteins identified in the KIS phosphoproteome would indicate that phosphorylation does not require stable protein-protein interactions. We have added these lines to the Discussion section (P10 L34-P11 L2). We completely agree with the reviewer that, in future work, it would be very interesting to test the possibility that KIS binding modulates the composition and functional properties of splicing complexes through ULM domains.
Figure EV2C. The S3A and S308A mutations clearly reduce phosphorylation. However, the effects of S178A and S434A are far less clear. Presumably the quantitation shown in the lower panel of EV2C relies on normalization to PTBP2 protein input, which appears quite variable in the Coomassie gel. It might be better to repeat the experiment with uniform protein inputs. Minimally, details of the quantitation approach should be added to Materials and Methods.
The different levels of reduction in 32P incorporation displayed by the single phosphonull mutants suggests that phosphorylation follows a hierarchical pattern, S308-P facilitating phosphorylation of the other two phosphosites. We have added this comment to the revised version of the paper (P5 L25-28). As mentioned by the reviewer, 32P incorporation was made relative to the total amount of PTBP2 present in the assay, which was deduced from cold Coomassie-stained gels run in parallel to the radioactive gels with same amount of proteins. We have added details of the quantification in the Methods section from 3 independent experiments
Fig 3D shows PTBP2 overexpression, but the main text (p7) states KIS overexpression.
The text and panel order in Fig 3D were misleading and have been corrected (P7 L9-14).
Fig 4B should have a scale bar for the FRET signal
Done (now Fig 5B)
Fig 4E should indicate the location of S178
Done (now Fig 6C)
Reviewer #2 (Significance (Required)):
Significance
This interesting, clear and concise manuscript provides important new insights into the way that a neuron specific kinase can regulate neuronal splicing networks by phosphorylating and thereby downregulating the known neuronal splicing regulator PTBP2. Alternative splicing is known to play a particularly important role in neurons, so this demonstration of an additional layer of regulation by post-translational modification should make the manuscript of wide interest to investigators of splicing regulation, neuronal differentiation and maturation.
Issues that are not addressed in the manuscript include; i) how does KIS specifically target PTBP2 and related proteins? The UHM domain can mediate interaction with ULM containing splicing factors (such as U2AF2, SF3B1), but none of the identified targets have known ULMs. ii) the consequences of individual phoshomimetic mutants upon protein-protein interactions and RNA binding could readily be explored further using computational and experimental methods already used in the manuscript.
For context, this reviewer has a direct interest in the mechanisms of regulation of alternative splicing, but not in the context of neurons (though I am familiar with a lot of the relevant literature), and I do not have expertise in neuronal cell biology.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In this manuscript, the authors explored the function of the protein kinase KIS in splicing regulation associated with neuronal differentiation in vitro. KIS is a serine threonine kinase known to phosphorylate splicing factors such as SF1 and SUGP1, and to be preferentially expressed in adult brain in mammals. Using an shRNA based approach, the authors characterize cassette exon usage upon partial KIS depletion in cultured mouse cortical neurons.
In parallel using mass spectrometry of proteins in KIS overexpressing HEK293 cells, they identify potential KIS substrates including the splicing regulator PTBP2. They confirm that recombinant KIS can phosphorylates PTBP2 in vitro. They show a correlation between KIS-activated and PTBP2-inhibited exons using published data for this factor. They report opposite effects of KIS and PTBP2 on CamKIIB splicing and Finally, coimmunoprecipitation and FRET experiments suggest that KIS inhibits the interactions of PTBP2 with known protein binders, hnRNPM and Matrin3 as well as with RNA. Altogether these data suggest that KIS downregulates PTBP2 during neuronal differentiation.
Majors comments:
Overall the manuscript is well written and the data are interesting.
However several points could have been more extensively studied or discussed to achieve a stronger demonstration of the role of KIS in PTBP2 phosphorylation and neuronal differentiation.
1) To minimize possible off target problems, the RNAseq analysis would be more convincing if replicated with a second shRNA to knockdown KIS.
The efficiency of the selected shRNA had been validated both by the supplier (Merck-Sigma) and in our previous work, which also included a complementation assay (see Fig. 4A-C in Pedraza et al 2014).
2) Part of the reported splicing changes might reflect an indirect consequence of an altered differentiation contributing to the correlation observed in figure 1F. It would be interesting to confirm splicing changes using shorter incubation times with the shRNA compared to the 11 days used in this study.
The levels of splicing regulators such as PTBP1 and PTBP2 change quite markedly during the initial phases of neuronal differentiation (Zheng et al 2012). However, we observed no change in their levels when comparing KIS knockdown to control conditions, suggesting no major upstream effects on the differentiation program per se. But, more important, whereas the splicing pattern of CamKIIβ transcript was clearly affected by KIS knockdown at 18 DIV (Fig 3B), we observed no changes at 14 DIV.
3) Standard deviation is more relevant to describe data dispersion in all figures.
For non-parametric statistics we prefer the interquartile range as a measure of dispersion. For the parametric statistics of 3 independent experiments we show the standard error of the mean as a measure of dispersion.
4) Previous papers of the group described a function of KIS in translation (Cambray et al 2009, Pedraza et al 2014). This is not discussed here. For example, the possibility that RBPs are regulated by KIS at the translation level is not excluded by the analysis in Fig EV2a.
It is an interesting comment by the reviewer that we have considered during the course of this work. Nevertheless, in our experiments coexpressing KIS and PTBP2 in HEK293 cells we did not observe any reduction in splicing factor levels. We have included a representative immunoblot (S5C Fig) of input samples from the experiments shown in the corresponding main figure (Fig 5A in the revised version).
Minor comments:
Figure 1: The authors state that "KIS...accumulates in nuclear sub-structures adjacent to those formed by splicing factors". As the figure presents in fact GFP-KIS, it should be mentioned, and how this localisation is relevant for endogenous KIS should be addressed.
We have corrected the text to mention that GFP-KIS was used to analyse its nuclear localization pattern as shown in Fig 1A (P4 L12-13). We had previously validated the nuclear localization (Boehm et al, 2002) of an N-terminal fusion of KIS to a fluorescent protein (Cambray et al, 2009).
Fig EV1: SI range in pannel D is very different from that in pannel C and Fig1E.
In this figure we plot the average SI obtained from bins with 2500 exons, which would necessarily narrow the SI range obtained from individual exons. Our data indicate that protein disorder would only constitute a minor, but significant, factor in exon usage.
On page 4 "KIS expression reached maximal levels in hippocampal cultures (Fig 1B)." However the figure legend indicate that this analysis was performed with cortical neurons. The use of cortical or hippocampal neurons along the manuscript should be clarified.
We apologize for the typing mistake, and it has been corrected in the revised version (P4 L17-18)
page 4 " KISK54A, a point mutant without kinase activity" The authors should indicate the reference.
The reference to Maucuer et al (1997) has been added (P5 L15)
Figure EV2C: It is not clear whether the Coomassie staining and autoradiography do correspond to the same gel.
32P incorporation was made relative to the total amount of PTBP2 present in the assay, which was deduced from cold Coomassie-stained gels run in parallel to the radioactive gels with same amount of proteins.
Figure 3C. The authors use a dual fluorescence reporter to analyse PSD95 exon 18 splicing. However the well to well variability in such experiments might be elevated. Not only the cells number in a single well but also the number of replicates should be indicated and well to well variability reported.
As stated in the figure legend, the dual fluorescence reporter experiment has been analyzed at a single-cell level. Using ImageJ software, we analyzed the fluorescence of 1054, 970 and 672 cells expressing KIS, KISK54A or none, respectively, from 3 independent experiments.
Figure 3D. The precise timing for the transfection and culture of cells before staining is unclear
Hippocampal neurons were transfected at 5DIV and fixed at 12 DIV. This description has been added to the legend in Fig 3D.
Figure 4A. The input should be loaded to evaluate the coIP efficiencies and ascertain that KIS does not downregulate Matrin3 and hnRNPM levels.
We agree with the reviewer. We have included a representative immunoblot (S5C Fig) of input samples from the experiments shown in the corresponding main figure (Fig 5A in the revised version).
Figure EV4A. No difference of Matrin3 binding is to be seen on the gel. In addition, the authors should confirm that PTBP2 or binders are phosphorylated by recombinant KIS. The preparation of GST-KIS is not described.
We agree with the reviewer that in the in vitro assays the differences in phosphorylation are not as clear as in the in vivo experiments. Fig S2C shows an in vitro kinase assay of PTBP2 by recombinant KIS. Finally, we include a reference (Pedraza et al, 2009) for the preparation of recombinant GST-KIS (P14 L7)
Page 6: "We found that PTBP2-inhibited exons are significantly (FDR=0.001) enriched in KIS knockdown neurons, supporting the notion that KIS acts on AS, at least in part, by inhibiting PTBP2 activity." This should be rephrased as in fact PTBP2-inhibited exons are enriched among KIS activated exons.
The sentence has been rephrased as “We found that PTBP2-inhibited exons are significantly (FDR=0.001) among KIS activated exons…” (P6 L17-18)
Page 10: "SUGP1 is one of the most enriched proteins in our KIS phosphoproteome (see Fig 2A)". Phosphorylation and interaction with KIS was already reported by Arfelli and coll. 2023 supplementary figure 2.
We have modified the Discussion section to add this information (P11 L14-15)
Page 10: " It forms part of the spliceosome complex, interacts with the general splicing factor U2AF2 and has been reported to play an important role in branch recognition by its association with SF3B1." A reference is needed there.
A reference to Zhang et al (2019) has been added.
Page 10: The authors previously reported a differentiation defect in cultured neurons 'Cambray et al, 2008' that was not observed by another group (Manceau et al., PLOS One 2012). This should be discussed in view of these more recent results. Is there any differentiation defect in the experiments reported there?
We have added a new results section showing the effects on spine maturation in hippocampal neurons expressing PTBP2 phosphomutants and in a PTBP2-KIS double knockdown scenario (Fig4 and S4 Fig; Results section: P6 L12-P7 L21). First, PTBP2-overexpression effects on post-synaptic protrusion density are exacerbated by the phosphoablated mutant. Related to the point raised by the reviewer, KIS knockdown also decreased spine emergence and maturation, but partially suppressed the defects produced by the loss of PTBP2. In all, these data support the notion of KIS being a phosphorylation-mediated inhibitor of PTBP2 activity during neuronal differentiation.
Statistical values are difficult to read in the figures. Please use larger fonts.
Done
Reviewer #3 (Significance (Required)):
This manuscript brings new elements supporting the function of the protein kinase KIS in splicing regulation in neurons. In particular it identifies for the first time the splicing regulator PTBP2 as a substrate for KIS.
It will be of interest to a specialized audience of researchers interested in splicing regulators in neuronal differentiation.
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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
Mouse models have contributed significantly to the description of the anti-tumor and the tumor-promoting activity of gd-T cells, which (in mouse) can be divided into an IFNg and an IL-17-producing subgroup based on their CD27 expression. The study describes Ly6C+ and Ly6C cells as "new" subsets of IFNg-producing murine CD27+ gd T cells and proposes Ly6C+ cells as mouse counterparts to human Vd2 T cells. The use of Ly6C expression as a potential marker for functionally distinct CD27+gd T cell subpopulations is based on single-cell transcriptomics of murine lung gd T cells and was validated by flow cytometry of surface antigens and assay of cells for proliferation/cell death after CD3/CD28 stimulation, killing of tumor cells in vitro and in vivo and analysis of the conversion of Ly6C- to Ly6C+ cells and vice versa. Ly6C cells were defined as "immature" and Ly6C cells as "mature". Ly6C+ cells show increased tumor cell kill in vitro and better IFNg production in vitro. In a tumor model, adoptively transferred ex vivo expanded Ly6C+ but not Ly6C- cells prolonged survival. Based on the data shown in Fig. 6, it is postulated that IL-27 is important for the maintenance of LyC6+ cells in vitro and in vivo and for the increased IFN-γ production of Ly6C+-derived cells. It also modulates killing of tumor cells in vitro. Finally, the effects of IL-27 on human Vd1 and Vd2 T cells are analyzed and Vg9Vd2 T cells are postulated as a human counterpart to Ly6C+ gd-T cells. At times, I found the work hard to read, and hope that as someone who doesn't work with mouse gd-T cells, I fully understood the work.
I agree with the characterization of LyC6+ cells as a "more mature/differentiated" subset of IFNg-producing CD27 gd T cells. My doubts mainly concern the interpretation of the transcriptomic data (Fig. 1) and the data shown in Fig.6 and 7 (erroneously referred to as Fig. 8) which described the effects of IL-27 and the postulated similarity of LyC6+ cells and human Vg9Vd2 T cells.
I find it confusing to talk about Ly6 expression of Ly6+ and Ly6- cells. Perhaps it would be better to talk about these expanded cells Ly6+ derived and Ly6- derived cells (or something similar).
Fig. 1 While the identification of clusters 0 and 1 helps to identify LyC6 expression as a marker for CD27+ gd T cell differentiation, I do not understand how the gene signatures and the expression patterns in human lymphocyte subgroups "highlight the high similarity between gd T cells between species", since Fig. 1 E and 1 F also show high similarities between both clusters and non-gd T cells. In fact, the greatest similarities exist between cluster-0 cells and NK cells, followed by mature Vd1 and finally by total Vg9Vd2 T cells and CD8 cells. Naïve Vd1 and naïve (ab T) lymphocytes cells show mainly similarities to cluster 1 cells.
Fig. 2 Please name the organ from which the cells were taken. Please also present in the extended data figures a representative staining with a complete gating strategy, starting with and live gating, to give a wider audience an idea of how the subpopulations of CD27+gd T cells were identified. I also wonder if the expression levels (not the frequency) of CD27 and Ly6C are the same for different organs.
Fig. 3. It appears that Ly6C+ -derived cells show an effect against the E0771 tumor. In the Kaplan Meyer plot these were compared only with the PBS control. Please show the P values for Ly6C+ and Ly6C - derived cells.
Fig. 4 Please indicate the absolute number of gd-T cells and CD27+gd-T cells in organs and tumors.
Fig. 5 Please indicate the absolute number of gd-T cells and CD27+gd-T cells in organs and tumors.
Fig. 6A-C What is meant by control? No cytokines or IL-2 and IL-15 as in the other experiments plus the indicated cytokines. Please specify.
Fig. 6D-G Please indicate the absolute cell counts. Please give also cell counts and frequencies of CD27-Ly6C+ and Ly6C- cells directly taken ex vivo for WT and IL-27R-/-.
Figs. 6E and G and extended data Fig. 4. Was the significance of the differences between WT and IL-27-/- also tested for Ly6C-derived cells, as it was done for L6C+-derived cells. At least in some cases, I would be surprised if such differences were not observed (e.g. Extended data Fig. 4A for NKG2A expression in LN and lung). In addition, in some cases, the lack of differences of for Ly6C- cells between WT and IL-27R-/- cells may be (in part) reflect to their low abundance, which makes it difficult to find statistically significant differences in Il-27 dependence.
Fig. 6F is aimed to show that there is no statistically significant difference in the Ly6C expression of Ly6C- derived cells between IL-27R/- and WT animals, while there is such a difference for the Ly6C+ derived cells. To me, it looks like the lack of significance of differences between the Ly6C-derived WT and IL-27R-/- cells is due to a single outlier. Especially, since only three animals were analyzed compared to five or more in most other experiments I am not convinced of the biological (non-) significance of these differences.
Fig. 6E is indeed "incongruent" since it contradicts the claim that IL-27 supports the anti-tumor response of CD27+ gdT cells, as stated in the ONE-SENTENCE-SUMMARY. To claim a positive effect of IL-27 on tumor control by CD27+ gdT cells, an experiment as shown in Fig. 3 with gd T cells from IL-27R/- and WT mice or with cells expanded with or without IL-27 is required. Another question concerns the purity of cells after CD3/CD28 stimulation. Could it be that contaminating cells (e.g. NK cells) contribute to killing and that IL-27 effects are due to such cells?
Fig. 7 (incorrectly referred to as 8) and extended data Fig. 5. Does the MFI indicate the MFI of the positively gated cells or the alll cells? Please specify. Please also indicate the proportion of Vd1+ and Vd2+ cells before and after expansion for all donors. Fig. 5 A shows an approximately 30% inhibitory effect of IL-27 on Vd1 expansion and a 70-75% reduction in expansion of Vd2 cells. This contradicts the postulated (functional) Vg9Vd2 T cell homology with Ly6C+ cells, since in mice ( Fig. 6) IL-27 reduces the expansion of Ly6C cells and accelerates the expansion of Ly6C+ cells. Another problem could be the comparison of the mouse subgroups with the total Vd1 and Vg9Vd2 T cells. In view of the data in Fig. 1, it would be better to compare mature and naïve cells from both populations.
Mat. and Meth. been mentions FTOC experiments but I did not find them?
Mat. and Meth. also mentions that WT and Tcrb/- mice were used as a source of CD27+Ly6C- and CD27+Ly6C+ cells in the in vivo expansion experiment. I didn't find any mention of the use of Tcrb/- mice in the text of the illustrations. Please explain.
Ly6C is encoded by the genes Ly6c1 and Ly6c2. Ly6c2 is the dominant expressed gene, but in some populations (CD4+ T cells), a significant proportion of Ly6C-positive cells express Ly6C1 https://doi.org/10.4049/immunohorizons.2100114. This should be mentioned in the text. An optional experiment would be to test the expression of mRNA for both genes in naïve and expanded gd-T cells.
With respect to possible revisions. For the core of the paper description of the Ly6C (derived populations (Fig. 1-5) 1-2 months should be sufficient. More complicated is to address the issue of Il-27. Fig. 6. A good part of my concerns are on the evidence of the lack of an Il-27 response of Ly6C- derived cells. Putting less emphasis on this point and might be largely sufficient, since in my eyes it is not so central anyway. A problem is the statement on the importance of IL-27 in control of tumor by Ly6C derived cells. This would need in vivo experiments which may take much longer (-6 months?).
Significance
The work is certainly of interest to most immunologists working with mouse gd-T cells. The characterization of the Ly6C-defined subgroups of CD27+gd-T cells is plausible except for details and supported by the results. The data on IL-27 in Fig. 6 and extended Fig. 4A are less clear, in particular the lack of effect of IL-27 on Ly6C cells may have been overestimated sometimes. Also the importance of IL-27 for tumor control by gd-T cells in vivo has yet to proven. Both would broaden the audience, nd may increase translational/clinical relevance.
The weakest part is the postulated similarity between human gd T cell groups and the two mouse gd T cell subgroups. The cell populations of the mouse could indeed have human counterparts, but this has yet to be shown. Given the strong species-specific differences between both species especially for unconventional T cells, it is also conceivable that non-gd T cells take over the functions of certain gd T cell populations.
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Referee #2
Evidence, reproducibility and clarity
The manuscript submitted by Wiesheu et al focuses on the intrinsic nature of murine gd T cells in tumor immune response. Authors used their previously published scRNA-seq data and using a refined strategy, they dissected the role of Ly6C subset-specific CD27+ gd T cells. The authors represented experimental evidence for the possible differentiation of CD27+Ly6C gd T cells and revealed the essential role of IL-27 for the maintenance of CD27+Ly6C+ gd T cells. Finally, the authors found parallels in cellular features of gd T-cell subsets between mice and humans. Overall, this study is well-designed and expands knowledge of gd T-cell biology. However, the authors are suggested to consider the following comments:
Major:
- One of the major concerns is that the submitted manuscript is the extension of their previous work (Edwards et al 2022) published in J Exp Med. This previous work clearly delineated the differential role of PD-1 and TIM-3 using the scRNA-seq approach. Using the same scRNA-seq data, authors are now defining gd T-cell subsets within the CD27+ population. While providing the functional basis for Ly6C expression, the author shall also define the role of PD-1 or TIM3 in this context. Additionally, this suggestion is based on the literature reports Zhu et al, 2015 Nature Communications (https://doi.org/10.1038/ncomms7072) and Huang et al, 2019 J Exp Med (https://doi.org/10.1084/jem.20190173). It would be interesting to find out if IRF1 also plays a mechanistic role in gd T cells like CD8 T cells. So further experiments are needed in this context, which will complete the functional features of CD27+ gd T cells.
- As stated in the manuscript, Ly6C is a marker for the myeloid compartment in mice. Also, the differentiation (as authors described "conversion") phenomenon of monocytes based on the Ly6C marker is already reported by Mildner et al 2017 Immunity (https://doi.org/10.1016/j.immuni.2017.04.018). In the case of monocytes, Mildner et al showed that Ly6C+ monocytes differentiate into Ly6C- cells. But, in the current manuscripts, authors are proposing the opposite differentiation trajectory for gd T cells. Also, in the corresponding analysis with human scRNA-seq data (fig. 1E-F), are there no analogous expression of genes in the monocyte cluster. Could you please explain? Sorry, this is a bit unclear to me!
- I think, it is a bit strong statement that CD27+Ly6C- gd T-cell converts into CD27+Ly6C+ gd T cells. This statement needs to be supported and validated by more experiments:
- (a) authors shall perform trajectory analysis in scRNA-seq data, which will, hopefully, support the conversion hypothesis;
- (b) authors shall represent the purity and sorting strategy of flow cytometry experiments. For such experiments, it is very important to have the highest purity without any 'false negative' sorting!
- (c) Usually, a conversion of one phenotype into another phenotype requires an intermediate stage. For example, Mildner et al 2017 Immunity reported three stages of monocytes (Ly6C+, Ly6Cint, Ly6C-). However, none of the flow cytometry plots in the current manuscript show any 'intermediate' population.
- (d) check for protein expression of homing receptors like Ccr7, SELL, and S1pr1 to completely exclude that there is no 'homing'
- (e) Can authors consider a third possibility that there is a loss/gain of Ly6C protein expression through molecule signal orchestrated by epigenetics mechanism? If yes, this possibility is worth testing!
- (f) Last, but not least: One way to prove the conversion would be tagging Ly6C protein with a marker (e.g. GFP) to track its fate during the differentiation. Would it be possible?
- The statement in line 137 is not entirely convincing as Fig. 1C-D shows some overlap of Ly6C and CCR7 gene expression in opposing clusters. At least, there are no clear 'black-and-white' opposing expressing levels. Sorry, this is my observation. Is there any other way of representation to make it clearer?
- Authors have nicely shown the cytotoxic potential of CD27+Ly6C+ gd T cells in the in vivo setting. Would they observe the similar cytotoxic potential of expanded CD27+Ly6C+ gd T cells? Also, it would be great if memory CD8 T cells were added to the experimental set-up as a positive control, in addition to naïve CD8 as a negative control.
Minor:
- Authors have used t-SNE representation. However, UMAP representation is preferred as UMAP preserves local and global structure in the data. Can authors show the similar segregation of clusters in UMAP representation?
- Authors are suggested to add a supplementary figure on Ly6C2 and CCR7 expression in CD27- gd T cells. This is just a supporting evidence.
- If I have understood the fig. 1E-F correctly, has the gene signatures mapped to the B-cell cluster as well, not only to naïve Vd1 and ab T cells?
- Line 281-283: I think, it is redundant and can be moved to supplement.
- Line 309: I am not sure if the authors are referring to Fig. 6F. It should be Fig. 6E, right?
- In all figures, labels need to be precise. For example, figure labels say "% Ly6C+ cells". I think, the authors want to represent "% Ly6C+ CD27+ gd cells", right? Similarly, for other protein markers.
- Fig. 3I needs to include a figure legend for color bars.
Significance
This study is a good example of single-cell genomic data can be used to find novel immune cell subsets and their functions. Such approaches can be combined with human immune cells. Thus, it brings direct significance and relevance to the basic understanding and translational approaches. However, such study needs in-depth examination of claims and observations.
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Referee #1
Evidence, reproducibility and clarity
Wiesheu et al. studied the maturation and functional maintenance of IFNg-producing γδ T cells in a mouse tumor model and by using NGS methods. The study provides insights into underlying mechanisms in the differentiation from Ly6C- to Ly6C+ γδ cells and their anti-tumor function in tissues. The study identifies IL-27 as one key cytokine that maintains these cytotoxic Ly6C+ γδ T cells. They further compare transcriptomes of murine and human γδ T cells. The study is well performed, and provides new insights into the functional differentiation of gd T cells in the tumor response. I have few remarks as outlined below.
Major comments:
Comparison of murine and human γδ T cells: This is an important point and should be addressed in more detail. In Figure 1e-f, the transcriptomes of mouse cells are nicely mapped to human transcriptomes. I agree that there are similarities between naive Vδ1 T cells and αβ T cells, but in Figure 1f it appears that Vγ9Vδ2 T cells lie between the two clusters. Could the authors analyse this in more detail? It is well known that Vγ9Vδ2 T cells are more innate than other γδ T cell subsets (e.g. Vd1 cells). I suggest that this should also be taken into account in the comparison. How do these results relate to the in vitro assays on human γδ T cells in Figure 7? Second, do the authors see common pathways to IL-27 responsiveness of murine and human γδ T cells? Are the molecules described in Fig. 6H also upregulated in human γδ T cells? Similarities and differences could be better described/discussed.
Survival of Ly6C+ γδ T cells and implications for functionality: In the adoptive transfer experiments, tumour-bearing mice receive repeated injections of Ly6C+ γδ T cells. Would the system work with one transfer to further test survival or survival supported by IL-27? Do the transferred cells remain functional or would they be exhausted?
Minor comments:
In Figure 2A the CCR7 staining looks very weak despite the use of FMO. In my opinion, an isotype control or a positive control for CCR7 (e.g. expression on ab T cells) should be included. The Figure 1f is poor in resolution.
Significance
The study provides new insights into the functional differentiation and maintenance of murine γδ T cells in the tumor response, and compares this to human γδ T cell subsets at steady-state. A special focus was set on Ly6C, which has been rarely addressed in the past (please see references within the manuscript). A more in depth comparison of murine and human γδ T cells would be beneficial to address a broad readership and an ongoing debate on the translation of findings in murine systems to human immunology.
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Reply to the reviewers
1. General Statements [optional]
We are happy to receive the comments from the reviewers and grateful for their suggestions on how to improve our manuscript. We note that both reviewers find the work extensive and meaningful.
Based on the comments from the reviewers, we have performed a comprehensive set of additional experiments, which will result in one additional figure and a substantial restructuring of two figures with new data, considerably expanding both the preclinical as well as the mechanistic findings of our manuscript.
In short, reviewer 1 finds that we have done extensive work to understand the role of CDK12/CDK13 in glioblastoma and would like to see additional mechanistic details. Reviewer 2 recognizes the value of our work in exploring the potential usefulness of CDK12/13 inhibition in treatment of aggressive brain tumors and would like to see additional experiments, which demonstrate the efficacy of CDK12/13 inhibition in complex environments to reinforce our proof-of-concept.
To address this feedback, our response plan includes two lines of experiments, which will strengthen both the preclinical and mechanistic parts of our work:
- A) We have established a migration assay using GSC G7 in organotypic mice brain slices and tested the effect of CDK12/CDK13 inhibition on glioma migration and we will include these data in the revised manuscript.
- B) To further understand the mechanisms involved in the transcriptional inhibition following CDK12/CDK13 inhibition on DNA replication in glioma cells, we have performed the following additional experiments:
- Comparative mass-spectrometry to identify changes in the total and phospho-proteome. This revealed that major regulators of DNA replication and repair are impaired following CDK12/CDK13 inhibition.
- iPond (Identification of proteins on nascent DNA) assays that demonstrate that CDK12/CDK13 inhibition changes the composition of replication forks, with a strong reduction PCNA abundance early after treatment. PCNA tethers the DNA polymerase catalytic unit to the DNA template ensuring rapid and processive DNA synthesis. This reduction of PCNA occurs before EdU incorporation/DNA replication is reduced, suggesting that loss of DNA polymerase clamping and processivity explains the subsequent arrest of DNA replication.
- DNA fiber assays showing that the origin firing is heavily downregulated in GSCs following CDK12/CDK13 inhibition. Further analyses using immunofluorescence microscopy reveal that the markers of DNA damage response and cell cycle progression are not affected following CDK12/CDK13 inhibition at early time-points, thereby ruling out activation of cell-cycle checkpoints and/or DNA damage response as potential explanation for replication block in GSCs following CDK12/CDK13 inhibition. The results from these experiments strengthen our main findings that inhibiting CDK12/CDK13 has a potential therapeutic value in glioblastoma treatment. Our work also offers mechanistic insights into how the glioblastoma stem cells have acquired transcriptional addiction to CDK12/CDK13 involving phosphorylation of RNAPII CTD, nascent RNA synthesis and DNA replication dependent on CDK12/CDK13 activity.
2. Description of the planned revisions
A point-by-point plan in blue is described below.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
*The authors in this manuscript studied the role of a transcriptional cyclin-dependent kinase CDK12/CDK13 in glioblastoma. These cyclin-dependent kinases phosphorylate at ser2 residue in the C-terminal of RNA Pol II. Pharmacological inhibition of CDK12/CDK13 kinase with inhibitor decreases cell proliferation in multiple glioma cell lines and in patient-derived organoids. The CDK12/CDK13 inhibitor also reduces tumor growth in a mouse xenograft model. Mechanistically, the authors showed that genome-wide inhibition of CDK12/CDK13 attenuates RNA Pol II phosphorylation, disrupting transcriptional elongation and decreasing cell cycle progression. So, the authors proposed that targeting CDK12/CDK13 kinases can be used as a therapeutic strategy in glioblastoma. The authors have done extensive work in this manuscript to understand the role of CDK12/CDK13 in glioblastoma, but it is still a descriptive paper lacking mechanistic details.
*
RESPONSE: We appreciate the reviewer’s recognition of the extensive efforts behind this manuscript, and we are thankful for being pointed towards strengthening the mechanistic insights. In brief, we would like to corroborate our key findings that inhibition of CDK12/CDK13 abrogates RNAPII phosphorylation, nascent RNA synthesis and DNA replication. We have expanded the mechanistic characterization using the following experiments:
- Using DNA fiber assay, we find that origin firing is heavily downregulated in GSCs following CDK12/CDK13 inhibition. Furthermore, we have done in-depth characterization of the effect of THZ531 treatment on cell cycle regulators and DNA damage response in GSCs, and found that these were not affected by CDK12/CDK13 inhibition within six hours. This indicates that activation of a cell cycle checkpoint or DDR machinery was not the reason for replication block.
- To further characterize the rapid effect of CDK12/CDK13 inhibition, we have done comparative mass spectrometry following CDK12/CDK13 inhibition in GSCs to identify changes in total and phosphorylated proteins and identified major regulators of DNA replication and repair machinery that are strongly affected.
- We have implemented iPOND (identification of proteins on nascent DNA) to study the effect of CDK12/CDK13 inhibition on protein composition at the replication fork. On this basis, we find that the abundance of the DNA clamp PCNA is substantially reduced after two hours of THZ531 treatment. PCNA tethers the DNA polymerases together on the fork and adds processivity to the speed of DNA replication. EdU incorporation was not affected by two hours of THZ531 treatment, and loss of PCNA from the replication fork is a likely explanation for the DNA replication block observed after six hours of THZ531 treatment.
*Comments: 1. Figure 1 shows that CDK12/CDK13 inhibitor decreases cell viability, colony-forming ability, cell competition assay, and cell migration. The rationale behind choosing CDK12/CDK13 inhibitor in glioma is unclear from the manuscript. What is the CDK12/CDK13 expression in multiple glioma cells vs non-glioma cells? The authors should include normal astrocytes as a control for cell viability assay. The p value is missing in numerous Figure panels. *
RESPONSE: We have investigated the possibility of targeting transcriptional regulation in glioma cells by using inhibitors targeting transcriptional cyclin-dependent kinases which included CDK7, CDK9 and CDK12/CDK13.
- We found that glioma cell proliferation was most sensitive to CDK12/CDK13 inhibitors compared to other cancer cells (Figure 1A), whereas there was no specificity for CDK7 and CDK9 inhibitors on glioma cell proliferation compared to other cancer cells (Supplementary figure 1D). The selective inhibition of glioma cells by CDK12/CDK13 inhibitors was the rationale for choosing CDK12/CDK13 inhibitors for further studies. This is mentioned in the introduction, and the result section has been updated to reflect this.
- We have performed expression analyses of CDK12/CDK13 at the mRNA levels using RT-qPCR in the cell lines that are used in the study, and we did not find any correlation of CDK12/CDK13 expression in glioma versus non-glioma cells (Supplementary figure 1B). Thus, the propensity of cells to become addicted to CDK12/13 signaling for their survival seems not related to total transcript levels, but must rely on the function of CDK12/CDK13 as a selective regulator of transcriptional program required for glioblastoma proliferation.
- We will perform the cell viability assays on normal astrocytes.
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p-values will be added in the figure panels.
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Figure 2A shows the expression of CDK12 by immunohistochemistry in glioblastoma tissues. Including the non-glioma tissue samples as another control and including a quantification graph with the statistics is essential. In Figure 2B-D, the authors discussed the treatment of glioma patient-derived organoids with CDK12/CDK13 inhibitors. From the Figure, the organoids are resistant to THZ531 and SR-4835 inhibitors. To rule out this possibility, the immunoblot assay with cleave PARP will be essential to execute. Again, statistics need to be included in Figure 2C-D. *
RESPONSE: We want to point out that the immunohistochemistry for non-glioma tissue and additional controls are shown in Figure 2A, top right panel and supplementary Figure 2A.
Regarding the next statement, we do not think that there is any indication that the organoids (GBOs) are resistant to THZ531 and SR-4835. We would like to stress that data presented on Fig 2B-D shows the efficacy of THZ531, abemaciclib and SR-4835 inhibitors in GBOs. GBOs showed high resistance only to lomustine. We apologize for any part of the figure which may lack clarity and lead to potential misconceptions. We would very much like to improve on this, if we are able to identify which figure component that may give the impression that the organoids are resistant to THZ531 and SR-4835. One option would be to remove the 0 hr time point in Figure 2B, if that is the cause for misinterpretation. To emphasize the drug efficacy better, we plan to perform the following amendments to the revised manuscript:
- We will provide statistical analysis of the IC50 and AUC analysis in the supplementary table xxx. These analyses will further highlight the robustness of the evaluation of drug responses in comparison to lomustine.
- We will provide one-way Annova comparison of the efficacy of the four assessed drugs in Fig 3D.
- The cell viability assay applied in GBOs is based on the CellTiterGlow technology, which is applicable to small organoid cultures of
- The mouse subcutaneous xenograft experiment was carried out in U87 cells with CDK12/CDK13 inhibitors. However, the glioma stem cells are a more appropriate model for glioma biology, and it is not clear why authors suddenly chose U87 cells. Again, statistics are absent in multiple sub-panels. *
* *RESPONSE: We note reviewer’s acknowledgement of using GSCs as a more appropriate model for glioma biology and we want to emphasize that in this work, we have used 15 different glioma patient derived glioma cells (11 GSCs in Figure-1 and 4 GBOs in Figure-2) from two different research environments to show that CDK12/CDK13 inhibition compromises glioma proliferation in vitro. GSCs/GBOs used in our study are xenografted orthotopically in the brain to model glioma in vivo and since our drugs do not sufficiently cross the BBB, the GSCs/GBOs were not considered for the in vivo validation and instead, a subcutaneous xenograft model was best to assess the efficacy of the drug(s). Considering that these models require a high number of cells (eight million cells per xenograft were used in our experiment), we had to base our decision on feasibility and chose a type of cells that could be propagated to the required extent. Considering the reviewer’s criticism, we are open to moving the xenograft data are presented to the supplementary section. Appropriate statistics will be done and shown.
- The authors have performed CUT & RUN experiments in G7 cells with CDK12/CDK13 inhibitors and decided to use 1hr and 6hr time points for the assay. Although the inhibitor THZ531 is supposed to inhibit RNA Pol II phosphorylation at the Ser2 residue, it decreases the Pol II phosphorylation at the ser5 residue quite a bit. Therefore, it is crucial to determine the effect coming from ser2 vs ser5 phosphorylation and gene expression regulation. **
*
RESPONSE: This is a good point. To address the relationship further, we will perform quantitation of Ser2 and Ser5 signals as well as the changes in these over time. We will then correlate this to the transcriptional changes to assess which of the relationships that are most strongly correlated. In addition, we will perform non-parametric statistical testing of significance of ranked data.
- There are a lot of supplementary Figures where axes are not labeled correctly or missing. **
*
RESPONSE: This will be addressed.
- The statistical section needs to be included in the manuscript. **
*
RESPONSE: This will be included.
*Reviewer #1 (Significance (Required)): **
In this manuscript, the authors studied the role of CDK12/CDK13 in glioblastoma and performed extensive studies to uncover the importance of these kinases in glioblastoma. Understanding more mechanistic details of how these kinases are involved in glioma progression will uncover more therapeutic opportunities in glioblastoma.
*
*Reviewer #2 (Evidence, reproducibility and clarity (Required)): **
*
*Summary: ** Lier et al. present a set of results showing that pharmacological inhibition of CDK12/13, cyclin-dependent kinases that phosphorylate RNA polymerase II (RNAPII), alters the proliferative behavior and transcriptional program of glioblastoma cells. A set of 2D and 3D cultures of patient-derived cell lines with stem-like properties (GSC), as well as subcutaneous xenografts of the U87 cell line, were used as in vitro and in vivo models, respectively. Among the CDKs tested, only CDK13 expression was found to be associated with worse patient survival, while CDK12-immunoreactive cells were detected in patient glioblastoma tissues. The response of GSCs to the CDK12 and CDK13 inhibitor TZH541 included cell cycle blockade and decreased migration. Reduction in RNAPII phosphorylation in TZH541-treated cells was verified using one of the GSC lines. Genome-wide exploration of the transcriptional consequences of TZH541 treatment of 2 GSCs using CUT&RUN and SLAM-seq technologies revealed major transcriptional repression, particularly of genes associated with cell proliferation. *
*Main comments: ** Although I found this study very interesting, I noted points requiring clarification, particularly in order to fully support the authors conclusions. My recommendations focus on the glioblastoma cell biology experiments, my area of expertise.
*
RESPONSE: We are grateful for the reviewer's keen interest in our manuscript and appreciate various insightful observations on the challenges within glioblastoma biology. Recognizing the necessity of validating CDK12/CDK13 requirements in complex environments, we have undertaken a migration assay using GSC, G7 cells in organotypic mice brain slices. The ongoing assessment of CDK12/CDK13 inhibition on glioma migration will be included in the revised manuscript. We have also more carefully explained how the organoid models used in this study address the requested need to recapitulate the complexity seen in the patient tissue and tumor environment. Moreover, we have related immunohistochemistry assessments of CDK12 levels to the proliferation marker Ki-67. Finally, we have strengthened the mechanistic insights provided in the manuscript by the inclusion of new proteomics data, iPond data on nascent chromatin, and chromatin fiber assays, altogether showing that replication origins firing as well as PCNA function is heavily reduced and identifying key proteins in DNA replication that are affected. These points are thoroughly discussed and explained in the comments below.
-
- The rationale for studying only CDK12 expression in patient glioblastoma tissues needs clarification. In contrast with CDK13, the authors found no association between CDK12 expression levels and patient survival (Sup Fig. 1A). Do the authors obtain similar results using independent datasets of glioblastoma tissue transcriptomes (e.g. CGGA)? With regard to the major effect of CDK12/13 inhibition on glioblastoma cell proliferation, determining whether CDK12/13 expression is observed in proliferating areas of the patients' tumor tissues (Ki67 IHC) would help support the authors' conclusion that their "results provide proof-of-concept for the potential of CDK12 and CDK13 as therapeutic targets for glioblastoma". The main data regarding CDK expression the status in patients' tumors and their possible association with patient survival should be rearranged in the same figure and described in the same paragraph of the results. * RESPONSE: We have performed our analyses on CGGA dataset, which matches with the TCGA data. We will show analyses from both TCGA and CGGA in Sup Fig. 1.
CDK12 and CDK13 are functionally redundant, which is one of the reasons that they do not score in genome-wide CRISPR/Cas9 dropout screens. As a result, GSC proliferation is only partially dependent on the individual expression of CDK12 and CDK13, as we observe in Figure 1E. However, GSCs are dependent on the combined CDK12/CDK13 activity and therefore are sensitive to inhibitors targeting both. Possibly, this functional redundancy makes the interpretation of the relationship between the individual expression of CDK12/CDK13 and glioma patient survival less straightforward.
With regards to the immunohistochemistry (IHC) staining evaluating the expression of CDK12 and CDK13 in glioma patient samples, we tested several antibodies for both CDK12 and CDK13. However, we were only able to identify an antibody for CDK12 which worked reliably in IHC.
We will perform Ki-67 IHC to test whether CDK12 expression matches with proliferative areas of the tumor tissues.
- Fig.1 caption "Inhibition of CDK12/13 specifically affects proliferation of glioma cells" is not entirely consistent with the results. This inhibition also appears to induce cell death, at least in some of the GSC tested, as indicated with cell counts (Fig. 1C., sup Fig.1 G) and an 8-fold increase in the % of apoptotic cells after a 24h-TZH treatment shown in Fig. 5E. All data concerning the effects of TZH on proliferation and survival (including detailed effects on the cell cycle) should be brought together rather than split between the 1st and last figure. *
RESPONSE: We appreciate these comments and will be addressed it in the manuscript.
*3. The reason for which serum-treated GSC were used should be explicated (sup Fig. 1C). Serum being usually used to trigger GSC "differentiation", did the authors want to verify whether CDK12/13 inhibitors affected GSC in a specific manner? If yes, it is necessary to demonstrate that serum-treated GSC have lost their stem-like properties. *
RESPONSE: This is a good point that we appreciate being able to expound on. GSCs are grown in serum-free media with N2 and B27 supplements together with EGF/FGFb whereas the control cells, including breast cancer and Hela/U2OS cells are grown in media containing serum. Serum-containing media was used to assess whether the diverse set of macromolecules present in serum would affect the bioavailability and/or response to the drug, and our data clearly demonstrated that this was not the case and that glioma stem cells are susceptible to the drug regardless of serum presence. In order to minimize the effect of serum on GSC differentiation, serum was added in the media immediately before the drug treatment.
- The viability of patient-derived 3D organoids (GBO) was assessed by measuring ATP production. It is therefore not possible to distinguish between decreased cell proliferation and increased cell death as responsible for the signal decrease. This limitation in the interpretation of the results needs to be made explicit. I was also misled by the use of GBO. This abbreviation is currently used to designate fragments of patient tumor tissue amplified in culture, which retain the cellular heterogeneity and the extracellular matrix of the original tumor and therefore provide an actual ex vivo model of the tumor. To avoid any misunderstanding, I recommend referring to experimental models obtained from dissociated patient-derived cell lines as "3D organoids" or "cellular spheroids", and avoiding to designate them as ex vivo models since they do not recapitulate the complexity of the tumor. *
RESPONSE: We apologize for providing insufficient details concerning our GBO modelling, and we have now updated the description in the methods to avoid misconceptions and unclarity. Our GBOs are not derived from cell lines. We derive GBOs from patient tumors by short-term culture of tissue fragments in 3D conditions. Such organoids are of a very primary nature and contain extracellular matrix and tumor microenvironment components. To avoid propagation in vitro, we perform implantation of GBOs to immunodeficient animals to create patient-derived orthotopic xenografts (PDOXs). We have established that serial propagation of patient material via series of short-term GBO cultures and PDOXs allow for multiplication of GBM patient tumors without major clonal selection and genetic/phenotypic adaptation (Golebiewska, 2020, DOI: 10.1007/s00401-020-02226-7). To perform robust drug screening ex vivo in GBOs, we further developed a specific protocol based on the material isolated directly from well-established and characterized PDOXs (Oudin, 2021, DOI: 10.1016/j.xpro.2021.100534). The protocol includes reconstitution of 3D GBOs of uniform size, which allows for reliable ex vivo readouts. Importantly, GBM primary cells are able to reassemble into 3D structures of heterogeneous nature, including reconstitution of extracellular matrix. In the revised manuscript, we will provide a clear description of the GBO modelling in the material and methods as well as in the associated results.
- Although the abstract contains a statement indicating that CDK12/13 genetic ablation inhibits cell migration, I did not find the corresponding results in the article. The demonstration that CDK12/13 inhibition decreases cell migration is weaker than the demonstration of its effect on proliferation. Contrary to the experiments evaluating cell proliferation, cell migration was assessed using a single technical approach. Moreover, the method used to assay TZH effects on cell migration rather measures cell motility than cell migration over long distances in a 3D and complex environment as observed in diffuse glioma. Since these data add nothing significant to the article, I would delete them. *
RESPONSE: We thank the reviewer for pointing out the comment in first sentence, which is addressed in the abstract now.
It is correct that strictly speaking our assay measured the effect of CDK12/CDK13 inhibition on glioma motility rather than migration, we have corrected this sentence in the abstract. We have however also now strengthened the methodology in the manuscript by establishing and using migration assays of GSC G7 cells on organotypic mouse brain slices. Organotypic mouse brain slices have a preserved cytoarchitecture that allows analysis of migration over longer distances in a physiological environment. We are currently analyzing the data. These results will be included in the revised manuscript.
- In my opinion, the information from the in vivo experiments is limited and should be presented in a supplementary rather than a main figure. The data were obtained with a single cell model, U87 cells of uncertain origin, and using subcutaneous xenografts that provide an environment totally different from the patient's actual tumor. In this context, the data obtained provide little information on the response of cancer cells in a complex and specific environment well known to promote tumor growth and resistance to therapies. I understand that the use of intracerebral xenografts is not feasible, since the inhibitor does not appear to reach the brain. With this technical limitation, an alternative would be to deliver the compound directly inside the brain tumor. A cannula can be implanted into the tumor after it has formed, and connected to an Alzet minipump filled with the drug. These experiments are technically difficult, however, and success is not guaranteed. Another alternative would be to use GBO, as described by Jacob et al (2019) as a surrogate for tumor tissue, provided the authors can obtain tissue fragments from patient surgical resections or intracerebral xenografts of patient-derived cell lines. These alternatives are optional. *
RESPONSE: We thank the reviewer for pointing out the difficulties in testing currently available compounds in vivo. Following the reviewers’ comments, we are open to placing the in vivo experiments in U87 xenografts in the supplementary material. We would like to reemphasize the clinical significance of our data in GBOs (please see the response above), which relies on models of equal complexity compared to the Jacob’s protocol and represent 3D compact and complex structures ex vivo derived from the GBM patient tumors propagated as orthotopic patient-derived xenografts.
Minor comments: ** - Fig. 4A and Fig. 5E-F: Results from a single experiment? If yes, they must be repeated at least once.
RESPONSE: They are representative of a minimum of three independent biological experiments, which will be mentioned in the manuscript.
*- For the sake of clarity, all y-axes in graphs presenting MTT or CellTiter-Glo assay results should be labeled "cell viability index", as they only provide a measure of overall cell or organoid metabolic activity, and thus an indirect assessment of cell viability. *
RESPONSE: We thank the reviewer for this suggestion and will incorporate it in the revision.
*- Statistical analyses are missing for 3 of the 4 cell lines presented in Figure 1F. *
RESPONSE: This will be addressed.
*- Some GO terms are truncated in sup Fig. 3. *
* *RESPONSE: This will be fixed in the revised ms.
- The legend to Fig. 5B-D shows the mean and SD of 2 replicates. Please show individual points.
RESPONSE: This suggestion will be addressed in the revision.
- Sup Fig1 D-F: unit of concentration is missing (M?) ** RESPONSE: This is addressed.
*Reviewer #2 (Significance (Required)): **
Significance: Despite growing interest in the roles of CDK12/13 roles in cancers and their targeting for cancer therapy, their involvement in glioblastoma growth remains unexplored. The results presented in this study outline the potential of CDK12/13 inhibition in controlling the growth of glioblastoma, at least in vitro, and thus provide meaningful information on its potential usefulness for this aggressive brain tumor with a high proliferation rate. Obtaining the full proof-of-concept that CDK12/13 constitute relevant targets for glioblastoma therapies will however require additional experiments demonstrating efficacy of CDK12/13 inhibition in complex environments, as encountered in the patients' tumor. *
3. Description of the revisions that have already been incorporated in the transferred manuscript
We have addressed following of the reviewers’ comments.
Reviewer-1:
- Major comment-1 is partially incorporated in the text.
-
Major comments-5 and 6 are incorporated. Reviewer-2:
-
Major comment 1 is partially addressed.
- Major comment 2, 3 and 4 are addressed in writing.
- Major comment 5 is partially addressed in writing.
- Major comment 6 is addressed.
- All minor comments are incorporated in writing.
4. Description of analyses that authors prefer not to carry out
-
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Referee #2
Evidence, reproducibility and clarity
Summary:
Lier et al. present a set of results showing that pharmacological inhibition of CDK12/13, cyclin-dependent kinases that phosphorylate RNA polymerase II (RNAPII), alters the proliferative behavior and transcriptional program of glioblastoma cells. A set of 2D and 3D cultures of patient-derived cell lines with stem-like properties (GSC), as well as subcutaneous xenografts of the U87 cell line, were used as in vitro and in vivo models, respectively. Among the CDKs tested, only CDK13 expression was found to be associated with worse patient survival, while CDK12-immunoreactive cells were detected in patient glioblastoma tissues. The response of GSCs to the CDK12 and CDK13 inhibitor TZH541 included cell cycle blockade and decreased migration. Reduction in RNAPII phosphorylation in TZH541-treated cells was verified using one of the GSC lines. Genome-wide exploration of the transcriptional consequences of TZH541 treatment of 2 GSCs using CUT&RUN and SLAM-seq technologies revealed major transcriptional repression, particularly of genes associated with cell proliferation.
Main comments:
Although I found this study very interesting, I noted points requiring clarification, particularly in order to fully support the authors conclusions. My recommendations focus on the glioblastoma cell biology experiments, my area of expertise.
- The rationale for studying only CDK12 expression in patient glioblastoma tissues needs clarification. In contrast with CDK13, the authors found no association between CDK12 expression levels and patient survival (Sup Fig. 1A). Do the authors obtain similar results using independent datasets of glioblastoma tissue transcriptomes (e.g. CGGA)? With regard to the major effect of CDK12/13 inhibition on glioblastoma cell proliferation, determining whether CDK12/13 expression is observed in proliferating areas of the patients' tumor tissues (Ki67 IHC) would help support the authors' conclusion that their "results provide proof-of-concept for the potential of CDK12 and CDK13 as therapeutic targets for glioblastoma". The main data regarding CDK expression the status in patients' tumors and their possible association with patient survival should be rearranged in the same figure and described in the same paragraph of the results.
- Fig.1 caption "Inhibition of CDK12/13 specifically affects proliferation of glioma cells" is not entirely consistent with the results. This inhibition also appears to induce cell death, at least in some of the GSC tested, as indicated with cell counts (Fig. 1C., sup Fig.1 G) and an 8-fold increase in the % of apoptotic cells after a 24h-TZH treatment shown in Fig. 5E. All data concerning the effects of TZH on proliferation and survival (including detailed effects on the cell cycle) should be brought together rather than split between the 1st and last figure.
- The reason for which serum-treated GSC were used should be explicated (sup Fig. 1C). Serum being usually used to trigger GSC "differentiation", did the authors want to verify whether CDK12/13 inhibitors affected GSC in a specific manner? If yes, it is necessary to demonstrate that serum-treated GSC have lost their stem-like properties.
- The viability of patient-derived 3D organoids (GBO) was assessed by measuring ATP production. It is therefore not possible to distinguish between decreased cell proliferation and increased cell death as responsible for the signal decrease. This limitation in the interpretation of the results needs to be made explicit. I was also misled by the use of GBO. This abbreviation is currently used to designate fragments of patient tumor tissue amplified in culture, which retain the cellular heterogeneity and the extracellular matrix of the original tumor and therefore provide an actual ex vivo model of the tumor. To avoid any misunderstanding, I recommend referring to experimental models obtained from dissociated patient-derived cell lines as "3D organoids" or "cellular spheroids", and avoiding to designate them as ex vivo models since they do not recapitulate the complexity of the tumor.
- Although the abstract contains a statement indicating that CDK12/13 genetic ablation inhibits cell migration, I did not find the corresponding results in the article. The demonstration that CDK12/13 inhibition decreases cell migration is weaker than the demonstration of its effect on proliferation. Contrary to the experiments evaluating cell proliferation, cell migration was assessed using a single technical approach. Moreover, the method used to assay TZH effects on cell migration rather measures cell motility than cell migration over long distances in a 3D and complex environment as observed in diffuse glioma. Since these data add nothing significant to the article, I would delete them.
- In my opinion, the information from the in vivo experiments is limited and should be presented in a supplementary rather than a main figure. The data were obtained with a single cell model, U87 cells of uncertain origin, and using subcutaneous xenografts that provide an environment totally different from the patient's actual tumor. In this context, the data obtained provide little information on the response of cancer cells in a complex and specific environment well known to promote tumor growth and resistance to therapies. I understand that the use of intracerebral xenografts is not feasible, since the inhibitor does not appear to reach the brain. With this technical limitation, an alternative would be to deliver the compound directly inside the brain tumor. A cannula can be implanted into the tumor after it has formed, and connected to an Alzet minipump filled with the drug. These experiments are technically difficult, however, and success is not guaranteed. Another alternative would be to use GBO, as described by Jacob et al (2019) as a surrogate for tumor tissue, provided the authors can obtain tissue fragments from patient surgical resections or intracerebral xenografts of patient-derived cell lines. These alternatives are optional.
Minor comments:
- Fig. 4A and Fig. 5E-F: Results from a single experiment? If yes, they must be repeated at least once.
- For the sake of clarity, all y-axes in graphs presenting MTT or CellTiter-Glo assay results should be labeled "cell viability index", as they only provide a measure of overall cell or organoid metabolic activity, and thus an indirect assessment of cell viability.
- Statistical analyses are missing for 3 of the 4 cell lines presented in Figure 1F.
- Some GO terms are truncated in sup Fig. 3.
- The legend to Fig. 5B-D shows the mean and SD of 2 replicates. Please show individual points.
- Sup Fig1 D-F: unit of concentration is missing (M?)
Significance
Despite growing interest in the roles of CDK12/13 roles in cancers and their targeting for cancer therapy, their involvement in glioblastoma growth remains unexplored. The results presented in this study outline the potential of CDK12/13 inhibition in controlling the growth of glioblastoma, at least in vitro, and thus provide meaningful information on its potential usefulness for this aggressive brain tumor with a high proliferation rate. Obtaining the full proof-of-concept that CDK12/13 constitute relevant targets for glioblastoma therapies will however require additional experiments demonstrating efficacy of CDK12/13 inhibition in complex environments, as encountered in the patients' tumor.
-
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Referee #1
Evidence, reproducibility and clarity
The authors in this manuscript studied the role of a transcriptional cyclin-dependent kinase CDK12/CDK13 in glioblastoma. These cyclin-dependent kinases phosphorylate at ser2 residue in the C-terminal of RNA Pol II. Pharmacological inhibition of CDK12/CDK13 kinase with inhibitor decreases cell proliferation in multiple glioma cell lines and in patient-derived organoids. The CDK12/CDK13 inhibitor also reduces tumor growth in a mouse xenograft model. Mechanistically, the authors showed that genome-wide inhibition of CDK12/CDK13 attenuates RNA Pol II phosphorylation, disrupting transcriptional elongation and decreasing cell cycle progression. So, the authors proposed that targeting CDK12/CDK13 kinases can be used as a therapeutic strategy in glioblastoma. The authors have done extensive work in this manuscript to understand the role of CDK12/CDK13 in glioblastoma, but it is still a descriptive paper lacking mechanistic details.
Comments:
- Figure 1 shows that CDK12/CDK13 inhibitor decreases cell viability, colony-forming ability, cell competition assay, and cell migration. The rationale behind choosing CDK12/CDK13 inhibitor in glioma is unclear from the manuscript. What is the CDK12/CDK13 expression in multiple glioma cells vs non-glioma cells? The authors should include normal astrocytes as a control for cell viability assay. The p value is missing in numerous Figure panels.
- Figure 2A shows the expression of CDK12 by immunohistochemistry in glioblastoma tissues. Including the non-glioma tissue samples as another control and including a quantification graph with the statistics is essential. In Figure 2B-D, the authors discussed the treatment of glioma patient-derived organoids with CDK12/CDK13 inhibitors. From the Figure, the organoids are resistant to THZ531 and SR-4835 inhibitors. To rule out this possibility, the immunoblot assay with cleave PARP will be essential to execute. Again, statistics need to be included in Figure 2C-D.
- The mouse subcutaneous xenograft experiment was carried out in U87 cells with CDK12/CDK13 inhibitors. However, the glioma stem cells are a more appropriate model for glioma biology, and it is not clear why authors suddenly chose U87 cells. Again, statistics are absent in multiple sub-panels.
- The authors have performed CUT & RUN experiments in G7 cells with CDK12/CDK13 inhibitors and decided to use 1hr and 6hr time points for the assay. Although the inhibitor THZ531 is supposed to inhibit RNA Pol II phosphorylation at the Ser2 residue, it decreases the Pol II phosphorylation at the ser5 residue quite a bit. Therefore, it is crucial to determine the effect coming from ser2 vs ser5 phosphorylation and gene expression regulation.
- There are a lot of supplementary Figures where axes are not labeled correctly or missing.
- The statistical section needs to be included in the manuscript.
Significance
In this manuscript, the authors studied the role of CDK12/CDK13 in glioblastoma and performed extensive studies to uncover the importance of these kinases in glioblastoma. Understanding more mechanistic details of how these kinases are involved in glioma progression will uncover more therapeutic opportunities in glioblastoma.
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The authors Martiěnez-Balsalobre and colleagues found that the regenerative capacity of the zebrafish caudal fin is not limited by the lack of telomerase and showed that the length of telomeres does not decrease substantially after repeated amputations in telomerase-deficient zebrafish. These findings prompt the authors to explore an alternative mechanism that would explain the maintenance of telomere length in this regeneration setting. They produced suggestive evidence for the role of the ALT (Alternative Lengthening of Telomeres) mechanism in the maintenance of telomere length in the absence of telomerase in a regeneration setting.
In my view, several points need to be addressed and clarified.
**There are three major points:**
1.When working with tert mutants, the age at which these fish show a telomere phenotype (namely, loss of body mass and reduced fertility) varies. Therefore, it would be important to state if the fish used in this study were already showing these phenotypic characteristics at each time point studied, namely 4, 8 and 11 months of age
The premature aging phenotype of tert mutant fish has been previously characterized in the paper by Anchelin et al 2013 referenced in the manuscript. We used young fish with no phenotype (4 months old), and aged fish (8 and 11 months old) presenting the already described premature aging phenotypes, such as spinal curvature, loss of fertility, loss of body mass and loss of pigmentation.
The following sentence regarding this has been included in the revised version of the manuscript.
“The fish used showed non-detectable aging phenotype at 4 months old, whereas at 8- and 11-months fish presented the typical tert mutant premature aging phenotypes, i.e. backbone curvature, loss of body mass and hypopigmentation”
2.The knockdown experiments were performed using morpholinos. To confidently use morpholinos it is fundamental to demonstrate first their knockdown efficiency and their specificity. This is lacking in the manuscript.
In this work we have used 3 different morpholinos; tert morpholino has been already used and characterized in the work by Imamura and collaborators in 2008. atr morpholino has been already used and characterized in the paper by Stern at al., 2005.
However, nbs1 morpholino has been designed for this work. A Supplemental Figure (Figure S2) and the following paragraph have been added in the revised version of the manuscript to show the knock-down efficiency of the nbs1 morpholino:
“The knock-down efficiency of the atr morpholino was characterized by Stern and colleagues (Stern et al, 2005). The injection of the nbs1 morpholino in zebrafish eggs resulted in the reduction of the expression of nbs1 mRNA at 3dpf (Fig. S2A). Furthermore, PCR using cDNA as a template detected nbs1 mRNA species that retained the intron one of the gene as a result of the morpholino effect in blocking the splicing (Fig. S2B).
“The tert morpholino knock-down efficiency has been already showed (Imamura et al, 2008)”
3.The involvement of ALT mechanism in the regeneration process in the absence of telomerase is only suggestive, as the authors show an increase of C-circles and heterogenous telomerase length in telomerase-deficient zebrafish but when trying to establish a functional link the authors resort to the knowndown of genes that may be associated with ATL. Looking at the levels of TERRA and the number of C-circles in the knowndown caudal fins would be essential for their claim.
We have now performed caudal fin regeneration experiments in tert mutant fish microinjected with mo-atr and mo-nbs1 and analyzed the levels of TERRA RNAs and C-circles amount. The results are shown in Supplemental Figure S4. As expected, regeneration capacity decreased in fish microionjected with both morpholinos compared to control fish (FigS4 F). Consistently, TERRA RNAs levels, as well as C-circles amount, increased in the regenerating tissue and this induction was lower when atr and nbs1 gene expression was downregulated by mo-injection (Fig S4 G-J). Taking altogether, these results indicate that ALT mechanism is induced upon amputation and operates in the regenerating tissue of tert deficient fish.
**And several other points:**
4.The regeneration experiments were performed at 32 degrees and this option was never explained nor discussed.
The regeneration experiments in zebrafish typically are performed at 32 °C to accelerate regeneration process. Otherwise, the amount of regenerated blastema at 48 hpa or 72hpa would not be enough to perform any kind of analysis. Furthermore, it could happen that some experimental modifications, for instance the effects of the morpholino injection, do not last if the regeneration process is kept more than 84-96hpa at 28 °C.
This procedure have been used previously by other laboratories (PMID: 8601496, Johnson and Weston,1995; PMID: 12015289 Nechiporuk et al.,2003 and PMID: 16273523 Thumnel et al 2006) to increase the rate of regeneration approximately two fold, a temperature of 33°C was used for the regeneration experiments. In addition, It has been demonstrated normal regeneration at 33°C in wild-type fish
5.When referring to the ALT mechanism, the authors state that "... in about 10% of tumors cells, telomere length is maintained by the Alternative Lengthening of Telomeres (ALT) mechanism ..." and I think it would be more accurate to talk about cancer cells instead of tumor cells.
This has been corrected in the revised version
6.The sentence about C-circles is incorrect. C-circles are mostly single-stranded and not double-stranded as stated.
This has been corrected in the revised version
7.After Figure 2, the authors never mention the age of the fish used.
All the fish used in the amputation experiments after Fig2 are 4 -6 months of age
8.In Figure 1A. The site of amputation does not fit the one described in Mat & Met that states 2 cm from the base of the caudal peduncle. The same stands for Figure 2A.
This is corrected in the new version with a new Figure 1A and 2A
9.In Figure 1B
The Y axis should be named regeneration area instead of rate as the values are a percentage of the area reached after a certain time point after amputation. The same stands for Figure 2B, C. It would be nice to see the real caudal fin images for the relevant time points: before amputation, 0 dpa, when the fins reach 50% of regeneration area and then the last time point.
This has been changed in the new version
The authors should discuss why are the caudal fins reaching more than 100% of regeneration are
This is an intriguing question for which we currently lack an answer. Nonetheless, it does not impact the focus of our ongoing study
10.In Figure 2B. The meaning of ". .. ." on the right side of the graph is not clear. The same stands for Figure 2D.
This has been a mistake when handling the figure folder and has been corrected in the revised version
11.In Figure 2C .Why is the clip 10, 11 and 12 missing from the tert+/- and tert-/- ?
This has been changed in the new version and recalculated the statistical significance. We appreciate the feedback
12.In Figure 2E The proximity of all points at the 12 Clip is indicative of lack of statistical significance, therefore the **** related to which comparisons?
We have modified the data of fig 2E and recalculated the statistical significance
13.In Figure 2D, E
For the measurement of telomere length, the authors state that "Data are average of at least 2 independent experiments." What does this mean exactly? How many animals were used in each experiment?
In the experiments in Fig2, 6 fish total were used per group sampled in at least 2 independent experiments. This has been included in the figure legend and in the Mat&Met section
14.In Figure 3
The authors state that "Data are average of at least 2 independent experiments." What does this mean exactly? How many animals were used in each experiment?
The experiment in Fig 3A was done 3 times with 2 fish per group pooled in each experiment. The telomere length experiment has been done 2 times. This has been added to the figure legend and to the Mat&Met section.
Why were the c-circles evaluated at hpa while the telomere length evaluated at dpa? This should be discussed.
We expect to observe an effect on telomere length after several days of continuous cell proliferation in order to completely regenerate the caudal fin. However, the presence of C-circles in the regenerating tissue is expected to be found as early as 24hpa as a consequence of the action of the ALT mechanism of telomere maintenance, which has to be active from the very beginning. The following sentence has been included in the Discussion section: “ALT activation is expected to happen, and in fact detected, very early in the regeneration process, and eventually results in telomere length heterogenicity several days after amputation, when a lot of cell divisions and telomere recombination have occurred”.
15.In Figure 3A
The meaning of ". .. ." on the top side of the graph is not clear.
t0 should be removed and replaced by 0 hpa and 24hpa and 48hpa for coherence.
This has been a mistake when handling the figure folder and has been corrected in the revised version.
16.In Figure 3B,C 0 hpa replace by 0 dpa
This has been replaced in the new version
17.In Figure 3B
The blue and red stainings in the panels are labelling exactly what? This should be stated in the image and in the legend.
Red staining represents the telomeres and the blue staining are the nuclei. It is shown in the Figure and stated in the figure legend.
18.In Figure 3D
There is a mistake in the legend the should be corrected as follows "Very long telomeres have a higher fluorescence of 200,000 AUF and very short telomeres have a lower fluorescence of 30,000 AUF."
This has been corrected
19.In Figure 4
t0 should be removed and replaced by 0 hpa.
This has been corrected
The meaning of ". .. ." on the top side of the graph is not clear.
This has been a mistake when handling the figure folder and has been corrected in the revised version
The title is an overstatement, as the genes studied are DNA damage genes that may associate with ALT.
The title has been corrected to “The expression of ALT-associated genes is modulated in regenerative tissue of”
20.In Figure 4A, B
The expression of nbs1 and atr in tert-/- increases at 48hpa but the same seems to be true for the tert+/+ and this is never discussed by the authors.
This result would support the idea that both telomerase-dependent and ALT mechanisms operate in the regeneration process in a wild type animal. A sentence in the results and discussion sections has been added to mention and discuss this point:
“These genes were quantified in the regenerated tissue at 24 and 48 hpa. nbs1 and atr mRNA levels increased in telomerase deficient fish at 48 hpa compared to time 0 (0hpa) (Fig. 4A, 4B). The same effect in the expression of these genes was found in wild type fish regenerating fins. Interestingly, atrx and daxx expression decreased (Fig. 4C, 4D) at 24 and 48 hpa, in agreement with published data on ALT in cells (Amorim et al., 2016; Ren et al., 2018; Yost et al., 2019).”
“Curiously we observed an increased expression of ALT activator proteins in both wild type and telomerase deficient zebrafish, and a decrease in ALT inhibitor proteins suggesting that the main players of ALT and their mechanisms are conserved during evolution, and that both mechanisms of telomere maintenance could co-exist in the regeneration process in wild type fish”..
21.In Figure 4C, D
The differences in the expression of atrx and daxx decreases over time in a in tert-/- and this is never discussed by the authors.
As mentioned, and referenced in the manuscript, the proteins are ALT inhibitors, and mutations in these proteins are described to be promoting the activation of ALT mechanisms. Thus, it is expected that in the regenerating fins where ALT is activated, their expression decreases.
22.In Figure 5
An ideal control would be the direct comparison between microinjected+electroporated mo-std in the ventral part of the fin while the dorsal part would be microinjected+electroporated with the mo-gene of interest. This would discard any effect of microinjection+electroporation in the regeneration efficiency.
These experiments are not convincing to show that there is an ALT mechanism is operating here. What this experiment shows if the relevance of these genes for the regenerative capacity of the caudal fin. To show that this is related to the ALT mechanism the authors should investigate the C-circles in these regenerating fins.
We have performed regeneration experiments using WT fish to address this issue. We analyzed the regenerated area of control and morpholino injected fish and then obtained regenerating blastema and analyzed the expression of tert and atr. The results are shown in Supplemental Figure S4 (A-E). The regeneration capacity is inhibited in tissues injected with a mix of mo-std+mo-ter, a mix of mo-std+mo-atr, or a mix of mo-tert+mo-atr compared with a control injected with a double dosis of mo-std (std 2x, Fig S4B). In addition, the expression of tert and atr is decreased in the regenerated blastema upon morpholino injection (Fig S4 C and D) indicating that the genetic inhibition of the expression of these genes was efficient. Finally, the levels of TERRA RNAs are increased upon amputation and this induction is reduced when we mo-atr or a combination of mo-atr+tert were microinjected (Fig S4E).
We have also performed caudal fin regeneration experiments in tert mutant fish microinjected with mo-atr and mo-nbs1 and analyzed the levels of TERRA RNAs and C-circles amount. The results are shown in Supplemental Figure S4. As expected, regeneration capacity of the caudal fins of fish microionjected with both morpholinos decreased compared to control fish (Fig S4 F-H). Consistently, TERRA RNAs levels, as well as C-circles amount, increased in the regenerating tissue and this induction was lower when atr and nbs1 gene expression was decreased by mo-injection (Fig S4 I and J). Taking altogether, these results indicate that ALT mechanism is induced upon an injury and operating in the regenerating tissue of both wild type and tert deficient fish.
The amputation red lines are not placed in the exact amputation position in some of the panels.
Regeneration rate should be regeneration area.
This has been corrected
23.In Figure 5C, E
Why is the mo-tert more inhibitory of regeneration (Figure 5E - around 30%) than the tert-/- mutant (Figure 5C - around 60%)? This should be discussed.
This point is now discussed: “
24.In Figure 6A
The 2 adult zebrafish shown in the tank with the ATR inhibitor IV should have an amputated caudal fin.
This has been modified
Control is exactly what? Untreated? Treated with vehicle?
The control is fish treated with the same amount of DMSO (vehicle). This is now shown in the panel
Why was the ATR inhibitor IY added immediately after fin amputation while the mo-atr was injected at 48 hpa?
The ATR inhibitor was added immediately after amputation because ALT is then inhibited from the starting of the regeneration process. However, in the case of the atr morpholino we need some regenerated tissue to perform the microinjection within and inhibit atr expression specifically in this tissue.
25.Figure 6D, E, F
These panels are a bit out of the focus of this paper. If presented should go to a supplementary figure.
These panels are now moved to the Supplemental Figure S5
26.In Figure S2
The relevant bands should be identified.
We have performed new regeneration experiments in wild type adult fish using ATR inhibitor. The results show that treating fish with ATR inhibitor provokes a clear decrease in the overall phosphorylation status of ATR/ATR substrates within the regenerated tissue (Figure 5B and C). In this case, the intensity of the whole lane was used for quantification.
The gel identifies DMSO, 10uM and 50 uM but the quantification graph identifies Control, 50uM and 100uM.
This has been corrected in the new version
There are no error bars
In the new experiments are now shown.
The authors say that the quantification of various western blot bands was done but how many exactly?
In the new experiments, 3 western blots are quantified
27.In Figure S3
The primers for rps11 are repeated twice.Were these primers design de novo by the authors or did they used previous reported primers, in this case the references should be given.
Tert F2 and R1 should be replaced for F and R for consistency.
This has been corrected and references for the primers used are added in the new Supplemental Figure S6
28.In Figure S4
The sequence of tert mo is missing.
This has been corrected
29.In the methods the genotyping protocol of tert mutants is not described.
A protocol for genotyping the tert deficient zebrafisn has been added in the Mat&Met section.
30.The method to calculate the area of the fin pre- and post-amputation is not described.
The method is already described in the Mat&Met section: “In order to calculate the percentage area of growth between the injected and non-injected part, the values were inserted in the following formula: (Dorsal 48 hpi - Dorsal 0 hpi)/(Ventral 48 hpi -Ventral 0 hpi)*100, where Dorsal is the regenerative area of the MO-treated tissue and Ventral is the regenerative area of the corresponding uninjected half”
Reviewer #1 (Significance (Required)):
The manuscript by Martiěnez-Balsalobre and colleagues deals with a very interesting question on the importance of telomere lengthening during regenerative processes and its relation to ageing. To this end the authors made use of the tert mutant, a telomerase-deficient zebrafish. The authors show a surprising phenotype that telomerase-deficient zebrafish can still regenerate their caudal fins and are able to maintain telomere length during consecutive amputations and I say surprising because it has been shown that telomerase-deficient zebrafish are unable to regenerate their hearts efficiently.
Taking these novel findings, the authors propose that in the zebrafish caudal fin and in the absence of telomerase, telomere length is maintained through the activation of an alternative mechanism called ALT. To my knowledge, the role of ALT as a mechanism of telomere lengthening has never been described in the context of regenerating organs in zebrafish.
We fully appreciate the reviewer´s comments on the significance of the manuscript!
**Referees cross-commenting**
I agree with the comments made by the other reviewers. I would stress the need to tone down the role of ALT during fin regeneration in zebrafish as all the experiments are only indicative of the possible of the involvement of ALT.
We have conducted additional experiments that further support the involvement of ALT. Please read the responses to the other reviewers for more details.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Using zebrafish as a model for regeneration, the authors find that telomere maintenance by recombination can occur in the absence of telomerase.
Title to Figure 4 perhaps may be too strong, 'ALT mechanism is activated', since only a few features of ALT are assessed. Perhaps, 'ALT features are activated'?
The title to Figure 4 has been changed to “The expression of ALT-related genes is modulated…”
mRNA levels of NBS, ATR are also increased in WT animals (Figure 4A and 4B), but ATRX and DAXX mRNA levels are not decreased in WT animals. Is the increase why the authors in part suggest that ALT is being used in WT animals. If so, what would be the trigger for the use of ALT, as opposed to the trigger to use ALT in tert-/- animals?
Our results indicate the utilization of both telomere maintenance mechanisms to support cell division in regenerative fins among wild-type animals. Consequently, we propose that the signals instigating regeneration are shared between both mechanisms and are present in both wild-type and tert-deficient animals, albeit with varying degrees of contribution.
In Figure 5C, if tert-/- animals are downregulated for nbs1 and atr, would it be expected that the effect on regeneration be more pronounced compared to tert+/+ downregulated for nbs1 and atr than what is observed?
We agree with the reviewer comment, and that is what actually happens. The inhibition of the regeneration in wild type fish is about 40% in mo-nbs1 injection and around 70% in mo-atr injected animals. However, in tert mutants, the decrease in regeneration observed in mo-nbs1 injection is about 56%, whereas is 82% in mo-atr injection.
What are the telomere lengths in tert-/- animals treated with mo-atr or mo-nbs1 or in tert+/+ animals treated with mo-tert and mo-atr compared to singly treated?
The telomere length does not change in mo-atr or mo-nbs1 injected tert mutants compared to mo-std animals.
The telomere length in mo-tert and mo-atr injected wild type animals does not change compared to mo-std injected animals.
This results are now shown in Supplemental Figure S3
Reviewer #2 (Significance (Required)):
Reported findings are novel, timely and model of possible therapeutic value for screening compounds for ALT and/or telomerase inhibitors. Mechanisms of co-existence of ALT and telomerase can also be explored using this model.
We fully appreciate the reviewer´s comments on the significance of the manuscript!
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
**Summary:**
Martinez-Balsalobre have examined caudal fin regeneration following surgical transection in WT and telomerase-deficient (tert+/- and tert-/-) zebrafish adults of several ages, and in one experiment, in embryos. They conclude: (1) regeneration efficiency decrease with aging in all genotypes (2) telomere length is maintained, even in a tert-mutant background (3) ALT (alternative lengthening of telomeres) is involved in supporting cell proliferation in tailfin regeneration. The experimental system employs a quantitative area-based measurement as a measure of the degree of regeneration. Functional studies used antisense morpholino gene knockdown and chemical inhibition to implicate ALT involvement.
**Major comments:**
The experimental logic is appropriate, and in general, the data support the conclusions. Strengths of the work include: (1) The quantitative measure of % regeneration appears to be quite objective; (2) the internally controlled experimental design of the morpholino knockdown experiments of Fig 5.
We thank the reviewer for the comment
The Western blot in Fig S2 has some issues. The image is a montage. The experiment appears to have been done only once. The band's identifications by kDa are imprecise (where is the 82 kDa band on the gel? - there are bands smaller and larger than 82 kDa, but none of 82kDa; the 50 kDa band is close to background; the DMSO lane is underloaded relative to the two test lanes (but as the observation is a reduction in the test samples, this does not result in a misinterpretation). What concentration of ATRinhIV were used? The blot has 10 and 50 microM, Fig S1B has 50 and 100 microM, and the text says 1-50 microM).
We have performed new regeneration experiments in wild type adult fish using ATR inhibitor. The results show that treating fish with ATR inhibitor results in a clear decrease in the overall phosphorylation status of ATR/ATR substrates within the regenerated tissue (Figure 5B and C). In this case, the intensity of the whole lane was used for quantification. As mentioned in the text, we used concentrations of 1, 10 and 50 microM, but we do not observe any difference with the 1 microM concentration, thus do not show it. Then we measured the regeneration capacity in both wild type and tert mutant fish using 10microM concentration
The MO-knockdown studies are interpreted as showing synergy of atr and tert knockdown.
There are two problems with them interpretation of synergy: (1) the single result of a greater effect with both MOs does not distinguish between an additive or synergistic effect (and synergistic action is by definition a greater than additive action;
We agree with the reviewer´s comment, and have removed the sentence “Interestingly a synergistic effect was observed when both mechanisms are inhibited” from the Results section.
(2) MO dose is not controlled by a group with an equal total MO doses (mo-std+mo-atr and mo-std+mo-tert). While acknowledging that the issues of using local MO delivery in an adult model are very different from global delivery in an embryonic model, the "synergy" interpretation still requires these experiments/controls be done. These experiments were not accompanied by any molecular evidence that either of the morpholinos targeted expression of the intended gene (which would likely have to be derived from their assessment in another system) - a control that can be challenging, but one that is regarded as essential in the field (https://doi.org/10.1242/dev .001115 ). While this will be difficult to do in the adult setting, it is still appropriate to validate the activity/molecular efficacy of the MO sequence in an experimentally tractable scenario. The specificity of this experiment and interpretation would also be enhanced and corroborated independently by undertaking the atr knockdown in the tert -/- mutant background. Overall, these experiments were preliminary and require further work that could be done withiin 3 months.
We have performed regeneration experiments using WT fish to address this issue. We analyzed the regenerated area of control and morpholino injected fish and then obtained regenerating blastema and analyzed the expression of tert and atr .The results are shown in Supplemental Figure S4 (A-E). The regeneration capacity is inhibited in tissues injected with a mix of mo-std+mo-ter, a mix of mo-std+mo-atr, or a mix of mo-tert+mo-atr compared with a control injected with a double dosis of mo-std (std 2x, Fig S4B). In addition, the expression of tert and atr is decreased in the regenerated blastema upon morpholino injection (Fig S4 C and D) indicating that the genetic inhibition of the expression of these genes was efficient. Finally, the levels of TERRA RNAs are increased upon amputation and this induction is reduced when we mo-atr or a combination of mo-atr+tert were microinjected (Fig S4E).
We have also performed caudal fin regeneration experiments in tert mutant fish microinjected with mo-atr and mo-nbs1, and analyzed the levels of TERRA RNAs and C-circles amount. The results are shown in Supplemental Figure S4. As expected, regeneration capacity of the caudal fins of fish microionjected with both morpholinos decreased compared to control fish (Fig S4 F-H). Consistently, TERRA RNAs levels, as well as C-circles amount, increased in the regenerating tissue and this induction was lower when atr and nbs1 gene expression was decreased by mo-injection (Fig S4 I and J). Taking altogether, these results indicate that ALT mechanism is induced upon an injury and operating in the regenerating tissue of both wild type and tert deficient fish.
Note - the tert MO sequence is missing from the table in Fig S4.
The sequence has been added
The adult experiments have used n=6-10 animals/group. There is no consideration of statistical power (is the analysis of Fig 1C adequately powered?).
The type of statistical test applied in Fig 1C (2-way ANOVA, plus Dunnett´s post-test) compares means of every clip among the 3 genotypes. This is the test that is recommended for this kind of data and experiment.
The degree and nature of replication is not clear in all cases. For example, in Fig 1, were the 6 fish run as one cohort of 6 animals in parallel (which would be just one experiment with 6 animals, each animal being a biological replicate), or were there 6 animals injured at different times (representing multiple independent experiments and represented a greater degree of reproducibility), or something in between. A similar question applies to the other figures.
In the experiments, 6 fish total were used per group sampled in at least 2 independent experiments. This has been included in the figure legend and in the Mat&Met section
For the experiment of Fig 6F, although there are >=100 larvae per group, it is not clear that this experiment has been done more than once.
In the conducted experiments, three independent trials were conducted. The total number of larvae per group utilized in each of the three distinct experiments surpassed 100 larvae per group (approximately 40 larvae in each independent experiment). This data has been incorporated into both the figure legend and the Materials and Methods section."
A few comments about data presentation. "Regeneration rate" and its derivatives are presented as mean +/- SEM. The parameter measured is correctly defined in methods as "Percent fin regeneration", however the graphs where it is plotted have the y-axis labelled as "regeneration rate (%)" (for example. Fig 1B), which is incorrect. The plotted parameter is not a rate - although there is a time dimension (x-axis), what is plotted at each time point is "% regeneration".
This has been corrected and y-axis is now labeled as Regeneration area (% of initial fin area.
Also, in most figures, such as Fig 1B and 1C, mean +/- SD would be more appropriate, as here each of the n=6 data points represents a single observation from one individual in the population, not the mean of 6 small samples of groups of individuals from the population. Furthermore, at these small n-values (6-10 through the report), scatter plots are considered a more appropriate way of displaying the data (some succinct references: DOI: 10.4103/2229-3485.100662 ; from a Nature group journal DOI: 10.1038/s41551-017-0079 ; from a PLOS journal https://doi.org/10.1371/journal.pbio.1002128 ).
This was a mistake in the figure legend, since Fig 1B was already showing mean +/- SD. Fig 1C is now showing mean+/- SD and has been represented with scatter plots.
The use of mean +/- SEM in Fig 4 could be appropriate, but as n is "at least two independent experiments" scatter plots would again be appropriate. Readers would then know which data sets had only two values.
In two instances, the same data are presented in two different ways (Fig 1B, 1C; the column graphs and arrows of Fig 3D).
Fig4 is presented now as scatter plot graphs
How does "data are average of at least 2 independent experiments" apply to Fig 3C?
In the experiments in Fig3C, “Data are average of 2 independent experiments of 3 fish per group pooled”. This has been included in the figure legend.
**Minor comments:**
The paper is written clearly overall. There are multiple minor grammatical/typographical errors, but these did not detract from understanding the manuscript. These were most abundant in the discussion.
A few points:
Discussion p1 - what is meant by "prematurely aged 11-month fish"
This refers to 11 months old tert-/- fish, which has been shown to present accelerated aging features at this age compared to wild type
Discussion p2 - you mean "doubled" rather than duplicated?
Yes; this has been corrected in the new version
tert +/+, tert +/- and tert -/- genotypes for experiments - how were these obtained and genotypically verified? (heterozygous incrosses? WT x homozygous mutant outcrosses?)
All the fish adult fish of the 3 genotypes were obtained from heterozygous incrosses. Then fish were genotyped by PCR. A protocol for genotyping has been added in the Mat&Met section. The wild type larvae used in the tail fin regeneration experiments inhibiting ATR were obtained by wild type cross, whereas the tert-/- were obtained by tert-/- incross
The last paragraph of the discussion makes some valid points, but it seemed out of place and I wondered if it was misplaced.
This paragraph is added to highlight that our work describes new in vivo model to perform drug screening to inhibit ALT mechanism of telomere maintenance, which is of particular importance for the survival of ALT positive tumor cells.
The rps11 primers appear in the Table of Fig S3 twice.
This has been corrected
Reviewer #3 (Significance (Required)):
The authors claim that this is the first in vivo model examining ALT in regeneration.
The paper contributes to the relatively small body of literature using adult zebrafish models (rather than embryonic larval models) in biomedical research. I cannot comment on the telomere/telomerase literature.
This report will be of interest to those working in regenerative medicine, telomere biology, cancer research, and those interested in zebrafish models of disease and physiological processes.
My expertise encompasses zebrafish disease models and functional studies; I do not have special expertise in telomerase or ALT pathways.
We fully appreciate the reviewer´s comments!
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #3
Evidence, reproducibility and clarity
Summary:
Martinez-Balsalobre have examined caudal fin regeneration following surgical transection in WT and telomerase-deficient (tert+/- and tert-/-) zebrafish adults of several ages, and in one experiment, in embryos. They conclude: (1) regeneration efficiency decrease with aging in all genotypes (2) telomere length is maintained, even in a tert-mutant background (3) ALT (alternative lengthening of telomeres) is involved in supporting cell proliferation in tailfin regeneration. The experimental system employs a quantitative area-based measurement as a measure of the degree of regeneration. Functional studies used antisense morpholino gene knockdown and chemical inhibition to implicate ALT involvement.
Major comments:
The experimental logic is appropriate, and in general, the data support the conclusions. Strengths of the work include: (1) The quantitative measure of % regeneration appears to be quite objective; (2) the internally controlled experimental design of the morpholino knockdown experiments of Fig 5.
The Western blot in Fig S2 has some issues. The image is a montage. The experiment appears to have been done only once. The band's identifications by kDa are imprecise (where is the 82 kDa band on the gel? - there are bands smaller and larger than 82 kDa, but none of 82kDa; the 50 kDa band is close to background; the DMSO lane is underloaded relative to the two test lanes (but as the observation is a reduction in the test samples, this does not result in a misinterpretation). What concentration of ATRinhIV were used? The blot has 10 and 50 microM, Fig S1B has 50 and 100 microM, and the text says 1-50 microM).
The MO-knockdown studies are interpreted as showing synergy of atr and tert knockdown. There are two problems with them interpretation of synergy: (1) the single result of a greater effect with both MOs does not distinguish between an additive or synergistic effect (and synergistic action is by definition a greater than additive action; (2) MO dose is not controlled by a group with an equal total MO doses (mo-std+mo-atr and mo-std+mo-tert). While acknowledging that the issues of using local MO delivery in an adult model are very different from global delivery in an embryonic model, the "synergy" interpretation still requires these experiments/controls be done. These experiments were not accompanied by any molecular evidence that either of the morpholinos targeted expression of the intended gene (which would likely have to be derived from their assessment in another system) - a control that can be challenging, but one that is regarded as essential in the field (https://doi.org/10.1242/dev.001115). While this will be difficult to do in the adult setting, it is still appropriate to validate the activity/molecular efficacy of the MO sequence in an experimentally tractable scenario. The specificity of this experiment and interpretation would also be enhanced and corroborated independently by undertaking the atr knockdown in the tert -/- mutant background. Overall, these experiments were preliminary and require further work that could be done withiin 3 months. Note - the tert MO sequence is missing from the table in Fig S4.
The adult experiments have used n=6-10 animals/group. There is no consideration of statistical power (is the analysis of Fig 1C adequately powered?).
The degree and nature of replication is not clear in all cases. For example, in Fig 1, were the 6 fish run as one cohort of 6 animals in parallel (which would be just one experiment with 6 animals, each animal being a biological replicate), or were there 6 animals injured at different times (representing multiple independent experiments and represented a greater degree of reproducibility), or something in between. A similar question applies to the other figures. For the experiment of Fig 6F, although there are >=100 larvae per group, it is not clear that this experiment has been done more than once.
A few comments about data presentation. "Regeneration rate" and its derivatives are presented as mean +/- SEM. The parameter measured is correctly defined in methods as "Percent fin regeneration", however the graphs where it is plotted have the y-axis labelled as "regeneration rate (%)" (for example. Fig 1B), which is incorrect. The plotted parameter is not a rate - although there is a time dimension (x-axis), what is plotted at each time point is "% regeneration". Also, in most figures, such as Fig 1B and 1C, mean +/- SD would be more appropriate, as here each of the n=6 data points represents a single observation from one individual in the population, not the mean of 6 small samples of groups of individuals from the population. Furthermore, at these small n-values (6-10 through the report), scatter plots are considered a more appropriate way of displaying the data (some succinct references: DOI: 10.4103/2229-3485.100662 ; from a Nature group journal DOI: 10.1038/s41551-017-0079 ; from a PLOS journal https://doi.org/10.1371/journal.pbio.1002128). The use of mean +/- SEM in Fig 4 could be appropriate, but as n is "at least two independent experiments" scatter plots would again be appropriate. Readers would then know which data sets had only two values. In two instances, the same data are presented in two different ways (Fig 1B, 1C; the column graphs and arrows of Fig 3D). How does "data are average of at least 2 independent experiments" apply to Fig 3C?
Minor comments:
The paper is written clearly overall. There are multiple minor grammatical/typographical errors, but these did not detract from understanding the manuscript. These were most abundant in the discussion. A few points: Discussion p1 - what is meant by "prematurely aged 11-month fish" Discussion p2 - you mean "doubled" rather than duplicated? tert +/+, tert +/- and tert -/- genotypes for experiments - how were these obtained and genotypically verified? (heterozygous incrosses? WT x homozygous mutant outcrosses?) The last paragraph of the discussion makes some valid points, but it seemed out of place and I wondered if it was misplaced. The rps11 primers appear in the Table of Fig S3 twice.
Significance
The authors claim that this is the first in vivo model examining ALT in regeneration.
The paper contributes to the relatively small body of literature using adult zebrafish models (rather than embryonic larval models) in biomedical research. I cannot comment on the telomere/telomerase literature.
This report will be of interest to those working in regenerative medicine, telomere biology, cancer research, and those interested in zebrafish models of disease and physiological processes.
My expertise encompasses zebrafish disease models and functional studies; I do not have special expertise in telomerase or ALT pathways.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #2
Evidence, reproducibility and clarity
Using zebrafish as a model for regeneration, the authors find that telomere maintenance by recombination can occur in the absence of telomerase.
Title to Figure 4 perhaps may be too strong, 'ALT mechanism is activated', since only a few features of ALT are assessed. Perhaps, 'ALT features are activated'?
mRNA levels of NBS, ATR are also increased in WT animals (Figure 4A and 4B), but ATRX and DAXX mRNA levels are not decreased in WT animals. Is the increase why the authors in part suggest that ALT is being used in WT animals. If so, what would be the trigger for the use of ALT, as opposed to the trigger to use ALT in tert-/- animals?
In Figure 5C, if tert-/- animals are downregulated for nbs1 and atr, would it be expected that the effect on regeneration be more pronounced compared to tert+/+ downregulated for nbs1 and atr than what is observed?
What are the telomere lengths in tert-/- animals treated with mo-atr or mo-nbs1 or in tert+/+ animals treated with mo-tert and mo-atr compared to singly treated?
Significance
Reported findings are novel, timely and model of possible therapeutic value for screening compounds for ALT and/or telomerase inhibitors. Mechanisms of co-existence of ALT and telomerase can also be explored using this model.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #1
Evidence, reproducibility and clarity
The authors Martínez-Balsalobre and colleagues found that the regenerative capacity of the zebrafish caudal fin is not limited by the lack of telomerase and showed that the length of telomeres does not decrease substantially after repeated amputations in telomerase-deficient zebrafish. These findings prompt the authors to explore an alternative mechanism that would explain the maintenance of telomere length in this regeneration setting. They produced suggestive evidence for the role of the ALT (Alternative Lengthening of Telomeres) mechanism in the maintenance of telomere length in the absence of telomerase in a regeneration setting. In my view, several points need to be addressed and clarified.
There are three major points:
1.When working with tert mutants, the age at which these fish show a telomere phenotype (namely, loss of body mass and reduced fertility) varies. Therefore, it would be important to state if the fish used in this study were already showing these phenotypic characteristics at each time point studied, namely 4, 8 and 11 months of age.
2.The knockdown experiments were performed using morpholinos. To confidently use morpholinos it is fundamental to demonstrate first their knockdown efficiency and their specificity. This is lacking in the manuscript.
3.The involvement of ALT mechanism in the regeneration process in the absence of telomerase is only suggestive, as the authors show an increase of C-circles and heterogenous telomerase length in telomerase-deficient zebrafish but when trying to establish a functional link the authors resort to the knowndown of genes that may be associated with ATL. Looking at the levels of TERRA and the number of C-circles in the knowndown caudal fins would be essential for their claim.
And several other points:
4.The regeneration experiments were performed at 32 degrees and this option was never explained nor discussed.
5.When referring to the ALT mechanism, the authors state that "... in about 10% of tumors cells, telomere length is maintained by the Alternative Lengthening of Telomeres (ALT) mechanism ..." and I think it would be more accurate to talk about cancer cells instead of tumor cells.
6.The sentence about C-circles is incorrect. C-circles are mostly single-stranded and not double-stranded as stated.
7.After Figure 2, the authors never mention the age of the fish used.
8.In Figure 1A The site of amputation does not fit the one described in Mat & Met that states 2 cm from the base of the caudal peduncle. The same stands for Figure 2A. The experimental procedure refers 1 dpa but this time point is not plotted in the graph in Figure 1B.
9.In Figure 1B The Y axis should be named regeneration area instead of rate as the values are a percentage of the area reached after a certain time point after amputation. The same stands for Figure 2B, C. It would be nice to see the real caudal fin images for the relevant time points: before amputation, 0 dpa, when the fins reach 50% of regeneration area and then the last time point. The authors should discuss why are the caudal fins reaching more than 100% of regeneration area.
10.In Figure 2B The meaning of ". .. ." on the right side of the graph is not clear. The same stands for Figure 2D.
11.In Figure 2C Why is the clip 10, 11 and 12 missing from the tert+/- and tert-/- ?
12.In Figure 2E The proximity of all points at the 12 Clip is indicative of lack of statistical significance, therefore the **** related to which comparisons?
13.In Figure 2D, E For the measurement of telomere length, the authors state that "Data are average of at least 2 independent experiments." What does this mean exactly? How many animals were used in each experiment?
14.In Figure 3 The authors state that "Data are average of at least 2 independent experiments." What does this mean exactly? How many animals were used in each experiment? Why were the c-circles evaluated at hpa while the telomere length evaluated at dpa? This should be discussed.
15.In Figure 3A The meaning of ". .. ." on the top side of the graph is not clear. t0 should be removed and replaced by 0 hpa and 24hpa and 48hpa for coherence.
16.In Figure 3B,C 0 hpa replace by 0 dpa
17.In Figure 3B The blue and red stainings in the panels are labelling exactly what? This should be stated in the image and in the legend.
18.In Figure 3D There is a mistake in the legend the should be corrected as follows "Very long telomeres have a higher fluorescence of 200,000 AUF and very short telomeres have a lower fluorescence of 30,000 AUF."
19.In Figure 4 t0 should be removed and replaced by 0 hpa. The meaning of ". .. ." on the top side of the graph is not clear. The title is an overstatement, as the genes studied are DNA damage genes that may associate with ALT.
20.In Figure 4A, B The expression of nbs1 and atr in tert-/- increases at 48hpa but the same seems to be true for the tert+/+ and this is never discussed by the authors.
21.In Figure 4C, D The differences in the expression of atrx and daxx decreases over time in a in tert-/- and this is never discussed by the authors.
22.In Figure 5 An ideal control would be the direct comparison between microinjected+electroporated mo-std in the ventral part of the fin while the dorsal part would be microinjected+electroporated with the mo-gene of interest. This would discard any effect of microinjection+electroporation in the regeneration efficiency. These experiments are not convincing to show that there is an ALT mechanism is operating here. What this experiment shows if the relevance of these genes for the regenerative capacity of the caudal fin. To show that this is related to the ALT mechanism the authors should investigate the C-circles in these regenerating fins. The amputation red lines are not placed in the exact amputation position in some of the panels. Regeneration rate should be regeneration area.
23.In Figure 5C, E Why is the mo-tert more inhibitory of regeneration (Figure 5E - around 30%) than the tert-/- mutant (Figure 5C - around 60%)? This should be discussed.
24.In Figure 6A The 2 adult zebrafish shown in the tank with the ATR inhibitor IV should have an amputated caudal fin. Control is exactly what? Untreated? Treated with vehicle? Why was the ATR inhibitor IY added immediately after fin amputation while the mo-atr was injected at 48 hpa?
25.Figure 6D, E, F These panels are a bit out of the focus of this paper. If presented should go to a supplementary figure.
26.In Figure S2 The relevant bands should be identified. The gel identifies DMSO, 10uM and 50 uM but the quantification graph identifies Control, 50uM and 100uM. There are no error bars. The authors say that the quantification of various western blot bands was done but how many exactly?
27.In Figure S3 The primers for rps11 are repeated twice. Were these primers design de novo by the authors or did they used previous reported primers, in this case the references should be given. Tert F2 and R1 should be replaced for F and R for consistency.
28.In Figure S4 The sequence of tert mo is missing.
29.In the methods the genotyping protocol of tert mutants is not described.
30.The method to calculate the area of the fin pre- and post-amputation is not described.
Significance
The manuscript by Martínez-Balsalobre and colleagues deals with a very interesting question on the importance of telomere lengthening during regenerative processes and its relation to ageing. To this end the authors made use of the tert mutant, a telomerase-deficient zebrafish. The authors show a surprising phenotype that telomerase-deficient zebrafish can still regenerate their caudal fins and are able to maintain telomere length during consecutive amputations and I say surprising because it has been shown that telomerase-deficient zebrafish are unable to regenerate their hearts efficiently.
Taking these novel findings, the authors propose that in the zebrafish caudal fin and in the absence of telomerase, telomere length is maintained through the activation of an alternative mechanism called ALT. To my knowledge, the role of ALT as a mechanism of telomere lengthening has never been described in the context of regenerating organs in zebrafish.
Referees cross-commenting
I agree with the comments made by the other reviewers. I would stress the need to tone down the role of ALT during fin regeneration in zebrafish as all the experiments are only indicative of the possible of the involvement of ALT.
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Referee #4
Evidence, reproducibility and clarity
Abdullah Alieh and colleagues generate comprehensive transcriptome annotations in FACS-sorted murine cortical neural stem cells, precursor cells and neurons by combining (existing) short-read RNA-seq (SRS) data with long-read sequencing (LRS) data. They identify around 50,000 novel transcripts and show that they are enriched in neural functions and have a strong tendency of increasing inclusion of differential splicing events during differentiation. Several examples are validated by PCR. They show, by means of AlphaFold2 prediction of protein structure, that many splice isoforms likely cause either overall structural differences or switches in secondary structure.
Major points:
- The authors generate data using a previously characterized mouse model. However, they need to reconfirm expression of markers for the three cell types they analyse, particularly since one is identified by lack of expression of fluorescent tags.
- Validation is only performed by RT-PCR on 11 novel splicing events and not at all on novel TSS and termination sites. It would greatly benefit the reliability of novel isoforms if the authors could compare them with those detected previously by LRS in neural cells, or overlay novel TSS with data such as CAGE or 3'-end sequencing.
- Are divergent structural regions between isoforms often within regions of low model confidence? This would impact the relevance of the discovered changes.
- In the Discussion, the authors assert that '...AS alone was revealed to have a much greater impact in remodeling the transcriptome [...] than previously thought and independently from changes in gene expression.' However, this latter aspect is not demonstrated. To what extent does apparent change in AS derive from differential expression of isoforms from alternative TSS?
- The statement in the Discussion that 'Our study supports this notion [that differential inclusion of disordered segments can affect protein-protein interaction] with a significant increase in disordered isoforms arising concomitantly with neurogenic commitment' is not supported by the results presented. The authors only show that alternatively spliced proteins in their dataset have a higher propensity for disordered regions than the proteome at large, which is not a new observation.
- The statement in the Discussion that structural changes ostensibly caused by alternative splicing were 'similarly the case both when the structural change occurred within the AS event as well, more remarkably, when the event was far away' is not supported by the results as presented.
- Supplementary material is mentioned but not included with the manuscript.
Minor points:
- Fig. 1A: Why are there two numbers for transcripts (70,658, 71,760) in the overlap of pipelines 1 and two?
- Fig. 2F: Statements that events either low in NSC and rising, or high in NSC and declining, represent the 'least represented' isoform in NSC or N, respectively, do not seem to take into account that there may be other transcript isoforms for which inclusion of the event in question stays constant (e.g., skipped). The authors could make use of their LRS to confirm that at least for selected events.
- p8: How many unique new transcription start and end sites were identified?
- Fig. 2C: were categories selected for display (and if so, how), or are these all the categories identified?
- Fig. 2F-H: How many of the detected AS events, including neural microexons, are novel?
- Was the propensity to elicit nonsense-mediated decay taken into account when AS events were mapped to transcripts that did not contain them?
- How did 212 genes selected for modeling in Fig. 3 correspond to 987 isoforms? When genes comprised more than two isoforms, how were the changes in quantified properties attributed to the splicing events for which they were selected vs other isoforms or alternative translation start and stop sites?
- Fig. 3D: Coloring the structures by chain would make this figure easier to interpret.
- Details of Alphafold modeling are not provided.
- The authors should acknowledge that integrating SRS and LRS is a standard approach to generating annotations in organisms for which no reliable annotation exists, as well as approaches aimed at doing so to improve annotations in mammals, such as PMID: 37779246, 35468141, 32461551 etc.
Significance
While a combination of SRS and LRS sequencing along stages of neuronal differentiation has not been used in the same way to identify novel transcript isoforms, substantial work has been done employing LRS in neural contexts, including in single cells (e.g., work from the Tilgner, Waldmann lab).
Although it is not entirely clear from the results presented how many of the detected AS events are novel, as opposed to transcript isoforms, their characteristics are similar to previously known neural-differential events, thus supporting their veracity. The main advance in this manuscript lies in the insights derived from structural modeling of splice isoforms, which supports the potential relevance of many splicing events. This is a question relevant for both fundamental research and clinical audiences. However, several of the author's claims are not well supported, or else are not novel (see major points).
This reviewers' expertise lies in the field of molecular biology of alternative splicing; they have experience with RNA-seq and structural modeling of splice variants.
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Referee #3
Evidence, reproducibility and clarity
Summary:
Haj Abdullah Alieh at al., describe re-analysis of an existing short read RNA-Seq dataset consisting of 3 replicates of 3 FAC sorted cell populations of the E14.5 Btg2::RFP/Tubb3::GFP mouse cortex: neural stem cells (NSC; RFP-/GFP-), neural precursors (NP; RFP+/GFP-) and neurons (N; GFP+), for the purpose of investigating alternative splicing isoform switching during neuronal cell-type specification. They generate a one replicate PacBio dataset of these same sorted cells, with the aim of identifying full-length transcript isoforms, which are difficult to discern with short-read data alone. The key conclusions are the discovery of ~50,000 novel transcript isoforms containing ~2,500 novel splice junctions; the discovery of isoform switches between NSC -> neuron that contain a high proportion of microexon inclusion events and the finding that many of these switches are predicted by Alphafold2 to have a structural impact.
The data is interesting and the bioinformatics approach of investigating potential impacts of splice variants on protein structure using Alphafold2 is also interesting, however at present the paper would be better presented as a resource, unless effort is undertaken to experimentally validate some potential biological findings. However, for the paper to be useful as a resource, links to newly generated data and analysis code need to be provided. The capacity for exploration of these newly identified splice isoforms, or further analysis using the new GTF, could then be one of the attractions of this work.
Major comments:
- Are the key conclusions convincing?
- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. Figure 1 The discovery of ~50,000 novel transcript isoforms containing ~2,500 novel splice junctions As far as I can see the description of novelty is based on them being not present in either Ensembl (GRCm38.p6), NCBI_RefSeq, or Gencode (vM10) - note here the numbers are genome assembly versions and do not refer to the GTF annotation versions compared against - these should be provided as they are frequently updated. The claim is that they are not present in these references because the unique cell samples have not been analysed before. For transcript isoforms to be included in these references they must have a good level of support. I have a couple of concerns about the support for these isoforms: The numbers in figure 1A do not add up. For long read sequencing two pipelines are used resulting in 76,077 and 80,782 isoforms - in the venn diagram 1A the overlapping circle has two numbers of isoforms in it: 70,658 and 71,760 so it is unclear, are 70,658 isoforms found by both pipelines or 71,760? Then we are told the union of these transcripts is taken forward to the next venn diagram. However this diagram is labelled with 82,046 transcript isoforms. Pipeline 1 has labelled 5419 unique isoforms, pipeline 2 has 9,022 unique isoforms so 5419 + 9022 + 70658(71760) = 85,099(86201) not 82,046 - perhaps some extra filtering has occurred that should be labelled/described? Again the final number of transcripts at the end of everything is off - if the 82,046 transcripts from long read are combined with the 16,070 unique to the short read this equals 98,116, not 97,240. The authors decide to use long read sequencing to assemble the isoforms as short-read sequencing is unreliable for assembling full length isoforms - however for their final list they merge isoforms assembled by StringTie from short read data with the isoforms assembled from the PacBio long read data, it seems likely that the isoforms detected only by short-read Stringtie assembly would be unreliable and shouldn't be included in the final total. The authors perform only one biological replicate of PacBio long read sequencing of three different samples, so it is not possible to easily determine the reproducibility of the findings. I appreciate PacBio is expensive, the authors could consider other ways to evaluate the reproducibility - perhaps by looking at the detection of transcripts expected to be uniformly expressed between the different conditions? The authors provide no quality information for their PacBio sequencing run - eg. length distribution of reads, how many reads are left after quality filtering, quality across the length of reads, ie. I do not know if most isoforms reported are supported by 5 full length isoform reads, or if it is rare in the dataset to get full length isoform reads .etc is the quality comparable across the three PacBio samples? How many of the novel isoforms are supported by both short read and long read data? How many of the novel isoforms are supported only by short reads? How many isoforms are found in all three PacBio samples? Does gene expression measured with the PacBio data match the previous results of measuring gene expression in the short read data? Adding these kinds of analyses would give more confidence in the results. This section of methods is confusing, I don't really understand what has been done or what part of the manuscript this refers to: "Events were assigned to an inclusion isoform if their coordinates overlapped, at least partially, with an exon or to an exclusion isoform if they were located within an intron. AS events without a corresponding inclusion or exclusion isoform were assigned to an Ensembl or NCBI_RefSeq isoform using the criteria above. Only AS events assigned to at least one inclusion and one exclusion isoform were considered for further analysis." VastDB is a splicing database created by Manuel Irimia/Ben Blencowe containing a lot of neural samples across development - how many of the 'novel' splice sites are present in VastDB? Similarly, how many of the 'novel' splice isoforms were previously detected by Zhang et al., 2016, Cell.
Figure 2: over neuronal maturation the major splicing change is for cassette exons to become more included, 50% of those measured being microexons Overall this section is strongest, the conclusions are well supported. Figure 2D - there are no genome coordinates given to allow the reader to check the highlighted events out for themselves. Figure 2F is very confusing, consider an alternative way to present this. Figure 2G, the premise of this analysis is interesting! But confused on the numbers - in 2F its shown that 226 exons become more included between both NSC->NP->N, so why are 441 exons plotted in 2G? Whilst I appreciate genes must be expressed in both NSCs and neurons to be able to calculate differential splicing, one thing not addressed is whether expression of a lot of these genes also goes up in neurons, i.e. could it be that when these genes are lowly expressed in NSCs their splicing is not particularly well regulated but it doesn't really matter because they are not really required in NSCs? This becomes relevant later where you start to address the functionality of isoform switches - if the gene is expressed to the same degree in NSC vs. N this would suggest that both isoforms are functional, if a gene is very lowly expressed in NSC but highly expressed in N, then maybe only the N isoform needs to be functional. Gene ontology methodology is not described in the methods. What were the spliced genes compared against? Given these are neural samples, lots of expressed genes will have neural functions, so is this really informing us about the alternatively spliced genes? The manuscript would benefit by integration of its data with other published datasets - especially with the microexons - how do these behave in other datasets of neuronal maturation (such as those from vastdb or zhang 2016)? The authors could consider looking at motifs around regulated microexons to try and establish if any specific RBPs might be involved in this regulation, although this would benefit from follow up experiments.
Figure 3: exon inclusion in neuronal specific transcripts confers different structures to translated proteins, suggesting these events are important functionally Here, Alphafold2 is used to predict the structures of switching isoforms, whilst an interesting approach to inform further experiments, presented alone, it remains hypothetical. Hook2 is highlighted as one example, where inclusion of a microexon introducing two amino acids to the translated protein is predicted to cause a structural change that will impact its binding to microtubules. It's hard to determine if this really will have a functional impact without doing experiments in the lab. For this manuscript to serve as a research (rather than resource) article, it would benefit from an example experiment expressing neuronal vs. NSC Hook2 isoform in a cell line and measuring co-localisation with microtubules via IF microscopy, or something similar to address the proposed function. In the second half of this figure, more subtle local structural changes are investigated and the example of an alpha-helix to beta-strand switch predicted in Kctd13 is presented. The figure would benefit from showing the splicing change at the RNA level and relating that to the change seen at the protein sequence level as it is a bit confusing - the region of deletion is labelled as 'AS REGION' however, two amino acids preceding this box are different between the two isoforms (KVEF vs. KVRG) - so presumably the splicing change starts earlier than denoted? In the discussion the authors state: "While these regions are long known to exist, their structural switch was assumed to be dependent on substantial changes in their structural and sequence contexts (Gendoo and Harrison, 2011; W. Li et al., 2015) as opposed to, as observed in our study, being triggered by small perturbations within nearly identical sequence contexts." It's not clear whether these small local predictions are accurate and would require some additional structural data to validate. - 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. Suggestions of additional computational analysis are very realistic and shouldn't take longer than a month or two. The addition of experimental data to support Figure 3 would take considerable time and resources, potentially collaboration with other labs. Perhaps focusing on making this dataset an accessible resource would be a better route to publication. - Are the data and the methods presented in such a way that they can be reproduced? No, no source code, software versions or supplementary data/materials is provided. - Are the experiments adequately replicated and statistical analysis adequate? Having one replicate of the PacBio experiment is a bit concerning, but I am aware that it is expensive. Given they have three samples of different conditions with PacBio data perhaps showing the quality control of the libraries, reproducibility of transcripts that don't change in the three conditions, etc. would give more confidence in the data.
Minor comments:
- Specific experimental issues that are easily addressable. Made above.
- Are prior studies referenced appropriately? Yes. Except for this section of introduction: "While great effort is being made to overcome these limitations, capturing cell type-specific AS dynamics that is both quantitative and comprehensive of full-length transcript information currently requires combination of both SRS and LRS performed in parallel on the same cell pool. This was seldom attempted (Gupta et al., 2018; Joglekar et al., 2021) and, to the best of our knowledge, never for specific cell types of the developing mammalian brain. Even more limiting, systematic assessment of the consequences of AS on protein structure and putative function in cell fate commitment is entirely lacking. "
LRS has allowed for whole transcriptome determination and quantification in a number of cases, especially in non-model organisms, below I mention some examples from human and mouse: Nanopore use in GTEX + short reads: Glinos et al., 2022 Nature https://www.nature.com/articles/s41586-022-05035-y PacBio SMRT-Seq + short reads human and mouse cortex: Leung et al. Cell Reports 2021 https://www.cell.com/cell-reports/pdf/S2211-1247(21)01504-7.pdf PacBio IsoSeq + short reads in human and mouse sperm: Sun et al., 2021 Nature Communications https://www.nature.com/articles/s41467-021-21524-6 Single cell long read RNA-Seq has also been described in several scenarios and is worth referencing in the introduction: Samples from various mouse and human sources: Tian et al., 2021 Genome Biology https://link.springer.com/article/10.1186/s13059-021-02525-6 differential isoform usage in myeloma cell lines: Phillpott et al., 2021, Nature Biotech https://www.nature.com/articles/s41587-021-00965-w Single cell long read isoform analysis in human immune cells: Volden and Vollmers, 2022, Genome Biology https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02615-z - Are the text and figures clear and accurate? Mostly, I've highlighted where numbers in figures don't make sense to me. Generally the text could use some going over and tightening up (eg. sentence on page 12 needs revising for clarity and typo "The fact that within this helical packing resides the protein domain essential for Hook2 function to bind microtubules, implies that such a negligible AS switch by two ammino acids may result in a completely altered function. ") - Do you have suggestions that would help the authors improve the presentation of their data and conclusions? I have made suggestions above about figures that are unclear to me.
Referees cross-commenting
After reading the reviews of other reviewers, it seems we are much in agreement over the main concerns relating to this manuscript. Namely: concerns over the PacBio being single replicate, concerns over indiscriminately merging PacBio and SRS transcripts, concerns about lack of validation of the structural changes predicted by AlphaFold2. On the question of novelty and significance we also seem to be aligned.
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
The main general findings of the work have been described elsewhere: that microexon inclusion increases in many transcripts during neuronal cell fate commitment has previously been described, the suggestions of important isoform structural changes in Hook2 and Kctd13 are not backed up by any experimental data and so are not reliable. The description of a huge number of novel isoforms is not particularly useful because it's not clear if these have been found by other similar studies, because the data is not compared, furthermore we have no information about these isoforms to be able to pursue further research about them. The main output of the work would be the data and transcript annotations for other people to follow up on, but this is not provided in any accessible way. The paper might be better reframed as a resource, if it is not possible to follow up on the biological conclusions. - Place the work in the context of the existing literature (provide references, where appropriate).
Previously, alternative splicing has been studied in purified cell types of the developing mouse cortex using short read sequencing eg. in Zhang 2016, Cell. In this previous study, VZ NPCs (EGFP−) and non-VZ cells (EGFP+) were isolated from E14.5 Tbr2-EGFP mouse cerebral cortex. The double reporter mouse model used in the present study allows for better cell sorting into NSC, NPC and neurons, and the long read sequencing allows for whole transcript identification, however the present study has made no effort to compare the data, so it's not clear how much new biology this leads to. In Zhang 2016, the authors also predict disruption to protein domains caused by AS, but go further to perform experiments to validate the impact of some of these predictions. - State what audience might be interested in and influenced by the reported findings.
Researchers of this cell fate transition might want to look at their favourite genes to see if there are novel isoforms reported (however this is currently not possible because this information is not provided). Researchers of Hook2 or Kctd13 may want to further explore the described predicted structural changes. Researchers generally studying alternative splicing may want to include the novel isoforms in their analyses (again currently not possible because they are not provided). Generally this paper would probably be best seen as a resource. - 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. Bioinformatics, Splicing, RBP biology
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Referee #2
Evidence, reproducibility and clarity
In this manuscript the authors attempt to characterize alternative splicing in neurogenic progenitors during corticogenesis and the consequence of such alternative splicing on protein conformation. To do this the authors used previously published short-read sequencing data from neural stem cells, neural progenitors, and neurons at E14.5 and expanded on this dataset by adding long-read sequencing data.
Major comments:
- According to the methods section, new long-read sequencing data was generated for each of the NSC, NP, and N cell types. It is unclear to me how these were processed in terms of replicates. From figure 1 is seems that the samples were sequenced individually but then pooled for transcriptome assembly. It would really be helpful to understand the quality of the samples better. Are there replicates for each of the cell types included? What did the read count and transcript detection look like for each of the individual samples? Are the 3 samples really equal enough to be pooled together or will 1 sample dominate when assembling the transcriptome?
- On page 9, end of 2nd paragraph the authors state: "... these findings highlight the extent of AS within the neurogenic lineage underscoring its potential to regulate corticogenesis to a much greater degree than previously appreciated." Would it be possible to do a direct comparison between the number of AS detected or the type of AS detected between published data and the current paper? The authors provide a very coarse description of AS events during corticogenesis based on GO terms. The GO terms to surface are not surprising and seem not very meaningful in distinguishing the three cell types. Are there lower level GO terms that are specific to a subset of the cell populations?
- The authors show that cell types moving from NSC to NP to N gain exons. This raises the questions whether there is a specific set of genes that gains exons during development and/or there are different RNA binding proteins present in the three cell populations that could contribute to the differential splicing patterns seen in the three cell populations?
Minor comments:
- What was the background chosen for gene ontology analysis?
- For this paper the focus was on development of neurons. Certain non-neuronal populations arise from NSC and it would be interesting to compare the non-neuronal lineage as well. To what extent is the splicing pattern a differentiation/maturation hallmark and to what extent is it specific to the neuronal lineage.
Significance
- General assessment:
- Strengths: This manuscript describes a potential strategy to investigate the effect of alternative splicing events on the protein output. By combining short- and long-read sequencing the authors are able to capture a wide variety of splicing events in the neuronal lineage at one timepoint during development. The modeling of potential protein structures that arise from the alternatively spliced transcripts is critical to start to understand the biological effects of alternative splicing in ever changing systems like the brain during development.
- Limitations: Main limitations are the wet-lab experimental setup. The analysis was performed on a limited number of samples (n=1?) per cell type for just 1 time point. It is not known what the variability in AS events between individuals is and will limit statistical testing.
- This manuscript is mostly a proof-of-concept but does not provide enough solid proof to claim new discoveries.
- This manuscript serves a specialized audience interested in alternative splicing and biological effects of splicing events.
- Filed of expertise: single cell transcriptomics, long-read, alternative splicing, mouse brain development.
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Referee #1
Evidence, reproducibility and clarity
The authors FACS-sorted neuronal cells and conducted both short- and long-read sequencing to delineate the process of neurogenic differentiation. They went on to verify certain new splice junctions via RT-PCR and employed AlphaFold2 to forecast the outcomes. There are several issues the authors need to address.
- It's unclear why the author decided to superimpose the GTF file created by StringTie (intended for SRS) onto those generated by two distinct LRS pipelines. Given that long-read sequencing doesn't match the accuracy of NGS, which could result in discrepancies in splice junction coordinates, this approach seems questionable. Additionally, the presence of alternative start sites or polyadenylation sites could further reduce the concordance rate, as evidenced by the mere 15% transcript overlap between the methods depicted in Figure 1A. The updated version of StringTie, StringTie2, offers an improved protocol for assembling short-reads using long-read data as a guide. The author should contemplate the use of these more advanced tools rather than combining them in a potentially incompatible manner.
- The main text and figure legends of Figure 1 do not specify the number of replicates used.
- The author needs to depict alternative splicing events with gene annotations, such as those seen in a sashimi plot in panel 1C. The existing panel does not adequately differentiate whether the splice junctions presented are novel. Furthermore, the author should provide the PSI for each splicing event and contrasts these with the PSI derived from RT-PCR data.
- In the discussion section, the author asserts that their methodology, combining Short Read Sequencing (SRS) and Long Read Sequencing (LRS), is novel. However, similar approaches have been reported in previous studies, for instance in references 10.1371/journal.pcbi.1009730 and 10.1098/rsob.220206.
Significance
While the sequencing data and the integration of AlphaFold2 are new, the authors fall short of experimentally demonstrating the biological significance of their findings.
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Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary:
The current study investigates the metabolic regulation of hematopoietic cell differentiation through chromatin modification and gene expression. Using the primary CD34+ human cord blood cells, the authors show that transient pharmacological inhibition of glycolysis, PPP, and glutamine/glutamate metabolism alters the dynamics of chromatin structures and gene expression, leading to the impacts on cell proliferation, morphology, and the long-term differentiation capacity. Following are specific comments:
Major:
- The rationale behind the selection of the metabolic targets and the working hypothesis regarding specific effects on cellular consequence is not explicitly conveyed, which makes it difficult to judge if the experiment design is appropriate and if the results address the questions:
- The operational definition of "Metabolic perturbation" or "Metabolic stress" needs to be provided and the validation of inhibitory effects needs to be clarified. Fig. 3D and S1 Fig are supposed to indicate the inhibition of targeted metabolic pathways but it is not clear if the authors believe the inhibitors exert expected metabolic effects based on the presented data. The author should explain why they target the selected pathways (i.e. glycolysis, PPP and glutamine/glutamate metabolism) and precisely point out which up or down regulation (in Fig. 3D and S1 Fig, for example) indicate sufficient and specific inhibitory effects for each inhibitor to operationally define "metabolic perturbation". Thank you for bringing this point to our attention. We extended the Introduction section (page 3) with a paragraph better explaining the notion of metabolic perturbation or stress. Indeed, a clear definition of the metabolic targets is also required. Consequently, the update includes a more detailed presentation of the metabolic steps and the rationale as to why we selected them as targets (pages 3 to 4). Additionally, we have also incorporated an extra figure (S1 Fig) to illustrate the major metabolic pathways affected by the various inhibitors.
In this study, we have used single time-point detections of steady-state metabolite levels. The single time-point detection of individual metabolite levels alone does not allow clear understanding of the precise metabolic alterations. The network of metabolic reactions is highly interconnected with complex regulatory loops that makes precise predictions difficult. More detailed metabolic flux studies will be required to characterize the perturbations. There are considerable challenges in carrying out such flux experiments with the limited amount of cells (which cannot all be from a single patient source), making such experiments well beyond the scope of this study. However, even with single time-point steady inhibitor studies, we observe significant and inhibitor-specific cellular reactions involving cell division rate, morphology, cell surface marker distribution and changes in bulk metabolite levels. Therefore, we interpret these changes as collectively reflecting the metabolic impact of the inhibitors, which can be qualified as metabolic perturbation or stress. The manuscript has been modified (page 5) to clarify this point.
- Given that the major goal of the study is to characterize the long-term effects of transient metabolic perturbation, it is particularly important to address how soon after the treatment (and how soon after removal) of the inhibitor, the authors observed the expected changes of the targeted metabolic pathways. *The cells were cultured in the presence of inhibitors for 4 days, with day 0 being the beginning of the experiment. The effect on chromatin was detectable by ATAC-seq as early as 12 hours. Given the dramatic changes observed at 24h and early changes (detected at the chromatin level and observed in Time-Lapse), it is reasonable to infer that changes occur almost immediately after the addition of the inhibitors. The first time point that was analyzed after the removal of inhibitors was on day 7 (i.e. 3 days culture without inhibitors), then on day 10 and 14. The cells of the four conditions exhibited distinct evolution even after the inhibitors were removed. *
The chromatin-independent and transcriptional-independent mechanisms are not considered. Intermediate metabolites are known to directly modify protein activity, alter cell signaling resulting changes in differentiation potentials. The authors should acknowledge this possibility and examining their data to speculate which specific gene expression and related cell-fate changes are likely (or not likely) the direct result of epigenetic modulation.
We completely agree with the reviewer that cellular memory mechanisms other than chromatin modifications were not investigated. Fluctuations of the energy metabolism can also impact the post-translational modifications of cellular proteins. However almost nothing is known so far on the role of these modifications in cellular memory processes, and in the consolidation of phenotypic characteristics of a cell lineage. This idea is of course very exciting, but studying this aspect would necessitate an entirely separate investigation, using alternative methods. At this stage we believe that this is well out of the scope of the present study. We have added the idea in the Discussion section (page 16).
The samples of primary cells have heterogenic cell populations. The cellular characterization in bulk may confound the results regarding cell-fate programming versus the cell selection effect.
In Fig 3 and Fig6, how would the authors determine whether the inhibitor or rescue treatments alter cell differentiation program or selectively allow proliferation or survival of non-differentiated cells?
The question of the first selective hit followed by the amplification of the surviving cells is highly relevant. The CD34+cell population is inherently very heterogenous, and we used inhibitor concentrations close to the IC50 values. Collectively, we observe that the surviving cells exhibited greater resistance, which is likely due to their more resistant metabolic state. Our metabolic MS analysis was conducted on a bulk population, precluding conclusions at the single-cell level. However, time-lapse, cytometry, single-cell ATAC and RNA-seq analyses all provide information at the single-cell level. ATAC-seq revealed initial differences between control and treated cells approximately 12 hours after stimulation. By 24 hours, 16 different subsets of cells were identified using single-cell ATAC-seq chromatin accessibility profiling. All four conditions were represented in all subsets in variable proportions. Previous studies [1,9] indicated that at 24 hours, these cells couldn't be clustered into distinct groups based on their gene expression patterns, suggesting that chromatin changes precede gene expression changes by several hours. Notably, at the time of analysis, these cells had not undergone division yet. Time-lapse microscopy revealed that the first division occurred in control and 2-DG cells 24 hours later, while in DON and AOA cells, it occurred only around 72 hours later. At this point, single-cell RNA-seq data clustering identified 17 different subsets of cells. Particularly, AOA cells exhibited a distinctly different gene expression pattern, forming separate clusters. Based on these observations, we think that although some selection occurs during the initial hours, the differences observed between the inhibitors cannot be solely explained by it. Instead, chromatin differences between cells appear before the first division of the cells surviving the initial shock. These differences then gradually develop over the initial 96 hours. The inhibitors were removed at this point, and the cells primed by the different inhibitors were subsequently cultured under identical conditions. It is likely that cells exhibiting differential gene expression patterns possessed varying proliferation capacities, contributing to the observed evolution of cell populations as detected on days 7, 10, and 14. We have added this paragraph to the manuscript in the Discussion section for better clarity (pages 14 and 15).
- Trajectory analysis may further elucidate that the effects of metabolic perturbation on cell differentiation program are permissive or more instructive (towards/against specific lineage commitment). Although we were able to identify 17 subsets of cells based on their transcriptome profiles, any of them could be assigned to a specific hematopoietic lineage. It is presumably too early. As it was shown (Moussy et al 2017), at this stage, just 96 hours after stimulation most of the cells are still “hesitant” with fluctuating gene expression profiles and morphology. Their commitment to a specific lineage is not robust making the definition of trajectories impossible.
Minor:
- Fig. 1A is missing figure legends. We clarified the legend (see page 40).
The cell clusters in fig 3 needs to be at least deconvoluted based on the differentiation or cell-identity markers and annotated accordingly in the main figure.
Indeed, we conducted this analysis, but the results weren't conclusive enough to be included in the manuscript. We extracted the list of differentially expressed genes for each cluster (for a more detailed description, refer to the answer to Reviewer 2's Question 2 regarding the analysis of cluster 8). The list of extracted biomarkers was studied, and the top 20 for each cluster are shown on the heat-map in S6 Fig. However, for many clusters, canonical markers couldn't be identified to easily match the clusters to known cell types. For others, a few markers were detected, but with inconsistent mixes, such as in cluster 7 (LYZ and CD14 associated with CD14+ Mono, CST3 associated with DC, NKG7 associated with NK, IL7R and S100A4 associated with Memory CD4+, and MS4A7 associated with B cells) or in cluster 12 (PPBP associated with platelets, S100A4 associated with memory CD4+ cells and FCER1A associated with DC). At this very early stage, the cells are just exiting the multi-lineage primed stage, and it's likely that their identity is not yet fully determined, explaining the mix of markers from different lineages. We also attempted a Gene Ontology analysis on the lists of biomarkers, but most terms were general cellular functioning terms, making it impossible to assign the cells in the various clusters to specific cell types.
The statements in abstract and introduction broadly mention the environmental changes and metabolic adaptation in the process of differentiation. The study, however, address only the setting in vitro. As the mobilization of the hematopoiesis process is not possible to be address with the data presented in the current study. The author should revise the manuscript to better introduce relevant questions of the study.
With all due respect, we do not agree with this comment. The question we are seeking the answer to is defined in the Introduction section (page 3): “Does the change of the metabolic setup of the cells precede and trigger the non-specific chromatin opening?”. For better clarity, now we extended this question by a second one (page 3). It is true that in vitro studies cannot reproduce faithfully all the in vivo conditions such as the mobilization of the hematopoiesis process. However, the objective of our study was only to ask if the external restriction of the energy metabolism modifies the cellular differentiation process. From this perspective, utilizing metabolic inhibitors is a possible way to model restricted access to some substrates in a stressful environment. Indeed, this is the entire philosophy and value of in vitro experiments. The time resolution used in this study is impossible to achieve currently in any in vivo setting. The use of human CD34+ cells was motivated by the fact that this is a very well-studied in vitro model that retains many characteristics of cell differentiation in general. We only hope that our hypothesis and the observations done here are robust enough to be generalizable to other models and to cell differentiation in general. Obviously, confirmation by complementary studies on various other cellular models will be required.
Reviewer #1 (Significance (Required)):
Overall, we appreciate the author using untrivial experiments with purified/primary human cells and highly parallel omics analyses to test an interesting hypothesis. However, we think the specific question(s) and objective(s) of the study need to be specified/clarified and to be better addressed by more conclusive results.
This study will be of fundamental interest to the field of stem cell biology, cell metabolism and developmental biology. Our expertise is adult stem cell biology and dietary research.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary:
The authors evaluate the impact of metabolic perturbations on chromatin structure and the transcriptional landscape of undifferentiated hematopoietic progenitor cells following stimulation with early acting cytokines. Of note, the authors find very early changes in chromatin structure, associated with more long-term changes in transcriptional profiles, modulating the differentiation potential of these progenitors.
Major Comments:
-The authors show a significantly larger impact of AOA than DON on the chromatin and transcription responses of CD34+ progenitors even though they are both impacting glutamine metabolism. Alpha-ketoglutarate rescued CD34+ progenitors from the effect of AOA but did not rescue DON-treated cells which should also have an attenuated generation of alpha-ketoglutarate. How do the authors interpret this apparent discrepancy? In this regard, the MS data are confusing to this reviewer; alpha ketoglutarate levels were much higher in AOA-treated cells than in DON (or even 2-DG-treated) cells, potentially suggesting that DON had more of an impact on glutamine metabolism than AOA. Additionally, glutamine levels are low in DON-treated cells (where GLS is inhibited) but not in AOA-treated cells (this reviewer would have expected higher levels in both) and lactate is high in 2-DG treated cells (low levels would have been expected).
We were surprised by the metabolite levels found by mass spectrometry in the cells at 24 hours. In many cases these levels were different than what one would intuitively expect. This is why we have repeated the experiments many times. One possible explanation is to consider that these metabolites are produced and consumed simultaneously by many different alternative biochemical reactions. Inhibiting one of them induces immediate compensations by others. The metabolic network is complex and its state at a given moment is difficult to predict. Our measurement provides only a snapshot (which are steady-state measurements at that time). The significant change in the abundance of many metabolic intermediates indicates the fact that the network function is perturbed. To understand in detail the exact nature of these perturbations a single time-point measurement is not sufficient, detailed metabolic flux studies will be able to identify the modified metabolic fluxes. This is at present challenging, because the sources of cells are from different patients, at different times, and will require overcoming substantial experimental challenges. More specifically, the reason why AOA had a greater impact on the chromatin than DON and could be rescued by alpha-ketoglutarate may reside in the structure of the glutamine metabolizing pathway. The effect of DON inhibition on alpha-ketoglutarate can be relatively easily compensated by other amino acids, given that glutamine is a non-essential amino acid. This aligns with the observed recovery of surviving cells after an initial setback, where they subsequently resume their proliferation and differentiation following a brief lag period. Conversely, compensating for the inhibition caused by AOA is more challenging due to the direct involvement of transaminases in αKG production.
*The manuscript has been completed in the Results section (page 5) and in the Discussion section (pages 15 and 16). *
-The authors' finding of a single cluster of cells following AOA treatment (cluster 8) is extremely impressive. Can the authors better define this cluster?
Indeed, scRNA-seq analysis at 96hrs revealed very specific transcriptomic profiles for the AOA condition (Fig.3BC). Although some cells appeared in small numbers in clusters common to other conditions (clusters 4, 7, 10 and 13), most were grouped in completely distinct clusters (clusters 8, 11, 14 and 15). In particular, cluster 8 contained 70.2% of the cells from the AOA condition, i.e. 3598 cells out of 5126 analyzed for this condition before normalization. Given the small size of clusters 11, 14 and 15, attention was focused on cluster 8 for further characterization.
*First, we were able to confirm that this cluster was real and significant because even at a lower resolution than that initially used for the study (resolution 0.6 in Fig.3B), the cluster persists, so it is not an artefact of the clustering algorithm (cluster 1 on the figure on the left corresponds to cluster 8 on Fig.3B). *
Overall, the analysis of gene expression profile revealed that the cluster 8 was better defined by the genes that were down regulated rather than those overexpressed compared to the other clusters. However, the Gene Ontology analysis conducted on these gene lists was inconclusive. The extracted biomarkers do not allow for associating the cells with a specific mature cell type, 96hours is too early in the differentiation process. We think that this observation is not sufficiently conclusive at this stage to be included in the manuscript. Deeper analyses would be necessary to better understand their specificity, but it was out of the scope of the present study.
*Here is the detailed description of the analysis: *
*We searched for specific markers to characterize this cluster using the FindAllMarkers function in the Seurat package. This analysis compares each cluster against all others, identifying genes with differential expression. In the generated output, pct.1 represents the proportion of cells within the cluster where a specific gene is detected, while pct.2 signifies the average proportion of cells across all other clusters where the gene is detected. To refine our results, we filter the positive markers, retaining those with a difference > 0.25 between pct.1 and pct.2, alongside a p_val_adj
ID
Ont.
Description
Gene Ratio
geneID
Count
GO:0071392
BP
cellular response to estradiol stimulus
45171
CRHBP/NRIP1
2
GO:0017046
MF
peptide hormone binding
45232
CRHBP/NPR3
2
GO:0042562
MF
hormone binding
45232
CRHBP/NPR3
2
*The study of genes overexpressed in this cluster 8 is therefore inconclusive. When we look at the heatmap with the top 20 markers for each cluster, it seems that cluster 8 is characterized by the under-expression of certain genes, genes that are also under-expressed in clusters 14 and 15 and over-expressed in clusters 11 and 16: GPNMB, LGALS3, MMP9, CTSD, CXCL8, CTSB, SOD2, IFI30, PSAP, CHI3L1, CYP1B1, CSTB, ACP5, MARCKS, S100A11, FCER1G, LIPA. We conducted a Gene Ontology analysis on this new list, and this time, 53 terms were identified. The figure below shows the top 25 terms. Several terms related to immune cells and neutrophils are observed. The standard analysis doesn't provide us with additional insights into the cells within cluster 8. *
-The authors find an increase in cells expressing the CD36 marker, especially following 2-DG treatment. However, they never discuss the functional significance of CD36 as a fatty acid translocase (FAT), serving as a receptor for long chain fatty acids, and potentially as a compensatory mechanism under conditions where glucose metabolism is inhibited. We thank the reviewer for drawing our attention to this omission. It is indeed highly relevant and important to mention it in the paper. It fits perfectly with the basic idea of metabolic adaptation as a driving force. We introduced this point with references in the manuscript in the Results section (page 11).
__Minor Comments: __
-A schematic showing the different inhibitors and metabolic pathways would be helpful. A schematic representation of the main metabolic pathways and the steps affected by inhibitors has been added as S1 Fig (see page 32 and 40). Consequently, the other supplementary figures have been renumbered.
Reviewer #2 (Significance (Required)):
General comments:
The impact of metabolic perturbations on a progenitor cell with the potential to differentiate to multiple lineages is of much interest to the field. The authors have performed extensive single cell analyses, incorporating both scATACseq and scRNAseq together with cell morphology analyses and cell surface protein evaluations, to monitor short- and long-term impacts. They find very rapid changes in chromatin structure with long-lasting effects, despite the cessation of the metabolic perturbation. This has important implications for our understanding of the crosstalk between metabolic alterations, chromatin structure, and gene expression, coming together to regulate progenitor cell survival, expansion, and differentiation.
Assessments: strengths and limitations
Strengths and Advances:
The authors should be commended for their use of primary hematopoietic progenitors and a close evaluation of the impact of metabolic perturbations during the first 24h of stimulation. Their studies have added significantly to our understanding of cell differentiation, showing that changes in metabolic circuits rapidly modulate cytokine-induced epigenetic chromatin states.
Limitations:
Because CD34+ progenitors represent a heterogeneous population, metabolic perturbations are likely impacting the different subsets in distinct manners. The single cell data presented here can be exploited to assess how these subsets (clusters) change at very early time points following perturbation. It will also be important to confirm the effects of different inhibitors on specific metabolites in a cell line(s) since the changes reported here do not appear to be specific. It is possible that these differences are due to an overall decrease in the activation state of a cytokine-stimulated progenitor leading to a global decrease in metabolites.
Audience: This study will be of much interest to scientists/clinicians studying stem cells, hematopoietic stem cells, metabolism, and epigenomic/transcriptomic landscapes. As such, it will be of interest to a large community.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The authors evaluate the impact of metabolic perturbations on chromatin structure and the transcriptional landscape of undifferentiated hematopoietic progenitor cells following stimulation with early acting cytokines. Of note, the authors find very early changes in chromatin structure, associated with more long-term changes in transcriptional profiles, modulating the differentiation potential of these progenitors.
Major Comments:
- The authors show a significantly larger impact of AOA than DON on the chromatin and transcription responses of CD34+ progenitors even though they are both impacting glutamine metabolism. Alpha-ketoglutarate rescued CD34+ progenitors from the effect of AOA but did not rescue DON-treated cells which should also have an attenuated generation of alpha-ketoglutarate. How do the authors interpret this apparent discrepancy? In this regard, the MS data are confusing to this reviewer; alpha ketoglutarate levels were much higher in AOA-treated cells than in DON (or even 2-DG-treated) cells, potentially suggesting that DON had more of an impact on glutamine metabolism than AOA. Additionally, glutamine levels are low in DON-treated cells (where GLS is inhibited) but not in AOA-treated cells (this reviewer would have expected higher levels in both) and lactate is high in 2-DG treated cells (low levels would have been expected).
- The authors' finding of a single cluster of cells following AOA treatment (cluster 8) is extremely impressive. Can the authors better define this cluster?
- The authors find an increase in cells expressing the CD36 marker, especially following 2-DG treatment. However, they never discuss the functional significance of CD36 as a fatty acid translocase (FAT), serving as a receptor for long chain fatty acids, and potentially as a compensatory mechanism under conditions where glucose metabolism is inhibited.
Minor Comments:
- A schematic showing the different inhibitors and metabolic pathways would be helpful.
Significance
General comments:
The impact of metabolic perturbations on a progenitor cell with the potential to differentiate to multiple lineages is of much interest to the field. The authors have performed extensive single cell analyses, incorporating both scATACseq and scRNAseq together with cell morphology analyses and cell surface protein evaluations, to monitor short and long term impacts. They find very rapid changes in chromatin structure with long-lasting effects, despite the cessation of the metabolic perturbation. This has important implications for our understanding of the crosstalk between metabolic alterations, chromatin structure, and gene expression, coming together to regulate progenitor cell survival, expansion, and differentiation.
Assessments: strengths and limitations
Strengths and Advances: The authors should be commended for their use of primary hematopoietic progenitors and a close evaluation of the impact of metabolic perturbations during the first 24h of stimulation. Their studies have added significantly to our understanding of cell differentiation, showing that changes in metabolic circuits rapidly modulate cytokine-induced epigenetic chromatin states. Limitations: Because CD34+ progenitors represent a heterogeneous population, metabolic perturbations are likely impacting the different subsets in distinct manners. The single cell data presented here can be exploited to assess how these subsets (clusters) change at very early time points following perturbation. It will also be important to confirm the effects of different inhibitors on specific metabolites in a cell line(s) since the changes reported here do not appear to be specific. It is possible that these differences are due to an overall decrease in the activation state of a cytokine-stimulated progenitor leading to a global decrease in metabolites.
Audience:
This study will be of much interest to scientists/clinicians studying stem cells, hematopoietic stem cells, metabolism, and epigenomic/transcriptomic landscapes. As such, it will be of interest to a large community.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
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Referee #1
Evidence, reproducibility and clarity
The current study investigates the metabolic regulation of hematopoietic cell differentiation through chromatin modification and gene expression. Using the primary CD34+ human cord blood cells, the authors show that transient pharmacological inhibition of glycolysis, PPP, and glutamine/glutamate metabolism alters the dynamics of chromatin structures and gene expression, leading to the impacts on cell proliferation, morphology, and the long-term differentiation capacity. Following are specific comments:
Major:
- The rationale behind the selection of the metabolic targets and the working hypothesis regarding specific effects on cellular consequence is not explicitly conveyed, which makes it difficult to judge if the experiment design is appropriate and if the results address the questions:
- i. The operational definition of "Metabolic perturbation" or "Metabolic stress" needs to be provided and the validation of inhibitory effects needs to be clarified. Fig. 3D and S1 Fig are supposed to indicate the inhibition of targeted metabolic pathways but it is not clear if the authors believe the inhibitors exert expected metabolic effects based on the presented data. The author should explain why they target the selected pathways (i.e. glycolysis, PPP and glutamine/glutamate metabolism) and precisely point out which up or down regulation (in Fig. 3D and S1 Fig, for example) indicate sufficient and specific inhibitory effects for each inhibitor to operationally define "metabolic perturbation".
- ii. Given that the major goal of the study is to characterize the long-term effects of transient metabolic perturbation, it is particular important to address how soon after the treatment (and how soon after removal) of the inhibitor, the authors observed the expected changes of the targeted metabolic pathways.
- The chromatin-independent and transcriptional-independent mechanisms are not considered. Intermediate metabolites are known to directly modify protein activity, alter cell signaling resulting changes in differentiation potentials. The authors should acknowledge this possibility and examining their data to speculate which specific gene expression and related cell-fate changes are likely (or not likely) the direct result of epigenetic modulation.
- The samples of primary cells have heterogenic cell populations. The cellular characterization in bulk may confound the results regarding cell-fate programming versus the cell selection effect.
- i. In Fig 3 and Fig6, how would the authors determine whether the inhibitor or rescue treatments alter cell differentiation program or selectively allow proliferation or survival of non-differentiated cells?
- ii. Trajectory analysis may further elucidate that the effects of metabolic perturbation on cell differentiation program are permissive or more instructive (towards/against specific lineage commitment).
Minor:
- Fig. 1A is missing figure legends.
- The cell clusters in fig 3 needs to be at least deconvoluted based on the differentiation or cell-identity markers and annotated accordingly in the main figure.
- The statements in abstract and introduction broadly mention the environmental changes and metabolic adaptation in the process of differentiation. The study, however, address only the setting in vitro. As the mobilization of the hematopoiesis process is not possible to be address with the data presented in the current study. The author should revise the manuscript to better introduce relevant questions of the study.
Significance
Overall, we appreciate the author using untrivial experiments with purified/primary human cells and highly parallel omics analyses to test an interesting hypothesis. However, we think the specific question(s) and objective(s) of the study need to be specified/clarified and to be better addressed by more conclusive results.
This study will be of fundamental interest to the field of stem cell biology, cell metabolism and developmental biology. Our expertise is adult stem cell biology and dietary research.
- The rationale behind the selection of the metabolic targets and the working hypothesis regarding specific effects on cellular consequence is not explicitly conveyed, which makes it difficult to judge if the experiment design is appropriate and if the results address the questions:
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Reply to the reviewers
The two reviewers are very positive and emphasize the relevance of this study. Reviewer 1 notes that “the humoral immune responses but also parasite transcriptomics data is examined for the first time”. Reviewer 2 notes that our study “tries to mimic the infection in nature by reinfecting the Aotus monkeys with different stains of the parasite and then assesses the immune response with main emphasis on antibody response to the infection. This model is important to facilitate vaccine development and understanding the immune response against particular vaccines.”
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
To elucidate whether humoral immunity and/or genetic polymorphisms contribute to the protection against P. vivax blood-stage infection, the Authors assessed whether P. vivax strain-transcendent immunity can be achieved by repeated infection in Aotus monkeys. They infected six Aotus monkeys with blood stages of the P. vivax Salvador 1 (SAL-1) strain until obtaining sterile immunity, and then challenged with the heterologous AMRU-1 strain. Sterile immunity was achieved after two homologous infections, and partial protection against a heterologous AMRU-1 challenge was also achieved. IgG levels against parasite lysate by ELISA and protein microarray increased with repeated infections and correlated with the level of homologous protection. Although there were transcriptional differences in the P. vivax gene repertoire between SAL-1 and AMRU-1, there was no evidence of major antigenic switching upon homologous or heterologous challenge. These findings suggest that the partial protection observed during heterologous challenge is caused by genetic polymorphism between strains, rather than immune evasion by antigenic switching.
Major Comments: 1) Title There are several non-human primate models, therefore, please specify "Aotus monkey model" in the title.
Concur: We have added “Aotus monkey model” to the title.
2) Protein array Lines 373-374 "against major immunogenic blood stage antigens (Ags)" Please add selection criteria for how they select these 244 antigens.
We have added the following paragraph in the methods to address this comment:
“All P. vivax sequences for the array used in this study were derived from the SAL-1 strain, which allowed the evaluation of greater breadth of antigens (but limited evaluation of antigenic variation). Antigens on this array were down selected from larger arrays probed with reactive sera derived from various endemic regions. Only antigens demonstrating seroreactivity across all tested sera were included [105].”
They prepared protein array (n=244) based on the SAL-1 sequence. Please add a discussion of how the data was affected by the sequence difference between SAL-1 and AMRU-1 strains. They described this point only on the top 7 targets (Lines 283-287). Any further difference in antibody reactivity between polymorphic and conserved antigens (SAL-1 and AMRU-1).
We agree with this concern and have added two sentences to the discussion.
*“In comparing the SAL-1 and AMRU-1 strains to the PvP01 reference strain, the sequence data demonstrated clear differences between the isolates in the whole genome analysis. Therefore, this suggests that the current iteration of the microarray (n=244) used in the study did not capture the sequence target(s) responsible for the partial protection observed.” *
Please also add a discussion on how they can interpret their protein microarray data because the E. coli-based IVTT proteins array detects antibody responses against linear epitopes of the printed antigens.
*The IVTT cell-free E.coli express system used to generate the protein microarrays represents an unbiased systems biology approach to antigen identification (Davies DH et al PMID: 26428458). The focus is intentionally on linear epitopes as attempting to capture correctly folded whole proteins is a notoriously difficult venture (Vedadi M et al Mol Biochem Parasitol. PMID: 17125854; Mehlin C et al. Mol Biochem Parasitol. PMID: 16644028). The system has shown proven utility across several disease in identifying important antigenic targets which can then be explored in greater detail using other methods (Wager LE et al. Nat Med. PMID: 33432170; Nakajima R et al. mSphere PMID: 30541779; Virgil A et al. Future Microbiol. PMID: 20143947; Vankatesh A et al Sci Rep. PMID: 35654904). *
The following text and references have been included into the discussion:
“This approach was supported by previous studies which demonstrated the utility of the IVTT platform in high throughput antigen discovery across several disease areas (Jan S et al. Front Immunol PMID: 37533862; Nakajima R et al. mSphere PMID: 30541779; King CL et al. Am J Trop Med Hyg PMID: 26259938; Vankatesh A et al. Methods Mol Biol. PMID: 34115357; Vankatesh A et al. Malar J PMID: 30995911).”
3) Weakness Please summarize the weak points of this study (i.e. small number of animals used) in the Discussion section.
We have added and combined a few phrases with limitations in the discussion section:
“The partial protection observed in the heterologous AMRU-1 challenges may therefore be due to major genetic differences and hence antibody epitope variation between the two strains [50]. In comparing the SAL-1 and AMRU-1 strains to the PvP01 reference strain, the sequence data demonstrated clear differences between the isolates in the whole genome analysis. Therefore, this suggests that the current iteration of the microarray (n=244) used in the study did not capture the sequence target(s) responsible for the partial protection observed. To overcome this limitation and induce high levels of protective antibodies, we propose use of an immunization regime with whole parasite antigen pools from a mixture of genetically diverse strains. Another limitation of this study is the small number of subjects. The study can be considered as exploratory (i.e. looking for patterns of response rather than hypothesis testing [95]), hence the number of subjects used in the only group studied is typical of such exploratory research with humans [35, 96] and NHP [38].”
Minor Comments: 4) Line 129 "inoculation level II" Please reword this to "2nd inoculation" throughout the manuscript because "inoculation level" is a bit confusing for the readers.
Do not concur: It is easier to understand, in the figures in particular. Unless the editor insists, we would rather keep as is.
5) Line 320 "pir genes" Please spell out because this is the first appearance in this manuscript.
Done. Plasmodium interspersed repeat (PIR) genes.
6) Line 373 "IVTT" Please spell out because this is the first appearance in this manuscript.
Done. in vitro transcription/translation reaction (IVTT)
7) Line 404 "VIR antigens" Please spell out because this is the first appearance in this manuscript.
Done. Plasmodium vivax interspersed repeat (VIR) antigens.
8) Line 498 "Goat anti-monkey Rhesus macaque)" This may be HRP-labelled? Please correct.
Concur: We have added HRP labelled to: "Goat anti-monkey Rhesus macaque HRP-labelled"
9) Line 512 "temperature Plates" should be "temperature. Plates". 10) Line 514 "sulphuric acid 2.5M" should be "2.5M sulphuric acid".
Concur: Changed to "2.5M sulphuric acid".
11) Line 516 "Plasmodium falciparum" should be "Plasmodium vivax".
Concur: Changed to "Plasmodium vivax".
12) Line 524 "Escherichia.coli" should be "Escherichia coli".
Concur: Changed to "Escherichia coli".
13) Line 604 "is spleen-dependent (ref)" Please add a reference.
This paragraph has been removed as the data are not included in this study.
14) Line 1099 "core genes" Please add a description of what core genes mean.
Has now been added in the text line 319.
15) Figure S2 Please label each panel in Figure S2 A&B. Maybe I, II, III, IV from the left. Please also revise the label of the X-axis in Figure S2C because "Inoculation level" is misleading.
We have added the labeling to S2A and B.
**Referees cross-commenting**
I agree with Reviewer#2 comments.
Reviewer #1 (Significance (Required)):
1) General assessment: This is a valuable and important study conducted by qualified experts in this research field. All the works were carefully designed, and clearly presented, and the manuscript is well written.
(1) Strongest and most important aspects? Aotus monkey study with intensive data acquisition including humoral immune response and detailed parasite transcriptomic investigation.
(2) Weakness The number of animals used is rather small.
2) Advance: Does the study extend the knowledge in the field and in which way? Not only the humoral immune responses but also parasite transcriptomics data is examined for the first time.
3) Audience: Malariologists will be interested in or influenced by this research The data in this study will be the basis of future whole-parasite-based vaccine development.
My field of expertise is malariology and malaria vaccine research.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
This study focuses on the development of the model which can be further used as the model for developing a vaccine for the malaria parasite Plasmodium vivax. The researchers infected Aotus monkeys with one strain, achieved immunity, and then exposed them to a different strain. Four monkeys became immune to the initial strain, and three showed partial protection against the second strain. The researchers found that differences in genetic diversity and gene expression between strains are responsible for the varying levels of protection. This study provides insights for testing candidate vaccines against P. vivax. This model is unique and important for facilitating vaccine developments.
- The researchers provide a clear methodology and suitable for the proposed research questions.
- Did researchers observed any gametocytes after inoculations especially in the asymptomatic one or the prolong parasitemia. If they found, whether those gametocyte are infectious?
*We did not focus on gametocytes in this study, hence no mosquito infection experiments were performed. *
Reviewer #2 (Significance (Required)):
The asymptomatic infections are common in malaria endemic areas but it is hard to identify the underlying immune mechanism in response to the disease. This model tries to mimic the infection in nature by reinfecting the Aotus monkeys with different stains of the parasite and then assesses the immune response with main emphasis on antibody response to the infection. This model is important to facilitate vaccine development and understanding the immune response against particular vaccines.
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Referee #2
Evidence, reproducibility and clarity
This study focuses on the development of the model which can be further used as the model for developing a vaccine for the malaria parasite Plasmodium vivax. The researchers infected Aotus monkeys with one strain, achieved immunity, and then exposed them to a different strain. Four monkeys became immune to the initial strain, and three showed partial protection against the second strain. The researchers found that differences in genetic diversity and gene expression between strains are responsible for the varying levels of protection. This study provides insights for testing candidate vaccines against P. vivax. This model is unique and important for facilitating vaccine developments.
- The researchers provide a clear methodology and suitable for the proposed research questions.
- Did researchers observed any gametocytes after inoculations especially in the asymptomatic one or the prolong parasitemia. If they found, whether those gametocyte are infectious?
Significance
The asymptomatic infections are common in malaria endemic areas but it is hard to identify the underlying immune mechanism in response to the disease. This model tries to mimic the infection in nature by reinfecting the Aotus monkeys with different stains of the parasite and then assesses the immune response with main emphasis on antibody response to the infection. This model is important to facilitate vaccine development and understanding the immune response against particular vaccines.
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Referee #1
Evidence, reproducibility and clarity
To elucidate whether humoral immunity and/or genetic polymorphisms contribute to the protection against P. vivax blood-stage infection, the Authors assessed whether P. vivax strain-transcendent immunity can be achieved by repeated infection in Aotus monkeys. They infected six Aotus monkeys with blood stages of the P. vivax Salvador 1 (SAL-1) strain until obtaining sterile immunity, and then challenged with the heterologous AMRU-1 strain. Sterile immunity was achieved after two homologous infections, and partial protection against a heterologous AMRU-1 challenge was also achieved. IgG levels against parasite lysate by ELISA and protein microarray increased with repeated infections and correlated with the level of homologous protection. Although there were transcriptional differences in the P. vivax gene repertoire between SAL-1 and AMRU-1, there was no evidence of major antigenic switching upon homologous or heterologous challenge. These findings suggest that the partial protection observed during heterologous challenge is caused by genetic polymorphism between strains, rather than immune evasion by antigenic switching.
Major Comments:
- Title There are several non-human primate models, therefore, please specify "Aotus monkey model" in the title.
- Protein array Lines 373-374 "against major immunogenic blood stage antigens (Ags)" Please add selection criteria for how they select these 244 antigens. They prepared protein array (n=244) based on the SAL-1 sequence. Please add a discussion of how the data was affected by the sequence difference between SAL-1 and AMRU-1 strains. They described this point only on the top 7 targets (Lines 283-287). Any further difference in antibody reactivity between polymorphic and conserved antigens (SAL-1 and AMRU-1). Please also add a discussion on how they can interpret their protein microarray data because the E. coli-based IVTT proteins array detects antibody responses against linear epitopes of the printed antigens.
- Weakness Please summarize the weak points of this study (i.e. small number of animals used) in the Discussion section.
Minor Comments:
- Line 129 "inoculation level II" Please reword this to "2nd inoculation" throughout the manuscript because "inoculation level" is a bit confusing for the readers.
- Line 320 "pir genes" Please spell out because this is the first appearance in this manuscript.
- Line 373 "IVTT" Please spell out because this is the first appearance in this manuscript.
- Line 404 "VIR antigens" Please spell out because this is the first appearance in this manuscript.
- Line 498 "Goat anti-monkey Rhesus macaque)" This may be HRP-labelled? Please correct.
- Line 512 "temperature Plates" should be "temperature. Plates".
- Line 514 "sulphuric acid 2.5M" should be "2.5M sulphuric acid".
- Line 516 "Plasmodium falciparum" should be "Plasmodium vivax".
- Line 524 "Escherichia.coli" should be "Escherichia coli".
- Line 604 "is spleen-dependent (ref)" Please add a reference.
- Line 1099 "core genes" Please add a description of what core genes mean.
- Figure S2 Please label each panel in Figure S2 A&B. Maybe I, II, III, IV from the left. Please also revise the label of the X-axis in Figure S2C because "Inoculation level" is misleading.
Referees cross-commenting
I agree with Reviewer#2 comments.
Significance
- General assessment: This is a valuable and important study conducted by qualified experts in this research field. All the works were carefully designed, and clearly presented, and the manuscript is well written.
- (1) Strongest and most important aspects? Aotus monkey study with intensive data acquisition including humoral immune response and detailed parasite transcriptomic investigation
- (2) Weakness The number of animals used is rather small.
- Advance: Does the study extend the knowledge in the field and in which way? Not only the humoral immune responses but also parasite transcriptomics data is examined for the first time.
- Audience: Malariologists will be interested in or influenced by this research The data in this study will be the basis of future whole-parasite-based vaccine development.
My field of expertise is malariology and malaria vaccine research.
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
1. General Statements
We express our gratitude to the reviewers for their time and insightful comments, which have significantly contributed to the enhancement of our manuscript. We believe that the thoughtful critiques and suggestions have substantially improved the overall quality of our work. The changes made in the revised manuscript were highlighted in red. Below, we provide a point-by-point response to each comment, addressing the concerns raised by the reviewers.
2. Point-by-point description of the revisions
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
*Summary: *
*In the current study, Li et al investigated how TGF-beta signaling is controlled by protein abundances. Computational modeling and experiments indicated that the abundance of TGFBR1 and TGFBR2 affects the signaling, and those with lower abundance affect the signaling more, resembling Liebig's law of the minimum. Specifically, they showed that by using multiple cell lines with a different abundance of receptors, modulation of expression of the less abundant receptor impacts the signaling, which is measured by SMAD2 nuclear-to-cytosol ratio and/or relative phospho-SMAD2 level. Also, by using a light-induced interaction system, they showed that the signaling is dependent on the concentration of receptor complex when both receptors are expressed at similar amounts. *
*Major comments: *
*Computational predictions support the authors' idea. The computation and the experiments are well-documented. And it would gain substantially if the authors fill the gap between the predictions and the experiments as follows. *
*In Figure 4, the authors showed that perturbation on receptors with lower expression levels in each cell line changes the phospho-SMAD2 level. Although the data looks consistent with their claim, the result is only qualitative. The authors established a computational model in the former sections, thus it would be of great interest to assess if the experimental results quantitatively match the computational prediction. *
Response: The reviewer suggests that our work could benefit from a quantitative comparison between computational predictions and experimental data shown in Figure 4. We appreciate this suggestion. Given the challenges in obtaining precise quantification of TGFBR1 protein due to antibody issues (see the response to comment #2 from reviewer 2), a direct quantitative comparison between model predictions and experimental results is difficult. Our model predictions about the control principle with Liebig's law of the minimum should be interpreted qualitatively, rather than a strict quantitative law. We have explicitly indicated in the revised manuscript that our siRNA knockdown experiments are to qualitatively test our model predictions.
*In Figure 5, the authors computationally predicted that the expression level of receptors is correlated with SMAD2 N2C levels 1 hour after stimulation, and the strength of negative feedback with SMAD2 N2C levels 8 hours after stimulation. Because the authors employed iRFP-SMAD2 system, the prediction could be verified experimentally, at least the prediction on SMAD2 N2C 1 hour after stimulation could be checked. (In a sense, this is partially verified by the data in Figure 7, where both receptors are expressed at similar levels). It would gain substantially if the authors could verify the computational prediction in Figure 6. Since the authors stated in the introduction that "The same TGF-beta ligand can initiate different signaling responses depending on the cellular context, but the underlying control principle remains unclear...Together, these results revealed an effect of the minimum control in the TGF-beta pathway, which may be an important principle of control in signaling pathways with context-dependent outputs.", experimental verification of the prediction done in Figures 4-6 will be very important. Or the authors should stress that these points are only predicted by computational models. *
__Response: __The reviewer recommends verifying the model predictions in Figure 6 experimentally, particularly regarding SMAD2 N2C levels 1 hour after stimulation. We appreciate this valuable suggestion, which was also raised by reviewer 2. In response, we conducted experiments as recommended by reviewer #2, in which imbalanced expression of TGFBR1 and TGFBR2 was achieved by transfecting optoTGFBR1 or optoTGFBR2 plasmids into optoTGFBRs-HeLa cells, which initially expressed similar levels of both receptors. Western blot analysis confirmed the desired imbalance (Figure S13).
Consistent with the model predictions (Figure 6), the strong correlation between SMAD2 N2C fold change response at 1h and optoTGFBR2-tdTomato expression levels persisted in single cells when optoTGFBR1 was overexpressed (Figure 8A). Conversely, the high correlation between nuclear SMAD2 signaling and optoTGFBR2-tdTomato expression levels vanished at single cell level when optoTGFBR2 was overexpressed (Figure 8B). These experimental results validate our model predictions, confirming that the SMAD2 signaling is determined by the low abundance TGF-beta receptor in single cells. Incorporating these experimental validations enhances the quantitative support for our model predictions and clarifies the relationship between TGF-beta receptor abundance and signaling outcomes in single cells.
*As written in the below "Significance" section, the result is, in a sense, obvious. It should be stated that because the study utilized a slightly high concentration of TGF-beta in the experiments, it might be natural that the low-abundance receptor becomes a bottleneck of the signaling. It would gain to assess how receptor abundance affects signaling with the stimulation of lower concentrations of TGF-beta, or to examine the computational model if the low abundance of a receptor becomes a bottleneck of signaling because of saturation. Also, it is highly recommended to discuss the physiological implication of the current study, taking into account the experimental conditions used. *
Response: We appreciate the reviewer's insightful comments regarding the concentration of TGF-beta used in our experiments and the potential influence on the model predictions. In our experiments and model simulations, we utilized 100 pM TGF-beta, equivalent to 2.5 ng/mL (not 4.4 ng/mL as calculated by the reviewer). This concentration is a widely used dose in TGF-beta signaling studies. The reviewer's suggestion to explore how varying TGF-beta concentrations might influence the minimum control concept prompted us to extend our computational simulations. We used the extended model to perform simulations with lower TGF-beta concentrations (25 pM, equivalent to 0.625 ng/mL, and 10 pM, equivalent to 0.25 ng/mL). The results, depicted in Figure S7 of the revised manuscript, reaffirm that even at lower TGF-beta stimulations, a low abundance of a TGF-beta receptor acts as a bottleneck for SMAD2 signaling.
Following the reviewer’s suggestion, we have incorporated additional paragraphs to discuss the physiological implications and potential limitations of our study (Page 16-17 in the Main text).
It is pertinent to note that while the concept of TGF-beta signaling response being dictated by the minimum abundance of TGF-beta receptors may seem intuitive or even obvious, theoretical and experimental validations are crucial. As demonstrated in Figure S1B, our new simulation results from the minimal model illustrate similar response profiles when a high binding affinity (K1) is set for ligand-receptor interactions (Figure S1A). However, with a small binding affinity (K1), the minimal model indicates that TGF-beta signal response remains proportional to the product of TGFBR1 and TGFBR2 abundance and can be sensitive to the change of high abundance receptor in some region (Figure S1B). This highlights that the observed response patterns aligning with Liebig's law of the minimum depend on the binding affinity of ligand-receptor interactions in our minimal model. Consequently, the intuitive idea about Liebig's law of the minimum is not necessarily true theoretically. Moreover, given the non-linearity of the TGF-beta network, this complexity introduces an additional layer of uncertainty regarding the applicability of the minimum control principle to TGF-beta responses. This uncertainty led us to develop an extended model, with parameter values either experimentally measured or estimated from time course experimental data. The extended model predicted a similar minimum control principle at the TGF-beta receptor level, inspiring us to validate this prediction through diverse experiments. While we acknowledge the intuitive nature of our findings, we believe it is important for the field to prove this expectation, as emphasized by reviewer 4.
Reviewer #1 (Significance (Required)):
*TGF-beta signaling is one of the most rigorously studied pathways both computationally and experimentally. As written in the introduction of the manuscript, it is still unknown how the variability of responses arises not only between cell types but also differences among cells of single cell type. Studies showed that protein abundance accounts at least partly for a source of cell variability in TGF-beta signaling. While former studies examined the variability in SMAD protein abundance, the uniqueness of this study is that it focused on the abundance of TGF-beta receptors. *
*Given that both TGFBR1 and TGFBR2 are involved in the signaling, however, it's not difficult to imagine that a less abundant receptor affects the signaling more than the other, and serves as a bottleneck for the signaling. Specifically, because a slightly high concentration (100pM = 4.4 ng/mL of TGF-beta; other studies used much lower conc., e.g. 0, 0.03, 0.04, 0.07, and 2.4 ng/mL in Frick et al, PNAS, 2017, and 0, 1, 2.5, 5, 25, and 100 pM in Strasen et al, Mol Syst Biol, 2017) is used throughout the experiments to check cell-cell variability and the effect of receptor abundance in the current study, the formation of the receptor-ligand complex may be quite fast and be saturated at the level where the receptor with lower abundance is exhausted. In the reviewer's humble opinion, the authors' statement that this is Liebig's law of the minimum sounds a bit exaggerated. *
Nevertheless, the study is of some value because it utilized both computational and experimental analysis to show it is indeed the case. Of note, the current study showed that the variability in the different proteins leads to the variability in different time points, namely, the variability in the receptor abundance leads to the variability 1 hour after stimulation, while that in negative feedback strength leads to the variability 8 hours after stimulation. If the authors fill a small gap between their computational analysis and experimental verification, the study will be of interest to the specialist in the field.
__Response: __We are grateful for the valuable feedback provided by the reviewer. The concerns related to the TGF-beta dose have been thoroughly addressed in our responses to previous comments. Regarding the observation that the term "Liebig's law of the minimum" may sound a bit exaggerated, we acknowledge this consideration. We have refined the title to "Liebig’s Law of the Minimum in the TGF-β/SMAD Pathway," specifying its relevance to SMAD signaling exclusively, as non-SMAD signaling was not within the scope of this study. We appreciate the reviewer's constructive feedback and hope these adjustments enhance the specificity and accuracy of our manuscript.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Li et al. present an interesting and intuitive concept for the sensitivity and heterogeneity of biological networks: When two or more proteins form a functional complex, it is the limiting component with the lowest concentration that is most sensitive to perturbations and whose fluctuations dictate cell-to-cell variability of complex function. The authors apply this concept to the TGFb pathway and discuss sensitivity of SMAD signaling towards TGFb receptor I and II fluctuations. The paper is clearly written and convincing, but some improvements in the experimental validation would be beneficial as detailed further below.
1) The authors claim that the ratio of TGFb receptor I and II is very different across cell lines (Fig. 1) and use this observation for the validation of their model in Fig. 4. However, the relative expression TGFb receptor levels are purely based on RNAseq data which does not necessarily imply similar behavior at the protein level, especially on the cell surface. To address this issue, the authors should ideally provide absolute Western blot measurements of TGFbRI at the protein level to complement their absolute quantification of TGFbRII (Fig. S2). At the very least they should show that the observed relative expression levels of TGFbRI and II at the protein level (Figure S7) are correlated to differences in RNA levels (Fig. 1) using protein quantification. They should also confirm that similar receptor ratios for these receptors at the RNA level are observed in other published RNAseq datasets of the same cell lines (e.g., ENCODE for HepG2 and published RNAseq studies in HaCaT). Furthermore, they might take into account published mass spec datasets for quantifications of TGFbR protein levels.
Response: We appreciate the reviewer's thorough evaluation and constructive suggestions.
(A) Absolute quantification of TGFBR1: We acknowledge the importance of obtaining absolute quantification of TGFBR1 protein similar as what we have done for TGFBR2 protein (Figure S2). Despite significant efforts, our attempts to achieve this were hindered by challenges with available TGFBR1 antibodies and recombinant TGFBR1 proteins. Many commercial antibodies failed negative controls with TGFBR1 knockdown samples, while others validated TGFBR1 antibodies could not recognize the available recombinant TGFBR1 protein standards.
Although many mass spectrometry proteomics data available for different cell lines, it is difficult to convert these MS quantitative values to absolute protein abundance as mentioned in a recent publication (Nusinow et al.,bioRxiv 2020.02.03.932384): “Importantly, these values are all relative values to the other values for that same protein and not absolute values. This means that comparing the levels of different proteins to each other without using something like a correlation to standardize values won’t produce meaningful results.”
We share the reviewer's concern and fully agree that obtaining this absolute quantification is crucial. However, at the present stage, technical limitations prevent us from providing this information for TGFBR1. We commit to pursuing this aspect when feasible in the future.
(B) Validation of relative TGF-beta receptor expression ratios: Following the reviewer's suggestion, we conducted additional analyses to validate the relative expression ratios of TGFBR1 and TGFBR2 using different RNA-Seq databases. The results, presented in Table S1, demonstrate consistent imbalances in TGFBR1-to-TGFBR2 ratios across HepG2 and RH30 cell lines from various data sources, reinforcing the reliability of our observations.
(C) Correlation between RNA and protein expression: We appreciate the reviewer highlighting the challenges associated with correlating RNA and protein expression. Indeed, the correlations between RNA and protein levels vary widely, and direct comparisons can be challenging. To address this, we referenced a recent study (Nusinow et al., Cell 2020, 180:387), which reported that the protein data of TGFBR1 and TGFBR2 were highly correlated with the corresponding RNA data from the same cell line (Spearman’s correlation: 0.672 for TGFBR1, 0.771 for TGFBR2) based on quantitative proteomics and RNA expression data from 375 cancer cell lines.
2) Figure 4: To better judge the reproducibility of the knockdown titration, it would be good to show the different siRNA concentrations as a color code- Alternatively, TGFBR expression could be plotted as a function of the siRNA concentration in a Supplemental Figure, showing the effects of individual replicates.
Response: We thank the reviewer for the suggestion to enhance the clarity of the knockdown titration data. In response, we have now presented the quantified experimental data from three replicates with different colors in Figure 4. Additionally, we have created Figure S9 that plots the expression levels of relative TGFBR1 and TGFBR2 as a function of siRNA concentration, providing a more detailed view of the effects across individual replicates.
3) The simulations in Figs. 5 and 6 show that SMAD signaling fluctuations are mainly determined by cell-to-cell variability of receptor levels when using the SMAD nucleocytoplasmic ratio as a readout, and this is especially true for early time points. For downstream cellular responses, the absolute concentration of phosphorylated SMAD (complexes) in the nucleus is likely more relevant. Based on the authors work and evidence from the literature, I expect that this quantity will likely be heavily be influenced by receptor levels as well, but fluctuations in SMAD expression will play an important role as well. The authors should discuss this issue, and clarify that normalized quantities like SMAD N2C and pSMAD/SMAD mostly characterize receptor-level fluctuations while filtering SMAD fluctuations.
__Response: __We acknowledge the importance of discussing the relevance of different readouts in our study. In the revised manuscript, we have incorporated a discussion addressing this issue. Specifically, we highlight that while the SMAD nucleocytoplasmic ratio is sensitive to cell-to-cell variability in low abundance receptor levels, the absolute concentration of phosphorylated SMAD in the nucleus may be more relevant for downstream cellular responses (e.g.: gene expression). We have cited the work by Lucarelli et al, which demonstrated that variations in SMAD abundance could modulate the balance of different SMAD complexes, thereby regulating heterogeneous gene expression in diverse cell types (Lucarelli et al., Cell Systems 2018).
4) The single-cell measurements in Fig. 7 are interesting, but can only partially be seen as a direct validation of the model predictions, as it seems expected that varying the total input by introducing co-fluctuations in both receptors heavily influence the SMAD level. Wouldn't it be possible to design more specific validation experiments, in which the receptor co-expression construct (Fig. 7C) is used for baseline optoTGFBR expression and combined with an individual expression construct for one of the opto-receptors? This way, the authors could establish different regimes, in which one of the two receptors becomes dominant, and the impact fluctuations could be analyzed in a larger receptor expression space. Of course, a full validation of all possible scenarios is not necessary, but it would, for instance, be valuable to see whether the strong dependency of SMAD signaling of TGFBR2 levels vanishes when TGFBR2 is expressed at a higher level than TGFBR1.
Response: We appreciate the insightful comments and suggestions provided by the reviewer. Based on these recommendations, we have conducted additional experiments to further validate our model predictions. Reviewer 1 also raised this point, we quote our aforementioned response here: “consistent with the model predictions (Figure 6), the strong correlation between SMAD2 N2C fold change response at 1h and optoTGFBR2-tdTomato expression levels persisted in single cells when optoTGFBR1 was overexpressed (Figure 8A). Conversely, the high correlation between nuclear SMAD2 signaling and optoTGFBR2 expression levels vanished at single cell level when optoTGFBR2 was overexpressed (Figure 8B). These experimental results validate our model predictions, confirming that the SMAD2 signaling is determined by the low abundance TGF-beta receptor in single cells. Incorporating these experimental validations enhances the quantitative support for our model predictions and clarifies the relationship between TGF-beta receptor abundance and signaling outcomes in single cells.”
**Referees cross-commenting**
Comments from R2: I agree with most comments of the other reviewers, and highlight the most important overlaps with my comments below.
I agree with R1 that the model validation in Fig. 7 is incomplete and think that this will be a key point to improve the quality of the manuscript (see also my reviewer comment 4)
In line with R3 and R4, I think that the SMAD N/C simulations do not necessarily imply effects on TGFb target gene expression, cell fate decisions or human pathologies. The significance of the results for cellular behavior should be discussed (see also my comment 3)
__Response: __We are grateful for the reviewer's thoughtful comments. These comments have been now addressed (see our responses to the corresponding comments).
Reviewer #2 (Significance (Required)):
The manuscript presents an interesting and intuitive concept for the sensitivity and heterogeneity of biological networks. The authors apply this concept to the TGFb pathway and discuss sensitivity of SMAD signaling towards TGFb receptor I and II fluctuations.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
*Summary: *
*This is an interesting study that examines the output of the TGF-Beta pathway and how abundance/dosage can determine the signaling response in single cells across multiple cell types. The study is primarily mathematical. The focus is on the Type 1 and 2 TGF-Beta receptors driving nuclear SMAD2 expression. The authors observe that SMAD2 phosphorylation is sensitive to variations in the lower levels of either receptor but robust at variations of high abundance of the receptor reflected through SiRNA experiments shown in Figure 4. Their conclusion is that the feature is consistent with Liebig's law of the minimum- where in this case- a low abundance of the receptor serves as the rate-limiting step in signaling for this pathway. *
*Major comments: *
*- While the data as presented are interesting, it is unclear as to whether the abundance regulates biological function. SMAD2 phosphorylation is shown with some nuclear translocation. However, TGF-Beta target gene activation is not shown, and this needs to be completed. *
Response: We appreciate the reviewer's constructive comment. We have conducted new experiments and included quantitative real-time PCR data in the revised manuscript to evaluate the impact of TGFBR1 and TGFBR2 knockdown on the expression of TGF-beta target genes, such as SMAD7, PAI1, and JUNB. The results, presented in Figure S11, demonstrate differential sensitivity of these genes to the downregulation of TGFBR1 and TGFBR2 in various cell lines (HaCaT, HepG2, and RH30). Specifically, the expression of SMAD7, PAI1, and JUNB is sensitive to TGFBR2 knockdown in RH30 cells, while it is sensitive to TGFBR1 knockdown in HepG2 cells. HaCaT cells, expressing similar levels of both receptors, show comparable sensitivities to reductions in both TGFBR1 and TGFBR2. These findings provide additional insights into the regulatory role of TGF-beta receptor abundance on downstream target gene activation, complementing our study's focus on SMAD2 phosphorylation and nuclear translocation.
*- In addition, it is unclear as to what happens to SMAD3 and SMAD4 which are expressed endogenously in this setting. How are these other TGF-Beta signaling molecules addressed by these observations? *
__Response: __Thank you for bringing up this important point. In our study, the expression levels of endogenous SMAD2 and SMAD4 were found to be similar across HaCaT, RH30, and HepG2 cells. However, SMAD3 expression was notably lower in RH30 and HepG2 compared to HaCaT cells. The central conclusion of our study is based on the observed common control principle, which hinges on the relative expression levels of TGFBR1 and TGFBR2. Consequently, the applicability of this principle is more pertinent when comparing signal responses within the same cell type.
We acknowledge the relevance of endogenous SMAD proteins, and in the revised manuscript, we have expanded our discussion on how differences in SMAD protein expression levels and potential mutations (page 16 in main text), as observed in certain cancers, could influence the formation of homo- and hetero-oligomeric SMAD complexes. These considerations contribute to a more comprehensive understanding of downstream gene expression responses, as discussed in the work of Lucarelli et al. (Cell Systems 2018).
*-Specific biological readouts- cell differentiation etc. are not examined and would need to be provided and discussed. Therefore, the claims put forward while interesting require additional experiments examining SMAD2 target gene activation and biological readouts. *
__Response: __We appreciate this valuable suggestion. While we acknowledge the importance of exploring long-term biological responses, including cell differentiation, it is crucial to note that specific biological readouts are not solely dependent on SMAD signaling; they also involve other non-SMAD signaling pathways. Additionally, these responses are highly cell type-specific. Undertaking extensive investigations into these responses would extend beyond the current scope of our work. Nevertheless, we have discussed this topic in the revised manuscript (page 16 in main text).
Following the reviewers’ suggestion on examining TGF-beta target genes, we have performed experiments examining the expression of SMAD7, PAI1, and JUNB with respect to the changes of TGFBR1 and TGFBR2, respectively (see our response to the first major comment of this reviewer).
*- Lastly, statistical analyses are not provided and would need to be provided. For instance, in Figure 4, how many experiments were replicated and statistical analysis performed for this Figure? *
__Response: __In addressing this concern, we conducted three siRNA knockdown titration experiments for each cell line, as detailed in the figure legend. Due to batch effects, different percentages of TGF-beta receptors were knocked down in different experiments using the same concentration of siRNA. To transparently present the data, we utilized a scatter plot. Following the suggestion from reviewer 2, we have further enhanced the clarity of our data presentation by labeling the results of different experiments with a color code. In addition, we have performed statistical analysis of TGF-β receptor fold-change effects leading to a 50% reduction in the P-Smad2 response compared to that in the non-targeting siRNA control group (EC50) during siRNA knockdown experiments (Figure S10). The results of this analysis unveil significant differences in the sensitivities of pSMAD2 responses to variations in TGFBR1 and TGFBR2 within RH30 and HepG2 cells.
Reviewer #3 (Significance (Required)):
*- Conceptually this is an important study because dosage is a prominent issue in TGF-Beta signaling. For instance, in my field of expertise- mouse models of TGF-beta signaling e.g. SMAD2 knockouts- the cancer phenotypes are evident in haploid animals. Yet how and why dosage plays such a large role in tumorigenesis remains unclear. *
__Response: __We sincerely appreciate your recognition of the conceptual importance of our study in addressing the dosage-related complexities of TGF-beta signaling. Your insights into dosage effects in mouse models, particularly in haploid animals, highlight the relevance of our work underlying tumorigenesis. We have incorporated relevant citations and expanded our discussion in the revised manuscript, providing additional context to the importance of dosage in tumorigenesis (page 18 in main text).
Reviewer #4 (Evidence, reproducibility and clarity (Required)):
Summary: In this study, Li and co-workers combined computational modeling and experimental analysis to study the dependence of the output of the TGF-beta pathway on the abundance of signaling molecules in the pathway, mainly the most upstream regulators of SMAD2, TGFbeta type I and type II receptors. They showed by a combination of biochemical studies (mainly pSmad2 WB and type I/II receptor expression profiling) in HaCaT and HeLa cells as well as stable optogenetical receptor variants expressed by those cell lines, that TGF-beta receptor abundance influences signaling outputs using the concept of Liebigs law of the minimum, meaning that the output-modifying factor is the signaling protein that is most limited, to determine signaling responses across cell types and in single cells.
*Major comments: *
The study is very interesting, the combination of biochemistry and computational modeling to better understand the compexity of the TGFbeta pathway is very much required in the field and should stimulate others to further expand this approach.
__Response: __Thank you for the positive evaluation of this work.
*However, the authors must further explain that the model depicted here to explain pathway kinetics and dynamics lacks multiple crossroads and feedbacks and is until now oversimplified in the manuscript. They have mentioned receptor internalization and recycling, nuclear import and export of SMAD protein, and the feedback regulations e.g. by SMADs regulating receptor expression. Beyond, there is non- SMAD signaling (Derynck et al.; SMAD Linker regulation, deRobertis et al.), different receptor oligomerization modes (Ehrlich/Henis et al.) and heteromeric receptor complexes of TGFbeta receptors known (Hill et al.), that further diversify beyond these mentioned mechanisms. It is understandable that the mathematical model cannot include those considerations to date, however, they must be further explained and commented on to allow that this model can be expanded in the future. *
Response: We acknowledge that there are multiple crossroads and feedbacks that exist in the TGF-beta signaling pathway that have not been explicitly incorporated into our model. We appreciate the reviewer's understanding that current model cannot include these considerations and his/her suggestions for potential future extensions. In the revised manuscript, we have mentioned one of the limitations of our model: non-Smad signaling and crosstalk with other signaling pathways were not considered for simplicity. We have also discussed how to expand this model by including these regulations when more quantitative data are available in the future (page 16-17 in main text).
*A myriad of research labs focus on these intricate fine tuning ot the TGFbeta pathway by those mechanisms which makes the difference between "good" TGFbeta signaling and "bad" TGFbeta signaling in different context and this complexity must be acknowledged by more introduction and discussion. *
Response: In the revised manuscript, we have added an introduction and discussion about the dual role of TGF-beta signaling (page 4 and page 18 in main text).
*The model here will be important to explain *
*A: the mode of heterooligomeric TGFbeta/BMP receptor assemblies as e.g. found in pathological conditions and *
B: Can maybe explain the formation of mixed SMAD complexes as activated by lateral signaling comprising TGFbeta *and BMP receptors once one receptor is of lower abundance to form a high affinity complex. *
*It is therefore required to comment on these aspects at multiple points in the manuscript. *
*It is very important that the visual model used in this manuscript depicts on the possibility, that a TGFbeta type I receptor can team up with e.g. another TGFbeta type I receptor together with two TGFbeta type II receptors but also with an activin type II receptor or that a BMP type I receptor (e.g. ALK1) can form heterooligomeric complexes with ALK5 (TGFbeta type I). *
__Response: __Thank you for this comment. We cited the relevant work (Ramachandran et al, eLife 2018; Szilagyi et al, BMC Biology 2022) and added a discussion about the complexity of the mode of heterooligomeric TGFbeta/BMP receptor assemblies and its effect on the induction of mixed SMAD complexes (page 17 in the main text).
*While the use of optogenetical TGFbeta receptor biosensors is highly interesting, their mode of oligomerization is not yet fully described. It is not known if those biosensors behave like wt receptors in terms of oligomerization and ligand binding. This should be mentioned somewehere. For this reason, the authors should also consider to draw the TGFbeta receptor complex in the cartoons with more detail towards the heterooligomeric assembly that is standard to the field. *
__Response: __The reviewer is correct that the optogenetic TGF-beta receptors might behave differently from the natural TGF-beta receptor system in terms of ligand binding. We have added this point in the Discussion part to highlight the potential difference between the optogenetic TGF-beta systems and the wild-type system (page 16 in the main text).
*While the general finding is not surprising (manipulating the receptor with the lowest abundancy has the biggest impact on signaling output) the methods and models used here are very important to the field to proof that this expectation is actually true and can be experimentally addressed by a combination of bioinformatics and biochemistry. The model developed will be valuable to expand to much more complex and interesting questions in TGFbeta signaling and possibly also BMP signaling e.g. in pathological context (see below). *
*Minor comments: *
*The authors should discuss their findings in the context of: *
- non-Smad signaling outputs (similar or different to the observations on pSMAD2)*
- What do these findings mean for e.g. human pathologies, where type I or type II receptor expression is altered? *
- Can those findings integrate into the "switch" in TGFbeta signaling? *
- How do these findings translate towards BMP SMAD 1/5/9 signaling? * Response: First, we sincerely appreciate the reviewer’s recognition that our work is very important to the field in proving that manipulating the receptor with the lowest abundance has the biggest impact on signaling output. The reviewer’s suggestions about discussing our work in the context of non-Smad signaling, BMP SMAD1/5/9 branch, and the relevance to the dual role of TGF-beta signaling are all constructive. We have incorporated these suggestions and discussed them in the revised manuscript (page 17 in the main text).
Reviewer #4 (Significance (Required)):
*The manuscript is novel and interesting, partiular the combination of bioinformatical and biochemical approaches. The use of optogenetics is state-of-art while some more care should be given to interpretation of results with optogenetical TGfbeta receptor biosensors, is is not known if they really behave similar in terms of receptor oligomerization and signaling. Also it is not shown how their interactome in terms of effector proteins looks like that can potentially influence SMAD signaling output (e.g. Phosphathases to SMADs known to interact with wt receptors). *
*The models drawn need to depict more accurately on the nature of type I and type II receptor complexes (heterotetrameric) and high affinity towards the ligand. The current versions are too oversimplified at this stage. The pathway crosstalks and feedbacks need to be more visible, in order for non experts to not draw too simple conclusions from the visual representations presented in this MS. Particularly the work by Hill and co-workers on receptor oligomerization and SMAD shuttling and feedback need to be included. *
Overall, the manuscript is very significant to the field.
__Response: __We would like to thank the reviewer again for his/her positive evaluation of the novelty and significance of our work. We have taken the reviewer's comments into consideration and made revisions to the manuscript. We now provide more information on the limitations of our current model and the optogenetic TGF-beta receptor biosensors in the Discussion section. We have also included more details about the receptor complex nature and the high affinity towards the ligand. The ligand receptor complex in the model is now drawn as heterotetrametric complex (1 ligand dimer with two TGFBR1s and two TGFBR2s). Additionally, we have incorporated information about pathway crosstalks and feedbacks, giving a more comprehensive view for non-experts. The work by Hill and co-workers on receptor oligomerization, SMAD shuttling, and feedback has been included in the revised manuscript to provide a more complete and accurate representation of the current knowledge in the field.
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Referee #4
Evidence, reproducibility and clarity
Summary:
In this study, Li and co-workers combined computational modeling and experimental analysis to study the dependence of the output of the TGF-β pathway on the abundance of signaling molecules in the pathway, mainly the most upstream regulators of SMAD2, TGFbeta type I and type II receptors.
They showed by a combination of biochemical studies (mainly pSmad2 WB and type I/II receptor expression profiling) in HaCaT and HeLa cells as well as stable optogenetical receptor variants expressed by those cell lines, that TGF-β receptor abundance influences signaling outputs using the concept of Liebigs law of the minimum, meaning that the output-modifying factor is the signaling protein that is most limited, to determine signaling responses across cell types and in single cells.
Major comments:
The study is very interesting, the combination of biochemistry and computational modeling to better understand the compexity of the TGFbeta pathay is very much required in the field and should stimulate others to further expand this approach.
However, the authors must further explain that the model depicted here to explain pathway kinetics and dynamics lacks multiple crossroads and feedbacks and is until now oversimplified in the manuscript. They have mentioned receptor internalization and recycling, nuclear import and export of SMAD protein, and the feedback regulations e.g. by SMADs regulating receptor expression. Beyond, there is non- SMAD signaling (Derynck et al.; SMAD Linker regulation, deRobertis et al.), different receptor oligomerization modes (Ehrlich/Henis et al.) and heteromeric receptor complexes of TGFbeta receptors known (Hill et al.), that further diversify beyond these mentioned mechanisms. It is understandable that the mathematical model can not include those considerations to date, however, they must be further explained and commented on to allow that this model can be expanded in the future. A myriad of research labs focus on these intricate fine tuning ot the TGFbeta pathway by those mechanisms which makes the difference between "good" TGFbeta signaling and "bad" TGFbeta signaling in different context and this complexity must be acknowledged by more introduction and discussion.
The model here will be important to explain
A: the mode of heterooligomeric TGFbeta/BMP receptor assemblies as e.g. found in pathological conditions and
B: Can maybe explain the formation of mixed SMAD complexes as activated by lateral signaling comprising TGFbeta nd BMP receptors once one receptor is of lower abundance to form a high affinity complex.
It is therefore required to comment on these aspects at multiple points in the manuscript.
While the use of optogenetical TGFbeta receptor biosensors is highly interesting, their mode of oligomerization is not yet fully described. It is not known if those biosensors behave like wt receptors in terms of oligomerization and ligand binding. This should be mentioned somewehere.
For this reason, the authors should also consider to draw the TGFbeta receptor complex in the cartoons with more detail towards the heterooligomeric assembly that is standard to the field.
It is very important that the visual model used in this manuscript depicts on the possibility, that a TGFbeta type I receptor can team up with e.g. another TGFbeta type I receptor together with two TGFbeta type II receptors but also with an activin type II receptor or that a BMP type I receptor (e.g. ALK1) can form heterooligomeric complexes with ALK5 (TGFbeta type I).
While the general finding is not surprising (manipulationg the receptor with the lowest abundancy has the biggest impact on signaling output) the methods and models used here are verxy important to the field to proof that this expactation is actually true and can be experimentally adressed by a combination of bioinformatics and biochemistry. The model developed will be valuable to expand to much more complex and interesting questions in TGFbeta signaling and possibly also BMP signaling e.g. in pathological context (see below).
Minor comments:
The authors should discuss their findings in the context of: 1. non- Smad signaling outputs (similar or different to the observations on pSMAD2) 2. What do these findings mean for e.g. human pathologies, where type I or type II receptor expression is altered? 3. Can those findings intergate into the "switch" in TGFbeta signaling? 4. How do these findings translate towards BMP SMAD 1/5/9 signaling?
Significance
The manuscript is novel and interesting, partiular the combination of bioinformatical and biochemical approaches. The use of optogenetics is state-of-art while some more care should be given to interpretation of results with optogenetical TGfbeta receptor biosensors, is is not known if they really behave similar in terms of receptor oligomerization and signaling. Also it is not shown how their interactome in terms of effector proteins looks like that can potentially influence SMAD signaling output (e.g. Phosphathases to SMADs known to interact with wt receptors).
The models drawn need to depict more accurately on the nature of type I and type II receptor complexes (heterotetrameric) and high affinity towards the ligand. The current versions are too oversimplified at this stage. The pathway crosstalks and feedbacks need to be more visible, in order for non experts to not draw too simple conclusions from the visual representations presented in this MS. Particularly the work by Hill and co-workers on receptor oligomerization and SMAD shuttling and feedback need to be included.
Overall, the manuscript is very significant to the field.
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Referee #3
Evidence, reproducibility and clarity
Summary:
This is an interesting study that examines the output of the TGF-Beta pathway and how abundance/dosage can determine the signaling response in single cells across multiple cell types. The study is primarily mathematical. The focus is on the Type 1 and 2 TGF-Beta receptors driving nuclear SMAD2 expression. The authors observe that SMAD2 phosphorylation is sensitive to variations in the lower levels of either receptor but robust at variations of high abundance of the receptor reflected through SiRNA experiments shown in Figure 4. Their conclusion is that the feature is consistent with Liebig's law of the minimum- where in this case- a low abundance of the receptor serves as the rate-limiting step in signaling for this pathway.
Major comments:
- While the data as presented are interesting, it is unclear as to whether the abundance regulates biological function. SMAD2 phosphorylation is shown with some nuclear translocation. However, TGF-Beta target gene activation is not shown, and this needs to be completed.
- In addition, it is unclear as to what happens to SMAD3 and SMAD4 which are expressed endogenously in this setting. How are these other TGF-Beta signaling molecules addressed by these observations?
- Specific biological readouts- cell differentiation etc. are not examined and would need to be provided and discussed.
- Therefore, the claims put forward while interesting require additional experiments examining SMAD2 target gene activation and biological readouts.
- Lastly, statistical analyses are not provided and would need to be provided. For instance in Figure 4, how many experiments were replicated and statistical analysis performed for this Figure?
Significance
- Conceptually this is an important study because dosage is a prominent issue in TGF-Beta signaling.
- For instance, in my field of expertise- mouse models of TGF-beta signaling e.g. SMAD2 knockouts- the cancer phenotypes are evident in haploid animals. Yet how and why dosage plays such a large role in tumorigenesis remains unclear.
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Referee #2
Evidence, reproducibility and clarity
Li et al. present an interesting and intuitive concept for the sensitivity and heterogeneity of biological networks: When two or more proteins form a functional complex, it is the limiting component with the lowest concentration that is most sensitive to perturbations and whose fluctuations dictate cell-to-cell variability of complex function. The authors apply this concept to the TGFb pathway and discuss sensitivity of SMAD signaling towards TGFb receptor I and II fluctuations. The paper is clearly written and convincing, but some improvements in the experimental validation would be beneficial as detailed further below.
- The authors claim that the ratio of TGFb receptor I and II is very different across cell lines (Fig. 1) and use this observation for the validation of their model in Fig. 4. However, the relative expression TGFb receptor levels are purely based on RNAseq data which does not necessarily imply similar behavior at the protein level, especially on the cell surface. To address this issue, the authors should ideally provide absolute Western blot measurements of TGFbRI at the protein level to complement their absolute quantification of TGFbRII (Fig. S2). At the very least they should show that the observed relative expression levels of TGFbRI and II at the protein level (Figure S7) are correlated to differences in RNA levels (Fig. 1) using protein quantification. They should also confirm that similar receptor ratios for these receptors at the RNA level are observed in other published RNAseq datasets of the same cell lines(e.g., ENCODE for HepG2 and published RNAseq studies in HaCaT). Furthermore, they might take into account published mass spec datasets for quantifications of TGFbR protein levels.
- Figure 4: To better judge the reproducibility of the knockdown titration, it would be good to show the different siRNA concentrations as a color code- Alternatively, TGFBR expression could be plotted as a function of the siRNA concentration in a Supplemental Figure, showing the effects of individual replicates.
- The simulations in Figs. 5 and 6 show that SMAD signaling fluctuations are mainly determined by cell-to-cell variability of receptor levels when using the SMAD nucleocytoplasmic ratio as a readout, and this is especially true for early time points. For downstream cellular responses, the absolute concentration of phosphorylated SMAD (complexes) in the nucleus is likely more relevant. Based on the authors work and evidence from the literature, I expect that this quantity will likely be heavily be influenced by receptor levels as well, but fluctuations in SMAD expression will play an important role as well. The authors should discuss this issue, and clarify that normalized quantitites like SMAD N2C and pSMAD/SMAD mostly characterize receptor-level fluctuations while filtering SMAD fluctuations.
- The single-cell measurements in Fig. 7 are interesting, but can only partially be seen as a direct validation of the model predictions, as it seems expected that varying the total input by introducing co-fluctuations in both receptors heavily influence the SMAD level. Wouldn't it be possible to design more specific validation experiments, in which the receptor co-expression construct (Fig. 7C) is used for baseline optoTGFBR expression and combined with an individual expression construct for one of the opto-receptors? This way, the authors could establish different regimes, in which one of the two receptors becomes dominant, and the impact fluctuations could be analyzed in a larger receptor expression space. Of course, a full validation of all possible scenarios is not necessary, but it would, for instance, be valuable to see whether the strong dependency of SMAD signaling of TGFBR2 levels vanishes when TGFBR2 is expressed at a higher level than TGFBR1.
Referees cross-commenting
Comments from R2: I agree with most comments of the other reviewers, and highlight the most important overlaps with my comments below.
I agree with R1 that the model validation in Fig. 7 is incomplete and think that this will be a key point to improve the quality of the manuscript (see also my reviewer comment 4)
In line with R3 and R4, I think that the SMAD N/C simulations do not necessarily imply effects on TGFb target gene expression, cell fate decisions or human pathologies. The significance of the results for cellular behavior should be discussed (see also my comment 3)
Significance
The manuscript presents an interesting and intuitive concept for the sensitivity and heterogeneity of biological networks. The authors apply this concept to the TGFb pathway and discuss sensitivity of SMAD signaling towards TGFb receptor I and II fluctuations.
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Referee #1
Evidence, reproducibility and clarity
Summary:
In the current study, Li et al investigated how TGF-beta signaling is controlled by protein abundances. Computational modeling and experiments indicated that the abundance of TGFBR1 and TGFBR2 affects the signaling, and those with lower abundance affect the signaling more, resembling Liebig's law of the minimum. Specifically, they showed that by using multiple cell lines with a different abundance of receptors, modulation of expression of the less abundant receptor impacts the signaling, which is measured by SMAD2 nuclear-to-cytosol ratio and/or relative phospho-SMAD2 level. Also, by using a light-induced interaction system, they showed that the signaling is dependent on the concentration of receptor complex when both receptors are expressed at similar amounts.
Major comments:
Computational predictions support the authors' idea. The computation and the experiments are well-documented. And it would gain substantially if the authors fill the gap between the predictions and the experiments as follows.
In Figure 4, the authors showed that perturbation on receptors with lower expression levels in each cell line changes the phospho-SMAD2 level. Although the data looks consistent with their claim, the result is only qualitative. The authors established a computational model in the former sections, thus it would be of great interest to assess if the experimental results quantitatively match the computational prediction.
In Figure 5, the authors computationally predicted that the expression level of receptors is correlated with SMAD2 N2C levels 1 hour after stimulation, and the strength of negative feedback with SMAD2 N2C levels 8 hours after stimulation. Because the authors employed iRFP-SMAD2 system, the prediction could be verified experimentally, at least the prediction on SMAD2 N2C 1 hour after stimulation could be checked. (In a sense, this is partially verified by the data in Figure 7, where both receptors are expressed at similar levels). It would gain substantially if the authors could verify the computational prediction in Figure 6.
Since the authors stated in the introduction that "The same TGF-β ligand can initiate different signaling responses depending on the cellular context, but the underlying control principle remains unclear...Together, these results revealed an effect of the minimum control in the TGF-β pathway, which may be an important principle of control in signaling pathways with context-dependent outputs.", experimental verification of the prediction done in Figures 4-6 will be very important. Or the authors should stress that these points are only predicted by computational models.
As written in the below "Significance" section, the result is, in a sense, obvious. It should be stated that because the study utilized a slightly high concentration of TGF-beta in the experiments, it might be natural that the low-abundance receptor becomes a bottleneck of the signaling. It would gain to assess how receptor abundance affects signaling with the stimulation of lower concentrations of TGF-beta, or to examine the computational model if the low abundance of a receptor becomes a bottleneck of signaling because of saturation. Also, it is highly recommended to discuss the physiological implication of the current study, taking into account the experimental conditions used.
Significance
TGF-beta signaling is one of the most rigorously studied pathways both computationally and experimentally. As written in the introduction of the manuscript, it is still unknown how the variability of responses arises not only between cell types but also differences among cells of single cell type. Studies showed that protein abundance accounts at least partly for a source of cell variability in TGF-beta signaling.
While former studies examined the variability in SMAD protein abundance, the uniqueness of this study is that it focused on the abundance of TGF-beta receptors.
Given that both TGFBR1 and TGFBR2 are involved in the signaling, however, it's not difficult to imagine that a less abundant receptor affects the signaling more than the other, and serves as a bottleneck for the signaling. Specifically, because a slightly high concentration (100pM = 4.4 ng/mL of TGF-beta; other studies used much lower conc., e.g. 0, 0.03, 0.04, 0.07, and 2.4 ng/mL in Frick et al, PNAS, 2017, and 0, 1, 2.5, 5, 25, and 100 pM in Strasen et al, Mol Syst Biol, 2017) is used throughout the experiments to check cell-cell variability and the effect of receptor abundance in the current study, the formation of the receptor-ligand complex may be quite fast and be saturated at the level where the receptor with lower abundance is exhausted. In the reviewer's humble opinion, the authors' statement that this is Liebig's law of the minimum sounds a bit exaggerated.
Nevertheless, the study is of some value because it utilized both computational and experimental analysis to show it is indeed the case. Of note, the current study showed that the variability in the different proteins leads to the variability in different time points, namely, the variability in the receptor abundance leads to the variability 1 hour after stimulation, while that in negative feedback strength leads to the variability 8 hours after stimulation. If the authors fill a small gap between their computational analysis and experimental verification, the study will be of interest to the specialist in the field.
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Reply to the reviewers
Reviewer #1
We thank this Reviewer for the time spent assessing our manuscript, and for suggesting approaches to strengthen the robustness of the differences (e.g., TL vs FL) reported in our results. We have carefully addressed each point raised by this and other reviewers, providing new analyses and data - see list below. Indeed, these analyses combined helped us to make our main results reproducible, corroborating the main findings and refining the message of the manuscript.
New analyses/data added:
- *Effect of batch due to different lanes - comparison of DEGs (TL/FL) obtained when samples in different lanes are tested individually (new Figure S15). *
- Effect of batch correction on our results - comparison of the DEGs (TL/FL) obtained with and without batch removal (new Figure S15).
- Sensitivity of our enrichment results for GWAS significance – we performed the enrichment of GWAS genes using different GWAS thresholds, 10-6, 10-7, 5x10-8 (new Figure S14).
- Expression analysis of GRIN2A and SLC12A5 in Allen Brain Atlas data and qPCR results of GRIN2A and SLC12A5 in patients with frontal and temporal lobe traumatic injury (new Figure S12, Table S3).
- Comparison of the DEGs (TL/FL) with DEGs (autism/Ctrl) obtained from single cell RNA seq (new Figure S16, Table S7).
- Comparison of the results using the GWAS genes derived from Trubetskoy et al. with our gene lists (new Figure S17).
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Description of the data quality (Figure S2) Major points:
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The main limitation of the work is the small starting sample size. The authors studied 1 frontal lobe sample and 2 temporal lobe samples. Although this information was in Table S3 it would be good to include upfront in the Methods. snRNA-seq was generated on the 10x platform. It would be helpful to know if the 10x step and sequencing was performed as one batch, or as individual batches. Similarly, were the sample libraries all sequenced on the same lane, or different lanes. The authors do not state in the Methods how many nuclei they were targeting and this should be included. Sample pre-processing was well described and standard. We now provide additional details about the sequencing step (nuclei, sample pre-processing, etc.) in the revised manuscript (see Methods, and text below). The potential batch effect of the lane is discussed and addressed in the next point.
‘10X Genomics uses a microfluidic system for cell sorting. Cells and enzymes, combined with Gel Beads, enter the oil phase to form GEMs. The resulting sample libraries were sequenced on separate lanes. To enhance sequencing depth, the primary target number of nuclei for the two samples from TL is set at 10,000, considering an RNA integrity number (RIN) of 6.5. In contrast, for the sample from FL, the target is set at 20,000 nuclei due to a higher RIN of 8.1.’
In relation to Batch correction - as with any batch correction method, it is unclear whether the correction is adjusting for biological differences or technical. Since this is a study of the differences between FL and TL, would it not be more appropriate not to correct for batch, particularly as the samples were analysed individually - particularly if batch effects were carefully controlled for in the initial study design. The authors should test whether the results are robust to batch correction or not.
Since the samples are sequenced by different lanes of 10X platform we can’t exclude potential batch effects. To address this, we corrected the batch by CCA (canonical correlation analysis) which enhanced the clustering and the UMAP visualization, which is now less affected by batch-specific variations.
Moreover, in an attempt to account for the sample size limitation, we employed 3 approaches to confirm the main transcriptional differences between the 2 regions, and that these are “robust” to batch correction, as is shown in new Figure S15: (1) Comparison of the gene expression differences (2 TL vs 1FL) with and without removing batches (new Figure S15. a, c); (2) The results obtained by comparing the differences between each individual TL sample (processed in different lanes) and FL sample are contrasted with the results after batch removal (new__ Figure S15. b, d__); (3) To confirm a limited effect of lane, we provide analysis of the expression similarity of three samples which demonstrates, consistently for each major cell type and neuronal sub-types, a strong correlation between the two TL samples (form different lanes) as compared with FL (new Figure S15. e).
As shown in panel a, c, below, the majority of DEGs (2TL vs FL) identified with batch effects largely overlap with the DEGs (2TL vs FL) without considering batch effects for both major cell types and neuronal sub-types. In panel b, d, we show that the majority of DEGs with batch correction (2TL vs FL) overlap with the individual DEGs found in each TL vs FL comparison. In panel e, we show that the transcriptomic profiles of 2 TL exhibit higher similarity compared with the sample from FL. Overall, based on these analyses we concluded that our results are robust to batch correction.
In addition, we highlight that, differently from other tissues, it is very difficult to obtain the “fresh” human samples of brain cortex, which most likely provides different transcriptome information than the more commonly used post-mortem brain samples. These analyses offered another evidence supporting the differences between TL and FL, which complement (and align with) the comparative analyses using the data from Allen Brain Atlas (see Figure S9, original results).
Figure S15. Comparison of biological (gene expression) differences in each major cell type and neuron-subtype between the 2 regions with and without batch effect removal. ____a, c. Comparison of the DEGs (2 TL vs 1FL) with and without removing batches (a, up-regulated in TL; c, up-regulated in FL). b, d. Comparison of the DEGs (2TL vs FL) following the removal of batch effects with the DEGs calculated by individual TL vs FL samples. __e. __Expression correlation between each sample (without batch correction for lane), showing higher transcriptional similarity within the same tissue type than across tissues, consistently in major cell-types and neuronal subtypes.
3.Differential gene expression analyses between the FL and TL was undertaken using edgeR. It is unclear if this was performed on aggregated counts or not - i.e., sum of counts per gene per cell type. If it was, then with such a small sample size (1 frontal lobe and 2 temporal lobe samples), it is unclear how well edgeR will perform. Similarly, if the DE analysis was performed using individual gene per cell counts, then there is a type 2 error risk due to pseudoreplication. It is reassuring that the primary results were replicated in a second dataset. Moreover, the downstream analyses (functional enrichment analysis, heritability enrichment analysis etc) are designed to cope with noisy data so I'm happy with the broad conclusions.
We acknowledge the reviewer’s point, and here we specify that edgeR performs differential expression analysis at the level of individual genes across individual cells, and we performed DE analysis for each cell type. We and others consider edgeR a robust tool for analyzing RNA-Seq data; edgeR has been extensively benchmarked alongside other widely used statistical methods, e.g., edgeR-LRT and edgeR-QLF which showed high performance1. Another study about different tools for differential expression in single cell data demonstrated that edgeR (and others) has usually higher precision, larger than 0.9, yielding lower false positive2. Therefore, based on previous formal assessments showing the robustness of edgeR, we select this approach for DE analysis.
Moreover, it has been previously documented that edgeR can be used also to analyses small samples due to several inherent features. First, edgeR uses an empirical Bayes framework to estimate dispersion, which is a measure of the biological variability in gene expression. This approach uses information across genes, helping to stabilize the variance estimates even when sample sizes are small. This makes edgeR more robust in cases with a limited number of replicates. Second, edgeR accounts for overdispersion, which can effectively handle small sample sizes and provide more accurate statistical tests. In the revised manuscript, we now discuss the advantages of edgeR in Methods, in particular for edgeR performance on small sample size in single cell RNA seq.
It is unclear if this was performed on aggregated counts or not - i.e., sum of counts per gene per cell type
We specify that edgeR performs differential expression analysis at the level of individual genes across individual cells, and we performed DE analysis for each cell type. This is now indicated in Methods.
*4.To calculate the enrichment of "genetic risk" associated with psychiatric disorders, the authors used a hypergeometric test for the overlap between cell type specific genes and the GWAS variant-mapped genes for each disease, which is widely used to evaluate the enrichment of genetic risk genes. To identified GWAS variant mapped genes the authors used a GWAS SNP threshold of To test the sensitivity of the enrichment analysis, we selected the GWAS genes with each threshold respectively: 10-6, 10-7, 5x10-8. The new results are largely consistent with those obtained using a P-value of 10-5. Susceptibility genes for neuropsychiatric disorders are enriched for expression in neuronal cell types for each P-value. With respect to neuronal subtypes, we found stronger enrichment in INH than in EX sub-clusters, with INH PVALB, SST and EX L5 being the neuronal sub-clusters mostly enriched for expression of GWAS genes (new __Figure S14).
Figure S14 Cell type for expression of neuropsychiatric disorder associated GWAS genes with each threshold respectively: 10-6, 10-7, 5x10-8. a-c. adjusted P-value of enrichment in each 7 major cell type. d-f. adjusted P-value of enrichment in each neuron subtype.
Moreover, the Reviewer suggests using an alternative tool, FUMA, which requires the whole set of SNP GWAS associations. While these can be available for single diseases and GWAS data (assuming the authors made all data available, and assuming one obtains approval by the consortia managing the GWAS data), unfortunately these SNPs data are not available for several diseases in the NHGRI-EBI GWAS catalog, which provides only SNPs with a max P=10-5. Since in our study we wanted to consider GWAS data from 7 neuropsychiatric diseases, we pragmatically opted for obtaining data from NHGRI-EBI GWAS catalog rather than seeking GWAS SNP data from individual studies.
We also acknowledge the limitations for the variant to gene mapping (revised Discussion, page 17, line 17), and we also highlight that several other studies rely on the variant to gene mapping from NHGRI-EBI GWAS catalog for enrichment analyses3-5. There are also studies that investigate the enrichment of mapped genes (from NHGRI-EBI GWAS catalog) in different cell types using the hypergeometric test 6-7, as we do in our study. Therefore, the methods used in our manuscript are consistent with approaches adopted in previously published studies. Perhaps more importantly, in the revised manuscript, we replicated the main GWAS enticement results (e.g., in INH neurons and in PVLAB from the temporal lobe) in the Brain Allen Atlas datasets, which shows that, despite these limitations of variant to gene mapping, our main enrichment results are replicable. We discussed these limitations in our paper (see Discussion, page 17, line 6).
However, where individual genes are mentioned then the authors may wish to confirm the results from edgeR for a few selected genes with a second technique such as qPCR. For example, GRIN2A and SLC12A5.
To address this point, first, we check the expression of the 2 genes using the data from Allen Brain Atlas data, which show significantly high expression in TL (new Figure S12. b, and below). In addition, we carried out new qPCR analysis, and found the mRNA expression levels of GRIN2A and SLC2A5 in patients with traumatic brain injury in the temporal lobe region were higher than those in patients with frontal lobe injury (new Figure S12. c).
Figure S12. b. Expression level of GRIN2A and SLC12A5 in 2 regions using Brain Allen Atlas. ***P-value-ΔΔCt method. Significance was determined through T-test (two-tailed). qPCR for each TL or FL sample was repeated 3 times.
Reviewer #2
We thank this Reviewer for the time spent evaluating our manuscript. In the revised manuscript we have now included several new analyses and data that allowed us to replicate and strengthen our main findings, and especially we considered the psychoactive drug target genes using the whole psychoactive drugs DB. We believe these new data helped us to refine the message and overall improve reproducibility of the main findings presented. We have carefully addressed each point raised by this and other reviewers, by providing revisions and explanations, and adding new data to our manuscript, as follows:
New analyses/data added:
- *Effect of batch due to different lanes - comparison of DEGs (TL/FL) obtained when samples in different lanes are tested individually (new Figure S15). *
- Effect of batch correction on our results - comparison of the DEGs (TL/FL) obtained with and without batch removal (new Figure S15).
- Sensitivity of our enrichment results for GWAS significance – we performed the enrichment of GWAS genes using different GWAS thresholds, 10-6, 10-7, 5x10-8 (new Figure S14).
- Expression analysis of GRIN2A and SLC12A5 in Allen Brain Atlas data and qPCR results of GRIN2A and SLC12A5 in patients with frontal and temporal lobe traumatic injury (new Figure S12, Table S3).
- Comparison of the DEGs (TL/FL) with DEGs (autism/Ctrl) obtained from single cell RNA seq (new Figure S16, Table S7).
- Comparison of the results using the GWAS genes derived from Trubetskoy et al. with our gene lists (new Figure S17).
- Description of the data quality (Figure S2) 1.The manuscript is unfortunately lacking (supplemental) figures showing the preprocessing, batch effect correction, and cell type annotation of single nucleus RNAseq data. Although this part is described in the methods in detail, it is hard to judge if these parts were done properly if data is not shown in any of the figures. Regarding the batch effect correction, it reads as if the batch effects have been removed for both brain regions separately. This potentially introduces a bias between brain regions that hugely questions the later performed analysis of differential expression analysis in FL vs TL. In any case, this analysis is not convincing since it has been performed on n=3 vs. n=3 samples and is thus tremendously underpowered.
We thank the reviewer for the suggestions. First, we added the cell type annotation process for the major cell type by showing the expression of known markers in Figure S2. f. To show the validity of our cell classification, we calculated the significance of overlap with major cell type markers derived from known study in Figure S2. e. __We also provide the distribution of nUMI, nGenes, percentage of mitochondrial genes after quality control in Figure S2. b __to show the large number of cells contributing to the overall quality and depth of the scRNA-seq dataset despite the small number of individual samples.
Figure S2. Description of snRNA-seq data. b. Distribution of nUMI, nGenes, percentage of mitochondrial genes after QC. e. Significance of overlap with major cell type markers derived from known study. f. Expression of known markers for each cell type.
Since the samples are sequenced by different lanes of 10X platform, therefore, we can’t exclude potential batch effects. To account for this potential batch effect, we corrected the batch by doing CCA (canonical correlation analysis) which enhanced the clustering and the UMAP visualization more biologically meaningful and less driven by batch-specific variations.
Moreover, in an attempt to account for the sample size limitation, we employed 3 approaches to confirm the main transcriptional differences between the 2 regions, and that these are “robust” to batch correction, as is shown in new Figure S15 (see next page): (1) Comparison of the gene expression differences (2 TL vs 1FL) with and without removing batches (new Figure S15. a, c); (2) The results obtained by comparing the differences between each individual TL sample (processed in different lanes) and FL sample are contrasted with the results after batch removal (new Figure S15. b, d); (3) To confirm a limited effect of lane, analysis of the expression similarity of three samples demonstrates, consistently for each major cell type and neuronal sub-types, a strong correlation between the two TL samples (form different lanes) as compared with FL (new__ Figure S15. e__).
As shown in panel a, c, below, the majority of DEGs (2TL vs FL) identified with batch effects largely overlap with the DEGs (2TL vs FL) without considering batch effects for both major cell types and neuronal sub-types. In panel b, d, we show that the majority of DEGs with batch correction (2TL vs FL) overlap with the individual DEGs found in each TL vs FL comparison. In panel e, we identified that the transcriptomic of 2 TL exhibit higher similarity compared with the sample from FL.
Overall, based on these analyses we concluded that the results are robust to batch correction.
Figure S15. Comparison of biological (gene expression) differences in each major cell type and neuron-subtype between the 2 regions with and without batch effect removal. a, c. Comparison of the DEGs (2 TL vs 1FL) with and without removing batches (a, up-regulated in TL; c, up-regulated in FL). b, d. Comparison of the DEGs (2TL vs FL) following the removal of batch effects with the DEGs calculated by individual TL vs FL samples. __e. __Expression correlation between each sample (without batch correction for lane), showing higher transcriptional similarity within the same tissue type than across tissues, consistently in major cell-types and neuronal subtypes.
In addition, we highlight that, differently from other tissues, it is very difficult to obtain the “fresh” human samples of brain cortex, which most likely provides different transcriptome information than the more commonly used post-mortem brain samples. These analyses offered another evidence supporting the differences between TL and FL, which complement (and align with) the comparative analyses using the data from Allen Brain Atlas (Figure S9, original results).
2.Furthermore, the way that the authors treat GWAS data for disease does not seem to follow best practices. For schizophrenia, last year the largest GWAS so far was published (Trubetskoy et al, Nature, 2022) with very careful prioritization of genes. The authors should re-analyze their data using the gene list from this paper (and similar from other disorders) rather than the gene list that they came up with using their approach. The approach to select genes from different GWAS introduced seems highly arbitrary and leaves the reader unsure about statistical rigor.
We have carefully considered the suggestion regarding the treatment of GWAS data, particularly with respect to the gene list derived from the recent schizophrenia GWAS by Trubetskoy et al. (Nature, 2022). In this paper, the author mainly identified 120 genes (106 protein-coding) that are likely to underpin associations with schizophrenia which implicate fundamental processes related to neuronal function including synaptic organization, differentiation and transmission.
With respect to our study, first, we found there is significant overlap between prioritized genes in Trubetskoy et al’ study and GWAS genes included in our study. We showed the P value for overlap significance below, and listed the 27 genes. Among the prioritized genes, GRIN2A is also identified to be important in neuropsychiatric disorder, which is also confirmed to differ between the 2 regions and dysregulated in disease brain.
Enrichment of genes obtained from the prioritized schizophrenia-associated genes in Trubetskoy et al. Significant overlap (P=0.013, hypergeometric test) between schizophrenia-associated genes (120 prioritized genes from Trubetskoy et al.) and our GWAS genes (from GWAS catalogue).
Second, we conducted a supplementary analysis focused on the 120 genes prioritized by Trubetskoy et al, as shown below. We found the 120 prioritized genes in this paper are significantly enriched in excitatory and inhibitory neurons (panel b, below), aligning with our main findings conducted by schizophrenia related genes in our previous GWAS gene lists. Within the neuronal subcluster, we found a significant enrichment in L4, LAMP5 and PVALB cells (panel c); L4 and PVALB are largely consistent with our previous results (shown in Figure 3. c). Furthermore, we also found the 120 schizophrenia-associated genes are highly significantly enriched in DEGs (TL/FL) in VIP and PVALB subtypes (panel d).
b-c. Enrichment of 120 prioritized schizophrenia-associated genes in major cell types and neuronal subtypes. d. For each cell type, the enrichment of 120 genes is calculated with respect to the set of DEGs (TL/FL). Approach used for enrichment analysis is hypergeometric test (significance level, P-valueThese results suggest that while new gene lists from larger GWAS studies (e.g., Trubetskoy et al) come up regularly, the lists of GWAS genes prioritized in our enrichment analysis has some overlap with the newest GWAS. We agree that including more (larger) GWAS studies will strengthen the manuscript, but based on the analyses above, we believe our GWAS enrichment results are robust. In the revised manuscript, the new analysis including the detailed comparison with schizophrenia GWAS by Trubetskoy et al. (Nature, 2022) are reported in new Figure S17.
To improve on the GWAS enrichment analysis, we carried out additional sensitivity analyses to support our GWAS enticement results. We selected additional thresholds to evaluate the robustness of our results to the choice of gene lists to test the sensitivity of the enrichment analysis, we selected the thresholds: 10-6, 10-7, 5x10-8. The new results are largely consistent with those obtained using P-value of 10-5. Susceptibility genes for neuropsychiatric disorders are enriched for expression in neuronal cell types for each P-value. With respect to neuronal subtypes, we found stronger enrichment in INH than in EX sub-clusters, with INH PVALB, SST and EX L5 being the neuronal sub-clusters mostly enriched for expression of GWAS genes. These results are reported in new Figure S14.
Figure S14. Enrichment of cell type expression of neuropsychiatric disorder-associated GWAS genes for different GWAS-thresholds. a-c. Adjusted P-value of enrichment in each 7 major cell type. d-f. Adjusted P-value of enrichment in each neuron subtype.
3.Similarly, the choice of data set for disease-related differentially expressed genes is unclear as much larger (two orders of magnitude) published data sets exist for many of the disorders. For three of those DEG analyses performed on bulk RNAseq data, for the remaining two the DEG list of papers is used directly -making a comparison complicated. One would have to run DEG analysis in a standardized way for all 5 datasets/ disorders. It would be good to also indicate the respective sample size in Fig. 5a. (On a different note, the OCD publication is Piantadosi et al. 2021, not Sean C.et.al..) In addition, the authors matched brain regions to their regions of interest (frontal and temporal lobe) as shown in Fig. 5a. Still, they vary across disorders, which makes it hard to compare their findings across disorders and does not allow for a general statement about frontal vs. temporal lobe. ____To generalize for any of those psychiatric disorders I would recommend including more RNA-seq studies of the same disorder. Nowadays there are getting more and more case-control single nuclei studies on such disorders published. The authors could also include those by transforming them to pseudo bulk datasets and running their DEG analysis with edgeR as documented.
We acknowledge there might be a bias introduced by using the DEGs from the original paper directly. In addition, there is a general limitation affecting all bulk-RNA studies in complex tissues with different anatomical structures (e.g., kidney, brain, etc.), which form a great part of the publicly available data sources. In brain research, it is also more difficult to collect fresh human brain samples from patients with psychiatric disorders, which poses additional tissue availability constraints. Despite these limitations, we argue that bulk-RNA studies in anatomically complex tissues, and the DEGs reported therein, can be useful for GWAS enrichment analysis and not all DEGs are due to spurious or artificial signals. Furthermore, due to the lower sequencing depth inherent in single-cell RNA sequencing compared to bulk RNA sequencing, we set up to contrast our findings with results found by bulk-RNA seq.
We agree with the Reviewer that “One would have to run DEG analysis in a standardized way for all 5 datasets/ disorders”, however this approach assumes that the raw data are directly available and/or that the authors are keen to share the raw data. Both these assumptions are – unfortunately – not valid in many cases. (In several instances, we did contact authors to have access to raw data, with no success). Furthermore, when a commonly shared gene set in the DE genes is identified when using “heterogenous DE gene lists”, this might suggest a strongest convergence, or a convergence that is “robust” despite the differences between the heterogeneous DE lists (from authors or newly generated by us). Therefore, despite the limitations, our approach was motivated by practical considerations.
In addition, the brain region differences can be more prevalent and have a larger impact for specific psychiatric disorders. In our manuscript, for MDD we specially looked at only the BA8/9 which come from dorsolateral prefrontal cortex. Regarding OCD, BP, and MDD, several studies showed that there are no significant functional differences clinically observed between the orbitofrontal cortex and dorsolateral prefrontal cortex (Schoenbaum G, Setlow B. Integrating orbitofrontal cortex into prefrontal theory: common processing themes across species and subdivisions. Learning & Memory, 20018. Golkar A, Lonsdorf T B, Olsson A, et al. Distinct contributions of the dorsolateral prefrontal and orbitofrontal cortex during emotion regulation. PloS one, 20129). In the case of ASD, Brodmann area 41, 42, 22 refers to a subdivision of the cytoarchitecturally defined temporal region of cerebral cortex, exhibiting similar functionality to the temporal gyrus. Therefore, ASD and SCHI may arise from specific regions within the temporal lobe, while OCD, MDD, and BP may be associated with regions within the frontal lobe.
To address the Reviewer’s point more directly - we carried out additional analyses to investigate the effect of this factor on our main results. One of our aims was to understand how regional gene expression differences (TL/FL) in PVALB neurons are associated with gene dysregulation in the brain of neuropsychiatric disease patients. We have now extended these analyses to a separate dataset, and tested whether the dysregulated genes in neuropsychiatric disease are expressed mainly in TL and FL using single cell data from Brain Allen Atlas (4 patients, each with 6 brain regions profiled). The new results are shown in new Figure S11 b-f (and reported in the next page).
Briefly, we found that the percentage of dysregulated genes in SCHI, BP, OCD, and MDD that are expressed in MTG (SCHI: 75%, BP: 81%, OCD: 68%, MDD: 71%) and CgG (SCHI: 77%, BP: 80%, OCD: 60%, MDD: 77%) is higher compared with those in all other regions included in Brain Allen Atlas dataset. The percentage of ASD dysregulated genes expressed in the 6 regions from Brain Allen Atlas are quite similar. This analysis suggests that, despite the potential impact of heterogeneity of regions, the DEGs in psychiatric conditions are typically expressed at higher level in MTG (TL) and CgG (FL) compared with other regions, therefore highlighting the potential role of these two regions in psychiatric conditions. Therefore, we believe that despite the heterogeneity of regions included in the published RNA-seq studies, the strongest signal of enrichment for DEGs is detected consistently in TL and FL, i.e., in the 2 brain regions where the DEGs are also most highly expressed compared with other regions. These new data, reported in a new Figure S11 of the revised manuscript, provide additional evidence to support our main conclusions.
Due to the difficulties obtaining the human sample of psychiatric disorders causing limited public data resource, we found one study about molecular changes of ASD revealed by single cell RNA seq coming from Velmeshev et al. Science. 2019; 364(6441):685-689 (PMID: 31097668), including 22 ASD samples and 19 control samples. We compared the DEGs (TL/FL) with the DEGs (ASD/Ctrl), and report the results in new Figure S16. Briefly, the results show that except LAMP5, Endo, and L4, ASD-associated dysregulated genes significantly overlap with DEGs between FL and TL in several cell types, especially in VIP and astrocytes. While PVALB is not the most apparent cluster reflecting regional differences contributing to ASD, we found a moderate association (R2 =0.11, P=0.04) between changes in TL/FL and those in ASD/Ctrl brain. These findings suggest that gene expression differences between the 2 regions may contribute to ASD disorder, providing additional evidence to support our main conclusions.
Figure S16. Overlap of genes dysregulated in ASD and genes differentially expressed between TL and FL in each major cell type and neural subtype. Venn diagram plots in a-m showing the number of overlapped genes. Dot plot in each panel shows the relationship between the log2FC(TL/FL) [our study] and log2FC(ASD/Ctrl) [Velmeshev et al. Science. 2019 study]. Significance of the overlap: *0.001-0.01, **0.0001-0.001, ***0.00001-0.0001, ****4.For cell type enrichment of disease signal based on GWAS signal several carefully controlled studies exist using more sophisticated statistical methods (Skene et al., Nature Genetics, 2018, Bryois et al., et al. Nature Genetics 2020, MJ Zhang et al Nature Genetics 2022 to mention a few). I applaud that the authors aim to go beyond this basic characterization but I think it is worrisome that by using less sophisticated (and importantly less controlled) statistical and genetic approaches they reach a different signal -and then they go on and analyze this signal. It is potentially interesting they reach a different conclusion, but they need to provide a careful statistical analysis to explain how the chosen method is superior or at least different to previous efforts.
The Reviewer suggests the use of alternative approaches to link GWAS variants to genes, like MAGMA, LDSC, FUMA to improve the gene mapping from GWAS signals, and are better than the gene mapping based on proximity alone. While these approaches can provide some advantages, most of these methods do require the whole set of SNP GWAS associations, including non-significant associations. While these can be available for single diseases and specific GWAS data (assuming the authors made all data available, and assuming one obtains approval by the consortia managing the GWAS data) these SNPs are not available for several diseases in the NHGRI-EBI GWAS catalog, which provides only SNPs with a max P=10-5. Since in our study we considered GWAS data from 7 neuropsychiatric diseases, we (pragmatically) opted for obtaining data from NHGRI-EBI GWAS catalog rather than seeking GWAS SNP data from individual studies.
We now acknowledge the limitations for the variant to gene mapping (revised Discussion, page 17, line 17), and we also report that several other studies rely on the variant to gene mapping from NHGRI-EBI GWAS catalog for enrichment analyses4-6. There are also studies that investigate the enrichment of mapped genes (from NHGRI-EBI GWAS catalog) in different cell types using the hypergeometric test 7-8, as we do in our study. Perhaps more importantly, in the revised manuscript, we replicated the main GWAS enticement results (e.g., in INH neurons and in PVLAB from the temporal lobe) in the Brain Allen Atlas datasets, which shows that, despite these limitations of variant to gene mapping, our main enrichment results are replicable.
(Other comments)
- only n=3, ~45 000 cells making it hard to generalize
- no supplementary figures for the methods (i.e. preprocessing, cell type annotations), thus hard to judge if done properly if they do not show any data - much higher level of transparency needed
- The methods part is not clear, in general, it is only descriptive, with no equations
- Unconvincing determination of DEGs for each disorder
- DEGs and pathways based on n1=1 vs n2=2 feals handwavy
- DEG analysis and cell type annotation are mixed up and it is unclear how DEGs were determined
While we acknowledge the limitation of sample size in our study, we also emphasize again the challenges in of availability of fresh human sample, which provide more transcriptomic information than postern sample. Despite the small number of individual samples, the large number of cells (~45,000) contributes to the overall quality and depth of the scRNA-seq dataset. Hence, our study provides a foundational perspective on the gene expression between the frontal lobe (FL) and temporal lobe (TL), and valuable data source for further investigations.
With respect to the additional description of the data processing and cell annotation process, in the revised manuscript we now elucidate the cell type annotation process by showing the expression of some known markers in new Figure S2. f, the significance of overlap with major cell type markers derived from known study in new Figure S2. e, the distribution of nUMI, nGenes, percentage of mitochondrial genes after quality control in new __Figure S2. b. __
To strengthen the differential gene expression analysis, we replicated our main findings through SMART RNA-seq from Brain Allen Atlas including the DEGs identified in our study (Figure S9).
More technical details are provided in the revised manuscript, as detailed below:
In the revised Methods section – (1) Differential expression analysis in FL vs TL and pathway enrichment analysis, we added more details about how the DEGs are identified and how this is robust to batch correction. (2) Replication analyses in human Brain Allen Atlas, we provide more details about how we replicated the DEGs using Allen Brain Atlas dataset. (3) Enrichment of neuropsychiatric disease GWAS genes in brain cell clusters, we now added more methodological details about the enrichment analysis.
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Reviewer #3
We thank the Reviewer for his/her overall positive comments. In the revised manuscript we have now included several new analyses requested by this and other reviewers (see list below), which allowed us to replicate and strengthen our main findings. We also add details of the method used in this paper. We believe these new analyses and data helped us to improve reproducibility and strengthen the main findings presented in our manuscript.
New analyses/data added:
- *Effect of batch due to different lanes - comparison of DEGs (TL/FL) obtained when samples in different lanes are tested individually (new Figure S15). *
- Effect of batch correction on our results - comparison of the DEGs (TL/FL) obtained with and without batch removal (new Figure S15).
- Sensitivity of our enrichment results for GWAS significance – we performed the enrichment of GWAS genes using different GWAS thresholds, 10-6, 10-7, 5x10-8 (new Figure S14).
- Expression analysis of GRIN2A and SLC12A5 in Allen Brain Atlas data and qPCR results of GRIN2A and SLC12A5 in patients with frontal and temporal lobe traumatic injury (new Figure S12, Table S3).
- Comparison of the DEGs (TL/FL) with DEGs (autism/Ctrl) obtained from single cell RNA seq (new Figure S16, Table S7).
- Comparison of the results using the GWAS genes derived from Trubetskoy et al. with our gene lists (new Figure S17).
- Description of the data quality (Figure S2) 1.The authors integrated the brain snRNA-seq data with GWAS data to annotate the cell type specific expression, which is one of the key points for this analysis, however a more detailed description of the method is lacking.
We have made changes to the text to improve and clarify this aspect. In the revised Methods section, we now specify: “To calculate the enrichment of genetic risk associated with psychiatric disorders, we used a hypergeometric test for the overlap between cell type specific genes (DEGs between one cell with other cell types, log2FC>0.5, adjusted.P __2.The authors found a set of genes which is associated with psychiatric disorders and specific cell types, for example inhibitory neurons are the most vulnerable cell type to genetic susceptibility through their analysis. The correlation of each cell type and each psychiatric disorders can be discussed.*__
We thank the Reviewer for this suggestion; we have now added more details discussing the relationship between other cell types with psychiatric disorders other than PVALB-neuron in this part – see Discussion in the revised manuscript, where we added: “Astrocyte, OPC are also associated with psychiatric disorders, and play essential roles in maintaining brain homeostasis, regulating synaptic transmission, and supporting neuronal function. Astrocytes also contribute to maintaining the integrity of the blood-brain barrier (BBB) and interact closely with neurons. Disruptions in this communication impact neural circuitry, which is relevant to many psychiatric disorders. OPCs generate oligodendrocytes, producing myelin crucial for signal conduction and brain structural integrity, which potentially impacts brain connectivity and communication between brain regions. Among neuronal subtypes, our data suggest that disruption of specific biological process in PVALB, SST and L5 neurons may contribute to neuropsychiatric disorders. PVALB cells are believed to activate pyramidal neurons only if the signal from excitatory neurons is sufficient and optimize the signaling in both EX and INH72. SST neurons gate excitatory input onto pyramidal neurons within cortical microcircuits, mainly coming from L5 layer of excitatory neuron which is involve in motor control, decision-making, and information transfer between the cortex and subcortical structures73. These signaling processes, when dysregulated, have been implicated in psychiatric diseases74. The relationship between psychiatric disorders and other layers of the cerebral cortex is still under investigation. *L2-3 neurons handle local processing, relevant to conditions like schizophrenia and autism. L6 neurons in thalamocortical circuits are crucial for sensory processing and information relay, involving sensory perception abnormalities.” *
3.The authors have found a group of interesting genes, such as GRIN2A, DGKI, and SHISA9 and confirmed them with the Allen Brain Atlases. Experimental validation would be helpful to confirm such findings.
In our manuscript, we emphasized that GRIN2A and SLC12A5 (both implicated in schizophrenia and bipolar disorder) were significantly upregulated in TL PVALB neurons and in psychiatric disease patients’ brain. To address this point, first, we check the expression of the 2 genes using the data from Allen Brain Atlas data, which showed significantly high expression in TL (new Figure S12. b). By means of new qPCR analysis in primary TL/FL samples, we found the mRNA expression levels of GRIN2A and SLC2A5 in patients with traumatic brain injury in the temporal lobe region were higher than those in patients with frontal lobe injury (new Figure S12. c).
Figure S12. b. Expression level of GRIN2A and SLC12A5 in 2 regions using Brain Allen Atlas. ***P-value-ΔΔCt method. Significance was determined through T-test (two-tailed). qPCR for each TL or FL sample was repeated 3 times.
Lastly, we want to highlight that since we believe in “Data Democratization” and sharing our data resources, upon publication, we will make all our data (including the single cell in “fresh” (surgically resected) brain tissue samples) and corresponding detailed results available to the scientific community.
We believe our study (which is focused on psychiatric diseases) will prompt other groups to use our single cell data and to dig deep into the role of temporal and frontal lobes in other neurogenerative diseases.
__ __
References
- Squair, J.W., Gautier, M., Kathe, C. et al. Confronting false discoveries in single-cell differential expression. Nat Commun 12, 5692 (2021).
- Wang, T., Li, B., Nelson, C.E. et al. Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data. BMC Bioinformatics 20, 40 (2019).
- Bhattacherjee A, Djekidel MN, Chen R, Chen W, Tuesta LM, Zhang Y. Cell type-specific transcriptional programs in mouse prefrontal cortex during adolescence and addiction. Nat Commun. 2019 Sep 13;10(1):4169.
- Grubman A, Chew G, Ouyang JF, Sun G, Choo XY, McLean C, Simmons RK, Buckberry S, Vargas-Landin DB, Poppe D, Pflueger J, Lister R, Rackham OJL, Petretto E, Polo JM. A single-cell atlas of entorhinal cortex from individuals with Alzheimer's disease reveals cell-type-specific gene expression regulation. Nat Neurosci. 2019 Dec;22(12):2087-2097
- Przytycki, P.F., Pollard, K.S. CellWalker integrates single-cell and bulk data to resolve regulatory elements across cell types in complex tissues. Genome Biol 22, 61 (2021).
- Swindell, William R., et al. "RNA-Seq analysis of IL-1B and IL-36 responses in epidermal keratinocytes identifies a shared MyD88-dependent gene signature." Frontiers in immunology 9 (2018): 80.
- Geirsdottir, Laufey, Eyal David, Hadas Keren-Shaul, Assaf Weiner, Stefan Cornelius Bohlen, Jana Neuber, Adam Balic et al. "Cross-species single-cell analysis reveals divergence of the primate microglia program." Cell 179, no. 7 (2019): 1609-1622.
- Schoenbaum G, Setlow B. Integrating orbitofrontal cortex into prefrontal theory: common processing themes across species and subdivisions[J]. Learning & Memory, 2001, 8(3): 134-147.
- Golkar A, Lonsdorf T B, Olsson A, et al. Distinct contributions of the dorsolateral prefrontal and orbitofrontal cortex during emotion regulation[J]. PloS one, 2012, 7(11): e48107
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Referee #3
Evidence, reproducibility and clarity
In the manuscript "Decoding frontotemporal and cell type-specific vulnerabilities to neuropsychiatric disorders and psychoactive drugs", the authors integrated brain with no history of neuropsychiatric disorder snRNA-seq data with public GWAS data among 7 psychiatric disorders to explore the heterogeneity between temporal lobe (TL) and frontal lobe (FL), the genetic risk factors and potential drug responsible genes. Multiple bioinformatics technics have been used in the manuscript. The authors found critical pathways and key genes that are related to the psychiatric disorders and GWAS genes enriched cells such as PVALB cells, which can help the understanding in the field. Overall, the manuscript is well written and organized, but there are some issues need to be addressed.
- The authors integrated the brain snRNA-seq data with GWAS data to annotate the cell type specific expression, which is one of the key points for this analysis, however a more detailed description of the method is lacking.
- The authors found a set of genes which is associated with psychiatric disorders and specific cell types, for example inhibitory neurons are the most vulnerable cell type to genetic susceptibility through their analysis. The correlation of each cell type and each psychiatric disorders can be discussed.
- The authors have found a group of interesting genes, such as GRIN2A, DGKI, and SHISA9 and confirmed them with the Allen Brain Atlases. Experimental validation would be helpful to confirm such findings.
Significance
Strength: this manuscript is strong in bioinformatics analysis. Limitation: wet-lab validation of some of the findings would be helpful.
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Referee #2
Evidence, reproducibility and clarity
Paper review: Decoding frontotemporal and cell type-specific vulnerabilities to neuropsychiatric disorders and psychoactive drugs
In their manuscript with the title Decoding frontotemporal and cell type-specific vulnerabilities to neuropsychiatric disorders and psychoactive drugs, the authors describe their work on integrating snRNAseq data from "fresh" human frontal and temporal lobe of three healthy donors with genetic risk factors of 7 psychiatric disorders, bulk RNAseq data from healthy and disease human cortex/ DEG lists from previous studies for 5of the psychiatric disorders, and gene targets for commonly prescribed psychoactive drugs. The authors claim that PVALB neurons in the temporal lobe are most vulnerable to genetic risk factors and even more to psychoactive drugs for psychiatric diseases and suggest GRIN2A and SLC12A5 as the genes that most contribute to their vulnerability.
According to my overall impression, the paper has major problems in terms of quality, clarity, and statistical power. I do not recommend publishing this manuscript in its current form.
The manuscript is unfortunately lacking (supplemental) figures showing the preprocessing, batch effect correction, and cell type annotation of single nucleus RNAseq data. Although this part is described in the methods in detail, it is hard to judge if these parts were done properly if data is not shown in any of the figures. Regarding the batch effect correction, it reads as if the batch effects have been removed for both brain regions separately. This potentially introduces a bias between brain regions that hugely questions the later performed analysis of differential expression analysis in FL vs TL. In any case, this analysis is not convincing since it has been performed on n=3 vs. n=3 samples and is thus tremendously underpowered.
Furthermore, the way that the authors treat GWAS data for disease does not seem to follow best practices. For schizophrenia, last year the largest GWAS so far was published (Trubetskoy et al, Nature, 2022) with very careful prioritization of genes. The authors should re-analyze their data using the gene list from this paper (and similar from other disorders) rather than the gene list that they came up with using their approach. The approach to select genes from different GWAS introduced seems highly arbitrary and leaves the reader unsure about statistical rigor. Similarly, the choice of data set for disease-related differentially expressed genes is unclear as much larger (two orders of magnitude) published data sets exist for many of the disorders. For three of those DEG analyses performed on bulk RNAseq data, for the remaining two the DEG list of papers is used directly -making a comparison complicated. One would have to run DEG analysis in a standardized way for all 5 datasets/ disorders. It would be good to also indicate the respective sample size in Fig. 5a. (On a different note, the OCD publication is Piantadosi et al. 2021, not Sean C.et.al..) In addition, the authors matched brain regions to their regions of interest (frontal and temporal lobe) as shown in Fig. 5a. Still, they vary across disorders, which makes it hard to compare their findings across disorders and does not allow for a general statement about frontal vs. temporal lobe. To generalize for any of those psychiatric disorders I would recommend including more RNAseq studies of the same disorder. Nowadays there are getting more and more case-control single nuclei studies on such disorders published. The authors could also include those by transforming them to pseudo bulk datasets and running their DEG analysis with edgeR as documented. For cell type enrichment of disease signal based on GWAS signal several carefully controlled studies exist using more sophisticated statistical methods (Skene et al., Nature Genetics, 2018, Bryois et al., et al. Nature Genetics 2020, MJ Zhang et al Nature Genetics 2022 to mention a few). I applaud that the authors aim to go beyond this basic characterization but I think it is worrisome that by using less sophisticated (and importantly less controlled) statistical and genetic approaches they reach a different signal -and then they go on and analyze this signal. It is potentially interesting that they reach a different conclusion, but they need to provide a careful statistical analysis to explain how the chosen method is superior or at least different to previous efforts.
Plus:
- Flash-frozen human tissue with little post-mortem delay
- TL and FL comparison: interesting
- Multiple comparison corrections
- Replication analysis included The drug target genes angle is interesting
Minus:
- only n=3, ~45 000 cells making it hard to generalize
- no supplementary figures for the methods (i.e. preprocessing, cell type annotations), thus hard to judge if done properly if they do not show any data - much higher level of transparency needed
- The methods part is not clear, in general, it is only descriptive, with no equations
- Unconvincing determination of DEGs for each disorder
- DEGs and pathways based on n1=1 vs n2=2 feals handwavy
- DEG analysis and cell type annotation are mixed up and it is unclear how DEGs were determined
Unclear:
- Is the background dataset used for enrichment of genetic risk calculation different for each region and cell type? If so? How is this a fair comparison?
- Which subset of GWAS genes is used for Gene co-expression networks
Significance
Mainly weaknesses
Advancement provided by the study remains modest due to low confidence in the findings. Potentially interesting approach but needs to utilize state-of-the-art methodology and data sets. A typical audience would be journals targeting molecular/biological psychiatry
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Referee #1
Evidence, reproducibility and clarity
The authors integrated snRNA-seq analysis with genetic risk and drug-specific signatures to investigate brain regional differences in risk for neuropsychiatric disease and drug response. To replicate the main findings, the authors also analyzed single nuclei data from the Brain Allen Atlas. Overall, the manuscript is very well written. The methods are comprehensive, clear and well-wrtten which is very welcome. The authors have undertaken a large number of bioinformatic investigations using appropriate methodology and careful design.<br /> The main limitation of the work is the small starting sample size. The authors studied 1 frontal lobe sample and 2 temporal lobe samples. Atlhough this information was in Table S3 it would be good to include upfront in the Methods. snRNA-seq was generated on the 10x platform. It would be helpful to know if the 10x step and sequencing was performed as one batch, or as individual batches. Similarly, were the sample libraries all sequenced on the same lane, or different lanes. The authors do not state in the Methods how many nuclei they were targeting and this should be included. Sample pre-processing was well described and standard. In relation to Batch correction - as with any batch correction method, it is unclear whether the correction is adjusting for biological differences or technical. Since this is a study of the differences between FL and TL, would it not be more appropriate not to correct for batch, particularly as the samples were analysed individually - particularly if batch effects were carefully controlled for in the initial study design. The authors should test whether the results are robust to batch correction or not. Differential gene expression analyses between the FL and TL was undertaken using edgeR. It is unclear if this was performed on aggregated counts or not - i.e., sum of counts per gene per cell type. If it was, then with such a small sample size (1 frontal lobe and 2 temporal lobe samples), it is unclear how well edgeR will perform. Similarly, if the DE analysis was performed using individual gene per cell counts, then there is a type 2 error risk due to pseudoreplication. It is reassuring that the primary results were replicated in a second dataset. Moreover, the downstream analyses (functional enrichment analysis, heritability enrichment analysis etc) are designed to cope with noisy data so I'm happy with the broad conclusions. However, where individual genes are mentioned then the authors may wish to confirm the results from edgeR for a few selected genes with a second technique such as qPCR. For example, GRIN2A and SLC12A5. To calculate the enrichment of "genetic risk" associated with psychiatric disorders, the authors used a hypergeometric test for the overlap between cell type specific genes and the GWAS variant-mapped genes for each disease, which is widely used to evaluate the enrichment of genetic risk genes. To identified GWAS variant mapped genes the authors used a GWAS SNP threshold of <10-5, and mapped SNPs to genes using the GWAS DB. The background set of genes is appropriate as is the statistical method. Given the small sample size however, I think it would be helpful to see a sensitivity analysis of the results that (a) uses different GWAS thresholds e.g., 10-6, 10-7, 5x10-8 and (b) uses an alternative SNP to gene mapping tool such as FUMA. Overall, whilst well written, the manuscript as a whole feels overly long. I think it could be improved by a more stringent focus on the most important biological and translational findings.
Significance
Overall, the manuscript is very well written. The methods are comprehensive, clear and well-wrtten which is very welcome. The authors have undertaken a large number of bioinformatic investigations using appropriate methodology and careful design.
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Reply to the reviewers
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Referee #3
Evidence, reproducibility and clarity
Summary:
Oxytocin (OXT) is a neuro-hypophysial hormone and exerts its effects through binding to the oxytocin receptor (OXTR). OXTR is expressed by various types of cells, including leukocytes and gastrointestinal cells. Previous studies demonstrated that OXT alleviates experimental colitis and regulates anti-inflammatory response. The authors' group reported that conditional deletion of OXTR in macrophages and dendritic cells exacerbated dextran sulfate sodium (DSS)-induced colitis. In the present study, they aimed to uncover the essential function of OXT signaling in colonic carcinogenesis and colitis by using intestinal epithelium cell (IEC)-specific OXTR knockout (KO) mice. IEC-specific KO mice exhibited markedly increased susceptibility to DSS-induced colitis and Azoxymethane (AOM)/DSS-induced colitis-associated colorectal cancer (CAC) compared to wild-type mice. Mechanistically, OXTR depletion in IECs impaired the inner mucus of the colon epithelium. Furthermore, oxytocin was found to regulate MUC2 maturation through B3GNT7-mediated fucosylation. In human colitis and CAC colon samples, there was a positive correlation between B3GNT7 expression and OXTR expression. Moreover, the administration of oxytocin significantly alleviated tumor burden. These results suggested oxytocin's promising potential as an effective therapeutic intervention for individuals affected by colitis and CAC.
Major comments:
- Figure 1: The expression levels of many genes are altered in cancer cells. It is unclear whether decreased OXTR expression is the cause or the consequence of CAC in both human cases and mouse experiments. In Figure 1B, the background staining on the control tissue is very high. On the CAC tissue section, the staining appears uneven and OXTR staining appears high where the background is high. Thus, the result is not convincing.
- OXTR is expressed by many types of cells, including leukocytes, and OXTR expressed by leukocytes is reported to have an anti-inflammatory activity (Mehdi et al., Front Immunol, 2022, 13:864007. doi: 10.3389/fimmu.2022.864007). In this study, the importance of OXTR expressed by leukocytes is not considered.
- Oxytocin is usually administered by injection. It is unclear how it was administered. Oral administration is probably not effective. There is also no description about the source of oxytocin.
- The effects of oxytocin could be different between males and females. It might be interesting to present data of males and females separately and comment on the finding. It was previously shown that OT plasma levels (pg / ml, mean {plus minus} SD) were significantly higher in women than in men (4.53 {plus minus} 1.18 vs 1.53 {plus minus} 1.19, p ˂ 0.001), and such differences might be related to behaviors, attitudes, as well as susceptibility to stress response, resilience and social emotions specific of women and men (Marazziti et al., Clin. Pract. Epidemiol. Ment. Health. 2019; 15: 58-63). Male mice are more susceptible to DSS-induced colitis and this could be due to different oxytocin levels.
- Figure S3: Is the antibody directed to sugar? In the absence of OXTR, MUC2 is almost absent. It is hard to believe that the expression/production of MUC2 is almost completely dependent on oxytocin.
- Figure 5: The authors indicated that reduced fucosylation was due to the decreased B3GNT7 expression. Addition of L-fucose may not result in increased fucosylation.
- The effects of fucose supplementation was studied using a colitis model, whereas the effects of oxytocin supplementation was studied using a colon cancer model. Thus, the effects by these two agents cannot be compared.
- Figure 6: Oxytocin treatment could activate OXTR expressed on both leukocytes and epithelial cells. There is no comment on this subject.
- Figure 6I: A few mice died after 20 days. Is it correct? What does "day 0" mean in this figure?
- Figure 7B: Again, the result is not convincing. Leukocytes are reported to express OXTR and many leukocytes are in the colon tissues, especially in colitis tissue. But, they are not positive.
- Figure 7M: It is good to have a summary figure; however, it appears not accurate. There is no data showing the floxed mice have "Tolerant immune response" and KO mice have "dysregulated immune response".
- The authors' group previously reported that OT activated IECs to release prostaglandin E2 that was required for the repair of intestinal epithelium after injury (Ref. 11 in this manuscript). What happened to this mechanism?
Minor comments:
- Mice: There is no reference for the floxed mice. It would be also helpful to add the strain number.
- Figure 1D: What was the expression level "1"? There is only a small difference between 6.4 (Control) and 6.1 (AOM/DSS).
- Figure 1J: What caused the increase in spleen weight? Is this a marker for increased inflammatory responses or cancer cell growth?
- Figure 1K: What was the meaning of the increased expression of each cytokine?
- Figure 1L: The photos are too small to see the detail.
- Figure 1M: What was the end point?
- Page 3: "OXTR Deficiency in IEC Facilitates CAC Depends on Inflammation" This is not a sentence.
- Figure 2A and I: "% weight loss" were less then 1%. Is it correct?
- Figure 2N: Hard to see any differences. Too small.
- Fig 5K: Labels are not complete.
- Figure 5: Silver color is used for lines and columns but difficult to see.
- It is unclear at which time point samples were prepared.
Significance
General assessment:
The role of OXT and its receptor OXTR in DSS-induced colitis was previously reported by using systemic or myeloid cell-specific OXTR KO mice. Here, the authors used IEC-specific OXTR KO mice and found that OXTR expressed by IEC cells plays an important role in inflammation-associated colitis and CAC by promoting the post-transcriptional modification of MUC2 via B3GNT7. This is a strength. The major imitation is that they used only IEC-specific KO mice and the relative importance of OXTR on IECs is unclear. In addition, important information necessary to understand the results is missing throughout the manuscript. There are other questions as listed in the comments.
Advance:
Interaction of OXT and OXTR has been demonstrated to protect mice from inflammation-associated colitis. This study went one step further by demonstrating that OXTR expressed by IEC cells plays a protective role in inflammation-associated colitis and CAC by promoting the post-transcriptional modification of MUC2 via B3GNT7.
Audience:
Basic research and translational/clinical.
Field of expertise:
The reviewer is expertized in the field of "cancer and inflammation" but not in "MUC2" or "glycosylation". Keywords: inflammation, chemokines, leukocyte trafficking, tumor microenvironment.
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Referee #2
Evidence, reproducibility and clarity
Summary: In their paper "Oxytocin alleviated colitis and colitis-associated colorectal tumorigenesis by targeting fucosylated MUC2" the authors describe the contribution of oxytocin (OXT) to colonic mucus formation, and its protective contribution to colitis and colon cancer. The authors also describe the mechanism by which OXT execute its alleviating effects on the colon mucus; enhanced B3GNT7-mediated fucosylation. The findings are demonstrated in cell cultures, various mice models, and samples from human patients.
Major comments:
- A conceptually confusing issue in this work is whether a decrease in OXTR expression is a predisposition, or a result of colonic illness. On one hand, the experiments with OXTR KO mice and cultured cells suggest that pre-existing lower levels of the receptor sensitize the tissue. However, in the AOM/DSS model the control mice present normal OXTR levels whereas mice that received AOM/DSS had lower expression, suggesting that changes in OXTR levels are not a predisposition but a result of the treatment/illness. Additionally, when tissue from CAC patients were analyzed, decreased levels of OXTR were found in sites of wounds but not in adjacent healthy tissue, implying that this decrease is not a genetic treat but a result of external cue. This inconsistency must be sorted out and clearly demonstrated.
- The study describes a new regulatory pathway for colonic mucin 2, and colon related conditions. Why did the author choose to generate mice lacking OXTR in the entire intestine (small+large) and not a large-intestine specific deficiency? And is there any way to demonstrate that the absence of OXTR in the small intestine does not interfere with the results presented here?
- The commonly used fixative for mucus and secreted mucins is Carnoy fixative (can be found in many of Hannson G.C and in Johansson M.E.V papers, and many other papers describing colon staining), while the use of formaldehyde and glutaraldehyde is less preservative for mucus layer. This raises a concern regarding the data obtained from aldehyde-fixed mucus samples.
- The authors found that mice lacking OXTR have lowered levels of B3GNT7, which leads to a decrease in mucin 2 fucosylation and to further damage in the colon. What is the mechanism by which supplementation with L-fucose alleviates these outcomes given that the enzyme that regulates the addition of the fucose to mucin 2 is downregulated?
Minor comments:
- Some IHC images don't show comparable or similar areas. Specifically, Figure 1 B, Figure 7 F, I.
- There is a discrepancy between the dosage of DSS used to induce chronic colitis in the text (2%) and in the methods (2.2%). In addition, the difference between concentrations of DSS used to induce chronic and acute colitis (2.2% vs. 2.5%, respectively) is significantly smaller than what is reported in many other papers using these models.
- In Figure 2 A, I, Y-axis labeling doesn't seem right (compare with Figure 5 G). It looks like the decimal point is a mistake.
- All Western blots presented in this study lack the molecular weight of the proteins. In many cases it would have been more convincing to see a larger portion of the membrane.
- Mucin 2 in a large protein (more than 5000 amino acids in human mucin 2), and many disulfide bonds. The authors do not mention if any reducing or denaturing agents were added to the lysis buffers, and whether any other special conditions were employed to separate this huge globular protein on SDS-PAGE gel.
- The following sentence should be revised: " To examine the effects of fucosylation regulated by OXT on LS174T cells and colonic organoids, we found that..."
Significance
Though the concept of OXT-mediated suppression of colon cancer has been reported (For example: PMID: 34528509, and 31920487), the regulatory pathway by which it exerts its alleviating effect, and all the mechanistic components described in this paper, were not known before. This pathway may be a potential target for therapeutic intervention in various colonic diseases. Moreover, additional mucins may be regulated by OXT in a similar manner, which can extend the importance of these findings to other organs and disease-conditions. This type of findings is of interest to the broad audience of general cell biology as well as to GI clinicians. However, as stated in my comments there are some major issues with the hypothesis, the way data is presented, and in key methods that fundamentally limits my ability to evaluate this paper.
Key words for my field of expertise: Disulfide catalysis, Golgi, mucin
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Referee #1
Evidence, reproducibility and clarity
Summary
In this study, Wang and colleagues present data linking oxytocin signaling to protection against colitis and colitis associated cancer development. To this end, they utilize a Villin-Cre line to specifically remove OXTR only from intestinal epithelia and make use of AOM and/or DSS treatment to induce colitis, colorectal cancer or CAC. OXTRdeltaIEC mice consistently develop worse symptoms and more severe colitis/CAC compared to non-Cre expressing littermates, which appears to be associated with defects in the mucus layer. Using RNA-Seq, they identify the glycosyltransferase B3GNT7 as a differentially expressed gene. Due to its role in O-linked glycosylation, they investigate whether B3GNT7 is involved in mucin production and are able to show that OXT-induced upregulation of MUC2 protein is abolished when B3GNT7 is knocked down. In vivo, co-treatment with oxytocin reduces experimental CAC, which is an interesting that OXT may present a potential treatment option in CAC. The study is quite interesting in that it provides a potential treatment option where the mucus layer, which is often disturbed in IBD, can be impacted in a positive way. Still, there are some things missing to really be able to interpret the full picture and these should be experimentally addressed.
Major comments
- The staining for OXTR in Fig 1B is very strong (especially as others have reported that they were not able to demonstrate OXTR in human samples, Ohlsson et al. PMID: 16678285) - it would be beneficial to confirm OXTR distribution in steady state in mice, especially as you have a great negative control were OXTR staining should be absent from the IECs. Given the observations further in the study, I would also be very curious to see whether OXTR expression is specific to goblet cells, so co-staining with Muc2 would be interesting to include in later figures. The information for the OXTR antibody is also missing from the supplementary methods.
- Fig 1D, Fig S2 - is this whole colon RNA or specifically epithelial cells? This can have major impact on observed expression levels as the relative amounts of epithelial vs. other cell types can drastically change, thereby falsely giving the impression that expression levels in the epithelial cells change, so this really needs to be analyzed in purified epithelial cells. In Fig S2 there is significant OXTR expression remaining in the deltaIEC mice, so this suggests to me that non-IEC cell types are also included.
- The interpretation of the study would benefit from including some steady state/untreated data for the OXTRdIEC mice. For example in Fig 2 the researchers report increased spleen size, increased cytokines etc upon CRC in these mice, but it is important to also show steady state data as these parameters may already be significantly increased in basal conditions in these OXTR deficient mice (especially seeing as Fig 3 claims that under basal conditions the mucus layer is extensively damaged you would expect some phenotype in these mice).
- Special care needs to be taken to preserve the mucus layer during fixation, and from the methods it is not clear whether the authors have taken these technical difficulties into account. Only PFA fixation is mentioned, but it is well-established that the golden standard for imaging the mucus layer is to fix tissues in water-free Carnoy's fixative, as the mucus layer tends to collapse using formaldehyde (see also Johansson & Hansson, PMID: 22259139).
- In Fig 5, the message and conclusions become a bit more fuzzy. Overall fucosylation is measured, but it is unclear whether MUC2 itself is increasingly fucosylated due to OXTR signaling, or that this represents more global changes in the secretory pathway that eventually lead to more efficient MUC2 production. Perhaps an IP using fucose-specific lectin combined with western blotting for MUC2 may be an option to demonstrate whether MUC2 itself becomes increasingly fucosylated due to OXTR signaling?
- The title broadly claims that OXT "alleviates" colitis and CAC through MUC2 fucosylation and Fig 6 indeed shows that OXT treatment affects the outcome of mice in a CAC model, which is very promising, but it also loses the link with the mechanistic insights surrounding MUC2 fucosylation in previous figures. To really definitively make the claim in the title, it's important to investigate whether these OXT treated mice indeed have restored B3GNT7 levels and a thicker mucus layer after AOS/DSS regimen compared to non-OXT treated mice (as one would expect based on the in vitro data using LS174T cells and organoids). Studying the effect of OXT treatment in the regular DSS colitis model would also provide additional support for this claim.
- Optional as I am not a specialist in OXT signaling: I would assume that there are quite some differences between males and females when it comes to OXT and OXTR. Have the authors ever observed differences in staining pattern or expression levels between males and females? The methods state that all groups are sex matched, but I wonder if it may be necessary to include gender as a variable in the analysis?
Minor comments
- I would suggest to include another reference in the introduction and/or discussion, as MUC2 deficient mice are also known to develop colorectal cancer (Velcich et al. PMID: 11872843) and this serves as additional support for why it is important to discover how we can positively impact the mucin layer in IBD patients.
- In Fig 1A GEO data is reanalyzed, but it's not immediately clear what the original samples were (i.e. colon biopsies). At first glance, the figure itself adds to this confusion with the titles 'hypothalamus' and 'hypophysis' - it's not very clear that these labels indicate synthesis location of the respective hormone and not the tissue where expression was measured.
- Fig 1C - I could not find in the methods what software was used for these quantifications.
- Fig 1C - N=5 is mentioned in the figure legend and there are 10 datapoints in each group. Were 2 biopsies quantified per patient then? Please state this more clearly.
- Fig 3A, B and all other western blots - please include molecular weight indications in the figure
- Several figures use light grey bars and datapoints, but this color was very hard to see after printing the manuscript.
- The conclusion statement for Fig 3 should be revisited, as the expression of Muc2 mRNA is not affected at all by OXTR genotype (Fig S2F). Conclusion should make it clear that specifically (mature) protein levels are affected.
- Fig 4A-D - would be nice to include the full list of DE genes in supplement, it's an important resource. For example, there are other factors known that influence the mucus layer (such as AGR2), so I would be interested to see how these are behaving in the knockout mice.
- In Fig 4 H-J it would be informative to show the MUC2 mRNA expression level in these cultures as this could provide support for the mice data - i.e. do the cultures also display normal MUC2 mRNA levels, with a specific defect in the mucin maturation (as appears to be the case in mice)?
- Fig S3H-K - this figure and the validation of the siRNA is not mentioned in the main text
- It is interesting that L-fucose seems to partly reverse the effect of DSS, but I wonder whether mechanistically this is explained by restoration of B3GNT7 expression?
- Please check the accession codes for the reanalyzed datasets, figure legends mention two accessions, while the Data availability statement mentions three accessions.
- The number of repeats for each experiment is a bit unclear. It is now buried in the statistics statement in the methods, but it may be more clear if it is included in each figure legend.
Significance
This study shows a -for me- quite unexpected link between oxytocin and protection against colitis and colitis-associated cancer development. Disturbances in the mucin layer are a very common phenomenon in IBD and colitis and there has been a great interest in this in the scientific community for quite some years (Johansson, PMID: 25025717, Yao et al. PMID: 34902790, and many others). Current IBD treatment options are generally aimed at reducing inflammation, but this does not necessarily restore the mucin layer quality. It is therefore quite interesting to see that this is apparently heavily influenced by oxytocin (which already has applications in human medicine), and this provides significant advance to our current fundamental understanding of mucin barrier regulation.
As mentioned in the comments, the study can be further improved. To me, a more detailed investigation into the steady state phenotype of these mice, and a more detailed confirmation of where the oxytocin receptor is expressed is necessary to fully put the results into a broader framework. Also Fig 6, where the actual interventional effect of oxytocin is evaluated, no longer demonstrates whether this indeed happens through the same mechanism as outlined in the previous figures and this should be developed more.
I expect this study to be of interest primarily to a basic research audience, though I assume that a more clinical audience would be intrigued by the findings as well.
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Reply to the reviewers
__Points raised by both reviewers in their cross-comments __
- “emphasizing the acute nature of the study is important as well as the use of only male rats” RESPONSE: Thank you for pointing this out. It has been clarified throughout the manuscript, including the abstract, limitations section, and conclusions.
“The need for improvement of the presentation cannot be stressed enough”.
RESPONSE: The manuscript has undergone extensive revisions to enhance the clarity of data presentation and discussion, and to highlight its novelty in comparison to our prior studies. We have reduced the use of technical terminology and abbreviations, and when they do appear, they are explained with their first use and in the new Glossary section. The manuscript has been better organized, ensuring a logical flow of data and conclusions.
Reviewer #1
Major comments
“the extensive statistical analysis done for the gene expression would require assistance unless the in-house expertise already existed. If these are in place the work could be reproduced with the details provided.”
RESPONSE: Terms and abbreviations used in statistical and correlation analyses are thoroughly explained in the text and in the newly added Glossary section in the revised manuscript to the extent acceptable in a biological paper.
All statistical codes are accessible in the public GitHub repository at https://github.com/YaromirKo/biostatistics-nms. These codes may be utilized for the purpose of replicating studies.
Minor comments
“It is not clear how the genes that were studied here were picked. It is clearly stated what groups the genes fall into and their relevance to the study but it isn't clear how these were decided upon. Clarifying this would be helpful.”
RESPONSE: There is currently no consensus regarding the classification of genes as related to neuroplasticity. In particular, there is no agreement on lists of genes consistently associated with neuroplasticity across studies, and providers of mRNA analysis platforms do not offer panels of neuroplasticity-related genes. Most companies, such as Thermofisher, Illumina, and Nanostring, provide "Neurological" or "Neuropathology Research" panels that contain genes related to neuroplasticity. However, these panels are not specifically designed for targeted analysis of neuroplasticity-related genes.
The gene selection is arbitrary, and the chosen genes may vary across different studies depending on their objectives. In the present study, genes were selected based on several significant works having determined that these genes were likely related to neuroplasticity. Each gene's selection is justified by citing these works in the "Materials and Methods" section and we made every effort to avoid any bias. We do not assert that the gene set is all-encompassing. This matter is addressed in the Limitations section of the revised manuscript.
“It is not always clear what had been done in the previous work and what is completely new in this work, that could be addressed better.”
RESPONSE: Thank you for emphasizing that. It has been thoroughly addressed in the revised manuscript. While our previous study has discovered a left-sided neuroendocrine system, the current work delves into its organizational principles, which are equally crucial. We have shown that this system is bipartite and mirror asymmetric, and that its left and right counterparts can be targeted differently by pharmacological means. Additionally, we have revealed the left-right side-specific gene regulatory networks that operate in the neuroendocrine system and which activities are laterally coordinated by this system along the neuraxis.
“The text and figures are quite complex and require thorough reading the knowledge of the background to understand, therefore not making this work for a general audience.”
“Given the complexity of the work the reading of the results is quite dense and difficult to maneuver unless you have some prior understanding. My suggestion would be to try to simplify this but I wouldn't know exactly how to go about this.”
RESPONSE: We appreciate the Reviewer’s comments here, and agree that this is a complex work. We have endeavored to find a balance between a comprehensive presentation of the methods and results while also providing a level of simplification that will allow the reader who is not versed in this field to still appreciate this work. However, because of the nature of the experimental designs and of the findings that we report, we believe it to be important to provide a comprehensive explanation of the work and results. We believe that we have struck a balance between simplification and comprehensiveness with this revision. We have simplified the presentation of the results, their statistical analysis, and the analysis of gene regulatory networks for easier understanding. We also provide detailed explanations of technical terms in the newly added Glossary section. Please also refer to our response to point 2.
We believe that the revised manuscript has a level of complexity in data presentation and density similar to that of most combined physiological and molecular studies, complemented with advanced statistical and bioinformatics analysis. See please, for example papers published in Plos Biology (doi.org/10.1371/journal.pbio.3002328; doi.org/10.1371/journal.pbio.3002282; doi.org/10.1371/journal.pbio.3001465) and eLIFE (doi.org/10.7554/eLife.85756; https://doi.org/10.7554/eLife.90511.1).
General assessment
“The limitation would be understanding exactly what was done before and how this work expands on that, often it required the reader to look up references and prior work.”
RESPONSE: The introduction and discussion have been modified accordingly in order to comply with this comment. We have clarified how this study expands upon our previous work. In addition, please see the response to Comment 5 that also addresses this issue.
“The audience would be rather specialized, although it does gear towards clinical translation, this aspect could be highlighted better in the introduction and discussion.”
RESPONSE: Clinical aspects of the findings have been further highlighted in the revised manuscript. In the introduction, we note that the discovered phenomenon could contribute to asymmetrical neurological deficits following stroke and TBI. In the discussion section, we examine mechanical similarities between hindlimb asymmetry in rats and spastic dystonia in patients and hypothesize that the rat asymmetries may model this human neuropathology. In the concluding remarks, we state that it is crucial to examine the balance between neural and endocrine pathways in their contribution to neurological impairments, and to establish pharmacological approaches targeting the neuroendocrine system to restore the disturbed neurohormonal equilibrium.
“Those interested in brain injury/neurodegeneration as well as how signaling of motor control could be affected by not just damage to electrical descending motor tracts but to neuroendocrine signaling would be the specific audience.”
RESPONSE: We agree that the experts in neurotrauma, stroke and motor control may be interested in this study. However, the left-right side-specific neuroendocrine signaling may be a general biological phenomenon essential for regulation of lateralized brain functions, and, in a broader biological perspective, regulation of the body plan along the left-right axis.
Furthermore, the study presents what, to the best of our knowledge, is the first evidence for the existence of the left and right side-specific gene regulatory networks in the CNS. They operate in the neuroendocrine system and its peripheral target, and are coordinated across them via the humoral pathway. This is a novel molecular dimension in asymmetric organization of the generally mirror-symmetric CNS.
We are confident that experts in the establishment of the body plan and functional and molecular brain asymmetries will be interested in the concept formulated in this study.
Reviewer #2
Major comments:
“It should be made clear in the introduction that an acute complete cervical SCI is used and the discussion should be extended to include advantages and disadvantages of the used model and the alternatives.”
RESPONSE: Thank you for your suggestions. The introduction and discussion have been supplemented with the requested information. Specifically, we have noted that hindlimb postural asymmetry, a proxy model for neurological deficits, has enabled the discovery and characterization of the left-right side-specific neuroendocrine system. It is a binary model with two qualitatively different responses generated on either the left or right side. On the other hand, it cannot be used to analyze awake animals, and knowledge of its mechanisms is limited. A role for the neuroendocrine phenomenon in the persistent left-right specific biological and pathophysiological processes requires further investigation. This can be addressed by analyzing the effects of unilateral TBI in subchronic experiments with awake animals whose spinal cords are completely transected to disable neural pathways. The methodology could involve an integrated evaluation of hindlimb function during body weight-supported stepping, utilizing behavioral, electrophysiological, and biomechanical measures.
“A similar concern poses the use of pentobarbital and the interpretation of the results of the deafferentation. Were timing of the application and dosage strictly controlled between the different groups? It's effects on somatosensory afferent transmission through presynaptic inhibition are a concern.”
RESPONSE: Thank you for the remark. We have paid special attention to this issue. The rats were deeply anesthetized with the same dose and timing of anesthesia. These parameters were thoroughly controlled in all of the experiments. The depth of pentobarbital anesthesia was characterized by a barely perceptible corneal reflex and a lack of overall muscle tone. Of note, the side and magnitude of postural asymmetry do not apparently depend on anesthesia and its type; the asymmetry was virtually the same after brain injury in rats under deep pentobarbital or isoflurane anesthesia (this study and Lukoyanov et al., 2021; Watanabe et al., 2020; Watanabe et al., 2021; Zhang et al., 2020) and also in decerebrate unanesthetized rats (Zhang et al., 2020). Similar left-right differences were observed in the rats with left and right brain injury which were deafferentated 3 days later, and then analyzed under isoflurane anesthesia (Zhang et al., 2020). This is discussed in the revised manuscript.
Furthermore, no nociceptive stimulation was applied and tactile stimulation was negligible in the course of the asymmetry analysis; the legs were stretched by pulling the threads glued to nails of the toes. The application of lidocaine to the toes, which were pulled during stretching, had no impact on the formation of asymmetry. After all, the stretch and postural limb reflexes are immediately abolished and remain so for several days, and markedly decreased under anesthesia as it was firmly established in many studies. As these reflexes likely do not play a role in the formation of the asymmetric hindlimb posture, their afferent mechanisms could not be a cause of variations in our experiments.
In summary, three main arguments speak against an interference of pentobarbital with asymmetry formation in rats after rhizotomy. First, a similar asymmetry phenomenon developed in pentobarbital anesthetized rats, isoflurane anesthetized rats, and decerebrate un-anesthetized rats. Second, in rats that underwent rhizotomy, the primary sensory nerve fibers were entirely severed. Thus, the hypothetical link between pentobarbital's impact on asymmetry through its effect on presynaptic inhibition could be eliminated. Third, although there may be some variability in the depth of anesthesia among animals, the probability of such strong and statistically significant differences in the effects of brain injury and deafferentation arising from bias in the depth of anesthesia among groups of animals likely to be negligible.
*“Only two test for the asymmetry of spinal processing were used and the two tests are likely measuring very similar phenomena (tonic flexor over activation). Additional reflex tests could shed light onto underlying mechanisms.” *
RESPONSE: We agree. In previous studies, we also analyzed asymmetry in withdrawal reflexes between the left and right hindlimbs as an indicator of the effects of brain injury (Lukoyanov et al., 2021; Watanabe et al., 2021; Zhang et al., 2020). In the present study, we do not focus on the neurophysiological mechanisms of postural asymmetry. We instead prioritize characterizing the phenomenology and organizational principle of the left-right side-specific neuroendocrine system using the postural asymmetry model as a "black box" and as a robust and reliable readout.
Of note, there are several other equally important issues that remain to be addressed, including the identification of signaling pathways from the injured cortex to the hypothalamic-pituitary system, the identification of signaling molecules in the blood that convey information about the side of the brain injury, and the dissection of encoding and decoding mechanisms in the hypothalamus and spinal cord, respectively. No single study could investigate all of these mechanisms.
Minor comments:
“Figure 3 shows only the magnitude of the postural asymmetry in response to the different opioid receptor antagonists, yet the directionality is of interest, especially in case of the control animals. Pre2 values are missing too.”
RESPONSE: We appreciate the reviewer's comment and apologize for any errors in our previous version. The legend for Figure 3 has been revised and simplified. It is unnecessary to include PAS (Postural Asymmetry Size) in addition to MPA as the direction of PAS in all animals in each group was the same. This is stated in the revised manuscript's Legend for Figure 3. MPA was used to compare the left and right UBI groups, which had positive and negative PAS values, respectively. This comparison could not be carried out with PAS.
“Too many abbreviations are used which makes the text and figures very difficult to read at times.” “Terminology is sometimes inconsistent (e.g., delta vs contrast).”
RESPONSE: The manuscript now features a reduced amount of abbreviations. Technical terms and abbreviations are defined upon their first use and are also included in the newly added Glossary section. Corrections have been made to the use of the term "contrast" and its abbreviation "delta" in Figures. Additionally, the term "deltaW" as the left-right difference is no longer utilized within the manuscript.
“The section "correlation patterns in the hypothalamus and spinal cord" was almost impossible for me to understand and could use rephrasing.”
RESPONSE: We apologize for the previous version, and have simplified the presentation of molecular data. We believe that the level of complexity in the revised manuscript's statistics and data presentation is now comparable to that of many other molecular studies featuring system-level analyses; please see also response to Comment # 6 of the first reviewer.
“Only male rats are used.”
RESPONSE: This limitation has been addressed in the Limitation section. It is important to investigate whether identical or distinct neurohormones are responsible for the outcomes of left and right brain injury in male and female rats. However, this requires prior identification of most hypothalamic neurohormones and neuropeptides that regulate the asymmetric processes. Their number may be considerable, given the constellation of left and right gene regulatory networks in the hypothalamus.
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Referee #2
Evidence, reproducibility and clarity
Watanabe et al. build on their previous work to show that the left-right side specific effect of unilateral brain injury after acute complete spinal transection is indeed mediated by side-specific endocrine signaling. This is done by looking at a cervical spinal transection as opposed to a thoracic as in previous work. They further characterize the side-specific humoral hypothalamus-lumbar spinal cord pathways using gene expression patterns, application of opioid receptor antagonists, and dorsal root rhizotomy. Overall the evidence is very convincing and excludes mediation through the sympathetic system in addition to central descending tracts. Curiously the deafferentation, while having an effect on both sides only reversed the postural asymmetry caused by left-sided brain injury, and gene-gene co-expression revealed ipsilateral coordination.
Major comments:
- It is possible that many of the observations in the paper are dependent on the acute state of the spinal cord injury. This is mentioned in the limitations section and it is clear that the presented experiments are important and advance our understanding of this curious phenomenon. Yet, it should be made clear in the introduction that an acute complete cervical SCI is used and the discussion should be extended to include advantages and disadvantages of the used model and the alternatives.
- A similar concern poses the use of pentobarbital and the interpretation of the results of the deafferentation. Were timing of the application and dosage strictly controlled between the different groups? It's effects on somatosensory afferent transmission through presynaptic inhibition are a concern.
- Only two test for the asymmetry of spinal processing were used and the two tests are likely measuring very similar phenomena (tonic flexor over activation). Additional reflex tests could shed light onto underlying mechanisms.
- All major comments shouldn't be seen as a request for additional data but only require discussion.
Minor comments:
- Figure 3 shows only the magnitude of the postural asymmetry in response to the different opioid receptor antagonists, yet the directionality is of interest, especially in case of the control animals. Pre2 values are missing too.
- Too many abbreviations are used which makes the text and figures very difficult to read at times.
- Terminology is sometimes inconsistent (e.g., delta vs contrast).
- The section "correlation patterns in the hypothalamus and spinal cord" was almost impossible for me to understand and could use rephrasing.
- Only male rats are used.
Referees cross-commenting
I agree with reviewer #1's comments; most of them are in line with mine. The need for improvement of the presentation cannot be stressed enough. This is excellent and important work, which makes it even more important to convey it in an accessible way (be clear about prior work and what the novel results add, reduce number of abbreviations, guide the reader in how to interpret the figures, etc.). Otherwise, the audience will be limited.
Significance
General assessment: The manuscript provides clear evidence that there is a side-specific effect of UBI that is mediated by humoral signaling. Specifically, the present work excludes the sympathetic system. This is a very important finding that was missing in previous work. Further characterization of this recently discovered non-neuronal component of UBI is of very high importance as the potential for clinical implications are high.
Advance: The study provides a clear advance of our understanding of side-specific endocrine signaling to the spinal cord.
Audience: This study should be of interest to a wide audience, particularly for neuroscientists and neurologists who deal with the motor system.
My field of expertise: Neural control of locomotion, spinal cord injury, motor control, sensorimotor integration.
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Referee #1
Evidence, reproducibility and clarity
Summary
Watanabe et al. show in "Bipartite left-right sided endocrine system: processing contralateral effects of brain injury" a continuation of previously published work, that hindlimb postural asymmetry (HL-PA) is due to the neuroendocrine signaling and not the cervical parasympathetic pathways in anesthetized spinal C6-C7 fully transected unilateral brain injured (UBI) rats. Further, this differential neuroendocrine control of the left-right side-specific hormonal signaling is affected differently by either right or left unilateral hindlimb sensorimotor cortex brain injuries. However, bilateral deafferentation (L1-S1) showed that only left-side brain injury was altered, indicating differing inputs. Adding to the previous finding that blocking opioid signaling in UBI non-injured spinal rats leads HL-PA, here they demonstrated this finding holds with right-left differences following a spinal transection. Furthering the previous findings of left-right lumbar spinal gene expression differences, this time they found hypothalmus and lumbar spinal cord gene expression differences that were ipsilaterally coordinated and affected by brain injury.
Major comments
- Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them? Yes, the claims are supported by the data presented in this manuscript.
- Are the data and the methods presented in such a way that they can be reproduced? The data and methods have been presented in a way that could be reproduced, however given the expertise of this laboratory in developing new systems not for purchase it is likely it would take a given expertise to replicate the data. Additionally, the extensive statistical analysis done for the gene expression would require assistance unless the in-house expertise already existed. If these are in place the work could be reproduced with the details provided.
- Are the experiments adequately replicated and statistical analysis adequate? Yes, the experiments have been adequately replicated and statistical analysis to my understanding is adequate.
Minor comments
- Specific experimental issues that are easily addressable. It is not clear how the genes that were studied here were picked. It is clearly stated what groups the genes fall into and their relevance to the study but it isn't clear how these were decided upon. Clarifying this would be helpful.
- Are prior studies referenced appropriately? It is not always clear what had been done in the previous work and what is completely new in this work, that could be addressed better. The references themselves are extensive and well-used throughout the work.
- Are the text and figures clear and accurate? The text and figures are quite complex and require thorough reading the knowledge of the background to understand, therefore not making this work for a general audience.
- Do you have suggestions that would help the authors improve the presentation of their data and conclusions? Given the complexity of the work the reading of the results is quite dense and difficult to maneuver unless you have some prior understanding. My suggestion would be to try to simplify this but I wouldn't know exactly how to go about this.
Referees cross-commenting
Reviewer #2 makes a crucial point that emphasizing the acute nature of the study is important as well as the use of only male rats. Otherwise, reviewer #2's comments overlap partially with my own in increasing the accessibility of the work. Neither recommended changes would require new experimental data.
Significance
General assessment:
I would this topic quite intriguing and a novel understanding of motor control. The multiple experiments that were performed that addressed various contingencies of HL-PA may occur after UBI were addressed here (ie. parasympathetic and sensory input). Further experiments expanded on previous findings of the involvement of opioids, the pituitary gland, and spinal gene networks. The limitation would be understanding exactly what was done before and how this work expands on that, often it required the reader to look up references and prior work.
Advance:
Although this is my first encounter with the work, it is a follow-up study on work that was published previously in eLife in 2021. Therefore, given some of the overlap it wouldn't be entirely conceptually new but it would be addressing open questions which arose from that work and further add to our understanding of the mechanism involved in this phenomenon.
Audience:
The audience would be rather specialized, although it does gear towards clinical translation, this aspect could be highlighted better in the introduction and discussion. Those interested in brain injury/ neurodegeneration as well as how signaling of motor control could be affected by not just damage to electrical descending motor tracts but to neuroendocrine signaling would be the specific audience. My expertise is in spinal cord injury, sensorimotor coordination of hindlimbs and gene expression. Although not an expert in brain injury or neuroendocrine signaling, my background allows me to understand the experiments performed here and the relevance of the work.
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Reply to the reviewers
We thank the reviewers for their constructive and detailed reviews. We have been able to resolve all issues raised by the reviewers with additional experiments and changes in the text:
- In response to two of the reviewers we've changed the nomenclature of the residues. As we would like to avoid assigning roles in the naming, we now use 'critical residue 3' and 'critical residue 4', with Cys and His forming critical residue number 1 and 2 respectively.
- We analyzed the role of the negative charge in the fourth critical residue of USP1, by mutating this Asp to Asn to assess the importance of a charged residue in these positions (Supplementary figure 2), resulting in complete loss of activity just like the alanine mutant. We also tested the effect of mutating the third critical residue to Asn in USP1, which causes a minor decrease in activity. This highlights the importance of the highly conserved aspartate (fourth critical residue), and shows that precise residue found in the position is important for catalysis. Additionally, these mutants address potential effects of the ‘holes’ left by the original Ala mutations.
- Importantly, we were able to perform single-turnover assays to expand on our analysis of the precise roles of the critical residues and give more fundamental insight in the defects of the mutants. These assays further elaborate on the variability observed between these USPs. In USP15, these experiments explain the defect in catalysis for the third critical residue mutant and provides insight how a successful nucleophilic attack is combined with defective catalysis (updated Figure 4), which is not observed in the other USPs we tested. In these other USPs, the single turnover experiments reveal that the nucleophilic attack performed by the third and fourth critical residue mutant of USP7 and USP40 happens with low efficiency, even lower efficiency for USP48 and that this ability is lost entirely in USP1.
- We included a number of important textual changes to better explain the choices and variation in USPs tested, highlight prior USP2 data and the implications for drug discovery.
- We updated Ub-PA conjugation assays (updated Figure 4) for better contrast, and repeated the Ub-PA assay for USP1 and USP48 with longer incubation (Supplementary Figure 6). More details are given in the point-by-point response below. All in all, we are convinced that this much improved manuscript is now ready for publication and hope that all reviewers will agree.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary: The authors study the functional role of two adjacent active site residues as candidates for polarising the catalytic histidine in the "Asn/Asp" box from five phylogenetically unrelated ubiquitin specific proteases (USP1, USP7, USP15, USP40 and USP48). One of these residues is more variable across USPs (Asn, Asp, Ser), whereas the second one is absolutely conserved (Asp). To this end they use alanine mutants in kinetic experiments and test their ability to crosslink to ubiquitin propargyl as a proxy for testing the nucleophilicity of the catalytic cysteine. They then further evaluate the activity of the USP1 mutants in processing PCNA-Ub in RPE1 cells. They find that the role of these two residues differs between the different USPs studied, which is in line with previous work that has shown that in USP7, the amongst USPs less conserved residue takes on the major role of polarising the histidine, whereas in the more distantly related USP2, the absolutely conserved Asp is more important (Zhang W, et al. Contribution of active site residues to substrate hydrolysis by USP2: insights into catalysis by ubiquitin specific proteases. Biochemistry. 2011 50(21):4775-85. doi: 10.1021/bi101958h). This study expands on these findings to evaluate the role of these residues in four other USPs.
Major comments: 1. The authors compare highly diverse USPs; USP1 requires UAF1 for full activity and the complex is used in the study, USP7 requires a C-terminal tail peptide for full activity, USP40 and USP48 belong to the CHN class, whereas USP7, USP15 and USP1 belong to the CHD class of USPs. The rationale for selecting this diverse set of USPs is therefore not clear and makes direct comparisons of the findings more difficult. It is certainly interesting that the previously published differences between USP2 and USP7 with respect to these residues are also found in four other divergent USPs, but for this reason it isn't as "surprising" as the title suggests. The title, omission of background knowledge on USP2 in the abstract and presentation of the findings in a graph that makes direct comparisons (Figure 5) are therefore a bit misleading, which needs addressing.
- We apologize that it seemed as if we had overlooked USP2, for which both critical residues are important, and we agree that our abstract previously focused too much on the perception of the field and its focus on USP7. We have changed the abstract and introduction to highlight the USP2 data for a more balanced perspective.
- The reviewer is correct that the set of USPs is diverse, but we see this as a strength, given that this is the first manuscript in which these residues are analyzed in a comparative side-by-side manner for multiple DUBs. We find that our results are not directly related to the CHN/CHD diversity (i.e. changes in the third catalytic residue), nor apparently to activation by a C-terminal tail (as both USP7 and USP40 have this mechanism). Since these are structurally conserved enzymes with a common fold, we do find the comparison is informative. Furthermore, we felt that it was important to clearly signal the variation in different steps of the mechanism, something which appears to largely remain unnoticed by the field. Figure 5 is helpful in understanding that these changes have multiple dimensions. We agree that it is important to signal the diversity as possible source for these differences and we have added the following sentences to paragraph 3 of the results: “These USPs vary in domain architecture and allosteric regulation, and therefore represent different aspects of the USP family. USP1, USP7 and USP15 both harbor two aspartates as third and fourth critical residue and USP40 and USP48 harbor an asparagine and aspartate as third and fourth critical residue respectively, allowing us to examine the importance of a negative charge in position of the third critical residue.”
- We used the word surprising in the title to indicate the variability we observed in the two dimensions of the mechanism, as indicated in Fig. 5.
The study relies on single alanine mutations, which will inevitably change the hydrogen bonding patterns and the local environment which could impact the conclusion. The authors should verify in kinetic assays at least for USP1, which is the main focus, that Asp to Asn mutants still display the same effects.
- We are thankful for this suggestion. We have made these additional USP1 mutants through insect cell expression and tested these in different assays. As expected, both Asn mutants follow the alanine mutations. The results are reported in Supplementary fig 2BC.
While neither mutant unfolds below 40 degrees, there are clear differences in thermal stability between some of the proteins used in the study (Supp. Fig. 1B). A full table of measured Tms by NanoDSF for all Wt and mutant proteins should be provided so that the reader can evaluate how the results may be impacted by local effects that impact the thermal stability. It is noticeable that USP40 and USP15 mutants in particular display large differences in thermal stability, which could directly affect the results. The authors should clearly discuss these limitations of the study.
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We have added supplemental table S2 to report the melting temperatures. The effect observed for USP15 is addressed in the results: “While both mutants of USP15 have a decreased thermal stability compared to USP15wt, these variants retain stability until 50 °C, indicating that they are still well-folded and suitable for kinetic assays at room temperature.”. For USP40, it is not the actual measured Tm that deviates a lot, but the measured 350/330 ratio, which is addressed in the legend of supplementary figure 1B “Ratios measured (350 mm/330 mm) varied between some of the mutants (Eg. USP40wt), but this did not affect the measured inflection points (Supplementary table 1)”.
Minor comments: 1. For USP48 and USP40 no published structures are available at present, so it isn't clear whether there are any differences in orientation of the studied residues. An unpublished USP40 structure is referred to but not shown. The general conclusion that structures do not reveal any differences in these residues may therefore not be valid for all the studied USPs. Please revise.
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We apologize if this was not clear. We did however not refer to a USP40 structure, but a USP40 manuscript in preparation that studies biochemistry USP40 activation through activation by its C-terminal tail.
- The existing structures do not show observable differences in the active site residues, nor in the immediate surrounding, and therefore do not give insight which residue is critical for catalysis. We now mention this more explicitly. “It was previously shown that there are no structural differences in the positioning of the catalytic triad and the fourth critical residue between USP2 and USP7, despite their third and fourth critical residues behaving differently (Zhang et al., 2011). We superimposed the currently available crystal structures of USP catalytic domains (Table 1, Figure 1E) and also found only minor differences in the positioning of these two adjacent residues.”
- As the AlphaFold predictions for USP40 and USP48 closely resemble the known structures in Figure 1E, we have added this information as follows : “While the structures of USP40 and USP48 have not been solved, they contain the conserved USP catalytic domain and AlphaFold predictions for USP40 (Uniprot: Q9NVE5) and USP48 (Uniprot: Q86UV5) do not suggest major changes in their catalytic domains."
The introduction of the new terms "critical residue 1 and 2" are confusing and partially disproved by the study itself (replace with e.g. less conserved versus absolutely conserved 3rd triad residue or similar), please revise.
- Thank you, this issue is also mentioned by reviewer 2. We aimed at a solution that would not make inferences on mechanisms. We settled on "critical residue 3" and "critical residue 4", with the active site Cys and His being the first two.
p. 3/4: please add pH information to buffers used in the stability studies. "Previous publication" and "manuscript in preparation" are contradictions.
- pH information has been added.
- Thank you for the comments, we've adjusted the text.
p. 4. Assay buffer for USP1, USP7 and USP48 pH information is missing
- We have corrected the omission.
p. 6: last heading: typo is dispensable
- Typo was corrected.
p. 8: please explain choice of USP1 C90R mutation
- Other mutations tend to increase affinity for free ubiquitin, and in cells this can change ubiquitin homeostasis. The Cys to Arg mutation was shown to avoid this problem in some DUBs. (Morrow et al, Embo Rep 2018 Oct;19(10):e45680. doi: 10.15252/embr.201745680). We have added the reference in both the methods and results sections.
Explain choice of pH range 7-9 studied with regards to anticipated pKas
- We primarily aimed to look at the catalytic cysteine, which needs to be deprotonated in order to allow for catalysis. The sentence on pKas has been removed to avoid confusion. Since the catalytic cysteine in USPs typically has a high pKa, we decided to look at an increased pH to favor partial Cys deprotonation. To that, we have added a reference on USP7, in which it was previously shown USP7 is activated by a higher pH, which holds true for both full-length and its catalytic domain (Faesen et al., (2011). Molecular Cell, 44(1), 147–159. https://doi.org/10.1016/j.molcel.2011.06.034).
Importance of mutagenesis for studying enzymatic mechanisms is clear but limitations also need to be discussed; introduction of local changes etc.. this should be added to the discussion
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We have extended the discussion of limitations as requested. Importantly, the new USP1 asparagine mutants relieve some of the limitations of using alanine substitutions, which we also addressed in this section of the discussion: “While alanine mutations leave open an empty space, or take away the negative charge whenever an aspartate is mutated, mutating both critical residues to asparagine in USP1 did not alleviate the decrease in catalytic competence. Additionally, all single critical residue mutants remained stable and some mutants retaining most of their catalytic competence suggests that these enzymes still function properly.”
Table 1: linear not lineair
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Thank you. We have made the change.
Table 2: add information for mutant names (exact residue numbers) these data correspond to to improve clarity
- Thank you. We have made the change.
Fig. 1D which structure is shown?
- USP7 (1NBF), we have adjusted the legend.
Fig. 4 bands for USP1/UAF1 D752A and USP15 WT/mutants very faint so difficult to see whether there is crosslinking or not, please comment
- We performed the experiment again and made new figures with better contrast.
Fig.5: please see above for comment about graph and remove or revise.
- We have adjusted the legend to make the diversity more clear: “These five USPs share the conserved USP catalytic domain but vary considerably in domain architecture and allosteric regulation, and therefore represent a part of the diversity found in the USP family.”
Suppl. Table 2: global fit analysis not appropriate for when a poor fit was obtained or where the mutants were barely active (Figs S2, S3). These constants should be removed from the table or more information on the fitting provided. There seems to be some correlation between barely active mutants and the thermal stability, please comment.
- We prefer to do the global fit analysis, as it enables us to share rate constants and get meaningful comparisons. All USP variants were fit simultaneously using the global fit approach where k1 and k-3 rate constants were fixed, k-1 and k3 were shared for all the data sets of the same USP and only k2 was fitted for each data set separately. The quality of the global fit correlates with standard errors of k-1 and k3 rate constants. So, the model we use fits reasonably well with all the data sets all together. Even though a few fitted curves are not aligned well with some of the data for mutants with low activity the value of k2 is still important to report since it gives an approximation of magnitude for the catalytic activity and high standard error reflects the quality of the fit for those specific data sets. In addition, kcat/Km values for all the proteins, including low activity mutants, calculated from global fit approach correlate well with the values calculated from Michaelis-Menten analysis. We clarified this in the legend of supplementary figure 3: “Our kinetic model fits the data well. No fit could be obtained for USP15D880A since no activity was detected. We got relatively poorer fits to USPs with low activity, USP1D752A, USP7D481A). Still, for these low activity USPs the reported Kcat/Km gives an approximation of the magnitude for the catalytic activity and the poorer fit is reflected by their relatively higher standard errors reported in supplementary table 3.”
Suppl. Fig. 1B: See above.
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See comment on 3.
**Referees cross-commenting**
reviewers' comments are balanced
Reviewer #1 (Significance (Required)):
The study builds on previous work on USP7 and USP2 and while not a conceptual advance, adds to our understanding and knowledge of USP mechanisms. The in cellulo work of probing critical residues in USP1 for processing PCNA-Ub adds a new dimension. However, the limitations of some of the experimental design, stability of mutants and choice of USPs (as outlined above) somewhat hamper the direct comparisons the study makes and previous work needs to be adequately represented (USP2). The work will be of interest to basic researchers and medicinal chemists in particular.
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We very much appreciate the enthusiasm of the reviewer for our cellular validation.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Dr. Sixma is a leading expert in DUB enzymology, especially the enzymology of USP family members. This manuscript is a welcome addition to the field and her body of work to date. Exploring the possibility of redundant or entirely new catalytic residues in USPs is indeed an important venture for differentiating these highly homologous enzymes. The paper is well-written, and the experiments are simplistic and understandable. However, as a whole, the work is not ground-breaking, and the mechanistic explanation of the experimental observation lacks substantiating evidence. The manuscript should be recommended for publication in an appropriate journal after some revision.
Major comments: - A major concern of the article is about the mechanistic explanation of the role of the second critical residue Asp. The authors proposed two different possible mechanisms, including 1. the residue is flexible to position itself to replace the role of the canonical general base "first" critical residue; 2. Cys/His forms a dyad as seen in other cysteine proteases, and the "second critical residue" Asp participates in the oxyanion hole to stabilize the activated substrate. However, as the authors argue in their discussion, both mechanisms are speculative and have major issues: mechanism #1 requires the catalytic His to flip, and the conformation of the His and "second" critical residue is not optimal for them to form a hydrogen bond directly. The author suggested it may be mediated by a water molecule. However, no such structure has been reported. Mechanism #2 also has the trouble of lacking experimental evidence, and since the tetrahedral oxyanion intermediate is negatively charged, the same negatively charged Asp would be unfavourable. Without mechanistic evidence, the observation of the second (more) critical residue Asp is a very interesting one but beyond that, most of the discussions are speculative. The activity-based labelling experiment using Ub-PA, and the cellular experiments using the mutants only confirmed the observation but can not approve any of these mechanisms.
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Indeed, we do not come with a full mechanistic explanation which explains catalysis in all USPs. Instead, we show that individual USPs have greatly different dependence on their catalytic residue, and thus display important mechanistic distinctions, both for nucleophilic attack and for completion of the reaction. The new Asn mutations do show that negative charge in the 4th critical residue is critical for USP1 function, while the new stopped-flow analysis reveals that USP15 is trapped after the first turnover when the 4th critical residue is lost, and that this is not the case for the other USPs tested.
- The possibility of substrate trapping in some mutants is of interest. Paragraph 5 of the discussion even mentions this. I think this should be investigated by single-turnover assay techniques.
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We are very thankful for this great suggestion. We performed fast kinetics assays (stopped flow) for all USP wildtype and alanine variants. Together with the Ub-PA labelling experiments these assays shed new light on the ability of these USPs to perform a nucleophilic attack. In terms of substrate trapping, it does indeed turn out that USP15 is inactivated after the first turnover (Figure 4B).
Minor general concern: - The naming of the Asp/Asn/Ser in the canonical triad is a bit confusing. It is called "the third catalytic residue" and then the "first critical residue" (Intro, last paragraph). This is confusing because, in the catalytic triad, Cys/His are also critical residues. Given the importance of the fourth Asp residue, maybe the authors should come up with a different naming system. One suggestion could be calling the Asp/Asn/Ser the **general base residue** (in the canonical triad terms, Cys is the nucleophile, His is the general acid-base residue, Asp is the general base residue), and the 4th Asp as the "alternative general base residue"?
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Reviewer 1 also did not like the naming. To address the issue we have settled on: "critical residue 3" and "critical residue 4", with the active site Cys and His being the first two. This avoids assigning mechanistic roles to particular residues, but still stresses their importance.
- The augment at the end of the discussion that this alternative Asp residue could lead to new inhibitors for this difficult class of cysteine proteases is a stretch. The majority, if not all, structurally defined inhibitors of USPs (USP7, USP1, USP14) are allosteric inhibitors that do not target the catalytic triad directly. I doubt the discovery of Asp will change that. The most variability of activity regulation of USPs comes from auxiliary domains of the FL USPs, or cofactor proteins, as the authors' lab has previously demonstrated for many of the USPs, including USP7, USP4, USP1, etc., and there lie more opportunities for new inhibitor discovery.
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We agree that current inhibitors would not make use of these variations, but we feel that our findings could spark an interest in developing new classes that would benefit from the variability. We have adjusted the discussion to make that point more explicitly: “The variety in catalytic mechanisms might allow for development of new types of inhibitors with improved specificities.”
- Similarly, it is a fancy term to cite of DUBTACs, but I don't see much relevance of this alternative residue applied to DUBTACs. The authors could explore the idea a bit if they decide to cite this.
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Indeed, only if the such new inhibitors can be made. We’ve removed the sentence on DUBTACS.
Minor comments and grammar: editing is difficult without the inclusion of line numbers. I have attempted to address errors the best I can, considering this.
- Synopsis: "..., the majority of USPs **does** not..." should be "**do**"
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Correction was made
- Synopsis: "..., either critical **residues** can..." should be "**residue**"
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Correction was made
- Intro: "Subsequently a tetrahedral..." should have a comma after subsequently
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Correction was made
- Intro: 2nd paragraph, line 6, be more specific to be "peptide bond."
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Correction was made
- Intro: in the 3rd paragraph, the residue numbers of the catalytic residues should be stated.
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The numbers were added
- Intro: the first line of paragraph 4. The statement is confusing and should be made clearer by simply stating, "The third catalytic residue in USPs is either Asp, Asn, or Ser."
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Correction was made
- Intro: second last paragraph, be a bit more specific on what "resembles USP15 and USP7" could be "... USP8, another USP whose catalytic triad resembles those of USP15 and USP7" because the domain structure of these FL USPs is very different, only the triad is similar.
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We agree and we apologize for this oversight, we have deleted the sentence on USP8 as it is not relevant in this context.
- Intro: the last paragraph mentions the loss of function USP15 mutation behaves like wild type and USP1. The term "loss of function" is misleading. If mutation to the canonical 3rd catalytic residue has no effect on activity, then it is not a loss of function mutant. Please specify the alanine mutation.
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We've made this change
- Intro: last paragraph, "Michaelis Menten," should have a hyphen in between.
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Correction was made
- Methods: please add a space between values and units; this comes up multiple times throughout the manuscript
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Corrections have been made
- Methods: all taxonomic names should be italicized, i.e., E. coli
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Correction was made
- Methods: protein stability section, "**build**-in" should be "**built**-in" (build-in is repeated elsewhere and needs to be fixed)
-
Correction was made
- Methods: structure superposition section, "... bound to ubiquitin were **use** whenever..." should be "...bound to ubiquitin were **used** whenever..."
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Correction was made
- Methods: pH analysis section, "duplo" should be duplicate
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Correction was made
- Methods: Expression of USP1 in RPE1 cells section, please briefly state how you determined the expression level of USP1 in transduced RPE1 USP1KO cells when selecting clones with comparable levels to RPE1 wt cells
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We have added an extended description on how we selected these single clones. “To select clones with similar USP1 levels compared to endogenous, single clones were incubated with 1 µg/ml doxycycline for 44 hours and were lysed using RIPA buffer (1% NP40, 1% sodium deoxycholate, 0.1% SDS, 0.15 M NaCl, 0.01 M sodium phosphate pH 7.5, 2 mM EDTA), containing cOmplete™, EDTA-free Protease Inhibitor Cocktail (Roche, 11873580001), 1 mM 2-chloroacetamide and 0.25 U/µl benzonase (SC-202391, Santa Cruz Biotechnology). Total protein concentration in the lysate was determined using a BCA assay (23227, Thermo Scientific) so that equal amounts could be loaded on gel. Samples were loaded on 4-12% Bolt gels (NW04127, Thermo Scientific), and run for 40 minutes at 180 V in MOPS running buffer (B0001, Thermo Scientific). Proteins were transferred to nitrocellulose membrane (10600002, Amersham Protran 0.45 NC nitrocellulose). Membranes were stained with a USP1 antibody (14346-1-AP, Proteintech). After incubation with HRP coupled secondary antibody the blots were imaged using a Bio-Rad Chemidoc XRS+. Using Bio-Rad ImageLab 5.1 software, USP1 levels were quantified by measuring the volume intensities of each USP1 band for each clone and compared this to endogenous USP1 levels in RPE1 cells. Clones with comparable expression levels were selected and used for further experiments.”
- Methods: tCoffee webserver should be "T-Coffee"
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We realized that multiple sequence alignment was performed using Clustal Omega, not T-Coffee, which has now been corrected. We apologize for this oversight.
- Methods: MSA. Can the authors provide more details on when doing BLAST, what were the criteria of selecting sequences from the result?
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Details have been added: “Catalytic domains as defined by Uniprot of the resulting human USPs were used for multiple sequence alignment. For USPs with multiple isoforms, the canonical isoform (isoform 1) was selected. In case of the USP17 gene family, USP17L2/DUB3 was selected (Komander et al., 2009). In order to properly align USP1, its inserts were removed from the catalytic domain following (Dharadhar et al., 2021). In order to properly align USP40, a shorter sequence was used (residues 250-480).”
- Methods: please provide the details for determining the concentration of the enzymes used.
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Details on how we determined the concentrations of enzymes have been added.
- Methods: Please provide the manufacturers of the Pherastar plate reader and the 384-well plate (please correct from "384 well-plate").
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Info on the manufacturers has been added.
- Results: In paragraph 1, "lies a **much better** conserved..." you should use "more highly."
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Correction was made
- Results: paragraph 1, "USP50 does not harbor either of" should be "USP50 harbors neither of"
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We corrected this: “This aspartate is present in all USPs except CYLD and USP50. The latter misses the third critical residue as well and therefore may be inactive.”
- Supp Fig 2: USP39 does not have glutamate in position of the first critical residue, it is glutamine (Q)
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Correction was made
- Results: second subsection title **"The first critical residue is dispenUSP1..."** needs to be fixed
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Correction was made as follows: The third critical residue is dispensable in USP1/UAF1, USP15, USP40 and USP48
- Results: pg. 8 last line "to crosslink", the word crosslink is not proper for the reaction between Ub-PA with USPs. It usually refers to a reactive linker that links two molecules. Words like "conjugate", "conjugation," or "covalent react with", and "activity-based labelling" are probably better choices depending on the context.
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We have corrected this throughout the manuscript.
- Figure 1: figure legend describing B, C, and D are mixed up.
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Correction was made
- Results: In paragraph 9, the statement that your data on 5 USPs is representative of most of the 57 members in that the third catalytic is dispensable is not a sound statement for the small sample size. I think more emphasis on the diversity of USP1, USP7, USP15, USP40, and USP48 needs to be stated to help bolster such a claim. The statement to follow, which mentions sequence analysis alone is not able to predict the catalytic residue, is also somewhat contradictory to the opening statement and insinuates that all active USPs should be tested, while you only examined 5.
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We have changed this to ”Our findings demonstrate that for the majority of tested USPs…”. The diversity of tested USPs is clarified earlier in the manuscript: “These USPs vary in domain architecture and allosteric regulation, and therefore represent different aspects of the USP family, known for its structural variety and modular architecture”. The statement about sequence analysis has been removed from the results section and is now only mentioned in the discussion. However, we do think that precise active site assignment for other USPs will require mutagenesis support.
- Figure 4: legend title, the critical residues are not responsible for **performing** nucleophilic attack per se; that is the job of Cys. The title of the figure should be altered to clear this up.
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Correction was made as follows: " Variation in the ability of USP critical residue mutants to successfully and efficiently facilitate a nucleophilic attack.”
- Discussion: paragraph 3, since the Hu 2002 USP7 mechanism is not valid for other USPs tested, the "consensus USP catalytic mechanism" should be referred to as the "canonical."
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Indeed! Correction was made.
- Discussion: paragraph 4, "USP7, USP15 and USP40 all **three** have misaligned..." should be "USP7, USP15 and USP40 all have misaligned..."
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Correction was made.
- Discussion: paragraph 8, "negative charge itself could **contributes**..." should be "negative charge itself could **contribute**..."
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Correction was made
- Discussion: pg. 10, 3rd paragraph. Is the first sentence a statement of fact or a hypothesis? The writing is not clear to differentiate the two possibilities.
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Parts of the discussion have been rewritten, but the corresponding sentence has been rewritten as follows: “Canonically, it is thought that the fourth critical residue is involved in oxyanion hole formation.”
- Discussion: pg. 10, 3rd paragraph, line 3, which "critical residue" does it refer to, the general base residue or the alternative residue?
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We've changed the text as follows: ". A dual role, with the third or fourth critical residue stabilizing catalytic histidine and oxyanion hole formation simultaneously is unlikely”.
- Discussion: pg. 10, second last paragraph. Can the statement that "inaccurate assumptions about the catalytic triad ... be substantiated with an example?
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We apologize for the possible confusion, but our point here was to point out that it could be misdirecting conclusions if you strictly follow the canonical assignment of the catalytic triad. We have rewritten the sentence to make that more clear: “Additionally, assumptions about the catalytic triad solely based on the canonical catalytic triad assignment in USP could affect conclusions made regarding loss of function mutations in genetic screens. For example, we find that some USPs retain full or most of their activity once their canonical third catalytic residue is mutated.”
- Table 1, "ubiquitin variant" is mostly often used in the literature to refer to the ubiquitin mutants generated by phage display pioneered by the Sidhu lab or designed mutants. "ubiquitin and homolog derivatives" is a better term for "ubiquitin variant" in this article.
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We have changed this to ubiquitin-like proteins
- Table 1, the USP21 line "Lineair" is a typo, it should be "linear."
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Correction was made
- References: citations for Cadzow, 2020. and Tsefou, 2021 do not appear in the bibliography.
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Correction was made
- Add a hyphen to "Ubiquitin-specific proteases."
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Correction was made
Reviewer #2 (Significance (Required)):
General assessment:
Based on the studies of prototypical ubiquitin-specific protease USP7, the field generally accepts that USPs are a class of cysteine proteases that contain a catalytic triad with a cysteine, a histidine and a general base residue (asparagine, aspartate, or serine). This manuscript described the importance of an alternative, highly conserved aspartate that plays a critical role in catalysis using an enzyme kinetics study on five out of 57 USPs. The work is a very interesting observation that could change the perception in the field. However, the atomic details of how this fourth, or alternative residue, plays its role in catalysis are not clear without the structure evidence of an intermediate/transition state-bound complex.
Advance:
The study provided the first systematic enzymology study of the role of a fourth conserved residue critical for the catalysis of USPs. It is a conceptual advance and a first step to elucidate possibly a new catalytic mechanism of USPs.
Audience: The manuscript will be of interest to biochemists in the field of ubiquitination and drug discovery.
Reviewers' expertise
The reviewers are structural biologists with expertise in the structure, function and enzymology of ubiquitin enzymes in general, with practical experience in drug discovery targeting the DUB and kinase families.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
The article by Keijzer and colleagues describes an interesting study comparing the active site of multiple USPs (the largest subfamily of deubiquitinases) and elucidating the importance of specific residues lining the active site for catalysis. The authors carried out a careful analysis of the kinetic properties of 5 representative USPs and mutants thereof revealing a remarkable variety in their function that highlights that the majority of USPs studied do not require the canonical third residue of the catalytic triad of USPs for activity but instead rely on a highly conserved second critical residue. Furthermore, the authors apply complementary experimental approaches (mutagenesis, pH dependence of activity, crosslinking with Ub-PA) to allow distinguishing between residues important for the nucleophilic attack versus oxyanion hole stabilisation.
This is a well-written, thorough enzymatic study of high technical quality. The experiments are described in sufficient detail to allow others to reproduce the experimental set up. The data presented fully support the claims of the paper and no additional experiments are required to further support the conclusions. It is great to see that the authors have carried out thermal stability assays on all WT and mutant proteins under investigation to ensure that any effects observed are not due to protein misfolding.
Minor comments:
- There are a few typos in the manuscript the authors should correct.
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Thank you, we have removed the typos from the manuscript.
- The panels/paper legends to Figure 1B/C/D are mixed up. Please correct.
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Correction was made
-It would be helpful to use different colours in the alignment shown in Supplementary Figure1 to indicate the position of the first and second critical residue.
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Thank you, we have highlighted these residues
- I wonder if the authors could comment on how representative the 5 USPs characterised in this work are of the entire family.
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We address the variation of these USPs in more detail, both in the results as in the legend of figure 5: “These USPs vary in domain architecture and allosteric regulation, and therefore represent different aspects of the USP family, known for its structural variety and modular architecture”
Reviewer #3 (Significance (Required)): Deubiquitinating enzymes (DUBs) play essential roles in many cellular processes and their activity is associated with a variety of diseases. There is a lot of interest in targeting DUBs for therapeutic purposes and a number of small molecule inhibitors are undergoing clinical studies. While the structure and mechanism of multiple DUBs have been studied over the years, many open questions about their detailed catalytic mechanism remain and the importance of specific residues might often have been inferred based on sequence conservation alone without accompanying experimental support. This work makes an important contribution to the field by systematically examining 5 members of the USP family and defining the precise role of the first and second critical residue for the catalytic cycle. This work will be of interest to those studying the mechanism of DUBs in general and those trying to target specific DUBs with small molecules. In addition, this study will also be interesting more generally for those studying enzyme kinetics as it highlights the importance of experimental validation of a catalytic mechanism that has been predicted based on sequence conservation or structural studies.
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Referee #3
Evidence, reproducibility and clarity
The article by Keijzer and colleagues describes an interesting study comparing the active site of multiple USPs (the largest subfamily of deubiquitinases) and elucidating the importance of specific residues lining the active site for catalysis. The authors carried out a careful analysis of the kinetic properties of 5 representative USPs and mutants thereof revealing a remarkable variety in their function that highlights that the majority of USPs studied do not require the canonical third residue of the catalytic triad of USPs for activity but instead rely on a highly conserved second critical residue. Furthermore, the authors apply complementary experimental approaches (mutagenesis, pH dependence of activity, crosslinking with Ub-PA) to allow distinguishing between residues important for the nucleophilic attack versus oxyanion hole stabilisation.
This is a well-written, thorough enzymatic study of high technical quality. The experiments are described in sufficient detail to allow others to reproduce the experimental set up. The data presented fully support the claims of the paper and no additional experiments are required to further support the conclusions. It is great to see that the authors have carried out thermal stability assays on all WT and mutant proteins under investigation to ensure that any effects observed are not due to protein misfolding.
Minor comments:
- There are a few typos in the manuscript the authors should correct.
- The panels/paper legends to Figure 1B/C/D are mixed up. Please correct.
- It would be helpful to use different colours in the alignment shown in Supplementary Figure1 to indicate the position of the first and second critical residue.
- I wonder if the authors could comment on how representative the 5 USPs characterised in this work are of the entire family.
Significance
Deubiquitinating enzymes (DUBs) play essential roles in many cellular processes and their activity is associated with a variety of diseases. There is a lot of interest in targeting DUBs for therapeutic purposes and a number of small molecule inhibitors are undergoing clinical studies. While the structure and mechanism of multiple DUBs have been studied over the years, many open questions about their detailed catalytic mechanism remain and the importance of specific residues might often have been inferred based on sequence conservation alone without accompanying experimental support.
This work makes an important contribution to the field by systematically examining 5 members of the USP family and defining the precise role of the first and second critical residue for the catalytic cycle. This work will be of interest to those studying the mechanism of DUBs in general and those trying to target specific DUBs with small molecules. In addition, this study will also be interesting more generally for those studying enzyme kinetics as it highlights the importance of experimental validation of a catalytic mechanism that has been predicted based on sequence conservation or structural studies.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #2
Evidence, reproducibility and clarity
Dr. Sixma is a leading expert in DUB enzymology, especially the enzymology of USP family members. This manuscript is a welcome addition to the field and her body of work to date. Exploring the possibility of redundant or entirely new catalytic residues in USPs is indeed an important venture for differentiating these highly homologous enzymes. The paper is well-written, and the experiments are simplistic and understandable. However, as a whole, the work is not ground-breaking, and the mechanistic explanation of the experimental observation lacks substantiating evidence. The manuscript should be recommended for publication in an appropriate journal after some revision.
Major comments:
- A major concern of the article is about the mechanistic explanation of the role of the second critical residue Asp. The authors proposed two different possible mechanisms, including 1. the residue is flexible to position itself to replace the role of the canonical general base "first" critical residue; 2. Cys/His forms a dyad as seen in other cysteine proteases, and the "second critical residue" Asp participates in the oxyanion hole to stabilize the activated substrate. However, as the authors argue in their discussion, both mechanisms are speculative and have major issues: mechanism #1 requires the catalytic His to flip, and the conformation of the His and "second" critical residue is not optimal for them to form a hydrogen bond directly. The author suggested it may be mediated by a water molecule. However, no such structure has been reported. Mechanism #2 also has the trouble of lacking experimental evidence, and since the tetrahedral oxyanion intermediate is negatively charged, the same negatively charged Asp would be unfavourable. Without mechanistic evidence, the observation of the second (more) critical residue Asp is a very interesting one but beyond that, most of the discussions are speculative. The activity-based labelling experiment using Ub-PA, and the cellular experiments using the mutants only confirmed the observation but can not approve any of these mechanisms.
- The possibility of substrate trapping in some mutants is of interest. Paragraph 5 of the discussion even mentions this. I think this should be investigated by single-turnover assay techniques.
Minor general concern:
- The naming of the Asp/Asn/Ser in the canonical triad is a bit confusing. It is called "the third catalytic residue" and then the "first critical residue" (Intro, last paragraph). This is confusing because, in the catalytic triad, Cys/His are also critical residues. Given the importance of the fourth Asp residue, maybe the authors should come up with a different naming system. One suggestion could be calling the Asp/Asn/Ser the general base residue (in the canonical triad terms, Cys is the nucleophile, His is the general acid-base residue, Asp is the general base residue), and the 4th Asp as the "alternative general base residue"?
- The augment at the end of the discussion that this alternative Asp residue could lead to new inhibitors for this difficult class of cysteine proteases is a stretch. The majority, if not all, structurally defined inhibitors of USPs (USP7, USP1, USP14) are allosteric inhibitors that do not target the catalytic triad directly. I doubt the discovery of Asp will change that. The most variability of activity regulation of USPs comes from auxiliary domains of the FL USPs, or cofactor proteins, as the authors' lab has previously demonstrated for many of the USPs, including USP7, USP4, USP1, etc., and there lie more opportunities for new inhibitor discovery.
- Similarly, it is a fancy term to cite of DUBTACs, but I don't see much relevance of this alternative residue applied to DUBTACs. The authors could explore the idea a bit if they decide to cite this.
Minor comments and grammar: editing is difficult without the inclusion of line numbers. I have attempted to address errors the best I can, considering this.
- Synopsis: "..., the majority of USPs does not..." should be "do"
- Synopsis: "..., either critical residues can..." should be "residue"
- Intro: "Subsequently a tetrahedral..." should have a comma after subsequently
- Intro: 2nd paragraph, line 6, be more specific to be "peptide bond."
- Intro: in the 3rd paragraph, the residue numbers of the catalytic residues should be stated.
- Intro: the first line of paragraph 4. The statement is confusing and should be made clearer by simply stating, "The third catalytic residue in USPs is either Asp, Asn, or Ser."
- Intro: second last paragraph, be a bit more specific on what "resembles USP15 and USP7" could be "... USP8, another USP whose catalytic triad resembles those of USP15 and USP7" because the domain structure of these FL USPs is very different, only the triad is similar.
- Intro: the last paragraph mentions the loss of function USP15 mutation behaves like wild type and USP1. The term "loss of function" is misleading. If mutation to the canonical 3rd catalytic residue has no effect on activity, then it is not a loss of function mutant. Please specify the alanine mutation.
- Intro: last paragraph, "Michaelis Menten," should have a hyphen in between.
- Methods: please add a space between values and units; this comes up multiple times throughout the manuscript
- Methods: all taxonomic names should be italicized, i.e., E. coli
- Methods: protein stability section, "build-in" should be "built-in" (build-in is repeated elsewhere and needs to be fixed)
- Methods: structure superposition section, "... bound to ubiquitin were use whenever..." should be "...bound to ubiquitin were used whenever..."
- Methods: pH analysis section, "duplo" should be duplicate
- Methods: Expression of USP1 in RPE1 cells section, please briefly state how you determined the expression level of USP1 in transduced RPE1 USP1KO cells when selecting clones with comparable levels to RPE1 wt cells
- Methods: tCoffee webserver should be "T-Coffee"
- Methods: MSA. Can the authors provide more details on when doing BLAST, what were the criteria of selecting sequences from the result?
- Methods: please provide the details for determining the concentration of the enzymes used.
- Methods: Please provide the manufacturers of the Pherastar plate reader and the 384-well plate (please correct from "384 well-plate").
- Results: In paragraph 1, "lies a much better conserved..." you should use "more highly."
- Results: paragraph 1, "USP50 does not harbor either of" should be "USP50 harbors neither of"
- Supp Fig 2: USP39 does not have glutamate in position of the first critical residue, it is glutamine (Q)
- Results: second subsection title "The first critical residue is dispenUSP1..." needs to be fixed
- Results: pg. 8 last line "to crosslink", the word crosslink is not proper for the reaction between Ub-PA with USPs. It usually refers to a reactive linker that links two molecules. Words like "conjugate", "conjugation," or "covalent react with", and "activity-based labelling" are probably better choices depending on the context.
- Figure 1: figure legend describing B, C, and D are mixed up.
- Results: In paragraph 9, the statement that your data on 5 USPs is representative of most of the 57 members in that the third catalytic is dispensable is not a sound statement for the small sample size. I think more emphasis on the diversity of USP1, USP7, USP15, USP40, and USP48 needs to be stated to help bolster such a claim. The statement to follow, which mentions sequence analysis alone is not able to predict the catalytic residue, is also somewhat contradictory to the opening statement and insinuates that all active USPs should be tested, while you only examined 5.
- Figure 4: legend title, the critical residues are not responsible for performing nucleophilic attack per se; that is the job of Cys. The title of the figure should be altered to clear this up.
- Discussion: paragraph 3, since the Hu 2002 USP7 mechanism is not valid for other USPs tested, the "consensus USP catalytic mechanism" should be referred to as the "canonical."
- Discussion: paragraph 4, "USP7, USP15 and USP40 all three have misaligned..." should be "USP7, USP15 and USP40 all have misaligned..."
- Discussion: paragraph 8, "negative charge itself could contributes..." should be "negative charge itself could contribute..."
- Discussion: pg. 10, 3rd paragraph. Is the first sentence a statement of fact or a hypothesis? The writing is not clear to differentiate the two possibilities.
- Discussion: pg. 10, 3rd paragraph, line 3, which "critical residue" does it refer to, the general base residue or the alternative residue?
- Discussion: pg. 10, second last paragraph. Can the statement that "inaccurate assumptions about the catalytic triad ... be substantiated with an example?
- Table 1, "ubiquitin variant" is mostly often used in the literature to refer to the ubiquitin mutants generated by phage display pioneered by the Sidhu lab or designed mutants. "ubiquitin and homolog derivatives" is a better term for "ubiquitin variant" in this article.
- Table 1, the USP21 line "Lineair" is a typo, it should be "linear."
- References: citations for Cadzow, 2020. and Tsefou, 2021 do not appear in the bibliography.
- Add a hyphen to "Ubiquitin-specific proteases."
Significance
General assessment:
Based on the studies of prototypical ubiquitin-specific protease USP7, the field generally accepts that USPs are a class of cysteine proteases that contain a catalytic triad with a cysteine, a histidine and a general base residue (asparagine, aspartate, or serine). This manuscript described the importance of an alternative, highly conserved aspartate that plays a critical role in catalysis using an enzyme kinetics study on five out of 57 USPs. The work is a very interesting observation that could change the perception in the field. However, the atomic details of how this fourth, or alternative residue, plays its role in catalysis are not clear without the structure evidence of an intermediate/transition state-bound complex.
Advance:
The study provided the first systematic enzymology study of the role of a fourth conserved residue critical for the catalysis of USPs. It is a conceptual advance and a first step to elucidate possibly a new catalytic mechanism of USPs.
Audience: The manuscript will be of interest to biochemists in the field of ubiquitination and drug discovery.
Reviewers' expertise
The reviewers are structural biologists with expertise in the structure, function and enzymology of ubiquitin enzymes in general, with practical experience in drug discovery targeting the DUB and kinase families.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #1
Evidence, reproducibility and clarity
Summary:
The authors study the functional role of two adjacent active site residues as candidates for polarising the catalytic histidine in the "Asn/Asp" box from five phylogenetically unrelated ubiquitin specific proteases (USP1, USP7, USP15, USP40 and USP48). One of these residues is more variable across USPs (Asn, Asp, Ser), whereas the second one is absolutely conserved (Asp). To this end they use alanine mutants in kinetic experiments and test their ability to crosslink to ubiquitin propargyl as a proxy for testing the nucleophilicity of the catalytic cysteine. They then further evaluate the activity of the USP1 mutants in processing PCNA-Ub in RPE1 cells. They find that the role of these two residues differs between the different USPs studied, which is in line with previous work that has shown that in USP7, the amongst USPs less conserved residue takes on the major role of polarising the histidine, whereas in the more distantly related USP2, the absolutely conserved Asp is more important (Zhang W, et al. Contribution of active site residues to substrate hydrolysis by USP2: insights into catalysis by ubiquitin specific proteases. Biochemistry. 2011 50(21):4775-85. doi: 10.1021/bi101958h). This study expands on these findings to evaluate the role of these residues in four other USPs.
Major comments:
- The authors compare highly diverse USPs; USP1 requires UAF1 for full activity and the complex is used in the study, USP7 requires a C-terminal tail peptide for full activity, USP40 and USP48 belong to the CHN class, whereas USP7, USP15 and USP1 belong to the CHD class of USPs. The rationale for selecting this diverse set of USPs is therefore not clear and makes direct comparisons of the findings more difficult. It is certainly interesting that the previously published differences between USP2 and USP7 with respect to these residues are also found in four other divergent USPs, but for this reason it isn't as "surprising" as the title suggests. The title, omission of background knowledge on USP2 in the abstract and presentation of the findings in a graph that makes direct comparisons (Figure 5) are therefore a bit misleading, which needs addressing.
- The study relies on single alanine mutations, which will inevitably change the hydrogen bonding patterns and the local environment which could impact the conclusion. The authors should verify in kinetic assays at least for USP1, which is the main focus, that Asp to Asn mutants still display the same effects.
- While neither mutant unfolds below 40 degrees, there are clear differences in thermal stability between some of the proteins used in the study (Supp. Fig. 1B). A full table of measured Tms by NanoDSF for all Wt and mutant proteins should be provided so that the reader can evaluate how the results may be impacted by local effects that impact the thermal stability. It is noticeable that USP40 and USP15 mutants in particular display large differences in thermal stability, which could directly affect the results. The authors should clearly discuss these limitations of the study.
Minor comments:
- For USP48 and USP40 no published structures are available at present, so it isn't clear whether there are any differences in orientation of the studied residues. An unpublished USP40 structure is referred to but not shown. The general conclusion that structures do not reveal any differences in these residues may therefore not be valid for all the studied USPs. Please revise.
- The introduction of the new terms "critical residue 1 and 2" are confusing and partially disproved by the study itself (replace with e.g. less conserved versus absolutely conserved 3rd triad residue or similar), please revise.
- p. 3/4: please add pH information to buffers used in the stability studies. "Previous publication" and "manuscript in preparation" are contradictions.
- p. 4. Assay buffer for USP1, USP7 and USP48 pH information is missing
- p. 6: last heading: typo is dispensable
- p. 8: please explain choice of USP1 C90R mutation
- Explain choice of pH range 7-9 studied with regards to anticipated pKas
- Importance of mutagenesis for studying enzymatic mechanisms is clear but limitations also need to be discussed; introduction of local changes etc.. this should be added to the discussion
- Table 1: linear not lineair
- Table 2: add information for mutant names (exact residue numbers) these data correspond to to improve clarity
- Fig. 1D which structure is shown?
- Fig. 4 bands for USP1/UAF1 D752A and USP15 WT/mutants very faint so difficult to see whether there is crosslinking or not, please comment
- Fig.5: please see above for comment about graph and remove or revise.
- Suppl. Table 2: global fit analysis not appropriate for when a poor fit was obtained or where the mutants were barely active (Figs S2, S3). These constants should be removed from the table or more information on the fitting provided. There seems to be some correlation between barely active mutants and the thermal stability, please comment.
- Suppl. Fig. 1B: See above.
Referees cross-commenting
reviewers' comments are balanced
Significance
The study builds on previous work on USP7 and USP2 and while not a conceptual advance, adds to our understanding and knowledge of USP mechanisms. The in cellulo work of probing critical residues in USP1 for processing PCNA-Ub adds a new dimension.
However, the limitations of some of the experimental design, stability of mutants and choice of USPs (as outlined above) somewhat hamper the direct comparisons the study makes and previous work needs to be adequately represented (USP2). The work will be of interest to basic researchers and medicinal chemists in particular.
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Reply to the reviewers
We would like to thank all reviewers for their valuable and constructive comments, which helped us a lot to improve the manuscript.
The followings are point-by-point responses to the reviewers' comments:
Reviewer #1
Strong points
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- The demonstration that pMAC-lncRNA accumulation depends upon Ema2 is convincing. This finding provides novel insights into the mechanism involved in TDSD in Tetrahymena. An important point that would be worth discussing is how ds pMAC-lncRNAs may pair with scnRNAs. An RNA helicase (Ema1?) may play an important role in this process.*
The requirement of Ema1 in the interaction between pMAC-lncRNAs and scnRNAs was reported previously by us (Aronica et al. 2008), which has been cited in this manuscript. Related to this point, we have added the following discussion in the revised manuscript (Page 10, Line 30):
“Although it is unclear whether lncRNAs are single or double stranded when Ema1 promotes the lncRNA-scnRNAs interaction, the less severe TDSD defect observed in the EMA2 KO cells compared to the EMA1 KO cells (Figure 3B) indicates that certain Ema1-dependent TDSD may be initiated by single-stranded lncRNAs or mRNAs that are transcribed independently of Ema2”.
- The manuscript is very well written. I noticed only a few typos (see minor comments below).*
The pointed typos have been corrected in the revised manuscript.
- The experiments are overall well done and well described. For non-Tetrahymena readers, it would be useful to clarify in the Results section (or in figure captions) whether the different KOs are in the MAC and/or also in the MIC*
We have indicated whether each KO line is somatic or germline (MAC+MIC) in the figure legends whenever these lines are referenced.
Responses for the suggestions:
Major concerns
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- The search for Ema2 targets using mass spectrometry was performed in a wild-type SMT3 background. This implies that endogenous wild-type Smt3 may have competed with His-Smt3 for protein sumoylation. To what extent may this have been a problem for the enrichment of sumoylated proteins on nickel columns? This point is critical, since the authors discuss that other proteins involved in pMAC-lncRNA transcription may be modified by Ema2 (p. 12). They should repeat the experiment in an SMT3 KO, or use anti-Smt3 antibodies to enrich for sumoylated proteins. If this is not possible, they should at least provide additional explanations.*
We agree that a competition between His-tagged and non-tagged Smt3 lowered the sensitivity for the identification of SUMOylated proteins and we might miss some Ema2-dependent SUMOylated protein in the current study. However, we believe such protein, if any, is SUMOylated at very low level and not highly likely to be involved in the genome-wide orchestration of lncRNA transcription. We rather think that a critical Ema2-dependent SUMOylation event might be missed because some other residues of the same protein are SUMOylated by Ema2-independent manner and it was detected as a protein that was SUMOylated in both wild-type and EMA2 KO condition. Therefore, as was explained in Discussion, it is important to identify individual residues that are SUMOylated in Ema2-dependent manner. We are on our way to set up an experimental system that allows us to detect individual SUMOylated residues in Tetrahymena and we hope to analyze the functions of Ema2-dependent SUMOylated residues in future studies.
- In Figure 7A, the authors only show the localization of Spt6 in early exconjugants. Since Spt6 is essential for vegetative growth, one can expect that it also localizes in the vegetative MAC. Is it also found in the new developing MACs? The authors should complete the figure with additional panels showing vegetative cells and exconjugants at later stages (with their new MAC).*
The Spt6 is indeed localized in the MAC during vegetative growth and in the new MAC at late conjugation stage in the wild-type condition. We did not detect any anomaly of Spt6 localization in the EMA2 KO cells at least at the cytological level. The immunostaining results at the late conjugation stage are shown in Figure EV4 in the revised manuscript and mentioned in the revised text (Page 11, Line 13). The immunostaining results of vegetatively growing cells are only attached below because Spt6 localization at vegetative stage when EMA2 is not expressed is not highly relevant to this study.
- Along the same line, the authors show that the non sumoylatable Spt6 mutant does not inhibit pMAC-lncRNA synthesis. No scnRNA analysis is shown under these conditions: does TDSD still take place? It would also be interesting to check whether lncRNAs are still produced in the new MACs.*
The nonSUMOylatable Spt6 mutant (we now call SUMOylation defective Spt6 mutant according to one of the Reviewer 3’s suggestions) show lower mating, making us difficult to investigate its effect on TDSD. Because we did not detect Spt6 SUMOylation prior to mating, we believe the low mating phenotype of this mutant is not directly due to the loss of SUMOylation but instead some of the 77 K to R mutations affect the functions of Spt6 in efficient initiation of mating. Therefore, to precisely measure the effect of Ema2-dependent Spt6 SUMOylation, we need to identity exact Ema2-dependent SUMOylated residues of Spt6 to produce another nonSUMOylatable Spt6 mutant with fewer number of mutations that does not affect the mating process. Engaging in such work demands a substantial time investment, and we believe that the reviewers will concur that these experiments are components of our future projects.
Long dsRNA accumulation in the new MACs detected by the J2 antibody was comparable between wild-type and the SUMOylation-defective Spt6 mutant, suggesting that Spt6 SUMOylation is not necessary to produce lncRNAs in the new MAC. The data have been shown in Figure EV9 and mentioned in the main text (Page 12, Line 24) in the revised manuscript.
- The experiment shown in Figure 4C indicates that high-molecular weight (possibly sumoylated) proteins decrease to 50% in the EMA2 KO: this suggests that another sumoylation activity exists in the cell. A search for other putative SUMO E3 ligases is missing in this study.*
A few other putative SUMO E3 ligases indeed encoded in the Tetrahymena genome. Moreover, it is known that some substrates are SUMOylated without any SUMO E3 ligase in other eukaryotes. These points have been described in the revised text as follows (Page 8, Line 22):
“The remaining Ema2-independent SUMOylation is likely mediated by other SUMO E3 ligases (including the SP-RING containing proteins TTHERM_00227730, TTHERM_00442270 and TTHERM_00348490) and/or E3-independent SUMOylation (Sampson et al. 2001).”
We agree that exploring the roles of other SUMO E3 ligases in Tetrahymena would be important and interesting, and we believe it will be one of our future projects.
- Can one exclude that Spt6 is sumoylated at other stages (vegetative or during new MAC development) in an Ema2-independent manner?*
We have now included western blot observation of Spt6 at different life stages of wild-type cells as Figure EV2. We did not detect any slower-migrating Spt6 species in vegetative cells. This has been mentioned in the revised text as follows (Page 9, Line 17):
“Then, to examine the timing of the appearance of the slower migrating Spt6 species, we introduced the same Spt6-HA-expressing construct into a wild-type strain and Spt6-HA was analyzed by western blotting (Figure EV2). Consistent with the Ema2-dependent appearance of the slower migrating Spt6-HA, they were not detected in growing and starved vegetative wild-type cells (Figure EV2, Veg and 0 hpm, respectively) when Ema2 was not expressed (Figure 1). The slower migrating Spt6-HA was also detected at 8 hpm when the new MAC was already formed (Figure EV2, 8 hpm) suggesting that Spt6 is possibly SUMOylated also in the new MAC.”
- In which nucleus does coding transcription take place between 4.5 and 6 hpm? Can we exclude that the weaker association of Rpb3 with chromatin in the EMA2 KO cross also impairs coding transcription?*
Coding transcription takes place in the parental MAC at 4.5 and 6 hpm in wild-type cells. Also, because EMA2 KO cells did not show obvious defect in the progression of the conjugation processes, any essential mRNA transcriptions for these processes must occur even in the absence of Ema2. These points prompted us to add the following discussion in the Discussion section (Page 13, Line 14):
“Moreover, as EMA2 KO cells did not significantly impede the progression of conjugation processes, any essential mRNA transcriptions for these processes must take place in the parental MAC during conjugation even in the absence of Ema2. Therefore, the observed loss of the majority of Spt6 and RNAPII from chromatin in the absence of Ema2 (Figure 7B) must be a temporal event during the mid-conjugation stage. This suggest that RNAPII might be specifically engaged in pMAC-lncRNA transcription at this particular time window in wild-type cells.”
Minor concerns
- The authors do not explain how they found Ema2. More information could be useful.*
Ema2 was identified as a protein involved in DNA elimination during our systematic genetic investigation of genes exclusively expressed during conjugation. This has been mentioned in the revised manuscript (Page 6, Lines 4-5).
- In Figures 2B and 3B: the statistical significance of the differences observed for the IES retention index and small RNA amounts should be evaluated using appropriate tests.*
The result shown in Figure 2B (IES retention analysis) has been tested by Welch two-sample t-test and outcomes have been shown in the revised Figure 2B.
The result shown in Figure 3B (small RNA seq) has been tested by Wilcoxon rank sum test and outcomes have been shown in the revised Figure 3B.
Figure 3 caption: define acronym "IQR"
The definition of IQR (the interquartile range) has now been mentioned in the figure legend in the revised manuscript.
Figure 5 caption (line 4): there may be a word missing ("from conjugating cells?")
We have corrected the sentence by adding “cells” after “from conjugating” in Page30-Line 34.
Figure 8C: what does the asterisk stand for?
We realized that the asterisk is not necessary in the figure and thus it have been removed in the revised figure.
- p. 10 (bottom): an "o" is missing in "Aronica et al 2008"*
We have corrected the error.
- p.13 (2nd line): remove final "s" in "mimic"*
We have corrected the error.
- p. 14: change "were" to "was" in "the production of the EMA2 KO strains was described previously"*
We have corrected the error.
- p. 14: remove capital letters in "Gorovsky"*
We have corrected the error.
- p. 15 (Viability test for progeny): what does "6-mp" stand for?*
It is 6-methylpurine. We have added this information to the revised manuscript.
- p. 17 (end of first paragraph): change "contracts" to "constructs"*
We have corrected the error.
- p. 17 (2nd line of last paragraph): change "was" to "were " in "EMA2 cells containing the BP6MB1-His-SMT3 construct were mated..."*
We have corrected the error.
- p. 19 (3rd line of 2nd paragraph"): "spined own" should be replaced by "spinned down"*
We have corrected the error.
Reviewer #2
Major comments
From Figure 4C, the authors conclude that "Ema2 is the major SUMO E3 ligase during the mid-conjugation stages.", yet in Figure 5 show that only Spt6-SUMOylation is affected in Ema2 mutants. These conclusions seem inconsistent and should be reconciled as it is a central point in the paper. E.g. is Spt6 protein abundance based on the MS data supporting that this protein constitutes a major fraction of the (high mol weight) SUMOylated proteins? Of note, the discussion contains a very balanced discussion of this but the current description in the results should be improved.
Some of the proteins detected from both the wild-type and EMA2 KO conditions were possibly poly-histidine-containing proteins that bound intrinsically to the nickel-NTA beads or proteins unpacifically bound to some of the bead material. Taking these possibilities into account, a control experiment with wild-type cells not expressing His-Smt3 in the same condition is now included in the study and any proteins that were also identified in this experiment with log2 LFQ score above 25 were excluded in the new Figure 5A. We also removed any identified proteins containing more than 6 consecutive histidine residues from the plot. After these filtering processes, it is now clear that Spt6 is the major SUMOylated protein detected in the wild-type (with His-Smt3) condition and the LFQ intensities of other proteins (except Smt3) were ~16 or more hold less than that of Spt6. Together with the fact that the molecular weight range of most of the SUMOylated proteins fits very well to that of SUMOylated Spt6, we are now more confident to conclude that Ema2 is the major SUMO E3 ligase during the mid-conjugation stages and Spt6 is the major target of Ema2. We have modified the corresponding figure and texts to explain this filtering and the outcomes (Page 9, Lines 2-9).
The western blots carried out for the chromatin fraction and presented in Figures 7B, 7C, and 8B have variable levels of histone H3 which serves as a fractionation control, thus indicating some experimental variability. To support the quantitative conclusions, the authors should indicate how many times were these fractionation experiments repeated and should also provide experimental replicate data in the supplements. These data are important to firmly support the quantitative conclusions the authors currently draw from the experiments.
Each of these fractionation experiments was done three times and gave comparative results. The replicate data have been shown in Figures EV5, EV6 and EV8.
Minor comments
Page 3: "Because small RNA-producing loci are also small RNA targets ... " It should be specified that this is the case specifically for the studied system as it is not generally the case for small RNA loci. Overall, this third intro paragraph is a bit hard to read and might be improved by first introducing Tetrahymena and its distinctive cellular biology and then moving to the observation that small RNA source and target loci are separated in this ciliate.
We have modified the description to “Because small RNA-producing loci are also small RNA targets in most of the studied small RNA-directed heterochromatin formation processes, it poses a challenge to separately investigate lncRNA transcription for small RNA biogenesis and that for small RNA-dependent recruitment of downstream effectors in these processes.” (Page 3, Lines 24-27). We believe this has improved overall readability of the paragraph.
Figure annotation and readability: The manuscript and figure labels are rich in abbreviation (and sometimes even abbreviations of abbreviations, e.g. na = new MAC = new macronucleus).
We agree that there are many abbreviations in this manuscript but we believe most of them are necessary to keep the text and figures concise. To increase readability, we have spelled out all “abbreviations of abbreviations” when they appear the first time in the text. In fact, “na” was used not as an abbreviation but as a mark in the figures. We have modified the corresponding figure legends to make this point clearer. Also, to make the abbreviation “TDSD” more generalizable, we modified the manuscript to used it as “target-directed small RNA degradation” instead of “target-directed scnRNA degradation”.
Also Figures 4, 5 - the addition of the protein name after α-HA, -GST or -His would make the interpretation of blots easier.
Because anti-GST is detecting both GST alone and GST-Ema2, in Figure 4B, we had indicated the names of the proteins next to the blots. These might be less visible due to the busy arrangement of the panels in the previous manuscript. We have made extra space to make these labeles more visible. For Figure 4C, Figure 5B and Figure 5C, we have followed the reviewer’s suggestion and changed the labels to show the proteins detected.
In Figure 4, it is unclear how the protein quantification was made (leading the the "reduced to ~50% in the EMA2 KO" statement). Please clarify.
The total signal intensities of HA-Smt3 in triplicated experiments were analyzed by western blotting and quantified. We now have included the data as a part of Figure 4C in the revised manuscript and explained the quantification procedure in the figure lagend and Materials and Method.
In some places, the current manuscript refers to implicit knowledge that some non-specialists may not take for granted. For example, dsRNA formation is important for scnRNA production, motivating detection using the J2 antibody. Editing for non-expert readability could help reach a broader readership.
In this study, we used the J2 antibody not because dsRNA formation is important for the scnRNA production but because it allows us to cytologically detect lncRNAs in the parental MAC. We have modified the related sentence (Page 10, Lines 17-20) in the revised manuscript to improve readability. We have also added a discussion about single vs double-strand nature of lncRNA in the parental MAC (Page 10, Lines 30-34) as mentioned in our reply for the first comment of Reviewer 1.
- Also, on Page 7, bottom, it would be helpful to briefly explain to the reader how SUMOylation works to motivate the conclusion from the Ubc9 interaction.*
We have added a brief explanation for the actions of E1 and E2 enzymes in SUMOylation in the revised text (Page 8, Line 6-7).
**Referees cross-commenting**
My report (rev #2) closely aligns with that of rev #3. While all reports are positive, rev #1 suggests several lines of additional work, such as the characterization of lncRNA expression in the new MAC (major concern 3) and a search for other SUMO E3 ligase (major concern 4). While several interesting ideas are brought up here, I see such added investigations as non-essential for the current paper. I would encourage to focus revision work on the substantiation of the already included experiments.
The lncRNA expression in the new MAC in the C-KR mutant has been analyzed and included in Figure EV9. We have included some discussion regarding other SUMO E3 ligases and reserved their functional investigations for our future studies as Reviewer #2 and #3 suggested.
Reviewer #3
It is not entirely clear why the transcripts of small RNA targets are necessarily non-coding. labelling them as nascent would be sufficient in my opinion
In the described examples of small RNA-directed heterochromatin formation processes in the various eukaryotes in Introduction, the targets of small RNAs are indeed lncRNAs. Therefore, to separately discuss small RNA targets from mRNA, we keep using the term lncRNA for the former.
It is unclear whether mRNAs can also be small RNA targets in the Tetrahymena DNA elimination process. We have added the following sentence in Introduction (Page 4, Line 30):
“Although mRNAs are transcribed in the parental MAC, it remains unclear if they also can induce TDSD and how mRNAs and pMAC-lncRNAs can be transcribed from overlapping locations.”
Nonetheless, because EMA2 KO did not show detectable defect in the progression of conjugation processes, we believe any essential mRNA transcriptions for these processes occur in the parental MAC in EMA2 KO (which are now mentioned in Discussion [Page 13, Lines 14-20] for replying to one of Reviewer 1’s suggestions) and thus believe that the defects of EMA2 KO observed/discussed in this manuscript are due to the loss of lncRNAs. Therefore, we believe using lncRNA to label the RNAs transcribed by Ema2-directed SUMOylation is valid.
the nomenclature of methylated H3K9 might need some adjustment. Consider the abbreviation H3K9me2/3 instead of H3K9me
We followed the suggestion and H3K9me2/3 or H3K9m3 have been used in the revised manuscript.
it would be desirable if the authors could cross reference to the Paramecium field where possible given that this is a second, powerful study system in small RNA-mediated genome elimination.
We have extensively modified Introduction to describe the small RNA-directed genome rearrangement process of Tetrahymena and Paramecium as much as possible in parallel.
Main text:
"The conjugation-specific expression and the localization switch from the parental to the new MAC are reminiscent of the factors involved in DNA elimination (Mochizuki et al, 2002; Coyne et al, 1999; Kataoka & Mochizuki, 2015; Liu et al, 2007; Yao et al, 2007)."
please name these other factors here.
We have added “such as the Piwi protein Twi1, which is loaded by scnRNAs, and PRC2 (Mochizuki et al. 2002; Liu et al. 2007; Noto et al. 2010)” at the end of this sentence (Page 6, Line 13).
Figure 5A: what is the author's interpretation of the finding that most identified proteins remain unchanged? are these Ema2 independent SUMOylated proteins or are these background proteins that are not SUMOylated?
As mentioned in our reply to Reviewer 2, some of the proteins detected from both WT and EMA2 KO were possibly poly-histidine-containing proteins that bound intrinsically to the nickel-NTA beads without His-Smt3 conjugation or proteins unpacifically bound to some of the bead material. Taking these possibilities into account, a control experiment with wild-type cells not expressing His-Smt3 in the same condition has now been included and any proteins that were also identified in this experiment with log2 LFQ score above 25 were excluded in the new Figure 5A. We also removed any proteins containing more than 6 consecutive histidine residues from the plot. After these filtering processes, it is now clear that Spt6 is the major SUMOylated protein detected in the wild-type (with His-Smt3 expression) condition and the LFQ intensities of other proteins (except Smt3) were ~16 or more hold less than that of Spt6. We have modified the corresponding figure and texts (Page 9, Lines 2-9) to explain this filtering procedure and the outcomes.
Even after this filtering, many proteins were identified similarly between wild-type and EMA2 KO conditions. As mentioned in our reply for one of the comments by Reviewer 1, these are most likely Ema2-independent SUMOylated proteins either mediated by another SUMO E3 ligase or by E3-independent SUMOylation. We have added these points in the revised manuscript (Page 8, Lines 22-25).
"However, the cells rescued by HA-SPT6N-KR and HA-SPT6-M-KR showed severe defects in meiotic progression and mating initiation, respectively, making their SUMOylation status during conjugation uninvestigable." Why can't you investigate the SUMOylation capacity of these variants in wildtype cells?
The suggested experiment is probably a valid way to investigate the SUMOylation of HA-Spt6N-KR and HA-Spt6-M-KR. However, in such experimental setting, SUMOylation of Spt6 might be blocked not by loss of SUMOylation sites but by competition between the wild-type and the mutant Spt6. Moreover, even if one of them is proved to be unSUMOylatable (we now decided to call it SUMOylation-defective mutant [please see below]), we cannot examine its effect on lncRNA transcription if it has to be co-expressed with the wild-type Spt6. Therefore, we decided not to further examine the SUMOylation of the two mutants.
"Therefore, Spt6-C-KR is an unSUMOylatable Spt6 mutant." How sure can you be about this given the dynamic range of the detection in this experiment?
Whatever the dynamic range is, it is not possible to conclude that there is zero SUMOylation on Spt6-C-KR in the experimental setting we used. So, we have decided to call it a “SUMOylation-defective mutant” and modified the corresponding sentence as follows (Page 12, Line 18):
“Therefore, Spt6-C-KR represents a SUMOylation-defective Spt6 mutant, exhibiting at least a reduced level of SUMOylation compared to Spt6 in the absence of Ema2 (compare Figure 8B and Figure 5B).”
Figure 1A: label the plot to make it more accessible. Axis labels are missing.
Axis labels and explanations for the stages have been added in the revised Figure 1A.
Figure 3A: can you speculate about the higher molecular weight signal in the northern blot that appears in the later time-points and that seems to be partially dependent on Ema2?
The appearance of these higher molecular weight signals correlates with the presence or absence of lncRNAs detected by the J2 antibody at 4.5 hpm (Figure 6B). However, their presence in EMA2 KO cells at 6 hpm, the time point before the development of the new MAC, does not fit well to the absence of J2 staining in the parental MAC in EMA2 KO cells. Therefore, we currently have no clear idea for the identity of the higher molecular weight signals.
Figure 3B: why are the scanRNA levels at 3h already so different between WT and mutant cells? Lane 1 versus lanes 3 and 5?
The following sentence has been added in the revised manuscript (Page 7, Line 20):
“Because TDSD takes place concurrently with the scnRNA production (Schoeberl et al. 2012), the increased abundance of MDS-complementary scnRNAs at 3 hpm in the EMA2 KO cells compared to the wild-type cells can also be attributed to the necessity of Ema2 in TDSD.”
Figure 5: could you comment on the weak Smt3 signal that remains for Spt6 in the Ema2 KO conditions. Is this due to other SUMO-ligases or is the Ema2 KO not a full loss of function condition?
The following sentence has been added in the revised manuscript (Page 9, Line 31):
“The remaining SUMOylation observed on Spt6 in the absence of Ema2 is likely facilitated by other SUMO E3 ligases and/or E3-independent SUMOylation, as discussed earlier for the other instances of Ema2-independent SUMOylations.”
Figure 6C: are the many arrowheads not confusing? Are they needed?
We have removed most of the arrowheads from the figure and marked only the parental MACs. In addition, we have used the same labeling for all immunofluorescent staining figures.
Figure 8A: the cartoon depicting different colors for the various Lysine residues is not immediately clear to the reader. Try to make this more accessible.
We have modified the drawing to make the markings for the mutated lysine residues more visible in the revised figure.
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Referee #3
Evidence, reproducibility and clarity
This study presents novel data and evidence for a critical involvement of protein SUMOylation in the process of noncoding RNA transcription during the process of conjugation in Tetrahymena. Loss of the critical SUMO E3 Ligase Ema2 leads to a loss of ncRNA transcription in the parental macronucleus, ultimately leading to the lack of scanRNA traget molecules on chromatin, and as a result a loss of heterochromatin formation as well as defective target-dependent small RNA degradation. The paper is very well written, the figures are mostly a treat, the data is well discussed and placed in context, and the claims are supported by robust data. The authors went a long way to nail the relevant target protein of Ema2 and provide on the one side compelling evidence that the transcription elongation factor Spt6 is a bona fide SUMOylation substrate for Ema2. Quite surprisingly, however, a mutant Spt6 construct that shows no sign of SUMOylation in cells does rescue the Spt6 loss of function phenotype. While this puts the relevance of Spt6 SUMOylation in the process slightly into question, the authors provide a compelling discussion as to how SUMOylation still might be essential for proper Spt6 function in stimulating ncRNA transcription. All in all, this is a great paper that reports important data for the ciliate community, for the transcription community, and the larger small RNA community.
the following comments hopefully help to further improve the paper. I do not recommend any additional experiments.
Introduction:
- It is not entirely clear why the transcripts of small RNA targets are necessarily non-coding. labelling them as nascent would be sufficient in my opinion
- the nomenclature of methylated H3K9 might need some adjustment. Consider the abbreviation H3K9me2/3 instead of H3K9me
- it would be desirable if the authors could cross reference to the Paramecium field where possible given that this is a second, powerful study system in small RNA-mediated genome elimination.
Main text:
- "The conjugation-specific expression and the localization switch from the parental to the new MAC are reminiscent of the factors involved in DNA elimination (Mochizuki et al, 2002; Coyne et al, 1999; Kataoka & Mochizuki, 2015; Liu et al, 2007; Yao et al, 2007)." please name these other factors here.
- Figure 5A: what is the author's interpretation of the finding that most identified proteins remain unchanged? are these Ema2 independent SUMOylated proteins or are these background proteins that are not SUMOylated?
- "However, the cells rescued by HA-SPT6N-KR and HA-SPT6-M-KR showed severe defects in meiotic progression and mating initiation, respectively, making their SUMOylation status during conjugation uninvestigable." Why can't you investigate the SUMOylation capacity of these variants in wildtype cells?
- "Therefore, Spt6-C-KR is an unSUMOylatable Spt6 mutant." How sure can you be about this given the dynamic range of the detection in this experiment?
- Figure 1A: label the plot to make it more accessible. Axis labels are missing.
- Figure 3A: can you speculate about the higher molecular weight signal in the northern blot that appears in the later time-points and that seems to be partially dependent on Ema2?
- Figure 3B: why are the scanRNA levels at 3h already so different between WT and mutant cells? Lane 1 versus lanes 3 and 5?
- Figure 5: could you comment on the weak Smt3 signal that remains for Spt6 in the Ema2 KO conditions. Is this due to other SUMO-ligases or is the Ema2 KO not a full loss of function condition?
- Figure 6C: are the many arrowheads not confusing? Are they needed?
- Figure 8A: the cartoon depicting different colors for the various Lysine residues is not immediately clear to the reader. Try to make this more accessible.
Referees cross-commenting
I agree with the comment from reviewer #2 that additional experiments are not required at this stage. Several constructive points have been raised by all three reviewers that will strengthen this already very mature work.
Significance
This is a very strong experimental study that reports very interesting findings that do go beyond the ciliate community. Spt6 is a major transcription elongation factor and understanding the various functions of this factor by studying in vivo processes is highly important. The paper opens up a new research niche. The findings are very well presented and the discussion does a great job in putting the somewhat surprising results n the non SUMOylatable mutant into context.
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Referee #2
Evidence, reproducibility and clarity
Summary
During conjugation (the sexual reproduction stage in the Tetrahymena ciliates), programmed DNA elimination guided by small RNAs termed scnRNAs results in the specific elimination of many repetitive sequences. This specificity relies on the target-directed scan RNA degradation (TDSD) pathway where scnRNAs matching the active parental macronucleus are eliminated.
The manuscript by Shehzada et al. identifies a novel player in Tetrahymena TDSD: SUMO E3 ligase Ema2. The authors show by northen and small RNA-seq that Ema2 is required for TDSD. Furthermore, the paper describes how Ema2 post-translationally modifies the transcription elongation factor Spt6 by SUMOylation and that Ema2 is required to produce long double-stranded scnRNA precursor transcripts from the parental macronucleus, possibly via its modification of Spt6.
Major comments
From Figure 4C, the authors conclude that "Ema2 is the major SUMO E3 ligase during the mid-conjugation stages.", yet in Figure 5 show that only Spt6-SUMOylation is affected in Ema2 mutants. These conclusions seem inconsistent and should be reconciled as it is a central point in the paper. E.g. is Spt6 protein abundance based on the MS data supporting that this protein constitutes a major fraction of the (high mol weight) SUMOylated proteins? Of note, the discussion contains a very balanced discussion of this but the current description in the results should be improved.
The western blots carried out for the chromatin fraction and presented in Figures 7B, 7C, and 8B have variable levels of histone H3 which serves as a fractionation control, thus indicating some experimental variability. To support the quantitative conclusions, the authors should indicate how many times were these fractionation experiments repeated and should also provide experimental replicate data in the supplements. These data are important to firmly support the quantitative conclusions the authors currently draw from the experiments.
Minor comments
Page 3: "Because small RNA-producing loci are also small RNA targets ... " It should be specified that this is the case specifically for the studied system as it is not generally the case for small RNA loci. Overall, this third intro paragraph is a bit hard to read and might be improved by first introducing Tetrahymena and its distinctive cellular biology and then moving to the observation that small RNA source and target loci are separated in this ciliate
Figure annotation and readability: The manuscript and figure labels are rich in abbreviation (and sometimes even abbreviations of abbreviations, e.g. na = new MAC = new macronucleus). Also Figures 4, 5 - the addition of the protein name after α-HA, -GST or -His would make the interpretation of blots easier.
In Figure 4, it is unclear how the protein quantification was made (leading the the "reduced to ~50% in the EMA2 KO" statement). Please clarify.
In some places, the current manuscript refers to implicit knowledge that some non-specialists may not take for granted. For example, dsRNA formation is important for scnRNA production, motivating detection using the J2 antibody. Editing for non-expert readability could help reach a broader readership. Also, on Page 7, bottom, it would be helpful to briefly explain to the reader how SUMOylation works to motivate the conclusion from the Ubc9 interaction.
Referees cross-commenting
My report (rev #2) closely aligns with that of rev #3. While all reports are positive, rev #1 suggests several lines of additional work, such as the characterization of lncRNA expression in the new MAC (major concern 3) and a search for other SUMO E3 ligase (major concern 4). While several interesting ideas are brought up here, I see such added investigations as non-essential for the current paper. I would encourage to focus revision work on the substantiation of the already included experiments.
Significance
Overall, the presented work is well-structured, well-executed experimentally and carefully interpreted. The manuscript in most places (see minor comments) is clear and easy to follow for the expected broad readership in the fundamental biology of small RNAs and programmed DNA elimination. The main weakness of the paper is the proposed mechanistic connection from the Ema2 KO phenotype to Spt6 SUMOylation function in TDSD. The authors, however, have a very balanced description of this aspect in the discussion. In addition, there are some important technical questions to address regarding protein quantification by western blotting.
The work presented elucidates the crucial role of SUMO E3 ligase Ema2 in the TDSD pathway for scnRNAs in Tetrahymena. This advance is significant as TDSD is the foundation for the specificity of programmed DNA elimination in Tetrahymena and as it is currently not well understood mechanistically.
This work will be of interest to a broad readership for two reasons: (i) it advances our understanding of programmed DNA elimination in Tetrahymena, which is a major mechanistic model system for eukaryotic programmed DNA elimination. And (ii) it makes mechanistic connections to small RNA-mediated transcriptional silencing in yeast and fruit flies with possible general implications for these processes across eukaryotes.
In sum, the paper presents interesting new findings about small RNA biology and DNA elimination and was a pleasure to read.
The reviewers' declared field of expertise: small RNAs, chromatin, transcription
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Referee #1
Evidence, reproducibility and clarity
The authors convincingly show that Ema2, a conjugation-specific SUMO E3 ligase, localizes in the parental MAC during early conjugation stages, then moves to the new MAC. Using somatic EMA2 KO strains, they show that Ema2 is necessary for IES elimination and the recovery of viable progeny. They demonstrate that MAC scnRNAs do not disappear in an EMA2 KO and conclude that Ema2 is required for TDSD. They also show that ds lncRNA amounts in the parental MAC drop to background levels in an EMA2 KO, while they remain similar to WT in meiotic MICs or the new MACs.
They also present evidence supporting that the transcription factor Spt6 is one of the targets of Ema2-mediated sumoylation. Spt6 is found in the parental MAC of conjugating cells, regardless of Ema2. However, Ema2 is crucial for the stable chromatin association of both Spt6 and Rpb3 (a subunit of RNA polymerase II). Unexpectedly, a non-sumoylatable Spt6 mutant is able to complement a SPT6 KO, since it maintains the synthesis of lncRNA in the parental MAC. Nonetheless, this mutant strongly impairs new MAC development and IES elimination. As a whole, the role of Spt6 sumoylation in programmed DNA elimination is not clearly established, and it probably affects another step than pMAC-lncRNA synthesis.
Strong points:
- The demonstration that pMAC-lncRNA accumulation depends upon Ema2 is convincing. This finding provides novel insights into the mechanism involved in TDSD in Tetrahymena. An important point that would be worth discussing is how ds pMAC-lncRNAs may pair with scnRNAs. An RNA helicase (Ema1?) may play an important role in this process.
- The manuscript is very well written. I noticed only a few typos (see minor comments below).
- The experiments are overall well done and well described. For non-Tetrahymena readers, it would be useful to clarify in the Results section (or in figure captions) whether the different KOs are in the MAC and/or also in the MIC
Major concerns:
- The search for Ema2 targets using mass spectrometry was performed in a wild-type SMT3 background. This implies that endogenous wild-type Smt3 may have competed with His-Smt3 for protein sumoylation. To what extent may this have been a problem for the enrichment of sumoylated proteins on nickel columns? This point is critical, since the authors discuss that other proteins involved in pMAC-lncRNA transcription may be modified by Ema2 (p. 12). They should repeat the experiment in an SMT3 KO, or use anti-Smt3 antibodies to enrich for sumoylated proteins. If this is not possible, they should at least provide additional explanations.
- In Figure 7A, the authors only show the localization of Spt6 in early exconjugants. Since Spt6 is essential for vegetative growth, one can expect that it also localizes in the vegetative MAC. Is it also found in the new developing MACs? The authors should complete the figure with additional panels showing vegetative cells and exconjugants at later stages (with their new MAC).
- Along the same line, the authors show that the non sumoylatable Spt6 mutant does not inhibit pMAC-lncRNA synthesis. No scnRNA analysis is shown under these conditions: does TDSD still take place? It would also be interesting to check whether lncRNAs are still produced in the new MACs.
- The experiment shown in Figure 4C indicates that high-molecular weight (possibly sumoylated) proteins decrease to 50% in the EMA2 KO: this suggests that another sumoylation activity exists in the cell. A search for other putative SUMO E3 ligases is missing in this study.
- Can one exclude that Spt6 is sumoylated at other stages (vegetative or during new MAC development) in an Ema2-independent manner?
- In which nucleus does coding transcription take place between 4.5 and 6 hpm? Can we exclude that the weaker association of Rpb3 with chromatin in the EMA2 KO cross also impairs coding transcription?
Minor concerns
- The authors do not explain how they found Ema2. More information could be useful.
- In Figures 2B and 3B: the statistical significance of the differences observed for the IES retention index and small RNA amounts should be evaluated using appropriate tests.
Figure 3 caption: define acronym "IQR"
Figure 5 caption (line 4): there may be a word missing ("from conjugating cells?")
Figure 8C: what does the asterisk stand for?
p. 10 (bottom): an "o" is missing in "Aronica et al 2008"
p. 13 (2nd line): remove final "s" in "mimic"
p. 14: change "were" to "was" in "the production of the EMA2 KO strains was described previously"
p. 14: remove capital letters in "Gorovsky"
p. 15 ({section sign} Viability test for progeny): what does "6-mp" stand for?
p. 17 (end of first paragraph): change "contracts" to "constructs"
p. 17 (2nd line of last paragraph): change "was" to "were " in "EMA2 cells containing the BP6MB1-His-SMT3 construct were mated..."
p. 19 (3rd line of 2nd paragraph"): "spined own" should be replaced by "spinned down"
Significance
In this manuscript Shehzada et al report important novel findings on the molecular mechanisms involved in RNA-mediated control of programmed DNA elimination in the ciliate Tetrahymena thermophila. In this organism, non-coding transcription takes place in distinct nuclei and produces double-stranded (ds) long non-coding RNAs (lncRNAs) at different stages during conjugation. First, bidirectional transcription in the MIC during meiosis produces ds lncRNAs that are processed to short scnRNAs. Second, lncRNAs from the parental MAC (pMAC-lncRNAs) are thought to drive the degradation of scnRNAs homologous to parental MAC DNA, in a process called TDSD (target-directed scnRNA degradation). Third, the remaining MIC-specific scnRNAs are imported to the new MACs, where their pair with lncRNAs and drive heterochromatin formation and DNA elimination.
The present study focuses on TDSD, a process that has been poorly described at the molecular level. The strongest part of the work is the demonstration that the SUMO E3 ligase Ema2 is necessary for the production of pMAC-lncRNAs, which in turn impairs the selective degradation of MAC scnRNAs. A less convincing part is the identification of Ema2 targets. The authors identify Spt6 as one of the Ema2-dependent sumoylated proteins. However, they show that Spt6 sumoylation is not necessary for pMAC-lncRNA transcription.
In principle, the results presented in this manuscript should be of broad interest for the scientific communities working on non-coding RNA biology and the epigenetic control of programmed genome rearrangements.
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Reply to the reviewers
Response to Referees Letter
We thank the reviewers for their constructive comments and their positive comments that this study provides insights into the non-canonical roles of Bcl-xL in cancer and may lead to therapeutic approaches to repress metastatic capacity. We have carefully read their comments and have extensively revised the manuscript accordingly. The specific points made by each reviewer are addressed below in blue color.
Response to Reviewer #1:
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary In this study the authors build on their previous work that Bcl-xL has a role in metastasis promotion independent of it's function in the mitochondrial apoptotic pathway. They show that Bcl-xL can be found in the nucleus of some human breast cancer cells and through a mass spec approach show that CtBP2 promotes the nuclear translocation of Bcl-xL. Using various knockdown/knockout methods they show that reduced levels of CtBP2 reduces metastasis, because of loss of Bcl-xL translocation to the nucleus. The authors map this interaction and show that this interaction modulates metastasis.
Major comments * Figure 1 - a more comprehsive analysis of nuclear Bcl-xL should be conducted. The data presented only shows 3 different samples, with no quantification. Perhaps the authors could stain a breast cancer TMA or similar?
__Response: __We performed breast cancer TMA staining experiment as suggested. This experiment provides further support to our conclusion. We have included the following information in the revised manuscript.
“We further evaluated human breast specimens in tissue microarrays (TMAs), consisting of 25 non-neoplastic breast tissues, 150 primary breast cancer, 55 lymph node metastases, and 99 metastatic breast cancer at various distant sites, for the expression and localization of Bcl-xL by immunohistochemistry. Compared to normal breast tissues, the intensity of Bcl-xL was significantly higher in breast cancer, including primary tumors, lymph node (LN) metastases, and distant metastases (Table 1a and 1b). The proportion of positive perinuclear/nuclear Bcl-xL cases was significantly increased in human breast cancer tissues compared to normal breast tissues (Table 1c and Figure 1d), and it showed an increasing trend towards metastases (Table 1d, p =0.004).”
* Figure 2 - could the authors show a graph with a representation of the mass spectrometry data, so the reader can get a sense of how many proteins were found to be associated with Bcl-xL?
__Response: __As suggested, we have included the mass spectrometry data in Supplemental Table 1. Forty proteins were commonly immunoprecipitated by anti-HA magnetic beads from all three cell lines overexpressing HA-tagged wt Bcl-xL and two Bcl-xL mutants but not from the parental cells overexpressing the control vector.
* Have the authors tried any other ways to verify the interaction between Bcl-xL and CtBP2? For instance, do they co-localise when imaged? Also, can the reverse IP be performed?
__Response: __We have verified the interaction between Bcl-xL and CtBP2 by several methods, including IP, reverse IP, and co-immunostaining. Please find HA-Bcl-xL IP and Western for endogenous CtBP2 (Figure 2a), co-immunostaining of endogenous Bcl-xL and CtBP2-V5 (Figure 2b and 2c), co-immunostaining of endogenous Bcl-xL and endogenous CtBP2 (Figure 4e), HA-Bcl-xL IP and Western for seven different constructs of V5 tagged CtBP2 (Figure 5b and 5c), and V5-CtBP2 IP and Western for seven different constructs of Myc tagged Bcl-xL (Figure 6b).
* Figure 2C - the authors claim that this data shows that Bcl-xL nuclear translocation is reduced in cells with reduced levels of CtBP2 - however, although they quantify this I simply do not see it from the images presented. I do not think this data supports the conclusion that knockdown of CtBP2 reduces Bcl-xL translocation to the nucleus. Furthermore, this data is only shown with overexpressed Bcl-xL - have the authors tried with endogenous staining of Bcl-xL?
Response: To assist Reviewer #1’s visualization, below are some marked RFP+ cells that responded to Dox-inducible shRNA expression from Figure 2e. Please note that these cells were not sorted by dsRed so that they gave us a unique opportunity to determine whether the knockdown of CtBP2 affected Bcl-xL nuclear localization by comparing subcellular localization of HA-Bcl-xL in the dsRed-positive cells and the neighboring dsRed-negative cells in the same images. The nuclear-to-cytosol ratio of HA-Bcl-xL was reduced in the dsRed-positive shCtBP2 cells compared to the dsRed-negative cells in both shCtBP2 #2260 and #2403 cultures on dox, not in shRLuc #713 control cells on dox.
In addition, we have performed endogenous staining of Bcl-xL and found that CtBP2 knockout reduced the nuclear to cytosol ratio of endogenous Bcl-xL (Figure 4f).
* Figure 2e-f - again these data are in cells with overexpressed Bcl-xL - does the same effect on invasion happen when only CtBP2 levels are reduced, without overexpression of Bcl-xL? What happens when Bcl-xL is knocked down? Also, doxycycline has been shown to affect mitochondrial function, which might confound this data - perhaps another way to knockdown CtBP2 (e.g. CRISPR which is used later in the study) would rule this out
Response: First, we have previously reported that CtBP2 knockdown reduced migration in cells without overexpression of Bcl-xL (Paliwal et al., 2007), and others have shown that siRNA knockdown of Bcl-xL reduces migration and invasion (Trisciuoglio et al., 2017).
Second, to control any effect of doxycycline, we have included the doxycycline-fed control cells that express doxycycline-inducible shRNA against Renilla Luciferase (shRLuc #713) in revised Figure 2g and 2h (original Figure 2e and 2f).
Third, the novelty of this study is that the discovery that Bcl-xL and CtBP2 interact with each other to promote metastasis. Our study showed that CtBP2 controls Bcl-xL in two ways: nuclear translocation and transcription. Because we found that knockout CtBP2 reduced transcription of endogenous Bcl-xL (Figure 4a-c), it will make the interpretation of the migration effect difficult. Using cells overexpressing HA-Bcl-xL, whose transcription is not regulated by CtBP2, we can evaluate whether the invasion effect of HA-Bcl-xL is mediated by CtBP2 when CtBP2 is knocked down. While overexpression of Bcl-xL promotes invasion (Choi et al., 2016), knockdown of CtBP2 can reverse the effect (Figure 2g).
* Figure 3c - these blots are not labelled, but ideally this would be shown with endogenous Bcl-xL, rather that just the overexpressed HA-Bcl-xL. However these data are more convincing than the images presented in Figure 2c
__Response: __We apologize for the missing labels in these blots of Figure 3c when we merged the graphs. We have now added them back.
* Figure 4 - the authors use CRISPR to knockout CtBP2 - logically this data would go with the shRNA data shown before, as it seems to just repeat what has already been shown?
__Response: __In Figure 4, we examined the effect of CtBP2 knockout on the endogenous Bcl-xL. We were pleased to see that CtBP2 knockout reduced the nuclear-to-cytosol ratio of endogenous Bcl-xL. Moreover, we observed that CtBP2 knockout reduced transcription of Bcl-xL. These knockout data (Figure 4) were logically presented after the knockdown data (Figure 2 and 3).
* Figure 4d - what does "SN" refer to? There is no loading control for this part of the fractionation - I assume this is supernatant? If so, why is there no loading control for this (same applies to figure 3c). Also, why are these not on the same blot? If CtBP2 knockdown reduces Bcl-xL mRNA level, does it also reduce Bcl-xL protein levels? We should be able to tell this from the blots in figure 4d, but since they are on different membranes this is impossible to deduce.
__Response: __We apologize for the missing information. We have added “SN: soluble nuclear fraction” in the figure legend of Figure 4d and re-run all the samples on the same blot. No detection of cytoplasmic proteins and chromatin-bound proteins in the soluble nuclear fraction suggested good fractionation as described (Méndez and Stillman, 2000, PMID: 11046155). CtBP2 knockout indeed reduced Bcl-xL protein levels, as shown in Figure 4a.
* Figure 5c - molecular weight markers should be included here.
__Response: __We apologize for the missing labels of the molecular weight markers, and we have added them in the revision.
* Figure 7a - the text says that MM102 treatment "significantly reduced" H3K4me3 levels - where is the quantification of this?
__Response: __We appreciate the suggestion, and we have now added the quantification in Figure 7a.
Minor comments * Some of the figures are not properly labelled * Some of the data are presented in an awkward manner - the authors should consider re-structuring either the manuscript or the figures so there is less "jumping around"
__Response: __We apologize for the missing labels again, and we have now labeled the figures properly. We hope that the revision (with additional data and properly labelled figures) has made the structure of the manuscript sound.
Reviewer #1 (Significance (Required)):
General assessment * Provides new insight into non-canonical roles of Bcl-xL in cancer * Relies heavily on over-expressed proteins to draw conclusions * If the data were stronger and supported the conclusions, this study could be of interest to a broad cancer audience
My expertise Cell biology, cell death, cancer, imaging
__Reviewer #2 (Evidence, reproducibility and clarity (Required)): ____ __ The manuscript describes a large number of experiments each of which describes a small part of the functional cascade of Bcl-xL in nuclear function and metastatic tumor behavior. No one experiment accomplishes a lot, but taken as a total, the story is compelling and fairly complete.
Major: Figure 1 shows Bcl-xL in one primary sample (a) but clearly not in a second one (c). The authors state 3 of 15. Can they make any comment about breast cancer subtype of these 3 or outcomes? This seems fairly thin evidence of Bcl-xL involvement in human tumorigenesis in general - a better survey might be performed with tissue microarrays of more than one cancer subtype. I'm not sure that this figure is compelling or necessary really for the rest of the manuscript. Really, the main weakness of this paper is some proof that this Bcl-xL-mediated pathway is significant in some proportion of human cancer and metastasis. Perhaps some RNASeq datasets on metastatic versus localized cancers could be mined to establish this relvance?
__Response: __We appreciate this suggestion. We have compared the breast cancer subtypes and the outcomes of the cases used in the original immunofluorescent study. No particular cancer subtype or outcome of these cases is associated with the presence of more nuclear Bcl-xL.
As suggested by the reviewer, we used breast cancer TMAs to investigate the involvement of Bcl-xL in human tumorigenesis in general. We have found that the cases positive of peri-nuclear and nuclear Bcl-xL showed an increasing trend of metastases (Table 1d). We have included the following information in the revised manuscript.
“We further evaluated human breast specimens in tissue microarrays (TMAs), consisting of 25 non-neoplastic breast tissues, 150 primary breast cancer, 55 lymph node metastases, and 99 metastatic breast cancer at various distant sites, for the expression and localization of Bcl-xL by immunohistochemistry. Compared to normal breast tissues, the intensity of Bcl-xL was significantly higher in breast cancer, including primary tumors, lymph node (LN) metastases, and distant metastases (Table 1a and 1b). The proportion of positive perinuclear/nuclear Bcl-xL cases was significantly increased in human breast cancer tissues compared to normal breast tissues (Table 1c and Figure 1d), and it showed an increasing trend towards metastases (Table 1d, p =0.004).”
Most other experiments and figures are well explained. The only one I have some trouble with is Figure 8 CUT and RUN data where we are only presented with peaks around six genes. Is there a way to summarize data for the rest of the genome? Or to display a composite of CUT and RUN data on promoters that are not predicted to be targets of Bcl-xL and MLL1 activity (compared to those that are)?
__Response: __We have deposited the entire CUT&RUN-Seq datasets in Gene Expression Omnibus (accession #GSE221629, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE221629), which will become publicly available when the manuscript is published.
It is very challenging to present 1,190 unique H3K4me3 histone modification regions, and we tried our best to present the CUT&RUN-Seq data in the revised manuscript. In addition to the differential H3K4me3 peaks around promoters of six genes, we have included genome browser view, including the whole gene body by zooming out in Supplementary Figure S7 and peaks for 9 regions that are not targets of Bcl-xL and MLL1 activity in Supplementary Figure S8. Furthermore, we used Hypergeometric Optimization of Motif EnRichment (HOMER) to perform motif analysis for the differential H3K4me3 peaks. Enrichment p-values of the motifs were between 1e-12 and 1e-2 (Supplementary Table S5). It is of note that motifs with a p-value of more than 1e-10 or even 1e-12 are likely to be false positives (http://homer.ucsd.edu/homer/introduction/basics.html). The result revealed the limitation to identify motifs around the H3K4me3 CUT&RUN peaks recognized by the nuclear Bcl-xL complex.
Minor: While the main future direction pointed out by the manuscript was made in the last sentence of the Discussion, it could be spelled out in more detail to enforce the manuscript's impact.
__Response: __We appreciate this suggestion and expanded the discussion in the revised manuscript to enforce the impact of this work.
Reviewer #2 (Significance (Required)):
The authors describe nuclear targets and functions of the anti-apoptotic protein TF Bcl-xL, which has long been of research interest to this group. Specifically, this manuscript follows up on Choi 2016 which established that nuclear localization seemed to be critical for promotion of metastatic/invasion properties of Bcl-xL independent of its anti-apoptotic function. Due to the membrane localization in cells, it was unclear how Bcl-xL entered the nucleus, simulating the current paper. Here the authors (i) demonstrate this nuclear localization happens without mutation to the protein, (ii) localization is promoted by binding to CtBP2 in co-precipitations, (iii) enforced loss of CtBP2 expression correlated with lower metastasis, (iii) specific domains within the two proteins are necessary for physical interaction and function (iii) the histone methyltransferase MLL is critical for downstream transcriptomic impacts which include upregulation of the TGFbeta pathway. Description of this pathway and the specific protein domains necessary may lead to therapeutic targets to repress metastatic capacity. This reviewer is an expert as a cancer biologist and epidemiologist.
__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __ Summary Zhang et al. investigated new roles of Bcl-xL and CtBP2 in cancer progression. They previously reported that Bcl-xL is nuclear localized and promotes cancer metastasis by inducing global histone H3 trimethyl Lys4 (H3K4me3) independent of its anti-apoptotic activity. In this study, they found that CtBP2 is a key factor for promoting the nuclear translocation of Bcl-xL. Furthermore, they showed that the binding between Bcl-xL and CtBP2 is required for MLL1 activation. MLL1 mediates the Bcl-xL-induced H3K4me3 activation and upregulation of TGFβ mRNA level. By global analysis of histone H3K4me3, the authors demonstrated that H3K4me3 modifications are enriched in the promoter regions of genes encoding TGFβ and related signaling pathways in cancer cells overexpressing Bcl-xL. Therefore, they concluded that Bcl-xL exerts its metastatic function by interacting with CtBP2 and MLL1. The mechanism for histone modification by Bcl-xL is interesting and this study expanded our current understanding of epigenetic regulation in cancer. However, the mechanism for MLL1 activation induced by Bcl-xL is not fully demonstrated.
Major points 1) Figure 1) The number of primary breast cancer and lymph node specimens is too small. The authors analyzed only two cases of primer breast cancer and one case of lymph node metastasis. They should also present the result of normal breast tissues to show increased nuclear enrichment during disease progression. In addition, quantification of nuclear signals and statistical analysis are necessary. More importantly, the expression of CtBP2 and MLL1 should be evaluated in these clinical samples because they claimed that the interaction of Bcl-xL/CtBP2/MLL1 is important for tumor metastasis in this study.
__Response: __We appreciate this suggestion to increase the number of the clinical samples. We have stained breast cancer TMAs and included normal breast tissues to show increased nuclear enrichment during disease progression (Table 1). We have included the following information in the revised manuscript. Although we would also like to co-stain these breast cancer TMAs with CtBP2 and MLL1, there are no suitable antibodies for co-staining these two proteins with Bcl-xL in these FFPE sections.
“We further evaluated breast cancer specimens in tissue microarrays (TMAs) for the expression and localization of Bcl-xL by immunohistochemistry. Compared to normal breast tissues, the intensity of Bcl-xL was significantly higher in breast cancer, including primary tumors, lymph node (LN) metastases, and distant metastases (Table 1a and 1b). Perinuclear/nuclear Bcl-xL is significantly increased in human breast cancer tissues compared to normal breast tissues (Table 1c and Figure 1d). The proportion of peri-nuclear and nuclear Bcl-xL positive cases showed an increasing trend towards metastasis (Table 1d).”
2) (Figure 2c) In this experiment, the expression of Bcl-xL is mainly observed in the cytoplasm even in the condition of shControl. Therefore, I think that the nuclear localization of Bcl-xL is not convincingly regulated by CtBP2 expression change. Overexpression of CtBP2 is also necessary to show CtBP2-dependent nuclear localization of Bcl-xL.
__Response: __We appreciate this suggestion to overexpress CtBP2. We have performed this experiment by transiently transfecting cells with CtBP2 and found that overexpression of CtBP2 increased the nuclear to cytosol ratio of Bcl-xL (new Figure 2b and 2c) and included the following information in the revised manuscript.
“To determine the role of CtBP2 in mediating Bcl-xL’s nuclear translocation, we employed overexpression and knockdown of CtBP2 approaches. To overexpress CtBP2, we transfected a V5-tagged CtBP2 construct (Paliwal et al., 2006) into 293T cells and performed immunofluorescent staining using anti-V5 and anti-Bcl-xL antibodies. We observed an increased nuclear-to-cytosol ratio of endogenous Bcl-xL in cells overexpressing CtBP2-V5 (Figure 2b and 2c).”
3) (Figure 6d-e) These results are important because the anti-apoptotic activity is not inhibited even if the interaction between CtBP2 and Bcl-xL is lost. I wonder whether the authors analyzed the cellular localization of each mutant protein (particularly, wt, construct #5 and #6) in the presence of CtBP2. In addition, the authors should examine how the histone K4me3 and MLL1 activity is affected by overexpressing construct #5 and #6 to elucidate the metastatic ability by these constructs (Figure 6e). The authors should describe whether wt Bcl-xL is constract #2 or not in the legends.
__Response: __We appreciate that the reviewer pointed out the importance of our finding that even if the interaction between CtBP2 and Bcl-xL is lost, the anti-apoptotic activity of Bcl-xL is not inhibited. As suggested by the reviewer, we described wt Bcl-xL as construct #2 in the manuscript, and we analyzed the subcellular localization of wt HA-Bcl-xL (construct #2, which binds to CtBP2), construct #5 (which binds to CtBP2), and construct #6 (which does not bind to CtBP2), in the presence of endogenous CtBP2 in N134 mouse PNET cells. We found that the nuclear to cytosol ratio of wt HA-Bcl-xL (construct #2) and construct #5 was similar to each other, and we observed a reduction in the nuclear-to-cytosol ratio of construct #6 (Figure 6f and 6g). This is in consistent of the reduction of the metastatic ability of construct #6.
Further, we examined H3K4me3 and MLL1 in these cells and found that H3K4me3 was reduced in construct #6 compared to wt HA-Bcl-xL (construct #2) and construct #5 (Figure 6c). We also found that H3K4me3 levels were reduced in the CtBP2 knockout cells (Supplementary Figure S5b).
Minor points 4) (Figure 2d) Labels for these graphs are lacking.
__Response: __We apologize for the missing labels when we merged the graphs. We have added them back (new Figure 2f).
5) (Figure 2e, f) The authors should label in these graphs whether these results are statistically significant or not.
__Response: __Thanks for the suggestion. We have labeled * for statistically significant (P 6) (Figure 3c) No labels for these blots.
__Response: __We apologize for the missing labels when we merged the graphs. We have added them back.
7) (Figure 3b) They should describe the full spell of n/a in the legends.
__Response: __Thanks for the suggestion. We have described “n/a: non-sorted parental cells” in the legends in the revision.
8) (Figure 4f) The label of Y-axis should be corrected.
__Response: __Thanks for the suggestion. We have corrected the label of Y-axis.
9) (Figure 8c) The location of gene transcriptional start site and ChIP signal level should be shown. In addition, the genome browser view including whole gene body by zooming out should be shown.
__Response: __In addition to the differential peaks around promoters of six genes in Fig. 8, we have included the whole gene body with the location of the gene transcriptional start site in Supplementary Figure S7.
Reviewer #3 (Significance (Required)):
It is interesting that Bcl-xL can be transported to the nucleus and modulate the entire epigenetic condition for promoting metastatic ability. In the previous study, this group highlighted the nuclear function of Bcl-xL in cancer cells. This concept, Bcl-xL functions independent of its anti-apoptotic activity (Choi et al. Nat Commun 2016;7:10384.), is highly original and will bring some impacts on cancer research. In this study, the authors revealed molecular mechanisms to elucidate this nuclear translocation of Bcl-xL and how Bcl-xL regulate the epigenetic condition. However, the authors should present more evidences to demonstrate the mechanism that CtBP2/Bcl-xL interaction with MLL1 regulate global K4me3 levels in the nucleus to promote metastasis. 1) First of all, there are insufficient data to demonstrate how the interaction with Bcl-xL is involved in MLL1 activation. In Figure 7e, the authors analyzed H3K4me3 level by only inhibiting MLL1 expression and activity. However, the authors should investigate whether Bcl-xL and CtBP2 knockdown or overexpression modulate MLL1-mediated histone H3K4me3 regulation.
Response: __We appreciate that Reviewer #3 considered our work to be highly original. As suggested, we investigated whether CtBP2 knockout affected H3K4me3 levels and found that H3K4me3 levels were reduced in the CtBP2 knockout cells (Supplementary Figure S5b). Conversely, we have reported that Bcl-xL overexpression increases H3K4me3 levels (Choi et al., 2016). The main take-home message of this study is the discovery of the nuclear translocation mechanism of Bcl-xL through a novel interaction with CtBP2. We have shown that Bcl-xL or CtBP2 binds to MLL1 only when Bcl-xL and CtB2 bind to each other (__Figure 5b, 5c, and__ 6b__).
2) (Figure 8) The authors should explain why MLL1 activation specifically affect the K4me3 levels of TGFβ signal-associated genes. I wonder whether Bcl-xL/MLL1/CtBP2 functions as cofactors by binding to certain transcription factors. In addition, Bcl-xL, CtBP2 and MLL1 ChIP-seq/CUT & RUN analysis would be preferable.
__Response: __We have tried but have not been able to successfully establish the CUT&RUN conditions using Bcl-xL, CtBP2, and MLL1 antibodies. Whether Bcl-xL/MLL1/CtBP2 functions as cofactors by binding to certain transcription factors is a very interesting question. Additional studies are required to identify the other components of this Bcl-xL/CtBP2/MLL1 protein complex, which is beyond the scope of this work. This is added in the Discussion of the revised manuscript.
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Referee #3
Evidence, reproducibility and clarity
Summary
Zhang et al. investigated new roles of Bcl-xL and CtBP2 in cancer progression. They previously reported that Bcl-xL is nuclear localized and promotes cancer metastasis by inducing global histone H3 trimethyl Lys4 (H3K4me3) independent of its anti-apoptotic activity. In this study, they found that CtBP2 is a key factor for promoting the nuclear translocation of Bcl-xL. Furthermore, they showed that the binding between Bcl-xL and CtBP2 is required for MLL1 activation. MLL1 mediates the Bcl-xL-induced H3K4me3 activation and upregulation of TGFβ mRNA level. By global analysis of histone H3K4me3, the authors demonstrated that H3K4me3 modifications are enriched in the promoter regions of genes encoding TGFβ and related signaling pathways in cancer cells overexpressing Bcl-xL. Therefore, they concluded that Bcl-xL exerts its metastatic function by interacting with CtBP2 and MLL1. The mechanism for histone modification by Bcl-xL is interesting and this study expanded our current understanding of epigenetic regulation in cancer. However, the mechanism for MLL1 activation induced by Bcl-xL is not fully demonstrated.
Major points
- Figure 1) The number of primary breast cancer and lymph node specimens is too small. The authors analyzed only two cases of primer breast cancer and one case of lymph node metastasis. They should also present the result of normal breast tissues to show increased nuclear enrichment during disease progression. In addition, quantification of nuclear signals and statistical analysis are necessary. More importantly, the expression of CtBP2 and MLL1 should be evaluated in these clinical samples because they claimed that the interaction of Bcl-xL/CtBP2/MLL1 is important for tumor metastasis in this study.
- (Figure 2c) In this experiment, the expression of Bcl-xL is mainly observed in the cytoplasm even in the condition of shControl. Therefore, I think that the nuclear localization of Bcl-xL is not convincingly regulated by CtBP2 expression change. Overexpression of CtBP2 is also necessary to show CtBP2-dependent nuclear localization of Bcl-xL.
- (Figure 6d-e) These results are important because the anti-apoptotic activity is not inhibited even if the interaction between CtBP2 and Bcl-xL is lost. I wonder whether the authors analyzed the cellular localization of each mutant protein (particularly, wt, construct #5 and #6) in the presence of CtBP2. In addition, the authors should examine how the histone K4me3 and MLL1 activity is affected by overexpressing construct #5 and #6 to elucidate the metastatic ability by these constructs (Figure 6e). The authors should describe whether wt Bcl-xL is constract #2 or not in the legends.
Minor points
- (Figure 2d) Labels for these graphs are lacking.
- (Figure 2e, f) The authors should label in these graphs whether these results are statistically significant or not.
- (Figure 3c) No labels for these blots.
- (Figure 3b) They should describe the full spell of n/a in the legends.
- (Figure 4f) The label of Y-axis should be corrected.
- (Figure 8c) The location of gene transcriptional start site and ChIP signal level should be shown. In addition, the genome browser view including whole gene body by zooming out should be shown.
Significance
It is interesting that Bcl-xL can be transported to the nucleus and modulate the entire epigenetic condition for promoting metastatic ability. In the previous study, this group highlighted the nuclear function of Bcl-xL in cancer cells. This concept, Bcl-xL functions independent of its anti-apoptotic activity (Choi et al. Nat Commun 2016;7:10384.), is highly original and will bring some impacts on cancer research. In this study, the authors revealed molecular mechanisms to elucidate this nuclear translocation of Bcl-xL and how Bcl-xL regulate the epigenetic condition. However, the authors should present more evidences to demonstrate the mechanism that CtBP2/Bcl-xL interaction with MLL1 regulate global K4me3 levels in the nucleus to promote metastasis.
- First of all, there are insufficient data to demonstrate how the interaction with Bcl-xL is involved in MLL1 activation. In Figure 7e, the authors analyzed H3K4me3 level by only inhibiting MLL1 expression and activity. However, the authors should investigate whether Bcl-xL and CtBP2 knockdown or overexpression modulate MLL1-mediated histone H3K4me3 regulation.
- (Figure 8) The authors should explain why MLL1 activation specifically affect the K4me3 levels of TGFβ signal-associated genes. I wonder whether Bcl-xL/MLL1/CtBP2 functions as cofactors by binding to certain transcription factors. In addition, Bcl-xL, CtBP2 and MLL1 ChIP-seq/CUT & RUN analysis would be preferable.
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Referee #2
Evidence, reproducibility and clarity
The manuscript describes a large number of experiments each of which describes a small part of the functional cascade of Bcl-xL in nuclear function and metastatic tumor behavior. No one experiment accomplishes a lot, but taken as a total, the story is compelling and fairly complete.
Major:
Figure 1 shows Bcl-xL in one primary sample (a) but clearly not in a second one (c). The authors state 3 of 15. Can they make any comment about breast cancer subtype of these 3 or outcomes? This seems fairly thin evidence of Bcl-xL involvement in human tumorigenesis in general - a better survey might be performed with tissue microarrays of more than one cancer subtype. I'm not sure that this figure is compelling or necessary really for the rest of the manuscript. Really, the main weakness of this paper is some proof that this Bcl-xL-mediated pathway is significant in some proportion of human cancer and metastasis. Perhaps some RNASeq datasets on metastatic versus localized cancers could be mined to establish this relvance?
Most other experiments and figures are well explained. The only one I have some trouble with is Figure 8 CUT and RUN data where we are only presented with peaks around six genes. Is there a way to summarize data for the rest of the genome? Or to display a composite of CUT and RUN data on promoters that are not predicted to be targets of Bcl-xL and MLL1 activity (compared to those that are)?
Minor:
While the main future direction pointed out by the manuscript was made in the last sentence of the Discussion, it could be spelled out in more detail to enforce the manuscript's impact.
Significance
The authors describe nuclear targets and functions of the anti-apoptotic protein TF Bcl-xL, which has long been of research interest to this group. Specifically, this manuscript follows up on Choi 2016 which established that nuclear localization seemed to be critical for promotion of metastatic/invasion properties of Bcl-xL independent of its anti-apoptotic function. Due to the membrane localization in cells, it was unclear how Bcl-xL entered the nucleus, simulating the current paper. Here the authors (i) demonstrate this nuclear localization happens without mutation to the protein, (ii) localization is promoted by binding to CtBP2 in co-precipitations, (iii) enforced loss of CtBP2 expression correlated with lower metastasis, (iii) specific domains within the two proteins are necessary for physical interaction and function (iii) the histone methyltransferase MLL is critical for downstream transcriptomic impacts which include upregulation of the TGFbeta pathway. Description of this pathway and the specific protein domains necessary may lead to therapeutic targets to repress metastatic capacity. This reviewer is an expert as a cancer biologist and epidemiologist.
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Referee #1
Evidence, reproducibility and clarity
Summary
In this study the authors build on their previous work that Bcl-xL has a role in metastasis promotion independent of it's function in the mitochondrial apoptotic pathway. They show that Bcl-xL can be found in the nucleus of some human breast cancer cells and through a mass spec approach show that CtBP2 promotes the nuclear translocation of Bcl-xL. Using various knockdown/knockout methods they show that reduced levels of CtBP2 reduces metastasis, because of loss of Bcl-xL translocation to the nucleus. The authors map this interaction and show that this interaction modulates metastasis.
Major comments
- Figure 1 - a more comprehsive analysis of nuclear Bcl-xL should be conducted. The data presented only shows 3 different samples, with no quantification. Perhaps the authors could stain a breast cancer TMA or simiilar?
- Figure 2 - could the authors show the a graph with a representation of the mass spectrometry data, so the reader can get a sense of how many proteins were found to be associated with Bcl-xL?
- Have the authors tried any other ways to verify the interaction between Bcl-xL and CtBP2? For instance, do they co-localise when imaged? Also, can the reverse IP be performed?
- Figure 2C - the authors claim that this data shows that Bcl-xL nuclear translocation is reduced in cells with reduced levels of CtBP2 - however, although they quantify this I simply do not see it from the images presented. I do not think this data supports the conclusion that knockdown of CtBP2 reduces Bcl-xL translocation to the nucleus.Furthermore, this data is only shown with overexpressed Bcl-xL - have the authors tried with endogenous staining of Bcl-xL?
- Figure 2e-f - again these data are in cells with overexpressed Bcl-xL - does the same effect on invasion happen when only CtBP2 levels are reduced, without overexpression of Bcl-xL? What happens when Bcl-xL is knocked down? Also, doxycycline has been shown to affect mitochondrial function, which might confound this data - perhaps another way to knockdown CtBP2 (e.g. CRISPR which is used later in the study) would rule this out
- Figure 3c - these blots are not labelled, but ideally this would be shown with endogenous Bcl-xL, rather that just the overexpressed HA-Bcl-xL. However these data are more convincing than the images presented in Figure 2c
- Figure 4 - the authors use CRISPR to knockout CtBP2 - logically this data would go with the shRNA data shown before, as it seems to just repeat what has already been shown?
- Figure 4d - what does "SN" refer to? There is no loading control for this part of the fractionation - I assume this is supernatant? If so, why is there no loading control for this (same applies to figure 3c). Also, why are these not on the same blot? If CtBP2 knockdown reduces Bcl-xL mRNA level, does it also reduce Bcl-xL protein levels? We should be able to tell this from the blots in figure 4d, but since they are on different membranes this is impossible to deduce
- Figure 5c - molecular weight markers should be included here
- Figure 7a - the text says that MM102 treatment "significantly reduced" H3K4me3 levels - where is the quantification of this?
Minor comments
- Some of the figures are not properly labelled
- Some of the data are presented in an awkward manner - the authors should consider re-structuring either the manuscript or the figures so there is less "jumping around"
Significance
General assessment
- Provides new insight into non-canonical roles of Bcl-xL in cancer
- Relies heavily on over-expressed proteins to draw conclusions
- If the data were stronger and supported the conclusions, this study could be of interest to a broad cancer audience
My expertise
Cell biology, cell death, cancer, imaging
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Referee #2
Evidence, reproducibility and clarity
Summary:
This article by Raphael Schleutker and Stefan Luschnig addresses the importance of S-palmitoylation of the proteolipid protein M6, one of the three components of tricellular septate junction along with Anakonda and Giotactin, in the assembly of tricellular junctions using Drosophila embryo as a model system. Using a combination of state-of-the-art genome engineering, live imaging and biochemistry, the authors demonstrated that M6 is palmitoylated in vivo, elegantly identified the cysteine residue that is palmitoylated, showed that this modification is essential for interaction with Anakonda and provided convincing evidence that palmytoylation is required for the initial assembly of tricellular junctions.
Major comments:
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Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them? The claims are largely supported by the very high quality data assembled in this article. I have just a few concerns that can be resolved by modifying the text or, optionally, by carrying out additional experiments:
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in the summary (lane 40) and all along the article it is stated that 'Abolishing M6 palmitoylation leads to delayed accumulation of M6 and Aka at vertices but does not affect the rate of TCJ growth or mobility of M6 or Aka.'<br /> However, whilst the data presented convincingly demonstrate the delayed localization of GFP::M6 delta Palm at TCJ, that of Aka at TCJ is not shown. Although I think this is a reasonable hypothesis, without showing Aka localization, this claim is too strong and should be toned down, or better (optional) show the dynamics of Aka localization. Lanes 184 and 197 'indicating that effcient TCJ formation depends on M6 palmitoylation.' TCJ formation is not assessed here, what is measured is the localization of M6 at vertex. I suggest to amend the text accordingly.
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Fig. 5C and lane 292' Lack of M6 palmitoylation reduces, although it does not completely abolish, the interaction with Aka, ....' In Fig. 5C, GFP::M6 efficiently co-precipitates three forms of Aka with different molecular weights. The two upper bands are highly enriched in the GFP::M6 coIp. In contrast, GFP::M6 delta Palm seems to coIp only the low molecular weight form of Aka. Could the authors explain what the three forms of Aka are, and provide potential explanations or interpretations of this result?
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Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether.
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Are the data and the methods presented in such a way that they can be reproduced? The data are of very high quality and the methods sufficiently described (with appropriate references where necessary) and presented in such a way that they can be reproduced.
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Are the experiments adequately replicated and statistical analysis adequate? Although the microscopy data are perfectly quantified and the appropriate statistical tests are used, unless I am mistaken, the number of replicates and the number of independent experiments carried out in biochemistry (Fig. 2 and Fig. 5) are not indicated.
Minor comments:
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Specific experimental issues that are easily addressable.
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The localization of M6 on living specimens, GFP::M6 enriched at the tricellular junction, differs from the localization of M6 detected by anti-M6 on fixed samples, i.e. M6 homogenous distributed at the bicellular junction, no enrichment at the tricellular junction. Please comment and possibly explain the reason for the difference in localisation. Is the anti-GFP staining on the GFP::M6 sample restricted to the bicellular junction without apparent TCJ enrichment?
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In Fig. S2, isoforms E and F are expressed at low level but fully rescue Gli localization but not Aka. These results are somewhat surprising if Gli localization relies on Aka and M6 localization at TCJ. Is localization of M6 at TCJ important or is it the expression of M6 that matters? Would it be possible to compare the expression levels of the different isoforms?.
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lane 118 'Notably, vertex enrichment varied between M6-GFP isoforms and was inversely proportional to overall signal intensity, suggesting that saturation effects upon overexpression impede vertex enrichment. Consistent with this notion, endogenous GFP::M6CA06602 showed higher vertex enrichment (7.8-fold; Fig. 1E, L) than the individual overexpressed isoforms.' To conclude that all isoforms contain the elements for vertex localization, it would be interesting to provide the level of expression (signal intensity) for all M6 isoform as well as M6deltaPAlm-GFP to appreciate the threshold above which saturation is achieved? Or better (optional) to express the different isoforms in a M6 mutant background. Could the authors exclude the possibility that the position of the GFP moiety affect the localization of M6 at TCJ?
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lanes 187 '...in a single spot that subsequently extends basally with a speed of 0.09 μm/min ' The images are presumably projections along the apical basal axis, so it is difficult to appreciate the apical to basal extension, perhaps an orthogonal section would help.
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Are prior studies referenced appropriately? yes
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Are the text and figures clear and accurate? yes
Significance
Provide contextual information to readers (editors and researchers) about the novelty of the study, its value for the field and the communities that might be interested.
The following aspects are important:
Tricellular junctions are hot spots integrating mechanical and chemical inputs that are essential to ensure epithelia homeostasis. It is therefore essential to understand how the components of tricellular junctions are located and assembled to form functional tricellular junctions. The authors brilliantly demonstrate the key role of S-palmitoylation in M6 localization and ability to interact with Aka in vivo. The fact that the role of palmitoylation appears to be conserved for the assembly of vertebrate TCJs, made up of components that are not conserved throughout evolution, indicates a fundamental function of palmitoylation in protein-protein interactions at the level of TCJs and in their vesicular trafficking. As palmitoylation is reversible, this work also raises the question of how palmitoylation is regulated in time and space to ensure the plasticity of TCJs in developing epithelia.
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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?
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Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).
This study follows on from that showing the role of Angulin1 palmitoylation in its localization to tricellular junctions in vertebrates. The present study demonstrates the conserved nature of the role of this post-translational modification in the assembly of complex membrane structures essential for epithelial homeostasis. In addition, it demonstrates the dynamic nature and temporality of the role of palmytoylation in the early stages of recruitment of M6 to the vertex, opening up numerous hypotheses for future work at the conceptual and fonctional levels, elegantly presented in the discssion.
- 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?
I believe this article is dedicated to a rather broad audience. Although this article may at first appear to be aimed at specialists, the findings go beyond the interest in tricellular junctions in Drosophila, since the role of palmytoylation of tricellular components appears to be conserved in vertebrates. In addition, this study will have an impact on the overall cell biology community, including membrane trafficking and the role of lipid modification additions on the subcellular dynamics of transmembrane proteins in a physiological context.
- 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. I am a developmental cell biologist, with an expertise in epithelial junctions and epithelial tissue homeostasis, using vertebrate and invertebrate model systems.
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Referee #1
Evidence, reproducibility and clarity
The epithelial diffusion barrier in triangular junctions is initially formed by a protein complex of Aka, Gli and M6. Aka and M6 act upstream of Gli. GPM6a, the vertebrate homolog of M6 is palmitoylated, whose functional implications have not thoroughly been analyzed, yet. It order to better define the function of M6 and especially the role of the palmitoyl moity, the authors conducted a genetic analysis of M6 in Drosophila embryos.
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They first establish a genetic system with defined mutants (deletion and CS mutants which are not palmitoylated), tagged protein at the genetic locus and an quantitative assay for protein enrichment at triangular junctions. Secondly they provide biochemical evidence that M6 is palmitoylated at a cluster of three conserved cysteine residues in vivo. With a palmitoylation-deficient mutant, thirdly the authors investigate the function of the palmitoyl moiety for protein localization at triangular junctions and complex formation with the other proteins at triangular junctions. The authors reveal a quantitative function of the palmitoyl moiety at triangular junctions with respect to enrichment and initial accumulation but not for later functions during growth of triangular junctions. The lower enrichment of the non-palmitoylated M6 mutant are sufficient for recruitment of Aka and Gli. Importantly, reducing Aka in combination with the non-palmitoylated mutant leads to a strong phenotype with respect to Gli localization and and leads to a genetically synthetic embryonic lethality. Fourthly, on a molecular level, the palmitoyl residue mediates binding to Aka but is not required for di/oligomerization of M6 itself as shown by immunoprecipitation from embryonic lysates.
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Though the function of M6 acting together with Aka and Gli has been demonstrated previously, molecular details of the interactions and targeting of the proteins to triangular junctions have remained unclear. Similarly, although palmitoylation of the vertebrate homologue has been previously demonstrated, its functional implications have not been investigated in a physiological context with stringent genetics. The current study provides convincing data about the role of the palmitoylated moiety of M6. Importantly, the authors manage to differentiate a function of the palmitoyl residue in initial accumulation of M6 at triangular junctions versus maintenance. Also the authors manage to reveal an essential function of M6 palmitoylation when the dose of Aka is reduced. In summary, the study provides novel and interesting insights into the detailed molecular requirements of epithelial barrier formation. Although the quality of the data and analysis provides an argument for publication on its own, it may be noted that similar mechanisms may underlie barrier formation at triangular junction in vertebrates given the conservation of the protein components.
Minor comments:
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L100: it is stated that "... is not detectable on other known TCJ components". What about Angulin-1, which is palmitoylated?
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L122 In my understanding all M6 isoforms contain an element which is sufficient. Not "required".
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L336. The allele designation "DeltaPalm" is misleading. A designation like "3xCS" would be more better because three defined cysteine residues are mutated.
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L329 A reference to FLYBASE is missing. Similarly not reference to stock centers are provides. To document the importance of the community services it is essential that their services are properly cited in a way that can be automatically tracked, e. g. by a literature citation.
Significance
In summary, the study provides novel and interesting insights into the detailed molecular requirements of epithelial barrier formation. Although the quality of the data and analysis provides an argument for publication on its own, it may be noted that similar mechanisms may underlie barrier formation at triangular junction in vertebrates given the conservation of the protein components.
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Reply to the reviewers
Reviewer #1:
Comment: The author investigated the role of the stress sensor pathway in the mechanism of tumor cell survival<br /> They identified a long noncoding RNA as JUNI that regulates antagonizing MAP phosphatase and favors the JUN transcription. JUNI correlated with the survival of several cancer histotypes, particularly in RCC, as a highly specific and correlated prognosis.
The abstract although not always required from the journal should be divided into methods used to reach the main findings and clear presentation of results
Response: We do not know yet to which Journal the paper will be sent. The format will be adjusted to the Journal requirements.
it is unclear whether JUNI is a positive or negative regulator of JUI (I assume the reviewer meant JUN)
Response: The text in the abstract was changed to” JUNI positively regulates the expression of its neighboring gene JUN, a key transducer of signals that regulate multiple transcriptional outputs.”
Hope it is clearer now
When the author indicates that JUNI antagonizes MAP PHOSPHATASE is not correct the term antagonism is related to receptors but the authors did not show any receptor.
Response: The term "antagonism" does not only refer to receptor drugs. In pharmacology, antagonism generally describes the interaction between a drug (or other molecule) and a receptor or biological target that results in the inhibition or blocking of the receptor's activity. However, this concept can extend beyond receptor drugs and apply to various biological interactions.
Outside of the realm of drugs and receptors, antagonism can also refer to antagonistic relationships between different biological processes, molecules, or organisms.
Overall, while antagonism is commonly discussed in the context of receptor drugs, the concept of antagonism can apply to a broader range of interactions in biology and other fields.
Response: The p values for the prognostic values of JUNI and DUSP14 in RCC were added to the abstract.
Generally, Jun oncogene correlated with poor overall survival while the table indicates promote survival so good prognosis?
Response: This manuscript describes for the first time the biological activity and cancer relevance of JUNI. It positively regulates stress induced c-Jun and can be used as prognostic marker in ccRCC.
The significance of JUNI and its interactome in ccRCC prognosis is unequivocal, according to data analysis of cancer relevant data (TCGA) regardless to its effects on c-Jun. The concern raised by reviewer 1 and 2 is whether the cancer-relevant effects are mediated by c-Jun regulation. We suggest that despite regulating stress induced c-Jun, they are not! This suggestion is based on three points: 1. We show in the manuscript that a large portion of JUNI dependent effects on cellular survival activity is c-Jun independent. 2. We describe many interacting proteins that may, in a JUN-independent manner, affect tumorigenesis. 3. In this study we examined JUNI’s functions which are cell-autonomous. However, neither the non -autonomous effects nor effects on cells that compose the tumor environment were studied. Reports that lncRNAs may have a role in immune responses and high expression of JUNI in CD8 cells may suggest this direction for future investigation (Carpenter, S et al. science, 341(6147), pp.789-792; Mickaël, M. et al https://doi.org/10.1101/2021.12.01.470587)
Therefore, we assume that direct correlations in every biological activity between JUNI and JUN is an over simplified consumption. Analogy for that can be found with another major regulator of c-Jun, JNK, which is stress induced, c-Jun regulator involved in stress-induced cell death, whereas c-Jun itself is contributing in many cases to drug resistance.
The introduction contains the main information to follow the role of JUN and renal carcinoma<br /> However, should be improved with background on the key role of stress genes in the pro-survival pathway of tumors during progression and hypoxia condition. Too many references on long noncoding compared to the JUN complex with AP-1 and transformation
Response: A section describing the major stress pathway in ccRCC, HIF 1 and its role in ccrCC was added. Due to the limitation of word count in most journals we cannot expend this section further
Results In Figure 1 the authors showed expression levels of JUNI and JUN that are clearly different after UV stimuli. they demonstrate that are both regulated by UV but the amount and the time are different. the author should comment on these data if they want to study the regulative mechanism
Response: The following comment was added at the end of the first section: Overall, these results suggested that JUNI is a stress-induced gene whose expression pattern resembles that of JUN, therefore, we investigated the potential existence of regulatory effects between the two genes, especially post exposure of cells to stress.
Figure 1 F the cellular distribution of JUNI which is the rational of this experiment to provide that is into nucleus while normally is into the cytoplasm? What adds this experiment?
Response: This is the first reported description of JUNI. We attempted to characterize it as much as possible. It’s localization was not described previously and we suggest that it is mainly nuclear. A novel important information that should be presented.
In Figure 2 the authors provided that the kinase pathway is important for Jun regulation but the effect on JUNI a Luciferase assay needs to be provided
Response: We respectfully disagree with the reviewer. We believe that examining the expression from a DNA fragment identical to the endogenous one is superior to artificial system, such as luciferase.
In Figure 3 for Migration assay is necessary to see cells on the other side of the filter by staining not a graphical representation
Response: The graphical representation is an accumulated result of at least 3 experiment. However, a figure representing a single experiment was added as a supplement figure s1.
The experiment on kinase does not add any data to what is already known on jun probably should be shifted in Figure 6
Response: We apologize, this question was not fully understood as there is no experiment on kinase in figure 3. If case the reviewer was referring to kinase inhibition in Fig 2A we do think it is needed as a positive control for the kinases activity.
Table 1 is cited two times once in the context of Figure 3 and then in Figure 6 indicating that the authors go forward and back on their experimental design
Response: Table 1 is indeed referred to in two places. It is first mentioned when we investigated the potential relevance of JUNI for human cancer, given its regulatory impact on the neighboring JUN gene and its influence on motility. Later, the types of cancers described in figure 1 were further processed in order to examine relations between JUNI and DUSP14 in human cancer. We do not see it as a flaw in experimental design but rather as further evolution of the story based on data discovered in earlier stages.
in figure 4 the apoptotic cells are not clearly visible a specific staining marker is necessary to provide the phenomenon
Response: Two corrections were made to demonstrate apoptosis clearly. The pictures in Figure 4 panel A were replaced with a better-quality image with addition of DNA staining to demonstrate the cell death clearer, appearance of cell blebbing and nuclear fragmentation. Panel B demonstrating increase in cleaved caspase 3 in JUNI silenced cells after all treatment was added.
Additionally XTT assay should be reported as the percentage of survival cells not staining incorporated compared to untreated cells over time
Response: We do apologize for the legend omission, but XTT assays, colonies formation and soft agar colonies formation are presented in Figure 4 H-J and Figure S3 for all cell lines
The data on prognosis and correlation of gene expression are not clearly presented and discussed
Response: Figure S4 was replaced by table S3 to demonstrate clearer the differences in Medians survival caused by JUNI of DUSP 14. Text was changed in the last section of results.
The western blot need to be quantified
Response: All blots were quantified
Reviewer #2:
- While the experimental data showed JUNI, like c-JUN, is pro-survival of cancer cells, the clinical sample analyses correlated it positively with patients' survival. This discrepancy casts doubts in significance of the findings. The authors need to re-evaluate their data and conclusion
Response: This manuscript describes for the first time the biological activity and cancer relevance of JUNI. It positively regulates stress induced c-Jun and can be used as prognostic marker in ccRCC.
The significance of JUNI and its interactome in ccRCC prognosis is unequivocal, according to data analysis of cancer relevant data (TCGA) regardless to its effects on c-Jun. The concern raised by reviewer 1 and 2 is whether the cancer-relevant effects are mediated by c-Jun regulation. We suggest that despite regulating stress induced c-Jun, they are not! This suggestion is based on three points: 1. We show in the manuscript that a large portion of JUNI dependent effects on cellular survival activity is c-Jun independent. 2. We describe many interacting proteins that may, in a JUN-independent manner, affect tumorigenesis. 3. In this study we examined JUNI’s functions which are cell-autonomous. However, neither the non -autonomous effects nor effects on cells that compose the tumor environment were studied. Reports that lncRNAs may have a role in immune responses and high expression of JUNI in CD8 cells may suggest this direction for future investigation (Carpenter, S et al. science, 341(6147), pp.789-792; Mickaël, M. et al https://doi.org/10.1101/2021.12.01.470587)
Therefore, we assume that direct correlations in every biological activity between JUNI and JUN is an over simplified consumption. Analogy for that can be found with another major regulator of c-Jun, JNK, which is stress induced, c-Jun regulator involved in stress-induced cell death, whereas c-Jun itself is contributing in many cases to drug resistance.
Response: The Western blotting data need at least triplicate biological experiments and quantification. This is particularly important for trivial differences, such as shown in Fig. 6.
Response: All westerns X=3. Representative experiments are depicted. Quantification was added.
The identification and gene structure of LINC01135 and its relevance to c-Jun need better clarity
Response: First result section. “According to ENCODE data, JUNI contains five main exons and has multiple isoforms. Twenty-seven different transcript isoforms were described according to LNCipedia ranging from 213 to 6213 bases {Volders, 2019 #2907}. The relevance to c-Jun was referred to in discussion: Both the effects of JUNI on c-Jun induction and cellular survival were demonstrated using under-expression conditions by targeting, the common, first, exon of JUNI. Nevertheless, this exon was also sufficient for c-Jun induction upon stress exposure, under conditions of overexpression.
Page 9-10, Line 198-199, there are no results in Fig. 1 showing that JUNI induction was dependent to serum stimulation of starved cells
Response: “ Similar to JUN, the induction was dose dependent (Fig 1C), and the rapid response to stress (Fig 1D) as well as to serum stimulation of starved cells, identified by others (36), qualifies it as an “immediate early” lncRNA.”
Serum stimulation is described in reference 36
What is the Y-axis in figures 2B, 4E-G
Response: Legend was added to Y-axis of Figures 2B and 4 E-G
In Fig. 3B, actin image is missing
Response: Actin was hidden in the graphic process. Corrected.
In Fig. 4. brightfield images are inaccurate for distinguishing apoptosis and necrosis. Additional molecular markers need to be used, such as caspase-3 cleavage and LDH release
Response: Two corrections were made to demonstrate apoptosis clearly. The pictures in Figure 4 panel A were replaced with a better-quality image with addition of DNA staining to demonstrate the cell death clearer, appearance of cell blebbing and nuclear fragmentation. Panel B demonstrating increase in cleaved caspase 3 in JUNI silenced cells after all treatment was added.
The inconsistency of using four cell types in each assay. For example, in Fig. 4A, B, E-G and Suppl Fig. 1, HMCB, MDA-MB-231 and CHL1 cells were used to test the short-term effect of JUNI knockdown on cell survival, whereas Hela, MDA-MB-231 and CHL1 cells were chosen to determine the long-term effect of JUNI knockdown. Similar case in other figures.
Response: Effects on Jun regulation and the effects on long term survival were tested in all four cell lines both by XTT and clonogenic assays whereas effects on short term survival were tested in three out of the four cell lines. It is practically impossible to perform a study of this magnitude were all assays were tested in all cell lines. Using four cell lines was applied to prove the major points.
In Fig. 5D, no difference of c-Jun expression between NS and siJUN groups
Response: Correct, the western in 5D was replaced by a more representative one
Cell survival in Fig. 5 lacked statistical analyses
Response: Error bars were mistakably omitted. The figure was corrected.
In Suppl Fig. 2C, there is no figure to show the reduced colonies formation in soft agar in MDA-MB-231 cells, contradicting to that stated in the manuscript
Response: Indeed Figure 4 J and S3 C presented colonies formation in HMCB and HeLa cells. The text was corrected.
Reviewer #3: "linc01135" - this is a human gene, should be capitalized
Response: linc01135 was capitalized
Please indicate primers in Fig1A and mention this in relevant part of Results
Response: The following section was added: “Importantly, ENCODE predicts that the first exon is shared by all, therefore, all primers to analyze JUNI’s expression as well as siRNAs to silence it, were targeted for this exon.”
Fig1C-F - please add a legend to explain the colors
Response: Legend was added into the Figure as well
Copy number: It is important to establish the approximate copy number of JUNI RNAs in the cell lines tested. FISH would be one appropriate method. This could also be referenced back to the RNA-seq TPM values. Are we talking about <1 copy /cell, or many? Quick inspection of ENCODE RNA-seq in the UCSC browser suggest an intermediate value that varies between cell lines. This value is very important when interpreting mechanistic experiments later on
Response: The copy number in HMCB and MDA-MB-231 was calculated by comparison of CT values obtained from RNAs from a known number of cells relative to calibration curve of known concentrations of JUNI. The following section was added to the first paragraph of the results: “quantitation of JUNI’s copy number in untreated HMCB and MBA-MD-231 cells revealed the presence of minimal amount of about 8 copies per cell”
Fig3 - again, no figure legends, difficult for reader
Response: Legend was added to Fig. 3A
In general, the figures could be much more clearly annotated and presented with more care. They do not do justice to the quality of the work itself. For example, Fig4E-G why not label each panel with the time course, the cell line tested etc etc to save us the work of digging through the Legends?
Response: We thank the reviewer for this remark. All figures were corrected, legends and proteins quantification was added.
Rescue experiments: The rescue experiments in Fig5D are nicely done and the results are interesting. However, I would request the authors to perform similar experiments with JUNI rescue. Specifically, to knock down JUNI with siRNA, and then reintroduce it from an 'immune' expression plasmid, where the siRNA site is mutated. This will further strengthen the claim that JUNI siRNA is acting through the intended target to cause observed effects on cell viability
Response: As the effects on survival are strongest in the longer term, 14 days after silencing, rescue experiments were performed to test the rescue in the survival of HMCB and HeLa cells using clonogenic assays. Results are presented in figure 4 L
IncPrint data: was Jun protein found to be an interactor? This might be mentioned in the text, whether it is yes or no
Response: c-Jun was screened and did not interact with JUNI. The text was changed as following” Interestingly, c-Jun itself does not interact with JUNI (Table S2, Normalized luciferase intensity MS2, RLU =0.44). By contrast, the dual specificity protein phosphatase 14….”
Expression: A key issue is the expression of JUNI in healthy and diseased cells and organs. Is JUNI ubiquitous (and essential to both healthy and tumor cells), or is it specific to tumor cells? Which tumor types? This would be straightforward to find out from public data. I would suggest a main figure panel. Also, is JUNI upregulated across tumors? Could find this out from GEPIA2 or other databases.
Response: Figure 7E describing the levels of JUNI in variety of normal and tumor samples was added.
Non-tumor cells: Like many studies, this one focusses on effect of LOF in transformed cells. However, therapeutic relevance is tied to specific effect in transformed cells. Therefore I believe the paper would be vastly strengthened, if knockdowns+viability assays were also performed in some non-transformed cells. Eg HEK293, immortalised fibroblasts, RPE1 etc
Response: Indeed discrimination between Normal and cancer cells is an essential point for further research and translation. We examined the affects of silencing on spontaneously immortalized keratinocytes, HaCat cells, and the results are depicted in Figure 4 K.
Alternative reagents: The siRNA experiments are well performed with two independent sequences. An important additional experiment would be to replicate these experiments with antisense oligonucleotides. This would both further strengthen the confidence in experiments, and open more lines of potential therapies. This experiment I would consider optional
Response: Stable CRISPR can not be formed. We are currently constructing inducible CRISPR but the construction consumes longer time than the scope of this revision.
Advanced models: All the present experiments are performed in monolayer cell lines. The authors will no doubt be aware that the paper would be substantially strenghtened if functional experiments could be replicated in more advanced models: spheroids, PDX, explants, mice...
Response: We examined the protective role of JUNI in Doxorubicin treated spheroids of HMCB and CHL1 cells. The results are depicted in figure 4 D and E.
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Referee #3
Evidence, reproducibility and clarity
Kumar and colleagues present an apparently novel, cancer-promoting lncRNA 'JUNI' and perform a rather thorough and careful analysis of its in vitro functions and molecular mechanisms.JUNI is located adjacent to the Jun protein coding gene, although intriguingly the two appear to be rather indepent of each other at the level of gene products. JUNI appears to be necessary for cancer cell line growth and survival (monolayer) in multiple contexts. Particularly interesting is the demonstration that JUNI appears to function in trans. Overall this is an excellent paper - work is solidly and carefully done, hypotheses are well formulated and thoroughly tested. The JUNI lncRNA is well supported in public annotations, seems to be highly expressed, and it is surprising that virtually no work has been carried out on it so far. Furthermore, the apparently essentiality of JUNI to cancer cells has potentially important therapeutic and mechanistic ramifications.
These are suggestions for improvement of the work.
"linc01135" - this is a human gene, should be capitalised.
Please indicate primers and ASOs in Fig1A and mention this in relevant part of Results.
Fig1C-F - please add a legend to explain the colors.
Copy number: It is important to establish the approximate copy number of JUNI RNAs in the cell lines tested. FISH would be one appropriate method. This could also be referenced back to the RNA-seq TPM values. Are we talking about <1 copy /cell, or many? Quick inspection of ENCODE RNA-seq in the UCSC browser suggest an intermediate value that varies between cell lines. This value is very important when interpreting mechanistic experiments later on.
Fig3 - again, no figure legends, difficult for reader.
In general, the figures could be much more clearly annotated and presented with more care. They do not do justice to the quality of the work itself. For example, Fig4E-G why not label each panel with the time course, the cell line tested etc etc to save us the work of digging through the Legends?
Rescue experiments: The rescue experiments in Fig5D are nicely done and the results are interesting. However, I would request the authors to perform similar experiments with JUNI rescue. Specifically, to knock down JUNI with siRNA, and then reintroduce it from an 'immune' expression plasmid, where the siRNA site is mutated. This will further strengthen the claim that JUNI siRNA is acting through the intended target to cause observed effects on cell viability.
IncPrint data: was Jun protein found to be an interactor? This might be mentioned in the text, whether it is yes or no.
Expression: A key issue is the expression of JUNI in healthy and diseased cells and organs. Is JUNI ubiquitous (and essential to both healthy and tumor cells), or is it specific to tumor cells? Which tumor types? This would be straightforward to find out from public data. I would suggest a main figure panel. Also, is JUNI upregulated across tumors? Could find this out from GEPIA2 or other databases.
Non-tumor cells: Like many studies, this one focusses on effect of LOF in transformed cells. However, therapeutic relevance is tied to specific effect in transformed cells. Therefore I believe the paper would be vastly strengthened, if knockdowns+viability assays were also performed in some non-transformed cells. Eg HEK293, immortalised fibroblasts, RPE1 etc.
Alternative reagents: The siRNA experiments are well performed with two independent sequences. An important additional experiment would be to replicate these experiments with antisense oligonucleotides. This would both further strengthen the confidence in experiments, and open more lines of potential therapies. This experiment I would consider optional.
Advanced models: All the present experiments are performed in monolayer cell lines. The authors will no doubt be aware that the paper would be substantially strenghtened if functional experiments could be replicated in more advanced models: spheroids, PDX, explants, mice...
Significance
This is an important advance in the cancer field. It reveals a potential new lncRNA oncogene, JUNI, which appears to be necessary for cancer cell survival in multiple contexts through mechanisms defined by the authors. Future work will be required to understand the degree to which JUNI's activity is cancer specific, and its functional effects will have to be replicated in more faithful cancer models.
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Referee #2
Evidence, reproducibility and clarity
This work identified a lncRNA JUNI located near c-JUN and investigated its relationships with c-JUN and stress response, survival, and cancer prognosis. Experiments are logically designed, and the research topic is novel. The main concern is weaknesses in data interpretation and significance. Additionally, the paper needs improvement in experimental rigor with statistical assessment of multiple data sets; data description and conclusion need better clarity.
Overall comments:
- While the experimental data showed JUNI, like c-JUN, is pro-survival of cancer cells, the clinical sample analyses correlated it positively with patients' survival. This discrepancy casts doubts in significance of the findings. The authors need to re-evaluate their data and conclusion.
- The Western blotting data need at least triplicate biological experiments and quantification. This is particularly important for trivial differences, such as shown in Fig. 6.
Specific comments:
- The identification and gene structure of LINC01135 and its relevance to c-Jun need better clarity.
- Page 9-10, Line 198-199, there are no results in Fig. 1 showing that JUNI induction was dependent to serum stimulation of starved cells.
- What is the Y-axis in figures 2B, 4E-G
- In Fig. 3B, actin image is missing.
- In Fig. 4. brightfield images are inaccurate for distinguishing apoptosis and necrosis. Additional molecular markers need to be used, such as caspase-3 cleavage and LDH release.
- The inconsistency of using four cell types in each assay. For example, in Fig. 4A, B, E-G and Suppl Fig. 1, HMCB, MDA-MB-231 and CHL1 cells were used to test the short-term effect of JUNI knockdown on cell survival, whereas Hela, MDA-MB-231 and CHL1 cells were chosen to determine the long-term effect of JUNI knockdown. Similar case in other figures.
- In Fig. 5D, no difference of c-Jun expression between NS and siJUN groups.
- Cell survival in Fig. 5 lacked statistical analyses
- In Suppl Fig. 2C, there is no figure to show the reduced colonies formation in soft agar in MDA-MB-231 cells, contradicting to that stated in the manuscript.
Significance
Experiments are logically designed, and the research topic is novel. The main concern is weaknesses in data interpretation and significance. Additionally, the paper needs improvement in experimental rigor with statistical assessment of multiple data sets; data description and conclusion need better clarity.
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Referee #1
Evidence, reproducibility and clarity
The author investigated the role of the stress sensor pathway in the mechanism of tumor cell survival<br /> They identified a long noncoding RNA as JUNI that regulates antagonizing MAP phosphatase and favors the JUN transcription. JUNI correlated with the survival of several cancer histotypes, particularly in RCC, as a highly specific and correlated prognosis.
The abstract although not always required from the journal should be divided into methods used to reach the main findings and clear presentation of results it is unclear whether JUNI is a positive or negative regulator of JUI. When the author indicates that JUNI antagonizes MAP PHOSPHATASE is not correct the term antagonism is related to receptors but the authors did not show any receptor. Correlated with prognosis ( negative or positive ) Statistical value should be reported in the abstract. Generally, Jun oncogene correlated with poor overall survival while the table indicates promote survival so good prognosis?
Major comments
The introduction contains the main information to follow the role of JUN and renal carcinoma<br /> However, should be improved with background on the key role of stress genes in the pro-survival pathway of tumors during progression and hypoxia condition. Too many references on long noncoding compared to the JUN complex with AP-1 and transformation.<br /> Results In Figure 1 the authors showed expression levels of JUNI and JUN that are clearly different after UV stimuli they demonstrate that are both regulated by UV but the amount and the time are different the author should comment on these data if they want to study the regulative mechanism figure 1 F the cellular distribution of JUNI which is the rational of this experiment to provide that is into nucleus while normally is into the cytoplasm? What adds this experiment?<br /> In Figure 2 the authors provided that the kinase pathway is important for Jun regulation but the effect on JUNI a Luciferase assay needs to be provided<br /> In Figure 3 for Migration assay is necessary to see cells on the other side of the filter by staining not a graphical representation the experiment on kinase does not add any data to what is already known on jun probably should be shifted in Figure 6. Table 1 is cited two times once in the context of Figure 3 and then in Figure 6 indicating that the authors go forward and back on their experimental design<br /> in figure 4 the apoptotic cells are not clearly visible a specific staining marker is necessary to provide the phenomenon additionally XTT assay should be reported as the percentage of survival cells not staining incorporated compared to untreated cells over time.<br /> The data on prognosis and correlation of gene expression are not clearly presented and discussed
Significance
The authors identified a long noncoding RNA as JUNI that regulates antagonizing MAP phosphatase and favors the JUN transcription. JUNI correlated with survival of several cancer histotypes In particular in RCC as a highly specific and correlated prognosis.
The data are not presented with a good rationale often the authors go forward and back on the experimental design. The data are not presented in the best way some data are shown as bar graph but need to be supported by cell staining of transwell staining and standard plot for survival rate The western blot need to be quantified
In general, the experimental design does not match the rational
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Reply to the reviewers
_We have underlined the important points in the reviewer's comments. All responses have been read and authorized by all authors of this manuscript. Authors would like to thank the reviewers and the editor for their valuable time. We believe that the comments and suggestions from both reviewers will significantly improve SMorph and the manuscript. _
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
First of all, I want to apologize the authors and editor for my delay. Secondly, for clarity, I want to disclose that I am the author of the Fiji's 'Sholl Analysis' plugin, that the authors cite extensively (Ferreira et al, Nat Methods, 2014).
In this study, Sethi et al introduce a software tool - SMorph - for bulk morphometric analysis of neurons and glia (astrocytes and microglia), based on the Sholl technique. The authors compare it to the state-of-the-art in a series of validation experiments (stab wound injury), to conclude that it is 1000 times faster that existing tools. Empowered by the tool, the authors show that chronic administration of a tricyclic antidepressant (DMI) leads to structural changes of astrocytes in the mouse hippocampus. The paper is well written, the description of the tool is clear, and the authors make all of the source code available, as well as most of the imagery analyzed in the manuscript. The latter on its own, makes me really appreciative of the authors work.
We thank reviewer #1 for their careful reading of the manuscript and their comments.
**Major comments:**
A major strength of SMorph is that it leverages the Python ecosystem, which allow the authors take advantage of powerful python packages such as sklearn, without the need for external packages or tools. However, I have strong criticisms for the claims that are made in terms of speed and broad-applicability of the software, including PCA.
Speed:
The 1000x speed gains, assumes - for the most part -- that the processing in Fiji cannot be automated. This is false. I read the source code of SMorph, and with exception of the PCA analysis, all aspects of SMorph can be automated in Fiji, using any of Fiji's scripting languages to make direct calls to the Fiji and
Sholl Analysis
plugin APIs (See https://javadoc.scijava.org/) . Now, perhaps the authors do not have experience with ImageJ scripting, or perhaps we Fiji developers failed to provide clear tutorials and examples on how to do so. Or perhaps, there is something inherently cumbersome with Fiji scripting that makes this hard (e.g., there is a current limitation with the ImageJ2 version of 'Sholl Analysis' that does not make it macro recordable). It such limitations do exist, it is perfectly fine to mention them, but do contact us at https://forum.image.sc, if something is unclear. We do strive to make our work as re-usable as possible. Unfortunately our own research does not always allow us the time required to do so. Case in point, our scripting examples (e.g., https://github.com/tferr/ASA/blob/master/scripting-examples/3D_Analysis_ImageStack.py; https://github.com/tferr/ASA/blob/master/scripting-examples/3D_Analysis_ImageStack.py) are not well advertised. That being said, I am still surprised that in their side-by-side comparisons the authors were not able to automate more the processing steps (e.g., the ImageJ1 version of 'Sholl Analysis' remains fully functional and is macro recordable). If I misunderstood what was done, please provide the ImageJ macros you used. Also, I wanted to mention that i) semi-manual tracing with Simple Neurite Tracer (now "SNT"), can also be scripted (see https://doi.org/10.1101/2020.07.13.179325); and that ii) Fiji commands and plugins can also be called in native python using pyimagej (https://pypi.org/project/pyimagej/), see e.g., https://github.com/morphonets/SNT/tree/master/notebooks#snt-notebooks). Arguably, the fact that SMorph handles blob detection and skeletonization-based metrics directly is more advantageous from a user point of view. In Fiji, blob detection, skeletonization and Strahler analysis (https://imagej.net/Strahler_Analysis) of the skeleton are handled by different plugins. However, those are also fully scriptable, and interoperate well. The point that topographic skeletonization in Fiji can originate loops is valid, however the authors should know that such cycles can be detected and pruned programmatically using e.g., pixel intensities (see https://imagej.net/AnalyzeSkeleton.html#Loop_detection_and_pruning and the original publication (https://pubmed.ncbi.nlm.nih.gov/20232465/)We completely agree with the reviewer’s assertion that most parts of the functionality of SMorph can be automated within imageJ as well, and in such comparison, the speed gains with SMorph will not be >1000X.
However, automating the analysis in imageJ is beyond the scope of the present manuscript. In fact, imageJ analysis comparison was not a part of our original manuscript at all. Upon presubmission inquiry to one of the affiliate journals of Review Commons, we were specifically asked to include a side-by-side comparison with “already available” methods. So, we decided to use ImageJ as it is, and automation, if any, was limited to simple macros to run a series of commands sequentially on batches of images. Although it is true that this analysis could be done much more efficiently with additional scripting, it would not have met the definition of “already available” tools. The imageJ analysis was performed in a way an average biologist with no programming experience would perform it, since that group will find SMorph most useful. In no way do we intend to imply that imageJ analysis can’t be made more efficient and automated. Perhaps it was not clear from the way the text was framed in the initial version of the manuscript. We will add additional text to make this point clearer.
On a side-note, in response to reviewer #2’s comments, we will perform the speed comparison on a per-image basis, so the speed gain (1080X) may change a little in the new comparison.
Broad applicability:
In our work, we made a significant effort to ensure that automated Sholl could be performed on any cell type: e.g., By supporting 2D and 3D images, by allowing repeated measures at each sampled distance, and by improving curve fitting. For linear profiles, we implemented the ability to perform polynomial fits of arbitrary degree, and implemented heuristics for 'best degree' determination. For normalized profiles, we implemented several normalizers, and alternatives for determining regression coefficients. We did not tackle segmentation of images directly (we did provide some accompanying scripts to aid users, see e.g. https://imagej.net/BAR) because in our case that is handled directly by ImageJ and Fiji's large collection of plugins. However, in SMorph, several of these parameters are hard-wired in the code. They may be suitable to the analyzed images, but they can be hardly generalized to other datasets. In detail: In terms of segmentation, SMorph is restricted to 2D images, scales data to a fixed 98 percentile, and uses a fixed auto-threshold method (Otsu). These settings are tethered to the authors imagery. They will give ill results for someone else using a different imaging setup, or staining method. In terms of curve fitting, the polynomial regression seems to be fixed at a 3rd order polynomial, which will not be suitable to different cell types (not even to all cells of 'radial morphology').
We have indeed hard-coded the parameters that the reviewer mentions, and we agree that we can perhaps give all options to the end-users to choose from. The decision was made to hard-code the parameters so that SMorph becomes very easy and minimalistic to use for the end-users. But the reviewer is right to point out that this may compromise the broad applicability and accuracy. We will update the code in the revised version of the manuscript to give the users control over choosing these parameters.
PCA:
The idea of making PCA analysis of Sholl-based morphometry accessible to a broader user base has merit and is welcomed. However, it has to be done carefully in a self-critic manner as opposed to a black-box solution. E.g., in the text it is mentioned that 2 principal components are used, in the tutorial notebook, 3. Why not provide intuitive scree plots that empower users with the ability to criticize choice? Also, it would be useful for users to understand which metrics correlate with each other, and their variable weights.
Reviewer #1’s suggestions would indeed make the PCA analysis more useful to the users. In the revised version of the code, we will provide additional data/plots to the user for making an informed choice of the significant principal components e.g. the elbow method, Ogive or Pareto plots, variable weights of different features in the principal components and correlation/covariance matrices.
When we showcased the utility of PCA to distinguish closely related morphology groups (as in Type-1 and Type-2 PV neurons), we had been unable to base the distinction on individual metrics, at least not in a robust manner (see Fig. S4 in Ferreira et al, 2014). A minor conundrum of the paper, is that it does not directly highlight the advantages of "analyzes in a multidimensional space". The differences between groups in the stab wound and DMI assays are such, that PCA is hardly needed: I.e., the differences depicted Fig2F,G are already significant, and already convey changes in "size and branch complexity" (as per PC1). The same argument applies to Fig. 5. The paper would profit from having this discussed.
PCA data indeed is not required to make any of the inferences we make in the paper and is superfluous. However, as mentioned in the discussion section of this manuscript, the low-dimensional PCA data can be used in future for other applications, e.g to cluster the astrocytes into morphometrically-defined subpopulations. SMorph can be further developed to perform real-time classification of these cells into morphometric clusters, which will allow the researchers to investigate clusters-specific gene expression, electrophysiology etc. Preliminary results from our lab do suggest that such clusters are differentially altered by stress and antidepressant treatments. However, these results are preliminary and are a part of a long-term future study. The data is really premature to publish at this stage, since it will require a lot of experimentation to show that these astrocyte subpopulations are indeed physiologically and functionally different. Nevertheless, we think that the utility of SMorph for such analyses may help others to come up with additional innovative ways to use the PCA data. Hence, we do believe that the community will benefit from the current release of SMorph having PCA. PCA data was shown in the figures just to demonstrate the functionality of SMorph. We will add additional text to make these points clearer.
Other:
- All metrics and parameters should be expressed in physical units (e.g.," radii increasing by 3 pixels", axes in Figure 2, 3, 5, S2) so that readers can directly interpret them.
In the revised manuscript, we will convert all units into actual physical distances.
- The paper would profit from the insights provided by Bird & Cuntz (https://pubmed.ncbi.nlm.nih.gov/31167149/)
We thank the reviewer for suggesting this paper. We will include this in the discussion of the manuscript.
**Minor comments:**
- Usage of RGB images (8-bit per channel) seems hardly justifiable. Aren't you loosing dynamic range of GFAP signal?
We agree that we could have captured the images at a higher dynamic range. However, for the changes we observe between treatment groups using GFAP immunoreactivity signal as presented in the manuscript, we do not see an advantage of using higher dynamic range. However, as the reviewer rightly pointed out, under certain conditions, imaging using a higher dynamic range may help and hence, we will include this recommendation in the materials and methods section.
- Please explain how MaxAbsScaler "prevents sub-optimal results"
Since morphometric features extracted from cell images either have different units or are scalar, we had to perform normalization before PCA. We will add further explanation in the methods section of the manuscript.
- The fact that automated batch processing can stall on a single bad 'contrast ratio' image seems rather cumbersome to deal with
This problem has been resolved in the current version of SMorph, which will be uploaded with the revised version of the manuscript.
- Please add a license to https://github.com/parulsethi/SMorph/. Without it, other projects may shy away from using SMorph
We will add a ____GPLv3 license
- "mounted on stereotax" should be "mounted on a stereotaxis device"?
We will make this change
- Ensure Schoenen is capitalized
We will make this change
Reviewer #1 (Significance (Required)):
I find the Desipramine results interesting. However, given the existing claims that DMI can modulate LTP, I regret that the authors did not look at structural modifications in hippocampal neurons (e.g., by performing the experiments in Thy1-M-eGFP animals). I understand, that doing so at this point would be a large undertaking.
Another manuscript from our lab__1, as well as work from other labs have shown that stress causes significant degenerative changes in hippocampal astrocytes__2,3__. In the light of these observations, we do believe that our observation of chronic antidepressant treatment inducing structural plasticity in astrocytes is significant. Structural alterations in neurons after DMI treatment are of interest. But in our experience, we have not seen gross morphological (dendritic arborization) changes in hippocampal neurons as a result of antidepressant drug treatments. Such changes are restricted to spine morphology and axonal varicosities, which is beyond the capabilities of SMorph. __
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
This paper addresses the challenge of automatic Sholl analysis of large dataset of multiple cell types such as neurons, astrocytes and microglia. The developed approach should improve the speed of morphology analysis compared to the state of the art without compromising on the accuracy. The authors present an interesting application of their tool to the morphological analysis of astrocytes following chronic antidepressant treatment. The paper is well written, and the tool presented could be beneficial for different applications and context. However, some major aspects should be addressed by the author concerning the description of the algorithms used and the quantification of the results.
We thank reviewer #2 for their careful reading of the paper and their comments.
**Major comments/Questions:**
- In the Results and/or Methods sections, the author should better describe how their approach is different from state-of-the-art approaches in terms of algorithms used and how these difference impacts on the speed and accuracy of the analysis.
We will add these descriptions in the methods section in response to this comment as well as some comments from reviewer #1.
Imaging was performed on a Zeiss LSM 880 airyscan confocal microscope. Is this method robust to other types of imaging techniques, other microscopes, variable levels of signal-to-noise? This should be tested and quantified.
We will demonstrate the results obtained from images taken using different microscopes and imaging techniques, and quantify the outcome.
Manual cropping of the cells with ImageJ was used. However, in the methods section, the authors mention that other machine learning tools could be used for this task. Why were these tools not implemented in this paper in order to propose a fully automated analysis approach in combination with SMorph?
We have tried both the machine learning tools cited in this paper (one for DAB images and other for confocal images). However, in our experience, we do not get robust performance from these tools with our datasets, and these tools will perhaps need more optimization for broad applicability. We are developing an auto-cropping tool in-house, but that is beyond the scope of the current study. Another point is that these tools are tailor-made for astrocytes, and their integration into SMorph will restrict its applicability to just one cell type.
In the methods section you state that cropped cells need to have a good contrast ratio for automated batch processing. Could you define what a good contrast ratio is and characterize the performance of your approach for different contrast ratio?
In the revised manuscript, we will compare the images taken from multiple microscopes and quantify the outcome. We will change the text accordingly. As such, the comment on rejected cells referred to really poor quality images. In the revised manuscript, we will make specific recommendations on imaging parameters so that this should not be an issue at all.
It is mentioned that the analysis routine can be interupted by a cell with lower contrast ratio. This is a major drawback of the approach (but I think that it could be easily improved), as such interruptions may not be= practicable for many applications that need to rely on automated processing.
We have already rectified this problem and the updated version of SMorph will be uploaded with the revised manuscript.
Also, you should precise how the contrast ratio should be enhanced without modifying raw data in order to be processed with your approach. You suggest removing cells with lower contrast ratio from the analysis, but can this impact on the findings especially if some treatments impact on the detected fluorescence signal? Can you propose ways to improve the robustness of your approach to variable signal ratios?
It is indeed possible that removing cells from analysis, may in certain cases, affect the results. To rectify this, we are testing the method on images obtained from different microscopes and under different imaging conditions. From these analyses, we will deduce minimum recommendations for imaging conditions so that images don’t have to be edited/altogether removed from analysis for the software to work. In the materials and methods section, we will add these recommendations to the users on the optimal range of imaging parameters. This way, rejection/modification of images should not be an issue.
In the Results section, you describe the time necessary to perform different analysis. However, giving a total time in hours is not very informative as this will likely vary a lot depending on the size of the dataset, complexity of the images, etc. You should compare the average time per image for both methods and types of analysis.
We compared the total time required for the entire dataset, since SMorph is meant for batch-processing all the images at once. However, we can change the comparisons to time taken per image. We can divide the total time taken by SMorph by the number of images analysed. However, in our opinion, the time taken to initiate SMorph will make these comparisons inaccurate.
You state that for the number of branch point, the lower value of the measured slope when comparing SMorph and ImageJ was related to a constant overestimation of this parameter with ImageJ. How was this quantified? I think you should stress out more the comparison of both approaches with the manually annotated dataset.
In the revised version of this manuscript, we will include some examples of skeletonized images that overestimate the number of forks. We have observed this to be a recurring problem with the skeletonization tools we have tried in imageJ. This can be rectified in imageJ itself as pointed out by reviewer #1. However, that’s beyond the scope of the present study and will not fit the definition of comparison with “already available” methods.
How can you explain the differences in the 2D-projected Area, total skeleton length and convex hull between SMorph and ImageJ, which all show a slope around 0.83? Can you quantify the performance of both methods by comparing them with your manually annotated dataset?
In the revised version, we will include the correlation data between completely manual and SMorph comparisons. We will discuss these comparisons further in the manuscript and make specific conclusions about the accuracy.
In the introduction and discussion, you mention that you present a method that works on neurons, astrocytes and microglia. However, I don't see in the paper the comparison between the accuracy for all these cell types as you seem to have analyzed only the morphology of astrocytes.
In the revised manuscript, we will include the Sholl analysis comparison (imageJ vs SMorph) from images of neurons and microglia.
You mention that your method is quite sensitive to variation in contrast ratio. You should quantify the contrast ratio throughout the experiments and ensure that this is not biasing the SMorph analysis for some of the treatments.
We thank both reviewers for highlighting this issue in the initial version of SMorph. As mentioned in our response to point #6, we will perform additional analyses to make specific recommendations to the end users regarding imaging parameters so that SMorph can work on images as they are. As such, our comments on contrast ratio applied only to very poor quality images. If images are acquired conforming to the imaging parameters we will recommend in the revised manuscript, images can be analysed without any issues.
**Minor Points :**
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Precise the exact inclusion and exclusion criteria for Soma detection and rephrase: "The high-intensity blobs were detected as a position of soma..." & "Boundary blobs coming from adjacent cells...".
We will add a complete explanation of blob detection and the exclusion criterion in the methods section.
Throughout the text, make sure to always refer to an analysis time per image or per cell and not only include absolute duration values without reference to the task at hand (e.g. in the discussion : SMorph took 40 second to complete the analysis... please state to which analysis you are exactly referring to and if applicable if it varies from cell to cell).
We will change all comparisons to time taken per cell. Text will be added to mention which datasets were used when any claims of speed are made.
When you state in the discussion that "Although some methods do allow Sholl analysis without manual neurite tracing, they still work on one cell at a time", please precise if the only aspect that is missing from this type of analysis is batch processing (looping through the data) or if there is a major obstacle to automate this technique. This is important a SMorph does proceed with the analysis one cell at a time but can work in a loop/batch.
We will elaborate further on our assertion regarding the challenges of using imageJ plugins for sholl analysis in large batches of cells.
Reviewer #2 (Significance (Required)):
This tool could very useful to researchers in the field of cellular neuroscience working with high-throughput analysis of microscopy data. The authors show some interesting improvements over existing methods. An improved quantitative characterization of the robustness of their approach would be of great importance to ensure the significance of this tool to a large community of researchers using different types of microscopes or studying different cell types.
My expertise is in the field of optical microscopy and high-throughput (automated) image analysis for neuroscience. My expertise to evaluate the biological findings in this study is very limited.
We thank reviewer #2 for their careful reading of the manuscript and their insightful comments. Growing evidence (clinical and preclinical) shows a significant reduction in astrocyte density in key limbic brain regions as a result of depression. We believe that the structural plasticity induced by chronic antidepressant treatment, as demonstrated in this manuscript, is an interesting novel plasticity mechanism that can negate deleterious effects of stress on astrocytes.
The improvements suggested by both reviewers will help us to greatly improve SMorph in the revised version of this manuscript.
References:
- Virmani, G., D’almeida, P., Nandi, A. & Marathe, S. Subfield-specific Effects of Chronic Mild Unpredictable Stress on Hippocampal Astrocytes. doi:10.1101/2020.02.07.938472.
- Czéh, B., Simon, M., Schmelting, B., Hiemke, C. & Fuchs, E. Astroglial plasticity in the hippocampus is affected by chronic psychosocial stress and concomitant fluoxetine treatment. Neuropsychopharmacology 31, 1616–1626 (2006).
- Musholt, K. et al. Neonatal separation stress reduces glial fibrillary acidic protein- and S100beta-immunoreactive astrocytes in the rat medial precentral cortex. Dev. Neurobiol. 69, 203–211 (2009).
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
**Summary:**
-The authors have carried out an extensive survey of dorso-ventral axis determination in the cricket Gryllus bimaculatus. They did this through analysing and knocking down key components of the two main pathways involved in D/V patterning, the toll pathway and BMP signalling. This analysis was placed in a comparative context, looking at published data on four other insect species, with the aim of contributing to our understanding of the evolution of D/V patterning.
-The authors find significant similarities between D/V patterning in Gryllus and in Drosophila - These similarities are both in the relative contributions of toll and BMP to D/V polarization and in the early ovarian activation of the toll pathway. Despite these similarities, a closer analyses of the molecular interactions uncovers some significant differences, most notably, the absence of several key modulators of BMP activity. These results lead the authors to conclude that the similarities in D/V patterning between Gryllus and Drosophila are due to convergence and not due to the conservation in Drosophila of an ancestral patterning mechanism that has been lost in almost all other lineages studied.
**Major comments:**
•All in all this is an excellent paper. There is a huge amount of data in here, and everything is done very meticulously and carefully. There is a good balance between mostly descriptive work (gene expression patterns, cell movements in WT embryos) and experimental work. I could find no obvious flaws with any of the results or methods, and I think the authors have made a convincing case to support their conclusions, without being too dogmatic.
•I don't see a need for any additional experiments beyond what the authors have done. They have covered all relevant aspects of D/V patterning, and make a convincing case with the data they have.
**Minor comments:**
The few comments I have are very minor and technical:
•Missing taxonomic names (families) in Fig. 1
•Missing label in Fig. 6 Panel A.
•Punctuation could be improved. There are several instances of missing commas, and other places with unnecessary commas.
*Reviewer #1 (Significance (Required)):
•The manuscript represents an admirable amount of work. One can say that in a single paper, the authors have provided nearly as much information about Gryllus D/V patterning as is available for other "second-order" insect model species such as Oncopeltus or Nasonia. A such, it provides an additional major phylogenetic anchor point for understanding the evolution of early patterning.
•In terms of significance to advancing our knowledge, the data in the manuscript is, as stated above, an anchor point. It does not on its own provide any major novel insight, but fits into an ever-expanding body of comparative knowledge, whose importance is greater than the sum of its parts. Perhaps the most interesting conclusion, is indeed the one the authors have chosen as the selling-point of their paper, the fact that there is functional convergence in certain aspects of D/V patterning between two widely diverged insect species, with very different oogenesis and early development. This is again, not a major advance on its own, but an important additional piece of the comparative picture of early insect development.
•This paper will be of significant interest to the research community of comparative insect development (the community to which this reviewer belongs). It will also be of interest to those interested in examples of convergence at the functional and molecular level, to those interested in the evolution of gene families and to those interested specifically in the signalling pathways discussed (even in a non-comparative context).*
Response
We thank the reviewer for the very positive response to our paper.
We added missing taxonomic names and labels in Figure 6A and improved the punctuation throughout the manuscript.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
In this paper Pechmann and colleagues investigate the molecular mechanisms of dorso-ventral patterning in Gryllus bimaculatus. As a basis for their study they carry out thorough RNAseq analyses of various embryonic stages. Gryllus is a member of the hemimetabolous insects and therefore of interest for comparison with holometabolous insects such as Drosophila, Tribolium and Nasonia. Previous work has shown that there are significant differences in the use of Toll and Sog in establishing the dorso-ventral gradient of BMP signaling among Drosophila and Nasonia. Pechmann et al find that in Gryllus Toll has a similar role as in Drosophila and is regulated via Pipe, so far only found in Drosophila. Furthermore, they show by RNAi knockdown studies that loss of BMP signaling has little impact on the differentiation of mesoderm in Gryllus, like in Drosophila, hence, BMP signaling has largely a role in dorsal fates. Ventral fates are under direct control of the Toll gradient. Surprisingly, they also find that the key antagonist of BMP signaling and shuttle for BMPs, Sog, has been lost in Ensifera, the lineage leading to Gryllus.
This is a thorough and detailed study involving a series of functional experiments, which highlights the flexibility and evolvability of GRN of the dorso-ventral body axis formation in insects. The major finding that Gryllus is more similar to Drosophila than is Nasonia and Tribolium is interesting and even somewhat unexpected, since Drosophila is often regarded as the derived odd ball. The authors discuss two obvious explanations: the situation found in Gryllus and Drosophila reflects the ancestral condition, or, alternatively, it is the result of convergent evolution. They tend to favor the latter hypothesis. This study is an important advancement to our understanding, as it shows the constraints and the evolvability of a key patterning system to establish a body axis.
Even though the authors show nicely that Toll signaling is required to establish the BMP signaling gradient, the loss of Sog in Gryllus leaves the question unanswered how the long range BMP gradient and its shape is established. In Drosophila and vertebrates, Sog/Chordin acts both as an antagonist close to its source and as a shuttling factor, promoting BMP signaling at a distance, which is crucially important for the long range and the shape of the BMP signaling gradient. It would be desirable to test the function of other potential BMP antagonists (follistatin, gremlin, noggin) or competing BMPs (BMP3, ADAMP) in this context.
As a minor suggestion, I would recommend to summarize the findings in a synthetic picture depicting the evolutionary scenarios of the two hypotheses.
Reviewer #2 (Significance (Required)):
This study is an important advancement to our understanding, as it shows the constraints and the evolvability of a key patterning system to establish a body axis.
Response
We thank the reviewer for the very positive response to our paper.
As the reviewer suggested we added a schematic representation (Figure 11) depicting the two scenarios, which explain the evolution of DV patterning.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
**Summary**
This manuscript continues a series of beautiful papers from Roth, Pechman, Lynch and colleagues analysing D/V patterning in a range of insects. The work started with Drosophila and has extended to other holometabolous and now hemimetabolous insect species.
This paper is in many ways one of the most remarkable of the series, for it shows that the mechanisms of D/V patterning in the cricket Gryllus are, in several striking respects, very similar to those known from Drosophila - much more so than in some of the other insects studied to date, even though Gryllus is phylogenetically the most distant from Drosophila.
Specifically, the authors present compelling data to show that the roles of Toll and dpp, as inferred from their knockdown phenotypes, are remarkably similar in Gryllus and Drosophila. This is very different from the consequences of toll and dpp knockdown in the hemipteran Oncopeltus, a species which almost certainly shares a more recent common ancestor with Drosophila.
The discussion, after summarising the results, addresses the interpretation of this surprising observation. The authors favour the hypothesis that the similarity between Drosophila and Gryllus is the result of convergence in the roles and regulation of Toll and dpp signalling, rather than an ancestral trait that has been lost to a greater or lesser extent in Oncopeltus, and in the two other insects previously studied. The argument for this interpretation is carefully made, on the basis of a thorough knowledge of the comparative embryological literature (including highly relevant recent work).
**Major comments**
The work depends on an analysis of candidate genes, not de novo functional searches. However, it builds on the well established understanding of the relevant genetic machinery in Drosophila, and on extensive knowledge of the genome and transcriptome of Gryllus, a dataset that has been substantially extended by new work reported in this paper, on ovary and embryonic transcriptomes. These data are sufficiently complete to give confidence that all orthologues of most of the known candidate genes have been identified, and to highlight the apparent absence from the Gryllus genome of any sog/chordin orthologue - a key dpp inhibitor widely involved in D/v patterning.
The embryology is beautifully described. The early stages of these very yolky eggs are not easy to handle, but the stainings reported here are almost all of high quality, as are the movies of live development using a nuclear GFP marked line.
The gene knockdowns appear to have been carried out carefully with due regard for the potential biases caused by sterility following parental RNAi. Phenotypes have been documented effectively by the expression of marker genes in fixed embryos, and by live imaging of development in knockdown embryos. Tables in the supplementary data show that sufficient numbers have been obtained. The work is carefully interpreted, and where inferences are less than certain, they are carefully phrased.
I find the results convincing, and therefore accept the conclusion of fundamental similarity between the roles of Toll and dpp in Drosophila and Gryllus.
Time will tell whether or not the authors favoured interpretation of these data as convergent is correct, but I certainly believe that the argument as here presented in the discussion is appropriate for publication in its current form. The abstract is, appropriately, more non-committal than the discussion itself on the interpretation of these results.
The paper is well written.
**Minor points**
Videos - please state orientation of the embryos, especially in videos 2 &4
Page 23 bottom "The early dorsal-to-ventral gradient of pMad (Figure 5AB) indicates that BMP signalling plays an important role ...." suggests would be better than indicates here, until functional data is considered.
Reviewer #3 (Significance (Required)):
The gene networks mediating patterning of the D/V body axis are related across the whole range of animals, with in particular the involvement of TGFb/dpp signalling being almost universal in this process. However, there are a great many variations on this theme. Even within the insects, the mechanisms that have been described for establishing localised TGFb and Toll signalling span the range from self organisation to effective maternal prelocalisation. This has made the GRN underlying D/V patterning a key model for studies of the evolution of gene regulatory networks.
This paper adds an interesting and important twist to the story. It is certainly not the result that any of us would have expected, based on prior published work from Oncopeltus.
If indeed it does turn out to be a case of convergence, a more detailed mechanistic analysis of that convergence will provide considerable insight into the reproducibility of evolution.
Other published work: There is no comparable work on D/V patterning in any other polyneopteran insect, to my knowledge.
Audience: Insect developmental biologists, evolutionary developmental biologists and others interested in the evolution of gene regulatory networks.
My expertise: Arthropod embryology, axial patterning, evolutionary developmental biology.
I have not reviewed in detail the presentation of the transcriptomic data and the phylogenetic analysis of gene sequences as presented in the supplementary info.
Response
We thank the reviewer for the very positive response to our paper.
We made the small textual corrections suggested by the reviewer.
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Reply to the reviewers
Overall, we were pleased that the reviewers found our study carefully designed and interesting. We have addressed their comments below.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The manuscript by Kern, et al., demonstrates that phagocytosis in macrophages is regulated in part by the intermolecular distance of phagocytosis-promoting receptors engaging phagocytic targets. Cells expressing chimeric receptors containing cytosolic domains of Fc receptors (FcR) and defined ligand-binding DNA domains were used to drive phagocytosis of opsonized glass beads coated with complementary DNA ligands of defined spacing and number. These so-called origami ligands allowed manipulation of receptor spacing following engagement, which allowed the demonstration that tight spacing of ligands (7 nm or 3.5 nm) optimized signaling for phagocytosis. The study is carefully performed and convincing. I have a few technical concerns and minor suggestions.
- __ It is assumed that the origami preparations were entirely uniform. How much variation was there? Is that supported by TIRF microscopy of origami preparations? Was the TIRF microscopy calibrated for uniformity of fluorescence (ie., shade correction)?__ Our laboratory, Dong et al., has extensively characterized the origami uniformity and robustness of these exact pegboards. This paper was just posted on bioRxiv (Dong et. al, 2021). We have also cited this paper in our revised manuscript in reference to the characterization of the DNA origami (Line 117).
We did not use any shade correction. Instead we only collected data from a central ROI in our TIRF field. To check for uniformity of illumination, we plotted the origami pegboard fluorescent intensity along the x and y axis. We observed very modest drop off in signal - the average signal intensity of origamis within 100 pixels of the edge is 76 ± 6% the intensity of origamis in a 100 pixel square in the center of the ROI. Fitting this data with a Gaussian model resulted in very poor R values. While this may account for some of the variation in signal intensity at individual points, we expect the normalized averages of each condition to be unaffected. We have amended the methods to describe this strategy (Lines 851-854).
(Image could not be uploaded)
__ Likewise, how much variation was there in the expression of the chimeric receptors? Large variation in receptor numbers per cell could significantly alter the quantitative studies. Aside from the flow sorting for cells expressing two different molecules, how were cells selected for analysis?__
We thank the reviewer for bringing up this point. We confirmed comparable receptor expression levels at the cell cortex of the DNA CAR-𝛾 and the DNA CAR-adhesion used throughout the paper. We also have confirmed that receptor levels at the cell cortex were similar for the large DNA CAR constructs used in Figure 6C-D. This data is now included in Figures S5 and S7. We have also altered the text to include this (lines 169-172):
Expression of the various DNA CARs at the cell cortex was comparable, and engulfment of beads functionalized with both the 4T and the 4S origami platforms was dependent on the Fc𝛾R signaling domain (Figure S5).
When quantifying bead engulfment, cells were selected for analysis based on a threshold of GFP fluorescence, which was held constant throughout analysis for each individual experiment. We have amended the “Quantification of engulfment” methods section to convey this (lines 921-923).
__ The scale of the origami relative to the cells is difficult to discern in Figures 2C and D. Additional text would be helpful to indicate, for example, that the spots on the Fig. 2D inset indicate entire origami rather than ligand spots on individual origami particles.__
Thank you for pointing this out, we see how the legend was unclear and have corrected it (lines 453-454), including specifically noting “Each diffraction limited magenta spot represents an origami pegboard.” We have also outlined the cell boundary in yellow to make the cell size more clear.
__ Figure 5 legend, line 482: How was macrophage membrane visualized for these measurements?__
We have added the following clarification (line 535-536): “The macrophage membrane was visualized using the DNA CAR𝛾, which was present throughout the cell cortex.”
__ line 265: "our data suggest that there may be a local density-dependent trigger for receptor phosphorylation and downstream signaling". This threshold-dependent trigger response was also indicated in the study of Zhang, et al. 2010. PNAS.__
The Zhang et al. study was influential in our study design, and we wish to give the appropriate credit. Zhang et al. found that a sufficient amount of IgG is necessary to activate late (but not early) steps in the phagocytic signaling pathway. In contrast, our study addresses IgG concentration in small nanoclusters. We find that this nanoscale density affects receptor phosphorylation. Thus, we think these two studies are distinct and complementary.
Lines 283-287 now read:
While this model has largely fallen out of favor, more recent studies have found that a critical IgG threshold is needed to activate the final stages of phagocytosis (Zhang et al., 2010). Our data suggest that there may also be a nanoscale density-dependent trigger for receptor phosphorylation and downstream signaling.
__ line 55: Rephrase, “we found that a minimum threshold of 8 ligands per cluster maximized FcgR-driven engulfment.” It is difficult to picture how a minimum threshold maximizes something.__
We now state “we found that 8 or more ligands per cluster maximized FcgR-driven engulfment.”
__ line 184: Rephrase, "we created... pegboards with very high-affinity DNA ligands that are predicted not to dissociate on a time scale of >7 hr". Remove "not".__
Thank you for pointing this out, it is now correct.
Reviewer #1 (Significance (Required)):
This study provides a significant advance in understanding about the molecular mechanisms of signaling for particle ingestion by phagocytosis.
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Reviewer #2 (Evidence, reproducibility and clarity (Required)):
The manuscript on “Tight nanoscale clustering of Fcg-receptors using DNA origami promotes phagocytosis" studies how clustering and nanoscale spacing of ligand molecules for a chimeric Fcg-receptors influence the phagocytosis of functionalized silicon beads by macrophage cell lines. The basis of this study is the design of a chimeric Fc-receptor (DNA-CARg) comprising an extracellular SNAP-tag domain that can be loaded with single-stranded (ss) DNA, the transmembrane part of CD86 and the cytosolic part of the Fc-receptor g-chain containing an immunoreceptor tyrosine-based activation motif (ITAM) as well as a C-terminal green fluorescent protein (GFP). As control the authors used a similar designed DNA-CAR that is lacking the intracellular ITAM-containing FCg tail. The chosen target for this chimeric DNA-CAR, are silicon beads covered by a lipid bilayer that contains biotin-labelled lipids that, via Neutravidin, can be loaded with a biotinylated DNA origami pegboard displaying complimentary ss-DNA as ligand for the DNA-CAR. The DNA origami pegboard contains four ATTO647N fluorescence for visualization and the ssDNA ligand in different quantities and spacing. Using these principles, the authors study how ligand affinity, concentration and spacing influence the activation of the DNA-CARg and the engulfment of the loaded beads.
The authors show that bead engulfment is increased between 2 till 8 ssDNA ligands on the pegboard. After this, ligand numbers do not play a role anymore in the engulfment. They then study the role of the ligand spacing using pegboards that either contain 4 single strand DNA ligands in close (7nm/3,5nm) proximity or a more spaced version using 21/17,5 nm or 35/38,5 nm. The authors find that the bead engulfment is maximally and positively affected by the close spacing of the ssDNA ligands. In their final experiments the authors vary the design of the DNA-CARs by tetramerization of the ITAM-containing Fcg-signaling subunit. In their discussion the authors mention different possibilities for the effect of spacing on the engulfment process.
I think that, in general, this is an interesting study. However, it has some caveats and open issues that should be clarified before its publication.
**Major comments**
- __ As a general comment, it is somewhat a pity that the authors did not use the endogenous FcR as a control. It would have been quite easy for the authors to place the SNAP-tag domain on the Fcg extracellular domain which would allow to do all their experiments in parallel, not only with the DNA-CAR, but also with a DNA-containing wild type receptor. Such a control would be important because, by using a CD86 transmembrane domain, the authors do not know whether the nanoscale localization of their chimeric receptors is reflecting that of the endogenous Fcg receptor.__
We agree with the reviewer completely. We have repeated experiments shown in Figure 4A with a DNA-CAR containing the Fc𝛾 transmembrane domain instead of CD86 as the reviewer suggests. We also included a DNA-CAR version of the Fc𝛾R1 alpha chain, although this construct was not expressed as well as the others. These data are now included in Figure S5, and referenced in lines 167-168.
__ An important issue that is discussed by the authors but not addressed in this manuscript is whether the different amount and spacing of the ligand is only impacting on signaling or also on the mechanical stress of the cells. Indeed, mechanical stress on the cytoskeleton arrangement could influence the engulfment process. For this, it would be very important to test that the different bead engulfment, for example, those shown in Fig. 4, is strictly dependent on signaling kinases. The authors should repeat the experiment of Fig. 4 a and b in the presence or absence of kinase inhibitors such as the Syk inhibitor R406 or the Src inhibitor PP2 to show whether the different phase of engulfment is dependent on the signaling function of these kinases. This crucial experiment is clearly missing from their study.__
We agree this is an interesting point. We find that ligand spacing affects receptor phosphorylation; however this does not preclude effects on downstream aspects of the signaling pathway. We will clarify this by adding the following comment to the manuscript (line 299-301):
While our data pinpoints a role for ligand spacing in regulating receptor phosphorylation, it is possible that later steps in the phagocytic signaling pathway are also directly affected by ligand spacing.
The DNA-CAR-adhesion in Figure 1 strongly suggests that intracellular signaling is essential for phagocytosis. We have now included additional controls using this construct as detailed in our response to point 3 below. Unfortunately, Src and Syk inhibitors or knockout abrogate Fc𝛾R mediated phagocytosis (for example, PMIDs 11698501, 9632805, 12176909, 15136586) and thus would eliminate phagocytosis in both the 4T and 4S conditions. This precludes analysis of downstream steps in the phagocytic signaling pathway.
__ Another problem of this study is that the authors show in Fig. 1A the control DNA-CAR-adhesion but then hardly use it in their study. For example, the crucial experiments shown in Fig. 4 should be conducted in parallel with DNA-CAR-adhesion expressing macrophage cells. This study could provide another indication whether or not ITAM signaling is important for the engulfment process.__
We have added this control. It is now included in Figure S5 and S7. Figure 3D also shows that the DNA-CAR-adhesion combined with the 4T origami pegboards does not activate phagocytosis and we have amended the text to make this more clear (line 152).
__ Another important aspect is how the concentration of the loaded origami pegboard is influencing the engulfment process. In particular, it would be interesting to show the padlocks with different spacings such as the 4T closed spacing versus 4s large spacing show a different dependency on the concentration of this padlock loading on the beads. This would be another important experiment to add to their study.__
We agree that this is an interesting question. We suspect that at a very high origami density, 4S signaling would improve, and potentially approach the 4T. However, we are currently coating the beads in saturating levels of origami pegboards. Thus we cannot increase origami pegboard density and address this directly.
**Minor comments:**
- __ The definition of the ITAM is Immunoreceptor Tyrosine-based Activation Motif and not "Immune Tyrosine Activation Motif" as stated by the authors.__ We have corrected this.
__ The authors discuss that it is the segregation of the inhibitory phosphatase CD45 from the clustered Fc receptors is the major mechanism explaining their finding that 4T closed spacing is more effective than 4s large spacing. With the event of the CRISPR/Cas9 technology it is trivial to delete the CD45 gene in the genome of the RAW264.7 macrophage cell line used in this study and I am puzzled why they author are not conducting such a simple but for their study very important experiment (it takes only 1-2 month to get the results).__
This experiment may be informative but we have two concerns about its feasibility. First, CD45 is a phosphatase with many different roles in macrophage biology, including activating Src family kinases by dephosphorylating inhibitory phosphorylation sites (PMID 8175795, 18249142, 12414720). Second, CD45 is not the only bulky phosphatase segregated from receptor nanoclusters. For example, CD148 is also excluded from the phagocytic synapse (PMID 21525931). CD45 and CD148 double knockout macrophages show hyperphosphorylation of the inhibitory tyrosine on Src family kinases, severe inhibition of phagocytosis, and an overall decrease in tyrosine phosphorylation (PMID 18249142). CD45 knockout alone showed mild phenotypes in macrophages. We anticipate that knocking out CD45 alone would have little effect, and knocking out both of these phosphatases would preclude analysis of phagocytosis. Because of our feasibility concerns and the lengthy timeline for this experiment, we believe this is outside of the scope of our study.
In our discussion, we simplistically described our possible models in terms of CD45 exclusion, as the mechanisms of CD45 exclusion have been well characterized. This was an error and we have amended our discussion to read (lines 335-343):
As an alternative model, a denser cluster of ligated receptors may enhance the steric exclusion of the bulky transmembrane proteins like the phosphatases CD45 and CD148 (Bakalar et al., 2018; Goodridge et al., 2012; Zhu, Brdicka, Katsumoto, Lin, & Weiss, 2008).
Reviewer #2 (Significance (Required)):
The innovative part of this study is the combination of SNAP-tag attached, chimeric Fc-receptor with the DNA origami pegboard technology to address important open question on receptor function.
**Referees cross-commenting**
I find most of my three reviewing colleagues reasonable
I also agrée to Reviewer #1 comments 2
Likewise, how much variation was there in the expression of the chimeric receptors? Large variation in receptor numbers per cell could significantly alter the quantitative studies. Aside from the flow sorting for cells expressing two different molecules, how were cells selected for analysis?
But I want to add it is not only the amount of receptors but ils the nanoscale location that is key to receptor function
We have ensured that all receptors are trafficked to the cell surface. We have also measured their intensity at the cell cortex as discussed in response to Reviewer 1.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
This is a very nicely done synthetic biology/biophysics study on the effect of ligands spacing on phagocytosis. They use a DNA based recognition system that the group has previously use to investigate T cell signaling, but express the SNAP tag linked transmembrane receptor in a macrophage cell line and present the ligands using DNA origami mats to control the number and spacing of complementary ligands that are designed to be in the typical range for low or high affinity FcR, a receptor that can trigger phagocytosis. The study offers some very nice quantitative data sets that will be of immediate interest to groups working in this area and, in the future, for design of synthetic receptors for immunotherapy applications. Other groups are working on similar platform for TCR. I don't feel there is any need for more experiments, but I have some questions and suggestions. Answering and considering these could clarify the new biological knowledge gained.
We thank the reviewer for their support of our manuscript. Given the reviewer’s statement that no new experiments are required, we have answered their questions to the best of our ability given the current data. Should the editor decide that any of these topics require experimental data to enhance the significance of the paper, we are happy to discuss new experiments.
Reviewer #3 (Significance (Required)):
I think the significance would be increased by addressing these questions, that would help understand how the synthesis system described related to other system directed as similar questions and more natural settings.
- __ The densities of the freely mobile DNA ligands required to trigger phagocytosis is quite high. Was the length of the DNA duplexes optimized? The entire complex for both the intermediate and high affinity duplexes seems quite short, perhaps The extracellular domain of the DNA-CAR (SNAP tag and ssDNA strand) are approximately 10 nm (PMID 28340336). The biotinylated ligand ssDNA is attached to the bilayer via neutravidin, resulting in a predicted 14 nm intermembrane spacing. The endogenous IgG FcR complex is 11.5 nm. Bakalar et al (PMID 29958103) tested the effect of antigen height on phagocytosis and found that the shortest intermembrane distance tested (approximately 15 nm) was the most effective. As the reviewer notes, the optimal distance between macrophage and target may be larger than our DNA-CAR. However we think the intermembrane spacing in our system is within the biologically relevant range.
We saw robust phagocytosis at 300 molecules/micron of ssDNA, which is similar to the IgG density used on supported lipid bilayer-coated beads in other phagocytosis studies (PMID 29958103, 32768386). As the reviewer noticed, this is significantly higher than ligand density necessary to activate T cells (PMID 28340336). We have added a comment on ligand density to lines 96-97.
__ Are the origami mats generally laterally mobile on the bilayers. If so, what is the diffusion coefficient? Can one detect the mats accumulating in the initial interface between the bead and cell, particularly in cased where there is no phagocytosis? Would immobility of the mats make them more efficient at mediating phagocytosis compared to the monodispersed ligands, which I assume are highly mobile and might even be "slippery".__
We have confirmed that our bead protocol generally produces mobile bilayers, where his-tagged proteins can freely diffuse to the cell-bead interface (see accumulation of a his-tagged FRB binding to a transmembrane FKBP receptor at the cell-bead synapse below). We can qualitatively say that the origamis appear mobile on a planar lipid bilayer (see Dong et. al 2021 and images below). Directly measuring the diffusion coefficient on the beads is extremely difficult because the beads themselves are mobile (both diffusing and rotating), and cannot be imaged via TIRF. We do not see much accumulation of the origami at cell-bead synapses. This could reflect lower mobility of the origamis, or could be because the relative enrichment of origamis is difficult to detect over the signal from unligated origamis.
Overall, we expect the origami pegboards (tethered by 12 neutravidins) are less mobile than single strand DNA (tethered by a single neutravidin, supported by qualitative images below). We are uncertain whether this promotes phagocytosis. At least one study suggests that increased IgG mobility promotes phagocytosis (PMID 25771017). However, the zipper model would suggest that tethered ligands may provide a better foothold for the macrophage as it zippers the phagosome closed (PMID 14732161). Hypothetically, ligand mobility could affect signaling in two ways - first by promoting nanocluster formation, and second by serving as a stable platform for signaling as the phagosome closes. Since our system has pre-formed nanoclusters, the effect of ligand mobility may be quite different than in the endogenous setting.
(Image could not be uploaded)
In the above images, a 10xHis-FRB labeled with AlexaFluor647 was conjugated to Ni-chelating lipids in the bead supported lipid bilayer. The macrophages express a synthetic receptor containing an extracellular FKBP and an intracellular GFP. Upon addition of rapamycin, FRB and FKBP form a high affinity dimer, and FRB accumulates at the bead-macrophage contact sites.
(Image could not be uploaded)
In the above images, single molecules were imaged for 3 sec. The tracks of each molecule are depicted by lines, colored to distinguish between individual molecules. The scale bar represents 5 microns in both panels.
__ Breaking down the analysis into initiation and completion is interesting. When using the non-signalling adhesion constructs, would they get to the initiation stage or would that attachment be less extensive than the initiation phase.__
This is an interesting question. While we did not include the DNA-CAR-adhesion in our kinetic experiments, we have now quantified the frequency of cups that would match our ‘initiation’ criteria in 3 representative data sets where macrophages were fixed after 45 minutes of interaction with origami pegboard-coated beads. We found that an average of 16/125 of 4T beads touching DNA-CAR-adhesion macrophages met the ‘initiation’ criteria and an average of 2/125 were eaten (14% total). In comparison, we examined 4T beads touching DNA CAR𝛾 macrophages and found that on average 23/125 met the ‘initiation’ criteria, and 45/125 were already engulfed (54%). This suggests that the DNA-CAR-adhesion alone may induce enough interaction to meet our initiation criteria, but without active signaling from the FcR this extensive interaction is rare. We have added this data in a new Figure S6 and commented on this in lines 213-215.
__ It would be interesting to put these results in perspective of earier work on spacing with planar nanoarrays, although these can't be applied to beads. For integrin mediated adhesion there was a very distinct threshold for RGD ligand spacing that could be related to the size of some integrin-cytoskeletal linkers (PMID: 15067875). On the other hand, T cell activation seemed more continuous with changes in spacing over a wide range with no discrete threshold (PMID: 24117051, 24125583) unless the spacing was increased to allow access to CD45, in which case a more discrete threshold was generated (PMID: 29713075). The results here for phagocytosis with the very small ligands that would likely exclude CD45 seems to be more of a continuum without a discrete threshold, although high densities of ligand are needed. This issue of continuous sensing vs sharp threshold is biologically interesting so would be good assess this by as consistent standards are possible across systems.__
We agree that this is an interesting body of literature worth adding to our discussion. We have added a paragraph that puts our study in the context of prior work on related systems, including these nanolithography studies (Line 364-382):
How does the spacing requirements for Fc𝛾R nanoclusters compare to other signaling systems? Engineered multivalent Fc oligomers revealed that IgE ligand geometry alters Fcε receptor signaling in mast cells (Sil, Lee, Luo, Holowka, & Baird, 2007). DNA origami nanoparticles and planar nanolithography arrays have previously examined optimal inter-ligand distance for the T cell receptor, B cell receptor, NK cell receptor CD16, death receptor Fas, and integrins (Arnold et al., 2004; Berger et al., 2020; Cai et al., 2018; Deeg et al., 2013; Delcassian et al., 2013; Dong et al., 2021; Veneziano et al., 2020). Some systems, like integrin-mediated cell adhesion, appear to have very discrete threshold requirements for ligand spacing while others, like T cell activation, appear to continuously improve with reduced intermolecular spacing (Arnold et al., 2004; Cai et al., 2018). Our system may be more similar to the continuous improvement observed in T cell activation, as our most spaced ligands (36.5 nm) are capable of activating some phagocytosis, albeit not as potently as the 4T. Interestingly, as the intermembrane distance between T cell and target increases, the requirement for tight ligand spacing becomes more stringent (Cai et al., 2018). This suggests that IgG bound to tall antigens may be more dependent on tight nanocluster spacing than short antigens. Planar arrays have also been used to vary inter-cluster spacing, in addition to inter-ligand spacing (Cai et al., 2018; Freeman et al., 2016). Examining the optimal inter-cluster spacing during phagosome closure may be an interesting direction for future studies.
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Additional experiments performed in revision
In addition to these reviewer comments, we have added additional controls validating the DNA-CAR-4x𝛾 used in Figure 6c,d. We compared the DNA-CAR-4x𝛾 to versions of the DNA-CAR-1x𝛾-3x𝛥ITAM construct with the functional ITAM in the second and fourth positions (see the schematics now included Figure S7). We found that four individual receptors with a single ITAM each were able to induce phagocytosis regardless of which position the ITAM was in. However the DNA-CAR-4x𝛾 construct, which also contains 4 ITAMs, was not. This further validates the experiment presented in 6c,d. We also fixed minor errors we discovered in the presentation of data for Figures 1C and S1A.
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Reply to the reviewers
__Reviewer #1 __ __1. One key citation missing from the current manuscript is from Hwang et al. 2014 (PMID 25288734). This study has already described that the isp-1 mutant strain survives longer during P. aeruginosa infection. This citation also describes that the gene expression profile of isp-1 mutants animals includes a considerable number of pathogen-responsive genes that are similarly induced during infection. While the current manuscript does go into the mechanism of this resistance with more detail, they should amend the language to more appropriately reflect previous work, notably the above reference.
__
We apologize for the oversight and have added the suggested citation. Hwang et al. show that isp-1 worms have increased resistance to bacterial pathogens that is dependent on HIF-1/HIF1 and AAK-2/AMPK. In future work, it will be interesting to examine whether HIF-1 and AAK-2 act in concert with, or independently of, ATFS-1 and the p38-mediated innate immune signaling pathway to mediate pathogen resistance and longevity in isp-1 worms. We will add these points to our discussion.
__2. The authors suggest that ROS activation of the p38 MAPK pathway is likely not the mechanism that explains the resistance of long-lived mitochondrial mutant animals due to their reduced food intake. However, is ROS production nonetheless involved? Does antioxidant treatment suppress the increased resistance during infection of isp-1 and/or nuo-6 mutant animals?
__
To address this question, we will treat wild-type, isp-1 and nuo-6 worms with antioxidant and then measure resistance to bacterial pathogens using the P. aeruginosa strain PA14 slow kill assay. For the antioxidant treatment, we will use 10 mM Vitamin C as we have previously shown that this concentration is effective at reducing ROS in isp-1 worms to decrease isp-1 lifespan (Van Raamsdonk and Hekimi 2012, PNAS). Although antioxidant treatment can have pleiotropic effects, if this decreases survival of bacterial pathogen exposure, it will suggest that the elevated ROS production in isp-1 and nuo-6 worms may contribute to their enhanced bacterial pathogen resistance.
__3. (line 278-282): the authors should elaborate on how the p38 MAPK pathway plays a permissive role. It is intriguing that ATFS-1 and ATF-7 are both bZIP transcription factors that could theoretically heterodimerize and that they share common immune gene targets. The authors do indicate that the binding sites for ATFS-1 and ATF-7 are very different and are likely acting distinctly but some speculation would nonetheless strengthen this statement.
__
While ATFS-1 and ATF-7 were shown to bind to the promoter regions of the same innate immunity genes, the apparent consensus binding sites are different suggesting that they bind to different regions of the promoter. One way in which the p38 MAPK pathway may be playing a permissive role is that ATF-7 binding and relief from its repressor activity is required for any transcription of p38-mediated innate immunity target genes to occur. This is consistent with our data showing that disruption of nsy-1, sek-1, pmk-1 or atf-7 decreases the expression of innate immunity genes in wild-type worms. In contrast, it may be that the role of ATFS-1 is for enhanced expression of innate immunity genes such that when ATFS-1 is bound to the promoter region, or perhaps enhancer elements, the baseline expression of innate immunity genes that results from the binding of ATF-7 is increased. This idea is supported by our data showing that disruption of atfs-1 does not affect the expression of innate immunity genes in wild-type worms but prevents nuo-6 mutants from having increased expression. We will update our manuscript to include these points.
__4. The authors suggest that reduced food consumption of nuo-6 and isp-1 animals may suppress ROS-induced activation of the p38 innate immune pathway. It is intriguing that dietary restriction was previously shown to increase resistance to infection, presumably through p38-independent mechanisms (PMID 30905669). It would be interesting to measure host survival of nuo-6 and isp-1 mutant animals that are dietary-restricted to see if the enhanced survival rates conferred by mitochondrial stress and DR are additive or not.
__
According to this suggestion, we will compare the bacterial pathogen resistance of wild-type, isp-1 and nuo-6 worms that have undergone dietary restriction to the same strains under ad libitum conditions. This will determine the extent to which their enhancement of pathogen resistance might be additive.
__5. Figure 2: It is intriguing that loss of p38 signaling appears to have different effects in nuo-6 versus isp-1 animals. Specifically, loss of p38 signaling in isp-1 mutants renders them more sensitive to infection than wild-type, whereas it generally suppresses survival rates back to wild-type levels in the nuo-6 mutant background. Even within the nuo-6 mutant group, loss of SEK-1 has more dramatic effects on nuo-6 mutant animals than does loss of NSY-1, PMK-1 or ATF-7(gf). This is despite the fact that the nsy-1, sek-1, and pmk-1 alleles that are used in this study are all reported to be null. Can the authors speculate on these differences?
__
While the isp-1 and nuo-6 mutations both alter mitochondrial function, they affect different components of the electron transport chain. isp-1 mutations affect Complex III (Feng et al. 2001, Dev. Cell), while nuo-6 mutations affect Complex I (Yang and Hekimi 2010, Aging Cell). Although these mutants both have increased lifespan and a similar slowing of physiologic rates, it is not uncommon to observe differences between these mutants. For example, while treatment with the antioxidant NAC completely reverts nuo-6 lifespan to wild-type, it only partially reduces isp-1 lifespan (Yang and Hekimi 2010, PLoS Biology), suggesting that nuo-6 lifespan may be more dependent on ROS than isp-1. We have recently shown that deletion of atfs-1 reduces nuo-6 lifespan, but completely prevents isp-1 worms from developing to adulthood (Wu et al. 2018, BMC Biology), suggesting that isp-1 worms are more dependent on ATFS-1 than nuo-6 worms. Disruption of sek-1 has a greater impact on pathogen resistance than nsy-1 and pmk-1 because SEK-1 is absolutely required for innate immune signaling, while some partial redundancy exists for NSY-1 and PMK-1. We will add these points to our manuscript.
__6. One of the main conclusions from this study is that ATFS-1 likely binds directly to innate immune genes that are in common with ATF-7. Since this is such a pivotal finding, the authors should validate some candidate genes from the referenced ChIP seq datasets using ChIP qPCR. Also, are there predicted ATFS-1 binding sites (PMID 25773600) in these promoters?
__
Our data shows that activation of ATFS-1 increases the expression of innate immunity genes without increasing activation of p38. The simplest explanation for this observation is that ATFS-1 can upregulate the same innate immunity genes as ATF-7. Accordingly, we hypothesized that ATFS-1 and ATF-7 can bind to the same promoter. Fortunately, two previous ChIP-Seq studies, from well-established laboratories who have extensive experience studying ATFS-1 and ATF-7, had already determined which genes are bound by these two transcription factors (Nargund et al. 2015, Molecular Cell; Fletcher et al. 2019, PLoS Genetics). Comparing the results of these two published studies confirmed our hypothesis by demonstrating that the same innate immunity genes are bound by both ATF-7 and ATFS-1 in vivo. In order to provide additional support for the conclusion that ATFS-1 and ATF-7 can bind to the same genes, we will examine the genetic sequence of innate immunity genes that were shown to be bound by both ATFS-1 and ATF-7 in the published ChIP-seq studies to identify predicted binding sites for ATFS-1 and ATF-7, while noting that the ATFS-1-associated sequence is an enriched motif and not an established binding site. If we are able to identify the predicted binding sites for these two transcription factors in the same gene, it will provide further support for the conclusion that these transcription factors can both bind to the same innate immunity genes.
__Reviewer #2:
(1) The authors state that the p38 MAPK PMK-1 is not activated in the long-lived mitochondrial mutants. However, it might be better to state that there is "no enhanced activation" of PMK-1, since they clearly show in nuo-6 and isp-1 mutants the presence of phosphorylated PMK-1 (Fig. 4A), which would indicate an activated form of PMK-1 in these mutants.__
According to this suggestion, we will change the text to indicate that there is no enhanced activation of PMK-1 in nuo-6 and isp-1 worms.
__(2) Are the food-intake behaviors of all mutants in liquid culture (Fig. 4B-F) the same as their food-intake behaviors on solid agar media, the environment where pathogen resistance was measured?
__
We previously compared assays measuring food intake on solid agar media versus the liquid culture approach used in the current study to determine which method is the most robust (Wu et al. 2019, Cell Metabolism). While both assays produced similar results, performing the food intake assay on solid agar plates was much more variable as it is challenging to scrape off all of the uneaten bacteria from solid plates in order to measure it. Since the approach of measuring food intake in liquid media produces more consistent and reliable results, we chose to use this assay for the current study. We will update our manuscript to include this justification.
(__3) Does the p38 pathway single mutant nsy-1 or sek-1 live shorter than wild type on dead E. coli OP50 (Fig. S9) than they do on live OP50 (Fig. 3)? If so, what might that mean? These mutants are also living shorter than wild type on PA14 (Fig. 2), but live as long as wild type on OP50 (Fig. 3). What is in the live OP50 that allows these mutants to live like wild type?
__In a previous publication, we found that sek-1 mutants live shorter than wild-type worms, and nsy-1 live slightly shorter than wild-type worms in a lifespan assay performed in liquid medium with dead OP50 bacteria (Wu et al. 2019, Cell Metabolism). In the current study, we performed lifespan assays on solid NGM plates with live OP50 bacteria and observed a wild-type lifespan in sek-1 and nsy-1 worms. Since there are multiple experimental variables that are different between the previous and current study, most notably liquid versus solid media, the lifespan results cannot be directly compared. In the case of measuring survival of these strains on PA14, the simplest explanation is that they are dying sooner because their innate immune signaling pathway is disrupted, and so they are less able to mount an immune response against the pathogenic bacteria. We will update our manuscript to include these points.
__At the same time, wouldn't it be simpler to call the multiple antibiotic-treated OP50 as "dead bacteria", instead of "non-proliferating bacteria"? Some of the antibiotics used to treat OP50 are bactericidal and not bacteriostatic.
__
We previously monitored the OD600 of the antibiotic-treated, cold-treated OP50 that we used in our experiment, and found that there is only a very small decrease in OD600 after 10 days (Moroz et al. 2014, Aging Cell). Since dead bacteria are rapidly broken down leading to a decrease in OD600, this result is consistent with the bacteria being alive but not proliferating. We will include this point in our manuscript.
__(4) Since nuo-6 and isp-1 do not always behave exactly the same in their dependence on certain genes (e.g., Fig. 2C vs Fig 2D), what happens in isp-1; atfs-1 double mutants? Do these mutants behave in the same manner as nuo-6; atfs-1?
__
This is an interesting question. Unfortunately, isp-1;atfs-1 mutants arrest during development (Wu et al. 2018, BMC Biology), which is why we only examined the effect of atfs-1 deletion in nuo-6 mutants. We will update the manuscript to note this point.
__Regarding nuo-6; atfs-1, why does the double mutant live shorter on PA14 than either single mutant (Fig. 6A)? Is this because atfs-1 is needed to activate the p38 MAPK-dependent and -independent pathways? __
It is possible that the nuo-6 mutation makes worms more sensitive to bacterial pathogens, perhaps due to decreased energy production, and that activation of ATFS-1 is required not only to enhance their resistance to pathogens but also to increase their resistance back to wild-type levels. In a previous study, we showed that loss of ATFS-1 slows down the rate of nuclear localization of DAF-16. Thus, loss of atfs-1 may also be decreasing resistance to bacterial pathogens by diminishing the general stress resistance imparted by the DAF-16-mediated stress response pathway. We will update the manuscript to include these points.
__In Fig. 7B, the atfs-1(gof) appears to have slightly more phosphorylated p38 compared to wild type, although it is not statistically significant?
__
While there is a trend towards a very modest increase in phosphorylated p38 in the constitutively-active atfs-1 mutant compared to wild-type, quantification of four biological replicates indicated that the difference is not significant. This result is consistent with the fact that the levels of phosphorylated p38 are not significantly increased in nuo-6 or isp-1 mutants, both of which show activation of ATSF-1. We have provided raw images of all of these Western blots in our supplementals. In addition, we will repeat these Western blots to determine if this difference becomes significant with additional replicates.
__In Fig. 6B, the atfs-1 loss-of-function single mutant also increases the expression of Y9C9A.8, but suppresses it in a nuo-6 mutant background? What might that mean?
__
It is possible that in wild-type animals disruption of atfs-1 causes a compensatory upregulation of specific stress response genes. We have previously shown that deletion of atfs-1 results in upregulation of chaperone genes involved in the cytoplasmic unfolded protein response (hsp-16.11, hsp-16.2; Wu et al. 2018; BMC Biology). Perhaps Y9C9A.8 is acting in a similar way. In nuo-6, the upregulation of Y9C9A.8 is driven by activation of ATFS-1, and thus is prevented by atfs-1 deletion. We will add these points to the manuscript.
__Reviewer #3:
1) Some studies propose that OP50 offers some toxicity to worms which is not observed in other bacterial strains like HT115. The authors should test the role of the p38-innate immune signaling pathway in nuo-6 and isp-1 lifespan using other non-pathogenic E. coli strains.
__
To determine if the effect of disrupting the p38-mediated innate immune signaling pathway on the lifespan of isp-1 and nuo-6 mutants was simply the result of losing protection against OP50 bacteria, we examined the effect of nsy-1, sek-1 and atf-7(gof) mutations on isp-1 and nuo-6 lifespan using non-proliferating bacteria. We found that even when no proliferating bacteria are present, disruption of the p38-mediated innate immune signaling pathway markedly decreases isp-1 and nuo-6 lifespan. This suggests that the p38-mediated innate immune signaling pathway is required for their long lifespan independently of its ability to protect against bacterial infection. Similarly, we have previously shown that lifespan extension resulting from dietary restriction is dependent on the p38-mediated innate immune signaling pathway even when non-proliferating bacteria are used (Wu et al. 2019, Cell Metabolism). We will clarify this important point in the manuscript.
__ 2) The authors should measure food intake in worms exposed to pathogenic bacteria, given that reduced bacterial intake may be related to reduced mortality.
__
Unfortunately, it is not feasible to perform the food intake assay using the pathogenic bacteria because the bacteria cause death thereby complicating the calculation of food consumed per worm (which requires at least 3 days to assess). As an alternative to measuring food intake, we will attempt to measure intestinal accumulation of P. aeruginosa, which is a balance between food intake and other factors. To do this we will use a P. aeruginosa strain that expresses GFP and quantify the amount of intestinal fluorescence in wild-type, isp-1 and nuo-6 worms that have been grown on the GFP-labelled P. aeruginosa.
__3) The authors should check if ROS is required for the activation of the p38-mediated innate immune signaling pathway and reduction in food intake.
__
To determine if the elevated ROS that is present in isp-1 and nuo-6 worms affects activation of the p38-mediated innate immune signaling pathway, we will treat wild-type, isp-1 and nuo-6 worms with Vitamin C and measure the ratio of phosphorylated p38 to total p38 by Western blotting. Similarly, to examine the effect of ROS on food intake, we will treat wild-type, isp-1 and nuo-6 worms with Vitamin C and then quantify its effect on food intake. For these experiments, we will use 10 mM Vitamin C as we have previously shown that this concentration is effective at reducing ROS in isp-1 worms to decrease isp-1 lifespan (Van Raamsdonk and Hekimi 2012, PNAS).
__4) Since ATFS-1 and the p38 pathway control food intake, how related to dietary restriction the phenotypes the authors are studying are?
__
While the lifespan extension that results from mild impairment of mitochondrial function and the lifespan extension resulting from dietary restriction are both dependent on the p38-mediated innate immune signaling pathway, these interventions modulate innate immunity gene expression in opposite directions. We previously reported that dietary restriction primarily downregulates innate immunity genes (Wu et al. 2019 Cell Metabolism). Here, we show that mutations in isp-1 or nuo-6 primarily result in upregulation of innate immunity genes. To more globally examine gene expression changes between dietary restriction and mild impairment of mitochondrial function, we compared differentially expressed genes. We found that there was very little overlap of either upregulated or downregulated genes between dietary restriction and isp-1/nuo-6 mutants. We will add a supplementary figure to demonstrate this, and add these points to our manuscript.
__ 5) Somewhat related to the previous points, I am not so sure whether the changes in food intake are cause or consequence of the alterations in the innate immunity-related genes. Reduced food intake is depicted in Fig. 8 as the cause of the activation of the p38 pathway, but there is not enough evidence to unequivocally prove that. In fact, food intake might be controlled by the p38 or ATFS-1 pathway or by a common regulator such as ROS.
__
We apologize that we didn’t make this clearer. In our previous work, we showed that dietary restriction results in decreased activation of the p38 pathway (Wu et al. 2019, Cell Metabolism). Here, we show that activation of ATFS-1 results in decreased food intake. Based on our previous study, this decrease in food intake should similarly decrease p38 pathway activation. In Figure 8, we have depicted ATFS-1 inhibiting food intake, and food intake activating the p38-mediated innate immune signaling pathway. Combined, our model suggests that activation of ATFS-1 should act to decrease p38-mediated innate immune signaling. We will clarify this in the figure legend.
__6) I am not so convinced of the role of DAF-16. In fact, in Fig. 5A daf-16 mutation reduces pathogen resistance and that could represent a toxic effect of the mutation. Furthermore, the results in Fig. 4D do not exclude the possibility that daf-16 and isp-1 act in parallel.
__
We agree that the role of DAF-16 could be non-specific. While we show that disruption of daf-16 leads to decreased bacterial pathogen survival in isp-1 worms, it also decreases bacterial pathogen survival in wild-type worms. Since DAF-16 is known to be required for general resistance to stress, the decreased survival when daf-16 is disrupted could be due to a general toxic effect of reducing general stress resistance. This conclusion is consistent with our observation that DAF-16 is not involved in the upregulation of innate immunity genes in isp-1 worms. We will emphasize these points in our manuscript.
__ 7) Loss of innate immunity related genes may result in toxicity and sensitize worms to pathogenic bacteria. This is further supported by an even lower resistance to pathogens in the double mutants mainly in Fig. 2D.
__
We agree. Our data confirms that disruption of the p38-mediated innate immune signaling pathway makes worms more susceptible to bacterial pathogens. We will emphasize this point.
__ 8) The blots are saturated, particularly in Fig. 4A, and this can be masking the differences in p38 phosphorylation. In fact, the fact that p38 phosphorylation is not changed is contradictory to the other results. How is p38 regulated by mitochondrial mutations then? I am concerned that p38 is actually not altered and the changes in gene expression are exclusively due to ATFS-1. The interaction with the p38 pathway demonstrated genetically could be due to the toxicity elicited by the loss of function mutations in this pathway.__
To address this concern, we will repeat the Western blotting experiment to compare the ratio of phosphorylated p38 to total p38 between wild-type, isp-1 and nuo-6 worms. We will take multiple exposures to ensure that the blots are not over-saturated. Having already completed four replicates, we believe that there is not a major change in p38 activation. Our data suggests that the p38-mediated innate immunity pathway is playing a permissive role such that it is required for baseline expression of innate immunity genes, but that activation of ATFS-1 is driving the enhanced expression of innate immunity genes that we observe in the long-lived mitochondrial mutants and constitutively active atfs-1 mutants. We will update our manuscript to clarify this.
__ **Minor concerns**
1) Lines 167 and 174: What are these p values referred to?
__
The p-values indicate the significance of the overlap between the two gene sets. Given the size of the two gene sets, this is the probability that the observed number of overlapping genes would result by picking genes at random. We will clarify this in the manuscript.
__2) Line 258: I partially agree with the conclusions, since the functions may not necessarily be associated with innate immune signaling but rather other functions of p38.
__
Since isp-1 and nuo-6 worms have extended longevity even when grown on non-proliferating bacteria this indicates that their long life is not dependent on their enhanced resistance to bacterial pathogens. Similarly, since disruption of genes in the p38-mediated innate immune signaling pathway decrease isp-1 and nuo-6 lifespan even when the worms are grown on non-proliferating bacteria, this suggests that this pathway enhances longevity independently of its ability to increase innate immunity.
__ 3) Why in figures 4D and E different mutants were used?
__
We only used isp-1 mutants to examine the effect of daf-16 because we were unable to generate nuo-6;daf-16 mutants due to close proximity of the two genes on the same chromosome. We only used nuo-6 mutants to examine the effect of atfs-1 because isp-1;atfs-1 worms arrest during development. We will include this explanation in our manuscript.
__ 4) Line 498: revise writing.
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We will rewrite this sentence to improve clarity.
__ 5) Show blots in Fig. 7B.
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We will provide an image of a representative Western blot in Figure 7, and will provide the raw images for all of Western blots in our supplementals.
__ 6) It would be interesting to know where the activation of the immune-related genes by the mitochondrial mutations is happening, whether this is a cell autonomous or cell non-autonomous mechanism.
__While it would be interesting to explore whether specific tissues are important in sensing mitochondrial impairment in order to upregulate genes involved in innate immunity, it is beyond the scope of this manuscript. Previous work has shown that knocking down the expression of the cytochrome c oxidase gene cco-1 in neurons can activate the ATFS-1 target gene hsp-6 in the intestine (Durieux et al., 2011). Based on this, one could hypothesize that a similar cell non-autonomous mechanism might be involved. We will note this possible future direction in our discussion.
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Reply to the reviewers
__Reviewer #1: __ __ **Major concerns:**
1) This manuscript has some overlap with another manuscript from the same group recently submitted to EMBO Reports. Although I believe both manuscripts have sufficient elements to justify publication of two papers, I strongly recommend that these publications are made back-to-back and they should be discussed in context with one-another.
__
We agree that this manuscript is distinct from but highly complementary to our manuscript on innate immunity in the long-lived mitochondrial mutants, which has been invited for revision at EMBO Reports. According to this suggestion, we have arranged for these papers to be considered for publication at the same time in EMBO Reports and Life Science Alliance. We have updated the discussions of both manuscripts to incorporate the findings of the other manuscript.
__ 2) How is ATFS-1 function regulated in long-lived worms or under multiple stress conditions? Is there a common regulator such as oxidative stress or mitochondrial dysfunction? Both manuscripts would benefit from a clear understanding on how ATFS-1 is controlled under conditions where mitochondrial function is altered. Is mitoUPR required for this activation? If so, is mitoUPR upregulated in all interventions where ATFS-1 has been shown to play a role in stress response. __
We have previously used a reporter strain to determine which external stressors activate ATFS-1. The reporter strain has a transgene that links the promoter of the ATFS-1 target gene hsp-6 to GFP (Phsp-6::GFP) such that these worms exhibit increased fluorescence whenever ATFS-1 is activated. After exposing these worms to heat, cold, osmotic stress, anoxia, oxidative stress, starvation, ER stress and bacterial pathogens, we only observed increased fluorescence after exposure to oxidative stress (Dues et al. 2016, Aging). Here, we show that constitutive activation of ATFS-1 results in increased resistance not only to oxidative stress but also ER stress, osmotic stress, anoxia and bacterial pathogens (fast kill assay). Thus, ATFS-1 activation does not just protect against stresses that lead to its activation. Notably, the constitutively active atfs-1 mutants (et15 and et17) exhibit activation of the mitoUPR under unstressed conditions (e.g. upregulation of hsp-6 in Fig. 1A; increased fluorescence of hsp-6 and hsp-60 reporter strains in Rauthan et al. 2013, PNAS; upregulation of many other stress pathway target genes Fig. 2). It is likely that the activation of the mitoUPR and downstream stress response pathways under unstressed conditions results in the increased resistance to stress that we observe. We have included these points in the revised manuscript.
__Is there any intervention that controls longevity and does not trigger ATFS-1 response?
__
When we compared RNA-seq data on a panel of long-lived mutants representing multiple pathways of lifespan extension to ATFS-1 target genes (defined as genes that are upregulated by spg-7 RNAi in an ATFS-1 dependent manner from Nargund et al. 2012, Science), we found that seven of the nine long-lived mutants that we examined showed enrichment of ATFS-1 target genes (clk-1, isp-1, nuo-6, daf-2, glp-1, ife-2) while two did not (eat-2, osm-5) (Fig. 5). Interestingly, in six of these seven strains (all except ife-2), there is an increase in reactive oxygen species (ROS) that contributes to their longevity (treatment with antioxidants decreases their lifespan; Yang and Hekimi 2010, PLoS Biology; Zarse et al. 2012, Cell Metabolism; Wei and Kenyon 2016, PNAS). This observation is consistent with the idea that ROS/oxidative stress is sufficient to activate ATFS-1/mitoUPR. We have previously shown that exposure to a mild heat stress (35°C, 2 hours) or osmotic stress (300 mM, 24 hours) can extend lifespan but does not increase expression of the ATFS-1 target gene hsp-6 (Dues et al. 2016, Aging). Thus, there are multiple examples in which a genetic mutation or intervention increases longevity but does not trigger upregulation of ATFS-1 target genes. We have updated the manuscript to include these points.
__3) In Fig. 3, some of these genes appear to be unspecifically associated with different stressors. Therefore, it is difficult to rule out the participation of ATFS-1 in specific stress responses without looking at specific stress-responsive genes or a wider range of genes. For example, the conclusion that ATFS-1 does not control osmotic stress gene expression response comes from looking at 3 genes: sod-3, gst-4 and Y9C9A.8. gst-4 does not appear to be directly controlled by ATFS-1 regardless of the stressor. sod-3 is also upregulated by oxidative stress and Y9C9A.8 by anoxia. On the other hand, somewhat contradicting the authors' conclusions that ATFS-1 does not participate in osmotic stress response based on these 3 genes, ATFS-1 appears to be required for osmotic stress resistance.
__
In this experiment, we treated wild-type and atfs-1 deletion mutants with six different stressors (oxidative stress, bacterial pathogens, heat stress, osmotic stress, anoxia, and ER stress), isolated mRNA and then examined the expression of 14 different stress response genes. To select these genes, we chose a combination of the most established target genes of the stress response pathways that we examined in Figures 1/2, and genes that we had previously shown to be upregulated by specific stresses using fluorescent reporter strains (Dues et al. 2016, Aging). These genes included hsp-6, hsp-4, hsp-16.2, sod-3, gst-4, nhr-57, Y9C9A.8, trx-2, ckb-2, gcs-1, sod-5, T24B8.5, clec-67 and dod-22. To determine if ATFS-1 is required for gene upregulation in response to any of the six different stressors, we first identified which of these stress genes is significantly upregulated in response to each stressor and then looked to see if this upregulation is reduced or prevented by atfs-1 mutation. We found that there were multiple examples of this for both oxidative stress and bacterial pathogen stress, but not for other stresses. We selected three representative genes to display in Figure 3. Nonetheless, it is possible that there are genes that we didn’t examine that are upregulated by the other four stressors in an ATFS-1-dependent manner. To definitively address this question, one would have to do RNA sequencing on wild-type and atfs-1(gk3094) worms comparing untreated and stressed, but this is beyond the scope of the current manuscript. We have updated the manuscript to include these points, and noted the possibility that there are genes, which we didn’t measure, that are upregulated by the other four stressors in an ATFS-1-dependent manner. We have also included the qPCR data for all 14 genes for each of the six external stressors in Supplemental Figures S3-S8.
__ **Minor concerns:**
1) The paragraph starting in line 107 is confusing. They write that "Constitutive activation of ATFS-1 in atfs-1(et 15) and atfs-1(et17) mutants resulted in upregulation of most of the same genes that are upregulated in nuo-6 mutants, except for gst-4" and later they state that "Activating the mitoUPR through the nuo-6 mutation, or through the constitutively-active ATFS-1 mutants did not significantly increase the expression of target genes from the ER-UPR (hsp-4; Fig. 1B) or the cyto-UPR (hsp-16.2; Fig. 1C)." I understand the upregulation of ER-UPR and cyto-UPR is not statistically significant (isn't it for hsp-16.2?), but the first sentence is not accurate if statistics is considered.
__
To clarify this, we have modified the first sentence to describe which genes are significantly upregulated in atfs-1(et15) mutants, and separately describe the findings for atfs-1(et17) mutants in the second sentence. The results for hsp-16.2 are not significant because this gene shows highly variable expression between replicates and can be induced 60-fold. We have noted this in the text as well.
__ 2) The authors should discuss why they think atfs-1(et15) gain-of-function mutant exhibited decreased resistance to chronic oxidative stress, while it is protected from acute oxidative stress. In fact, the et15 allele differs in many aspects in relation to the et17 and in some cases it behaves similarly to the gk3094 loss-of-function allele.
__
While atfs-1(et15) and atfs-1(et17) mutants generally show similar results, they also exhibit differences. We previously used RNA sequencing to examine gene expression in these two strains. We found that atfs-1(et15) mutants have far more extensive changes in gene expression than atfs-1(et17) mutants (6227 differentially expressed genes versus 958 differentially expressed genes). It is possible that the et15 mutation is more disruptive to the mitochondrial targeting sequence than et17, thereby resulting in increased nuclear localization and more gene expression changes. The additional gene expression changes in the atfs-1(et15) mutant may contribute to their decreased resistance to chronic oxidative stress. We have included these points in the revised manuscript.
__ 3) Fig 4I is very similar to Fig. 6A of the other manuscript which strengthen the notion that ATFS-1 is not required (it is rather detrimental) for bacterial pathogen response when no underlying stress (most likely oxidative) occurs.
__
Yes, our results indicate that ATFS-1 is not required for wild-type survival of bacterial pathogen exposure. This is consistent with our findings in the other manuscript that baseline expression of innate immunity genes does not depend on ATFS-1 (innate immunity gene expression is similar between wild-type and atfs-1(gk3094) mutants). We have updated the manuscript to emphasize these points.
__ 4) In the paragraph starting in line 213, the authors conclude that "ATFS-1 is sufficient to protect against oxidative stress, osmotic stress, anoxia, and bacterial pathogens but not heat stress". The results do not unequivocally support a participation of ATFS-1 in oxidative stress or bacterial pathogen response, given the responses vary depending on the allele or condition.
__
We have modified this sentence by replacing “activation of ATFS-1 is sufficient to protect” with “activation of ATFS-1 can protect” to indicate that we didn’t observe protection in all cases.
__ 5) "Combined, this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background despite having an important role in stress resistance." It actually does, since ATFS-1 gain-of-function decreases lifespan.
__
We have rewritten this sentence to say that constitutive activation of ATFS-1 does not extend lifespan, despite increasing resistance to multiple stresses.
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__ __
__6) Paragraph starting in line 359 needs to be discussed in light of the results of the other manuscript submitted by the authors to EMBO.
__
Combined these two manuscripts indicate that baseline levels of innate immunity are dependent on the p38-mediated innate immune signaling pathway, and not dependent on ATFS-1. This idea is supported by the fact that deletion of atfs-1 does not decrease resistance to bacterial pathogens and does not reduce the expression of innate immunity genes. In contrast, disrupting genes involved in the p38-mediated innate immune signaling pathway does decrease resistance to bacterial pathogens and does decrease the expression of innate immunity genes. We have updated this paragraph to include these points and reference the findings from our manuscript on innate immunity in the long-lived mitochondrial mutants.
__ 7) In Fig. 1C, it appears that atfs-1 loss of function increases hsp-16.2. Is that significant?
__
While there is a strong trend towards increased hsp-16.2 expression in atfs-1(gk3094) mutants, this difference did not reach significance because this gene shows highly variable expression and can be induced 60-fold.
__ 8) In Fig. 2, 5 and S1, it would be interesting to build one single Venn Diagram with all the lists of genes to see if there are common genes associated with multiple pathways and if there are many ATFS-1 target genes not associated with these classical stress or longevity pathways.
__
While we would be very interested in performing this type of visualization, weighted Venn diagrams with more than 3 or 4 groups are challenging to generate and more challenging to interpret. Instead, we have generated an UpSetR plot to demonstrate the number of overlapping genes between each of the stress response pathways, as well as how many ATFS-1 target genes are not involved in stress response. We have included this plot in Figure 2, Panel I. We have also generated simpler figure to show the overlap between pairs of stress response pathways (Figure S1). In addition, we have also added Table S4 with these gene lists.
__ 9) In Fig. 2, 5 and S1: What are the p values referred to?
__
The p-values indicate the significance of the difference between the observed number of overlapping genes between the two gene sets, and the expected number of overlapping genes if the genes were picked at random. We have clarified this in the manuscript.
__ 10) In paragraph starting in line 85, the authors should include references that evidence the genes are bona fide markers of the stress response pathways.
__
We have added references for each of the genes that we examined to link it to the associated stress response pathway.
__ 11) Tables S2 and S3 are missing. __
Tables S2 and S3 were uploaded as Excel spreadsheets, not included with the supplemental figures as the other supplementary Tables were. We apologize that these were difficult to locate. In the revision, Table S1 is in the manuscript file, while Table S2 to S6 will be uploaded as separate files.
__ __
__Reviewer #2:
**Major comments:**
The only major conclusion that I would qualify is "ATFS-1 serves a vital role in organismal survival of acute stresses through its ability to activate multiple stress response pathways"-the data, as presented, does not make clear whether ATFS-1 directly activates these pathways (ie, by binding response elements in genes in those pathways), or indirectly influences them by altering the physiology of the worm).
__
We agree that our data does not determine precisely how ATFS-1 acts to modulate the expression of the different stress response pathways. To determine the extent to which ATFS-1 might be able to bind directly to the target genes of other stress response pathways, we have compared the ChIP-seq results for ATFS-1 to ChIP-seq studies for other stress responsive transcription factors (DAF-16, SKN-1, HSF-1, HIF-1, ATF-7). We found that in each case there are sets of genes that can be bound by both transcription factors. This suggests that ATFS-1 may be direct regulating at least some of the target genes from other stress response pathways. We have updated our manuscript to include these points and included the ChIP-seq data comparisons in Figure S2.
__ **Minor comments:**
In abstract, consider broadening/re-wording "Gene expression changes resulting from the activation of the mitoUPR are mediated by the transcription factor ATFS-1/ATF-5." Because a naïve reader may understand this to suggest that ATFS-1 is activated only by mitochondrial protein misfolding.
__
In this sentence we are describing the role of ATFS-1 in mediating the gene expression changes resulting from the activation of the mitoUPR. We would be happy to modify the sentence if this is unclear.
__Please indicate whether strains were outcrossed, and how often.
__
We have added these details to our materials and methods.
__ How was "young adult" defined? Were worms synchronized, and if so, how?
__
Young adult worms are picked on day 1 of adulthood before egg laying begins. The worms were not synchronized, but picked visually as close to the L4-adult transition as possible. We have added these details to our method section.
__ For the gene expression experiments, do I understand correctly that FUDR was used only for oxidative stress and adult day 2 experiments? Please clarify.__
Yes, that is correct. FUdR was used for these samples because (1) with the 2-day duration of this stress, worms can produce progeny which would complicate the collection of the experimental worms; and (2) 4 mM paraquat often results in internal hatching of progeny when FUdR is absent, which might have affected the results. The control worms for the 48-hour 4 mm paraquat stress were also treated with FUdR. We have clarified this in the manuscript and noted that the presence of FUdR has the potential to alter gene expression.
__ Important: Please make clear how many replicates were performed for each experiment, and where relevant, how many worms were measured per replicate (e.g., stress survival and lifespan). __
We have added a spreadsheet (Table S6) to include the number of replicates and number of worms per replicate for all experiments.__
For 2-way ANOVA analyses, please specify p values of both main factors as well as interaction terms and posthoc analyses where relevant.
__
We have included these additional details from our statistical analyses in Table S6.
__ In the second paragraph of the introduction, I suggest broadening slightly the description of why normal mitochondrial function is required for ATFS-1 important and degradation, because this helps the reader understand that any one of many perturbations to mitochondrial function (decreased bioenergetics, membrane potential, protein degradation, protein import; increased ROS; etc.) could prevent or reduce ATFS-1 import and degradation.
__
We have added these additional factors that might prevent ATFS-1 import and degradation in paragraph one of our introduction and broadened the description in paragraph two.
__ For Figure 1: The authors present their choice of genes to analyze as if, and interpret their results assuming, that each of these gene is ONLY regulated by the indicated stress response pathways. I think this is very unlikely. For example: is it certain that sod-3 and trx-2 are not also skn-1 regulated? How is "antioxidant" distinguished from the skn-1 pathway? Further clouding the water is the likelihood that nuo-6 and atfs-1 manipulations alter physiology in such a way that there are secondary/indirect stress pathways activated (for example: the authors show that ATFS-1 overexpression shortens lifespan. Perhaps this is why it appears that ATFS-1 overexpression also appears to cause a strong, although variable, upregulation of the cytosolic UPR?). The likelihood (in my opinion) that these genes are in fact regulated by more than one type of response element, and that the manipulations used to study these relationships have pleiotropic effects, do not invalidate the general conclusion that these pathways interact-but they do mean that the results should be discussed with more caveats regarding HOW they interact.
__
These are excellent points. The genes that we selected for Figure 1 are the genetic targets that in our reading of the literature have been most often used to represent a particular stress response pathway. We have added references to justify the association of each gene with the indicated stress response pathway. We have also noted that in at least some cases the stress response genes that have been typically used to represent a specific pathway can be activated by multiple pathways. We agree that the selection of genes for Figure 1 is not a comprehensive approach, and that it is possible that if we chose a different gene from each of these pathways, the results might be different. We have updated our manuscript to specifically note these limitations. To avoid these limitations, we examined the overlap between all of the genes significantly upregulated by ATFS-1 activation and all of the genes significantly upregulated by the different stress response pathways in Figure 2. In addition, to gain a better understanding of the overlap between these different stress response pathways globally, we have compared gene expression between each of the stress response pathways studied in Figure S1.
__Figure 1 also illustrates why a more detailed description of sample size and statistical analysis should be provided. What was the "n"? What were the main effects and interaction terms of each 2-way ANOVA? The design is not full factorial and therefore does not permit a simple 2-way ANOVA (i.e., not all condition combinations are performed)-which responses precisely were compared to which? Were 2 2-way ANOVAs performed per mRNA?
__
For Figure 1 we used a one-way ANOVA to compare all of the groups to wild-type with a Bonferroni’s Multiple Comparison post-hoc test. We have updated the manuscript to include the sample size and statistical details in Table S6.
__ The work shown in Figure 2 is a very nice way to leverage previous data to further explore this idea of cross-talk. I would suggest including a bit more meta-data in the supplemental data files related to each dataset. For example, what lifestages were used (were they all young adult?), was FUDR used, etc.
__
We have added these details to Table S3, which includes the lists of target genes from each stress response pathway.
__ However, again, I don't understand how the authors can reach this conclusion: "Combined, this indicates that activation of ATFS-1 is sufficient to upregulate genes in multiple stress response pathways." (lines 152-153 but similar phrasing occurs multiple times) Could it not simply be that one form of cellular stress often eventually triggers broader cellular dysfunction, thus activating other cell stress pathways? Ie-how do we know whether these genes are directly regulated by atfs-1 binding regulatory elements, as implied by this phrasing?
__
This conclusion is derived from our data showing that constitutively active ATFS-1 mutants have significant upregulation of target genes from multiple stress response pathways (Figure 2). As the worms in those experiments were not exposed to stress, we don’t have reason to believe that they are experiencing cellular stress or dysfunction. We think it is more plausible that activation of ATFS-1, which normally occurs in response to stress, leads to the activation of other stress response pathways, either directly or indirectly, and that these pathways are recruited to help regain mitochondrial homeostasis. We don’t mean to imply that activated ATFS-1 binds directly to the target genes of other stress response pathways. We have clarified this in the revised manuscript.
__ The stress response experiments are very nicely done and very interesting. I appreciate that the authors did not shy away from describing counterintuitive results (eg et15 mutants showing increased sensitivity to chronic oxidative stress), and think that these results should also be briefly considered in the Discussion.
__
We have updated our manuscript to discuss the observation that atfs-1(et15) mutants have increased sensitivity to chronic oxidative stress.
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__Figure 3: please report ANOVA interaction terms-these are what tell whether the inductions are in fact dependent on atfs-1 (not the post-hoc analyses). Again, it also appears that in some cases, there is an upregulation of certain genes with atfs-1 knockdown-please report all p-values (because there will be many, I recommend a supplemental table with all main and interaction and posthoc analyses). Again, the "n" also needs to be specified.
__
We have added Table S6 to include all of these statistical details.
__ Figure 4 A-C appear to be lacking error bars? Please add. Perhaps relatedly-the effect size for 4A looks much larger than for 4B, but this does not come across in the text.
__
We have added error bars to Figure 4A-C. We think the difference in effect size might result from the fact that 4A is an acute assay and 4B is a chronic assay. We speculate that the negative effect of the et15 and et17 mutations on lifespan might be a stronger factor in the chronic assay. We have updated the text to comment on the relative effect sizes.
__ For Figures 4 and 6, please indicate sample size-number of independent experimental replicates, and number of worms per replicate (or range per replicate).
__
We have added the number of replicates and sample size in Table S6.
__ Lines 224-225 re. sod-2 mutants: these may also act by decreasing ROS signaling (less conversion of superoxide anon to hydrogen peroxide); also, why would this strain not be considered another long-lived mitochondrial mutant (like clk-1, isp-1 and nuo-6, to which it is contrasted)?
__
We think the sod-2 mutation extends lifespan by increasing ROS signaling, as treatment with antioxidants decreases their lifespan. The increased superoxide from the loss of sod-2 may be converted to H2O2 by sod-3 or sod-1, which are also present in the mitochondria. We don’t include sod-2 with the mitochondrial mutants because the mutation does not directly impact the mitochondrial electron transport chain, but may do so secondarily due to elevated ROS.
__ The confirmation that atfs-1 overexpressing strains are short-lived is very interesting. However, I think this statement "Combined, this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background despite having an important role in stress resistance." (lines 265-267 and similar in several places throughout the Discussion, eg line 279) should be altered to indicate that this was observed under controlled laboratory conditions. Eg, "...this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background under optimized laboratory conditions..."
__
This is an interesting point. It is possible that constitutive activation of ATFS-1 may be beneficial for lifespan in an environment where worms are exposed to external stressors. We have noted that our lifespan results were obtained under lab conditions, which are believed to be relatively unstressful.
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__Discussion: consider adding in a consideration of dose-response, both of knockdown of mitochondrial genes (eg, k/d of many mitochondrial genes promotes lifespan at low levels, but decreases lifespan with greater knockdown) and of stressors (chemicals, heat, etc; for chemicals, at the least, dose-response is very important, with low levels not infrequently triggering apparently beneficial stress responses, and higher levels causing toxicity).
__
It is possible that the magnitude of ATFS-1 activation will impact its effect on stress resistance and lifespan. Perhaps, a milder activation of ATFS-1 will be more beneficial with respect to lifespan. The degree of ATFS-1 activation may also account for differences that we observe between atfs-1(et15) and atfs-1(et17) mutants. atfs-1(et15) has more differentially expressed genes than atfs-1(et17) suggesting the possibility that it has more ATFS-1 activation. We have updated our manuscript to include these points.
__ Section beginning on line 384 "ATFS-1 upregulates target genes of multiple stress response pathways"-again, please revise to make clear that this work does not demonstrate direct regulation.
__
We have clarified that our results don’t demonstrate direct regulation. In addition, we have examined published ChIP-seq datasets to determine if there is evidence of direct regulation.
__ It seems to me that our reviews are in pretty good agreement. I agree with Reviewers 1 and 3 where they commented on things that I did not. While I did not consider the manuscripts as overlapping in the sense of being redundant, I very much like Reviewer 1's suggestion that they be published back to back and that the Discussion of each incorporate consideration of the Results of the other.
__According to this suggestion, we have arranged for these papers to be considered for publication at the same time in EMBO Reports and Life Science Alliance. We have updated the discussions of both manuscripts to incorporate the findings of the other manuscript.
__ Reviewer #3:
**Major comments**
1.The authors mention that activation of the UPRmt by nuo-6 mutants or atfs-1(gf) do not activate the ER UPR or cyto-UPR gene expression targets (lines 111-113). However, they also find that atfs-1(gf) animals have 25% overlap with the ER UPR pathway (line 146-147). Is 25% overlap not substantial?
__
The genes that we are referring to in lines 111-113 are the genetic targets that in our reading of the literature have been most often used to represent the ER-UPR or Cyto-UPR. This is not a comprehensive approach, and it is possible that if we chose a different gene from each of these pathways, the result might be different. We have updated our manuscript to include this limitation. To avoid this limitation, we examined the overlap between all of the genes significantly upregulated by ATFS-1 activation and all of the genes significantly upregulated by the ER-UPR or Cyto-UPR in Figure 2. In both cases, we find the overlap is significant, indicating that activation of ATFS-1 leads to activation of ER-UPR and Cyto-UPR target genes.
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__To determine whether ATFS-1 mediates any protective effect during ER stress, authors should test atfs-1(gf) and atfs-1(lf) animals' resistance to ER stress.
__
To examine the effect of ATFS-1 on resistance to ER stress, we exposed wild-type, atfs-1(gk3094), atfs-1(et15) and atfs-1(et17) worms to 50 µM tunicamycin beginning at young adulthood and monitor survival daily. We found that both constitutively active atfs-1 mutants, et15 and et17, have increased resistance to ER stress compared to wild-type worms, while atfs-1 deletion mutants have a similar survival to wild-type. We have added this new data to Figure 4.
__ Authors should comment on the difference in outcomes with atfs-1(et17) and atfs-1(et15) animals to chronic oxidative stress (line 184-187).
__
We have updated our manuscript to discuss the observation that atfs-1(et15) mutants have increased sensitivity to chronic oxidative stress.
__ Lines 258-260. The authors should make clear in this section that a previous study had already measured lifespans of atfs-1(gf) animals and found that it was reduced (PMID 24662282). Also, an elaboration on why this experiment was repeated would be warranted.
__
We have referenced the lifespan results from this previous study in our introduction (line 53-54, Bennett et al), in our results section (lines 342-343; “which is consistent with a previous study finding shortened lifespan in atfs-1(et17) and atfs-1(et18) worms”) and in our discussion (lines 429-431; “as well as previous results using constitutively active atfs-1 mutants (et17 and et18) show that constitutive activation of ATFS-1 in wild-type worms results in decreased lifespan”). The reasons that we repeated this result are (1) because the lifespan of the atfs-1(et15) mutant had not been measured and this was the allele that we used in our paper; and (2) because the shortened lifespan is a surprising result given the beneficial effect of ATFS-1 on stress resistance, we thought it was important to repeat this experiment under the same conditions that we measured stress resistance.
__ The authors find that atfs-1(gk3094) animals lived longer during infection with PA14 (line 208-211). Another study found that atfs-1(gk3094) animals died faster on PA14 (PMID 28283579), which should be mentioned and commented on.
__
We have added this finding to our discussion. We have also compared the protocols used by Jeong et al. (who observed decreased survival in atfs-1(gk3094) deletion mutants), Pellegrino et al. (who observed wild-type survival in atfs-1(tm4919) deletion mutants and our manuscript (in which we observed slightly increased survival in atfs-1(gk3094) deletion mutants), to see which parameters might account for the observed differences.
__**Minor comments**
Line 38: "Inside the mitochondria, ATFS-1 is degraded by the Lon protease CLPP-1/CLP1". The phrasing suggests that CLPP-1/CLP1 is a Lon protease, when in fact they are independent proteases.
__
We have removed the word “Lon” to clarify this.
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Referee #3
Evidence, reproducibility and clarity
The manuscript by Soo et al. investigates the role of the mitochondrial unfolded protein response (UPRmt) during different organismal stresses. Using both loss-of-function and gain-of -function alleles of atfs-1, the gene encoding the transcription factor and main regulator of the UPRmt, the authors discover that ATFS-1 is both required and sufficient for the expression of genes associated with different types of cellular stresses including hypoxia, innate immunity, and antioxidant defense. Consistent with these gene regulations, gain-of-function atfs-1 animals were more resistant to specific cellular stresses, while loss of ATFS-1 animals were generally more sensitive.
Major comments
1.The authors mention that activation of the UPRmt by nuo-6 mutants or atfs-1(gf) do not activate the ER UPR or cyto-UPR gene expression targets (lines 111-113). However, they also find that atfs-1(gf) animals have 25% overlap with the ER UPR pathway (line 146-147). Is 25% overlap not substantial?
To determine whether ATFS-1 mediates any protective effect during ER stress, authors should test atfs-1(gf) and atfs-1(lf) animals' resistance to ER stress.
- Authors should comment on the difference in outcomes with atfs-1(et17) and atfs-1(et15) animals to chronic oxidative stress (line 184-187).
- Lines 258-260. The authors should make clear in this section that a previous study had already measured lifespans of atfs-1(gf) animals and found that it was reduced (PMID 24662282). Also, an elaboration on why this experiment was repeated would be warranted.
- The authors find that atfs-1(gk3094) animals lived longer during infection with PA14 (line 208-211). Another study found that atfs-1(gk3094) animals died faster on PA14 (PMID 28283579), which should be mentioned and commented on.
- At the request of the Editor, I was asked to comment on potential overlap between this manuscript and a recently submitted article submitted by the current authors (RC-2021-00651). There is only minor overlap in my opinion, with the finding in the current manuscript that the UPRmt is associated with stimulation of a pathogen defense program (innate immunity). Manuscript RC-2021-00651 goes into more detail regarding the mechanism of the UPRmt/innate immunity association and regulation.
Minor comments
Line 38: "Inside the mitochondria, ATFS-1 is degraded by the Lon protease CLPP-1/CLP1". The phrasing suggests that CLPP-1/CLP1 is a Lon protease, when in fact they are independent proteases.
Significance
The finding that the UPRmt regulates other cellular stress response pathways which provides resistance to a variety of stressors is of interest. However, associations of the UPRmt with increased resistance to exogenous stresses such as hypoxia and pathogen infection have been reported before (PMID 26234215, 25274306, 28283579), which might reduce the impact of the current manuscript to some degree.
This work would be interest to those in the fields of mitochondria, stress responses, and longevity.
My expertise is in stress responses, longevity, and host-pathogen interactions.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The authors carried out experiments, and mine published datasets, to further characterize the role of the ATFS1 transcription factor in mediating survival and lifespan in laboratory or stressed conditions. The role of ATFS-1 was assessed by using a loss-of-function deletion and two constitutive gain-of function mutants in which the mitochondrial leader sequence is not functional, resulting in continual nuclear translocation. The effect of ATFS1 loss or constitutive activation was assessed in both wild-type and mutant (mitochondrial function and long-lived mutants) strains, and either under standard laboratory conditions or in the context of a variety of physical, chemical, and pathogen stressors. Constitutive ATFS-1 activation upregulated genes from a number of stress-response pathways, and the loss of atfs-1 blocked upregulation of some stress-response genes by a variety of exogenous stressors, with little or no effect on baseline expression of those genes. Loss of atfs-1 also increased sensitivity to many exogenous stressors (not all mitochondria-targeting), and overexpression was generally protective. However, overexpression also decreased lifespan in the absence of exogenous stressor.
Major comments:
- Are the key conclusions convincing? Mostly, assuming sample size was adequate (see below). The only major conclusion that I would qualify is "ATFS-1 serves a vital role in organismal survival of acute stresses through its ability to activate multiple stress response pathways"-the data, as presented, does not make clear whether ATFS-1 directly activates these pathways (ie, by binding response elements in genes in those pathways), or indirectly influences them by altering the physiology of the worm).
- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? No.
- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. No.
- Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. N/A
- Are the data and the methods presented in such a way that they can be reproduced? Mostly; see below.
- Are the experiments adequately replicated and statistical analysis adequate? Unclear; see below.
Minor comments:
- Specific experimental issues that are easily addressable:
In abstract, consider broadening/re-wording "Gene expression changes resulting from the activation of the mitoUPR are mediated by the transcription factor ATFS-1/ATF-5." Because a naïve reader may understand this to suggest that ATFS-1 is activated only by mitochondrial protein misfolding. Please indicate whether strains were outcrossed, and how often.
How was "young adult" defined? Were worms synchronized, and if so, how?
For the gene expression experiments, do I understand correctly that FUDR was used only for oxidative stress and adult day 2 experiments? Please clarify. Important: Please make clear how many replicates were performed for each experiment, and where relevant, how many worms were measured per replicate (e.g., stress survival and lifespan).
For 2-way ANOVA analyses, please specify p values of both main factors as well as interaction terms and posthoc analyses where relevant. - Are prior studies referenced appropriately? Yes. - Are the text and figures clear and accurate? Yes. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
Yes:
In the second paragraph of the introduction, I suggest broadening slightly the description of why normal mitochondrial function is required for ATFS-1 important and degradation, because this helps the reader understand that any one of many perturbations to mitochondrial function (decreased bioenergetics, membrane potential, protein degradation, protein import; increased ROS; etc.) could prevent or reduce ATFS-1 import and degradation.
For Figure 1: The authors present their choice of genes to analyze as if, and interpret their results assuming, that each of these gene is ONLY regulated by the indicated stress response pathways. I think this is very unlikely. For example: is it certain that sod-3 and trx-2 are not also skn-1 regulated? How is "antioxidant" distinguished from the skn-1 pathway? Further clouding the water is the likelihood that nuo-6 and atfs-1 manipulations alter physiology in such a way that there are secondary/indirect stress pathways activated (for example: the authors show that ATFS-1 overexpression shortens lifespan. Perhaps this is why it appears that ATFS-1 overexpression also appears to cause a strong, although variable, upregulation of the cytosolic UPR?). The likelihood (in my opinion) that these genes are in fact regulated by more than one type of response element, and that the manipulations used to study these relationships have pleiotropic effects, do not invalidate the general conclusion that these pathways interact-but they do mean that the results should be discussed with more caveats regarding HOW they interact.
Figure 1 also illustrates why a more detailed description of sample size and statistical analysis should be provided. What was the "n"? What were the main effects and interaction terms of each 2-way ANOVA? The design is not full factorial and therefore does not permit a simple 2-way ANOVA (i.e., not all condition combinations are performed)-which responses precisely were compared to which? Were 2 2-way ANOVAs performed per mRNA?
The work shown in Figure 2 is a very nice way to leverage previous data to further explore this idea of cross-talk. I would suggest including a bit more meta-data in the supplemental data files related to each dataset. For example, what lifestages were used (were they all young adult?), was FUDR used, etc.
However, again, I don't understand how the authors can reach this conclusion: "Combined, this indicates that activation of ATFS-1 is sufficient to upregulate genes in multiple stress response pathways." (lines 152-153 but similar phrasing occurs multiple times) Could it not simply be that one form of cellular stress often eventually triggers broader cellular dysfunction, thus activating other cell stress pathways? Ie-how do we know whether these genes are directly regulated by atfs-1 binding regulatory elements, as implied by this phrasing?
The stress response experiments are very nicely done and very interesting. I appreciate that the authors did not shy away from describing counterintuitive results (eg et15 mutants showing increased sensitivity to chronic oxidative stress), and think that these results should also be briefly considered in the Discussion.
Figure 3: please report ANOVA interaction terms-these are what tell whether the inductions are in fact dependent on atfs-1 (not the post-hoc analyses). Again, it also appears that in some cases, there is an upregulation of certain genes with atfs-1 knockdown-please report all p-values (because there will be many, I recommend a supplemental table with all main and interaction and posthoc analyses). Again, the "n" also needs to be specified.
Figure 4 A-C appear to be lacking error bars? Please add. Perhaps relatedly-the effect size for 4A looks much larger than for 4B, but this does not come across in the text.
For Figures 4 and 6, please indicate sample size-number of independent experimental replicates, and number of worms per replicate (or range per replicate).
Lines 224-225 re. sod-2 mutants: these may also act by decreasing ROS signaling (less conversion of superoxide anon to hydrogen peroxide); also, why would this strain not be considered another long-lived mitochondrial mutant (like clk-1, isp-1 and nuo-6, to which it is contrasted)?
The confirmation that atfs-1 overexpressing strains are short-lived is very interesting. However, I think this statement "Combined, this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background despite having an important role in stress resistance." (lines 265-267 and similar in several places throughout the Discussion, eg line 279) should be altered to indicate that this was observed under controlled laboratory conditions. Eg, "...this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background under optimized laboratory conditions..."
Discussion: consider adding in a consideration of dose-response, both of knockdown of mitochondrial genes (eg, k/d of many mitochondrial genes promotes lifespan at low levels, but decreases lifespan with greater knockdown) and of stressors (chemicals, heat, etc; for chemicals, at the least, dose-response is very important, with low levels not infrequently triggering apparently beneficial stress responses, and higher levels causing toxicity).
Section beginning on line 384 "ATFS-1 upregulates target genes of multiple stress response pathways"-again, please revise to make clear that this work does not demonstrate direct regulation.
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. The mitoUPR has generally been viewed and tested as an isolated mitochondrial stress-specific response; the authors have built upon previous work to convincingly show that it is integrated with a variety of other stress response pathways. This is an important contribution to the field.
- Place the work in the context of the existing literature (provide references, where appropriate). The authors have done a nice job of this in their discussion.
- State what audience might be interested in and influenced by the reported findings. Researchers interested in stress response in general, and mitochondrial homeostasis and stress response in particular, as well as the relation of these to lifespan.
- Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Mitochondrial response to exogenous stressors, particularly pollutants.
Referees cross-commenting
It seems to me that our reviews are in pretty good agreement. I agree with Reviewers 1 and 3 where they commented on things that I did not. While I did not consider the manuscripts as overlapping in the sense of being redundant, I very much like Reviewer 1's suggestion that they be published back to back and that the Discussion of each incorporate consideration of the Results of the other.
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Referee #1
Evidence, reproducibility and clarity
Summary:
In this manuscript, Soo et al explore the role of the mitoUPR-associated transcription factor ATFS-1 as a hub in multiple stress response pathways controlling gene expression changes and resistance to a variety of exogenous and endogenous stressors. They found that ATFS-1 gain-of-function is sufficient to upregulate a number of genes involved in oxidative stress response, DAF-16-related response, hypoxia and innate immunity. Moreover, they found that many ATFS-1-responsive genes are upregulated under multiple stress conditions and by interventions that prolong lifespan. They also showed that ATFS-1 is required for stress response and resistance under different stress conditions. Finally, they demonstrate that ATFS-1 is not necessary for normal lifespan, but ATFS-1 gain-of-function decreases lifespan.
Major concerns:
1) This manuscript has some overlap with another manuscript from the same group recently submitted to EMBO J. Although I believe both manuscripts have sufficient elements to justify publication of two papers, I strongly recommend that these publications are made back-to-back and they should be discussed in context with one-another. While the main focus of the other manuscript is how mitochondrial mutations lead to improved bacterial pathogen response, it concludes ATFS-1 is key to explain how genes involved in this particular stress response are upregulated upon mitochondrial dysfunction. Their model is that mitochondrial mutations lead to upregulation of innate immunity genes via ATFS-1-mediated transcriptional activation. Here they show that ATFS-1 controls many other stress response pathways in addition to the innate immunity response. Somewhat contradicting their model in the other manuscript, here they show that ATFS-1 is not necessarily required for bacterial pathogen response. In contrast, they even found protection against PA in atfs-1 loss-of-function mutants. This could be explained in light of the fact that ATFS-1 appears to have a protective role under oxidative stress conditions (e.g., mitomutants or paraquat) whereas in worms that have no underlying stress, high ATFS-1 levels may be detrimental. This is consistent with the results in Figure 6. These aspects considered, I believe both manuscripts need to be revised back-to-back so that the data can be reconciled and discuss in context.
2) How is ATFS-1 function regulated in long-lived worms or under multiple stress conditions? Is there a common regulator such as oxidative stress or mitochondrial dysfunction? Both manuscripts would benefit from a clear understanding on how ATFS-1 is controlled under conditions where mitochondrial function is altered. Is mitoUPR required for this activation? If so, is mitoUPR upregulated in all interventions where ATFS-1 has been shown to play a role in stress response. Is there any intervention that controls longevity and does not trigger ATFS-1 response?
3) In Fig. 3, some of these genes appear to be unspecifically associated with different stressors. Therefore, it is difficult to rule out the participation of ATFS-1 in specific stress responses without looking at specific stress-responsive genes or a wider range of genes. For example, the conclusion that ATFS-1 does not control osmotic stress gene expression response comes from looking at 3 genes: sod-3, gst-4 and Y9C9A.8. gst-4 does not appear to be directly controlled by ATFS-1 regardless of the stressor. sod-3 is also upregulated by oxidative stress and Y9C9A.8 by anoxia. On the other hand, somewhat contradicting the authors' conclusions that ATFS-1 does not participate in osmotic stress response based on these 3 genes, ATFS-1 appears to be required for osmotic stress resistance.
Minor concerns:
1) The paragraph starting in line 107 is confusing. They write that "Constitutive activation of ATFS-1 in atfs-1(et 15) and atfs-1(et17) mutants resulted in upregulation of most of the same genes that are upregulated in nuo-6 mutants, except for gst-4" and later they state that "Activating the mitoUPR through the nuo-6 mutation, or through the constitutively-active ATFS-1 mutants did not significantly increase the expression of target genes from the ER-UPR (hsp-4; Fig. 1B) or the cyto-UPR (hsp-16.2; Fig. 1C)." I understand the upregulation of ER-UPR and cyto-UPR is not statistically significant (isn't it for hsp-16.2?), but the first sentence is not accurate if statistics is considered.
2) The authors should discuss why they think atfs-1(et15) gain-of-function mutant exhibited decreased resistance to chronic oxidative stress, while it is protected from acute oxidative stress. In fact, the et15 allele differs in many aspects in relation to the et17 and in some cases it behaves similarly to the gk3094 loss-of-function allele.
3) Fig 4I is very similar to Fig. 6A of the other manuscript which strengthen the notion that ATFS-1 is not required (it is rather detrimental) for bacterial pathogen response when no underlying stress (most likely oxidative) occurs.
4) In the paragraph starting in line 213, the authors conclude that "ATFS-1 is sufficient to protect against oxidative stress, osmotic stress, anoxia, and bacterial pathogens but not heat stress". The results do not unequivocally support a participation of ATFS-1 in oxidative stress or bacterial pathogen response, given the responses vary depending on the allele or condition.
5) "Combined, this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background despite having an important role in stress resistance." It actually does, since ATFS-1 gain-of-function decreases lifespan.
6) Paragraph starting in line 359 needs to be discussed in light of the results of the other manuscript submitted by the authors to EMBO.
7) In Fig. 1C, it appears that atfs-1 loss of function increases hsp-16.2. Is that significant?
8) In Fig. 2, 4 and S1, it would be interesting to build one single Venn Diagram with all the lists of genes to see if there are common genes associated with multiple pathways and if there are many ATFS-1 target genes not associated with these classical stress or longevity pathways.
9) In Fig. 2, 4 and S1: What are the p values referred to?
10) In paragraph starting in line 85, the authors should include references that evidence the genes are bona fide markers of the stress response pathways.
11) Tables S2 and S3 are missing.
Significance
Nature and significance of the advance:
The study advances our knowledge about the role of ATFS-1 - a transcription factor involved in mitoUPR - in multiple stress response pathways.
Compare to existing published knowledge:
The role of ATFS-1 has been previously studied in the context of mitoUPR, although the present manuscript expands it to a variety of other stress response pathways. It is yet to be defined whether mitoUPR itself is promiscuously activated in response to different kinds of stressors or ATFS-1 may be activated independently of mitoUPR. As mentioned before, the present manuscript has considerable overlap with a manuscript from the same group under review in EMBO J. These manuscripts need to be discussed in light of one-another.
Audience:
The audience interested in this study is expected to be aging biologists, mitochondrial biologists, as well as researchers using C. elegans as a model organism.
Expertise:
I am interested in mechanisms of aging and their association with metabolism.
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Reply to the reviewers
Reviewer comments:
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
In this paper, the authors examine the relationship between the transcription factor Ribbon, its ribosomal protein gene (RPG) targets, and cell growth during the process of salivary gland tubulogenesis in the Drosophila embryo. This study builds upon previous work they published in 2016 (Loganathan et al., 2016). While the previous study identified RPGs as potential targets of Ribbon from ChIP-Seq analysis, they did not delve into the role of these targets in salivary gland morphogenesis. Here, the authors demonstrate that mutation of ribbon results in decreased cell volumes via immunostaining and image analysis. They identify and confirm RPGs as ribbon transcriptional targets using ChIP-SEQ, Microarray data, in situ hybridization, and qRT-PCR. They analyze these targets in an effort to identify a Rib consensus binding sites by MEME and find that Rib binding is not specific using EMSA. They suggest specificity arises from association with transcriptional cofactors. Binding with cofactors was confirmed by CO-IP and in vivo RNAi experiments demonstrated the requirement of these cofactors in mediating changes in cell volume during salivary gland tubulogenesis. They demonstrate that Ribbon regulation of cell growth via transcription of RPGs is not a universal mechanism for Ribbon function, as Ribbon regulates transcription of other genes in the context of tracheal development.
**Major comments:**
Results of all experiments are conclusive, and significant numbers of samples were noted for most figure panels. For a few panels the sample number/number of replicates was not noted, and it is recommended that the authors add this information (Figure 1F; 5B,C; 7B).
Additional experiments are not needed to support the conclusions presented in this work. The data and methods are presented clearly and the statistical analyses performed were appropriate.
In regard to microarray data, Figure 4E shows fold change as log2 values, but it is unclear if this is the case for Table S2. This should be clarified. The authors note in the text on page 7 that few targets show a greater than 1.5-fold change. Based on Figure 4E, this is a log2 value, and should be specified as such.
As the Rib antibody was generated in this study, it would be helpful to include data illustrating a confirmation of antibody specificity. This could include Rib antibody staining on rib mutant embryos, or showing a lack of band for ribbon in ribbon mutants on a Western blot. If the specificity has been published elsewhere, please add a reference.
**Minor Comments:**
As the microarray data was previously published in Loganathan et al 2016, as mentioned in the results section, this citation should also be included in the Methods section describing the Microarray data.
In the discussion section on page 15, a list of factors in the gene network are listed. What is viz.?
Reviewer #1 (Significance (Required)):
•As described in the introduction, the role of cell growth during embryonic tissue morphogenesis is a relatively unexplored topic. The authors point out that most previous studies describing regulation of tissue growth have focused on the role of mitosis and increased polyploidy, as in the gut (https://doi.org/10.1016/S0925-4773(00)00512-8 ), as primary mechanisms. In the case of the salivary gland, only a single endocycle occurs during embryogenesis and cells are post-mitotic, suggesting another mechanism is at play. This study identifies Ribbon as a mediator of cell growth and demonstrates that Ribbon mediates this function through transcriptional regulation of RPGs. In addition, they identify Ribbon cofactors that are important for salivary gland cell growth and tissue morphogenesis. Interestingly, they find that this mechanism for cell growth may be tissue specific, as Ribbon appears to regulate different genes in the trachea.
•This work has implications for the regulation of cell growth in other tissues and organisms and would be of broad interest to those studying organ development.
•In order to contextualize my review, I am a developmental biologist that works with Drosophila.
**Referees cross-commenting**
In regard to the comments by reviewer #2: I agree that point # 2 should be addressed to more thoroughly describe the method, but as the authors have looked at DNA Amplification at a time point following the normal endocycle, which occurs at stage 12, and DNA content is not significantly different, I don't think analysis of earlier stages would influence their conclusions.
Given that the authors do include some RNAi data for RPGs and Trf2, it would enhance the paper further to include M1BP and Dref RNAi data if quality reagents are available as described in point 5. Point 6 can be easily addressed. In regard to point 8, the effects of rib overexpression alone would be interesting to see given the ability of this construct to rescue the phenotype.
While I think points 3 and 7 are excellent ideas for a follow up study, I think they are outside of the scope of this paper. I do not view point 4 as essential to this study, as the study focuses on the regulation of transcription of the RPGs by Rib.
In regard to the comments by reviewer #3, I agree that points 1 and 2 should be addressed. It would be extremely difficult to address point #3 by dissecting out the tissue, but it could be addressed via further explanation in the text, as could point #4. I don't think minor points 4-6 need to be addressed, but the minor points 1-3 should addressed to improve the paper. For minor point #3, I would suggest the number of genes be included in Supplementary Table 1.
As reviewer #1, I think my comments should be addressed to improve the quality and clarity of the paper.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
This paper reported a role for the BTB/POZ-domain transcription factor rib in mediating early cell growth of embryonic salivary gland (SG) cells. the authors show that during tubulogenesis of the salivary glands, rib binds the transcription start site of almost all SG-expressed ribosomal protein gene (RPG) and promotes their transcription, thus providing a material foundation for cell growth. Interestingly, in embryo trachea cells, rib targets do not include RPGs, which indicates that rib may use different mechanisms to regulate cell growth of different organs. In general, this is a well-written, well designed research article with many conclusions well-supported by experimental evidence. Listed below are a few issues (mostly minor/unessential) for the authors to consider.
**Major comments:**
1.Although in Figure 1G, the nucleus size is indistinct in rib mutant and wt cells at stage 15 and 16, Figure 1C appeared to look like that the rib mutant nuclei at stage 11, 13 and 14 are significantly smaller than those in wild type cells. The authors need to make sure that the rib phenotype has nothing to do with DNA amplification.
2.Please describe the details on calculating DNA volume by DAPI staining in the method session.
3.The authors have demonstrated weak DNA binding ability of Rib, and physical interactions between Rib with the known regulators of RPG transcription (Trf2, M1BP, and Dref), but what is the functional relationships between Rib and the known RPG regulators? e.g., does Rib function to promote DNA binding and transcriptional activity of Trf2, M1BP, and Dref, or vice versa?
4.To confirm the rib function on RPG translation, it is recommended to examine ribosomal proteins by western, and comparing the total protein content would also be helpful.
5.As Trf2, M1BP and Dref are physically interacted with Rib, it would be helpful to determine Whether M1BP and Dref knockdown can phenocopy the cell growth deficit observed in rib mutant SGs.
6.Page12, paragraph 3, "Thus, despite the shared requirement for Rib in embryonic cell growth of both tubular organs, Rib-dependent growth in the trachea is likely through regulation of alternative growth-promoting factors." Please list the potential growth-promoting factors targeted by Rib according to the Chip-seq data, if possible.
7.It would be interesting to determine whether rib mutation differently affect the secretory function of salivary gland at embryo, larva, pupa or adult stage.
8.Does Rib overexpression have any effects to SG development? Considering the authors adopted GAL4-UAS system to rescue Rib under Rib-KO, it would be interesting to see if Rib overexpression could cause an opposite overgrowth phenotype.
Reviewer #2 (Significance (Required)):
This paper discovered a new mechanism underlying organ-specific cell growth regulation during a specific time-window of animal development, which should be of interest to the field of cell and developmental biology.
Drosophila genetics; Developmental biology
**Referees cross-commenting**
I agree with all the other referees that the comments raised by reviewer #1 should be addressed entirely.
In regard to the comments by reviewer #3, all of the 4 major points are excellent and should be addressed, but it is okay to address points #3 and 4 by simple explanation or re-wording. I find the minor point #6 is nice to have but not essential, the rest should be addressed.
In case of my comments (reviewer 2), points #1,2,5,8 should be addressed, others are nice to have.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In the manuscript "The Ribb-osome: Ribbon boosts ribosomal protein gene expression to coordinate organ form and function" the authors show evidence that Ribbon mediates early cell growth in Drosophila embryonic salivary gland through direct interaction with ribosomal protein genes. The manuscript is well written while presenting novel and solid data. The data could be strengthened by some further analysis and clarification, but none of the issues raised represent major flaws.
**Key points:**
1.Cell segmentations: The way the cell segmentations / volume quantifications are presented it is impossible to judge their quality. The authors should provide the extracted geometries as Supplementary Data. The methods could be clearer on how the segmentations for cell and DNA volume were done; were the surfaces done manually, were there any image preprocessing steps etc.? In Figure 7C, it is not clear from the images whether cells or nuclei were segmented. Also, it would strengthen the work if the authors analysed the cell shapes (in particular cell height, and apical cell shape bias), considering that they mention it to be different in the Rib mutant. In addition, it would add to the manuscript if the authors could quantify the volume of the luminal space, of the epithelial layer in wt and mutant, and the bias in tube outgrowth.
2.The authors show nicely that the rib mutants have a smaller overall cell size, can this be the reason why the secretory tube in figure is smaller? In addition, if the overall size of the mutant and the WT is the same as suggested in figure 1H then why does the mutant larvae in figure 1f appear so much smaller than the WT in the same panel?
3.In figure 4f the authors see 4 out of 7 RPGs been significantly down-regulated, do they have an explanation for that? Why are not all 7 tested RPGs significantly down-regulated? Can it be that the results will be significantly improved by dissecting the tissue of interest instead of using whole embryos? Finally with what criteria were these 7 genes selected?
4.The authors state in their manuscript the limitations of the chip-seq and the fact that the 11 unbound RPGs are essentially a technical artifact. I suggest that the authors either perform ChIP on some of these RPGs to prove their point or that they ton down their statements about chip-seq limitations and Rib binding all SG-expressed RPGs
**Minor points**
The authors need to clarify in the text what is early and late stage of tubulogenisis.
In figure 1c the Mipp1 staining is of low quality and although the white lines help the reader on where to focus, noise vs signal is almost indistinguishable. Furthermore, the authors claim that they only take under consideration SG cells that show uniform membrane staining but Figure 1c does not show such uniform staining.
Figure 1d needs the addition of statistical analysis WT vs rib mutant st12 look very similar.
In their ChIP-seq data the authors identify 436 peaks that correspond to 413 genes. It is worth to add a pie chart depicting how many of those 413 are RPGs and how may are non-ribosomal.
Throughout the manuscript the authors exhibit nicely the effects of rib mutants. What happens to the tested genes in panel 4f when rib is overexpressed?
RPls are known to be involved in size regulation. If the authors use another driver than fkh to express Rib, Rpl19 etc will they still see similar phenotypes or not?
Figure 7b is hard to follow, the IP panels should be in agreement with the order that they appear in the text e.g., first experiment then controls
Reviewer #3 (Significance (Required)):
In the manuscript "The Ribb-osome: Ribbon boosts ribosomal protein gene expression to coordinate organ form and function" the authors show evidence that Ribbon mediates early cell growth in Drosophila embryonic salivary gland through direct interaction with ribosomal protein genes. As I am only vaguely familiar with the field, I would leave it to someone who is closer to judge the advance and relevance. But with the additional quantifications, the paper should be of interest more generally to developmental biologists who are interested in tubulogenesis, and if the authors make the 3D cell geometries available, the work should also be of interest to computational modellers with an interest in epithelial organization as segmented 3D cell geometries are still rare.
**Referees cross commenting**
Looking at all 3 referee reports, I find all points made by referee 1 either essential and/or easy to fix. As such, I would insist on all points made.
With regard to referee 2, I see points 1,5,8 as essential, and point 2 is too easy to do to not request it. The others I would consider nice-to-have, but not essential.
In case of my own report, I would insist on points 1 & 2. Among the minor points, points 4 & 6 are NOT essential. The others are either important or easy enough to fix.
I look forward to the views of my colleagues.
Our response to reviewer comments
We thank the reviewers for their very positive comments regarding the importance of this paper and for the constructive feedback they have provided. Indeed, we would be delighted to address every suggestion raised, but since we would also like to have this work published in a timely manner, it is quite helpful to have consensus among the three reviewers regarding which changes and experiments are the most important to include. Since all three reviewers felt it important to address all of the comments from Reviewer #1, we will do so. For the comments raised by reviewers #2 and #3, we will follow the consensus opinion and address those comments by changes in the text or by including more experiments. In this revision plan, we also address the comments that were considered to be beyond the scope of the current study.
Points raised by Reviewer #1
Include N values for all the figure panels: We will provide sample number information for those panels currently missing that information: Figures 1F; 5B, C; and 7B.
Microarray fold-change clarification: We will clarify that we are reporting the fold-change values in Table S2. As is standard with Volcano plots for reporting microarray data, Figure 4E is plotted as Log2 data.
Antibody validation: We will provide a supplemental figure with information about the Rib antiserum and its specificity.
Add citation regarding the microarray data: We will add the citation referring to the microarray data to the Methods section.
Uncommon word usage pg 15: We will remove “viz.”—contraction of a Latin phrase “videre licet” to mean “namely” or “specifically”—from the discussion of factors in the gene network, since it was clearly distracting.
Points raised by Reviewer #2
Appearance of Nuclei and Calculation of DNA volume: The rib mutant nuclei shown in Fig. 1C depict CrebA staining and were used only for identification of SG secretory cells – we did not measure nuclear volume in these samples. To eliminate any potential confusion, we have re-labelled the last column “3D cell volume”. All of the calculations of nuclear size (as a measure of DNA amplification) were carried out with DAPI-staining as shown In Fig 1G, which revealed no difference between WT and rib mutant SG secretory cells. Measurement of entire nuclear volume is critical, since, in any single focal plane, how much of the nucleus is captured varies. We will provide information detailing how DNA volume was obtained in the methods section.
SG cell size phenotypes of M1BP and Dref RNAi Knockdowns: We agree with the reviewers that determining if M1BP and Dref SG-specific RNAi also phenocopy the cell growth deficit observed in the rib mutant SGs is a meaningful experiment and could strengthen our conclusions. We will, therefore, perform this experiment. It should be noted, however, that whereas rib and Trf2 do not have significant levels of maternal mRNA or protein, both M1BP and Dref have high levels of both [based on ModEncode data; Flybase]. Thus, it may be challenging to deplete these genes with only SG driven expression of the RNAi constructs.
List of potential Rib-dependent growth promoting factors in the trachea: In the revised version, we provide the list of candidate growth genes bound by Rib from the tracheal Chip-Seq data as requested by reviewer #2 (and agreed upon by reviewer #1 as important) in the supplement.
Effects of Rib overexpression on SG cell growth: All of the reviewers agree that testing for a SG secretory cell over-growth phenotype with Rib overexpression is worthwhile and we will do this experiment. Nonetheless, we recognize that we may not see overgrowth phenotypes based on a few observations. Our ChIP-Seq data indicate that Rib binds neither the promoters of ribosomal RNAs [rRNAs; the other essential component of ribosomes] nor the promoters of known rRNA transcription factors. Based on a study from another group, it seems likely that Myc upregulates rRNA expression (Grewal et al., 2005). Correspondingly, myc is transcriptionally upregulated in the embryonic SG (supplemental panel 7C) and myc expression in the SG is independent of rib (i.e. Rib does not bind the myc gene based on the SG ChIP-Seq and myc levels in the embryonic SG do not change in rib null embryos based on microarray and whole mount in situs). Also based on ChIP-Seq, Rib binds its own promoter and, based on qRT-PCR experiments, represses its own expression (Loganathan et al., 2016). Thus, over-expression of Rib with GAL4:UAS driven expression may reduce rib transcription from the endogenous locus. Nonetheless, this experiment is still worth doing.
Points raised by Reviewer #3
Information on cell segmentations: In the revised manuscript, we will provide sample 3D views of cell volume quantifications as movie files. In the methods section, we will also make it clear that the surfaces were manually segmented and that no image preprocessing steps were performed. We will also provide the excel spread sheets on size calculations in a supplement. We will provide information in the legend for figure 7 that whole secretory cells were segmented for the calculations done for panel C. The information on cell shapes, apical membrane dynamics, and luminal volumes (including the assessment of developmental dynamics of tube elongation based on live-imaging construction of computational elastic and analytical viscoelastic models) has been presented in previous publications from our lab (Cheshire et al., 2008; Loganathan et al., 2016) and from work in other labs (Blake et al., 1998). We will include this information in the revised discussion and will include the appropriate citations.
Panel 1F and comment on the apparent smaller size of the rib mutant shown: rib mutant embryos show characteristic head invagination defects along with amioserosa and dorsal closure defects [Bradley and Andrew, 2001]. The partial embryo image in Panel 1F captures the head invagination defect making the embryo appear smaller. We will include images of whole embryos in the revised version to clarify that whole embryo volumes of rib mutants are comparable to WT for the representations shown in Fig. 1F.
Clarify early vs. late Tubulogenesis: Early SGs are stage 11, 12 – when the SG cells are internalizing. Late SGs are stages 13 – 16, when the glands are fully internalized. We will clarify this in the figure legend.
Statistics on Panel 1D: We will perform statistical analysis of growth profiles shown in Fig 1D as suggested by the reviewer and include the results in the figure or figure legend.
Pie-chart for RPG fraction: Given how crowded the figures currently are, instead of providing pie charts, we simply provide the fraction of the bound genes that are RP genes in the text. Using our set cut-off of 4.0: 12.9% of genes bound by Rib (with both drivers) were RP genes. Using the IDR platform for peak calling, 12.8% of bound genes were RP genes. In Fig 4A, we also include genes above the cut-off with one GAL4 driver, but not the other, as described in the legend.
Effects of Rib Overexpression: As discussed earlier, we will perform this experiment (please also see our response to the last comment by reviewer #2)
Order of presentation of co-IP results in Panel 7B: As requested, we will reorder the IP results in Fig. 7B as suggested by the reviewer to present first the results from the experiments and then the results from controls in accord with how we discuss the data in the results section.
Testing the functional relationships between Rib and known RPG regulators: We will not determine if Rib promotes DNA binding and transcriptional activity of Trf2, M1BP, and Dref, as this experiment was considered to not be critical for this paper by any of the three reviewers.
Panel 4F and tissue-specific RT-qPCR: We agree that it would be ideal to have tissue-specific qRT-PCR, but it is not technically feasible to dissect out enough embryonic SGs for analysis (as acknowledged by Reviewer 1). In future studies, we do plan to get that kind of information from single cell RNA sequencing (scRNA-Seq) of WT and rib mutant embryos, but there are a few hurdles to overcome before those experiments. In selecting the RP genes for qRT-PCR, we chose sample RpL and RpS genes, making sure to include at least one gene (RpS9) that was “not bound” by Rib based on ChIP-Seq criteria.
Determine Rib function on RPG translation: We will not examine levels of RP proteins by Western since this experiment was deemed be unnecessary for the current study by the three reviewers.
Effects of rib on the secretory function of the SG at the embryo, larva, pupa, or adult stage: We agree with the reviewer that these data would be interesting to have; as pointed out by reviewer #1, however, it’s a question for a future follow-up study.
Chip-Seq technical artifact / limitations: We don’t think we are incorrect in suggesting that the failure to detect Rib binding to all RP genes could be a technical artifact because of the following: (1) a direct examination of the binding tracts associated with every RP gene reveals a peak at/near the TSS. The values associated with those peaks do not always reach the cut-off, but when the peak values are lower than the cut-off, the signals in the flanking DNA are often also much lower than average (for details, see Supplemental Figure 1). (2) Among the RP genes whose expression went down significantly by qRT-PCR is RpS9 – an RP gene “not bound” by Rib, based on the cut-offs we followed.
Using another SG driver: We agree with reviewer #1 that the results obtained using the fkh-GAL4 driver for RNAi of RP regulators and RP genes are robust and sufficient to support the conclusion that Rib binds RPGs to regulate SG secretory cell size. Thus, we will not redo these experiments using another SG driver.
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Referee #3
Evidence, reproducibility and clarity
In the manuscript "The Ribb-osome: Ribbon boosts ribosomal protein gene expression to coordinate organ form and function" the authors show evidence that Ribbon mediates early cell growth in Drosophila embryonic salivary gland through direct interaction with ribosomal protein genes. The manuscript is well written while presenting novel and solid data. The data could be strengthened by some further analysis and clarification, but none of the issues raised represent major flaws.
Key points:
1.Cell segmentations: The way the cell segmentations / volume quantifications are presented it is impossible to judge their quality. The authors should provide the extracted geometries as Supplementary Data. The methods could be clearer on how the segmentations for cell and DNA volume were done; were the surfaces done manually, were there any image preprocessing steps etc.? In Figure 7C, it is not clear from the images whether cells or nuclei were segmented. Also, it would strengthen the work if the authors analysed the cell shapes (in particular cell height, and apical cell shape bias), considering that they mention it to be different in the Rib mutant. In addition, it would add to the manuscript if the authors could quantify the volume of the luminal space, of the epithelial layer in wt and mutant, and the bias in tube outgrowth.
2.The authors show nicely that the rib mutants have a smaller overall cell size, can this be the reason why the secretory tube in figure is smaller? In addition, if the overall size of the mutant and the WT is the same as suggested in figure 1H then why does the mutant larvae in figure 1f appear so much smaller than the WT in the same panel?
3.In figure 4f the authors see 4 out of 7 RPGs been significantly down-regulated, do they have an explanation for that? Why are not all 7 tested RPGs significantly down-regulated? Can it be that the results will be significantly improved by dissecting the tissue of interest instead of using whole embryos? Finally with what criteria were these 7 genes selected?
4.The authors state in their manuscript the limitations of the chip-seq and the fact that the 11 unbound RPGs are essentially a technical artifact. I suggest that the authors either perform ChIP on some of these RPGs to prove their point or that they ton down their statements about chip-seq limitations and Rib binding all SG-expressed RPGs
Minor points
The authors need to clarify in the text what is early and late stage of tubulogenisis.
In figure 1c the Mipp1 staining is of low quality and although the white lines help the reader on where to focus, noise vs signal is almost indistinguishable. Furthermore, the authors claim that they only take under consideration SG cells that show uniform membrane staining but Figure 1c does not show such uniform staining.
Figure 1d needs the addition of statistical analysis WT vs rib mutant st12 look very similar.
In their ChIP-seq data the authors identify 436 peaks that correspond to 413 genes. It is worth to add a pie chart depicting how many of those 413 are RPGs and how may are non-ribosomal.
Throughout the manuscript the authors exhibit nicely the effects of rib mutants. What happens to the tested genes in panel 4f when rib is overexpressed?
RPls are known to be involved in size regulation. If the authors use another driver than fkh to express Rib, Rpl19 etc will they still see similar phenotypes or not?
Figure 7b is hard to follow, the IP panels should be in agreement with the order that they appear in the text e.g., first experiment then controls
Significance
In the manuscript "The Ribb-osome: Ribbon boosts ribosomal protein gene expression to coordinate organ form and function" the authors show evidence that Ribbon mediates early cell growth in Drosophila embryonic salivary gland through direct interaction with ribosomal protein genes. As I am only vaguely familiar with the field, I would leave it to someone who is closer to judge the advance and relevance. But with the additional quantifications, the paper should be of interest more generally to developmental biologists who are interested in tubulogenesis, and if the authors make the 3D cell geometries available, the work should also be of interest to computational modellers with an interest in epithelial organization as segmented 3D cell geometries are still rare.
Referees cross commenting
Looking at all 3 referee reports, I find all points made by referee 1 either essential and/or easy to fix. As such, I would insist on all points made.
With regard to referee 2, I see points 1,5,8 as essential, and point 2 is too easy to do to not request it. The others I would consider nice-to-have, but not essential.
In case of my own report, I would insist on points 1 & 2. Among the minor points, points 4 & 6 are NOT essential. The others are either important or easy enough to fix.
I look forward to the views of my colleagues.
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Referee #2
Evidence, reproducibility and clarity
This paper reported a role for the BTB/POZ-domain transcription factor rib in mediating early cell growth of embryonic salivary gland (SG) cells. the authors show that during tubulogenesis of the salivary glands, rib binds the transcription start site of almost all SG-expressed ribosomal protein gene (RPG) and promotes their transcription, thus providing a material foundation for cell growth. Interestingly, in embryo trachea cells, rib targets do not include RPGs, which indicates that rib may use different mechanisms to regulate cell growth of different organs. In general, this is a well-written, well designed research article with many conclusions well-supported by experimental evidence. Listed below are a few issues (mostly minor/unessential) for the authors to consider.
Major comments:
1.Although in Figure 1G, the nucleus size is indistinct in rib mutant and wt cells at stage 15 and 16, Figure 1C appeared to look like that the rib mutant nuclei at stage 11, 13 and 14 are significantly smaller than those in wild type cells. The authors need to make sure that the rib phenotype has nothing to do with DNA amplification.
2.Please describe the details on calculating DNA volume by DAPI staining in the method session.
3.The authors have demonstrated weak DNA binding ability of Rib, and physical interactions between Rib with the known regulators of RPG transcription (Trf2, M1BP, and Dref), but what is the functional relationships between Rib and the known RPG regulators? e.g., does Rib function to promote DNA binding and transcriptional activity of Trf2, M1BP, and Dref, or vice versa?
4.To confirm the rib function on RPG translation, it is recommended to examine ribosomal proteins by western, and comparing the total protein content would also be helpful.
5.As Trf2, M1BP and Dref are physically interacted with Rib, it would be helpful to determine Whether M1BP and Dref knockdown can phenocopy the cell growth deficit observed in rib mutant SGs.
6.Page12, paragraph 3, "Thus, despite the shared requirement for Rib in embryonic cell growth of both tubular organs, Rib-dependent growth in the trachea is likely through regulation of alternative growth-promoting factors." Please list the potential growth-promoting factors targeted by Rib according to the Chip-seq data, if possible.
7.It would be interesting to determine whether rib mutation differently affect the secretory function of salivary gland at embryo, larva, pupa or adult stage.
8.Does Rib overexpression have any effects to SG development? Considering the authors adopted GAL4-UAS system to rescue Rib under Rib-KO, it would be interesting to see if Rib overexpression could cause an opposite overgrowth phenotype.
Significance
This paper discovered a new mechanism underlying organ-specific cell growth regulation during a specific time-window of animal development, which should be of interest to the field of cell and developmental biology.
Drosophila genetics; Developmental biology
Referees cross-commenting
I agree with all the other referees that the comments raised by reviewer #1 should be addressed entirely.
In regard to the comments by reviewer #3, all of the 4 major points are excellent and should be addressed, but it is okay to address points #3 and 4 by simple explanation or re-wording. I find the minor point #6 is nice to have but not essential, the rest should be addressed.
In case of my comments (reviewer 2), points #1,2,5,8 should be addressed, others are nice to have.
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Referee #1
Evidence, reproducibility and clarity
In this paper, the authors examine the relationship between the transcription factor Ribbon, its ribosomal protein gene (RPG) targets, and cell growth during the process of salivary gland tubulogenesis in the Drosophila embryo. This study builds upon previous work they published in 2016 (Loganathan et al., 2016). While the previous study identified RPGs as potential targets of Ribbon from ChIP-Seq analysis, they did not delve into the role of these targets in salivary gland morphogenesis. Here, the authors demonstrate that mutation of ribbon results in decreased cell volumes via immunostaining and image analysis. They identify and confirm RPGs as ribbon transcriptional targets using ChIP-SEQ, Microarray data, in situ hybridization, and qRT-PCR. They analyze these targets in an effort to identify a Rib consensus binding sites by MEME and find that Rib binding is not specific using EMSA. They suggest specificity arises from association with transcriptional cofactors. Binding with cofactors was confirmed by CO-IP and in vivo RNAi experiments demonstrated the requirement of these cofactors in mediating changes in cell volume during salivary gland tubulogenesis. They demonstrate that Ribbon regulation of cell growth via transcription of RPGs is not a universal mechanism for Ribbon function, as Ribbon regulates transcription of other genes in the context of tracheal development.
Major comments:
Results of all experiments are conclusive, and significant numbers of samples were noted for most figure panels. For a few panels the sample number/number of replicates was not noted, and it is recommended that the authors add this information (Figure 1F; 5B,C; 7B).
Additional experiments are not needed to support the conclusions presented in this work. The data and methods are presented clearly and the statistical analyses performed were appropriate.
In regard to microarray data, Figure 4E shows fold change as log2 values, but it is unclear if this is the case for Table S2. This should be clarified. The authors note in the text on page 7 that few targets show a greater than 1.5-fold change. Based on Figure 4E, this is a log2 value, and should be specified as such.
As the Rib antibody was generated in this study, it would be helpful to include data illustrating a confirmation of antibody specificity. This could include Rib antibody staining on rib mutant embryos, or showing a lack of band for ribbon in ribbon mutants on a Western blot. If the specificity has been published elsewhere, please add a reference.
Minor Comments:
As the microarray data was previously published in Loganathan et al 2016, as mentioned in the results section, this citation should also be included in the Methods section describing the Microarray data.
In the discussion section on page 15, a list of factors in the gene network are listed. What is viz.?
Significance
•As described in the introduction, the role of cell growth during embryonic tissue morphogenesis is a relatively unexplored topic. The authors point out that most previous studies describing regulation of tissue growth have focused on the role of mitosis and increased polyploidy, as in the gut (https://doi.org/10.1016/S0925-4773(00)00512-8 ), as primary mechanisms. In the case of the salivary gland, only a single endocycle occurs during embryogenesis and cells are post-mitotic, suggesting another mechanism is at play. This study identifies Ribbon as a mediator of cell growth and demonstrates that Ribbon mediates this function through transcriptional regulation of RPGs. In addition, they identify Ribbon cofactors that are important for salivary gland cell growth and tissue morphogenesis. Interestingly, they find that this mechanism for cell growth may be tissue specific, as Ribbon appears to regulate different genes in the trachea.
•This work has implications for the regulation of cell growth in other tissues and organisms and would be of broad interest to those studying organ development.
•In order to contextualize my review, I am a developmental biologist that works with Drosophila.
Referees cross-commenting
In regard to the comments by reviewer #2: I agree that point # 2 should be addressed to more thoroughly describe the method, but as the authors have looked at DNA Amplification at a time point following the normal endocycle, which occurs at stage 12, and DNA content is not significantly different, I don't think analysis of earlier stages would influence their conclusions.
Given that the authors do include some RNAi data for RPGs and Trf2, it would enhance the paper further to include M1BP and Dref RNAi data if quality reagents are available as described in point 5. Point 6 can be easily addressed. In regard to point 8, the effects of rib overexpression alone would be interesting to see given the ability of this construct to rescue the phenotype.
While I think points 3 and 7 are excellent ideas for a follow up study, I think they are outside of the scope of this paper. I do not view point 4 as essential to this study, as the study focuses on the regulation of transcription of the RPGs by Rib.
In regard to the comments by reviewer #3, I agree that points 1 and 2 should be addressed. It would be extremely difficult to address point #3 by dissecting out the tissue, but it could be addressed via further explanation in the text, as could point #4. I don't think minor points 4-6 need to be addressed, but the minor points 1-3 should addressed to improve the paper. For minor point #3, I would suggest the number of genes be included in Supplementary Table 1.
As reviewer #1, I think my comments should be addressed to improve the quality and clarity of the paper.
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Reply to the reviewers
1. General Statements [optional]
On behalf of the authors, I would like to thank the three reviewers for providing a valuable feedback on our manuscript. We appreciate that they all considered that the first part of our study conveys novelty to the field of neuroblastoma research, while being coherent with recent studies aimed at identifying the NB cells-of-origin through transcriptomics and single-cell RNA-sequencing approaches. We indeed identified a transcriptional signature that distinguishes LR-NBs from HR-NBs, revealing that these two NB subgroups are better discriminated by the core transcriptional signature shared by the distinct SA cell types, rather than by the transcriptional specificities of any of these cell types, as recently debated. Of note, our findings unveil that the sympatho-adrenal transcriptional program facilitates NB formation but concomitantly restricts its malignant potential. We also wish to thank the reviewers for acknowledging that, in contrast to previous studies, we pursued further by testing the functional relevance of this signature through a combination of in vitro and in vivo experiments. We thereby identified NXPH1 and its receptor α-NRXN1 as ones of the very first factors showing an anti-metastatic activity in the context of NB. Uncovering NXPH1/α-NRXN signaling as a possible target to treat metastatic HR-NBs gives our study considerable clinical relevance.
We consider that the critics and recommendations provided by the three reviewers are positive and pertinent. We are thus willing to address nearly all the reviewers’ concerns and suggestions within the scope of a revision, including performing additional in vivo experiments, as explained in details in the following section. We hope that the planned revisions will be sufficient to make our manuscript suitable for publication.
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2. Description of the planned revisions
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
**Summary:**
In the reviewed manuscript, the authors aimed to characterize the sympathoadrenal (SA) transcriptional landscape that defines low- and high-risk neuroblastomas (LR-NBs and HR-NBs respectively). In particular, they analyze previously published Affymetrix U219 expression profiles of 18 low- (n=8) and high-risk (n=10) neuroblastomas, and 2 fetal human adrenal glands. The authors define transcriptional signatures of LR- and HR-NBs, and further unbiasedly classified them in 4 clusters defining groups of patients with different prognosis (as tested using the 498 SEQC cohort). Within these transcriptional signatures, the authors delineated a SA signature using a human fetal adrenal gland transcriptional profile recently published (Kildisiute et al. 2021), that can discriminate low-risk neuroblastomas. From these genes, the authors further select NXPH1 and NRNX1-2 as promising targets for extensive experimental in vitro and in vivo validation, including validation in cell cultures, and xenografts, to determine that NXPH1/alpha-NRXN1-2 signaling is sufficient for NB tumor growth, and that the expression of either stimulates the metastatic potential of NB cells.
**Major comments:**
1- The cohorts and data used by the authors to conduct the main analysis of the paper are already published, and thus the contribution of the analysis is incremental. In particular, the authors analyzed arrays from a limited cohort size, in comparison with others available sequenced with RNA-seq (e.g. 176 HR- and 322 non-HR NB in 498 SEQC; 224 HR- and 342 non-HR NBs included in the Westermann-genecode19 cohort; and 80 HR- and 20 non-HR in the Jagannathan cohort). Furthermore, the 12,000 most-expressed genes (out of ~20,000 available) were analyzed by the authors, as opposed to more than 40,000 (coding and non-coding) included in a normal RNA-seq study. Only the 498 SEQC dataset provides 12,000 genes significantly up-regulated in either high-risk (n~5,500) or non-high-risk (n~7,000). The differences between datasets could influence the results of the study. For example, in the reviewed manuscript, genes with a high expression in LR-NB (compared to fAG) included DNMT3B, SEMA5A, SOX5, and TET1, all of which have a significantly higher expression in HR-NBs of the 498 SEQC cohort. The quality of the manuscript will be enhanced with consistent results obtained by conducting the same reported analysis in larger cohorts.
Reply: To define the transcriptional signatures associated to the aetiology of LR- and HR-NBs and to the malignant behavior of HR-NBs, we first needed to compare the transcriptomic signatures of LR-NB and HR-NB samples to the one of a non-tumoral, healthy tissue such as the fetal adrenal gland. The online databases cited by the reviewer #1 do not contain any healthy samples, thus precluding the possibility to use it for the first step of our analysis. This is why we decided to use as a starting point the cohort of samples from the Hospital Sant Joan de Déu (HSJD cohort), which further provided the advantage of comparing samples from patients all diagnosed and treated at the same facility. We then used the SEQC database in different steps of our analytic strategy (Fig.S1F, 2F-H, 3A-B, S3) to test the relevance and coherence of the results obtained with the HSJD cohort in the context of a larger cohort. As mentioned by the reviewer #1, we indeed focused our analysis on the 12,000 most-expressed genes. We did so to follow the recommendations of the software Phantasus for transriptomic analyses of mammalian datasets.
2- In the reviewed manuscript, PHOX2A and PHOX2B are significantly more expressed in both LR-NB and HR-NB compared to fAG. This is also the case for other adrenergic markers including TH and DBH. Oppositely, the expression of cortex markers (i.e. STAR and CYP11A1) is significantly higher in fAG. Nevertheless PNMT is not significantly up-regulated in fAG in comparison to LR-NB nor HR-NB. Is it possible that the fetal adrenal glands analyzed include a large proportion of cortex that confounds the transcriptional signals? The quality of the manuscript will be enhance if the authors could establish what proportion of the fAG transcriptional signal belongs to cortex, and if they account for its influence in the analysis.
Reply: The samples of human fetal adrenal gland from which RNA was extracted were obtained from donations (samples staged at 22 weeks post-conception or 2 days after birth) and evaluated by a pathologist who confirmed their correct preservation before sample processing. As fetal adrenal glands are very small tissues, successfully separating the cortical and adrenal regions requires micro-dissection, which was not applied. These samples were instead processed entirely, presenting a ratio of medulla/cortex tissue according to their developmental stage.
3- A recent published paper (Bedoya-Reina et al. 2021) study the differences of HR-NB and LR-NB from a single-cell perspective. In the published manuscript, the authors conclude that LR-NBs are enriched in cells that resemble chromaffin and sympathoblast cells, while the high-risk neuroblastomas are enriched in undifferentiated cells that resemble cells with progenitor characteristics in post-natal adrenal gland. This is broadly consistent with the conclusion reached by the authors in the manuscript under review. It will enhance the content of the reviewed manuscript if the authors compare their transcriptional signatures with the recently published transcriptional signatures in this paper to answer the following questions:
-1) to what extent the transcriptional signatures for HR-NBs (4 hierarchical clusters incl.) in the manuscript under review resembles that from the published undifferentiated cluster (nC3) enriched in HR-NBs, and the progenitor cluster (hC1) in post-natal adrenal gland;
-2) to what extent the transcriptional signatures for LR-NBs (4 hierarchical clusters incl.) in the manuscript under review resembles that from the published NOR (nC7, nC8, and nC9) enriched in LR-NBs, and that from the chromaffin cells (hC4) enriched in post-natal adrenal gland;
-3) how is the expression of NXPH1 and alpha-NRXN1-2 in the reported LR- and HR-NBs, and adrenal gland.
Reply: As stated by the reviewer #1, our findings are indeed in agreement with the main conclusions recently reported by Bedoya-Reina et al (Bedoya-Reina et al, 2021). We agree with the reviewer #1’s suggestion that a detailed comparison of our transcriptomic signatures with those of Bedoya-Reina et al would be interesting. We will thus perform this comparison and provide these complementary results in the revised version of the manuscript.
4- In the discussion, the authors indicate that they do not aim to identify the transcriptional signature associated to NB origin but rather use the component of the SA lineage that distinguish LR- and HR-NBs. This statement implies that neuroblastoma can originate from any cell in the developing SA lineage (i.e. SCP, bridge, chromaffin and sympathoblast), a controversial assumption that requires further proof. In particular, when discussing about the core sympathoadrenal signatures enriched in LR-NBs and HR-NBs, the authors obtained a SA signature of genes shared by at least 3 of the 4 SA cell signatures. Further justification needs to be provided as for why (in particular) one of these SA cell signatures exclude the sympathoblast/neuroblast contribution.
Reply: We decided to use as a core SA signature the genes shared by at least 3 of the 4 SA cell types to avoid being too restrictive, the SCP signature being particularly distant from the 3 others. As such, the list of genes shared by all 4 cell types consists of 663 genes, of which only 51 are retrieved in our list of 503 LR vs HR DEGs. Conversely, the list of genes shared by Bridge cells, Chromaffin cells and Sympathoblasts (but not shared by SCPs) consists 3,530 genes, of which 199 are retrieved in the list of 503 LRvsHR DEGs. These complementary results, which can be discussed and provided if required, therefore suggest that the core SA signature discriminating LR-NBs from HR-NBs represents mostly the Bridge-Chromaffin-Sympathoblast lineage and excludes the SCP identity. They are therefore in agreement with the recent notion that NB cells-of-origin derive from the sympathoblast-chromaffin lineage.
5- Some of the most interesting results in the paper are limited to proportions in a subset of top-ranked genes. It will be valuable to set the analysis in an hypothesis driven context, add probabilities, test names, and corrected p-values to the results.
Reply: Our study is based on the initial hypothesis that LR- and HR-NBs might differ in the way they exploit the transcriptional program underlying their developmental origin. To get a deeper insight into this notion we performed a sequential differential expression analysis of primary samples of LR-NBs, HR-NBs and human fetal adrenal gland using the web-based Phantasus software. This software identifies differentially expressed genes between groups using the Limma R package, as detailed in the Methods section. As such, basic statistics for significance analysis were performed using a modetared T-test (as specified in the Limma R package) and FDR-adjusted P-values were set to PAdditionally, top-ranked genes of SA clusters were selected as part of a heuristic approach aimed at highlighting the clinical implication of the transcriptional clusters retrieved in our analysis. The relationship between their expression levels and patient survival was further analyzed using the SEQC database (Fig. S1E). Next, and in contrast to previous studies, we tested experimentally the validity of our analytical findings correlating the expression of SA-c1 genes with a better patient prognosis. To this aim, we selected the candidate gene NXPH1, one of the top-ranked genes from the SA-c1 subset, on the basis of several complementary arguments (listed in response to the reviewer #2’ comment #4). We thereby analyzed how modulating the expression of NXPH1 or that of its receptor α-NRXN1 affect the growth and metastatic potential of human NB cells. The results obtained argue for the validity of our model, by proposing that the neural crest-derived sympatho-adrenal developmental program, in particular the SA-c1 signature, plays a complex role in NB tumorigenesis: it facilitates tumor growth but blocks metastasis formation, hence opposing NB malignancy.
**Minor comments.**
6- In comparison with other cohorts that include low- and intermediate-risk NBs as non-HR NBs, the reviewed data specifically includes low- and high- risk NBs. It is important that the authors include a characterization of intermediate-risk neuroblastomas in their analysis.
Reply: The samples forming the HSDJ cohort were all obtained from NB patients diagnosed and treated at the Hospital Sant Joan de Déu. Several clinical and biological parameters were used for classification, among which the age of the patient. If we apply the cut-off point of 1 year (used at the time the samples were obtained), our cohort does not include any intermediate-risk NB. If we apply the cut-off of 18 months which is now more usual, our cohort would contain only one case (#HSJD-NB14 - aged 16 months at the time of diagnosis) that could be classified as intermediate-risk.
7- Further details and figures on what precise criteria was used to remove the sample #LR-08 is required. How including this sample changes the reported results?
Reply: As explained in the Methods section, before comparing the transcriptomic landscapes of LR-NBs and HR-NBs, we first assessed the sample dispersion by performing a principal component analysis. This analysis identified one outlier (#LR-08) that was clearly distant from all the other NB samples (both LR- and HR-NBs). We thus removed this sample to limit the dispersion and variability that would have impacted the subsequent analyses, as recommended by the Phantasus guidelines. We will provide an illustration of the PCA including this outlier. We believe that performing de novo the whole bioinformatical analysis including this outlier would not bring any novel significant conclusion.
8- GO-term distribution was assessed using the 50 most-enriched GO-terms. How would the results change if all the significant GO terms were analyzed?
Reply: We will re-analyse the GO-term distribution by including all the significant terms.
9- Was the SEQC 498 (GSE62564) dataset obtained with microarrays (as indicated in the methods) or with RNA-seq (i.e. Illumina HiSeq 2000)?
Reply: Similar results were obtained using either the SEQC database obtained with microarrays or the one obtained with RNAseq. The data presented in our manuscript correspond to the ones obtained with the RNA-seq SEQC database.
10- In methods, the first quartile (Q1) in SA-c1 has a higher limit in 487 samples and the fourth quartile the lowest limit in 4, how many samples (out of 498 NBs) were excluded and why?
Reply: As explained in the Methods section and in Fig. 2F, the complete SEQC cohort was included in this survival analysis. To subdivide the 498 samples of the SEQC cohort into 4 expression quartiles, we evaluated whether the expression level of each of the 242 SA-c1 genes (corresponding to 573 ref-seq IDs) in a given patient sample was above or below the mean expression of that gene in the complete cohort. Samples were then distributed into quartiles based on the number of genes presenting an expression level above the mean. As detailed in the Methods section, the resulting sample distribution was as follows: 487≤Q1≤360 (124 patients); 359≤Q2≤250 (125 patients); 249≤Q3≤135 (126 patients) and 134≤Q4≤4 (123 patients).
11- In the 503 DEGs between LR-HR NBs, NTRK2 and MYCN are not included, even if the HR samples included MYCN amplified tumors. Can the authors comment on this?
Reply: MYCN did not pass the cut-off when comparing its expression levels in LR-NB and HR-NB samples (showing an adjusted P=0.10226 for a cut-off of adjusted PNTRK2 was present in our initial 12,000 gene dataset, but it was not differentially expressed in any of the comparisons made (LR vs fAG: adj-P=0.4916; HR vs fAG: adj-P=0.63757; HRvsLR: adj-P=0.90431)
12- The authors mention that the top 30 genes found in cluster c1 (and also in c2) are correlated with favorable patient prognosis. Is it the case that *all* the genes in c1 (and also c2, c3 and c4) are significantly associated with a favorable or else unfavorable prognosis?
Reply: As presented in the datasheet “KM analyses” of Table S3:
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32 out of the 33 genes (97%) of the cluster c2 correlate to an unfavorable prognosis, the 33th gene showing no particular correlation.
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29 out of the 32 genes (91%) of the cluster c3 correlate to a favorable prognosis, 1 gene correlates to an unfavorable prognosis and the last 2 do not show any particular correlation.
For the clusters c1 and c4, we focused on the top 30 genes because these clusters contain numerous genes (338 and 100, respectively). The results obtained showed:
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All the top 30 genes (100% of the genes tested) of the cluster c1 correlate to a favorable prognosis
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23 out of the top 30 genes (77% of the genes tested) of the cluster c4 correlate to an unfavorable prognosis, whereas the other 7 genes do not show any particular correlation
We believe that these results are convincing enough. However, if considered mandatory we will assess the prognosis of all the genes found in clusters c1 and c4.
13- The high expression of a (significant?) number of genes in cluster c4 is observed in patients with worst outcome (i.e. lower event-free survival), including ATR, HIF1A, ING2, POLR2L, SRPRB (498 SEQC, analyzed with R2).
Reply: Indeed, 23 out of the top 30 genes (77% of the genes tested) of the cluster c4 correlate to an unfavorable prognosis, which appears to us as a significant number of genes. We will test whether the expression of the remaining genes forming the cluster c4 also correlate to an unfavorable prognosis.
14- Regarding the 242 genes in the core SA signature, although its a smaller number, the expression of several genes in the core SA signature with a higher expression in HR compared to LR belonging to clusters 2, 3, and 4 is observed in worst outcome patients in the 498 SEQC cohort (CHD7, DNMT1, HMGA1, HSD17B12, LBR, LSM7, MCM4, NKAP, POLA1, and others). Is this small fraction significant?
Reply: The genes presenting a higher expression in HR-NBs than in LR-NBs are found either in cluster c2 or cluster c4. The core SA signature retrieved 262 genes of the 503 LR vs HR DEGs, of which 14 belong to cluster c2 (including CHD7, DNMT1, HMGA1, LBR, LSM7, MCM4, and POLA1) and 3 to cluster c4 (UQCRFS1, NKAP and HSD17B12). As shown in the “KM analyses” datasheet of Table S6, these 17 genes all correlated with an unfavorable prognosis.
15- In Kildisiute et al. 2021, NRXN1 is expressed in SCPs, while NXPH1 is expressed in bridge, chromaffin and sympathoblastic cells. How are the microenviroment of these cells regulating the expression of these genes in a developmental context (particularly as sympathoblastic cells are know to have larger proliferative capabilities than SCPs)? how is this cell heterogeneity replicated by a NB cell line? are mesenchymal and adrenergic cells expressing differentially NRXN1 and NXPH1?
Reply: Unfortunately, the literature about NXPH1 remains very limited (less than 40 articles referenced in Pubmed) and nothing is known about the regulation of its expression during development. The data from Kildisiute et al (Kildisiute et al, 2021) indeed identified NXPH1 in the signatures of bridge cells, chromaffin cells and sympathoblasts, while its receptors NRXN1 and NRXN2 were found in the transcriptomic signatures of all 4 SA cell types. Interestingly, the data provided by Kildisiute et al further established that the expression of NXPH1, NRXN1 and NRXN2 is specifically enriched during the pseudo-time transition from bridge cells to sympathoblasts. This suggests that NXPH1/α-NRXN signaling might be particularly important at that stage and could participate in regulating this transition. But this remains purely speculative and it will need further investigation.
We initially used a panel of 10 human NB cell lines harboring distinct characteristics in terms of genetic profile and morphological properties. We did not find any specific correlation between NXPH1 or α-NRXN1/2 expression and the different types of NB cell lines. We will provide an illustration of this observation in the revised version of the manuscript.
NB cells can convert or be reprogrammed from an adrenergic state, which is less chemoresistant in vitro, to a mesenchymal state (van Groningen et al, 2019). As asked by the reviewer #1, we investigated the expression of NXPH1 and α-NRXN1 in relation with the mesenchymal vs adrenergic status of NB cells (using the dataset GSE90803 from (van Groningen et al, 2019). We found that both genes are expressed at higher levels in cells of the adrenergic phenotype, suggesting that NXPH1/α-NRXN signaling might be particularly relevant for the maintenance of this phenotype. If needed, we will provide an illustration of this observation in the revised version of the manuscript.
16- Figure 1B and C, 2B,D: might the information provided be enhanced? otherwise these inserts might be excluded.
Reply: We thought that the panels presented as Fig.1B, C and 2B, D would be helpful to the readers. We could remove them if the editors and reviewers consider it mandatory.
17- Figure 3D: Kildisiute et al. 2021 data and GTEX available at human protein atlas indicate expression of NRXN1 and NXPH1 in developing and adult adrenal gland. Might the results illustrated suggest a confounding effect in the sampled fetal adrenal glands, perhaps from cortex?
Reply: The samples of human fetal adrenal gland from which RNA was extracted were obtained from donations (samples staged at 22 weeks post-conception or 2 days after birth) and evaluated by a pathologist who confirmed their correct preservation before sample processing. As fetal adrenal glands are very small tissues, successfully separating the cortical and adrenal regions requires micro-dissection, which was not applied. These samples were instead processed entirely, presenting a ratio of medulla/cortex tissue according to their developmental stage.
18- The authors conduct extensive experiments in NRXN1, and make conclusions about its role in for instance metastasis, nevertheless the LR-NB/HR-NB SA signal only includes NRXN2. Can the authors comment on the differences between NRXN1s and NRXN2?
Reply: NRXN1 and NRXN2 were both found to be differentially expressed between LR vs fAG and HR vs fAG, and were thus retrieved among the list of 3.096 common DEGs (Table S3). NRXN2 was further found in the list of 503 LR vs HR DEGs (adjusted P=0.037), showing higher expression levels in LR-NBs than in HR-NBs (see Fig.3D). NRNX1 presented an expression profile comparable to that of NRXN2 (Fig.3D) but did not pass the cut-off (adjusted P=0.38) due to an increased variability in HR-NB samples and was thus absent from the list of LR vs HR DEGs. In vitro, the expression levels of NRXN1 and NRXN2 showed comparable patterns. When we initiated the functional experiments there was no fluorescence-conjugated antibody available to detect and sort α-NRXN2, but there was for α-NRXN1. This is the practical reason that led us to focus on α-NRXN1 in the second part of our study.
Reviewer #1 (Significance (Required)):
The significance of the study relies in investigating the role of selected targets in neuroblastomas within a risk group. In particular, HR-NBs have poor outcomes and are generally metastatic at the time of diagnosis.
The results of the manuscript are somehow consistent with a recently published manuscript analyzing LR- and HR-NBs from a single-cell perspective. The manuscript will be enhanced by conducting the suggested comparison between the reviewed and the reported results. The authors further need to comment why HR-NBs markers, particularly MYCN is not recovered in the LR-NB/HR-NB and the LR-NB/HR-NB SA signals. Also they need to comment on possible confounding effects in the fetal adrenal gland.
The paper is directed to a broader audience of cancer and developmental biologists, and computational biologist. Yet further statistical support needs to be provided.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
**Summary:**
Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.
In this paper, Fanlo-Escudero et al., determines gene signatures differentiating low-risk (LR) neuroblastoma (NB) from high-risk neuroblastoma (HR-NB), as well as LR- and HR-NB as an entity from human fetal adrenal gland. They identify a transcriptional signature corresponding to a core sympathoadrenal lineage that can discriminate between LR-NB and HR-NB. This signature is composed of genes associated with favorable patient outcome. The authors further choose one gene, NXPH1, for functional analysis and investigates the effects this gene has on NB progression using in vitro assays, chick CAM assay and mouse in vivo models. The authors conclude that this transcriptional signature can distinguish LR-NB from HR-NB and that NXPH1 is involved in NB cell growth.
**Major comments:**
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Are the key conclusions convincing? The key conclusions are 1) a core SA lineage signature can discriminate between LR-NB and HR-NB, and 2) NXPH1 represses NB malignancy (in terms of metastatic capacity) and is a therapeutic target. The first conclusion is indeed convincing, and not contradictive to common beliefs. The second conclusion is poorly supported by data. The authors perform a range of experiments using in vitro and in vivo settings, but lack some fundamental experiments and overstate their findings.
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Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? Several claims should be softened, re-phrased and/or clearly marked as preliminary or speculative. No data nor claims need to be removed.
For example, the following statements need to be changed:
1- Page 10, second paragraph: The authors state "Remarkably, NXPH1 and α-NRXN1/2 levels increased in all the NB cell lines harbouring a sphere-forming capacity (Fig. 3E), thereby revealing a strong positive correlation between the expression of NXPH1 and α-NRXN1/2 and the acquisition of a NCC stem cell identity". The authors only show that NXPH1 is expressed in 8 out of 10 NB cell lines. Sphere-forming capacity is displayed in a relative and not absolute scale which makes it difficult to assess which cell lines that do form spheres and to what extent. The capacity to form spheres (from low to high) does not correlate to the levels of NXPH1 in the different cell lines.
Reply: In its current form, Fig.3E presents via a heatmap representation how the expression of selected genes changes after growing cell lines in sphere-forming conditions as compared to basal (normal) ones. We understand from various reviewers’ comments that this representation has been misleading. We will change it, showing more explicitly the expression levels both in basal and sphere-forming conditions and will bring further details on how the sphere-forming ability of each cell line was assessed and characterized.
2- Page 13, paragraph 1. The authors write "...these data revealed that NXPH1/α-NRXN1 signaling is necessary and sufficient for NB tumor growth in vivo". This is an overstatement. Tumors still form, meaning that NXPH1 signaling is not sufficient.
Reply: Indeed, tumors still form after xenografting sh-NXPH1 or sh-NRNX1 cells but they form with a decreased frequency. Specifically, sh-NXPH1 cells formed tumors in 4 out of 6 xenografted mice, and the 4 tumors all showed a markedly reduced volume. Tumors formed from sh-NRNX1 cells were observed in only 2 out of 5 xenografted mice, with 1 of the 2 tumors showing a markedly reduced volume. We consider that these results support the conclusion that inhibiting NXPH1/α-NRXN1 signaling impairs tumor growth, affecting both tumor initiation and tumor growth. We however understand the reviewer #2’s comment and will thus rephrase this part accordingly. In addition, we will provide complementary data showing the mean volume of the tumors generated in the distinct experimental conditions.
3- Throughout the text, the authors convert their statements. One example is page 15, first paragraph. They write "...growth of NB cells but markedly restrict their metastatic potential", but they do not show this. Instead, they only address the opposite situation - Knockdown enhances metastasis. This is not equal to their statement. See other experiments in other sections. The authors need to go through the manuscript and make sure that they explain their conclusions to actually fit their experiments.
Reply: We will follow the reviewer #2’s recommendation and will rephrase the conclusions whenever needed to better fit to the experimental results.
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Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary to evaluate the paper as it is, and do not ask authors to open new lines of experimentation.
**Issues to respond to:**
4- The authors should more clearly explain why they choose to further study NXPH1 (second highest on the list), involved in synaptogenesis and neurotransmission, instead of SOX6, highest on the list, that is highly relevant in neural crest development.
Reply: The combination of several reasons led us to choose NXPH1 for functional studies:
- NXPH1 is a secreted factor, whose activity might be much more easy to target and modulate in future pharmacological/clinical assays than that of a transcription factor like SOX6
- NXPH1 and its receptor NRNX2 both came out in the list of SA-c1 genes, and NRXN1 presented a comparable expression profile (higher levels in LR-NBs than in HR-NBs, although it did not pass the cut-off required to appear in the SA-c1 list), emphasizing the putative importance of this signaling pathway in NB tumor biology
- The functional involvement of NXPH1 or that of its receptors has never been addressed in cancer formation or progression to date
5- When the authors investigate the expression of NXPH1 and other genes and compare that with sphere-forming capacity (Fig. 3E and page 10) they analyze expression in cells cultured in basal medium while sphere-forming capacity is measured after 5 weeks in restricted medium. How does the expression of the analyzed genes change under these conditions?
Reply: In its current form, our Fig.3E is actually showing how the expression of the analyzed genes changes after growing cell lines in sphere-forming conditions as compared to basal (normal) ones. As explained above (reviewer #1, point #1), we understand that this representation has been misleading and we will modify it.
6- Is the subpopulation of NRXN1+ cells low (1.5%) because these samples are from aggressive ("High-risk") cell lines?
Reply: All the NB cell lines used in this study derive from HR-NB tumors. As mentioned by the reviewer #3, there is no cell line modeling low-risk neuroblastoma to date. We indeed observed that the proportion α-NRXN1+ cells, as detected by FACS, was very low in all three cell lines tested. A similar observation was made using cells dissociated from three different patient-derived xenografts. We believe that a similar observation made in 6 samples of distinct origins highlights the consistency of finding a low proportion of α-NRXN1+ cells in NB samples.
7- The authors state "...the number of cells quantified per tumor section was decreased by ~50% for the α-NRXN1+-deprived cells relative to their control (Fig. 3J, K), thus revealing that α-NRXN1+ cells are required to support NB tumor growth in vivo". This is not a correct conclusion. This experiment shows that a-NRXN1- cells do not grow and expand to the same extent as control cells. They cannot say that a-NRXN1+ cells support NB growth without comparing growth between a pure a-NRXN1+ and control cells.
Reply: Unfortunately we could not assess the growth of purified α-NRXN1+ cells, given the low number of α-NRXN1+ cells that could be sorted as compared to the numbers of cells required to perform a CAM assay. We thus opted for comparing the growth of total SK-N-SH cells with that of SK-N-SH cells in which the α-NRXN1+ subpopulation had been experimentally removed. We believe that the results obtained convincingly argue for the importance of the α-NRXN1+ subpopulation in promoting NB proliferation and growth. Nevertheless, we understand the reviewer #2’s comment and will rephrase the conclusion.
8- The authors use shRNAs to knock down NXPH1. They enrich their cells by two means - puromycin or doxocycline. This results in equal cell populations. The authors however state that they use doxocycline to circumvent the growth arrest they observe with puromycin selection. They need to elaborate on this and show why this would be the case and what difference the two methods do and show.
Reply: We generated two types of knock-downs: a constitutive one and an inducible one. Puromycin was used to select constitutive knock-downs, whereas doxycycline was used to trigger sh-RNA production in an inducible manner, in stable clones previously established through neomycin selection. We apologize if this was not stated clearly enough in the manuscript and we will correct it.
9- The major flaw of this paper is that the authors use one cell line in total, and even more that they use the same cell line for both knockdown and activation. Since they do show that different NB cell lines have different expression levels (ranging from high to absent), they should choose one cell line for KD and one for overexpression. The authors could also do a rescue experiment with knockout and gain-of-function (e.g., construct that will not be targeted by the shRNA) in the same cells.
Reply: Since NXPH1 is a secreted protein, we needed a cell line that expresses both NXPH1 and its receptors to expect noticing effects on NB cell behavior when their expression is reduced. We reasoned that performing a gain-of-function of NXPH1 in a cell line that does not express its receptors would have no interest, and vice versa. We also believe that it is more conclusive to perform gain- and loss-of-function experiments in the same cell line, because of the likely differences in cell behavior and aggressiveness of distinct cell lines. We however agree that our conclusions would be strengthened if similar conclusions were reached using different cell lines. We will thus perform growth and metastasis assays both in vivo and in the CAM using NXPH1 and α-NRXN1 shRNAs in an additional cell line. We will moreover consider performing rescue experiments and will think about the best methodology to do so.
10- They only use one shRNA after trying several (Fig. S4). The efficiency is substantially variable and not convincing. As stated also elsewhere in this review, they need to check protein level. And to ensure that their results are not off-target they should perform at least some crucial experiments with two shRNAs.
Reply: We agree that the decreased mRNA levels caused by NXPH1 and α-NRXN1 shRNAs showed variability. Yet, they were sufficient to significantly reduce cell viability, which was impaired by two distinct shRNA constructs, both for NXPH1 and α-NRXN1. To complete these experiments as recommended by the reviewer #2, we will assess how NXPH1 and NRXN1 expression is altered at the protein level by western-blotting. We will moreover address possible off-target effects by RT-qPCR.
11- Why don't the authors add BrdU post-implantation? This is easily done in the egg considering the accessibility and would better reflect the proliferation in vivo.
Reply: Adding BrdU pre-implantation allowed us to get a read-out of the global proliferative behavior of NB cells over the whole post-implantation duration. Adding it at the end of the post-implantation would have only allowed us to assess the proliferative behavior of cells at the end of the experiment. We believe that this would have been less informative.
12- Why do the authors switch between CAM and mouse xenografts? I understand that the mouse model must be employed for "metastasis", but can it be explained why and when they perform the different "tumor growth" experiments?
Reply: The CAM assay was used for 2 reasons: 1) when cell numbers were limiting (i.e. testing the importance of the α-NRXN1+ subpopulation for tumor growth), and 2) to perform a gain-of-function strategy using a recombinant rNXPH1 protein and testing its effects on tumor growth over a duration of 1 week. Such experiment would not have been possible using mouse xenografts, due to the extended experimental duration (7-8 weeks) of this assay and to the need for repeated rNXPH1 injections. The rNXPH1 gain of function experiment in the CAM and the NXPH1/α-NRXN1 loss-of-function experiment using mouse xenografts were performed concomitantly.
13- Why do the authors do left ventricle injections for metastatic studies and not AG implantations?
Reply: We reasoned that cell injections into the left ventricle were ideal to test the organotropism of metastatic NBs, as this methodology facilitates cell dissemination and colonization of organs targeted by NB metastasis such as the liver and bone marrow. Cell implantation into the adrenal gland is especially useful to study cell growth in one of the primary sites of NB growth, which was not our experimental purpose at that stage of the study.
14- The measurement of bioluminescence is very difficult to interpret. The authors discuss metastatic spread, but the images show only large blobs covering the heart and areas surrounding it, especially for the sh-αNRXN1. To be able to say that the cells have colonized specific organs the authors need to dissect these organs and perform staining. I see it as tumors recur rather than particularly metastasize when they re-appear after 6 weeks.
Reply: The parameters for bioluminescence detection were set equally for all experimental conditions. The differences in bioluminescence intensities observed in mice injected with control cells compared to those injected with shNXPH1 and shNRXN1 cells explain why it is so intense in the case of shNXPH1 and shNRXN1 cells.
As suggested by the reviewer #2, we had further dissected the mice and recovered the organs of interest. We will provide additional data confirming that an intense bioluminescent signal was effectively detected in the liver and hind legs (containing bone marrow) of mice injected with shNXPH1 and shNRXN1 cells, but not in those injected with control cells.
15- How do the authors explain results presented in Fig. S5: There are more HuNu+ cells but tumor size is unchanged?
Reply: In our experimental CAM setup, 5·105 SK-N-SH cells were embedded in 10μl of Matrigel, which served as a matrix facilitating tridimensional NB cell proliferation. The addition of rNXPH1 increased the number of HuNu+ (NB) cells per section and per mm3 (Fig.4G, H and Fig.S5C), without significantly increasing the tumor area (Fig.S5D) nor the tumor/matrigel volume (data not shown in the current version, but it will be included in the revised version). As illustrated in Fig. 4G, the matrigel was not totally filled with NB cells, even at the time of recovery. We thus deduced from these observations that more NB cells progressively filled the matrigel, without reaching the point where they significantly altered the tumor/matrigel volume. Nevertheless, we can provide additional data revealing that the addition of rNXPH1 caused a slight, yet reproducible increase in the tumor/matrigel weight, in agreement with the increased cell density already shown in Fig.S5C.
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Are the suggested experiments realistic for the authors? It would help if you could add an estimated cost and time investment for substantial experiments.
Some suggested experiments take time (orthotopic implantations, rescue experiments, adding cell lines and # of shRNAs).
However, as long as the authors discuss and address their methods for in vivo growth (in ovo vs in vivo vs their choice of metastasis model), an orthotopic AG model is not necessary. The authors should however consider it for future studies.
Experiments required to support their conclusions: A rescue experiment and use of different cell lines for KD and overexpression is somewhat time-consuming, as well as ensuring KD at protein level and include an additional shRNA in crucial experiment. I expect that this would take 2-4 months.
- Are the data and the methods presented in such a way that they can be reproduced? 16- The authors should elaborate on their methods, but in general they are reproducible.
Reply: As requested, we will bring further details on the experimental setups.
Are the experiments adequately replicated and statistical analysis adequate?
17- In several places the number of replicates is questionable. Especially at the end of page 26, the authors state that they have performed n=1-4 replicates. N=1 replicate is never ok. In several instances they do n=2 replicates. This can be acceptable but the authors could address this.
Reply: When assessing the sphere-forming capacity of the different NB cell lines, some cell lines only produced spheroids in 1 of the 4 replicates tested. This is a result in itself, which helped us classifying cell lines based on their sphere-forming capacity. We understand that this Methods paragraph was elusive. We apologize for it and will clarify this aspect.
**Minor comments:**
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Specific experimental issues that are easily addressable. Mainly text changes can be seen as minor. For experiments and other issues, see other sections.
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Are prior studies referenced appropriately? Yes. 18- The reference list is not coherently styled.
Reply: We understand that using numbered references can be annoying. We will adapt the reference format to stick to the guidelines of the specific journal to which the manuscript will be addressed.
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Are the text and figures clear and accurate?
19- Overall, the paper is written in a complex way and difficult to easily comprehend. For example, the authors need to clarify several issues on the material they use and experiments they perform. I suggest substantial re-writing to better convey their messages. Sentences should be short and clear, data explained in the context of it was derived.
Reply: We will take into consideration the reviewer #2’s suggestion and modify the manuscript to facilitate its comprehension.
The following text edits and clarifications are required:
20- Page 3. The authors write "cell-of-origin" in several places, this should be changed to "cells-of-origin" (i.e., plural). The view that all NBs originate from only one cell is too simplistic, and the authors should definitely edit this considering that they are investigating different subgroups of NB.
Reply: We will correct this mistake.
21- Page 5. The authors MUST define where the material from the 18 NB patients as well as fetal AG derive from. There is no reference, and taken from the material&methods section, the transcriptome data from these data has not been generated by the authors themselves?
Reply: The transcriptomic data used herein have indeed been previously generated by two co-authors of the study, and the corresponding reference is cited (ref #20; (Gomez et al, 2015). All the NB samples included in our transcriptomic analysis were obtained at the time of diagnosis from patients attended at Hospital Sant Joan de Déu (HSJD, Barcelona, Spain). Tumors were evaluated by a pathologist and only the snap-frozen pre-treatment samples showing at least 70% of viable tumor content were included for analysis. Neuroblastoma risk assessment was defined by the International Neuroblastoma Staging System (INSS). Samples of normal fetal adrenal gland (n=2) were used as a non-tumoral reference tissue. Total RNA from frozen samples was extracted by TRIzol® Reagent. High quality RNA (RINe>7.00) was hybridized to Human Genome U219 microarray plates at the Functional Genomic Unit, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS, Barcelona, Spain) according to Affymetrix standard protocols. Microarray data are deposited in NCBI (#GSE54720). We will include this information in the Methods section of the revised version of the manuscript.
22- Why do the authors choose to work with the data set from Jansky et al in particular?
Reply: Actually, we chose to work with the data from Kildisiute et al. (ref #16 (Kildisiute et al, 2021). We selected this study because: 1) they identified the transcriptional signatures of the 4 SA subtypes relevant to our study and additional related signatures of interest (adrenal cortex, mesenchyme…), 2) these data were obtained from human tissues, and 3) these signatures were easily accessible from their supplementary data.
23- Page 7, paragraph 1. The authors write "Remarkably, 67% of the 503 genes found in this signature, which formed the cluster c1, are both associated with a neural identity and a better patient outcome". I do not agree that this is remarkable and the authors should remove this word.
Reply: We will remove the term “Remarkably”.
24- Page 10, second paragraph. The authors should clarify that the expression levels of the displayed genes are derived from qPCR analysis (if I have understood that correctly, I had to find and guess this from the M&M section), as well as explain how they have set the scale for sphere-forming capacity and what this corresponds to. What do the colors actually represent?
Reply: We apologize for this information lacking in the Results section. We will precise that the expression levels were assessed by RT-qPCR, we will add a representation of their expression levels in basal culture conditions and will improve the explanation of the assessment of the sphere-forming capacity of the NB cell lines.
25- Page 11 and onwards. The authors write "deprived of their a-NRXN1+ subpopulation". This is highly confusing and difficult to read. The authors should write "a-NRXN1- subpopulation"
Reply: We will follow the reviewer #2’s recommendation and change the text for "α-NRXN1- subpopulation".
26- Page 11, end of paragraph 2. The authors write "...arguing that NXPH1/α-NRXN signaling could control NB growth and/or aggressiveness". Number of cells do not directly correlate to aggressiveness, and this needs to be re-phrased to only state what the experiment actually shows - proliferation of a-NRXN1- cells.
Reply: We will rephrase this sentence according to the reviewer #2’s recommendation.
27- I am in favor of using the CAM assay as a complementary system. The authors however use this to define "...required to support tumor growth in vivo". The CAM assay using NB (i.e., transformed cells) shows the growth of these cells in response to presence of blood vessels and not a full tumor micro-environment. This should be clarified.
Reply: We will clarify this aspect.
28- As stated in the previous comment, the authors write "...required to support tumor growth in vivo". The next paragraph has the following headline "NXPH1/α-NRXN signaling stimulates NB growth". This is to me the same thing. The authors should elaborate on how these differ, or if they use them to show the same thing, clearly state that.
Reply: We will better explain how these distinct pieces of evidence are complementary and reinforce the conclusion that NXPH1/α-NRXN signaling stimulates NB growth.
29- The authors conclude that NXPH signaling can be used as a therapeutic target. This would however be extremely difficult considering the opposing effects shown on growth vs metastasis. I agree that it is important to find means to inhibit metastasis, but that does not mean we can allow for enhanced growth of the primary tumor. A better reflection would be to use this as a biomarker, but this can only be predictive/speculative since the authors do not perform for example a tissue microarray to show this at IHC protein level, something that is currently the practice in the clinic.
Reply: While we agree with reviewer #2 that we cannot allow for enhanced growth of the primary tumor, we believe that having identified a secreted factor whose activity inhibits NB metastatic potential is a novel and important finding. We did not wish to suggest that our experimental setup could be directly applied to inhibit the metastatic dissemination of HR-NB tumors. However, we believe that our findings can set the basis of a therapeutic design in which NXPH1/α-NRXN signaling would be enhanced locally to prevent/block metastases from HR-NB tumors.
As mentioned in the Discussion section (page 17), one study reported that NXPH1 can be used as a DNA methylation biomarker associated with a good prognosis for NB patients (reference #54, (Decock et al, 2016).
**Material and Methods:**
30- The authors have misspelled the cell line SK-N-BE(2)c.
Reply: Indeed. We will correct this mistake.
31- Why are some cell lines grown in 20% FBS? This is not standard and could impact the results.
Reply: The 3 cell lines of our panel which were grown in 20% FBS correspond to the 3 cell lines of the mixed subtype, including SK-N-SH, SK-N-Be(2)c and IMR-32 cells. These cell lines have been established and originally grown in presence of a high FBS content (Tumilowicz et al, 1970; Biedler et al, 1973; Ciccarone et al, 1989). In our hands and as recommended by the colleagues that provided us with these cell lines, growing the cell lines in presence of 20% FBS was indeed crucial to sustain their morphological heterogeneity. While we agree with the reviewer #2 that culture conditions could affect cell behavior in vitro, we wish to emphasize that our main conclusions are derived from in vivo experiments. We are thus convinced that our main findings were not impacted by in vitro culture conditions.
32- The authors should state what the tumor volume limit in their ethical permit is (page 30).
Reply: The tumor volume limit was set at 1,500 mm3 as specified by our ethical committee.
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Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
33- Fig. 3E. As discussed elsewhere, the authors should clarify the scale/meaning for sphere-forming capacity
Reply: As explained above, we will modify the representation of the results shown in Fig.3E to improve their clarity and facilitate their comprehension.
34- Fig. 4D. What do the numbers (i - vi) refer to? I cannot find this in the figure, figure legend, text or material&methods.
Reply: These numbers were used to call different tumors and show their GFP content, as appearing in the lower part of this panel. We will precise this information in the corresponding figure legend.
35- The authors do not present the tumor volume in Fig. 4. The authors discuss tumor growth in the text and this data should be included.
Reply: We agree with the reviewer #2’ suggestion and will present tumor volumes in the revised version of the manuscript.
36- Fig. S4. Knockdown efficiency is variable, and efficiency does not correlate to growth capacity. Especially because of this, the authors need to investigate this at protein level.
Reply: As requested by the reviewer #2, we will investigate the knockdown efficiency at the protein level.
Reviewer #2 (Significance (Required)):
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Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. This paper is partly within the scope of the ongoing debate of NB origin (see references below). This paper is however not as extensive, provides no new patient material (applies data from Jansky et al), and do not address the actual cell-of-origin (which the authors themselves also clearly states). This paper provides new gene signatures that can be used to define low-risk vs high-risk patients which is highly important to the field, but these signatures are not unexpected and does not add a significant advance to the field. The authors also do not address how these signatures could be applied clinically. The authors do not use any new methodology. With this said, with revision of the paper, it will still add to the current focus on NB biology research.
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Place the work in the context of the existing literature (provide references, where appropriate). 37- There is a recently initiated, important and extensive debate about the cells-of-origin for NB. The authors indeed bring this up in the paper and also state that they do not intend to add to this debate. They use data from one paper from referenced debate above, and I would argue that because of this fact, and that they extract gene signatures from it, they do, at least partly, touch on the NB cells-of-origin debate, and the authors should put their results into context, from a big picture perspective. As of now, they dodge this complex issue.
References: Dong et al., Cancer Cell 2020; Hanemaaijer et al., PNAS 2021; Jansky et al., Nat Genet 2021; Kameneva et al., Nat Genet 2021; Kildisiute et al., Sci Adv 2021; Furlan et al., Science 2017).
Reply: We understand the reviewer #2’s argument. We will thus try to put our results in perspective regarding the NB cells-of-origin.
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State what audience might be interested in and influenced by the reported findings.
This paper will be interesting for scientists within the neuroblastoma field, in particular those working on defining NB subgroups in correlation to developmental stages.
- Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Reviewer's expertise: Neuroblastoma, neural crest, trunk neural crest, chick embryos, mouse models, in vitro models
Parts of paper outside expertise of the reviewer: Analysis of the bioinformatics data (i.e., extracting signatures).
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
**Summary: short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).**
Dr. Le Dréau and colleagues provide a transcriptional analysis comparing low versus high-risk neuroblastomas using a cohort of 18 patients. By comparing them to normal transcriptional signatures obtained from human fetal adrenal glands, they conclude that low risk tumors share a specific core sympatho-adrenal developmental program, which is associated with favorable patient prognosis. Among this signature, they specifically assess the role of NXPH1/a-NRXN in vitro and in vivo using different models (cell lines, PDX-derived cell lines, mouse and chick xenografts). They propose that NXPH1/a-NRXN axis promotes neuroblastoma tumor growth via cell proliferation, and inhibits metastases.
**Major comments:**
- Are the key conclusions convincing?
There are major points that need to be addressed before being convinced by the conclusions.
1- The choice for using the SKNSH cell line, among others, to study the role of NXPH1/a-NRXN axis need to be explained. Indeed, whereas there is no cell line modeling low-risk neuroblastoma, authors should properly illustrate the basal expression of NXPH1/a-NRXN in the cell lines and not the ratio provided in Fig3E. If I understood well, according to Fig3F, only 1.5% of SKNSH cells express NRXN1. Could the authors provide FACS plots? And explain how they can sort high and low expressing cells among 1.5%? And then how do they justify using shRNA approach in a cell line in which only 1.5% are concerned cells? Also, the authors should prove the efficiency of the shRNA used on each specific target for both in vitro and in vivo studies by WB.
Reply: We chose the SK-N-SH cell line for experimental assays based on the following facts: 1) the expression levels of NXPH1 and α-NRXN1/2, 2) this cell line showed the highest sphere-forming capacity, 3) this cell line harbored the highest percentage of α-NRXN1+ cells detected by FACS among the different cell lines tested, and 4) this cell line is of a mixed type, which is supposed to encompass more cell heterogeneity than other (N, I and S) types, thus reproducing more faithfully the complex heterogeneity of primary NB tumors.
In a revised version of the manuscript we will provide data on NXPH1 and a-NRNX1/2 expression levels in basal culture conditions, and will improve the representation of Fig.3E to facilitate its comprehension. The method used to sort α-NRXN1+high, α-NRXN1+low and α-NRXN1- cells is explained in the Methods section (page 25). As requested by the reviewer #3, we will provide FACs plots to illustrate the sorting method.
As requested by the reviewers #2 and #3, we will assess the shRNA efficiency by testing the knockdown at the protein level.
2- The conclusion of inhibition of metastatic process is not supported by enough data. To achieve such a conclusion, authors should provide more than one in vivo experiment (which need to be completed already with the proof of protein deregulation). Some in vitro characterization of metastatic properties such as invasion and migration assays and/or transcriptional analyses could be done.
Reply: We agree with the reviewer #3 that that our conclusion on the anti-metastatic ability of NXPH1/α-NRXN signaling would be reinforced by complementary experiments. To this aim, we propose to test how NXPH1/α-NRXN knockdown affects the metastatic potential of the SK-N-SH cell line and of another cell line in the CAM assay. This assay can indeed be used to assess not only NB tumor growth (as we already did), but also NB cell invasion in target organs (such as the liver and the bone marrow), thus mimicking a metastatic dissemination.
3- Why don't the authors use the transcriptome dataset of 498 patients to realize a more powerful comparative study of low versus high risk tumors (Zhang et al, Genome biology, 2015)? The authors should show the expression plot of NXPH1/a-NRXN in low versus high risk patients, in their cohort of 18 patients but also in the cohort of 498 patients. How do the authors reconciliate the idea that NXPH1/a-NRXN could be associated to stem cell identity but low risk tumors?
Reply: As explained in response to the reviewer #1’ (comment #1), our strategy entailed comparing the transcriptomic signatures of LR-NB and HR-NB samples to the one of a non-tumoral, healthy tissue such as the fetal adrenal gland. The online SEQC database cited by reviewers #1 and #3 does not contain healthy samples, and therefore could not be used for this initial step of the analysis. This is why we decided to use as a starting point the cohort of samples from the Hospital Sant Joan de Déu, which further provided the advantage of comparing samples from patients all diagnosed and treated at the same facility. We then used the SEQC database at different steps of our analytic strategy (Fig.S1F, 2F-H, 3A-B, S3) to test the relevance and coherence of the results obtained with the HSJD cohort in the context of a larger cohort.
As requested by the reviewer #3, we will provide a dot-plot representation of the expression levels of NXPH1 and its receptors in both the HSDJ and SEQC cohorts.
At this point we can only speculate on the association between NXPH1/α-NRXN expression and stem cell identity. This correlation might simply reflect the fact that cells from human NB cell lines return to a transcriptional program closer to their neural crest-derived identity when grown in sphere-forming conditions (as suggested by the increased expression of p75/NTR). Alternatively, this correlation might reflect the ability of NXPH1/α-NRXN signaling to retain cells in an immature state. Such ability could explain how NXPH1/α-NRXN signaling participates in promoting primary tumor growth, and the fact that their expression is increased in LR-NBs as compared to normal fetal adrenal gland. On the other hand, NXPH1/α-NRXN expression is higher in LR-NBs than in HR-NBs, and our findings suggest that this is linked to the anti-metastatic ability of NXPH1/α-NRXN. We could further imagine that by favoring stem cell identity NXPH1/α-NRXN signaling might also provide LR-NB cells with an enhanced ability to “re-enter” a normal developmental path or to be eliminated. In this regard, it is worth reminding that LR-NBs are detected earlier during development than HR-NBs, and sometimes show the puzzling ability to regress spontaneously.
- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
Details of experiments and supplementary experiments have to be provided (see previous question).
- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.
4- Additional details of the experiments have to be provided (cf above): levels of expression of NXPH1/a-NRXN in cell lines, FACS analyses to prove enrichment and/or depletion, WB for validation of shRNA knock-down etc...Moreover, the conclusion that NXPH1/a-NRXN axis inhibits metastatic potential of neuroblastoma is supported by only one in vivo experiment using SKNSH cell line with shRNAs anti-NXPH1/a-NRXN. It should be completed with invasion/migration assays in vitro for example, and/or transcriptional signature of tumors obtained +/- shRNAs anti-NXPH1/a-NRXN. Ideally, these results could be validated in an additional model.
Reply: As mentioned above, we will perform additional experiments following the reviewer #3’s recommendations, including assessing NXPH1 and α-NRXN1 knockdown efficiency at the protein level, assessing how knocking down NXPH1 and α-NRXN1 expression alters the in vivo growth and metastatic potential of an additional cell line, and studying how knocking down NXPH1 and α-NRXN1 alters the metastatic potential of NB cells using a second and complementary metastasis assay (CAM assay).
- Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.
Yes
- Are the data and the methods presented in such a way that they can be reproduced?
Yes, Material and methods section is well described. But, several points are missing:
5- The paragraphs describing how RNAs from 18 patients and fetal adrenal glands were obtained, how good was the quality, and how transcriptomes have been realized and sequenced are missing.
Reply: The transcriptomic data used herein have indeed been previously generated by two co-authors of the study, and the corresponding reference is cited (ref #20; (Gomez et al, 2015). All the NB samples included in our transcriptomic analysis were obtained at the time of diagnosis from patients attended at Hospital Sant Joan de Déu (HSJD, Barcelona, Spain). Tumors were evaluated by a pathologist and only the snap-frozen pre-treatment samples showing at least 70% of viable tumor content were included for analysis. Neuroblastoma risk assessment was defined by the International Neuroblastoma Staging System (INSS). Samples of normal fetal adrenal gland (n=2) were used as a non-tumoral reference tissue. Total RNA from frozen samples was extracted by TRIzol® Reagent. High quality RNA (RINe>7.00) was hybridized to Human Genome U219 microarray plates at the Functional Genomic Unit, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS, Barcelona, Spain) according to Affymetrix standard protocols. Microarray data are deposited in NCBI (#GSE54720). We will include this information in the Methods section of the revised version of the manuscript.
6- The sequences of shRNAs have to be provided.
Reply: We will provide the oligo sequences used to generate shRNAs against NXPH1 and aNRNX1.
-Are the experiments adequately replicated and statistical analysis adequate?
Yes
**Minor comments:**
- Specific experimental issues that are easily addressable.
7- The authors should show the basal expression of their genes of interest in the patients, cell lines and PDX-derived cell lines, to justify the choice to work then with SKNSH cell line. In addition to the ratio they show in Fig3.E.
Reply: As explained above, we will provide more details on the expression levels of NXPH1 and α-NRXN1/2 in the HSJD and SEQC cohorts and in the NB cell lines used in our study.
8- Authors should also illustrate the FACS analyses they realized in this study, in order to appreciate the quantity of positive cells that are either enriched or depleted.
Reply: The methodology used to sort α-NRXN1+high, α-NRXN1+low and α-NRXN1- cells is explained in the Methods section (page 25). As requested by the reviewer #3, we will provide FACs plots to illustrate this methodology.
9- Authors could precise in their schemes that DOX is maintained in vivo.
Reply: We will follow the reviewer #3’s suggestion.
10- The number of mice need to be integrated in each experiment.
Reply: The numbers of mice used to assess growth and metastasis in vivo were included in the corresponding figure legends (Figs. 4D, 4E and 5C) and appear discreetly on the panel 4D (to the right). We will follow the reviewer #3’s recommendation and add these details on the corresponding figure panels.
- Are prior studies referenced appropriately?
11- No some elements are not right in the introduction:
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Mutations of PHOX2B are not associated to poor prognosis.
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Original publications could be provided instead of reviews.
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Genetic alterations in NB are not only 16%, the authors forgot to mention TERT and ATRX.
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Maybe the NXPH1 methylation in ref 54 could be more explicit, if DNA methylation is detected on NXPH1, it would be of poor prognosis because driving low expression ..?
Reply: We will follow the reviewer #3’s critics and correct the mistakes and information lacking in the introduction and discussion sections.
- Are the text and figures clear and accurate?
Text is very clear and well written, as are the figures.
- Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
Already mentioned before: FACS and WB are needed.
Reviewer #3 (Significance (Required)):
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. This work aimed first at better understanding the fundamental transcriptional differences between two risk groups in Neuroblastoma. The authors defend the conceptual idea that these two groups represent two distinct diseases, which is not new in the area but requires attention, and indeed supported here by distinct transcriptional signatures.
Using published signatures of fetal sympatho-adrenal system, they define a core transcriptional program that is more strongly expressed in low-risk tumors, but we already know that tumors of this category are often more differentiated, and by definition expressed strong markers of SA differentiation, whereas high-risk tumors have a more undifferentiated phenotype.
Not surprisingly, several genes as well as a specific gene signature could be associated to better prognosis, a well-known characteristic of low-risk tumors. However, among them, a novel axis (NXPH1/a-NRXN) is proposed to explain the proliferation but absence of metastasis that define the low-risk group.
- Place the work in the context of the existing literature (provide references, where appropriate).
Neuroblastoma is a rare and very heterogeneous disease, in terms of biology and clinical presentation. To try to decipher such a heterogeneity, recent works have allied single cell transcriptome analyses on tumors and on human fetal cells during development. These studies are well cited in the discussion, and I agree that they yielded discrepant conclusions concerning the cell(s) of origin of Neuroblastoma. By comparing the normal developing human adrenal gland cells to cells from series of neuroblastomas, most of the studies converge towards that the tumors resembled differentiating adrenal neuroblasts. In one study, MYCN-amplified neuroblastoma cells (high risk group) were most similar to normal neuroblasts from seven- or eight-week post-conception, while lower-risk neuroblastomas included more cells resembling late neuroblasts (Janksy et al, 2021).
During the submission of this work, another paper using single cell technologies was published and supported the idea of two distinct tumor entities (Bedoya-reina et al, 2021), with also a stronger signature of sympatho-adrenal cells in low risk tumors.
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State what audience might be interested in and influenced by the reported findings. As the current clinical classification based on various criteria already allows clinicians to identify well low-risk tumors, I think this work would mainly attract fundamental researchers on the molecular differences between low-risk and high-risk tumors.
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Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. I'm specialized on pediatric cancers, and especially Neuroblastoma for the past 3 years. I'm interested in tumor cell identity and cell plasticity, particularly in response to treatment. I think that I have sufficient expertise to evaluate all parts of the manuscript.
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3. Description of the revisions that have already been incorporated in the transferred manuscript
Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.
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4. Description of analyses that authors prefer not to carry out
1: The reviewers #1 and #3 suggested using a large dataset (the SEQC database containing 498 samples) to realize a more powerful comparative transcriptomic study. However, to define the transcriptional signatures associated to the aetiology of LR- and HR-NBs and to the malignant behavior of HR-NBs, we first needed to compare the transcriptomic signatures of LR-NB and HR-NB samples to the one of a non-tumoral, healthy tissue such as the fetal adrenal gland. The online SEQC does not contain any healthy samples, thus precluding the possibility to use it for the first step of our analysis. This is why we decided to use as a starting point the cohort of samples from HSJD cohort, which further provided the advantage of comparing samples from patients all diagnosed and treated at the same facility. In different steps of our analytic strategy (Fig.S1F, 2F-H, 3A-B, S3), we already used the SEQC database to test the relevance of the results in the context of a larger cohort. In doing so we obtained coherent results. As recommended by the reviewer #1 (comment #3), we will further compare our data with the data from another study (Bedoya-Reina et al, 2021). We thus believe that the request of the reviewers #1 and #3 does not need to be addressed within the scope of a revision.
2: The reviewer #2 suggested that some crucial functional experiments should be repeated with another shRNA construct to ensure that our results are not off-target and because the knock-down efficiency of the shRNAs used was variable and not convincing. We have tested two distinct shRNA constructs for both NXPH1 and α-NRXN1, which all comparably reduced cell viability. Following this reviewer’s suggestion we will assess possible off-target effects of the sh-NXPH1 and sh-aNRXN1 constructs by RT-qPCR. Moreover, we will also test the effects of these sh-NXPH1 and sh-aNRXN1 constructs in another cell line and using a novel and complementary metastasis assay (CAM assay).
3: The reviewer #2 suggested to study the metastatic potential of NB cells by performing orthotopic implantations of NB cells into the mouse adrenal gland instead of performing cell injections into the mouse left cardiac ventricle. We reasoned that cell injections into the left cardiac ventricle were ideal to test the organo-tropism of metastatic NBs, as this methodology facilitates cell dissemination and colonization of organs targeted by NB metastasis such as the liver and bone marrow, as shown in Fig.5B. Cell implantation into the adrenal gland is especially useful to study cell growth in one of the primary sites of NB formation. We thus believe that our current experimental approach is more relevant to the question we wish to address within the scope of a revision.
References:
Bedoya-Reina OC, Li W, Arceo M, Plescher M, Bullova P, Pui H, Kaucka M, Kharchenko P, Martinsson T, Holmberg J, et al (2021) Single-nuclei transcriptomes from human adrenal gland reveal distinct cellular identities of low and high-risk neuroblastoma tumors. Nat Commun 12: 1–15
Biedler JL, Helson L & Spengler BA (1973) Morphology and Growth, Tumorigenicity, and Cytogenetics of Human Neuroblastoma Cells in Continuous Culture. Cancer Res 33: 2643–2652
Ciccarone V, Spengler BA, Meyers MB, Biedler JL & Ross RA (1989) Phenotypic Diversification in Human Neuroblastoma Cells: Expression of Distinct Neural Crest Lineages. Cancer Res 49: 219–225
Decock A, Ongenaert M, Cannoodt R, Verniers K, Wilde B De, Laureys G, Van Roy N, Berbegall AP, Bienertova-Vasku J, Bown N, et al (2016) Methyl-CpG-binding domain sequencing reveals a prognostic methylation signature in neuroblastoma. Oncotarget 7: 1960–72
Gomez S, Castellano G, Mayol G, Sunol M, Queiros A, Bibikova M, Nazor KL, Loring JF, Lemos I, Rodriguez E, et al (2015) DNA methylation fingerprint of neuroblastoma reveals new biological and clinical insights. Epigenomics 7: 1137–1153
van Groningen T, Akogul N, Westerhout EM, Chan A, Hasselt NE, Zwijnenburg DA, Broekmans M, Stroeken P, Haneveld F, Hooijer GKJ, et al (2019) A NOTCH feed-forward loop drives reprogramming from adrenergic to mesenchymal state in neuroblastoma. Nat Commun 10: 1–11
Kildisiute G, Kholosy WM, Young MD, Roberts K, Elmentaite R, van Hooff SR, Pacyna CN, Khabirova E, Piapi A, Thevanesan C, et al (2021) Tumor to normal single-cell mRNA comparisons reveal a pan-neuroblastoma cancer cell. Sci Adv 7: eabd3311
Tumilowicz JJ, Nichols WW, Cholon JJ & Greene AE (1970) Definition of a continuous human cell line derived from neuroblastoma. Cancer Res 30: 2110–2118
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Referee #3
Evidence, reproducibility and clarity
Summary: short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).
Dr. Le Dréau and colleagues provide a transcriptional analysis comparing low versus high-risk neuroblastomas using a cohort of 18 patients. By comparing them to normal transcriptional signatures obtained from human fetal adrenal glands, they conclude that low risk tumors share a specific core sympatho-adrenal developmental program, which is associated with favorable patient prognosis. Among this signature, they specifically assess the role of NXPH1/a-NRXN in vitro and in vivo using different models (cell lines, PDX-derived cell lines, mouse and chick xenografts). They propose that NXPH1/a-NRXN axis promotes neuroblastoma tumor growth via cell proliferation, and inhibits metastases.
Major comments:
- Are the key conclusions convincing? There are major points that need to be addressed before being convinced by the conclusions. • The choice for using the SKNSH cell line, among others, to study the role of NXPH1/a-NRXN axis need to be explained. Indeed, whereas there is no cell line modeling low-risk neuroblastoma, authors should properly illustrate the basal expression of NXPH1/a-NRXN in the cell lines and not the ratio provided in Fig3E. If I understood well, according to Fig3F, only 1.5% of SKNSH cells express NRXN1. Could the authors provide FACS plots? And explain how they can sort high and low expressing cells among 1.5%? And then how do they justify using shRNA approach in a cell line in which only 1.5% are concerned cells? Also, the authors should prove the efficiency of the shRNA used on each specific target for both in vitro and in vivo studies by WB. • The conclusion of inhibition of metastatic process is not supported by enough data. To achieve such a conclusion, authors should provide more than one in vivo experiment (which need to be completed already with the proof of protein deregulation). Some in vitro characterization of metastatic properties such as invasion and migration assays and/or transcriptional analyses could be done. • Why don't the authors use the transcriptome dataset of 498 patients to realize a more powerful comparative study of low versus high risk tumors (Zhang et al, Genome biology, 2015)? The authors should show the expression plot of NXPH1/a-NRXN in low versus high risk patients, in their cohort of 18 patients but also in the cohort of 498 patients. How do the authors reconciliate the idea that NXPH1/a-NRXN could be associated to stem cell identity but low risk tumors?
- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? Details of experiments and supplementary experiments have to be provided (see previous question).
- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. Additional details of the experiments have to be provided (cf above): levels of expression of NXPH1/a-NRXN in cell lines, FACS analyses to prove enrichment and/or depletion, WB for validation of shRNA knock-down etc...
Moreover, the conclusion that NXPH1/a-NRXN axis inhibits metastatic potential of neuroblastoma is supported by only one in vivo experiment using SKNSH cell line with shRNAs anti-NXPH1/a-NRXN. It should be completed with invasion/migration assays in vitro for example, and/or transcriptional signature of tumors obtained +/- shRNAs anti-NXPH1/a-NRXN.
Ideally, these results could be validated in an additional model.
- Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. Yes
- Are the data and the methods presented in such a way that they can be reproduced? Yes, Material and methods section is well described. But, several points are missing:
- The paragraphs describing how RNAs from 18 patients and fetal adrenal glands were obtained, how good was the quality, and how transcriptomes have been realized and sequenced are missing.
- The sequences of shRNAs have to be provided.
- Are the experiments adequately replicated and statistical analysis adequate? Yes
Minor comments:
- Specific experimental issues that are easily addressable. The authors should show the basal expression of their genes of interest in the patients, cell lines and PDX-derived cell lines, to justify the choice to work then with SKNSH cell line. In addition to the ratio they show in Fig3.E.
Authors should also illustrate the FACS analyses they realized in this study, in order to appreciate the quantity of positive cells that are either enriched or depleted.
Authors could precise in their schemes that DOX is maintained in vivo. The number of mice need to be integrated in each experiment. - Are prior studies referenced appropriately? No some elements are not right in the introduction: - Mutations of PHOX2B are not associated to poor prognosis. - Original publications could be provided instead of reviews. - Genetic alterations in NB are not only 16%, the authors forgot to mention TERT and ATRX. - Maybe the NXPH1 methylation in ref 54 could be more explicit, if DNA methylation is detected on NXPH1, it would be of poor prognosis because driving low expression ..? - Are the text and figures clear and accurate? Text is very clear and well written, as are the figures. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions? Already mentioned before: FACS and WB are needed.
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.. This work aimed first at better understanding the fundamental transcriptional differences between two risk groups in Neuroblastoma. The authors defend the conceptual idea that these two groups represent two distinct diseases, which is not new in the area but requires attention, and indeed supported here by distinct transcriptional signatures.
Using published signatures of fetal sympatho-adrenal system, they define a core transcriptional program that is more strongly expressed in low-risk tumors, but we already know that tumors of this category are often more differentiated, and by definition expressed strong markers of SA differentiation, whereas high-risk tumors have a more undifferentiated phenotype.
Not surprisingly, several genes as well as a specific gene signature could be associated to better prognosis, a well-known characteristic of low-risk tumors. However, among them, a novel axis (NXPH1/a-NRXN) is proposed to explain the proliferation but absence of metastasis that define the low-risk group.
- Place the work in the context of the existing literature (provide references, where appropriate). Neuroblastoma is a rare and very heterogeneous disease, in terms of biology and clinical presentation. To try to decipher such a heterogeneity, recent works have allied single cell transcriptome analyses on tumors and on human fetal cells during development. These studies are well cited in the discussion, and I agree that they yielded discrepant conclusions concerning the cell(s) of origin of Neuroblastoma. By comparing the normal developing human adrenal gland cells to cells from series of neuroblastomas, most of the studies converge towards that the tumors resembled differentiating adrenal neuroblasts. In one study, MYCN-amplified neuroblastoma cells (high risk group) were most similar to normal neuroblasts from seven- or eight-week post-conception, while lower-risk neuroblastomas included more cells resembling late neuroblasts (Janksy et al, 2021).
During the submission of this work, another paper using single cell technologies was published and supported the idea of two distinct tumor entities (Bedoya-reina et al, 2021), with also a stronger signature of sympatho-adrenal cells in low risk tumors.
- State what audience might be interested in and influenced by the reported findings. As the current clinical classification based on various criteria already allows clinicians to identify well low-risk tumors, I think this work would mainly attract fundamental researchers on the molecular differences between low-risk and high-risk tumors.
- Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. I'm specialized on pediatric cancers, and especially Neuroblastoma for the past 3 years. I'm interested in tumor cell identity and cell plasticity, particularly in response to treatment. I think that I have sufficient expertise to evaluate all parts of the manuscript.
-
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Referee #2
Evidence, reproducibility and clarity
Summary:
Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.
In this paper, Fanlo-Escudero et al., determines gene signatures differentiating low-risk (LR) neuroblastoma (NB) from high-risk neuroblastoma (HR-NB), as well as LR- and HR-NB as an entity from human fetal adrenal gland. They identify a transcriptional signature corresponding to a core sympathoadrenal lineage that can discriminate between LR-NB and HR-NB. This signature is composed of genes associated with favorable patient outcome. The authors further choose one gene, NXPH1, for functional analysis and investigates the effects this gene has on NB progression using in vitro assays, chick CAM assay and mouse in vivo models. The authors conclude that this transcriptional signature can distinguish LR-NB from HR-NB and that NXPH1 is involved in NB cell growth.
Major comments:
• Are the key conclusions convincing?
The key conclusions are 1) a core SA lineage signature can discriminate between LR-NB and HR-NB, and 2) NXPH1 represses NB malignancy (in terms of metastatic capacity) and is a therapeutic target. The first conclusion is indeed convincing, and not contradictive to common beliefs. The second conclusion is poorly supported by data. The authors perform a range of experiments using in vitro and in vivo settings, but lack some fundamental experiments and overstate their findings.
• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
Several claims should be softened, re-phrased and/or clearly marked as preliminary or speculative. No data nor claims need to be removed.
For example, the following statements need to be changed:
- Page 10, second paragraph: The authors state "Remarkably, NXPH1 and α-NRXN1/2 levels increased in all the NB cell lines harbouring a sphere-forming capacity (Fig. 3E), thereby revealing a strong positive correlation between the expression of NXPH1 and α-NRXN1/2 and the acquisition of a NCC stem cell identity". The authors only show that NXPH1 is expressed in 8 out of 10 NB cell lines. Sphere-forming capacity is displayed in a relative and not absolute scale which makes it difficult to assess which cell lines that do form spheres and to what extent. The capacity to form spheres (from low to high) does not correlate to the levels of NXPH1 in the different cell lines.
- Page 13, paragraph 1. The authors write "...these data revealed that NXPH1/α-NRXN1 signaling is necessary and sufficient for NB tumor growth in vivo". This is an overstatement. Tumors still form, meaning that NXPH1 signaling is not sufficient.
- Throughout the text, the authors convert their statements. One example is page 15, first paragraph. They write "...growth of NB cells but markedly restrict their metastatic potential", but they do not show this. Instead, they only address the opposite situation - Knockdown enhances metastasis. This is not equal to their statement. See other experiments in other sections. The authors need to go through the manuscript and make sure that they explain their conclusions to actually fit their experiments. • Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary to evaluate the paper as it is, and do not ask authors to open new lines of experimentation.
Issues to respond to:
- The authors should more clearly explain why they choose to further study NXPH1 (second highest on the list), involved in synaptogenesis and neurotransmission, instead of SOX6, highest on the list, that is highly relevant in neural crest development.
- When the authors investigate the expression of NXPH1 and other genes and compare that with sphere-forming capacity (Fig. 3E and page 10) they analyze expression in cells cultured in basal medium while sphere-forming capacity is measured after 5 weeks in restricted medium. How does the expression of the analyzed genes change under these conditions?
- Is the subpopulation of NRXN1+ cells low (1.5%) because these samples are from aggressive ("High-risk") cell lines?
- The authors state "...the number of cells quantified per tumor section was decreased by ~50% for the α-NRXN1+-deprived cells relative to their control (Fig. 3J, K), thus revealing that α-NRXN1+ cells are required to support NB tumor growth in vivo". This is not a correct conclusion. This experiment shows that a-NRXN1- cells do not grow and expand to the same extent as control cells. They cannot say that a-NRXN1+ cells support NB growth without comparing growth between a pure a-NRXN1+ and control cells.
- The authors use shRNAs to knock down NXPH1. They enrich their cells by two means - puromycin or doxocycline. This results in equal cell populations. The authors however state that they use doxocycline to circumvent the growth arrest they observe with puromycin selection. They need to elaborate on this and show why this would be the case and what difference the two methods do and show.
- The major flaw of this paper is that the authors use one cell line in total, and even more that they use the same cell line for both knockdown and activation. Since they do show that different NB cell lines have different expression levels (ranging from high to absent), they should choose one cell line for KD and one for overexpression. The authors could also do a rescue experiment with knockout and gain-of-function (e.g., construct that will not be targeted by the shRNA) in the same cells.
- They only use one shRNA after trying several (Fig. S4). The efficiency is substantially variable and not convincing. As stated also elsewhere in this review, they need to check protein level. And to ensure that their results are not off-target they should perform at least some crucial experiments with two shRNAs.
- Why don't the authors add BrdU post-implantation? This is easily done in the egg considering the accessibility and would better reflect the proliferation in vivo.
- Why do the authors switch between CAM and mouse xenografts? I understand that the mouse model must be employed for "metastasis", but can it be explained why and when they perform the different "tumor growth" experiments?
- Why do the authors do left ventricle injections for metastatic studies and not AG implantations?
- The measurement of bioluminescence is very difficult to interpret. The authors discuss metastatic spread, but the images show only large blobs covering the heart and areas surrounding it, especially for the sh-aNRXN1. To be able to say that the cells have colonized specific organs the authors need to dissect these organs and perform staining. I see it as tumors recur rather than particularly metastasize when they re-appear after 6 weeks.
- How do the authors explain results presented in Fig. S5: There are more HuNu+ cells but tumor size is unchanged? • Are the suggested experiments realistic for the authors? It would help if you could add an estimated cost and time investment for substantial experiments.
Some suggested experiments take time (orthotopic implantations, rescue experiments, adding cell lines and # of shRNAs).
However, as long as the authors discuss and address their methods for in vivo growth (in ovo vs in vivo vs their choice of metastasis model), an orthotopic AG model is not necessary. The authors should however consider it for future studies.
Experiments required to support their conclusions: A rescue experiment and use of different cell lines for KD and overexpression is somewhat time-consuming, as well as ensuring KD at protein level and include an additional shRNA in crucial experiment. I expect that this would take 2-4 months.
• Are the data and the methods presented in such a way that they can be reproduced?
The authors should elaborate on their methods, but in general they are reproducible. • Are the experiments adequately replicated and statistical analysis adequate?
In several places the number of replicates is questionable. Especially at the end of page 26, the authors state that they have performed n=1-4 replicates. N=1 replicate is never ok. In several instances they do n=2 replicates. This can be acceptable but the authors could address this.
Minor comments:
• Specific experimental issues that are easily addressable.
Mainly text changes can be seen as minor. For experiments and other issues, see other sections. • Are prior studies referenced appropriately? Yes. The reference list is not coherently styled. • Are the text and figures clear and accurate?
Overall, the paper is written in a complex way and difficult to easily comprehend. For example, the authors need to clarify several issues on the material they use and experiments they perform. I suggest substantial re-writing to better convey their messages. Sentences should be short and clear, data explained in the context of it was derived.
The following text edits and clarifications are required: * Page 3. The authors write "cell-of-origin" in several places, this should be changed to "cells-of-origin" (i.e., plural). The view that all NBs originate from only one cell is too simplistic, and the authors should definitely edit this considering that they are investigating different subgroups of NB. * Page 5. The authors MUST define where the material from the 18 NB patients as well as fetal AG derive from. There is no reference, and taken from the material&methods section, the transcriptome data from these data has not been generated by the authors themselves? * Why do the authors choose to work with the data set from Jansky et al in particular? * Page 7, paragraph 1. The authors write "Remarkably, 67% of the 503 genes found in this signature, which formed the cluster c1, are both associated with a neural identity and a better patient outcome". I do not agree that this is remarkable and the authors should remove this word. * Page 10, second paragraph. The authors should clarify that the expression levels of the displayed genes are derived from qPCR analysis (if I have understood that correctly, I had to find and guess this from the M&M section), as well as explain how they have set the scale for sphere-forming capacity and what this corresponds to. What do the colors actually represent? * Page 11 and onwards. The authors write "deprived of their a-NRXN1+ subpopulation". This is highly confusing and difficult to read. The authors should write "a-NRXN1- subpopulation" * Page 11, end of paragraph 2. The authors write "...arguing that NXPH1/α-NRXN signaling could control NB growth and/or aggressiveness". Number of cells do not directly correlate to aggressiveness, and this needs to be re-phrased to only state what the experiment actually shows - proliferation of a-NRXN1- cells. * I am in favor of using the CAM assay as a complementary system. The authors however use this to define "...required to support tumor growth in vivo". The CAM assay using NB (i.e., transformed cells) shows the growth of these cells in response to presence of blood vessels and not a full tumor micro-environment. This should be clarified. * As stated in the previous comment, the authors write "...required to support tumor growth in vivo". The next paragraph has the following headline "NXPH1/α-NRXN signaling stimulates NB growth". This is to me the same thing. The authors should elaborate on how these differ, or if they use them to show the same thing, clearly state that. * The authors conclude that NXPH signaling can be used as a therapeutic target. This would however be extremely difficult considering the opposing effects shown on growth vs metastasis. I agree that it is important to find means to inhibit metastasis, but that does not mean we can allow for enhanced growth of the primary tumor. A better reflection would be to use this as a biomarker, but this can only be predictive/speculative since the authors do not perform for example a tissue microarray to show this at IHC protein level, something that is currently the practice in the clinic.
Material and Methods:
The authors have misspelled the cell line SK-N-BE(2)c. Why are some cell lines grown in 20% FBS? This is not standard and could impact the results. The authors should state what the tumor volume limit in their ethical permit is (page 30).
• Do you have suggestions that would help the authors improve the presentation of their data and conclusions? * Fig. 3E. As discussed elsewhere, the authors should clarify the scale/meaning for sphere-forming capacity * Fig. 4D. What do the numbers (i - vi) refer to? I cannot find this in the figure, figure legend, text or material&methods. * The authors do not present the tumor volume in Fig. 4. The authors discuss tumor growth in the text and this data should be included. * Fig. S4. Knockdown efficiency is variable, and efficiency does not correlate to growth capacity. Especially because of this, the authors need to investigate this at protein level.
Significance
• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
This paper is partly within the scope of the ongoing debate of NB origin (see references below). This paper is however not as extensive, provides no new patient material (applies data from Jansky et al), and do not address the actual cell-of-origin (which the authors themselves also clearly states). This paper provides new gene signatures that can be used to define low-risk vs high-risk patients which is highly important to the field, but these signatures are not unexpected and does not add a significant advance to the field. The authors also do not address how these signatures could be applied clinically. The authors do not use any new methodology. With this said, with revision of the paper, it will still add to the current focus on NB biology research.
• Place the work in the context of the existing literature (provide references, where appropriate).
There is a recently initiated, important and extensive debate about the cells-of-origin for NB. The authors indeed bring this up in the paper and also state that they do not intend to add to this debate. They use data from one paper from referenced debate above, and I would argue that because of this fact, and that they extract gene signatures from it, they do, at least partly, touch on the NB cells-of-origin debate, and the authors should put their results into context, from a big picture perspective. As of now, they dodge this complex issue. References: Dong et al., Cancer Cell 2020; Hanemaaijer et al., PNAS 2021; Jansky et al., Nat Genet 2021; Kameneva et al., Nat Genet 2021; Kildisiute et al., Sci Adv 2021; Furlan et al., Science 2017)
• State what audience might be interested in and influenced by the reported findings.
This paper will be interesting for scientists within the neuroblastoma field, in particular those working on defining NB subgroups in correlation to developmental stages.
• Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
Reviewer's expertise: Neuroblastoma, neural crest, trunk neural crest, chick embryos, mouse models, in vitro models
Parts of paper outside expertise of the reviewer: Analysis of the bioinformatics data (i.e., extracting signatures).
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Referee #1
Evidence, reproducibility and clarity
Summary:
In the reviewed manuscript, the authors aimed to characterize the sympathoadrenal (SA) transcriptional landscape that defines low- and high-risk neuroblastomas (LR-NBs and HR-NBs respectively). In particular, they analyze previously published Affymetrix U219 expression profiles of 18 low- (n=8) and high-risk (n=10) neuroblastomas, and 2 fetal human adrenal glands. The authors define transcriptional signatures of LR- and HR-NBs, and further unbiasedly classified them in 4 clusters defining groups of patients with different prognosis (as tested using the 498 SEQC cohort). Within these transcriptional signatures, the authors delineated a SA signature using a human fetal adrenal gland transcriptional profile recently published (Kildisiute et al. 2021), that can discriminate low-risk neuroblastomas. From these genes, the authors further select NXPH1 and NRNX1-2 as promising targets for extensive experimental in vitro and in vivo validation, including validation in cell cultures, and xenografts, to determine that NXPH1/alpha-NRXN1-2 signaling is sufficient for NB tumor growth, and that the expression of either stimulates the metastatic potential of NB cells.
Major comments:
The cohorts and data used by the authors to conduct the main analysis of the paper are already published, and thus the contribution of the analysis is incremental. In particular, the authors analyzed arrays from a limited cohort size, in comparison with others available sequenced with RNA-seq (e.g. 176 HR- and 322 non-HR NB in 498 SEQC; 224 HR- and 342 non-HR NBs included in the Westermann-genecode19 cohort; and 80 HR- and 20 non-HR in the Jagannathan cohort). Furthermore, the 12,000 most-expressed genes (out of ~20,000 available) were analyzed by the authors, as opposed to more than 40,000 (coding and non-coding) included in a normal RNA-seq study. Only the 498 SEQC dataset provides 12,000 genes significantly up-regulated in either high-risk (n~5,500) or non-high-risk (n~7,000). The differences between datasets could influence the results of the study. For example, in the reviewed manuscript, genes with a high expression in LR-NB (compared to fAG) included DNMT3B, SEMA5A, SOX5, and TET1, all of which have a significantly higher expression in HR-NBs of the 498 SEQC cohort. The quality of the manuscript will be enhanced with consistent results obtained by conducting the same reported analysis in larger cohorts.
In the reviewed manuscript, PHOX2A and PHOX2B are significantly more expressed in both LR-NB and HR-NB compared to fAG. This is also the case for other adrenergic markers including TH and DBH. Oppositely, the expression of cortex markers (i.e. STAR and CYP11A1) is significantly higher in fAG. Nevertheless PNMT is not significantly up-regulated in fAG in comparison to LR-NB nor HR-NB. Is it possible that the fetal adrenal glands analyzed include a large proportion of cortex that confounds the transcriptional signals? The quality of the manuscript will be enhance if the authors could establish what proportion of the fAG transcriptional signal belongs to cortex, and if they account for its influence in the analysis.
A recent published paper (Bedoya-Reina et al. 2021) study the differences of HR-NB and LR-NB from a single-cell perspective. In the published manuscript, the authors conclude that LR-NBs are enriched in cells that resemble chromaffin and sympathoblast cells, while the high-risk neuroblastomas are enriched in undifferentiated cells that resemble cells with progenitor characteristics in post-natal adrenal gland. This is broadly consistent with the conclusion reached by the authors in the manuscript under review. It will enhance the content of the reviewed manuscript if the authors compare their transcriptional signatures with the recently published transcriptional signatures in this paper to answer the following questions: 1) to what extent the transcriptional signatures for HR-NBs (4 hierarchical clusters incl.) in the manuscript under review resembles that from the published undifferentiated cluster (nC3) enriched in HR-NBs, and the progenitor cluster (hC1) in post-natal adrenal gland; 2) to what extent the transcriptional signatures for LR-NBs (4 hierarchical clusters incl.) in the manuscript under review resembles that from the published NOR (nC7, nC8, and nC9) enriched in LR-NBs, and that from the chromaffin cells (hC4) enriched in post-natal adrenal gland; and 3) how is the expression of NXPH1 and alpha-NRXN1-2 in the reported LR- and HR-NBs, and adrenal gland.
In the discussion, the authors indicate that they do not aim to identify the transcriptional signature associated to NB origin but rather use the component of the SA lineage that distinguish LR- and HR-NBs. This statement implies that neuroblastoma can originate from any cell in the developing SA lineage (i.e. SCP, bridge, chromaffin and sympathoblast), a controversial assumption that requires further proof. In particular, when discussing about the core sympathoadrenal signatures enriched in LR-NBs and HR-NBs, the authors obtained a SA signature of genes shared by at least 3 of the 4 SA cell signatures. Further justification needs to be provided as for why (in particular) one of these SA cell signatures exclude the sympathoblast/neuroblast contribution.
Some of the most interesting results in the paper are limited to proportions in a subset of top-ranked genes. It will be valuable to set the analysis in an hypothesis driven context, add probabilities, test names, and corrected p-values to the results.
Minor comments.
1) In comparison with other cohorts that include low- and intermediate-risk NBs as non-HR NBs, the reviewed data specifically includes low- and high- risk NBs. It is important that the authors include a characterization of intermediate-risk neuroblastomas in their analysis.
3) Further details and figures on what precise criteria was used to remove the sample #LR-08 is required. How including this sample changes the reported results?
4) GO-term distribution was assessed using the 50 most-enriched GO-terms. How would the results change if all the significant GO terms were analyzed?
5) Was the SEQC 498 (GSE62564) dataset obtained with microarrays (as indicated in the methods) or with RNA-seq (i.e. Illumina HiSeq 2000)?
6) In methods, the first quartile (Q1) in SA-c1 has a higher limit in 487 samples and the fourth quartile the lowest limit in 4, how many samples (out of 498 NBs) were excluded and why?
7) In the 503 DEGs between LR-HR NBs, NTRK2 and MYCN are not included, even if the HR samples included MYCN amplified tumors. Can the authors comment on this?
8) The authors mention that the top 30 genes found in cluster c1 (and also in c2) are correlated with favorable patient prognosis. Is it the case that all the genes in c1 (and also c2, c3 and c4) are significantly associated with a favorable or else unfavorable prognosis?
9) The high expression of a (significant?) number of genes in cluster c4 is observed in patients with worst outcome (i.e. lower event-free survival), including ATR, HIF1A, ING2, POLR2L, SRPRB (498 SEQC, analyzed with R2).
10) Regarding the 242 genes in the core SA signature, although its a smaller number, the expression of several genes in the core SA signature with a higher expression in HR compared to LR belonging to clusters 2, 3, and 4 is observed in worst outcome patients in the 498 SEQC cohort (CHD7, DNMT1, HMGA1, HSD17B12, LBR, LSM7, MCM4, NKAP, POLA1, and others). Is this small fraction significant?
11) In Kildisiute et al. 2021, NRXN1 is expressed in SCPs, while NXPH1 is expressed in bridge, chromaffin and sympathoblastic cells. How are the microenviroment of these cells regulating the expression of these genes in a developmental context (particularly as sympathoblastic cells are know to have larger proliferative capabilities than SCPs)? how is this cell heterogeneity replicated by a NB cell line? are mesenchymal and adrenergic cells expressing differentially NRXN1 and NXPH1?
12) Figure 1B and C, 2B,D: might the information provided be enhanced? otherwise these inserts might be excluded.
13) Figure 3D: Kildisiute et al. 2021 data and GTEX available at human protein atlas indicate expression of NRXN1 and NXPH1 in developing and adult adrenal gland. Might the results illustrated suggest a confounding effect in the sampled fetal adrenal glands, perhaps from cortex?
14) The authors conduct extensive experiments in NRXN1, and make conclusions about its role in for instance metastasis, nevertheless the LR-NB/HR-NB SA signal only includes NRXN2. Can the authors comment on the differences between NRXN1s and NRXN2?
Significance
The significance of the study relies in investigating the role of selected targets in neuroblastomas within a risk group. In particular, HR-NBs have poor outcomes and are generally metastatic at the time of diagnosis.
The results of the manuscript are somehow consistent with a recently published manuscript analyzing LR- and HR-NBs from a single-cell perspective. The manuscript will be enhanced by conducting the suggested comparison between the reviewed and the reported results. The authors further need to comment why HR-NBs markers, particularly MYCN is not recovered in the LR-NB/HR-NB and the LR-NB/HR-NB SA signals. Also they need to comment on possible confounding effects in the fetal adrenal gland.
The paper is directed to a broader audience of cancer and developmental biologists, and computational biologist. Yet further statistical support needs to be provided.
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Referee #2
Evidence, reproducibility and clarity
Summary:
In this manuscript, Dennis et al. identify different secretory routes and cell exit sites involved in basement membrane secretion and diversification in epithelial cells. Using the follicular epithelium of the Drosophila ovary as their model system coupled with genetics, imaging, and image analysis approaches, they show that two previously identified RabGTPases, Rab8 and Rab10, work in parallel routes for basement membrane secretion. These two small GTPases work in a partially redundant manner, where Rab8 promotes basal secretion leading to a homogenous basement membrane, while Rab10 promotes lateral and planer-polarized secretion, leading to the formation of fibrils. The authors also show that Rab10 and the dystrophin-associated protein act together to regulate lateral secretion, and dystrophin (Dys) is necessary for dystroglycan (Dg) to recruit Rab10. Furthermore, DAPC is shown to be essential for fibril formation and is sufficient to reorient Collagen IV to the Rab10-dependent secretory route. Dys was also shown to interact directly with exocyst subunit Exo70. Using overexpression and loss of function approaches the authors claim that Exo70 limits the planer polarization of Dys, and as a result, Rab10, hence limiting basement membrane fibril formation. Finally, the authors state that the Exocyst (Exo70) is also required for the Rab8-dependent basement membrane route. Overall, the data described in this manuscript are convincing and the authors' claims are supported by the presented data. We have mainly minor comments and only a few major comments that need to be addressed.
Major Comments:
• In the text for Figure 1G-H (page 4), the authors stated that the basal secretion was not restored in Rab8, 10, and 11 triple KD, in our opinion, it is unclear how the authors came to this strong conclusion from the presented data. It would be good if the authors explicitly explain how they come to this conclusion. Is it only based on the weak Coll-IV-GFP signal in the Rab8, 10, and 11 triple KD data compare to the control? If so, the authors should statistically quantify the difference with the control. In Figure 1H, no statistical analysis is provided between the control and triple KD conditions.
• From the data presented in Figure S1B, the authors state that the basement membrane mislocalization observed in Rab8/10KD has no major impact on polarity maintenance. They based this statement only on the localization of the apical marker aPKC. Although the aPKC data are convincing, it would be more compelling if the authors observe the distribution of other polarity proteins such as Dlg, E-Cadherin, and armadillo to better assess if the overall epithelial polarity is maintained in this condition.
Minor Comments:
General comments:
• In the text describing their data, we recommend that the authors clearly indicate which panel(s) they are referring to.
• The authors should also be consistent with the diction throughout the manuscript when referring to the cortical domain or region of the cell (back/rear/trailing edge/leading edge).
• Several references are missing in the manuscript.
The following specific comments are in order of appearance in the manuscript.
Introduction Section:
The following statements in the introduction should be supported by specific references:
• "BM is critical for tissue development, homeostasis and regeneration, as exemplified in humans by its implication in many congenital and chronic disorders."
• "BM is assembled from core components conserved throughout evolution: type IV collagen (Col IV), the heparan sulfate proteoglycan perlecan, and the glycoproteins laminin and nidogen."
• "During development, the dynamic interplay between cells and BM participates in sculpting organs and maintaining their shape."
• "BM protein secretion shows some specificities, mainly because of the large size of the protein complexes (e.g., procollagen) that must transit from the endoplasmic reticulum to the cell surface". This statement could be supported with references including specific Drosophila references. Additionally, the authors need to clarify what they mean by "some specifies".
Results section:
• In the text describing Fig. 2 (page 5), the authors describe two different basement membrane types: fibrils and homogenous. Moreover, the manuscript focuses on the role of Rab8 and Rab10 in the formation of these two structures. Thus, the authors must better describe the two different types of basement membrane structures and their known roles. This will be helpful for the readers to analyze the presented data, especially for those that are not familiar with the system. In Figure 2A, the authors describe stage 3 basement membrane as uniform BM, do they mean homogenous?
• In the text describing the data for Fig. 3 (page 6), the authors should clearly explain the reason to use anti-GFP antibodies in a non-permeabilized condition (i.e., to detect specifically the extracellular secretion of BM proteins). This will help the readers to interpret the data presented.
• On page 9, the authors stated that the precise localization of Dg in follicle cells is unknown. This statement is incorrect. It has been shown, using a Dg antibody, that Dg localizes at a high level at the basal side of the follicle cells and at a lower level at the apical side (Deng et al, 2003 and Denef et al. 2008).
Discussion Section:
• The following statement is not clear: "Thus, three different Rab proteins are targeted towards the three distinct domains of epithelial cells defined by apical basal polarity, and at least of them is also planar polarized". The authors should rephrase and describe specifically which Rabs they are talking about.
• This statement is vague: "These three Rab GTPases have been jointly involved in different processes (Knödler et al, 2010; Sato et al, 2014; Vogel et al, 2015; Eguchi et al, 2018; Häsler et al, 2020)". The authors could also mention the processes in which Rab8, 10, and 11 are involved.
• The following statements need to be supported by references. "Therefore, more investigations are required to define exactly how the DAPC allows the formation of BM fibrils. Nonetheless, given the importance of the DAPC and BM proteins in muscular dystrophies, our results will pave the way to determine whether a similar function is present also in muscle cells. Interestingly, the extracellular matrix is different between the myotendinous junction and the interjunctional sarcolemmal basement membrane and may provide another developmental context where several routes targeted to different subcellular domains may be implicated".
Experimental Procedure Section:
• In the dissection and immunostaining section (p14), there is a typo: it should be for "20 min" instead of "2for 0 min"
• For the GST pulldown experiments, the authors mention that they use a standard protocol to produce S35 Exo 70 and the GST pulldown experiments. The authors should provide references.
Figure and Figure Legend: • General comment: The orientation of the images showing the rotation and leading and trailing edges need to be consistent in the different figures (e.g., In Figures 3 and 7, the leading edge is oriented to the top while in Figures 4, S4, 5, 6, the leading edge is oriented to the bottom). This will help the readers to analyze the data.
• In Figure 1 C-G the scale bars are missing and should be added as Fig. 1B.
• Figure S1A: The data presented in Figure S1A is convincing. However, a control panel should be added showing the absence of apical Coll IV for comparison. This information will help with the interpretation of the data.
• In Figure 3 legend: it should be "immunostained" for GFP instead of stain for f-actin and GFP.
• In Figure 4, some scale bars are missing.
• In Figure 4 legend: it should be "(A, E)" after (i.e 0.8 µm above the basal surface) instead of "(C, G)"
• In Figure 5A-E, the authors show quantification of the fibril fraction for Dys-, Rab10 OE, and Rab10OE+Dys, Rab8KD, and Rab8KD+Dys-, and images of the collagen fibril for all the conditions except Dys-, it will be informative that the authors present a representative image of the Coll IV fibril in Dys- condition for comparison. The above comment also applies to Figure 5F-J, and it will be also informative to have a representative image of Dys- condition.
• In Figure 5 legend (p23), it should be "plane" and not "plan".
• Overall, the legend for Fig. S5 is not clear and we recommend the authors to clearly described the different panels. (e.g., it should be "(D)" instead of "(H-J)")
• In Figure 6, some scale bars are missing.
Significance
Despite the important roles of the basement membrane for mechanical support, tissue and organ development, and function, the mechanisms that control the polarized deposition of basement membrane proteins are largely unknown. The contribution of Rab 8 and Rab 10 in the polarized deposition of the basement membrane was previously shown. However, by identifying two competitive secretory routes for the basal secretion of the basement membrane proteins that required these two different RabGTPases, controlled by the DAPC and the exocyst complexes, the authors make a novel contribution to our understanding of the mechanism that leads to the polarized secretion of basement membrane proteins (in that case Collagen IV). Since the basement membrane has critical roles in tissue and organ morphogenesis and functions, and its misregulation has been associated with developmental defects and pathological conditions, this research sheds light on the mechanisms important in these morphogenetic processes and will give insights into their deregulations in pathological conditions.
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