Author Response

Reviewer #1 (Public Review):

How morphogens spread within tissues remains an important question in developmental biology. Here the authors revisit the role of glypicans in the formation of the Dpp gradient in wing imaginal discs of Drosophila. They first use sophisticated genome engineering to demonstrate that the two glypicans of Drosophila are not equivalent despite being redundant for viability. They show that Dally is the relevant glypican for Dpp gradient formation. They then provide genetic evidence that, surprisingly, the core domain of Dally suffices to trap Dpp at the cell surface (suggesting a minor role for GAGs). They conclude with a model that Dally modulates the range of Dpp signaling by interfering with Dpp's degradation by Tkv. These are important conclusions, but more independent (biochemical/cell biological) evidence is needed.

As indicated above, the genetic evidence for the predominant role of Dally in Dpp protein/signalling gradient formation is strong. In passing, the authors could discuss why overexpressed Dlp has a negative effect on signaling, especially in the anterior compartment. The authors then move on to determine the role of GAG (=HS) chains of Dally. They find that in an overexpression assay, Dally lacking GAGs traps Dpp at the cell surface and, counterintuitively, suppresses signaling (fig 4 C, F). Both findings are unexpected and therefore require further validation and clarification, as outlined in a and b below.

a) In loss of function experiments (dallyDeltaHS replacing endogenous dally), Dpp protein is markedly reduced (fig 4R), as much as in the KO (panel Q), suggesting that GAG chains do contribute to trapping Dpp at the cell surface. This is all the more significant that, according to the overexpression essays, DallyDeltaHS seems more stable than WT Dally (by the way, this difference should also be assessed in the knock-ins, which is possible since they are YFP-tagged). The authors acknowledge that HS chains of Dally are critical for Dpp distribution (and signaling) under physiological conditions. If this is true, one can wonder why overexpressed dally core 'binds' Dpp and whether this is a physiologically relevant activity.

According to the overexpression assay, DallyDeltaHS seems more stable than WT Dally (Fig. 4B’, E’, 5H, I). As the reviewer suggested, we addressed the difference using the two knock-in alleles and found that DallyDeltaHS is more stable than WT Dally (Fig.4 L, M inset), further emphasizing the insufficient role of core protein of Dally for extracellular Dpp distribution.

(During the revising our figure, we found labeling mistake in Fig. 4M, N and Fig. 4Q, R and corrected the genotypes.)

In summary, we showed that, although Dally interacts with Dpp mainly through its core protein from the overexpression assay (Fig. 4E, I), HS chains are essential for extracellular Dpp distribution (Fig. 4R). Thus, the core protein of Dally alone is not sufficient for extracellular Dpp distribution under physiological conditions. These results raise a question about whether the interaction of core protein of Dally with Dpp is physiologically relevant. Since the increase of HS upon dally expression but not upon dlp expression resulted in the accumulation of extracellular Dpp (Fig. 2) and this accumulation was mainly through the core protein of Dally (Fig. 4E, I), we speculate that the interaction of the core protein of Dally with Dpp gives ligand specificity to Dally under physiological conditions.

To understand the importance of the interaction of core protein of Dally with Dpp under physiological conditions, it is important to identify a region responsible for the interaction. Our preliminary results overexpressing a dally mutant lacking the majority of core protein (but keeping the HS modified region intact) showed that HS chains modification was also lost. Although this is consistent with our results that enzymes adding HS chains also interact with the core protein of Dally (Fig. 4D), the dally mutant allele lacking the core protein would hamper us from distinguishing the role of core protein of Dally from HS chains.

Nevertheless, we can infer the importance of the interaction of core protein of Dally with Dpp using dally[3xHA-dlp, attP] allele, where dlp is expressed in dally expressing cells. Since Dally-like is modified by HS chains but does not interact with Dpp (Fig. 2, 4), dally[3xHA-dlp, attP] allele mimics a dally allele where HS chains are properly added but interaction of core protein with Dpp is lost. As we showed in Fig.3O, S, the allele could not rescue dallyKO phenotypes, consistent with the idea that interaction of core protein of Dally with Dpp is essential for Dpp distribution and signaling and HS chain alone is not sufficient for Dpp distribution.

b) Although the authors' inference that dallycore (at least if overexpressed) can bind Dpp. This assertion needs independent validation by a biochemical assay, ideally with surface plasmon resonance or similar so that an affinity can be estimated. I understand that this will require a method that is outside the authors' core expertise but there is no reason why they could not approach a collaborator for such a common technique. In vitro binding data is, in my view, essential.

We agree with the reviewer that a biochemical assay such as SPR helps us characterize the interaction of core protein of Dally and Dpp (if the interaction is direct), although the biochemical assay also would not demonstrate the interaction under the physiological conditions.

However, SPR has never been applied in the case of Dpp, probably because purifying functional refolded Dpp dimer from bacteria has previously been found to be stable only in low pH and be precipitated in normal pH buffer (Groppe J, et al., 1998)(Matsuda et al., 2021). As the reviewer suggests, collaborating with experts is an important step in the future.

Nevertheless, SPR was applied for the interaction between BMP4 and Dally (Kirkpatrick et al., 2006), probably because BMP4 is more stable in the normal buffer. Although the binding affinity was not calculated, SPR showed that BMP4 directly binds to Dally and this interaction was only partially inhibited by molar excess of exogenous HS, suggesting that BMP4 can interact with core protein of Dally as well as its HS chains. In addition, the same study applied Co-IP experiments using lysis of S2 cells and showed that Dpp and core protein of Dally are co-immunoprecipitated, although it does not demonstrate if the interaction is direct.

In a subsequent set of experiments, the authors assess the activity of a form of Dpp that is expected not to bind GAGs (DppDeltaN). Overexpression assays show that this protein is trapped by DallyWT but not dallyDeltaHS. This is a good first step validation of the deltaN mutation, although, as before, an invitro binding assay would be preferable.

Our overexpression assays actually showed that DppDeltaN is trapped by DallyWT and by dallyDeltaHS at similar levels (Fig. 5H-J), indicating that interaction of DppDeltaN and HS chains of Dally is largely lost but DppDeltaN can still interact with core protein of Dally.

(Related to this, we found typo in the sentence “In contrast, the relative DppΔN accumulation upon DallyΔHS expression in JAX;dppΔN was comparable to that upon DallyΔHS expression in JAX;dppΔN (Fig. 5H-J).” and corrected as follows, “In contrast, the relative DppΔN accumulation upon Dally expression in JAX;dppΔN was comparable to that upon DallyΔHS expression in JAX;dppΔN (Fig. 5H-J).”

We thank the reviewer for the suggesting the in vitro experiment. Although we decided not to develop biophysical experiments such as SPR for Dpp in this study due to the reasons discussed above, we would like to point out that our result is consistent with a previous Co-IP experiment using S2 cells showing that DppDeltaN loses interaction with heparin (Akiyama2008).

However, in contrast to our results, the same study also proposed by Co-IP experiments using S2 cells that DppDeltaN loses interaction with Dally (Akiyama2008). Although it is hard to conclude since western blotting was too saturated without loading controls and normalization (Fig. 1C in Akiyama 2008), and negative in vitro experiments do not necessarily demonstrate the lack of interaction in vivo. One explanation why the interaction was missed in the previous study is that some factors required for the interaction of DppDeltaN with core protein of Dally are missing in S2 cells. In this case, in vivo interaction assay we used in this study has an advantage to robustly detect the interaction.

Nevertheless, the authors show that DppDeltaN is surprisingly active in a knock-in strain. At face value (assuming that DeltaN fully abrogates binding to GAGs), this suggests that interaction of Dpp with the GAG chains of Dally is not required for signaling activity. This leads to authors to suggest (as shown in their final model) that GAG chains could be involved in mediating the interactions of Dally with Tkv (and not with Dpp. This is an interesting idea, which would need to be reconciled with the observation that the distribution of Dpp is affected in dallyDeltaHS knock-ins (item a above). It would also be strengthened by biochemical data (although more technically challenging than the experiments suggested above). In an attempt to determine the role of Dally (GAGs in particular) in the signaling gradient, the paper next addresses its relation to Tkv. They first show that reducing Tkv leads to Dpp accumulation at the cell surface, a clear indication that Tkv normally contributes to the degradation of Dpp. From this they suggest that Tkv could be required for Dpp internalisation although this is not shown directly. The authors then show that a Dpp gradient still forms upon double knockdown (Dally and Tkv). This intriguing observation shows that Dally is not strictly required for the spread of Dpp, an important conclusion that is compatible with early work by Lander suggesting that Dpp spreads by free diffusion. These result show that Dally is required for gradient formation only when Tkv is present. They suggest therefore that Dally prevents Tkv-mediated internalisation of Dpp. Although this is a reasonable inference, internalisation assays (e.g. with anti-Ollas or anti-HA Ab) would strengthen the authors' conclusions especially because they contradict a recent paper from the Gonzalez-Gaitan lab.

Thanks for suggesting the internalization assay. As we discussed in the discussion, our results suggest that extracellular Dpp distribution is severely reduced in dally mutants due to Tkv mediated internalization of Dpp (Fig. 6). Thus, extracellular Dpp available for labelling with nanobody is severely reduced in dally mutants, which can explain the reduced internalization of Dpp in dally mutants in the internalization assay. Therefore, we think that the nanobody internalization assay would not distinguish the two contradicting possibilities.

The paper ends with a model suggesting that HS chains have a dual function of suppressing Tkv internalisation and stimulating signaling. This constitutes a novel view of a glypican's mode of action and possibly an important contribution of this paper. As indicated above, further experiments could considerably strengthen the conclusion. Speculation on how the authors imagine that GAG chains have these activities would also be warranted.

Thank you very much!

Reviewer #2 (Public Review):

The authors are trying to distinguish between four models of the role of glypicans (HSPGs) on the Dpp/BMP gradient in the Drosophila wing, schematized in Fig. 1: (1) "Restricted diffusion" (HSPGs transport Dpp via repetitive interaction of HS chains with Dpp); (2) "Hindered diffusion" (HSPGs hinder Dpp spreading via reversible interaction of HS chains with Dpp); (3) "Stabilization" (HSPGs stabilize Dpp on the cell surface via reversible interaction of HS chains with Dpp that antagonizes Tkv-mediated Dpp internalization); and (4) "Recycling" (HSPGs internalize and recycle Dpp).

To distinguish between these models, the authors generate new alleles for the glypicans Dally and Dally-like protein (Dlp) and for Dpp: a Dally knock-out allele, a Dally YFP-tagged allele, a Dally knock-out allele with 3HA-Dlp, a Dlp knock-out allele, a Dlp allele containing 3-HA tags, and a Dpp lacking the HS-interacting domain. Additionally, they use an OLLAS-tag Dpp (OLLAS being an epitope tag against which extremely high affinity antibodies exist). They examine OLLAS-Dpp or HA-Dpp distribution, phospho-Mad staining, adult wing size.

They find that over-expressed Dally - but not Dlp - expands Dpp distribution in the larval wing disc. They find that the Dally[KO] allele behaves like a Dally strong hypomorph Dally[MH32]. The Dally[KO] - but not the Dlp[KO] - caused reduced pMad in both anterior and posterior domains and reduced adult wing size (particularly in the Anterior-Posterior axis). These defects can be substantially corrected by supplying an endogenously tagged YFP-tagged Dally. By contrast, they were not rescued when a 3xHA Dlp was inserted in the Dally locus. These results support their conclusion that Dpp interacts with Dally but not Dlp.

They next wanted to determine the relative contributions of the Dally core or the HS chains to the Dpp distribution. To test this, they over-expressed UAS-Dally or UAS-Dally[deltaHS] (lacking the HS chains) in the dorsal wing. Dally[deltaHS] over-expression increased the distribution of OLLAS-Dpp but caused a reduction in pMad. Then they write that after they normalize for expression levels, they find that Dally[deltaHS] only mildly reduces pMad and this result indicates a major contribution of the Dally core protein to Dpp stability.

Thanks for the comments. We actually showed that compared with Dally overexpression, Dally[deltaHS] overexpression only mildly reduces extracellular Dpp accumulation (Fig. 4I). This indicates a major contribution of the Dally core protein to interaction with Dpp, although the interaction is not sufficient to sustain extracellular Dpp distribution and signaling gradient.

The "normalization" is a key part of this model and is not mentioned how the normalization was done. When they do the critical experiment, making the Dally[deltaHS] allele, they find that loss of the HS chains is nearly as severe as total loss of Dally (i.e., Dally[KO]). Additionally, experimental approaches are needed here to prove the role of the Dally core.

Since the expression level of Dally[deltaHS] is higher than Dally when overexpressed, we normalized extracellular Dpp distribution (a-Ollas staining) against GFP fluorescent signal (Dally or Dally[deltaHS]). To do this, we first extracted both signal along the A-P axis from the same ROI. The ratio was calculated by dividing the intensity of a-Ollas staining with the intensity of GFP fluorescent signal at a given position x. The average profile from each normalized profile was generated and plotted using the script described in the method (wingdisc_comparison.py) as other pMad or extracellular staining profiles.

Although this analysis provides normalized extracellular Dpp accumulation at different positions along the A-P axis, we are more interested in the total amount of Dpp or DppDeltaN accumulation upon Dally or dallyDeltaHS expression. Therefore, we plan to analyze the normalized total amount of Dpp against GFP fluorescent signal (Dally or Dally[deltaHS]) in the revised ms. In this case, normalization will be performed by dividing total signal intensity of extracellular Dpp staining (ExOllas staining) divided by GFP fluorescent signal (Dally or Dally[deltaHS]) in ROI in each wing disc.

We agree with the reviewer that additional experimental approaches are needed to address the role of the core protein of Dally. As we discussed in the response to the reviewer1, to understand the importance of the interaction of core protein of Dally with Dpp, it is important to identify a region responsible for the interaction. Our preliminary results overexpressing a dally mutant lacking the majority of core protein (but keeping the HS modified region intact) showed that HS chains modification was also lost. Although this is consistent with our results that enzymes adding HS chains also interact with the core protein of Dally (Fig. 4D), the dally mutant allele lacking the core protein would hamper us from distinguishing the role of the core protein of Dally from HS chains.

Nevertheless, we can infer the importance of the interaction of core protein of Dally with Dpp using dally[3xHA-dlp, attP] allele, where dlp is expressed in dally expressing cells. Since Dally-like is modified by HS chains but does not interact with Dpp (Fig. 2, 4), dally[3xHA-dlp, attP] allele mimics a dally allele where HS chains are properly added but interaction of core protein with Dpp is lost. As we showed in Fig.3O, S, the allele could not rescue dallyKO phenotypes, consistent with the idea that interaction of core protein of Dally with Dpp is essential for Dpp distribution and signaling.

Prior work has shown that a stretch of 7 amino acids in the Dpp N-terminal domain is required to interact with heparin but not with Dpp receptors (Akiyama, 2008). The authors generated an HA-tagged Dpp allele lacking these residues (HA-dpp[deltaN]). It is an embryonic lethal allele, but they can get some animals to survive to larval stages if they also supply a transgene called “JAX” containing dpp regulatory sequences. In the JAX; HA-dpp[deltaN] mutant background, they find that the distribution and signaling of this Dpp molecule is largely normal. While over-expressed Dally can increase the distribution of HA-dpp[deltaN], over-expression of Dally[deltaHS] cannot. These latter results support the model that the HS chains in Dally are required for Dpp function but not because of a direct interaction with Dpp.

Our overexpression assays actually showed that both Dally and Dally[deltaHS] can accumulate Dpp upon overexpression and the accumulation of Dpp is comparable after normalization (Fig. 5H-J), consistent with the idea that interaction of DppdeltaN and HS chains are largely lost. As the reviewer pointed out, these results support the model that the HS chains in Dally are required for Dpp function but not because of a direct interaction with Dpp.

In the last part of the results, they attempt to determine if the Dpp receptor Thickveins (Tkv) is required for Dally-HS chains interaction. The 2008 (Akiyama) model posits that Tkv activates pMad downstream of Dpp and also internalizes and degrades Dpp. A 2022 (Romanova-Michaelides) model proposes that Dally (not Tkv) internalizes Dpp.

To distinguish between these models, the authors deplete Tkv from the dorsal compartment of the wing disc and found that extracellular Dpp increased and expanded in that domain. These results support the model that Tkv is required to internalize Dpp.

They then tested the model that Dally antagonizes Tkv-mediated Dpp internalization by determining whether the defective extracellular Dpp distribution in Dally[KO] mutants could be rescued by depleting Tkv. Extracellular Dpp did increase in the D vs V compartment, potentially providing some support for their model. However, there are no statistics performed, which is needed for full confidence in the results. The lack of statistics is particularly problematic (1) when they state that extracellular Dpp does not rise in ap>tkv RNAi vs ap>tkv RNAi, dally[KO] wing discs (Fig. 6E) or (2) when they state that extracellular Dpp gradient expanded in the dorsal compartment when tkv was dorsally depleted in dally[deltaHS] mutants (Fig. 6I). These last two experiments are important for their model but the differences are assessed only visually. In fact, extracellular Dpp in ap>tkv RNAi, dally[KO] (Fig. 6B) appears to be lower than extracellular Dpp in ap>tkv RNAi (Fig. 6A) and the histogram of Dpp in ap>tkv RNAi, dally[KO] is actually a bit lower than Dpp in ap>tkv RNAi, But the author claim that there is no difference between the two. Their conclusion would be strengthened by statistical analyses of the two lines.

We will provide the statistical analyses in the revised ms.

Strengths:

1) New genomically-engineered alleles

A considerable strength of the study is the generation and characterization of new Dally, Dlp and Dpp alleles. These reagents will be of great use to the field.

Thanks. We hope that these resources are indeed useful to the field.

2) Surveying multiple phenotypes

The authors survey numerous parameters (Dpp distribution, Dpp signaling (pMad) and adult wing phenotypes) which provides many points of analysis.

Thanks!

Weaknesses:

1) Confusing discussion regarding the Dally core vs HS in Dpp stability. They don't provide any measurements or information on how they "normalize" for the level of Dally vs Dally[deltaHS]? This is important part of their model that currently is not supported by any measurements.

We explained how we normalized in the above section. We will update the analysis in the revised ms.

2) Lacking quantifications and statistical analyses:

a) Why are statistical significance for histograms (pMad and Dpp distribution) not supplied? These histograms provide the key results supporting the authors' conclusions but no statistical tests/results are presented. This is a pervasive shortcoming in the current study.

Thanks. We will provide statistics in the revised ms.

b) dpp[deltaN] with JAX transgene - it would strengthen the study to supply quantitative data on the percent survival/lethal stage of dpp[deltaN] mutants with or without the JAK transgene

In this study, we are interested in the role of dpp[deltaN] during the wing disc development. Therefore, we decided not to perform the detailed analysis on the percent survival/lethal stage of dpp[deltaN] mutants with or without the JAX transgene in the current study. Nevertheless, the fact that dpp[deltaN] allele is maintained with a balanced stock and JAX;dpp[deltaN] allele can be maintained as homozygous stock indicates that the lethality of dpp[deltaN] allele comes from the early stages. Indeed, our preliminary results showed that pMad signal is severely lost in the dpp[deltaN] embryo without JAX (data not shown), indicating that the allele is lethal at early embryonic stages.

c) The graphs on wing size etc should start at zero.

Thanks. We corrected this in the current ms.

d) The sizes of histograms and graphs in each figure should be increased so that the reader can properly assess them. Currently, they are very small.

Thanks. We changed the sizes in the current ms.

The authors' model is that Dally (not Dlp) is required for Dpp distribution and signaling but that this is not due to a direct interaction with Dpp. Rather, they posit that Dally-HS antagonize Tkv-mediated Dpp internalization. Currently the results of the experiments could be considered consistent with their model, but as noted above, the lack of statistical analyses of some parameters is a weakness.

Thanks. We will perform the statistical analyses in the revised ms.

One problematic part of their result for me is the role of the Dally core protein (Fig. 7B). There is a mis-match between the over-expression results and Dally allele lacking HS (but containing the core). Finally, their results support the idea that one or more as-yet unidentified proteins interact with Dally-HS chains to control Dpp distribution and signaling in the wing disc.

Our results simply suggest that Dpp can interact with Dally mainly through core protein but this interaction is not sufficient to sustain extracellular Dpp gradient formation under physiological conditions (dallyDeltaHS) (Fig. 4Q). We find that the mis-match is not problematic if the role of Dally is not simply mediated through interaction with Dpp. We speculate that interaction of Dpp and core protein of Dally is transient and not sufficient to sustain the Dpp gradient without HS chains of Dally stabilizing extracellular Dpp distribution by blocking Tkv-mediated Dpp internalization.

There is much debate and controversy in the Dpp morphogen field. The generation of new, high quality alleles in this study will be useful to Drosophila community, and the results of this study support the concept that Tkv but not Dally regulate Dpp internalization. Thus the work could be impactful and fuel new debates among morphogen researchers.

Thanks.

The manuscript is currently written in a manner that really is only accessible to researchers who work on the Dpp gradient. It would be very helpful for the authors to re-write the manuscript and carefully explain in each section of the results (1) the exact question that will be asked, (2) the prior work on the topic, (3) the precise experiment that will be done, and (4) the predicted results. This would make the study more accessible to developmental biologists outside of the morphogen gradient and Drosophila communities.

Thanks. We will modify our texts to help non-experts understand our story in the revised ms.

Author Response

Reviewer #2 (Public Review):

Major points:

1). This study does not provide any evidence about the cell death of the transplanted cells. The immunostaining of the Caspase-3 or TUNEL staining should be used to address this issue.

We have conducted immunostaining of Caspase-3 at 7 days after transplantation using the human-specific STEM121 antibody to demonstrate the transplanted cells. We have added the results to Figure 3A and modified the text accordingly (Page 8, Line 156-165).

2). The authors showed that the neurological functions (evaluated by balance beam, ladder lung, rotarod test and Modified Neurological Severity Score (mNSS) up to 8 weeks after treatment (Figure 1C)) were significantly improved in the NES+Exo group compared to their control groups. However, these cells (transplanted cells) are progenitors (Nestin+) or undifferentiated cells (Tuj1+) at this stage (Figure 3). Thus, I was curious about that how can the immature neurons play neurological functions? This point should be explained.

We agree with the reviewer’s insightful comments. We have performed immunostaining using antibodies against the post-mitotic mature neuron marker RBFOX3/NeuN, post-synaptic marker PSD-95 and human-specific STEM121 at 4 weeks after transplantation. The results confirmed that NeuN+/STEM121+ and PSD-95+/STEM121+ mature neurons appeared in NSC group and increased in NSC+Exo group (Figure 3B and Figure 3 - supplement 1D). Furthermore, our additional data showed that the expression of presynaptic marker SYN1 was increased in both NSC and NSC+Exo groups at 8 weeks after treatment. Therefore, we believe that there are mature neurons and newly formed synapses involved in neurological functions.

3). The authors used the Golgi staining to show the NES+Exo can improve dendritic density and length. How do you know these neurons are transplanted cells?

Our data show that mature neurons and synapses are generated by the transplanted cells (please also see response to reviewer #2-major ponts #2). We believe that the newly generated neurons partly contribute to the improved dendritic density and length. However, we agree that the neurons with increased dendritic density and length may be both survived local neurons and those generated by the transplanted cells.

4). The cell morphology of tdTomato+ cells is fuzzy and it is difficult to distinguish the cell body. It looks like that these cells out of whack.

We have immunostaining using the human-specific STEM121 antibody to demonstrate the transplanted cells and more neuronal markers such as RBFOX3/NeuN to identify NSC differentiation (Figure 3A and 3B; Figure 3 - supplement 1C and 1D).

Author Response

Reviewer #1 (Public Review):

Lemerle et al utilize elegant imaging and molecular biology approaches to convincingly demonstrate the presence of Bin1 and caveolae containing rings capable of tubulation in developing muscle. The data is of fundamental potential significance as it advances our understanding of t-tubule biogenesis, which represents a major knowledge gap in muscle biology. The paper will be of broad interest to skeletal and cardiac muscle biologists and physiologists. The paper is well written, with a comprehensive yet concise introduction, clearly presented results, and an appropriate discussion. The imaging is spectacular, and the use of CLEM provides compelling validation of the protein constituents of ring structures identified via EM. When combined with time-lapse imaging, the combination of approaches provides powerful nanoscale structural information alongside temporal dynamics and live-cell confirmation of tubulating ability by Bin1-Cav3 containing rings. The data indicate that Bin1 is sufficient to generate circular structures that are subsequently decorated by caveolae which facilitate tubule formation at the membrane, and they support the requirement of both Bin1 and Cav3 for efficient tubule initiation and elongation. The authors also utilize myotubes from patients with cav3 mutations to explore whether altered ring formation may contribute to muscle pathology - however, this section requires additional controls and validation to confer pathological insight. Further, additional quantification of imaging data across the study is required to increase the rigor and strength of the conclusions of this work.

We would like to thank reviewer #1 for his appreciation of our work, in particular the imaging experiments and for deeming our overall conclusions convincing. We have now performed additional experiments on patient myotubes including a rescue of Cav3, performed rigorous quantifications of rings and tubules under our different experimental conditions and re-wrote corresponding parts of the of the discussion to increase the strength of our conclusions.

Reviewer #2 (Public Review):

In this work Lemerle et al. provide long-awaited insight into how transverse tubules develop in skeletal muscle. Together with the sarcoplasmic reticulum transverse tubules form the triad, a specialized structure required for excitation-contraction coupling in skeletal muscle. Defects in transverse tubules or the triad can lead to problems such as muscular dystrophy. Whilst the involvement of specialist membrane structures (caveolae) and the membrane-bending protein Bin1 have long been recognized the precise mechanism of how caveolae and Bin1 cause transverse tubules to form and extend has remained unknown. This work provides compelling evidence, correlating antibody labelling with electron microscopy, to support the concept that caveolae rings form underneath the cell membrane which is surrounded by the endo/sarcoplasmic reticulum. These rings contain caveolin-3 and Bin1 and the authors show Bin1 enriched tubes extend from multiple points on these rings. Their data suggest that Bin1 assembles to initially form these scaffolds that then recruit the caveolae to form the ring. In addition, tubules appear continuous with the extracellular environment which is necessary for their function of facilitating calcium release during excitationcontraction coupling. In patients with mutations in caveolin-3 the caveolin ring formation as well as Bin1 tubulation were defective which may play a role in the pathology. The elegant experiments including time-lapse work clearly support the conclusions of the authors.

The ability of the authors to combine labelling studies with advanced microscopy to show the underlying structures provides very strong evidence for the proposed mechanisms. The authors suggest that the muscle-specific isoforms of BIN1 are key to tubule extension from caveolae rings but it would be interesting for them to discuss how this fits with studies suggesting that constitutive Bin1 isoforms can also form transverse tubules. It would also be interesting to understand the authors' views on whether caveolae rings are involved in the turnover of transverse tubules in adult myotubes as well as the initial formation and, additionally, if the caveolae rings are restricted to the region just under the surface membrane.

Insight into how transverse tubules are formed sets the groundwork for future therapies. This is clearly important for skeletal muscle myopathies but should also be considered in the heart. Cardiac transverse tubule loss and disorder play an important role in dysfunction in heart failure and atrial fibrillation and as such lessons learned in skeletal muscle may be successfully applied to the heart.

We would like to thank reviewer #2 for this appreciation of our work. We agree with the points raised and have updated our discussion section to highlight these points.

Reviewer #3 (Public Review):

T-tubules are an elaborate series of membrane invaginations that bring membrane voltageactivated Ca2+ channels in close apposition to the sarcoplasmic reticulum containing RyR, allowing for Ca2+-induced Ca2+ release. They serve as critical hubs of excitation-contraction coupling and play a central role in myopathies and inherited and acquired cardiomyopathies. Several membrane structures and proteins have been implicated in striated muscle t-tubule biogenesis, but the specific mechanisms of early t-tubule biogenesis are not defined. Lemerle et al here investigate the biogenesis of transverse tubules in skeletal muscle. They use skeletal myoblasts from murine and human muscle as well as sophisticated high-resolution microscopy, live cell imaging, and adenoviral targeting to forward a model of BIN1 mediated caveolae ring formation which give rise to DHPR enriched t-tubules and associate with SR. While they demonstrate that BIN1 and Cav3 enriched caveolae act together to form t-tubules, the precise pathophysiological mechanisms by which this process acts in disease remain unclear. Strengths of the study consist in the use of both murine and human skeletal muscle experiments, suggesting a conserved molecular mechanism; the innovative approach of correlative light and electron microscopy, and the use of pathological specimens. The live cell timelapse provides imaging evidence of Cav3-enriched caveolae-rings forming in centers of high BIN1 enrichment, from which t-tubules emanate. This is novel evidence in support of the biogenesis model proposed by the authors. The pathological correlation of their model is promising but limited. Specifically, while the study of Cav3 mutant specimens is used to show the Cav3 dependence of BIN 1 action (in experiments using BIN 1 overload), the authors have not tested the sufficiency of their proposed mechanism by rescuing the pathologic state. Moreover, the conditions of development likely have an important effect on the studied mechanism - such as mechanical loading, contractile state, neurohormonal environment, and so on. Furthermore, a more complete description of the precise molecular binding sites between BIN1 and Cav3 would be important. While exon11 is required for tubulation, BIN1 not expressing exon 11 appears sufficient to assemble caveolar rings, suggesting this is mediated by other specific BIN1 regions.

Overall, the study provides new details on early t-tubule biogenesis in skeletal muscle (likely shared with other striated muscle) and lays the foundations for further definition of the precise molecular mechanisms.

We would like to thank reviewer #3 for the appreciation of our work. We have now performed additional experiments on patient myotubes including rescue experiments, analysis of key excitationcontraction coupling proteins by Western blot and quantification of caveolae rings and tubules to strengthen our claims with patient myotubes.

Author Response:

Reviewer #1 (Public Review):

In this manuscript, Mastrototaro et al. perform a series of experiments in transgenic murine models assessing the function of Palladin (PALLD) in the heart. Global PALLD KOs are embryonic lethal, precluding the assessment of the roles of this protein in adulthood. To circumvent this limitation, the authors generated a floxed Palld allele and ablated it with two cardiomyocyte-specific Cres: the constitutively active Myh6-Cre and the tamoxifen-inducible aMHC-MerCreMer. Interestingly, ablation with the constitutive Cre (cKO) did not produce any overt phenotype, but ablation in adulthood (cKOi) resulted in compromised cardiac function. These observations suggest a compensation mechanism that takes place when cardiomyocytes develop in the complete absence of this protein but not when cardiomyocytes develop in a wild-type background and are deprived of this protein after achieving full maturation. These experiments were complemented with yeast two-hybrid techniques to identify novel partners that bind to a region of PALLD for each no interactants had been previously identified. Experiments in human samples revealed an upregulation of PALLD transcripts in the hearts of patients.

This manuscript adds important information to our understanding of sarcomeric proteins. Data are generally of good quality and well presented in figures. The numbers of animals in echocardiographic studies are also adequate for proper conclusions. Authors achieve most of their goals, including the identification of novel partners of PALLD and the identification of a requirement for PALLD in cardiomyocytes for normal heart function. However, given that all experiments performed in this study were focused on the loss-of-function of PALLD, it is not clear what is the relevance of the PALLD upregulation observed in human patients. Authors should clearly state this limitation in their results.

Considering that authors have observed evidence for nuclear PALLD, which could hint at potential major gene expression changes when this protein is ablated, it would be interesting to perform an unbiased assessment of transcriptional alterations (RNA-seq) in cardiomyocytes isolated from control and cKOi hearts. In addition, to test if the compensation observed in the embryonic cKO involves mechanisms of transcriptional adaptation, it would be interesting to compare RNA-seq results from cKOi and cKO (genes encoding proteins similar to PALLD that are upregulated in cKO but not cKOi cardiomyocytes would be very strong candidates). However, these transcriptomic data are not essential to support current findings and can be performed in follow-up studies.

We agree with the reviewer that it would be interesting to perform RNA-Seq on isolated cardiomyocytes from cPKOi mice and we are in fact planning to do this in a follow-up study.

Reviewer #2 (Public Review):

The role of the actin-binding protein palladin (PALLD) in cardiomyocyte development, growth, and function has not been defined. In order to address this question, the authors first identified that CARP and FHOD1 interact with PALLD in cardiomyocytes. They then performed cardiomyocyte selective deletion of PALLD in embryonic and adult mice and discovered that deletion of PALLD in adult mice leads to dilated cardiomyopathy (DCM) and intercalated disc ultrastructural changes. In contrast, embryonic deletion of cardiomyocyte PALLD did not cause a cardiomyopathy phenotype in neonatal or adult animals.

1. The divergent cardiac phenotypes of the embryonic deletion of cardiomyocyte PALLD (no cardiomyopathy) versus the adult deletion of cardiomyocyte PALLD (dilated cardiomyopathy(DCM)) is an interesting result. The authors speculate that embryonic deletion of PALLD induces compensatory pathways that prevent the development of adult cardiomyopathy in these mice. However, these compensatory pathways remain unexplored.<br /> 2. The authors discovered that mice with adult cardiomyocyte deletion of PALLD had significant changes in the cardiomyocyte intercalated disc (ICD) ultrastructure. They suggest these changes in ICD ultrastructure contribute to DCM formation in the adult PALLD deletion mice (line 270). However, it remains unclear if these changes in ICD ultrastructure are specific to mice with adult deletion of PALLD.<br /> 3. The different transgenic Cre mouse lines may be an alternative explanation for the divergent cardiac phenotypes in the embryonic versus adult deletion of cardiomyocyte PALLD. The tamoxifen dose administered for the inducible Myh6:MerCreMer mice was 30mg/kg/day x 5 which has been reported to lead to the induction of cardiomyocyte DNA damage response pathways (Dis Model Mech. 2013 Nov; 6(6): 1459-1469, J Cardiovasc Aging 2022;2:8). The electron micrograph experiments in Figure 5 did not include a group of Myh6:MerCreMer mice administered tamoxifen. The authors only compared PALLD fl/fl and Myh6:MerCreMer/PALLD fl/fl mice.

In the papers that the Reviewer refers to it was shown that administration of tamoxifen to Myh6:MerCreMer mice at a dose of 30 mg/kg/day for 3 (Bersell et al., Dis Model Mech. 6, 1459-1469, 2013) or 5 days (Rouhi et al., J Cardiovasc Aging 2, 8, 2022) is not associated with apoptosis. Bersell et al., found that amounts ≥40 mg/kg/day for 3 days is associated with apoptosis, and Rouhi et al., showed that injection of 30 mg/kg/day for 5 days causes transient minor changes in gene expression with no discernible effects on cardiac function, myocardial fibrosis, apoptosis, or induction of double-stranded DNA breaks. The reason that we chose to inject tamoxifen at an amount of 30 mg/kg/day for 5 days was in fact that this amount has been shown not to be associated with severe effects and has been widely used in the literature.

4. The apoptosis assessment was performed 24 weeks after administration of tamoxifen to the Myh6:MerCreMer/PALLD fl/fl mice. However, cardiomyocyte apoptosis may have occurred much earlier if it was secondary to Myh6:MerCreMer tamoxifen-induced cardiotoxicity (or related to PALLD deletion).<br /> 5. The animal studies in Fig 3D show a DCM phenotype in mice with adult deletion of cardiomyocyte 200kDa PALLD which suggests a potential loss of function mechanism for DCM formation. However, the authors then report in Fig 6 that human DCM heart tissue samples have a ~2.5fold increase in mRNA expression of the 200kDa PALLD transcript which would suggest a possible gain of function mechanism for DCM formation. How do the authors reconcile these divergent results with regard to palladin's role in cardiomyocyte homeostasis and cardiomyopathy formation?

In the revised manuscript we demonstrate that the transcriptional changes in PALLD expression are not reflected at the protein level.

Reviewer #3 (Public Review):

This study shows for the first time changes in palladin expression under disease conditions and mRNA alterations in human samples. The authors have identified novel binding partners for the protein as a first step toward determining how palladin mediates its effects in the heart. Finally, through the use of mouse models to decrease palladin expression they identify a crucial role for palladin in the cardiac response to pathological stress, with some interesting findings that show the effects of palladin depend on when the protein is altered.

We appreciate that the Reviewer finds our study interesting. However, we did not show a role of PALLD in the cardiac response to pathological stress. On the contrary, we demonstrated that mice with constitutive knockout of PALLD in the heart (cPKO mice) show no pathological cardiac phenotype either under basal conditions or in response to mechanical pressure overload by transaortic constriction. On the other hand, deletion of PALLD in adult mice resulted in DCM under basal conditions within 8 weeks after tamoxifen induction.

The novel findings of the study are supported by the data presented, but there are several instances where clarification is needed of the conclusions drawn from the data reach beyond what is presented in the Results section.

The focus on only male mice is a significant limitation of the paper, as it is well known that there are profound sex differences in the response to pathological stressors. While the ability to obtain sufficient heart samples from male and female patients may be a reasonable justification for focusing on males, the preclinical mouse model should have been examined in both sexes and the limitation of this choice should be clearly noted in the paper.

Due to the three Rs and the high costs associated with the breeding of the high amount mice required for the project, we chose to focus only on male mice.

In line 537-539, we stated. “All experiments were performed on male mice as females often develop a less severe cardiac phenotype due to the cardioprotective role of estrogen (Brower, Gardner, & Janicki, 2003; Du, 2004).

The changes in myopalladin expression were not measured in the disease model (TAC), which limits the ability to determine if myopalladin was altered in the disease state. This addition would strengthen the study.

We have previously demonstrated that myopalladin protein levels are significantly reduced after TAC in wildtype mice (Figure 6K, L in Filomena et al., eLife 10:e58313, 2021). We did not measure myopalladin levels in cPKO subjected to TAC and unfortunately don’t have tissue from cPKO mice to perform the measurements.

Finally, the myofilament data are presented as evidence that changes in the contractile apparatus are contributors to the observed contractile dysfunction at the organ level. But these studies were conducted using levels of calcium that far exceed what is seen in vivo and, therefore, do not support the conclusion drawn.

The reviewer is right that the myofibril experiments were conducted at Ca2+ concentrations that cannot be reached under the physiological conditions of cardiac contraction. However, the result clearly demonstrates that the intrinsic force generating capacity of the cardiac sarcomeres of cPKOi mice is impaired 8 weeks after TAM independently from any changes in myofilament Ca2+ sensitivity and cardiomyocyte Ca2+ handling. Experiments at lower (more physiological) Ca2+ concentrations would have produced less clear results in the absence of a full investigation of the relation between force and [Ca2+]. Since data demonstrate that cross bridge mechanics and kinetics are not affected, the reported finding supports the idea that a myofibril structural defect is responsible for the lower maximal force of the KO sarcomeres.

Author Response:

Reviewer #1 (Public Review):

This study presents a resource aiming to unify language and rules used in the literature to describe, curate and assess biology experiments, published or not. Focusing on host-pathogen interactions, the work presents a new ontology and controlled vocabulary, as well as rules to describe 'metagenotypes', a term coined for the joint description of interacting host-pathogen genotypes. 'PHI-Canto' extends a previous resource by also enabling using UniProtKB IDs to curate proteins. Among other important by-products, PHI-Canto could contribute to damping proliferating names and acronyms for genes, processes, and interactions; a chronic annoyance in the biosciences.

The tool does give the impression that, with sufficient time and usage, it could become a rich and robust resource. Just addressing the Uniprot IDs issue is a nice move.

We thank the reviewer for their positive comments and acknowledgement of the importance of using unified language in literature curation. We are pleased to see that our effort to improve interoperability and use existing resources has been recognized. We are also pleased that this reviewer recognizes the additional benefits of choosing to use UniProtKB accession numbers. 

Reviewer #2 (Public Review):

In this paper, the authors propose a system for annotating and curating scientific publications in the context of interspecies host-pathogen interactions. This system, called PHI-Canto (the Pathogen-Host Interaction Community Annotation Tool), is an extension of an existing tool (called Canto). In addition, they present the development of new concepts, controlled vocabularies, and an ontology for annotating relevant aspects in this domain, called PHIPO (Pathogen-Host Interaction Phenotype Ontology).

The approach has been empirically validated by annotating ten publications. The application's source code is available, as well as the associated ontologies and vocabularies and an example of the data resulting from the annotation process.

We thank the reviewer for their positive comments on our framework for curating interspecies interactions literature. We are pleased that the reviewer has recognized that the source code, associated ontologies and curated data are freely available for others to use. We are delighted that the reviewer found the curation of ten trial publications in PHI-Canto informative and benefited from the worked curation examples.

Reviewer #3 (Public Review):

In this work, the authors have built a framework for the annotation of interactions between species. The framework includes ontologies, methodologies, and an annotation tool called PHI-Canto. The framework makes use of multiple existing ontologies that are in wide use in the biocuration community. In addition, the authors have built their own project-specific controlled vocabularies and ontologies for the capture of pathogen-host interaction phenotypes (PHIPO), diseases (PHIDO), and environmental conditions (PHI-ECO). Their work builds on and extends methods that have been developed within the Gene Ontology Consortium and model organism databases. The tool PHI-Canto is an extension of the tool Canto developed by PomBase for curation. The authors used this framework to annotate pathogen-host interactions within the Pathogen-Host Interactions Database.

Strengths: The manuscript is well-written and includes significant detail regarding curation policies/methods and the use of the actual PHI-Canto tool. The appendices are very detailed and provide useful illustrations of the annotation practices and tool interface. The work has built upon and extended well-established standards and methods that have proven their utility over many years of use in the biocuration community. The authors have rigorously tested their framework with the curation of a variety of publications providing a diverse assortment of annotation challenges. The concept of a "metagenotype" is important and providing such a structured system for the capture of this information is useful. All of the materials produced by the work are completely freely available for use by the wider community.

Weaknesses: There are some areas of the manuscript and appendices which are a bit confusing and could be improved. The authors have developed their own set of disease terms (PHIDO) but do not comment on why existing disease terminologies (such as Mondo or DO) were not used or if the PHIDO terms relate to those other vocabularies. There is no discussion of the possible use of a graph representation for the capture of this complex information (which is being done in many settings including the Gene Ontology with GO Causal Activity Models (GO-CAMs)) or why such a structure was not used. Although the abstract talks about the use of the framework within the PHI database as a test case for broader use regarding interspecies interactions, there is no mention of extending the use of the tool to other species interaction communities beyond pathogen-host interactions.

We thank the reviewer for their detailed response. We are pleased that the reviewer found the manuscript to be well-written and informative with useful examples. We thank the reviewer for their helpful suggestions to improve the appendices and manuscript text.

We would like to clarify that PHIDO is not intended to compete with existing disease ontologies: it is instead being used as a placeholder, until the time when its terms can be replaced with terms from existing disease ontologies. PHIDO was an expedient solution, in the sense that it provided the fastest way for us to test the process of curating diseases with PHI-Canto. This is because we only had to convert the existing list of disease names already in PHI-base into a controlled vocabulary, thus removing the need to wait for maintainers of other ontologies to add terms for us (as reported in Urban et al., 2022).

Additionally, we were required to use terms from PHIDO due to the lack of representation for plant and animal diseases in existing ontologies or vocabularies. Plant disease, in particular, is very underrepresented, with the ontologies we surveyed having either inappropriate semantics (e.g. the Plant Trait Ontology focusing on traits related to disease, rather than the diseases themselves) or still being in development (e.g. the Plant Stress Ontology). The majority of source ontologies used by MONDO are human-centric, and DO is exclusively for human disease, yet human disease represents only part of the focus of PHI-base (~35%). Furthermore, our choice of vocabularies is limited by the fact that Canto currently only supports ontologies in OBO format (for historical reasons).

We have begun the process of harmonizing disease names in PHI-base with terms from existing disease ontologies – such as MONDO, DO, and the National Cancer Institute Thesaurus – with the ultimate aim of using terms from those ontologies in curation, instead of terms from PHIDO. As general vocabularies for animal and plant disease emerge or are identified, we will extend this procedure to those diseases.

With regards to a graph representation of the data, we are aware of the examples the reviewer described, and we agree that this type of representation could be preferable. However, our data model is currently constrained by the developers of Canto, who use a relational data model and currently have no plans to implement a graph data model or a graph representation. We acknowledge that query languages like GraphQL can provide a graph-based interface to an existing relational data model, but we believe this would require a significant technological investment. For PHI-base, we plan to enable a graph representation of the data by integrating with existing knowledge graph tools, such as KnetMiner (www.knetminer.com;doi.org/10.1111/pbi.13583), which will provide graph-based queries on PHI-base (albeit only on select species for which knowledge graphs will be provided, i.e. Arabidopsis, rice, wheat, eight plant and human infecting fungal ascomycete pathogens, and two non-pathogenic yeast species). We will also use KnetMiner integration to embed subgraphs of the complete knowledge graph into the gene-centric pages on the PHI-base 5 website.

We acknowledge the lack of discussion about extending the tool for broader interspecies interactions. These examples may have been omitted from a previous draft due to journal word count limits. Possible future uses of the PHI-Canto schema could include insect–plant interactions (both beneficial and detrimental), endosymbiotic relationships such as mycorrhiza–plant rhizosphere interactions, nodulating bacteria–plant rhizosphere interactions, fungi–fungi interactions, plant–plant interactions or bacteria–insect interactions, and non-pathogenic relationships in natural environments, such as bulk soil, rhizosphere, phyllosphere, air, freshwater, estuarine water or seawater, and tissues or organs (e.g. the gut, lungs, and skin of humans, birds, or other animals). The schema could also be extended to situations where phenotype relations to genes or genotypes have been established for predator–prey relationships, or where there is competition in herbivore–herbivore, predator–predator, or prey–prey relationships in the air, on land or in the water. Customizing Canto to use other ontologies and controlled vocabularies is as simple as editing a configuration file within the source code.

Author Response:

We appreciate the Reviewers’ feedback. The manuscript was extensively revised and ultimately accepted for publication (Petrican and Fornito, 2023, Developmental Cognitive Neuroscience). The revisions address the Reviewers’ key concerns, including the theoretical basis of the link between MDD and AD, the rationale for studying this link in adolescence, clear references to significant genetic associations between the two, detailed assessment of CCA and PLS model generalisability and reliability, quantification of resilience, residualization of confounders, and corrections for multiple comparisons. We also note that the details concerning the receptor density maps we use in our analysis have now been published (Hansen et al., 2022, Nature Neuroscience; Markello et al., 2022, Nature Methods).

Author Response

Reviewer #1 (Public Review):

By performing immunopeptidomics of macrophages infected with virulent M. tuberculosis, the authors were able to appropriately address whether Mtb proteins are able to enter the MHC-I antigen processing pathway. Their interrogation provides convincing evidence that substrates of Mtb's type VII secretion systems (T7SS) are a significant contributor to the Mtb-derived peptides presented on MHC-I. Compelling data are provided to demonstrate that ESX-1 activity is required for the MHC-1 presentation of these newly identified peptides.

Strength

Employing a virulent strain of Mtb for infection of human monocyte-derived macrophages to identify Mtb proteins that access the MHC-I antigen processing pathways and the associated mechanisms.

Weakness

The immunogenicity of at least some of the identified peptides should have been evaluated.

Although obtaining T cells from a cohort of TB-exposed patients was not within the scope of this study, we are also eager to assess the immunogenicity of the epitopes we identified in future work. In addition to the references we made in our initial submission to prior work showing that many of the proteins from which the epitopes we identified derive elicit T cell responses in Mtb-exposed humans, we’ve added references to prior studies that show that a few of the specific epitopes we identified are immunogenic, providing at least a preliminary indication that MHC-I peptides identified by MS can be immunogenic T cell epitopes (lines 420-423): “Individual peptides we identified by MS have also been previously shown to be recognized by human T cells, including EsxJ24-34 (Grotzke et al., 2010; Lewinsohn et al., 2013) and EsxA28-36 (Tully et al., 2005), providing a proof of concept that particular epitopes identified by MS can be immunogenic.”

Author Response

Reviewer #1 (Public Review):

The authors have performed scATACseq on multiple timepoints during mouse male gonadogenesis and germ cell maturation during the fetal to neonatal transition (E18.5 and postnatal days 1,2,5). Clustering of thousands of cells revealed striking cellular diversity and led to the identification of cell populations that were not known before. This work may have far reaching implications, but additional validation is needed.

We would like to start by expressing our appreciation to the reviewer’s valuable comments and feedback on our manuscript. We would also like to express our sincere apologies for the delay in submitting our revised manuscript. The COVID-19 pandemic has had a significant impact on academic research and publication, and we encountered several challenges during this time. Both co-first authors of this manuscript were promoted to new roles, which required additional time and effort to transition into these new positions. Furthermore, we experienced significant delays in obtaining the necessary research materials due to longer shipment times for antibodies and other reagents during the pandemic, which further contributed to the delay. We understand that our delay may have caused inconvenience but we want to assure you that we have carefully addressed all of the reviewer comments and we deeply appreciate your understanding and patience during these challenging times.

The identification of novel transitional spermatogonia population in Figure 4D is intriguing. Independent validation by flow cytometry or in testis cross section to better allow the colocalization of nr5a1 and Oct4 and other germ cell markers would be important. Additional validation is needed to ensure that populations 1 and 2 in figure 4d are not to doublets. Providing violin plots for both soma and germ cell markers will be helpful. Is SF1 the only gene expressed in this unique germ cell population or are many other somatic markers expressed in the population. Do these cells express well recognized SPG markers like Oct4+ , PLZF, GFRA?

We have performed immunostaining of NR5A1 in testicular sections and showed that NR5A1+ germ cells (TRA98+ cells) exist in P5.5 testis (Figure 4D). We appreciate the reviewer's comment and understand the concern regarding potential doublets in figure 4d. We examined the expression of various markers in both scATAC-seq (gene score) and scRNA-seq (mRNA) datasets and provided violin plots. Sertoli cell markers and germ cell markers showed variable levels in unknown 1 and 2 populations while the Leydig cell marker did not (Supplementary figure S6D).

As additional evidence supporting our finding that a subset of somatic markers are expressed in the unique germ cell population we identified, we reference a study where cells in the spermatogonial signature 3 cluster showed high levels of mRNAs characteristic of Sertoli cells, including Nr5a1, Sox9, and Wt1 (PMID: 25568304). This indicates that cells with germ cell identity can express somatic cell genes, which is consistent with our findings. Additionally, another study reported the expression of the somatic cell marker WT1 in some germ cells through immunostaining (Figure 3B, PMID: 34815802). We have included this information in the revised manuscript to further support our conclusion (line 301). In addition, as we have isolated nuclei rather than whole cells, it is less likely that germ cells and sertoli cells are sticking together during single cell capture. We hope that the additional evidence and analysis provided will help to ease the reviewer's concerns and further support the conclusions drawn from our data.

The IF validation in 5F is not as convincing that these cells are potentially Sertoli stem cells. IF in cross-sections will be easier to interpret- especially when co-stained with several germ, somatic, or novel markers of that population. purification of these cells and further characterization is needed. A hallmark of fetal Sertoli cells is to mediate the migration of endothelial cells to the seminiferous tubules during testicular cord formation. Is it possible to purify these cells to determine whether they have functional Sertoli cells properties in vitro using human umbilical vein endothelial cells (HUVECs). Do these cells have immune privilege properties - can they suppress proliferation of Jurkat E6 cells.

Following the reviewer’s suggestions, we conducted further immunostaining of MBD3 and AMH in Sertoli cells (Figure 5F). The observed staining results not only confirm the properties of MBD3+ cells (MBD3-high/AMH-high) but also highlight the heterogeneity of Sertoli cells, as evidenced by the presence of various expression patterns such as MBD3-low/AMH-high (cluster SC3 in Figure 5A) and MBD3-low/AMH-low (cluster SC2/4/5/6 in Figure 5A). This further emphasizes the complexity and diversity within the Sertoli cell population.

However, we understand that it is premature to definitively conclude that MBD3-high cells are Sertoli stem cells without functional studies. We appreciate the suggestion of using additional functional assays such as in vitro co-culture with HUVECs and immune privilege assays to further characterize the potential Sertoli stem cell population. These are valuable experiments to consider for future research in order to gain a deeper understanding of the properties and functions of these cells. To more accurately reflect the scope of our study and avoid potential misinterpretation, we have revised the language to reflect that we have identified subpopulations of Sertoli cells with unique characteristics, rather than using the term "stem cell". We hope that our revised data adequately addresses the reviewer’s concerns.

Reviewer #2 (Public Review):

Liao et at performed single cell ATAC sequencing to reveal chromatin status in various cell types in the perinatal mouse testes. The chromatin status was then used to define cell types and identify potential transcription factors that control the progress of differentiation. This work could provide new insights into how various cell types acquire their fate in early testis development and establish a genomic framework that can be used to correlate with human data for infertility. The strength lies on the novelty of single cell analyses. The weaknesses include a lack of statistical power, the uncertainty on the correlation between chromatin status, gene expression, and transcription factor activity, and insufficient information and confirmation on some of the experiments and results.

We would like to start by expressing our appreciation to the reviewer’s valuable comments and feedback on our manuscript. We would also like to express our sincere apologies for the delay in submitting our revised manuscript. The COVID-19 pandemic has had a significant impact on academic research and publication, and we encountered several challenges during this time. Both co-first authors of this manuscript were promoted to new roles, which required additional time and effort to transition into these new positions. Furthermore, we experienced significant delays in obtaining the necessary research materials due to longer shipment times for antibodies and other reagents during the pandemic, which further contributed to the delay. We understand that our delay may have caused inconvenience but we want to assure you that we have carefully addressed all of the reviewer comments and we deeply appreciate your understanding and patience during these challenging times.

Author Response

Reviewer #1 (Public Review):

The manuscript by Lujan and colleagues describes a series of cellular phenotypes associated with the depletion of TANGO2, a poorly characterized gene product but relevant to neurological and muscular disorders. The authors report that TANGO2 associates with membrane-bound organelles, mainly mitochondria, impacting in lipid metabolism and the accumulation of reactive-oxygen species. Based on these observations the authors speculate that TANGO2 function in Acyl-CoA metabolism.

The observations are generally convincing and most of the conclusions appear logical. While the function of TANGO2 remains unclear, the finding that it interferes with lipid metabolism is novel and important. This observation was not developed to a great extent and based on the data presented, the link between TANGO2 and acyl-CoA, as proposed by the authors, appears rather speculative.

We thank you for your advice and now include additional data that lends support to the role of TANGO2 in lipid metabolism. We have changed the title accordingly.

1) The data with overexpressed TANGO2 looks convincing but I wonder if the authors analyzed the localization of endogenous TANGO2 by immunofluorescence using the antibody described in Figure S2. The idea that TANGO2 localizes to membrane contact sites between mitochondria and the ER and LDs would also be strengthened by experiments including multiple organelle markers.

We agree that most of the data on TANGO2 localization are based on the overexpression of the protein. As suggested by the reviewer and despite the lack of commercial antibodies for immunofluorescence-based evaluation, see the following chart, we tested the commercial antibody described in Figure 2 on HepG2 and U2OS cells. Moreover, we used Förster resonance energy transfer (FRET) technology to analyze the proximity of TANGO2 and Tom20, a specific outer mitochondrial membrane protein. In addition, we visualized cells expressing tagged TANGO2 and tagged VAP-B, an integral ER protein in the mitochondria-associated membranes (doi:10.1093/hmg/ddr559) or tagged TANGO2 and tagged GPAT4-Hairpin, an integral LD protein (doi:10.1016/j.devcel.2013.01.013). These data strengthen our proposal and are presented in the revised manuscript.

As suggested by the reviewer, we have also visualized two additional cell lines (HepG2 and U2OS) with the anti-TANGO2( from Novus Biologicals) that have been used for western blot (see chart above). As shown in the following figure, the commercial antibody shows a lot of staining in addition to mitochondria, especially in U2OS cells, where it also appears to label the nucleus.

2) The changes in LD size in TANGO2-depleted cells are very interesting and consistent with the role of TANGO2 in lipid metabolism. From the lipidomics analysis, it seems that the relative levels of the main neutral lipids in TANGO2-depleted cells remain unaltered (TAG) or even decrease (CE). Therefore, it would be interesting to explore further the increase in LD size for example analyze/display the absolute levels of neutral lipids in the various conditions.

We agree with the reviewer and now present the absolute levels of lipids of interest in the various conditions of the lipidomics analyses (Figure S 3).

3) Most of the lipidomics changes in TANGO2-depleted cells are observed in lipid species present in very low amounts while the relative abundance of major phospholipids (PC, PE PI) remains mostly unchanged. It would be good to also display the absolute levels of the various lipids analyzed. This is an important point to clarify as it would be unlikely that these major phospholipids are unaffected by an overall defect in Acyl-CoA metabolism, as proposed by the authors.

As stated above, we have now included the absolute levels of lipids of interest in the various conditions of the lipidomics analyses (Figure S 3).

Author Response

Reviewer #1 (Public Review):

This is a well-performed and carefully executed and quantified study. There is however a point that needs clarification:

We thank the reviewer for these motivating comments and appreciate the careful reflection of our work.

The authors state that acute regeneration occurs between 5-10dpt. However, the graphs in Fig 1D, F, and 2F indicate that most PC generation occurs from 20-30 days. What happens in this period? Does proliferation increase? Can the authors perform BrdU incorporation between 6 days and 1 month?

The reviewer is right that PC regeneration seems to be more intense from 20-30 days. Yet during this stage also wildtype larvae add a number of PCs to their PC population pool, thus we would consider only PCs being added in surplus to the number of regularly added PCs as a contribution to regeneration, and here we see in quantified samples the largest increase of regenerating PCs during 8-10 days post-treatment with 20,9 and 23,2 additional (surplus) PCs on average respectively.

This question also relates to the first comment of reviewer 3 who asked for a combined BrdU and EdU labeling approach to address the cell cycle length of PC progenitors. We have therefore performed this experiment with the first pulse of BrdU-labeling at 18 days after PC-ablation to include the request stated here for a BrdU-labeling at later stages of regeneration. Again, no significant difference between BrdU-positive PC progenitors was found at this later stage of PC regeneration, but a small number of PC progenitors underwent additional rounds of proliferation compared to controls, which provide an explanation of how the entire PC population is replenished and why complete PC regeneration requires several months. Please see also our answer to question 1 of reviewer 3. These new findings are now presented in an additional Supplementary Figure (Figure 1-figure supplement 3) and have been added to the last paragraph of the section reporting the findings presented in Figure 1.

Related to this, as the authors indicate in lines 129-131, the regeneration of new PCs overlaps with normal development. Are other neuronal cell types generated in appropriate numbers?

This is an interesting question raised by the reviewer. But it is very general relating to all cerebellar neuronal cell types, which is out of our possibilities to address. We considered eurydendroid cells as the most likely cell population, which could be affected in their numbers by PC ablation and regeneration, because eurydendroid cells share the same ptf1a+-expressing progenitor cells with Purkinje cells. Eurydendroid cells – the zebrafish equivalents to deep nuclei neurons in mammals – can be identified by their expression of olig2. We have therefore quantified the number of eurydendroid cells in the cerebellum of double transgenic PC-ATTAC/olig2:GFP larvae 15 days after PC ablation. No significant difference in olig2:GFP positive cells could be observed between PC-regenerating and control zebrafish suggesting that eurydendroid cells are not affected in their quantity and are generated in appropriate numbers in PC regenerating larvae. These findings are presented in a new Supplementary Figure (Figure 3-figure supplement 3) and are described together with findings about eurydendroid cells presented in the main Figure 3.

Author Response

Reviewer #1 (Public Review):

In this manuscript, Gonzalez et al investigated the dynamics of dopamine signals, measured with optophysiological methods in the lateral shell of the nucleus accumbens (LNAc), in response to different types of visual stimuli. Contrary to most current theories of dopamine signaling, the authors found that LNAc dopamine transients tracked sensory transitions in visual stimulation rather than any immediately apparent motivational variable. This unorthodox finding is of potential interest to the field, as it suggests that dopamine in this particular area of the striatum supports a very different, albeit unclear behavioral function than what has been previously attributed to this neuromodulator. Many of the approaches used by the authors were very elegant, like the careful selection of visual stimuli parameters and the use of Gnat1/2 KO mice to demonstrate that the dopamine responses were directly dependent on the visual stimulation of rods and cones. That said, the authors did not discuss how their findings relate to much previously published work, many of which offer potential alternative explanations for their results. It is also not clear from the manuscript text which mice were used for which experiments, and how testing history might affect the results.

We would like to thank the reviewer for their careful review of our manuscript. In our revised manuscript, we reworked our Materials and Methods to better detail the experimental workflow, which is highlighted in yellow. We have also added new data in stimulus-naïve animals to better examine the effect of exposure history on the dopaminergic response to light. To provide validation of our recording sites, we have included a new figure (Figure 1-Figure Supplement 1) that contains a representative histological image showing the location of the optical fiber/virus expression, as well as a schematic demonstrating optical fiber placements. Finally, the reviewer’s point about discussing the current results in the context of previous literature is well taken, and we have added three new paragraphs of text in the Discussion to highlight these findings.

Reviewer #2 (Public Review):

In this elegant work, the authors investigated dopamine release (measured by dLight sensor fiber photometry) in the nucleus accumbens shell, in response to salient luminance change. They show that abrupt visual stimuli - including stimuli not detectable by the human eye - can evoke robust dopamine release in the accumbens shell.

The fact that dopamine signals can be evoked by salient sensory stimuli is not itself novel, but the paper manages to make several important and new findings:

1) The authors show that the dopamine signal is not related to the level of threat evoked by the visual stimuli.

2) They provide important detail about the stimuli parameters relevant to dopamine release. For instance, they show that the rate of luminance change (or abruptness) is a key factor in evoking dopamine responses.

3) They show that robust dopamine responses can be evoked by visual stimuli of low intensity, including stimuli not perceptible by the human eye.

4) They show that these dopamine responses can be evoked by all wavelengths in the visible spectrum (with some higher sensitivity at certain wavelengths).

5) Finally, by recording dopamine responses in two knockout mice strains, the authors show that the light-evoked dopamine release critically relies on rod and cone photoreceptors, but not melanopsin phototransduction.

These results add to a series of recent findings showing that dopamine signals are not restricted to the encoding of reward prediction error, but instead contribute to signaling environmental changes more broadly. The study has been skillfully executed, the results are clear and appropriately analyzed, and the manuscript is very well written. Although the work did not include control mice lacking the dLight sensor, the fact that light-evoked dopamine responses were not observed in mice lacking cone + rod phototransduction is strong evidence that the fiberphotometry signals were not due to direct light artifacts.

We would like to thank the reviewer for taking their valuable time over the holidays to review our manuscript. We appreciate their feedback and have responded to their concerns below.

Comment/concerns are minor:

1) The authors show that the dopamine response evoked by a brief visual stimulus is drastically reduced when the visual stimulus is repeated in rapid succession (stimulus train). The authors interpret this as evidence for the HABITUATION of this light-evoked dopamine release. An alternative explanation is that it is the prediction of the stimulus that is responsible for canceling the dopamine response (i.e. sensory prediction error). The authors should discuss this alternative explanation for this finding.

This is a valid point, which we have now addressed in the revised Discussion section (Paragraph 3).

2) Although the study largely focuses on dopamine responses to visual stimuli, the results are largely consistent with previous studies showing dopamine signals encoding value-neutral changes in sensory inputs (i.e. sensory prediction errors) in different modalities (taste or odors; cf. Takahashi et al., 2017, Neuron; Howard & Kahnt, 2018, Nat. Comm.). The authors might want to cite those papers (note that I am not affiliated with those papers).

This is similar to the point brought up by Reviewer 1, namely that several key pieces of literature were not discussed in the original manuscript. We agree that this was an oversight and hope we have remedied it in the revised Discussion, as detailed in the response to Reviewer 1. We have included both citations in the new text.

Author Response

Reviewer #1 (Public Review):

This manuscript describes efforts to understand how independence from ribonucleotide reduction might evolve in obligate intracellular bacterial pathogens using E. coli as a model for this process. The authors successfully deleted the three ribonucleotide reductase (RNR) operons present in E. coli and showed that growth of this knockout strain can be achieved with deoxyribonucleotide supplementation. They also performed evolutionary experiments and analysis of cell growth and morphology under conditions of low nucleotide availability. In this work, they established that certain genes are consistently mutated to compensate for the loss of RNR activity and the low availability of deoxynucleotides. Comparison to genomes of intracellular pathogens that lack RNR genes shows that these patterns are largely conserved.

While the experimental results support the conclusions of the study, the authors do report changes in cell morphology upon the growth of the RNR knockout strains with low concentrations of nucleotides. It would be ideal to note this complication earlier in the manuscript. And to clarify how the possibility of cell elongation might affect the OD measurements in Figure 3 describing the experiments to establish that dC is necessary for growth in the knockout strain. It would also be ideal to provide a more detailed explanation for that observation in the discussion.

Thank you for the feedback. We have now added mention of cell morphology in the final paragraph of the introduction, where we summarise key findings.

For establishing if there is either growth or no growth under various conditions, as we have done, a qualitative assessment such as the one presented in Figure 3 is sufficient. The issue of whether OD is impacted by cell elongation has been documented by Stevenson et al. (https://www.nature.com/articles/srep38828), and becomes a problem if trying to quantify parameters such as doubling time or when trying to estimate cell counts. We do not do either of these, as calculation of both requires an assumption of normal cell morphology in E. coli. We have added a note to clarify this in the first paragraph of the Discussion section, as per the suggestion from Reviewer #1.

Reviewer #2 (Public Review):

Ribonucleotide reductase (RNR) is crucial for de novo synthesis of the dNTP building blocks needed for DNA synthesis and is essential in nearly all organisms. In the current study, all three E. coli RNRs have been removed and the essential function of the enzyme is bypassed by the introduction of an exogenous deoxyribonucleoside kinase that enables dNTP production via salvage synthesis. This leads to a complete dependency on exogenously supplied deoxyribonucleosides (dNs), loss of control of dNTP regulation, and a highly increased mutation rate. The bacteria could also grow with only supplied deoxycytidine (and no other dNs), indicating that all dNTPs could be synthesized from deoxycytidine. An evolutionary analysis of the recombinant E. coli strain grown in multiple generations showed that mutations accumulated in genes involved in the catabolism of deoxycytidine and deoxyribose-1-P, supporting a model that all the other deoxyribonucleosides can be produced by a phosphorylase using nucleobases and deoxyribose-1-P as substrates and that the deoxycytidine (besides being a precursor of dCTP) could be a substrate to produce the deoxyribose-1-P needed by the phosphorylase working in the opposite direction.

The story is very interesting with novel findings, and the experiments are well performed. There are a few missing pieces of information, but on the other hand, it is many steps to cover if everything is going to be shown in a single paper and I came to the conclusion that the data is enough at this stage. One of the missing points for future research is to check what happens with the dNTP pools. RNR is a very important enzyme to control the dNTP levels and it is likely that it is unbalanced dNTP pools that lead to the increased mutation rates. However, it would be interesting to really measure the dNTP pools and connect them to the mutations reported. Another missing piece is to identify which nucleoside phosphorylase is involved and investigate its substrate specificity to better understand why the cells can live on deoxycytidine but not other dNs.

We thank the reviewer for these comments. It is certainly possible that the mutational biases we observe across the genomes of our evolved lines are related to skewed pools. We hope to examine this in a follow-up study. Likewise, it will be interesting to investigate the biochemical basis for our lines being able to grow solely on deoxycytidine, and to ascertain how this might also impact mutation.

Reviewer #3 (Public Review):

The study focuses on a compelling question focusing on a largely indispensable mechanism, ribonucleotide reduction. The authors generate a unique specific bacterial strain where the ribonucleotide reducatase operon, entirely, is deleted. They grow the mutant strain in environments that have various amounts of the necessary deoxyribonucleoside levels, further, they perform evolution experiments to see whether and how the evolved lines would be able to adapt to the limited deoxyribonucleosides. Finally, researchers identify key mutations and generate key isogenic genetic constructs where target mutants are deleted. A summary postulation based on the evolutionary trajectory of ribonucleotide reduction by bacteria is presented. Overall, the study is well presented, well-justified, and builds on fairly classic genetic and evolution experiments. The select question and hypotheses and the overall framing of the story are fairly novel for the respective communities. The results should be interesting to evolutionary biology researchers, especially those interested in RNA>DNA directional evolution, as well as molecular microbiologists interested in the ribonucleotide reception dependence and selection by the environment. A discussion on the limitations of the laboratory study for the broader understanding of the host dependence during endosymbiosis and parasitism would be a good addition given the emphasis on this phenomenon as a part of the broader impacts of the study.

We thank the reviewer for suggestion that we consider the broader implications of our work. We have now added a final paragraph which addresses the question of why loss of ribonucleotide reduction appears so rare.

Author Response:

What is novel here is that we calculated the time-varying retinal motion patterns generated during the gait cycle using a 3D reconstruction of the terrain. This allows calculation of the actual statistics of retinal motion experienced by walkers over a broad range of normal experience. We certainly do not mean to claim that stabilizing gaze is novel, and agree that the general patterns follow directly from the geometry as worked out very elegantly by Koenderink and others.  We spend time describing the terrain-linked gaze behavior because it is essential for understanding the paper. We do not claim that the basic saccade/stabilize/saccade behavior is novel and now make this clearer.

The other novel aspect is that the motion patterns vary with gaze location which in turn varies with terrain in a way that depends on behavioral goals. So while some aspects of the general patterns are not unexpected, the quantitative values depend on the statistics of the behavior.  The actual statistics require these in situ measurements, and this has not previously been done, as stated in the abstract.

The measured statistics provide a well-defined set of hypotheses about the pattern of direction and speed tuning across the visual field in humans. Points of comparison in the existing literature are hard to find because the stimuli have not been closely matched to actual retinal flow patterns, and the statistics will vary with the species in question. However, recent advances allow for neurophysiological measurements and eye tracking during experiments with head-fixed running, head-free, and freely moving animals. These emerging paradigms will allow the study of retinal optic flow processing in contexts that do not require simulated locomotion. While the exact the relation between the retinal motion statistics we have measured and the response properties of motion-sensitive cells remains unresolved, the emerging tools in neurophysiology and computation make similar approaches with different species more feasible.

A more detailed description of the methods including the photogrammetry and the reference frames for the measurements has been added primarily to the Methods section.

Reviewer #1 (Public Review):

Much experimental work on understanding how the visual system processes optic flow during navigation has involved the use of artificial visual stimuli that do not recapitulate the complexity of optic flow patterns generated by actual walking through a natural environment. The paper by Muller and colleagues aims to carefully document "retinal" optic flow patterns generated by human participants walking a straight path in real terrains that differ in "smoothness". By doing so, they gain unique insights into an aspect of natural behavior that should move the field forward and allow for the development of new, more principled, computational models that may better explain the visual processing taking place during walking in humans.

Strengths:

Appropriate, state-of-the-art technology was used to obtain a simultaneous assessment of eye movements, head movements, and gait, together with an analysis of the scene, so as to estimate retinal motion maps across the central 90 deg of the visual field. This allowed the team to show that walkers stabilize gaze, causing low velocities to be concentrated around the fovea and faster velocities at the visual periphery (albeit more the periphery of the camera used than the actual visual field). The study concluded that the pattern of optic flow observed around the visual field was most likely related to the translation of the eye and body in space, and the rotations and counter-rotations this entailed to maintain stability. The authors were able to specify what aspects of the retinal motion flow pattern were impacted by terrain roughness, and why (concentration of gaze closer to the body, to control foot placement), and to differentiate this from the impact of lateral eye movements. They were also able to identify generalizable aspects of the pattern of retinal flow across terrains by subsampling identical behaviors in different conditions.

Weaknesses:

While the study has much to commend, it could benefit from additional methodological information about the computations performed to generate the data shown. In addition, an estimation of inter-individual variability, and the role of sex, age, and optical correction would increase our understanding of factors that could impact these results, thus providing a clearer estimate of how generalizable they are outside the confines of the present experiments.

Properties of gait depend on the passive dynamics of the body and factors such as leg length and subject specific cost functions which are influenced by image quality and therefore by optical correction. In this experiment all subjects were normal acuity or corrected to normal (with no information regarding their uncorrected vision). This is now noted in the Methods. The goal of the present work was to calculate average statistics over a range of observers and conditions in order to constrain the experience-dependent properties one might see in neurophysiology. We have added between-subjects error bars to Figure 2 and added gaze angle distributions as a function of terrain for individual observers in the Supplementary materials. Figure 4 b and d now show standard errors across subjects. Individual subject plots are shown in the Supplementary materials. For Figure 2, most variability between subjects occurs in the Flat and Bark terrains where one might expect individual choices of energetic costs versus speed and stability etc might come into play. This is supported by our subsequent unpublished work on factors influencing foothold choice. We have also found that leg length determines path choices and thus will influence the retinal motion. Differences between observers are now noted in the text. These individual subject differences should indicate the range of variability that might be expected in the underlying neural properties and perhaps in behavioral sensitivity. Because of the size of our dataset (n=11) it is not feasible to make comparisons of sex or age. There were equal numbers of males and females and age ranged from 24 to 54. Now noted in the Methods section.

Reviewer #2 (Public Review):

The goal of this study was to provide in situ measurements of how combined eye and body movements interact with real 3D environments to shape the statistics of retinal motion signals. To achieve this, they had human walkers navigate different natural terrains while they measured information about eyes, body, and the 3D environment. They found average flow fields that resemble the Gibsonian view of optic flow, an asymmetry between upper and lower visual fields, low velocities at the fovea, a compression of directions near the horizontal meridian, and a preponderance of vertical directions modulated by lateral gaze positions.

Strengths of the work include the methodological rigor with which the measurements were obtained. The 3D capture and motion capture systems, which have been tested and published before, are state-of-the-art. In addition, the authors used computer vision to reconstruct the 3D terrain structure from the recorded video.

Together this setup makes for an exciting rig that should enable state-of-the-art measurements of eye and body movements during locomotion. The results are presented clearly and convincingly and reveal a number of interesting statistical properties (summarized above) that are a direct result of human walking behavior.

A weakness of the article concerns tying the behavioral results and statistical descriptions to insights about neural organization. Although the authors relate their findings about the statistics of retinal motion to previous literature, the implications of their findings for neural organization remain somewhat speculative and inconclusive. An efficient coding theory of visual motion would indeed suggest that some of the statistics of retinal motion patterns should be reflected in the tuning of neural populations in the visual cortex, but as is the present findings could not be convincingly tied to known findings about the neural code of vision. Thus, the behavioral results remain strong, but the link to neural organization principles appears somewhat weak.

We agree, but we think that strengthening the neural links requires future studies. As mentioned above, it is very difficult to relate the measured statistics to existing neurophysiological literature and we have tried to make this clearer in the Discussion (p14, 15, 16). This is because the stimuli chosen are typically arbitrary and not chosen to be realistic examples of patterns consistent with natural motion across a ground plane. Other stimuli are simply inconsistent with self-motion together with gaze stabilization (eg not zero velocity at the fovea). It has also been technically difficult to map cell properties across the visual field. We have made the comparisons we thought were useful. The point of the paper is to provide a hypothesis about the pattern of direction and speed tuning across the visual field. So the challenge for neurophysiology is to show how the observed cell properties vary across the visual field. Note also that the motion patterns will be influenced by the body motion of the animal in question, and because of this we are now collaborating with a group who are attempting to record from monkey MT/MST during locomotion while tracking eyes and body. Similarly we are training neural networks to learn the patterns generated by human gait to develop more specific hypotheses about receptive field properties.

Reviewer #3 (Public Review):

Gaze-stabilizing motor coordination and the resulting patterns of retinal image flow are computed from empirically recorded eye movement and motion capture data. These patterns are assessed in terms of the information that would be potentially useful for guiding locomotion that the retinal signals actually yield. (As opposed to the "ecological" information in the optic array, defined as independent of a particular sensor and sampling strategy).

While the question posed is fundamental, and the concept of the methodology shows promise, there are some methodological details to resolve. Also, some terminological ambiguities remain, which are the legacy of the field not having settled on a standardized meaning for several technical terms that would be consistent across laboratory setups and field experiments.

Technical limits and potential error sources should be discussed more. Additional ideas about how to extend/scale up the approach to tasks with more complex scenes, higher speed or other additional task demands and what that might reveal beyond the present results could be discussed.

This issue is addressed in more detail in the Discussion, second paragraph, and also the second last paragraph.

Author Response

Reviewer #1 (Public Review):

This work presents a unification model (of sorts) for explaining how the flow of evidence through networks can be controlled during decision-making. The authors combine two general frameworks previously used as neural models of cortical decision-making, dynamic normalization (that implement value encoding via firing activity) and recurrent network models (which capture winner-take-all selection processes) into a unified model called the local disinhibition-based decision model (LDDM). The simple motif of the LDDM allows for the disinhibition of excitatory cells that represent the engagement of individual actions that happens through a recurrent inhibitory loop (i.e., a leaky competing accumulator). The authors show how the LDDM works effectively well at explaining both decision dynamics and the properties of cortical cells during perceptual decision-making tasks.

All in all, I thought this was an interesting study with an ambitious goal. But like any good study, there are some open issues worth noting and correcting.

MAJOR CONCERNS

1. Big picture

This was a comprehensive and extremely well-vetted set of theoretical experiments. However, the scope and complexity also made the take-home message hard to discern. The abstract and most of the introduction focus on the framing of LDDM as a hybrid of dynamic normalization models (DNM) and recurrent network models (RNMs). This is sold as a unification of value normalization and selection into a novel unified framework. Then the focus shifts to the role of disinhibition in decision-making. Then in the Discussion, the goal is stated as to determine whether the LDDM generates persistent activity and does this activity differ from RNMs. As a reader, it seems like the paper jumps between two high- level goals: 1) the unification of DNM and RNM architectures, and 2) the role of disinhibition. This constant changing makes it hard to focus as the reader goes on. So what is the big picture goal specifically?

Also, the framing of value normalization and WTA as a novel computational goal is a bit odd as this is a major focus of the field of reinforcement learning (both abstractly at the computational level and more concretely in models of the circuits that regulate it). I know that the authors do not think they are the first to unify value judgements with selection criteria. The writing just comes across that way and should be clarified.

We thank the Reviewer for their thoughtful consideration of the overall framing of the big picture goals of the paper. Upon reflection, we agree that the paper really centers on the importance of incorporating disinhibition into computational circuit-based models of decision-making. Thus, we have significantly revised the Introduction and Discussion to focus on the theoretical and empirical importance of incorporating disinhibition into computational models of decision-making, and use the integration of value normalization and WTA selection as an example of how disinhibition increases the richness of circuit decision models. Please see the response to recommendations below for more detail on the changes.

1. Link to other models

The LDDM is described as a novel unification of value normalization and winner-take-all (WTA) selection, combining value processing and selection. While the authors do an excellent job of referencing a significant chunk of the decision neuroscience literature (160 references!) the motif they end up designing has a highly similar structure to a well-known neural circuit linked to decision-making: the cortico-basal ganglia pathways. Extensive work over the past 20+ years has highlighted how cortical-basal ganglia loops work via disinhibition of cortical decision units in a similar way as the LDDM (see the work by Michael Frank, Wei Wei, Jonathan Rubin, Fred Hamker, Rafal Bogacz, and many others). It was surprising to not see this link brought up in the paper as most of the framing was on the possibility of the LDDM representing cortical motifs, yet as far as I know, there does not exist evidence for such architectures in the cortex, but there is in these cortical-basal ganglia systems.

We thank the Reviewer for the suggestion to link the LDDM to disinhibition in CBG models; this is indeed an important body of empirical and computational work that we overlooked in the original manuscript. We have now added text to the Discussion to highlight the link between LDDM and these CBL disinhibition models, focusing on how they are conceptually similar and how they differ. Please see our response to recommendations below for a more detailed discussion of the revisions.

1. Model evaluations

The authors do a great job of extensively probing the LDDM under different conditions and against some empirical data. However, most of the time there is no "control" model or current state-of-the-art model that the LDDM is being compared against. In a few of the simulation experiments, the LDDM is compared against the DNM and RNM alone, so as to show how the two components of the LDDM motif compare against the holistic model itself. But this component model comparison is inconsistently used across simulation experiments.

Also, it is worth asking whether the DNM and RNM are appropriate comparison models to vet the LDDM against for two reasons. First, these are the components of the full LDDM. So these tests show us how the two underlying architectural systems that go into LDDM perform independently, but not necessarily how the LDDM compares against other architectures without these features. Second, as pointed out in my previous comment, the LDDM is a more complex model, with more parameters, than either the DNM or RNM. The field of decision neuroscience is awash in competing decision models (including probabilistic attractor models, non-recurrent integrators, etc.). If we really want to understand the utility of the LDDM, it would be good to know how it performs against similarly complex models, as opposed to its two underlying component models.

We greatly appreciate the Reviewer’s comments on the point of model comparison, which points out that our original manuscript failed to clearly convey a very important difference between the LDDM and the existing RNM(s). In the revision, we now make it clearer that the fundamental difference between the LDDM and the RNMs is the architecture of disinhibition (see the revised Introduction, especially p. 8 lines 164-168). The LDDM is not simply a combination of the DNM model with RNM architecture (a point we may have mistakenly conveyed in the original manuscript): the introduction of disinhibition separates LDDM inhibition into option-selective subpopulations, as opposed to the single pooled inhibition of RNM models. Given this fact, the LDDM predicts unique selectiveinhibition dynamics shown in recent optogenetic and calcium imaging results, a finding inconsistent with the common-pooled and non-selective inhibition assumed in the existing RNMs and many of its variants. Thus, we believe that a comparison between the LDDM and the RNM, which share similar level of complexity and numbers of parameters, is important.

We also appreciated the Reviewer’s concern about testing the LDDM against alternative models. In order to better connect to the existing literature, we now compare the LDDM to another standard circuit model of decision-making - the leaky competing accumulator (LCA) model. The LCA is a circuit model that captures many of the aspects of perceptual decision-making seen in the mathematical drift diffusion model (DDM), but with a construction that allows for fitting to behavioral data and comparison of underlying unit activities. Please see our response to recommendations below for further detail.

1. Comparison to physiological data

I quite enjoyed the comparisons of the excitatory cell activity to empirical data from the Shadlen lab experiments. However, these were largely qualitative in nature. In conjunction with my prior point on the models that the LDDM is being compared against, it would be ideal to have a direct measure of model fits that can be used to compare the performance of different competing "control" models. These measures would have to account for differences in model complexity (e.g., AIC or BIC), but such an analysis would help the reader understand the utility of the LDDM in connecting with empirical data much better.

We agree with the Reviewer that a quantitative comparison of the match between model neural predictions and empirical neurophysiological data is important. First, we wish to clarify that the model neural predictions are simulated from models fit to the behavioral (choice and RT data), not from fits to the neural activity traces – a point we now clarify in the text. While directly fitting dynamic models (LDDM, RNM, or LCA) to the neurophysiological data is appealing, there are currently several obstacles to this approach. The first problem is the complexity of the dynamic neural traces. Despite the long history of the random-dot motion paradigm, detailed features of the dynamics are still not understood. For example, the stereotyped initial dip after stimulus onset may reflect a reset of the network state to improve signal to noise ratio (Conen and Padoa-Schioppa, 2015) or simply reflect a surround suppression-like lateral inhibition in visual processing. A second problem is that the primary difference between the models is the activity of inhibitory (and disinhibitory) neurons, which are typically not recorded in neurophysiological experiments; thus, there is a lack of empirical data to which to fit the models. In the revision, we clarified that the model fitting to the Roitman & Shadlen data is for behavioral data only, and model unit activity traces are derived from models fit to behavioral data.

That being said, we agree that a quantitative comparison of model activity predictions is helpful. Because the models are fit not to the neural data but to the behavioral data, rather than using likelihood-based measures like AIC and BIC we used a simple RMSE measure to compare the match between predicted and neural activity patterns (revised Fig. 6E, Fig 6-S4E, Fig 6-S5E). Please see response to recommendations below for details.

Reviewer #2 (Public Review):

The aim of this article was to create a biologically plausible model of decision-making that can both represent a choice's value and reproduce winner-take-all ramping behavior that determines the choice, two fundamental components of value- based decision-making. Both of these aspects have been studied and modeled independently but empirical studies have found that single neurons can switch between both of the aspects (i.e., from representing value to winner-take-all ramping behavior) in ways that are not well described by current biological plausible models of decision making.

The current article provides a thorough investigation of a new model (the local disinhibition decision model; LDDM) that has the goal of combining value representations and winner-takes-all ramping dynamics related to choice. Their model uses biologically plausible disinhibition to control the levels of inhibition in a local network of simulated neurons. Through a careful series of simulation experiments, they demonstrate that their network can first represent the value of different options, then switch to winner-takes-all ramping dynamics when a choice needs to be made. They further demonstrate that their single model reproduces key components of value-based and winner-takes-all dynamics found in both neural and behavioral data. They additionally conduct simulation studies to demonstrate that recurrent excitatory properties in their network produce value-persistence behavior that could be related to memory. They end by conducting a careful simulation study of the influence of GABA agonists that provide clear and testable predictions of their proposed role of inhibition in the neural processes that underlie decision-making. This last piece is especially important as it provides a clear set of predictions and experiments to help support or falsify their model.

There are overall many strengths to this paper. As the authors note, current network models do not explain both value- based and ramping-like decision-making properties. Their thorough simulation studies and their validation against empirical neural and behavioral data will be of strong interest to neuroscientists and psychologists interested in value- based decision-making. The simulations related to persistence and the GABA-agonist experiments they propose also provide very clear guidelines for future research that would help advance the field of decision-making research.

Although the methods and model were generally clear, there was a fair amount of emphasis on the role of recurrence in the LDDM, but very little evidence that recurrence was important or necessary for any of the empirical data examined. The authors do demonstrate the importance of recurrence in some of their simulation studies (particularly in their studies of persistence), but these would need to be compared against empirical data to be validated. Nevertheless, the model and thorough simulation investigations will likely help develop more precise theories of value-based decision-making.

We appreciate the Reviewer’s thoughtful comments. These comments - especially about anatomic recurrence and its relationship to the parameter 𝛼 - inspired us to think more about the uniqueness of the current circuit to others, especially the implications related to the parameters 𝛼 (i.e., self-excitation) and 𝛽 (i.e., local disinhibition). Recurrence is required to drive winner-take-all competition in the standard RNM of decision-making. However, we show here with both analytical and numerical approaches that recurrence helps WTA competition but is not necessary in our model. Instead, the key feature of the LDDM is to utilize disinhibition in conjunction with lateral inhibition to realize winner-take-all competition. That leads to many different predictions of the current model from the existing models, such as selective inhibition and flexible control of dynamics.

In response to the Reviewer’s points and after careful consideration of the differential equations, we realized that in our model fitting, the 𝛼 parameter fitting to zero does not necessarily mean recurrence should be zero. The 𝛼 parameter shares a lot of similarity to the baseline gain control (parameter BG in our revision), and thus is unidentifiable in the current dataset. In the interest of parsimony, we did not include the parameter BG in the original manuscript, but now include it because it reveals the difficulty of interpreting fit 𝛼 values as simply the level of recurrence.

Overall, disinhibition (𝛽) in the LDDM is required for WTA activity while recurrence (𝛼) can contribute but is not necessary; however, 𝛼 is theoretically important for generating persistent activity, with the caveat that in the current framework there is an unclear relationship between fit 𝛼 and recurrence. Regardless, we agree that the contribution of 𝛼 to the LDDM framework is worth further testing and examining with future empirical data.

Reviewer #3 (Public Review):

Shen et al. attempt to reconcile two distinct features of neural responses in frontoparietal areas during perceptual and value-guided decision-making into a single biologically realistic circuit model. First, previous work has demonstrated that value coding in the parietal cortex is relative (dependent on the value of all available choice options) and that this feature can be explained by divisive normalization, implemented using adaptive gain control in a recurrently connected circuit model (Louie et al, 2011). Second, a wealth of previous studies on perceptual decision-making (Gold & Shadlen 2007) have provided strong evidence that competitive winner-take-all dynamics implemented through recurrent dynamics characterized by mutual inhibition (Wang 2008) can account for categorical choice coding. The authors propose a circuit model whose key feature is the flexible gating of 'disinhibition', which captures both types of computation - divisive normalization and winner-take-all competition. The model is qualitatively able to explain the 'early' transients in parietal neural responses, which show signatures of divisive normalization indicating a relative value code, persistent activity during delay periods, and 'late' accumulation-to-bound type categorical responses prior to the report of choice/action onset.

The attempt to integrate these two sets of findings by a unified circuit model is certainly interesting and would be useful to those who seek a tighter link between biologically realistic recurrent neural network models and neural recordings. I also appreciate the effort undertaken by the authors in using analytical tools to gain an understanding of the underlying dynamical mechanism of the proposed model. However, I have two major concerns. First, the manuscript in its current form lacks sufficient clarity, specifically in how some of the key parameters of the model are supposed to be interpreted (see point 1 below). Second, the authors overlook important previous work that is closely related to the ideas that are being presented in this paper (see point 2 below).

1) The behavior of the proposed model is critically dependent on a single parameter 'beta' whose value, the authors claim, controls the switch from value-coding to choice-coding. However, the precise definition/interpretation of 'beta' seems inconsistent in different parts of the text. I elaborate on this issue in sub-points (1a-b) below:

1a). For instance, in the equations of the main text (Equations 1-3), 'beta' is used to denote the coupling from the excitatory units (R) to the disinhibitory units (D) in Equations 1-3. However, in the main figures (Fig 2) and in the methods (Equation 5-8), 'beta' is instead used to refer to the coupling between the disinhibitory (D) and the inhibitory gain control units (G). Based on my reading of the text (and the predominant definition used by the authors themselves in the main figures and the methods), it seems that 'beta' should be the coupling between the D and G units.

1b). A more general and critical issue is the failure to clearly specify whether this coupling of D-G units (parameterized by 'beta') should be interpreted as a 'functional' one, or an 'anatomical' one. A straightforward interpretation of the model equations (Equations 5-8) suggests that 'beta' is the synaptic weight (anatomical coupling) between the D and G units/populations. However, significant portions of the text seem to indicate otherwise (i.e a 'functional' coupling). I elaborate on this in subpoints (i-iii) below:

(1b-i). One of the main claims of the paper is that the value of 'beta' is under 'external' top-down control (Figure 2 caption, lines 124-126). When 'beta' equals zero, the model is consistent with the previous DNM model (dynamic normalization, Louie et al 2011), but for moderate/large non-zero values of 'beta', the network exhibits WTA dynamics. If 'beta' is indeed the anatomical coupling between D and G (as suggested by the equations of the model), then, are we to interpret that the synaptic weight between D-G is changed by the top-down control signal within a trial? My understanding of the text suggests that this is not in fact the case. Instead, the authors seem to want to convey that top-down input "functionally" gates the activity of D units. When the top-down control signal is "off", the disinhibitory units (D) are "effectively absent" (i.e their activity is clamped at zero as in the schematic in Fig 2B), and therefore do not drive the G units. This would in- turn be equivalent to there being no "anatomical coupling" between D and G. However when the top-down signal is "on", D units have non-zero activity (schematic in Fig 2B), and therefore drive the G units, ultimately resulting in WTA-like dynamics.

(1b-ii). Therefore, it seems like when the authors say that beta equals zero during the value coding phase they are almost certainly referring to a functional coupling from D to G, or else it would be inconsistent with their other claim that the proposed model flexibly reconfigures dynamics only through a single topdown input but without a change to the circuit architecture (reiterated in lines 398-399, 442-444, 544-546, 557-558, 579-590). However, such a 'functional' definition of 'beta' would seem inconsistent with how it should actually be interpreted based on the model equations, and also somewhat misleading considering the claim that the proposed network is a biologically realistic circuit model.

(1b-iii). The only way to reconcile the results with an 'anatomical' interpretation of 'beta' is if there is a way to clamp the values of the 'D' units to zero when the top-down control signal is 'off'. Considering that the D units also integrate feed- forward inputs from the excitatory R units (Fig 2, Equations 1-3 or 5-8), this can be achieved either via a non-linearity, or if the top-down control input multiplicatively gates the synapse (consistent with the argument made in lines 115-116 and 585-586 that this top-down control signal is 'neuromodulatory' in nature). Neither of these two scenarios seems to be consistent with the basic definition of the model (Equations 1-3), which therefore confirms my suspicion that the interpretation of 'beta' being used in the text is more consistent with a 'functional' coupling from D to G.

We thank the reviewer for pointing out this confusion. We apologize that the original illustrations (Fig. 2A) and the differential equations in Methods (Eqs. 5-8) did not convey very well our ideas. 𝛽 is intended to reference the coupling from R to D, not a change in the weights between D and G units. We realize there was some confusion on this part due to inconsistency between our original figures, text, and supplementary material.

Given the lack of clarity in the previous version as well as the Reviewer’s questions, we now emphasize that 𝛽 represents a functional coupling between the R and D neurons. The biological assumption of the disinhibitory architecture is built based on recent findings that VIP neurons in the cortex always inhibit other neighboring inhibitory cells, such as SST and PV neurons, and consequently disinhibit the neighboring primary neurons (e.g., Fu et al., 2014; Karnani et al., 2014, 2016). We did not see evidence in the literature of fast-changing (anatomic) connections between VIP and SST/PV. However, there is evidence that the responsiveness of VIP neurons to excitatory neurons can be modulated by changing the concentrations of neuromodulators, such as acetylcholine and serotonin (Prönneke et al., 2020). While the stereotype of neuromodulator action is slow dynamics, recent findings show that for example basal forebrain cholinergic neurons respond to reward and punishment with surprising speed and precision (18 ± 3ms) (Hangya et al., 2015) to modulate arousal, attention, and learning in the neocortex. Given the large number of studies that identify long-term projections and neuromodulatory inputs to VIP neurons (e.g., Pfeffer et al., 2013; Pi et al., 2013; Alitto & Dan, 2013; Tremblay et al., 2016), we believe that it will be more plausible to assume the connection weights between R and D in our case is quickly modulated within a trial.

To clarify this issue in the revised manuscript, we made the following corrections:

1. We repositioned the 𝛽 parameter in Fig. 2A between the connection from R to D, to align the description of 𝛽 modulating R to D in the main text.

2. We modified the differential equations 5-8 (now numbered as Eqs. 28-32) in Methods (pp. 61) to include the disinhibitory unit D as an independent control from the inhibitory unit I, in order to be consistent with the disinhibitory D units in LDDM. Such a change makes tiny differences in the model predictions (please see dynamics simulated after the change in Fig. 2-figure supplement 1B).

3. We updated the neural circuit motif in Fig. 2 -figure supplement 1A accordingly.

2) The main contribution of the manuscript is to integrate the characteristics of the dynamic normalization model (Louie et al, 2011) and the winner-take-all behavior of recurrent circuit models that employ mutual inhibition (Wang, 2008), into a circuit motif that can flexibly switch between these two computations. The main ingredient for achieving this seems to be the dynamical 'gating' of the disinhibition, which produces a switch in the dynamics, from point-attractor-like 'stable' dynamics during value coding to saddle-point-like 'unstable' dynamics during categorical choice coding. While the specific use of disinhibition to switch between these two computations is new, the authors fail to cite previous work that has explored similar ideas that are closely related to the results being presented in their study. It would be very useful if the authors can elaborate on the relationship between their work and some of these previous studies. I elaborate on this point in (a-b) below:

2a) While the authors may be correct in claiming that RNM models based on mutual inhibition are incapable of relative value coding, it has already been shown previously that RNM models characterized by mutual inhibition can be flexibly reconfigured to produce dynamical regimes other than those that just support WTA competition (Machens, Romo & Brody, 2005). Similar to the behavior of the proposed model (Fig 9), the model by Machens and colleagues can flexibly switch between point-attractor dynamics (during stimulus encoding), line-attractor dynamics (during working memory), and saddle-point dynamics (during categorical choice) depending on the task epoch. It achieves this via a flexible reconfiguration of the external inputs to the RNM. Therefore, the authors should acknowledge that the mechanism they propose may just be one of many potential ways in which a single circuit motif is reconfigured to produce different task dynamics. This also brings into question their claim that the type of persistent activity produced by the model is "novel", which I don't believe it is (see Machens et al 2005 for the same line-attractor-based mechanism for working memory)

We thank the Reviewer for pointing out the conceptual similarities between the LDDM and the Machens Romo Brody model, and now include a discussion of the link between the two early in the revised Discussion (p. 38, lines 826-837). Please see response to recommendations below for a more detailed discussion of this point.

2b) The authors also fail to cite or describe their work in relation to previous work that has used disinhibition-based circuit motifs to achieve all 3 proposed functions of their model - (i) divisive normalization (Litwin-Kumar et al, 2016), (ii) flexible gating/decision making (Yang et al, 2016), and working memory maintenance (Kim & Sejnowski,2021)

The Reviewer notes several relevant papers, and we have now discussed them and their relationship to the LDDM in a revised Discussion section (pp. 35-36). Please see response to recommendations below for a more details.

Author Response

Reviewer #2 (Public Review):

The two new micropeptides are well characterized in the manuscript and appear to be functionally important with some chromatin-level consequences of their loss (which can be either direct or indirect), but the finding that lincRNA sequences encode micropeptides is not novel, and the two described in the paper appear to be zebrafish-specific and their function was tested only in zebrafish, which limits the interest in these genes. The use of ribosome profile data along behavioral screening to identify micropeptides is interesting and important, but the scope of the screen, the candidates selected for testing, etc. are not clear enough as presented. The ChIP-seq analysis of the new proteins is very interesting but is not described in any detail. Overall, the experimental part is well designed and the phenotypes reported by the authors appear to be strong and convincing, but the mechanistic understanding of what the two new proteins do and how, and the general interest in the results given the current scope of understanding of micropeptide is limited.

We apologize for the misunderstanding that these genes are zebrafish-specific. In this revision, we have clarified throughout the text and with additional data that these genes are not zebrafish-specific, but that linc-mipep and linc-wrb are homologous to human Hmgn1.

Author Response

Reviewer #1 (Public Review):

Francou et al. examine the dynamics of cell ingression at the primitive streak during mouse gastrulation and correlate this with the localization of elements of the apical Crumbs complex and the actomyosin cytoskeleton. Using time-lapse live imaging, they show that cells at the primitive streak ingress in a stochastic manner, by constricting their apical surface through a ratcheting shrinkage of individual junctions. Meticulous evaluation of immunofluorescent staining for many elements of the actomyosin contractile process as well as junctional and apical domain elements reveals anisotropic localization of Crumbs2, ZO1, and ppMLC. In addition, the localization of two groups of proteins showed a close correlation - actomyosin regulators and apical and junctional components - but there was a lack of correlation of localization of these two groups of proteins to each other. The localization of actomyosin and its activity, was altered and more homogeneous in Crumbs2-/- embryos, and there was a significant decrease in aPKC and Rock1. The authors conclude from these observations that Crumbs2 regulates anisotropic actomyosin contractility to promote apical constriction and cell ingression.

The strengths of this manuscript are the very detailed observations on the process of apical constriction and the meticulous evaluation of the localization of the many proteins likely to be involved in the process. While many of the general observations are not new, Francou et al. provide a much richer understanding of this process, as well as a paradigm with which to evaluate the effects of mutations on the gastrulation process. The figures are beautiful, clear, and informative, and support the conclusions made by the authors. The data provide a very compelling picture of both the dynamics of cell behavior and the anisotropies in protein localization associated with it.

However, much of the Crumbs2 mutant phenotype is not sufficiently explained by the authors' data or conclusions. First, the loss of Crumbs2 does not prevent ingression, as there are mesoderm cells evident between the epiblast and endoderm (Ramkumar et al., 2016, Xiao et al., 2011). There are certainly fewer, and the biggest effect appears to be during the elongation of the axis from E7.75 onward and not during the earlier migratory period (E6.5-E7.75) according to data from both previously published work (Xiao et al., 2011; Ramkumar et al., 2015, 2016) and the data presented here.

• The reviewer makes a good point regarding the defects observed in Crumbs2 mutant embryos. It is true that in this mutant, a first wave of gastrulation EMT, taking place around E6.5, does not appear to be affected. We interpret this to mean that the gastrulation EMT is a sequential process under differential regulation, and that Crumbs2 is not required for the first wave of cells ingression through the primitive streak, at the onset of gastrulation. Consequently, a small number of early mesodermal cells are produced in Crumbs2 mutants. However, within 24hours of the onset of gastrulation, corresponding to around E7.75, ingression defects are evident in Crumbs2 mutant embryos.

• For simplicity, these distinct sequential phases of gastrulation regulation, initially independent of Crumbs2, but subsequently dependent, were not initially discussed in our manuscript. We have now elaborated these details in the revised manuscript.

Nor does the loss of Crumbs2 prevent apical constriction. Ramkumar et al. in their 2016 paper show by live imaging that the major effect of the Crumbs2 mutation is to prevent the cells from detaching from the epithelium, but that the apical domain does undergo constriction, leading to many elongated flask-shaped cells still attached at the apical end. These observations do not fit well with the model proposed by the authors of Crumbs2 regulating anisotropic actomyosin contractility to promote apical constriction and suggest a more complicated story.

• We thank the reviewer for bringing this up, as it is an important point that we now discuss in greater detail and clarify in the revised manuscript.

• Importantly, we do not believe our data are in disagreement with the previous study of Ramkumar et al. The precise details of the defect observed in Crumbs2 mutants are still not totally clear. However, we would like to point out that in Ramkumar et al., the timelapse imaging data did not depict cells constricting their surfaces, but rather these data revealed that cells having small apical surfaces failed to detach and delaminate out of the epiblast layer. Thus, this previous study focused on the subsequent step in the process of ingression (delamination), to that being addressed in the present work.

• Furthermore, epiblast cells outside the domain occupied by the primitive streak, and even some cells positioned on the lateral sides of the embryo, were reported by Ramkumar and colleagues to exhibit abnormally small apical surfaces in Crumbs2 mutants. These cells, at a distance from the primitive streak, will not normally constrict their apical surfaces, since they are not going to undergo the gastrulation EMT, a behavior restricted to the region of the primitive streak. Thus, these previous data do not directly address nor demonstrate that epiblast cells in Crumbs2 mutants undergo apical constriction.

• Moreover, in Crumbs2 mutants a large number of cells were reported to fail to ingress at the primitive streak, and consequently they were seen to accumulate within the epiblast epithelial layer. Indeed, we believe that the small apical surfaces first reported in Crumbs2 mutants by Ramkumar and colleagues, most likely result from the crowding/jamming of cells within the epiblast layer, and that this causes changes in the shape and volume of cells due to them being spatially constrained. Thus, increased crowding of epithelial cells within a spatially constrained tissue, likely drives a reduction in apical surface area and extensive apico-basal elongation, as observed in Crumbs2 mutants.

However, the complications of the Crumbs2 mutant do not detract from the value of the basic observations presented in this manuscript, which are solid and well-documented, and will be a valuable resource for the field.

Reviewer #2 (Public Review):

In their manuscript, Francou and colleagues study the delamination of epiblast cells into the mesodermal layers using live imaging of mouse embryos cultured ex vivo. By segmenting the apical area of delaminating cells, they quantify extensively the dynamic behavior of delaminating cells. Using immunostaining and crumbs2 mutants, they propose that apical constriction of cells results from pulsed contractions, which could be guided by crumbs2 signals.

The manuscript is interesting and provides extremely valuable data for our understanding of mouse gastrulation. Occasionally, the manuscript can be a bit confusing and contains a few inaccuracies.

However, the main issues I have are with some of the interpretations from the authors, which may be incorrect due to limited time resolution (with a 5 min time resolution that was used, it might be difficult to distinguish pulses from measurement noise) and the analysis of immunostaining data, which would require more rigorous quantification.

• We acknowledge the reviewer’s comments and agree that a shorter time resolution would be ideal to facilitate the detection of constriction pulses of apical surfaces. However, we need to consider that imaging the apical surface of cells within the epiblast layer, which constitutes the most internal surface inside the embryo, is technically challenging in a gastrulating mouse embryo.

• As suggested by the reviewer, we attempted to image with a shorter time interval than 5min on several different microscope systems and modalities available at our institution (including two different laser point scanning confocals, a spinning disc system, as well as light-sheet microscopes with both upright and inverted configurations) and were not successful in acquiring usable images (having a shorted time-resolution) with the ZO1GFP knock-in reporter. We also need to consider that single-copy GFP knock-in reporters are often dim, thereby exacerbating the issue. In our hands, a high-speed resonant scanning confocal (Nikon A1RHD25) was the system that gave us the best signal-to-noise ratio, spatial resolution and temporal resolution, and was the set-up we used for our most recent live imaging experiments. Using this system, we were able to acquire a limited number of time-lapses with a time resolution of 2min, but none with a shorter time interval, and from our analyses, we determined that movies with a 2min time interval did not yield increased detail over movies with 5min time intervals to warrant a detailed reanalysis. We have provided additional detail relating to these technical issues within the revised manuscript and edited some of the conclusions.

• We acknowledge that immunostaining is not the most quantitative method, but we were unable to come up with alternative methods that can be used with our samples. We believe the junctional reduction of Myosin, aPKC and Rock1 is generally due to a nonrecruitment or activation of these proteins at junctions, and do not reflect their reduced expression at the gene or protein level. We do not believe that methods such as RTqPCR or Western blotting would be informative in the context in which we are looking, especially since they do not yield spatial resolution. Furthermore, we would need to isolate primitive streak cells to consider applying these methods, and we do not believe they would provide a sufficient improvement over immunostaining.

• By contrast to the live imaging, which was performed by placing the objective at the posterior side of the embryo in closest proximity to the outer visceral endoderm layer, for fixed tissue imaging, embryos were microdissected to recover the posterior side containing the primitive streak. Microdissected posterior regions were imaged on the side of the cavity by placing the objective in closest proximity to the inner epiblast layer, which permitted direct access to the apical surface of epiblast cells at the primitive streak. In this fixed tissue imaging configuration, the apical surfaces of cells in WT and Crumbs2 mutants were in closest proximity to the imaging objective and thus directly accessible. Thus, any difference in tissue thickness on the other side of the epithelium did not interfere with light penetration. We have edited the figures and include schematics to clarify how the objective positions are flipped with respect to the primitive streak regions at the embryo’s posterior for live vs. fixed tissue imaging.

• We have now measured the signal intensity in the cytoplasmic region of WT and Crumbs2 mutant embryos, and junctional intensity measurements have been normalized to cytoplasmic intensities.

Reviewer #3 (Public Review):

The manuscript by Francou et al investigated cellular mechanisms of epiblast ingression during mouse gastrulation. The authors wanted to know whether/how epiblast cell-cell junctional dynamics correlate with apical constriction and subsequent ingression. Because mouse gastrula adopts an inverted-cup morphology (as a result of differential invasive behavior of polar and mural trophoblast cells), epiblast cells are located in the innermost position and are difficult to image. This is more so when one wants to perform live imaging of epiblast cells' apical surface. The authors tackled such problems/limitations by using a combination of ZO-1 GFP line, confocal time-lapse microscopy, fixed embryo immunostaining, and Crumbs2 mutant embryos. The authors observed that apical constriction was associated with cell ingression, that this constriction occurred in a pulsed fashion (i.e., 2-4 cycles with phases of contraction and expansion, eventually leading to reduction of apical surface and ingression), that this constriction took place asynchronously (i.e., neighboring epiblast cells did not exhibit coordinated behavior) and that junctional shrinkage during apical constriction also occurred in a pulsed and asynchronous manner. The authors also investigated localization/co-localization of several apical proteins (Crumbs2, Myosin2B, pMLC, ppMLC, Rock1, F-actin, PatJ, and aPKC) in fixed samples, uncovering somewhat reciprocal distribution of two groups of proteins (represented by Myosin2B in one group, and Crumbs2 in the other). Finally, the authors showed that Crumbs2 -/- embryos had disturbed actomyosin distribution/levels without affecting junctional integrity (partially explaining the ingression defect reported in Crumbs2 -/- mutant embryos). Overall, this manuscript offers high-quality live imaging data on the dynamic remodeling of epiblast apical junctions during mouse gastrulation.

It would be interesting to see whether phenomena reported in this manuscript can be extended to the entire primitive streak (or are they specific only to a subset of mesoderm precursors) and to the entire period of mesendoderm formation. More importantly, it would be interesting to see whether the ingression behavior seen here is representative of all eutherian mammals regardless of their gastrular topography.

• The reviewer raises a very interesting and important point. We focused our data analysis on a middle region in the proximo-distal axis of the embryo, because this is the most optically accessible and the flattest region of the posterior of the embryo to analyze. We also focused on the E7.5 stage of development when the primitive streak is fully elongated, so as to capture as many ingression events within a single time-lapse experiment as possible. Due to the difficulties associated with live imaging the apical epiblast layer of embryos at these stages, we chose to focus our analysis on a defined region of the embryo and a defined period of time. We acknowledge that it will be important to analyze different regions of the primitive streak and at different stages of gastrulation to glean any general versus more distinct modes of epiblast cell ingression, but given the technical difficulties discussed we believe that any extended analysis is beyond the scope of the current study.

• We also agree that it would be interesting to know if the ingression behavior we observe in the mouse embryo is representative of all mammals, and even more generally of amniotes, but this is beyond the scope of our study.

Author Response

Reviewer #2 (Public Review):

Throughout the manuscript, the authors aim to distinguish signal from the lack of it. All conclusions depend on the success of this process. In such an endeavor, the sensitivity of the applied methods is critical. Thus, the authors must use the most sensitive tools to draw meaningful conclusions. The latest iGluSnFR has amazing sensitivity allowing the detection of single AP-evoked responses. This is not the case for vGpH, which requires hundred APs to get a meaningful signal. Similar, synthetic Ca2+ dyes have much better dynamic range, linearity and sensitivity compared to GCaMP6f.

The rate of silent boutons at 2 mM [Ca2+]e is lower for a single AP compared to 20 or 200 APs. The overall failure rate cannot be increased with increasing the number of APs. This clearly indicates a technical issue (e.g. insufficient sensitivity of vGpH and GCaMP6f).

We thank the reviewer for raising this concern. We attribute the relatively lower rate of silencing with 1 AP in [Ca2+]e 2.0 mM in neurons expressing iGluSnFr to its sensitivity to detect glutamate exocytosed from neighboring, possibly non-transfected terminals. This limitation is described in the manuscript (page 7, line 26 – page 8, line 5). The overall agreement in the proportion of silencing with iGluSnFr compared to physin-GCaMP or vGpH at lower [Ca2+]e, where the contributions from neighboring terminals is likely greatly diminished, supports this interpretation.

The authors used three different measuring tools and used three different stimulation protocols, making the interpretation of the data challenging. It is impossible to tell how the failure rate changes from 1 to 20 APs without knowing the release probability, the pool size, depletion, recovery of SVs, and facilitation. These are all unknown.

In an ideal world, a measure of release probability during a train of stimuli at varied [Ca2+]e would provide the most insight, but this is difficult to achieve with any of the existing methods, including the remarkable new iGluSnFR. The challenge we face is, for our approach, it is impossible to exclude signals from neighboring axons that are closely packed near the axon harboring the indicator. This limitation is described in the manuscript (page 7, line 26 – page 8, line 5). Given this, we felt that showing that silencing can be revealed with all the different techniques was the most conservative approach to address the issue. Because we have focused on this phenomenon, the number of APs is experimentally important only to ensure an adequate response could be detected. We have also included, in the discussion, an acknowledgement of the possibility that we are failing to detect minimal Ca2+ entry (see response to #8 from the synthesized review).

The last experiment with the GABAB agonist has little novelty in its present form. The authors demonstrate that GABAB agonism increases the rate of silent terminals. The interesting issue would be to reveal how the effect of GABAB activation depends on the [Ca2+]e. This information is essential to see whether there is indeed a shoulder in its effectiveness curve.

We are grateful to the reviewer for this recommendation and we have performed additional experiments (see response to #7 from the synthesized review).

The authors refer to a theoretical set-point in [Ca2+]e below which the function of the terminals is fundamentally different. From the presented experiments, the reviewer does not see any data that is inconsistent with a continuum. 'Thus, as with Ca2+ influx, SV recycling is modulated in an all-or-none manner by modest changes in [Ca2+]e around the physiological set point.' This statement is not supported by the data. The reviewer cannot see a set point.

We appreciate the reviewer’s criticism and wish to clarify that we mean the normal physiologic [Ca2+]e in the CSF. We have changed the text to clarify this point (page 7, line 20).

Author Response

Reviewer #1 (Public Review):

While the mechanism about arm-races between plant and specialist herbivores has been studied, such as detoxification of specific secondary metabolites, the mechanism of the wider diet breadth, so-called generalist herbivores have been less studied. Since the heterogeneity of host plant species, the experimental validation of phylogenetic generalism of herbivores seemed as hard to be conducted. The authors declared the two major hypotheses about the large diet breadth ("metabolic generalism" and "multi-host metabolic specialism"), and carefully designed the experiment using Drosophila suzukii as a model herbivore species.

By an untargeted metabolomics approach using UHPLC-MS, authors attempted to falsify the hypotheses both in qualitative- and quantitative metabolomic profiles. Intersections of four fruit (puree) samples and each diet-based fly individual samples from the qualitative data revealed that there were few ions that occur as the specific metabolite in each diet-based fly group, which could reject the "multi-host metabolic specialism" hypothesis. Quantitative data also showed results that could support the "metabolic generalism" hypothesis. Therefore, the wide diet breadth of D. suzukii seemed to be derived from the general metabolism rather than the adaptive traits of the diverse host plant species. On the other hand, the reduction of the metabolites (ions) set using GLM seemed logical and 2-D clustering from the reduced ions set showed that quantitative aspects of diet-associated ions could classify "what the flies ate". These interesting results could enhance the understanding of the diet breadth (niche) of herbivorous insects.

The authors' approach seemed clear to falsify the hypotheses based on the appropriate data processing. The intersection of shared ions from the qualitative dataset could distinguish the diet-specific metabolites in flies and commonly occurring metabolites among flies and/or fruits. Also, filtering on the diet-specific ions seemed to be a logical and appropriate way. Meanwhile, the discussion about the results seemed to be focused on different points regarding the research hypotheses which were raised in the introduction part. Discussion about the results mainly focused on the metabolism of D. suzukii itself, rather than the research hypotheses and questions that were raised from the evolution of the wide diet breadth of generalist herbivores. In particular, the conclusion seems to be far from the main context of the authors' research; e.g. frugivory. It makes the implication of the study weaker.

We wish to thank Reviewer #1 for their appreciation of our study. As recommended, we now focus our discussion more on the general aspect of our findings (relevant to insects, herbivores, or frugivores), and less on the peculiarities of the metabolism of D. suzukii itself. Specifically, we now only mention D. suzukii in one section (two sentences) of our Discussion, to serve as an example (l.387-396). Thanks to this comment, the Discussion may interest a broader readership, on the evolution of diet breadth in generalist herbivorous species and offers a better understanding of the general implications of our findings.

Reviewer #2 (Public Review):

The manuscript: "Metabolic consequences of various fruit-based diets in a generalist insect species" by Olazcuaga et al., addresses an interesting question. Using an untargeted metabolomics approach, the authors study how diet generalism may have evolved versus diet specialization which is generally more commonly observed, at least in drosophila species. Using the phytophagous species Drosophila suzukii, and by directly comparing the metabolomes of fruit purees and the flies that fed on them, the authors found evidence for "metabolic generalism". Metabolic generalism means that individuals of a generalist species process all types of diet in a similar way, which is in contrast to "multi-host metabolic specialism" which entails the use of specific pathways to metabolize unique compounds of different diets. The authors find strong evidence for the first hypothesis, as they could easily detect the signature of each fruit diet in the flies. The authors then go on to speculate on the evolutionary ramifications of this for how potentially diet specializations may have evolved from diet generalism. Overall, the paper is well written, the experiments well documented, and the conclusions convincing.

We thank Reviewer #2 for their comments and appreciation of our work.

Reviewer #3 (Public Review):

Laure Olazcuaga et al. investigated the metabolomes of four fruit-based diets and corresponding individuals of Drosophila suzukii that reared on them using comparative metabolomics analysis. They observed that the four fruit-based diets are metabolically dissimilar. On the contrary, flies that fed on them are mostly similar in their metabolic response. From a quantitative point of view, they find that part of the fly metabolomes correlates well with that of the corresponding diet metabolomes, which is indicative of insect ingestive history. By further focusing on 71 metabolites derived from diet-specific fly ions and highly abundant fruit ions, the authors show that D. suzukii differentially accumulates diet metabolism in a compound-specific manner. The authors claim that the data support the metabolic generalism hypothesis while rejecting the multi-host metabolic specialism hypothesis. This study provides a valuable global chemical comparison of how diverse diet metabolites are processed by a generalist insect species.

Strengths:

The rapid advances in high-resolution mass spectrometry have recently accelerated the discovery of many novel post-ingestive compounds through comparative metabolomics analysis of insect/frass and plant samples. Untargeted metabolomics is thus a very powerful approach for the systematic comparison of global chemical shifts when diverse plant-derived specialized metabolites are further modified or quantitatively metabolized after ingestion by insects. The technique can be readily extended to a larger micro- or macro-evolutionary context for both generalist and specialist insects to systematically investigate how plant chemical diversity contributes to dietary generalism and specialism.

We would like to thank Reviewer #3 for their insightful comments on the power of untargeted metabolomics to evaluate the fate of plant metabolites and their use by herbivores. We also agree that these techniques can be used to tackle eco-evolutionary issues, such as the origin and maintenance of dietary generalism and specialism here. We hope that our study will inspire other researchers to explore such techniques and experiments to gain a global overview of biochemistry fluxes and their evolution. We now mention it in the conclusion (L454-459).

Weaknesses:

The authors claim that their data support the hypothesis of metabolic generalism, however, a total analysis of insect metabolism may not generate a clean dataset for direct comparison of fruit-derived metabolites with those metabolized by D. suzukii, given that much of these metabolites would be "diluted" proportionally by insect-derived metabolites. If the insect-derived metabolites predominate, then, as the authors observed, a tight clustering of D. suzukii metabolomes in the PCA plot would be expected. It is therefore very difficult to interpret these patterns.

We agree with Reviewer #3 that a careful examination of the different possible origins of metabolites should take place to distinguish between our two competing hypotheses.

The only source of metabolites for insects in our experimental setup is a mixture of (i) a large proportion of fruit purees and (ii) a minor proportion of artificial medium consisting mainly of yeast. Our goal is thus to understand the fate of (i) “fruit-derived” metabolites (transformed and untransformed), while controlling for (ii) “artificial media-derived” metabolites, that constitute a nuisance signal but are necessary for a complete development in our system.

By “fruit-derived” and “insect-derived” metabolites, it is our understanding that Reviewer #3 means “fruit” metabolites (when in insects, untransformed “fruit-derived” metabolites) and “artificial medium-derived” metabolites. It is true that we do wish to avoid a predominance of “artificial medium-derived” metabolites and focus on “fruit-derived” metabolites in insects. We also want to note that it is of primary importance in our study to distinguish between “fruit” metabolites that are carried as is (“fruit” metabolites present in insects, ie untransformed “fruit-derived” metabolites), and “fruit” metabolites that are used after transformation by the insect (i.e., transformed “fruit-derived” metabolites).

We agree with Reviewer #3 that the presence of “artificial medium-derived” metabolites could be problematic in direct comparisons of fruits and insects (and not among fruits or among insects’ comparisons).

However, we took some steps to avoid such problems:

1. We included control fly samples in our experiment: at each experimental generation, flies developed only on artificial medium (without fruit puree) were collected and processed simultaneously with flies that developed on fruit media. Results using these artificial medium-reared flies as controls (by subtracting their ions levels and removing ions that were similar, respective of their generation) were similar to results using raw data and conclusions were identical (see below).

2. We lowered the proportion of artificial medium in our fruit media so that it was kept to a minimum, compatible with larval development and adult survival.

Consistent with the low impact of this “artificial medium” component on our conclusions, we also wish to point out the presence pattern of metabolites found only in flies and never in fruits when using raw data (Figure 3, yellow stack). Even in the most conservative hypothesis of 100% of these metabolites originating from our artificial medium (which is probably not the case), we observe that it constitutes only a minor proportion of metabolites common to all flies (15.7%).

For your consideration, we include below the main Figures, using both raw data and artificial medium-controlled:

Figure 2, left = raw data; right = artificial-media controlled:

Figure 3, left = raw data; right = artificial-media controlled:

Figure 3S1, left = raw data; right = artificial-media controlled:

Figure 4, above = raw data; below = artificial-media controlled:

We hope that we convinced the Editor/Reviewers that raw data and artificial-medium controlled data provide a single and same answer to all our analyses. We chose to present only raw data, to simplify the Materials & Methods section.

We however modified the current version of the manuscript to inform the reader that proper controls were done and that their inclusion do not modify any of our conclusions (l.110-113 and l.583-589).

We also wish to point out two additional comments:

• As Reviewer #1 also recommended, we modified the expectations drawn in Fig1G to better consider the general comment of “insect derived” metabolites being fundamentally different from plant metabolites (even if we do show in our study that only approx. 9% of metabolites are private to flies).

• The main part of our care in the use of this global PCA analysis is that it follows two other analyses (global intersection and comparison of intersections among fruits and among flies) and precedes another one (fly-focused PCA). We hope that all these analyses help the readers get a comprehensive overview of the dataset and associated results, avoiding reliance on a single analysis.

• We also help readers to explore and visualize all analyses presented in our manuscript by setting up a shiny application (in addition to our available dataset and R code), at https://fruitfliesmetabo.shinyapps.io/shiny/. This is now mentioned in the main text (l.588-589).

We thank the Reviewer for their comment that greatly improved the manuscript.

The authors generated a qualitative dataset using the peak list produced by XCMS which contains quantitative peak areas, it is unclear how the threshold was selected to determine if a peak is present or absent in a given sample. The qualitative dataset would influence the output of their data analysis.

The referee is right in pointing out that the threshold used to determine if a peak is present or absent in a given sample was not clearly specified. This has now been corrected in the “Host use” section of the Materials & Methods (l.513-516). Briefly, a given replicate of a compound was considered present if the corresponding peak area following XCMS quantification was > 1000. This threshold was selected to be close to the practical quantification threshold of the Thermo Exactive mass spectrometer used in this study. This threshold was selected in order to allow the quantification of low-abundance compounds, as many plant-derived diet compounds were expected to be present in trace amounts in flies. We additionally applied a stringent rule for presence of any given compound (presence in at least 3 biological replicates).

The authors reply on in-source fragmentation for peak annotation when authentic standards are not available. The accuracy of the annotation thus requires further validation.

The Supplementary Table 1 was unfortunately omitted in the first submission of the manuscript. This oversight has been now corrected and the Supplementary Table 1 details all information used for metabolite annotation. In particular, MS/MS data comparison with mass spectral databases as well as with published literature have been added to substantiate metabolite identifications. This MS/MS data was produced thanks to the comment of the Reviewer. We also provide four more annotations from standards to attain 30 / 71 identifications validated through chemical standards.

Author Response

Reviewer #1 (Public Review):

Part 1: Type 2 deiodinase

Table I is supposed to clarify and summarize the results but brings confusion. The text says that table I supports the claim that "in the cerebellum, Luc-mRNA was lower in the Ala92-Dio2 mice" whereas figure 1G does not show any difference. It is unclear whether Table I and figure 1 report the same data, and what the statistical tests are actually addressing (effect of genotype vs effect of treatment, whereas what matters here is only the interaction between genotype and treatment). Overall, it is not acceptable to present quantitative data without giving numbers, standard deviation, p-value, etc. as in Table I.

Thank you. We agree with the reviewer. We intended to minimize the amount of data presented, which was already very large, and therefore only presented the ratios of thr/alaDio2 and which created confusion. This part was removed from the new version of the MS.

Also, evaluating T3 signaling by only looking at the luc reporter and the Hprt housekeeping gene is not always sufficient (many T3 responsive genes can be found in the literature and more than one housekeeping gene should be used as a reference).

Thank you. The advantage of using the THAI mouse is that the Luciferase reporter gene is driven by a promoter that is only sensitive to T3, which is not the case for any other T3-responsive responsive gene. The Hprt housekeeping signal was stable among the samples, and the differences observed were not caused by differences in the housekeeping gene expression. This part was removed from the new version of the MS.

Another important weakness is that the wild-type mice have a proline at position 92. Why not include them? In absence of structural prediction, one wonders whether the mouse models are relevant to the human situation and whether the absence of the proline reduces the enzymatic activity when substituted for an Ala or Thr. This might have been addressed in previous work, but the authors should explain.

The position 92 in DIO2 is occupied by Thr in humans. Its Km(T4) is indistinguishable from mouse Dio2 which has a Pro in the position 92 (4nM vs. 3.1nM) [PMID 8754756; PMID: 10655523]. Humans also carry an Ala in position 92. Comparing the two human alleles is the purpose of the study.

Experiment 2: Ala92-Dio2 Astrocytes Have Limited Ability to Activate T4 to T3

Here, the authors use primary cell cultures from different areas of the brain to measure the in vitro conversion of T4 to T3 by Dio2. They find that hippocampus astrocytes are less active, notably if they come from Ala92-Dio2 mice.

This part has the following weaknesses:

• This result correlates with the results from Fig 1F however the difference between Ala92-Dio2 and Thr92-Dio2 is significant in vitro, but not in vivo.

From a deiodinase perspective, TH signaling in vivo depends on the presence of D2 (expressed in glial cells) and D3 (expressed in neurons), whereas in vitro it only depends on D2. In fact, D2 and D3 are known for a reciprocal regulation to preserve TH signaling [PMID: 33123655]. Thus, it is conceivable that the differences observed between the two models are explained by the intrinsic differences in the models.

What matters is not the activity/astrocytes, but the total activity of the brain area, which depends on the number of astrocytes x individual activity. This is not measured.

We respectfully disagree with the reviewer. The total D2 activity in a brain area depends fundamentally on the number of astrocytes in that area and on the intrinsic activity of the enzyme. The reviewer is suggesting that having an area denser in astrocytes expressing a catalytically less active D2 preserves a normal local T3 production. This is unlikely to be the case because we have no evidence that the density of astrocytes is different in Ala-DIo2 mice. Please keep in mind that the intimate relationship between astrocytes and neurons is what defines the microenvironment that surrounds the neuron. By separating astrocytes from neurons we are able to measure T3 production that is occurring in the neuronal microenvironment and show that cells obtained from AlaDio2 mouse produce less T3.

• What the authors called 'primary astrocytes' is an undefined mixed population of glial cells, (including radial glial cells, stem cells, ependymal cells, progenitor cells, etc...) that proliferated differentially for more than a week in culture, among which an unknown ratio expresses Dio2. The cellular model is thus poorly characterized, and the interpretation must be prudent.

• Again, wild-type mice are not included.

Thank you. We now include a reference to illustrate the types and percentages of cells present in our cultures. Given that the study is to compare the Thr92 and the Ala92 alleles, which are both present in humans, we did not believe it was necessary to include them here. Please note (as explained above) the Km(T4) for Thr92 and Pro92-Dio2 is indistinguishable.

Part 2: Neuronal response to T3 Involves MCT8 and Retrograde TH transport

The authors next move to primary neuronal cultures, prepared from the fetal cortex which they grow in the microfluidic chamber to study axonal transport. This is a surprising move: the focus is not on Dio2 anymore, but on the MCT8 transporter, which is known in humans to play an important role to transfer TH into the brain. It is expressed mainly in glia, but also in neurons. They study the influence of endosomes and type 3 deiodinase on the trafficking and metabolism of TH.

Thank you.

It would be useful to perform an experiment, in which radioactive T3 is introduced in the "wrong" side of the chamber, in an attempt to detect a possible anterograde transport. This would address the possibility that Mct8 also promotes efflux and control so that the chamber is not leaking.

Thank you. To satisfy the reviewer, we have conducted three new experiments adding 125IT3 in the MC-CS. The first experiment verified that the T3 transport in the cortical neurons also occurs anterogradely. The second experiment showed that the anterograde transport depends on mct8. The third experiment shows that D3 activity in the neuronal soma is limiting the amount of T3 transported along axons. We have included a new paragraph in the results section describing these experiments (Line 154 to 167), and a new supplementary figure (Figure 3—figure supplement 3). We have also discussed these new findings. Line 383 to 386. In every experiment, we have controlled for the possibility of leaking using one device without neurons that received radioactive T3. After 24 and 72h samples from the opposite side were obtained but did not contain any radioactive T3. We refer the reviewer to figure 1, where this is explained.

The authors use sylichristin as an inhibitor of Mct8, to demonstrate that transport is Mct8 dependent. They do not provide indications or references that would clearly indicate that this drug is a fully selective antagonist of Mct8 (but not of Oatp1c1, Mct10, Lat1, Lat2, etc., the other TH transporters). A good alternative would be to use Mct8 KO mice as controls.

Thank you. We refer the reviewer to reference 27 [J. Johannes et al., Silychristin, a Flavonolignan Derived from the Milk Thistle, Is a Potent Inhibitor of the Thyroid Hormone Transporter MCT8. Endocrinology 157, 1694-1701 (2016)] clearly indicating that Silychristin has a remarkable specificity toward MCT8. While using mct8 KO is interesting, it would have prevented us from testing some of our hypotheses. Being able to selectively inhibit Mct8 either in the MC-CS or in the MC-AS was a clear advantage. For example, pls see the experiment in which we add T3 in the MC-AS and the silychristin in the MC-CS (Fig. 3F). Here, we discovered new roles of mct8, such as its involvement in the release of T3 from the endosomes (line 228 to 231).

The B27 used in primary neuronal culture might contain TH. This is not easy to know, but at least some batches do.

Thank you. While the neurons were cultured in B27, all experiments were performed in cells incubated with neurobasal only (B27 was removed 24 earlier). This was not clear in the initial version, where there was only a vague reference in the legend of figure 3F. Now, this has been explained in the footnote of figure 3 and in line 207.

The presence of astrocytes, probably expressing Mct8 and Dio2 is inevitable in primary neuronal cultures, and is not mentioned, but might interfere with TH metabolism.

Thank you. We were aware that, under normal conditions, primary neuronal culture contains 25% of astrocytes. This was however minimized/eliminated by 2-day culture with the anti-mitotic cytosine arabinoside, which restricts astrocytes and microglia to <0.01 in this type of culture. This was explained in the initial version of the manuscript in the material and methods section (lines x to x) and supported with reference 53 (reference 57 in the previous version).

Part 3: T3 Transport Triggers Localized TH Signaling in the Mouse Brain

The authors return to in vivo experiments, implanting T3 crystals, labeled or not with radioactive iodine. They do so in the hypothalamus, where they address the retrograde transport of TH in TRH neurons, and in the cortex, looking for contralateral transport. These data are the most difficult to interpret. - First, T3 is hydrosoluble and would probably migrate without active transport.

Thank you. Please note that at no point we characterized the T3 transport “active transport”, which by definition is an ATP-dependent process. Please note that to address the issue raised by the reviewer “migrate without active transport”, in both experimental approaches, we included controls to assess the random diffusion of T3.

In hypothalamic studies, we used the (i) cerebral cortex and (ii) the lateral hypothalamus, a region that is immediately adjacent to the PVN. Neither region exhibit an axonal connection to the median emminence. The results, in both cases, show that the presence of radioactive T3 in the control areas was minimal when compared to the PVN (Fig. 5C).

In the cerebral cortical studies, we included ipsi- and contra-lateral hypothalamic measurements that served as controls given the absence of a connection between the cortex and the hypothalamus. Accordingly, T3 signaling was not detected in any of the control regions (Fig. 6C previous version; now figure 5). Thus, these controls indicate that it is unlikely that the results could be explained by “migrate without active transport” of T3.

• The authors do not demonstrate that these specific neuronal populations contain Mct8, and that these observations are connected to the previous in vitro observation (which used cortical neurons prepared from the fetus).

Thank you. In the previous version, we did not make it abundantly clear that the EM pictures in Fig. 3D-G (previous version; now figure 2 D-G) were from neurons in the mouse motor cortex (this information is now explained in lines 149 to 151), which is where we inserted the T3 crystals. In addition, we have done more histological work on the brain M1 (cortex) of adult mice and found that many neurons in the M1 express D3 and Mct8—lines 433-434 and Figure 5 G-K (along with histological studies showing the specificity of the ab against D3 Fig S6).

The possibility that astrocytes are involved, as reported in the literature, is not considered.

• Here again, using Mct8KO mice would greatly help to interpret the data. In particular, the experiments with cold T3 involve a 48h delay which is very long in comparison to the 30 minutes required for long-distance transfer of radioactive T3.

Thank you. We are unsure about the question posed by the reviewer. We are wondering how would astrocytes play a role in inter-hemispheric transport of T3? Given that astrocytes are not known to project across long distances, we have not considered this possibility. We agree that using the Mct8KO mouse could have provided supporting evidence of the role played by Mct8 in this process, but please keep in mind that the Mct8KO mouse does not have or exhibits a very mild brain phenotype, indicating that during development compensatory mechanisms have occurred that obviate the function of the transporter. This compensatory mechanism most likely involved Oatp1c1, given that only the double Mct8 and Oatp1c1 KO mouse develops a significant phenotype. This consideration directed us to the utilization of sylycristin, the highly selective Mct8 inhibitor, which disrupts the Mct8 pathway in a mouse that developed normally.

The two approaches used to demonstrate neuronal T3 transport in vivo are fundamentally different. The hypothalamus experiments employed radioactive T3, whereas T3 crystals were used in the cerebral cortex. The first approach studied T3 transport whereas the second studied downstream T3 effects, logically requiring more time. The solid T3 implant requires time to release T3 and activate gene expression. In the original paper that utilized T3 implants in the rodent brain, samples were processed after 4 days. (Dyess et al. 1988 Endo; PMID 3139393)

Discussion

Considering the diversity of questions that are addressed in the study, it is not surprising that the discussion is not covering all aspects. The authors implicitly consider that their conclusions can be extended to all neurons, while they use in their experiments a variety of different populations coming from either the fetal cortex, hippocampus, adult cortex, or hypothalamus. The claim that they discovered a mechanism applying to all neurons is not supported by the data.

Thank you. We agree with the reviewer: the high number of neuronal subtypes might include different mechanisms in T3 transport. Our studies involved cortical (central) and dorsal root ganglia (peripheral) neurons in vitro and cortical and hypothalamic neurons in vivo. Thus we think that the described mechanism is not confined to specific neuronal subtypes. The discussion has been modified accordingly (lines 402 to 411).

Moreover, we have done immunofluorescence studies to characterize the neurons present in the MC-CS better. We have found that all the neurons residing in the MC-CS are excitatory, expressing the vesicular glutamate transporter 1 (Vglut1). But no neurons were expressing GAD67, a marker for inhibitory neurons Figure 5—figure supplement 5). This is supported by the fact that during the mouse's brain development, the embryonic days 14.5 to 17.5 is the birth date of layer 4 and 2/3 excitatory neurons (PMID: 34163074). These neurons are migrating and have not extended their cellular processes, making them more likely to survive the isolation protocol from the cortex. On the other hand, the neurons (mostly excitatory) already residing in the cortex may have expanded their processes and changed their morphology, making them less capable of surviving the isolation process.

Some highly relevant literature is not cited. In particular:

• Mct8 KO mice do not have marked brain hypothyroidism (PMID: 24691440) which at least suggests that the pathway discovered by the authors can be efficiently compensated by alternative pathways.

We agree with the reviewer. As mentioned above, a compensatory mechanism triggered during development “compensates” for the inactivation of Mct8. That, however, does not mean that mct8 is not critically important. We have added that limitation to the discussion (lines 342); ref 46.

• Dio3 KO only increases T3 signaling in a few brain areas and only in the long term (PMID: 20719855).

Thank you. That is now included in the ms; ref 25.

• Anterograde transport of T3 has been reported for some brainstem neurons (PMID: 10473259).

Thank you. This was our mistake, indeed. We had worked on several versions of the manuscript that included references to her seminal work but unfortunately deleted it from the final version. This is now included in refs 48 and 49.

Reviewer #2 (Public Review):

Salas-Lucia et al. investigated two main questions: whether the Thr92Ala-DIO2 mutation impairs brain responsiveness to T4 therapy under hypothyroidism induction and the mechanisms of neuronal retrograde transport of T3. They find that the Thr92Ala-DIO2 mutation reduces T4-initiated T3 signaling in the hippocampus, but not in other brain regions. Using neurons cultured in microfluidic chambers, they further describe a novel mechanism for retrograde transport of T3 that depends on MCT8 and endosomal loading (possibly protecting T3 from D3-mediated cytosolic degradation) and microtubule retrotransport. Finally, they present evidence of retrograde transport of T3 through hypothalamic projections and interhemispheric connections in vivo. The main novelty of this study is the delineation of the mechanism of T3 retrograde transport in neurons. This is interesting from the cell biology perspective. The notion of impaired hippocampal T3 signaling is relevant for the cognitive outcomes of hypothyroidism and its associated therapy.

Thank you.

Although the data are exciting and relevant for the community, some issues need to be addressed so that conclusions are more clearly justified by data:

1) The title and the abstract mean that dissecting this novel mechanism of T3 retrograde transport may help improve cognition or brain responsiveness in patients taking T4 or L-T3 therapy. However, how initial results (Figs 1 and 2) connect to later data is not essentially clear. For example, do Thr92Ala-DIO2 mice present altered retrograde transport of T3? Would stimulation of retrograde transport in Thr92Ala-DIO2 mice rescue neurological phenotypes? Can the authors address this experimentally?

Thank you. These are all interesting points raised by the reviewer. However, the three reviewers felt that a connection between the studies in astrocytes and the studies in neurons was missing, and complained about the disjoint nature of the manuscript. To satisfy the reviewers we removed from the MS the experiments with astrocytes and DIO2 polymorphism, and focused on the neuronal transport of T3.

2) Although the authors present in vivo evidence of retrograde T3 transport in the hypothalamus and motor cortex, given the select susceptibility of the hippocampus to hypothyroidism, it would be especially interesting to test whether this mechanism also happens in a hippocampal circuit (CA3-CA1 Schaffer collaterals, mossy fibers or perforant pathway).

Thank you. We agree that this would be interesting, but technically challenging. Nonetheless, we intend to study this in the future.

3) Table 1 should present the raw values for Ala92-DIO2 mice and treatments instead of only displaying the direction of change and statistical significance. From Panels 1E-J, it is unclear if Thr92Ala-DIO2 mice or treatments caused any real change in brain regions other than the hippocampus.

Thank you. These experiments were removed from the new version of the MS.

4) The authors put forward the notion that a rapid nondegradative endosome/lysosome incorporation protects T3 from D3 degradation in the cytosol. Their experiments with pharmacological modulation of MCT8, lysosomes, and microtubules are in this direction. However, they do not represent an unequivocal demonstration of this mechanism. Therefore, the authors should be more cautious in their interpretation and discuss the limitations of their approaches.

Thank you. The manuscript was edited to reflect these important points.

Reviewer #3 (Public Review):

Initially, Salas-Lucia et al examined the effect of deiodinase polymorphism on thyroid hormone-medicated transcription using a transgenic animal model and found that the hippocampus may be the region responsible for altered behavior. Then, by changing to topic completely, they examined T3 transport through the axon using a compartmentalized microfluid device. By using various techniques including an electron microscope, they identified that T3 is uptaken into clathrin-dependent, endosomal/non-degradative lysosomes (NDLs), transported in the axon to reach the nucleus and activate thyroid hormone receptor-mediated transcription.

Although both topics are interesting, it may not be appropriate to deal with two completely different topics in one paper. By deleting the topic shown in Table 1, Figure 1, and Figure 2, the scope of the manuscript can be more clear.

Thank you. We did as suggested by the reviewer. These studies were removed from the present version of the ms.

Their finding showing that triiodothyronine is retrogradely transported through axon without degradation by type 3 deiodinase provides a novel pathway of thyroid hormone transport to the cell nucleus and thus can contribute greatly to increasing our understanding of the mechanisms of thyroid hormone action in the brain.

Thank you.

Author Response

Reviewer #2 (Public Review):

In their study the authors aimed to investigate the dissemination of Enterobacterales plasmids between geographically and temporally restricted isolates recovered from different niches, such as human blood stream infections, livestock, and wastewater treatment works. By using a very strict similarity threshold (Mash distance < 0.0001) the authors identified so-called groups of near-identical plasmids in which plasmids from different genera, species, and clonal background co-clustered. Also, 8% of these groups contained plasmids from different niches (e.g., human BSI and livestock) while in 35% of these cross-niche groups plasmids carried antimicrobial resistance (AMR) genes suggesting recent transfer of AMR plasmids between these ecological niches.

Next, the authors set-out to examine the wider plasmid population structure by clustering plasmids based on 21-mer distributions capturing both coding and non-coding plasmid regions and using a data-driven threshold to build plasmid networks and the Louvain algorithm to detect the plasmid clusters. This yielded 247 clusters of which almost half of the clusters contained BSI plasmids and plasmids from at least one other niche, while 21% contained plasmids carrying AMR genes. To further assess cross-niche plasmids similarities, the authors performed an additional plasmid pangenome-like analysis. This highlighted patterns of gain and loss of accessory plasmid functions in the background of a conserved plasmid backbone.

By comparing plasmid core gene or plasmid backbone phylogenies with chromosome core gene phylogenies, the authors assessed in more detail the dissemination of plasmids between humans and livestock. This indicated that, at least for E. coli, AMR dissemination between human and livestock-associated niches is most likely not the result of clonal spread but that plasmid movement plays an important role in cross-niche dissemination of AMR.

Based on these data the authors conclude that in Enterobacterales plasmid spread between different ecological niches could be relatively common, even might be occurring at greater rates than estimated, as signatures of near-identity could be transient once plasmids occupy and adept to a different niche. After such a host jump, subsequent acquisition, and loss of parts of the accessory plasmid gene content, as a result of plasmid evolution after inter-host transfer, may obscure this near-identity signature. As stated by the authors, this will raise challenges for future One Health-based genomic studies.

Strengths

The article is well written with a clear structure. The authors have used for their analysis a comprehensive collection of more than 1500 whole genome sequenced and fully assembled isolates, yielding a dataset of more than 3600 fully assembled plasmids across different bacterial genera, species, clonal backgrounds, and ecological niches. A strong asset of the collection, especially when analyzing dissemination of AMR contained on plasmids, is that isolates were geographically and temporally restricted. Bioinformatic analyses used to discern plasmid similarity are beyond state-of-the-art. The conclusions about dissemination of plasmids between genera, species, clonal background and across ecological niches are well supported by the data. Although conclusions about inter-host plasmid dissemination patterns may have been drawn before, this is to my knowledge the first time that patterns of dissemination of plasmids have been studied at such a high-level of detail in such a well selected dataset using so many fully assembled genomes.

Weaknesses

One conclusion that is not entirely supported by the data is the general statement in the discussion that "cross-niche plasmid in not driven by clonal lineages". From the tanglegram, displaying the low congruence between the plasmid and chromosome core gene phylogeny in E. coli, this conclusion is probably valid for E. coli, but this not necessarily means that this is also the case for the other Enterobacterales genera and species included in this study. For these other genera, the data supporting this conclusion are not given, probably because total number of isolates for certain genera were low, or because certain niches were clearly underrepresented in certain genera.

Thank you for reviewing our manuscript.

We agree that this statement in the conclusion was too general, and have adapted it (lines 407-409):

“By examining plasmid relatedness compared to bacterial host relatedness in E. coli, we demonstrated that plasmids seen across different niches are not necessarily associated with clonal lineages”

In the limitations section of the Discussion, we have also referenced this specifically as a limitation (lines 422-424):

“Although we evaluated four bacterial genera, 72% (1,044/1,458) of our sequenced isolates were E. coli, and so our analyses and findings are particularly focused on this species.”

Furthermore, the BSI as well as the livestock niches were analyzed as single niches while the BSI niche included both nosocomial and community-derived BSI isolates and the Livestock niche included samples from different livestock-related hosts. Given the fact that a substantial number of plasmids were available from cattle, sheep, pigs, and poultry, it would be interesting to see whether particular livestock hosts were more frequently found in the cross-niche plasmid clusters than other livestock hosts and whether the BSI plasmids in these cross-niche clusters were predominantly of community or nosocomial origin.

We agree that analyses which distinguish between nosocomial/community acquired BSI isolates would be interesting further work, but are beyond the scope of this study. Our analysis of the BSI/livestock cross-niche near-identical plasmid groups details the livestock hosts involved (lines 144-154). Briefly, of the n=8 BSI/livestock cross-niche groups, these involved

• pig/poultry (1/8)

• poultry (1/8)

• pig (2/8)

• sheep (3/8)

• cattle/pig/poultry (1/8)

We have added a note of explanation in the methods to explain how the distance threshold we use for near-identical clustering is maximally conservative at small plasmid sizes (a single SNP produces a new plasmid cluster) but remains highly conservative (tens of SNPs) at large plasmid sizes.

We have carefully considered the point about whether particular hosts were more frequently found in cross-niche plasmid clusters. However, we do not think it is easy to infer whether a particular livestock host is represented more frequently in these cross-niche events than would be expected from chance, given the low density of the sampling.

We have reorganised the paragraph in lines 144-154 to provide more clarity on the groups’ niches.

“Sharing between BSI and livestock-associated isolates was supported by 8/17 cross-niche groups (n=45 plasmids). Of these, n=3/8 groups contained BSI/sheep plasmids: one group contained mobilisable Col-type plasmids, the remaining two groups contained conjugative FIB-type plasmids. Of these, one group contained plasmids carrying the AMR genes aph(3'')-Ib, aph(6)-Id, blaTEM-1, dfrA5, sul2, and the other group contained plasmids carrying the MDR efflux pump protein robA (see Materials and Methods). A further n=2/8 groups contained BSI/pig mobilisable Col-type plasmids, of which one group other carried the AMR genes aph(3'')-Ib, aph(6)-Id, dfrA14, and sul2. Lastly, n=1/8 groups contained BSI/poultry non-mobilisable Col-type plasmids, n=1/8 contained BSI/pig/poultry/influent non-mobilisable Col-type plasmids, and n=1/8 contained BSI/cattle/pig/poultry/influent mobilisable Col-type plasmids.”

We have also added this as a limitation in the discussion (lines 424-426):

“Additionally, we did not sample livestock-associated niches densely enough to explore individual livestock types (cattle/pigs/poultry/sheep) sharing plasmids with BSI isolates (see Appendix 1 Fig. 9).”

We have already recognised that our culture methods may have affected our sensitivity to detect Klebsiella spp. isolates in the livestock/environmental samples – we have expanded on this to explicitly highlight that this may have affected our capacity to evaluate Klebsiella-associated plasmids (lines 443-444):

“This limited our ability to study the epidemiology of livestock Klebsiella plasmids.”

Author Response

Reviewer #1 (Public Review):

Although the authors have identified some properties/molecular markers of canine H3N2 influenza viruses that highlight the potential for infecting humans, it needs to be cautious to emphasize the threat of these viruses to public health. One fact is that despite the increasing prevalence of these viruses in dogs and the close proximity between dogs and humans, there is so far no report of human infection with canine H3N2 influenza viruses. The authors are wished to discuss this in their manuscript so that the readers can have a more comprehensive understanding of their findings and the public health importance of canine influenza viruses.

We agree with the reviewer. We added the related discussion and revised some words to not emphasize the threat of these viruses to public health (lines 342-346).

Reviewer #3 ( Public Review):

1) The investigators should run neuraminidase inhibition assays to established the level of cross reactivity of human sera to the canine origin NA (one of reasons proposed as to the lower impact of the H3N2 pandemic was the presence of anti0N2 antibodies in the human population).

We performed neuraminidase inhibition assays as suggested for both ferret sera against human H3N2 virus and human sera. The results showed that the NI titers of ferret sera against human H3N2 virus to canine H3N2 viruses were <10 (lines 147- 148, Supplementary file 2). Additionally, 2.0%–3.0% of the children's serum samples, 1.0%–2.0% of the adult's serum samples, and 1.0%–2.0% of the elderly adult's serum samples had NI antibody titers of ≥10 to canine origin NA (lines 158-161, Table 1, and lines 435-445).

2) Please tone down the significance of ferret-to-ferret transmission as a predictor of human-to-human transmission. Although flu viruses that transmit among humans do show the same capacity in ferrets, the opposite is NOT always true.

We agree with the reviewer. To tone down the significance of ferret-to-ferret transmission as a predictor of human-to-human transmission, we added the related discussion and deleted or revised some words (lines 342-346, line 37, line 302, line 308, line 322, and line 341).

Author Response

Reviewer #2 (Public Review):

In this manuscript, Vias and co-authors develop HGSOC PDOs and characterized their genomes, transcriptomes, drug sensitivity, and intra-tumoural heterogeneity. They show that PDOs represent the high variability in copy number genotypes observed in HGSOC patients. Drug sensitivity was reproducible compared to parental tissues and the ability of these models to grow in vivo.

Overall, the manuscript lacks sufficient novelty. Several pieces of information and a number of conclusions that are presented here have been previously published by other groups (Nina Maenhoudt, Stem cell reports, 2020; Shuang Zhang, Cancer Discov, 2021).

We agree that several important papers on HGSOC organoids have been published. However, we disagree about your assessment of “lacks sufficient novelty”. Our MS addresses critical questions about conservation of mechanisms of chromosomal instability, how PDOs can be selected as clinical relevant models based on patterns of CIN and their comparative drug response. These questions are vital to using PDOs for therapeutic development and have not been explored before. By contrast, Maenhoudt et al. performed many analyses on several organoids (whole-genome sequencing, whole exome sequencing) but did not analyse the relationships between copy number profiles, mutational signatures or drug sensitivity between donor tissues and derived organoids and did not perform transcriptomic or scDNA analyses. A major novelty of our approach is to provide robust clinical validation of individual HGSOC PDOs by analysing how our PDOs are statistically representative of the various CN subclasses of HGSOC. Maenhoudt et al and Zhang et al classify their models only using infrequent recurrent mutations in driver genes. We do not understand how the Zhang MS overlaps with our MS as it describes the CRISPR-engineering of mouse cells to model HGSOC and investigates drivers of the mouse tumour microenvironment.

Reviewer #3 (Public Review):

1) The manuscript adequately demonstrates that genomic instability is maintained in HGSOC tumourspheres. The use of 3-dimensional HGSOC models to more greatly resemble the in vivo environment has been used for more than a decade, but this is the first demonstration using a variety of genomic assessment tools to show genomic instability in the HGSOC tumoursphere model. It is clearly demonstrated that these HGSOC tumourspheres represent copy number variations similar to information in public datasets (TCGA, PAWG, BriTROC-1) and that cellular heterogeneity is present in these tumourspheres. The simple steps outlined to establish and passage tumourspheres will benefit the field to further study mechanisms of genomic instability in HGSOC.

Thank you for these positive comments.

2) A weakness of the manuscript is the lack of operational definitions for what constitutes an organoid and an appropriate definition to distinguish genomic instability from chromosomal instability (a distinct type of genomic instability). Line 147 states "As PDOs consist of 100% tumour cells...", although this does not appear to have been established by any assessment. This limited characterization of the 3D model is a weakness since no data is provided on whether the tumourspheres constitute only a single cell type (as indicated on line 147) or multiple cell types (e.g., HGSOC cell, mesothelial cells) using markers beyond p53 expression. Based on this information, this model cannot be called a PDO, rather it should be referred to as a tumoursphere.

We define continuous PDO models on page 3 stating our criteria based on passage > 5 and successful reculture after thawing (previous publications have not defined whether their models are continuous or finite). As shown in our targeted-gene mutation analysis, all our PDOs contain a TP53 mutation allele fraction between 80–95%. Moreover, in our single cell DNA-Seq data we do not observe any normal copy number profiles that would indicate normal cells. This information is now included in the text for clarification. Our reasons not to use the term spheroids or tumourspheres are:

1. The word spheroid comes from the in vitro spheroid formation assay which was originally designed to overcome the difficulties found in functional in vivo serial transplantations. This method generates colony-forming units in suspension. Our patient-derived cells are not growing in suspension but within an extra-cellular matrix.

2. Spheroids are clonally expanded from a single-cell as part of the colony-forming assay; our patient-derived organoids were not clonally expanded in any way.

3. Organoids derived from patient-tumours have been named PDOs in multiple publications where pure tumour cellularity was stated for the PDOs [Vlachofiannis et al. Science (2018) 359, 920; Li et al. Nat. Comm.(2018) 9, 2983; Lee et al. Cell (2018)173, 515; Kopper et al. Nat Med (2019) 25, 838]. Use of other terms will cause confusion for readers and prevent important comparisons between PDO from different researchers.

3) Chromosome instability (CIN) is a type of genomic instability that is broadly defined as an increased rate of chromosome gains or losses and is best identified through analysis of single cells (e.g., karyotype analysis), something that bulk whole genome sequencing cannot determine since it is a reflection of cell populations and not individual cells. While the data demonstrate genomic instability is retained in the tumourspheres, and chromosome losses or copy-number amplifications were observed using single-cell whole genome sequencing, evaluation of samples from the same patient over time was not evaluated. While there is evidence to support CIN in these samples, in agreement with other published work that has demonstrated CIN in >95% of HGSOC samples analyzed at the single-cell level, this work is not conclusive. The title of the manuscript should be modified to more accurately represent what the evidence supports.

We have discussed the ambiguity of CIN in our recent publication “A pan-cancer compendium of chromosomal instability” Drews et al Nature 2022.

“CIN has complex consequences, including loss or amplification of driver genes, focal rearrangements, extrachromosomal DNA, micronuclei formation and activation of innate immune signalling. This leads to associations with disease stage, metastasis, poor prognosis and therapeutic resistance. The causes of CIN are also diverse and include mitotic errors, replication stress, homologous recombination deficiency (HRD), telomere crisis and breakage fusion bridge cycles, among others.

Because of the diversity of these causes and consequences, CIN is generally used as an umbrella term. Measures of CIN either divide tumours into broad categories of high or low CIN, are restricted to a single aetiology such as HRD, are limited to a particular genomic feature such as whole-chromosome-arm changes, or can only be quantified in specific cancer types. As a result, there is no systematic framework to comprehensively characterize the diversity, extent and origins of CIN pan-cancer, or to define how different types of CIN within a tumour relate to clinical phenotypes. Here we present a robust analysis framework to quantitatively measure different types of CIN across cancer types.”

Many authors use CIN to include the consequences of CIN and other specifically use CIN to indicate ongoing numerical and structural change. We do not think our usage of CIN in the title and text is controversial and is consistent with previous peer reviewed publications, including our own.

4) An additional weakness is missing information (e.g., Figure 1d, Supplementary Figure 3b, and Supplementary Table 4 were not included in the manuscript; the 13 anticancer compounds used to test drug sensitivity are not indicated) making an assessment of the data impossible, and assessment of some conclusions difficult.

We apologise for this misunderstanding as a typo suggested that there was a Figure 1d (it should have referred to Figure 1c) or Figure 1-Figure supplement 3B (the label of which was missing); we also apologise for the omission of Supplementary Table 4. These errors have been corrected and the list of compounds is now included in the Methods section.

Author Response

Reviewer #1 (Public Review):

We would like to thank reviewer #1 for her helpful comments and would like to respond to these as follows:

1) “Editing efficiencies were variable (99% to 0%) depending on the species, being worst for L. major.”

It is true that the editing efficiency was different in each species and worst for L. major. However, it is important to note that these efficiencies varied not only for each species but also amongst genes and especially chosen sgRNA sequences. Variations in efficiency across sgRNAs targeting the same gene and locus is a common problem in any CRISPR approach. We made this clearer in our revised manuscript (line 670 – 673).

2) “The use of premature termination codons also clearly raises issues for false positives and negatives, especially as there is no evidence for nonsense-mediated mRNA decay in Leishmania.”

We have now included in our revised manuscript that it is currently unclear whether a classical nonsense-mediated decay pathway is present in Leishmania or not. If such a pathway would be present, mutant mRNAs in which a termination codon is present within the normal open reading frame would be removed (Clayton, Open Biology 2019; Delhi et al., PLoS One 2011). But if not, remaining N-terminal protein parts could be functional and may lead to false positive and negative results. However, as reviewer #2 pointed out, this may also provide extra information about functional domains of the targeted protein and highlights that our tool can not only be used to create functional null mutants by inserting premature STOP codons but also to pursue targeted mutagenesis screens (line 674 - 683).

3) “There are already two genome-wide screening options for Leishmania, so the advantages and disadvantages of the method proposed here need to be discussed in a much more detailed and balanced way.”

We have revised our manuscript to include in our introduction (line 36 - 73) and discussion (line 658 - 697) a better comparison of all potential tools for genome-wide screening in Leishmania, including RNAi, bar-seq and base editing screening. We highlight why we think that base editing has unique advantages.

4) “In the "LeishGEM" project (http://www.leishgem.org) all Leishmania mexicana genes will be knocked out and each KO will be bar-coded. At the end, 170 pooled populations of 48 bar-coded mutants will be publicly available. The only real reason the authors of the current paper give for not using this approach is that it is labour-intensive. However, LeishGEM is funded and underway, with several centres involved, so that argument is weak.”

In our original manuscript we gave multiple reasons why we think that the LeishGEdit method, which is being used for the LeishGEM screen and has been developed by the lead author of our here presented study, has clear disadvantages compared to base editing.

As written in our original manuscript (line 709 – 716): “However, for a bar-seq screen, each barcoded mutant needs to be created individually by replacing target genes with drug selectable marker cassettes (20,21), making them extremely labour intensive and most likely “one-offs” on a genome-wide scale. Furthermore, aneuploidy in some Leishmania species can be a major challenge for gene replacement strategies as multiple rounds of transfection or isolation of clones may be required to target genes on multi-copy chromosomes. Using gene replacement approaches it is also not feasible to study multi-copy genes that have copies on multiple chromosomes. These are major disadvantages of bar-seq screening.”

Therefore, we still think that the main disadvantage of bar-seq screening is that it is labour-intensive as each mutant needs to be created individually. The fact that LeishGEM requires five years and several research centres to knockout all genes in just one Leishmania species is proof for this argument.

However, to clarify our position about this further, we have listed other disadvantages of the LeishGEM screen, including difficulties of sharing mutant pools between labs, possible problems in expanding mutant pools without losing uniformity, no ability to change the composition of generated pools and limited ability to distinguish between technical failures and essentiality. If any of these problems would occur, it would require a de novo generation of barcoded mutants and therefore this is an extremely labour-intensive method for large-scale screening. We also added that bar-seq screens are not feasible in Leishmania species that display extreme cases of aneuploidy, such as L. donovani (line 59 – 73).

Despite all these disadvantages of the LeishGEdit approach for the LeishGEM project, there are of course also clear advantages, which we also point out in our introduction (line 52 – 55).

5) “There is also a preprint describing RNAi for functional analysis in Leishmania braziliensis.”

Although our original manuscript included the pre-print about RNAi screening in Leishmania braziliensis already (line 706-709), we understand that this deserves a stronger discussion. We have therefore highlighted now RNAi as a possible tool for genome-wide screening in selected Leishmania species in our revised introduction (line 36 - 43). However, we also argue that RNAi approaches are at the moment only available to Leishmania of the Viannia subgenus and that RNAi activity greatly varies between the species (line 36 – 43 and 665 - 669). In addition, we discuss that the use of RNAi genome-wide screens is much less specific, as usually randomly sheared genomic DNA is used to generate RNAi libraries (line 687 - 689). Since the pre-print is now published, we have replaced the pre-print publication with the peer-reviewed one.

Reviewer #2 (Public Review):

We would like to thank reviewer #2 for helpful comments and would like to respond to those as follows:

1) “Line 482 - the authors wrote 'As expected, the proportion of cells showing a motility phenotype in the IFT88 targeted L. infantum population decreased further' Why is this result expected? Presumably, this is due to the fact that cells without a functional IFT system lack flagella and grow slower so can be outcompeted by faster-growing mutants. This speaks to the major caveat highlighted by the authors in the discussion and the final small-scale screen. In a population of cells, those with deleterious mutations in an essential gene or one whose disruption results in slower growth will be outcompeted by cells in which a non-deleterious mutation has occurred, which feeds into the issue of timing.”

As the reviewer highlighted himself, deleterious mutations that result in slower growth will be outcompeted by cells in which a non-deleterious mutation has occurred. We have stated that the complete deletion of IFT88 in Leishmania mexicana has been shown to have reduced doubling time (Beneke et al., PLoS Pathogens 2019) and are therefore most likely outcompeted from the pool (line 529 – 532 and 767 - 769).

2) “The authors show with CRK3 this process of non-deleterious mutants outcompeting deleterious mutants does result in a detectable drop in the number of parasites with specific CRK3 guides but not in those with IFT88. Is this due to the fact that the outgrowth of the non-deleterious IFT88 mutants occurs rapidly or that the mutation of the targets in IFT88 was ineffective? The data presented in Figure 5 shows that for some species at least a mutation of the IFT88 gene was possible. This might mean that for certain genes the outgrowth occurs within the first 12 days after transfections so will not be seen using this approach, without a wider study, which is beyond the scope of this manuscript it will be difficult to know.”

As we stated in our discussion, we did not test IFT88 guides individually in L. mexicana. Therefore, the editing rate observed for the IFT88 guides in L. major and L. infantum (Fig. 5) may differ from the editing rate in L. mexicana, which is the species we used for the pooled transfection screen. It is therefore difficult to conclude why IFT88 was not depleted from the pool. This may be due to lower guide activity in L. mexicana or rapid selection of non-deleterious mutations (line 769 - 774). We are therefore planning to further optimize our system by streamlining the editing efficiency and eliminating species-specifics effects (line 735 - 745). As the reviewer highlighted, this is beyond the scope of this study.

However, the reviewer raises a fair point about the exact timing of isolating DNA from pools, which might influence when exactly parasites with a deleterious mutation are depleted from the pool. This may differ between guides and may even be gene specific. We have added this point to our discussion (776 - 780).

3) “The authors highlight that this base editing approach will leave potentially functional regions of the NT of proteins, which is true and may mean genes are missed. However, this may also provide extra information about the protein's function/domain structure if STOP codons in certain positions showed an effect on function whereas those in others don't.”

We thank reviewer #2 for pointing out that functional parts of truncated proteins following base editing may actually allow to draw additional conclusions. We have included this in the manuscript (681 - 683).

Author Response

Reviewer #1 (Public Review):

This umbrella review aims to synthesize the results of systematic reviews of the impact of the COVID-19 pandemic on various dimensions of cancer care from prevention to treatment. This is a challenging endeavor given the diversity of outcomes that can be assessed in cancer care.

Search and review methods are good and are in line with recommendations for umbrella reviews. Perhaps one weakness of the search strategy was that only one database (Pubmed) was searched. The search strategy appears adequate, though perhaps some more search terms related to reviews and cancer could have been included. It is therefore possible that some reviews may have been missed by the search strategy.

It is challenging to perform a good umbrella review that yields novel insights, as it is difficult to combine results from different reviews which themselves combine results from different studies with different methodologies. However, I think perhaps one of the main weaknesses of this study is that it is not clear to me what is the core objective of the umbrella review, and how analyses relate to that core objective. In other words, I do not understand based on the introduction what new information the authors are hoping to learn from their umbrella review that could not be learned from reading the individual systematic reviews, beyond a vague objective of "synthesizing" the literature. Because of this, it is not very clear to me how the data extracted and the analysis fits into the larger objectives, and what the new knowledge generated by this review is. Based on the reported results, it would appear that one of the main goals is to assess the quality of systematic reviews and of the underlying studies in the reviews, but it is hard to tell. I think there are potentially important insights this review could tell us, but the message and implications of current evidence remain for me a little confused in the current manuscript.

We thank the reviewer for the encouraging remarks on our work, and for the useful feedback. We have now addressed all concerns as outline below.

Reviewer #2 (Public Review):

This umbrella review summarizes the results of systematic reviews about the impact of the COVID-19 pandemic on cancer care. PRISMA checklist is used for reporting. The literature search was performed in PubMed and systematic reviews published until November 29th, 2022 were included. The quality of included systematic reviews was appraised using the AMSTAR-2 tool and data were reported descriptively due to the high heterogeneity of 45 included studies. Based on the results of this paper, regardless of the low quality of included evidence, COVID-19 affected cancer care in many ways including delay and postponement of cancer screening, diagnosis, and treatment. Also, patients with cancer had been affected psychologically, socially, and financially during the COVID-19 pandemic.

The main limitation of the current study is that the authors have searched only one database, which might have missed some relevant systematic reviews. Also, most of the included reviews in this paper had low and medium methodological quality.

We thank the reviewer for this excellent remark. Guideline on umbrella reviews suggest PubMed, reference screening and an additional bibliographic database for an optimal database combination for searching systematic reviews (Goossen K et al. 2020). To follow the guidelines, and considering the specialized focused on COVID-19, in addition to Pubmed and reference screening, we also performed a search in the WHO COVID-19 Database. Furthermore, we revised the search strategy in Pubmed to include mesh terms. The search was performed by a specialized librarian with experiences in systematic review searches. Overall, we retrieve 485 new references, and found 6 new studies that met out inclusion criteria to be included in final analysis. We have now revised the manuscript to reflect the above changes, and also highlighted this as a strength of our work. In addition, we added the new detailed search strategy in the supplemental material.

Author Response

Reviewer #2 (Public Review):

The authors describe in the nematode C. elegans the effects of perturbed organization of Intermediate filaments (IFs), which form the cytoskeleton of animal cells together with actin filaments. They focus on a previously identified mutant of the kinase SMA-5, which when mutated leads to disorganized IF structure in intestinal cells of C. elegans. The authors found that the phenotypes caused by the mutated SMA-5 kinase concerning gut morphology and animal health can be reversed by removing IF network components such as the protein IFB-2. This finding is extended to other components of the IF network, which also display a certain degree of sma-5 phenotype alleviation when depleted.

Strength:

The finding that suppressing the intestinal phenotypes caused in sma-5 mutants can be suppressed by removing functional IF components is an interesting observation. It confirms a previous study showing that bbln-1 mutation-caused IF phenotypes can be suppressed by depleting IFB-2.

Weakness:

1) The finding of suppressing the intestinal phenotypes caused in sma-5 mutants can be considered a minor conceptual advancement. However, the study comes short of providing insight into the molecular processes of how deranged IF networks and its consequence can be rescued/suppressed by removing e.g. the IFB-2 filaments. Many statements concerning the relationship between SMA-5 and the IFs are based on assumptions. The study requires protein biochemical analysis to show whether SMA-5 phosphorylates the IF proteins - mainly the IFB-2 polypeptide. The relationship between SMA-5 / IFB-2 is a central aspect of this study but the main conclusions are based on the notion that IFB-2 and other IF proteins may be phosphorylated by SMA-5. Mutating putative phosphorylation sites of IFB-2 without having shown any proof that the modification occurs by SMA-5 is futile. This important open question needs to be addressed. And will allow statements whether the ifb-2(kc20) mutant allele-encoded shorter IFB-2 protein lacks phosphorylation or not.

We have addressed the major concern of the Reviewer by performing phosphorylation analyses of IFB-2 showing that loss of SMA-5 induces phosphorylation of multiple sites throughout the IFB-2 molecule. The results are presented in new Figs. 5 and S5.

2) No quantification of the morphological defects such as using fluorescent-labeled IF proteins as in previous studies is provided in the manuscript. The EM pictures are not sufficient to provide information on how often the IF network perturbations and morphology defects occur. Also, the rescue of the actual morphological gut defects was not quantified. The assessment of development time and arrest, body length, lifespan, oxidative stress resistance, and others should be related to intestinal tube defects. They are useful and important but are an indirect measure of intestine defects and rescue.

We provide the requested data on IF localization and intestinal morphology in new Figs. S2 and S3, respectively.

3) It is not clear how exactly the mutant ifb-2 allele kc20 was identified. In the Materials and methods section, the authors provide information on the specific primers for the ifb-2 locus. But how did they know that the mutation lies within this region? Was there mutation mapping or whole-genome sequencing applied?

The requested information is included in the revised Result section (first paragraph).

Author Response

Reviewer #2 (Public Review):

In this manuscript, the authors use an embedding of human olfactory perceptual data within a graph neural network (which they term principal odor map, or POM). This embedding is a better predictor of a diverse set of olfactory neural and behavior data than methods that use chemical features as a starting point to create embeddings. The embedding is also seen to be better for comparison of pairwise similarities (distances of various sorts) - the claim is that proximity of pairs of odors in the POM is predictive of their similarity in neural data from olfactory receptor neurons.

A major strength of the paper is the conceptualization of the problem. The authors have previously described a graph neural net (GNN) to predict verbal odor descriptors from molecular features (here, a 2019 preprint is cited, but a newer related one in 2022 describing the POM is not cited). They now use the embedding created by that GNN to predict similarities in large and diverse datasets in olfactory neuroscience (which the authors have curated from published work). They show that predictions from POM are better than just generic chemical features. The authors also present an interesting hypothesis that the underlying latent structure discovered by the GNN relates to metabolic pathway proximity, which they claim accounts for the success in the prediction of a wide range of data (insect sensory neuron responses to human behavior). In addition to the creativity of the project, the technical aspects, are sound and thorough.

There are some questions about the ideas, and the size of the effects observed.

1) The authors frame the manuscript by invoking an analogy to other senses, and how naturalstatistics affect what's represented (and how similarity is defined). However, in vision or audition, the part of the world that different animals "look at" can be very different (different wavelengths, different textures and spatial frequencies, etc). It is still unresolved why any given animal has the particular range of reception it has. Each animal is presumably adapted for its ecological niche, which can have different salient sensory features. In vision, different animals pick different sound bandwidths or EM spectra. Therefore, it is puzzling to think that all animals will somehow treat chemicals the same way.

Our assumption (an assumption of the broader interpretation, not of the analyses themselves) that all terrestrial animals have a correlated odor environment is certainly only true for some values of “correlated”. One could imagine, for example, that some animals are able to exploit food energy sources that humans cannot (for example, plants with high cellulose content), and that they might therefore be adapted to smell metabolic signatures of such plants, whereas humans would not be so adapted. This seems quite reasonable and there are probably many such examples. In future work they might be used to test the theory directly: representations might be more likely to differ across species on tasks when the relevant ecological niches are non-overlapping. We have updated the discussion to propose such future tests. However, it is also apparent that the odor environment overall is nonetheless highly correlated across species. Recent work (Mayhew et al, PNAS) showed that nearly all molecules that pass simple mass transport requirements (that should apply to all mammals, at the least) are likely to have an odor to humans, so it seems unlikely that the “olfactory blind spots” are intrinsically large.

2) The performance index could be made clearer, and perhaps raw numbers shown beforeshowing the differences from the benchmark (Mordred molecular descriptor). For example, can we get a sense of how much variance in the data does it explain, what percent of the hold-out tests does it fit well, etc.?

The performance index in Figure 1 is required to compare across different types of tasks, which are in turn dictated by the nature of the data (e.g. continuous vs categorical). Regression tasks yields an R2 value and categorical tasks yield an AUROC. We normalized and placed these on a single scale in order to show all of the tasks clearly together. We have added a table to the shared code (from link in Methods section, go to predictive_performance/data/dataset_performance_index_raw.csv) that shows the original (non-normalized) values, for both the POM and the benchmark(s) across multiple seeds and various metrics with the model hyper-parameters that generate the best performance.

3) The "fitting" and predictions are in line with how ML is used for classification and regression inlots of applications. The end result is a better fit (prediction), but it's not actually clear whether there are any fundamental regularities or orders identified. The metabolic angle is very intriguing, but it looks like Mordred descriptor does a very good job as well (extended figure 5 [now Figure 2-figure supplement 5]). Is it possible to show the relation between metabolic distance and Mordred distance in Figure 2c? In fact, even there, cFP distance looks very well correlated with metabolic distance (we are talking about r= 0.9 vs r = 0.8). This could simply be due to a slightly nonlinear mapping between chemical similarity and perceptual similarity (which was used to get POM distance).

We show additional “showdown” comparisons between metabolic distance, POM distance, and alternative distance metrics in the new Figure 2-figure supplement 3 and Figure 2-figure supplement 4. Indeed, the Mordred descriptors perform well; after all, metabolic reactants and products must be at least somewhat structurally related. But POM (derived only from human perceptual data) outperforms it significantly. Visual inspection of Figure 2c also reveals that the dispersion of structural distances (at a given metabolic distance) is just much higher than the dispersion of POM distances. This won’t change if one uses a non-linear curve fit, as it is a property of the data itself.

It’s also worth noting while r=0.8 and r=0.9 might seem close, in terms of variance unexplained (1 - r2) they are approximately two-fold different. Reducing the unexplained variance by half seems like a meaningful difference. Alternatively, if one simulates scatter plots with correlation r=0.8 vs r=0.9, it is apparent that the latter is simply a much tighter relationship.

4) How frequent are such examples shown in Fig 2d? Pentenal and pentenol are actually verysimilar in many ways, and it may be that Tanimoto distance is not a great descriptor of chemical similarity. cFP edit distance is quite small, just like metabolic distance. The thiol example on the right is much better. Also, even in Fig 2C POM vs metabolic distance, the lowest metabolic distances have large variations in the POM values - so there too, metabolic reactions that create very different molecules in 1 step can vary widely in POM distance as well.

We agree that Tanimoto distance is not perfect. We were unable to find a measure of structural distance that agreed with human intuitions about “structural distance” in all cases; indeed that intuition is often generated by an understanding of odor/flavor characteristics of function in metabolic networks, which would beg the question! To answer the question about the frequency of examples like the ones shown in Figure 2d, we created a new density map (Figure 2-figure supplement 4) showing the number of one-step metabolite pairs for a given range of POM vs cFP edit/Tanimoto distance. We found >25 pairs of metabolites in the same “small POM distance” and “large structural distance” quadrant from which we found the original examples shown in Figure 2d..

5) A major worry is that Mordred descriptors are doing fine, and POM offers only a smallimprovement (but statistically significant of course). Another way to ask this question is this: if you plot pairwise correlation/distance of pairs of odors from POM against that for Mordred, how correlated does this look? My suspicion is that it will be highly correlated.

It will look highly correlated (as shown in the new Figure 2-figure supplement 3). The reason is that metabolic reactions cannot make arbitrary transformations to molecules (the reactants must have some structural relationship to the products) or similarly that olfactory receptors (in any species) cannot have arbitrary tuning – at the end of the day receptors mostly bind to similar-looking classes of molecules. As stated above, we believe that the improvement here is not just statistically significant but meaningful – a 2-fold drop in unexplained variance is large – and that it is important to identify principles by which the nervous system can be tuned, above and beyond the physical constraints imposed by basic rules of chemistry.

Also, the metabolic distances that we constructed from available data are themselves noisy, since not all metabolic pathways and the compounds that compose them are known, which places an upper bound on the correlation that we could have obtained. Despite that, we still found a correlation of r>0.9.

6) The co-occurrence in mixtures and close POM distance may arise from the way theembedding was done - with perceptual descriptors used as a key variable. Humans may just classify molecules that occur in a mixture as similar just from experiencing them together. Can the authors show that these same molecules in Fig 4d,e have very similar representations in neural data from insects or mice?

We have added a new Figure 4-figure supplement 1 to show this. One constraint is that the neural datasets must contain molecules that are also in the natural substance datasets used in Figure 4. In all cases where the data is sufficient to be powered to test the hypothesis (i.e. more than five co-occuring pairs of molecules in essential oil), we observe an effect in the predicted direction.

Author Response

Reviewer #1 (Public Review):

This work focuses on the characterization of neutralizing antibodies from humans survivors of SNV and ANDV hantavirus infections, including the mapping of epitopes located in the Gn and/or Gc glycoproteins, and their mechanism of viral interference blocking receptor binding or membrane fusion. It also confirms previous data on broadly neutralizing epitopes allowing inhibition of different hantavirus species. The work covers for the first time in vivo evidence of cross-protection against HNTV infection by a broadly neutralizing antibody prepared from SNV infection using a prophylaxis animal model and compares the data with protection from ANDV lethal challenge using ANDV-specific neutralizing antibodies. The work provides valuable information for the development of therapeutic measures that cross-protect against several hantavirus species which seems a promising strategy to rise pharmaceutical interest against a group of viruses causing orphan disease.

The strength of the work is based on the impressive amount of work and versatility of methods to identify residues involved in the binding and/or escape from seven different neutralizing antibody clones that allow for important conclusions on species-specific antigenic regions and confirm data on a region that seems broadly conserved among different hantavirus species. At the same time, the weakness of the work is that data processing does not allow for readers data analysis (Figs. 1b, 2a, 2c, Ext. Data Fig. 4).

The authors clearly achieve their aim of characterizing the antigenic sites of neutralizing antibodies. Yet, the presented data on binding to ANDV mutant constructs and negative-staining EM does not allow for the conclusion that the epitope of the broadly neutralizing antibodies ANDV-44 and SNV-53 involved the Gn capping loop. An alternative explanation of the escape mutations in the Gn capping loop could be produced by an allosteric effect on the Gc fusion loop region, and a role in structuring the Gc fusion loop has been previously demonstrated (References 7 and 9). In addition, it is not clear why SNV-24 has no broad neutralizing activity although escape mutations occurred at the highly conserved residues K833 and D822 in Gc domain I.

. . . it would be important to show viral RNA levels in lungs and kidneys in the lethal ANDV animal model (Fig. 7) to allow for comparison with the prophylaxis from HTNV infection (Fig. 6).

ANDV does not necessarily cause significant viremia but this challenge model does allow detection of substantial virus load in organs. To monitor virus in organs, a separate animal study would be required with serial euthanasia. All treated animals survived and were kept until day 28. The previous study (DOI: 10.1016/j.celrep.2021.109086) demonstrated that virus was not detected in animals that survived until day 28. Here, we would have to perform another ABSL3 animal experiment with euthanasia and harvest organs at the expected peak for viral replication to confirm this finding. We do not believe repeating such a study is justified at this point, since the key endpoint for the experiment here is survival, and the study provided clear results. Increasing the number of animals in study in order to euthanize a subset in order to collect organs on a specific day makes more sense in a drug discovery effort where a candidate drug is not expected to protect the animals but might have some impact on the virologic endpoint only (e.g., reduce viremia in blood or organs). Thus, we do not believe repeated studies are justified to obtain this additional confirmatory data point.

Author Response

Reviewer #1 (Public Review):

Collins et al use mesoscopic two-photon imaging to simultaneously record activity from basal forebrain cholinergic or noradrenergic axons in several distant regions of the dorsal cortex during spontaneous behavior in head-fixed awake mice. They find that activity in axons from both neuromodulatory systems is closely correlated with measures of behavioral state, such as whisking, locomotion and face movements. While axons were globally correlated with these behavioral state-related metrics across the dorsal cortex, they also find evidence of behavioral state independent heterogenous signals.

The use of simultaneous multiarea optical recordings across a large extent of dorsal cortex with single axon resolution for studying the coherence of neuromodulatory afferents across cortical areas is novel and addresses important questions regarding neuromodulation in the neocortex. The manuscript is clearly written, the data is well presented and, for the most part, carefully analyzed. Parts of the manuscript confirm previous results on the influence of behavioral state on norepinephrine and acetylcholine cortical afferents. However, the observation that these modulations are globally broadcasted to the dorsal cortex while behavioral state independent heterogenous signals are also present in these axons is novel and important for the field.

While the evidence for a behavioral state driven global modulation of activity in both neuromodulatory systems is quite clear, I have concerns that the apparent heterogeneity in axonal responses might be driven by movement-induced artifacts. Moreover, even in the case that the heterogeneity in calcium activity across axons is confirmed, it might not be driven by differences in spiking activity across neuromodulatory axons as concluded, but by other mechanisms that are not explicitly discussed or considered.

1) Motion artifacts are always a concern when imaging from small structures in behaving animals. This issue is addressed in the manuscript in Fig 2A-C by comparing axonal responses to "autofluorescent blebs that did not have calcium-dependent activity" (line 1011). Still, as calcium-dependent activity and motion artifacts can both be locked to behavioral variables the "bleb" selection criterion seems biased and flawed with a circular logic. "Blebs" presenting motion-induced changes in fluorescence that may pass as neural activity will be wrongly excluded when from the "bleb" control group using this criterion. This will result in an underestimation of the extent of the contamination of the GCaMP signals by movement-induced artifacts. This potential confound might generate apparent heterogeneity across axons and regions as some axons and some cortical areas might be more prone to movements artifacts than others.

Thank you for the suggestion. We agree that motion artifacts are a reasonable concern. We rigorously addressed this concern by introducing non-calcium-dependent mCherry into cholinergic cortical axons and demonstrating that motion cannot explain our results (see Fig. 2F, Fig. 4H,L,P, Fig. 4 - figure supplement 1G, Video 3, and response above). These axons were chosen for analysis based solely on their ability to be imaged, in a manner identical to that of GCaMP6s containing axons.

We agree that the observed evidence of heterogeneity is not as clear as the evidence of a common signal. We now carefully present our evidence. Heterogeneity may arise from variations in activity between single axons that is not explained by a common signal such as behavioral state. Heterogeneity could also be signaled by variations in correlated activity between axons. We now address these two possibilities in our manuscript. Our new analysis reveals that the correlated activity between axons is as expected for axons that are variably correlated to a common signal, such as behavioral state. Although we do find some evidence of correlation outside this common signal, we are not able to discern if this is related to imaging axon segments that are part of the same axon, or if it truly represents an independent signal. This is now stated in the text. On the other hand, strong variations in axonal activity from trial to trial that appear to be separate from the common signal is also prevalent. We now point out this variation as a possible source of heterogeneity. Since we do not know the source or meaning of this heterogeneous activity, we discuss only the possibility that it may hold behaviorally relevant information in these modulatory systems.

2) In the case that the heterogeneity is indeed due to differences in calcium activity, it might be not due to modularity in spiking activity within the LC or the BF as interpreted and discussed in the manuscript. As calcium signaling in axons not only relates to spiking activity but can also reflect presynaptic modulations, the observed heterogeneity might be due to local action of presynaptic modulators in a context of global identical broadcasted activity. The current dataset does not allow distinguishing which of the two different mechanisms underlies the observed signal heterogeneity.

It is true that our data set is unable to determine whether presynaptic modulations contribute to any observed heterogeneity. We have adjusted our interpretation of heterogeneity throughout the manuscript and have specifically addressed this comment in the discussion by presenting the possibility that a global signal could be locally modulated.

Reviewer #3 (Public Review):

Acetylcholine and Norepinephrine are two of the most powerful neuromodulators in the CNS. Recently developments of new methods allow monitoring of the dynamic changes in the activity of these agents in the brain in vivo. Here the authors explore the relationship between the dynamic changes in behavioral states and those of ACh and NE in the cortex. Since neuromodulatory systems cover most of the cortical tissue, it is essential to be able to monitor the activity of these systems in many cortical areas simultaneously. This is a daunting task because the axons releasing NE and ACh are very thin. To my knowledge, this study is the first to use mesoscopic imaging over a wide range of the cortex at the single axon resolution in awake animals. They find that almost any observable change in behavioral state is accompanied by a transient change in the activity of cortical ACh and NE axonal segments. Whisking is significantly correlated with ACh and NE. The authors also explore the spatial pattern of activity of ACh and NE axons over the dorsal cortex and find that most of the dynamics is synchronous over a wide spatial scale. They look for deviation from this pattern (which I will discuss later). Lastly, the authors monitor the activity of cortical interneurons capable of releasing ACh.

Comments:

1) On a broad overview, I find the discussion of behavioral states, brain states, and neuromodulation states quite confusing. To begin with, I am not convinced by the statement that "brain states or behavioral states change on a moment-to-moment basis." I find that the division of brain activity into microstates (e.g., microarousal) is counterproductive. After all, at the extreme, going along this path, we might eventually have an extremely high dimensional space of all neuronal activity, and any change in any neuron would define a new brain state. Similarly, mice can walk without whisking, can whisk without walking, can walk and whisk, are all these different behavioral states? And if so, are they all associated with different brain states? And if so, are they all associated with different brain states? Most importantly, in the context of this manuscript, one would expect that different states (brain, behavior) would be associated with at least four potential states of the ACh x NE system (high ACh and High NE, High ACh and Low NE, etc.). However, the reported findings indicate that the two systems are highly synchronized (or at least correlated), and both transiently go on with any change from a passive state to an active state. Therefore, the manuscript describes a rather confined relationship of the neuromodulation systems with the rather rich potential of brain and behavioral states. Of course, this is only my viewpoint, and the authors are not obliged to accept it, but they should recognize that the viewpoint they take for granted is not shared by all and consider acknowledging it in the manuscript.

We thank this reviewer for this thoughtful comment. While it is clear that animals do in fact exhibit distinct and clear brain and behavioral states (e.g. sleep, waking, grooming, still, walking, etc.), it is beyond the scope of the present manuscript to attempt to tackle this complex field - rather, we refer the reader to a recent review that we have published on this important topic (McCormick, Nestvogel, and He 2020). We agree that properly delineating brain and behavioral states is of great importance, as it could significantly impact experimental design and interpretation of results. Since all of the relevant substates that a mouse may exhibit have not yet been determined, we decided to use changes in whisking and walking behaviors to differentiate between distinct behavioral states owing to: 1) historical use of these measures in behavioral and neural states in head-fixed mice, 2) relative ease of measurement of these variables, 3) a clearly observable relationship with cholinergic and noradrenergic activity with these measures of behavior, and, arguably most importantly, 4) assumed relevance to the animal (Musall et al. 2019; Reimer et al. 2016; Salkoff et al. 2020; Stringer et al. 2019).

Our manuscript seeks to simply relate the activity of cholinergic and noradrenergic axons across the dorsal surface of the cortex in comparison to these commonly used measures of spontaneous behavior in head-fixed mice to discern to what relative degree there are common, global signals in these two modulatory systems and how they relate to changes in the measured behaviors. Somewhat surprisingly, previous studies have found that neural activity throughout the dorsal cortex of mice is strongly related to movements of the face and body as well as behavioral arousal (Stringer et al. 2019; Musall et al. 2019; Salkoff et al. 2020). Here we determine to what degree these commonly used measures of “state” are already reflected in the GCaMP6s activity of cholinergic and noradrenergic axons (and local cortical interneurons).

We agree with the interpretation that our results suggest a confined relationship between spontaneous cholinergic and noradrenergic activity in the cortex within the spontaneous behaviors that we observe. We, by no means, mean to suggest that this confined relationship is the only relationship cholinergic and noradrenergic systems exhibit to each other or to behavior. It seems very likely that in the wide variety of behavior exhibited by freely moving mice in their lifetime, there are times in which the activity of cholinergic and noradrenergic systems exhibit a radically different relationship to each other and to behavior. We simply cannot know this without experimental examination. We now mention this possibility in the discussion and give a few appropriate references.

2) Most of the manuscript (bar one case) reports nearly identical dynamics of ACh and NE. Is that a principle? What makes these systems behave so similarly? Why have two systems that act nearly the same? Still, if there is a difference, it is the time scale of the ACh compared to the NE. Can the authors explain this difference or speculate what drives it?

Perhaps one of the most striking findings in recent years from examination of mouse brain activity is the prominence and prevalence of a general signal in nearly all neural systems that relates to movement and arousal of the animal (Stringer et al. 2019; Salkoff et al. 2020). Here we report that this signal is also strongly present within the cholinergic and noradrenergic systems. Perhaps this is unsurprising, since everywhere one looks, one finds this global signal. However, we feel that understanding the presence and nature of this large signal is critical to deciphering behavior-related signals in these systems in the future. We discuss this point in the discussion. The one difference we did find is in the more transient nature of NE axonal activity versus both behavior and cholinergic axon activity. We now speculate on this difference in the discussion.

3) Whisker activity explains most strongly the neuromodulators dynamics, but pupil dilation almost does not (in contrast to many previous reports including reports of the same authors). If I am not mistaken, this was nearly ignored in the presentation of the results and the discussion section. Could the author elaborate more on what is the reason for this discrepancy?

We apologize for the misleading presentation of our results. In Fig. 3C and D it is clear that pupil diameter is highly coherent with both cholinergic and noradrenergic axon activity, as published previously. In the present study, this coherence peaks at 0.4 to 0.5 for both. In our previous study (Reimer et al. 2016), the cholinergic activity also peaked in coherence at low frequencies at around 0.4 to 0.5 (Reimer et al., Fig. 1H) while the noradrenergic activity coherence peaked at 0.6 to 0.7. The present study was not optimized for pupil diameter examination, since we kept the light levels as low as possible (resulting in low dynamic range of pupil dilations since they were nearly always enlarged to near maximum) in order to increase the S/N of cortical axon activity. We now mention these similarities and differences and caveats in the manuscript. An additional important point is that the kinetics of pupil diameter changes are slow in comparison to whisker movements, reducing the ability of pupil dilation to accurately track changes in axonal activity at frequencies greater than approximately 0.2 Hz (Fig. 2 - figure supplement 2). This is now mentioned in the text.

4) I find the question of homogenous vs. heterogenous signaling of both the ACh and NE systems quite important. It is one thing if the two systems just broadcast "one bit" information to the whole brain or if there are neuromodulation signals that are confined in space and are uncorrelated with the global signal. However, the way the analysis of this question is presented in the manuscript is very difficult to follow, and eventually, the take-home message is unclear. The discussion section indicates that the results support that beyond a global synchronized signal, there is a significant amount of heterogeneous activity. I think this question could benefit from further analysis. I suggest trying to demonstrate more specific examples of axonal ROIs where their activity is decorrelated with the global signal, test how consistent this property is (for those ROIs), and find a behavioral parameter that it predicts.

Also, in the discussion part, I am missing a discussion of the potential mechanism that allows this heterogeneity. On the one hand, an area may receive NE/ACh innervation from different BF/LC neurons, which are not completely synchronized. But those neurons also innervate other areas, so what is the expected eventual pattern? Also, do the results support neuromodulation control by local interneuron circuits targeting the axons (as is the case with dopaminergic axons in the Basal Ganglia)?

Our results clearly demonstrate a robust global signal that is common across cholinergic and noradrenergic axons which is related to behavioral state. We have less strong, but still present, evidence for a heterogeneous signal in addition to this global signal. This evidence is based largely upon the large variation in activities in different axon segments during behavioral events that appear similar. This result suggests that the axon segments we monitored do not all act as if they are members of the same axon. We now discuss the strong evidence for the global signal present in our data, and leave open the possibility of a heterogeneous signal whose mechanisms and importance remains to be determined.

5) The axonal signal seems to be very similar across the cortex. I am not sure this is technically possible, but given that NE axons are thin and non-myelinated and taking advantage of the mesoscopic scale, could the author find any clue for the propagation of the signal on the rostral to caudal axis?

We were unable to detect propagation across the cortical sheet and believe this is beyond the scope of the present study.

6) While the section about local VCIN is consistent with the story, it is somehow a sidetrack and ends the manuscript on the wrong note. I leave it to the authors to decide but recommend them to reconsider if and where to include it. Unfortunately, the figure attached was on a very poor resolution, and I could not look into the details, so I am afraid that I could not review this section properly.

We believe this adds to the manuscript and therefore have decided to include this data.

Author Response

Reviewer #1 (Public Review):

In this study, the authors aim to identify the cell state dynamics and molecular mechanisms underlying melanocyte regeneration in zebrafish. By analyzing thousands of single-cell transcriptomes over regeneration in both wild-type and Kit mutant animals, they provide thorough and convincing evidence of (1) two paths to melanocyte regeneration and (2) that Kit signaling, via the RAS/MAPK pathway, is a key regulator of this process. Finally, the authors suggest that another proliferative subpopulation cells, expressing markers of a separate pigment cell type, constitute an additional population of progenitors with the ability to contribute to melanocytes. The data supporting this claim are not as convincing, and the authors failed to show that these cells did indeed differentiate into melanocytes. Despite the challenges of describing this third cell state, this study offers compelling new findings on the mechanisms of melanocyte regeneration and provides paths forward to understanding why some animals lack this capacity.

The majority of the main conclusions are well supported by the data, but one claim, in particular, should be revisited by the authors.

(1) Provided evidence that the aox5(hi)mitfa(lo) population of cells contributes to melanocyte regeneration is inconclusive and somewhat circumstantial. First, the transcriptional profiles of these cells are much more consistent with the xanthophore lineage. Indeed, xanthophores have been shown to express mitfa (in embryos in Parichy, et al. 2003 (PMID: 10862741), and in post-embryonic cells in Saunders, et al. 2019). Second, while the authors address this possibility in Supplemental figure 7 by showing that interstripe xanthophores fail to divide following melanocyte ablation, they fail to account for the stripe-resident xanthophores/xanthoblasts. The presence and dynamics of aox5+ stripe-resident xanthophores/xanthoblasts are detailed in McMenamin, et al., 2014 (PMID: 25170046) and Eom, et al., 2015 (PMID: 26701906). Without direct evidence that the symmetrically-dividing, aox5+ cells measured in this study do indeed differentiate into melanocytes, it is more likely that these cells are a dividing population of xanthophores/xanthoblasts. The authors should revise their claims accordingly.

We agree with the editor and reviewers that the identities of the mitfa+aox5hi cells and the interplay between these cells and the mitfa+aox5lo cells is a fascinating, and originally unexpected, aspect of this manuscript. The issue, as we see it, is whether mitfa+aox5hi cells that arise via cell division during regeneration are multipotent pigment cell progenitors or ‘cryptic’ xanthophores. The experiments we have performed to address this ambiguity have not worked for technical reasons, so we have tempered text in the relevant Results and Discussion sections to leave both options open. We have backed off from calling these cells progenitors but have included additional data showing that they (i.e. the mitfa+aox5hi subpopulation of cells that we believe are daughters of mitfa+aox5hi cycling cells) express multiple markers associated with multipotent pigment cell progenitors that have been characterized in developing zebrafish. Our expanded Discussion is as follows:

“Heterogeneity may also be evident by the additional mitfa+aox5hi G2/M adj subpopulation that likely arises via cell divisions during regeneration. There are reasons to think that this could be a progenitor subpopulation. Firstly, these cells arose in response to specific ablation of melanocytes. Secondly, this subpopulation expresses markers that are associated with multipotent pigment progenitors cells found during development (Budi, et al., 2011; Saunders, et al., 2019). Thirdly, although this subpopulation expresses aox5 and some other markers associated with xanthophores, we showed that differentiated xanthophores are not ablated by the melanocyte-ablating drug neocuproine and this mitfa+aox5hi subpopulation does not make new pigmented xanthophores following neocuproine treatment. However, current observations cannot definitively determine the potency and fates adopted by these cells. One possibility is that these cells are indeed progenitors that arise through cell divisions, are in an as yet undefined way lineally related to MP-0 and MP-1 subpopulations, and ultimately give rise to new melanocytes during additional rounds of regeneration. Given their expression of markers associated with multipotent pigment cell progenitors, these cells could be multipotent but fated toward the melanocyte lineage following melanocyte-specific ablation. However, we cannot exclude the possibility that these cells are another cell type. For example, there is a type of partially differentiated xanthophores that populate adult melanocyte stripes (McMenamin, et al., 2014). At least some of these cells arise from embryonic xanthophores that transitioned through a cryptic and proliferative state (McMenamin, et al., 2014). That the descendants remain partially differentiated could indicate that they are in more of a xanthoblast state and maintain proliferative capacity (Eom, et al., 2015). It is possible that some or all of the cells in question are melanocyte stripe-resident, partially-differentiated xanthophores that arise: a) from cell divisions that are triggered by loss of interactions with melanocytes or, b) simply to fill space that is vacated due to melanocyte death. Such causes for partially-differentiated xanthophore divisions have not been documented, but nonetheless this possibility must be considered given the mitfa and aox5 expression and proliferative potential of these cells. Transcriptional profiles of ‘cryptic’ xanthophores are not available to help clarify the nature of these cells. Lastly, the relationship between adult progenitor populations – MP-0, MP-1 and, potentially, mitfa+aox5hi G2/M adj – and other progenitors present at earlier developmental stages is unclear and could be defined through additional long-term lineage tracing studies. In particular, previous examinations of pigment cell progenitors in developing zebrafish have identified dorsal root ganglion-associated pigment cell progenitors in larvae that contribute to adult pigmentation patterns (Singh, et al., 2016; Dooley, et al., 2013; Budi, et al., 2011). It is possible that these cells give rise to the adult progenitors we have identified. The further alignment of cell types that have been observed in vivo and cell subpopulations defined through expression profiling is a necessary route for understanding the complex relationship between stem and progenitor cells in development, homeostasis, and regeneration.”

(1) At line 140, it is noted that Xanthophores are pteridine-producing, but they also get their yellow color from carotenoids (especially in adults). This should be noted as well, especially since the authors display the xanthophore marker, scarb1, which plays a key role in xanthophore carotenoid coloration.

[Mapping expression levels onto UMAP space for scarb1 and perhaps other markers of xan, irid, or proliferation would be helpful as a supplement to the dot plot in Fig 1 and could help to clarify the transcriptomic signature of mitfa+ aox5-hi cells and plausibility of the model that they are an McSC population. -Parichy]

We thank the reviewer for the suggestion, and we have changed the text to include the carotenoid coloration facts of xanthophores as follows:

“aox5 is expressed in differentiated xanthophores, a pteridine- and carotenoid-producing pigment cell type of zebrafish, and in some undifferentiated pigment progenitor cells”

Additionally, we have also added a new Figure Supplement to Figure 1 (Figure 1 – figure supplement 3) with feature plots demonstrating the expression of xanthophore markers scarb1 and bco2b, iridophore markers lypc and cdh11, and proliferation markers pcna and mki67. As noted above, there is some heterogeneity within the large grouping of mitfa+aox5hi cells. Whereas some markers associated with xanthophores are broadly expressed in this grouping (e.g. scarb1), others have more restricted expression (e.g. bco2b). The heterogeneity could reflect multiple differentiation states of xanthophores, multiple types of differentiated xanthophores, xanthophore progenitors and/or less fate-restricted pigment cell progenitors that cluster in this grouping.

(2) The authors should provide the list of genes that comprise their cluster signatures (line 252) as part of the supplementary tables.

We have now included a table of genes in the cluster signatures. The Supplementary Table is called “Supplementary File 2.”

(3) The authors should more clearly describe how they performed lineage tracing (line 339). Additionally, for the corresponding figure 4E, the authors should list the number of cells traced. The source data only contains calculated percentages rather than counts for each type of differentiation. My understanding is that the number listed in the figure legend is the number of fish (i.e. n = 4), but this should be clarified as well.

[A supplementary figure of labeled cells is important here with enough context to show that cells can be re-identified unambiguously. Additionally note that "lineage tracing" will typically be assumed to mean single-cell labeling and tracking, so if that is not the case for these experiments it would be preferable to use an alternative descriptor. -Parichy]

We have included additional detail in our revised manuscript. In Figure 4E we now include the number of cells imaged and have included a breakdown of the raw numbers in the Source Data. We have also included Supplementary Animations as examples of the single-cell tracing that we perform through serial imaging.

Additionally, the point about using ‘lineage tracing’ is well taken. We have replaced this with ‘serial imaging’ through the text.

(4) Line 321, the authors list the mean regeneration percentages for the kita and kitlga(lf) mutants, but these differences are not significantly different according to Figure 4B. By listing the means (which should be noted), the authors seem to be highlighting the differences but then do not comment on them. The description and integration of this result into the main text should be clarified.

We have changed the wording in the text to clarify that the mean percentage is being listed. We have also reworded the text to de-emphasize the mean percentage difference between kita(lf) and kitlga(lf) mutants, instead highlighting that their defects are similar. In the figure legend we have clarified that the mean percentage regeneration is being shown.

(5) In Figure 6E, the RNA-velocity result is not particularly consistent with the authors' claims. Visually, the arrows seem fairly randomly directed. The data in 6B, showing gene expression associated with the S phase and G2/M phase much more clearly convey the directionality of the loop (S phase, followed by G2/M). I suggest that the authors weaken their claim about the RNA-velocity result or remove it altogether and focus on the cell cycle-related gene expression signatures.

We thank the reviewer for their careful eye here. We have decided to remove the RNA-velocity result previously displayed in Figure 6E. As the reviewer points out the results are more clearly demonstrated by Figure 6B.

Author Response

Reviewer #1 (Public Review):

This study addresses the role of the general transcription factor TBP (TATA-binding protein), a subunit of the TFIID complex, in RNA polymerase II transcription. While TBP has been described as a key component of protein complexes involved in transcription by all three RNA polymerases, several previous studies on TBP loss of function and on the function of its TRF2 and TRF3 paralogues have questioned its essential role in RNA polymerase II transcription. This new study uses auxin induced TBP degradation in mouse ES cells to provide strong evidence that its loss does not affect ongoing polymerase II transcription or heat-shock and retinoic acid-induced transcription activation, but severely inhibits polymerase III transcription. The authors coupled TBP degradation with TRF2 knock out to show that it does not account for the residual TBP-independent transcription. Rather the study provides evidence that TFIID can assemble and is recruited to promoters in the absence of TBP.

All together the study provides compelling evidence for TBP-independent polymerase II transcription, but a better characterization of the residual TFIID complex and recruitment of other general transcription factors to promoters would strengthen the conclusions.

We thank the reviewer for their accurate summary of our findings and the public assessment of our manuscript.

Reviewer #2 (Public Review):

The paper is intriguing, but to me, a main weakness is that the imaging experiments are done with overexpressed protein. Another is that the different results for the different subunits of TFIID would indicate that there are multiple forms of TFIID in the nucleus, which no one has observed/proposed before. Otherwise, the experimental data would have to be interpreted in a more nuance way. Additionally, there is no real model of how a TBP-depleted TFIID would recruit Pol II. Do the authors suggest that when TBP is present, it is not playing a role in Pol II transcription, despite being at all promoters? Or that in its absence an alternative mechanism takes over? In the latter case, are they proposing that it is just based on the rest of TFIID? How? The authors do not provide a mechanistic explanation of what is actually happening and how Pol II is being recruited to promoters.

We thank the reviewer for their public review of our manuscript. Although the reviewer poses many interesting questions raised from our findings, they would be a great focus for future directions.

We agree that our imaging experiments using over-expressed constructs have limitations. Though they provide insight that is unique and orthogonal to the genomics analyses, we agree that they are still preliminary, and therefore we have removed them from the manuscript, with the hope of further developing these experiments into a follow-up manuscript.

While we cannot exclude different forms of TFIID in the cell, previous studies have identified different TAF-containing complexes. Indeed, we referenced several of these studies in our manuscript, including TFTC/SAGA. Furthermore, in our Discussion section, we speculated how a large multi-subunit complex like TFIID may not behave as a monolith but rather have distinct dynamics/behavior among the subunits. Some studies are now revealing that biochemically defined complexes behave more as a hub, with subunits having distinct dynamics coming in and out of the complex, but in a way such that a snapshot at any given time would show a stably formed complex.

What TBP does for Pol II is an intriguing question, and one that we had thought we could answer with our rapid depletion system. One possibility is that Pol II initiation has evolved to have so many redundant mechanisms such that removal of one factor (TBP) would not disrupt the whole system. And yet, TBP remains a highly essential gene (perhaps mostly for its essential role in Pol III transcription), and therefore, its binding to Pol II gene promoters has been maintained, almost in a vestigial way. Of course, this is speculative, and our rapid depletion system only shows us that TBP is not required for Pol II transcription, not what it does when it binds to promoters.

Lastly, we believe that our study tested 3 potential mechanisms that could explain TBP-independence for Pol II transcription. 1) We tested the possibility that TBP is only needed for induction and not for subsequent re-initiation. We provide evidence using two orthogonal induction systems that this is not the case. 2) We tested whether the TRF2 paralog could functionally replace TBP, and show that this is also not the case. 3) We show that TFIID can form in the absence of TBP. While we agree that there are more mechanisms to test, addressing all of them would require a re-examination of over 50 years of research that would not be feasible to report in a single manuscript, especially for a system as complex as Pol II initiation.

Reviewer #3 (Public Review):

In this study, the authors set out to study the requirement of the TATA binding protein (TBP) in transcription initiation in mESCs. To this end they used an auxin inducible degradation (AID) system. They report that by using the AID-TBP system after auxin degradation, 10-20% of TBP protein is remaining in mESCs. The authors claim that as, the observed 80-90% decrease of TBP levels are not accompanied by global changes in RNA polymerase II (Pol II) chromatin occupancy or nascent mRNA levels, TBP is not required for Pol II transcription. In contrast, they find that under similar TBP-depletion conditions tRNA transcription and Pol III chromatin occupancy were impaired. The authors also asked whether the mouse TBP paralogue, TBPL1 (also called TRF2) could functionally replace TBP, but they find that it does not. From these and additional experiments the authors conclude that redundant mechanisms may exist in which TBP-independent TFIID like complexes may function in Pol II transcription.

The major strengths of this manuscript are the numerous genome-wide investigations, such as many different CUT&Tag experiments, and NET-seq experiments under control and +auxin conditions and their analyses. Weaknesses lie in some experimental setups (i.e. overexpression of Halo-tagged TAFs), mainly in the overinterpretation (or misinterpretation) of the data and in the lack of a fair discussion of the obtained data in comparison to observations described in the literature. As a result, very often the interpretation of data does not fully support the conclusions. Nevertheless, the findings that 80-90% decrease in cellular TBP levels do not have a major effect on Pol II transcription are interesting, but the manuscript needs some tuning down of many of the authors' very strong conclusions, correcting several weaker points and with a more careful and eventually more interesting Discussion.

We thank the reviewer for their public review of our manuscript. We would like to add that, in addition to testing the TBP paralog for redundancy, we also tested a mechanism in which TBP would be required for the initial round of transcription but not for subsequent ones. We show that data from orthogonal experiments that this mechanism is not the case. As in our response to Reviewer 2, we agree that our over-expression imaging experiments are still somewhat preliminary, and therefore we have removed these experiments and potential over/misinterpretation of these results from the manuscript.

Author Response

Reviewer #3 (Public Review):

This manuscript by Pendse et al aimed to identify the role of the complement component C1q in intestinal homeostasis, expecting to find a role in mucosal immunity. Instead, however, they discovered an unexpected role for C1qa in regulating gut motility. First, using RNA-Seq and qPCR of cell populations isolated either by mechanical separation or flow cytometry, the authors found that the genes encoding the subunits of C1q are expressed predominantly in a sub-epithelial population of cells in the gut that Cd11b+MHCII+F4/80high, presumably macrophages. They support this conclusion by analyzing mice in which intestinal macrophages are depleted with anti-CSF1R antibody treatment and show substantial loss of C1qa, b and c transcripts. Then, they generate Lyz2Cre-C1qaflx/flx mice to genetically deplete C1qa in macrophages and assess the consequences on the fecal microbiome, transcript levels of cytokines, macromolecular permeability of the epithelial barrier, and immune cell populations, finding no major effects. Furthermore, provoking intestinal injury with chemical colitis or infection (Citrobacter) did not reveal macrophage C1qa-dependent changes in body weight or pathogen burden.

Then, they analyzed C1q expression by IHC of cross-sections of small and large intestine and find that C1q immunoreactivity is detectable adjacent to, but not colocalizing with, TUBB3+ nerve fibers and CD169+ cells in the submucosa. Interestingly, they find little C1q immunoreactivity in the muscularis externa. Nevertheless, they perform RNA-sequencing of LMMP preparations (longitudinal muscle with adherent myenteric plexus) and find a number of changes in gene ontology pathways associates with neuronal function. Finally, they perform GI motility testing on the conditional knockout mice and find that they have accelerated GI transit times manifesting with subtle changes in small intestinal transit and more profound changes in measures of colonic motility.

Overall, the manuscript is very well-written and the observation that macrophages are the major source of C1q in the intestine is well supported by the data, derived from multiple approaches. The observations on C1q localization in tissue and the strength of the conclusions that can be drawn from their conditional genetic model of C1qa depletion, however, would benefit from more rigorous validation.

1) Interpretation of the majority of the findings in the paper rest on the specificity of the Lyz2 Cre for macrophages. While the specificity of this Cre to macrophages and some dendritic cells has been characterized in the literature in circulating immune cells, it is not clear if this has been characterized at the tissue level in the gut. Evidence demonstrating the selectivity of Cre activity in the gut would strengthen the conclusions that can be drawn.

As indicated by the reviewer, Cre expression driven by the Lyz2 promoter is restricted to macrophages and some myeloid cells in the circulation (Clausen et al., 1999). To better understand intestinal Lyz2 expression at a cellular level, we analyzed Lyz2 transcripts from a published single cell RNAseq analysis of intestinal cells (Xu et al., 2019; see Figure below). These data show that intestinal Lyz2 is also predominantly expressed in gut macrophages with limited expression in dendritic cells and neutrophils.

Figure. Lyz2 expression from single cell RNAseq analysis of mouse intestinal cells. Data are from Xu et al., Immunity 51, 696-708 (2019). Analysis was done through the Single Cell Portal, a repository of scRNAseq data at the Broad Institute.

Additionally, our study shows that intestinal C1q expression is restricted to macrophages (CD11b+MHCII+F4/80hi) and is absent from other gut myeloid cell lineages (Figure 1E-H). This conclusion is supported by our finding that macrophage depletion via anti-CSF1R treatment also depletes most intestinal C1q (Figure 2A-C). Importantly, we found that the C1qaDMf mice retain C1q expression in the central nervous system (Figure 2 – figure supplement 1). Thus, the C1qaDMf mice allow us to assess the function of macrophage C1q in the gut and uncouple the functions of macrophage C1q from those of C1q in the central nervous system.

2) Infectious and inflammatory colitis models were used to suggest that C1qa depletion in Lyz2+ lineage cells does not alter gut mucosal inflammation or immune response. However, the phenotyping of the mice in these models was somewhat cursory. For example, in DSS only body weight was shown without other typical and informative read-outs including colon length, histological changes, and disease activity scoring. Similarly, in Citrobacter only fecal cfu were measured. Especially if GI motility is accelerated in the KO mice, pathogen burden may not reflect efficiency of immune-mediated clearance alone.

We have added additional results which support our conclusion that C1qaDMf mice do not show a heightened sensitivity to acute chemically induced colitis. In Figure 3 – figure supplement 1 we now show a histological analysis of the small intestines of DSS-treated C1qafl/fl and C1qaΔMφ mice. This analysis shows that C1qaDMf mice have similar histopathology, colon lengths, and histopathology scores following DSS treatment. Likewise, our revised manuscript includes histological images of the colons of Citrobacter rodentium-infected C1qafl/fl and C1qaΔMφ mice showing similar pathology (Figure 3 – figure supplement 2).

3) The evidence for C1q expression being restricted to nerve-associated macrophages in the submucosal plexus was insufficient. Localization was shown at low magnification on merged single-planar images taken from cross-sections. The data shown in Figure 4C is not of sufficient resolution to support the claims made - C1q immunoreactivity, for example, is very difficult to even see. Furthermore, nerve fibers closely approximate virtually type of macrophage in the gut, from those in the lamina propria to those in the muscularis….Finally, the resolution is too low to rule out C1q immunoreactivity in the muscularis externa.

Similar points were raised by Reviewer 2. Our original manuscript claimed that C1q-expressing macrophages were mostly located near enteric neurons in the submucosal plexus but were largely absent from the myenteric plexus. However, as both Reviewers have pointed out, this conclusion was based solely on our immunofluorescence analysis of tissue cross-sections.

To address this concern we further characterized C1q+ macrophage localization by performing a flow cytometry analysis on macrophages isolated from the mucosa (encompassing both the lamina propria and submucosa) and the muscularis, finding similar levels of C1q expression in macrophages from both tissues (Figure 4 – figure supplement 1 in the revised manuscript). Although the mucosal macrophage fraction encompasses both lamina propria and submucosal macrophages, our immunofluorescence analysis (Figure 4 B and C) suggests that the mucosal C1q-expressing macrophages are mostly from the submucosal plexus. This observation is consistent with the immunofluorescence studies of CD169+ macrophages shown in Asano et al., which suggest that most C169+ macrophages are located in or near the submucosal region, with fewer near the villus tips (Fig. 1e, Nat. Commun. 6, 7802).

Most importantly, our flow cytometry analysis indicates that the muscularis/myenteric plexus harbors C1q-expressing macrophages. To further characterize C1q expression in the muscularis, we performed RNAscope analysis by confocal microscopy of the myenteric plexus from mouse small intestine and colon (Figure 4D). The results show numerous C1q-expressing macrophages positioned close to myenteric plexus neurons, thus supporting the flow cytometry analysis. We note that although the majority of C1q immunofluorescence in our tissue cross-sections was observed in the submucosal plexus, we did observe some C1q expression in the muscularis by immunofluorescence (Figure 4B and C). We have rewritten the Results section to take these new findings into account.

Is the 5um average on the proximity analysis any different for other macrophage populations to support the idea of a special relationship between C1q-expressing macrophages and neurons?

We agree that the proximity analysis lacks context and have therefore removed it from the figure. The other data in the figure better support the idea that C1q+ macrophages are found predominantly in the submucosal and myenteric plexuses and that they are closely associated with neurons at these tissue sites.

There are many vessels in the submucosa and many associated perivascular nerve fibers - could the proximity simply reflect that both cell types are near vessels containing C1q in circulation?

Our revised manuscript includes RNAscope analysis showing C1q transcript expression by macrophages that are closely associated with enteric neurons (Figure 4D). These findings support the idea that the C1q close to enteric neurons is derived from macrophages rather than from the circulation.

4) A major disconnect was between the observation that C1q expression is in the submucosa and the performance of RNA-seq studies on LMMP preparations. This makes it challenging to draw conclusions from the RNA-Seq data, and makes it particularly important to clarify the specificity of Lyz2-Cre activity.

Our revised manuscript provides flow cytometry data (Figure 4 – figure supplement 1) and RNAscope analysis (Figure 4D) showing that C1q is expressed in macrophages localized to the myenteric plexus. This accords with the results of our RNAseq analysis, which indicates altered LMMP neuronal function in C1qa∆Mφ mice (Figure 6A and B). Since neurons in the myenteric plexus are known to govern gut motility, it also helps to explain our finding that gut motility is accelerated in C1qa∆Mφ mice.

Finally, the pathways identified could reflect a loss of neurons or nerve fibers. No assessment of ENS health in terms of neuronal number or nerve fiber density is provided in either plexus.

Reviewers 1 and 2 also raised this point. Our revised manuscript includes a comparison of the numbers of enteric neurons in C1qafl/fl and C1qaΔMφ mice. There were no marked differences in neuron numbers in C1qaDMf mice when compared to C1qafl/fl controls (Figure 5A and B). There were also similar numbers of inhibitory (nitrergic) and excitatory (cholinergic) neuronal subsets and a similar enteric glial network (Figure 5C-E). Thus, our data suggest that the altered gut motility in the C1qaΔMφ mice arises from altered neuronal function rather than from an overt loss of neurons or nerve fibers. This conclusion is further supported by increased neurogenic activity of peristalsis (Figure 6H and I), and the expression of the C1q receptor BAI1 on enteric neurons (Figure 6 – figure supplement 4).

5) To my knowledge, there is limited evidence that the submucosal plexus has an effect on GI motility. A recent publication suggests that even when mice lack 90% of their submucosal neurons, they are well-appearing without overt deficits (PMID: 29666241). Submucosal neurons, however, are well known to be involved in the secretomotor reflex and fluid flux across the epithelium. Assessment of these ENS functions in the knockout mice would be important and valuable.

Our revised manuscript provides new data showing C1q expression by muscularis macrophages in the myenteric plexus. We analyzed muscularis macrophages by flow cytometry and found that they express C1q (Figure 4 – figure supplement 1). These findings are further supported by RNAscope analysis of C1q expression in wholemounts of LMMP from small intestine and colon (Figure 4D and E). These results are thus consistent with the increased CMMC activity and accelerated gut motility in the C1qaDMf mice. As suggested by the reviewer, our finding of C1q+ macrophages in the submucosal plexus indicates that C1q may also have a role controlling the function of submucosal plexus neurons. We are further exploring this idea through extensive additional experimentation. Given the expanded scope of these studies, we are planning to include them in a follow-up manuscript.

6) Immune function and GI motility can be highly sex-dependent - in all experiments mice of both sexes were reportedly used but it is not clear if sex effects were assessed.

This is a great point, and as suggested by the reviewer we indeed did encounter differences between male and female mice in our preliminary assays of gut motility. We therefore conducted our quantitative comparisons of gut motility between C1qafl/fl and C1qaDMf mice in male mice and now clearly indicate this point in the Materials and Methods.

Author Response

Reviewer #3 (Public Review):

Dominant pathogenic variants of the Aac2/Ant1 ATP transporter cause disease by an unknown mechanism. In this manuscript the authors aim to reveal how these gain of function mutants impair cellular and mitochondrial health. To characterize the phenotype of Aac2 mutants in yeast, the authors use a series of single and double Aac2 mutations, within the 2nd and 3rd transmembrane domains that are associated with human diseases. Aac2A128P,A137D mutant, which caused high toxicity and damaged the mitochondrial DNA was selected for further analysis. This mutant was not imported efficiently into mitochondria and exhibited an increased association with TOM, suggesting that it clogs the TOM translocase. As a result, expression of Aac2A128P,A137D led to impaired import of other mitochondrial proteins. Several findings suggested that the single mutant Aac2A128P impaired mitochondrial import in a similar manner: 1. Mass spec analysis revealed its increased association with cytosolic chaperones, TOM and TIM22 subunits, 2. Aac2A128P overexpression led to global mitochondrial protein import deficiency, demonstrated by HSP60 precursor accumulation and activation of stress responses (transcription of chaperons, proteosome induction, and CIS1). Parallel mutants of human Ant1 (AntA114P and Ant1A114P,A123D) were ectopically expressed in HeLa cells. The mutants were demonstrated to clog TOM and cause a global defect in mitochondrial protein import. This was confirmed in tissues from Ant1A114P,A123D/+ knock-in mice. The Ant1A114P,A123D/+ mice exhibited decreased maximal mitochondrial respiration in muscles. Examination of the skeletal muscle myofiber diameter and COX and SDH activity revealed that Ant1A114P,A123D expression in heterozygous mice acts dominantly and causes a myopathic phenotype and in some case neurodegeneration.

Major strengths -

The ability of proteins to clog TOM and sequentially disrupt protein import into mitochondria was demonstrated in recent years. However, till now this was achieved using chemicals, artificial cloggers and overexpression of mitochondrial proteins. This study reveals, for the first time, that disease associated variants of native mitochondrial proteins can clog the entry into the organelle. Thus, this work demonstrates that TOM clogging is a physiological relevant phenomenon that is involved in human diseases.

The manuscript is well-written and the experiments are well-designed, presenting convincing data that mostly support the conclusions. The methods used are well-establish and suitable techniques that are often used in the field. This work took advantage of 3 different biological systems/model organism, yeast, cell culture, and mice tissues, to validate the results, show conservation, and exploit the strengths of each system.

Overall, this study is impactful, greatly contributes to the field and should be of interest to the general scientific community. The work sheds light of the mechanisms by which Ant1 pathogenic mutants impact cellular health and provides evidence for the involvement of translocases clogging and impaired protein import in human diseases. The gain of function Aac2/Ant1 mutants will provide a new and powerful tool for future studies of mitochondrial quality control and repair mechanisms.

Major weaknesses -

1) The evidence for clogging of mitochondrial translocases and for general defect in protein import are solid. However, there are not enough evidence to conclude that all phenotype seen in mice and yeast are directly connected to clogging.

We completely agree with the reviewer that it is unreasonable to ascribe all phenotypes seen in mice and yeast directly to clogging. We are very open to the possibility that other unknown mechanisms contribute as well. The language in the manuscript has been modified to reflect this.

2) This work implies that Aac2/Ant1 variants can clog TOM, TIM22, or both. Clogging of TIM22 is novel and interesting but is not fully discussed in the manuscript, as well as the possibility that clogging of different translocases can result in different defects.

We thank the reviewer for this comment, and have directly addressed this in the revised manuscript. We added some speculation but overall, we prefer to keep this brief because the precise mechanism of carrier protein import and IMM insertion by the TIM22 complex remains unresolved, making an extensive discussion on its clogging premature.

Author Responses

Reviewer #1 (Public Review):

This work aimed at investigating how a BMI decoding performance is impacted by changing the conditions under which a motor task is performed. They recorded motor cortical activity using multielectrode arrays in two monkeys executing a finger flexion and extension task in four conditions: normal (no load, neutral wrist position), loaded (manipulandum attached to springs or rubber bands to resist flexion), wrist (no load, flexed wrist position) or both (loaded and flexed wrist). They found, as expected, that BMI decoders trained and tested on data sets collected during the same conditions performed better at predicting kinematics and muscle activity than others trained and tested across conditions. They also report that the performance of monkeys a BMI task involving the online control of a virtual hand was almost unaffected by changing either the actual manipulandum conditions as above or switching between decoders trained from data collected under different conditions. As for the neuronal activity, they found a mix of changes across task contexts. Interestingly, a principal component analysis revealed that activity in each context falls within well-aligned manifolds, and that the context-dependent variance in neuronal activity strongly correlated to the amplitude of muscle activity.

Strengths

The current study expands on previous findings about BMI decoders generalizability and contributes scientifically in at least three important ways.

First, their results are obtained from monkeys performing a fine finger control task with up to two degrees of freedom. This provides a powerful setting to investigate fine motor control of the hand in primates. The authors use the accuracy of BMI decoders between data sets as a measure of stationarity in the neuronsto-fingers mapping, which provides a reliable assessment. They show that changes in wrist angle or finger load affect the relationship between cortical neurons and otherwise identical movements. Interestingly, this result holds up for both kinematics and muscle activity predictions, albeit being stronger for the latter.

Second, their results confirming that neuronal activity recorded during different task conditions lies effectively within a common manifold is interesting. It supports prior observations, but in the specific context of finger movements.

Third, the dPCA results provide interesting and perhaps unexpected information about the fact that amplitude of muscle activity (or force) is clearly present in the motor cortical activity. This is possibly one of the most interesting findings because extracting a component from neural activity that can related robustly to muscle activity across context would provide great benefits to the development of BMIs for functional electrical stimulation.

Overall, the analyses are well designed and the interpretation of the results is sound.

Weaknesses

I found the discussion about the possible reasons why offline decoders are more sensitive to context than online decoders very interesting. Nonetheless, as the authors recognize, the possibility that the BMI itself causes a change in context, "in the plant", limits their interpretation. It could mean for the monkeys to switch from one suboptimal decoder to another, causing a ceiling effect occluding generalization errors.

Overall, several new and original results were obtained through these experiments and analyses. Nonetheless, I found it difficult to extract a clear unique and strong take-home message. The study comes short of proposing a new way to improve BMIs generalizability or precisely identifying factors that influence decoders generalizability.

We thank the reviewer for the positive comments. Relating these results to BMI design and interpreting the adaptation to contexts during online trials comprised a bulk of the essential revisions from the eLife editorial staff. More details can be found in common response #2 and essential revisions #1-3. To summarize, we added an analysis of neural activity during online trials to provide insight into how the monkeys were adapting. We have expanded the discussion of online adaptation, as detailed in essential revision #2. We also expanded discussion of how both the online and offline results might affect BMI design, as detailed in essential revision #3.

Reviewer #2 (Public Review):

The authors motivate this study by the medical need to develop brain-machine interfaces (BMIs) to restore lost arm and hand function, for example through functional electrical stimulation. More specifically, they are interested in developing BMI decoding algorithms that work across a variety of "contexts" that a BMI user would encounter out in the real world, for example having their hand in different postures and manipulating a variety of objects. They note that in different contexts, the motor cortex neural activity patterns that produce the desired muscle outputs may change (including neurons' specific relationship to different muscles' activations), which could render a static decoder trained in a different context inaccurate.

To test whether this potential challenge is indeed the case, this study tested BMI control of virtual (onscreen) fingers by two rhesus macaques trained to perform 1 or 2 degree-of-freedom non-grasping tasks either by moving their fingers, or just controlling the virtual finger kinematics with neural activity. The key experimental manipulations were context shifts in the form of springs on the fingers or flexion of the wrist (or both). BMI performance was then evaluated when these context changes were present, which builds on this group's previous demonstration of accurate finger BMI without any context shifts.

The study convincingly shows the aforementioned context shifts do cause large changes in measured firing rates. When neural decoding accuracy (for both muscle and position/velocity) is evaluated across these context changes, reconstruction accuracy is substantially impaired. The headline finding, however, is that that despite this, BMI performance is, on aggregate, not substantially reduced. Although: it is noteworthy that in a second experiment paradigm where the decoder was trained on the spring or wrist-manipulated context and tested in a normal context, there were quite large performance reductions in several datasets as quantified by multiple performance measures; this asymmetry in the results is not really explored much further. The changes in neural activity due to context shifts appear to be relatively modest in magnitude and can be fit well as simple linear shifts (in the neural state space), and the authors posit that this would make it feasible (in future work) to find context-invariant neural readouts that would result in more robust muscle activity decoders.

An additional novel contribution of this study is showing that these motor cortical signals support quite accurately decode muscle activations during non-prehensile finger movements (and also that the EMG decoding was more negatively affected by context shifts than kinematics decoding); previous work decoded finger kinematics but not these kinetics. Note that this was demonstrated with just one of the two monkeys (the second did not have muscle recordings).

This is a rigorous study, its main results are well-supported, and it does not make major claims beyond what the data support.

One of its limitations is that while the eventual motivating goal is to show that decoders are robust across a variety of tasks of daily living, only two specific types of context shifts are tested here, and they are relatively simple and potentially do not result in as strong a neural change as could be encountered in realworld context shifts. This is by no means a major flaw (simplifying experimental preparations are a standard and prudent way to make progress). But the study could point this out a bit more prominently that their results do not preclude that more challenging context shifts will be encountered by BMI users, and this study in its current form does not indicate how strong a perturbation the tested context shifts are relative to the full possible range of hand movement context shifts that would be encountered during human daily living activities.

A second limitation is that while the discrepancy between large offline decoding performance reduction and small online performance reduction are attributed to rapid sensorimotor adaptation, this process is not directly examined in any detail.

Third, the assessment of how neural dynamics change in a way that preserves the overall shape of the dynamics is rather qualitative rather than quantitative, and that this implementation of a more contextagnostic finger BMI is left for future work.

We thank the reviewer for the positive comments. We agree that the paper could discuss how this work impacts a wider range of movements and we now include more discussion to that point as detailed in the responses to feedback below. We also acknowledge that the paper did not directly examine online adaptation and we have now included an analysis aimed at answering how the monkeys adapted to the context changes during online tasks.

Reviewer #3 (Public Review):

In this manuscript the authors ask whether finger movements in non-human primates can be predicted from neural activity recorded from the primary motor cortex. This question is driven by an ultimate goal of using neural decoding to create brain-computer interfaces that can restore upper limb function using prosthetics or functional electrical stimulation systems. More specifically, since functional use of the hand (real or prosthetic) will ultimately require generating very different grasp forces for different objects, these experiments use a constant set of finger kinematics, but introduce different force requirements for the finger muscles using several different techniques. Under these different conditions (contexts), the study examines how population neural activity changed and uses decoder analyses to look at how these different contexts affect offline predictions of muscle forces and finger kinematics, as well as the animals' ability to use different decoders to control 1 or 2-DOF online. In general, the study found that when linear models were trained on one context from offline data, they did not generalize well to the other context. However, when performance was tested online (monkeys controlling a virtual hand in real time using neural activity related to movement of their own hands) with a ReFIT Kalman filter, the animals were able to complete the task effectively, even with a decoder trained without the springs or wrist perturbation. The authors show data to support the idea that neural activity was constrained to the same manifold in the different contexts, which enabled the animals to rapidly change their behavior to achieve the task goals, compared to the more complex requirement of having to learn entirely new patterns of neural activity. This work takes studies that have been conducted for upper-limb movements and extends them to include hand grasp, which is important for creating decoders for brain-computer interfaces. Finally, the authors show using dPCA can extract features during changes in context that may be related to the activity of specific muscles that would allow for improved decoders.

Strengths

The issue of hand control, and how it compares to arm control, is an important question to tackle in sensorimotor control and in the development of brain-computer interfaces. Interestingly, the experiments use two very different ways of changing the muscle force requirements for achieving the same finger movements; springs attached to a manipulandum and changes in wrist posture. Using both paradigms the decoder analysis clearly shows that linear models trained without any manipulation do not predict muscle forces or finger kinematics well, clearly illustrating the limitations of common linear decoders to generalize to scenarios that might encompass real grasping activities that require forceful interactions. Using a welldescribed real-time decoder (ReFIT Kalman Filter), the authors show that this performance decrease observed offline is easily overcome in online testing. The metrics used to make these claims are welldescribed, and the likely explanations for these findings are described well. A particular strength of this manuscript is that, at least for these relatively simple movements and contexts, a component of neural activity (identified using dPCA) is identified that is significantly modulated by the task context in a way that sensibly represents the changes in muscle activity that would be required to complete the task in the new contexts. We thank the reviewer for the positive comments.

Weaknesses

The differences between exemplar data sets and comprehensively tested contexts was difficult to follow. There are many references to how many datasets or trials were used for a particular experiment, but overall, this is fragmented across the manuscript. As a result, it is difficult to assess how generalizable the results of the manuscript were across time or animal, or whether day-to-day variations, or the different data collection schedules had an effect.

Thank you for the comment, we have added in the number of sessions in results in multiple places throughout the paper. For example, starting line 274 in the results:

"During these 10 sessions the context changes were tested 15 times: four times for the wrist context, seven times for the spring context, and four times for the combined wrist and spring context."

The introduction allocates a lot of space to discussing the concepts of generating (computing) movements as opposed to representing movements and relates this to ideas of neural dynamics. The distinction between these as described in the introduction is not very clear, nor is it clear what specific hypothesis this leads to for these experiments. Further, this line of thinking is not returned to in the discussion, so the contribution of these experiments to ideas raised in the introduction are unclear.

Thank you for the comment, we have written a new paragraph relating these results to the concept of generating movement. Starting line 452 of the discussion:

"During the offline tasks, many channels changed neural activity with context, with 20.9% to 61.7% of tuned SBP channels modulating activity with context (Table I). The magnitude of these shifts were relatively small, especially when compared to the large changes in required muscle activation (Figure 2D-E), with weak trends to require greater activation for resisted flexion and lesser for assisted extension (Figure 7B-C). Additionally, the neural manifolds underlying movements in each context were well-aligned (Figure 7D). Using dPCA we found that while a large proportion of neural variance was explained by dPCA components that did not change with context, a significant proportion of the neural variance is associated with components that are context-dependent (Figure 8B). Visually, the context components are shifting the trajectories without changing the overall shape and the shift in neural activity is strongly correlated with muscle activations in new contexts (Figure 8C). This agrees with other studies which found lower variance activity may be related to the actual motor commands (Gallego et al., 2018; Russo et al., 2018; Saxena et al., 2022)."

The complexity of the control that was possible in this task (1 or 2 DOF finger flexion/extension) was low. Further, the manipulations that were used to control context were simple and static. Both these factors likely contribute to the finding that there was little change in the principal angles of the high-variance principal components. While this is not a criticism of the specific results presented here, the simplicity of the task and contexts, contrasted with the complexity of hand control more generally, especially for even moderately dexterous movements, makes it unclear how well the finding of stable manifolds will scale. On a related point, it is unclear whether the feature, identified using dPCA, that could account for changes in muscle activity, could be robustly captured in more realistic behaviors. It is stated that future work is needed, but at this point, the value of identifying this feature is highly speculative.

Thank you for the comment, we have included more discussion to relate these results to decoder development in general as described in essential revision #3 from the editor.

The maintained control in online BMI trials could also be explained by another factor, which I don't think was explicitly described by either of the two suggestions. Prism goggle experiments introduce a visual shift can be learned quickly, and some BCI experiments have introduced simple rotations in the decoder output (e.g. Chase et. al. 2012, J Neurophys). This latter case is likely similar in concept to in-manifold perturbations. Regardless, the performance can be rapidly rescued by simply re-aiming, which is a simple behavioral adaptation. In a 1DOF or 2DOF control case like used in these experiments, with constant visual feedback on performance, the change in context could likely be rapidly learned by the animals, maybe even within a single trial. In other words, the high performance in the online case may be a consequence of the relatively simple task demands, and the simple biomechanical solution to this problem (push harder). What is the expectation that the results seen in these experiments would be relevant to more realistic situations that require grasp and interaction?

Thank you for the suggestion, we agree that the quick adaptation is likely related to re-aiming. To this end, we have included a re-aiming analysis, as described in essential revisions #1 and #2 from the editor and common response #2, to look into the quick adjustment.

Some of the figures were difficult to read and the captions contained some minor incorrect information. The primary purpose of some of the figures was not immediately clear from the caption. For example, the bar plots in Figures 5 and 6 were very small and difficult to read. This also made distinguishing the data from the two different animals challenging.

Thank you for the comments, multiple figures have been edited to increase legibility and a review of text has been done to fix errors and improve interpretability.

There is no specific quantification of the data in Figures 4D and 5D. In Figure 4D it seems apparent that the vast majority of the points are below the unity line. But, it remains unclear, particularly in Figure 5D whether the correlations between the two contexts truly are different or not in a way that would allow conclusive statements.

Thank you for the comments, Figure 4D has been moved to the supplement and 5D has now been replaced by figures analyzing the neural activity patterns during the online task.

Author Response

Reviewer #1 (Public Review):

This is thorough, quantitative microbial ecology research on one of the most important problems of species coexistence in infection biology. The intermediate disturbance hypothesis is supported once again, and they show unsurprisingly that nutrition matters for their ratio of coexistence, but more specifically as a novel function of the ratio of metabolic fueling to reproductive rate, which the authors term absolute growth. I like this study for its care and completeness even though the results are fairly intuitive to those in the field of cystic fibrosis microbial ecology.

We would like to thank the reviewer for acknowledging the importance, care, and completeness of our original manuscript. We have continued to employ our standards of rigor for this revision.

Reviewer #2 (Public Review):

The authors present a manuscript that addresses an important topic of bacterial co-existence. Specifically modeling infection-relevant scenarios to determine how two highly antibiotic-resistant pathogens will develop over time. Understanding how such organisms can persist and tolerate therapeutic interventions has important consequences for the design of future treatment strategies.

We would like to thank the reviewer for acknowledging the importance of our work.

A major strength of this paper is the methodical approach taken to assess the dynamics between the two bacterial species. Using carbon sources to regulate growth to test different community structures provides a level of control to be able to directly assess the impact of one dominant pathogen over another.

The modeling aspect of this manuscript provides a basis for testing other disturbances and/or the impact of additional incoming pathogens. This could easily be applied to other infection settings where multiple microbes are observed ( for example viral/bacterial interactions in the lung).

Thank you for acknowledging the rigor in our experimental and modeling approaches.

The authors clearly show that by altering the growth rate and metabolism of various carbon sources, population structure can be modified, with one out-competing the other. Both modeling and experimental approaches support this.

The exploration of the role of virulence factors is less clear, for example how strains unable to produce virulence factors are impacted in regard to their overall growth and whether S. aureus is able to sense virulence factors without transcriptional assays here. Although the hypothesis is strong, the experimental data does not fully support this conclusion.

In addressing your comments below, we hope that we have increased your confidence in our hypotheses presented in our manuscript as it pertains to the involvement of virulence factors.

Spatial disturbance has a significant impact on community structure. Although using one approach to assess this, it is not clear if the spatial structure is impacted without the comparable microscopy evaluation.

We have indeed acknowledged this short coming in our revised manuscript. In the discussion, we write:

“While we did not explicitly quantify spatial organization experimentally owing to technical limitations of our microplate reader and microscope setups, in theory, co-culture in an undisturbed condition should facilitate the creation of spatial organization.”

In fact, we would really like to be able to track the position of each bacterium during shaking events. However, the plate reader cannot accommodate a microscope setup. While we could remove the plate from the plate reader and transport it to the microscope (two floors down), we cannot be certain that the position of the bacterium would not be altered during transport. We have thought about fixing the bacterium in place prior to transport. However, the injection of liquid for the purposes of fixation would likely alter the positioning of bacteria. Thus, we chose a modeling approach using an agent based model that is parametrized based on our experimental approach. Accordingly, we agree that this is a limitation of our current study. We hope that acknowledging this limitation in the discussion sits well with the reviewer.

Overall this paper highlights the use of modeling approaches in combination with wet lab experiments to predict microbial interactions in changing environments.

Reviewer #3 (Public Review):

This is an intriguing manuscript with a rigorous experimental and computational methodology looking at the interaction of Pseudomonas aeruginosa (Pa) and Staphylococcus aureus (Sa). These two pathogens frequently co-habit infections but in standard liquid media often show a winner-take-all outcome. This study seeks to be mechanistically predictive as to the outcome of the co-culture based on the addition of specific carbon sources as filtered through the lens of metabolic efficiency or, as the authors term - absolute growth. Overall, the study is sound, but there are some specific caveats that I would like to present:

We would like to thank the reviewer for acknowledging the rigor of our work.

1) The study undersells the knowledge in the literature of what allows or prohibits the stability of the Pa and Sa co-cultures. While most of the correct papers are cited, the outcomes of those studies are downplayed in favor of the current predictive study. While the current study is indeed more "predictive", it strays exceedingly far from an infection-relevant media, whereas other studies show reasonable co-existence in host-relevant media.

We have addressed this comment two different ways. First, we have included an entire paragraph in the discussion that acknowledges previous work and how our results fit into previous findings. We write:

“Given the clinical importance of co-infection with both P. aeruginosa and S. aureus, multiple previous studies have identified mechanisms of co-existence. Indeed, long term co-existence of both species can result in physiological changes that reduce their competitive interactions. Strains of P. aeruginosa isolated from patients that enter into a mucoid state show reduced production of siderophores, pyocyanin, rhamnolipids and HQNO, which facilitates the survival of S. aureus [23, 24]. These strains can also overproduce the polysaccharide alginate, which in itself is sufficient to decrease the production of these virulence factors. Moreover, exogenously supplied alginate can reduce the production of pyoverdine and expression from the PQS quorum sensing system, which is responsible for the production of HQNO [25]. Changes in the physiology of S. aureus can also facilitate co-existence. Strains of S. aureus isolated from patients with cystic fibrosis show multiple changes in the abundance of proteins including super oxide dismutase, the GroEL chaperone protein, and multiple surface associated proteins [26]. Interestingly, the majority of proteins that show changes in abundance in S. aureus are related to central metabolism, which is consistent with our findings demonstrating that metabolism can influence the co-existence of both species. While it is unclear as to how long-term co-culture would affect the ratio of absolute growth, our findings provide an additional mechanism that can determine the co-existence of these bacterial species.”

Second, as noted in our response in the ‘essential revisions’ section, we have tested the relationship between the final density ratio and the absolute growth ratio in SCFM medium, which we believe is host relevant. Our findings were fully consistent with the trends that we saw in our original submission. This data is presented in Fig. 3 and Figure 5 – figure supplement 3.

2) The major weakness in the ability of this study to be extrapolatable to infection conditions is the basal media selected for this analysis. The authors choose TSB, which is an incredibly rich media from the start, and proceed to alter only 11% of the available carbon (per mass) with their carbon source manipulations. This suggests an underappreciation for the amino acid metabolism routes of these two pathogens that are taking advantage of the roughly 89% of carbon as amino acid content in the TSB components of tryptone and soytone (17g and 3g, respectively vs the 2.5g carbon source). There are a few major issues with this basal formulation:

a) Comparison to all extant literature on Pa - The media historically used to assess Pa include (rich) LB, BHI, MH; (minimal) MOPS, M63, M9; (host-associated) ASM, SCFM, SCFM2, Serum, and DMEM. TSB is not a historically evaluated formulation for Pa (though it is often for non-mammalian pathogenic Pseudomonads and environmental species). Thus, this study is not inherently integrated into the Pa literature and presents an offshoot study for which a direct connection to extant literature is difficult. Explicitly testing these predictions in the most minimal media possible and then in a host-relevant model would be optimal.

We would truly like to thank the reviewer for their rigor in reviewing our manuscript. We, admittedly, overlooked how amino acids might be influencing the growth of P. aeruginosa in TSB medium. We originally chose TSB medium as previous studies that have examined the co-culture of S. aureus and P. aeruginosa, or their mechanisms of interaction, have used this medium (e.g., [29-34]).

To address this comment directly, we grew co-cultures in AMM minimal medium. This medium, to our knowledge, is the only minimal medium that allows growth of S. aureus. We, and others, have not reported growth of S. aureus in M9 or MOPS minimal medium despite the addition of components such as casamino acids and increases in the concentration of thiamine.

While AMM as reported is quite complex relative to media such as MOPS and M9, we removed several vitamins (nicotinic acid, thiamine, calcium pantothenate, biotin), decreased the concentration of some salts, used a low concentration of casamino acids (0.01%), and used a higher concentration of carbon source (0.04%). In doing so, we hoped to reduce any ‘background effect’ of media components and thus absolute growth could be driven more by carbon source.

Importantly, in using AMM medium, we continue to find a strong and significant relationship between the final density ratio and the absolute growth ratio. This data is presented in the Figure 3 and is described in a standalone paragraph in the results, along with our findings using SCFM.

b) TSB is not remotely host-relevant. The Whiteley lab has done monumental work evaluating in vitro models that mimic human infection (scrupulously matching transcriptomes) and TSB is about as far as you can get. Thus, the ability to extrapolate from the current work to infection without testing in host-relevant media is limited.

As noted above, we repeated our core experimental analysis in SCFM. The results are fully consistent with our original submission. This data is presented Figure 3 and in Figure 5- figure supplement 3.

c) The experimental situation has a component that is both good and bad- O2 tension. By overlaying with mineral oil, the authors immediately bias Staph (a more versatile fermenter) to success, whereas Pa deals with most of these carbon sources better at body level or higher O2 levels. The benefit of this is that many of the infection sites in which these two species co-occur are low in O2.

This was an interesting observation that we have partially addressed experimentally and acknowledged in the discussion.

First, we acknowledged the limitations of our experimental approach as it pertains to O2 levels in the discussion as follows:

“We note that our findings may be relevant to infections occurring in both high and low O2 environments. While P. aeruginosa is limited in its ability to perform fermentation [35], we have provided evidence that the absolute growth ratio can affect community composition in both aerobic (Figures 2-5) and more anaerobic environments (Figure 2 - figure supplement 1, panel H). The limited ability of P. aeruginosa to grow in anaerobic environments was apparent in SCFM as we could not obtain reliable or robustly quantifiable growth of this bacteria when succinate or -ketoglutarate was provided as a carbon source.”

Second, we tested the effect of placing mineral oil over top of the co-culture experiments, thus increasing the anaerobic nature of the environment. We found that, in general, as the ratio of absolute growth increased, so did the dominance of P. aeruginosa in the growth medium. This new data is presented in Figure 2 - figure supplement 1, panel H.

Taken together, we hope that these two modifications meet the Reviewer’s expectations.

d) Some of the tested metabolites are osmotically active (sucrose), while others are not (acetate), confounding the interpretation of what absolute metabolism means in the context of this study since the concentrations of all tested metabolites vary from above to below physiologic-dependent on the metabolite. A much better approach would have been to vary a single metabolite or combination to alter 'absolute metabolism' and test whether the stability of the co-culture held.

e) The manuscript never goes into the fact that for some of these "the carbon source" sources, they are catabolite repressed compared to the basal TSB amino acids (or not). Both organisms show exquisite catabolite repression control, yet this is not addressed at all within the text of the manuscript. Since this response in both organisms is sensitive to relative proportions of the various C-sources, failure to vary C-sources or compare utilization compared to the massive excess tryptone and soytone in the media makes the 'absolute metabolism' difficult to interpret.

To address comments d and e, and to acknowledge the potential limitations of our findings, we have included the following in the discussion. In this paragraph, we acknowledge the osmotic activity of the different carbon sources and preferential consumption of amino acids in TSB medium.

“One drawback of our approach in using different carbon sources to manipulate absolute growth is that some carbon sources are osmotically active, whereas others are not, which could have additional physiological effects on the bacteria outside of changing growth and metabolism. Moreover, both species of bacteria have different carbon source preferences; as above S. aureus tends to prefer carbon sources such as glucose [36] whereas P. aeruginosa prefers organic and amino acids [37]. Given the carbon source preferences of each species, in complex medium such as TSB, there is the potential that P. aeruginosa consumes amino acids prior to consuming the supplied carbon source. This is perhaps less of a concern in AMM medium or SCFM where the concentration of amino acids and additional nutrient components is reduced as compared to TSB medium. Along this line, it is certainly worth investigating how each nutrient component and its ordered utilization by both species contributes to changes in absolute growth. Minor or transient changes in absolute growth owing to preferential nutrient consumption may provide windows of opportunity for one species to increase its relative density to the other.”

f) The authors left out the 'favorite' sources of Pa that are known to be relevant in vivo - the TCA intermediates: citrate, succinate, fumarate (and directly relevant to host-pathogen interactions, itaconate)

We have included the analysis of succinate as a carbon source in both TSB medium (Figs. 1 and 2) and AMM medium (Fig. 3). However, we could not achieve reliable or a quantifiable growth rate of P. aeruginosa in SCFM medium supplemented with succinate in our experimental setup. Accordingly, this carbon source was not used in SCFM.

3) Statistics: Most of the experiments presented are comparisons in which there are more than two experimental groups and the t-tests employed therefore need to be corrected for multiple comparisons. The standard way to do this is to employ an ANOVA with the appropriate multiple-comparison-corrected post-test. These appear to be appropriate for Dunnett's post-testing but the comparator group is not directly defined within the figure legends. Multiple comparison testing is critical for this analysis, as the H0 is that all are the same - the more samples potentially pulled from the same distribution will result in a higher likelihood that one or more will appear as from a distinct population (i.e. H0 rejected). Multiple comparisons correct for this and are absolutely critical for the evaluation of the data presented in this manuscript.

We have addressed this comment two different ways.

First, where there was a clear control group, we performed either a Dunnett’s (for normally distributed data) or a Dunn’s (for non-parametric data sets) following either an ANOVA or Kruskal-Wallis, respectively. These tests were applied to the data presented in Figure 2B, 5H (top and bottom panels) and in Figure 2 - figure supplement 1, panels K-L.

Second, we did not broadly perform multiple comparisons across all data sets. The reason is that this approach would test the significance of relationships that are not relevant to the central premise of the manuscript. For example, a multiple comparison for figure 1B would test the growth rate of all carbon sources against all carbon sources. However, we are only interested if S. aureus or P. aeruginosa grows faster than one another. However, we do understand the need for a corrected P value to reduce the occurrence of Type 1 errors. To accomplish this, we applied a Benjamini-Hochberg Procedure [38] with a 8.5% discovery rate to all P values in the manuscript, including those that tested the distribution of data. This reduced the P value to indicate significance at < 0.0472. We have updated all claims and indications of significance in the figures based on this adjusted P value.

4) The authors missed including Alves et Maddocks 2018 in relation to priority effects and other contributing factors to stable Pa/Sa co-culture.

We have indeed included this manuscript and its findings in the introduction where we write:

“While S. aureus can initially aid in the establishment of the P. aeruginosa population [8], production of N-acetylglucosamine from S. aureus augments…..”

Author Response

Reviewer #3 (Public Review):

The authors examine the role of secreted BAFF in senescence phenotypes in THP1 AML cells and primary human fibroblasts. In the former, BAFF is found to potentiate the inflammatory phenotype (SASP) and in the latter to potentiate cell cycle arrest. This is an important study because the SASP is still largely considered in generic and monolithic terms, and it is necessary to deconvolute the SASP and examine its many components individually and in different contexts.

Although the results show differences for BAFF in the two cell models, there are many places where key results are missing and the results over-interpreted and/or missing controls.

1) Figure 1. Test whether the upregulation of BAFF is specific to senescence, or also in reversible quiescence arrest.

We appreciate the Reviewer’s requests. We performed the experiments in fibroblasts and THP-1 cells to assess BAFF levels in quiescence. As shown below in the figure for Reviewers, we induced quiescence in fibroblasts by serum starvation (0.1%) for 96 h and confirmed the quiescent state by measuring two markers of quiescence (reduction of CCND1 mRNA and reduction of phopho-S6, when compared to cycling cells, following markers established previously (PMID 25483060) (panel A). In this case, the level of BAFF mRNA was increased upon quiescence (panel B).

In THP-1 cells, we tried to induce quiescence by serum starvation and glutamine depletion for 96 h. Unfortunately, however, inducing quiescence in THP-1 cells was rather challenging, likely because they are cancer cells. Thus, we observed a reduction of cell proliferation in both conditions, but we observed a reduction in phospho-S6 only in the samples without glutamine (panel C). We failed to see increased BAFF mRNA levels in quiescent THP-1 cells after either serum starvation or glutamine depletion (panel D).

In summary, further studies will be necessary to fully understand if the increased expression of BAFF seen in senescent cells is also observed in other conditions of growth suppression (such as quiescence or differentiation), as well as whether this effect is specific to different cell types.

2) Figure 1, Supplement 1G. Show negative control IgG for immunofluorescence.

We thank the Reviewer for this suggestion. Along with other changes during the revision, we decided to remove the immunofluorescence data in order to include more informative data.

3) All results with siRNA should be validated with at least 2 individual siRNAs to eliminate the possibility of off-target effects.

We agree with the Reviewer on the importance of testing individual siRNAs. For BAFF, we originally tested two independent siRNAs (BAFF#1 and BAFF#2) individually, but we also pooled them for additional analysis (and referred to simply as “BAFFsi” along the manuscript). In the revised version of our manuscript, we included the key experiments performed with these two individual BAFF siRNAs. Upon BAFF silencing in THP-1 cells, we observed a reduction of SASP factors and SA-β-Gal activity levels with each individual siRNA (Figure 4-Figure Supplement 1D-F) and with the pooled siRNAs (Figure 4C). For WI-38 cells, we observed a reduction of p53 levels with individual and pooled siRNAs (Figure 7-Figure Supplement 1A), as well as a reduction in IL6 levels and SA-β-Gal activity (Figure 6-Figure Supplement 1D,E). After IRF1 silencing, we observed a reduction in BAFF pre-mRNA with two different pairs of CTRLsi and IRF1si pools (Figure 2I and supplementary Figure 2E). For the data on BAFF receptors, we used SMARTpools from Dharmacon, which are combinations of 4 siRNAs designed by the company to minimize off-target effects. These additions and clarifications are indicated in the revised manuscript.

4) To confirm a role for IRF1 in the activation of BAFF, the authors should confirm the binding of IRF1 to the BAFF promoter by ChIP or ChIP-seq.

We thank the Reviewer for this suggestion. We performed ChIP-qPCR analysis in THP-1 cells that were either proliferating or rendered senescent after exposure to IR (Figure 2H, Materials and methods section), and we confirmed the binding of IRF1 to the proximal promoter region of BAFF. As anticipated, this interaction was stronger after inducing senescence.

5) Key antibodies should be validated by siRNA knockdown of their targets, for example, TACI, BCMA, and BAFF-R in Figure 5. Note that there is an apparent discrepancy between BCMA data in Figure 5B vs 5C.

We fully agree with the Reviewer on this point and we thank him/her for helping us to improve this part of our manuscript. To address the discrepancy regarding BCMA western blot analysis and flow cytometry data, we silenced BCMA in THP-1 cells and tested two different antibodies advertised to recognize BCMA. This experiment allowed us to identify the correct band for BCMA by western blot analysis. We then confirmed that BCMA is upregulated in senescence, as observed by both western blot and flow cytometry analyses. We have modified the manuscript to reflect these changes. Please find these data in Figure 5A,B and Figure 5-Figure Supplement 1A of the revised manuscript.

6) Figure 5E. Negative/specificity controls for this assay should be shown.

We thank the reviewer for this comment and regret that we were unable to provide a negative control. The kit only provides a competitive wild-type oligomer used to test the specificity of the binding. For each sample (CTRLsi, BAFFsi, CTRLsi IR, BAFFsi IR) and each antibody tested (p65, p50, p52, RelB and c-Rel), we evaluated the reductions in signal upon addition of excess competitive oligomer per well (20 pmol/well) compared to wells with an inactive oligomer. However, the negative control was performed only as single replicate, due to the limited quantity of nuclear extracts and the high number of samples and antibodies analyzed. We therefore considered this control as being ‘qualitative’ rather than fully ‘quantitative’.

7) Hybridization arrays such as Figure 5H, Figure 6 - Supplement 1I, and Figure 6H should be shown as quantitated, normalized data with statistics from replicates.

We appreciate this request. We have included the quantification and statistics to the phosphoarrays used for THP-1 and WI-38 cells, which had been performed in triplicate (Figure 7A, Figure 5-Figure Supplement 1D). The original arrays are shown in the respective Source Data Files. In the interest of space, we removed the cytokine array performed on IMR-90 cells and left instead the quantitative ELISA for IL6 (Figure 6-Figure Supplement 1F). The data obtained from the cytokine array analysis in Figure 4F and Figure 4-Supplemental Figure 1C are supported by quantitative multiplex ELISA measurements (Figure 4E and Figure 4C).

8) Figure 6B - Supplement 1. Controls to confirm fractionation (i.e., non-contamination by cytosolic and nuclear proteins) should be shown.

We thank the Reviewer for this suggestion. We tested the efficiency of fractionation and we did in fact observe some degree of contamination from cytosolic proteins using the earlier version of the kit (Pierce, cat. 89881). We therefore purchased an improved version of the kit (Pierce, cat. A44390) and repeated the surface fractionation assay, which this time showed improved fractionation (Figure 7-Figure Supplement 1B). Interestingly, with the improved fractionation strategy, we observed that BAFF receptors in fibroblasts were almost exclusively localized inside the cell and not on the surface, as we found in THP-1 cells. Further validation of BAFF receptor antibodies has been provided in Figure 5-Figure Supplement 1A. As described in the text, the intracellular localization of BAFF receptors was previously reported in other cell types and conditions (PMID 31137630, PMID 19258594, PMID 30333819, PMID 10903733), and thus it is possible that BAFF may act through non-canonical mechanisms in WI-38 cells. Nonetheless, we did detect a small amount of BAFFR on the cell surface, and furthermore, BAFFR silencing reduced the level of p53 in fibroblasts. Therefore, we propose that BAFFR may be the primary receptor involved in p53 regulation in fibroblasts (Figure 7-Figure Supplement 1B,C). Our data on BAFF receptors deserve deeper characterization in a future study of the functions of BAFF receptors in senescence.

9) Figure 6A. Knockdown of BAFF should be shown by western blot.

Yes, definitely. We appreciate this comment and have included BAFF knockdown data in fibroblasts by western blot analysis (Figure 7B).

10) Figure 6G. Although BAFF knockdown decreases the expression of p53, p21 increases. How do the authors explain this?

We thank the Reviewer for the interesting question. We too were surprised to observe that the p53-dependent transcripts regulated by BAFF did not include CDKN1A (p21) mRNA, as confirmed by western blot analysis. The accumulation of p21 in senescence can be also regulated by p53-independent pathways and in p53-/- cells, for example by p90RSK, SP1, and ZNF84 (PMID 24136223, PMID 25051367, PMID 33925586). Eventually, we removed the data relative to p21 and γ-H2AX in favor of other data and to streamline the content of this manuscript for the reader.

Author Response

Reviewer #1 (Public Review):

The authors present data identifying the role of the bacterial enhancer binding protein (bEBP) SypG in the regulation of the Qrr1 small RNA, which is known to be a key regulator of Vibrio fischeri bioluminescence production and squid colonization. Previously, only the bEBP LuxO was known to activate Qrr1 expression. LuxO and Qrr1 are conserved in the Vibrionaceae, and the authors show that SypG is conserved in ~half of the Vibrio family, suggesting that this Qrr1 regulatory OR gate controlled by LuxO or SypG may play important roles in physiology processes in other species.

Successful squid colonization by Vibrio fischeri is a complex process, known to be influenced by several factors, including the formation of and dispersal from cellular aggregates prior to entering squid pores, and inoculation of the light organ crypts, and biofilm formation within the crypts. Previously, it was shown that strains lacking qrr1 were at a deficit for crypt colonization in the presence of wild-type V. fischeri. Conversely, cells lacking binK, which encodes a hybrid histidine kinase, were at an advantage for crypt colonization in the presence of wild-type cells. However, the authors identified BinK as a negative regulator of Qrr1 expression in a transposon screen. The authors used genetic epistasis experiments and found that Qrr1 transcription can be activated by either phosphorylated LuxO at low cell densities (in the absence of quorum sensing signals) or by SypG, presumably by binding to the two upstream activation sequences in the promoter of qrr1 to activate transcription by the required alternative sigma factor sigma-54. The competition between these bEBPs has not been tested. The model proposed is an OR gate through which quorum sensing and aggregation signals control Qrr1. However, there are several untested aspects of this model. First, the role of phosphorylation in SypG activity, and the connection to BinK, are not addressed in this manuscript, which may confound the observed effects observed on qrr1 transcription. Further, the authors did not test whether SypG directly binds to the qrr1 promoter, nor did they assess the individual role of LuxO binding to the two LuxO binding sites in the absence of SypG. The study is lacking an in vivo assessment of SypG and LuxO binding/competition at the Qrr1 promoter based on the authors' model of the OR gate.

Major comments:

• What is known about the connection between BinK and SypG? BinK is a hybrid HK (intro states this). Does BinK phosphorylate/dephosphorylate SypG - directly or indirectly? I saw a published paper (Ludvik et al 2021) with a diagram suggesting BinK does inhibit SypG, but the connection is unclear. This diagram also suggested that SypG needs to be phosphorylated. Can the authors comment - does SypG need to be phosphorylated to be active? Because SypG has the same sequence as the LuxO linker (Fig. S2), then I presume that SypG would also need to be phosphorylated to be active (like LuxO)? The authors utilize a phosphomimic of LuxO to test function under constitutive activity (Fig. S3), but they do not use a phosphomimic of SypG (Fig 4). If the authors used a constitutive allele, would those assays reveal more about the competition between SypG and LuxO, in the presence of phosphorylated LuxO at low cell density? The authors should include a putative cartoon model for how BinK HK activity connects to SypG, based on what is already in the literature, to aid the reader.

We have added information & corresponding cartoon model in the results section about the signaling pathway involving BinK and SypG, including that SypG must be phosphorylated to be active and that BinK acts as a phosphatase towards SypG. We have also generated a SypGD53E mutant and found increased Pqrr1 activity, which suggests that phosphorylation of SypG has a major impact on SypG-dependent activation of Pqrr1.

• Line 246: Figure S3: nucleotide substitutions in both UAS regions showed loss of Pqrr1-gfp, but this could be due to binding/activation by SypG or LuxO. This should be tested in a sypG- strain to determine the sole effect of LuxO binding to these two UASs. In Figures 4G and 7, the luxO- sypG- Ptrc-sypG strain backgrounds allow the independent analysis of the two bEBPs. It is important to test which of these two sites is critical for LuxO-dependent activation of Pqrr1, given the conservation of the LuxO-Qrr1 region in other Vibrios (line 327, Fig. S5). Thus, the authors could also discuss whether these two proteins would compete at both sites. Further, the authors should comment that they have not shown biochemical evidence that SypG binds to the two UASs in the Qrr1 promoter. The regulation of this locus by SypG is only shown by genetic assays in this manuscript.

We have added a paragraph in the discussion highlighting how useful protein-DNA assays would be to address competition along with the barriers encountered with approaches to purify SypG. Regarding the contribution of each UAS to LuxO-dependent activation, we refer to the phosphomimic data of LuxO (Fig. S4) in the supplement that highlight G-131 and G-97 do not affect LuxO-dependent activation (as pointed out by reviewer #2), which has contributed to our test of a G-131T mutant in the co-colonization experiment.

• Examination of the binding of LuxO and SypG (e.g., ChIP-seq) in combination with their transcriptional reporter under varying conditions (low cell density vs high cell density, with or without rscS* overexpression) would be extremely beneficial in testing the model proposed.

We agree but have not had success in our attempts to perform ChIP due to protein instability. For example, we have tried SypG with a C-terminal TAP tag, which my colleague Dr. Lu Bai at Penn State has used extensively for ChIP, ChIP-seq, and ChIP-exo, but we could not observe a signal even when RscS* allele was included in the strain.

Reviewer #2 (Public Review):

The study by Surrett et al. uncovers a novel regulatory axis in Vibrio fischeri that controls the expression of the qrr1 small RNA, which post-transcriptionally controls various quorum-dependent outputs. This study is timely and addresses a major question about the physiology of this important model symbiosis and potentially other Vibrio species. The results should be of broad interest within the field of microbiology.

While it was previously believed that qrr1 expression is under the strict control of the LuxO-dependent quorum sensing cascade, the authors demonstrate that qrr1 expression can be induced by another bEBP, SypG, in a manner that is quorum-independent. It was previously shown that qrr1 is important for colonization, and the authors recapitulate and extend this finding here. However, bacteria are likely at high cell density prior to entry into the crypts, which would repress qrr1 expression. Thus, despite the importance of qrr1 expression for crypt colonization, it would counterintuitively be repressed. The discovery of the SypG quorum-independent induction of qrr1 in this study may help resolve this conundrum. The authors take a largely genetic approach to characterize this novel regulatory pathway in combination with a squid colonization model. The experiments performed are generally well controlled and the data are clearly presented. The authors, however, fail to provide experimental evidence to support the physiological relevance of SypG-dependent control of qrr1 expression during host colonization.

Fig. 2 - It is unclear why there is a disconnect between qrr1 expression and qrr1-dependent effects. Data in 2B, indicate that qrr1 is induced in the ∆binK mutant according to the Pqrr1-gfp reporter but this expressed qrr1 does not have any effect on phenotypes like bioluminescence according to the data presented in 2C. While the authors reveal an effect of the binK deletion when rscS is overexpressed, it is unclear why this is necessary since simple deletion of bink without rscS is sufficient to induce qrr1 in 2B. Could this discrepancy be due to the fact that experiments in 2B are done on solid media while the experiments in 2C are performed in liquid media? Do cells in liquid not express qrr1? Or conversely, perhaps testing the bioluminescence of cells scraped off of plates could reveal a phenotype for the binK mutant similar to those seen in the rscS background in liquid. Or alternatively, if cells in a liquid culture still express qrr1, perhaps there is a posttranscriptional mechanism that prevents qrr1 from exerting an effect on bioluminescence? The latter possibility would alter the proposed model.

To help explain why we chose to overexpress RscS, we have added the cartoon in Fig. 2C, which highlights how BinK dephosphorylates SypG. We believe that the conditions used in the bioluminescence assay do not phosphorylate SypG, which prevents an effect by BinK. However, overexpression of RscS permits phosphorylation of SypG, which enables a phenotype to emerge in a binK mutant. We have tested the bioluminescence of cells within spots but did not detect a difference.

The authors propose a model in which sypG dependent activation of qrr1 is required for appropriate temporal regulation of this small RNA and contributes to optimal fitness of V. fischeri during colonization, however, this was not directly tested, and experimental evidence to support a physiological role for spyG-dependent regulation of qrr1 remains lacking. Data in Fig. S3 and Fig. 4G-H suggest that the Gs at -131 and -97 in Pqrr1 are largely dispensable for LuxO-dependent activation, but are important for SypG-dependent activation of Pqrr1. Also, the Pqrr1 mutations at C -130 and -96 completely prevent sypG-dependent activation while only partially reducing LuxO-dependent activation. If SypG-dependent activation of qrr1 is critical for the fitness of V. fischeri, a strain harboring these Pqrr1 promoter mutations should be attenuated in a manner that resembles the qrr1 deletion mutant as shown in Fig. 3C.

We thank the reviewer for this suggestion, which led us to generate and test a G-131T mutant in vivo.

Fig. S4 - these data suggest that LuxO cannot enhance transcription of PsypA and PsypP at native expression levels. But sypG-dependent induction of qrr1 was largely tested with Ptrc-dependent overexpression of SypG. Would overexpression of LuxO induce PsypA and PsypP? The authors should at least acknowledge this possibility in the text.

As requested, we have added text that acknowledges this possibility.

The authors adopt three distinct strategies to induce sypG-dependent activation of qrr1 in distinct figures throughout the manuscript: deletion of binK, overexpression of rscS (rscS*), and direct overexpression of sypG. It is not entirely clear why distinct approaches are used in different figures. This is particularly true for Fig. 5 since the authors already demonstrated that the direct overexpression of sypG can be used, which is a more direct way of addressing this question. Similarly, sypG overexpression should inhibit bioluminescence in Fig. 2 based on the proposed model, which would have tested the claims made more directly. Additional text to clarify this would be helpful.

As requested, we have added Fig. 2C and text to describe how SypG is regulated, which provides ways to test SypG-dependent activation of qrr1.

The Fig. 5D legend indicates that the strains harbor a Ptrc-GFP reporter. However, the text would suggest that these strains should harbor a Pqrr1-GFP reporter to test the question posed.

This has been corrected.

The conclusion that SypG and LuxO share UASs in the qrr1 promoter is based on fairly limited genetic evidence where point mutations were introduced into 3 bp of the predicted LuxO UASs within the qrr1 promoter. This conclusion needs to be qualified in the text or additional experimental evidence is needed to support this claim. For example, in vivo ChIP-exo could be used to map the SypG and LuxO binding sites. Or SypG and LuxO could be purified to assess binding to the qrr promoter in vitro (to map binding sites or test competitive interactions of these proteins to the qrr promoter).

As described above and in the text, we have not been able to construct a functional tagged SypG that would enable these types of studies.

On a related note, SypG binding to the qrr1 promoter is speculated based on indirect genetic evidence. But the authors do not directly demonstrate this. This should be acknowledged in the text or additional experimental evidence should be provided to support this claim.

As requested, we have added text in the discussion that highlights this problem.

Reviewer #3 (Public Review):

In this manuscript, Surrett and coworkers aimed to identify the mechanism that regulates the transcription of Qrr1 sRNA in the squid symbiont Vibrio fischeri. In many Vibrio species, Qrr1 transcription is regulated by quorum sensing (QS) and activated only at low cell density. Qrr1 is important for V. fischeri to colonize the squid host. In the QS systems that have been studied so far, LuxO is the only known response regulator that activates Qrr sRNA transcription. However, the authors argued that since V. fischeri forms aggregates before entering into the light organ of the squid, Qrr1 would not be made as high cell density QS state is likely induced within the aggregates. Therefore, they hypothesized that additional regulatory systems must exist to allow Qrr1 expression in V. fischeri to initiate colonization of the light organ. In turn, the authors identified that disruption of the function of the sensor kinase BinK allowed Qrr1 expression even at high cell density. Through a series of cell-based reporter assays and an in vivo squid colonization assay, they concluded that BinK is also involved in Qrr1 regulation within the squid light organ. They went on to show that another sigma54-dependent response regulator SypG is also involved in controlling Qrr1 expression. The authors propose dual regulation of LuxO and SypG on Qrr could be a common regulatory mechanism on Qrr expression in a subset of Vibiro species.

Overall, the experiments were carefully performed and the findings that BinK and SypG are involved in Qrr1 regulation are interesting. This paper is of potential interest to an audience in the field of QS and Vibrio-host interaction. However, experimental deficiencies and alternative explanations of the results have been identified in the manuscript that prevents a thorough mechanistic understanding of the interplay between QS and these new regulators.

1) The premise that Qrr1 expression in the light organ has to be regulated by systems other than QS is unclear. In lines 108-109, it was stated that "...prior to entering the light organ, bacterial cells are collected from the environment and form aggregates that are densely packed", however, in lines 184-185, it was stated that "The majority of crypt spaces each contained only one strain type (Fig. 3B), which is consistent with most populations arising from only 1-2 cells that enter the corresponding crypt spaces". So, if the latter case is true (i.e., 1-2 cells/crypt), why Qrr1 could not be made at that time point as predicted by a QS regulation model?

We have not changed this section because if Qrr1 is expressed only after the cells have already entered the crypt space, then the Δqrr1 mutant would colonize a number of crypt spaces comparable to that of wild type cells.

2) The involvement of the rscS allele for the ∆binK mutant to show an altered bioluminescence phenotype is confusing. It is unclear why a WT genetic background was sufficient to show the high Qrr1 phenotype in the original genetic screen that identified BinK (Fig. 2A-B), while the rcsS allele is now required for the rest of the experiments to show the involvement of BinK in bioluminescence regulation (Fig 2C). Is the decreased bioluminescence phenotype observed in rcsS* ∆binK mutant (fig. 2C) dependent on LuxU/LuxO/Qrr1/LitR? Could it be through another indirect mechanism (e.g., SypK as discussed in line 403)? A better explanation of the connection between RcsS/Syp and BinK and perhaps additional mutant characterization are necessary to interpret the observed phenotypes.

As described above, we have added a cartoon that illustrates the pathway involving BinK (Fig. 2C) and additional justification in the results section, which better explains why RscS overexpression was used.

3) In squid colonization competition assays (Fig. 3), it was concluded that the ∆qrr1 allele is epistatic to the ∆binK allele (line 204), and the enhanced colonization of the ∆binK mutant is dependent on Qrr1 (section title, line 162). This conclusion is hard to interpret. The results can be interpreted as ∆qrr1 mutation lowers the colonization efficiency of the ∆binK mutant which could imply BinK regulates Qrr1 in vivo. Alternatively, it could be interpreted that the ∆binK mutation increases the colonization efficiency of the ∆qrr1 mutant. Direct competition between single and double mutants in the same animals may resolve the complexity. And direct comparison of Qrr1 expression of WT and ∆binK mutants inside the animals, if possible, will also help interpret these results.

We thank the reviewer for the suggestion and were able to test the ΔbinK and ΔbinK Δqrr1 mutants directly (Fig. S2). We were unable to interpret the data using the Pqrr1 reporter due to unexpected heterogeneity in Pqrr1 activity throughout the crypt spaces.

4) Similar concern to above (#2), in Fig. 4, the link between BinK and Qrr1 regulation is not fully explored. What connects BinK and Qrr1 expression? Does BinK function via LuxU (or other HPT) to control SypG like the other QS kinases? And what is the role of other known kinases (e.g., SypF) in the signaling pathway? And did the authors test other bEBPs found in V. fischeri for their role in Qrr1 regulation?

We have added to the discussion content that highlights examining LuxU as a direction worthwhile to pursue to understand how BinK affects signaling that activates Qrr1.

5) In addition to the genetic analysis, additional characterization of SypG is required to demonstrate the proposed regulatory mechanism: What is the expression level (and phosphorylation state) of SypG and LuxO at different cell densities? Does purified SypG directly bind to the qrr1 promoter region? c. How do these two bEBPs compete with each other if they are both made and active?

We agree that these are interesting questions, but as described above, we were unable to purify SypG to address the biochemistry.

6) The molecular OR logic gate is used to describe the relationship between LuxO and SypG, but this logic relationship is not always true in all conditions (if at all). In WT, deletion of luxO completely abolished Qrr1 expression (Fig. 4C). Even in the binK mutant, LuxO still seems to be the more prominent regulator (Fig. 4D) as deletion of luxO already caused a smaller but significant drop in Qrr1 expression. The authors may need to use this term more precisely.

We note that in wild-type cells, SypG is not active under the conditions tested, so SypG would not contribute to activating Qrr1 expression. The level of Pqrr1 activity by the SypG(D53E) variant surpasses the basal level of LuxO, which suggests that LuxO does not always serve as the prominent regulator. We have added content to the discussion to highlight how LuxO may contribute more to the regulation.

Author Response

Reviewer #2 (Public Review):

In this manuscript, Berryer et al describe a fully automated, scalable approach to quantify the number of synaptic inputs formed onto human iPSC-derived neurons (hNs) in 2D culture. They validate the sensitivity of their approach by synapsin1 knock-down and test almost 400 small molecules for their effect on synapses, and the role of astrocytes. They identify BET inhibitors as strong modifiers of synapse numbers in hNs and performed follow-up experiments to confirm the finding, characterize the effect further and demonstrate the critical role of astrocytes.

Every step of the protocol is automated to achieve high reproducibility and homogeneity throughout the experiments. This automated approach has great potential for scaling up drug screening, genetic perturbations, and disease modeling experiments related to synapses.

The authors successfully identified, in two independent hNs lines, three small-molecule inhibitors of transcription modifiers of the BET family as the strongest positive modifiers of synaptic inputs. The initial study performed with immunofluorescence was then validated by Western blot analysis and mRNA-seq analysis, which showed an increase in the expression of trans-synaptic signaling genes.

While accessing the molecular mechanisms of BET inhibitors, the authors observed that the increased synaptic inputs occurred only in cocultures of astrocytes and neurons, and not in hNs monoculture. Finally, the authors report that the presence of astrocytes alone is a major driving force to promote synaptic inputs.

Overall, the experiments are well conducted, and the conclusions are supported by the data. The new approach reaches beyond the current state of the field, especially in the first steps of automation and the identified modulators (BET inhibitors) are interesting and novel, and the subsequent validation is convincing.

On the other hand, the manuscript does not yet define the exact resolution and power of the new methods, and does not convincingly show that the observed synapsin-puncta are synapses and that the data of the validation experiments can be improved.

MAJOR POINTS:

1) Although the manuscript contains a lot of quantitative data on variance, the current manuscript stops short of an exact definition of the resolution of the assay and its statistical power. With the real (measured) variance of the assay, the power to detect certain effects can be computed. To be relevant for other applications than the current (e.g. genetic perturbations and disease modelling), it is relevant to define this for smaller effects too: can this assay detect a 25% effect with reasonable numbers of observations? Such assessments can also provide important recommendations on when it makes sense to add more repeated measures of the same specimens (wells, ROIs) and when more independent inductions are required (and how much this adds to overall power). The manuscript would also benefit from a short discussion on how to optimize future study designs (repeated measures, independent inductions, number of subjects).

As mentioned above, we have now calculated Cohen’s d for: (1) the primary screen overall as well as for compound included in the primary screen, (2) validation experiments performed in neuron monocultures and (3) validation experiments performed in neuron + astrocyte co-cultures, and these data have been added to Figure 5, Figure 5-figure supplement 1 and Supplementary File 2. For the validation experiments, we have also added a discussion of study design, given the observed effect sizes. These analyses are discussed in depth on pages 19-20 of the Results section and page 26 of the Discussion section in the PDF. In brief, we obtained a Cohen’s d of -0.18 for the primary screen where individual small molecules increased as well as decreased synaptic density. Also from the primary screen, we obtained a Cohen’s d of 2.914 for JQ1 and 3.710 for I-BET151, indicating large effects for the BET inhibitors. We also noted large effects for BET inhibitors in the co-culture validation experiments, where we could have scaled down on the number of fields and wells analyzed. While we were reasonably powered to detect changes in the monoculture validation experiments, here, effect sizes were much smaller and required the 50+ wells that we analyzed in order to achieve 95% power. Example from Figure 5 below shows well level data for the co-culture and monoculture validation experiments -

2) It is widely recognized that synapses formed in networks of NGN2-induced excitatory neurons only, may not model synapses in the real human brain very well (yet), especially not at DIV21. First, the authors can be more open/precise about this, e.g., in line 156 the authors indicate they use hNs at DIV21 because they are "electrophysiologically active" based on three references. However, (a) these references indicate that hNs cultures start to mature from DIV21 onwards but are not really mature yet, and (b) being "electrophysiologically active" seems not the most relevant criterion. Synaptic parameters like initial release probability, rise/decay time, and synchronicity are more relevant (none of which indicate synapses are mature at DIV21). Second, especially in the light of the claims the authors make regarding the effects of compounds on "synaptic connectivity" it seems essential to test, at least in a set of validation experiments, the distribution of postsynaptic markers. Synapsin-positive puncta may not be accompanied by a postsynaptic specialization and rather represent (mobile) vesicle clusters and/or release sites without postsynaptic partners. In addition, the authors claim synapsin1 is a pan-neuronal synapse marker. This is not yet validated for human neurons. A few control stainings with synaptic vesicle and active zone markers will secure this claim.

We thank the reviewer for this comment and have now updated the text to indicate and expand on the fact that we are looking at immature synapses at day 21 in vitro (e.g., please see pages 8 and 12 of the Results section in the PDF).

As mentioned above, we also tested conditions for four additional postsynaptic antibodies, drawing from those used in published studies of human cellular models (and species that would not cross-react with antibodies used for Synapsin1 and MAP2). Specifically, we tested antibodies against PSD-95, NLGN4, Homer1 and BAIAP2 at a range of concentrations in co-cultures generated from two independent cell lines. Of these antibodies, we only obtained quantifiable signal for PSD-95, while NLGN4, Homer1 and BAIAP2 appeared to be of poor quality in our culture systems (e.g., nonspecific signal, high signal in astrocytes, etc.). As shown below and in Figure 1-figure supplement 1, analysis of PSD-95 revealed that 43.1% of PSD-95 puncta on MAP2 also colocalized with synapsin1, and 28.8% of synapsin1 puncta on MAP2 also colocalized with PSD-95. Discussions of these data and limitations have been significantly elaborated upon on pages 10-11 of the Results section and pages 24 and 29 of the Discussion section in the PDF. For example, we discuss how the partial colocalization could be due both to the relative immaturity of the synapses discussed above (presynaptic assembly preceding postsynaptic assembly at this early stage of neuronal development) as well as the overall poorer quality of the PSD-95 signal in human cellular material (PSD-95 signal was of insufficient quality and consistency for screening applications and was generally quite difficult to resolve as compared to Synapsin1).

Additionally, we tested two additional presynaptic antibodies, including synaptophysin and SV2A. Of these antibodies, we obtained reasonable quality signal for synaptophysin, which we have quantified in Figure 1-figure supplement 1. While SV2A also gave some signal, it was of poorer quality and difficult to reliably quantify. We observed roughly half of the Synapsin1 signal on MAP2 colocalizing with synaptophysin, and vice versa. Lack of complete colocalization could be due to reports that synapsin1 expression precedes synaptophysin expression in the cortex (e.g., Pinto et al 2013), reports that synaptophysin is also expressed at extra synaptic sites (e.g., Micheva et al 2010), or the reduced quality of staining for synaptophysin that we obtained compared with synapsin1. These data are now elaborated upon on pages 10-11 of the Results section and page 24 of the Discussion section in the PDF.

We have also expanded our discussion of Synapsin1 as a presynaptic marker including additional references on the use of Synapsin1 to label cortical glutamatergic synapses in rodent (e.g., Micheva 2010) and the use of Synapsin1 on MAP2 as a pan-synaptic marker in human neurons (e.g., Chanda et al 2019, Pak et al 2015, Yi et al 2016; page 10). We have also included the use of Synapsin1 on MAP2 as a specific Limitation on page 29 where we discuss that reliance on this system in developing neurons may be capturing sites which do not then develop into fully functional synapses with postsynaptic partners.

3) The analysis of the transcriptional effects of BET inhibitors is rather basic, especially given the rather strong claim: "BET inhibitors enhance synaptic gene expression programs". Which programs? Differentially expressed transcripts can at least be analysed further in terms of subcellular localization (pre/post) or synaptic functions, e.g. using SYNGO, also to address point 2 above.

We thank the reviewer for this comment and have now incorporated SynGO analysis into Figure 6 to examine the synaptic ontology terms. As shown below, Figure 6g now includes the top 5 significantly enriched terms and Figure 6h shows the gene counts by cellular component. Here, we focused on genes upregulated after both JQ1 and Birabresib treatment compared with a background list of expressed genes. The most enriched synaptic ontology terms related to the post-synaptic membrane, so we also validated protein level changes in two postsynaptic proteins (Homer1 and BAIAP2) by Western blot analysis in Figure 6. In addition to Figure 6, these data are now included in Supplementary File 5 and discussed on page 22 of the Results section.

Author Response:

Reviewer #1 (Public Review):<br /> <br /> Roberts et al have developed a tool called "XTABLE" for the analysis of publicly available transcriptomic datasets of premalignant lesions (PML) of lung squamous cell carcinoma (LUSC). Detection of PMLs has clinical implications and can aid in the prevention of deaths by LUSC. Hence efforts such as this will be of benefit to the scientific community in better understanding the biology of PMLs.

The authors have curated four studies that have profiled the transcriptomes of PMLs at different stages. While three of them are microarray-based studies, one study has profiled the transcriptome with RNA-seq. XTABLE fetches these datasets and performs analysis in an R shiny app (a graphical user interface). The tool has multiple functionalities to cover a wide range of transcriptomic analyses, including differential expression, signature identification, and immune cell type deconvolution.

The authors have also included three chromosomal instability (CIN) signatures from literature based on gene expression profiles. They showed one of the CIN signatures as a good predictor of progression. However, this signature performed well only in one study. The authors have further utilised the tool XTABLE to identify the signalling pathways in LUSC important for its developmental stages. They found the activation of squamous differentiation and PI3K/Akt pathways to play a role in the transition from low to high-grade PMLs

The authors have developed user-friendly software to analyse publicly available gene expression data from premalignant lesions of lung cancer. This would help researchers to quickly analyse the data and improve our understanding of such lesions. This would pave the way to improve early detection of PMLs to prevent lung cancer.

Strengths:

1. XTABLE is a nicely packaged application that can be used by researchers with very little computational knowledge.<br /> 2. The tool is easy to download and execute. The documentation is extensive both in the article and on the GitLab page.<br /> 3. The tool is user-friendly, and the tabs are intuitively designed for successive steps of analysis of the transcriptome data.<br /> 4. The authors have properly elaborated on the biological interest in investigating PMLs and their clinical significance.

Weaknesses:

The article is focused on the development and the utility of the tool XTABLE. While the tool is nicely developed, the need for a tool focussing only on the investigation of PMLs is not justified. Several shiny apps and online tools exist to perform transcriptomic analysis of published datasets. To list a few examples - i) http://ge-lab.org/idep/ ; ii) http://www.uusmb.unam.mx/ideamex/ ; iii) RNfuzzyApp (Haering et al., 2021); iv) DEGenR (https://doi.org/10.5281/zenodo.4815134); v) TCC-GUI (Su et al., 2019). While some of these are specific to RNA-seq, there are plenty of such shiny apps to perform both RNA-seq and microarray data analysis. Any of these tools could also be used easily for the analysis of the four curated datasets presented in this article. The authors could have elaborated on the availability of other tools for such analysis and provided an explanation of the necessity of XTABLE. Since 3 of the 4 datasets they curated are from microarray technology, another good example of a user-friendly tool is NCBI GEO2R. This is integrated with the NCBI GEO database, and the user doesn't need to download the data or run any tools. iDEP-READS (http://bioinformatics.sdstate.edu/reads/) provide an online user-friendly tool to download and analyse data from publicly available datasets. Another such example is GEO2Enrichr (https://maayanlab.cloud/g2e/). These tools have been designed for non-bioinformatic researchers that don't involve downloading datasets or installing/running other tools.

Two of these tools (IDEP and TCC-GUI) were reviewed in a literature review covering 20 Shiny apps performed two years ago prior to work on XTABLE starting. Three of the suggested tools (IDEP, RNFuzzyApp, TCC-GUI) are for processing only RNA-seq datasets. IDEAMEX appears to be for RNA-seq data only and is severely limited in its downstream analysis capabilities. DEGenR appears to handle microarray datasets and features an option to retrieve data directly from GEO. However, it appears to be based on GEO2R (with additional downstream analyses) where it automatically logtransforms already log-transformed data and unlike GEO2R, you do not have the option to not apply a log-transformation. A refreshed literature search focusing on microarray datasets highlighted three additional tools. iGEAK which hasn’t been updated in three years and seems to have compatibility issues running on new Windows and Mac machines. sMAP, an upcoming Shiny app for microarray data published in bioRxiv on 29 May 2022. MAAP which has the same issue of log-transforming already log-transformed data. iDEP-READS does not list the datasets used in XTABLE. GEO2Enrichr appears to require the counts table and experimental design in one file, performs a “characteristic direction” DEG test and outputs enriched pathways. These apps require not just downloading of datasets but reformatting and renaming of expression data files and creation of additional files for setting up the DEG analysis which is not practical for the number of samples we have (122, 63, 33, 448) even if these apps handled microarray data. XTABLE also incorporates AUC metrics, which is appropriate given the number of samples in each dataset and tool known for adequately controlling FDR, which is not seen in other apps as well as emphasis on individual gene results and interrogation.

A new paragraph on the discussion section (lines 361-370) of the discussion addresses the potential use of existing applications instead of XTABLE

Secondly, XTABLE doesn't provide a solution to integrate the four datasets incorporated in the tool. One can only analyse one dataset at a time with XTABLE. The differences in terms of methodology and study design within these four datasets have been elaborated on in the article. However, attempts to integrate them were lacking.

We repeatedly considered different strategies of integrating the analysis of the four datasets and we always reached the conclusion that it was hardly going to offer any advantage, or that it might be counterproductive.

Integration can occur at multiple levels. One possibility is to carry out the same analysis (e.g. expression of a given gene in two groups of samples) in all datasets. Since the design and methodologies of the four studies differ substantially (different stages, different definitions of progression status, etc), a unique stratification for all datasets is not possible. Moreover, interrogating the four datasets simultaneously would slow the analysis, with no significant advantage in terms of speed. Another possibility is the integration of results in the same output. For instance, obtain a single chart with the expression of a given gene in multiple subgroups of the four datasets. We think that the results from each cohort should be kept separately and then compared with a similar analysis from other datasets due to differences in design. Scientifically, this is the best way to proceed as it avoids confusions.

Nevertheless, XTABLE allows the export of data for further analysis. The user can use this option to integrate data using other applications or statistical packages.

We do understand the attractiveness of integration between the four datasets is and we seriously considered it. But there is a fine balance between user-friendliness, flexibility, and scientific rigour. We think that XTABLE achieves this balance. Increasing integration of datasets might lead to error and wrong conclusions due to biological and methodological differences between studies. We believe that comparing analyses obtained independently from the four cohorts is the most sensible way to proceed.

We propose to discuss these aspects accordingly.

The integrative analysis of two or more datasets has been discussed in a new paragraph (382-391)

The tool also lacks the flexibility for users to add more datasets. This would be helpful when there are more datasets of PMLs available publicly.

This was also a permanent topic for discussion while designing XTABLE. Creating a tool that could be used to analyse other cohorts of precancerous lesions, while maintaining the ease of use was certainly a challenge. We had to adapt XTABLE to the characteristics of each one of the four databases: specific stratification criteria, different nomenclatures for the different sample types, etc. Designing a shiny app that can be adapted to other present or future datasets without the need of changing the code is simply not practical.

The flexibility that these other Shiny apps incorporate to analyse any RNA-seq dataset requires the contrasts used for the differentially expressed gene analysis be manually defined. IDEP requires an experimental design file where sample names in the counts file must match exactly the sample names in this experimental design file and pre-processing visualisation is limited to the first 100 samples. RNFuzzyApp is similar but we could not format the experimental design file in a way that did not result in the app crashing upon upload. TCC-GUI requires all the sample names to be renamed to the contrast group with the addition of the replicate number. Apps that allow datasets to be uploaded do not have a practical or easy way to set up the DEG analysis of more than a couple dozen samples.

Future versions of XTABLE can be updated to include additional curated PML datasets that would enhance hypothesis generation upon request. Importantly, the code is freely available and can be modified by other scientists to add their cohorts of interest, although we agree that a high level of expertise in coding will be needed. We propose to add these considerations to the text.

The possibilities of expansion of XTABLE to new databases are discussed in lines 392-398

Understanding the biology of PML progression would require a multi-omics approach. XTABLE analyses transcriptome data and lacks integration of other omics data. The authors mention the availability of data from whole exome, methylation, etc from the four studies they have selected. However, apart from the CIN scores, they haven't integrated any of the other layers of omics data available.

Only one dataset (GSE108104) contains whole-exome sequencing and methylation data. We considered that a multi-omics approach in XTABLE would result in an overcomplicated application. As far as early detection and biomarker discovery is concerned, transcriptomic data is the most interesting parameter.

Also discussed in lines 382-391

Lastly, the authors could have elaborated on the limitations of the tool and their analysis in the discussion.

We propose to raise these limitations accordingly in the discussion.

See above.

Reviewer #2 (Public Review):

In this manuscript, Roberts et al. present XTABLE, a tool to integrate, visualise and extract new insights from published datasets in the field of preinvasive lung cancer lesions. This approach is critical and to be highly commended; whilst the Cancer Genome Atlas provided many insights into cancer biology it was the development of accessible visualisation tools such as cbioportal that democratised this knowledge and allowed researchers around the world to interrogate their genes and pathways of interest. XTABLE is trying to do this in the preinvasive space and should certainly be commended as such. We are also very impressed by the transparency of the approach; it is quite simple to download and run XTABLE from their Gitlab account, in which all data acquisition and analysis code can be easily interrogated.

We would however strongly advocate deploying XTABLE to a web-accessible server so that researchers without experience in R and git can utilise it. We found it a little buggy running locally and cannot be sure whether this is due to my setup or the code itself. Some issues clearly need development; Progeny analysis brings up a warning "Not working for GSE109743 on the server and not sure why". GSEA analysis does not seem to work at all, raising an error "Length information for genome hg38 and gene ID ensGene is not available". In such relatively complex software, some such errors can be overlooked, as long as the authors have a clear process for responding to them, for example using Gitlab issue reporting. Some acknowledgement that this is an ongoing development would be helpful.

We thank the reviewer for these comments. We will inspect the code to address those warnings, implement a system for issue reporting, and add the acknowledgements suggested by the reviewer. Regarding the deployment of XTABLE to a web-accessible server, this could present a challenge in the long term as computing resources need to be allocated for years and the economic cost involved.

The code has been inspected to remove the warning and errors pointed out by the reviewer.

The authors discuss some very important differences between the datasets in the text. Most notably they differ in endpoints and in the presence of laser capture. We would advocate including some warning text within the XTABLE application to explain these. For example, the "persistent/progressive" endpoint used in Beane et al (next biopsy is the same or higher grade) is not the same as the "progressive" endpoint in Teixeira et al (next biopsy is cancer); samples defined as "persistent/progressive" may never progress to cancer. This may not be immediately obvious to a user of XTABLE who wishes to compare progressive and regressive lesions. Similarly, the use of laser capture is important; the authors state that not using laser capture has the advantage of capturing microenvironment signals, but differentiating between intra-lesional and stromal signals is important, as shown in the Mascaux and Pennycuick papers. The authors cannot do much about the different study designs, but as the goal is to make these data more accessible We think some brief description of these issues within the app would help to prevent non-expert users from drawing incorrect conclusions.

The authors themselves illustrate this clearly in their analysis of CIN signatures in progression potential. They observe that there is a much clearer progressive/regressive signal in GSE108124 compared to GSE114489 and GSE109743. This does not seem at all surprising, since the first study used a much stricter definition of progression - these samples are all about to become cancer whereas "progressive" samples in GSE109743 may never become cancer - and are much enriched for CIN signals due to laser capture. Their discussion states "CIN scores as a predictor of progression might be limited to microdissected samples and CIS lesions"; you cannot really claim this when "progression" in the two cohorts has such a different meaning. To their credit, the authors do explain these issues but they really should be clearly spelled out within the app.

This is a very good point. We will add the warning text about the differences between studies regarding the definition of progression potential and the differences and sample processing (LCM or o not) so that the user is permanently aware of the differences between cohorts.

A new tab (Dataset) has been added table with the methodologies used in each of each study, and the differences in progression status definitions. Additionally, we emphasized these differences in the main text of the manuscript (lines 296-300 and 403-409).

We are not sure we agree with their analysis of CDK4/Cyclin-D1 and E2F expression in early lesions. The authors claim these are inhibited by CDKN2A and therefore are markers of CDKN2A loss of function. But these genes are markers of proliferation and can be driven by a range of proliferative processes. Histologically, low-grade metaplasias and dysplasias all represent proliferative epithelium when compared to normal control, but most never become cancer. It is too much of a leap to say that these are influenced by CDKN2A because that gene is inactivated in LUSC; do the authors have any evidence that this gene is altered at the genomic level in low-grade lesions?

We are grateful for this comment. There is currently not evidence that CDKN2A mutations occur in low-grade lesions and therefore, we cannot argue that the of CDK4/Cyclin-D1 and E2F expression signature are the result of CDKN2A inactivation in low-grade lesions. We propose to modify the text to introduce these caveats to our conclusion an make our interpretations more accurate.

We have modified the discussion (lines 443-454) to address the interpretation of our results regarding the connection between CDKN2A inactivation and the CDK4/cyclin-D1 and E2F signatures. We now focus our conclusions on the pathway itself and we mention Cyclin-D1 and CDKN2A alterations as a potential modulator of the changes in the pathway, but leaving the discussion open to other drivers.

Overall this tool is an important step forwards in the field. Whilst we are a little unconvinced by some of their biological interpretations, and the tool itself has a few bugs, this effort to make complex data more accessible will be greatly enabling for researchers and so should be commended. In the future, we would like to see additional molecular data integrated into this app, for example, the whole genome and methylation data mentioned in line 153. However, we think this is an excellent start to combining these datasets.

Author Response

Reviewer #1 (Public Review):

Determination of the biomechanical forces and downstream pathways that direct heart valve morphogenesis is an important area of research. In the current study, potential functions of localized Yap signaling in cardiac valve morphogenesis were examined. Extensive immunostainings were performed for Yap expression, but Yap activation status as indicated by nuclear versus cytoplasmic localization, Yap dephosphorylation, or expression of downstream target genes was not examined.

We thank the reviewer for appreciating the significance of this work, and we also thank the reviewer for the constructive suggestions. Following these suggestions, we have improved analysis of YAP activation status and used nuclear versus cytoplasmic localization to quantify YAP activation. To address the reviewer’s concerns, we have conducted extra qPCR analysis of YAP downstream target genes and YAP upstream genes in Hippo pathway. Please find the detailed revisions in our responses to the Recommendations for authors.

The goal of the work was to determine Yap activation status relative to different mechanical environments, but no biomechanical data on developing heart valves were provided in the study.

We appreciate the reviewer for raising this concern. We have previously published the biomechanical data of developing chick embryonic heart valves in the following study:

Buskohl PR, Gould RA, Butcher JT. Quantification of embryonic atrioventricular valve biomechanics during morphogenesis. Journal of Biomechanics. 2012;45(5):895-902.

In that study, we used micropipette aspiration to measure the nonlinear biomechanics (strain energy) of chick embryonic heart valves at different developmental stages. Here in this study, we used the same method to measure the strain energy of YAP activated/inhibited cushion explants and compared it to the data from our previous study. Our findings were summarized in the Results: “YAP inhibition elevated valve stiffness”, and the detailed measurements, including images and data, are presented in Figure S4.

There are several major weaknesses that diminish enthusiasm for the study.

1) The Hippo/Yap pathway activation leads to dephosphorylation of Yap, nuclear localization, and induced expression of downstream target genes. However, there are no data included in the study on Yap nuclear/cytoplasmic ratios, phosphorylation status, or activation of other Hippo pathway mediators. Analysis of Yap expression alone is insufficient to determine activation status since it is widely expressed in multiple cells throughout the valves. The specificity for activated Yap signaling is not apparent from the immunostainings.

We thank the reviewer for pointing out this weakness. We have now implemented nuclear versus cytoplasmic localization as recommended to quantify YAP activation. We have also conducted additional experiments to analyze via qPCR YAP downstream target genes and YAP upstream genes in Hippo pathway. Please see the detailed revisions in our responses to the Recommendations for authors.

2) The specific regionalized biomechanical forces acting on different regions of the valves were not measured directly or clearly compared with Yap activation status. In some cases, it seems that Yap is not present in the nuclei of endothelial cells surrounding the valve leaflets that are subject to different flow forces (Fig 1B) and the main expression is in valve interstitial subpopulations. Thus the data presented do not support differential Yap activation in endothelial cells subject to different fluid forces. There is extensive discussion of different forces acting on the valve leaflets, but the relationship to Yap signaling is not entirely clear.

We thank the reviewer for these important questions. The region-specific biomechanics have been well mapped and studied, thanks to the help from Computational Fluid Dynamics supported by ultrasound velocity and pressure measurements. For example:

Yalcin, H.C., Shekhar, A., McQuinn, T.C. and Butcher, J.T. (2011), Hemodynamic patterning of the avian atrioventricular valve. Dev. Dyn., 240: 23-35.

Bharadwaj KN, Spitz C, Shekhar A, Yalcin HC, Butcher JT. Computational fluid dynamics of developing avian outflow tract heart valves. Ann Biomed Eng. 2012 Oct;40(10):2212-27. doi: 10.1007/s10439-012-0574-8.

Ayoub S, Ferrari G, Gorman RC, Gorman JH, Schoen FJ, Sacks MS. Heart Valve Biomechanics and Underlying Mechanobiology. Compr Physiol. 2016 Sep 15;6(4):1743-1780.

Salman HE, Alser M, Shekhar A, Gould RA, Benslimane FM, Butcher JT, et al. Effect of left atrial ligation-driven altered inflow hemodynamics on embryonic heart development: clues for prenatal progression of hypoplastic left heart syndrome. Biomechanics and Modeling in Mechanobiology. 2021;20(2):733-50.

Ho S, Chan WX, Yap CH. Fluid mechanics of the left atrial ligation chick embryonic model of hypoplastic left heart syndrome. Biomechanics and Modeling in Mechanobiology. 2021;20(4):1337-51.

Those studies have shown that USS develops on the inflow surface of valves while OSS develops on the outflow surface of valves, CS develops in the tip region of valves while TS develops in the regions of elongation and compaction. Here in this study, we mimic those forces in our in-vitro and ex-vivo models. This allows us to study the direct effect of specific force on the YAP activity in different cell lineages. The results showed that OSS promoted YAP activation in VECs while USS inhibited it, CS promoted YAP activation in VICs while TS inhibited it. This result well explained the spatiotemporal distribution of YAP activation in Figure 1. For example, nuclear YAP was mostly found in VECs on the fibrosa side, where OSS develops, and YAP was not expressed in the nuclei in VECs of the atrialis/ventricularis side, where USS develops. It is also worth noting that formation of OSS on the outflow side is slower, and thus the side specific YAP activation in VECs was not in effect at the early stage, from E11.5 to E14.5.

3) The requirement for Yap signaling in heart valve remodeling as described in the title was not demonstrated through manipulation of Yap activity.

With respect, it is unclear what the reviewer is asking for given no experiments are suggested nor an elaboration of alternative interpretations of our results that emphasize against YAP requirement. It has been previously shown that YAP signaling is required for early EMT stages of valvulogenesis using conditional YAP deletion in mice:

Zhang H, von Gise A, Liu Q, Hu T, Tian X, He L, et al. Yap1 Is Required for Endothelial to Mesenchymal Transition of the Atrioventricular Cushion. Journal of Biological Chemistry. 2014;289(27):18681-92.

Signaling roles for early regulators at these later fetal stages are different, sometimes opposite early EndMT stages, thus contraindicating reliance on these early data to explain later events:

Bassen D, Wang M, Pham D, Sun S, Rao R, Singh R, et al. Hydrostatic mechanical stress regulates growth and maturation of the atrioventricular valve. Development. 2021;148(13).

However, embryos with YAP deletion failed to form endocardial cushions and could not survive long enough for the study of its roles in later cushion growth and remodeling into valve leaflets. In this work,

We first showed the localization of YAP activity and its direct link with local shear or pressure domains. Then we explicitly applied controlled gain and loss of function of YAP via specific molecules. We also applied critical mechanical gain or loss of function studies to demonstrate YAP mechanoactivation necessity and sufficiency to achieve growth and remodeling.

Reviewer #2 (Public Review)

This study by Wang et al. examines changes in YAP expression in embryonic avian cultured explants in response to high and low shear stress, as well as tensile and compressive stress. The authors show that YAP expression is increased in response to low, oscillatory shear stress, as well as high compressive stress conditions. Inhibition of YAP signaling prevents compressive stress-induced increases in circularity, decreased pHH3 expression, and increases VE-cadherin expression. On the other hand, YAP gain of function prevents tensile stress-induced decreases in pHH3 expression and VE-cadherin expansion. It also decreases the strain energy density of embryonic avian cushion explants. Finally, using an avian model of left atrial ligation, the authors demonstrate that unloaded regions within the primitive valve structures are associated with increased YAP expression, compared to regions of restricted flow where YAP expression is low. Overall, this study sheds light on the biomechanical regulation of YAP expression in developing valves.

We thank the reviewer for the accurate summary and their enthusiasm for this work.

Strengths of the manuscript include:

• Novel insights into the dynamic expression pattern of YAP in valve cell populations during post-EMT stages of embryonic valvulogenesis.

• Identify the positive regulation of YAP expression in response to low, oscillatory shear stress, as well as high compressive stress conditions.

• Identify a link between YAP signaling in regulating stress-induced cell proliferation and valve morphogenesis.

• The inclusion of the atrial left atrial ligation model is innovative, and the data showing distinguishable YAP expression levels between restricted, and non-restricted flow regions is insightful.

We thank the reviewer for appreciating the strengths of this work.

This is a descriptive study that focuses on changes in YAP expression following exposure to diverse stress conditions in embryonic avian cushion explants. Overall, the study currently lacks mechanistic insights, and conclusions based on data are highly over-interpreted, particularly given that the majority of experimental protocols rely on one method of readout.

We thank the reviewer for constructive suggestions.

Reviewer #3 (Public Review)

In this manuscript, Wang et al. assess the role of wall shear stress and hydrostatic pressure during valve morphogenesis at stages where the valve elongates and takes shape. The authors elegantly demonstrate that shear and pressure have different effects on cell proliferation by modulating YAP signaling. The authors use a combination of in vitro and in vivo approaches to show that YAP signaling is activated by hydrostatic pressure changes and inhibited by wall shear stress.

We thank the reviewer for their enthusiasm for the impact of our work.

There are a few elements that would require clarification:

1) The impact of YAP on valve stiffness was unclear to me. How is YAP signaling affecting stiffness? is it through cell proliferation changes? I was unclear about the model put forward:

• Is it cell proliferation (cell proliferation fluidity tissue while non-proliferating tissue is stiffer?)

• Is it through differential gene expression?

This needs clarification.

We thank the reviewer for raising this important question. Cell proliferation can affect valve stiffness but is a minor factor compared with ECM deposition and cell contractility Our micropipette aspiration data showed that the higher cell proliferation rate induced by YAP activation did lead to stiffer valves when compared to the controls. This may be because at the early stages, cells are more elastic than the viscous ECM. However, the stiffness of YAP activated valves were only about half of that of YAP inhibited valves, showing that the transcriptional level factor plays a more important role. This also suggests that YAP inhibited valves exhibited a more mature phenotype. An analogous role of YAP has also been found in cardiomyocytes. Many theories propose that in cardiomyocytes when YAP is activated the proliferation programs are turned on, while when YAP is inhibited the proliferation programs are turned off and maturation programs are released. Similarly, here we hypothesize that YAP works like a mechanobiological switch, converting mechanical signaling into the decision between growth and maturation. We have revised the Discussion to include this hypothesis.

2) The model proposes an early asymmetric growth of the cushion leading to different shear forces (oscillatory vs unidirectional shear stress). What triggers the initial asymmetry of the cushion shape? is YAP involved?

Although the initial geometry of the cushion model is symmetric, the force acting on it is asymmetric. The detailed numerical simulation of how the initial forces trigger the asymmetric morphogenesis can be found in our previous publication:

Buskohl PR, Jenkins JT, Butcher JT. Computational simulation of hemodynamic-driven growth and remodeling of embryonic atrioventricular valves. Biomechanics and Modeling in Mechanobiology. 2012;11(8):1205-17.

The color maps represent the dilatation rates when a) only pressure is applied, b) only shear stress is applied, and c) both pressure and shear stress are applied. It is such load that initiates an asymmetric morphological change, as shown in d). In addition, we believe YAP is involved during the initiation because it is directly nuclear activated by CS and OSS or cytoplasmically activated by TS and LSS.

3) The differential expression of YAP and its correlation to cell proliferation is a little hard to see in the data presented. Drawings highlighting the main areas would help the reader to visualise the results better.

We thank the reviewer for this helpful suggestion, we have improved the visualization of Figure 3C and Figure 4C with insets of higher magnification.

4) The origin of osmotic/hydrostatic pressure in vivo. While shear is clearly dependent upon blood flow, it is less clear that hydrostatic pressure is solely dependent upon blood flow. For example, it has been proposed that ECM accumulation such as hyaluronic acid could modify osmotic pressure (see for example Vignes et al.PMID: 35245444). Could the authors clarify the following questions:

• How blood flow affects osmotic pressure in vivo?

• Is ECM a factor that could affect osmotic pressure in this system?

We thank the reviewer for sharing this interesting study. The osmotic pressure plays a critical role in mechanotransduction and the development of many tissues including cardiovascular tissues and cartilage. As proposed in the reference, osmotic pressure is an interstitial force generated by cardiac contractility. Here in our study, the hydrostatic pressure is different, which is an external force applied by flowing blood. According to Bernoulli's law, when an incompressible fluid flows around a solid, the static pressure it applies on the solid is equal to its total pressure minus its dynamic pressure.

Despite the difference, the osmotic pressure can mimic the effect of hydrostatic pressure in-vitro. The in-vitro osmotic pressure model has been widely used in cartilage research, for example:

P. J. Basser, R. Schneiderman, R. A. Bank, E. Wachtel, and A. Maroudas, “Mechanical properties of the collagen network in human articular cartilage as measured by osmotic stress technique.,” Arch. Biochem. Biophys., vol. 351, no. 2, pp. 207–19, 1998.

D. a. Narmoneva, J. Y. Wang, and L. a. Setton, “Nonuniform swelling-induced residual strains in articular cartilage,” J. Biomech., vol. 32, no. 4, pp. 401–408, 1999.

C. L. Jablonski, S. Ferguson, A. Pozzi, and A. L. Clark, “Integrin α1β1 participates in chondrocyte transduction of osmotic stress,” Biochem. Biophys. Res. Commun., vol. 445, no. 1, pp. 184–190, 2014.

Z. I. Johnson, I. M. Shapiro, and M. V. Risbud, “Extracellular osmolarity regulates matrix homeostasis in the intervertebral disc and articular cartilage: Evolving role of TonEBP,” Matrix Biol., vol. 40, pp. 10–16, 2014.

When maturing cushions shift from GAGs dominated ECM to collagen dominated ECM, the water and ion retention capacity of the tissue would be greatly changed, and thus reducing the osmotic pressure. This could in turn accelerate the maturation of cushions. By contrast, the ECM of growing cushions remain GAGs dominated, which would delay maturation and prolong the growth.

The revised second section of Results is as follows:

Shear and hydrostatic stress regulate YAP activity

In addition to the co-effector of the Hippo pathway, YAP is also a key mediator in mechanotransduction. Indeed, the spatiotemporal activation of YAP correlated with the changes in the mechanical environment. During valve remodeling, unidirectional shear stress (USS) develops on the inflow surface of valves, where YAP is rarely expressed in the nuclei of VECs (Figure 2A). On the other side, OSS develops on the outflow surface, where VECs with nuclear YAP localized. The YAP activation in VICs also correlated with hydrostatic pressure. The pressure generated compressive stress (CS) in the tips of valves, where VICs with nuclear YAP localized (Figure 2B). Whereas tensile stress (TS) was created in the elongated regions, where YAP was absent in VIC nuclei.

To study the effect of shear stress on the YAP activity in VECs, we applied USS and OSS directly onto a monolayer of freshly isolated VECs. The VEC was obtained from AV cushions of chick embryonic hearts at HH25. The cushions were placed on collagen gels with endocardium adherent to the collagen and incubated to enable the VECs to migrate onto the gel. We then removed the cushions and immediately applied the shear flow to the monolayer for 24 hours. The low stress OSS (2 dyn/cm2) promoted YAP nuclear translocation in VEC (Figure 2C, E), while high stress USS (20 dyn/cm2) restrained YAP in cytoplasm.

To study the effect of hydrostatic stress on the YAP activation in VICs, we used media with different osmolarities to mimic the CS and TS. CS was induced by hypertonic condition while TS was created by hypotonic condition, and the Unloaded (U) condition refers to the osmotically balanced media. Notably, in-vivo hydrostatic pressure is generated by flowing blood, while in-vivo osmotic pressure is generated by cardiac contractility and plays a critical role in the mechanotransduction during valve development (30). Despite the different in-vivo origination, the osmotic pressure provides a reliable model to mimic the hydrostatic pressure in-vitro (31). We cultured HH34 AV cushion explants under different loading conditions for 24 hours and found that the trapezoidal cushions adopted a spherical shape (Figure 2D). TS loaded cushions significantly compacted, and the YAP activation in VICs of TS loaded cushions was significantly lower than that in CS loaded VICs (Figure 2F).

Author Response

Reviewer #2 (Public Review):

The idea of using fluorescently labeled tandem SH2 domains to target tagged RTKs is brilliant and could potentially provide a powerful new way to assess the activation of RTKs in situ and in multiple physiological contexts. Thus, it was disappointing that there was insufficient characterization of the system to be able to interpret the data it generates. Although the paper shows that tagging the EGFR appears to have minimal impact on its biological activity, the readout for receptor kinase activity is % clearance of the fluorescent reporter tag from the cytosol. Such clearance is likely to depend on a variety of different factors, including the ratio of tagged receptors to probe, the number of functional pools in which the probe exists, the exchange rate between these pools, and the affinity of the probes for the tagged receptor. Without determining how each of these factors impacts % clearance, it is difficult to interpret either the dose-response curves or response kinetics.

We appreciate the reviewer’s point that the paper would be improved by a thorough analysis of how membrane translocation depends on our biosensor’s expression levels. We have attempted to address this thoroughly in our response to the Editor’s summary comments above. Briefly, we have now added 3 new supplementary figures (Figures S2-S4) in which we quantify ZtSH2 translocation as a function of expression levels. We find that the ratio of EGFR/ZtSH2 expression predicts the extent of ZtSH2 translocation in both NIH3T3 and HEK293T cells, matching results from our computational model. We have also added a new section to the main text to clearly explain these results (Lines 190-235). We hope that these data clarify the design constraints for two-component biosensors of this type.

For example, the difference in activation kinetics between EGFR and ErbB2 is very interesting, but the almost instantaneous rise (Fig S4B) is very surprising. The kinetics of activation of the EGFR have been extensively studied by mass-spectrometry and are generally limited by ligand binding, which has a characteristic time of several minutes, not seconds (pmid: 26929352; pmid: 1975591). Thus, such a response is suggestive of a freely exchanging ZtSH2 reporter pool that is mostly depleted in seconds with the slow secondary kinetics reflecting a slowly exchanging ZtSH2 reporter pool. Alternately, the cells could be accumulating an intracellular pool of activated receptors over time. That the authors are using concentrations of EGF >100-fold physiological levels (pmid: 29268862) further complicates the interpretation of these experiments.

We thank the reviewer for bringing these papers to our attention. However, we strongly disagree with their interpretation of the results. In a paper cited by the reviewer (PMID:26929352), phosphotyrosine responses are extremely fast, with phosphorylation occurring within tens of seconds even in response to 20 nM EGF (see Figure 2 from Reddy et al PNAS 2016). Reddy et al further claim in their abstract “Significant changes were observed on proteins far downstream in the network as early as 10 s after stimulation.” While the timescale of EGFR phosphorylation may be of some debate, the response timescale we observe is consistent with previously published observations.

It is also important to point out that the secondary gradual rise of ZtSH2 recruitment is only observed upon treatment with EGF, not EREG or EPGN (Figure 3A). The gradual rise can also be observed upon treatment with EREG in the presence of a GBM-associated EGFR mutation that alters receptor dimerization (Figure 3E). These data indicate that the secondary rise is not an intrinsic feature of the ZtSH2 reporter, and instead represents a feature of ligand-receptor activation itself.

The reviewer suggests that perhaps there is some internal pool of ZtSH2 or EGF, but we find no evidence for such a pool in our microscopy imaging. To clarify this point to the reader, we have now added a new supplementary figure (Figure S6) showing representative cells for all stimulation conditions used in Figure 3A, showing consistent, high levels of EGFR and ZtSH2 enrichment at the plasma membrane and uniform cytosolic intensity for at least 30 min after stimulation across all ligands.

Finally, while the reviewer mentions the use of high EGF doses in our paper, we would like to point out that we performed extensive experiments at other doses in the manuscript, testing 14 total doses of three EGFR ligands in Figure 3, and present additional data at 20 ng/mL EGF throughout Figures 2, S2, and S7. It is also very important to test high input doses for our negative controls to ensure that the ZtSH2 biosensor retains specificity for ITAM sequences and fails to show recruitment to untagged EGFR even under saturating conditions. It is also quite customary in the field: for example, the Erk KTR paper that the reviewer mentions in a later comment (Regot et al, Cell 2014) exclusively tests their biosensors using saturating doses of 50 ng/mL anisomycin, 100 ng/mL FGF, and 10 μM forskolin to characterize p38, Erk and PKA biosensor responses.

There is also insufficient attention paid to either controlling or measuring important parameters, such as expression levels of tagged receptors or levels of endogenous receptors. 3T3 cells, contrary to the statement of the authors, do not have "negligible" numbers of EGFR: they have ~40K, which is typical for mouse fibroblasts. This is much higher than MCF7 cells, which are frequently used as a model system to study EGFR responses. Yet they do not see transactivation of their ErbB2 construct in 3T3 cells without expressing additional EGFR (Fig. 4C), suggesting low sensitivity of the assay. Conversely, they show a significant response mediated by endogenously tagged EGFR in HEK 293 cells, which are frequently used as an EGFR-negative cell line (PMID: 26368334). This indicates that their assay is extremely sensitive. Which is it? As mentioned above, it likely depends on the expression level and affinity of the different components of their system.

After extensive searching we have not found any publications with an estimate as high as 40K EGFR receptors/cell in NIH3T3 cells. Livneh et al 1986 report that NIH3T3 cells express as little as 500 EGFR receptors per cell and do not respond mitogenically to EGF, and subsequent Schlessinger lab papers use NIH3T3 cells as an EGFR-null background for introduction of receptor variants. Eierhoff et al PLOS Pathogens 2010 use NIH3T3s as an EGFR-null control, showing immunoblot data of undetectable pEGFR responses. The paper we found with the highest stated EGFR expression per cell in NIH3T3 cells is Verbeek et al, FEBS Lett 1998, which reports a value of 3,000 receptors per cell, but does so without any literature citation or measurement. These references are consistent with our experience: over nearly a decade of MAPK signaling experiments in the lab, we have only seen weak or undetectable EGF-stimulated responses in unmodified NIH3T3s, depending on the assay. We are quite confident that more potent responses are elicited in HEK293T cells, where we observe EGFR expression by fluorescence imaging of CRISPR-tagged cells, immunofluorescence staining, and immunoblotting, and where we observe robust signaling responses using biosensors. We also now cite some of these references to support our claim (Line 144).

The reviewer makes an excellent point in the last sentence of their comment: indeed, it is essential to match the expression level of our SH2-based biosensor to the expression level of EGFR in any system in order to observe potent membrane translocation! This was imperative for visualizing any translocation in our CRISPR-tagged HEK293Ts: we had to switch to an exceptionally bright fluorophore and select cells with very low ZtSH2 expression to observe translocation. The ZtSH2/EGFR ratio is a crucial design parameter, which we now present extensive data and modeling to support (Figure S2-S4; Lines 190-235). Our data suggests that quite sensitive biosensor responses are possible with appropriate balance between ZtSH2 and EGFR expression levels (Figure 6) and, in general, biosensor responses can be matched to a dynamic range of interest by scaling ZtSH2 expression with EGFR levels.

A great advantage of using the EGFR system as a test case for the new system is that thousands of investigations have been performed over the last four decades. This provides a strong foundation for determining whether the new technology is working correctly. For example, the dynamics of EGFR activation and trafficking at the single cell level have been documented in many studies, which show a remarkable consistency (e.g. see pmid: 24259669; pmid: 11408594; pmid: 25650738). Unfortunately, instead of using differences between the new results and previously reported data as a basis for refining their technique, the authors attempt to apply their raw data to address complex questions of EGFR dynamics, with less than satisfactory results.

For example, they attempt to use their technique to understand the basis of different signaling dynamics between EGFR ligands. Rather than being a relatively recent observation, differences in EGFR ligand signaling have been explored for over 30 years (pmcid: PMC361851), and are generally ascribed to differences in trafficking (pmid: 7876195). Based on these observations and resulting mathematical models, novel EGFR ligands have been designed with enhanced potency (pmid: 8195228 , pmid: 9634854 ). All this work was done over 20 years ago. Since then, new natural ligands for the EGFR have been discovered from sequence analysis and differences in their potency have similarly been ascribed to differences in their intracellular trafficking patterns (pmid: 19531065 - cited by the authors). An alternate hypothesis was proposed more recently by Freed et al (2017) as described by the authors, but that is what it is: an alternative hypothesis.

We thank the reviewer for pointing out many excellent, classic studies on EGFR endocytosis and trafficking. We agree that this is a well-established field and that EGFR is certainly internalized, recycled, and degraded in a manner that depends on ligand affinity on the cell surface and in endosomes. These seminal studies lead the reviewer to propose an alternative hypothesis to explain our kinetic data in Figure 3: that differences in trafficking and maintenance of EGFR levels at the plasma membrane are the source of the altered kinetics between high- and low-affinity ligands. To address this question, we have now included new supplementary data examining endocytosis and trafficking in multiple contexts.

First, we examine membrane EGFR levels in 3T3 cells overexpressing our EGFR-pYtag system (or ITAM-less EGFR as a control) after EGF stimulation (Figure S5A-C). We find that EGFR membrane intensity is virtually unchanged after 60 min of saturating EGF stimulation, a response that does not depend on whether ITAMs are appended to the receptor. We also now include still images of cells at every concentration examined in our dose-response experiments for all 3 ligands (Figure S6), which do not show clear differences in the subcellular distribution of EGFR before and after stimulation as a function of ligand identity. We also remind the reviewer that our interpretation is not simply an untested hypothesis – we experimentally tested a GBM-associated EGFR variant whose effect on receptor dimerization has been quantified, and observe EGF-like response kinetics even after EREG stimulation, a result predicted by our model (Figure 3D-E).

We believe that the sustained membrane-localized signaling we observe might be ascribed to two factors: our choice of cell line and its expression level of EGFR. This conjecture is supported by some data: in contrast to our EGFR-overexpressing NIH3T3 cells, HEK293Ts harboring endogenous or low EGFR levels exhibit a dramatic redistribution of EGFR after EGF stimulation (Figure S3, Figure 6). This is clearly a context where transient versus sustained signaling might depend on the choice of ligand and its consequences on internalization.

We also note that our data identify ligand-specific signaling differences that are distinct from prior studies, which focused on transient vs sustained signaling downstream of different EGFR ligands. In contrast, we identify a biphasic increase in EGFR activity after stimulation with EGF versus a rapid approach to steady state after stimulation with EREG or EPGN, despite the continued presence of high levels of membrane-localized EGFR in each case.

Unfortunately, the model that the authors use to test this hypothesis does not even include endocytosis or receptor trafficking but instead uses variable "scaling" factors to see if the data can fit the dimerization hypothesis. In the supplement, they state that "Since our simulations were run on relatively short time scales (~30 min post-stimulation), we did not consider trafficking and degradation of receptors." However, the half-life of EGFR internalization is generally ~3-4min (pmid: 1975591) and degradation ~1hr, so most of the signal shown in Figure 3 is likely to come from internalized rather than surface-associated ligand-EGFR complexes. A further complication is that internalization rates are strongly influenced by receptor expression levels (pmid: 3262110), which are not controlled for here. Thus, the omission of trafficking in their model is not appropriate. This does not mean that the authors are wrong, it simply means that without validation or calibration, their new technology is not ready to resolve current problems in the field.

We thank the reviewer for pointing out ways to improve our modeling (endocytosis) and discussion of its parameterization (scaling factors). We address both points below:

Scaling factors: We thank the reviewer for their comments & agree that our discussion of model parameterization was lacking. To clarify: our base-case model for EGF includes 9 parameters, 6 of which are obtained from literature and 3 which reflect lumped kinetic processes of EGFR dimerization and activation and which we set to match our data. We then used experimentally-determined values to change the base-case model to simulate low-affinity ligand stimulation: a fold-change in ligand affinity and a fold-change in receptor dimerization. This is why we simulate EREG with β=50 and γ=100, reflecting the 10-to-100-fold differences in binding affinity and receptor dimerization that have been experimentally measured for this low-affinity ligand. Similar experimentally defined values constrain β and γ in the case of GBM-associated mutations. A more thorough explanation of our model and these scaling parameters is now included in Lines 334-362.

Endocytosis: We wholeheartedly agree that our model is quite simplified, and a thorough treatment of endocytosis and trafficking would be essential for capturing nuances associated with these steps of the cascade. However, while we appreciate the 3-4 min rule of thumb for EGFR internalization that the reviewer mentions, it is simply not reflective of the membrane-associated EGFR levels we observe in our cells. Examples can be observed in Figure 1C, Figure 2A, Figure 5F, Figure S1B, Figure S2A-B, Figure S5A, and Figure S6, as well as in the quantification of membrane associated EGFR at 0 and 60 min in Figure S5B. It is quite likely that endocytosis and trafficking are operating throughout this time course, but are balanced to maintain similarly high level of EGFR at the cell surface. We wholeheartedly agree with the reviewer’s helpful note that EGFR expression levels heavily influence internalization, which our data also support, and may explain our results. For example, we also see rapid EGFR membrane clearance in HEK293T CRISPR cells (Figure 6) and in HEK293Ts that express low levels of EGFR but not high levels of EGFR (Figure S3A).

In sum, we argue that our inclusion of additional data showing sustained EGFR protein levels and ZtSH2 recruitment at the plasma membrane should help justify our assumption of membrane-associated signaling in our model. However, we happily concede that this is a highly simplified model, and that endocytosis is a very important process that should be accounted for in future studies (e.g., Line 344-346: “However, we expect that internalization and trafficking can play a crucial role in EGFR dynamics in many contexts, which would need to be included in future models to adequately assess those scenarios”).

Author Response

Reviewer #3 (Public Review):

Over the past decade, Cryo-EM analysis of assembling ribosomes has mapped the major intermediates of the pathway. Our understanding of the mechanisms by which ATPases drive the transitions between states has been slower to develop because of the transient nature of these events. Here, the authors use cryo-EM and biochemical and molecular genetic approaches to examine the function of the DEAD-box ATPase Spb4 and the AAA-ATPase Rea1 in RNP remodeling. Spb4 works on the pre-60S in an early nucleolar state. The authors find that Spb4 acts to remodel the three-way junction of H62/H63/H63a at the base of expansion segment ES27. Interestingly, Spb4 appears to interact stably with a folding intermediate in the ADP rather than ATP-bound form. This work represents one of the few cases in which an RNA helicase of ribosome biogenesis has been captured and engaged with its substrate. The authors then show that the addition of the AAA-ATPase Rea1 to Spb4-purified particles results in the release of Ytm1, a known target of Rea1. However, they did not observe an efficient release of Ytm1 when particles were affinity purified via Ytm1, suggesting that the recruitment of Spb4 is important for this step. Cryo-EM analysis of Spb4-particles treated with Rea1 revealed the previously characterized state NE particles but no additional intermediates. Consequently, this analysis of Rea1 is less informative about its function than is their work on Spb4 helicase activity. In general, the data support the authors' conclusions and the data are well presented.

Major points

1) The Erzberger group has recently published work regarding the function of Spb4. They similarly found that Spb4 is necessary for remodeling the 3-way junction at the base of ES27. Although it was posted to Biorxiv in Feb 2022, it was not formally published until Dec 2022. The authors should cite this work and include a brief discussion comparing conclusions.

We are now citing this study in the introduction and discussion and are briefly comparing the conclusions.

2) L311. The heading "Coupled pre-60S dissociation of the Ytm1-Erb1 complex and RNA helicase Has1" should be changed. Coupling implies a mechanistic interplay. Although the release of Ytm1 and Has1 both depend on Rea1, the data do not support the conclusion of mechanistic coupling. In fact, the authors write in lines 328-329 "Thus, the Rea1-dependent pre-60S release of the Ytm1-Erb1 complex occurs before and independently of Has1..." Independently cannot also imply coupling.

We have changed the heading into “Ytm1–Erb1 release promotes the dissociation of the RNA helicase Has1”.

3) L339-342 Combining data sets for uniform processing was a great idea! This approach should be used more often in cryo-EM analyses of in vitro maturation reactions.

We agree with the reviewer that this approach is appropriate to analyse such reactions.

4) L428 The authors need to amend their comment that this is the first structure of Spb4-bound to the substrate as this has recently been published by the Erzberger group and was first posted as a preprint in early 2022.

We have removed the statement regarding the first structure of Spb4 and added a citation of the study published by Cruz et al.

Author Response

Reviewer #1 (Public Review):

This manuscript builds on data from the same group showing that Lphn2 functions cell-autonomously as a receptor in CA1 pyramidal axons and cell-non-autonomously as a ligand in the neurons of the subiculum. In either case, binding of teneurin-3 to Lphn2 mediates repulsive events, and since different populations of neurons within each region express differing levels of both proteins, this mechanism allows proximal CA1 pyramidal axons to preferentially project to distal subiculum and distal CA1 pyramidal axons to project to proximal subiculum. The authors now ask mechanistic questions about the role of Lphn2 signaling in these wiring processes.

The authors demonstrate that G-protein signaling downstream of Lphn2, which is mediated by the tethered agonist, is necessary for the ability of ectopically expressed Lphn2 to redirect proximal CA1 axons from distal to proximal subiculum. Moreover, the authors show that while autoproteolytic activity of Lphn2 facilitates G-protein signaling, possibly by making the tethered agonist more available for signaling, it is not necessary for axonal mistargeting. Thus, the authors conclude that tethered agonistdependent G-protein signaling is required for Lphn2-mediated hippocampal neural circuit assembly. Most of the data shown in support of these conclusions are convincing, though I have some concerns about the expression levels and/or effects of the tethered agonist mutants in CA1, which is important since the analyses assume that any defects are in the repulsive interactions described above.

We thank Reviewer 1 for their suggestion to incorporate data on the expression levels of the tethered agonist mutants in CA1. We have now performed additional experiments and included a new Figure 1—figure supplement 2A-B to address this concern.

The authors also use heterologous cells to determine that Lphn2 couples to Ga12/13, but not other heteromeric G-proteina-subunits. Within the context of heterologous cells, these experiments are well controlled and exhaustive, as every mutant used in vivo is carefully analyzed. One potential criticism of this work, however, is that perhaps the authors assume too much in simply translating their results in heterologous cells to neurons, especially when one of the most interesting conclusions of this paper (see below) is that Lphn2 signaling is context-dependent. Without further data to confirm the results of these experiments in the neuronal populations studied, these data primarily illustrate possibilities, but don't exclude other possibilities.

We are grateful to Reviewer 1 for bringing this potential criticism to our attention. We have now included clarification of this point in the text and discussion of the manuscript, as noted in our response to Essential Revision #3 above.

Finally, the authors test the role of Lphn2 functioning as a ligand in the subiculum by driving its expression in the normally Lphn2-low dorsal subiculum. As they reported before, this alteration decreases the ability of proximal CA1 axons to project to this area. Interestingly, and in contrast to the role of Lphn2 as a receptor above, neither Lphn2 autoproteolysis nor tethered agonist function are required for this effect. This finding is very interesting and will merit follow-up, though I agree with the authors that this manuscript does not require this for publication.

In summary, this is an interesting paper that addresses timely and pressing issues in the adhesion-GPCR field.

Reviewer #2 (Public Review):

This is an intriguing study investigating the molecular mechanisms of the adhesion G-protein coupled receptor latrophilin-2 control of neural circuit developmental organization. Using the model CA1 to subiculum hippocampal circuit with its spatially segregated axon targeting, the authors experiments find that ectopic Lphn2 expression in CA1 neurons that normally do not express it, leads to axon mistargeting. The authors detail these circuitry alterations with Lphn2 genetic manipulations, finding that axon targeting is dependent on its GPCR signaling, likely through Galpha12/13 coupling.

Strengths: Building off the author's previous studies, the experiments are well designed and analyzed. The advance in this study is finding that Lphn2 expression in CA1 cells that normally do not express impacts its axon targeting. They go on to show compelling data that implicates this mistargeting is dependent on Lphn2 GPCR signaling properties, identified as likely Galpha12/13 dependent.

Weaknesses: The system used is a "misexpression system". By forcing cells with ordinally low levels to overexpress Lphn2, circuitry alterations are observed. While this gain of function assay demonstrates the importance as to why Lphn2 is not expressed in certain cell types, it isn't a physiologically relevant system to investigate Lphn2 dependent circuit development.

We thank Reviewer 2 for the appreciation of our study. We wish to clarify, in response to the critiques of the artificial nature of misexpression system, that experiments involving loss-of-function of endogenous Lphn2 have been described in our previous study (Pederick et al., 2021). When we conditionally deleted Lphn2 in CA1, Lphn2+ mid-CA1 axons spread to distal, Ten3+ subiculum. Thus, both the gain-of-function experiment described in this study and the loss-of-function experiment described in Pederick et al., 2021 support the notion that Lphn2 acts in axons as a repulsive receptor for the Ten3 ligand.

To strengthen this study, the following specific points could use addressing:

1) While the data is strong, some of the terminology used is unclear, including use of terms "repulsive receptor" and "repulsive ligand". The authors use "repulsive receptor" to describe Lphn2 action for axon targeting, but repulsion and attraction processes are simultaneous. Is Lphn2 really by acting as a repulsive receptor, or alternatively, by acting to shift axon attraction to Lphn2 expressing subiculum neurons?

We apologize for the lack of clarity. The terms “receptor” and “ligand” are used to refer to a molecule’s role in axons or target neurons, respectively, a common usage in the axon guidance field (Kolodkin and Tessier-Lavigne, 2011; PMID 21123392). Using a series of loss and gain of function manipulations, our previous data support a role for Lphn2 both as a repulsive receptor in axons and repulsive ligand in target neurons. When Lphn2 is deleted in CA1 axons they invade Ten3 subiculum target neurons. Similarly, deletion of Ten3 in the subiculum results in Lphn2-positive axons invading the Ten3 KO area. Unlike its partner Ten3, which can serve as an attractive receptor when the ligand is Ten3 and repulsive receptor when the ligand is Lphn2, Lphn2 only serves as a repulsive receptor to the Ten3 ligand. We (and others) have shown that Lphn2 does not bind homotypically (Boucard et al., 2014 and Pederick et al., 2021). We have clarified these points in the revised manuscript (2nd paragraph of Introduction).

2) For their proposed axon guidance model to work, Lphn2 has to be signaling through Ga12/13 proteins near the axon growth cone to induce its collapse and retraction. By using Flag-tagged Lphn2 constructs in their assays, this should be visible. Clear Flag-Lphn2 signal is observed in the dendrites of infected cells (Figure1-figure supplement 1; Figure5- figure supplement 1). But does Flag-Lphn2 also localize to the pCA1 axons that are projecting to the subiculum?

Thank you for this important question. We have added new data to show that FLAG-tagged Lphn2 is indeed found in CA1 axons. Please see our response in “Essential Revision #2” above.

3) With their previous work, pCA1 to dSub circuit patterning is dependent on Ten3+ to Ten3+ homophilic attraction that exists between the two regions. Its unclear how ectopic Lphn2 is able to override this Ten3+ to Ten3+ connection patterning. Does ectopic Lphn2 outcompete Ten3 function in these neurons? Or alternatively, is Ten3 expression/localization impacted by the presence of ectopic Lphn2?

We believe it is the former. Regarding the latter, please see our response in “Essential Revision #1” above.

Author Response

Reviewer #1 (Public Review):

Idiosyncratic drug-induced liver injury is a disease that appears to be linked to mitochondrial DNA (mtDNA), but there is a lack of model cell lines for the study of this link. To help address this problem, the authors developed ten cybrid HepG2 cell lines that have had their mitochondrial DNA replaced with the mitochondrial DNA of ten human donors. Analysis of single nucleotide polymorphisms in all of the patients' mtDNA allowed the authors to assign the donors to two haplogroups (H and J) with five patients each. The authors also present the results of several assays (e.g. oxygen consumption, ATP production) performed on all ten cell lines in the absence and presence of five clinically-relevant drugs (or drug metabolites). Significant attention was paid to differences observed between the cell lines in the H and J haplogroups. The work is methodologically and scientifically rigorous, ethically conducted, and objectively presented according to the appropriate community standards.

While I feel that the manuscript will be useful to the research field and is an important step towards improving patient outcomes, I feel that the work lacks a broad interest. Much of the paper is spent discussing small and/or statistically insignificant differences between haplogroups H and J. While some interesting interpretations and suggestions are presented in the discussion, the authors didn't perform follow-up experiments to try to nail down any particular mechanistic insights that would be useful to the broader community. I also didn't feel a strong sense that the paper produced any specific suggestions for how clinical outcomes could be improved. Accordingly, any clear insights that would be interesting to a broad scientific community would probably require follow-up studies.

Again, we strongly believe that the subject is of broad interest to researchers in both academia and the pharmaceutical industry. Evidence of the level of interest in this subject can be quantified by the access metrics of the 3 publications we have recently published on this topic (Biochem Soc Trans, 2020, PMID: 32453388; Arch Toxicol, 2021, PMID: 33585966; Front Genetics, 2021, PMID: 34484295), which have been accessed >6000 times.

The structure of the paper is also not friendly to a broad audience; the results are presented without interspersed commentary that could help the reader understand the meaning or utility of the results as they are being presented. Accordingly, I often felt unsure about how the results being presented were relevant to solving the broader problem established nicely in the introduction.

We thank the reviewer for this comment and have revised the manuscript to now contain a combined results and discussion section.

Finally, it wasn't clear that the generated cell lines were made available for anyone to purchase through a cell bank (perhaps the authors did do this, but I don't recall seeing a mention of it). As these cell lines appear to be the primary output of this work, it seems important to better highlight the extent to which they are being made accessible to the scientific community.

The cells are currently in the process of being deposited under licence with XimBio. This will allow other researchers to easily access them. They are also available upon request from me. This has been conveyed in the revised manuscript (pg 18, lines 1-2).

Reviewer #2 (Public Review):

In this work, Ball et al. investigated the possibility to generate a novel set of HepG2 liver cell lines to generate "mitochondrial DNA-personalized" models as novel tools to study idiosyncratic drug-induced liver injury related to mitochondrial variation. This work represents the generation of a comprehensive collection of n=10 HepG2 lines, half reflecting haplogroup H and half reflecting haplogroup J. The authors then assessed their impact on basic mitochondrial function in liver cells. Interestingly, they find a greater respiratory complex activity driven by complex I and II of the haplogroup J lines relative to haplogroup H. Finally, the authors make an attempt at using this novel set of lines to probe the consequential effects of mitochondrial genotype on drug-induced liver toxicity. This work provides an interesting proof-of-concept study and is a starting point towards studying and predicting idiosyncratic drug-induced liver injury in a personalized manner. This technique may be broadly extrapolated to other commonly used liver cell models within the toxicology field.

Strengths:

1) This work presents an exciting initiative to study interindividual variability in idiosyncratic drug-induced liver injury focusing on mitochondrial haplotypes. In further follow-ups, this work could be extended to also represent other different haplogroups to establish a thorough "biobank". The established lines allow for future in-depth characterization and testing of many putative hepatotoxic compounds through a variety of toxicity measures that could shed further light on the impact of mitochondrial DNA variation on (idiosyncratic) drug-induced liver injury.

2) This technique may be broadly extrapolated to other commonly used liver cell lines within the toxicology field (e.g. HepaRG cells or iPSC-derived cells) that are potentially also more metabolically competent. A short discussion on this could be added to the current manuscript.

We thank the reviewers for this comment, which we agree with. We have now incorporated this into the conclusion (pg 18, lines 23 - 27).

Weaknesses:

1) The major weakness of the current manuscript is the rather large variation across sample measurements regarding the proof-of-concept experiments to study drug effects (fig. 3-6). This makes much of the data rather hard to interpret and to infer conclusions. As an example, proton leak (fig. 3f/4f) seems to 2-fold increase in the J group even under basal conditions (0 uM flutamide/metabolite), while this is not observed in fig. 2a and this effect seems to be also absent under 0 uM tolcapone (fig. 5f). Unfortunately, the current data do not allow us to draw confident conclusions about whether the tested drugs have effects on the mitochondrial respiration of the different haplogroups. This may well be linked to the methods used for measuring mitochondrial activity, but since this is the predominant method needed in the current paper, either increasing the number of experiments (across more lines) or identifying a more rigorous methodological manner to obtain consistencies of experiments would help the authors to make more confident claims about their data.

The reviewers have noted the inherent variability in the respiratory measurements from plate to plate. To counter this, experiments were designed so that for each cybrid cell line the control and treated cells were always positioned on the same plate. However, we believe that the reporting of such data, and their limitations, is a fundamental aspect of unbiased science reporting feeding into the principles of data reproducibility. In this resubmission, we have updated the methodology of our data analysis, which better accounts for this variability. The new figures plot each cybrid as a distinct point to easily visualise the variation across haplogroups dependent upon each cybrid within the group. We have included this limitation in the conclusion (pg 18, lines 15 – 19).

2) The data on the effects of inhibition of complex I/II activity are not sufficiently convincing to support the claim that haplogroup J is more susceptible to flutamide/metabolite (fig. 6). Both seem to respond rather identical to flutamide or its metabolite, i.e. at higher concentrations complex I/II activity decreases, but with the sole difference that the haplogroups represent different basal activity (not influenced by the drug). Estimating fold changes, for example, for both haplogroups, complex I and II activity decreases ca. 2-fold at the highest concentration of the metabolite (fig. 6c-d), therefore concluding that there is no difference between haplogroup susceptibility unlike the authors claim. It is furthermore unclear what the statistical significance currently represents: it should represent whether at different/increasing concentrations the activity of the complexes significantly differs vs. the previous/basal conditions from the same haplogroup. If it represents (which it seems to be) the significance of the haplogroup J vs. the haplogroup H, it is non-informative as it is obvious that haplogroup J presents with a higher baseline.

Thank you for this comment, we agree with the shortcomings of statistical analysis in fig 6 and have reanalysed the dataset using a more appropriate statistical methodology, see response 2.2.

3) It would help to mention how many lines per haplogroup H/J were used in the analyses across all figures. This should be clarified, as the error bars for most experiments are rather high and therefore statistical significance is lacking, making data interpretation complex. It could be helpful if the authors present at least for some analyses single plots of data obtained across different lines from the same haplogroup to evaluate the consistency of the effects of the genotypes as supplementary figures. If only 1-2 lines were used per group, it would help to perform additional experiments to assess consistencies across groups.

We apologise that the number of lines per haplogroup that were employed in the analyses is unclear. In every case, we included 5 cybrid lines per haplogroup. We have further clarified this point in the methods and results. Furthermore, in the new figures, each cybrid is now represented as a single data point.

Author Response

Reviewer #2 (Public Review):

1) A major point of the manuscript is the description of Hrc+ fibroblasts (Fibroblast 3) as profibrogenic in diabetes. However, fibroblast 3 expresses several cardiomyocyte markers Nppa, Ryr2, Ttn alongside Hrc which is described to play a role in Ca2+ handling at the sarcoplasmic reticulum in cardiomyocytes (Fig. 4C) and shows a low correlation with other fibroblast clusters (Fig. 4B). A possible explanation is technical, e.g. if two nuclei (one fibroblast, one cardiomyocyte) were captured together in one droplet (barcode collisions or doublets). Unfortunately, this uncertainty makes interpretation of all following snRNA-seq analyses based on this fibroblast subpopulation impossible.

Thank you very much for the precious comments of the reviewer. We went over scRNA-seq results carefully. Firstly, for quality of cells, we used a relatively high threshold to ensure that we have filtered out the most of barcodes associated with empty partitions or doublet cells. We quantified the number of genes and UMIs, and kept high quality cells with the detection threshold of 500-2,500 genes and 600-8,000 UMIs. Then cells with unusually high detection rate of mitochondrial gene expression (≥10%) were excluded in this study. Taking into account the multicellular effects as you mentioned, we tried to identify doublets cells by applying the DoubletFinder (v2.0.3) by the generation of artificial doublets, using the PC distance to find each cell’s proportion of artificial k nearest neighbors (pANN) and ranking them according to the expected number of doublets. We finded that 3.20% cells (19 cells) were labeled as doublets in fibroblast-3 (594 cells). Then 19 doublet cells were removed, the trends of cell proportion and the Hrc gene expression trend in fibroblast-3 was the same as before. Therefore, our data analysis results do not affect the conclusions in this study, and it was also validated by Hrc and vimentin double immunostaining experiments (Figure 4E). Thanks again to the reviewer for these professional comments.

2) To follow the study and be able to appreciate the data quality, individual sample metadata and UMAPs colored based on a sample and/or condition (diabetes or control) would be helpful. The paper would benefit from an analysis to show if the differences in the number of detected genes are due to the number of nuclei per cluster or if the bigger clusters are really also the ones with the most dramatic changes. Instead of showing expression levels of differentially regulated genes in distinct clusters (Fig1 S2), the differential expression could be displayed with violin plots or heatmaps that illustrate values for both conditions. Clusters that did not reveal any differential expressed genes, e.g. Adipo can be removed. Fig 1F these KEGG enrichments are hard to interpret since they can be confounded by highly expressed cardiomyocyte genes that are detected in all clusters (1B) and thus drive the GO enrichment of e.g. "cardiac muscle contraction" in T cells.

Thanks to the reviewer for these comments. Fig1 S2 shows top 10 upregulated genes in different cell populations and the expression characteristics of these genes in a concise way. More detailed expressions levels of differentially regulated genes in distinct clusters can be seen in supplemental file 2-5. At the same time, if we use violin plot or heat maps to show the differential expression information of top 10 upregulated genes, we need too many supplement figures in the main text and therefore take up too much space. On the other hand, cell populations without differentially expressed genes in Figure 1E have been removed as you suggested.

3) The study looks into the pathogenesis of cardiac fibrosis in diabetic mice. The authors show that downregulation of Itgb1 with siRNA (Fig 6I) leads to less fibrosis in diabetic mice. This effect might be expected since Itgb1 is an extracellular matrix-linked gene and might indicate that downregulation could be beneficial. Given this, it is confusing to see the following analysis which links several genetic variants associated with Type 2 Diabetes to Itgb1 (one leading to premature stop) and its ligand. This analysis seems out of place in relation to the remainder of the study which focuses to identify the downstream effects of diabetes on cardiac fibrosis.

Thank you very much for the precious comments of the reviewer. We have deleted the results of the association of Itgb1 variants with diabetic cardiac fibrosis in the revised manuscript.

Author Response

Reviewer #1 (Public Review):

Han et al use sophisticated genetic approaches to investigate leptin-responsive neural circuits. Overall, this is an impressive series of studies that provide fairly convincing evidence for a key inhibitory pathway downstream of AGRP neurons. A few data sets require additional validation or explanation.

We appreciate the reviewer’s strong interests and support of this manuscript and these valuable comments below. We have revised the manuscript accordingly to incorporate reviewer’s suggestions and critiques.

Reviewer #2 (Public Review):

Using a novel genetic system to conditionally ablate Lepr from Agrp neurons in adults, the authors discovered that leptin-AgRP neuron signaling strongly modulates the DMH and sought to understand the DMH targets and mechanisms of action in the response to AgRP neuron signaling. GABA signaling likely underlies the effects of AgRP neuron-mediated hyperphagia (etc). DMH Mc4R neurons appear to lie downstream of Agrp neurons. GABA in the DMH appears to mediate many of the effects of AgRP neurons on feeding and body weight. Furthermore, Deletion of Lepr from AgRP neurons increases DMH GABA-ARa3, and modulation of this receptor in the DMH alters food intake and the response to leptin.

Unfortunately, there is little quantification or other validation data from many of the systems deployed, and the analysis jumps around a fair amount, without really uniting the results in a way that paints a convincing picture of the final model that they build.

Thanks for these positive comments on our studies. In the revised manuscript, we have added substantial amount of new experimental data, more controls, and data validation that significantly strengthen our proposed model.

Reviewer #3 (Public Review):

The manuscript by Han et al characterizes a pathway from AgRP(LepR) neurons to DMH(MC4R) neurons that is involved in energy balance control. They use a conditional knockout strategy to show that AgRP(LepR) knockout increases body weight and this effect was reversible by blocking GABA signaling. They also showed that activation of AgRP-DMH projection increases food intake, and highlighted a role for alpha3-GABAA receptor signaling in the DMH for regulating feeding behavior. While these data highlight a potential circuit that modulates feeding, there are concerns about the paper in its current form that diminish enthusiasm. The lack of proper controls in many of the experiments raises doubts about the findings.

Strengths: The authors use new tools to characterize a new circuit for leptin-mediated energy balance control. The conditional knockout has several advantages over previous techniques that are described within the manuscript. Further, the authors use combinations of different techniques (gene knockout, optogenetic manipulation, in vivo activity monitoring) to make observations at multiple levels of analysis.

Weaknesses: Several experiments within the paper have worrisome caveats or lack proper controls, raising concerns about the overall conclusions made.

We appreciate the reviewer’s positive comments. We added more control and validation data in our updated manuscript to support our conclusion.

Author Response

Reviewer #1 (Public Review):

Demographic inference is a notoriously difficult problem in population genetics, especially for non-model systems in which key population genetic parameters are often unknown and where the reality is always a lot more complex than the model. In this study, Rose et al. provided an elegant solution to these challenges in their analysis of the evolutionary history of human specialization in Ae. aegypti mosquitoes. They first applied state-of-the-art statistical phasing methods to obtain haplotype information in previously published mosquito sequences. Using this phased data, they conducted cross-coalescent and isolation-with-migration analyses, and they innovatively took advantage of a known historical event, i.e., the spread of Ae. aegypti to South America, to infer the key model parameters of generation time and mutation rate. With these parameters, they were able to confirm a previous hypothesis, which suggests that human specialists evolved at the end of the African Humid Period around 5,000 years ago when Ae. aegypti mosquitoes in the Sahel region had to adapt to human-derived water storage as their breeding sites during intense dry seasons. The authors further carried out an ancestry tract length analysis, showing that human specialists have recently introgressed into Ae. aegypti population in West African cities in the past 20-40 years, likely driven by rapid urbanization in these cities.

Given all the complexities and uncertainties in the system, the authors have done outstanding jobs coming up with well-informed research questions and hypotheses, carrying out analyses that are most appropriate to their questions, and presenting their findings in a clear and compelling fashion. Their results reveal the deep connections between mosquito evolution and past climate change as well as human history and demonstrate that future mosquito control strategies should take these important interactions into account, especially in the face of ongoing climate change and urbanization. Methodologically, the analytical approach presented in this paper will be of broad interest to population geneticists working on demographic inference in a diversity of non-model organisms.

In my opinion, the only major aspect that this paper can still benefit from is more explicit and in-depth communication and discussion about the assumptions made in the analyses and the uncertainties of the results. There is currently one short paragraph on this in the discussion section, but I think several other assumptions and sources of uncertainties could be included, and a few of them may benefit from some quantitative sensitivity analyses. To be clear, I don't think that most of these will have a huge impact on the main results, but some explicit clarification from the authors would be useful.

Below are some examples:

Thank you very much for your kind words and your feedback! We have expanded our discussion of assumptions and uncertainties – we have responded to each point below:

1) Phasing accuracy: statistical phasing is a relatively new tool for non-model species, and it is unclear from the manuscript how accurate it is given the sample size, sequencing depth, population structure, genetic diversity, and levels of linkage disequilibrium in the study system. If authors would like to inspire broader adoption of this workflow, it would be very helpful if they could also briefly discuss the key characteristics of a study system that could make phasing successful/difficult, and how sensitive cross-coalescent analyses are to phasing accuracy.

We agree that this is an important topic to expand on. We have clarified as follows:

Results, Page 4, last paragraph: “Over 95% of prephase calls had maximal HAPCUT2 phred-scaled quality scores of 100 and prephase blocks (i.e. local haplotypes) were 728bp long on average (interquartile range 199-1009bp). We then used SHAPEIT4.2 to assemble the prephase blocks into chromosome-level haplotypes, using statistical linkage patterns present across our panel of 389 individuals (25).”

Discussion, Page 8, last paragraph: “Overall linkage disequilibrium is relatively low in Ae. aegypti, dropping off quickly over a few kilobases and reaching half its maximum value within about 50kb (37); this is likely sufficient for assembling shorter, high-confidence prephase blocks into longer haplotypes in many cases. However, phase-switch errors may be common across longer distances – potentially affecting inferences in the most recent time windows. Nevertheless, the similar results we obtain using different proxy populations (and thus different input haplotype structures) for human-specialist and generalist lineages (see Figure S1) suggest that our results are robust to potential mistakes in long-range haplotype phasing.”

Discussion, Page 9, paragraph 2: “Here, we take advantage of a continent-wide set of genomes, combined with read-based prephasing and population-wide statistical phasing to develop a phasing panel that should enable future studies in Ae. aegypti with a lower barrier to entry. The same approach may work for other study organisms with similar population genomic properties; high levels of diversity are helpful for prephasing and at least moderate levels of linkage disequilibrium are important for the assembly of prephase blocks.”

2) Estimation of mutation rate and generation time: the estimation of these importantparameters is made based on the assumption that they should maximize the overlap between the distribution of estimated migration rate and the number of enslaved people crossing the Atlantic, but how reasonable is this assumption, and how much would the violation of this assumption affect the main result? Particularly, in the MSMC-IM paper (Wang et al. 2020, Fig 2A), even with a simulated clean split scenario, the estimated migration rate would have a wide distribution with a lot of uncertainty on both sides, so I believe that the exact meaning and limitations of such estimated migration rate over time should be clarified. This discussion would also be very helpful to readers who are thinking about using similar methods in their studies. Furthermore, the authors have taken 15 generations per year as their chosen generation time and based their mutation rate estimates on this assumption, but how much will the violation of this assumption affect the result?

This is a great point. We have expanded our discussion of how this assumption affects our conclusions (see Discussion page 9, first paragraph): “Furthermore, we chose a scaling factor that maximized overlap between the peak of estimated Ae. aegypti migration and the peak of the Atlantic Slave Trade (Fig. 2B). If we instead consider alternative scenarios where peak migration occurred at the very beginning of the slave trade era, around 1500, then our inferred mutation rate would be lower (about 2.4e-9, assuming 15 generations per year), pushing back the split of human-specialist lineages to about 10,000 years before present. This scenario seems less plausible, in part because our isolation-with-migration analyses suggest a gradual onset of migration between continents rather than a single, early-pulse model. It would also make it harder to explain the timing of the bottleneck we see in invasive populations; the first signs of this bottleneck occur at the beginning of the slave trade (~500 years ago) with our current calibration (Fig. S1A), but would be pushed to a pre-trade date in this alternative scenario. We can also consider a scenario in which peak Ae. aegypti migration occurred more recently, perhaps around 1850, corresponding to increased global shipping traffic outside the slave trade alone. In this case, our inferred mutation rate would be higher (or generation time lower), and the split of human-specialist lineages would be placed at about 3,000 years ago. Overall, the best match between the existing literature and our data corresponds to our main estimates, but alternative scenarios could gain support if future research finds evidence for a different time course of invasion than is suggested by the epidemiological literature.”

We have slightly expanded our description of calibration in Results, page 5, last paragraph: “The fact that we see good overlap between the two distributions (yellow–white color) across a wide range of reasonable mutation rates and generation times for Ae. aegypti is consistent with our understanding of the species’ recent history and supports our approach. For example, if we take the common literature value of 15 generations per year (0.067 years per generation) (17, 20), the de novo mutation rate that maximizes correspondence between the two datasets is 4.85x10-9 (black dot in Figure 2A, used in Figure 2B), which is on the order of values documented in other insects. We chose to carry forward this calibrated scaling factor (corresponding to any combination of mutation rate and generation time found along the line in Figure 2A) into subsequent analyses.”

We have also expanded on the uncertainty of our analyses (see Discussion page 8, last paragraph): “First, the temporal resolution of our inferences is relatively low, and both previously published simulations (39) and our own bootstrap replicates (Figure 2B–D, grey lines) suggest relatively wide bounds for the precise timing of events.”

3) The effect of selection: all analyses in this paper assume that no selection is at play,and the authors have excluded loci previously found to be under selection from these analyses, but how effective is this? In the ancestry tract length analysis, in particular, the authors have found that the human-specialist ancestry tends to concentrate in key genomic regions and suggested that selection could explain this, but doesn't this mean that excluding known loci under selection was insufficient? If the selection has indeed played an important role at a genome-wide level, how would it affect the main results (qualitatively)?

We have clarified that we excluded those loci from our timing estimates for both MSMC and ancestry tract analyses, but then re-ran the ancestry tract analysis with all regions included to visualize and assess how tracts were distributed along chromosomes. See Methods, page 12, paragraph 2: “Since selection associated with adaptation to urban habitats could shape lengths of admixture tracts, we masked regions previously identified as under selection between human-specialists and generalists when estimating admixture timing—namely, the outlier regions in (2). However, we used an unmasked analysis to determine and visualize the genome-wide distribution of ancestries (Fig. 3).”

We have also added additional discussion of the expected effects of selection on our analyses (see Discussion, page 9, last paragraph): “Positive selection during adaptive introgression can increase tract lengths and make admixture appear to be more recent than it actually is. For this reason, we masked regions of the genome thought to underlie adaptation to human habitats before running our analysis. Nevertheless, if selection has acted outside these regions, admixture may be somewhat older than we estimate.”

Author Response

Reviewer #1 (Public Review):

The authors have tried to correlate changes in the cellular environment by means of altering temperature, the expression of key cellular factors involved in the viral replication cycle, and small molecules known to affect key viral protein-protein interactions with some physical properties of the liquid condensates of viral origin. The ideas and experiments are extremely interesting as they provide a framework to study viral replication and assembly from a thermodynamic point of view in live cells.

The major strengths of this article are the extremely thoughtful and detailed experimental approach; although this data collection and analysis are most likely extremely time-consuming, the techniques used here are so simple that the main goal and idea of the article become elegant. A second major strength is that in other to understand some of the physicochemical properties of the viral liquid inclusion, they used stimuli that have been very well studied, and thus one can really focus on a relatively easy interpretation of most of the data presented here.

There are three major weaknesses in this article. The way it is written, especially at the beginning, is extremely confusing. First, I would suggest authors should check and review extensively for improvements to the use of English. In particular, the abstract and introduction are extremely hard to understand. Second, in the abstract and introduction, the authors use terms such as "hardening", "perturbing the type/strength of interactions", "stabilization", and "material properties", for just citing some terms. It is clear that the authors do know exactly what they are referring to, but the definitions come so late in the text that it all becomes confusing. The second major weakness is that there is a lack of deep discussion of the physical meaning of some of the measured parameters like "C dense vs inclusion", and "nuclear density and supersaturation". There is a need to explain further the physical consequences of all the graphs. Most of them are discussed in a very superficial manner. The third major weakness is a lack of analysis of phase separations. Some of their data suggest phase transition and/or phase separation, thus, a more in-deep analysis is required. For example, could they calculate the change of entropy and enthalpy of some of these processes? Could they find some boundaries for these transitions between the "hard" (whatever that means) and the liquid?

The authors have achieved almost all their goals, with the caveat of the third weakness I mentioned before. Their work presented in this article is of significant interest and can become extremely important if a more detailed analysis of the thermodynamics parameters is assessed and a better description of the physical phenomenon is provided.

We thank you for the comments and, in particular, for being so positive regarding the strengths of our manuscript and for raising concerns that will surely improve it. We have taken the following actions to address your concerns:

1) Extensive revisions have been made to the use of English, particularly in the abstract and introduction. Key terms are defined as they are introduced in the text to enhance the clarity of the argument. This is a significant revision that is highlighted within the text, but it is too extensive to detail here.

2) In the results section, we improved and extended the discussion of our graphs to the extent possible. However, we found that attempting to explain the graphs' meanings more thoroughly would detract from our manuscript's main focus: identifying thermodynamic changes that could potentially lead to alterations in material properties, specifically aspect ratio, size, and Gibbs free energy. As a result, we introduced the type of information we could obtain from our analyses in the introduction (Lines 112-125) and briefly commented on it in the ‘results’ section (Lines 304-306, sentences below).

From introduction – lines 112-125:

“In addition, other parameters like nucleation density determine how many viral condensates are formed per area of cytosol. Overall, the data will inform us if changing one parameter, e.g. the concentration, drives the system towards larger condensates with the same or more stable properties, or more abundant condensates that are forced to maintain the initial or a different size on account of available nucleation centres (Riback et al., 2020:Snead, 2022 #1152). It will also inform us if liquid viral inclusions behave like a binary or a multi-component system. In a binary mixture, Cdilute is constant (Klosin et al., 2020). However, in multi-component systems, Cdilute increases with bulk concentration (Riback et al., 2020). This type of information could have direct implications about the condensates formed during influenza infection. As the 8 different genomic vRNPs have a similar overall structure, they could, in theory, behave as a binary system between units of vRNPs and Rab11a. However, a change in Cdilute with concentration would mean that the system behaves as a multi-component system. This could raise the hypothesis that the differences in length, RNA sequence and valency that each vRNP has may be relevant for the integrity and behaviour of condensates.”.

From results lines 304-306:

This indicates that the liquid inclusions behave as a multi-component system and allow us to speculate that the differences in length, RNA sequence and valency that each vRNP may be key for the integrity and behaviour of condensates.

3) The reviewer has drawn our attention to the absence of phase separation analysis in our study. We believe that the formation of influenza A virus condensates is governed by phase separation (or percolation coupled to phase separation). However, we must exercise caution at this point because the condensates we are studying are highly complex, and the physics of our cellular system may not be adequate to claim phase separation without being validated by an in vitro reconstitution system. IAV inclusions contain a variety of cellular membranes, different vRNPs, and Rab11a. While we have robust data to propose a model in which the liquid-like properties of IAV inclusions arise from a network of interacting vRNPs that bridge multiple cognate vRNP-Rab11 units on flexible membranes, similar to what occurs in phase-separated vesicles in neurological synapses, our model for this system still lacks formal experimental validation. As a note, the data supporting our model includes: the demonstration of the liquid properties of our liquid inclusions (Alenquer et al. 2019, Nature Communications, 10, 1629); and impairment of recycling endocytic activity during IAV infection Bhagwat et al. 2020, Nat Commun, 11, 23; Kawaguchi et al. 2012, J Virol, 86, 11086-95; Vale-costa et al. 2016, J Cell Sci, 129, 1697-710. This leads to aggregated vesicles seen by correlative light and electron microscopy (Vale-Costa et al., 2016 JCS, 129, 1697-710) and by immunofluorescence and FISH (Amorim et al. 2011,. J Virol 85, 4143-4156; Avilov et al. 2012, Vaccine 30, 7411-7417; Chou et al. 2013, PLoS Pathog 9, e1003358; Eisfeld et al. 2011, J Virol 85, 6117-6126 and Lakdawala et al. 2014, PLoS Pathog 10, e1003971.

To be able to explore the significance of the liquid material properties of IAV inclusions, we used the strategy described in this current work. By developing an effective method to manipulate the material properties of IAV inclusions, we provide evidence that controlled phase transitions can be induced, resulting in decreased vRNP dynamics in cells and a negative impact on progeny virion production. This suggests that the liquid character of liquid inclusions is important for their function in IAV infection. We have improved our explanation addressing this concern in the limitations of our study (as outlined below in the box and in manuscript in lines 857-872).

We are currently establishing an in vitro reconstitution system to formally demonstrate, in an independent publication, that IAV inclusions are formed by phase separation (or percolation coupled to phase separation). For this future work, we teamed up with Pablo Sartori, a theorical physicist to derive in-depth analysis of the thermodynamics of the viral liquid condensates in the in vitro reconstituted system and compare it to results obtained in the cell. This will provide means to establish comparisons. We think that cells have too many variables to derive meaningful physics parameters (such as entropy and enthalpy) and models that need to be complemented by in vitro systems. For example, increasing the concentration inside a cell is not a simple endeavour as it relies on cellular pathways to deliver material to a specific place. At the same time, the 8 vRNPs, as mentioned above, have different size, valency and RNA sequence and can behave very differently in the formation of condensates and maintenance of their material properties. Ideally, they should be analysed individually or in selected combinations. For the future, we will combine data from in vitro reconstitution systems and cells to address this very important point raised by the reviewer.

From the paper on the section ‘Limitations of the study’:

“Understanding condensate biology in living cells is physiological relevant but complex because the systems are heterotypic and away from equilibria. This is especially challenging for influenza A liquid inclusions that are formed by 8 different vRNP complexes, which although sharing the same structure, vary in length, valency, and RNA sequence. In addition, liquid inclusions result from an incompletely understood interactome where vRNPs engage in multiple and distinct intersegment interactions bridging cognate vRNP-Rab11 units on flexible membranes (Chou et al., 2013, Gavazzi et al., 2013, Sugita et al., 2013, Shafiuddin and Boon, 2019, Haralampiev et al., 2020, Le Sage et al., 2020). At present, we lack an in vitro reconstitution system to understand the underlying mechanism governing demixing of vRNP-Rab11a-host membranes from the cytosol. This in vitro system would be useful to explore how the different segments independently modulate the material properties of inclusions, explore if condensates are sites of IAV genome assembly, determine thermodynamic values, thresholds accurately, perform rheological measurements for viscosity and elasticity and validate our findings. The results could be compared to those obtained in cell systems to derive thermodynamic principles happening in a complex system away from equilibrium. Using cells to map how liquid inclusions respond to different perturbations provide the answer of how the system adapts in vivo, but has limitations.

Reviewer #2 (Public Review):

During Influenza virus infection, newly synthesized viral ribonucleoproteins (vRNPs) form cytosolic condensates, postulated as viral genome assembly sites and having liquid properties. vRNP accumulation in liquid viral inclusions requires its association with the cellular protein Rab11a directly via the viral polymerase subunit PB2. Etibor et al. investigate and compare the contributions of entropy, concentration, and valency/strength/type of interactions, on the properties of the vRNP condensates. For this, they subjected infected cells to the following perturbations: temperature variation (4, 37, and 42{degree sign}C), the concentration of viral inclusion drivers (vRNPs and Rab11a), and the number or strength of interactions between vRNPs using nucleozin a well-characterized vRNP sticker. Lowering the temperature (i.e. decreasing the entropic contribution) leads to a mild growth of condensates that does not significantly impact their stability. Altering the concentration of drivers of IAV inclusions impact their size but not their material properties. The most spectacular effect on condensates was observed using nucleozin. The drug dramatically stabilizes vRNP inclusions acting as a condensate hardener. Using a mouse model of influenza infection, the authors provide evidence that the activity of nucleozin is retained in vivo. Finally, using a mass spectrometry approach, they show that the drug affects vRNP solubility in a Rab11a-dependent manner without altering the host proteome profile

The data are compelling and support the idea that drugs that affect the material properties of viral condensates could constitute a new family of antiviral molecules as already described for the respiratory syncytial virus (Risso Ballester et al. Nature. 2021)

Nevertheless, there are some limitations in the study. Several of them are mentioned in a dedicated paragraph at the end of a discussion. This includes the heterogeneity of the system (vRNP of different sizes, interactions between viral and cellular partners far from being understood), which is far from equilibrium, and the absence of minimal in vitro systems that would be useful to further characterize the thermodynamic and the material properties of the condensates.

There are other ones.

We thank reviewer 2 for highlighting specific details that need improving and raising such interesting questions to validate our findings. We have addressed the comments of Reviewer 2, we performed the experiments as described (in blue) below each point raised.

1) The concentrations are mostly evaluated using antibodies. This may be correct for Cdilute. However, measurement of Cdense should be viewed with caution as the antibodies may have some difficulty accessing the inner of the condensates (as already shown in other systems), and this access may depend on some condensate properties (which may evolve along the infection). This might induce artifactual trends in some graphs (as seen in panel 2c), which could, in turn, affect the calculation of some thermodynamic parameters.

The concern of using antibodies to calculate Cdense is valid, and we thought it was very important. We addressed this concern by performing the same analyses using a fluorescent tagged virus that has mNeon Green fused to the viral polymerase PA (PA-mNeonGreen PR8 virus). Like NP, PA is a component of vRNPs and labels viral inclusions, colocalising with Rab11 when vRNPs are in the cytosol. However, per vRNP there is only one molecule of PA, whilst of NP there are 37-96 depending on the size of vRNPs. As predicted, we did observe changes in the Cdilute, Cdense and nucleation density. However, the measurements and values obtained for Gibbs free energy, size, aspect ratio detecting viral inclusions with fluorescently tagged vRNPs or antibody staining followed the same trend and allow us to validate our conclusion that major changes in Gibbs free energy occur solely when there is a change in the valency/strength of interactions but not in temperature or concentration (Figure 1 below). Given the extent of these data, we show here the results but, in the manuscript, we will describe the limitations of using antibodies in our study within the section ‘Limitations of the study’ from lines 881-894. Given the importance of the question regarding the pros and cons of the different systems for analysing thermodynamic parameters, we have decided to systematically assess and explore these differences in detail in a future manuscript.

For more information. This reviewer may be asking why we did not use the PA-fluorescent virus in the first place to evaluate inclusion thermodynamics and avoid problems in accessibility that antibodies may have to get deep into large inclusions. Our answer is that no system is perfect. In the case of the PA-fluorescent virus, the caveats revolve around the fact that the virus is attenuated (Figure 1a below), exhibiting a delayed infection as demonstrated by reduced levels of viral proteins (Figure 1b below). Consistently, it shows differences in the accumulation of vRNPs in the cytosol and viral inclusions form later in infection and the amount of vRNPs in the cytosol does not reach the levels observed in PR8-WT virus. After their emergence, inclusions behave as in the wild-type virus (PR8-WT), fusing and dividing (Figure 1c below) and displaying liquid properties.

As the overarching goal of this manuscript is to evaluate the best strategies to harden liquid IAV inclusions and given that one of the parameters we were testing is concentration, we reasoned that using PR8-WT virus for our analyses would be reasonable.

In conclusions, both systems have caveats that are important to systematically assess, and these differences may shift or alter thermodynamic parameters such as nucleation density, inclusion maturation rate, Cdense, Cdilute in particular by varying the total concentration. As a note, to validate all our results using the PA-mNeonGreen PR8 virus, we considered the delayed kinetics and applied our thermodynamic analyses up to 20 hpi rather than 16 hpi.

However, because of the question raised by this reviewer, on which is the best solution for mitigating errors induced by using antibodies, we re-checked all our data. Not only have we compared the data originated from attenuated fluorescently tagged virus with our data, but also made comparisons with images acquired from Z stacks (as used for concentration and for type/strength of interactions) with those acquired from 2D images. Our analysis revealed that there is a very good match using images acquired with Z-stacks and analysed as Z projections with between antibody staining and vRNP fluorescent virus. Therefore, we re-analysed all our thermodynamic data done with temperature using images acquired from Z stacks and altered entirely Figure 2. We believe that all these comparisons and analyses have greatly improved the manuscript and hence we thank all reviewers for their input.

Figure 1 – The PA-mNeonGreen virus is attenuated in comparison to the WT virus and data obtained is consistent for Gibbs free energy with analyses done with images processed with antibody fluorescent vRNPs. A. Representation of the PA-mNeonGreen virus (PA-mNG; Abbreviations: NCR: non coding region). B. Cells (A549) were transfected with a plasmid encoding mCherry-NP and co-infected with PA-mNeonGreen virus for 16h, at an MOI of 10. Cells were imaged under time-lapse conditions starting at 16 hpi. White boxes highlight vRNPs/viral inclusions in the cytoplasm in the individual frames. The dashed white and yellow lines mark the cell nucleus and the cell periphery, respectively. The yellow arrows indicate the fission/fusion events and movement of vRNPs/ viral inclusions. Bar = 10 µm. Bar in insets = 2 µm. C-D. Cells (A549) were infected or mock-infected with PR8 WT or PA-mNG viruses, at a multiplicity of infection (MOI) of 3, for the indicated times. C. Viral production was determined by plaque assay and plotted as plaque forming units (PFU) per milliliter (mL) ± standard error of the mean (SEM). Data are a pool from 2 independent experiments. D. The levels of viral PA, NP and M2 proteins and actin in cell lysates at the indicated time points were determined by western blotting. (E-G) Biophysical calculations in cells infected with the PA-mNeonGreen virus upon altering temperature (at 10 hpi, evaluating the concentration of vRNPs (over a time course) in conditions expressing native amounts of Rab11a or overexpressing low levels of Rab11a and upon altering the type/strength of vRNP interactions by adding nucleozin at 10 hpi during the indicated time periods. All data: Ccytoplasm/Cnucleus; Cdense, Cdilute, area aspect ratio and Gibbs free energy are represented as boxplots. Above each boxplot, same letters indicate no significant difference between them, while different letters indicate a statistical significance at α = 0.05 using one-way ANOVA, followed by Tukey multiple comparisons of means for parametric analysis, or Kruskal-Wallis Bonferroni treatment for non-parametric analysis.

2) Although the authors have demonstrated that vRNP condensates exhibit several key characteristics of liquid condensates (they fuse and divide, they dissolve upon hypotonic shock or upon incubation with 1,6-hexanediol, FRAP experiments are consistent with a liquid nature), their aspect ratio (with a median above 1.4) is much higher than the aspect ratio observed for other cellular or viral liquid compartments. This is intriguing and might be discussed.

IAV inclusions have been shown to interact with microtubules and the endoplasmic reticulum, that confers movement, and undergo fusion and fission events. We propose that these interactions and movement impose strength and deform inclusions making them less spherical. To validate this assumption, we compared the aspect ratio of viral inclusions in the absence and presence of nocodazole (that abrogates microtubule-based movement). The data in figure 2 shows that in the presence of nocodazole, the aspect ratio decreases from 1.42±0.36 to 1.26 ±0.17, supporting our assumption.

Figure 2 – Treatment with nocodazole reduces the aspect ratio of influenza A virus inclusions. Cells (A549) were infected with PR8 WT for 8 h and treated with nocodazole (10 µg/mL) for 2h, after which the movement of influenza A virus inclusions was captured by live cell imaging. Viral inclusions were segmented, and the aspect ratio measured by imageJ, analysed and plotted in R.

3) Similarly, the fusion event presented at the bottom of figure 3I is dubious. It might as well be an aggregation of condensates without fusion.

We have changed this (check Fig 5A and B in the manuscript), thank you for the suggestion.

4) The authors could have more systematically performed FRAP/FLAPh experiments on cells expressing fluorescent versions of both NP and Rab11a to investigate the influence of condensate size, time after infection, or global concentrations of Rab11a in the cell (using the total fluorescence of overexpressed GFP-Rab11a as a proxy) on condensate properties.

We have included a new figure, figure 5 with the suggested data.

Author Response

Reviewer #1 (Public Review):

In this paper, the authors present evidence from studies of biopsies from human subject and muscles from young and older mice that the enzyme glutathione peroxidase 4 (GPx4) is expressed at reduced levels in older organisms associated with elevated levels of lipid peroxides. A series of studies in mice established that genetic reduction of GPx4 and hindlimb unloading each elevated lipid peroxide levels and reduced muscle contractility in young animals. Overexpression of GPx4 or N- acetylcarnosine blocked atrophy and loss of force generating capacity resulting from hindlimb unloading in young mice. Cell culture experiments in C2C12 myotubes were used to develop evidence linking elevated lipid peroxide levels to atrophy using genetic and pharmacologic approaches. Links between autophagy and atrophy were suggested.

Experiments on GPx4 expression levels, lipid peroxide levels, muscle mass and muscle force generating capacity were internally consistent and convincing. I thought the experiments supporting the view that autophagy contributed to atrophy were convincing. The hypothesis that altered lipidation of autophagy factors contributed was tested or supported in my view. Evidence for muscle atrophy in response to genetic or pharmacologic manipulations is a bit inconsistent throughout the paper, possibly because the small N of some experiments does not provide sufficient power to detect observed numeric differences in the means. The pattern of muscle fiber atrophy by fiber type is consistent throughout the paper but there is variability in which comparisons reached the threshold for significance, again, possibly because of the small N of the experiments. I agree with the authors that altered activity of enzymes in the contractile apparatus provides one explanation for the observed weakness but respectfully wish to point out there are others such as impaired excitation-contraction coupling which is well known to occur in aging.

We thank Dr. Cardozo for taking time to carefully review our manuscript, and for providing an enthusiastic feedback for the significance of our work. We are grateful for additional suggestions and modified our manuscript accordingly.

Reviewer #2 (Public Review):

This is a well-written paper that reports that the accumulation of LOOH with age and disuse contributes to the loss of skeletal muscle mass and strength. Moreover, the authors report that LOOH neutralization attenuates muscle atrophy and weakness. The mechanism via which LOOH contributes to these phenotypes remains unclear but seems to be mediated by the autophagy- lysosomal axis. In addition, the paper also reports the efficacy of N-acetylcarnosine treatment in ameliorating muscle atrophy in mice.

We thank the reviewer 2 for their positive response to our manuscript. Very much appreciated! Below please find our response to your specific comments.

The authors should consider the following points to improve the manuscript:

• The authors showed that inhibition of the autophagy-lysosome axis by ATG3 deletion or BafA1 was sufficient to reduce LOOH levels induced by GPx4 deletion, erastin, or RSL3. Moreover, they found that 4-HNE co-localizes with LAMP2. However, it remains unclear the precise mechanism via which LOOH contributes to muscle atrophy and how it is amplified by the autophagy-lysosomal axis. The authors could further test the functional interaction of 4-HNE with LAMP2 with additional experiments such as immunoprecipitation.

Thank you for these comments. We agree with the reviewer that our observations on autophagy-lysosomal axis is yet backed by a tangible mechanism. To clarify, we only show 4HNE and LAMP2 colocalization to show that they are proximate to each other. We do not necessarily claim that LAMP2 is the protein that becomes 4-HNE-ylated. We are currently developing a proteomic platform to detect 4-HNE conjugations on peptides, and this should hopefully shed light to the nature of interaction between LOOH and the autophagy-lysosomal axis. We now include additional discussion on autophagy-lysosomal axis with LOOH in lines 280-291.

• A weak point of the paper is not having performed the experiments on 24-month-old-mice. At 20 months of age, the mice do not display any muscle wasting and myofiber atrophy compared to young mice that have completed postnatal muscle growth (=6-month-old-mice). It would be interesting to see the levels of 4-HNE in 24- or 30-month-old mice, and if N-acetylcarnosine treatment in older mice is able to rescue muscle atrophy induced by aging.

This is a nuanced but a very important point. We initially set out to study mice in the 24 months old mice, but these mice did not tolerate the hindlimb unloading procedure well and ended up using the 20 months old mice instead. While mice at this age tolerated our HU procedure well, they did not manifest significant reduction in muscle mass compared to young. We included additional discussions in lines 298-300 and 310-314. To address this point, we are currently performing a 6-month N-acetylcarnosine intervention in 24 months old mice, and examine the effect that this compound has on the effect of aging (without HU) in multiple organ systems. We have thus completed 2 cohorts for this preclinical trial. Results on the effects of long-term N- acetylcarnosine treatment on muscle will be included in the separate manuscript.

Previous studies have shown that inhibition of autophagy accelerates (rather than protect) from sarcopenia, and that autophagy is required to maintain muscle mass (Masiero 2009, PMID: 19945408; Castets 2013, PMID: 23602450; Carnio 2014, PMID: 25176656). On this basis, the authors should test whether their findings are valid only in the context of disuse atrophy or also in the context of sarcopenia (=24-30-month-old mice).

We agree with the reviewer that the role of autophagy and muscle mass is likely complex. In the current study, we only showed that a SHORT-TERM inhibition of autophagy by ATG3 deletion prevents muscle atrophy induced by a SHORT-TERM disuse intervention. Inhibition of autophagic machinery long-term will likely be detrimental, and as shown in references provided by the reviewer, accelerates sarcopenia. We now include these discussions in lines 280-287. We respectfully request that the experiments in 24-30 month old ATG3-MKO mice be beyond the scope of the study. As discussed above, there is much more to study regarding the nature of interaction between the autophagy-lysosomal axis and LOOH.

• In Fig.2 the authors report that GPx4 KD, erastin, and RSL3 reduce the diameter of myotubes. For how long and when was the treatment done? Looking at the images, it seems that there are some myoblasts in the cultures treated with GPx4 KD, erastin, and RSL3. Is it possible that these compounds reduce myotube size by inhibiting myoblast fusion rather than by inducing myotube atrophy?

Thank you for point this out. We now provide further details in the method section (lines 439- 443). For KD experiments, we treat myoblasts with virus simultaneous to differentiation, due to lower infection efficiency in myotubes. This is certainly a caveat. However, erastin and RSL3 experiments were done on fully differentiated myotubes. It is common to have non- differentiated myoblasts under differentiated myotubes.

• MDA quantification was done in the gastrocnemius although all the experiments in this paper were performed in the soleus and EDL. It would be good if the authors could explain the reason for this.

MDA and 4-HNE WB were done on gastroc for all mouse models because some soleus and EDL muscles are below 7 mg and provided insufficient materials to perform MDA or 4-HNE. Soleus and EDL were used for contractile experiments (gastr0c cannot be used for this experiment) and for histological analyses.

Author Response

Reviewer #1 (Public Review):

In this study, Jigo et al. measured the entire contrast sensitivity function and manipulated eccentricity and stimulus size to assess changes in contrast sensitivity and acuity for different eccentricities and polar angles. They found that CSFs decreased with eccentricity, but to a lesser extent after M scaling while compensating for striate-cortical magnification around the polar angle of the visual field did not equate to contrast sensitivity.

In this article, the authors used classic psychophysical tests and a simple experimental design to answer the question of whether cortical magnification underlies polar angle asymmetries of contrast sensitivity. Contrast sensitivity is considered to be the most fundamental spatial vision and is important for both normal individuals and clinical patients in ophthalmology. The parametric contrast sensitivity model and the extraction of key CSF attributes help to compare the comparison of the effect of M scaling at different angles. This work can provide a new reference for the study of normal and abnormal space vision.

The conclusions of this paper are mostly well supported by data, but some aspects of data collection and analysis need to be clarified and extended.

1) In addition to the key CSF attributes used in this paper, the area under the CSF curve is a common, global parameter to figure out how contrast sensitivity changes under different conditions. An analysis of the area under the CSF curve is recommended.

– We have added the area under the CSF (AULCSF) [lines 305-319, Fig 5 E-F; lines 339-343, Fig 6 E-F]. Differences for non-magnified and magnified stimuli are not eliminated.

2) In Figure 2, CRFs are given for several SFs, but were the CRFs at the cutof-sf well-fitted? The authors should have provided the CRF results and corresponding fits to make their results more solid.

– As reported in Fig 4A,C,E, the group data fits were very high (≥.98).

3) The authors suggested that the apparent decrease in HVA extent at high SF may be due to the lower cutoff-SF of the perifoveal VM. Analysis of the correlation between the change in HVA and cutoff SF after M scaling may help to draw more comprehensive conclusions.

– We have rephrased our explanation [lines 453-460]. As per your suggestion, we correlated the change in HVA and the cutoff SF after M scaling and found these correlations to be non significant.

4) In Figure 6, it would be desirable to add panels of exact values of HVA and VMA effects for key CSF attributes at different eccentricities, as shown in Figures 4B, D, and F, to make the results more intuitive.

– We have added these panels [FIG 6] and the corresponding analysis in the text [lines 321-343]

5) More discussions are needed to interpret the results. 1) Due to the different testing distances in VM and HM, their retinae will be in a different adaptation state, making any comparison between VM and HM tricky. The author should have added a discussion on this issue.

– Note that the mean luminance of the display (from retina to monitor) was 23 cd/m2 at 57cm and 19 cd/m2 at 115 cm. The pupil size difference for these two conditions is relatively small (< 0.5 mm) and should not significantly affect contrast sensitivity (Rahimi-Nasrabadi et al., 2021) [lines 483-491]. Moreover, the differences we get here are consistent with the asymmetries we (e.g., Carrasco, Talgar & Cameron, 2001; Cameron, Tai & Carrasco, 2002; Fuller, Park & Carrasco, 2009; Abrams, Nizam & Carrasco, 2012; Corbett & Carrasco, 2012; Himmelberg, Winawer & Carrasco, 2020) and many others (e.g., Baldwin et al., 2012; Pointer & Hess, 1989; Regan and Beverley, 1983; Rijsdijk et al., 1980; Robson and Graham, 1981; Rosén et al., 2014; Silva et al., 2008) have observed for contrast sensitivity when the vertical and horizontal meridian are tested simultaneously at the same distance.

6) In Figure 4, the HVA extent appears to change after M-scaling, although the analysis shows that M-scaling only affects the HVA extent at high SF. In contrast, the range of VMA was almost unchanged. The authors could have discussed more how the HVA and VMA effects behave differently after M-scaling.

– We had commented on this pattern and have further clarified it [lines 436-451]

7) The results in Figure 4 also show that at 11.3 cpd, the measurement may be inaccurate. This might lead to an inaccurate estimate of the M scaling effect at 11.3 cpd. The authors should discuss this issue more.

– We have explained why this data point is at chance [FIG 4 caption]

8) The different neural image-processing capabilities among locations, which is referred to as the "Qualitative hypothesis", is the main hypothesis explaining the differences around the polar angle of the visual field. To help the reader better understand this concept, the author should provide further discussions.

– We have expanded the discussion of the qualitative hypothesis of differences in polar angle (lines 86-92; lines 476-481).

9) The authors should also provide more details about their measures. For example, high grayscale is crucial in contrast sensitivity measurements, and the authors should clarify whether the monitor was calibrated with high grayscale or only with 8-bit. Since the main experiment was measuring CS at different locations, it should also be clarified whether the global uniformity of the display was calibrated.

– The monitor was calibrated with 8-bit at the center of the display [lines 607].

– Regarding global uniformity, although we only calibrated at the center of the display, please note that the asymmetries are not due to the particular monitor we used. We have obtained these asymmetries in contrast sensitivity in numerous studies using multiple monitors over 20 years (e.g., Carrasco, Talgar & Cameron, 2001; Cameron, Tai & Carrasco, 2002; Fuller, Park & Carrasco, 2009; Abrams, Nizam & Carrasco, 2012; Corbett & Carrasco, 2012; Hanning et al., 2022a; Himmelberg et al., 2020) and other groups have reported these visual asymmetries as well (Baldwin et al., 2012; Pointer and Hess, 1989; Rosén et al., 2014). Also important, as we had mentioned in the Introduction [lines 55-59], the HVA and VMA asymmetries shift in-line with egocentric referents, corresponding to the retinal location of the stimulus, not with the allocentric location (Corbett & Carrasco, 2011).

10) In addition, their method of data analysis relies on parametric contrast sensitivity model fitting. One of the concerns is whether there are enough trials for each SF to measure the threshold. The authors should have included in their method the number of trials corresponding to each SF in each CSF curve.

– We have specified number of trials [lines 637-644]

Reviewer #2 (Public Review):

This is an interesting manuscript that explores the hypothesis that inhomogeneities in visual sensitivity across the visual field are not solely driven by cortical magnification factors. Specifically, they examine the possibility that polar angle asymmetries are subserved by differences not necessarily related to the neural density of representation. Indeed, when stimuli were cortically magnified, pure eccentricity-related differences were minimized, whereas applying that same cortical magnification factor had less of an effect on mitigating polar angle visual field anisotropies. The authors interpret this as evidence for qualitatively distinct neural underpinnings. The question is interesting, the manuscript is well written, and the methods are well executed.

1) The crux of the manuscript appears to lean heavily on M-scaling constants, to determine how much to magnify the stimuli. While this does appear to do a modest job compensating for eccentricity effects across some spatial frequencies within their subject pool, it of course isn't perfect. But what I am concerned about is the degree to which the M-scaling that is then done to adjust for presumed cortical magnification across meridians is precise enough to rely on entirely to test their hypothesis. That is, do the authors know whether the measures of cortical magnification across a polar angle that are used to magnify these stimuli are as reliable across subjects as they tend to be for eccentricity alone? If not, then to what degree can we trust the M-scaled manipulation here? In an ideal world, the authors could have empirically measured cortical surface area for their participants, using a combination of retinotopy and surface-based measures, and precisely compensated for cortical magnification, per subject. It would be helpful if the authors better unpacked the stability across subjects for their cortical magnification regime across polar angles.

–– We note that the equations by Rovamo and Virsu are commonly used to cortically magnify stimulus size. This paper has many citations, and the conclusions of many studies are based on those calculations [lines 115-128].

–– In response to Rev’s 3 comment, “In lieu of carrying out new measurements, it could also suffice to compare individual cortical magnification factors to the performance to quantify the contribution to the psychophysical performance”, we found a significant correlation between the surface area and contrast sensitivity measures at the horizontal, upper-vertical and lower-vertical meridians. However, we found no significant correlation between the cortical surface with the difference in contrast sensitivity for fixed-size and magnified stimuli at 6 deg at each meridian. These findings suggest that surface area plays a role but that individual magnification is unlikely to equalize contrast sensitivity [lines 366-380; Fig 7; lines 511-529].

2) Related to this previous point, the description of the cortical magnification component of the methods, which is quite important, could be expanded on a bit more, or even placed in the body of the main text, given its importance. Incidentally, it was difficult to figure out what the references were in the Methods because they were indexed using a numbering system (formatted for perhaps a different journal), so I could only make best guesses as to what was being referred to in the Methods. This was particularly relevant for model assumptions and motivation.

–– We now detail M-scaling in the Introduction [lines 115-135], and we have fixed the references in the Methods section.

3) Another methodological aspect of the study that was unclear was how the fitting worked. The authors do a commendably thorough job incorporating numerous candidate CSF models. However, my read on the methods description of the fitting procedure was that each participant was fitted with all the models, and the best model was then used to test the various anisotropy models afterwards. What was the motivation for letting each individual have their own qualitatively distinct CSF model? That seems rather unusual.

Related to this, while the peak of the CSF is nicely sampled, there was a lack of much data in the cutoff at higher spatial frequencies, which at least in the single subject data that was shown made the cutoff frequency measure seem like it would be unreliable. Did the authors find that to be an issue in fitting the data?

–– We have further clarified that we fit all 9 models to the grouped data [lines 177-178] and in Methods [lines 693, 716, 725], and that the fit in Figure 3 corresponds to the grouped data [Fig 3 caption]. As reported in Fig 4A,C,E, the group data fits were very high (≥.98). Please note that the cutoff spatial frequency is reliable. The data point (11.3 cpd) in the differences which does not follow the same function (Fig 4D,F) reflects the fact that for both magnified and not-magnified stimuli, performance was at chance, consistent with the fact that high SF are harder to discriminate at peripheral locations [Fig 4 caption].

4) The manuscript concludes that cortical magnification is insufficient to explain the polar angle inhomogeneities in perceptual sensitivity. However, there is little discussion of what the authors believe may actually underlie these effects then. It would be productive if they could offer some possible explanation.

–– We have expanded the discussion of qualitative hypothesis of differences in polar angle [lines 86-92; lines 476-481].

–– We have expanded the discussion of possible mechanisms [lines 496-529].

–– We have explained why having assessed the VM and HM and different distances does not significantly influence our measures [lines 483-491].

–– We have expanded the discussion of how the HVA and VMA effects behave differently after M-scaling [lines 435-450].

–– We have clarified that the fits are reliable and made explicit that the highest SF data point is at chance in both conditions [FIG 4 caption].

Reviewer #3 (Public Review):

Jigo, Tavdy & Carrasco used visual psychophysics to measure contrast sensitivity functions across the visual field, varying not only the distance from fixation (eccentricity) but also the angular position (meridian). Both parameters have been shown to affect visual sensitivity: spatial visual acuities generally fall off with eccentricity, it is now widely accepted that it is superior along the horizontal than the vertical meridian, and there may also be differences between the upper and lower visual field, although this anisotropy is typically less pronounced. The eccentricity-dependent decrease in performance is thought to be due to reduced cortical magnification in peripheral compared to central vision; that is, the amount of brain tissue devoted to mapping a fixed amount of visual space. The authors, therefore, include a crucial experimental condition in which they scale the size of their stimuli to account for reduced cortical magnification. They find that while this corrects for reduced performance related to stimulus eccentricity, it does not fully explain the variation in performance at different visual field meridians. They argue that this suggests other neural mechanisms than cortical magnification alone underlie this intra-individual variability in visual perception.

The experiments are done to an extremely high technical standard, the analysis is sound, and the writing is very clear. The main weakness is that as it stands the argument against cortical magnification as the factor driving this meridional variability in visual performance is not entirely convincing. The scaling of stimulus size is based on estimates in previous studies. There are two issues with this: First, these studies are all quite old and therefore used methods that cannot be considered state-of-the-art anymore. In turn, the estimates of cortical magnification may be a poor approximation of actual differences in cortical magnification between meridians.

–– We note that the equations by Rovamo and Virsu are commonly used to cortically magnify stimulus size. This paper has many citations, and the conclusions of many studies are based on those calculations [lines 115-128].

–– In response to Rev’s 3 comment, “In lieu of carrying out new measurements, it could also suffice to compare individual cortical magnification factors to the performance to quantify the contribution to the psychophysical performance”, we found a significant correlation between the surface area and contrast sensitivity measures at the horizontal, upper-vertical and lower-vertical meridians. However, we found no significant correlation between the cortical surface with the difference in contrast sensitivity for fixed-size and magnified stimuli at 6 deg at each meridian. These findings suggest that surface area plays a role but that individual magnification is unlikely to equalize contrast sensitivity [lines 366-380; Fig 7; lines 511-529].

Second, we now know that this intra-individual variability is rather idiosyncratic (and there could be a wider discussion of previous literature on this topic). Since these meridional differences, especially between upper and lower hemifields, are relatively weak compared to the variance, a scaling factor based on previous data may simply not adequately correct these differences. In fact, the difference in scaling used for the upper and lower vertical meridian is minute, 7.7 vs 7.68 degrees of visual angle, respectively. This raises the question of whether such a small difference could really have affected performance.

That said, there have been reports of meridional differences in the spatial selectivity of the human visual cortex (Moutsiana et al., 2016; Silva et al., 2017) that may not correspond one-to-one with cortical magnification. This could be a neural substrate for the differences reported here. This possibility could also be tested with their already existing neurophysiological data. Or perhaps, there could be as-yet undiscovered differences in the visual system, e.g., in terms of the distribution of cells between the ventral and dorsal retina. As such, the data shown here are undoubtedly significant and these possibilities are worth considering. If the authors can address this critique either by additional experiments, analyses, or by an explanation of why this cannot account for their results, this would strengthen their current claims; alternatively, the findings would underline the importance of these idiosyncrasies in the visual cortex.

We now include discussion of the different points that the reviewer raised here in our new section 'What mechanism might underlie perceptual polar angle asymmetries' [lines 497-530].

Author Response

Reviewer #1 (Public Review):

• The statistical procedures used are not completely described and may not be appropriate.

We revised the text in Methods and Results sections to give more details about the methods used.

-As only two levels of delay were tested, it is not possible to directly test whether the subjective discounting function is hyperbolic or exponential and hence whether the delay is encoded subjectively or objectively.

We agree with the reviewer. A higher number of task parameters may offer a better resolution to evaluate the discounting functions. Fortunately, this does not affect our main results.

• The task has several variable interval lengths (hold in: 1.2-2.8 s, short delay: 1.8-2.3 s, long delay: 3.5-4s) that frustrate interpretation. The distribution of these delays is not described, for example as it reads it seems possible that some long delay rewards are delivered with shorter latency between cue and reward than some short delay rewards (1.2 + 3.5 = 4.7s vs. 2.8+2.3 = 5.1 s).

We revised the text to address that ambiguity. In the new version of the manuscript, we describe short versus long delays considering the total delay intervals between instruction cue onset and reward delivery [short delay (3.5-5.6s) and long delay (5.2-7.3s)]. Within each delay category, individual delays were distributed in a gaussian fashion such that the two delay ranges overlapped for 9% of trials. These details are now described in the revised Methods section (pg. 22).

-The authors have not considered that if the delay value is encoding, then the value, both objectively and subjectively, may be changing as the delay elapses. The variation of these task intervals may have an effect on the value of delay.

In the present study, we report a dynamic integration between the desirability of the expected reward and the imposed delay to reward delivery across the waiting period. Our results (e.g. see Fig. 6) do not fit with simple linear (or logarithmic) effects corresponding to continuous regular changes as the delay elapses. We found different types of interactions (Discounting± and Compounding±) at different periods of the hold period and in different single units. We did not find a way to model all these types of interactions with this type of approach.

Reviewer #2 (Public Review):

• Plots of "rejection rate" (trials where the monkeys failed to wait until the rewards) as a function of delay and reward size seem to indicate that the monkeys understood the visual cue. The rejection rates were very low (less than 4% for almost all conditions) which indicates that the monkeys did not have a hard time inhibiting their behavior. It also meant that the authors could not compare trials where the monkeys successfully waited with trials where they failed to wait. This missing comparison weakens the link between the neurophysiological observations and the conclusions the authors made about the signals they observed.

Here, our main goal was to describe the dynamic STN signals engaged during the waiting period without studying action-related activities. In the discussion (pg. 20), we clearly wrote ‘Further research is needed to determine whether the neural signals identified here causally drive animals’ behavior or rather just participate to reflect or evaluate the current situation.’ Consequently, our conclusions were already tempered by that point.

In addition, we address the same limitation by writing (pg. 20): “An important avenue for future research will be to determine how STN signals, such as those described here, change when animals run out of patience and finally decide to stop waiting. To do this, however, smaller reward sizes and longer delays might be used to promote more escape behaviors during the delay interval.”

• The authors examined the STN activity aligned to the start of the delay and also aligned to the reward. Most of the "delay encoding" in the STN activity was observed near the end of the waiting period. The trouble with the analysis is that a neuron that responded with exactly the same response on short and long trials could appear to be modulated by delay. This is easiest to see with a diagram, but it should be easy to imagine a neural response that quickly rose at the time of instruction and then decayed slowly over the course of 2 seconds. For long trials, the neuron's activity would have returned to baseline, but for short trials, the activity would still be above baseline. As such, it is not clear how much the STN neurons were truly modulated by delay.

We agree with the reviewers. Our original analyses using two-time windows had the potential to introduce biases in the detection of neuronal activities modulated by the delay. To overcome this issue, we modified the time frame of all of our analyses (neuronal activity, eye position, EMG). Now, the revised version of the manuscript only reports activities across one-time window aligned to the time of instruction cue delivery (i.e., -1 to 3.5s relative to instruction cue onset). This time frame corresponds to the minimum possible interval between instruction cues and reward delivery. We have revised all of the figures and we re-calculated all of the statistics using that one analysis window. Despite these major modifications, our key findings were not changed substantially. We found the same pattern in STN activities, with a strong encoding of reward (48% of neurons) preceding a late encoding of delay (39% of neurons). We also updated the text in Methods and Results sections to reflect the revised analyses.

• Another concern is the presence of eye movement variables in the regressions that determine whether a neuron is reward or delay encoding. If the task variables modulated eye movements (which would not be surprising) and if the STN activity also modulated eye movements, then, even if task variables did not directly modulate STN activity, the regression would indicate that it did. This is commonly known as "collider bias". This is, unfortunately, a common flaw in neuroscience papers.

Because the presence of eye variables did not influence how neurons were selected by the GLM, we do not think it likely that our analysis was susceptible to “collider bias”. Nonetheless, to control for that possibility directly, we have now repeated the GLM analyses with eye movement variables excluded. Results are shown in a new figure (Fig.4 – supplementary 1). Exclusion of eye parameters produced results that are very similar to those from the GLM that included eye parameters (differences <3 degrees). We have added text to the manuscript describing this added control analysis.

Author Response

Reviewer #2 (Public Review):

The work integrated genomic and transcriptomic data to reconstruct the origin of the svPDE gene from the ancestral ENPP3 gene. The authors also analyzed the expression of svPDE along different snake lineages and different tissues in three species of venomous snakes. Finally, they purified an svPDE from the venom of Naja atra and analyzed its crystallographic structure and enzymatic function. The experiments are adequately designed and carefully planned and the conclusions made by the authors are well supported by evidence.

I have the following suggestions:

1) I could not find a section where the authors provided information regarding the origin of the analyzed venom and tissues. i.e. muscle tissue from Naja atra and venom for purification of svPDE. It is important to include this information.

We thank the reviewer for mentioning this.

The information for the venom purification has been described in Results (LINE 116) as “This svPDE was directly purified from the crude venom of Naja atra captured in Taiwan”. The information for the tissues of sequencing data has been included in Results (LINE 117) as “… with publicly available RNA-Seq data and compared them with the corresponding genomes available in the NCBI Assembly database (SI Appendix, Table S1)”, and Material and Methods (Line 403) as “DNA was extracted from the muscle tissue of a male Naja atra …”.

Also, the SI Appendix Table S1 summarized all samples used for sequence analysis with their tissue origins.

We are still grateful for this comment and have updated the text to make it clearer as follows:

“The target genomes included the draft one of Naja atra sequenced from a muscle tissue (ongoing internal project, see Material and Methods for detail) and the complete one of its sister species, Naja naja, from the public data (Suryamohan et al., 2020).”

We have also updated the text when the first time mentioning the comparative genomics and transcriptomes analysis to indicate where the information is described.

“To test our hypothesis, we comprehensively de novo assembled transcriptomes from the species across 13 clades of Toxicofera (Fig. 1B) with publicly available RNA-Seq data and compared them with the corresponding genomes available in the NCBI Assembly database (see SI Appendix, Table S1 for sample details).”

2) The authors mention (Line 156) that "the genomic sequences of svPDE-E1a were present in all species of Serpentes but not in the species of Dactyloidae, Varanidae, and Typhlopidae.". As I understand it, the family Typhlopidae is included in the Suborder Serpentes. The conclusions stand of course, but I believe it is worth revising, for accuracy.

We thank the reviewer for noticing this issue.

We have updated the text as follows to prevent misleading:

From “the genomic sequences of svPDE-E1a were present in all species of Serpentes but not in the species of Dactyloidae, Varanidae, and Typhlopidae. This suggests an early emergence of svPDE-E1a in the common ancestor of Serpentes and became …”

To

“the genomic sequences of svPDE-E1a were present in all species of Serpentes except for the earliest diverged Typhlopidae. This suggest an early emergence of svPDE-E1a in the Serpentes evolution and became …”

3) During the discussion (Line 315), it is stated that the expression of svPDE in Lamprophiidae is probably associated with the adaptation of prey selection as a dietary generalist compared to Viperidae and Elapidae. Provided that both of these clades have several species considered dietary generalists, I believe this statement is not strongly supported.

We agreed with the reviewer’s comment that we overstated it without solid support. However, here we believe it is worth mentioning and providing a hint for future studies that Lamprophiidae, a less-known clade, has svPDE expression and is not lower than several species of Elapidae. Therefore, we have revised this paragraph to include the finding without further speculations.

“Comparative transcriptomics is a powerful tool to reveal species-specific or tissue-specific novel transcripts, providing new insights for further studies. For example, the svPDE expression of Lamprophiidae, even higher than several species of Elapidae, indicates the worth of further study for this less-known clade to fill the knowledge gap.”

4) Also in the discussion (Line 320), the authors mention that Colubridae is traditionally regarded as a non-venomous clade. This statement is far from accurate given that Colubridae is a very diverse clade and several species within it have been shown to be at least moderately venomous. Various species have been shown to produce secretions comparable to those of front-fanged snakes. Furthermore, despite their difference in morphology, I believe there is little to no evidence that suggests Duvernoy's glands in colubrids have any functions differing from the venom glands of front-fanged snakes.

We thank reviewer’s comment for revising the interpretation. This paragraph has been rewritten to as follows:

“Interestingly, the svPDE expression in Duvernoy’s glands of Colubridae, although low, several species within the diverse Colubridae clade have been shown to be moderately venomous. The expression of svPDE in the Duvernoy’s glands also highlights its potential function despite that Duvernoy’s glands exhibit morphological difference from the venom glands of front-fanged snakes”

Author Response

Reviewer #1 (Public Review):

The manuscript "Interplay between PML NBs and HIRA for H3.3 dynamics following type I interferon stimulus" by Kleijwegt and colleagues describes a study that's set out to explore the details of the PML-HIRA axis in H3.3 deposition at ISGs upon IFN-I stimulation. First, the authors establish that HIRA colocalized at PML NBs upon TNFa and TNFb treatment. This process is SUMO-dependent and facilitated by at least one of the identified SIM domains of HIRA. Next, the authors set out to determine whether interferon responsive genes (ISGs) are dependent on HIRA or PML. By knocking-down either HIRA or PML, only an effect on ISGs was observed when PML was knocked down. In fact, immune-FISH showed that PML NBs are in close proximity of ISGs upon TNFb treatment. To address the histone chaperone function of HIRA, the deposition of the replication-independent H3.3 on ISGs is tested. In specific, the enrichment of H3.3 across the ISG gene body. ChIP-seq data (Fig 5B) showed an enrichment around the TES, whereas qPCR (Fig 5A) showed less convincing enrichment (for details see below). When either HIRA or PML are knocked down, a mild loss of H3.3 enrichment was observed (Fig 5E). Interestingly, when HIRA is sequestered away from PML NBs by Sp100, an increased enrichment of H3.3 was observed. To understand the interplay between H3.3 deposition and HIRA's role in this process in the presence of PML NBs, H3.3 was overexpressed. Two population of cells were observed: low or high levels of H3.3. In the former, HIRA formed foci and the latter, HIRA did not form foci. Surprisingly, when HIRA is overexpressed, PML NBs form in the absence of TNFb. Finally, a two-sided model is proposed, where PML NBs is required for ISG transcription promoting H3.3 loading. The second side is that PML NBs function as a "storage center" for HIRA to regulate its availability.

Overall, it the model is intriguing, but the data presented seems insufficient to support the current claims.

We thank the reviewer for his/her constructive comments. We want to point out that there is a confusion in the reviewer's statement (highlighted in red here above) between TNFb and IFNb, because it is IFNb that was mostly used in our study. We suppose it is a typo error. Concerning the sentence: "when HIRA is overexpressed, PML NBs form in the absence of TNFb", it is inaccurate. Indeed, PML NBs are present in our cells with or without IFNb treatment. Overexpression of HIRA triggers accumulation of the ectopic HIRA in the PML NBs in absence of IFNb, probably as part of a buffering mechanism.

Major concerns:

• The suggested function of HIRA at the PML NBs as storage is interesting. Ideally, this would be tested by real-time single molecule tracking.

While surely interesting, we believe that the real-time single molecule tracking is beyond the scope of our article. In addition, with our hypothesis that PML NBs act as buffering places for HIRA, HIRA might come in and out of PML NBs depending on its concentration and/or the availability of free binding sites and single molecule tracking might not be informative for long- term possible storage functions of PML NBs.

• The link between PML NBs containing HIRA and H3.3 deposition is very intriguing and indeed the ChIP-seq data shown in Figure 5B shows a clear increase in the H3.3 signal around the TES. This distribution is very intriguing as recent work (Fang et al 2018 Nat Comm) showed that H3.3 deposition across the gene body was diverse and dynamic. Ideally, the qPCR of some select ISGs would confirm the ChIP-seq data. Here a more complex picture emerges. Just as with the ChIP-seq, a modest decrease of H3.3 at the TSS was observed, but only in 2 of the 3 genes shown is H3.3 enriched at the TES and only in 1 gene (ISG54) is H3.3 enriched at the gene body. As qPCR is later used in the manuscript (Fig 5E and 5G), it is essential that the results of two different techniques give similar results. With regards to Fig 5E and 5G, it is unclear why certain gene regions are shown, but not others.

We agree with the reviewer that distribution of H3.3 on active genes follows a diverse and dynamic pattern. H3.3 is enriched on gene bodies but several papers have shown an important increase of H3.3 loading on the TES region of actively transcribed genes (Tamura et al. 2009; Sarai et al. 2013). Our ChIP-qPCR data (Figure 6A) and our ChIP-Seq data (Figure 6B) are consistent and show a moderate increase of H3.3 on gene bodies, eg on MX1 mid or ISG54 mid regions shown by qPCR on Figure 6A (this enrichment is reproducible but not necessarily statistically significant) and on gene bodies of the 48 core ISGs as shown in our ChIP-Seq data (see the light blue line between TSS and TES on figure 6B). In addition, our ChIP-qPCR and ChIP-Seq data also consistently show a higher enrichment of H3.3 on the TES regions of ISGs (see the significant enrichment found in ChIP-qPCR in the TES regions of MX1, OAS1 and ISG54, as well as the strong increase in H3.3 deposition with IFN seen by the light blue line for ChIP- Seq data on figure 6B).

Since the strongest enrichment for H3.3 was found on the TES region, we focused on this region to evaluate the impact of HIRA or PML knock-down. Our ChIP-Seq data (now added in main Figure 6F for the whole ISG region, or with a zoom on the TES region in Figure 6G) shows that the strongest effect of HIRA or PML knock-down is indeed visible in the TES region of ISGs. Our ChIP-qPCR presented on Figure 6E data totally supports this effect.

Overall, the link between HIRA and PML in H3.3 loading is only mildly affected (Fig 5E and 5F). The conclusion that HIRA and PML are essential (Page 12, line 8) is not represented by the presented data. The authors propose that DAXX could play a role. Indeed, work on another H3 variant, CENP-A, showed that non-centromeric localization is dependent on both HIRA and DAXX (Nye et al 2018 PLoS ONE). It would be interesting to learn if a double knock-down of HIRA and DAXX can prevent the enrichment of H3.3 at TES of ISGs upon TNFb treatment.

To address the first part of the comment, we have now added 3 things :

(1) we have tuned-down our conclusion by saying that HIRA and PML are 'important' for the long-lasting deposition of H3.3 on ISGs,

(2) we provide new data of time-ChIP qPCR experiments suggesting that HIRA is important for H3.3 recycling during transcription of ISGs. We believe that these results strengthen the importance of HIRA for the global H3.3 enrichment on ISGs (by acting both in the de novo deposition and/or recycling of H3.3).

We agree with the reviewer that it could be interesting to study the impact of the double knock-down of DAXX and HIRA on H3.3 enrichment at ISGs. However, we decided to focus our attention on SP100 since it could help us to better tease apart the role of HIRA localization in PML NBs, versus its role in H3.3 deposition at ISGs. In addition, since SP100 knock-down unleashes ISGs transcription, it also provided us with the opportunity to study the impact of an elevated ISGs transcription on H3.3 deposition and whether this is also mediated by HIRA.

(3) we thus now also provide data of the double knock-down of SP100 and HIRA showing that the increase in H3.3 loading on ISGs seen upon SP100 knock-down is mediated by HIRA. This new result also strengthens the importance of HIRA for H3.3 enrichment on ISGs upon transcription.

• In Figure 6B, two versions of HIRA are overexpressed and the authors conclude that the number of PML NBs goes up. Earlier in the manuscript, the authors showed that PML NB formation upon IFNb exposure brings HIRA into the PML NBs via a SUMO-dependent mechanism. Is overexpression of HIRA and its accumulation in PML NBs also SUMO-dependent or SUMO-independent? Overexpressing the SIM mutants from Figure 3F would address this question. In addition, the link between the proposed HIRA being stored at PML NBs could be strengthened by overexpressing HIRA and see at both short and late time points whether H3.3 is enriched on ISG genes.

We want to clarify the first point: we do not conclude that the number of PML NBs goes up upon overexpression of HIRA. The number of PML NBs seems stable, although we have not quantified it. The aim of Figure 4A (previously Figure 6B) is to show that upon overexpression, ectopic forms of HIRA localize in PML NBs without IFN-I treatment, as part of a buffering mechanism.

The SIM mutant of HIRA from Figure 3F is indeed overexpressed and does not localize in PML NBs upon IFN-I treatment. We have now added an IF (Figure 3- figure supplement 1C) showing that it does not localize either in PML NBs in non-treated cells. Thus, this underscores that accumulation of ectopic HIRA in PML NBs is SUMO-SIM-dependent regardless of the IFN-I treatment.

• BJ cells are known to senesce rather easily. Did the authors double-check what fraction of their cells were in senescence and whether this correlated with the high or low expression of ectopic H3.3?

BJ cells can indeed enter into senescence, but there are less prone to senesce than other human primary cells such as IMR90 for example. Nevertheless, we checked EdU incorporation both in BJ cells (Figure 1 - Figure supplement 1F) and BJ eH3.3i cells with expression of ectopic H3.3, with or without IFN-I treatment (Figure R2 for reviewer). We could clearly see that in our conditions (Dox addition for 24h maximum, IFNb at 1000U/mL for 24h), there is no significant difference in the number of EdU+ cells (ie proliferating cells), thus excluding effects due to senescence entry. As positive control, we have treated BJ cells with etoposide, a known senescence-inducing drug (Kosar et al., 2013; Tasdemir et al., 2016) which indeed reduces the number of EdU positive cells. We have now added a sentence in the main text as well to underscore that cells are not senescent.

• In Figure 6 - figure supplement D, it appears that the levels of HIRA go up upon TSA and IFNb treatment. Rather than relying on visual inspection, ideally, all Western blots should be quantified to confirm the assessment that protein levels are not affected by different experimental procedures.

We now provide quantification of all WBs below each WB. In addition, we have removed data on TSA since it could appear too preliminary.

Reviewer #2 (Public Review):

HIRA chaperone complex has been previously shown to localize at PML Nuclear Bodies upon various stress or stimuli (senescence, viral infections, interferon treatment). The authors have previously unraveled an anti-viral role of PML NBs through the chromatinization of HSV-1 viral genome by H3.3 chaperones. Here, the authors identify SUMOylation, as well as a SIM-like sequence in HIRA, as drivers for HIRA recruitment at PML Nuclear Bodies upon interferon-I treatment. These HIRA-containing PML NBs localize close to interferon-stimulated gene (ISG) loci. Although the functional role of HIRA/PML interaction is yet not solved, HIRA and PML regulate H3.3 loading at transcriptional end sites of IGS upon Interferon-I treatment. The authors propose that PML NBs play a buffering role for HIRA, regulating its availability depending on H3.3 level or chromatin dynamics.

Strength:

The authors used primary human diploid BJ fibroblasts, a relevant cell line for studying physiological regulation upon inflammatory cytokines. The role of SUMO/SIM on HIRA localization upon interferon beta treatment was assessed using interesting - but already described - tools, such as SUMO-specific affimers. The authors provide convincing results on the requirement of PML SUMOylation and a putative SIM sequence on HIRA for its localization at PML Nuclear Bodies. Other interesting observations are described, such as some PML or HIRA-dependent long-lasting H3.3 loading at transcription end site of ISGs upon interferon beta treatment, as shown by ChIP analyses of ISG loci, but also by endogenous H3.3 ChIPseq analysis.

Weakness:

The authors claim HIRA partitioning at PML NBs correlates with increase in "PML valency" upon interferon-I. The "valency" refers to the number of interaction domains, but the number of SUMOs conjugated on PML is not explored here (nor the number of SIMs on HIRA). Although the authors have proposed interested hypothesis and discussion, the inhibitory role of H3.3 overexpression or acetylation inhibition on HIRA localization at PML Nuclear Bodies clearly deserves further investigations.

More generally, the manuscript explores many directions, but the links between the various observations remain unclear and limit firm conclusions.

We thank the reviewer for his/her constructive comments.

We have now addressed these 3 weaknesses pointed out by the reviewer.

• Our claims on PML valency have been removed. We have now underscored the link between HIRA accumulation in PML NBs and the increase in PML and SP100 protein levels, without lingering on the valency aspects which was not the focus of our paper.

• The role of H3.3 overexpression in inhibition of HIRA localization in PML NBs has been moved in the first part of the paper describing the mechanistic for accumulation of HIRA in PML NBs. We feel that these data are still of importance and support the role of PML NBs as a buffering place for HIRA depending on DAXX levels (new data) as well as H3.3 levels.

We agree that the acetylation inhibition would deserve further investigations and we have thus removed the part on TSA treatment.

• Thanks to the reviewer's comments, we have now remodeled the article to better convey two main conclusions : (1) PML NBs serve as a buffering site for HIRA. Accumulation of HIRA in PML NBs depends both on PML and SP100 concentration (and on PML SUMOylation) as well as DAXX and H3.3 levels and (2) upon IFN-I treatment, PML regulates ISGs transcription and thus indirectly regulates HIRA loading on ISGs, which controls H3.3 deposition and recycling during transcription. HIRA-mediated H3.3 deposition/recycling is highly dependent on ISGs transcription levels and is thus increased upon SP100 knock-down which unleashes ISGs transcription.

Author Response

Reviewer #1 (Public Review):

This manuscript provides the first cellular analysis of how neuronal activity in axons (in this case the optic nerve) regulates the diameter of nearby blood vessels and hence the energy supply to neuronal axons and their associated cells. This is an important subject because, in a variety of neurological disorders, there is damage to the white matter that may result from a lack of sufficient energy supply, and this paper will stimulate work on this important subject.

Axonal spiking is suggested to release glutamate which activates NMDA receptors on myelin-making oligodendrocytes wrapped around the axons: the oligodendrocytes - either directly or indirectly via astrocytes - then generate prostaglandin E2 which relaxes pericytes on capillaries, thus decreasing the resistance of the vascular bed and (presumably) increasing blood flow in the nerve.

Strengths of the paper

The paper identifies some important characteristics of axon-vascular coupling, notably its slow temporal development and long-lasting nature, the involvement of PgE2 in an oxygen-dependent manner, and a role for NMDARs. Rigorous criteria (constriction and dilation of capillaries by pharmacological agents) are used to select functioning pericytes for analysis.

Weaknesses of the paper

The study focuses exclusively on pericytes. It would have been interesting to assess whether arteriolar SMCs also contribute to regulating blood flow

We thank reviewer #1 for his/her positive comment on our manuscript. We also share the future interest in the optic nerve’s arteriole (there is only one main arteriole covered by SMC). However, it is not always visible in the preparation due to the orientation of the nerve - if not on the surface and directly under the microscope it is not possible to image it.

Reviewer #2 (Public Review):

This paper describes a new concept of "axo-vascular coupling" whereby action potential traffic along white matter axons induces vasodilation in the mouse optic nerve. This is an initial report dissecting some of the mechanisms that are undoubtedly complex as in gray matter NVC. I like the novel AVC concept.

We really appreciate the reviewer’s positive comments.

Author Response

Reviewer #1 (Public Review):

This manuscript reports a systematic study of the cortical propagation patterns of human beta bursts (~13-35Hz) generated around simple finger movements (index and middle finger button presses).

The authors deployed a sophisticated and original methodology to measure the anatomical and dynamical characteristics of the cortical propagation of these transient events. MEG data from another study (visual discrimination task) was repurposed for the present investigation. The data sample is small (8 participants). However, beta bursts were extracted over a +/- 2s time window about each button press, from single trials, yielding the detection and analysis of hundreds of such events of interest. The main finding consists of the demonstration that the cortical activity at the source of movement related beta bursts follows two main propagation patterns: one along an anteroposterior directions (predominantly originating from pre central motor regions), and the other along a medio- lateral (i.e., dorso lateral) direction (predominantly originating from post central sensory regions). Some differences are reported, post-hoc, in terms of amplitude/cortical spread/propagation velocity between pre and post-movement beta bursts. Several control tests are conducted to ascertain the veracity of those findings, accounting for expected variations of signal-to-noise ration across participants and sessions, cortical mesh characteristics and signal leakage expected from MEG source imaging.

One major perceived weakness is the purely descriptive nature of the reported findings: no meaningful difference was found between bursts traveling along the two different principal modes of propagation, and importantly, no relation with behavior (response time) was found. The same stands for pre vs. post motor bursts, except for the expected finding that post-motor bursts are more frequent and tend to be of greater amplitude (yielding the observation of a so-called beta rebound, on average across trials).

Overall, and despite substantial methodological explorations and the description of two modes of propagation, the study falls short of advancing our understanding of the functional role of movement related beta bursts.

For these reasons, the expected impact of the study on the field may be limited. The data is also relatively limited (simple button presses), in terms of behavioral features that could be related to the neurophysiological observations. One missed opportunity to explain the functional role of the distinct propagation patterns reports would have been, for instance, to measure the cortical "destination" of their respective trajectories.

In response to this comment, we would like to highlight two important points.

First, our work constitutes the first non-invasive human confirmation of invasive work in animals (Balasubramanian et al., 2020; Roberts et al., 2019; Rule et al., 2018; (Balasubramanian et al., 2020; Best et al., 2016; Rubino et al., 2006; Takahashi et al., 2011, 2015) and patients (Takahashi et al., 2011). Thus, these results bridges between recordings limited to the size of multielectrode arrays (roughly 0.16 cm2; Balasubramanian et al., 2020; Best et al., 2016; Rubino et al., 2006; Takahashi et al., 2011, 2015) and human EEG recordings spanning across large areas of the cortex and several functionally distinct regions (Alexander et al., 2016; Stolk et al., 2019). The ability to access these neural signatures non- invasively is important for cross-species comparison. This further enables us, to provide an in-depth analysis of the spatiotemporal diversity of human MEG signals and a detailed characterisation of the two propagation directions, which significantly extends previous reports. We note that their functional role remains undetermined also in these animal studies, but being able to identify these signals now in humans can provide a steppingstone for identifying their role.

Second, and related, the reviewers are correct that we did not observe distinct propagation directions between pre- and post-movement bursts, nor a relationship with reaction time. However, such a null result would be relevant, in our view, towards understanding what the functional relevance of these signals, if any, might be. Recent work in macaques indicates that the spatiotemporal patterns of high-gamma activity carry kinematic information about the upcoming movement (Liang et al 2023). The functional role of beta may therefore be more complex and not relate to reaction times or kinematics in a straightforward manner. We believe this is a relevant observation, and in keeping with the continued efforts to identify how sensorimotor beta relates to behaviour. It is increasingly clear that spatiotemporal diversity in animal recordings and human E/MEG and intracranial recordings can constitute a substantial proportion of the measured dynamics. As such, our report is relevant in narrowing down what these signals may reflect.

Together, we think that our work provides new insights into the multidimensional and propagating features of burst activity. This is important for the entire electrophysiology community, as it transforms how we commonly analyse and interpret these important brain signals. We anticipate that our work will guide and inspire future work on the mechanistic underpinnings of these dominant neural signals. We are confident that our article has the scope to reach out to the diverse readership of eLife.

Reviewer #2 (Public Review):

The authors devised novel and interesting experiments using high precision human MEG to demonstrate the propagation of beta oscillation events along two axes in the brain. Using careful analysis, they show different properties of beta events pre- and post movement, including changes in amplitude. Due to beta's prominent role in motor system dynamics, these changes are therefore linked to behavior and offer insights into the mechanisms leading to movement. The linking of wave-like phenomena and transient dynamics in the brain offers new insight into two paradigms about neural dynamics, offering new ways to think about each phenomena on its own.

Although there is a substantial, and recent, body of literature supporting the conclusions that beta and other neural oscillations are transient, care must be taken when analyzing the data and the resulting conclusions about beta properties in both time and space. For example, modifying the threshold at which beta events are detected could alter their reported properties and expression in space and time. The authors should therefore performing parameter sweeps on e.g. the thresholds for detection of oscillation bursts to determine whether their conclusions on beta properties and propagation hold. If this additional analysis does not change their story, it would lend confidence in the results/conclusions.

We thank the reviewing team for this comment. As suggested, we evaluated the effect of different burst thresholds on the burst parameters.

The threshold in the main analysis was determined empirically from the data, as in previous work (Little et al., 2019). Specifically, trial-wise power was correlated with the burst probability across a range of different threshold values (from median to median plus seven standard deviations (std), in steps of 0.25, see Figure 6-figure supplement 1). The threshold value that retained the highest correlation between trial-wise power and burst probability was used to binarize the data.

We repeated our original analysis using four additional thresholds, i.e., original threshold - 0.5 std, -0.25 std, +0.25 std, +0.5 std. As one would expect, burst threshold is negatively related to the number of bursts (i.e., higher thresholds yield fewer bursts, Figure R4a [top]), and positively related to burst amplitude (i.e., higher thresholds yield higher burst amplitudes, Figure R4a [bottom]).

Similarly, the temporal duration of bursts and apparent spatial width are modulated by the burst threshold: lowering the threshold leads to longer temporal duration and larger apparent spatial width while increasing the threshold leads to shorter temporal duration and smaller apparent spatial width Figure R4b. Note that for the temporal and spectral burst characteristics, the difference to the original threshold can be numerically zero, i.e., changing the burst threshold did not lead to changes exceeding the temporal and spectral resolution of the applied time-frequency transformation (i.e., 200ms and 1Hz respectively).

Importantly, across these threshold values, the propagation direction and propagation speed remain comparable.

We now include this result as Figure 6-figure supplement 2and refer to this analysis in the manuscript (page 28 line 717).

“To explore the robustness of the results analyses were repeated using a range of thresholds (Figure 6-figure supplement 2).”

Determining the generators of beta events at different locations is a tricky issue. The authors mentioned a single generator that is responsible for propagating beta along the two axes described. However, it is not clear through what mechanism the beta events could travel along the neural substrate without additional local generators along the way. Previous work on beta events examined how a sequence of synaptic inputs to supra and infragranular layers would contribute to a typical beta event waveform. Although it is possible other mechanisms exist, how might this work as the beta events propagate through space? Some further explanation/investigation on these issues is therefore warranted.

Based on this and other comments (i.e., comments 7 and 8) we re-evaluated the use of the term ‘generator’ in this manuscript.

While the term generator can be used across scales, from micro- to macroscale, ifor the purpose of the present paper, we believe one should differentiate at least two concepts: a) generator of beta bursts, and b) generator of travelling waves.

We realised that in the previous version of the manuscript the term ‘generator’ was at times used without context. We removed the term where no longer necessary.

Further, the previous version of the manuscript discussed putative generators of travelling waves (page 19f.) but not generators of beta bursts. We now address this as follows:

“Studies using biophysical modelling have proposed that beta bursts are generated by a broad infragranular excitatory synaptic drive temporally aligned with a strong supragranular synaptic drive (Law et al., 2022; Neymotin et al., 2020; Sherman et al., 2016; Shin et al., 2017) whereby layer specific inhibition acts to stabilise beta bursts in the temporal domain (West et al., 2023). The supragranular drive is thought to originate in the thalamus (E. G. Jones, 1998, 2001; Mo & Sherman, 2019; Seedat et al., 2020), indicating thalamocortical mechanisms (page 22f).”

Once the mechanisms have been better understood, a question of how much the results generalize to other oscillation frequencies and other brain areas. On the first question of other oscillation frequencies, the authors could easily test whether nearby frequency bands (alpha and low gamma) have similar properties. This would help to determine whether the observations/conclusions are unique to beta, or more generally applicable to transient bursts/waves in the brain. On the second issue of applicability to other brain areas, the authors could relate their work to transient bursts and waves recorded using ECoG and/or iEEG. Some recent work on traveling waves at the brain-wide level would be relevant for such comparisons.

We appreciate the enthusiasm and the suggestions. To comment on the frequency specificity of the observed effects we conducted the same analysis focusing on the gamma frequency range (60-90 Hz). For computational reasons, we limited this analysis to one subject. Figure R1 shows the polar probability histogram for the beta frequency range (left) and the gamma frequency range (right). In contrast to the beta frequency range, no dominant directions were observed for the gamma range and von Mises functions did not converge. These preliminary results suggest some frequency specificity of the spatiotemporal pattern in sensorimotor beta activity. We believe this paves the way for future analysis mapping propagation direction across frequency and space.

Here we did not investigate the spatial specificity of the effects, as the beta frequency range is dominant in sensorimotor areas. Investigating beta bursts in other cortical areas would have likely resulted in very few bursts. We discuss our results across spatial scales in the section: Distinct anatomical propagation axes of sensorimotor beta activity. However, please note that most of the previous literature operates on a different spatial scale (roughly 4mm; Balasubramanian et al., 2020; Best et al., 2016; Rubino et al., 2006; Rule et al., 2018; Takahashi et al., 2011, 2015) and different species (e.g., non-human primates). Non-invasive recordings in humans capture temporospatial patterns of a very different scale, i.e., often across the whole cortex (Alexander et al., 2016; Roberts et al., 2019). Comparing spatiotemporal patterns, across different spatial scales is inherently difficult. Work

investigating different spatial scales simultaneously, such as Sreekumar et al. 2020, is required to fully unpack the relationship between mesoscopic and macroscopic spatiotemporal patterns.

Figure R1: Spatiotemporal organisation for the beta (β, 13-30Hz) and gamma (γ, 60-90) frequency range for one exemplar subject. Same as Figure 4a, but for one exemplar subject.

If the source code could be provided on github along with documentation and a standard "notebook" on use other researchers would benefit greatly.

All analyses are performed using freely available tools in MATLAB. The code carrying out the analysis in this paper can be found here: [link provided upon acceptance]. The 3D burst analyses can be very computationally intensive even on a modern computer system. The analyses in this paper were computed on a MacBook Pro with a 2.6 GHz 6-Core Intel Core i7 and 32 Gb of RAM. Details on the installation and setup of the dependencies can be found in the README.md file in the main study repository.

This information has been added to the paper in the methods section on page 35.

Author Response

Reviewer #2 (Public Review):

Understanding the molecular mechanism of obesity-associated OA is highly in clinical demand. Overall, the current study is well-designed and illustrated that down-regulated GAS6 impairs synovial macrophage efferocytosis and promotes obesity-associated osteoarthritis. Based on the patient's sample, the data indicated synovial tissues are highly hyperplastic in obese OA patients and infiltrated with more polarized M1 macrophages than in non-obese OA patients. Further authors proved that obesity promotes synovial M1 macrophage accumulation and GAS6 was inhibited in synovitis during OA development in mice models. The sample size, data collection, and quality of the IHC and immunofluorescent histological sections are outstanding. The results were well presented with appropriate interpretation. But the following major questions should be addressed.

Major:

1) Animal model: Ten-week-old animals received DMM surgery and were fed a standard/HFD diet for 4 or 8 weeks prior to specimen harvest. Since Wang J and other studies have shown that male ApoE(-/-) and C57BL/6J wild-type (WT) mice fed with a high-fat diet for 12 or 24 weeks, and the ApoE(-/-) mice gained less body weight and had less fat mass and lower triglyceride levels with better insulin sensitivity and lower levels of inflammatory markers in skeletal muscle than WT (Wang J, et al. Atherosclerosis. 2012 Aug;223(2):342-9. PMID: 22770993; Hofmann SM, et al. Diabetes. 2008 Jan;57(1):5-12. PMID: 17914034; Kypreos KE et al. J Biomed Res. 2017 Nov 1;32(3):183-90. PMID: 29770778). Thus, it is very important to provide the data on the final body weight gained in your groups and provide a relative background of the animal model chosen in the introduction or discussion. Please explain why ApoE-/- mouse model, and how this animal model is clinically relevant. Does a high-fat diet induced obsess OA available in C57BL/6 WT?

Thank you for your valuable comment. We have added the body weight change data for each group of mice in Revised Figure 2-figure supplement 3. We also provided a relative background of the animal model in paragraph 2 of the Discussion section, which reads, “ApoE plays an important role in maintaining the normal levels of cholesterol and triglycerides in serum by transporting lipids in the blood. Mice lacking ApoE function develop hypercholesterolemia, increased very low-density lipoprotein (VLDL) and decreased high-density lipoprotein (HDL), exhibiting chronic inflammation in vascular disease and nonalcoholic steatohepatitis.”.

Epidemiological study results suggest obesity is an independent risk factor for OA pathological progression. Gierman et al. found that increased plasma cholesterol levels play a vital role in the development of OA1,2. Mice deficient in ApoE-/- showed naturally high levels of LDL-cholesterol independent of gender and age, which could additionally be increased by a cholesterol-rich diet3,4. Moreover, recent studies found that ApoE-/- mice feeding with HFD gained more body weight than those feeding standard chow-diet groups5–7. We have re-analyzed the body weight statistics and found that ApoE-/- fed with HFD (19.81±1.33g) gained more body weight than the control (16.89±0.75g). These manuscripts indicated that feeding HFD to ApoE-/- mice for a short period could accelerate the increase in LDL cholesterol levels and cause more body weight gain. ApoE-/- mice may be partially clinically relevant to pathological progression in obese osteoarthritis patients with elevated plasma LDL cholesterol levels. As Reviewer #2 mentioned, an HFD induced obesity is available in C57BL/6 WT according to our weight gain data. However, the effect of obesity on OA progression in these two kinds of animals deserves further study.

References：

1. Gierman LM, Kühnast S, Koudijs A, et al. Osteoarthritis development is induced by increased dietary cholesterol and can be inhibited by atorvastatin in APOE*3Leiden.CETP mice—a translational model for atherosclerosis. Ann Rheum Dis. 2014;73(5):921-927.

2. Gierman LM, van der Ham F, Koudijs A, et al. Metabolic stress-induced inflammation plays a major role in the development of osteoarthritis in mice. Arthritis Rheum. 2012;64(4):1172-1181.

3. Wu D, Sharan C, Yang H, et al. Apolipoprotein E-deficient lipoproteins induce foam cell formation by downregulation of lysosomal hydrolases in macrophages. J Lipid Res. 2007;48(12):2571-2578.

4. Naura AS, Hans CP, Zerfaoui M, et al. induces lung remodeling in ApoE-deficient mice: an association with an increase in circulatory and lung inflammatory factors. Lab Invest. 2009;89(11):1243-1251.

5. Tung MC, Lan YW, Li HH, et al. Kefir peptides alleviate high-fat diet-induced atherosclerosis by attenuating macrophage accumulation and oxidative stress in ApoE knockout mice. Sci Rep. 2020;10(1):8802.

6. Bao M hua, Luo H qing, Chen L hua, et al. Impact of high fat diet on long non-coding RNAs and messenger RNAs expression in the aortas of ApoE(−/−) mice. Sci Rep. 2016;6(1):34161.

7. Cao X, Guo Y, Wang Y, et al. Effects of high-fat diet and Apoe deficiency on retinal structure and function in mice. Sci Rep. 2020;10(1):18601.

2) Control group: The DMM surgery was performed on the right leg, and the contralateral knee joint should be used as a baseline to show the level of M1 macrophage infiltration under the obsess microenvironment.

Thank you for this insightful comment. The reason why we used the right lower limb as the control group in our experiment was mainly because we considered the impact of right knee surgery on the left lower limb. A book published in 2014 described a series of method for inducing mouse osteoarthritis model, authors noted that sham-operated left knee joints would develop OA-like symptoms after right knee joints received DMM. Thus, Lorenz et al. strongly recommend using a separate control group for sham surgeries.

References：

1. Lorenz, J., Grässel, S. (2014). Experimental Osteoarthritis Models in Mice. In: Singh, S., Coppola, V. (eds) Mouse Genetics. Methods in Molecular Biology, vol 1194. Humana Press, New York, NY.

Author Response

Reviewer #1 (Public Review):

The goal of this study was to investigate the mechanisms that lead to the release of photosynthetically fixed carbon from symbiotic dinoflagellate alga to their coral host. The experimental approach involved culturing free-living Brevolium sp dinoflagellates under "Normal" and "Low pH" conditions (respective target pH of 7.8 and 5.50) and measuring the following parameters: (Fig.1) cell growth rate over ~28 days, photosynthetic activity, glucose and galactose secretion at day 1; (Fig. 2) Cell clustering, external morphology (using SEM), and internal morphology (using TEM) after 3 weeks; (Fig. 3) Transcriptomic analyses at days 0 and 1; and (Fig. 4) glucose and galactose concentration in Normal culturing medium after 24h incubation with a putative cellulase inhibitor (PSG).

The paper reports decreased growth at Low pH coupled with decreased photosynthetic rates and increased glucose and galactose release in 1-day Breviolum sp. cultures. At this same time point, genes related to cellulase were upregulated, and after 3 weeks morphological changes on the cell wall were reported. The addition of the cellulase inhibitor PSG to cells in pH 7.8 media decreased the release of glucose and galactose.

The paper concludes that acidic conditions mimicking those reported for the coral symbiosome -the intracellular organelle that hosts the symbiotic algae- upregulate algal cellulases, which in turn degrade the algal cell wall releasing glucose and galactose that can be used as a source of food by the coral host. However, there are some methodological issues that hamper the interpretation of results and conclusions.

We appreciate your helpful comments and apologize the confusion caused by insufficient descriptions in the previous manuscript. In the revised manuscript we clarify what we originally intended to demonstrate including the followings:

(1) Most analyses including SEM and TEM were done at day 0 and 1, except for a few, i.e. growth rate over 28 days and cell clumping assay done 3 weeks after the inoculation, which is summarized as a schematic panel and clarified in the revised manuscript.

(2) Inhibitor assay for secreted celluloses was done in pH 5.5.

(3) We do not intend to suggest that low pH medium mimics symbiosomes, as these organelles are far more complex than simple culture media and how symbiosomes are maintained and what the interior environment is like are not fully understood in general. Based on previous studies, presumably they are featured by low pH, high CO2, host-derived nutrients. Among these, we focus on low pH, which is a stressor for dinoflagellates to go through in not only symbiosomes but also natural environments, e.g. animal gut.

In this study, we clarified how algae respond to low pH as an environmental stressor, which can also provide insights into how they interact with the host inside the guts as well as symbiosomes.

Reviewer #2 (Public Review):

Ishii and colleagues investigated the process of monosaccharide release from algae in low-pH environmental conditions, mimicking the acidic lysosomal-like intracellular compartment where the algae reside symbiotically and transfer nutrients to their hosts, namely corals and other animals. Upon exposure of cultured algae to low pH, subsequent physiological changes as well as the increased presence of glucose and galactose were measured in the surrounding media. Concurrently, photosynthetic activity was decreased, and further experiments employing the photosynthetic inhibitor DCMU to cultures also replicated the increased monosaccharide release. Transcriptomic comparison of algae in low pH to controls showed differential expression in glycolytic pathways and, interestingly, a strong upregulation of signal-peptide-containing isoforms of cellulases. Finally, the elegant use of a cellulase inhibitor on the cultured algae revealed a decrease in monosaccharides in the media. This led the authors to propose a pathway of sugar release in which acidic conditions trigger a cellulase-driven cascade of cell wall degradation in the algae and their consequent release of monosaccharides. These results have interesting implications on the molecular mechanisms of coral-algae symbiosis, contributing to the understanding of how these important symbioses function on the cellular level.

Overall the conclusions of this manuscript are supported by the data presented, but clarification and elaboration are needed to fully justify the proposed mechanisms and better situate the results in a broader context of the field.

We thank the reviewer for the positive comments. In the revised the manuscript we show that the results could be better explained with the proposed mechanisms in a broader context.

Author Response

Reviewer #2 (Public Review):

1) Mechanistic details of how FCA regulates FLC have been extensively studied, and both transcriptional and co-transcriptional regulations occur. I understand that FCA affects the 3'end processing of antisense COOLAIR RNAs, which regulate FLC. FCA also physically interacts with COOLAIR RNAs and other proteins, including chromatin-modifying complexes, which establish epigenetic repression of FLC regardless of vernalisation. In addition, FCA appears to function to resolve R-loop at the 3' end FLC, and FLC preferentially interacts with m6A-modified COOLAIR by forming liquid condensates. FCA is also alternatively spliced in an autoregulatory manner, and fca-1 mutant was reported to be a null allele as fca-1 cannot produce the functional form of FCA transcripts (r-form).

However, I could not find any information on the fca-3 allele, which was reported to exhibit a weaker phenotype in terms of flowering time (Koornneef et al., 1991). In this manuscript, the authors showed that the level of FLC expression is lower than fca-1 and higher than Ler WT, but I could not find any other relevant information on the nature of the fca-3 allele. Given the known details on the function of FCA, the authors should explain how fca-3 shows an "intermediate" phenotype, which is highly relevant to the argument for an "analog" mode of regulation in fca-3. Therefore, the nature of the fca-3 mutant should be described in detail.

We thank the reviewers for pointing out this omission. We have added much more information on the genotypes in the methods of the manuscript. We emphasise, however, that the rationale for selecting fca-3 as an intermediate mutant was empirical: namely, it generates an intermediate level of FLC expression (Fig. 1C and Fig. 1S1).

2) The authors used a transgene (FLC-venus) in which an FLC fragment from ColFRI was used. Both fca-1 and fca-3 is Ler background where FLC sequence variations are known. I understand that the authors introgressed the transgenic in Ler background to avoid the transgene effect, but it is not known whether fca-1 or fca-3 mutations have the same impact on Col- FLC.

We tested the expression of both endogenous (Ler) and FLC-Venus (Col-FLC) copies in these mutants by qPCR and found similar results (Fig. 1S1C,D), indicating that the fca-1 and fca-3 mutations have similar effects in both cases.

3) Fig. 3A: I understand that Fig 3A is the qRT-PCR data using whole seedlings, and the gradual reduction of FLC from 7 DAG to 21 DAG was used to test the "analog" vs. "digital" mode of gene regulation in fca-1 and fca-3. I am not sure whether this is biologically relevant.

Indeed, Ler is the only line that has transitioned to flowering during the experiment, with both fca lines being late flowering mutants. We totally agree that for Ler, later timepoints may be biologically irrelevant. It is used in this case as a negative control for the imaging, since FLC in Ler was already mostly OFF from the first timepoint and no biological conclusions are drawn from the later times. We have added a comment to this effect in the results section, also clarifying in the discussion that our focus is on the early regulation of FLC. Therefore, by looking at the young seedling in wildtype Ler, as we and others have previously, we are already looking too late to capture the switching of FLC to OFF. However, we expect that this combination of analog and digital regulation will be highly

relevant to FLC regulation in wild-type plants in different accessions, partly leading to the differences in autumn FLC levels that were shown to be so important in the wild (Hepworth et al. 2020).

3-a) The authors wrote that "This experiment revealed a decreasing trend in fca-3 and Ler (Fig. 3A)". But, I do also see a "decreasing trend" in fca-1 as well (although I understand that they may not be statistically significant). I also noticed that the level of FLC in fca-1 at 7 day has a greater variation. Is there any explanation?

The level of FLC in fca-1 at 7 days is indeed more variable in these experiments. However, in a new second experiment, this is not the case (Fig. 3S2). In addition, a similar effect has not been observed in the ColFRI genotype (Fig. S9F of Antoniou-Kourounioti et al. 2018). Therefore, we believe this greater variation in one data set may simply be due to random fluctuations.

For the decreasing trend in fca-1 in Fig. 3A, as the reviewer says, this is not significant. However, in the second experiment, we again see a decrease, which is now slow but significant. The decrease could be due to a subset of fca-1 ON cells switching off (in tissue that we have not imaged) and we comment on this slow decrease in the text.

3-b) The decreasing trend observed in Ler (although the expression of FLC is already relatively low in Ler) may be the basis for the biological relevance. But Fig. 3D shows that the FLC-venus intensity in Ler root is not "decreasing". The authors interpreted that "root tip cells in Ler could switch off early, while ON cells still remain at the whole plant level that continue to switch off, thereby explaining the decrease in the qPCR experiment." Does this mean that the root tip system with FLC-venus cannot recapitulate other parts of plants (especially at the shoot tip where FLC function is more relevant)?

The authors utilize the root system with transgenes in mutant backgrounds to observe and model the gene repression (transgene repression, to be exact). If the root tip cells behave differently from other parts of plants, how could the authors use data obtained from the root tip system?

We now show that FLC-Venus in Ler, fca-3, fca-1 in young leaves have similar expression patterns to roots, thus validating the root system as an appropriate one to study the switching dynamics, see response to Essential comment 3. Nevertheless, in Fig. 3A, we show that FLC expression declines even in Ler. However, the levels here are low, so if it is indeed a subfraction of late-switching cells that are responsible, these cells cannot form a large proportion of the plant. We now make this clear in the text.

4) I do see both fca-1 and fca-3 can express FCA at a comparable level (Fig. 3B); thus, I guess that the authors are measuring total FCA transcripts and that fca-3 may result in different levels of "functional form" of FCA. But this is not clearly discussed.

We have now added yellow boxes in Fig. 2S3 to show additional examples of short files of ON cells in fca-3 and fca-4. To further improve the interpretation of this image (and all others in the manuscript) we have changed the presentation of the imaging using a different colourmap to enhance clarity.

5) Quantification based on image intensity needs to be carefully controlled. Ideally, a threshold to call "ON" or "OFF" state should be based on the comparison to internal control and it is not clear to me how the authors determined which cells are ON or OFF based on image intensity (especially in fca-3).

For the wild-type and fca-1 situations there is no switching in the model, and hence no dynamical changes in the FLC protein levels. As the FLC levels in the ON or OFF states are simply fit to the data using log-normal distributions, this would simply be a fitting exercise for fca-1 and Ler, and little would be learnt. Hence, we have not pursued this line of analysis.

6) In many parts, I had to guess how the experiments were performed with what kind of tissues/samples. The methods section can benefit from a more thorough description.

We have now gone through and added the missing information.

Related to Public review #2. What is the phenotype (flowering time) of FLC-venus in fca-1 and fca-3? In addition, how many independent lines were used? Do they behave similarly?

It was observed that with the additional FLC gene (in the form of the FLC-Venus), flowering is delayed as expected. However, this was not quantified in this work. Instead, we validated that the expression of the transgene was equivalent to endogeneous between genotypes, as shown in Fig. 1S1, supporting that this is an appropriate readout for FLC expression. One line for each genotype was selected and used in this work. In addition, we also now use fca-4, which has similar expression to fca-3, and where FLC-Venus also behaves similarly to the fca-3 case (Fig. 1S1, 2S3).

Reviewer #3 (Public Review):

1) The way the authors define ON and OFF cells sounds a bit arbitrary to me and, in my understanding, can affect a lot the outcomes and derived conclusions. The authors define ON cells to those cells having more than one transcript, or when they are above the value of 0.5 of the Venus intensity measure - what would it happen if the thresholds are slightly above these levels? And why such thresholds should be the same for the studied lines Ler, fca-3 and fca-1? By looking at the distributions of mRNAs and Venus intensities in Ler and fca-3 plants, one could argue that all cells are in an OFF, 'silent' state, and that what is measured is some 'leakage', noise or simply cell heterogeneity in the expression levels. If there is a digital regulation, I would expect to see this bimodality more clearly at some point, as it was captured in Berry et al (2015) - perhaps cells in fca-1 show at a certain level of bimodality? When seeing bimodality, one could separate ON and OFF states by unmixing gaussians, or something in these lines that makes the definition less arbitrary and more robust.

As explained in Essential comment 5, we have removed arbitrary thresholding from the manuscript and only used absolute thresholds from smFISH (now changed to >3, and shown that our results are robust to varying these thresholds, Fig. 2S2). If all cells are in the OFF state and fca-3 just has higher noise/heterogeneity, then this does not explain the reduction in expression over time. Nor can such heterogeneity explain the short files of ON cells and longer files of OFF cells in Fig. 2S3: the cells should just be a random mix of varying FLC levels. Our results are much more compatible with switching into a heritable silenced state. Finally, with bimodality, this is difficult to see as clearly as before due to the wide levels of expression in fca-3, but we believe it is present: a well-defined OFF state together with a broad ON state. This broadness makes extracting the ON cells quite difficult as a completely rigorous unmixing of the two states is just not possible.

2) The authors use means in all their plots for histograms and data, and perform tests that rely on these means. However, many of these plots are skewed right distributions, meaning that mean is not a good measure of center. I think using median would be more appropriate, and statistical tests should be rather done on medians instead of means. If tests using medians were performed, I believe that some of the pointed results will be less significant, and this will affect the conclusions of this work.

Highly expressing FLC lines and mutants, such as ColFRI and fca-9, often used for vernalization studies, are late flowering, but do eventually flower even with no decrease in FLC levels (and so no switching). This is not an artifact of using roots versus shoots, and presumably arises from there being multiple inputs into the flowering decision which can allow the FLC-mediated flowering inhibition to eventually be overcome.

3) Some data might require more repeats, together with its quantification. For instance, the expression levels for fca-1 in Fig 2E and Fig 3D at 7 days after sowing look qualitatively different to me - not just the mean looks different, but also the distribution; fca-1 in Fig 3D looks more monomodal, while in Fig 2E it looks it shows more a bimodal distribution. Having these two different behaviours in these two repeats indicates that, more ideally, three repeats might be needed, together with their quantification. Fig. 2C would also need some repeats. In Fig 1S1 C and D, it would be good to clarify in which cases there are 2 or more repeats -3 repeats might be needed for those cases in Fig 1S1 C-D that have large error bars.

The data in Figs. 2C and 2E are both based on two independent experiments, with the results combined. The data in Fig. 3D is almost entirely based on three independent experiments. We have now stated this in the legend. The Venus imaging was performed on separate microscopes for Fig. 2 and Fig. 3 and this possibly accounts some of the observed differences. However, we do not think that the data in Fig 2E for fca-1 supports a bimodal distribution: the slight peak at higher levels is, we believe, much more likely to be a statistical fluctuation. For Fig. 1S1 C and D, we now clarify in the legend that n=2 biological replicates for fca-3 and n=3 for others.

Also, when doing the time courses, I find it would be very beneficial to capture an earlier time point for all the lines, to see whether it is easier to capture the digital nature of the regulation. Note that the authors have already pointed that 7 days after sowing might be too late for Ler line to capture the switch.

We agree that capturing earlier time points for Ler in particular is interesting and important. However, we have found that this requires specialist imaging in the embryo and we feel that this is really beyond the scope of this manuscript and will instead form the basis of a future publication.

Author Response

Reviewer #1 (Public Review):

The authors use what is potentially a novel method for bootstrapping sequence data to evaluate the extent to which SARS-CoV-2 transmissions occurred between regions of the world, between France and other European countries, and between some distinct regions within France. Data from the first two waves of SARS-CoV-2 in Europe were considered, from 2020 into January 2021. The paper provides more detail about the specific spread of the virus around Europe, specifically within France, than other work in this area of which I am aware.

First of all, we would like to thank reviewer #1 for their evaluation and their various comments which, in our opinion, have allowed us to considerably improve the manuscript.

An interesting facet of the methodology used is the downsampling of sequence data, generating multiple bootstraps each of around 500-1000 sequences and conducting analysis on each one. This has the strength of sampling, in total, a large number of sequences, while reducing the overall computational cost of analysis on a database that contains in total several hundred thousand sequences. A question I had about the results concerns the extent of downsampling versus the rate of viral migration: If between-country movements are rapid, a reduced sample could be misleading, for example characterising a transmission path from A to B to C as being from A to C by virtue of missing data. I acknowledge that this would be a problem with any phylogeographic analysis relying on limited data. However, in this case, how does the rate of migration between locations compare to the length of time between samples in the reduced trees? Along these lines, I was unclear to what extent the reported proportions of intra- versus inter-regional transmissions (e.g. line 223) would be vulnerable to sampling effects.

This question is indeed a very important one. Between-country movement rate can be high but the contagious period for a SARS-CoV-2-infected individual is short (a bit less than two weeks in average). In our subsamples, the dated trees have a median branch length around 20 days. To ensure that our subsamples did not introduce errors in estimating the exchange events between locations, we conducted a simulation. Briefly, we generated a tree of 1,000,000 tips with a five-states discrete trait. We then took 100 subsampled 1000-leaves trees, reconstructed the ancestry for the discrete trait and assess transitions between states. The error rate is less than 3% on average: it comprises the missing data, as you pointed out, and the errors in reconstructing the ancestry for the trait deeper in the tree.

We think that overall, less than 3% is a satisfying error rate.

The results of this specific simulation were added to the paper (lines 150-157) and as Figure 2—figure supplement 1.

A further question around the methodology was the use of an artificially high fixed clock rate in the phylogenetic analysis so as to date the tree in an unbiased way. Although I understood that the stated action led to the required results, given the time available for review I was unable to figure out why this should be so. Is this an artefact of under-sampling, or of approximations made in the phylogenetic inference? Is this a well-known phenomenon in phylogenetic inference?

We thank reviewer #1, who was, as reviewer #2 and the editor, disturbed by the use of an artificially fast and fixed molecular clock. It was an artifact to correct a mistake in our code that has been fixed. See the answer to point (3) of the editor.

The value of this kind of research is highlighted in the paper, in that genomic data can be used to assess and guide public health measures (line 64). This work elucidates several facts about the geographical spread of SARS-CoV-2 within France and between European countries. The more clearly these facts can be translated into improved or more considered public health action, through the evaluation of previous policy actions, or through the explication of how future actions could lead to improved outcomes, the more this work will have a profound and ongoing impact.

This is a very interesting point to emphasize indeed. We are currently discussing with public health specialists in our institution on how to assess past public health actions using phylodynamics data in a statistically valid manner.

Reviewer #2 (Public Review):

This study represents an important contribution to our understanding of SARS-CoV-2 transmission dynamics in France, Europe and globally during the early pandemic in 2020 and the authors should be congratulated for tackling this important question. Through evaluation of the contributions of intra- and inter-regional transmission at global, continental, and domestic levels, the authors provided compelling, although as of yet correlative and incomplete, evidence towards how international travel restrictions reduced inter-regional transmission while permitting increased transmission intra-regionally. Unfortunately, however this work suffers from a number of serious analytical shortcomings, all of which can be overcome in a major revision and re-analysis.

We would like to thank the reviewer #2 for their evaluation and their various comments. We want to point that reviewer #2 was contacted for advice on strategy for the molecular clock since she performed a study on a similar topic describing SARS-CoV-2 epidemics in Canada during 2020. We strongly believe that all reviewer #2 comments drastically contributed to improve the quality of this work.

With this genomic epidemiology analysis, the authors disentangled the relative contributions of different geographic levels to transmission events in France and in Europe in the first two COVID-19 waves of 2020. By partitioning the analysis into three complementary, but distinct, geographic levels, the migration flows in and out of continents, countries in Europe, and regions in France were inferred using maximum likelihood ancestral state reconstruction. The major strengths of this paper were the inclusion of multiple geographic levels, the comparison of different rate symmetries in the ancestral character estimation, and the comprehensive qualitative descriptions of comparisons over time and geographies. However, there were also major weaknesses that need to be addressed and are described in more detail below. They include summing across replicates that were drawn with replacement and were not independent; inadequate justification for excluding underrepresented geographies; the assertion that positive correlation between intra-regional transmission and deaths validates the accuracy of the analysis; considering the framework the authors have chosen for this analysis the analysis would accommodate and benefit strongly from increasing the size of the sequence sets selected for analysis in each replicate; and the sparsity of quantitative (over qualitative or exploratory) comparisons and statistics in the reporting of results. In particular, it would greatly strengthen the paper if the authors could better evaluate the effect of travel restrictions on importations and exportations by testing hypotheses, quantifying changes in the presence of restrictions, or estimating inflection points in importation rates.

We are grateful for this comprehensive listing of the strengths and weaknesses of our study. Regarding the limitations of this study, these will be detailed specifically for each dedicated remark of the reviewer. We would like to emphasize that all the remarks and limitations reported here by reviewer #2 are in our opinion fully justified. We hence have tried to bring additional analyses (study of the Pango lineages, averaging of the subsamples, simulation study to justify the size of the sampling), a modification of the methodology (in particular concerning the molecular clock) and a thorough rewriting of the “Results” section.

General comments on the Background: Need to elaborate on how this study fits into the big picture in the first paragraph. Should discuss how phylodynamics contributes to understanding of viral outbreaks, SARS-CoV-2 epidemiology and viral evolution.

We have added in the “Introduction” section some elements to better understand why phylodynamics is an important field in the epidemiology of SARS-CoV-2 and its evolution.

The authors should consider a hypothesis driven framework for their analyses, for example considering the geographically central position of France what hypotheses stem from this considering sources of viral importations and destinations of exportations from/to Europe vs other international? Or other a priori expectations.

We agree with reviewer #2 about this remark. Indeed, given the central position of France, we can hypothesize that it has strongly participated in the dissemination of the virus within Europe. This hypothesis has been included in the "Introduction" section of the revised version (lines 102-105).

To address the computational limits of phylogenetic reconstruction, 100 replicates of fewer than 1000 sequences each were sampled for each epidemic wave at each level. The inter- and intra-regional transmissions were averaged and then summed across replicates in order to compare the relative roles played by each geography towards transmission. While we see the logic in using the sum across replicates, this is highly likely to bias results, especially since in the methods, this is described as sampling with replacement between replicates (LX). The validity of summing replicates needs to be discussed and are likely most appropriately presented as mean or median. Also, these samples are quite small considering the computational capacity of the maximum likelihood tools being used. We recommend repeating the analysis with a substantially larger number of sequences per sample.

We thank reviewer #2 for this relevant remark. We initially summed the subsamples, a strategy that may possibly bias the results. In the new version of the manuscript, we averaged the subsamples by region and by week as recommended (and stated in the methods, line 536-537).

About the size of our subsamples, it made no difference to use 1,000, 2,000 or 5,000 genomes in each subsample. To get a more definitive and scientifically sound answer, we performed a simulation assay that has been included in the manuscript and is shown is what is now figure 2 (and figure 2—figure supplement 1). These simulations show that our subsampling strategy allows for an accurate estimate of transition rates for a discrete parameter (lines 107-160).

Author Response

Reviewer #1 (Public Review):

The paper addresses an interesting question - how genetic changes in Y. pestis have led to phenotypic divergence from Y. pseudotuberculosis - and provides strong evidence that the frameshift mutation in rcsD is involved. Overall, I found the data to be clearly presented, and most of the conclusions well supported by the data. The authors convincingly show that (i) the frameshift mutation in rcsD alters the regulation of biofilm formation, (ii) this effect depends upon expression of a small protein that corresponds to the C-terminal portion of RcsD, and (iii) the frameshift mutation in rcsD prevents loss of the pgm locus. I felt that the discussion/conclusions about what phosphorylates/dephosphorylates RcsB and how this impacts biofilm formation are overstated, as there are no experiments that directly address this question. I also felt that the authors' model for what phosphorylates/dephosphorylates RcsB in Y. pestis should be more clearly articulated, even if it is only presented as speculation. Lastly, the authors propose that full-length RcsD is made in Y. pestis and contributes to phosphorylation of RcsB, but the evidence for this is weak (faint band in Figure 2d). It may be that the N-terminal domain of RcsD is functional. I recommend either softening this conclusion or testing this hypothesis further, e.g., by introducing an in-frame stop codon early in rcsD after the frame-shift.

Thanks for your comments. We have provided a model and revised the discussion about phosphorylation/dephosphorylation of RcsB and how this impacts biofilm formation (Figure 8 and Supplementary Figure 4). In addition, we have introduced an in-frame stop codon in rcsD before the frameshift and showed that full-length RcsD is only made in wildtype Y. pestis but not in the rcsDpe-stop mutant (Supplementary Figure 1g).

Reviewer #2 (Public Review):

Guo et al. have investigated the consequences of a frameshift mutation in the rcsD gene in the Yersinia pseudotuberculosis progenitor that is conserved in modern Y. pestis strains. Interestingly, they identify a start codon with a ribosome binding site that enables production of an Hpt-domain protein from the C-terminus in Y. pestis. Targeted deletion of this Hpt-domain increased biofilm production in Y. pestis. They find that the ancestral RcsDpstb (full length) is a positive regulator of biofilm in Y. pestis while the Hpt-domain version (RcsDYP) represses biofilm in vitro. When fleas were infected with Y. pestis expressing the ancestral RcsDPSTB protein, there was no difference in bacterial survival or rate of proventricular blockage. This strain also killed mice the same rate (in a different Y. pestis strain background). However, replacing RcsDYP with RcsYPTB dramatically increases the frequency of pgm locus deletion (containing Hms ECM and yersiniabactin genes) during flea infection. The authors predict that this would reduce the invasiveness of the bacteria in mammals and/or flea blockage in subsequent flea-rodent-flea transmission cycles. They also measured global gene expression differences between RcsDPSTB compared to the wild-type strain. They argue that the frameshift of RcsD maintaining the Hpt-domain (RcsDYP) was needed to regulate biofilm while limiting loss of the pgm locus.

Loss of the pgm locus was not tested in the Y. pestis rcsD mutant strain (lacking the entire gene or just the C-terminal Hpt domain). Therefore, the claim that maintaining the Hpt-domain protein was important lacks convincing evidence. Additionally, it is possible that the population of rcsDpe::rcsDpstb after in vitro growth for 6 days would still be proficient at infecting and blocking fleas, even though many of the bacteria would have lost the pgm locus. Production of Hms polysaccharide by pgm+ could trans-complement those that are pgm-. The nature of the pgm locus loss is assumed to be due to recombination between IS elements. This is certainly the likeliest explanation but not the only one. The authors checked for pgm loss by phenotype (CR binding) and by two sets of primers, one targeting the hmsS gene and another set that is unspecified. Loss of the entire pgm (especially yersiniabactin genes) should be clarified.

Thanks for your comments. We have now provided the data to show that deletion of RcsD-Hpt resulted in increased loss of the pgm locus (Figure 5d) to strengthen the claim that maintenance of the Hpt-domain is significant for retention of the pgm locus. We also agree that 6-day old cultures of a mixture of pgm+ and pgm- rcsDpe::rcsDpstb will still be capable of infecting and blocking fleas. However, these strains will be less efficient at causing disease in the vertebrate host in the absence of the pgm locus. We agree that recombination between IS elements might not be the only cause of loss of the pgm locus. To verify the loss of the pgm locus, we have used two sets of primers. One set targets the hmsS gene and another set targets the upstream and downstream sequences of the pgm locus (Supplementary Table 3). We have clarified this in the revised manuscript (Line 610-613).

Reviewer #3 (Public Review):

The Rcs phosphorelay plays an important role in regulating gene expression in bacteria; most of the current knowledge about the Rcs proteins is from E. coli. Yersinia pestis, carrying mutations in two central components of the Rcs machinery, provides an interesting example of how evolution has shaped this system to fit the life cycle of this bacteria. In bacteria other than Y. pestis, most Rcs activating signals are sensed via the outer membrane lipoprotein RcsF; from there, signalling depends on inner membrane protein IgaA, a negative regulator of RcsD. Histidine kinase RcsC is the source of the phosphorylation cascade that goes from the histidine kinase domain of RcsC to the response regulator domain of RcsC, from there to the histidine phosphotransfer (Hpt) domain of RcsD, and finally to the response regulator RcsB. RcsB, alone or with other proteins, regulates transcription of many genes, both positively and negatively. These authors have previously shown that RcsA, a co-regulator that acts with RcsB at some promoters, is functional in Y. pseudotuberculosis but mutant in Y. pestis, and that this leads to increased biofilm in the flea. The authors also noted that rcsD in Y. pestis contains a frameshift after codon 642 in this 897 aa protein; in theory that should eliminate the Hpt domain from the expressed protein. However, they found evidence that the frame-shifted gene had a role in regulation. This paper investigates this in more depth, providing clear evidence for expression of the Hpt domain (without the N-terminal domain), and demonstrating a critical role for this domain in repressing biofilm formation. The Y. pseudotuberculosis RcsD does not express a detectable amount of the Hpt domain nor does it repress biofilm formation. The ability of the Hpt domain protein to keep biofilm formation low explains most of what is observed for the full-length frame-shifted protein.

1) The authors provide a substantial amount of data supporting the expression of the C-terminus of RcsD is sufficient and necessary for low biofilm levels, and that this is dependent upon the active site His in the RcsD Hpt domain (H844A) as well as other components of the basic phosphorelay (RcsC and RcsB). However, it is only possible to see this protein by Western blot in 100-fold "Enriched" lysates (Figure 2). No small protein was detected in the RcsDpstb strain, although the enriched lysate was not shown for this. Without that experiment, it is not possible to evaluate whether the small protein is also made from the rcsDpstb gene. Either answer would be interesting, and would allow other conclusions to be drawn. Is the RBS and start codon the same for the HPT region of this rcsD gene (it could be added to Supplementary Table 6). If the small protein is made, is its ability to function blocked by the excess full length protein in terms of interactions with RcsC? Or is the expression of the small protein dependent upon loss of overlapping translation from the upstream start?

The small Hpt protein may be produced from expression of the epitope tagged rcsDpstb gene as it can be detected in an enriched isolation of this sample (Supplementary Figure 1f). Because only a small amount of the RcsD-Hpt is produced from the rcsDpstb substitution, it might only function at low levels in the presence of large amounts of RcsDpstb. The RBS and start codon are the same for the RcsD-Hpt in Y. pestis and Y. pseudotuberculosis, we have added them in the Supplementary Table 6. In addition, we have provided a model to show the function and regulation of RcsD and Hpt (Supplementary Figure 4).

2) In many phosphorelays, the protein kinase also acts as a phosphatase, and which direction P flows is critical for regulation. It is often difficult to follow what the model for this is in this paper, and that is important to understand for evaluating the results. Most of this paper uses two assays, biofilm formation and crystal violet staining (also related to biofilm formation) to assess the functioning of the Rcs phosphorelay. Based on the behavior of the rcsB mutant, it would seem that functional Yersinia pestis Rcs (RcsDpe) represses this behavior, and this correlates with RcsB phosphorylation (Figure4). What is the basis (Line 443-44) for saying that RcsD phosphorylates RcsB while RcsDHpt dephosphorylates? Yersinia pseudotuberculosis RcsD(pstb) shows no difference with the rcsB mutant. Doesn't that suggest that RcsDpstb is no longer repressing (phosphorylating)? In the presence of the RcsDpstb as well as multicopy RcsF, an activating signal in other organisms, RcsDpstb seems able to phosphorylate. This all suggests that the full-length protein, like the Hpt domain, is capable of phosphorylating, but that it may be doing nothing in the absence of signal (or dephosphorylating). Given these results, saying that RcsDpstb is positively regulating biofilm formation (Fig.1 title, and elsewhere) is somewhat misleading. What it presumably does is prevent the Hpt domain, expressed from the chromosomal locus in Figure1b, from signalling to RcsB. By itself, it is not clear it is doing anything. Understanding this clearly is important for interpreting this system and the tested mutants. A clear model and how phosphate is flowing in the various situations would help a lot. Currently Supplementary Figure3 seems to reflect the appropriate directional arrows, but the text does not. Moving the rcsB data earlier in the paper (after Figure1, 2, or maybe earlier, before Figure3) would certainly help.

RcsD dephosphorylates RcsB while RcsD-Hpt phosphorylates RcsB. Expression of RcsDpstb in the wild type strain and the N-term deletion mutant resulted in increased biofilm, indicating RcsB is less phosphorylated (Figure 1b and 1c). While over-expression of RcsD-Hpt resulted in decreased biofilm formation, indicating RcsB is more phosphorylated. In addition, the Phos-tag experiments showed that the RcsDpstb strain has a lower level of phosphorylated RcsB (Figure 4b). Expression of RcsDpstb in the wild type strain showed similar results as a rcsB mutant indicating a lower level of phosphorylated RcsB in the presence of RcsDpstb.

It is possible that the RcsDpstb interferes with the ability for RcsD-Hpt to phosphorylate RcsB. However, plasmid expression of the rcsDpstb-H844A mutant in the Y. pestis rcsDN-term deletion mutant formed significantly less biofilm than wild type rcsDpstb indicating H844 might be important for RcsD to dephosphorylate RcsB (Supplementary Figure 2b and Line 180-183). In addition, it is known that RcsD plays a dual role in phosphorylation and dephosphorylation of RcsB in other organisms (Majdalani N, et al., 2005, J. Bacteriol. https://doi.org/10.1128/JB.187.19.6770-6778.2005; Wall EA, et al., 2020, Plos Genetics, https://doi.org/10.1371/journal.pgen.1008610; Takeda S., et al., 2001, Mol. Microbiol., https://doi: 10.1046/j.1365-2958.2001.02393.x). We therefore think it is safe to say that the full length RcsD might function to dephosphorylate RcsB. We have modified the model in the revised manuscript (Supplementary Figure 4 and Figure 8). Regulation of RcsB has been investigated previously. The main finding of our manuscript is regulation of RcsB by the mutated RcsD (RcsD-Hpt). Thus, we have moved the known rcsB deletion mutant data to Figure 1 in the revised manuscript as suggested. We kept the rest of data in Figure 4 the same. We think it might be better to first show the mutation of rcsD alters Rcs signaling and then show how this occurs (by affecting RcsB phosphorylation).

3) The authors show (in their pull-down) that there is a bit of full-length RcsD even in the frame-shifted protein. Is there any clear evidence this does anything here? Does the N-terminus (truncated after the frame-shift) have a function?

We have introduced a stop codon in rcsDpe and showed that full-length RcsD is made by rcsDpe but not by rcsDpe with the stop codon (Supplementary Figure 1g). RcsDN-term seems do not have a function in our tested condition (Figure 1e).

4) While the RNA seq data is useful addition here, it is difficult to interpret without a bit more data on the strain used for the RNA seq, including the biofilm phenotypes of the WT and mutant derivatives, as well as the relevant rcsD sequences, and maybe expression of a few genes or proteins (Hms or hmsT). Are these similar in the parallel strains used earlier in the paper and the one for RNA seq, in WT, rcsB- and the RcsDpstb derivative? It would appear that rcsB- and rcsDpstb have opposite effects, at least at 25{degree sign}C, while in Figure4, these two derivatives have similar effects on biofilm. Is this due to temperature, strains, or biofilm genes that are not shown here? It is certainly possible that the ability of the full-length RcsD changes its kinase/phosphatase balance as a function of temperature, or dependent on other differences in these Y. pestis strains.

The strain used for RNA seq is a derivative of the biovar Microtus strain 201 which has a similar in vitro phenotype as the strain KIM6+ (Line 297-298). We used this strain for RNA seq because it has the virulence plasmid pCD1 and we wanted to analyze the gene expression of this plasmid, which is required for virulence, as well. RNAseq data showed that rcsB- and rcsDpstb have opposite effects on mRNA level of some genes. However, no significant change in expression of biofilm genes was noted in the RNAseq data set. In fact, our previous data has shown that the biofilm related (hmsT and hmsD) genes are only moderately (Less than 2-fold change between wild type and rcsB mutant) regulated by RcsB based on RT-PCR and β-gal analysis (Sun YC, et al., 2012, J. Bacteriol. https:// doi: 10.1128/JB.06243-11and Guo XP, et al., 2015, Sci. Rep. https://doi: 10.1038/srep08412 and Figure 4c).

Author Response

Reviewer #1 (Public Review):

Sex determination and dosage compensation are two fundamental mechanisms in organisms with distinct sexes. These mechanisms vary greatly across the various model organisms in which they have been studied. Comparisons across more closely related members of the same genus have already proven productive in the past, to understand how these essential mechanisms evolve. In this study, the authors compare some aspects of the dosage compensation and sex determination mechanisms across two Caenorhabditis species that diverged ~15-30 MYA.

Previously, the authors have studied dosage compensation and sex determination extensively in C. elegans. Here, they first identify the homologs of some key factors in C. briggsae, a species that independently evolved hermaphroditism. The authors show that some of the key players in these processes play the same roles in C. briggsae as they do in C. elegans. Namely, they show that the nematode-specific SDC-2 protein plays a role in both dosage compensation and sex determination also in C. briggsae, they find the homologs of some of the SMC protein complex that performs dosage compensation also in C. elegans and they study the binding specificity on the X chromosome.

Overall, the work is thorough and compelling and is very clearly presented. The authors generate a number of genetic tools in C. briggsae and the careful genetic analyses together with a number of binding assays in vivo and in vitro, support the authors' main conclusions: that the main players and genetic regulatory hierarchy are conserved between these two nematodes, but the binding sites for the DCC on the X chromosome have diverged and the mode of binding has changed as well. Whereas in C. elegans the DCC binds sites in the X chromosome that contain multiple sequence motifs in a synergistic manner, in briggsae they seem to do so additively. This latter point is supported by the data, but it could be explored a bit more deeply using the available ChIP-seq data that the authors have generated. In addition, it would be interesting to discuss the possible implications of this difference.

One minor weakness of this work is that it could be better put in the context of other related comparisons of these mechanisms. For example, the comparison of sex determination pathway by Haag et al. in Genetics 2008, and the comparison of dosage compensation across Drosophila species (Ellison and Bachtrog, Plos Genetics, 2019), and possibly others. The other point that the authors could provide deeper insight into, is the rate of divergence of proteins like SDC-2 (which is thought to be the protein that contacts DNA), versus some other proteins in the DCC and in general other proteins not involved in sex determination or dosage compensation (this doesn't need to be limited to comparing elegans and briggsae as there are numerous Caenorhabditis genomes available). This would provide a more complete view of the evolution of these processes.

Regarding the comparison of our studies to those of the C. briggsae sex determination pathway described by Haag and others, we have included the following in our revised manuscript:

Pages 8-9. "Within the Caenorhabditis genus, similarities and differences occur in the genetic pathways governing the later stages of sex determination and differentiation (Haag, 2005). For example, three sex-determination genes required for C. elegans hermaphrodite sexual differentiation but not dosage compensation, the transformer genes tra-1, tra-2, and tra-3, are conserved between C. elegans and C. briggsae and play very similar roles. Mutation of any one gene causes virtually identical masculinizing somatic and germline phenotypes in both species (Kelleher et al., 2008). Moreover, the DNA binding motif for both Cel and Cbr TRA-1 (Berkseth et al., 2013), a Ci/GL1 zincfinger transcription factor that acts as the terminal regulator of somatic sexual differentiation (Zarkower and Hodgkin, 1992), is conserved between the two species.

At the opposite extreme, the mode of sexual reproduction, hermaphroditic versus male/female, dictated the genome size and reproductive fertility of Caenorhabditis species diverged by only 3.5 million years (Yin et al., 2018; Cutter et al., 2019). Species that evolved self-fertilization (e.g. C. briggsae or C. elegans) lost 30% of their DNA content compared to male/female species (e.g. C. nigoni or C. remanei), with a disproportionate loss of male-biased genes, particularly the male secreted short (mss) gene family of sperm surface glycoproteins (Yin et al., 2018). The mss genes are necessary for sperm competitiveness in male/female species and are sufficient to enhance it in hermaphroditic species. Thus, sex has a pervasive influence on genome content. In contrast to these later stages of sex determination and differentiation, the earlier stages of sex determination and differentiation had not been analyzed in C. briggsae."

Regarding the comparison to Drosophila dosage compensation, including the work of Ellison and Bachtrog (2019), we included the following in the Discussion of our revised manuscript (page 22) and included related remarks in the abstract.

"In contrast to the divergence of X-chromosome target specificity between Caenorhabditis species, X-chromosome target specificity has been conserved among Drosophila species. A 21-bp GA-rich sequence motif on X is utilized across Drosophila species to recruit the dosage compensation machinery, although it may not be the sole source of X target specificity (Alekseyendo, 2008; Kuzu, 2016, Ellison, 2008; Alekseyendo, 2013)."

Regarding a comparison of our work to that of other rapidly evolving processes, we have made the following revision to our Discussion (page 22):

"Conservation of DNA target specificity among species is also a common theme among developmental regulatory proteins that participate in multiple, unrelated developmental processes, such as Drosophila Dorsal in body-plan specification (Schloop et al., 2020) or Caenorhabditis TRA-1 in hermaphrodite sexual differentiation and male neuronal differentiation (Berkseth et al., 2013; Bayer et al., 2020). Typically, for such multi-purpose proteins, target-site specificity is evolutionarily constrained: protein function is changed far more by changes in the number and location of conserved cis-acting target sequences than by changes in the target sequences themselves (Carroll, 2008; Nitta et al., 2015). Hence, the divergence in X-chromosome target specificity across the Caenorhabditis genus is atypical among developmental regulatory complexes with highly diverse target genes and could have been an important factor for establishing reproductive isolation between species. Our finding is reminiscent of the discovery that centromeric sequences and their corresponding centromere-binding proteins have co-evolved rapidly as a consequence of hybrid incompatibilities (Malik and Henikoff, 2001; Henikoff et al., 2001; Talbert and Henikoff, 2022). Occurrence of rapidly changing DNA targets and their corresponding DNA-binding proteins (see also Lienard et al., 2016; Ting et al., 1998; Ting et al., 2004; Sun et al., 2004) is an increasingly dominant theme contributing to reproductive isolation."

A brief comment about all three comparisons is also made in the beginning of the Discussion on page 18.

Author Response

Reviewer #1 (Public Review):

The authors set out to extend modeling of bispecific engager pharmacology through explicit modelling of the search of T cells for tumour cells, the formation of an immunological synapse and the dissociation of the immunological synapse to enable serial killing. These features have not been included in prior models and their incorporation may improve the predictive value of the model.

Thank you for the positive feedback.

The model provides a number of predictions that are of potential interest- that loss of CD19, the target antigen, to 1/20th of its initial expression will lead to escape and that the bone marrow is a site where the tumour cells may have the best opportunity to develop loss variants due to the limited pressure from T cells.

Thank you for the positive feedback.

A limitation of the model is that adhesion is only treated as a 2D implementation of the blinatumomab mediated bridge between T cell and B cells- there is no distinct parameter related to the distinct adhesion systems that are critical for immunological synapse formation. For example, CD58 loss from tumours is correlated with escape, but it is not related to the target, CD19. While they begin to consider the immunological synapse, they don't incorporate adhesion as distinct from the engager, which is almost certainly important.

We agree that adhesion molecules play critical roles in cell-cell interaction. In our model, we assumed these adhesion molecules are constant (or not showing difference across cell populations). This assumption made us to focus on the BiTE-mediated interactions.

Revision: To clarify this point, we added a couple of sentences in the manuscript.

“Adhesion molecules such as CD2-CD58, integrins and selectins, are critical for cell-cell interaction. The model did not consider specific roles played by these adhesion molecules, which were assumed constant across cell populations. The model performed well under this simplifying assumption”.

In addition, we acknowledged the fact that “synapse formation is a set of precisely orchestrated molecular and cellular interactions. Our model merely investigated the components relevant to BiTE pharmacologic action and can only serve as a simplified representation of this process”.

While the random search is a good first approximation, T cell behaviour is actually guided by stroma and extracellular matrix, which are non-isotropic. In a lymphoid tissue the stroma is optimised for a search that can be approximated as brownian, or more accurately, a correlated random walk, but in other tissues, particularly tumours, the Brownian search is not a good approximation and other models have been applied. It would be interesting to look at observations from bone marrow or other sites to determine the best approximating for the search related to BiTE targets.

We agree that the tissue stromal factors greatly influence the patterns of T cell searching strategy. Our current model considered Brownian motion as a good first approximation for two reasons: 1) we define tissues as homogeneous compartments to attain unbiased evaluations of factors that influence BiTE-mediated cell-cell interaction, such as T cell infiltration, T: B ratio, and target expression. The stromal factors were not considered in the model, as they require spatially resolved tissue compartments to represent the gradients of stromal factors; 2) our model was primarily calibrated against in vitro data obtained from a “well-mixed” system that does not recapitulate specific considerations of tissue stromal factors. We did not obtain tissue-specific data to support the prediction of T cell movement. This is under current investigation in our lab. Therefore, we are cautious about assuming different patterns of T cell movement in the model when translating into in vivo settings. We acknowledged the limitation of our model for not considering the more physiologically relevant T-cell searching strategies.

Revision: In the Discussion, we added a limitation of our model: “We assumed Brownian motion in the model as a good first approximation of T cell movement. However, T cells often take other more physiologically relevant searching strategies closely associated with many stromal factors. Because of these stromal factors, the cell-cell encounter probabilities would differ across anatomical sites.”

Reviewer #3 (Public Review):

Liu et al. combined mechanistic modeling with in vitro experiments and data from a clinical trial to develop an in silico model to describe response of T cells against tumor cells when bi-specific T cell engager (BiTE) antigens, a standard immunotherapeutic drug, are introduced into the system. The model predicted responses of T cell and target cell populations in vitro and in vivo in the presence of BiTEs where the model linked molecular level interactions between BiTE molecules, CD3 receptors, and CD19 receptors to the population kinetics of the tumor and the T- cells. Furthermore, the model predicted tumor killing kinetics in patients and offered suggestions for optimal dosing strategies in patients undergoing BiTE immunotherapy. The conclusions drawn from this combined approach are interesting and are supported by experiments and modeling reasonably well. However, the conclusions can be tightened further by making some moderate to minor changes in their approach. In addition, there are several limitations in the model which deserves some discussion.

Strengths

A major strength of this work is the ability of the model to integrate processes from the molecular scales to the populations of T cells, target cells, and the BiTE antibodies across different organs. A model of this scope has to contain many approximations and thus the model should be validated with experiments. The authors did an excellent job in comparing the basic and the in vitro aspects of their approach with in vitro data, where they compared the numbers of engaged target cells with T cells as the numbers of the BiTE molecules, the ratio of effector and target cells, and the expressions of the CD3 and CD19 receptors were varied. The agreement with the model with the data were excellent in most cases which led to several mechanistic conclusions. In particular, the study found that target cells with lower CD19 expressions escape the T cell killing.

The in vivo extension of the model showed reasonable agreements with the kinetics of B cell populations in patients where the data were obtained from a published clinical trial. The model explained differences in B cell population kinetics between responders and non-responders and found that the differences were driven by the differences in the T cell numbers between the groups. The ability of the model to describe the in vivo kinetics is promising. In addition, the model leads to some interesting conclusions, e.g., the model shows that the bone marrow harbors tumor growth during the BiTE treatment. The authors then used the model to propose an alternate dosage scheme for BiTEs that needed a smaller dose of the drug.

Thank you for the positive comments.

Weaknesses

There are several weaknesses in the development of the model. Multiscale models of this nature contain parameters that need to be estimated by fitting the model with data. Some these parameters are associated with model approximations or not measured in experiments. Thus, a common practice is to estimate parameters with some 'training data' and then test model predictions using 'test data'. Though Supplementary file 1 provides values for some of the parameters that appeared to be estimated, it was not clear which dataset were used for training and which for test. The confidence intervals of the estimated parameters and the sensitivity of the proposed in vivo dosage schemes to parameter variations were unclear.

We agree with the reviewer on the model validation.

Revision: To ensure reproducibility, we summarized model assumptions and parameter values/sources in the supplementary file 1. To mimic tumor heterogeneity and evolution process, we applied stochastic agent-based models, which are challenging to be globally optimized against the data. The majority of key parameters was obtained or derived from the literature. Details have been provided in the response to Reviewer 3 - Question 1. In our modeling process, we manually optimized sensitive coefficient (β) for base model using pilot in-vitro data and sensitive coefficient (β) for in-vivo model by re-calibrating against the in-vitro data at a low BiTE concentration. BiTE concentrations in patients (mostly < 2 ng/ml) is only relevant to the low bound of the concentration range we investigated in vitro (0.65-2000 ng/ml). We have added some clarification/limitation of this approach in the text (details are provided in the following question). We understand the concerns, but the agent-based modeling nature prevent us to do global optimization.

The model appears to show few unreasonable behaviors and does not agree with experiments in several cases which could point to missing mechanisms in the model. Here are some examples. The model shows a surprising decrease in the T cell-target cell synapse formation when the affinity of the BiTEs to CD3 was increased; the opposite should have been more intuitive. The authors suggest degradation of CD3 could be a reason for this behavior. However, this probably could be easily tested by removing CD3 degradation in the model. Another example is the increase in the % of engaged effector cells in the model with increasing CD3 expressions does not agree well with experiments (Fig. 3d), however, a similar fold increase in the % of engaged effector cells in the model agrees better with experiments for increasing CD19 expressions (Fig. 3e). It is unclear how this can be explained given CD3 and CD19 appears to be present in similar copy numbers per cell (~104 molecules/cell), and both receptors bind the BiTE with high affinities (e.g., koff < 10-4 s-1).

Thank you for pointing this out. The bidirectional effect of CD3 affinity on IS formation is counterintuitive. In a hypothetical situation when there is no CD3 downregulation, the bidirectional effect disappears (as shown below), consistent with our view that CD3 downregulation accounts for the counterintuitive behavior. We have included the simulation to support our point. From a conceptual standpoint, the inclusion of CD3 degradation means the way to maximize synapse formation is for the BiTE to first bind tumor antigen, after which the tumor-BiTE complex “recruits” a T cell through the CD3 arm.

We agree that the model did not adequately capture the effect of CD3 expression at the highest BiTE concentration 100 ng/ml, while the effects at other BiTE concentrations were well captured (as shown below, left). The model predicted a much moderate effect of CD3 expression on IS formation at the highest concentration. This is partly because the model assumed rapid CD3 downregulation upon antibody engagement. We did a similar simulation as above, with moderate CD3 downregulation (as shown below, right). This increases the effect of CD3 expression at the highest BiTE concentration, consistent with experiments. Interestingly, a rapid CD3 downregulation rate, as we concluded, is required to capture data profiles at all other conditions. Considering BiTE concentration at 100 ng/ml is much higher than therapeutically relevant level in circulation (< 2 ng/ml), we did not investigate the mechanism underlying this inconsistent model prediction but we acknowledged the fact that the model under-predicted IS formation in Figure 3d. Notably, this discrepancy may rarely appear in our clinical predictions as the CD3 expression is low level and blood BiTE concentration is very low (< 2 ng/ml).

Revision: we have made text adjustment to increase clarity on these points. In addition, we added: “The base model underpredicted the effect of CD3 expression on IS formation at 100 ng/ml BiTE concentration, which is partially because of the rapid CD3 downregulation upon BiTE engagement and assay variation across experimental conditions.”

The model does not include signaling and activation of T cells as they form the immunological synapse (IS) with target cells. The formation IS leads to aggregation of different receptors, adhesion molecules, and kinases which modulate signaling and activation. Thus, it is likely the variations of the copy numbers of CD3, and the CD19-BiTE-CD3 will lead to variations in the cytotoxic responses and presumably to CD3 degradation as well. Perhaps some of these missing processes are responsible for the disagreements between the model and the data shown in Fig. 3. In addition, the in vivo model does not contain any development of the T cells as they are stimulated by the BiTEs. The differences in development of T cells, such as generation of dysfunctional/exhausted T cells could lead to the differences in responses to BiTEs in patients. In particular, the in vivo model does not agree with the kinetics of B cells after day 29 in non-responders (Fig. 6d); could the kinetics of T cell development play a role in this?

We agree that intracellular signaling is critical to T cell activation and cytotoxic effects. IS formation, T cell activation, and cytotoxicity are a cascade of events with highly coordinated molecular and cellular interactions. Compared to the events of T cell activation and cytotoxicity, IS formation occurs at a relatively earlier time. As shown in our study, IS formation can occur at 2-5 min, while the other events often need hours to be observed. We found that IS formation is primarily driven by two intercellular processes: cell-cell encounter and cell-cell adhesion. The intracellular signaling would be initiated in the process of cell-cell adhesion or at the late stage of IS formation. We think these intracellular events are relevant but may not be the reason why our model did not adequately capture the profiles in Figure 3d at the highest BiTE concentrations. Therefore, we did not include intracellular signaling in the models. Another reason was that we simulated our models at an agent level to mimic the process of tumor evolution, which is computationally demanding. Intracellular events for each cell may make it more challenging computationally.

T cell activation and exhaustion throughout the BiTE treatment is very complicated, time-variant and impacted by multiple factors like T cell status, tumor burden, BiTE concentration, immune checkpoints, and tumor environment. T cell proliferation and death rates are challenging to estimate, as the quantitative relationship with those factors is unknown. Therefore, T cell abundance (expansion) was considered as an independent variable in our model. T cell counts are measured in BiTE clinical trials. We included these data in our model to reveal expanded T cell population. Patients with high T cell expansion are often those with better clinical response. Notably, the T cell decline due to rapid redistribution after administration was excluded in the model. T cell abundance was included in the simulations in Figure 6 but not proof of concept simulations in Figure 7.

In Figure 6d, kinetics of T cell abundance had been included in the simulations for responders and non-responders in MT103-211 study. Thus, the kinetics of T cell development can’t be used to explain the disagreement between model prediction and observation after day 29 in non-responders. The observed data is actually median values of B-cell kinetics in non-responders (N = 27) with very large inter-subject variation (baseline from 10-10000/μL), which makes it very challenging to be perfectly captured by the model. A lot of non-responders with severe progression dropped out of the treatment at the end of cycle 1, which resulted in a “more potent” efficacy in the 2nd cycle. This might be main reason for the disagreement.

Variation in cytotoxic response was not included in our models. Tumor cells were assumed to be eradicated after the engagement with effecter cells, no killing rate or killing probability was implemented. This assumption reduced the model complexity and aligned well with our in-vitro and clinical data. Cytotoxic response in vivo is impacted by multiple factors like copy number of CD3, cytokine/chemokine release, tumor microenvironment and T cell activation/exhaustion. For example, the cytotoxic response and killing rate mediated by 1:1 synapse (ET) and other variants (ETE, TET, ETEE, etc.) are supposed to be different as well. Our model did not differentiate the killing rate of these synapse variants, but the model has quantified these synapse variants, providing a framework for us to address these questions in the future. We agree that differentiate the cytotoxic responses under different scenarios cell may improve model prediction and more explorations need to be done in the future.

Revision: We added a discussion of the limitations which we believe is informative to future studies.

“Our models did not include intracellular signaling processes, which are critical for T activation and cytotoxicity. However, our data suggests that encounter and adhesion are more relevant to initial IS formation. To make more clinically relevant predictions, the models should consider these intracellular signaling events that drive T cell activation and cytotoxic effects. Of note, we did consider the T cell expansion dynamics in organs as independent variable during treatment for the simulations in Figure 6. T cell expansion in our model is case-specific and time-varying.”

References:

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Author Response

Reviewer #1 (Public Review):

Following previous publications showing that NR2F2 controls atrial identity in the mouse and human iPS cells, the authors address in the fish the role of the transcription factor Nr2f1a, which is specific to the atrial chamber. This had been initiated in a previous publication (Duong et al, 2018) and is extended in this manuscript. In mutant fish, the atrial chamber is smaller and mispatterned. Markers of the atrioventricular canal and of the pacemaker are expanded. Transcriptomic analyses and electrophysiological measures further support this observation. A putative enhancer of nkx2.5 is identified by ATAC-seq and shown to be repressed in nr2f1a mutants, suggesting that Nkx2.5, a known repressor of pacemaker identity, may be a mediator of Nr2f1a. Overexpression of nkx2.5 delays the appearance of pacemaker cells, and is proposed to partially rescue the absence of nr2f1a.

Overall, this work provides novel insight into the mechanism of atrial chamber patterning in the fish and discusses the conservation of the role of nr2f1a. However, the claim that atrial cells switch their identity into ventricular and pacemaker cells is currently not demonstrated. Alternative hypotheses of mispatterning, cell number changes by proliferation, survival, or ingression are not ruled out by the data presented. The claim that "Nr2f1a maintains atrial nkx2.5 expression" or of a "progressive loss of Nkx2.5 within the ACs" needs to be further supported. The definition of "atrial cells (AC)" varies between figures.

Major comments:

1) The definition of "AC" varies from figure to figure: amhc+ in Fig 1A, amhc+vmhc- in Fig.1S1A, amhc+fgf13a- in Fig. 2 and 5, morphological area in Fig. 3. Please clarify how the atrial chamber is delineated in mutants in Fig. 3 since the avc constriction is not obvious.

a. As stated in the response to Essential Revisions comment 1.B, we have tried to clarify the definitions of the cardiomyocytes populations in the revised text by indicating the specific markers used in the text and the figures. We then provide our interpretation for what this means regarding the different cardiomyocyte populations.

b. Since the analysis of the electrophysiology cannot be performed with markers or the transgenic zebrafish embryos using GFP, we chose areas for analysis closer to the middle of the morphological atrium in the nr2f1a mutant and WT sibling control embryo hearts that would be consistent with having Amhc+ expression and fgf13a:EGFP+ transgenic and Isl1 markers that were found from the analysis with immunohistochemistry. This strategy was schematized in Figure 3A and is now explicitly stated on lines 266 and 267 of the revised manuscript.

2) The claim of a switch in cell identity or transdifferentiation is not demonstrated. This would require cell tracking or single-cell transcriptomics. I don't see how "AVC (..) [is] resolving to ventricular identity", since amhc seems to be maintained throughout the atrial chamber at all stages. The claim that "the number of vmhc+ only cardiomyocytes progressively increased" is not supported by Fig1S1. The expansion of pacemaker cells may result from cell ingression at the arterial pole. This hypothesis is in keeping with the expression of nr2f1a outside the heart tube in putative atrial progenitors (Duong, 2018). The phenotype upon nkx2.5 overexpression may also be interpreted along this line: ingression of pacemaker cells is delayed. The claim that "PC identity progressively expands throughout nr2f1a mutant atria" is not supported by the quantifications of a mean of 12 fgf13a+amhc+ cells at 96hpf (Fig. 2H), which is as many as fgf13a-amhc+ cells (Fig. 2G) and a quarter of the total amhc+ cells in Fig. 1J. The schema in Fig 6 does not reflect quantifications at 96hpf, which indicate the persistence of amhc+vmhc+ cells, amhc+ only, or amhc+fgf13a- in Fig 1S1 and 2G.

"We did not observe effects on cell death or proliferation in the hearts of nr2f1a mutants": please provide the data, since proliferation was shown to be affected in mouse mutants (Wu, 2013).

a. As indicated above in our response to the Essential Revisions comment 1.D, our quantification of cardiomyocytes indicates there are progressively fewer Amhc+/Vmhc+ cardiomyocytes in the nr2f1a mutant hearts (Figure 1J-L). The total number of Vmhc+ cardiomyocytes (Amhc+/Vmhc+ and Amhc-/Vmhc+) cardiomyocytes is increased in the nr2f1a mutant hearts relative to the WT sibling hearts. However, the number of Vmhc+-only (Amhc-/Vmhc+) cardiomyocytes, which reflect the ventricles, does not increase significantly in the n2f1a mutants and are not statistically different than their WT siblings at each of the stages, despite their trending that way (Figure 1 – figure supplement 2C). The total number of cardiomyocytes in the nr2f1a mutant hearts also is not increasing during these stages (Figure 1L). Along with the lack of cardiomyocyte death or proliferation (Figure 1 – figure supplements 3 and 4), this suggests that these hearts have more total Vmhc+ cardiomyocytes and the addition of Vmhc+-only cardiomyocytes is primarily coming from the cardiomyocytes in the Vmhc+/Amhc+ atrioventricular canal progressively losing Amhc expression. As indicated in the response to Essential Revisions comment 1.D, we have provided the individual image channels in a revised Figure 1 – figure supplement 1 and proportions of Vmhc+ cardiomyocytes in Figure 1 – figure supplement 2D to help clarify this issue.

b. Regarding the transdifferentiation vs ingression of newly-differentiating cardiomyocyte hypotheses for the expansion of pacemaker markers, was addressed in the response to Essential Revision comment 2. Please see that comment for how we addressed this concern.

3) The claim that "Nr2f1a maintains atrial nkx2.5 expression" or of a "progressive loss of Nkx2.5 within the ACs" needs to be further supported by quantification of the number of nkx2.5 positive cells in nr2f1a mutants. It seems that some cells in Fig. 4 co-express nkx2.5 and pacemaker markers in the mutant, which questions the repressive role of Nkx2.5. Following the observation of an nkx2.5 enhancer active next to pacemaker cells in control heart but absent in nr2f1a mutants, shouldn't we expect a gap of nkx2.5 expression next to pacemaker cells in mutants? It is unclear why pacemaker cells express nr2f1a (Fig. 6S1) but not nkx2.5. This needs clarification.

a. The repressive role of Nkx2.5 with respect to pacemaker identity has been well documented in zebrafish and mice (Colombo et al., 2018). Nkx2.5 and Isl1 expression at the venous pole of zebrafish hearts are predominantly mutually exclusive, although there are a few cardiomyocytes at their borders that the express both Nkx2.5 and pacemaker markers. We recgonize that there are still some Nkx2.5-expressing cardiomyocytes that overlap with the pacemaker maker cardiomyocytes in the nr2f1a mutant hearts, as shown in Figure 4F. However, the majority of these cardiomyocytes have lower expression than the adjacent cardiomyocytes that form a border and do not have overlapping expression. Furthermore, as shown in Figure 4D-F and Figure 4 – figure supplement 2, the overall effect appears to be a regression of Nkx2.5+ expression in cardiomyocytes and corresponding expansion of pacemaker markers from the venous pole from 48 though 96 hpf in the nr2f1a mutant hearts, consistent with the established role of Nkx2.5 in repressing pacemaker identity. In the revised manuscript, we have provided each of the individual channels for the images in Figure 4 to better allow visualization of the different cardiomyocyte markers and a new supplemental figure showing the predominantly mutually exclusive expression of Nkx2.5 and Isl1 at the venous pole of zebrafish embryo hearts (Figure 4 – figure supplement 1).

b. The expression of Nkx2.5 within the heart, like any gene, is likely controlled by multiple different regulatory elements. It is not clear to us why Reviewer #1 feels one would expect to see a gap in expression between Nkx2.5+ and pacemaker cardiomyocytes in the nr2f1a mutant hearts, unless Nkx2.5 was not required to repress pacemaker identity or there was a significant delay between loss of Nkx2.5 and gain of pacemaker markers. As indicated in the response to Essential Revisions comment 3.C, in the revised manuscript, we show experiments in which we have deleted the putative nkx2.5 enhancer element and found there is a loss of Nkx2.5+ and gain of fgf13a:EGFP+ cardiomyocytes in the atrium, as one might expect if the enhancer promotes or maintains Nkx2.5 expression in atrial cardiomyocytes that border the pacemaker cardiomyocytes. In the revised manuscript, this experiment is described in the Results (lines 348-364 and included in a revised Figure 6 and new Figure 6 – figure supplement 2.

c. Please see our response to Essential Revision comment 3.A regarding the issue of Nr2f1a expression in pacemaker cardiomyocytes.

Author Response

Reviewer #1 (Public Review):

The manuscript by Warren et al., presents evidence suggesting that aberrant Yap signaling plays a role in epithelial progenitor cell dysregulation in lung fibrosis. This work builds on a body of work in the literature that Hippo signaling is aberrantly regulated in idiopathic pulmonary fibrosis. They use a combination of single nuclear and spatial transcriptomics, together with in vivo conditional genetic perturbations of Hippo signaling in mice, to investigate roles for Yap/Taz signaling in alveolar epithelial homeostasis and remodeling associated with exposure to a fibrosing agent, bleomycin. They show that Taz and Tead1/4 are most abundantly expressed by alveolar type 1 (AT1) cells, but Nf2 immunoreactivity (upstream activator of Hippo) is observed predominantly within airway and AT2 cells. Bleomycin exposure was associated with reduced p-Mst in regenerating alveolar epithelium, that inactivation of Yap/Taz arrested AT2>AT1 differentiation, and inactivation of either Nf2 or Mst1/2 promoted AT1 differentiation after bleomycin exposure and reduced matrix deposition/fibrosis. They go on to show that compromised alveolar regeneration resulting from inactivation of Yap/Taz results in enhanced bronchiolization of injured alveoli. Experiments are well designed and include quantitative endpoints where appropriate, data of high quality, and results are generally supportive of conclusions. These studies provide valuable new data relating to roles for the Hippo pathway in regulation of alveolar homeostasis and epithelial regeneration/remodeling in injury/repair and fibrosis.

We thank the reviewer for their enthusiastic and constructive comments.

Reviewer #2 (Public Review):

The authors explored non-redundant, and potentially contrasting, roles of the Hippo effector transcription factors, YAP and TAZ, in the epithelial regenerative response to non-infectious lung injury. The strength of the work is the use of genetic mouse models that explored inducible loss of function of YAP and/or TAZ in an alveolar epithelial type 2 (AT2) specific manner. The main weakness of the work is that gene(s) inactivation was performed prior to lung injury and, therefore, does not take into account the contextual and dynamic nature of YAP/TAZ signaling; for example, work by other groups have shown that YAP/TAZ is activated early following injury followed by a decrease in activity, thus balancing proliferation and differentiation of AT2 cells (for review, see PMID: 34671628).

We thank the reviewer for their enthusiastic and constructive comments.

We agree that knocking out genes prior to injury might not take into account the contextual and dynamic nature of YAP/TAZ signaling. However, the Hippo pathway allows cells to sense changes in their environment. We have published that in the airway epithelium the Hippo pathway becomes inactivated upon naphthalene injury in surviving airway epithelial cells sensing the loss of their neighbors, to induce Wnt7b expression which then induces Fgf10 expression in airway smooth muscle cells to drive airway epithelial regeneration. Normally when regeneration is complete and cell density is restored the Hippo pathway reactivates and the repair cascade is inactivated. Knocking out Mst1/2 in airway epithelium chronically activates this cascade and leads to overproliferation of the airway epithelium. Interestingly, upon inactivation of Mst1/2 in the airway epithelium some airway epithelial cells also turn into AT1 cells.

However, AT1 cells do not proliferate. As such we believe that inactivation of Mst1/2 or Nf2 in AT2 cells will not result in overproliferation but mainly promote AT1 cell differentiation. That being said there are other pathways and molecules that affect Yap/Taz nuclear localization. So inactivation of Mst1/2 or Nf2 in AT2 cells most likely primes/activates AT2 cells to regenerate AT1 cells but this decision is likely not binary.

Reviewer #3 (Public Review):

The manuscript entitled "Hippo signaling impairs alveolar epithelial regeneration in pulmonary fibrosis" is a rigorous and timely report detailing the significance of Hippo signaling, Taz and Yap in AT2/AT1 differentiation and the subsequent impact on the progression of lung fibrosis versus repair/ regeneration. The authors experimental design and results support their conclusions. The identification of the distinct effects of Taz and Yap in these processes highlight the pathway and specific molecules as potential therapeutic targets.

The major strengths of these studies lie in the rigor of the elegant transgenic developmental/adult injuryrepair mouse models combined with spatial transcriptomics and analyses. The weaknesses include a lack of detail presented in the methods, some legends and discussion.

We thank the reviewer for their enthusiastic and constructive comments. And have addressed the issues raised.

Author Response

Reviewer #1 (Public Review):

This is a very interesting paper showing that during amino acid starvation of Neurospora, the general amino acid control factors CPC-1 and CPC-3 are crucial to maintaining circadian rhythm at the levels of rhythmic growth and transcription of the FRQ gene. They show that deleting both genes leads to reduced and arrhythmic cell growth and FRQ transcription that can be accounted for by severely reduced occupancy of the FRQ promoter by the key transcription factor WCC. This defect in turn appears to result from diminished H3 acetylation of the FRQ promoter that was observed at least in the cpc-1 mutant, which is mediated by Gcn5. Thus, they show that Gcn5 occupancy at FRQ is rhythmic and impaired by cpc-1 knock-out, that CPC-1 occupies the FRQ promoter, and provide coIP evidence that Cpc-1 interacts with Gcn5 and Ada2 and, hence, could act directly to recruit these cofactors to the FRQ promoter. Importantly, they show that knock out of GCN5 eliminates rhythmic cell growth and FRQ expression (although surprisingly not FRQ mRNA abundance), as well as reducing H3ac levels and WCC binding at FRQ. They further show that TSA treatment can reverse the effects of histidine starvation on the circadian period in WT cells, and can partially restore rhythmic growth to histidine-starved cpc-3 cells, and that elimination of HDAC Hda1 increases H3ac at FRQ in WT cells. They provide some evidence that transcriptional activation of certain aa biosynthetic genes by CPC-1 is also rhythmic, although the evidence for this is not strong and it's unclear whether CPC-1 occupancy or its activation function would be periodic. They also did not address whether CPC-1 occupancy at FRQ is rhythmic.

This work is important in providing convincing evidence that CPC-1-mediated induction of transcription factor CPC-3 in starved Neurospora cells mediates CPC-1-mediated recruitment of Gcn5 and acetylation of the FRQ promoter, which counteracts the function of histone deacetylase HDA1 to maintain high occupancy of the transcription factor WCC and attendant circadian rhythm of FRQ transcription. Although the work does not identify new regulatory circuits, such as rhythmic transcription of FRQ, the role of Gcn5, Hda1, and promoter histone acetylation in supporting transcriptional activation, and the general amino acid control response to amino acid starvation are all well-established mechanisms, the work is significant in showing how these pathways and mechanisms are integrated to maintain circadian rhythm in the face of amino acid limitation.

There is an abundance of convincing experimental evidence provided to support the key claims just summarized above. However, there are a few instances in which additional experiments might be required to resolve a discrepancy in the data or provide stronger evidence to support a claim.

Thanks for the comments. We have revised the manuscript as suggested.

Reviewer #2 (Public Review):

This study by Liu et al. investigates the mechanism that enables the Neurospora circadian clock to maintain robust molecular and physiological rhythms under conditions of nutrient stress. The authors showed that the nutrient-sensing GCN2 signaling pathway is required to maintain robust circadian clock function and output rhythms under amino acid starvation in the filamentous fungus Neurospora. Specifically, they observed that under amino acid starvation conditions, knocking out GCN2 pathway components GCN4 (CPC-1) and GCN2 (CPC-3) severely disrupts rhythmic transcription of core clock gene frequency (frq) and clock-regulated conidiation rhythm. They provided data to indicate that the observed disruptions are due to reduced binding of the White Collar (WC) complex to the frq promoter stemming from lower histone H3 acetylation levels. This prompted the authors to propose a model in which GCN2 (CPC-3) and GCN4 (CPC-1) are activated upon sensing amino acid starvation, recruit GCN-5 containing SAGA acetyltransferase complex to maintain robust histone acetylation rhythm at the frq promoter. They then performed a battery of assays to show that both GCN-5 and ADA-2 are necessary for maintaining robust H3ac, frq mRNA, and conidiation rhythms under normal conditions. To support that low H3ac level at the frq promoter is the cause for impaired WC binding and frq transcription, they demonstrated they can partially rescue the observed rhythm defects of the knockout mutants under amino acid starvation using an HDAC inhibitor. Finally, the authors used RNA-seq to identify genes and pathways that are differentially activated by GCN4 (CPC-1) under amino acid starvation conditions. Many of these genes are involved in amino acid metabolism and they showed that 3 of them exhibit rhythmic expression in WT but low and non-rhythmic expression in the CPC-1 KO strain.

Strength: The 24-hour period length of the circadian clock is known to be stable over a range of environmental and metabolic conditions because of circadian compensation mechanisms. Whereas temperature compensation (maintenance of circadian period length over a physiological range of temperature) has been studied extensively in multiple model organisms, the phenomenon of nutritional compensation and its underlying mechanisms are poorly understood. This study provides new insights into this important yet understudied area of research in chronobiology. In addition to advancing our understanding of fundamental mechanisms governing clock compensation mechanisms, this study also adds to our understanding of metabolic regulation of rhythmic biology and the relationship between nutrition and healthy biological rhythms. Given that the GCN2 nutrient-sensing pathway is broadly conserved beyond Neurospora, findings from this study will likely be relevant to other eukaryotic systems.

The authors provided strong evidence supporting their claims that the GCN2 signaling pathway is important for maintaining the robustness of the Neurospora clock under conditions of amino acid starvation. The authors performed parallel experiments in normal (no 3-AT) vs amino acid-starved conditions (+3-AT). Their observations of relatively minor disruptions of molecular and conidiation rhythms in cpc-3 and cpc-1 KO strains in normal nutrient conditions compared to starvation conditions support their model that sensing of amino acid starvation by GCN2 pathway-induced changes at the chromatin and transcriptional level that are necessary to maintain a robust frq oscillator. Without the comparison between normal vs amino acid starved conditions, this part of their model will not be as strong.

Previously Karki et al. (2020) showed that rhythmic activation of GCN2 kinase is regulated by the clock, resulting in clock-control rhythmic translation initiation. This study uncovers an additional mechanism through which GCN2 pathway modulates circadian rhythms by regulating histone acetylation of rhythmic genes. RNA-seq as described in Figure 7 provides some potential targets.

Thanks for the comments and suggestions. We have revised the manuscript as suggested.

Weakness:

(1) The authors propose a model (Figure 8) in which the GCN2 pathway is ,activated by amino acid starvation and recruits the SAGA complex to promote histone acetylation level at the frq promoter. There is however no data in this study showing that the GCN2 pathway is activated in amino acid-starved conditions, only that it is required to maintain robust frq and conidiation rhythms. The authors should clarify how they are defining "activation of the GCN2 pathway" in this study. For example, is it recruitment of GCN-5 and SAGA complex to frq promoter?

Thanks for the question. CPC-3, the GCN2 homolog in Neurospora, is the only eIF2α kinase responsible for eIF2α phosphorylation at serine 51(Karki S et al. 2020, PMID: 32355000). As shown in the revised Figure 1-figure supplement 1A, the eIF2α phosphorylation and CPC-1 were induced by 3-AT treatment in the WT but not in the cpc-3KO strain. These results demonstrate that the GCN2 pathway is activated by amino acid starvation, and as a result, the CPC-1 expression is activated to recruit the SAGA complex to the frq promoter.

(2) The experiments to examine the involvement of GCN-5 and ADA-2 were performed in normal conditions (no amino acid starvation). Unlike cpc-1 and cpc-3 KO strains, gcn-5 and ada-2 KO strains showed severely disrupted frq rhythms in normal nutrient conditions, suggesting they are normally required for robust circadian rhythms. If GCN-5 and the SAGA complex are normally involved in regulating H3ac rhythms in the frq loci, how does GCN2 pathway modulates the activity of GCN-5 and SAGA complex in conditions of amino acid starvation? Are the interactions between GCN2/4 with GCN-5 and SAGA complex different in normal vs amino acid starved conditions? The authors should clarify their model.

As mentioned above, our data suggested that GCN-5 and ADA-2 are required for robust circadian rhythms under normal conditions. As suggested, we did detect dampened rhythmic expression of frq in the gcn-5KO and ada-2KO strains under amino acid starvation (Figure 5D and 5E and Figure 5–figure supplement 1E and 1F). We also performed Co-IP to compare the difference of interactions between CPC-1 with ADA-2 and GCN5 with ADA-2 under normal and amino acid starved conditions. The results showed that although the Myc.GCN-5, MYC.CPC-1 or Flag.ADA-2 protein level was repressed by 3 mM 3-AT treatment (likely due to global translational inhibition by induced eIF2α phosphorylation) (Karki S et al. 2020, PMID: 32355000), the interactions between CPC-1 with ADA-2 and GCN-5 with ADA-2 were almost the same under normal and amino acid starved conditions (IP was normalized with Input) (Figure 4B and 4C). These results indicated that amino acid starved conditions had little impact on the protein interactions between CPC-1 with GCN-5 and SAGA complex.

In our model, we proposed that amino acid starvation resulted in compact chromatin structure (due to decreased H3ac) in the frq promoter in the WT strain (Figure 3B), likely due to activation of histone deacetylases or inhibition of histone acetyltransferases. Amino acid starvation activates GCN2 pathway and induces CPC-1 expression. The induced CPC-1 can recruit GCN5-containing SAGA complex to the frq promoter to loosen the chromatin structure, promoting frq rhythmic transcription under starvation conditions. However, in the cpc-3KO mutants, CPC-1 could not effectively recruit GCN5 containing SAGA complex to frq promoter, resulting in arrhythmic frq transcription. We have now clarified our model in the revised discussion.

(3) Given that the GCN2 pathway is important for nutrient sensing, the authors should not disregard the alternative hypothesis that the GCN2 pathway may be important for nutrient compensation and plays a role in maintaining the robustness of rhythms in a range of nutrient conditions.

Thanks for the suggestion. We now discussed the alternative hypothesis in the revised manuscript. “Because GCN2 signaling pathway is important for nutrient sensing, it may be important for nutrient compensation and plays a role in maintaining the robustness of rhythms in a range of nutrient conditions”.

(4) The authors should use circadian statistics to compute the phase and amplitude of the mRNA, DNA binding of the WC complex, and H3Ac rhythms. This will allow them to compare between rhythms and provide statistical significance values, rather than just providing qualitative descriptions. This will be valuable when comparing rhythms between strains and between nutrient conditions.

As suggested, we used CircaCompare to analyze our data.

Reviewer #3 (Public Review):

This is an important paper anchored by the observation that cultures of Neurospora undergoing amino acid starvation lose circadian rhythmicity if orthologs in the classic GCN2/CPC-3 cross-pathway control system are absent. Data convincingly show that Neurospora orthologs of Saccharomyces GCN2 and GCN4 (CPC-3 and CPC-1 respectively) are needed to promote histone acetylation at the core clock gene frequency to facilitate rhythmicity. While the binding of CPC-1 and thereby GCN-5 are plainly rhythmic, the explanation of exactly where rhythmicity enters the pathway is incomplete.

Figure 1 shows that inhibition of the HIS-3 activity affected by 3-AT, which should trigger cross-pathway control, is correlated with a graded reduction in the amplitude of the rhythm, and eventually to arrhythmicity at 3 mM 3-AT. While normalized data are shown in Figure 1B, raw data should also be provided in the Supplement as sometimes normalization hides aspects of the data. Ideally, this would be on the same scale in wt and in mutant strains.

We revised as suggested and added the raw data. The results are now shown in Figure 1–figure supplement 2A and 2B and Figure 5–figure supplement 1B and 1C.

Figure 2. The logical conclusion from Fig 1 is that circadian frq expression driven by the WCC has been impacted by amino acid starvation in the mutants. If so, either WC-1/WC-2 levels might be low, or else they might not be able to bind to DNA. When this was assessed, ChIP assays showed a loss of DNA binding. Although documented, an interesting result is that WCC protein amounts are sharply increased, especially for WC-1. The authors could comment on possible causes for this.

Line 176, "hypophosphorylation of WC-1 and WC-2 (which is normally associated with WC activation . . . )". While the authors are correct that this is often the case it is not always the case and this introduces a potentially interesting caveat. That is, the overall phosphorylation status of WCC does not always reflect its activity in driving frq transcription. This was first noticed by Zhou et al., (2018 PLOS Genetics) who reported that even though WCC is always hyperphosphorylated in ∆csp-6, the core clock maintains a normal circadian period with only minor amplitude reduction. This should be noted, cited, and discussed.

Thanks for the suggestion. We revised the manuscript as suggested, “It should be noted that the overall phosphorylation status of WCC does not always reflect its activity in driving frq transcription, possibly due to the unknown function of multiple key phosphosites on WCC (Wang et al., 2019; X. Zhou et al., 2018)”.

Figure 2 and Figure 2 Suppl. report different gel conditions and show that the sharply increased WC1/WC-2 levels seen in Fig 2 resulting from 3-AT treatment of the cpc pathway mutants are due to the accumulation of hypophosphorylated WC-1/2. The conclusion would be stronger if the gels in the Supplement showed the same degree of difference between wt and mutants as seen in Fig 2. In any case, these hypophosphorylated WC should be active and able to bind DNA but plainly are not based on Fig 2.

Thanks for the comments. It’s correct that WC-1/WC-2 were hypo-phosphorylated and their protein levels were increased (Figure 2 and Figure 2-figure supplement 1). However, the reduced binding of WC-1/WC-2 at the frq promoter explains for the reduced frq transcription in the cpc-1KO or cpc-3KO mutants under amino acid starvation.

Figure 3 correlates the unexpected loss of DNA binding by hypophosphorylated WCC with reduced histone H3 acetylation at frq. The 3 mM 3-AT reported to result in arrhythmicity in cpc mutants in Figures 1 and 2 results in a small (~20%?) and not statistically significant reduction in H3 acetylation in wt, compatible with the sustained rhythms seen in wt in Figure 1, but in a substantial (~5 fold) loss of binding in the ∆cpc-1 background; so CPC-1 is needed for H3 acetylation at frq to sustain the rhythm during amino acid starvation. The simplest explanation here then is that the hypophosphorylated WCC cannot bind to DNA because the chromatin is closed due to decreased AcH3.

Thanks for the comments.

Figure 4. Title:" Figure 4. CPC-1 recruits GCN-5 to activate frq transcription in response to amino acid starvation"; the conditions of amino acid starvation should be mentioned here for the reader's benefit. (In the unlikely case that there was no amino acid starvation here then many things about the manuscript need to be reconsidered.)

Based on the model from yeast where amino acid starvation activates GCN2 (aka CPC-3 in Neurospora) kinase which activates the transcriptional activator GCN4 (aka CPC-1) which recruits the SAGA complex containing the histone acetylase GCN5 to regulated promoters, CPC-1 was tagged and shown by ChIP to bind rhythmically at frq. Co-IP experiments establish the interaction of components of the SAGA complex in Neurospora and Neurospora GCN-5 indeed is bound to frq, likely recruited by CPC-1. This part all follows the Saccharomyces model with the interesting twist that the binding CPC-1 is weakly rhythmic and GCN-5 strongly rhythmic in a CPC-1-dependent manner. Based on the figure legend title, these cultures should always be starved for amino acids (although as noted this should be made explicit in the figure legend). In any case, given this, from where does the rhythmicity in GCN-5-binding arise? This question is developed more below.

Line 224, "low in the cpc-1KO strain, suggesting that CPC-1 rhythmically recruit GCN-5". Because ChIP was done only for a half circadian cycle (DD10-22), it is hard to conclude "rhythmically". The statement should be modified.

To address the concern, we performed the ChIP assay using the CPC-1 antibody instead of Myc antibody (revised Figure 4A). Analysis of the ChIP results with CircaCompare showed that CPC-1 binding at the frq promoter was rhythmic without 3-AT (Figure 4A) or with 3 mM 3-AT treatment (Figure 4-figure supplement 1A). Due to the ADA-2-GCN5 and CPC-1-ADA-2 interactions with/without 3-AT treatment (Revised Figure 4B-C), CPC-1 should be able to recruit GCN-5-containing SAGA complex to activate frq transcription in response to amino acid starvation. We have now clarified this model in the revised manuscript. Please also see response to Reviewer 2/point 5.

It was previously reported that the CPC-3/CPC-1 signaling pathway was rhythmically controlled by circadian clock, as indicated by CPC-3-mediated rhythmic eIF2α phosphorylation at serine 51 (Karki S et al. 2020, PMID: 32355000). Our data showed rhythmic CPC-1 and GCN-5 binding at the frq promoter in the WT strain and decreased GCN-5 binding in the cpc-1KO mutant (Figure 4A and 4D). These results suggested that the circadian clock controlled the CPC-3/CPC-1 signaling pathway rhythmically, which in turn promoted the rhythmic frq transcription through recruiting GCN5 containing SAGA complex under amino acid starvation. We clarified the model and description in the discussion.

As suggested by the reviewer, we modified the statement "suggesting that CPC-1 recruits GCN-5-containing SAGA complex to the frq promoter".

Figure 5 shows that rhythmicity in general and of frq/FRQ specifically requires GCN-5 even under conditions of normal amino acid sufficiency, and that normal levels of H3 acetylation and its rhythm at frq require GCN-5. Not surprisingly, high H3 acetylation at frq correlated with high WC-2 DNA binding, and ADA-2 is required for SAGA functions.

As earlier, raw bioluminescence data corresponding to panel B should be provided in the figure or Supplement.

Also, if CPC-3 and CPC-1 regulate frq transcription through GCN-5, why is the frq level extremely low in the cpc-3KO or cpc-1KO(Fig.1D) but remains normal in gcn-5KO (Fig. 5D)?

Raw bioluminescence data are listed in Figure 5–figure supplement 1B and 1C. For frq transcription in the WT and gcn-5KO mutant, please see response to Essential Revisions point 4.

Figure 6 documents the counter effects of TSA which inhibits histone deacetylation and shortens the period versus 3-AT which decreases (via CPC-3 to CPC-1 to GCN-5) histone acetylation and causes period lengthening or arrhythmicity. HDA-1 is necessary for histone deacetylation at frq.

Thanks for the comments.

Figure 7 documents extensive changes in gene expression associated with 3-AT-induced amino acid starvation and the CPC-3 to CPC-1 pathway. How do these results compare with other previously studied systems, particularly Saccharomyces, where similar experiments have been done? Are the same genes regulated to the same extent or are there some interesting differences?

Thanks for the suggestion. We revised our manuscript by comparing the difference of these genes in Saccharomyces. GCN4/CPC-1 targets are similar. “Similar to Saccharomyces cerevisiae (Natarajan et al., 2001), genes in amino acid biosynthetic pathways, vitamin biosynthetic enzymes, peroxisomal components, and mitochondrial carrier proteins were also identified as CPC-1 targets”.

Figure 8 provides a model consistent with the role of the CPC-3/GCN2 pathway in regulating genes in response to amino acid starvation. It seems this could be any gene responding to amino acid starvation.

Not accounted for in the model is the data from Fig 4 which show the rhythmic binding of CPC-1 and stronger rhythmic binding of GCN-5 to frq, both under amino acid starvation. In the presence of 3-AT, amino acid starvation is constant, which should mean that CPC-3 and CPC-1 would always be "on". Why doesn't CPC-1 recruit GCN5 at the same level at all times leading to constant high H3 acetylation rather than rhythmic H3 acetylation as seen in Figure 3? Perhaps, unlike the statement in lines 345-34, it is WCC that regulates rhythmic GCN-5 binding and facilitates rhythmic histone acetylation at frq. Or perhaps the clock introduces rhythmicity upstream from GCN5. Without an answer to the question of where rhythmicity comes into the pathway, the story is only about how the CPC-3/GCN2 pathway in regulating genes in response to amino acid starvation; without explaining the rhythmicity the story seems incomplete.

It was previously reported that the CPC-3/CPC-1 signaling pathway was rhythmically controlled by circadian clock, as indicated by CPC-3-mediated rhythmic eIF2α phosphorylation at serine 51 (Karki S et al. 2020, PMID: 32355000). Our data showed rhythmic CPC-1 and GCN-5 binding at the frq promoter in the WT strain and decreased GCN-5 binding in the cpc-1KO mutant (Figure 4A and 4D). These results suggested that the circadian clock controlled the CPC-3/CPC-1 signaling pathway rhythmically, which in turn promoted the rhythmic frq transcription through recruiting GCN5 containing SAGA complex under amino acid starvation. We clarified the model and description in the discussion.

Author Response

Reviewer 2 (Public review):

A quasi-experimental before and after design as the methodological intention should be stated in the article. Although there are equally powerful alternatives with arguably less-stringent requirements that are appropriate and well-tested for natural experiments such as that intervened by the COVID-19 pandemic given the simulation methods, as of now obtaining the actual stage distribution of cancer and the cancer-specific mortality rates before and after the pandemic is possible for making scientifically valid conclusions based on observed data to support the simulation study.

We agree with the reviewer that a modelled before-and-after analysis would have been informative. However, the required mortality and cancer stage distribution data to inform this analysis is not yet available for Australia. In future, our modelled predictions can be compared to emergent observed national stage and mortality data. The current paper presents estimates that were modelled during rapid-response modelling commissioned by the Australian Government early in the pandemic. Findings therefore demonstrate what could be done with the information available at that time. We have amended, as shown in bold below, the end of the introduction as follows:

“We demonstrate what could be estimated by a rapid response evaluation based on information available early in the pandemic, and discuss how these estimates relate to subsequent observed disruptions to screening. In future, our modelled predictions can be compared to emergent observed national stage and mortality data.”

The screening disruption is the only concerned parameter in modelling the change of cancer progression in this study. But delayed diagnosis after screening as another concern could be possibly affected by the pandemic. This should be taken into consideration in the simulation. The authors also claimed the cancer treatment could also be affected by the pandemic, the evaluation on mortality is therefore not feasible. However, the impacts of COVID-19 pandemic on the delayed treatment and cancer treatment are important issues which should be covered by simulation study.

We clearly state that this is a limitation of the current study. We have added the following sentence to the discussion, lines 377-379.

‘These effects will be incorporated in future modelled evaluations, after careful calibration and validation to observed data, with a view to extending the modelled outcomes to mortality estimates.’

By simulations, the confident intervals for the outcomes should be provided as the requirement to determine the required reliability for the estimates.

The manuscript aims to present indicative estimates for a range of scenarios, with numerous simplifying assumptions as indicated. In this context, generating meaningful uncertainty intervals is not feasible or appropriate.

Author Response

Reviewer #1 (Public Review):

There has been a lot of work showing that multi-peaked tuning curves contain more information than single peaked ones. If that's the case, why are single-peaked tuning curves ubiquitous in early sensory areas? The answer, as shown clearly in this paper, is that multi-peaked tuning curves are more likely to produce catastrophic errors.

This is an extremely important point, and one that should definitely be communicated to the broader community. And this paper does an OK job doing that. However, it suffers from two (relatively easily fixable) problems:

I) Unless one is an expert, it's very hard to extract why multi-peaked tuning curves lead to catastrophicerrors.

II) It's difficult to figure out under what circumstances multi-peaked tuning curves are bad. This isimportant, because there are a lot of neurons in the sensory cortex, and one would like to know whether multi-peaked tuning curves are really a bad idea there.

And here are the fixes:

I) Fig. 1c is a missed opportunity to explain what's really going on, which is that on any particular trialthe positions of the peaks of the log likelihood can shift in both phase and amplitude (with phase being more important). However Fig. 1c shows the average log likelihood, which makes it hard to understand what goes wrong. It would really help if Fig. 1c were expanded into its own large figure, with sample log likelihoods showing catastrophic errors for multi-peaked tuning curves but not for single peaked ones. You could also indicate why, when multi-peaked tuning curves do give the right answer, the error tends to be small.

We thank the reviewer for this suggestion. We have now split the first figure into two.

In the new Figure 1, we provide an intuitive explanation of local vs catastrophic errors and single-peaked / periodic tuning curves. We have also added smaller panels to illustrate how the distribution of errors changes with decoding time (using a simulated single-peaked population).

The new Figure 2 shows sampled likelihoods for 3 different populations. We hope this provides some intuitive understanding of the phase shifts. Unfortunately, it proved difficult not to normalize the “height” of each module’s likelihood as they can differ by several orders of magnitude across the modules. However, due to the multiplication, the peak likelihood values can (approximately) be disregarded in the ML-decoding. Lastly, we have also added more simulation points (scale factors) compared to what we had in the earlier version of the figure (see panels d-e).

II) What the reader really wants to know is: would sensory processing in real brains be more efficient ifmulti-peaked tuning curves were used? That's certainly hard to answer in all generality, but you could make a comparison between a code with single peaked tuning curves and a good code with multi-peaked tuning curves. My guess is that a good code would have lambda_1=1 and c around 0.5 (you could use the module ratio the grid cell people came up with -- I think 1/sqrt(2) -- although I doubt if it matters much). My guess is that it's the total number of spikes, rather than the number of neurons, that matters. Some metric of performance (see point 1 below) versus the contrast of the stimulus and the number of spikes would be invaluable.

We thank the reviewer for this comment and the suggestions. We agree, ideally such an expression would be useful. However, as you note it is a very challenging task due to the large parameter space (number of neurons, peak amplitude, spontaneous firing rate, width of tuning, stimulus dimensionality etc) and is beyond the scope of the present study. We have instead included a new figure (see Figure 7 in the manuscript) detailing the minimal decoding times for various choices of parameter values. We believe this gives an indication to how minimal decoding time scales with various parameters.

Author Response:

Reviewer #1 (Public Review):

[…] This novel system could serve as a powerful tool for loss-of-function experiments that are often used to validate a drug target. Not only this tool can be applied in exogenous systems (like EGFRdel19 and KRASG12R in this paper), the authors successfully demonstrated that ARTi can also be used in endogenous systems by CRISPR knocking in the ARTi target sites to the 3'UTR of the gene of interest (like STAG2 in this paper).

We thank the referee for highlighting the novelty and potential of the ARTi system.

ARTi enables specific, efficient, and inducible suppression of these genes of interest, and can potentially improve therapeutic target validations. However, the system cannot be easily generalized as there are some limitations in this system:

• The authors claimed in the introduction sections that CRISPR/Cas9-based methods are associated with off-target effects, however, the author's system requires the use CRISPR/Cas9 to knock out a given endogenous genes or to knock-in ARTi target sites to the 3' UTR of the gene of interest. Though the authors used a transient CRISPR/Cas9 system to minimize the potential off-target effects, the advantages of ARTi over CRISPR are likely less than claimed.

We thank the reviewer for raising these very valid concerns about potential off-target effects related to the CRISPR/Cas9-based gene knockout or engineering of endogenous ARTi target sites. In contrast to conventional RNAi- and CRISPR-based approaches, such off-target effects can be investigated prior to loss-of-function experiments through comparison between parental and engineered cells, which in the absence of CRISPR-induced off-target events are expected to be identical. Subsequent ARTi experiments provide full control over RNAi-induced off-target activities through comparison of target-site engineered and parental cells. However, we agree that undetected CRISPR/Cas9-induced off-target events cannot be ruled out in a definitive way, which we will point out in our revised manuscript.

• Instead of generating gene-specific loss-of-function triggers for every new candidate gene, the authors identified a universal and potent ARTi to ensure standardized and controllable knockdown efficiency. It seems this would save time and effort in validating each lost-of-function siRNAs/sgRNAs for each gene. However, users will still have to design and validate the best sgRNA to knock out endogenous genes or to knock in ARTi target sites by CRISPR/Cas9. The latter is by no-means trivial. Users will need to design and clone an expression construct for their cDNA replacement construct of interest, which will still be challenging for big proteins.

We fully agree that the required design of gene-specific sgRNAs and subsequent CRISPR-engineering steps are by no means trivial. However, we believe that decisive advantages of the method, in particular the robustness of LOF perturbations and additional means for controlling off-target activities, can make ARTi an investment that pays off. In our experience, much time can be lost in the search for effective LOF reagents, and even when these are found, questions about off-target activity remain. While ARTi overcomes many of these challenges by providing a standardized experimental workflow, we do not propose to replace all other LOF approaches by this method. Instead, we would position ARTi as a unique orthogonal approach for the stringent validation and in-depth characterization of candidate target genes, as we will highlight in our revised discussion.

• The approach of knocking-out an endogenous gene followed by replacement of a regulatable gene can also be achieved using regulated degrons, and by tet-regulated promoters included in the gene replacement cassette. The authors should include a discussion of the merits of these approaches compared with ARTi.

We thank the reviewer for pointing out these alternative LOF methods. We had already included a brief discussion of chemical-genetic LOF methods based on degron tags. While we certainly share the current excitement about degron technologies, they inevitably require changes to the coding sequence of target proteins, which can alter their regulation and function in ways that are hard to control for. In our revised discussion, we will add a brief comparison to conventional tet-regulatable expression systems, which unlike ARTi require the use of ectopic tet-responsive promoters. Overall, we would position ARTi as an orthogonal tool that enables inducible and reversible LOF perturbations without changing the coding sequence and the endogenous transcriptional control of candidate target genes.

Reviewer #2 (Public Review):

[…] The system is very innovative, likely easy to be established and used by the scientific community and thus very meaningful.

We thank the reviewer for their enthusiasm about ARTi.

Author Response

Reviewer #1 (Public Review):

Starrett, Gabriel et al. investigated 43 bladder cancers (primary tumors), 5 metastases and 14 normal tissues from 43 solid organ transplant recipients of 5 Transplant Cancer Match Study participating registries (US) for the presence of viral genetic signatures, their host genome integration and possible contribution in carcinogenesis. They isolated DNA and RNA from FFPE tissues to perform state of the art whole genome and transcriptome sequencing. They find that 20 of the primary tumors, 3 of the metastases and 7 of the normal tissues harbor viral signatures with BKPyV and JCPyV being the most prevalent viruses detected. The bulk of the experiments focuses on the 9 BKPyV-positive primary tumors. They report that several of the BKPyV-positive tumors show host genome integration of BKPyV with associated focal amplifications of adjacent host chromosome regions, with chromosome 1 being the most prevalent. Furthermore, BKPyV-positive tumors show a distinct transcriptomic signature with gene expression changes related to DNA damage responses, cell cycle progression, angiogenesis, chromatin organization, mitotic spindle assembly, chromosome condensation/separation and neuronal differentiation. The authors only touch the features of other virus-positive tumors, e.g. those with JCPyV and HPV signals, without offering further detail or thought. The overall mutation signature analysis reveals no clear correlation between presence of viral sequences and tumor mutation burden suggesting that many different, virus-unrelated, factors possibly contribute to bladder cancer genesis and progression. Most striking are cases potentially linked to aristolochic acid, ABOBUCK3 and SBS5. Thus, while the approach is state-of-the-art, the causality of viral signatures and oncogenesis and vice versa remains unsolved.

Strengths:

1) The study assesses 43 primary tumors, 5 metastases and 14 normal tissues from 43 solid organ transplants of different kinds (24x kidney, 4x liver, 14x heart and/or lung, 1x pancreas) rather than just focusing on a particular organ.

2) The study makes use of whole genome sequencing and transcriptomics and the assayed material is extracted from FFPE tissue, which shows a high level of practical, technical and computational skills and expertise.

Weaknesses:

1) There have been multiple inconsistencies in sample number and figure references throughout the publication. Is it 19 or 20 cases that have viral sequences detected? A comprehensive checker board table showing all cases, the available tissue samples and respective analyses would be in order.

We would like to thank the reviewer for their detailed assessment of the manuscript. A checkerboard table of all samples tissues and analysis has been added as supplemental table 1 (Supplementary file 1a).

2) The overall low coverage of the whole genome sequencing, which the authors mention, and the relatively big variation in coverage in both datasets (WGS, transcriptomics) are major limitations of the study. Possibly, this was done to increase specificity, but sorting out and discarding reads may also be problematic. Please comment.

Besides performing quality and adapter trimming as described in the methods, we did not discard any reads. Experimental design and analysis were conducted to be as inclusive as possible considering the rarity of these specimens.

Reviewer #2 (Public Review):

Starrett et al performed whole genome and transcriptome sequencing of bladder cancers from 43 organ transplant recipients. They found that most of these tumors contained DNA from one of four viruses (BKPyV, JCPyV, HPV, and TTV). Viral genomes are most often integrated into the genomes of these tumor cells and the authors provide evidence that the integration utilized the POL theta-mediated end joining pathway. In most cases, viral RNA was detected in tumors with viral DNA. This suggests that the viruses are actively altering the cellular environment. Frequently, this resulted in similarities for overall gene expression patterns in the tumors that were grouped by the type of virus present in the tumor. Moreover, the changes in expression linked with viral gene expression were found in genes relevant to tumorigenesis. Immunohistochemical detection of viral proteins in these tumors also demonstrated active viral gene expression. However, the presence of viral proteins was heterogenous within the tumor, with between 1 and 100% of the tumor staining positive for BKPyV large T antigen. An analysis of mutational signatures in these tumors indicate that the viruses are also shaping the tumor genome by inducing mutations. Evidence that specific viruses are contributing to tumorigenesis in organ transplant patients has fundamental implications for preventing tumorigenesis in these patients.

The conclusions of this paper are generally well supported by the data provided. Indeed, there is little doubt that viral infections are more likely in these tumors. However, there are aspects of the paper that could be improved and or clarified. Most importantly, despite the strong evidence that the viruses are altering the tumor cell environment, it is unclear if these changes are necessary for tumorigenesis or less excitingly the result of an even more immune suppressive environment within the tumor. The heterogeneity of the LT expression suggests that the presence of the viral DNA and RNA may not be enough to assess whether it is actively contributing to the tumor. Is an increased frequency of viral protein staining linked with any evidence of an active contribution to tumorigenesis (fewer tumor-suppressor/oncogene mutations). that they reduced mutations in tumor suppressors. This might be easiest to assess with the tumors that have oncogenic HPV DNA. If those tumors lacked p53 and RB mutations, it would support a causative role for the virus.

We thank the reviewer for their thoughtful review. Indeed, in Figure 6 we show that no BKPyV-positive or HPV-positive tumor harbored mutations in RB1. Additionally, only one BKPyV-positive tumor and none of the HPV-positive tumors had a mutation in TP53. We have added further emphasis to this point on page 14, “None of the HPV-positive tumors with WGS harbored mutations in TP53 or RB1. Similarly, none of the polyomavirus-positive tumors harbored mutations in RB1 and only TBC08 had a frameshift mutation in TP53.”

Author Response

Reviewer #1 (Public Review):

Buglak et al. describe a role for the nuclear envelope protein Sun1 in endothelial mechanotransduction and vascular development. The study provides a full mechanistic investigation of how Sun1 is achieving its function, which supports the concept that nuclear anchoring is important for proper mechanosensing and junctional organization. The experiments have been well designed and were quantified based on independent experiments. The experiments are convincing and of high quality and include Sun1 depletion in endothelial cell cultures, zebrafish, and in endothelial-specific inducible knockouts in mice.

We thank the reviewer for their enthusiastic comments and for noting our use of multiple model systems.

Reviewer #2 (Public Review):

Endothelial cells mediate the growth of the vascular system but they also need to prevent vascular leakage, which involves interactions with neighboring endothelial cells (ECs) through junctional protein complexes. Buglak et al. report that the EC nucleus controls the function of cell-cell junctions through the nuclear envelope-associated proteins SUN1 and Nesprin-1. They argue that SUN1 controls microtubule dynamics and junctional stability through the RhoA activator GEF-H1.

In my view, this study is interesting and addresses an important but very little-studied question, namely the link between the EC nucleus and cell junctions in the periphery. The study has also made use of different model systems, i.e. genetically modified mice, zebrafish, and cultured endothelial cells, which confirms certain findings and utilizes the specific advantages of each model system. A weakness is that some important controls are missing. In addition, the evidence for the proposed molecular mechanism should be strengthened.

We thank the reviewer for their interest in our work and for highlighting the relative lack of information regarding connections between the EC nucleus and cell periphery, and for noting our use of multiple model systems. We thank the reviewer for suggesting additional controls and mechanistic support, and we have made the revisions described below.

Specific comments:

1) Data showing the efficiency of Sun1 inactivation in the murine endothelial cells is lacking. It would be best to see what is happening on the protein level, but it would already help a great deal if the authors could show a reduction of the transcript in sorted ECs. The excision of a DNA fragment shown in the lung (Fig. 1-suppl. 1C) is not quantitative at all. In addition, the gel has been run way too short so it is impossible to even estimate the size of the DNA fragment.

We agree that the DNA excision is not sufficient to demonstrate excision efficiency. We attempted examination of SUN1 protein levels in mutant retinas via immunofluorescence, but to date we have not found a SUN1 antibody that works in mouse retinal explants. We argue that mouse EC isolation protocols enrich but don’t give 100% purity, so that RNA analysis of lung tissue also has caveats. Finally, we contend that our demonstration of a consistent vascular phenotype in Sun1iECKO mutant retinas argues that excision has occurred. To test the efficiency of our excision protocol, we bred Cdh5CreERT2 mice with the ROSAmT/mG excision reporter (cells express tdTomato absent Cre activity and express GFP upon Cre-mediated excision (Muzumdar et al., 2007). Utilizing the same excision protocol as used for the Sun1iECKO mice, we see a significantly high level of excision in retinal vessels only in the presence of Cdh5CreERT2 (Reviewer Figure 1).

Reviewer Figure 1: Cdh5CreERT2 efficiently excises in endothelial cells of the mouse postnatal retina. (A) Representative images of P7 mouse retinas with the indicated genotypes, stained for ERG (white, nucleus). tdTomato (magenta) is expressed in cells that have not undergone Cre-mediated excision, while GFP (green) is expressed in excised cells. Scale bar, 100μm. (B) Quantification of tdTomato fluorescence relative to GFP fluorescence as shown in A. tdTomato and GFP fluorescence of endothelial cells was measured by creating a mask of the ERG channel. n=3 mice per genotype. ***, p<0.001 by student’s two-tailed unpaired t-test.

2) The authors show an increase in vessel density in the periphery of the growing Sun1 mutant retinal vasculature. It would be important to add staining with a marker labelling EC nuclei (e.g. Erg) because higher vessel density might reflect changes in cell size/shape or number, which has also implications for the appearance of cell-cell junctions. More ECs crowded within a small area are likely to have more complicated junctions. Furthermore, it would be useful and straightforward to assess EC proliferation, which is mentioned later in the experiments with cultured ECs but has not been addressed in the in vivo part.

We concur that ERG staining is important to show any changes in nuclear shape or cell density in the post-natal retina. We now include this data in Figure1-figure supplement 1F-G. We do not see obvious changes in nuclear shape or number, though we do observe some crowding in Sun1iECKO retinas, consistent with increased density. However, when normalized to total vessel area, we do not observe a significant difference in the nuclear signal density in Sun1iECKO mutant retinas relative to controls.

3) It appears that the loss of Sun1/sun1b in mice and zebrafish is compatible with major aspects of vascular growth and leads to changes in filopodia dynamics and vascular permeability (during development) without severe and lasting disruption of the EC network. It would be helpful to know whether the loss-of-function mutants can ultimately form a normal vascular network in the retina and trunk, respectively. It might be sufficient to mention this in the text.

We thank the reviewer for pointing this out. It is true that developmental defects in the vasculature resulting from various genetic mutations are often resolved over time. We’ve made text changes to discuss viability of Sun1 global KO mice and lack of perduring effects in sun1 morphant fish, perhaps resulting from compensation by SUN2, which is partially functionally redundant with SUN1 in vivo (Lei et al., 2009; Zhang, et al., 2009) (p. 20).

4) The only readout after the rescue of the SUN1 knockdown by GEF-H1 depletion is the appearance of VE-cadherin+ junctions (Fig. 6G and H). This is insufficient evidence for a relatively strong conclusion. The authors should at least look at microtubules. They might also want to consider the activation status of RhoA as a good biochemical readout. It is argued that RhoA activity goes up (see Fig. 7C) but there is no data supporting this conclusion. It is also not clear whether "diffuse" GEF-H1 localization translates into increased Rho A activity, as is suggested by the Rho kinase inhibition experiment. GEF-H1 levels in the Western blot in (Fig. 6- supplement 2C) have not been quantitated.

We agree that analysis of RhoA activity and additional analysis of rescued junctions strengthens our conclusions, so we performed these experiments. New data (Figure 6IJ) shows that co-depletion of SUN1 and GEF-H1 rescues junction integrity as measured by biotin-matrix labeling. Interestingly, co-depletion of SUN1 and GEF-H1 does not rescue reduced microtubule density at the periphery (Figure 6-figure supplement 3BC), placing GEF-H1 downstream of aberrant microtubule dynamics in SUN1 depleted cells. This is consistent with our model (Figure 8) describing how loss of SUN1 leads to increased microtubule depolymerization, resulting in release and activation of GEF-H1 that goes on to affect actomyosin contractility and junction integrity. In addition, we include images of the junctions in GEF-H1 single KD (Figure 6-figure supplement 3BC) and quantify the western blot in Figure 6-figure supplement 3A.

We performed RhoA activity assays and new data shows that SUN1 depletion results in increased RhoA activation, while co-depletion of SUN1 and GEF-H1 ameliorates this increase (Figure 6-figure supplement 2D). This is consistent with our model in which loss of SUN1 leads to increased RhoA activity via release of GEF-H1 from microtubules. In addition, we now cite a recent study describing that GEF-H1 is activated when unbound to microtubules, with this activation resulting in increased RhoA activity (Azoitei et al., 2019).

5) The criticism raised for the GEF-H1 rescue also applies to the co-depletion of SUN1 and Nesprin-1. This mechanistic aspect is currently somewhat weak and should be strengthened. Again, Rho A activity might be a useful and quantitative biochemical readout.

We respectfully point out that we showed that co-depletion of nesprin-1 and SUN1 rescues SUN1 knockdown effects via several readouts, including rescue of junction morphology, biotin labeling, microtubule localization at the periphery, and GEFH1/microtubule localization. We’ve moved this data to the main figure (Figure 7B-C, E-F) to better highlight these mechanistic findings. These results are consistent with our model that nesprin-1 effects are upstream of GEF-H1 localization. We also added results showing that nesprin-1 knockdown alone does not affect junction integrity, microtubule density, or GEF-H1/microtubule localization (Figure 7-figure supplement 1B-G).

Reviewer #3 (Public Review):

Here, Buglak and coauthors describe the effect of Sun1 deficiency on endothelial junctions. Sun1 is a component of the LINC complex, connecting the inner nuclear membrane with the cytoskeleton. The authors show that in the absence of Sun1, the morphology of the endothelial adherens junction protein VE-cadherin is altered, indicative of increased internalization of VE-cadherin. The change in VE-cadherin dynamics correlates with decreased angiogenic sprouting as shown using in vivo and in vitro models. The study would benefit from a stricter presentation of the data and needs additional controls in certain analyses.

We thank the reviewer for their insightful comments, and in response we have performed the revisions described below.

1) The authors implicate the changes in VE-cadherin morphology to be of consequence for "barrier function" and mention barrier function frequently throughout the text, for example in the heading on page 12: "SUN1 stabilizes endothelial cell-cell junctions and regulates barrier function". The concept of "barrier" implies the ability of endothelial cells to restrict the passage of molecules and cells across the vessel wall. This is tested only marginally (Suppl Fig 1F) and these data are not quantified. Increased leakage of 10kDa dextran in a P6-7 Sun1-deficient retina as shown here probably reflects the increased immaturity of the Sun1-deficient retinal vasculature. From these data, the authors cannot state that Sun1 regulates the barrier or barrier function (unclear what exactly the authors refer to when they make a distinction between the barrier as such on the one hand and barrier function on the other). The authors can, if they do more experiments, state that loss of Sun1 leads to increased leakage in the early postnatal stages in the retina. However, if they wish to characterize the vascular barrier, there is a wide range of other tissue that should be tested, in the presence and absence of disease. Moreover, a regulatory role for Sun1 would imply that Sun1 normally, possibly through changes in its expression levels, would modulate the barrier properties to allow more or less leakage in different circumstances. However, no such data are shown. The authors would need to go through their paper and remove statements regarding the regulation of the barrier and barrier function since these are conclusions that lack foundation.

We thank the reviewer for pointing out that the language used regarding the function and integrity of the junctions is confusing, although we suggest that the endothelial cell properties measured by our assays are typically equated with “barrier function” in the literature. However, we have edited our language to precisely describe our results as suggested by the reviewer.

2) In Fig 6g, the authors show that "depletion of GEF-H1 in endothelial cells that were also depleted for SUN1 rescued the destabilized cell-cell junctions observed with SUN1 KD alone". However, it is quite clear that Sun1 depletion also affects cell shape and cell alignment and this is not rescued by GEF-H1 depletion (Fig 6g). This should be described and commented on. Moreover please show the effects of GEF-H1 alone.

We thank the reviewer for pointing out the effects on cell shape. SUN1 depletion typically leads to shape changes consistent with elevated contractility, but this is considered to be downstream of the effects quantified here. We updated the panel in Figure 6G to a more representative image showing cell shape rescue by co-depletion of SUN1 and GEF-H1. We present new data panels showing that GEF-H1 depletion alone does not affect junction integrity (Figure 6I-J). We also present new data showing that co-depletion of GEF-H1 and SUN1 does not rescue microtubule density at the periphery (Figure 6-figure supplement 3B-C), consistent with our model that GEF-H1 activation is downstream of microtubule perturbations induced by SUN1 loss.

3) In Fig. 6a, the authors show rescue of junction morphology in Sun1-depleted cells by deletion of Nesprin1. The effect of Nesprin1 KD alone is missing.

We thank the reviewer for this comment, and we now include new panels (Figure 7figure supplement 1B-G) demonstrating that Nesprin-1 depletion does not affect biotin-matrix labeling, peripheral microtubule density, or GEF-H1/microtubule localization absent co-depletion with SUN1. These findings are consistent with our model that Nesprin-1 loss does not affect cell junctions on its own because it is held in a non-functional complex with SUN1 that is not available in the absence of SUN1.

References

Azoitei, M. L., Noh, J., Marston, D. J., Roudot, P., Marshall, C. B., Daugird, T. A., Lisanza, S. L., Sandί, M., Ikura, M., Sondek, J., Rottapel, R., Hahn, K. M., Danuser, & Danuser, G. (2019). Spatiotemporal dynamics of GEF-H1 activation controlled by microtubule- and Src-mediated pathways. Journal of Cell Biology, 218(9), 3077-3097. https://doi.org/10.1083/jcb.201812073

Denis, K. B., Cabe, J. I., Danielsson, B. E., Tieu, K. V, Mayer, C. R., & Conway, D. E. (2021). The LINC complex is required for endothelial cell adhesion and adaptation to shear stress and cyclic stretch. Molecular Biology of the Cell, mbcE20110698. https://doi.org/10.1091/mbc.E20-11-0698

King, S. J., Nowak, K., Suryavanshi, N., Holt, I., Shanahan, C. M., & Ridley, A. J. (2014). Nesprin-1 and nesprin-2 regulate endothelial cell shape and migration. Cytoskeleton (Hoboken, N.J.), 71(7), 423–434. https://doi.org/10.1002/cm.21182

Lei, K., Zhang, X., Ding, X., Guo, X., Chen, M., Zhu, B., Xu, T., Zhuang, Y., Xu, R., & Han, M. (2009). SUN1 and SUN2 play critical but partially redundant roles in anchoring nuclei in skeletal muscle cells in mice. PNAS, 106(25), 10207–10212.

Muzumdar, M. D., Tasic, B., Miyamichi, K., Li, L., & Luo, L. (2007). A global doublefluorescent Cre reporter mouse. Genesis, 45(9), 593-605. https://doi.org/10.1002/dvg.20335

Ueda, N., Maekawa, M., Matsui, T. S., Deguchi, S., Takata, T., Katahira, J., Higashiyama, S., & Hieda, M. (2022). Inner Nuclear Membrane Protein, SUN1, is Required for Cytoskeletal Force Generation and Focal Adhesion Maturation. Frontiers in Cell and Developmental Biology, 10, 885859. https://doi.org/10.3389/fcell.2022.885859

Zhang, X., Lei, K., Yuan, X., Wu, X., Zhuang, Y., Xu, T., Xu, R., & Han, M. (2009). SUN1/2 and Syne/Nesprin-1/2 complexes connect centrosome to the nucleus during neurogenesis and neuronal migration in mice. Neuron, 64(2), 173–187. https://doi.org/10.1016/j.neuron.2009.08.018.

Author Response

Reviewer #1 (Public Review):

In mammals, a small subset of genes undergoes canonical genomic imprinting, with highly biased expression in function of parent of origin allele. Recent studies, using polymorphic mouse embryos and tissues, have reevaluating the number of allele-specific expressed genes (ASE) to 3 times more than previously thought, however with most of these novel genes showing a very low ASE (50%-60% bias toward one parental allele). Here, the authors undergo a comparison of 4 datasets and complete bioinformatic reanalysis of 3 recent allele specific RNAseq to study potential novel imprinted genes, using recently released iSoLDE pipeline. Very few genes have been confirmed with true ASE in the different studies and/or validated by pyrosequencing analysis, However, the authors show that most of the newly discovered ASE genes are lying in close proximity of already known imprinted loci and could be co-regulated by these imprinted clusters. This is important to understand how and to which extent imprinted control regions control gene expression.

This manuscript highlights the number of potential false discovered imprinted genes in previous datasets that could result to either lack of replicates, weak allelic ratio or low gene expression and lack of read depth. But the lack of overlap in the ASE called genes (at the exception to the known imprinted genes) between the different datasets is worrying and important to discuss, as the authors did. I would have appreciated more details into the differences between the different datasets that could explain the lack of reproducibility : library preparation protocol, sequencer technology, SNP calling, number of reads per SNP, bioinformatics pipeline.

We agree and a comparison of all the studies is included in the methods section. In particular, we have now included more information on SNP calling and sequencer technology.

Studying allele specific expression of lowly expressed genes is difficult by technology based on PCR amplification (library preparation, pyrosequencing) and could result on a bias expression only due to the random amplification of a small pool of molecules. Could the author compare the level of expression of their different classes of genes? The more robust ASE genes in their study could be the more highly expressed? Several genes were identified only in one or two of the previous studies, were they expressed in the other studies when not define as ASE? This would also allow defining a threshold of expression to study allelic bias in the future. To conclude, this study is an important resource for the epigenetic field and better understand genomic imprinting.

We thank-you for this suggestion. We have now taken all RNAseq data that we had run through the ISoLDE pipeline and extracted the transcripts per million (TPM) expression levels for each of the genes called in the original studies. We find no over representation of lowly expressed genes in the novel biased genes compared with known imprinted genes. We also looked specifically at the expression levels of the genes tested by pyrosequencing in these datasets and saw no relationship between validation and expression levels. Expression levels are consistent between studies, especially in the same tissue, indicating the lack of reproducibility between studies is not due to differing expression. These observations have been added to the manuscript.

Reviewer #2 (Public Review):

This work aims to understand genomic imprinting in the mouse and provide further insight to challenges and patterns identified in previous studies.

Firstly, genomic imprinting studies have been surrounded by controversy especially ~10 years ago when the explosion of sequencing data but immature methods to analyze it lead to highly exaggerated claims of widespread imprinting. While the methods have improved, clear standards are not set and results still have some inconsistencies between studies. The authors first do a meta-analysis of previous studies, comparing their results and doing a useful reanalysis of the data. This provides some valuable insights into the reasons for inconsistencies and guides towards better study designs. While this work does not exactly set a common standard for the field, or provide a full authoritative catalog of imprinted loci in mouse tissues, it provides a step in that direction. I find these analyses relatively simple and straightforward, but they seem solid.

Previous studies have described a relatively common pattern of subtle expression bias towards one parental allele, rather than the classical imprinting pattern of fully monoallelic expression. This work digs deeper into this phenomenon, using first the meta-analysis data and then also targeted pyrosequencing analysis of selected loci. The analysis is generally well done, although I did not understand why gDNA amplification bias was not systematically corrected in all cases but only if it was above a given (low) threshold. I doubt this would affect the results much though. To some extent the results confirm previously observed patterns (bimodal distribution of either subtle or full bias, and effect of distance from the core of the imprinted locus). The novel insights mostly concern individual loci, with discovery and validation of some novel genes, typically with a subtle or context-specific parental bias.

The study also provides some insights into mechanisms, especially by analysis of existing mouse models with a deletion of the ICR of specific loci. The change in the parental bias pattern was then used to infer potential methylation and chromatin-related mechanisms in these imprinted loci, including how the subtle bias further away is achieved. There are interesting novel findings here, as well as hypotheses for further research. However, this is an area where the conclusions rely quite heavily on published research especially as this study doesn't include single-cell resolution, and it's not entirely clear how much of e.g. the Figure 7 mechanisms part is based on discoveries of this study.

We agree that Figure 7 does not illustrate models based exclusively on data generated in this study: instead, it serves as hypotheses to be tested in the coming years

Imprinting is a fascinating phenomenon that can be informative of mechanisms of genome regulation and parental effects in general. It is a bit of a niche area though, and the target audience of this study is likely going to be limited to specialists doing research on this specific topic. As the authors point out, the functional importance of the findings is unknown.

Author Response

Reviewer #3 (Public Review):

In this manuscript, the authors studied the erythropoiesis and hematopoietic stem/progenitor cell (HSPC) phenotypes in a ribosome gene Rps12 mutant mouse model. They found that RpS12 is required for both steady and stress hematopoiesis. Mechanistically, RpS12+/- HSCs/MPPs exhibited increased cycling, loss of quiescence, protein translation rate, and apoptosis rates, which may be attributed to ERK and Akt/mTOR hyperactivation. Overall, this is a new mouse model that sheds light into our understanding of Rps gene function in murine hematopoiesis. The phenotypic and functional analysis of the mice are largely properly controlled, robust, and analyzed.

A major weakness of this work is its descriptive nature, without a clear mechanism that explains the phenotypes observed in RpS12+/- mice. It is possible that the counterintuitive activation of ERK/mTOR pathway and increased protein synthesis rate is a compensatory negative feedback. Direct mechanism of Rps12 loss could be studied by ths acute loss of Rps12, which is doable using their floxed mice. At the minimum, this can be done in mammalian hematopoietic cell lines.

We thank the reviewer for pointing this out. We have addressed this question by developing a new inducible conditional knockout Rps12 mouse model (see response below to major point 1).

Below are some specific concerns need to be addressed.

1) Line 226. The authors conclude that "Together, these results suggest that RpS12 plays an essential role in HSC function, including self-renewal and differentiation." The reviewer has three concerns regarding this conclusion and corresponding Figure3. 1) The data shows that RpS12+/- mice have decreased number of both total BM cells and multiple subpopulations of HSPCs. The frequency of HSPC subpopulations should also be shown to clarify if the decreased HSPC numbers arises from decreased total BM cellularity or proportionally decrease in frequency. 2) This figure characterizes phenotypic HSPC in BM by flow and lineage cells in PB by CBC. HSC function and differentiation are not really examined in this figure, except for the colony assay in Figure 3K. BMT data in Figure4 is actually for HSC function and differentiation. So the conclusion here should be rephrased. 3) Since all LT-, ST-HSCs, as well as all MPPs are decreased in number, how can the authors conclude that Rps12 is important for HSC differentiation? No experiments presented here were specifically designed to address HSC differentiation.

We thank the reviewer for this excellent point. We think that the main defect is in HSC and progenitor maintenance, rather than in HSC differentiation. This is consistent with the decrease in multiple HSC and progenitor populations, as observed both by calculating absolute numbers and by frequency of the parent population (see new Supplementary Figures S2C-S2C). We have removed any references to altered differentiation from the text.

We added data on the population frequency in the Supplementary Figure 2. And in the corresponding text. See lines 221-235.

2) Figure 3A and 5E. The flow cytometry gating of HSC/MPP is not well performed or presented, especially HSC plot. Populations are not well separated by phenotypic markers. This concerns the validity of the quantification data.

We chose a better representative HSC plot and included it in the Figure 3A

3) It is very difficult to read bone marrow cytospin images in Fig 6F without annotation of cell types shown in the figure. It appears that WT and +/- looked remarkably different in terms of cell size and cell types. This mouse may have other profound phenotypes that need detailed examination, such as lineage cells in the BM and spleen, and colony assays for different types of progenitors, etc.

The purpose of the bone marrow cytospin images in Figure 6F was to show the high number of apoptotic cells in the bone marrow of Rps12 KO/+ mice compared with controls. The differences in apoptosis in the LSK and myeloid progenitor populations are quantified in the flow cytometry data shown in Figure 6G-H. A detailed quantitative analysis of different bone marrow cell populations and their relative frequencies is also shown in Figures 2 and 3. In Rps12 KO/+ bone marrow, we observed a significant decrease in multiple stem cell and progenitor populations.

4) For all the intracellular phospho-flow shown in Fig7, both a negative control of a fluorescent 2nd antibody only and a positive stimulus should be included. It is very concerning that no significant changes of pAKT and pERK signaling (MFI) after SCF stimulation from the histogram in WT LSKs. There are no distinct peaks that indicate non-phospho-proteins and phosphoproteins. This casts doubt on the validity of results. It is possible though that Rsp12+/- have very high basal level of activation of pAKT/mTOR and pERK pathway. This again may point to a negative feedback mechanism of Rps12 haploinsufficiency.

It is true that we did not observe an increase in pAKT, p4EBP1, or pERK in control cells in every case. This is often an issue with these specific phospho-flow cytometry antibodies, as they are not very sensitive, and the response to SCF is very time-dependent. We did observe an increase in pS6 with SCF in both LSK cells and progenitors (Figure 7B, E). However, the main point of this experiment was to assess the basal level of signaling in Rps12 KO/+ vs control cells. We did not observe hypersensitivity of RpS12 cells to SCF, but we did observe significant increases in pAKT, pS6, p4EBP1, and pERK in Rsp12 KO/+ LSK cells.

To address the concern about the validity of staining, please see the requested flow histograms for unstained vs individual Phospho-antibodies (Ab): p4EBP1, pERK, pS6 and pAKT (Figure R1 for reviewers) below. Additionally, since staining with the surface antibodies potentially can change the peak, we are including additional an control of the cell surface antibodies vs full sample with surface antibodies and Phospho-Ab: p4EBP1, pERK, pS6 and pAKT. We can include this figure in the Supplementary Data if requested.

5) The authors performed in vitro OP-Puro assay to assess the global protein translation in different HSPC subpopulations. 1) Can the authors provide more information about the incubation media, any cytokine or serum included? The incubation media with supplements may boost the overall translation status, although cells from WT and RpS12+/- are cultured side by side. Based on this, in vivo OP-Puro assay should be performed in both genotypes. 2) Polysome profiling assay should be performed in primary HSPCs, or at least in hematopoietic cell lines. It is plausible that RpS12 haploinsufficiency may affect the content of translational polysome fractions.

We are including these details in the methods section: for in vitro OP-Puro assay (lines 555565) cells were resuspended in DMEM (Corning 10-013-CV) media supplemented with 50 µM β-mercaptoethanol (Sigma) and 20 µM OPP (Thermo Scientific C10456). Cells were incubated for 45 minutes at 37°C and then washed with Ca2+ and Mg2+ free PBS. No additional cytokines were added.

We did not perform polysome profiles. Polysome profiling of mutant stem and progenitor cells would be very challenging, as their numbers are much reduced. We now deem this of reduced interest, given the conclusion of the revised manuscript that RpS12 haploinsufficiency reduces overall translation. Also, because in RpS12-floxed/+;SCL-CRE-ERT mouse model with acute deletion of RpS12 we observed the expected decrease in translation in HSCs using the same ex vivo OPP protocol, we did not follow up with in vivo OPP treatment,

Author Response:

Reviewer #1 (Public Review):

1) All feeding data presented in the manuscript are from the interactions of individual flies with a source of liquid food, where interaction is defined as 'physical contact of a specific duration.' It would be helpful to approach the measurement of feeding from multiple angles to form the notion of hedonic feeding since the debate around hedonic feeding in Drosophila has been ongoing for some time and remains controversial. One possibility would be to measure food intake volumetrically in addition to food interaction patterns and durations (e.g. via the modified CAFE assay used by Ja).

We acknowledge that our FLIC assays address only one dimension of feeding behavior, physical interaction with liquid food. However, there is clear evidence that interactions are strongly predictive of consumption, and it would be technically difficult to measure feeding durations at the resolution of milliseconds using a Café assay.  Nevertheless, we appreciate the spirit of this comment and agree that expanding our inference to other measures of feeding, as well as feeding environments, is an important next step. To this end, we will include measures of feeding on more traditional solid food, using the ConEx assay, and find that flies in the hedonic environment consume twice as much sucrose volume compared to flies in the control environment. These will be added as supplemental data (Figure 1 – Figure Supplement 1A), and the text will be updated to reflect our findings.

2) Some of the statistical analyses were presented in a way that may make understanding the data unnecessarily difficult for readers. Examples include:

a) In Table I the authors present food interaction classifications based on direct observation. These are helpful. However, the classification system is updated or incompletely used as the manuscript progresses, most importantly changing from four categories with seven total subcategories to three categories and no subcategories. In subsequent data analyses, only one or two of these categories are assessed. It would be helpful, especially when moving from direct observation to automated categorization, to quantify the exact correspondences between all of the prior and new classifications, as well as elaborate on the types of data that are being excluded.

We appreciate the feedback on our usage of the behavioral classification system and will make several adjustments to improve it. We will rename some of the behaviors to make them more intuitive (see Reviewer #2, comment #1), and update the main text and Table 1 to reflect these changes. We will update the text and figures to be more transparent about when we group subcategories into main categories for quantification and when we quantify all subcategories separately. Because these videos required manual scoring by an experimenter, after our initial characterizations we opted to score only main categories (which contain subcategories). We agree that it would be useful to quantify correspondence between subcategories and the automated FLIC signal. However, we believe this task is better suited for more advanced and automated video tracking software, and, incidentally, more sophisticated analysis of FLIC data, which has a very high-dimensional character that has yet to be properly exploited. At the moment, therefore, we are not confident in the ability to understand the data at the desired resolution.

b) The authors switch between a variety of biological and physiological conditions with varying assays, which makes following the train of reasoning nearly impossible to follow. For example, the authors introduce us to circadian aspects of feeding behavior to introduce the concept of 'meal' and 'non-meal' periods of the day. It is then not clear in which of the subsequent experiments this paradigm is used to measure food interactions. Is it the majority of the subsequent figure panels? However, the authors also use starved flies for some assays, which would be incompatible with circadian-locked meals. The somewhat random and incompletely reported use of males and females, which the authors show behave differently, also makes the results more difficult to parse. Finally, the authors are comparing within-fly for the 'control environment' and between flies for their 'hedonic environment' (Figure 3A and subsequent panels), which I believe is not a good thing to do.

We apologize for our difficulties conveying our inference, which was also noted by Reviewer #2.  We will work hard to improve this component in the revision. With respect to the confusion about circadian feeding, we introduced circadian meal-times to complement starvation as a second (perhaps more natural) way to measure behaviors associated with hunger. Importantly, we do not use circadian meal-times beyond Figure 1; all subsequent FLIC experiments were conducted during non-meal times of day for 6 hours, which avoids confounding our data with circadian-locked meals even when we use starved flies. We will clarify this point in the revision.

The reviewer also points out that we make both within-fly and between-fly comparisons, which is a point that we note. Perhaps some concern arises, again, from the challenges that we faced in properly delineating our inferences about different types of feeding measures (and motivations). Inference about homeostatic feeding was made using within-fly measures, comparing events on sucrose vs. those on yeast. Inference about hedonic feeding was made using between fly measures (average durations of different flies on 2% vs. 20% sucrose). Treatment comparisons to control always used measures of the same type, such that inference was not made using between-fly measures for treatment and within-fly for control (i.e., all of our figure panels were either within-fly or between fly). We will clarify this in the revision.

Importantly, our approach to all experiments avoided confounding by used randomized design at multiple levels (e.g., randomizing control and hedonic environments to FLIC DFMs, alternating food choice sidedness in the DFMs), by ensuring that flies in both environments are sibling flies that came from the same vial environment before being tested, and by performing each experiment multiple times.

c) Statistical analyses are not always used consistently. For example, in Figures 3B and C, post hoc test results are shown for sucrose vs. yeast interactions, but no such statistics are given for 3E and 3F, preventing readers from assessing if the assay design is measuring what the authors tell us it is measuring.

We report p-values for two-way ANOVA interaction terms for all appropriate experiments. If (and only if) the interaction term is significant, we conduct post-hoc tests for more detailed statistical analysis and report the p-values. The reviewer points out that we do not perform post-hoc tests in figures 3E and 3F. These figures had a non-significant interaction term, and thus, we did not feel a post-hoc test was warranted.

Reviewer #2 (Public Review):

1) The dissection of feeding into distinct behavioral elements and its correlation with electrical FLIC signals that allow interpreting feeding types is a fundamental new method to dissect feeding in flies. However, the categories of micro-behaviors in Table 1 are not intuitive.

We agree and will update the Table, figures, and main text. Please see also our response to Reviewer #1, comment #1.

2) The details for the behavioral data analysis are not clear and should be made more obvious. For example, how many males and females were used in each experiment? Were any of the females mated or were they all virgins? If all virgins, why not use mated females? Mating status may have an effect on the feeding drive. If mated and virgin females were used, are there any differences between them? Similarly, for diurnal feeding experiments, it is not immediately clear from the graphs how many animals were used and how the frequencies were obtained (Fig. 1F, presumably averages for each category per fly but that is inconsistent with the legend in the supplement for this figure). Why does the transition heat map not include all micro-behaviors (Fig. 1E, no LQ data which are significant in diurnal feeding)?

We will clarify the number of flies and events for each behavioral experiment in Figure 1, and we will update the figure legend appropriately. We note that these behavioral datasets are non-overlapping, and each time we mention the number of events scored in the text, that number includes only “new” videos. Female and male flies for all experiments were mated, and we will clarify this in the main text and methods.

For the diurnal experiment in Figure 1F, we scored over 700 events from new (non-overlapping) video compilations and updated the number of flies and event number in the figure legend. The diurnal data we present in the supplement for this figure is a separate experiment conducted on 38 flies, intended only to demonstrate the circadian nature of fly feeding.

For the transition heat map, analysis of this sort seems to require a large amount of data to have sufficient power to return a transition matrix. LQ events are relatively low in frequency, so we opted to combine them with L events for this analysis. We have updated the figure and figure legend to reflect this.

3) The CaMPARI images do not look great, particularly in the pan-neuronal condition (Fig. 5A). It would be useful to include the movie of the stack. Did any other brain regions show activity differences, such as SEZ or PI? These regions are known to be involved in feeding so it seems surprising they show no effect.

We find that CaMPARI imaging is subject to high levels of noise and background, especially when using a broad driver as the reviewer has pointed out. This is why we opted to follow-up our pan-neuronal CaMPARI experiment using a more specific mushroom body driver and to test our correlational findings of increased MB activity in hedonic environments with genetic approaches in the remainder of Figure 5. We will include movies of the confocal stacks for both CaMPARI experiments, as requested.

Author Response

Reviewer #1 (Public Review):

This paper describes the accrual of RSV mutations in a severely immunocompromised child with persistent infection and demonstrates that ribavirin increases the observed mutation rate with base pair changes (C to U and G to A) compatible with its known mechanism. The paper utilizes a mathematical model to explain the counterintuitive finding that viral load does not decrease despite loss of viral fitness and clinical improvement. Positive selection is observed but does not keep pace with deleterious mutations induced by ribavirin. Overall, though the data is restricted and limited to a single person, the analysis is rigorous and supports the paper's interesting conclusions.

The paper is fascinating, but its generalizability is somewhat limited by the single study participant. Nevertheless, comparisons of therapy-induced deleterious mutations versus adaptive mutations over time is potentially important for multiple viruses.

We thank the reviewer for their comments. Although we acknowledge that this is only a single case of infection, we believe that it is an interesting case, and we are keen to share our findings with the broader scientific community.

Reviewer #2 (Public Review):

In this work, Illingworth et al. investigate the effectiveness of ribavirin and favipiravir on the treatment of a paediatric patient with chronic RSV. These drugs cause mutations and the authors tested whether they could observe this effect through deep sequencing viruses from nasal aspirates over the course of treatment. They found an increase in mutations caused by ribavirin but favipiravir appeared to have no additional mutagenic effect. Despite the lack of change in viral load, the authors suggest that the ribavirin reduced viral fitness and did not lead to adaptive escape mutations. The authors modelled how generation time and fitness interacted with mutational load. They also estimated fitness for different haplotypes generated from the mutational data.

Strengths of the paper:

Using mutagenic drugs to treat viruses is generally accepted but results have been mixed with severe viral infections and specific evidence of the precise effects of the drugs is often lacking. This paper is especially valuable for demonstrating that despite in vitro evidence that favipiravir had some effect against RSV, there was no evidence for favipiravir having an effect in a patient. This differs from the authors previous work showing a clear clinical benefit to favipiravir in treating influenza. This paper also appears to be the first to sequence RSV from a patient having been exposed to ribavirin which is important for demonstrating that the drug is having a measurable effect.

Weaknesses in the paper:

I think there is a conceptual problem with the paper. Ribavirin is supposed to increase the mutational rate of the virus which would increase the mutational load. Mutational load has been calculated by summing up the frequencies of minor alleles. However, if a particular mutation rises in frequency, it does not mean that ribavirin has caused additional mutations at the same site but rather viruses containing the mutation have risen in frequency. If a subpopulation containing mutations rises through drift or selection to a relatively high percentage that will bias the mutational load. The authors provide ~75 mutations which were at significant percentages across multiple different timepoints. It seems that these mutations contribute significantly to the mutational load but changes in mutation percentages between samples do not reflect changes in mutational events but changes in viral haplotypes/subpopulations. In a previous study Lumby et al. 2020, the authors removed mutations at >5% from their analysis but there is no indication that they performed this step similarly here. Summing many small changes will give an indication of background mutational rate (though counting only a single mutation at each locus is perhaps the only method to remove the effect of viral clonal expansion).

The mutational load is defined as the mean number of mutations per virus with respect to the consensus, equal to the sum of minor allele frequencies across the genome. We filter variant frequencies prior to calculating mutational load to compensate for noise arising from genome sequencing.

We use a deterministic model of mutation-selection balance to describe the overall dynamics of mutational load, but are conscious that the dynamics of individual variants are complex. Genetic drift could contribute to these dynamics, as might hidden structure in the viral population, with stochastic observations of viruses from distinct subpopulations. As we make clear, our key assumption regarding mutational load is that all variants from the consensus are at least mildly deleterious; under this assumption calculating the sum of allele frequencies is an appropriate measurement of mutational load. Our model accounts for the possible presence of variants under stronger and weaker selection being observed at lower and higher frequencies respectively.

We note that, in a case where distinct variants occurred in subpopulations, these variants would be observed in a mixture at lower frequencies than they existed in the subpopulations. This would lead to the observation of more variants overall, with each variant being at a reduced frequency. While stochastic effects would alter the frequencies of mutations in individual samples, if mutational load acted equally on each subpopulation, the total mutational load would be preserved across samples. The existence of subpopulations would not of itself invalidate the calculation of mutational load as we have performed it.

Our previous study Lumby et al, 2020 considered a case where favipiravir was given for a short period of time in a case of influenza B infection. In that case we did not make an assessment of the total mutational load in a population, although we did remove mutations at >5% when considering the spectrum of mutations i.e. the proportion of mutations of each type C to T, G to A, etc. We are still working on different approaches to measuring mutational load, but we are not convinced that removing high frequency mutations is always a good idea when evaluating the total mutational load. Cutting out higher frequencies is potentially a useful means to study mutational spectra under viral mutagenesis, but in a measurement of mutational load it could exclude deleterious mutations.

While ribavirin appears to have shown an effect, many questions remain. Why does the mutational load only increase for 3 points before plateauing? The authors would likely argue that this is the new saturation point for mutation load but they don't test it. Sequencing points from after the cessation of treatment would be expected to show lower mutational load but this data was not collected. Furthermore, questions remain over the methodology. It is thought that Ribavirin should only increase transitions and a transition/transversion ratio for the different samples would have been helpful. The absolute numbers of many mutation classes appear to have increased including transversions e.g AU. There isn't a good reason why nucleoside analogues should have caused this effect and perhaps it is an artefact.

Ribavirin has been shown to increase C to T and G to A mutations; these are both transitions, but T to C and A to G mutations are also transitions; the proportion of these was found to decrease under treatment. We have included a new figure showing Ts/Tv ratios but we do not find a significant pattern of change in these statistics over time.

The plateauing of the observed mutational load is consistent with the theory of mutationselection balance. Following a change in the mutation rate we would expect a shift to a new equilibrium U/s.

Sequencing was conducted as part of an investigation that was secondary to treatment of the patient: All of the samples that were collected were sequenced. We agree that upon the cessation of mutagenic drugs we would expect to see a fall in mutational load.

I don't think that the authors can reasonably determine how many haplotypes there are in the population from short read sequencing data. I think that the sequencing data very clearly shows subpopulations due to the large changes in mutation frequencies between different time points. The authors say that their analysis assumes a well-mixed population which is clearly not the case. Therefore, determining fitness of different haplotypes or mutations is likely not accurate.

Although we have short read sequencing data, some of the reads we have span more than one locus, providing some information about linkage between variants. As noted in the Methods section our inference approach provides a minimal reconstruction of haplotypes: Our reconstruction details the smallest set of distinct haplotypes necessary to explain the data.

Where we use a haplotype-based model to reconstruct the within-host evolution of the population, we neglect the potential presence of subpopulations by assuming a well-mixed population, then fully discuss the implications of this assumption for our result.

Our basic question is whether within-host adaptation leads to a gain in viral fitness in excess of the loss of fitness imposed by an increase in mutational load. In this comparison we make a conservative (i.e. low) estimate for the extent of the loss of fitness through mutational load.

When we look at within-host evolution our assumption of a well-mixed population attributes all of the systematic change in the viral population to the effects of selection. If some of this change arises through stochastic differences in emissions from a structured population, the influence of selection would be less than our inference. Thus, our estimate of the gain in fitness through within-host adaptation is a high estimate. As our high estimate of within-host fitness gain is less than a low estimate of the fitness lost through mutational load, our result is robust to our assumption.

The authors construct a model to estimate viral fitness and suggest that viral fitness decreased with the drug. This is somewhat problematic to me as viral load has not changed so it would be reasonable to say that viral fitness was likely unaffected by the drug. The authors define fitness in terms of the number of mutations that each virus likely has and assumes that these mutations are deleterious. The authors then use this to claim that mutagenic drugs reduce fitness. This seems very circular to me. If the drugs reduce fitness, it should be observed as a property of the virus population. As the only measure was viral load, which didn't change, it is difficult to claim ribavirin reduced viral fitness. There are other reasons why there could be an increase in the number of mutations e.g. sequencing more subpopulations which would have nothing to do with fitness.

We have discussed our assumption that variants in the viral population are deleterious; this lies behind the use of a model of mutation-selection balance. Under this assumption, the accumulation of a greater number of mutations following ribavirin treatment is indicative of a loss of viral fitness, although we cannot precisely quantify the magnitude of this loss. The link between an increased mutation rate and lower viral fitness is intrinsic to the concept of mutagenic drugs; our data show an increase in mutational load coincident with the therapeutic use of ribavirin.

A change in viral fitness does not necessarily lead to a substantial and clearly observable drop in viral load; we say more about this in the response to comments below.

At various points, the paper assumes that there is no selection taking place but immunoglobulin was being applied weekly and palivizumab monthly. The timing of when these drugs were given should be included. How did the palivizumab affect selection? The K272E mutation seems to go up and down but it is not clear if this was in response to drug infusion timing or if this mutation was present in a subpopulation.

Our approach assumes that selection could act at two distinct levels: Firstly, we assume that the observed increase in mutational load correlates to a reduction in viral fitness; the link between viral fitness and mutational load is intrinsic to the equation of Haldane. Secondly we use a haplotype-based model to infer how selection is acting on the level of higherfrequency mutations; under the assumption of a well-mixed model we identify a signal of within-host adaptation.

We have added details of the timing of palivizumab treatment to Figure 1. Immunoglobulin was given throughout; details of treatment have been given in Supporting Data. As we have now clarified in the Methods, our identification of potentially selected alleles was a two stage process, with the first assessing the level of noise in the data. Our model of noise envisages nonuniformity arising from multiple sources, including a situation whereby the viral population was divided in subpopulations, and in which reads comprised stochastic samples from these subpopulations. Given our model for noise, the observation of the K272E mutation at generally higher frequencies in earlier samples and generally lower frequencies in later samples was sufficient to call this as a potentially selected variant. We did not explore more complex models of drug-dependent selection.

I think the main impact of the paper will be that favipiravir will not be used in the future to treat RSV. Given that the EC50 of favipiravir against RSC is ~100x that of influenza, favipiravir was unlikely to reach a therapeutic level in the patient. Nucleoside analogues have a mixed record at treating serious viral infections. Hopefully, this work will spur on future studies to precisely measure the effect that ribavirin has on RSV.

Favipiravir was used in this patient following its successful experimental use against a case of influenza B infection (Lumby et al., 2020). We would be happy if our work inspires future research in this area.

Author Response

Reviewer #1 (Public Review):

This manuscript explores how biliary epithelial cells respond to excess dietary lipids, an important area of research given the increasing prevalence of NAFLD. The authors utilize in vivo models complemented with cultured organoid systems. Interesting, E2F transcription factors appear important for BEC glycolytic activation and proliferation.

We thank this reviewer for his/her comments and for finding the E2F-mediated mechanism of interest.

Much of the work utilizes the BEC-organoid model, which is complicated by the fact that liver cell organoid models often fail to maintain exclusive cell identity in culture. The method used by the authors (Broutier et al., 2016) can lead to organoids with a mixture of ductal and hepatocyte markers. It would be helpful for the authors to further demonstrate the cholangiocyte identity of the organoid cells.

We understand the concern of this reviewer. Indeed, this method can give rise to biliary cells or more hepatocyte-like cells. However, this choice depends on the culture media used. Our experiments used BEC-organoids in an undifferentiated state with a biliary expression profile. Please see point 1 above for a detailed answer.

The authors suggest that BECs form lipid droplets in vivo by detecting BODIPY immunofluorescence of liver cryosections. While confocal microscopy would ensure that the BODIPY fluorescence signal is within the same plane as the cell of interest, the authors use a virtual slide microscope that cannot exclude fluorescence from a different focal plane. The conclusion that BECs accumulate lipids does not seem to be fully supported by this analysis.

We fully agree with this criticism. To address this concern, we decided to use FACS analysis, a quantitative and independent method, to further confirm our initial findings. To this end, we stained sorted EPCAM+ BECs isolated from livers of CD- or HFD-fed mice with BODIPY, quantified the number of BODIPY+/EPCAM+ BECs in each experimental condition, and confirmed that these cells accumulate more lipids after HFD feeding (New Figure 1I, page 5, lines 112-115, and see also reply rebuttal to point 4).

Several mouse experiments rely heavily on rare BEC proliferation events with the median proliferation event per bile duct being 0-1 cell. While the proliferative effect appears consistent across experiments, a more quantitative approach, such as performing Epcam+ BEC FACS and flow cytometry-based cell cycle analyses, would be helpful.

Following this suggestion, we quantified proliferative EdU+ BEC cells by FACS in a new cohort of C57BL/6J mice fed CD or HFD. These data, now included in the revised manuscript (New Figure 2G, page 7, lines 143-147), strongly confirm that immunofluorescence quantification mirrors the FACS quantification and reinforce the initial finding that EPCAM+ BECs proliferate more in the livers of HFD-fed mice. Please see point 6 above for a detailed answer.

Finally, it is not yet clear how relevant the findings in this study are to ductular reaction, which is a non-specific histopathologic indicator of liver injury in the context of severe liver disease. In NAFLD, the ductular reaction is uncommon in benign steatosis, and if seen at all, occurs in the setting of substantial liver inflammation and fibrosis (Gadd et al., Hepatology 2014). The authors use a dietary model containing 60 kcal% fat, which causes adipose lipid accumulation as well as subsequent liver lipid accumulation. This diet does not cause overt inflammation or fibrosis that would represent experimental NASH, which typically requires the addition of cholesterol in dietary lipid NASH models (Farrell et al., Hepatology, 2019). While the E2F-driven proliferation may be important for physiologic bile duct function in the setting of obesity, the claim that E2Fs mediate DR initiation would require an additional pathophysiologic model or human data to demonstrate relevance. The authors could clarify this point in their discussion.

We agree with this reviewer that 15 weeks of HFD on C57BL/6J feeding are insufficient to trigger a ductular reaction. For this purpose, we used the term “BEC activation” in our manuscript, which refers to the first mandatory step for the ductular reaction to initiate. We apologize if our initial manuscript did not sufficiently emphasize this point. However, as suggested by the reviewer we investigated the ductular reaction in our model. In order to further characterize the livers after 15 weeks of CD or HFD feeding, we stained the bile ducts for pancytokeratin (PANCK) and osteopontin (OPN) and asked a pathologist (Dr. Christine Gopfert at EPFL) to evaluate these sections with a particular focus on the bile ducts. She concluded that the livers of HFD-fed mice showed steatosis and inflammation but no apparent fibrosis (New Figure 1 – figure supplement 1E). The shape of bile ducts was similar in the livers of CD- and HFD-fed mice (New Figure 1 – figure supplement 1I), concomitant with the absence of portal fibrosis and inflammation. In addition, we checked the expression levels of several established markers of ductular reaction in our RNA sequencing data and observed that, of all these genes, only Ncam1 was significantly upregulated with HFD feeding in EPCAM+-BEC cells (New Figure 2 – figure supplements 1D and 1E, Page 6, lines 127-131). Overall, these data support our conclusion that HFD triggers BEC activation without signs of an established ductular reaction and might suggest Ncam1 as a marker for this initial BEC activation process. Please see point 3 above for a detailed answer.

Reviewer #2 (Public Review):

The manuscript by Yildiz et al investigates the early response of BECs to high fatty acid treatment. To achieve this, they employ organoids derived from primary isolated BECs and treat them with a FA mix followed by viability studies and analysis of selected lipid metabolism genes, which are upregulated indicating an adjustment to lipid overload. Both organoids with lipid overload and BECs in mice exposed to a HFD show increased BEC proliferation, indicating BEC activation as seen in DR. Applying bulk RNA-sequencing analysis to sorted BECs from HFD mice identified four E2F transcription factors and target genes as upregulated. Functional analysis of knock-out mice showed a clear requirement for E2F1 in mediating HFD induced BEC proliferation. Given the known function of E2Fs the authors performed cell respiration and transcriptome analysis of organoids challenged with FA treatment and found a shift of BECs towards a glycolytic metabolism. The study is overall well-constructed, including appropriate analysis. Likewise, the manuscript is written clearly and supported by high-quality figures.

We appreciate that this reviewer finds our study well-constructed, clear, and with high-quality figures.

My major point is the lack of classification of the progression of DR, since the authors investigate the early stages of DR associated with lipid overload reminiscent of stages preceding late NAFLD fibrosis. How are early stages distinguished from later stages in this study? Molecularly and/or morphologically? While the presented data are very suggestive, a more substantial description would support the findings and resulting claims.

We thank the reviewer for the suggestion. We would like to emphasize that instead of ductular reaction, we used the term “BEC activation” in our revised manuscript, referring to the first mandatory step for initiating the ductular reaction. Both reviewers criticized the poor characterization of the ductular reaction process in the first version of our study; we put substantial effort into further clarifying this point. Our response to this point can be read in our reply to the last comment of reviewer 1 and point 3 of the rebuttal.

Author Response

Reviewer #1 (Public Review):

It is now widely accepted that the age of the brain can differ from the person's chronological age and neuroimaging methods are ideally suited to analyze the brain age and associated biomarkers. Preclinical studies of rodent models with appropriate neuroimaging do attest that lifestyle-related prevention approaches may help to slow down brain aging and the potential of BrainAGE as a predictor of age-related health outcomes. However, there is a paucity of data on this in humans. It is in this context the present manuscript receives its due attention.

Comments:

1) Lifestyle intervention benefits need to be analyzed using robust biomarkers which should be profiled non-invasively in a clinical setting. There is increasing evidence of the role of telomere length in brain aging. Gampawar et al (2020) have proposed a hypothesis on the effect of telomeres on brain structure and function over the life span and named it as the "Telomere Brain Axis". In this context, if the authors could measure telomere length before and after lifestyle intervention, this will give a strong biomarker utility and value addition for the lifestyle modification benefits. 2) Authors should also consider measuring BDNF levels before and after lifestyle intervention.

Response to comments 1+2: we agree that associating both telomere length and BDNF level with brain age would be interesting and relevant. However, we did not measure these two variables. We would certainly consider adding these in future work. Regarding telomere length, we now include a short discussion of brain age in relation to other bodily ages, such as telomere length (Discussion section):

“Studying changes in functional brain aging is part of a broader field that examines changes in various biological ages, such as telomere length1, DNA methylation2, and arterial stiffness3. Evaluating changes in these bodily systems over time allows us to capture health and lifestyle-related factors that affect overall aging and may guide the development of targeted interventions to reduce age-related decline. For example, in the CENTRAL cohort, we recently reported that reducing body weight and intrahepatic fat following a lifestyle intervention was related to methylation age attenuation4. In the current work, we used RSFC for brain age estimation, which resulted in a MAE of ~8 years, which was larger than the intervention period. Nevertheless, we found that brain age attenuation was associated with changes in multiple health factors. The precision of an age prediction model based on RSFC is typically lower than a model based on structural brain imaging5. However, a higher model precision may result in a lower sensitivity to detect clinical effects6,7. Better tools for data harmonization among dataset6 and larger training sample size5 may improve the accuracy of such models in the future. We also suggest that examining the dynamics of multiple bodily ages and their interactions would enhance our understanding of the complex aging process8,9. “

And

“These findings complement the growing interest in bodily aging indicated, for example, by DNA methylation4 as health biomarkers and interventions that may affect them.”

Reviewer #2 (Public Review):

In this study, Levakov et al. investigated brain age based on resting-state functional connectivity (RSFC) in a group of obese participants following an 18-month lifestyle intervention. The study benefits from various sophisticated measurements of overall health, including body MRI and blood biomarkers. Although the data is leveraged from a solid randomized control set-up, the lack of control groups in the current study means that the results cannot be attributed to the lifestyle intervention with certainty. However, the study does show a relationship between general weight loss and RSFC-based brain age estimations over the course of the intervention. While this may represent an important contribution to the literature, the RSFC-based brain age prediction shows low model performance, making it difficult to interpret the validity of the derived estimates and the scale of change. The study would benefit from more rigorous analyses and a more critical discussion of findings. If incorporated, the study contributes to the growing field of literature indicating that weight-reduction in obese subjects may attenuate the detrimental effect of obesity on the brain.

The following points may be addressed to improve the study:

Brain age / model performance:

1) Figure 2: In the test set, the correlation between true and predicted age is 0.244. The fitted slope looks like it would be approximately 0.11 (55-50)/(80-35); change in y divided by change in x. This means that for a chronological age change of 12 months, the brain age changes by 0.11*12 = 1.3 months. I.e., due to the relatively poor model performance, an 80-year-old participant in the plot (fig 2) has a predicted age of ~55. Hence, although the age prediction step can generate a summary score for all the RSFC data, it can be difficult to interpret the meaning of these brain age estimates and the 'expected change' since the scale is in years.

2) In Figure 2 it could also help to add the x = y line to get a better overview of the prediction variance. The estimates are likely clustered around the mean/median age of the training dataset, and age is overestimated in younger subs and overestimated in older subs (usually referred to as "age bias"). It is important to inspect the data points here to understand what the estimates represent, i.e., is variation in RSFC potentially lost by wrapping the data in this summary measure, since the age prediction is not particularly accurate, and should age bias in the predictions be accounted for by adjusting the test data for the bias observed in the training data?

Response to comment 1+2: we agree with the reviewer that due to the relatively moderate correlation between the predicted and observed age, a large change in the observed age corresponds to a small change in the predicted age. We now state this limitation in Results section 2.1:

“Despite being significant and reproducible, we note that the correlations between the observed and predicted age were relatively moderate.”

And discuss this point in the Discussion section:

“In the current work, we used RSFC for brain age estimation, which resulted in a MAE of ~8 years, which was larger than the intervention period. Nevertheless, we found that brain age attenuation was associated with changes in multiple health factors. The precision of an age prediction model based on RSFC is typically lower than a model based on structural brain imaging5. However, a higher model precision may result in a lower sensitivity to detect clinical effects6,7. Better tools for data harmonization among dataset6 and larger training sample size5 may improve the accuracy of such models in the future.”

Moreover, , we now add the x=y line to Fig. 2, so the readers can better assess the prediction variance as suggested by the reviewer:

We prefer to avoid using different scales (year/month) in the x and y axes to avoid misleading the readers, but the list of observed and predicted ages are available as SI files with a precision of 2 decimals point (~3 days).

We note that despite the moderate precision accuracy, we replicated these results in three separate cohorts.

Regarding the effect of “age bias” (also known as “regression attenuation” or “regression dilution” 10), we are aware of this phenomenon and agree that it must be accounted for. In fact, the “age bias” is one of the reasons we chose to use the difference between the expected and observed ages as the primary outcome of the study, as this measure already takes this bias into account. To demonstrate this effect we now compute brain age attenuation in two ways: 1. As described and used in the current study (Methods 4.9); and 2. By regressing out the effect of age on the predicted brain age at both times separately, then subtracting the adjusted predicted age at T18 from the adjusted predicted age at T0. The second method is the standard method to account for age bias as described in a previous work 11. Below is a scatter plot of both measures across all participants:

The x-axis represents the first method, used in the current study, and the y-axis represents the second method, described in Smith et al., (2019). Across all subjects, we found a nearly perfect 1:1 correspondence between the two methods (r=.998, p<0.001; MAE=0.45), as the two are mathematically identical. The small gap between the two is because the brain age attenuation model also takes into account the difference in the exact time that passed between the two scans for each participant (mean=21.36m, std = 1.68m).

We now note this in Methods section 4.9:

“We note that the result of computing the difference between the bias-corrected brain age gap at both times was nearly identical to the brain age attenuation measure (r=.99, p<0.001; MAE=0.45). The difference between the two is because the brain age attenuation model takes into account the difference in the exact time that passed between the two scans for each participant (mean=21.36m, std = 1.68m).”

3) In Figure 3, some of the changes observed between time points are very large. For example, one subject with a chronological age of 62 shows a ten-year increase in brain age over 18 months. This change is twice as large as the full range of age variation in the brain age estimates (average brain age increases from 50 to 55 across the full chronological age span). This makes it difficult to interpret RSFC change in units of brain age. E.g., is it reasonable that a person's brain ages by ten years, either up or down, in 18 months? The colour scale goes from -12 years to 14 years, so some of the observed changes are 14 / 1.5 = 9 times larger than the actual time from baseline to follow-up.

• The questions above should be investigated and addressed in the context of potential challenges with using brain age as a marker (see e.g., https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.25837, https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.26144).

We agree that our model precision was relatively low, especially compared to the period of the intervention, as also stated by reviewer #1. We now discuss this issue in light of the studies pointed out by the reviewer (Discussion section):

“In the current work, we used RSFC for brain age estimation, which resulted in a MAE of ~8 years, which was larger than the intervention period. Nevertheless, we found that brain age attenuation was associated with changes in multiple health factors. The precision of an age prediction model based on RSFC is typically lower than a model based on structural brain imaging5. However, a higher model precision may result in a lower sensitivity to detect clinical effects6,7. Better tools for data harmonization among datasets6 and larger training sample size5 may improve the accuracy of such models in the future.”

Again, we note that despite the moderate precision accuracy, we replicated these results in three separate cohorts and found that both the correlation and the MAE between the predicted and observed age were significant in all of them.

RSFC for age prediction:

1) Several studies show better age prediction accuracy with structural MRI features compared to RSFC. If the focus of the study is to use an accurate estimate of brain ageing rather than specifically looking at changes in RSFC, adding structural MRI data could be helpful.

We focused on brain structural changes in a previous work, and the focus of the current work was assessing age-related functional connectivity alterations. We now added a few sentences in the Introduction section that would hopefully better motivate our choice:

“We previously found that weight loss, glycemic control, lowering of blood pressure, and increment in polyphenols-rich food were associated with an attenuation in brain atrophy 12. Obesity is also manifested in age-related changes in the brain’s functional organization as assessed with resting-state functional connectivity (RSFC). These changes are dynamic13 and can be observed in short time scales14 and thus of relevance when studying lifestyle intervention.”

2) If changes in RSFC are the main focus, using brain age adds a complicated layer that is not necessarily helpful. It could be easier to simply assess RSFC change from baseline to follow up, and correlate potential changes with changes in e.g., BMI.

We are specifically interested in age-related changes as we described a-priori in the registration of the study: https://clinicaltrials.gov/ct2/show/NCT03020186

Moreover, age-related changes in RSFC are complex, multivariate and dependent upon the choice of theoretical network measures. We think that a data-driven brain age prediction approach might better capture these multifaceted changes and their relation to aging. We now state this in the Introduction section:

“Studies have linked obesity with decreased connectivity within the default mode network15,16 and increased connectivity with the lateral orbitofrontal cortex17, which are also seen in normal aging18,19. Longitudinal trials have reported changes in these connectivity patterns following weight reduction20,21, indicating that they can be altered. However, findings regarding functional changes are less consistent than those related to anatomical changes due to the multiple measures22 and scales23 used to quantify RSFC. Hence, focusing on a single measure, the functional brain age, may better capture these complex, multivariant changes and their relation to aging. “

The lack of control groups

1) If no control group data is available, it is important to clarify this in the manuscript, and evaluate which conclusions can and cannot be drawn based on the data and study design.

We agree that this point should be made more clear, and we now state this in the limitation section of the Discussion:

“We also note that the lack of a no-intervention control group limits our ability to directly relate our findings to the intervention. Hence, we can only relate brain age attenuation to the observed changes in health biomarkers.”

Also, following reviewers’ #2 and #3 comments, we refer to the weight loss following 18 months of lifestyle intervention instead of to the intervention itself. This is now made clear in the title, abstract, and the main text.

Reviewer #3 (Public Review):

The authors report on an interesting study that addresses the effects of a physical and dietary intervention on accelerated/decelerated brain ageing in obese individuals. More specifically, the authors examined potential associations between reductions in Body-Mass-Index (BMI) and a decrease in relative brain-predicted age after an 18-months period in N = 102 individuals. Brain age models were based on resting-state functional connectivity data. In addition to change in BMI, the authors also tested for associations between change in relative brain age and change in waist circumference, six liver markers, three glycemic markers, four lipid markers, and four MRI fat deposition measures. Moreover, change in self-reported consumption of food, stratified by categories such as 'processed food' and 'sweets and beverages', was tested for an association with change in relative brain age. Their analysis revealed no evidence for a general reduction in relative brain age in the tested sample. However, changes in BMI, as well as changes in several liver, glycemic, lipid, and fat-deposition markers showed significant covariation with changes in relative brain age. Three markers remained significant after additionally controlling for BMI, indicating an incremental contribution of these markers to change in relative brain age. Further associations were found for variables of subjective food consumption. The authors conclude that lifestyle interventions may have beneficial effects on brain aging.

Overall, the writing is concise and straightforward, and the langue and style are appropriate. A strength of the study is the longitudinal design that allows for addressing individual accelerations or decelerations in brain aging. Research on biological aging parameters has often been limited to cross-sectional analyses so inferences about intra-individual variation have frequently been drawn from inter-individual variation. The presented study allows, in fact, investigating within-person differences. Moreover, I very much appreciate that the authors seek to publish their code and materials online, although the respective GitHub project page did not appear to be set to 'public' at the time (error 404). Another strength of the study is that brain age models have been trained and validated in external samples. One further strength of this study is that it is based on a registered trial, which allows for the evaluation of the aims and motivation of the investigators and provides further insights into the primary and secondary outcomes measures (see the clinical trial identification code).

One weakness of the study is that no comparison between the active control group and the two experimental groups has been carried out, which would have enabled causal inferences on the potential effects of different types of interventions on changes in relative brain age. In this regard, it should also be noted that all groups underwent a lifestyle intervention. Hence, from an experimenter's perspective, it is problematic to conclude that lifestyle interventions may modulate brain age, given the lack of a control group without lifestyle intervention. This issue is fueled by the study title, which suggests a strong focus on the effects of lifestyle intervention. Technically, however, this study rather constitutes an investigation of the effects of successful weight loss/body fat reduction on brain age among participants who have taken part in a lifestyle intervention. In keeping with this, the provided information on the main effect of time on brain age is scarce, essentially limited to a sign test comparing the proportions of participants with an increase vs. decrease in relative brain age. Interestingly, this analysis did not suggest that the proportion of participants who benefit from the intervention (regarding brain age) significantly exceeds the number of participants who do not benefit. So strictly speaking, the data rather indicates that it's not the lifestyle intervention per sé that contributes to changes in brain age, but successful weight loss/body fat reduction. In sum, I feel that the authors' claims on the effects of the intervention cannot be underscored very well given the lack of a control group without lifestyle intervention.

We agree that this point, also raised by reviewer #2, should be made clear, and we now state this in the limitation section of the Discussion:

“We also note that the lack of a no-intervention control group limits our ability to directly relate our findings to the intervention. Hence, we can only relate brain age attenuation to the observed changes in health biomarkers.”

Also, following reviewers #2 and #3, we refer to the weight loss following 18 months of lifestyle intervention instead of to the intervention itself. This is now explicitly mentioned in the title, abstract, and within the text:

Title: “The effect of weight loss following 18 months of lifestyle intervention on brain age assessed with resting-state functional connectivity”

Abstract: “…, we tested the effect of weight loss following 18 months of lifestyle intervention on predicted brain age, based on MRI-assessed resting-state functional connectivity (RSFC).”

Another major weakness is that no rationale is provided for why the authors use functional connectivity data instead of structural scans for their age estimation models. This gets even more evident in view of the relatively low prediction accuracies achieved in both the validation and test sets. My notion of the literature is that the vast majority of studies in this field implicate brain age models that were trained on structural MRI data, and these models have achieved way higher prediction accuracies. Along with the missing rationale, I feel that the low model performances require some more elaboration in the discussion section. To be clear, low prediction accuracies may be seen as a study result and, as such, they should not be considered as a quality criterion of the study. Nevertheless, the choice of functional MRI data and the relevance of the achieved model performances for subsequent association analysis needs to be addressed more thoroughly.

We agree that age estimation from structural compared to functional imaging yields a higher prediction accuracy. In a previous publication using the same dataset12, we demonstrated that weight loss was associated with an attenuation in brain atrophy, as we describe in the introduction:

“We previously found that weight loss, glycemic control and lowering of blood pressure, as well as increment in polyphenols rich food, were associated with an attenuation in brain atrophy 12.”

Here we were specifically interested in age-related functional alterations that are associated with successful weight reduction. Compared to structural brain changes aging effect on functional connectivity is more complex and multifaced. Hence, we decided to utilize a data-driven or prediction-driven approach for assessing age-related changes in functional connectivity by predicting participants’ functional brain age. We now describe this rationale in the introduction section:

“Studies have linked obesity with decreased connectivity within the default mode network15,16 and increased connectivity with the lateral orbitofrontal cortex17, which are also seen in normal aging18,19. Longitudinal trials have reported changes in these connectivity patterns following weight reduction20,21, indicating that they can be altered. However, findings regarding functional changes are less consistent than those related to anatomical changes due to the multiple measures22 and scales23 used to quantify RSFC. Hence, focusing on a single measure, the functional brain age, may better capture these complex changes and their relation to aging.”

We address the point regarding the low model performance in response to reviewer #2, comment #2.

Author Response

Reviewer #1 (Public Review):

IRF8 is a key transcription factor in the differentiation of hematopoietic cell lineages including dendritic cell (DC) and monocyte/macrophage lineages. The promoter and enhancer regions of Irf8 have been a focus of intense research in recent times. In the submitted study Xu H. et. Al., have first time reported a lncRNA transcribed specifically in the pDC subtype from +32Kb which is also the region for the enhancer for Irf8 specifically in the cDC1 subtype. Authors have employed modern-day tools for an in-depth understanding of the role of lncIrf8, its promoter region, and crosstalk with Irf8 promoter to identify that it is not the lncIRF8 itself but its promoter region is crucial for pDC and cDC1 differentiation conferring feedback inhibition of Irf8 transcription. In the attempt to decipher the crosstalk between the promoter regions of IRF8 and lncIRF8 by employing various in vitro artificial systems, the study falls short of identifying the real significance of the lncIRF8 which is specifically expressed in pDC subtype.

We appreciate the public review made by the reviewer. We agree with the reviewer that most of the experiments on the identification of the negative feedback regulation of IRF8 via the lncIRF8 promoter element were carried out in vitro. But we would like to point out also our in vivo work: (i) transplantation lncIRF8 promoter KO cells into mice demonstrates that pDC and cDC1 development were compromised (Figure 3); (ii) lncIRF8 is expressed in in vivo BM and spleen pDC (new Figure 1-figure supplement 3). We also would like to emphasize that (i) in vivo studies on the identification of the negative feedback regulation of IRF8 via the lncIRF8 promoter element and (ii) mechanistic studies with CRISPR activation and CRISPR interference would have been difficult to perform in vivo with current tools available in mice.

According to our current understand lncIRF8 act as an indicator of +32 kb enhancer activity and we agree with the reviewer that further potential functions of lncIRF8 still need to be explored. We added a sentence on page 13, lines 427 and 428 on potential additional functions of lncIRF8:

"However, lncIRF8 might have additional functions in DC biology, which are not revealed in the current study and remain to be identified."

Reviewer #2 (Public Review):

The manuscript of Xu and colleagues examines in detail the regulation of the important transcription factor IRF8 in dendritic cell (DC) subsets. They identify a long noncoding RNA arises from the +32kb enhancer of IRF8 specifically in plasmacytoid DCs (pDCs)and show clearly that this lncIRF8 marks the activity of a region of this enhancer but the RNA itself does not appear to have any function. Deletion of the promoter of the lncIRF8 ablated cDC1 and pDC differentiation using an in vitro cell differentiation model. The authors propose an innovative model that the lncIRF8 promoter sequences act to limit IRF8 expression in cDC1, but are inactive in pDCs, resulting in their characteristically very high IRF8 expression.

This is a conceptually interesting study that makes excellent use of an extensive set of genomic data for the DC subsets. There has been a lot of recent research investigating the regulation of the IRF8 gene in hematopoiesis and this study provides an important new aspect to the work. The use of an in vitro model of DC differentiation is a powerful practical approach to investigating IRF8 regulation, as is the innovative use of CRISPR technology. Perhaps the biggest limitation of this study is that the authors have not conformed to the in-cell system data by creating a mouse strain lacking the lncIRF8 element. Such approaches by others, most notably the Murphy lab, have been instrumental in pushing this field forward. Nevertheless, Xu et al. significantly add to our current knowledge of the regulation of IRF8, a critical step in forming the dendritic cell network.

We appreciate the public review made by the reviewer and the positive assessment of our work. We agree with the review that extending our in-cell system data to lncIRF8 promoter KO mice will further strengthen our data and this will be subject of our future work.

Author Response

Reviewer #1 (Public Review):

Using health insurance claims data (from 8M subjects), a retrospective propensity score matched cohort study was performed (450K in both groups) to quantify associations between bisphosphonate (BP) use and COVID- 19 related outcomes (COVID-19 diagnosis, testing and COVID-19 hospitalization. The observation periods were 1-1-2019 till 2-29-2020 for BP use and from 3-1-2020 and 6-30-2020 for the COVID endpoints. In primary and sensitivity analyses BP use was consistently associated with lower odds for COVID-19, testing and COVID-19 hospitalization.

The major strength of this study is the size of the study population, allowing a propensity-based matched- cohort study with 450K in both groups, with a sizeable number of COVID-19 related endpoints. Health insurance claims data were used with the intrinsic risk of some misclassification for exposure. In addition there probably is misclassification of endpoints as testing for COVID-19 was limited during the study period. Furthermore, the retrospective nature of the study includes the risk of residual confounding, which has been addressed - to some extent - by sensitivity analyses.

In all analyses there is a consistent finding that BP exposure is associated with reduced odds for COVID-19 related outcomes. The effect size is large, with high precision.

The authors extensively discuss the (many) potential limitations inherent to the study design and conclude that these findings warrant confirmation, preferably in intervention studies. If confirmed BP use could be a powerful adjunct in the prevention of infection and hospitalization due to COVID-19.

We thank the reviewer for this overall very positive feedback. We appreciate the reviewer's comments regarding the potential risks associated with misclassification of exposure and other potential limitations, which we have sought to address in a number of sensitivity analyses and are also addressing in the discussion of our paper. In addition, as noted by the reviewer, the observed effect size of BP use on COVID-19 related outcomes is large, with high precision, which we feel is a strong argument to explore this class of drugs in further prospective studies.

Reviewer #2 (Public Review):

The authors performed a retrospective cohort study using claims data to assess the causal relationship between bisphosphonate (BP) use and COVID-19 outcomes. They used propensity score matching to adjust for measured confounders. This is an interesting study and the authors performed several sensitivity analyses to assess the robustness of their findings. The authors are properly cautious in the interpretation of their results and justly call for randomized controlled trials to confirm a causal relationship. However, there are some methodological limitations that are not properly addressed yet.

Strengths of the paper include:

(A) Availability of a large dataset.

(B) Using propensity score matching to adjust for confounding.

(C) Sensitivity analyses to challenge key assumptions (although not all of them add value in my opinion, see specific comments)

(D) Cautious interpretation of results, the authors are aware of the limitations of the study design.

Limitation of the paper are:

(A) This is an observational study using register data. Therefore, the study is prone to residual confounding and information bias. The authors are well aware of that.

(B) The authors adjusted for Carlson comorbidity index whereas they had individual comorbidity data available and a dataset large enough to adjust for each comorbidity separately.

(C) The primary analysis violates the positivity assumption (a substantial part of the population had no indication for bisphosphonates; see specific comments). I feel that one of the sensitivity analyses 1 or 2 would be more suited for a primary analysis.

(D) Some of the other sensitivity analyses have underlying assumptions that are not discussed and do not necessarily hold (see specific comments).

In its current form the limitations hinder a good interpretation of the results and, therefore, in my opinion do not support the conclusion of the paper.

The finding of a substantial risk reduction of (severe) COVID-19 in bisphosphonate users compared to non- users in this observational study may be of interest to other researchers considering to set up randomized controlled trials for evaluation of repurpose drugs for prevention of (severe) COVID-19.

We thank the reviewer for the insightful comments and questions related to our manuscript. Our response to the concerns regarding limitations of our study is as follows:

(A) We agree that there is likely residual confounding and information bias due to use of US health insurance claims datasets which do not include information on certain potentially relevant variables. Nonetheless, given the large effect size and precision of our analysis, we feel that our findings support our main conclusion that additional prospective trials appear warranted to further explore whether BPs might confer a meaure of protection against severe respiratory infections, including COVID-19. We have added a sentence on the second page of our Discussion (line 859-860) to emphasize this point: "Specifically, there is the potential that key patient characteristics impacting outcomes could not be derived from claims data."

(B) The progression of this study mirrors the real-world performance of the analysis where we initially used the CCI in matching to control for comorbidity burden on a broader scale. This was our a priori approach. After observing large effect sizes, we performed more stringent matching for sensitivity analyses 1 and 2. Irrespective of the matching strategy chosen, effect sizes remained similar for all outcome parameters. Therefore, we elected to include both the primary analysis and the sensitivity analyses with more stringent matching in order to more transparently show what was done in entirety during our analyses, as we feel it displays all of the efforts taken to identify sources of unmeasured confounding which could have impacted our results.

(C) We agree that the positivity assumption is a key factor to consider when building comparable treatment cohorts. We also agree that it is the important to separately perform the analysis for either all patients with an indication for use of BPs and for other anti-osteoporosis medications, as we have done in our analysis of the Osteo-Dx-Rx cohort and Bone-Rx cohort, respectively. However, we did not have sufficient data, a priori, to determine whether BP users would be more similar in their risk of COVID-19 outcomes to non- users or to other users of anti-resorptive medications. In addition, we believe that this specific limitation does not negate our findings in the primary analysis for the following reasons: (1) ‘Type of Outcome’: the outcomes in this study are related to infectious disease and are not direct clinical outcomes of any known treatment benefits of BPs. The clinical benefits being assessed - impact of BP use on COVID-19-related outcomes - were essentially unknown at the time of the study data; this fact mitigates the impact of any violation of the positivity assumption; and (2) ‘Clinical Population’: after propensity score matching, both the BP user and the BP non-user group in the primary analysis mainly consisted of older females (90.1% female, 97.2% age>50), which is the main population with clinical indications for BP use. According to NCHS Data Brief No. 93 (April 2012) released by the CDC, ~75% and 95% of US women between 60-69 and 70-79 suffer from either low bone mass or osteoporosis, respectively, and essentially all women (and 70% of men) above age 80 suffer from these conditions, which often go undiagnosed (https://www.cdc.gov/nchs/data/databriefs/db93.pdf). Women aged 60 and older make up ~75% of our study population (Table 1). Although bone density measurements are not available for non- BP users in the matched primary cohort, there is a high probability that the incidence of osteoporosis and/or low bone mass in these patients was similar to the national average. This justifies the assumption that BP therapy was indicated for most non-BP users in the matched primary cohort. Arguably, for these patients the positivity assumption was not violated.

(D) We will discuss in detail below the specific issues raised by the reviewer regarding our sensitivity analyses. In general we acknowledge that individual analytical and/or matching approaches may each have their own limitations, but the analyses performed herein were done to test in a systematic fashion the different critical threats to the validity of our initial results in the primary cohort analysis, which were based on a priori-defined methods and yielded a large and robust effect size. Thus, the individual sensitivity analyses should be considered in the greater context of the entire project.

Specific comments (in order of manuscript):

Methods:

Line 158: it is unclear how the authors dealt with patients who died during the follow-up period. The wording suggests they were excluded which would be inappropriate.

When this study was executed, we were unable to link the patient-level US insurance claims data with patient-level mortality data due to HIPAA concerns. Therefore, line 158 (now 177) defines continuous insurance coverage during the observation period as a verifiable eligibility criterion we used for patient inclusion. It was necessary to disqualify individuals who discontinued insurance coverage for a variety of reasons, e.g. due to loss or change of coverage, relocation etc., but our approach also eliminated patients who died. Appendix 3 (line 2449ff) describes methods we employed post hoc to assess how censoring due to death could have impacted our analyses. We discuss our conclusions from this post hoc analysis in the main text (lines 1053-1058) as follows: "An additional limitation is potential censoring of patients who died during the observation period, resulting in truncated insurance eligibility and exclusion based on the continuous insurance eligibility requirement. However, modelling the impact of censoring by using death rates observed in BP users and non-users in the first six months of 2020 and attributing all deaths as COVID-19-related did not significantly alter the decreased odds of COVID-19 diagnosis in BP users (see Appendix 3)."

Why did the authors use CCI for propensity matching rather than the individual comorbid conditions? I presume using separate variables will improve the comparability of the cohorts. The authors discuss imbalances in comorbidities as a limitation but should rather have avoided this.

CCI was the a priori approach defined at the study outset and was chosen due to the widespread use and understanding of this score. The general CCI score was originally planned for matching in order to have the largest possible study population since we did not know how many patients would meet all criteria as well as have an event of interest. After realizing we had adequate sample size to power matching using stricter criteria, we proceeded to perform subsequent sensitivity analyses on more stringently matched cohorts (sensitivity analysis 2).

Line 301-10: it seems unnecesary to me to adjust for the given covariates while these were already used for propensity score matching (except comorbidities, but see previous comment). The manuscript doesn't give a rationale why did the authors choose for this 'double correction'.

The following language was added to the methods section (lines 325-327): “Demographic characteristics used in the matching procedure were also included in the final outcome regressions to control for the impact of those characteristics on outcomes modelled.”

The following language was added to the Discussion section regarding the potential limitations of our srudy (lines 1078-1085): “Another limitation in the current study is related to a potential ‘double correction’ of patient characteristics that were included in both the propensity score matching procedure as well as the outcome regression modelling, which could lead to overfitting of the regression models and an overestimation of the measured treatment effect. Covariates were included in the regression models since these characteristics could have differential impacts on the outcomes themselves, and our results show that the adjusted ORs were in fact larger (showing a decreased effect size) when compared to the unadjusted ORs, which show the difference in effect sizes of the matched populations alone.”

In causal research a very important assumption is the 'positivity assumption', which means that none of the individuals has a probability of zero or one to be exposed. Including everyone would therefore not be appropriate. My suggestion is to include either all patients with an indication (based on diagnosis) or all that use an anti-osteoporosis (AOP) drug (or one as the primary and the other as the sensitivity analysis) instead of using these cohorts as sensitivity analyses. The choice should in my opinion be based on two aspects: whether it is likely that other AOP drugs have an effect on the COVID-19 outcomes and whether BP users are deemed to be more similar (in their risk of COVID-19 outcomes) to non-users or to other AOP drug users. Or alternatively, the authors might have discussed the positivity assumption and argue why this is not applicable to their primary analysis.

The following text has been added to the Discussion section addressing potential limitations of our study (lines 987-1009): " Another potential limitation of this study relates to the positivity assumption, which when building comparable treatment cohorts is violated when the comparator population does not have an indication for the exposure being modelled 56. This limitation is present in the primary cohort comparisons between BP users and BP non-users, as well as in the sensitivity analyses involving other preventive medications. This limitation, however, is mitigated by the fact that the outcomes in this study are related to infectious disease and are not direct clinical outcomes of known treatment benefits of BPs. The fact that the clinical benefits being assessed – the impact of BPs on COVID-related outcomes – was essentially unknown clinically at the time of the study data minimizes the impact of violation of the positivity assumption. Furthermore, our sensitivity analyses involving the “Bone-Rx” and “Osteo-Dx- Rx” cohorts did not suffer this potential violation, and the results from those analyses support those from the primary analysis cohort comparisons. Moreover, we note that the propensity score matched BP users and BP non-users in the primary analysis cohort mainly consisted of older females. According to the CDC, ~75% and 95% of US women between 60-69 and 70-79 suffer from either low bone mass or osteoporosis, respectively (https://www.cdc.gov/nchs/data/databriefs/db93.pdf). Essentially all women (and 70% of men) above age 80 suffer from these conditions, which often go undiagnosed. Women aged 60 and older represent ~75% of our study population (Table 1). Although bone density measurements are not available for non-BP users in the matched primary cohort, there is a high probability that the incidence of osteoporosis and/or low bone mass in these patients was similar to the national average.Thus, BP therapy would have been indicated for most non-BP users in the matched primary cohort, and arguably, for these patients the positivity assumption was not violated."

Sensitivity Analysis 3: Association of BP-use with Exploratory Negative Control Outcomes: what is the implicit assumption in this analysis? I think the assumption here is that any residual confounding would be of the same magnitude for these outcomes. But that depends on the strength of the association between the confounder and the outcome which needs not be the same. Here, risk avoiding behavior (social distancing) is the most obvious unmeasured confounder, which may not have a strong effect on other health outcomes. Also it is unclear to me why acute cholecystitis and acute pancreatitis-related inpatient/emergency-room were selected as negative controls. Do the authors have convincing evidence that BPs have no effect on these outcomes? Yet, if the authors believe that this is indeed a valid approach to measure residual confounding, I think the authors might have taken a step further and present ORs for BP → COVID-19 outcomes that are corrected for the unmeasured confounding. (e.g. if OR BP → COVID-19 is ~ 0.2 and OR BP → acute cholecystitis is ~ 0.5, then 'corrected' OR of BP → COVID-19 would be ~ 0.4.

We appreciate the reviewer’s thoughtful comments regarding the differential strength of the association between unmeasured confounders and outcome. We had initially selected acute cholecystitis and pancreatitis-related inpatient and emergency room visits as negative controls because we deemed them to be emergent clinical scenarios that should not be impacted by risk avoiding behavior. However, upon further search, we identified several publications that suggest a potential impact of osteoporosis and/or BPs on gallbladder diseases (DOIhttps://doi.org/10.1186/s12876-014-0192-z; http://dx.doi.org/10.1136/annrheumdis-2017-eular.3900), thus calling the validity our strategy into question. We therefore agree that the designation of negative control outcomes is problematic and adds relatively little to the overall story. Therefore, we have removed these analyses from the revised manuscript.

Sensitivity Analysis 4: Association of BP-use with Exploratory Positive Control Outcomes: this doesn't help me be convinced of the lack of bias. If previous researchers suffered from residual confounding, the same type of mechanisms apply here. (It might still be valuable to replicate the previous findings, but not as a sensitivity analysis of the current study).

We agree that the same residual confounding in previous research papers could be present in our study. Nonetheless, it was important to assess whether our analysis would be potentially subject to additional (or different) confounding due to the nature of insurance claims data as compared to the previous electronic record-based studies. Therefore, it was relevant to see if previous findings of an association between BP use and upper respiratory infections are observable in our cohort.

The second goal of sensitivity analysis #4 (now #3) was to see whether associations could be found on different sets of respiratory infection-based conditions, both during the time of the pandemic/study period as well as during the pre-pandemic time, i.e. before medical care in the US was significantly impacted by the pandemic. In light of these considerations, we feel that sensitivity analysis 4 adds value by showing consistency in our core findings.

Sensitivity Analysis 5: Association of Other Preventive Drugs with COVID-19-Related Outcomes: Same here as for sensitivity analysis 3: the assumption that the association of unmeasured confounders with other drugs is equally strong as for BPs. Authors should explicitly state the assumptions of the sensitivity analyses and argue why they are reasonable.

The following sentence was added to the Discussion section (lines 1019-1020): “ "These analyses were based on the assumption that the association of unmeasured confounders with other drugs is comparable in magnitude and quality as for BPs."

Results: The data are clearly presented. The C-statistic / ROC-AUC of the propensity model is missing.

Unfortunately, a significant amount of time has passed since execution of our original analysis of the Komodo dataset by our co-authors at Cerner Enviza. To date, our ability to perform follow-up studies with the Komodo dataset (which is exclusively housed on Komodo's secure servers) has become limited because business arrangements between these companies have been terminated, and the pertinent statistical software is no longer active. This issue prevents us from attaining the original C-statistic and ROC-AUC information, however, we were able to extract the actual; propensity scores themselves for the base cohort matching (BP-users versus non-users). The table below illustrates that the distribution of propensity scores for the base cohort match ranged from <0.01 to a max of 0.49, with 81.4% of patients having a propensity score of 10-49%, and 52.9% of patients having a propensity score of 20-49%. This distribution is unlikely to reflect patients who had a propensity score of either all 0 or all 1.

Discussion:

When discussing other studies the authors reduce these results to 'did' or 'did not find an association'. Although commonly practiced, it doesn't justify the statistical uncertainty of both positive and negative findings. Instead I encourage the authors to include effect estimates and confidence intervals. This is particularly relevant for studies that are inconclusive (i.e. lower bound of confidence interval not excluding a clinically relevant reduction while upper bound not excluding a NULL-effect).

We appreciate the reviewer’s suggestion and have added this information on p.21/22 in the Discussion.

Line 1145 "These retrospective findings strongly suggest that BPs should be considered for prophylactic and/or therapeutic use in individuals at risk of SARS-CoV-2 infection." I agree for prophylactic use but do not see how the study results suggest anything for therapeutic use.

We have removed “and/or therapeutic use” from this sentence (line 1088-1090).

The authors should discuss the acceptability of using BPs as preventive treatment (long-term use in persons without osteoporosis or other indication for BPs). This is not my expertise but I reckon there will be little experience with long-term inhibiting osteoblasts in people with healthy bones. The authors should also discuss what prospective study design would be suitable and what sample size would be needed to demonstrate a reasonable reduction. (Say 50% accounting for some residual confounding being present in the current study.)

Although BPs are also used in pediatric populations and in patients without osteoporosis (for example, patients with malignancy), we do recognize the lack of long-term safety data in use of BPs as preventative treatments. We tried to partially address this concern in our sub-stratified analysis of COVID-19 related outcomes and time of exposure to BP. Reassuringly, we observed that patients newly prescribed alendronic acid in February 2020 also had decreased odds of COVID-19 related outcomes (Figure 3B), suggesting that the duration of BP treatment may not need to be long-term. This was further discussed in the last paragraph of our Discussion where we state that " BP use at the time of infection may not be necessary for protection against COVID-19. Rather, our results suggest that prophylactic BP therapy may be sufficient to achieve a potentially rapid and sustained immune modulation resulting in profound mitigation of the incidence and/or severity of infections by SARS- CoV-2."

We agree that a future prospective study on the effect of BPs on COVID-19 related outcomes will require careful consideration of the study design, sample size, statistical power etc. However, we feel that a detailed discussion of these considerations is beyond the scope of the present study.

The authors should discuss the fact that confounders were based on registry data which is prone to misclassification. This can result in residual confounding.

Some potential sources of misclassification have been discussed on line 932-948. In addition, the following language was added (line 970-985): "Additionally, limitations may be present due to misclassification bias of study outcomes due to the specific procedure/diagnostic codes used as well as the potential for residual confounding occurring for patient characteristics related to study outcomes that are unable to be operationalized in claims data, which would impact all cohort comparisons. For SARS- CoV-2 testing, procedure codes were limited to those testing for active infection, and therefore observations could be missed if they were captured via antibody testing (CPT 86318, 86328). These codes were excluded a priori due to the focus on the symptomatic COVID-19 population. Furthermore, for the COVID-19 diagnosis and hospitalization outcomes, all events were identified using the ICD-10 code for lab-confirmed COVID-19 (U07.1), and therefore events with an associated diagnosis code for suspected COVID-19 (U07.2) were not included. This was done to have a more stringent algorithm when identifying COVID-19-related events, and any impact of events identified using U07.2 is considered minimal, as previous studies of the early COVID-19 outbreak have found that U07.1 alone has a positive predictive value of 94%55, and for this study U07.1 captured 99.2%, 99.0%, and 97.5% of all COVID-19 patient-diagnoses for the primary, “Bone-Rx”, and “Osteo-Dx-Rx” cohorts, respectively."

Author Response:

We thank the reviewers and editor for their feedback, which we will carefully consider as we revise the manuscript. We aim to provide more detail on how this technique could be used with other probes, ideally showing experimental data to support this use. We will add further detail of the histology from our ex vivo ovine and porcine and in vivo porcine testing. We will also provide a more thorough comparison of our technique to other recently developed lesioning techniques. In order to provide more complete evidence that our technique perturbs local neuron populations, we will refine the action potential analysis presented before and after lesions in non-human primates. In addition to providing further clarity of the method, we will include more non-human primate data where possible.

Author Response:

We are very glad that the reviewers found our paper of broad interest to the community of population, evolutionary, and ecological genetics. We thank them for their positive feedback and insightful comments and suggestions. We are preparing a revision of the preprint that will address these points. 

One issue raised by the reviewers was that it is important to acknowledge possible limitations of the demographic model used in simulation in capturing different aspects of genomic variation. In particular, different demographic models inferred for the same species using different methods or sets of samples may have different strengths and weaknesses, and this should be considered when selecting a demographic model for simulation. This is an important point that we intend to discuss in the revised version of our manuscript. We also plan to expand the documentation of the stdpopsim catalog to include more information about  the type of data used to fit every demographic model. Below we provide an outline of our thoughts on the topic.

First of all, it is important to acknowledge that demographic models inferred from genomic data cannot fully capture all aspects of the true demographic changes in the history of a species. As a result, these models do a good job in capturing some aspects of genetic variation, but not all of them. This is primarily determined by two factors: the method used for demographic inference, and the samples whose genomes were used in inference. Regardless of the method applied, the inferred demographic model can only reflect the genealogical ancestry of the sampled individuals, and this will typically make up a small portion of the complete genealogical ancestry of the species (albeit the genealogy of any set of sampled individuals includes many ancestors). Thus, demographic models inferred from larger sets of samples from diverse ancestry backgrounds may provide a more comprehensive depiction of genetic variation within a species, as long as a sufficiently realistic demographic model can be fit. That said, the choice of samples used for inference will mostly influence recent changes in genetic variation. This is because the genealogy of even a single individual consists of numerous ancestors in each generation in the deep past (which is the premise behind PSMC-style inference methods).

The computational method used for inference also affects the way genetic variation is reflected by the demographic model, because different methods derive their inference from different features of genomic variation. Some methods make use of the site frequency spectrum at unlinked single sites (e.g., dadi, Stairway plot), while other methods use haplotype structure (e.g., PSMC, MSMC, IBDNe). This, in turn, may influence the accuracy of different features in the inferred demography. For example, very recent demographic changes, such as recent admixture or bottlenecks, are difficult to infer from the site frequency spectrum, but are more easily inferred by examining shared long haplotypes (as demonstrated by the demographic model inferred for Bos Taurus by MacLeod et al. (2013)). There have been several studies that compare different approaches to demography inference (e.g., Biechman et al. (2017); Harris and Nielsen (2013)), but unfortunately, there is currently no succinct handbook that describes the relative strengths and weaknesses of different methods. Indeed, we hope that the standardized simulations provided by stdpopsim will facilitate systematic comparisons between methods, which will, in turn, provide valuable insights for researchers when selecting demographic models for simulation.

It is important to note that inclusion of a demographic model in the stdpopsim catalog does not involve any judgment as to which aspects of genetic variation it captures. Any model that is a faithful implementation of a published model inferred from genomic data can be added to the stdpopsim catalog. Thus, potential users of stdpopsim should use the implemented models with the appropriate caution, keeping in mind the limitations discussed above. Scientists contributing a new model to the catalog are required to write a brief summary, which is added to the documentation page of the catalog: https://popsim-consortium.github.io/stdpopsim-docs/ latest/catalog.html. This summary includes a graphical description of the model (such as the one shown for Anopheles gambiae in Fig. 2B of the paper), as well as a description of the data and method used for inference. We will mention this in the revised manuscript to help users of stdpopsim navigate through this resource.

Author Response:

First of all, we would like to thank the reviewers for their work. We appreciate the constructive review comments and useful suggestions to further improve our article.

The main criticism on our manuscript, from both reviewers, is that the cryo-EM structures are of low resolution and that the fit of the crystallographic structures of the PAD and the stalk domain into these low-resolution structures is questionable. We would like to point out that the cryo-EM data, and the conclusions from it, are not essential for the main conclusions of the article. All mutants that we made in this study were designed based on the structural data obtained from the high-resolution X-ray structures, with no input from the low-resolution cryo-EM docked models. We chose to include the cryo-EM data since it allowed us to speculate about the interaction between the PAD and the stalk domain of PrgB, domains that we have separately determined the structures of via X-ray crystallography. We agree with the reviewers that further experiments are needed to verify this potential interaction. Therefore, we will perform additional biochemical assays to investigate the proposed interaction. We will also try to optimize the cryo-EM data to hopefully allow for a more reliable fit of our high-resolution crystallographic structures. Once that is done, we will submit a revised version of the manuscript.

On behalf of all authors,

Ronnie Berntsson

Author Response:

We’d like to thank the three reviewers for reviewing our work in depth and providing insightful comments and suggestions.

Reviewer 1

1. The in vivo efficacy of MS023 does not seem to be very great. The mice treated with MS023 display a very small reduction in ADMA levels and a small increase in SDMA levels (Fig S6A).

REPLY: We have quantified proteins with ADMA and SDMA by Western blotting tail clippings from mice treated with vehicle (n=6) and MS023 (n=6). These were normalized for equal loading to b-actin levels. The average ADMA relative expression was 0.92 for vehicle treated mice and 0.86 for MS023 treated mice (p < 0.044). The average SDMA relative expression was 0.89 for vehicle treated mice and 0.98 for MS023 treated mice (p < 0.000019). These whole-body measurements show MS023 promotes the decrease of proteins with ADMA and increasing proteins with SDMA, as observed before with inhibition of PRMT1 (Dhar et al, 2013).

Reviewer 2

1. Two weaknesses are noted which lie in overstatements of the findings. There are six type I PRMTs (PRMT1, 2, 3, 6, 8, and CARM1), all of which are inhibited by MS023. While the authors demonstrate that their observations are not due to the inhibition of CARM1, they do not demonstrate that it is due to the inhibition of PRMT1, as they suggest.