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:

Chen W, Yang F, Wang C, Narula J, Pascua E, Ni I, Ding S, Deng X, Chu ML, Pham A, Jiang X, Lindquist KC, Doonan PJ, Blarcom TV, Yeung YA, Chaparro-Riggers J. 2021. One size does not fit all: navigating the multi-dimensional space to optimize T-cell engaging protein therapeutics. MAbs 13:1871171. DOI: 10.1080/19420862.2020.1871171, PMID: 33557687

Dang K, Castello G, Clarke SC, Li Y, AartiBalasubramani A, Boudreau A, Davison L, Harris KE, Pham D, Sankaran P, Ugamraj HS, Deng R, Kwek S, Starzinski A, Iyer S, Schooten WV, Schellenberger U, Sun W, Trinklein ND, Buelow R, Buelow B, Fong L, Dalvi P. 2021. Attenuating CD3 affinity in a PSMAxCD3 bispecific antibody enables killing of prostate tumor cells with reduced cytokine release. Journal for ImmunoTherapy of Cancer 9:e002488. DOI: 10.1136/jitc-2021-002488, PMID: 34088740

Gong C, Anders RA, Zhu Q, Taube JM, Green B, Cheng W, Bartelink IH, Vicini P, Wang BPopel AS. 2019. Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors. Frontiers in Oncology 8:649. DOI: 10.3389/fonc.2018.00649, PMID: 30666298

Mejstríková E, Hrusak O, Borowitz MJ, Whitlock JA, Brethon B, Trippett TM, Zugmaier G, Gore L, Stackelberg AV, Locatelli F. 2017. CD19-negative relapse of pediatric B-cell precursor acute lymphoblastic leukemia following blinatumomab treatment. Blood Cancer Journal 7: 659. DOI: 10.1038/s41408-017-0023-x, PMID: 29259173

Samur MK, Fulciniti M, Samur AA, Bazarbachi AH, Tai YT, Prabhala R, Alonso A, Sperling AS, Campbell T, Petrocca F, Hege K, Kaiser S, Loiseau HA, Anderson KC, Munshi NC. 2021. Biallelic loss of BCMA as a resistance mechanism to CAR T cell therapy in a patient with multiple myeloma. Nature Communications 12:868. DOI: 10.1038/s41467-021-21177-5, PMID: 33558511

Xu X, Sun Q, Liang X, Chen Z, Zhang X, Zhou X, Li M, Tu H, Liu Y, Tu S, Li Y. 2019. Mechanisms of relapse after CD19 CAR T-cell therapy for acute lymphoblastic leukemia and its prevention and treatment strategies. Frontiers in Immunology 10:2664. DOI: 10.3389/fimmu.2019.02664, PMID: 31798590

Yoneyama T, Kim MS, Piatkov K, Wang H, Zhu AZX. 2022. Leveraging a physiologically-based quantitative translational modeling platform for designing B cell maturation antigen-targeting bispecific T cell engagers for treatment of multiple myeloma. PLOS Computational Biology 18: e1009715. DOI: 10.1371/journal.pcbi.1009715, PMID: 35839267

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. 

REPLY: MS023 has been shown to have in vitro activity for several type I enzymes (Eram et al, 2016) and the same goes for GSK3368712 (Fedoriw et al, 2019). MS023 IC50 in vitro 30nM PRMT1, 119 nM PRMT3, 83 nM CARM1, 4 nM PRMT6, and 5 nM PRMT8 (Eram et al., 2016).  It was documented early that PRMT1 is the major cellular type I enzyme (Pawlak et al, 2000) and this is why PRMT1 and PRMT5, major type II, are embryonic lethal in mice (Guccione & Richard, 2019). In vivo data using MS023 is paralleled by using siPRMT1 (Gao et al, 2019; Plotnikov et al, 2020; Wu et al, 2022; Zhu et al, 2019). Thus in vivo, MS023 targets the main type I PRMT, PRMT1. Further, in support of our claim that MS023 targets PRMT1 in MuSCs is our previous observation that deleting PRMT1 stimulates MuSC proliferation. Since this effect was irreversible (Blanc et al, 2016) we pursued studies with the reversible MS023, the only compound to have significant activity towards PRMT1 in vivo. For these reasons, we are convinced that the effect of MS023 is mainly mediated by inhibiting PRMT1 in the MuSC.

To be thorough we should test all other type I PRMT inhibitors as they become available. CARM1 was shown to be a player in MuSC (Kawabe et al, 2012), but we excluded it using a CARM1 inhibitor TP-064 (Nakayama et al, 2018). PRMT6 mice that we generated are perfectly viable without overt phenotypes, suggesting PRMT6 is not involved (Neault et al, 2012), and PRMT8 is brain specific (Taneda et al, 2007).

2. Furthermore, this study suggests that the switch and elevated cellular metabolism in muscle stem cells due to MS023 enhanced self-renewal and engraftment capabilities but does not demonstrate this fact directly as stated. 

REPLY: Agreed. The link between cellular metabolism and MS023 enhanced self-renewal and engraftment capabilities is correlative and we will edit the revised text to reflect this.

Reviewer 3

1. However, the proposed underlying mechanism, which is claimed to rely on the expansion of MuSC and 'reprograming' of MuSCs towards a "unique and previously uncharacterized identity" is not sufficiently supported. The extent of the description of scRNA-seq data is inappropriate. Some conclusions from the scRNA-seq data appear to be overinterpreted or are rather trivial.

REPLY: We presented the top marker genes for each subpopulation that was identified in our scRNAseq to aid the reader in establishing a broad view of whether a given subpopulation was quiescent-like, proliferating, or differentiating. M1-M5 clusters were all enriched for cell cycle markers (Mki67, Cdk1, etc), indicating a proliferative identity. The unique finding in our data is that treatment with MS023 resulted in a shift in identity as compared to the DMSO-treated proliferating MuSCs (M1, M2 and M4), creating transcriptionally distinct M3 and M5 clusters. M3 and M5 had elevated markers for metabolism (E.g. Eno1, Atp5k, etc) and early activation (E.g. Fos, Jun), while the untreated MuSCs in clusters M1, M2 and M4 did not. Furthermore, M3 and M5 had higher baseline levels of Pax7 expression when compared to untreated cells. Together, these findings describe a transitional subpopulation of MuSCs unique to MS023 treatment which not only harbour stem like/early activation markers Pax7, Fos and Jun, but also elevated proliferative markers related to cell cycle and energy metabolism. This particular combination of characteristics is unique to the MS023-treated MuSCs, thus identifying a novel subtype of MuSC identity. In accordance with our scRNAseq data, we validated experimentally that MS023-treated cells have higher energy metabolism and increased self-renewal potential, thereby confirming that the unique transcriptomic signature of these cells also lead to a different cell fate decision.

2. It remains completely unclear whether the MS023-stimulated increase of metabolic pathway activity (OXPHOS, glycolysis) plays any role for preserving stem cell properties of MuSC during expansion and improves engraftment. Additional functional and mechanistic studies are required to explore the underlying molecular processes.

REPLY: Agreed. The link between cellular metabolism and MS023 enhanced self-renewal and engraftment capabilities is correlative and we will edit the revised text to reflect this.

3. Furthermore, it remains completely unclear whether the acclaimed increase in grip and tetanic strength of mdx mice after MS023 treatment relies on enhanced expansion of MuSC mediated by PRMT1 inhibition. 

REPLY: Agreed. We cannot exclude if the effect is mediated by an expansion of the MuSC pool or by an effect on other cell types, such as a direct impact on the myofibers. The goal of this figure was to provide a therapeutic perspective for the use of type I PRMT inhibitor for the treatment of DMD. Muscle wasting/weakness in DMD is a complex and multifactorial process (e.g., myofiber fragility, MuSC defects, chronic inflammation, fibrofatty accumulation). If MS023 can target multiple aspects of the physiopathology of the disease it would increase its therapeutic applicability. Further studies will be needed to determine the exact mechanism by which MS023 mediate its beneficial effect. The manuscript will be modified to reflect this.

References

• Blanc RS, Vogel G, Li X, Yu Z, Li S, Richard S (2016) Arginine methylation by PRMT1 regulates muscle stem cell fate. Mol Cell Biol 37: e00457-00416

• Dhar S, Vemulapalli  V, Patananan AN, Huang GL, Di Lorenzo A, Richard S, Comb MJ, Guo A, Clarke SG, Bedford MT (2013) Loss of the major Type I arginine methyltransferase PRMT1 causes substrate scavenging by other PRMTs. Scientific reports 3: 1311

• Eram MS, Shen Y, Szewczyk M, Wu H, Senisterra G, Li F, Butler KV, Kaniskan HU, Speed BA, Dela Sena C et al (2016) A Potent, Selective, and Cell-Active Inhibitor of Human Type I Protein Arginine Methyltransferases. ACS Chem Biol 11: 772-781

• Fedoriw A, Rajapurkar SR, Brien SO, Gerhart SV, Lorna H, Pappalardi B, Shah N, Laraio J, Liu Y, Butticello M et al (2019) Anti-tumor activity of the first-in-class type I PRMT inhibitor, GSK3368715, synergizes with PRMT5 inhibition through MTAP loss. Cancer cell XX: XX

• Gao G, Zhang L, Villarreal OD, He W, Su D, Bedford E, Moh P, Shen J, Shi X, Bedford MT et al (2019) PRMT1 loss sensitizes cells to PRMT5 inhibition. Nucleic acids research 47: 5038-5048

• Guccione E, Richard S (2019) The regulation, functions and clinical relevance of arginine methylation. Nat Rev Mol Cell Biol 20: 642-657

• Kawabe Y, Wang YX, McKinnell IW, Bedford MT, Rudnicki MA (2012) Carm1 regulates Pax7 transcriptional activity through MLL1/2 recruitment during asymmetric satellite stem cell divisions. Cell Stem Cell 11: 333-345

• Nakayama K, Szewczyk MM, Dela Sena C, Wu H, Dong A, al. e (2018) TP-064, a potent and selective small molecule inhibitor of PRMT4 for multiple myeloma. Oncotarget 9: 18480-18493

• Neault M, Mallette FA, Vogel G, Michaud-Levesque J, Richard S (2012) Ablation of PRMT6 reveals a role as a negative transcriptional regulator of the p53 tumor suppressor. Nucleic acids research 40: 9513-9521

• Pawlak MR, Scherer CA, Chen J, Roshon MJ, Ruley HE (2000) Arginine N-Methyltransferase 1 Is Required for Early Postimplantation Mouse Development, but Cells Deficient in the Enzyme Are Viable. Mol Cell Biol 20: 4859-4869

• Plotnikov A, Kozer N, Cohen G, Carvalho S, Duberstein S, Almog O, Solmesky LJ, Shurrush KA, Babaev I, Benjamin S et al (2020) PRMT1 inhibition induces differentiation of colon cancer cells. Scientific reports 10: 20030

• Taneda T, Miyata S, Kousaka A, Inoue K, Koyama Y, Mori Y, Tohyama M (2007) Specific regional distribution of protein arginine methyltransferase 8 (PRMT8) in the mouse brain. Brain Res 1155: 1-9

• Wu Q, Nie DY, Ba-Alawi W, Ji Y, Zhang Z, Cruickshank J, Haight J, Ciamponi FE, Chen J, Duan S et al (2022) PRMT inhibition induces a viral mimicry response in triple-negative breast cancer. Nature chemical biology 18: 821-830

• Zhu Y, He X, Lin YC, Dong H, Zhang L, Chen X, Wang Z, Shen Y, Li M, Wang H et al (2019) Targeting PRMT1-mediated FLT3 methylation disrupts maintenance of MLL-rearranged acute lymphoblastic leukemia. Blood 134: 1257-1268

Author Response

Reviewer #2 (Public Review):

1) The main limitation of this study is that the results are primarily descriptive in nature, and thus, do not provide mechanistic insight into how Ryr1 disease mutations lead to the muscle-specific changes observed in the EDL, soleus and EOM proteomes.

An intrinsic feature of the high-throughput proteomic analysis technology is the generation of lists of differentially expressed proteins (DEP) in different muscles from WT and mutated mice. Although the definition of mechanistic insights related to changes of dozens of proteins is very interesting, it is a difficult task to accomplish and goes beyond the goal of the high-throughput proteomic analysis presented here. Nevertheless, the analysis of DEPs may indeed provide arguments to speculate on the pathogenesis of the phenotype linked to recessive RyR1 mutations. In the unrevised manuscript, we pointed out that the fiber type I predominance observed in congenital myopathies linked to recessive Ryr1 mutation are consistent with the high expression level of heat shock proteins in slow twitch muscles. However, as suggested by Reviewer 3, we have removed "vague statements" from the text of the revised manuscript, concerning major insights into pathophysiological mechanisms, since we are aware that the mechanistic information, if any, that we can extract from the data set, cannot go over the intrinsic limitation of the high-throughput proteomic technology.

b) Results comparing fast twitch (EDL) and slow twitch (soleus) muscles from WT mice confirmed several known differences between the two muscle types. Similar analyses between EOM/EDL and EOM/soleus muscles from WT mice were not conducted.

We agree with the point raised by the Reviewer. In the revised manuscript we have changed Figure 2. The new Figure 2 shows the analysis of differentially expressed proteins in EDL, soleus and EOMs from WT mice. We have also added 2 new Tables (new Supplementary Table 2 and 3) and have inserted our findings in the revised Results section (page, 7, lines 157-176, pages 8 and 9).

c) While a reactome pathway analysis for proteins changes observed in EDL is shown in Supplemental Figure 1, the authors do not fully discuss the nature of the proteins and corresponding pathways impacted in the other two muscle groups analyzed.

We have now included in the revised manuscript a new Figure 2 which includes the Reactome pathway analysis comparing EDL with soleus, EDL with EOM and soleus with EOM (panels C, F and I, respectively). We have also inserted into the revised manuscript a brief description of the pathways showing the greatest changes in protein content (page 7 line 156-175, pages 8 and 9). We agree that the data showing changes in protein content between the 3 muscle groups of the WT mice are important also because they validate the results of the proteomic approach. Indeed, the present results confirm that many proteins including MyHCIIb, calsequestrin 1, SERCA1, parvalbumin etc are more abundantly expressed in fast twitch EDL muscles compared to soleus. Similarly, our results confirm that EOMs are enriched in MyHC-EO as well as cardiac isoforms of ECC proteins. This point has been clarified in the revised version of the manuscript (page 8, lines 198-213; page 9 lines 214-228). Nevertheless, we would like to point out that the main focus of our study is to compare the changes of protein content induced by the presence of recessive RyR1 mutations.

Reviewer #3 (Public Review):

a) it would be useful to determine whether changes in protein levels correlated with changes in mRNA levels …….

We performed qPCR analysis of Stac3 and Cacna1s in EDL, Soleus and EOM from WT mice (see Figure 1 below). The expression of transcripts encoding Cacna1s and Stac3 is approximately 9-fold higher in EDL compared to Soleus. The fold change of Stac3 and Cacna1s transcripts in EDL muscles is higher compared to the differences we observed by Mass spectrometry at the protein level between EDL and Soleus. Indeed, we found that the content of the Stac3 protein in EDL is 3-fold higher compared to that in soleus. Although there is no apparent linear correlation between mRNA and protein levels, we believe that a few plausible conclusions can be drawn, namely: (i) the expression level of both transcripts and proteins is higher EDL compared to EOM and soleus muscles, respectively, (ii) the expression level of transcripts encoding Stac3 correlate with those encoding Cacan1s and confirm proteomic data. In addition, the level of Stac3 transcript does not changes between WT and dHT, confirming our proteomic data which show that Stac3 protein content in muscles from dHT is similar to that found in WT littermates. Altogether these results support the concept that the differences in Stac3 content between EDL and soleus occur at both the protein and transcript levels, namely high Stac3 mRNA level correlates with higher protein content (EDL) and low mRNA levels correlated with low Stac3 protein content in Soleus muscles (see Figure 1 below).

Figure 2: qPCR of Cacna1s and Stac3 in muscles from WT mice. The expression levels of the transcripts encoding Cacna1s and Stac3 are the highest in EDL muscles and the lowest in soleus muscles (top panels). There are no significant changes in their relative expression levels in dHT vs WT. Each symbol represents the value from of a single mouse. * p=0.028 Mann Whitney test qPCR was performed as described in Elbaz et al., 2019 (Hum Mol Genet 28, 2987-2999).

….and whether or not the protein present was functional, and whether Stac3 was in fact stoichiometrically depleted in relation to Cacna1s.

We thought about this point but think that there are no plausible arguments to believe that Stac3 is not functional, one simple reason being that our WT mice do not have a phenotype which would be associated with the absence of Stac3 (Reinholt et al., PLoS One 8, e62760 2013, Nelson et al. Proc. Natl. Acad. Sci. USA 110:11881 2013).

b) In the abstract, the authors stated that skeletal muscle is responsible for voluntary movement. It is also responsible for non-voluntary. The abstract needs to be refocused on the mutation and on what we learn from this study. Please avoid vague statements like "we provide important insights to the pathophysiological mechanisms..." mainly when the study is descriptive and not mechanistic.

The abstract of the revised manuscript has been rewritten. In particular, we removed statements referring to important “pathophysiological mechanistic insight”.

c) The author should bring up the mutation name, location and phenotype early in the introduction.

In the revised manuscript we provide the information requested by the Reviewer (page 2 lines 36-38 and page 4, lines 98-102).

d) This reviewer also suggests that the authors refocus the introduction on the mutation location in the 3D RyR1 structure (available cryo-EM structure), if there is any nearby ligand binding site, protomers junction or any other known interacting protein partners. This will help the reader to understand how this mutation could be important for the channel's function

The residue Ala4329 is present inside the TMx (Auxiliary transmembrane helices) domain which spans from residue 4322 to 4370 and interposes structurally (des Georges A et al. 2016 Cell 167,145-57; Chen W, et al. 2020 EMBO Rep. 21, e49891). Although the structural resolution of the region has been improved (des Georges et al, 2016), parts of the domain still remain with no defined atomic coordinates, especially the region encompassing a.a. E4253 – F4540. Because of such undefined atomic coordinates of the region E4253-F4540, we are not able to determine the real orientation and the disposition of the amino acids in this region, including the A4329 residue. As reference, structure PDB: 5TAL of des Georges et al, 2016 was analyzed with UCSF Chimera (production version 1.16) (Pettersen et al. J. Comput. Chem. 25: 1605-1612. doi: 10.1002/jcc.20084).

Author Response

Reviewer #1 (Public Review):

This manuscript describes a relatively novel approach to discovering combinations of herbal medications that may help modulate immune responses, and in turn help treat diseases such as cancer. The authors use breast plasma call mastitis as a disease in which they present results from a non-blinded clinical trial with modest results. The main shortcomings are a lack of rigor around standardizing the control group given steroids versus the treatment group given the combinations of herbal medications. There needs to be a detailed statistical analysis of the comparison in tumor size, stage, invasiveness, etc. as well as consideration of confounding disease states (autoimmune disease, prior cancers, diabetes, etc.). While the results are interesting in that the use of herbal medications is often overlooked in Western medicine, the manuscript needs great detail in the clinical comparison in order to provide convincing evidence for an effect.

Many thanks for your very kind words about our work. We are excited to hear that you think our manuscript is relatively novel with considerable translational impact to the field of herbal medications. We are grateful for your valuable time and efforts you have spent to provide your very insightful comments, which are of great help for our revision.

Reviewer #2 (Public Review):

The work is rather interesting and novel because for the first time, the authors employed knowledge graph, a cutting-edge technique in the domain of artificial intelligence, to identify a novel herbal drug combination for the treatment of PCM. The results of the clinical trial study clearly demonstrated that the drug combination is effective to ameliorate the symptoms of PCM patients and improve the general health status of the patients. Overall, the strategy of this manuscript may provide a paradigm for the design of drug combination towards many other human disorders.

We are truly grateful for your very kind words about our work. It is very encouraging to know that you think our work is novel and of significance for the field. We sincerely appreciate the valuable time and kind efforts that you have spent on the thorough review of our manuscript.

Reviewer #3 (Public Review):

The major merit of the manuscript is that the authors introduced the concept of knowledge graph into the domain of herbal drugs or TCM. Namely, the authors designed a knowledge graph towards systematic immunity or immunotherapy based on massive data mining techniques. The authors successfully identified an herbal drug combination for PCM with the help of a scoring system. Moreover, the authors conducted a clinical trial study and the clinical data showed that the herbal drug combination holds great promise as an effective treatment for PCM. The weakness of the manuscript is that some details for the herbal drug combination and the clinical trial study are missing.

Many thanks for your very kind words about our work. We are excited to hear that you think our work is relatively novel and holds great promise as an effective remedy for PCM. We are truly thankful for your valuable time and efforts you have spent to provide your very insightful comments, which are of great help for our revision.

Author Response

Reviewer #1 (Public Review):

After giving a very accessible introduction to cellular processes during brain development, the authors present the computational model used in this study. It combines the kinematics of cell proliferation with the mechanic of brain tissue growth and is essentially equal to their model presented in Zarzor et al (2021), but extended for the outer subventricular zone (OSVZ), see for example Figs. 2 in the present manuscript and in Zarzor et al (2021). This zone, which is specific to humans, provides a second zone of cell proliferation. The division rate in the OSVZ is smaller and at most equal to that in the ventricular zone.

The authors present two main findings: The distance between sulci in the cortex is decreased whereas the cell density in the ventricular zone is increased in presence of the OSVZ. Furthermore, the "folding evolution", which is the ratio between the outer perimeter at time t and the initial perimeter increases in presence of the OSVZ. The strongest effect is seen, when division rates in both proliferating zones are equal. The authors compare the cases of varying and constant cortical stiffness, which they had also done in Zarzor et al (2021). Finally, they consider the feedback of cortical folding on OSVZ thickness.

The computational model provides a sound description of how cell proliferation and migration combined with tissue mechanics yield cortical folding patterns. However, only a few parameter values are varied in a limited range. Also, it remains unclear to me, how important the specific functional dependencies of, for example, the cell division rate on the radial coordinate are. This point seems of particular importance because the effect of the presence of the OSVZ on the folding patterns seems rather minute, see Fig. 5. The authors do not propose experiments that could be used to test their description and results. Finally, the analysis is restricted to 2 dimensions.

Thank you very much for the valuable suggestions. We agree that we are only able to show limited parameter studies in the manuscript. Therefore, we have now implemented a user interface that can be downloaded from Github (https://github.com/SaeedZarzor/BFSimulator) and will allow interested readers to directly change the parameter values and run the simulations.

To better emphasize the effect of the presence of the OSVZ on the folding patterns, we have edited the corresponding section and figure in the revised manuscript to include a quantification of the distance between sulci:

“In general, the distance between neighboring sulci decreases with increasing Gosvz, as marked in Figure 7. For the displayed cases, the distance decreases from d = 8.796 mm for Gosvz = 0 to d = 8.67 mm for Gosvz = 10 and finally d = 8.2 mm for Gosvz = 20. Interestingly, the cortical thickness and effective stiffness ratio at the first instability point (denoted by w in Figure 5) are the same for all these cases. Therefore, we attribute the observed differences to the faster increase in the cell density and thus cortical growth, cortical stiffness and the effective stiffness after the instability has been initiated.”

In addition, we have added a new figure to show that the observed trends also hold true for 3D simulations:

“Figure 8 demonstrates that the observed trends also hold true when extending the model to 3D. For the case of varying stiffness with a stiffness ratio of 3, a growth ratio of 3, and an initial division rate in the ventricular zone Gvz = 600, the folding complexity increases with increasing initial division rate in the OSVZ Gosvz.”

Reviewer #2 (Public Review):

Weaknesses

• To account for the complexity of biological phenomena, the model relies on a large number of ad hoc choices whose consequences are difficult to predict.

We fully agree that there are quite a number of model assumptions that we have to make. Still, we have achieved great agreement with the data from fetal brain sections, which in our opinion justified the assumptions made.

To better explain the choice of parameters, we have now included the following paragraph in the manuscript: “The mechanical and diffusion parameters are adapted from the literature Budday et al. (2020); de Rooij and Kuhl (2018), while the geometry parameters are estimated based on histologically stained human brain sections and magnetic resonance images. For instance, to determine the MST factor, we measured the relative distance between the ISVZ and OSVZ in histologically stained images. The final value adopted is the result of dividing the measured distance by the expected time. When determining the growth problem parameters, numerical stability and algorithm convergence were major criteria.”

• The physical model description is highly technical and out of reach for a non-specialist.

Thank you for making this point! We have now adapted the model description to better emphasize the main features of the model and the feedback mechanisms between the mechanical growth problem and the cell density problem:

“...is the Cauchy stress tensor formulated in terms of the elastic deformation tensor, as only the elastic deformation induces stresses. The Cauchy stress describes the three dimensional stress state in the spatial (grown and deformed) configuration and is computed by deriving the strain energy function…”

“Through Equation 6, the cell density problem controls the effective stiffness ratio between cortex and subcortex (as the cortical stiffness changes while the subcortical stiffness remains constant) and thus also the emerging cortical folding pattern Budday et al. 2014; Zarzor et al. 2021.”

“Through Equation 8, the amount of growth is directly related to the cell density - the higher the cell density, the more growth.”

“The vector n represents the normalized orientation of radial glial cell fibers in the spatial configuration and controls the migration direction of neurons. As the brain grows and folds, the fiber direction changes. Through this feedback mechanism, the mechanical growth problem affects how neurons migrate and the cell density evolves locally.”

“By applying Equation 16 for the VZ, we ensure that the division rate decreases from its initial value G_vz to a smaller value as the maximum stretch value s in the domain increases, i.e., with increasing gestational age. This constitutes an additional feedback mechanism between the mechanical growth problem and the cell density problem: As the maximum stretch and thus the deformation increases due to constrained cortical growth, the division rate in the VZ decreases, resulting in less newborn cells” and “G^s_osvz is the division rate in the OSVZ that decreases with increasing maximum stretch s in the domain”

• The description of neurogenesis shows three zones of cell proliferation, each inhabited by a specific cell type. Despite its realism, the proposed model does not take into account the ISVZ where the intermediate progenitors operate.

Indeed, in our model we have focused on two original sources of the cells which are radial glial cells and ORGCs. As we know so far, the intermediate progenitor cells are produced from those two cell types, so they are indirectly included in the model as a resulting cell density.

• The experiment of comparing several regimes derived from the relative importance of proliferation in the VZ and OSVZ is not very clear. It leads to the observation of the evolution of cell density maxima over time, which seems insufficient to conclude the importance of the OSVZ for folding. One wonders whether the key parameter that leads to folding is the rate of OSVZ proliferation or simply the total quantity of neurons generated by the two or even the three zones.

Thank you for this remark. We fully agree with the Reviewer that a key factor is the total quantity of neurons generated. However, the major question we intend to address here is where these neurons originate from and how the different proliferating zones interact. In other words, we do not question the existence of the OSVZ, but we are trying to build a computational model that can mimic all relevant cellular processes during brain development - to then study their individual effect on cortical folding. Therefore, we do not argue that the OSVZ is necessary for folding, but that it plays a crucial role in the speed of generating these folds and their complexity in the Conclusion section:

“Our results show that the existence of the OSVZ particularly triggers the emergence of secondary mechanical instabilities leading to more complex folding patterns. Furthermore, the proliferation of outer radial glial cells (ORGCs) reduces the time required to induce the mechanical instability and thus cortical folding.”

• The experiment on the heterogeneity of proliferation in the OSVZ is a bit frustrating. I would like to see a set-up corresponding to the mosaics found in ferrets and closely associated with folding patterns.

This is a valuable point, thank you! We have now added new results showing a more distinct regional variation of the OSVZ and have adapted our conclusions regarding this point:

“Also in the ferret brain, where a region close in structure to the primate's OSVZ was found, this region shows a unique mosaic-like structure Fietz et al. (2010b); Reillo and Borrell (2012). In this section, we aim to assess the effect of regional proliferation variations in the OSVZ on the emerging cortical folding pattern. We discuss two different heterogeneous patterns here, but have included more variations online through our user interface on GitHub, as described in the Data availability section. In the first case, the OSVZ division rate gradually decreases along the circumferential direction. In the second case, the division rate varies in a more random pattern. Figures 13 and 14 show how cortical folds develop in both cases for the varying cortical stiffness case, a division rate in the VZ of G_vz = 120 and an initial division rate in the OSVZ of G_osvz = 20. As expected, the evolving folding patterns slightly differ. In both cases, the first folds appear, where the cell proliferation rate is highest. Expectedly, those regions also show a higher cell density in the cortex than regions nearby. However, both cases lead to final patterns with similar distances between sulci and folding complexity (one period doubling pattern). In addition, gyri and sulci are distributed equally -- regardless of the division rate. Therefore, we may conclude that inhomogeneous cell proliferation in the OSVZ controls the location of first gyri and sulci but does not necessarily affect the distance between sulci (also referred to as folding wavelength) and the overall complexity of the emerging folding pattern. This agrees well with our previous finding that the characteristic wavelength of folding remains relatively stable for inhomogeneous cortical growth patterns Budday and Steinmann (2018). The simulation results are also consistent with the previously found remarkable surface expansion above the regions with higher proliferation in the OSVZ Llinares-Benadero and Borrell (2019).”

“Finally, our simulations reveal that inhomogeneous cell proliferation patterns in the OSVZ can control the location of first gyri and sulci but do not necessarily affect the distance between sulci and the overall complexity of the emerging folding pattern.”

Furthermore, in our code, we have added a user interface with multiple options for different OSVZ regional variations. The link to the code with the user interface shown below is now updated in the Data availability section.

• It would be interesting to elaborate a little on the possibility of extending the model in 3D, which seems imperative to evaluate the nature of the folding pattern generated. Comparing them to reality is an essential step in gauging the credibility of the model. For instance, it would be interesting to test to which extent the model can father the type of variability observed in the general population (Mangin et al.). It will also be particularly interesting to work on the inverse model between the real folding patterns and the heterogeneous proliferation maps that can generate them.

We fully agree with the Reviewer. Unfortunately, to the best of the Author’s knowledge, there is currently no data set providing both the 3D evolution of the folding pattern and the corresponding distribution of the cell density. Therefore, the validation of 3D results is difficult. Promisingly, our model achieved good agreement with data from histologically stained fetal brain sections regarding the local gyrification index, final cortical thickness, and cell density distribution, as presented in Zarzor, et al (2021). We have indeed initiated the collection of additional data, ideally for the 3D validation. However, this will take some time and is out of the scope of the current work. It is also a great suggestion to compare our 3D simulation results with the variability found in the general population. Indeed, we plan to do such work in the future but consider this out of the scope of the current work, which focuses more on the OSVZ.

To still show that our model can be extended to 3D, we have now included the following results: “Figure 8 demonstrates that the observed trends also hold true when extending the model to 3D. For the case of varying stiffness with a stiffness ratio of 3, a growth ratio of 3, and an initial division rate in the ventricular zone G_vz = 600, the folding complexity increases with increasing initial division rate in the OSVZ G_osvz.”

Reviewer #3 (Public Review):

Zarzor et al. developed a new multifield computational model, which couples cell proliferation and migration at the cellular level with biological growth at the organ level, to study the effect of OSVZ on cortical folding. Their approach complements the classical experimental approach in answering open questions in brain development. Their simulation results found the existence of OSVZ triggers the emergence of secondary mechanical instabilities that leads to more complex folding patterns. Also, they found that mechanical forces not only fold the cortex but also deepen subcortical zones as a result of cortical folding. Their physics-based computational modeling approach offered a novel way to predictively assess the links between cellular mechanisms and cortical folding during early human brain development, further shedding light on identifying the potential controlling parameters for reverse brain study.

Strengths:

The newly developed physics-based computational model has several advantages compared to previous existing computational brain models. First, it breaks the traditional double-layer computational brain model, gray matter layer and white matter layer, by introducing the outer subventricular zone. Second, it develops multiscale computational modeling by bringing the cellular level features, cell diffusion, and migration, into the macroscale biological growth model. Third, it could provide a cause-effect analysis of cortical folding and axonal fiber development. Finally, their approach could complement, but not substitute, sophisticated experimental approaches to answer some open questions in brain science.

Weaknesses:

The cellular diffusion and migration seem determined and controlled by a single variable, cell density, which is one-way coupled with the deformation gradient of the brain model. However, cell migration and diffusion should be potentially coupled with stress and vice versa. Also, the current computational model can be improved by extending it to a 3D model. Finally, they can further improve the study of regional proliferation variation by introducing fully-randomized heterogenous cell density and growth in their model.

Thank you. We apologize for the lack of clarity in the original submission. There are indeed more coupling mechanisms, which we have now better emphasized when introducing the model:

“Through Equation 6, the cell density problem controls the effective stiffness ratio between cortex and subcortex and thus also the emerging cortical folding pattern Budday et al. 2014; Zarzor et al. 2021.”

“Through Equation 8, the amount of growth is directly related to the cell density - the higher the cell density, the more growth.”

“The vector n represents the normalized orientation of radial glial cell fibers in the spatial configuration and controls the migration direction of neurons. As the brain grows and folds, the fiber direction changes. Through this feedback mechanism, the mechanical growth problem affects how neurons migrate and the cell density evolves locally.”

“By applying Equation 16 for the VZ, we ensure that the division rate decreases from its initial value Gvz to a smaller value as the maximum stretch value s in the domain increases, i.e., with increasing gestational age. This constitutes an additional feedback mechanism between the mechanical growth problem and the cell density problem: As the maximum stretch and thus the deformation increases due to constrained cortical growth, the division rate in the VZ decreases, resulting in less newborn cells” and “Gosvzs is the division rate in the OSVZ that again decreases with increasing maximum stretch s in the domain”

In addition, we have added a new figure to show that the observed trends also hold true for 3D simulations:

“Figure 8 demonstrates that the observed trends also hold true when extending the model to 3D. For the case of varying stiffness with a stiffness ratio of 3, a growth ratio of 3, and an initial division rate in the ventricular zone Gvz = 600, the folding complexity increases with increasing initial division rate in the OSVZ Gosvz.”

Finally, we have added new results showing a more distinct regional variation of the OSVZ. Furthermore, in our code, we have added a user interface with multiple options for different OSVZ regional variations. The link to the code with user interface is available in the paper:

“Also in the ferret brain, where a region close in structure to the primate's OSVZ was found, this region shows a unique mosaic-like structure Fietz et al. (2010b); Reillo and Borrell (2012). In this section, we aim to assess the effect of regional proliferation variations in the OSVZ on the emerging cortical folding pattern. We discuss two different heterogeneous patterns here, but have included more variations online through our user interface on GitHub, as described in the Data availability section. In the first case, the OSVZ division rate gradually decreases along the circumferential direction. In the second case, the division rate varies in a more random pattern. Figures 13 and 14 show how cortical folds develop in both cases for the varying cortical stiffness case, a division rate in the VZ of G_vz = 120 and an initial division rate in the OSVZ of G_osvz = 20. As expected, the evolving folding patterns slightly differ. In both cases, the first folds appear, where the cell proliferation rate is highest. Expectedly, those regions also show a higher cell density in the cortex than regions nearby. However, both cases lead to final patterns with similar distances between sulci and folding complexity (one period doubling pattern). In addition, gyri and sulci are distributed equally -- regardless of the division rate. Therefore, we may conclude that inhomogeneous cell proliferation in the OSVZ controls the location of first gyri and sulci but does not necessarily affect the distance between sulci (also referred to as folding wavelength) and the overall complexity of the emerging folding pattern. This agrees well with our previous finding that the characteristic wavelength of folding remains relatively stable for inhomogeneous cortical growth patterns Budday and Steinmann (2018). The simulation results are also consistent with the previously found remarkable surface expansion above the regions with higher proliferation in the OSVZ Llinares-Benadero and Borrell (2019).”

Author Response

Reviewer #1 (Public Review):

The authors push a fresh perspective with a sufficiently sophisticated and novel methodology. I have some remaining reservations that concern the actual make-up of the data basis and consistency of results between the two (N=16) samples, the statistical analysis, as well as the “travelling” part.

I previously commented on the fact that findings from both datasets were difficult to discern and more effort should be made to highlight these. Also, a major conclusion “the directionality effect [effect of attention on forward waves] only occurs for visual stimulation” only rested on a qualitative comparison between studies. The authors have improved on this here, e.g., by toning down this conclusion. One thing that is still missing is a graphical representation of the data from Foster et al. (the second dataset analysed here) that would support the statistical results and allow the reader a visual comparison between the sets of findings.

We are glad that the reviewer recognizes the improvement in the presentation of the conclusions. According to the suggestions, we have modified figure 2, not only by including a third dataset (see point below), but also in a way that allows a direct comparison between the three datasets. Specifically, the results from the three datasets are now shown in three columns next to each other. The first row shows the FW and BW waves in contra and ipsilateral lines of electrodes for each dataset: our dataset and the one from Feldmann-Wustefeld and colleagues (the first and the second column in the figure, both with visual stimulation) shows a clear interaction between direction and laterality, as confirmed by the statistical analysis. The dataset from Foster and colleagues (the third column, no visual stimulation) shows a laterality effect only in the backward waves but not in the forward ones, in line with the hypothesis that FW waves are modulated only in the presence of visual stimulation. The second row shows a schematic representation of the task, and the third row illustrate the electrodes’ lines used in each dataset. We hope the reviewer will be satisfied with the current data presentation.

Also, for any naive reader, the concept of travelling waves may be hard to grasp in the way data are currently presented - only based on the results of the 2D-FFT. Can forward and backward-travelling waves be illustrated in a representative example to make this more intuitive?

We thank the reviewer for the suggestion. We included in figure 1 an additional panel E that represents a schematic example of forward and backward waves in the temporal domain (i.e., in the EEG data). We hope this example will provide a better understanding of the data and the traveling wave concept.

Finally, the way Bayes Factors from the Bayesian ANOVA are presented, especially with those close to the ‘meaningful boundaries’ ⅓ and 3, as defined in the ‘Statistical analysis’ section, requires some unification/revision. For example, here: “We found a positive correlation between contra- and ipsi- lateral backward waves, and occipital (all Pearson’s r~=0.4, all BFs 10 ~=3) and -to a smaller extent- frontal areas (all Pearson’s r~=0.3, all BFs 10 ~=2).”, where the second part should strictly be labelled as inconclusive evidence. In the same vein, there is occasional mention of “negative effects”, where it should say that evidence favours the absence of an effect.

We agree with the reviewer and apologize for the inaccuracies in reporting the statistical analysis. We corrected as suggested (see below), replacing ‘negative effects’ with ‘evidence favors the absence of an effect’.

From the updated manuscript :

"We found moderate evidence of a positive correlation between contra- and ipsi- lateral backward waves, and occipital (all Pearson’s r~=0.4, all BFs10~=3) but inconclusive evidence in the frontal areas (all Pearson’s r~=0.3, all BFs10~=2)."

From the revised ‘Results’ section, now it reads:

[…] whereas all other factors and their interactions revealed evidence in favor of the absence of an effect (BFs10<0.3).

[…] but not in the forward waves (BF10=0.231, error<0.01%, supporting evidence in favor of the absence of an effect).

Reviewer #2 (Public Review):

The present manuscript takes a new perspective and investigates the functional relevance of traveling alpha waves’ direction for visual spatial attention. While the modulation of alpha oscillatory power - and especially the lateralization of alpha power - has been associated with spatial attention in the literature, the present investigation offers a new perspective that helps understand and differentiate the functional roles of alpha oscillations in the ipsi- versus contralateral hemisphere for spatial attention.

The present study uses a straightforward approach and provides an analysis of two EEG datasets, which are convergingly in line with the authors’ claim that two patterns of travelling alpha waves need to be differentiated in visual spatial attention. First, backward waves in the ipsilateral hemisphere, and second, forward waves in the contralateral hemisphere, which are only observed during visual stimulation. Importantly, the authors test the relation of these patterns of traveling waves to the overall power of alpha oscillations and to the hemispheric lateralization of alpha power. Furthermore, to test the functional significance, the authors demonstrate that the pattern of forward and backward waves around stimulus onset differentiates between hits and misses in task performance.

Although the results are in line with the conclusions drawn, some questions remain. The authors investigate the relationship between traveling alpha waves and the hemispheric lateralization of alpha power, which is a well-established neural signature of spatial attention. Surprisingly, the lateralization of alpha power shown in Figure 3B appears relatively weak in the present dataset (by visual inspection), which raises the question of whether the investigation of a relation between lateralized alpha power and alpha traveling waves is warranted in the first place.

We agree with the reviewer that the effect seems reduced compared to other studies, despite the topography of alpha-band lateralization in our data is in line with the literature. In order to quantify the effect, we performed an analysis similar to (Thut et al., 2006), defining a laterality index as:

We computed such index for occipital electrodes and their average (in red in figure R1). The results reveal that for most electrodes, including their average, the laterality index is significantly larger than 0, confirming the presence of alpha-band lateralization. However, we also note that the amplitude of the effect (~0.04) is reduced compared to the study by Thut and colleagues, which was between 0.05 and 0.10.

Figure R1 – Laterality index for occipital electrodes, quantifying alpha-band lateralization during attention allocation. All electrodes go in the expected direction, revealing an increase of alpha-band power in the ipsilateral occipital hemisphere.

Furthermore, the authors employ between-subject correlations (with N = 16) to test the relationship between alpha traveling waves and (lateralized) alpha power. However, as inter- individual differences in patterns of travelling waves are not the main focus here, within- subject analyses of the same relations would be able to test the authors’ hypotheses much more directly.

As suggested, we included the recommended within-subject analysis in the revised manuscript by computing a trial-by-trial correlation between alpha power and traveling waves for each participant. First, we obtained a correlation coefficient and a p-value for each subject. Then, we tested whether the correlation coefficients had an overall positive or negative distribution (i.e., according to our previous results, we expected a positive correlation between backward waves and alpha power). Additionally, we combined the p-values to test for overall significance (using the Fisher method, see Methods section below). Our results corroborate the between-subject correlation, supporting the conclusion that alpha-band power correlates mostly with backward waves (especially contro-lateral to the attended location). The other correlations (i.e., forward waves and alpha power) were statistically inconclusive. We included in the revised manuscript these new results, as shown in the following.

From the Results section:

“To further investigate the relation between alpha-band travelling waves and alpha power, we performed the same analysis focusing on the correlation within each participant. In particular, we correlated trial-by-trial forward and backward waves with alpha-band power for each subject, obtaining correlation coefficients ‘r’ and their respective p-values. As in the previous analysis, we correlated forward and backward waves with frontal and occipital electrodes in both contro- and ipsilateral hemispheres. We applied the Fisher method (Fisher, 1992, see Methods for details) to combine all subjects' p-values in every conditions. Overall, we found a significant effect of all combined p-values (p<0.0001), except in the lateralization condition (contra- minus ipsilateral hemisphere), similar to our previous analysis. Additionally, we tested for a consistent positive or negative distribution of the correlation coefficients. As shown in figure 3C, the results support a significant correlation between backward waves and alpha- power in the hemisphere contralateral to the attended location (BF10=10.7 and BF10=7.4 for occipital and frontal regions, respectively; all other BF10 were between 1 and 2, providing inconclusive evidence). Interestingly, this analysis also revealed a small but consistent effect in the correlation between lateralization effects, as we reported a consistently positive correlation in the contra- minus ipsilateral difference between forward waves and alpha power (BF10~5 for both frontal and occipital electrodes). However, it’s important to notice that the combined p-values obtained using the Fisher method did not reach the significance threshold in the lateralization condition, reducing the relevance of this specific result.“

From the Methods section:

“Additionally, we computed trial-by-trial correlations between waves and alpha power for all participants. First, we tested the correlation coefficient against zero in all conditions. Then, we obtained a combined p-value per condition using the log/lin regress Fisher method (Fisher, 1992), as shown in (Zoefel et al., 2019). Specifically, we computed the T value of a chi- square distribution with 2*N degrees of freedom from the pi values of the N participants as:

It needs to be appreciated that the authors analyze two datasets in the present study. However, the question remains whether the absence of the forward waves effect in paradigms without visual stimulation is a general one and would replicate in other datasets. Moreover, the manuscript would benefit from a discussion of the potential implications of traveling waves for functional connectivity between posterior and anterior regions.

We have now included a third dataset in the paper. In this dataset, from (Feldmann-Wüstefeld & Vogel, 2019), participants performed a visual working memory task by attending either the left or the right side of the screen where a stimulus was displayed. We analyzed the amount of waves during stimulus presentation, and we found the same results as in our own dataset: very strong evidence in favor of an interaction between LATERALITY (contra- and ipsilateral) and DIRECTION (FW and BW). We now included the results in figure 2 (see point above) and in the results section of the manuscript. Unfortunately, we couldn't find any other publicly available EEG dataset in which participants attend to either side of the screen without ongoing visual stimulation.

In addition, we re-analyzed our main findings (i.e. the interaction between LATERALITY and DIRECTION) in all three datasets using a classic ANOVA to report the effect size as 𝜂2 (see point above). Unlike the Bayesian ANOVA (which -in JASP- is based on linear mixed models), the classic one does not model the slope of the random effects. Yet, we observed that the LATERALITY x DIRECTION interaction in the Foster dataset proved very significant, with a large effect size (F(1,16)=9.81, p=0.003, 𝜂2=0.13). Supposedly, modeling the slope of the random effects in the Bayesian ANOVA lowered its statistical sensitivity. For the sake of completeness, we reported both results in the manuscript.

Concerning the potential implications of traveling waves on functional connectivity, we consider the interpretation based on the Predictive Coding scheme in the one before the last paragraph of the discussion (reported below for the reviewer’s convenience). In this framework, top-down connections have inhibitory functions, suppressing the predicted activity in lower regions. These interpretations align with our findings, relating the inhibitory role of backward travelling waves to visual attention. Similarly, in the same paragraph, we refer to the work of Spratling, which extensively investigates the relationship between selective attention and Predictive Coding.

From the Results section:

"To confirm our previous results, we replicated the same traveling waves analysis on two publicly available EEG datasets in which participants performed similar attentional tasks (experiment 1 of Foster et al., 2017 and experiment 1 of Feldmann-Wüstefeld and Vogel, 2019). In the first experiment from the Feldmann-Wüstefeld and Vogel dataset, participants were instructed to perform a visual working memory task in which, while keeping a central fixation, they had to memorize a set of items while ignoring a group of distracting stimuli. We focused our analysis on those trials in which the visual items to remember were placed either to the right or the left side of the screen, while the distractors were either in the upper or lower part of the screen (we pulled together the trials with either 2 or 4 distractors, as this factor was irrelevant for the purposes of our analysis). The stimuli were shown for 200ms, and we computed the amount of forward and backward waves in the 500ms following stimulus onset. As shown in figure 2 (central column), the analysis confirmed our previous results, demonstrating a strong interaction between the factors DIRECTION and LATERALITY (BF10=667, error~2%; independently, the factors DIRECTION and LATERALITY had BF10=0.2 and BF10=0.4, respectively). These results confirmed that, in the presence of visual stimulation, spatial attention modulates both forward and backward waves. Next, we analyzed another publicly available dataset from Foster et al., 2017. [...]"

"Remarkably, as shown in figure 2 (right panel), our analysis demonstrated an effect of the lateralization (LATERALITY: BF10=3.571, error~1%), revealing more waves contralateral to the attended location, but inconclusive results regarding the interaction between DIRECTION and LATERALITY (BF10=2.056, error~1%). However, using a classical ANOVA (i.e., without modeling the slope of the random terms), the interaction between DIRECTION and LATERALITY proved significant (F(1,16)=9.81, p=0.003, 𝜂2=0.13)."

From the Methods section:

"We included two additional datasets in this study. In both studies, participants performed a visual attention task while keeping their fixation in the center of the screen. Regarding the Feldmann-Wüstefeld and Vogel, 2019 study, participants were asked to memorize the colors of two stimuli while ignoring a set of distractors stimuli. We analyzed uniquely those trials in which the visual stimuli were presented to the left or right side of the screen, while the distractors were placed above or below the fixation cross. After 500ms of the fixation cross, two colored 'target' stimuli were presented for 200ms. Participants were asked to memorize these stimuli, and a new 'probe’ stimulus was shown after an additional second. Participants reported whether the probe matched the target stimuli or not. We analyzed the traveling waves in the 500ms following the target stimulus onset. Participants performed a spatial attention task in the second dataset from Foster et al. 2017. First, the fixation cross cued participants to covertly attend one of eight possible spatial positions uniformly distributed around the center of the screen. After one second, a digit was displayed either in the cued location or in any other one. The remaining locations were filled with letters. Participants were instructed to report the only displayed digit. We analyzed the waves the second before the stimuli onset when participants attended to the locations cued to the left or right side of the screen (we discarded trials in which participants attended locations above or below the fixation cross). For additional details about both experimental procedures, we refer the reader to Foster et al., 2017 and Feldmann-Wüstefeld and Vogel, 2019.”

From the discussion:

"Our previous work proposed an alternative cause for the generation of cortical waves (Alamia and VanRullen, 2019). We demonstrated that a simple multi-level hierarchical model based on Predictive Coding (PC) principles and implementing biologically plausible constraints (temporal delays between brain areas and neural time constants) gives rise to oscillatory traveling waves propagating both forward and backward. This model is also consistent with the 2-dipoles hypothesis (Zhigalov and Jensen, 2022), considering the interaction between the parietal and occipital areas (i.e., a model of 2 hierarchical levels). However, dipoles in parietal regions are unlikely to explain the observed pattern of top-down waves, suggesting that more frontal areas may be involved in generating the feedback. This hypothesis is in line with the PC framework, in which top-down connections have an inhibitory function, suppressing the activity predicted by higher-level regions (Huang and Rao, 2011). Interestingly, Spratling proposed a simple reformulation of the terms in the PC equations that could describe it as a model of biased competition in visual attention, thus corroborating the interpretation of our finding within the PC framework (Spratling, 2008, 2012)."

Author Response

Reviewer #1 (Public Review):

The authors developed a new concept: Skeletal age, which is chronological age + years lost due to suffering a low-energy fracture. There seem to be conceptual problems with this concept: It is not known if the years lost are lost due to the fracture or co-morbidities.

The Reviewer raises an important point, and we are happy to discuss it as follows. While it is not possible to show the causal relationship between a fragility fracture and excess mortality, it has been shown repeatedly that a fracture is associated with an increased risk of pre-mature mortality after accounting for comorbidities and frailty. Indeed, we and others have found that comorbidities contribute little to the increased risk10,11. Moreover, in a previous study using the ‘relative survival analysis’ technique12, we have shown that hip and proximal fractures were associated with reduced life expectancy after accounting for time-related changes in background mortality in the population, suggesting that hip and proximal fractures are an independent clinical risk factor for mortality.

In this study, we used a multivariable Cox’s proportional hazards model to adjust for confounding effects of age and severity of comorbidities, and our result clearly indicated that a fracture is associated with years of life lost. Moreover, comorbidities were considered a factor in an individual's risk profile for estimating skeletal age. As a result, skeletal age reflects the common real-world scenario that the combination of comorbidities and proximal or lower leg fractures compounded post-fracture excess mortality, much greater than each alone13.

Technically, there are two steps to individualise skeletal age for each individual with a specific risk profile. First, we used the statistical approach recommended for the individualisation of survival time prediction using statistical models14 to individualise specific mortality risk for each participant with a specific risk profile. Specifically, we calculated the prognostic risk index as a single-number summary of the combined effects of his/her specific risk profile of a specific fracture site and the severity of comorbidity. His/her individualised fracture-mortality association was then computed as the difference between his/her prognostic index and the mean prognostic index of “typical” people in the general population. In the second step, we used the Gompertz law of mortality and the Danish national lifetable data to transform the individualised association into life expectancy loss as a result of a fracture15.

We have modified part of the description of the methodology as follows:

“For the second aim, we determined skeletal age for individual based on the individual’s specific risk profile. First, we calculated the prognostic risk index as a single-number summary of the combined effects of his/her specific fracture site and the severity of comorbidity51. The prognostic index is a linear combination of the risk factors with weights derived from the regression coefficients. The individualised fracture-mortality association for an individual with a specific risk profile is then the difference between the individual's prognostic index and the mean prognostic index of 'typical' people in the general population51. In the second step, we used the Gompertz law of mortality and the Danish national lifetable data to transform the excess mortality into life expectancy loss as a result of a fracture49.”.

In addition, with the possible exception of zoledronate after hip fracture, we have no evidence that this increased risk of mortality can be changed with interventions.

We agree that there is a lack of strong evidence from randomised controlled trials supporting the benefit of anti-resorptive therapy on post-fracture survival. As mentioned above, the mention of zoledronic acid was simply for illustrating the use of skeletal age to convey a treatment benefit. We have decided to remove the section related to the benefit of pharmacological treatment on post-fracture mortality.

Furthermore, it is not clear why the authors think that patients and doctors will better understand the implications of older "skeletal age", on future fracture risk and the need for prevention, for example, the 10-year risk of MOF? Knowing that my bones are older than me, could make a patient feel even more fragile and afraid of being physically active. The treatment will reduce the risk of future fractures, but this study provides no information about the effect on mortality of preventing the subsequent fracture or the risk of mortality associated with recurrent fractures.

The risk of fracture is typically conveyed to patients and the public in terms of absolute risk metric (e.g., probability) or relative risk metrics (e.g., risk ratio). However, patients and doctors often struggle to comprehend probabilistic statements such as 'Your risk of death over the next 10 years is 5% if you have suffered from a bone fracture'. The underappreciation of post-fracture mortality's gravity has caused patients to be hesitant towards treatment and prevention, contributing to the current crisis of osteoporosis treatment.

We consider that skeletal age will make doctor-patient risk communication more intuitive and probably more effective. For example, for the same 2-fold increased mortality risk of hip fracture, telling a 60-year man with a hip fracture that his skeletal age would be 66 years old, equivalent to a 6-year loss of life is much more intuitive. The patient might be thus more likely to accept the recommended pharmacological treatment, ultimately improving health benefits. However, we have not had RCT evidence for the effectiveness of skeletal age, and this will be one of our future research focus. We would like to point out that there is RCT evidence that effective age (such as 'Heart Age', 'Lung Age') could improve the uptake of preventive actions. For example, informing patients about their heart age, as shown by Lopez-Gonzalez et al16 was found to better improve their cardiovascular risk compared to informing the Framingham probabilistic risk score.

Introduction:

The statement that treatment reduces the risk of dying, needs modification as the majority of clinical trials have not demonstrated reduced mortality with treatment.

We have modified the statement as follows: “In randomised controlled trials, treating high-risk individuals with bisphosphonates or denosumab reduces the risk of fracture4, though whether the reduction translates into reduced mortality risk remains contentious5, 6.”

It is not clear how the skeletal age captures the risk of a future fracture. The other difference between the idea of "skeletal age" and for example "heart age" is that there are treatments available for heart disease that reduce the risk of mortality, as mentioned above this has not been shown consistently in clinical trials in osteoporosis.

We take the Reviewer's point, but we would like to point out that there are at least two RCTs on zoledronic acid showing that treating patients with a fragility fracture reduces their risk of mortality17,18.

Because the risk profile that is associated with a post-fracture mortality is also associated with the risk of fracture, skeletal age can be seen as a measure of the decline of the skeleton due to a fracture or exposure to risk factors that raise the risk of fracture. Thus, a 60-year-old with a skeletal age of 66 is in the same risk category as a 66-year-old with 'favourable risk factors' or at least the ones that are potentially modifiable. Hence, an older skeletal age means a greater risk of fracture.

Neither the “Skeletal Age” nor the “Heart Age”16,19,20 has the treatment intervention incorporated into its calculator. We have added details to explain how the assessment of skeletal age would provide the conceptual risk of both fracture and post-fracture mortality as follows:

“Unlike the current fracture risk assessment tools17 which estimate the probability of fracture over a period of time using probability-based metrics, such as relative risk and absolute risk, skeletal age quantifies the consequence of a fracture using a natural frequency metric. A natural frequency metric has been consistently shown to be easier and more friendly to doctors and patients than the probability-based metrics9 11 30. It is not straightforward to appreciate the importance of the two-fold increased risk of death (i.e., relative risk = 2.0) without knowing the background risk (i.e., 2 folds of 1% would remarkably differ from 2 folds of 10%). By contrast, for the same 2-fold mortality risk of hip fracture, telling a 60-year man with a hip fracture that his skeletal age would be 66 years old, equivalent to a 6-year loss of life, is more intuitive. The skeletal age can also be interpreted as the individual being in the same risk category as a 66-year-old with 'favorable risk factors' or at least the ones that are potentially modifiable. Hence, an older skeletal age means a greater risk of fracture.”.

Discussion:

The prevalent comorbidities; cardiovascular diseases, cancer, and diabetes, suggest that fracture patients die from their comorbidities and not their fractures.

Please refer to the above response for more detail. Briefly, the multivariable Cox’s proportional hazards regression adjusted for the confounding effect of age and the severity of comorbidities, indicating the association between fracture and mortality was independent of aging and comorbidity severity. On the other hand, skeletal age is a measure of excess mortality related to either fracture or co-morbidities or both.

The discussion should be more balanced as there is a number of clinical trials demonstrating reductions in vertebral and non-vertebral fractures without effect on mortality. There may be specific effects of zoledronate on mortality, but that has not been shown for the vast majority of treatments.

Please refer to the above response for more detail. Specifically, as the study primarily aimed at introducing skeletal age as a new metric for risk communication, we have decided to omit the paragraph discussing the potential benefit of zoledronic acid on post-fracture mortality risk in order to maintain the clarity and focus of the study.

It is not correct that FRAX does not take mortality into account? It does not tell you specifically how high the risk of dying and how high the risk of a fracture is but integrates the two. "Skeletal age" does not provide either information, it just tells you that your skeleton is older than your chronological age - most patients and doctors will not associate that with an increased risk of dying - only of frailty.

Although it is commonly believed that FRAX accounts for competing risk of death, it does not provide the risk of post-fracture mortality. Indeed, none of the current fracture risk assessment tools was designed to provide post-fracture mortality risk5. Skeletal age fills the gap by providing the excess mortality following a fracture for an individual with specific risk profile.

The statement that zoledronate reduces the "skeletal age" by 3 years, has not been demonstrated and it is not clear how this can be demonstrated by the analysis reported here. As the reduced mortality has only been shown for the Horizon RFT, this cannot be inferred for other treatments and other fracture types. The information provided by the "skeletal age" is only that the fracture you already had took x years of your remaining lifetime. With the exception of perhaps zoledronate after hip fracture, we have no indication from clinical trials that the treatment of osteoporosis will change this.

The current study was not designed to examine the effectiveness of an intervention. The statement related to the survival benefit of zoledronate is used to illustrate how skeletal age is used to convey the treatment benefit in real-world doctor-patient risk communication. Given the hazard ratio of 0.72 for zoledronate-mortality association17, a patient might find the statement “Zoledronic acid treatment helps a patient with a hip fracture gain (back) 3 years of life” much easier to understand and probably more persuasive than the traditional statement of “Zoledronic acid treatment reduced the risk of death by 28%”.

Reviewer #2 (Public Review):

The paper of Tran et al. introduces the concept of 'skeletal age' as a means of conveying the combined risk of fracture and fracture-associated mortality for an individual. Skeletal age is defined as the sum of chronological age and the number of years of life lost associated with a fracture. Using the very comprehensive Danish national registry and employing Cox's proportional hazards model they estimated the hazard of mortality associated with a fracture. Skeletal age was estimated for each age and fracture site stratified by gender. The authors propose to replace the fracture probability with skeletal age for individualized fracture risk assessment.

Strengths of the study lie in the novelty of the concept of 'skeletal age' as an informative metric to internalize the combined risks of fracture and mortality, the very large and well-described Danish National Hospital Discharge Registry, the sophisticated statistical analysis and the clear messages presented in the manuscript. The limitations of the study are acknowledged by the authors.

We appreciate your positive remark that captures the essence of our work.

References:

1. Lujic S, Simpson JM, Zwar N, Hosseinzadeh H, Jorm L. Multimorbidity in Australia: Comparing estimates derived using administrative data sources and survey data. PloS one 2017; 12(8): e0183817.
2. Andersen TF, Madsen M, Jorgensen J, Mellemkjoer L, Olsen JH. The Danish National Hospital Register. A valuable source of data for modern health sciences. Dan Med Bull 1999; 46(3): 263-8.
3. Vestergaard P, Mosekilde L. Fracture risk in patients with celiac Disease, Crohn's disease, and ulcerative colitis: a nationwide follow-up study of 16,416 patients in Denmark. Am J Epidemiol 2002; 156(1): 1-10.
4. Hundrup YA, Hoidrup S, Obel EB, Rasmussen NK. The validity of self-reported fractures among Danish female nurses: comparison with fractures registered in the Danish National Hospital Register. Scand J Public Health 2004; 32(2): 136-43.
5. Beaudoin C, Moore L, Gagne M, et al. Performance of predictive tools to identify individuals at risk of non-traumatic fracture: a systematic review, meta-analysis, and meta-regression. Osteoporos Int 2019; 30(4): 721-40.
6. Spiegelhalter D. How old are you, really? Communicating chronic risk through 'effective age' of your body and organs. BMC Med Inform Decis Mak 2016; 16: 104.
7. Vestergaard P, Rejnmark L, Mosekilde L. Osteoporosis is markedly underdiagnosed: a nationwide study from Denmark. Osteoporos Int 2005; 16(2): 134-41.
8. Roerholt C, Eiken P, Abrahamsen B. Initiation of anti-osteoporotic therapy in patients with recent fractures: a nationwide analysis of prescription rates and persistence. Osteoporos Int 2009; 20(2): 299-307.
9. Cummings SR, Lui LY, Eastell R, Allen IE. Association Between Drug Treatments for Patients With Osteoporosis and Overall Mortality Rates: A Meta-analysis. JAMA Int Med 2019; 179(11): 1491-500.
10. Chen W, Simpson JM, March LM, et al. Comorbidities Only Account for a Small Proportion of Excess Mortality After Fracture: A Record Linkage Study of Individual Fracture Types. J Bone Miner Res 2018; 33(5):795-802
11. Vestergaard P, Rejnmark L, Mosekilde L. Increased mortality in patients with a hip fracture-effect of pre-morbid conditions and post-fracture complications. Osteoporos Int 2007; 18(12): 1583-93.
12. Tran T, Bliuc D, Hansen L, et al. Persistence of Excess Mortality Following Individual Nonhip Fractures: A Relative Survival Analysis. J Clin Endocrinol Metab 2018; 103(9): 3205-14.
13. Tran T, Bliuc D, Ho-Le T, et al. Association of Multimorbidity and Excess Mortality After Fractures Among Danish Adults. JAMA Netw Open 2022; 5(10): e2235856.
14. Henderson R, Keiding N. Individual survival time prediction using statistical models. J Med Ethics 2005; 31(12): 703-6.
15. Kulinskaya E, Gitsels LA, Bakbergenuly I, Wright N. Calculation of changes in life expectancy based on proportional hazards model of an intervention. Insur Math Econ 2020; 93: 27-35. 16 Lopez-Gonzalez AA, Aguilo A, Frontera M, et al. Effectiveness of the Heart Age tool for improving modifiable cardiovascular risk factors in a Southern European population: a randomized trial. Eur J Prev Cardiol 2015; 22(3): 389-96.
16. Lyles KW, Colon-Emeric CS, Magaziner JS, et al. Zoledronic acid and clinical fractures and mortality after hip fracture. N Engl J Med 2007; 357(18): 1799-809.
17. Reid IR, Horne AM, Mihov B, et al. Fracture Prevention with Zoledronate in Older Women with Osteopenia. N Engl J Med 2018; 379(25): 2407-16.
18. Bonner C, Batcup C, Cornell S, et al. Interventions Using Heart Age for Cardiovascular Disease Risk Communication: Systematic Review of Psychological, Behavioral, and Clinical Effects. JMIR Cardio 2021; 5(2): e31056.
19. Svendsen K, Jacobs DR, Morch-Reiersen LT, et al. Evaluating the use of the heart age tool in community pharmacies: a 4-week cluster-randomized controlled trial. Eur J Public Health 2020; 30(6): 1139-45.
20. Suissa S. Immortal time bias in pharmaco-epidemiology. Am J Epidemiol 2008; 167(4): 492-9.

Author Response

Reviewer #1 (Public Review):

The authors use a newly developed object-space memory task comprising of a "Stable" version and "Overlapping" version where two objects are presented in two locations per trial in a square open field. Each version consists of 5 training trials of 5-min presentations of an object-space configuration, with both object locations staying constant across training trials in the Stable condition, and only one object location staying fixed in the Overlapping condition. Memory is tested in a test trial 24 hours later where the opposite configuration is presented - overlapping configuration presented for the Stable condition and stable configuration presented for the Overlapping condition - with the thesis that memory in this test trial for the Overlapping condition will depend on the accumulated memory of spatial patterns over the training trials, whereas memory for the test trial in the Stable condition can be due to episodic memory of last trial or accumulated memory. Memory is quantified using a Discrimination Index (DI), comparing the amount of time animals spend exploring the two object locations.

Here, animals in other groups are also presented with an interference trial equivalent to the test trial, to test if the memory of the Overlapping condition can be disrupted. The behavioral data show that for RGS14 over-expressing animals, memory in the Overlapping condition is diminished compared to controls with no interference or controls where over-expression is inhibited, whereas memory in the Stable condition is enhanced. This is interpreted as interference in semantic-like memory formation, whereas one-shot episodic memory is improved. The authors speculate that increased cortical plasticity should lead to increased and larger delta waves according to the sleep homeostasis hypothesis, and observe that instead increased cortical plasticity leads to less non-REM sleep and smaller delta waves, with more prefrontal neurons with slower firing rates (presumably more plastic neurons). They further report increased hippocampal-cortical theta coherence during task and REM sleep, increased NonREM oscillatory coupling, and changes in hippocampal ripples in RGS14 over-expressing animals.

While these results are interesting, there are several issues that need to be addressed, and the link between physiology and behavioral results is unclear.

1) The behavioral results rely on the interpretation that the Overlapping condition corresponds to semantic-like memory and the Stable condition corresponds to episodic-like memory. While the dissociation in memory performance due to interference seen in these two conditions is intriguing, the Stable condition can correspond not just to the memory of the previous trial but also accumulated memory of a stable spatial pattern over the 5 testing trials, similar to accumulated memory of a changing spatial pattern in the Overlapping pattern.

Yes! We completely agree on this. We do not claim the stable condition corresponds to episodic-like memory, instead we refer to it as simple memory, since it can be solved either way (one trial memory or cumulative memory). We now expanded this in the discussion to make it clearer.

Here, it is puzzling that in the behavioral control with no interference (Figure 1D), memory in the Stable and Overlapping condition is unchanged in the test trial, with the DI statistically at 0 in the test trial. In the original description of the Object Space task by the authors in the referenced paper, the measure of memory was a Discrimination Index significantly higher than 0 in both the Stable and Overlapping conditions. This discrepancy needs to be reconciled. Is the DI for the interference trial shown in Fig. S1 significantly different than 0? No statistics or description is provided in the figure legend here.

As mentioned above, we apologize that we oversimplified the description. The 24h interference trial would be what corresponds to the original test trial. We added a clarifying figure for comparison in S1 (bar graph in addition to the violin plot) and stats. Performance was for all groups and conditions above chance, replicating our previous results.

2) The physiology experiments compare Home cage (HC) conditions to the Object Space task (OS) throughout the manuscript. While some differences are seen in the control and RGS14 over-expressing animals, there is no comparison of the Stable vs. Overlapping condition in the physiology experiments. This precludes making explicit links between physiological observations and behavioral effects.

As also mentioned above, we have now added analysis exploring the detailed OS conditions. We would like to thank the reviewers for giving us the opportunity of doing so.

3) The authors speculate that learning will result in larger and more delta waves as per the synaptic homeostasis hypothesis. It should be noted here that an alternative hypothesis is that there should also be a selective increase in synaptic plasticity for learning and consolidation. The authors do observe that control animals show more frequent and higher-amplitude delta waves, but rather than enhancing this process, RGS14 animals with increased plasticity show the opposite effect. How can this be reconciled and linked with the behavioral data in the Stable and Overlapping condition?

In the context of the Object Space Task, we would expect all behavioural conditions (Stable and Overlapping) to induce synaptic changes since learning does occur also in the Stable condition (see also performance on 24h trial). Thus, especially homeostatic responses such as increase in delta amplitude, we would expect for all experiences independent if subtle statistical rules are presented or not. In contrast, detailed processing, extracting underlying regularities is rather proposed by the Sleep for Active Systems Consolidation Hypothesis to occur during hippocampal-cortical interactions in form of delta/ripple/spindle interactions (with different theories emphasising different types of interactions). As mentioned above, we now add a more specific analysis in this regards, where we can show that the two OS conditions that involve moving objects (where thus potentially statistical regularities can be extracted) show a higher percentage of ripples occurring after large slow oscillations in comparison to home cage or the simple learning condition Stable. In contrast, RGS14 already has higher participation in both control conditions, emphasising that in these animals all experiences are treated by the brain as significant learning condition, explaining the behavioural effect (increased interference due to better memory for the interference). Further, we expanded in the discussion how in RGS we sometimes see an enhancement of learning effects but sometimes see a more complex interaction of what we would expect from physiological learning.

Similarly, there is an increase in slower-firing neurons in RGS14 over-expressing animals. Slower-firing neurons have been proposed to be more plastic in the hippocampus based on their participation in learned hippocampal sequences, but appropriate references or data are needed to support the assertion that slower-firing neurons in the prefrontal cortex are more plastic.

As described above, we have expanded the discussion including other citations that also consider the cortex. We can show that our changes would be expected if one turns the cortex as plastic as the hippocampus.

4) It is noted that changing cortical plasticity influences hippocampal-cortical coupling and hippocampal ripples, suggesting a cortical influence on hippocampal physiological patterns. It has been previously shown that disrupting prefrontal cortical activity does alter hippocampal ripples and hippocampal theta sequences (Schmidt et al., 2019; Schmidt and Redish, 2021). The current results should be discussed in this context.

We would like to thank the reviewer for these suggestions, they are now incorporated in the manuscript.

Reviewer #2 (Public Review):

In this paper, the authors provide evidence to support the longstanding proposition that a dual-learning system/systems-level consolidation (hippocampus attains memories at a fast pace which are eventually transmitted to the slow-learning neocortex) allows rapid acquisition of new memories while protecting pre-existing memories. The authors leverage many techniques (behavior, pharmacology, electrophysiology, modelling) and report a host of behavioral and electrophysiological changes on induction of increased medial prefrontal cortex (mPFC) plasticity which are interesting and will be of significant interest to the broad readership.

The experimental design and analyses are convincing (barring some instances which are discussed below). The following recommendations will bolster the strength/quality of the manuscript:

1) Certain concerns regarding the interpretation and analysis of the behavioral data remain. The authors need to clarify if increased mPFC plasticity leads to only an increase in one-shot memory or 'also' interference of previous information. It seems that the behavioral results could also be explained by the more parsimonious explanation that one-shot memory is improved. Do the current controls tease apart these two scenarios?

We agree we cannot disentangle if one memory is just stronger than the other or if its an overwriting effect. We added this now to the discussion. Of note, we do not think it actually would be possible to distinguish these two effects behaviourally in rodents, or at least we cannot think of a fitting study design that would enable the contrast.

Additionally, the authors need to clarify why the 'no trial' and 'anisomycin' controls for the stable task perform at chance levels on exposure to a new object-place association on test day (Fig 1D).

Violin plots are sometimes hard to see. Here simple bar plots where you can see that the animals are not at chance at the 72h test in the control conditions.

Finally, further description of how the discrimination index (exploration time of novel-exploration time of familiar/sum of both) is recommended i.e., in the stable condition, which 'object' is chosen as 'novel' (as both are in the same locations) for computing the index (Fig 1). Do negative DI values imply a neophobia to novel objects (and thus are a form of memory; this is also crucial because the modelling results (Fig 1E) use both neophilia and neophobia while negative discrimination indexes are considered similar to 0 for interpreting the behavioral results, as stated on page 3, lines 84-86?

We added this now to the methods (For Overlapping it is moved location – stable location, for Stable it is location-to-be-moved-at-test – stable location and for random which is assigned as moved and stable is random, and then for each divided by total time). We agree that neophilia/neophobia (especially changes in the distribution) can be an issue and have discussed it in detail in Schut et al NLM 2020 where we see difference in absolute beta values (thus controlling for philia/phobia differences). We also discuss there why it is difficult to control for this in the DI in more detail. In short, one could use absolute values but then it is difficult to determine what a group chance-level would look like. However, luckily here there is not issue since we did not observe difference in neophilic or phobic tendencies while running the experiments. Critically the interference trial (that can also function as simple test trial) confirms that as a group animals show positive DI and neophilia.

2) The authors report lower firing rates in RGS14414 animals during the task in Fig 2F. It is indeed remarkable how large the reported differences are. The authors need to rule out any differences in the behavioral state of the animals in the two groups during the task, i.e., rest vs. active exploration/movement dynamics. Are only epochs during the task while the animals interact with the objects used for computing the firing rates (same epochs as Fig 1)? If not, doing so will provide a useful comparison with Fig 1. Additionally, although the authors make the case for slow firing rate neurons being important for plasticity (based on Grosmark and Buzsaki, 2016), it is crucial to note that the firing rate dynamic (slow vs. fast) in that study for the hippocampus is defined based on the whole recorded session (predominated by sleep), indeed the firing rates of the two groups (slow vs. fast/plastic vs. rigid) during the task/maze-running do not differ in that study. Therefore, the results here seem incongruent with the Grosmark and Buzsaki paper. Since this finding is central to the main claim of the authors, it either warrants further investigation or a re-interpretation of their results.

As mentioned in the main points, we now added the firing rate analysis (including new groups splits) for wake in the sleep box, NREM and REM separately. Each time the same results are obtained. Currently, we do not yet have the tracking and video synchronization set-up, therefore we cannot split the task for specific behaviours.

However, we now also cite Buzsaki’s original log-normal brain review, where he first proposed the idea. There he also shows same effects as we do, in that the general firing rate distribution is the same for task and different sleep stages, just overall shifted. The analysis from Grosmark included more strigent subselection of neurons to be able to also argue that incorporation into run/replay-sequences could not have been biased by firing rate per se (instead of plasticity). However, the original proposition from Buzsaki does fit to our results. He further presents hippocampus vs cortex firing rates, which also confirm the idea (hippocampus more plastic and has slower firing rates). We included this figure above in the general comments. Further, we now expanded the discussion in this point.

3) A concern remains as to how many of the electrophysiological changes they observe (firing rate differences, LFP differences including coupling, sleep state differences, Figs. 2-4) support their main hypothesis or are a by-product of injection of RGS14414 (for instance, one might argue that an increased 'capability' to learn new information/more plasticity might lead to more NREM sleep for consolidation, etc.). The authors need to carefully interpret all their data in light of their main hypothesis, which will substantially improve the quality/strength of the manuscript.

We now expanded the discussion, included more structure and also include that we cannot disentangle if the cellular changes or sleep oscillation changes or an interaction of both is the cause of the result. Furthermore, we added that we cannot distinguish if the interference memory is stronger or actually overwrites the original training memory.

Reviewer #3 (Public Review):

The authors set out to test the idea that memories involve a fast process (for the acquisition of new information) and a slow process (where these memories are progressively transferred/integrated into more-long term storage). The former process involves the hippocampus and the latter the cerebral cortex. This 'dual-learning' system theoretically allows for new learning without causing interference in the consolidation of older memories. They test this idea by artificially increasing plasticity in the pre-limbic cortex and measuring changes in different learning/memory tasks. They also examined electrophysiological changes in sleep, as sleep is linked to memory formation and synaptic plasticity.

The strengths of the study include a) meticulous analyses of a variety of electrophysiological measurements b) a combination of neurobiological and computational tools c) a largely comprehensive analysis of sleep-based changes. Some weaknesses include questions about the technique for increasing cortical plasticity (is this physiological?) and the absence of some additional experiments that would strengthen the conclusions. However, overall, the findings appear to support the general idea under examination.

This study is likely to be very impactful as it provides some really new information about these important neural processes, as well as data that challenges popular ideas about sleep and synaptic plasticity.

We would like to thank the reviewer for these positive comments. Answers to the weaknesses are presented below in the recommendations for the authors.

Author Response

Reviewer #1 (Public Review):

I noticed 2 weaknesses, the first related to the killing assays: considering that WT IgG less efficiently promotes complement-mediated phagocytosis of bacteria, one would assume that the ingested bacteria (to be killed) would be lower in neutrophils exposed to this IgG, to begin with - which is not accounted for in the analyses shown.

We now included a better explanation of our opsonophagocytic killing assay.

A second weakness in my mind pertains to the in vivo experiment: the model used obviously requires a very high number of bacteria (the inoculum), somehow indicating that this specific bacterial strain does not lead to progressive infection (i.e. with replicating bacteria) but mice experience a severe acute inflammatory response followed by the rapid elimination of bacteria. This explains the high mortality - and indicates that mice succumb to acute inflammation, rather than the progressive replication of bacteria. To conclusively prove the therapeutic value of those modified antibodies, a clinically more relevant S. pneumoniae model would be helpful.

The inoculum used in our mouse model was based on a dose finding study. Although the initial starting dose was 5x106 bacteria (based on previously published mouse infection models with S. pneumoniae serotype 6A), we needed a higher dose (1x108 bacteria) to reach 80-100% mortality. While we agree that the final dose was relatively high, this does not mean that capsule type 6 is not a clinically relevant strain. It is well known that clinically relevant serotypes in humans are not always invasive in mice (doi: 10.1128/iai.60.1.111-116.1992). This is the exact reason why we chose to perform in vivo experiments with serotype 6A, which is known to be more invasive in mice (while serotype 6B is more virulent in humans). Of course, while our in vivo data provide an important proof-of-concept for the capacity of hexamer-enhancing mutations to improve protection by anti-capsular antibodies, future studies are needed to verify the potential use of mAbs against other serotypes.

A third aspect, which should be addressed in the discussion, unless tested and not shown, is how anti-pneumococcal IgM antibodies compare to hexamerized IgGs. Is there any advantage, or do they perform similarly with regards to complement activation?

We have now generated and tested IgM against CPS6 (Figure 2g). Although anti-CPS6 IgM can induce complement-dependent phagocytosis to some extent, but IgM was less potent than IgG variants with hexamer-enhancing mutations. This suggests that complement activation via pre-assembled IgM oligomers was less effective than via IgG hexamers that are formed after target binding.

These new data are now included in the revised manuscript as figure 2g, supplemental figure 9 and commented in results section lines 172-179.

Reviewer #2 (Public Review):

The results are intriguing, and one consideration is whether enhancing complement activation is beneficial or harmful for a therapeutic antibody. Based on these results is there the possibility of a natural selection against strong levels of complement activation?

We appreciate the positive feedback to our presented work. Indeed, it is believed there is a natural selection against these mutations to avoid uncontrolled complement activation by naturally occurring IgGs in solution. It is important to realize that formation of IgG hexamers is a surface-dependent process. When IgG molecules bind to surface-bound antigens (via Fab), they can organize into higher-ordered hexamers via Fc-Fc interactions. The specific point mutations used in this paper increase hexamer formation after antigen binding on the cell surface. However, at high concentrations of IgG (as those occurring in our blood (>10 mg/ml), IgG hexamers might be formed independent of target binding (van Kampen et al Journal of Pharmaceutical Sciences Volume 111, Issue 6, June 2022, Pages 1587-1598). If naturally occurring IgGs would have hexamer-enhancing mutations, IgG hexamers could be formed in solution resulting in massive complement activation and depletion of the complement system.

The study clearly shows that the introduction of the hexamerisation mutations affects the ability of the antibodies to bind and activate complement. The studies in Fig 2 examining the role of Fc are particularly elegant. One issue is that it is surprising that the WT IgG1 and IgG3 monoclonals have a minimal capacity to fix and activate complement, despite IgG1/3 to other antigens being efficient isotypes at fixing complement. In the absence of data showing whether IgG1/3 from normal human sera against capsule fixes complement then it is difficult to contextualise these results or to assess if other changes, such as in glycosylation, contribute to the results presented. Related to this, there is reasonable evidence that antibodies induced to capsules can be protective yet the data in Fig 5 suggests that without the mutations then the monoclonals are not effective at all for 6B and only effective at the highest concentration for 19A.

As mentioned in Essential revision 3 our data with S. aureus antibodies demonstrate that this is not a consequence of how these mAbs are produced or differences in their Fc glycosylation profile. We agree with the fact that there are reasonable evidence that antibodies induced to capsules can be protective. However, not all vaccine serotypes are able to induce a strong immune protection. Serotype 6B, for instance, which is covered by current vaccines, is found to be poorly immunogenic (manuscript lines 101-103). For further studies, it would be really interesting to find out what makes this difference between mAbs and, specifically in our case between anti-CPS antibodies.

The adoptive transfer experiments demonstrate that the antibodies can moderate bacteraemia. The mechanism of this is not explored and the importance of hexamerisation and complement activation not demonstrated, especially as it is not clear if human antibodies and mouse complement are a productive combination in this context.

We have now included additional phagocytosis assays with mouse sera (supplemental figure 15) that demonstrate that human antibodies and mouse complement are a productive combination.

Author Response

Reviewer #1 (Public Review):

The manuscript by Silva et al. "Evaluation of the highly conserved S2 hairpin hinge as a pan-coronavirus target" seeks to evaluate a new epitope target on the S2 domain of SARS-CoV2 Spike protein and evaluate its potential as a pan-coronavirus target. This is an impressive combination of extensive structural, HDXMS-based dynamics and antibody engineering approaches. What is missing is a detailed correlation of HDXMS with Spike dynamics. The authors have not examined the allosteric effects of 3A3 binding to the Spike trimer, specifically cooperativity in antibody binding. Does binding of one Fab positively or negatively impact the subsequent binding of antibody? In this regard, readers would benefit from HDXMS spectral envelopes in figures, at least for the epitope locus peptides. Further, what is the effect of the intrinsic ensemble behavior of the Spike protein on 3A3 interactions? In a broader sense antibody binding is assisted by intrinsic trimer ensemble behavior, as observed by the lowered binding to the omicron variant- but are there induced binding effects? It would help to better integrate HDXMS with cryo-EM and antibody engineering. It is a novel, less explored epitope target on the S2 domain. Overall, a more definitive mechanistic conclusion for how targeting the S2 hinge can advance future pan-coronavirus strategies is missing.

1) Given that the authors have demonstrated ensemble switching behavior from 4 ℃ to 37 ℃ (Costello et al. (2021)) why is this not factored in how the HDXMS is carried out? The samples were stored, frozen at -80 ℃, thawed, and equilibrated for 20 min at 20 ℃ with or without antibody present and analyzed by HDXMS. However, the reported t1/2 for trimer tightening at 37 ℃ is t1/2 = 2.5 h (Supplementary Fig. 7). The samples should ideally be analyzed under standardized conditions with the stable conformer. Sample heterogeneity from HDXMS is likely due to any of the following contributing factors:

i) Intrinsic ensemble heterogeneity (Costello et al. (2021)), Kinetics of RBD- up and down conformational switching

ii) Cooperativity of Fab binding.

iii) Partial occupancy of trimer epitopes with bivalent IgG.

iv) Combination of cooperativity effects and partial binding effects

I would predict for any of the above reasons, it is intriguing why are there no bimodal kinetics of deuterium exchange reported. Partial occupancy should be evident from HDXMS paratope analysis.

2) Pan-coronavirus neutralization potential is clearly evident. It is intriguing that the antibodies were isolated after immunization with an authentic MERS S2 domain but showed better selectivity to full-length 6P-engineered Spike. How is cooperativity built into antibody binding, given that the epitope site is occluded to various extents by the S1 domain and access is contingent upon RBD up-down kinetics?

3) I am surprised that there is no allostery described for 3A3 (Supplementary figures 5, 6).

The HDX-MS experiments presented in this work were carried out by the D’Arcy lab and published in a preprint on bioRxiv (originally posted on February 1, 2021) prior to publication of Costello et al. (first posted to bioRxiv July 11, 2021, epub March 2, 2022). Indeed, our bioRxiv posting inspired the Marqusee lab to request 3A3 for inclusion in their work focused on the conformational heterogeneity of the spike protein. Without prior knowledge of the conformational heterogeneity, we carried out these epitope mapping experiments at 25Ç, which allowed us to successfully mapped the epitope without determining which conformation the antibody prefers.

The data presented in Costello et al. further confirms the location of 3A3’s epitope presented here and provides additional information about its preference for different conformational states within the spike protein. We have included an additional comment in the methods section (lines 660-661) stating, “The location of the 3A3 epitope was confirmed in a separate experiment carried out over the temperature range of 4 to 37 °C (Costello et al. 2022).”

This is a clear example of the value of pre-prints to stimulate timely scientific collaboration. While Costello et al. used 3A3 as a tool to probe spike dynamics, here we highlight the original work that identified the epitope.

Spectral envelopes have been provided (Supplementary Fig. 4b and Supplementary Table 3).

The HDX-MS data provides limited insight into possible cooperative or allosteric binding of the 3A3 antibody because of other sources of heterogeneity such as spike dynamics and partial occupancy of the spike epitopes. However, no difference in occupancy was detected when HDX-MS with 3A3 Fab was compared to the same experiment with bivalent 3A3 IgG. It should be noted that in this HDX system, the antibody is not bound so tightly that the spectra are bimodal, showing the exchange of bound and unbound populations separately. Though HDX-MS experiments were performed in slight Fab or IgG excess of 1:1 Fab:spike monomer stoichiometry, the absolute stoichiometry in the context of the spike trimer is unclear.

Reviewer #2 (Public Review):

The authors report a conserved spike S2 hinge epitopes and two conformationally selective antibodies that help elucidate spike behavior. This work defines a third class of S2 antibody and provides insights into the potency and limitations of targeting this S2 epitope for future pan-coronavirus strategies.

Thank you for your review of this manuscript.

Reviewer #3 (Public Review):

The study by Silva et al details the discovery and evaluation of a third class of broadly cross-reactive anti-Spike antibody that binds a conserved hinge region in the S2 domain. After immunizing mice with a stabilized S2 protein from MERS and generating scFv phage libraries, the authors were able to identify antibody 3A3, which showed broad cross-reactivity with SARS2 (including Omicron BA.1), SARS1, MERS, and HKU1 spike proteins. Using a combination of a low-resolution cryo-EM structure and HDX mass spectrometry, the authors were able to map amino acids in the antibody paratope and spike epitope, the latter of which is the hinge region of the Spike S2 domain (residues 980-1005) that plays a critical role in pre- to -post-fusion conformational changes. Through well-executed and comprehensive mutagenesis, binding, and functional assays, the authors further validated critical residues that lead to antibody escape, which centered around the 2P residues and diminished viral entry. While 3A3 and an affinity-enhanced engineered version, RAY53, did not show potent in vitro neutralization against the authentic virus, the antibody was shown to recruit Fc effector functions for viral clearance, in vitro.

Overall, the conclusions of this paper are well supported by the data, but the usefulness of such antibodies is likely limited. The work can be strengthened by extending the analysis of 3A3-like antibodies in the context of human immune responses and in vivo effectiveness.

1) Isolation of 3A3 was achieved after the generation of scFv-phage libraries following immunization with a MERS S2-domain immunogen in a mouse model. The fact that 3A3 binds well to 2P-stabilized sequences and binding/neutralization is diminished upon reversion of 2P mutations back to the native spike sequence (Figures 3a, 4c, and 5b), suggest that such antibodies would likely not arise from natural infection. This contrasts the isolation of fusion peptide and stem helix-directed antibodies, which were isolated from both immunized animals and convalescent individuals. To make their results more solid regarding the use of such antibodies in future vaccine strategies, the authors should provide evidence that 3A3-like antibodies can be identified in human donors. For example, they could enrich donor-derived S2-specific antibodies that bind both MERS and SARS2 S2 domains and evaluate the fraction of antibodies that recognize the hinge-epitope using competition binding assays (either ELISA or BLI), which have commonly been used to map epitope-specific sera responses. This could also be achieved with nsEMPEM of polyclonal IgGs bound to S2 proteins.

2) The authors speculate in the discussion that strategies to enhance access to the hinge epitope, which may include ACE2-mimicking antibodies, could promote enhanced viral clearance. In addition to ACE2-mimicking antibodies, several antibodies have been described that bind the RBD and promote S1 shedding (see for instance mAb S2A4 - Piccoli et al, 2020, Cell). Several 2nd generation vaccine platforms utilize RBD-only immunogens that are likely to induce high titers of ACE2-mimicking and cross-reactive S1-shedding antibodies. Thus, adding in vitro neutralization and ADCC experiments to assess synergy between 3A3/RAY53 and such antibodies would booster this speculative claim and be of interest to many in the field developing strategies for pan-coronavirus therapies.

3) The authors provide in vitro evidence in Figure 5c,d for Fc-mediated viral clearance. While in vivo data to show effectiveness in animal models is ideal, additional in vitro data that utilize engineered constructs that modulate effector function (e.g., DLE (+) or LALA (-)) would boost the authors' claims regarding Fc-mediated viral clearance mechanisms by 3A3/RAY53.

1) Though we do not plan to isolate 3A3-like antibodies from human donors, there is evidence that these antibodies are elicited in infected humans via analysis of polyclonal responses in Claireaux et al 2022. We also know of several studies on naturally occurring S2 hinge targeting antibodies from colleagues that are in preparation. Understanding the therapeutic role of this antibody class is relevant to the study of broadly-reactive S2 antibodies, even if that role is limited.

2) We agree that synergy between S2 hinge epitope binding antibodies and ACE2 mimicking antibodies will be very interesting to investigate. We hope to pursue this in future work.

3) We agree these are excellent controls to include, in addition to isotype controls already shown. In accordance with the eLife COVID research policy, we minimized our claims around Fc-effector functions elicited by RAY53 and stated that further experiments to confirm our preliminary findings are needed.

The existing description of the effector function experiments states in lines 392-392 “These results indicate that RAY53 binding is compatible with ADCP and ADCC,” which is already a very limited claim.

We also added in line 450 that S2 core-binding antibodies “require further validation” of their ability to recruit effector functions.

We appreciate the importance of controls providing effector function modulation and will include the LALAPG mutations as a standard component of our future ADCC evaluation. However, given our focus on the relevance of the epitope and consistency of the Fc regions across the antibodies, we felt that the isotype and positive control antibodies (target binding controls) were the most relevant controls to include in this study.

Author Response

eLife assessment

Germline inactivation of NPHP2, which encodes a protein that localizes to the transition zone at the base of the primary cilium, results in infantile kidney cysts and fibrosis. In this study, the authors provide solid evidence that increased cell proliferation and fibrosis precede cyst formation in Nphp-2 mouse models, that mutant renal epithelial cells are responsible for the phenotype, and that genetic inhibition of ciliogenesis in this model reduces disease severity. They also show that valproic acid, a drug that affects a number of cellular targets and is used to treat other human conditions, slows disease progression. One limitation of the study is that it provides limited insights into the mechanisms responsible for any of its interesting observations.

To our knowledge, our study is the first to pinpoint defective epithelial cells as the main driver for both epithelial cysts and interstitial fibrosis in a NPHP model. The discovery that abnormal signaling from epithelial cells triggered a profibrotic response in the absence of cyst formation is also novel. Our Ift88 Nphp2 double mutant results, combined with tissue-specific function of NPHP2, suggest that NPHP2 functions as a negative regulator of a profibrotic and pro-cystic pathway that interacts with cilia-mediated signaling in epithelial cells and that abnormal signaling from epithelial cells triggers interstitial fibrosis. Moreover, we identified the HDAC inhibitor VPA as a potential candidate drug for treating NPHP. Although the precise molecular function of NPHP2 remains undefined, our results suggest that epithelial specific function and epithelial-stromal crosstalk underlie NPHP like phenotypes in Nphp2 mutant kidneys. Furthermore, although whether NPHP2 interacts with polycystin-mediated signaling remains an outstanding question, our results ruled out the involvement of NPHP2 in ciliary localization of PC2.

Reviewer #1 (Public Review):

Nephronophthisis (Nphp) is a multigenic, recessive disorder of the kidney presenting in childhood that is characterized by cysts predominantly at the cortico-medullary junction and progressive fibrosis. An infantile form of the disease presents earlier with more diffuse cystic change. The condition is considered a ciliopathy because most of the genes linked to the condition encode proteins involved in ciliary biogenesis or function. Germline mutations in NPHP2 are associated with a particularly severe, infantile form of the disease. Given that interstitial fibrosis is a more prominent feature of Nphp compared to many other forms of polycystic kidney disease, the authors sought to determine the mutant cell types responsible for the phenotype.

In the current study, the authors generated and characterized mouse lines with Nphp2 selectively inactivated in either renal epithelial cell or stromal cell lineages and found that inactivation in renal epithelial cells was both necessary and sufficient to cause disease. They further showed that markers of interstitial fibrosis and proliferation increase in mutants prior to the onset of histologically evident cystic disease, suggesting that aberrant epithelial-stromal cell signaling is an early and primary feature of the condition (Figures 1-4). The study design was straightforward and appropriate to address the question, and the results support their conclusions.

They next tested whether the cilia-dependent cyst-activating pathway (CDCA) that is "unmasked" by loss of other PKD-related genes is similarly active in Nphp2 mutants by generating Nphp2/Ift88 double mutants. Their studies found that the severity of cystic disease and markers of proliferation and fibrosis was attenuated in double-mutants (Fig 5, 6). These studies were also appropriate for testing the hypothesis and the results were similarly consistent with their interpretation.

In the last set of studies, they tested whether valproic acid (VPA), a drug that has multiple modes of action including acting as a broad inhibitor of HDACs and previously used by the investigators in other forms of polycystic kidney disease, would have similar effects in Nphp2 mutants. The authors tested daily injection from days P10 through P28 in both control and Nphp2 mutant mice with VPA or an appropriate vehicle control and found that VPA was beneficial (Fig 7). The study design was acceptable and the results generally support their conclusions. The one perplexing result is shown in Fig 7B. The Nphp2 mutants, regardless of treatment status, have body weights (BW) that are significantly lower than the controls, with treated mutants even trending lower than their untreated mutant counterparts. This is unexplained and should be addressed. In the mutants with more widespread epithelial cell knock-out of Nphp2 (Ksp-Cre, Fig 1), total body weight decreased as mice became more severely cystic with renal impairment. In the milder form of disease produced with the Pkhd1- Cre (Fig 7), total body weight is inexplicably approx. 2g lower on average despite having much more modestly elevated KBWs and BUNs. Moreover, one might have expected that mutants treated with VPA would have had BWs intermediate between untreated mutants and controls since the severity of the disease was moderately attenuated. These differences raise the question as to whether body weight differences are due to factors independent of disease status, the most likely of which would be that the controls were not littermates. This prompted a careful review of the text for descriptions of the control mice. Throughout the study, the investigators describe selecting animals from the same "cohort", but this term is imprecise.

There is little information provided about background strains, whether any of the lines were congenic, or whether any of the studies were done using littermate controls. This must be addressed. It would help if the investigators identified the litter status in their plots. This would clearly show relationships between animals and the number of litters that had animals with these properties. If littermates were not used for each study, the authors must explain both why they didn't do so and how they then selected which animals to use. This information is especially important for interpreting the results of their genetic interaction (fig 5) and drug treatment studies (fig 7).

We thank the reviewer for the multiple positive comments.

To address the issue of body weight, we examined the time course of body weight change more carefully and added Figure 7-figure supplement 1 to present the results. Although Nphp2flox/flox;Pkhd1-Cre mice displayed reduced body weight at P28 in comparison to controls, this reduction was more moderate than that of Nphp2flox/flox;Ksp-Cre mice (Figure 7-figure supplement 1A). Notably, the trend of body weight difference started at around P21 in both Nphp2flox/flox;Pkhd1-Cre and Nphp2flox/flox;Ksp-Cre mice, coinciding with weaning (Figure 7-figure supplement 1B). It is possible that mutants with compromised kidney function were less capable to thrive and gain weight at around this transition time. In terms of VPA treatment, body weight trended down in both wild type and mutant mice subjected to the treatment, although the difference did not reach statistical significance (Fig. 7B). We cannot rule out the possibility that side effect of VPA contributed to weight loss in treated mice. In addition, VPA may affect body weight increase through HDAC: the HDAC inhibitor Trichostatin A was shown to inhibit adipogenesis (PMID: 34232916) and 4-hexylresorcinol, another HDAC inhibitor, reduced body weight in treated rats (PMID: 34445640). To include the additional data and references, we added the following in the Results section:

"We analyzed body weight change of Nphp2flox/flox;Pkhd1-Cre mice in more detail and compared it to Nphp2flox/flox;Ksp-Cre mice. At P28, the reduction of body weight in Nphp2flox/flox;Pkhd1-Cre mice in comparison to control mice was more moderate than that in Nphp2flox/flox;Ksp-Cre mice (Figure 7-figure supplement 1)."

" However, the reduced body weight phenotype in mutant mice was not suppressed by VPA treatment (Fig. 7B). We cannot rule out the possibility that the side effects of VPA contributed to weight loss in treated mice. In addition, VPA may reduce body weight through inhibiting HDAC during the growth period: the HDACI Trichostatin A was shown to inhibit adipogenesis (51)."

Regarding genetic background, all mice analyzed in figures 5 and 7 are in the same genetic background (C57/BL6J). We added more detailed description of genetic background in the Materials and Methods section. Littermate status is now also indicated in figure legends.

In Figure 5, multiple genotypes (i.g. Nphp2flox/flox;Ksp-Cre, Nphp2flox/flox;Ift88flox/flox;Ksp-Cre and Ift88flox/flox;Ksp-Cre) were analyzed. Because of the limited number of animals per litter and low yield of desired genotypes, non-littermates had to be included in some cases. Littermate status is now highlighted by colors in the data tables of Figure 5 source data.

In Figure 7, because of the limited number of animals per litter and the need to subject each genotype to VPA and vehicle treatment, non-littermates had to be included in some cases. Littermate status is now indicated by highlight colors in the data tables of Figure 7 source data.

Several other considerations. The authors state that the effects of VPA are mediated through the drug's inhibition of HDACs and suggest that future studies could be directed at refining the specific HDAC. While this is certainly possible, the authors should acknowledge that VPAs have been reported to have numerous pharmacologic effects and targets and which of these is mediating the effects in their model is unknown (text). They would need mechanistic studies to show this, though it doesn't discount their possible efficacy as a therapy for PKD.

We agree that it is an important point to clarify and added in Discussion: "It is also worth noting that VPA could affect targets other than HDACs and testing newly approved HDACIs will provide useful insight."

The authors also state in their abstract that their double knock-out studies "support a significant role of cilia in Nphp2 function in vivo." It is not clear to me how their studies show this nor how they can exclude that ciliary activity is operating in an Nphp2-independent, parallel fashion that modulates some common downstream pathways.

We agree with the reviewer that our results do not exclude the possibility that NPHP2 and ciliary activity feed into a common downstream pathway, i.e., a cilia-dependent cyst-activating pathway could operate outside of cilia. We changed the sentence in abstract to "supporting a significant interaction of cilia and Nphp2 function in vivo." In addition, we added "Although cilia-dependent, the downstream pathway could potentially operate outside of cilia and receive parallel signals from both ciliary activity and Nphp2." to Discussion to clarify and reflect the results and model more precisely.

Reviewer #2 (Public Review):

The manuscript by Li et al demonstrates the role of Nphp2/Invs in renal epithelia in preventing NPHP-like phenotypes, such as epithelial/stromal proliferation and stromal fibrosis, in mice. Previously, mutants of the Nphp2 allele in mice, generated by insertional mutagenesis, showed severe cystic kidney disease and fibrosis in neonates.

The authors nicely show that the NPHP-like phenotypes in mutant kidneys arise from abnormal signaling specifically within and from renal epithelial cells. Furthermore, the fibrotic response and abnormal increase of cell proliferation precede cyst formation and could be initiated independently of cyst formation. The authors also show that the removal of cilia reduces the severity of Nphp2 phenotypes. The authors suggest that similar to polycystins, NPHP2 inhibits a cilia-dependent cyst and fibrosis-activating pathway. Finally, the histone deacetylase (HDAC) inhibitor valproic acid (VPA) reduces these phenotypes and preserves kidney function in Nphp2 mutant mice, supporting HDAC inhibitors as potential candidate drugs for treating NPHP.

Overall, understanding the mechanisms driving NPHP phenotypes is important and drugging relevant pathways in treating this disease is an important unmet need in patients. The authors have provided insights into both these aspects in this study. The manuscript is nicely written, and the assays shown are rigorous and insightful.

We thank the reviewer for the positive comments.

Reviewer #3 (Public Review):

In this manuscript, Li et. al, investigate whether epithelial or stromal Nphp2 loss, a gene causative of nephronophthisis (NPHP), drives polycystic kidney disease (PKD) and kidney fibrosis in a novel floxed model of Nphp2. The authors found that only epithelial and not stromal Nphp2 loss results in NPHP-like phenotypes in their mouse model. In addition, the authors show that concurrent cilia, via Ift88 loss, and Nphp2 loss within the kidney epithelium as well as HDAC inhibition results in less severe PKD/kidney fibrosis, as has been shown in mouse models of other non-syndromic forms of PKD, such as autosomal dominant PKD caused by mutations to Pkd1 or Pkd2.

The authors aimed to understand (1) whether the published NPHP phenotype (kidney cysts and fibrosis), known from the global Nphp2 knockout mouse, is driven by the function of NPHP2 in the kidney epithelium or stromal cells; (2) if kidney fibrosis in NPHP is linked to kidney damage caused by cysts, or independent and preceding of the PKD phenotype; (3) whether cilia are required, causative, or prohibitive of NPHP cystogenesis; and (4) if a broad spectrum HDAC inhibitor is a potential therapeutic approach for NPHP.

With the provided results, the authors established that epithelial Nphp2 loss is likely a predominant driver of PKD in their model; however, they cannot exclude that stromal NPHP2 does not play a role in cysts growth post-initiation because the authors failed to directly compare their cell type-specific models to a global cre knockout (e.g. Cagg-cre).

We agree with the reviewer that we cannot rule out the possibility that stromal NPHP2 plays a role post cyst initiation and added "However, our result does not rule out functional significance of interstitial cells once a pro-cystic and fibrotic response is triggered in mutant epithelial cells." to the Discussion section.

A direct comparison between epithelial specific and global knockout models is an attractive idea, but technically challenging. For an interpretable comparison, it is essential that the stage and knockout efficiency in epithelial cells are equivalent between the two models. However, Ksp-Cre is expressed in the distal nephron specifically, sparing epithelial cells in other segments, while epithelial cells in all segments would be affected by Cagg-Cre. In addition, global knockout of Nphp2 leads to peri-natal lethality. Inducible Cagg-Cre could potentially be used to bypass earlier functional requirements. But matching stage and knockout efficiency in renal epithelial cells between Ksp-Cre and inducible Cagg-Cre mediated knockout remains challenging. These factors make a direct comparison problematic. Finally, our results revealed the role of defective epithelial cells in triggering the phenotypes but did not rule out a role of interstitial cells once abnormal signaling is initiated in epithelial cells. To clarify this point, we added " However, our result does not rule out functional significance of interstitial cells once a pro-cystic and fibrotic response is triggered in mutant epithelial cells." to the Discussion section.

In addition, it is possible that cyst initiation/growth upon stromal Nphp2 loss occurs substantially slower compared to epithelial Nphp2 loss. However, it seems the authors did not look at kidney phenotypes beyond 28 days of age. Publications from the ADPKD field suggest, that stromal Pkd1 loss initiates cystogenesis much slower than epithelial Pkd1 loss.

We have expanded our analysis to 8-week-old mice. We now show that Nphp2flox/flox;Foxd1-Cre mice show normal kidney weight, kidney/body weight ratio, kidney function and histology at P56, supporting our original conclusion that deletion of Nphp2 in interstitial cells fails to trigger obvious renal phenotypes, up to young adult stage. These results were presented in Figure 4- figure supplement 1 and the Results section.

Further, while the authors suggest that kidney fibrosis precedes cyst development, the results supporting this conclusion are limited to one time point, analyzing IF staining of a single marker that can be compared between non-cystic and cystic time points. These analyses need to be extended to make any firm conclusions.

At the precystic kidney stage (P7), we analyzed SMA and vimentin levels via immunostaining. Their mRNA levels were additionally quantified via RT-qPCR. We have now analyzed vimentin levels at multiple timepoints (P9, 14 and 21) and results were added to Figure 2. Combined, these data support the initiation of a fibrotic response prior to cyst formation.

The most interesting finding of the manuscript, and likely most impactful to the field, is, that loss of cilia within the setting of epithelial Nphp2 loss reduces PKD severity. This finding parallels published findings for Pkd1 and Pkd2 which are suggested to function in a cilia- dependent cyst-activation mechanism. Unfortunately, the here shown studies, do not add to the mechanistic insight beyond showing the descriptive finding. Most importantly, it remains unclear whether NPHP2 functions in the same pathway as polycystin-1 or -2 (the Pkd1, Pkd2 gene products) or in a separate independent pathway.

Our Ift88 Nphp2 double mutant results, combined with tissue-specific function of NPHP2, which to our knowledge is completely novel in a NPHP model, suggest that NPHP2 functions as a negative regulator of a profibrotic and pro-cystic pathway that interacts with cilia-mediated signaling in epithelial cells and that abnormal signaling from epithelial cells triggers interstitial fibrosis. We agree with the reviewer that whether NPHP2 functions in the same pathway as polycystins is an interestingly question. However, we feel it is out of the scope of this manuscript and would pursue this research direction in our future studies.

With respect to the HDAC preclinical studies, the authors show supporting data that a broad- spectrum HDAC inhibitor may be suitable for slowing cyst growth in their model of NPHP. Overall, these studies are not novel to the field, as HDAC inhibition has been shown to slow PKD progression in various models of PKD al while not in NPHP specifically. Further, the studies seem like an add-on, which does not directly link to the prior cell type-specific studies of NPHP2, and no mechanisms linking the two concepts are provided.

Although we and others showed that HDACIs slow cyst progression in other PKD models, this study is the first to show its impact on a NPHP model. Given the current lack of treatment for NPHP, we feel it important to communicate the results to the research community even though the molecular mechanism remains to be defined.

Author Response

Reviewer #1 (Public Review):

The article "Identification of a weight loss-associated causal eQTL in MTIF3 and the effects of MTIF3 deficiency on human adipocyte function" explored the functional roles of MTIF3 during adipocyte differentiation. In persons living with obesity, genetic variation at the MTIF3 locus associates with body mass index and responses to weight loss interventions. MTIF3 regulates mitochondrial protein expression and gene knockouts cause cardiomyopathy in mice. This paper provides insight into the impacts of MTIF3 knockout on adipocyte differentiation and the expression effects of the eQTL on MTIF3 levels. The authors implement a CRISPR/Cas9 gene editing approach coupled with an in vitro platform to detect influences of MTIF3 on adipocyte glucose metabolism and gene expression. This method may serve as a platform to explore knockouts in human cell lines, so it may allow the discovery of new gene x environment influences on in vitro outcomes related to differentiation, growth, and metabolism.

The conclusions of this paper are mostly well supported by data, but some experimental conditions and data analysis needs to be clarified and extended.

1) The authors use CRISPR/Cas9 to generate the rs1885988 variant in the human white adipocyte cell line and performed a comprehensive validation analysis of gene editing (Figure 1). qPCR analysis showed reduced MTIF3 expression during human adipocyte differentiation (Figure 1E, F). To expand the importance of the rs1885988 variant, the authors should have provided target gene measurements to verify the canonical differentiation profile (e.g., FABP4, ADIPOQ) and help readers understand the overall impact of gene editing at the MTIF3 locus.

Thank you for your suggestions. As you requested, we have quantified several adipocyte differentiation markers in the allele-edited cells after 12 days of adipogenic differentiation. The data (Figure 1-figure supplement 1) shows no significant difference between cells with the different genotypes. We have added more information about this in lines 100-101, and also in another context in lines 105-116.

Notably, the intra-group variation of the marker gene expression is large (Figure 1-figure supplement 1), which makes it difficult to clearly state how much the allele editing, as opposed to random variation resulting from single cell cloning, contributes to the differentiation outcome. However, if we also consider MTIF3 knockout cells (that do not need to be single-cell cloned), their differentiation marker expression also appears unaffected (Figure 3-figure supplement 1). Taken together then, it is unlikely the allele editing with the consequent effect on MTIF3 expression affects adipogenic differentiation in our experiments. We mention the absence of effect of MTIF3 knockout on differentiation in the paragraph starting on line 137.

2) The direct mechanistic influences of MTIF3 on adipocyte function remain unclear. MTIF3 regulates the translation initiation of mitochondrial protein synthesis. Western blots of OXPHOS proteins do not per se underscore supercomplex formation, which is also a process mediated by MTIF3. Blue native gel electrophoresis may prove a better method to establish the effects of MTIF3 loss-of-function on supercomplex formation.

As suggested, we have run blue native gel electrophoresis to detect the formation of OXPHOS respiration complexes. In the revised manuscript (lines: 158-168 and Figure 4 E,F), we show how MTIF3 knockout indeed interferes with the complex formation, with lower abundance of complexes V/III2+IV1, III2/IV2 and IV1. Additionally, although the blot signal for complex I+III2+IVn is diffuse, it appears higher in scrambled control cells than in MTIF3 knockout cells. Interestingly, complex II content is slightly higher in MTIF3 knockouts, which may result from a compensatory regulation mechanism, as none of the subunits of complex II is encoded by mitochondrial DNA. We also found several faster-migrating (“undefined bands” in the figure) in the MTIF3 knockout samples, although it is hard to determine whether those are single chain proteins, or degradation or mistranslation products. Overall though, the native gel blots show impaired OXPHOS complex assembly in MTIF3 knockout samples.

In addition, we performed western blots for other mitochondrial proteins, including COX II (subunit of OXPHOS complex IV), ND2 (subunit of OXPHOS complex I), ATP8 (subunit of OXPHOS complex V), and CYTB (subunit of OXPHOS complex III). The data (Figure 4 A,B), show decreased ND2 and COX II, trending decrease of CYTB, and unaffected ATP8 content in MTIF3 knockout adipocytes.

The methods (paragraph starting at line 479), results (paragraph starting at line 145), and discussion (lines: 261-263, 274-277) were incorporated in the revised manuscript.

3) Based on the findings, the authors argue that MTIF3 knockout alters the function of adipocytes. However, many of the experiments show fairly small effect sizes (Figure 5A, Figure 6A). How does the MTIF3 knockout explicitly perform functions related to body weight regulation? Gene editing in vivo would have helped to substantiate the authors' conclusions.

In the paper we are looking at the consequences of MTIF3 deficiency in one cell type, over short time, in vitro. The outcome of body weight regulation, e.g. during weight loss, would result from long-term effects of MTIF3-altered metabolism in more than one tissue. We envisage that small changes in energy metabolism in not only fat, but also in e.g. muscle, would make a substantial difference over time in vivo (this, we cannot capture in in vitro models). We have added this discussion to lines 294-311.

As for in vivo genomic editing, the alleles of interest are specific to the human genome. Ideally, a genotype-based recall study in humans would be appropriate, but due to time and resource limitation, we are not able to conduct such a study at the moment (although we certainly hope to perform such a study in the future). As for modeling the MTIF3 deficiency in mice – the MTIF3 knockout mice are not viable [1], and certainly other options (e.g. overexpression, tissue-specific knockouts) are possible and tempting to investigate. This, however, would require considerable additional work which we could only perform in a future project.

4) In several instances, the authors refer to 'feeding' cells with glucose (line 206, line 171). Feeding experiments often imply complex nutrient interventions in animal models and people, which cannot be easily recapitulated in cell culture. The in vitro experiments simply alter levels of glucose and more precise language would state the specific challenges accurately.

In the revised manuscript, we have substituted “feeding” for exact glucose concentration, or “glucose concentration” where appropriate. (paragraph starting at line 215, and lines 577-578, 597, 873-879)

Reviewer #2 (Public Review):

Huang Mi, et al. investigated the role of MTIF3, the mitochondrial translation initiation factor 3, in the function of adipocytes. They first detected the expression of the obesity-related MTIF3 variants based on the GTEx database and found two variants lead to an increase in MTIF3 expression. Then they knockout MTIF3 in differentiated hWAs adipocytes and characterized the mitochondrial function. They found loss of MTIF3 decrease mitochondrial respiration and fatty acid oxidation. They further treated cells with low glucose medium to mimic weight loss intervention and found MTIF3 knockout adipocytes lose fewer triglycerides than control adipocytes. This paper provides new information about MTIF3 in adipocytes and the potential functional role of MTIF3 in mitochondrial function.

1) The authors provided sufficient data to show those two genetic variants increase MTIF3 expression. Their CRISPR/Cas9 knockin cell line is also convincing. But they didn't show if the genetic variants affect adipogenesis. Adipogenesis is an important process for weight gain and fat deposition. In lines 103-107, the authors mentioned that the "allele-edited cells have some problem in differentiated state, e.g. triglyceride or mitochondrial content", so they used an inducible Cas9 system. However, the issue of differentiated allele-edited cells may be the functional effect of MTIF3 genetic variants, such as interrupting adipogenesis, decreasing triglyceride, or affecting mitochondrial number. The authors should provide that information.

Thank you for all your suggestions. We think we were not clear regarding this issue. We did not mean that the allele-edited cells have problem in differentiated state, which then definitely could be (as you point out) due to the functional effect of MTIF3 genetic variants. The problem relates to the process of single-cell cloning itself, which inherently introduces random variation. As a consequence, the data on adipogenic differentiation in allele-edited cells has relatively high intra-group variation. We have added more clarifying text in lines 104-116.

To provide the data on this, per your request, in the revised manuscript we include the results for the rs67785913-edited cells in Figure 1-figure supplement 1. As shown, we observed no differences in the expression of adipogenic markers (ADIPOQ, PPARG, CEBPA, SREBF1 and FABP4) or in mitochondrial content between the two rs67785913 genotypes. Since the intra-group variation is often high, it is hard to conclude how much the rs67785913 eQTL affects the quantified variables. Much of the variation could instead be ascribed to the effects of single cell cloning.

The cloning per se introduces random variation, but is required to obtain homozygous allele-edited cells. Because of this dilemma, and to clarify how much MTIF3 expression can actually influence adipogenic differentiation, we have, during the revision, also used the hWAs-iCas9 cells to generate MTIF3 knockouts at the preadipocyte stage and then tested their differentiation capacity. As we show in Figure 3-figure supplement 1, we found no apparent differences in adipogenic marker gene expression between scrambled control and MTIF3 knockout cells (we mention that in lines 137-144). Taken together, our results may indicate that the rs67785913 genotype, through affecting MTIF3 expression, is unlikely to regulate adipogenic differentiation.

2) In Figure 4, the author mentioned that MTIF3 knockout does not affect the expression of adipogenic differentiation markers. They need to provide more evidence to prove their point. Oil-red O staining is a clearer way to quantify adipocyte differentiation in cell culture. In addition, in Fig. 4B western blot, the author should include MTIF3 as a control to show the knockout efficiency. It is not clear the meaning of plus and minus in that panel. The author should also compare the total triglyceride levels in MTIF3 knockout cells and control cells.

We have now included Oil-red O staining results and total triglyceride levels (Figure 3 F,G), which show no apparent differences between scrambled control and MTIF3 knockout cells (method: lines 427-431; results: lines 137-144). We also added the MTIF3 blots to figure 4A as a control, showing high and consistent MTIF3 knockout efficiency in independent experiments. In the original manuscript, the plus and minus referred to control and knockout, respectively. To clarify that, we have changed the expression to SC and KO in the revised manuscript.

With regards to Oil-red O vs. quantification of adipogenic markers, we actually prefer the latter method, as it gives more accurate and less variable results than Oil-red O (at least in the cell line we use). We have, however, performed Oil-red O as well to address your question.

3) MTIF3 is a translation initiation factor in mitochondria and is involved in the protein synthesis of mitochondrial DNA-encoding genes. The authors should check protein levels rather than the mRNA levels of mitochondrial DNA-encoding genes (Fig. 6E). It's interesting to see the increase of mRNA levels of ND1 and ND2, which might be feedback of lower translation. Since ND1 and ND2 are in OXPHOS complex I, the expression levels of complex I in MTIF3 KO cells would be worth checking. Additionally, the author should also check the mitochondria copy number.

As suggested, we have detected several mitochondrial encoding proteins which are subunits of each mitochondrial OXPHOS complex. As shown in figure 4A, ND2 (subunit of OXPHOS complex I) and COX II (subunit of OXPHOS complex IV) expression were significantly reduced, CYTB (subunit of OXPHOS complex V) expression tended to decrease, and ATP8 expression was not affected in the MTIF3 knockout adipocytes. We also detected the formation of the OXPHOS respiration complex in extracted mitochondrial proteins and found MTIF3 perturbation affect mitochondrial complex assembly. The detailed methods (lines: 479-490), results (lines: 145-169) and discussion (lines: 260-262, 274-277) were incorporated in the revised manuscript.

We have also added the mitochondrial copy number data (Figure 3A), showing that MTIF3 knockout has lower mitochondrial content (methods: lines 491-500; results: 156-157)

4) MTIF3 knockout adipocytes retain more triglycerides under glucose restriction is interesting. It may link to the previous result of lower fatty acid oxidation in MTIF3 knockout adipocytes. However, the authors then showed there is no difference in lipolysis. The author should discuss those results in the manuscript.The authors could also check lipolysis in glucose restriction conditions. It's also necessary to include the triglyceride levels of KO cell lines at full medium

We have now examined the glycerol release in glucose restriction condition, and found no differences between control and MTIF3 knockouts (Figure 6-figure supplement 1). Interestingly, in 1 mM glucose, both genotypes released less glycerol than at 25 mM glucose, and this has been observed before in SGBS cell line [2] According to your suggestion, we have added the total triglyceride content at 25 mM glucose condition (Figure 6C), which also was not different between control and MTIF3 knockout cells. We speculate the higher retention of triglycerides in the knockouts could be due to higher re-esterification of lipolytically released fatty acids, since, as we observed, fatty acid oxidation is impaired in the knockouts. In the revised manuscript, we added that to the discussion (lines: 289-293).

References

1. Rudler, D.L., et al., Fidelity of translation initiation is required for coordinated respiratory complex assembly. Sci Adv, 2019. 5(12): p. eaay2118.
2. Renes, J., et al., Calorie restriction-induced changes in the secretome of human adipocytes, comparison with resveratrol-induced secretome effects. Biochim Biophys Acta, 2014. 1844(9): p. 1511-22.

Author Response

Reviewer #2 (Public Review):

The idea that decidualization is related to or evolved from wound healing, including fibroblast activation, is old, going back all the way to Creighton 1878 who pointed to the similarity between granulation tissue and decidual tissue, and is supported by the fact that embryo implantation is a compensated form of the endometrial lesion. Nevertheless, the mechanistic connection between FB activation and decidualization is an important fact necessary for understanding decidualization, a fact that is reflected in previous work, for instance, Kim et al., 1999 (Hum Reprod 14 Suppl 2), their reference 20, and Oliver et al., 1999 (Humn Reprod 14), their reference 56 a.o.m. More specifically, a recent single-cell study of in vitro decidualization has shown that a myofibroblast-like cell state is a transient state in the process of decidualization, i.e. decidual cells themselves are not so much activated fibroblasts, but rather decidual cells differentiate after endometrial stromal fibroblasts undergo a FB activation like process, and the decidual re-programming happens from these activated FB like states (Stadtmauer et al., 2021, Biol. of Reprod. 1-18).

Yes, the paper from Stadtmauer DJ and Wagner GP (2022) was cited in revised version.

The above assessment of how the current study fits into the conceptual landscape of mammalian reproductive biology does not diminish the importance of the paper under consideration. The study contributes a large amount of observational and experimental facts to the understanding of how FB activation and decidualization are related. The authors suggest, in particular, that blastocyst-derived TNF activates the cLPA- producing Arachidonic acid (AA), activating PGI2 and PPARd signaling pathway (more about this later).

Other major comments:

The authors suggest that luminal epithelial cells signal through the release of arachidonic acid (AA) in response to TNF. That is interesting and supported by in vitro experiments inducing decidualization and FB activation by AA. What makes this conclusion a little problematic is that it is known that luminal epithelial cells also express COX2/PTGS2 and thus the synthesis of prostaglandins is already starting in the LE and thus LE can also signal to the stoma via PGE2, PGI2 as well as PGL2 rather than AA directly. The in vitro experiments can not exclude the possibility that the ESF is producing some prostaglandin and then having an autocrine effect.

Yes, we agree with you. It is possible that PGI2 and PGE2 from luminal epithelial cells may also induce fibroblast activation. Based on the data from in situ hybridization, COX-2, mPGES, PGIS and PPARδ are mainly expressed in subluminal stromal cells at mouse implantation site on day 5 of pregnancy (Lim et al, 2000; Ni et al, 2002; Wang et al, 2004). Therefore, PGI2 from stromal cells should be the dominant one compared to that from luminal epithelial cells. In the future, we will examine the effects of AA on COX-2, mPGES and PGIS in luminal epoithelial cells.

Lim H, Dey SK. PPAR delta functions as a prostacyclin receptor in blastocyst implantation. Trends Endocrinol Metab. 2000 May-Jun;11(4):137-42.

Ni H, Sun T, Ding NZ, Ma XH, Yang ZM. Differential expression of microsomal prostaglandin e synthase at implantation sites and in decidual cells of mouse uterus. Biol Reprod. 2002 Jul;67(1):351-8.

Wang H, Ma WG, Tejada L, Zhang H, Morrow JD, Das SK, Dey SK. Rescue of female infertility from the loss of cyclooxygenase-2 by compensatory up-regulation of cyclooxygenase-1 is a function of genetic makeup. J Biol Chem. 2004 Mar 12;279(11):10649-58.

344: here the authors report that PGE2 has no effect on FB activation marker expression, but the problem with that is, that (at least in human ESF), progesterone is causing a change in the expression of the PGE2 receptors from EP4 to EP2, and it is only the EP2 receptor that activates cAMP/PKA pathway.

Yes, we agree with you. PGES is highly expressed in stromal cells at implantation site. Previous studies also show that PGE2 is important during decidualization. In our study, PGES showed no significant changes after stromal cells were treated with AA. PGE2 also had no significant effects on fibroblast activation. Therefore, we focused on PGI2-PPAR pathway. It is possible that PGE2 may regulate decidualization through an alternative way rather than fibroblast activation.

The fact that the authors show an effect of PGI2 is interesting because PGI2 receptors are among the strongest expressed PTG receptors in mammalian ESF. Prostacyclin receptor is a GPCR rather than a nuclear receptor. So the question is really why the authors have not pursued the role of prostacyclin receptor and instead have focused on PPARd?

Yes, we agree with you. When mouse stromal cells were treated with AA, there was no significant change for the protein level of prostacyclin receptor (Figures 4E, 4F). When mouse stromal cells were treated with the agonist SELEXIPAG of prostacyclin receptor, the markers of fibroblast activation showed lower changes compared with treatments with PPARδ (Figure 3D). Therefore, we focused on PPARδ. Yes, we agree with you. Although prostacyclin receptor is less responsive than PPARδ in activating fibroblast activation, it should contribute to fibroblast activation. In the future, we will pursue the effect of prostacyclin receptor on fibroblast activation. Thank you very much for your suggestion.

Reviewer #3 (Public Review):

This manuscript postulates that uterine stroma cells undergo a stage of activation between the resting state and the differentiated decidual state in order to support embryo implantation. Using in vivo mouse and in vitro mouse and human stroma cells they demonstrate that during decidualization the stroma cells express the marker genes for activated stroma. They then trace an axis from the embryo-producing TNF to prostaglandin production and activin A that is required for this process. They propose data to show that activation of the stroma is altered in infertility due to fetal trisomy 16.

The strengths of this manuscript are:

1) This is a comprehensive study using both in vivo and in vitro studies and in both mouse and human stroma cells.

2) The experiments use a combination of ligands, agonists, and inhibitors to map the signaling axis regulating stroma activation.

3) The data shown support the conclusions in this manuscript.

The weaknesses of this manuscript are:

1) The conclusion that Acitvin A is the regulator of stroma activation as mentioned by this manuscript is correlative. What is needed is a knockdown of Activin A and then assess stroma activation to prove Activin A is the major regulator and not one of many TGFb family members.

Yes, the data from Activin A knockdown were provided.

2) The use of uterine epithelial cells is problematic. The in vitro co-culture approach is not a state-of-the-art co-culture. Removal of epithelial cells from the uterus results in loss of the epithelial phenotype. If the manuscript used an epithelial organoid stroma cell coculture approach it may better reflect the role of the epithelial cells in this process. Otherwise, it is not clear that the epithelial cells are actual participants in the signaling axis. The treatments could be directly on the stroma cells.

Yes, we agree with you. According to your suggestions, we established a culture system for epithelial organoid. When the epithelial organoids were treated with TNF, a similar response was obtained compared with in vitro cultured mouse epithelial cells.

3) Ishikawa cells are endometrial cancer cells. They do not really reflect uterine epithelium and it is not clear that any epithelial cell could be substituted for these cells.

Thank you very much for your comments. It is true that Ishikawa cells should be different from in vivo endometrial epithelial cells. However, several studies showed that Ishikawa cell line possess apical adhesiveness to JAR trophoblast cells and expresses many of the same enzymes and structural proteins found in normal human endometrium (Castelbaum AJ et al, 1997).. Because both estrogen and progesterone receptors are expressed in Ishikawa cells, Ishikawa cells show a good response to both estrogen and progesterone (Castelbaum AJ et al, 1997). Therefore, Ishikawa cells are used as a model for receptive endometrial epithelial cells (Hannan NJ et al, 2010).

Castelbaum AJ, Ying L, Somkuti SG, Sun J, Ilesanmi AO, Lessey BA. Characterization of integrin expression in a well differentiated endometrial adenocarcinoma cell line (Ishikawa). J Clin Endocrinol Metab 1997; 82:136-142.

Hannan NJ, Paiva P, Dimitriadis E, Salamonsen LA. Models for study of human embryo implantation: choice of cell lines? Biol Reprod. 2010; 82:235-245.

Lessey BA, Ilesanmi AO, Castelbaum AJ, Yuan L, Somkuti SG, Chwalisz K, Satyaswaroop PG. Characterization of the functional progesterone receptor in an endometrial adenocarcinoma cell line (Ishikawa): progesterone-induced expression of the alpha1 integrin. J Steroid Biochem Mol Biol. 1996; 59:31-39.

4) The activation of stroma cells in the fetal trisomy 16 experiments at the end is very superficial. Data should show that these cells decidualize with decidual markers. This appears to be an experiment to show the translational value of the signaling axis. This experiment, again, is not well developed, does not add much to the manuscript, and should be omitted.

Yes, we agree with you. The description on human trisomy 16 was deleted.

In summary, the concept of stroma cell activation as part of decidualization is nicely developed and will add to the field. Normally investigators consider decidualization a mesenchymal to epithelial transition while some consider it stromal activation. This manuscript demonstrates that stroma cell activation is a critical part of the process of decidualization.

Author Response

Reviewer #1 (Public Review):

The authors screen large libraries of small proteins to identify three proteins of <50 aa that rescue the growth of an auxotrophic serB deletion Escherichia coli strain. They convincingly show that the growth rescue is due to the small proteins increasing expression of the his operon by reducing transcriptional attenuation. The authors argue that the small proteins function by directly binding the his RNA 5' UTR to alter RNA secondary structure.

The conclusion that the three small proteins reduce his operon attenuation is well supported by the data. A previous study suggested this mechanism for a somewhat larger, randomly selected protein, but the current study extends this prior work by firmly establishing that the proteins modulate attenuation. The suggestion that the small proteins function by directly binding the his RNA is less well supported by the data. The RNase T1 mapping data are not straightforward to interpret, and there is no assessment of protein-RNA interactions in vivo.

Major comments:

1) The RNase T1 probing data are not straightforward to interpret, and hence are insufficient to conclude that Hdp1 binding to the his 5' UTR is the mechanism by which it reduces attenuation. Specifically, G96 has reduced cleavage in the presence of Hdp1, inconsistent with the antiterminator conformation. The authors argue that G96 could be within the site of Hdp1 binding. This is certainly possible but would require additional experimental evidence to draw a confident conclusion. Also, the increased cleavage of bases around the start codon and Shine-Dalgarno sequence is inconsistent with a shift from the terminator to the antiterminator conformation. One confounding issue here is the lack of replicates and the lack of quantification. Additional probes could be tested, which would provide complementary structural information.

We agree that the RNase T1 probing data alone does not provide sufficient resolution to fully assess changes in terminator/anti-terminator conformations. Therefore, we have clarified our interpretation of the data, addressed its limitations, and have softened the conclusions that can be drawn from it in the text (lines 419-431). We have also included two additional T1 probing experimental replicates in Supplementary Fig. S11 which are in agreement with the cleavage patterns presented in the main text Figure 3D. Based on the revised conclusions and the consistency of the cleavage patterns between the experimental replicates, we do not think that quantification of the probing data would provide any additional information.

2) There are no experiments to test whether Hdp1 binds the his RNA in vivo. The in vitro data show that Hdp1 can bind the his RNA, but they do not show that this occurs in vivo, or that this is the mechanism by which Hdp1 regulates the expression of the his operon.

As addressed in the Essential Revisions section, we have now performed and included data from co- immunoprecipitation assays, in which we were able to successfully detect and demonstrate enrichment of his operator-regulated RNA transcripts in HA-tagged Hdp1 pull-down samples. We were also able to demonstrate less enrichment (i.e. reduced interaction/specificity) for thr operator-regulated RNA transcripts in the Hdp1 pull-downs as well as lower enrichment for all his operator-regulated target RNA transcripts in pull-downs performed with the HA-tagged Hdp1 L27Q mutant. These data are presented in Fig. 3A and discussed in lines 313-337.

Reviewer #2 (Public Review):

In this work, Babina et al. address a central question in molecular evolution that is only partially answered: how does cellular novelty emerge in evolution? The authors focus here on small proteins, whose importance to various cellular functions has become more appreciated recently. Babina et al. ask if functional small proteins can emerge from random sequences, a question that is mostly unresolved with only a small number of examples in the published literature for such functions. In this study, the authors demonstrate that proteins selected from random, synthetic libraries can rescue auxotrophy in E. coli. Namely, the authors find three small, random proteins (<50 amino acids) that allow E. coli cells with a ΔserB genetic background to grow in a medium without the amino-acid serine. They then show that this rescue is based on the up-regulation of HisB, an enzyme that can compensate for the serB deletion. Finally, using different molecular biology techniques, the authors propose a model in which up-regulation of HisB is achieved by physical interactions between the random proteins and the his operator that regulates the transcription of the his operon in E. coli.

Notably, as the authors themselves point out, a previous study has already shown that semi-random proteins can result in up-regulation of HisB levels to rescue ΔserB cells. Thus, most of the novelty comes from the attempt to figure out the molecular mechanism of the three random proteins. The idea that a random protein binds the 5' of an mRNA which results in up-regulated expression levels is interesting and can benefit the field. However, some clarification on existing data and additional control experiments are needed to support the authors' claims:

1) Growth data are not presented in the current form of the manuscript, which makes it impossible to evaluate many of its claims. Especially, the extent of rescue and fitness gain achieved by these random proteins compared to cells harboring the serB gene.

We thank the reviewer for pointing out this discrepancy. We have now added all relevant growth data under non-permissive conditions (Figure 1G, Supplementary Figures S2, S3, S5) and have also included data on the fitness effects exerted by Hdp expression in cells harboring serB under permissive conditions (LB medium), to allow for comparison with the empty plasmid control strain (Supplementary Figure S1).

2) The authors have screened their library on other auxotrophic strains, however, they could only find random proteins that rescue growth in the ΔserB background. Currently, they do not address this point, but it might be relevant to the molecular mechanism of those random proteins.

The reviewer raises an interesting point. We have added a paragraph to our Discussion addressing why we believe that the serB-model with a complementary enzyme is an ideal target for the selection of de novo genes (lines 536-543).

3) Central to the authors' claims is the up-regulation of HisB, however, they mostly work with an alternative LacZ system to assess the effects of the random proteins on expression. The paper will benefit from some more work measuring actual HisB levels as expressed by the various constructs used along the paper. The authors did provide an important proteomic analysis to show that HisB (along with other proteins in the his operon) is up- regulated as a result of the expression of one of the random proteins. However, it is unclear if the reported ~3- fold increase in HisB levels is enough to allow the growth of ΔserB cells in a medium without serine.

We thank the reviewer for raising this concern and allowing the opportunity to clarify. It is well established that upregulation of HisB can rescue growth of a SerB-deficient strain on minimal medium (for examples, see Patrick, et al. PMID: 17884825, Digianantonio and Hecht PMID: 26884172). We have now performed additional proteomics analyses that show a specific upregulation of the his operon upon expression of Hdp1 and Hdp3. We have also added a control experiment overexpressing HisB from our expression vector, showing that it restores growth of the auxotrophic ΔserB mutant. It is also clear that histidine starvation itself does not de-repress HisB sufficiently to allow growth of a ΔserB mutant, as this strain does not grow on minimal medium lacking histidine (such as M9 minimal medium that was used for the functional selection in our study). In addition to upregulation of HisB, we show that the rescue is dependent on presence of HisB and provide additional experiments showing a specific interactions in vitro and in vivo of Hdp1 with the his operator RNA. Our results clearly show that rescue depends on HisB and that Hdp expression upregulates HisB, and we do believe our central claim is substantiated beyond reasonable doubt. The reviewer’s main concern, that it is unclear if expression levels of HisB are high enough to allow growth is, in our opinion, resolved by the observation that Hdp-dependent upregulation of HisB does restore growth.

We respectfully disagree with the reviewer’s suggestion that an exact determination of the level of upregulation is relevant and needed, as outlined above. In addition, we would like to point out that it is not possible to measure HisB upregulation compared to an empty plasmid control strain under non- permissive conditions. Comparing HisB levels in a ΔserB strain expressing Hdp vs. the empty plasmid control in minimal medium is not possible, since the empty plasmid control strain is not able to grow, and the corresponding baseline of HisB expression cannot be determined in a non-growing strain. To circumvent this, we determined HisB levels in rich medium, which does not necessarily reflect the exact amount of upregulation occurring under non-permissive conditions, but still allows us to detect a physiological activity. Alternative experimental setups, such as comparing HisB levels in a strain carrying serB in minimal medium also suffer severe shortcomings as it no longer reflects the cellular physiology of the auxotoph under non-permissive conditions, where growth is dependent on HisB upregulation.

4) It is unclear how noisy and statistically significant some of the critical experiments in the manuscript are, especially the EMSA and T1-digestion experiments. The authors should try to find a different operator with a similar RNA structure and attenuation function, but a different nucleotide sequence, to the his operator, and show that this control operator is unaffected by the random proteins. Demonstrating the lack of phenotypes using the LacZ system, EMSA experiments, and T1-digestion patterns will much support the authors' claims.

We thank the reviewer for suggesting this important control and agree that its inclusion significantly strengthens our claims. We used the threonine operon (thr) operator, which is regulated by terminator/anti-terminator formation similar to that of to the his operon with the his operator. We show that Hdp1 does not cause de-repression of this operator using a lacZ reporter construct. Strongly supporting this is the fact that our whole proteome analysis showed specific upregulation of the his operon. Any other off target de-repression would be detected in this assay. Furthermore, we now include the thr operator RNA as a control in the EMSAs, which demonstrates reduced binding with Hdp1 in comparison to the his operator RNA. We also added an in vivo pull-down experiment using tagged Hdp1, showing marked enrichment of his operator-regulated RNA transcripts, whereas the observed enrichment of the control thr RNA transcripts is substantially less.

Author Response

Reviewer #1 (Public Review):

Thakkar et al describe the immune effects of 3rd and 4th doses of COVID-19 monovalent vaccines in a diverse cohort of immunocompromised cancer patients. They describe augmentation of anti-Spike antibodies after dose 3, especially seroconversion in 57% of patients, followed by a durable response over six months. The fourth dose was associated with increased anti-Spike antibodies in 67% of patients. T-cell responses were seen in 74% and 94% of patients after the third and fourth doses respectively. Strikingly, neutralization of Omicron was absent in all patients after the third dose but increased to 33% after the fourth dose.

Strengths:

Diverse cohort (34% Caucasian, 31% AA, 25% Hispanic 8% Asian) including 106 cancer patients after dose 3, of which 47 patients were longitudinally assessed for six months, as well as eighteen patients assessed after the fourth dose. Seronegative as well as seropositive patients benefit from a third dose of vaccination. Assessment of cellular (T cell) immune responses and viral neutralization against wild-type as well as Omicron variant is commendable.

Weaknesses:

The efficacy of the bivalent vaccine (Omicron specific) is not studied here, since the fourth dose of vaccine was a monovalent vaccine. This should be clarified in the discussion.

We have added text in the discussion section regarding this comment, lines 470-472

“The bivalent COVID-19 vaccine was introduced after the enrollment for our study was closed however it is reassuring to see that the bivalent vaccine has better neutralization activity against Omicron sub-variants”

The authors describe an increase in anti-S titers after monoclonal antibodies. Were any of the patients receiving IVIG, and what was the effect, if any on Anti-S antibodies? Characteristics of breakthrough infections, particularly if they had prolonged duration, would be important to include.

We have added text in the results section for IVIG (lines 382-383) and characteristics of breakthrough infections (lines 341-344)

“No patients were on intravenous immunoglobulin (IVIG) at the time of study participation” “Of the 4 breakthrough infections, 1 patient had no symptoms, and 3 had mild symptoms”

Reviewer #2 (Public Review):

In this manuscript, Thakkar and colleagues evaluate the immunogenicity of 3rd and 4th doses of SARS-CoV2 vaccinations in patients with cancer. The authors find that additional vaccine doses are able to seroconvert a subset of patients and that antibody levels correlate with T-cell responses and viral neutralization.

The main strengths of this manuscript are:

1) The authors systemically performed a broad array of immunological assessments, including assessments of antibody levels, T cell activity, and neutralization assays, in a large cohort of patients with cancer receiving 3rd and 4th doses of COVID vaccines.

2) The authors recruited an ethnically diverse cohort of patients with diverse cancer types, though enrolled participants were enriched for hematological malignancies.

3) Prior to FDA/CDC guidance supporting a 4th vaccine dose, the authors recruited participants with no or inadequate responses into a prospective clinical trial of a 4th dose, the results of which are outlined here.

4) The authors' findings that patients with hematologic malignancies and those receiving anti-CD20/BTK inhibitors have lower immunological responses to SARS-CoV-2 vaccines are consistent with multiple prior studies, including prior studies from these authors.

5) The authors also find that 3rd and 4th COVID vaccine doses are able to seroconvert a subset of patients with no or "inadequate" responses, though it's unclear whether seroconversion is enough for true protection from SARS-CoV-2 infection.

The main weaknesses of the manuscript include:

1) The study cohorts disproportionately enrolled patients with hematological malignancies who have been previously shown to mount lower immunological responses to COVID-19 vaccines; thus, the findings may not be representative of a typical oncology patient population.

We have clarified this in the discussion (lines 465-466)

“However, caution should be exercised in generalizing these results to the broader immunosuppressed population given the small sample size of our cohort and the disproportionately high representation of hematologic malignancy patients”

2) The subgroup analyses were relatively small.

The discussion text in line 464-465 is in concordance with this observation

“However, caution should be exercised in generalizing these results to the broader immunosuppressed population given the small sample size of our cohort and the disproportionately high representation of hematologic malignancy patients”

3) The nomenclature used in the manuscript was confusing when it came to "baseline" assessments and boosters versus additional doses of vaccines.

We have clarified the nomenclature throughout the manuscript

4) Ultimately, the major limitation of this manuscript is that antibody levels/T-cell responses/neutralization are surrogates for immune protection against SARS-CoV-2, but it's unclear what defines the ideal cutoffs for protection. Simply seroconverting may still be insufficient. The authors don't provide data showing antibody levels as relates to breakthrough infection, likely because they are underpowered for this analysis.

We have added text to expand on this further lines 475-482

“Further efforts are also needed to better determine cut-off values at which anti-S antibody levels provide protection from symptomatic COVID-19. At the present time, this data exists only for neutralizing antibody titers[36, 44] and the commercially available anti-S antibody assays are quite heterogenous with efforts being made to improve equivalency in titer reporting[45]. Our study while providing a correlation between anti-S antibody titer and neutralizing antibody titer supports that the higher the titer, the better neutralization is expected and by extrapolation, less likelihood of symptomatic infection however this needs to be confirmed in larger, systematic studies”.

Author Response

Reviewer #3 (Public Review):

Zhang, Q. et al. developed a two-photon fluorescence microscope (2PFM) by incorporating direct wavefront sensing adaptive optics (AO), which is optimized for mouse in vivo retinal imaging. By using the same 2PFM with the option of using or not using the incorporated AO system, this team compared the in vivo retinal images and convincingly demonstrated that AO correction acquired brighter and higher resolution images of retinal ganglion cells (RGCs) and their axons in both densely and sparse labeled transgenic mouse lines, normal and defected capillary vasculatures, and RGC spontaneous activities detected by genetic Ca2+ sensor. Interestingly and importantly, this team found that a global correction by removing the common aberration from the entire FOV enhances imaging signals throughout the entire large FOV, indicating a preferable AO imaging strategy for large FOVs. The potential applications of the in vivo retinal imaging techniques and strategies developed by this study will certainly inspire further investigation of the dynamic morphological and functional changes of retinal vasculatures and neurons during disease progression and before and after treatments. It would be beneficial to the manuscript and the readers if the authors can elaborate on optic design a little bit more. For example, whether the incorporation of AO adversely affects the 2PFM optic design? If the 2PFM can be further optimized by uncompromised optic design without incorporating AO, the quality of in vivo images will comparable to the AO-2PFM or not?

We thank the reviewer for these thoughtful questions.

Whether the incorporation of AO adversely affects 2PFM optical design may be a matter of perspective. As we demonstrated in the retina and elsewhere, AO substantially improves the achievable spatial resolution. Its incorporation does not reduce the temporal resolution of the system, as the ocular aberrations are temporally stable in the anesthetized mouse due to the lack of eye movement and do not require repeated aberration measurements throughout the imaging session. Signal enhancement by AO can increase the frame rate by reducing pixel dwell time required to achieve desired signal-to-noise ratio (SNR). The deformable mirror used for wavefront correction has high reflectivity, thus does not reduce the power throughput of the 2PFM. Using similar lenses for conjugation of the AO path to those employed by the 2PFM itself, we also maintain the scanning field of view size.

However, the incorporation of AO, including the direct wavefront sensing module (the “L10-L11-SH-sensor” path in Fig. 1A) and the deformable mirror (together with a pair of lenses for optical conjugation), does increase the complexity of the imaging system. Maintaining the optimal performance of AO also requires advanced optical knowledge that may not be possessed by most biological users.

For this reason, we carefully designed the 2PFM path for optimal imaging performance without AO, characterized its performance (“AO two-photon fluorescence microscope (AO-2PFM)” and “System correction” sections of Materials and Methods, Fig. S1), and optimized sample preparation including designing our own contact lens (“In vivo imaging” section of Materials and Methods, Fig. S2). Our efforts, which we believe to have led to the best possible performance of a 2PFM sans AO, allowed us to resolve retinal capillaries and cell bodies (in 2D) in vivo. Therefore, our 2PFM (sans AO) design and sample preparation procedure should benefit users who do not plan to implement AO.

Hypothetically, if the ocular aberrations of all mouse eyes were similar, it would be possible to add a static corrective element to a conventional 2PFM to improve image resolution (in the same spirit as the non-prescription reading glasses for far-sighted human eyes). However, as shown in Fig. S6 (“Zernike decompositions and corrective wavefronts for all experiments”), ocular aberrations are variable. These variabilities may arise from alignment differences (e.g., different angles between the optical axis of the ocular optics and the optical axis of the 2PFM), which can be minimized by establish a procedure to reproducibly position the eyes of different mice in similar ways. In this case, a static corrective element may be designed for substantial aberration reduction. However, the variations also arise from optical differences in the ages [1] or strains [2] of the mice. To have a 2PFM that always performs at the diffraction limit, an adaptive element as employed by AO is necessary to maintain optimal performance regardless of the specifics of the sample.

References

1. C. Cheng, J. Parreno, R. B. Nowak, S. K. Biswas, K. Wang, M. Hoshino, K. Uesugi, N. Yagi, J. A. Moncaster, W.-K. Lo, B. Pierscionek, and V. M. Fowler, "Age-related changes in eye lens biomechanics, morphology, refractive index and transparency," Aging (Albany. NY). 11(24), 12497–12531 (2019).
2. C. Tan, H. na Park, J. Light, K. Lacy, and M. Pardue, "Strain differences in mouse lens refractive indices when measured with OCT," Invest. Ophthalmol. Vis. Sci. 54(15), 1917 (2013).

Authoor Response

Reviewer #1 (Public Review):

This manuscript investigates the question of how polylysogeny impacts competition with a sensitive non-lysogen, and how this is shaped by phage resistance. This is an important and timely question, as lysogeny can be a strategy to invade new niches, and prophages are important vehicles for the acquisition of a range of virulence factors by pathogens including Klebsiella. The authors use a polylysogenic Klebsiella clone in competition with a non-lysogen that is sensitive to at least some of the prophages produced by the polylysogen. They compete these strains over a 30-day period and measure host population dynamics and evolution of phage resistance and lysogenic conversion in the (initially) sensitive competitor. Overall, the experiment shows that lysogen formation is relatively rare and short-lived. Instead, phage resistance through complete loss of the capsule is the primary mechanism evolving, but other resistant capsule mutants, with more subtle mutations affecting capsule expression, emerge as well. The authors have collected a very impressive amount of data and made some very interesting observations.

My main problem with this paper is that the manuscript lacks a clear narrative, making it very hard to extract the key message this paper wants to convey. Related to this, (some of) the conclusions that the authors make do not appear to be well supported by the data. For example, the authors conclude that selection favours more subtle capsule mutations because they are less costly than capsule-loss mutants (lines 497-500). However, there are no data to support this conclusion, as fitness costs of the various resistance phenotypes analysed were not measured. Apart from the genotypes, the data that are presented in this show that these subtle mutants have more subtle decreases in capsule production compared to the mutants that show a complete loss of capsule. But this does not tell us their relative cost. It also doesn’t tell us how the emergence of these different mutants relates to phage pressure, because whilst bacterial population dynamics data are monitored meticulously, phage dynamics data are missing (I have not found them in the supplemental information either). This makes it impossible to directly relate the emergence of the various resistance mechanisms to phage infection pressure during the coevolution experiment, even though this appears to be a hypothesis the authors wish to test.

Overall I think the overarching question of the manuscript is important and the model system is a very relevant one to study this question, but in my view, the current data don’t support the conclusions of the paper. Apart from these criticisms, the manuscript is very well written and the figures are overall easy to interpret.

We thank the reviewer for the critical assessment of our work and the time invested in the process. We have modified our manuscript following the recommendations, provided new data and we are convinced that our main results are now fully supported by the data.

Reviewer #2 (Public Review):

This manuscript presents data on multiple experiments regarding the co-evolution of poly-lysogenic and phage-susceptible Klebsiella pneumoniae strains. In particular, the manuscript aimed to determine the mechanisms of resistance that would shape bacterial competition over co-evolutionary timescales. The major finding is that the potential for lysogenization as a phage resistance mechanism is narrow and only likely to occur given certain circumstances. Moreover, the manuscript again reinforces the importance of receptor changes -initially loss, but modification in structure or expression over longer time scales- as a major mechanism of phage resistance that influences bacterial competition.

Strengths

A major strength of this manuscript is the care in designing experiments and conducting follow-up experiments to isolate the essential elements to support each of the conclusions. This includes using orthogonal methods such as sequencing and modeling to support or expand the findings from culturing and experimental evolution. The study features results that were beautifully replicated (e.g. Figure 3) lending confidence to the findings.

Weaknesses

Two weaknesses of the manuscript in its current form are: 1) a need to discuss other studies that also have found context-dependent results and 2) more focus on delivering the key overall "message" of the paper to the reader. Finally, not a weakness, but a (necessary) limitation is the study system, but this manuscript sets a bar for other groups to test in their systems to probe the generality of the findings.

The support for the conclusions is compelling. The findings were counter to the initial expectation (lysogenization as a major feature) and the manuscript does an admirable job of supporting the unexpected conclusion with thorough experimental work, supplemented with modeling.

This manuscript will be of great significance in microbial evolution, both for its implications in limiting the scope of lysogenization as a viable phage resistance mechanism in the long term and for its significant experimental rigor, particularly with regard to the co-evolutionary timescale studied. The study has very important implications for the evolution of antimicrobial resistance and phage therapy.

We thank the reviewer for the time spent and enthusiasm towards our experimental set-up.

Author Response

Reviewer #1 (Public Review):

The authors conducted a thorough analysis of the correlation between height and measures of cognitive abilities (what are essentially IQ test components) across four cohorts of children and adolescents in the UK measured between 1957 and 2018. The authors find the strength of the association between height and cognitive measures declined over this time frame--for example, among 10- and 11-year-olds born in 1958, height explained roughly 3% of the variation in verbal reasoning scores; this dropped to approximately 0.6% among those born in 2001. These associations were further attenuated after accounting for proxy measures of social class.

The authors' analyses were performed carefully and their observations regarding declining height / cognitive measure associations are likely to be robust if we interpret their results with an important caveat: these results reflect measurements aimed at assessing cognition rather than cognition itself. The importance of this distinction is evidenced by the changing correlation structure of the cognitive measures over time. For example, age 11 verbal / math scores were correlated at >= 0.75 at the first two time points but dropped to 0.33 at the most recent time point. Similar patterns are present for the other cognitive measures and time points. The authors' conclude that such changes are unlikely to impact their primary findings, but I'm less certain. For example, one interpretation of this finding is that older cognitive measures were simply worse at indexing distinct cognitive domains and instead reflected a combination of cognitive ability together with non-specific factors relating to opportunity, health, class, etc. Further, height was historically a stronger proxy for class and economic status than it is today (e.g., by capturing adequate nutritional intake, risk for childhood disease, etc.). Together, then, previously high height / cognitive measure correlations might reflect the fact that both phenotypes previously indexed socio-economic factors to a greater extent than they might today (which is still non-negligible).

We agree, it is possible that our results could in principle be explained by changes to the measures. We have provided further analysis to attempt to inform the likelihood of this suggestion and have expanded our discussion of this issue (Discussion, explanation of findings section; copied below).

First, we conducted additional sensitivity analysis repeating our main analysis using cognition measures in which the number of response options was set to be the same for each test (the lowest common denominator across all cohorts). This was tested in two separate approaches: 1) by reducing the number of categories to the same number in each cohort; and 2) or by picking a random sample of question items for each category. Our main findings were unchanged: described in “Additional and sensitivity analyses” section, Figs S20-S21.

Regarding the suggestion that “high height / cognitive measure correlations might reflect the fact that both phenotypes previously indexed socio-economic factors to a greater extent than they might today” – we sought to account for this by adjustment for measured indicators of socioeconomic position, and found the trend remained after adjustment (Fig 1 panel 2). As in other observational studies we cannot fully rule out the possibility of residual confounding however (Discussion, Explanation of findings paragraph 2).

“The multi-purpose and multidisciplinary cohorts used cognition tests which differed slightly in each cohort. It is therefore possible that differences in testing could have either: 1) entirely generated the pattern of results we observed, such that if identical tests were used the association between cognition and height would otherwise have been identical in each cohort; in contrast to previous findings which reported using identical tests20; or 2) biased our results, such that if identical tests were used the decline in association between cognition and height would have been less marked than we reported. While we cannot directly falsify this alternative hypothesis given our reliance on historical data sources, a number of lines of reasoning suggest that the first scenario is unlikely. First, our results were similar when using 4 different cognitive tests (spanning mathematical and verbal reasoning); any bias which generated the results we observed should be similarly present across all 4 tests. Other things being equal, one would expect that more discriminatory tests (i.e., those with a greater number of responses) would have higher accuracy and thus better index cognition. Our results were similar when the youngest cohort had similar numbers of unique scores in cognitive tests compared with the oldest cohort (Verbal @ 11 years: n=41 in 1946c, n=40 in 2001c) and fewer unique scores (Maths @ 7/11: n=51 in 1946c, n=21 in 2001c). Our results were also similar in sensitivity analyses in which the number of response options were set to be the same in each cohort. Higher random measurement error in the independent variable (cognition) would lead to weakened observed associations with the outcome (height),52 yet we do not a-priori anticipate that this such error was higher in younger across all tests in such a manner that would have led to the correlation we observed. Ensuring comparability of exposure is a major challenge across such large timespans. Reassuringly, our results are consistent with those from a previous study which reported consistent tests being used (from 1939-1967).20 However, even seemingly identical require modification across time (e.g., for verbal reasoning/vocabulary there is typically a need to adapt question items due to societal and cultural changes over time in vocabulary and numerical use); further, changes to education such as increases in testing may have led to increasing preparedness and familiarity with testing than in the past even where identical tests are used.

Interestingly, we observed a marked reduction in the correlation between cognitive tests across time (e.g., between verbal and maths scores). This trend has been reported in previous studies53 54 and warrants future investigation; it is consistent with evidence that IQ gains across time seemingly differ by cognitive domain,45 potentially capturing differences across time in cognitive skill use and development in the population. Previous studies using three (1958-2001c) of the included cohorts have also reported changing associations between cognition (verbal test scores at 10/11 years) and other traits: a declining negative association with birth weight19 and a change in direction of association with maternal age (from negative to positive);55 each finding has plausible explanations based on changes across time in relevant societal phenomena (improved medical conditions19 and changes in parental characteristics,55 respectfully), yet also cannot conclusively falsify the notion that differences in tests used influences the results obtained. In this paper, we used multiple tests and sensitivity analyses to attempt to address this.”

Additionally, their findings add an interesting data point to a collection of recent results suggesting that the relationship between cognitive and anthropometric measures is complex and difficult to interpret. For example, studies using genetic markers to examine shared genetic bases have virtually all relied on methods assuming mating is random, which is not the case empirically. Howe et al. (doi.org/10.1038/s41588-022-01062-7) recently reported that the ostensible genetic correlation of -.32 between years of education and BMI attenuates to -.05 when using direct-effect estimates, which should theoretically be immune to the effects of non-random mating and other confounding variables. Likewise, Keller et al. (doi.org/10.1371/journal.pgen.1003451) and Border et al. (doi.org/10.1101/2022.03.21.485215) used very different approaches to arrive at the same conclusion that ~50% of the nominal genetic correlation between IQ and height could be attributed to bivariate assortative mating rather than shared causal biological factors. Given that assortative mating on both IQ measures and height involves many other traits (not just two as assumed in such bivariate models), the true extent to which height / IQ correlations reflect causal factors is plausibly even lower than these estimates suggest. For these reasons, I do not entirely agree with the authors' review of previous findings in the introduction, where they write "recent studies have suggested that links between higher cognition and taller height can be largely explained by genetic factors", though it is certainly true that this claim has been made.

We have revised our introduction to better reflect the complexity of previous findings and to note that this claim.

Reviewer #2 (Public Review):

The authors use birth cohorts with extensive cognitive assessments and height measurements along with data on parental height and socioeconomic status. The authors estimate that the correlation between height and cognitive ability has approximately halved in the last 60 years.

Quantile regression results suggest that this is due to a stronger association between low cognitive ability and short stature in older cohorts, potentially due to environmental factors that cause both and that have been removed by improvements in the environment in the last 60 years.

While this is a plausible hypothesis, the evidence presented in the manuscript is unable to rule out alternative hypotheses, such as changes in assortative mating.

The results in the manuscript will be of interest to researchers investigating how genetics and environment lead to correlations between cognitive and physical/health traits, and to researchers interested in the relationship between social and health inequalities.

While my sense of the evidence presented is that there is fairly solid statistical evidence for a trend where the correlation between cognitive ability and height declines over time, there is no formal quantification of this trend nor measurement of the uncertainty in the trend.

We now include additional statistical tests to compare estimates in each cohort (Fig S6). We have opted to include this in supplemental material given the large number of tests included already.

Similarly, the quantile regression plots in Figure 2 appear to show a trend across the height deciles for the two oldest cohorts, but no quantification of how strong this is nor what uncertainty exists is calculated. Furthermore, if the apparent trend in the quantile regression plots is true, wouldn't this imply a non-linear association between height and cognitive ability for the older cohorts? Can this be seen in the scatterplots or in a non-linear regression?

We included 95% confidence intervals in our quantile regression analyses which provide an indication of uncertainty. We believe that given the substantial amount of analyses (across 4 historical cohorts and 4 cognition tests; 23 supplemental results) further work would be best placed to undertake additional statistical exploration of both quantile regression and non-linear associations. We would be happy to reconsider this if requested.

I think the authors could have done more with their data to investigate the contribution of assortative mating to the observed trend. Looking at Figure S4, it looks like the correlation between mother's education and father's height in the 2001 cohort is substantially lower than for previous cohorts. While cognitive ability may not be available for parents, one could look at, for example, father's education and mother's height across the cohorts and see if there is a downward trend in correlation.

We now include in Figure S5 cross-cohort investigation of the correlation between parental height and maternal education. We find that the correlation is similar across 1946c, 1958c, and 1970c, yet is weaker in 2001c (Fig S5). We comment on this in the paper (see revised discussion, explanation of findings section). Interpretation of these results is complicated by measurement error in parental education (typically reported for both parents by mothers). Further, interpretation may be further complicated by reductions in the socioeconomic patterning of height across time (see https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(18)30045-8/fulltext). Future would which focuses on assortative mating could investigate these issues.

Reviewer #3 (Public Review):

A difficulty with the paper is the different cognitive tests used in the different cohorts; the authors address this at some length in the discussion. However, I am afraid that this matter makes the results hard or impossible to interpret along the lines of their research question. One would need to know that, if these cognitive tests were administered in a single cohort at one time, they would have the same correlation with height.

Please see our responses to Reviewer 1 and our revised Discussion. We are reliant upon imperfect historical data to make inferences on long-run trends, in the absence of ideal data for this paper (eg, the same tests used in all cohorts born in 1946, 1958, 1970 and millennium; though even in this instance some changes would be required (eg, to the words chosen in verbal reasoning tasks; see Discussion, explanation of findings section)).

I judge that the main limitation of the method is the fact that different cognitive tests are used in the different cohorts. The tests in themselves are valid tests of cognitive functions. However, given that the focus of the study is on the change in correlations across time, then it is a worry that the tests are different; that is, the authors have the burden of proving to us that, if the environmental/social changes had NOT been operative across time, then the height-cognitive test correlations would be the same. What can the authors do to prove to us that if, say, all of these different-cohort verbal tests had been given to a single cohort on a single occasion, then they would have the same correlations with height? The same goes for the mathematics based tests. I note the tests' somewhat different distributions in Figure 1, but that is not the only thing that could lead to different correlations with, say, height. I am aware that all cognitive tests tend to correlate positively and that they all have loadings on general intelligence; however, different tests will not necessarily have the same correlations with outside variables (e.g. height). This will depend on things such as their content, their reliability/internal consistency etc.

In the Results the authors state: "Cognitive test scores were strongly-moderately positively correlated with each other, with the size of the correlation weakening across time." That's true, but perhaps, also a major concern for this study. One possible reason for the decline in verbal-maths test correlations across cohorts (old to recent) is that the nature of these tests has changed across time, either/both in terms of content (what capabilities are assessed) or something such as reliability/internal consistency/ceiling-or-floor effects (how well the capabilities are assessed). That is, given that the height-cognitive test correlations show a similarly declining pattern of correlations over cohorts, it could be that the tests' contents (of the different tests) is partly or wholly responsible. I raise that as a possibility only, and I appreciate that it might be correct, as the authors prefer, that there is an inherent lowering of intelligence-height correlations over time, but I do not think that one can rule out-with the present study's design-that it might have been due to the change in tests. For example, a reading-math correlation of 0.74 in 1946 lowered to a correlation of .32 in 2001, in the face of different tests. To show that this is not due to the different tests being used would require more information. If this is a true result, it is big news.

Please see our responses to Reviewer 1. This includes additional analysis and an expanded discussion of this possible cause of bias. We hope our manuscript now provides further evidence and discussion to inform the likelihood of this possibility.

I have a suggestion: if the authors wish to rule out the possibility that the lowering intelligence-height correlations across cohorts are due to different cognitive tests being used, they should take all the cognitive tests used here and apply them cross-sectionally to single-year-born samples (of 11- and 16-year olds) that have also been measured for height. If the cognitive tests all correlate at the same level with height within each of these two samples (they needn't do so across the 11- and 16-year olds), then one could proceed more safely with between-cohorts (1946, 1958, 1970, 2001) comparisons of the correlations.

We thank the reviewer for this suggestion. However we are unsure that we understood the suggested analysis or whether it was tractable given our data—the cohorts we used were born in either 1946, 1958, 1970, or around 2000. We do not have cross-sectional samples of 11 and 16 year olds at the same time.

Author Response:

Dear eLife Editorial Board, dear reviewers, dear readers,

We very much thank the eLife editors and reviewers for their overall very positive review and encouraging assessment of our manuscript, and for highlighting our study’s innovation and relevance for using genomic approaches for the conservation of biodiversity.

We very much thank the reviewers for pointing out parts of the manuscript that could be described more clearly or in more detail to make the study fully reproducible, and have therefore rewritten parts of the manuscript. We importantly follow reviewer 1’s specific recommendation to focus the main text on clearly understandable results, and therefore now only showcase the application of selective nanopore sequencing (aka adaptive sampling) to one soil sample, which we hope will make the flow of the manuscript easier to understand.

We further agree that parts of the study could have been conducted more extensively (e.g. include more samples and thereby showcase the broad applicability of the approach), which was unfortunately not feasible since I as the lead author left New Zealand to take up another position abroad. We are, however, following up on this work with another controlled large-scale study.  

We further agree that both qPCR and metabarcoding have their advantages and disadvantages. Metabarcoding approaches, however, importantly deliver more information about the biodiversity of a location than just the presence of a single species; this, in our case, includes other endangered species and evidence of kākāpō predators. We further show that the chosen marker gene region (12S rRNA) is species-specific enough to distinguish kākāpō from its two closest relatives. While qPCR has been shown to be more sensitive for some species, the difference is often minimal (see e.g., Harper et al., Ecol Evol. 2018 Jun; 8(12): 6330–6341), and for some species has been shown to be equally sensitive (Schneider et al., PLoS ONE 2016, 11, e0162493). qPCR approaches further require the careful design of species-specific primers, and herewith the access to samples and DNA of the target species and of closely related species – all of which are not necessarily at hand, especially not for conservationists who want to use these approaches regularly in the future, and in countries like New Zealand where genomic work with material from any “treasured” species has to be approved in a long and detailed process according to national regulations and the Nagoya Protocol. Given all these reasons, and the general good performance of our metabarcoding approach (also in detecting our species of interest), we do not see the necessity of applying a qPCR approach in this study.

To avoid any confusion, we now also describe the samplings sites in more detail and use their labels consistently throughout the manuscript. Briefly, the sites were always sampled directly at the site, and at 4m and 20m distance, and all in replicates, as described in detail in the manuscript. Specifically, the “abandoned nests” had only been abandoned ~30 days before sampling, as described in the Methods, and this is why kākāpō DNA is still present.

We further thank reviewer 2 for suggesting to discuss the impact of selective nanopore sequencing on pore efficiency in more depth, and added a respective sentence to the Discussion. We in general added more references and the broader scientific context to the Discussion.

Thank you again for this very helpful review of our work.

With best regards,<br /> Lara Urban

Author Response:

We are grateful for the detailed feedback provided by the two anonymous reviewers. We provide a point-by-point response to their reviews below:

Reviewer #1 (Public Review):

Medwig-Kinney et al perform the latest in a series of studies unraveling the genetic and physical mechanisms involved in the formation of C. elegans gonad. They have paid particular attention to how two different cell fates are specified, the ventral uterine (VU) or anchor cell (AC), and the behaviors of these two cell types. This cell fate choice is interesting because the anchor cell performs an invasive migration through a basement membrane. A process that is required for correct C. elegans gonad formation and that can act as a model for other invasive processes, such as malignant cancer progression. The authors have identified a range of genes that are involved in the AC/VC fate choice, and that imparts the AC cell with its ability to arrest the cell cycle and perform an invasive migration. Taking advantage of a range of genetic tools, the authors show that the transcription factor NHR-63 is strongly expressed in the AC cell. The authors also present evidence that NHR-63 is could function as a transcriptional repressor through interactions with a Groucho and also a TCF homolog, and they also suggest that these proteins are forming repressive condensates through phase separation.

The authors have produced an extensive dataset to support their two primary claims: that NHR-67 expression levels determine whether a cell is invasive or proliferative, and also that NHR-67 forms a repressive complex through interactions with other proteins. The authors should be commended for clearly and honestly conveying what is already known in this area of study with exhaustive references. But absent data unambiguously linking the formation and dissolution of NHR-67 condensates with the activation of downstream genes that NHR-67 is actively repressing, the novelty of these findings is limited.

Response 1.1: We thank the reviewer for recognizing the extensive dataset we provide in this manuscript in support of our claims that, (1) NHR-67 expression levels are important for distinguishing between AC and VU cell fates, and (2) NHR-67 interacts with transcriptional repressors in VU cells. We acknowledge that a complete mechanistic understanding of the functional significance of NHR-67 puncta is not possible without knowing direct targets of NHR-67 in the AC. Unfortunately, tools to identify transcriptional targets in individual cells or lineages in C. elegans do not exist, and generation of such tools would be beyond the scope of this work. This is evidenced by the fact that the first successful attempt to transcriptionally profile the AC was only posted as a preprint one month ago (Costa et al., doi: 10.1101/2022.12.28.522136). It is our hope that the findings we present here can be integrated with future AC- and VU-specific profiling efforts to provide a more complete picture of the functional significance of NHR-67 subnuclear organization.

Reviewer #2 (Public Review):

Medwig-Kinney et al. explore the role of the transcription factor NHR-67 in distinguishing between AC and VU cell identity in the C. elegans gonad. NHR-67 is expressed at high levels in AC cells where it induces G1 arrest, a requirement for the AC fate invasion program (Matus et al., 2015). NHR-67 is also present at low levels in the non-invasive VU cells and, in this new study, the authors suggest a role for this residual NHR-67 in maintaining VU cell fate. What this new role entails, however, is not clear. The model in Figure 7E shows NHR-67 switching from a transcriptional activator in ACs to a transcriptional repressor in VUs by virtue of recruiting translational repressors. In this model, NHR-67 actively suppresses AC differentiation in VU cells by binding to its normal targets and acting as a repressor rather than an activator. Elsewhere in the text, however, the authors suggest that NHR-67 is "post-translationally sequestered" (line 450) in nuclear condensates in VU cells. In that model, the low levels of NHR-67 in VU cells are not functional because inactivated by sequestration in condensates away from DNA. Neither model is fully supported by the data, which may explain why the authors seem to imply both possibilities. This uncertainty is confusing and prevents the paper from arriving at a compelling conclusion. What is the function, if any, of NHR-67 and so-called "repressive condensates" in VU cells?

Response 2.1: As the reviewer correctly notes, we present two possible models in this manuscript. The interaction between NHR-67 and the Groucho/TCF complex in the VU cells could (1) switch the role of NHR-67 from a transcriptional activator to a transcriptional repressor, or (2) sequester NHR-67 away from its transcriptional targets. Indeed, we cannot definitively exclude the possibility of either model. In our resubmission, we will attempt to make this more clear in the text and by presenting both possible models in the summary figure (Fig. 7E).

Below we list problems with data interpretation and key missing experiments:

1) The authors report that NHR-67 forms "repressive condensates" (aka. puncta) in the nuclei of VU cells and imply that these condensates prevent VU cells from becoming ACs. Fig. 3A, however, shows an example of an AC that also assemble NHR-67 puncta (these are less obvious simply due to the higher levels of NHR-67 in ACs). The presence of NHR-67 puncta in the AC seems to directly contradict the author's assumption that the puncta repress the AC fate program. Similarly, Figure 5-figure supplement 1A shows that UNC-37 and LSY-22 also form puncta in ACs. The authors need to analyze both AC and VU cells to demonstrate that NHR-67 puncta only form in VUs, as implied by their model.

Response 2.2: The puncta formed by NHR-67 in the AC are different in appearance than those observed in the VU cells and furthermore do not exhibit strong colocalization with that of UNC-37 or LSY-22. The Manders’ overlap coefficient between NHR-67 and UNC-37 is 0.181 in the AC, whereas it is 0.686 in the VU cells. Likewise, the Manders’ overlap coefficient between NHR-67 and LSY-22 is 0.189 in the AC compared to 0.741 in the VU cells. We speculate that the areas of NHR-67 subnuclear enrichment in the AC may represent concentration around transcriptional targets, but testing this would require knowledge of direct targets of NHR-67.

2) While a pool of NHR-67 localizes to "repressive condensates", it appears that a substantial portion of NHR-67 also exists diffusively in the nucleoplasm. This would appear to contradict a "sequestration model" since, for such a model to work, a majority of NHR-67 should be in puncta. What proportion of NHR-67 is in puncta? Is the concentration of NHR-67 in the nucleoplasm lower in VUs compared to ACs and does this depend on the puncta?

Response 2.3: The proportion of NHR-67 localizing to puncta versus the nucleoplasm is dynamic, as these puncta form and dissolve over the course of the cell cycle. However, we estimate that approximately 25-40% of NHR-67 protein resides in puncta based on segmentation and quantification of fluorescent intensity of sum Z-projections. We also measured NHR-67 concentration in the nucleoplasm of VU cells and found that it is only 28% of what is observed in ACs (n = 10). We disagree with the notion that the majority of NHR-67 protein should be located in puncta to support the sequestration model. As one example, previously published work examining phase separation of endogenous YAP shows that it is present in the nucleoplasm in addition to puncta (Cai et al., 2019, doi: ​​10.1038/s41556-019-0433-z). In our system, it is possible that the combination of transcriptional downregulation and partial sequestration away from DNA is sufficient to disrupt the normal activity of NHR-67.

3) The authors do not report whether NHR-67, UNC-37, LSY-22, or POP-1 localization to puncta is interdependent, as implied in the model shown in Fig. 7.

Response 2.4: It is difficult to test whether localization of these proteins to puncta is interdependent, as perturbation of UNC-37, LSY-22, and POP-1 result in ectopic ACs. Trying to determine if loss of puncta results in VU-to-AC transdifferentiation or vice versa becomes a chicken-egg argument. It is also possible that UNC-37 and LSY-22 are at least partially redundant in this context. We based our model, shown in Fig. 7E, on known or predicted protein-protein interactions, which we confirmed through yeast two-hybrid analyses (Fig. 7D; Fig. 7-figure supplement 1).

4) The evidence that the "repressor condensates" suppress AC fate in VUs is presented in Fig. 4D where the authors deplete the presumed repressor LSY-22. First, the authors do not examine whether NHR-67 forms puncta under these conditions. Second, the authors rely on a single marker (cdh-3p::mCherry::moeABD) to score AC fate: this marker shows weak expression in cells flanking one bright cell (presumably the AC) which the authors interpret as a VU AC transformation. The authors, however, do not identify the cells that express the marker by lineage analyses and dismiss the possibility that the marker-positive cells could arise from the division of an AC-committed cell. Finally, the authors did not test whether marker expression was dependent on NHR-67, as predicted by the model shown in Fig. 7.

Response 2.5: For the auxin-inducible degron experiments, strains contained labeled AID-tagged proteins, a labeled TIR1 transgene, and a labeled AC marker. Thus, we were limited by the number of fluorescent channels we could co-visualize and therefore could not also visualize NHR-67 (to assess for puncta formation) or another AC marker (such as LAG-2). We could have generated an AID-tagged LSY-22 strain without a fluorescent protein, but then we would not be able to quantify its depletion, which this reviewer points out is important to measure. We did visualize NHR-67::GFP expression following RNAi-induced  knockdown of POP-1 and observed consistent loss of puncta in ectopic ACs. However, this again becomes a chicken-egg argument as far as whether cell fate change or loss of puncta causes the other.

5) Interaction between NHR-67 and UNC-37 is shown using Y2H, but not verified in vivo. Furthermore, the functional significance of the NHR-67/UNC-37 interaction is not tested.

Response 2.6: We attempted to remove the intrinsically disordered region found at the C-terminus of the endogenous nhr-67 locus, using CRISPR/Cas9, as this would both confirm the NHR-67/UNC-37 interaction in vivo and allow us to determine the functional significance of this interaction. However, we were unable to recover a viable line after several attempts, suggesting that this region of the protein is vital.

6) Throughout the manuscript, the authors do not use lineage analysis to confirm fate transformation as is the standard in the field.

Response 2.7: The timing between AC/VU cell fate specification and AC invasion (the point at which we look for differentiated ACs) is approximately 10-12 hours at 25 °C. With our imaging setup, we are limited to approximately 3-4 hours of live-cell imaging. Therefore, lineage tracing was not feasible for our experiments. Instead, we relied on visualization of established markers of AC and VU cell fate to determine how ectopic ACs arose. In Fig. 6B,C we show that the expression of two AC markers (cdh-3 and lag-2) turn on while a VU marker (lag-1) get downregulated within the same cell. In our opinion, live-imaging experiments that show in real time changes in cell fate via reporters was the most definitive way to observe the phenotype.

There are 4 multipotential gonadal cells with the potential to differentiate into VUs or ACs. Which ones contribute to the extra ACs in the different genetic backgrounds examined was not determined, which complicates interpretation. The authors should consider and test the following possibilities: disruption of NHR-67 regulation causes 1) extra pluripotent cells to directly become ACs early in development, 2) causes VU cells to gradually trans-fate to an AC-like fate after VU fate specification (as implied by the authors), or 3) causes an AC to undergo extra cell division(s)?? In Fig. 1F, 5 cells are designated as ACs, which is one more that the 4 precursors depicted in Fig. 1A, implying that some of the "ACs" were derived from progenitors that divided.

Response 2.8: When trying to determine the source of the ectopic ACs, we considered the three possibilities noted by the reviewer: (1) misspecification of AC/VU precursors, (2) VU-to-AC transdifferentiation, or (3) proliferation of the AC. We eliminated option 3 as a possibility, as the ectopic ACs we observed here were invasive and all of our previous work has shown that proliferating ACs cannot invade and that cell cycle exit is necessary for invasion (Matus et al., 2015; Medwig-Kinney & Smith et al., 2020; Smith et al., 2022). Specifically, NHR-67 is upstream of the cyclin dependent kinase CKI-1 and we found that induced expression of NHR-67 resulted in slow growth and developmental arrest, likely because of inducing cell cycle exit. For our experiment using hsp::NHR-67, we induced heat shock after AC/VU specification. For POP-1 perturbation, we explicitly acknowledged that misspecification of the AC/VU precursors could also contribute to ectopic ACs (Fig. 6A; lines 364-402). We could not achieve robust protein depletion through delayed RNAi treatment, so instead we utilized timelapse microscopy and quantification of AC and VU cell markers (Fig. 6B,C; see response 2.7 above).

In conclusion, while the authors report on interesting observations, in particular the co-localization of NHR-67 with UNC-37/Groucho and POP-1 in nuclear puncta, the functional significance of these observations remains unclear. The authors have not demonstrated that the "repressive condensates" are functional and play a role in the suppression of AC fate in VU cells as claimed. The colocalization data suggest that NHR-67 interacts with repressors, but additional experiments are needed to demonstrate that these interactions are specific to VUs, impact VU fate, and sequester NHR-67 from its targets or transform NHR-67 into a transcriptional repressor.

Response 2.9: We agree that, at this time, we cannot pinpoint the precise mechanism through which NHR-67 puncta function (i.e., by sequestering NHR-67 from DNA or switching the role of NHR-67 from activating to repressing). However, identification of NHR-67 puncta and their colocalization with UNC-37, LSY-22, and POP-1 in VU cells allowed us to discover an undescribed role for the Groucho/TCF complex in maintaining VU cell fate. This, combined with our evidence demonstrating that NHR-67 transcriptional regulation is important for distinguishing between AC and VU cell fate, are the main contributions of our study.

Author Response:

Reviewer #1 (Public Review):

Vaparanta et al propose a new bioinformatic algorithm for pathway discovery from multi-omics data sources at one time point, and validate some of their algorithm's predictions using functional experiments. The authors should be commended for their detailed experimental work and comprehensive data collection around TYRO3 signaling in melanoma, which will likely be of value to that field. They also provide a mature software package that is well documented for implementing their bioinformatic methods. The reviewer's experience with the software was that it is computationally efficient/fast with well written code. The biological data (both multiomics and functional validation studies) will be of interest to melanoma research as well as scientists interested in TYRO3 signaling.

The authors wish to thank the Reviewer for the positive comments.

At this time, however, the bioinformatics algorithm proposed is of unclear utility to the broader multiomics community for the following reasons:

First, the algorithm itself has numerous hyperparameters, which can make it challenging to use and potentially highly sensitive to these user inputs. Just the regulatory complex inference step has 10 hyperparameters/settings required to be selected.

We have now reduced the number of parameters in the code by automating the choice for 2 of the parameters. The manuscript is now accompanied by a sensitivity analysis on all the key parameters in the code (new Supplementary Figures 5-11) and we have created a script to inform the choice of the key parameter S (suggest parameter S value for regulatory complex inference, new Supplementary Figure 10). We have additionally thoroughly revised the accompanying documentation in helping the user choose the right settings for their datasets (available in Mendeley data: https://data.mendeley.com/datasets/m3zggn6xx9/draft?a=71c29dac-714e-497e-8109-5c324ac43ac3).

Second, the algorithm is presented in an ad hoc manner without mathematical/statistical justifications of the many design decisions and steps in the analysis. For example, the authors write "The inference of regulatory complexes from the combined score follows the nearest neighbor principle, assuming that while a single high combined score can be random chance, the combination of combined scores between 3 cell signaling molecules would be predictive". It is mathematically unclear that this is true…

We have now tested the effect of the design decisions of the algorithm on the ability to discover known associations in omics datasets (new Supplementary Figure 4). Adhering to the design decision of the algorithm greatly improves the amount of known associations found in real omics data.

…and thus this reviewer attempted to test the algorithm using simulated uncorrelated Gaussian noise (see code/outputs at end of the review) in 10K genes and 10 samples using a best attempt at hyperparameter selection per the code comments and documentation. It appears that nearly 1/3 of all genes (i.e., 3205 of 10K) were erroneously grouped into complexes (assuming no mistakes in reviewer's usage of the code). In general, "unbiased" pathway analysis in multiomics that is not relying on prior knowledge will require solving the extraordinarily challenging task of estimating a very large covariance matrix from statistically small sample sizes. This puts the method at high risk of producing spurious results.

The Reviewer raises an important topic that should be considered in de novo analyses. However, the test dataset the reviewer used is not truly representative of the omics datasets that should be used to evaluate the performance of the algorithm. First, the algorithm should be only used with positive expression values due to the way the stoichiometry score is calculated. This is now more clearly indicated in the accompanying documentation (available in Mendeley data: https://data.mendeley.com/datasets/m3zggn6xx9/draft?a=71c29dac-714e-497e-8109-5c324ac43ac3). The Gaussian noise used by the reviewer does not represent any positive expression values of any omics datasets.

Second, the way the algorithm is constructed it will try to find an association to all features in the dataset if so instructed by the parameters. To this end, we have now added a new parameter (parameter S) into the algorithm to better control this setting. If correctly used in the test dataset used by the reviewer the algorithm now returns 0 complexes. The authors also wish to point out that they strongly believe that the amount of features in the dataset that have no real association with other features in real omics data is very low since most intracellular molecules have common upstream regulators. This poses a problem only if the dataset has a very limited amount of features.

Third, it seems to the authors that instead of testing the limits of the algorithm with totally randomized data, it would be more valuable to assess whether the algorithm can find true positives among randomized data. To this end we estimated the true positive and false positive rate with normally, negative binomial and beta distributed simulated data (new Supplementary Figures 7-9). Indeed, the algorithm can discover only the true positives among the false positives as long as the S parameter is not set too low. We now provide a separate script (suggest parameter S value for regulatory complex inference, new Supplementary Figure 10) that will help the user to choose the parameter S for their data so that the amount of false positives in the inference is minimized.

Fourth, the data produced by the standard normal distribution has a relatively low variance, already 68% values fall between -1 and 1 and 95% values between -2 and 2. If you simulate 10000 random rows with a sample size of 10 of such low variance parameter you are at high chance of creating highly correlating rows that actually would be representative of true positives in the dataset due to the generally high variation within omics data. Therefore, it is exceedingly hard to interpret whether the features were erroneously assigned into complexes or not because the chosen simulation method could have by chance created associations that represent true positives in the dataset.

Fifth, we also analyzed the standard normal distributed simulated data with WGCNA, which is still the most widely used module discovery method. WGCNA assigned almost all the features into modules. However, I think it is clear due to the wide us that the analysis still can offer valuable insight into biological processes. Therefore, the authors are not sure how concerned they should be about the results of this test.

Third, pathway analysis has long been a bioinformatic goal in the literature, with the authors citing a landmark paper for the WGCNA method from 2008. As such, there are numerous and long-standing discussions in the literature regarding challenges of pathway analysis (i.e., omics data often has dimensionality D far larger than sample size N, and correlation matrix estimation requires D^2 >> N parameters to be estimated) and its potential for spurious correlations. Some authors use sophisticated statistical tools (e.g., "Biological network inference using low order partial correlation" 2014, "Learning Large‐Scale Graphical Gaussian Models from Genomic Data" 2005, "Incorporating prior knowledge into Gene Network Study" 2013) to attempt to deal with this issue.

The authors agree that if by spurious the Reviewer means non causal indirect associations like in the paper by Zuo et al. (Zuo et al., 2014. Biological network inference using low order partial correlation. Methods 69:266-73. doi: 10.1016/j.ymeth.2014.06.010.), then, indeed, the algorithm has not been designed to find directed networks. Instead, the algorithm has been designed to find common upstream regulators.

Furthermore, the authors indicate that their approach is the first to attempt pathway analysis in multi-omics setting, stating "Integrative approaches combining more than one robust molecular association measure, however, have not been explored", but one can find attempts such as "An Integrative Transcriptomic and Metabolomic Study of Lung Function in Children With Asthma" to build on WGCNA for work in multiomics datasets.

Indeed, the Reviewer is correct that correlation networks and WGCNA have been previously used with multi-omics datasets. What the authors meant to convey is that these previous approaches rely only on one measure of molecular association, which in the case of correlation networks is correlation and WGCNA covariation, while our method is the first that combines two measures of molecular association, the correlation and stoichiometry score. We have now amended the sentence in the manuscript (lines 51-52).

The 2020 review paper "Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources" seems to identify multiple published methods dealing with pathway estimation in multiomics datasets. As the paper stands, this reviewer cannot adequately assess the impact of the proposed bioinformatic algorithm and its results against the existing body of literature for pathway inference.

We have now benchmarked our method against existing module discovery, network and multi-omics integration methods and provide evidence that our method outperforms these methods (new Figure 4).

Reviewer #2 (Public Review):

The authors describe a bioinformatic platform that allows for unbiased pathway analysis from multiomics data. The concept is based on correlation, stoichiometry scores and their combination to evidence interaction between two proteins, transcripts or phosphosites in an omic dataset. This platform was developed and validated on both previously published and in house omics data. I really appreciate that the paper is well written and clear, and I would like to acknowledge the amount of work generated to produce the in-house dataset.

The authors wish to thank the Reviewer for the encouraging words.

Author Response:

Reviewer #1 (Public Review):

The authors' conclusions presented herein are supported by a well-established mouse genetic conditional approach and an extensive array of phenotypic analyses.

Strengths:

1. The authors utilized well-described genetic tools, AdipoQCre, to target preadipocyte-like progenitor cell populations in bone marrow, as well as Csf1 floxed alleles. They further sifted through the cell population by showing that mature lipid-laden adipocytes express Csf1 at a much lower level, and determined that AdipoQCre-marked progenitor cell population presents a major cellular source of M-CSF,

2. The reanalysis of published scRNAseq datasets in Figure 1, as well as the following phenotypic analyses of the mutant mice are well-conducted. The analyses include a broad range of experiments both in vivo (3DmicroCT, histology, flow cytometry) and ex vivo (osteoclastogenesis assay in bone marrow cell culture). The confidence of the reported findings is high.

3. The data presented in this manuscript are of very high quality.

Weaknesses:

1. The role of AdipoQ-lineage progenitors as a source of M-CSF is overstated. The authors claim in many instances that "mature bone adipocytes do not express M-CSF", "These cells however do not produce Csf1", "...these peripheral AdipoQ+ cells nearly do not produce M-CSF". However, the authors' qPCR experiments only show four times differences in Csf1 expression. Therefore, the claim that AdipoQ-lineage progenitors are an exclusive source of M-CSF is not well substantiated. In line with this, some of the recent literature reporting conditional deletion of M-CSF in other bone cells (JBMR Plus. 4:e10080., Nature. 590:457-462) are not included.

We thank the reviewer for this important question. We have performed the below experiments to further clarify and support our conclusion:

1) We increased the replicates of each group cells in Fig. 3A (the old Fig. 1E) to five/group and based on reviewer 3’ recommendation on housekeeping gene usage, we found that the mRNA expression of Csf1 in bone marrow AdipoQ-lineage progenitor cells is 20-30 fold higher than those in mature adipocytes. This result has been updated in Fig. 3A.

2) We further performed immunofluorescence staining of M-CSF on bone slices, and found that the majority of bone marrow AdipoQ-expressing progenitor cells express M-CSF (Fig. 3B, 1865 cells out of 2001 cells counted, n=3 mice, 93.2%). In contrast, M-CSF expression was not detected in mature bone marrow adipocytes (Perilipin1+) (Fig. 3C, 0 cells out of 115 cells counted, n=3 mice, 0%), indicating that mature bone marrow adipocytes are unlikely a significant source of M-CSF.

3) We performed western blot to analyze M-CSF protein expression in peripheral adipose. As shown in Fig. 3D, the stromal vascular fraction (SVF) cells in adipose, which contain multiple cell populations including adipogenic progenitors, express M-CSF. On the contrary, M-CSF was nearly undetectable in the peripheral mature adipocytes isolated from adipose (Fig. 3D).

These data collectively support that mature adipocytes are not a significant source of M-CSF as evidenced by nearly undetectable M-CSF expression compared to the Adipoq-lineage progenitors. The results were described on pg. 5. However, the reviewer’s comment on ‘exclusive source’ is well taken as osteocytes and osteo lineage also express certain levels of M-CSF. We deleted ‘exclusive source’ in the manuscript, have added relevant literature and discussion in the Results and Discussion section on pp. 5 and 9.

2. Some of the phenotypic analyses are still incomplete. The authors did not report whether CHet (AdipoQCre Csf1(flox/+)) showed any bone phenotype. Further, the authors did not show that Csf1 mRNA or M-CSF protein is expressed in AdipoQ-lineage progenitors using histological methods. Current evidence is only based on scRNAseq and qPCR of isolated cells. Whether there was any change in circulating bone resorption markers in CKO mice was not shown. Cortical bone parameters were not included in the 3D-microCT analyses. These missing pieces of information would be important to correctly interpret the phenotypes.

The het mice (Csf1f/+;AdipoQ Cre) do not show abnormal bone phenotype, which is now shown in Fig. 4-figure supplement 4. We performed immunofluorescence staining of M-CSF on bone slices, and found that the majority of bone marrow AdipoQ-expressing progenitor cells express M-CSF (Fig. 3B, 1865 cells out of 2001 cells counted, n=3 mice, 93.2%). We tested serum TRAP level in mice, and found that the Csf1 deficiency in Csf1∆AdipoQ mice significantly decreased the TRAP level in serum, compared to that in the WT control mice (Fig. 5B). Csf1∆AdipoQ mice do not exhibit abnormal cortical bone phenotype. The cortical bone parameters are now included in Fig. 4G.

3. Which bone marrow cell population(s) are marked by AdipoQCre remain largely unclear. It is possible that AdipoQCre also marks at least part of MSPC-osteo cluster in addition to MSPC-adipo. Adipo-lineage progenitors may not stay entirely as adipoprogenitors and drift toward osteoblasts or their precursor cells.

We thank the reviewer for the insightful comment on this interesting mystery and complicated question, which is drawing more attention in the field.

In addition to Adipoq-lineage progenitors, Adipoq Cre also labels other clusters. However, the expression levels of Adipoq and frequency of Adipoq+ cells in other cell populations are relatively low. For example, the integrated scRNAseq dataset we analyzed shows that Adipoq is expressed at a low level (with scaled mean expression at 0.68, (27)) in a small proportion of MSPC-osteo cells (Fig. 1), and small amounts (31, 37) (about 4%) of osteoblasts in 8 or 12-week-old mice are Adipoq-lineage. A recent report found that in 24-week-old mice, about 15-40% of osteoblasts are marked with Adipoq Cre (37). This raises a few important possibilities that will need to be distinguished in future work. One possibility is that the Adipoq-lineage cells (adipo-CAR cells/MALPs) have minor or latent osteogenic potential that may become more evident under specific conditions, such as in older animals. However, balanced against this is the alternative that Adipoq-cre could primarily target a population of solely adipogenic adipo-CAR cells but that its specificity is imperfect, leading to progressive low levels of deletion in a separate population expressing very low levels of Adipoq, such as osteo-CAR cells. An additional possibility is that the Adipoq-lineage cells may themselves actually be further subdivided into multiple component cell types, including a major adipogenic and a separate minor osteogenic subpopulation. Ultimately, at the root of these issues is that Adipoq cre primarily defines one or possibly more lineages of cells rather than a cell type within those lineages. Therefore, application of further markers to fractionate the adipoq-lineage into its component cell types will be needed to resolve these possibilities, focusing on whether any potential osteogenic activity present can be fractionated away from the primary adipogenic activity present.

Of note, the Adipoq expression level and positive cell proportion are much higher in bone marrow Adipoq lineage progenitors than the levels seen in osteoblast lineage (Fig.1, Fig.2, (22, 27, 31)) or endothelial cells in bone marrow (38, 39). For example, the MSPC-Adipo cluster (Adipoq-lineage progenitors) has 6441 cells with the highest level (scaled mean expression level at 3.01 per (27) at Single Cell Portal) of Adipoq seen among bone marrow cells analyzed. In contrast, the MSPC-osteo cluster consists of 2247 cells with a very low Adipoq expression level (scaled mean expression level at 0.68 per (27) at Single Cell Portal). Taken together with both average expression level and cell numbers in each cluster, the relative overall contribution to Adipoq expression by MSPC-osteo vs the Adipoq-lineage progenitors is 7.8% ((2247 x 0.68)/(6441 x 3.01)). Therefore, the expression of Adipoq in MSPC-osteo cluster is marginal compared to that in the Adipoq-lineage progenitors. These data make Adipoq as an important marker to identify bone marrow Adipoq lineage progenitors. Overall, our work not only validates prior research identifying adipoq-lineage cells, identified as MALPs (22, 31), as a key osteoclast regulatory population, but also further extends the scope of their functions to encompass M-CSF production and regulation of macrophages.

These points have been added to the Discussion sections on pp. 9-10.

4. The OVX data in Figure 5 are not very well explained. The data do not seem to support the authors' conclusion that M-CSF deficiency in AdipoQ-lineage progenitors alleviates estrogen-deficiency induced osteoporosis. The CKO mice lose bone mass almost to the same extent as WT mice upon OVX.

To address the reviewer’s question, we calculated the changes of the uCT parameter values between Sham and OVX groups in the WT control and Csf1∆AdipoQ mice. Significant changes were identified between the control and Csf1∆Adipoq mice in several μCT parameters. For example, a decrease in trabecular BV/TV after OVX: 35.1% in the control vs 20.9% in Csf1∆Adipoq mice; a decrease in Tb. N after OVX:11.34% in the control vs 7.97% in Csf1∆Adipoq mice; a decrease in Conn-Dens after OVX: 39.7% in the control vs 14.56% in Csf1∆Adipoq mice; an increase in Tb. Sp after OVX: 12.51% in the control vs 1.97% in Csf1∆Adipoq mice. These results support our conclusion that M-CSF deficiency in AdipoQlineage progenitors alleviates estrogen-deficiency induced osteoporosis. These value changes have been included in Fig. 7C and discussed on pg. 7.

Reviewer #3 (Public Review):

Macrophage colony-stimulating factor (M-CSF) plays key roles in the differentiation of myeloid-lineage cells, including monocytes, macrophages and osteoclasts. The latter mediate bone resorption, which is important for physiological bone remodelling but, unrestrained, contributes to bone loss in conditions such as in post-menopausal osteoporosis. M-CSF production within the bone marrow is implicated in the maintenance of myeloid and skeletal homeostasis, but the cellular source of bone marrow M-CSF has remained elusive. In this study, Inoue et al address this issue through advanced transcriptomic and gene targeting approaches. They conclude that a population of Adipoq-expressing progenitors within the bone marrow, designated "AdipoQ-lineage progenitors", is the key cellular source of M-CSF. Consistent with this, they find that transgenic deletion of M-CSF from these cells disrupts macrophage and osteoclast development, leading to osteopetrosis and possibly preventing bone loss following ovariectomy. However, they have not adequately addressed the possibility that M-CSF production from other cell types, particularly adipocytes in peripheral adipose tissues, may also be influencing these phenotypes. Specific strengths and weaknesses are as follows:

Strengths:

1. The manuscript is written in a clear, succinct manner and the data are generally nicely presented. It is therefore a pleasure to read.

2. The analysis of single-cell transcriptomic data is clear and convincing, and the skeletal phenotyping has been done to a high standard.

Weaknesses:

1. The authors underplay the potential contribution of M-CSF production from other cell types, particularly from adipocytes in peripheral adipose tissues. They show that M-CSF expression from these cells is lower than from the bone marrow progenitors that they focus on; however, based on this they allude to "no expression" of M-CSF from these other adipocytes. This overlooks the findings of other studies showing that peripheral adipocytes produce M-CSF and that this has biological functions. Whether their knockout model alters M-CSF expression in peripheral adipose tissue, whether for whole tissue or for isolated adipocytes, has not been tested.

We performed western blot to analyze M-CSF protein expression in peripheral adipose. As shown in Fig. 3D, the stromal vascular fraction (SVF) cells in adipose, which contain multiple cell populations including adipogenic progenitors, express M-CSF. On the contrary, M-CSF was nearly undetectable in the peripheral mature adipocytes isolated from adipose (Fig. 3D). These data collectively support that mature adipocytes are not a significant source of M-CSF as evidenced by nearly undetectable M-CSF expression compared to the Adipoq-lineage progenitors. However, we understand that current techniques may have limitation in identification of trace amount of M-CSF. We thus deleted descriptions such as ‘exclusive’ or ‘do not produce/express…’ in the revised manuscript.

2. The decreases in M-CSF have been assessed at the transcript level, but not for M-CSF protein. Whether their knockout model

We performed immunofluorescence staining of M-CSF on bone slices, and found a drastic decrease in M-CSF protein in bone marrow AdipoQ+ cells in Csf1∆AdipoQ mice compared to the WT control mice. The results are shown in Fig. 4B, and Fig. 3B-D.

3. It is also unclear if the Adipoq-lineage progenitors consist exclusively of adipogenic cells, or if osteogenic progenitors are also part of this population.

We thank the reviewer for the insightful comment on this interesting mystery and complicated question, which is drawing more attention in the field.

In addition to Adipoq-lineage progenitors, Adipoq Cre also labels other clusters. However, the expression levels of Adipoq and frequency of Adipoq+ cells in other cell populations are relatively low. For example, the integrated scRNAseq dataset we analyzed shows that Adipoq is expressed at a low level (with scaled mean expression at 0.68, (27)) in a small proportion of MSPC-osteo cells (Fig. 1), and small amounts (31, 37) (about 4%) of osteoblasts in 8 or 12-week-old mice are Adipoq-lineage. A recent report found that in 24-week-old mice, about 15-40% of osteoblasts are marked with Adipoq Cre (37). This raises a few important possibilities that will need to be distinguished in future work. One possibility is that the Adipoq-lineage cells (adipo-CAR cells/MALPs) have minor or latent osteogenic potential that may become more evident under specific conditions, such as in older animals. However, balanced against this is the alternative that Adipoq-cre could primarily target a population of solely adipogenic adipo-CAR cells but that its specificity is imperfect, leading to progressive low levels of deletion in a separate population expressing very low levels of Adipoq, such as osteo-CAR cells. An additional possibility is that the Adipoq-lineage cells may themselves actually be further subdivided into multiple component cell types, including a major adipogenic and a separate minor osteogenic subpopulation. Ultimately, at the root of these issues is that Adipoq cre primarily defines one or possibly more lineages of cells rather than a cell type within those lineages. Therefore, application of further markers to fractionate the adipoq-lineage into its component cell types will be needed to resolve these possibilities, focusing on whether any potential osteogenic activity present can be fractionated away from the primary adipogenic activity present.

Of note, the Adipoq expression level and positive cell proportion are much higher in bone marrow Adipoq lineage progenitors than the levels seen in osteoblast lineage (Fig.1, Fig.2, (22, 27, 31)) or endothelial cells in bone marrow (38, 39). For example, the MSPC-Adipo cluster (Adipoq-lineage progenitors) has 6441 cells with the highest level (scaled mean expression level at 3.01 per (27) at Single Cell Portal) of Adipoq seen among bone marrow cells analyzed. In contrast, the MSPC-osteo cluster consists of 2247 cells with a very low Adipoq expression level (scaled mean expression level at 0.68 per (27) at Single Cell Portal). Taken together with both average expression level and cell numbers in each cluster, the relative overall contribution to Adipoq expression by MSPC-osteo vs the Adipoq-lineage progenitors is 7.8% ((2247 x 0.68)/(6441 x 3.01)). Therefore, the expression of Adipoq in MSPC-osteo cluster is marginal compared to that in the Adipoq-lineage progenitors. These data make Adipoq as an important marker to identify bone marrow Adipoq lineage progenitors. Overall, our work not only validates prior research identifying adipoq-lineage cells, identified as MALPs (22, 31), as a key osteoclast regulatory population, but also further extends the scope of their functions to encompass M-CSF production and regulation of macrophages.

These points have been added to the Discussion section on pp. 9-10.

If these weaknesses are addressed then this work has potential to yield firm conclusions and new insights into the regulation of myeloid and skeletal homeostasis, both in normal physiology and in clinically relevant conditions.

Yes, we have addressed the above 3 major questions.

Author Response

Reviewer #1 (Public Review):

The current study proposed a drug discovery pipeline to accelerate the process of identifying drug candidates for LCA10 patients using cells from mouse retinal organoid for initial screening, human patient iPSC-derived retinal organoid for further testing, and then mouse mutants for in vivo validation. Reserpine was identified as the top candidate, possibly through modulating proteostasis and autophagy to promote cilium assembly. The study was with high translational value. However, the rationale using dissociated cells from mouse retinal organoid for initial drug screening needs to be justified. In addition, the consistency of phenotypic characteristics in human patient iPSC-derived retinal organoid needs to be reported. It was unclear if the rescued phenotypic changes were from the drug effects or a result of phenotypic variations in organoids.

We thank the reviewer for the comments and suggestions. Please see the response provided in the “Essential Revisions” earlier. Briefly, the use of single-cell cultures for screening is to compensate for the variations of the Nrl-GFP signal in rd16 organoids so that each compound was present to homogenous cells. In addition, we performed a large-scale screening with 11 concentrations and 2 duplicates of over 6000 compounds. It was thus not feasible to manually perform the screening. We used a semi-automatic electronic dispenser to set up the screens in 1536-well plates and a liquid handling system to add the compounds. Intact mouse retinal organoids are too big to be dispensed and would be damaged during the process. They are also too big to fit into one well of a 1536-well plate or even in a 384-well plate. Therefore, single-cell cultures outweigh intact organoids in this application. We understand the potential pitfalls and thus the positive hits were verified in intact organoids in the secondary assays.

We have now tested reserpine on retinal organoids derived from 2 clones of each (a total of 4) of LCA1 and LCA2 patients. As suggested by the reviewers, we quantified the fluorescence intensity of rod marker rhodopsin staining in multiple sections of at least two batches of differentiation (Figure 3C and Figure 3—figure supplement 2). Although showing variability as predicted, reserpine treatment significantly increased the fluorescence intensity of rhodopsin in retinal organoids differentiated from multiple lines (Figure 3C), further validating the rescue effect of reserpine.

Reviewer #2 (Public Review):

In this manuscript, a drug discovery pipeline was developed using a human iPSC derived organoid-based high-throughput screening platform to be used to identify drug candidates for maintaining photoreceptor survival in LCA10 retinopathies. Reserpine proved effective in patient organoids and in mutant mouse retina in vivo to improve photoreceptor survival and outer segment structure. Protein homeostasis was restored after reserpine treatment by increasing p62 levels, decreasing the 20S proteasome, and increasing proteasome activity. The manuscript is clearly written, contains a large amount of valuable and high-quality data and demonstrates that rebalancing proteostasis can stabilize photoreceptor overall homeostasis in the presence of a mutation that causes retinal degeneration.

The manuscript may lack functional in vivo data on the treatment by reserpine in RD16 mice such as ERG measurements or other functional tests (the authors also refer to it as future direction). Nevertheless, in my view, the study provides a solid and convincing set of data and substantially advances our understanding on the neuroprotective effects of reserpine beyond the scope of the retina and therefore can be expected to have widespread influence on a readership interested in the principles of neuroprotection rebalancing proteostasis.

We sincerely thank the reviewer for the positive comments and suggestions. This study has taken many years to materialize. We agree and have now performed full-field electroretinogram (ERG) of untreated and reserpine-treated rd16 retina (as stated in response to an earlier comment). Scotopic a-wave was only marginally increased, yet scotopic b-wave displayed a significant higher amplitude, suggesting improved rod photoreceptor function (Figure 6D).

Reviewer #3 (Public Review):

Chen et al. perform an innovative screen using retinal organoids derived from rd16 mice to identify small molecules to treat CEP290 hypomorphic mutations linked to ciliopathies such as LCA. The authors identify reserpine which promotes photoreceptor development and viability in retinal organoids derived from LCA patient iPSCs and rd16 mouse retinas. The authors finally propose a mechanistic model where reserpine restores proteostasis thereby improving ciliogenesis.

The authors present a highly effective drug screen that utilizes the benefits of retinal organoids while also accounting for the inherent variability of retinal organoids by performing a screen on 2D cultures derived from the organoids. This is an innovated approach to using retinal organoids in drug screens and is of interest to the greater community. The success of the screen is reflected in the effectiveness of reserpine in the in vivo rd16 mouse retinal model where it promotes photoreceptor survival. However there are multiple issues with the LCA patient organoid screen that must be resolved.

We are grateful to the reviewer for generous comments. We have incorporated the suggestions and performed additional work to resolve the issues, as mentioned earlier in this response as well as below.

The patient derived iPSC lines are not controlled sufficiently enough to make conclusions stated in the manuscript. The authors rely on single iPSC clones from disease patients to perform experiments, and it is not clear whether karyotyping to validate normal chromosomal integrity was performed. In the case of the RNAseq experiment one patient clone does not show any differences calling into question the findings from the other clone. Patient derived iPSC studies would benefit from the use of multiple independently derived iPSC clones per patient, or rescuing the LCA10 mutation using CRISPR editing to validate the correlation of the mutation with the differences observed.

This study could be strengthened by parallel RNAseq studies is the rd16 mouse retina and patient iPSC retinal organoids.

Thanks for the suggestions. As mentioned earlier in “Essential Revisions” and response to other reviewers, we have performed additional experiments using multiple iPSC clones and from three patients (2 each from LCA1 and LCA2). These iPSC lines have been characterized previously (Shimada et al. 2017). We have now provided more details on iPSC derivation, iPSC maintenance, and differentiation. Karyotypes of all human and mouse iPSC lines were provided in Figure 1—figure supplement 1. Retinal organoids were generated using iPSC lines within 10 passages of test cells.

The purpose of the RNA-seq data is to provide primers on the signaling pathways modulated by reserpine treatment. The rescue effect of reserpine suggests that these pathways might be implicated in disease pathogenesis. Based on our RNA-seq data, we have validated the dysregulation of proteostasis pathway in patient-derived retinal organoids and in vivo rd16 retina. Further investigations are needed to validate other pathways but are beyond the scope of this manuscript. Although RNA-seq studies have advantages, more detailed molecular and functional assays are needed to validate the findings of RNA-seq studies and therefore we argue that performing additional RNA-seq on different clones or cell lines or mouse retina would provide more solid information.

According to our quantification of rhodopsin staining intensity (Figure 3C and Figure 3—figure supplement 2), LCA1 organoids are more responsive to reserpine compared to LCA2, which is not surprising based on the variations of patient responsiveness to drug treatments in previous clinical studies. We note that reserpine is not a transcription factor, thus the differentially expressed genes in reserpine treatments are secondary effects and the change of gene profiles upon reserpine treatment could vary in time and intensity, which could explain the few differentially expressed genes observed in LCA-2. Nevertheless, the action mechanisms of reserpine we found based on LCA1 could be validated on LCA2 (Figure 5—figure supplement 3), further strengthening our findings.

The reason why we performed RNA-seq on treated organoids but not treated mice was to identify the signaling pathways modulated by reserpine in a well-controlled manner in order to catch the small changes. Compared to reserpine treatment on organoid cultures, in which the organoids have stable and constant contact with reserpine, intravitreal injection of reserpine into P7 mice is technically challenging and leads to substantial variations. In this case, some small changes might be missed and masked by the variations.

Author Response

Reviewer #2 (Public Review):

The authors sought to be able to examine what cellular mechanisms underlie increases in mature blood cell production upon immune challenge. To this end they devised a new in vitro organ culturing system for the lymph gland, the main hematopoietic organ of the fruit fly Drosophila melanogaster; the fly serves as an excellent model for studying fundamental questions in immunology, as it allows live imaging combined with genetic manipulation, and the molecular pathways and cellular functions of its innate immune system are highly conserved with vertebrates.

The authors provide compelling evidence that the cultured lymph gland shows a similar time scale, dynamics, and capacity for cell division as was observed in vivo, and does not undergo undue oxidative stress in their optimized culture conditions. This technique will prove extremely useful to the large community studying the fly lymph gland, and potentially vertebrate immunologists seeking to expand the models they utilize.

In these cultured glands, the authors identify progenitors undergoing symmetric cell divisions and provide some evidence that is consistent with, but does not prove, that these two cells maintain their proliferative capacity. They detect equivalent levels in the two equally sized daughter cells of dome-Meso-GFP, a marker for JAK-STAT activity; however, this could be due to an equal inheritance of the protein from the mother, not an equivalent maintenance of a proliferative capacity.

This is an interesting question. A close look at the our movie (Video 4) of the dome-Meso-GFP marker shows the following sequence of events: the marker is nuclear, the mother cell divides and the nuclear envelope breaks down, cell division is completed, the dome-Meso-GFP re-accumulates at the nucleus of the daughter cells. This sequence of events implies that JAK-STAT is still active in the daughter cells. But as the reviewer points out there is a possibility of inheritance of the signal from the mother. If one of the cells were to differentiate, we would expect two things to occur, a differentiation marker to turn on in one of the daughter cells, and likely a slow decrease in the signal level of dome-Meso-GFP in one of the cells over time. We failed to mention that we accounted for both of those possibilities in our experiments such as the one shown in Video 5. We did this by first, including the eater-dsRed in the genetic background (see Figure 2 figure legend) in which these experiments were undertaken, if differentiation took place dsRed level would go up, an occurrence which we did not observe. Second, long-term tracking of dome-Meso-GFP levels for extended periods of time after completion of cell division did not show divergence or significant decrease of protein levels in the two daughter cells (Figure 2 - figure supplement 2). In any case, to directly make readers aware of this important caveat raised by the reviewer concern we added to the Results section in line 225-230 an explanation mentioning the possibility of inheritance of the marker and why we did not think this was the case.

The authors develop a technique to conduct tracking of progenitor cell size over time in the cultured lymph glands and identify a switch increase in growth after division, as well as two orientations of the divisions, with the main one occurring 90% of the time.

They show that bacterial infection results in a significant decrease in the division of Blood progenitors and the elimination of the minor orientation of division, but no obvious change in the rate of division.

By imaging two markers, Dome-GFP for the progenitor state and Eater dsRed for the differentiated one, they examine the trajectories by which differentiation occurs in the wild-type lymph gland. They describe two main categories of fate transitions. In one that they call linear, the blood cells express high levels of the differentiation marker along with the progenitor marker before turning off the progenitor marker. The dynamics of how these progenitor cells get to the state of expressing both the differentiation and progenitor marker at high levels is not described. In the other, which they call sigmoidal, cells express only high levels of the progenitor marker, and the differentiation marker increases after or as the progenitor marker decreases. The authors show that upon infection there is a large increase in the amount of the linear type of differentiation. But how this change in the type of differentiation upon infection explains the increased amount of differentiation is not clear.

A potential explanation comes from an aspect of their data that the authors don't comment upon. In their live analysis of lymph glands at a distinct time point in the uninfected state (Fig 7M-N), 95% of the cells they analyze traversing the sigmoidal path are in the intermediate step. This would predict that the cells on this path spend a much longer time stuck in this intermediate state before traversing to the final differentiated one, or that only a small fraction of the cells that become sigmoidal intermediate cells progress onwards to full differentiation. But this does not match the trajectories observed in the real-time analysis for uninfected cultured lymph glands (Fig 7A'-D') marker. Perhaps their algorithm discarded traces from the live imaging in which the differentiation marker did not come up quickly and was thus not analyzed in the trajectories.

If my interpretation of the single time point analysis is true, this would argue that the linear path is actually much faster/more fruitful than the sigmoidal one and this would explain why a higher level of total progenitor differentiation infection is the result of infection-inducing more differentiation by the linear path. Otherwise, I don't understand how their data explains that observation.

We understand the reviewer concern here and would like to state categorically that we did not use an algorithm to “discard” traces. As the reviewer outlines there is a large concentration of cells in the Dome-Meso-GFP (low expressing), eater-dsRed (low expressing) state. This is an intermediate state for the sigmoid differentiation trajectory. The reviewer suggests two scenarios to explain this. The first scenario is that this is the slowest (and thus rate limiting) step in the sigmoid differentiation trajectory. But, also as the reviewer notes, our tracking of individual cell trajectories doesn't show that cells spend a lot of time in this state. This leaves the second scenario the reviewer outlines, that only a small fraction of the cells that are in the Dome-Meso-GFP (low expressing), eater-dsRed (low expressing) state go on to differentiate (at least in the larval stage). We favor this model, because it is consistent with our observations, mainly that manipulating the sigmoid pathway is not a good way to induce the production of mature blood cells following infection, compared to manipulating the linear pathway. As the reviewer correctly points out the linear pathway provides a powerful way to change the rate of production of mature blood cells, with a few hours of infection the number of cells that are found in the intermediate state for this trajectory (Dome-Meso-GFP (high expressing), eater-DSred (high expressing)) increases 5-6 times. We now mention this specifically in the Discussion in line 532-539.

Author Response

Reviewer #1 (Public Review):

Single-cell sequencing technologies such as 10x, in conjunction with DNA barcoded multimeric peptide MHCs (pMHCs) has enabled high throughput paring of T cell receptor transcript with antigen specificity. However, the data generated through this method often suffers from the relatively high background due to ambient DNA barcodes and TCR transcripts leaking into "productive" GEMs that contain a 10X bead and a T cell decorated with antigen-specific barcoded proteins. Such contaminations can affect data analysis and interpretation and have the potential to lead to spurious results such as an incorrect assessment of antigen-TCR pairs or TCR cross-reactivity. To address this problem, Povelsen and colleagues have described a data-driven algorithm called "Accurate T cell Receptor Antigen Pairing through data-driven filtering of sequencing information from single-cells" (ATRAP) that supplies a set of filtering approaches that significantly reduces background and allows for accurate pairing of T cell clonotypes with cognate pMHC antigens.

This paper is rigorously conducted and will be useful for the field - there are some areas where further clarifications and comparisons will benefit the reader.

Strengths:

1) Povelsen and colleagues have systematically evaluated the extent to which parameters in the experimental metadata can be used to assess the likelihood of a GEM to correctly identify the antigen specificity of the associated T cell clonotype.

2) Povelsen and colleagues have provided elegant data-driven scoring metrics in the form of concordance score, specificity score, and an optimal ratio of pMHC UMI counts between different pMHCs on a GEM, which allows for easy identification of poor quality data points.

3) Based on the experimental goals, ATRAP allows for customizable filters that could achieve appropriate data quality while maximizing data retention.

Weakness:

1) The authors mention that 100% of the 6,073 "productive" GEMs contained more than one sample hashing barcode, and 65% contained pMHC multiplets. While the rest of the paper elaborates on the steps taken to deal with pMHC multiplets issue, not much is said about the extent of multiplet hashing issue and how was it dealt with when assigning cells to individual donors. How is this accounted for? Even a brief explanation would be beneficial.

We agree that the issue of multiplet hashing was only very briefly discussed in the manuscript. The reason for this is that although cell hashing multiplets exist for every GEM, it is generally a much simpler issue to solve than pMHC multiplets, because one hashing entry most often has much higher counts compared to the others (see supplementary fig. 3). Moreover, in the experimental design, only one hashing antibody is added to each sample. It is therefore given that only a single hashing signal should be associated with each GEM, i.e. this does not mirror the complex nature of the pMHC data, where cross-reactivity could result in more than one pMHC being a true binder to a given TCR. Given the simplicity associated with the hashing signal, we have here opted for utilizing an existing tool to annotate cell hashing. We have elaborated the description of this in the revised manuscript (line 384).

2) It would be helpful for the authors to describe how experimental factors such as the quality of the input MHC protein may affect the outputted data (where different proteins may have different degrees of non-specific binding), and to what degree the ATRAP approach is robust to these changes. As an example, the authors mention that RVR/ A03 was present at high UMI counts across all GEMs and RPH/ B07 was consistently detected at low levels. Are these observations the property of the pMHCs or the barcoded dextran reagent? Furthermore, are there differences in the frequency of each of these multimers in the starting staining library which manifests in consistent high vs low read counts for the pMHC barcodes?

We understand the reviewers' concern. We have extensive experience from staining with large libraries of different pMHCs in a bulk setting (Bentzen et al 2016), where it is part of the routine analyses to include an aliquot of the barcoded pMHC library taken prior to incubation with cells (input sample). From this data, we know that even if pMHCs are present in uneven amounts prior to cell incubation, this unevenness is not translated to the final output. I.e. if a given barcode (associated with a specific pMHC) is present at levels up to 2x higher than the remaining barcodes, this does not result in that barcode also being enriched after cell incubation if T cells do not recognize the corresponding pMHC. And vice versa, a barcode present at lower levels in the input can still be enriched after incubation with cells.. From the same type of data, we also have experience with differences in the background associated with different MHC/HLA molecules, i.e. a general higher level of background related to a certain MHC irrespectively of the peptide bound in this. We agree that this potentially could be a confounding factor influencing our results (as it will influence any other results related to the potential different background signal associated with different MHC/HLA molecules). We are currently in other studies investigating in a broader sense whether these differences reflect a biological inherent MHC association or are experimental artifacts. In the current work, we have opted for not defining pHLA specific UMI count threshold to ensure that any biological relevance remains unmasked, but still ensure that we can at the same time filter the data to identify the most likely true pMHC specific interaction.

3) It would be helpful for the authors to further explain how ATRAP handles TCRs that may be present in only one (or a small number) of GEMs, as seen in Figure 7b, and potentially for the large number of relatively small clonotypes observed for the RVR/A03 peptide in Figure 6 (it is difficult to know if the long tail of clonotypes for RVR is in the range of 1 or 10 GEMs based on the scale bar). Beyond that, is there any effect on expected (or observed) clonal expansion on these data analyses, for example, if samples are previously expanded with a peptide antigen ex vivo or not?

ITRAP removes any GEM that does not meet the criteria of the selected filters. Small clones are only removed if all GEMs in a clone fail to meet the selected filter criteria. As ITRAP is based on combinations of filters which are user-defined, one can choose to filter away singlet specificities, i.e. a TCR-pMHC pair only observed in a single GEM. However, this might not be relevant in all cases. We believe that it is a strength of the method that it is flexible and adaptable to the needs of individual users. This also allows for additional filters to be imposed by the user, if one for instance wishes to remove clones of fewer than a certain number of GEMs. With respect to figure 6, we agree that it was difficult to estimate the number of clonotypes within a given peptide plateau, and have updated the figure to include a clonotype count in the x-axis. In relation to the effect on clonotype expansion, we would first like to refer to figure 7. Here, we in figure a) and b) display the observed T cell frequencies towards the individual pMHCs as obtained by the two different experiment approaches a) conventional fluorescent multimer staining, and b) GEMs counts as obtained using the single-cell pipeline described here. This analysis demonstrates a very high concordance between the two approaches of the T cell populations, reflected by the vast majority of the responses detected by fluorescent multimer staining also being captured in the single-cell screening, (recall of 0.95). This result suggests that sensitivity of the SC approach, in the context of the current pMHC epitope set, is comparable to that of conventional fluorescent multimer staining. With regard to clonotype expansion, we would next like to refer back to figure 3. Even though we have not expanded the clones in vitro, this figure shows how the specificity of a TCR clone can be more confidently assigned when there are more GEMs mapped to a given TCR clone. Hence, to identify a single TCR-pMHC match, it could in many cases be valuable to expand a given clone prior to the experiments. However, since the 10x pipeline can only include a limited number of cells, we argue that it is valuable to identify pMHC TCR pairs on unexpanded/unmanipulated material to include as many different pairs as possible.

4) The authors mention a second method, ICON, for conducting these types of analyses, and that the approach leads to significantly more data loss. However, given there could be differences in dataset quality themselves, and given the dataset, ICON is publicly available, it would be helpful for a more explicit cross-comparison to be conducted and presented as a figure in the paper.

We have conducted such a comparative analysis in a separate manuscript (available at BioRxiv doi.org/10.1101/2023.02.01.526310). The overall conclusion is that both methods allow for effective denoising of the provided data, with an overall advantage in favor of iTRAP. We have extended the discussion in the current manuscript with a brief summary of the main findings from this study.

Reviewer #2 (Public Review):

The study by Povlsen, Bentzen et al. describes certain computational pipelines authors used to analyze the results from a single-cell sequencing experiment of pMHC-multimer stained T cells. DNA-barcoded pMHC multimers and single-cell sequencing technologies provide an opportunity for the high-throughput discovery of novel antigen-specific TCRs and profiling antigen-specific T-cell responses to multiple epitopes in parallel from a single sample. The authors' goal was to develop a computational pipeline that eliminates potential noise in TCR-pMHC assignments from single-cell sequencing data. With several reasonable biological assumptions about underlying data (absence of cross-reactivity between these epitopes, same specificity for different T-cells within a clonotype, more similarity for TCRs recognizing the same epitope, HLA-restriction of T cell response) authors identify the optimal strategy and thresholds to filter out artifacts from their data.

It is not clear If the identified thresholds are optimal for other experiments of this kind, and how the violation of authors' assumptions (for example, inclusion of several highly similar pMHC-multimers recognized by the same clone of cross-reactive T cells) will impact the algorithm performance and threshold selection by the algorithm. The authors do not discuss several recent papers featuring highly similar experimental techniques and the same data filtering challenges:

https://www.science.org/doi/10.1126/sciimmunol.abk3070

https://www.nature.com/articles/s41590-022-01184-4

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184244/

As described above, we have investigated the use of ITRAP on the large data set provided by 10X Genomics, and here further compared the result to that obtained by ICON in an independent publication [BioRxiv doi.org/10.1101/2023.02.01.526310]. We have included a brief summary of the findings in study in the current manuscript. The overall results and conclusions between the two studies align very well. UMI count filtering and donor-HLA matching are in both cases driving the strongly denoising signal. However, when it comes to the identified UMI thresholds, they were found to differ between the two data sets. As stated above, this we however believe to be a strength of the ITRAP framework, since it demonstrates that the tools can be robustly applied to data originating from very different technical and/or biological settings.

We acknowledge that ITRAP is highly dependent on the data containing a set of “large” clonotypes for which a single pMHC target can be assigned using the statistical approach outlined in the manuscript. This since the UMI filtering thresholds are defined based on these clonotypes and associated peptide annotations. However, other than this, the method does not exclude identification of cross-reactive TCR (in contrast to for instance ICON). We have expanded the discussion to make this point more clear.

When it comes to the papers mentioned by the reviewer, these are clearly of high interest to us, and we are currently in the process of analyzing these data using the ITRAP framework. We however believe these analyses are beyond the score of the current publication, in particular since we have conducted the parallel benchmark study on the 10X Genomics data mentioned above.

Unfortunately, I was unable to validate the method on other datasets or apply other approaches to the authors' data because neither code nor raw or processed data were available at the moment of the review.

All data sets and code has been made publicly available at https://services.healthtech.dtu.dk/suppl/immunology/ITRAP

One of the weaknesses of this study is that the motivation for the experiment and underlying hypothesis is unclear from the manuscript. Why these particular epitopes were selected, why these donors were selected, are any of the donors seropositive for EBV/CMV/influenza is unclear. Without particular research questions, it is hard to evaluate pipeline performance and justify a particular filtering strategy: for some applications, maximum specificity (i.e. no incorrect TCR specificity assignments) is crucial, while for others the main goal is to retain as many cells as possible.

We understand this concern and have elaborate our motivation for the experimental design in the text. The overall motivation for this study was to generate TCR-pMHC data complementing what was available in the public domain at the start of the project. This with the purpose of generating novel data for training of TCR specificity prediction models. This is also the reason why we explicitly “deselected” T cells specific for the 3 negative control peptides, since these already are covered with large amounts of TCR sequences in the public databases.

We do not know the serostatus of the donors included, but have determined the antigen-specificities present in the donors prior to initiating the study (evaluated for T cell recognition against 945 common viral specificities, using barcoded pMHC multimers in a bulk setting). The 945 peptides were selected from prevalent epitopes within IEDB. This means that the T cell specificities for the donors selected to be included in the current study was known a priori. We have updated the motivation for performing the study (lines 122-126).

Author Response

Reviewer #2 (Public Review):

The manuscript "Optimal Cancer Evasion in a Dynamic Immune Microenvironment Generates Diverse Post-Escape Tumor Antigenicity Profiles" by George and Levine describes TEAL - a mathematical model for the dynamics of cancer evolution in response to immune recognition. The authors consider a process in which tumor cells from one clone are characterized by a set of neoantigens that may be recognized by the immune system with a certain probability. In response to the recognition, the tumor may adapt to evade immune recognition, by effective removal of recognizable neoantigens. The authors characterize the statistics of this adaptive process, considering, in particular, the evasion probability parameter, and a possibility of an adaptive strategy when this parameter is optimized in each step of the evolution. The dynamics of the latter process are solved with a dynamic programming approach. In the optimal case, the model captures the tradeoff between a cancer population's need for adaptability in hostile immune microenvironments and the cost of such adaptability to that population. Additionally, immune recognition of neoantigens is incorporated. These two factors, antitumor vs pro-tumor IME as quantified by the Beta penalty term, and the level of immune recognition as quantified by the rate q, form the basis of a characterization of tumors as 'hot' or 'cold'.

I think this framework is a valuable attempt to formally characterize the processes and conditions that result in immunologically hot vs cold tumors. The model and the analytical work are sound and potentially interesting to a major audience. However, certain points require clarification for evaluation of the relevance of the model:

1) Tumor clonality

My main concern is about the lack of representation of the evolutionary process in the model and that the heterogeneity of the tumor is just glossed over.

The single mention of the problem occurs in Section 2, p2: "Our focus is on a clonal population, recognizing that subclonal TAA distributions in this model may be studied by considering independent processes in parallel for each clone."

I don't think this assumption resolves the impact of tumor heterogeneity on the immune evasion process. Furthermore, I would claim that the process depicted in Fig 1A is very rare and that cancers rarely lose recognizable neoantigens - typically it would be realized via subclonal evolution, with an already present cancer clone without the neoantigens picking up. Similarly, the adaptation of a tumor clone is an evolutionary process - supposedly the subclones that manage to escape recognition via genetic or epigenetic changes are the ones that persist. It is not clear what the authors assume about the heterogeneity of the adapting/adapted population between different generations, n->(n+1). Is the implicit assumption that the n+1 generation is again clonal, i.e. that the fitness advantage of the resulting subclone was such that the remaining clones were eliminated? Or does the model just focuses on the fittest subclone? A discussion on whether these considerations are relevant to the result would clarify the relevance of the result.

We thank the reviewer for these helpful clarifying points. Empirical evidence in lung cancer exists for genomic changes manifesting as lost neoantigens in treatment-resistant clones (and Anagnostou et al. Cancer Discovery 2017) showed that those lost antigens were also shown to generate functional immune responses). Similar results for melanoma have also been shown (Verdegaal et al. Nature 2016), with loss of neoantigens associated with reactivity in TILs. Recent observations (Jaeger et al. Clinical Cancer Research 2020) even show that mutated peptides may be hid by protein stabilization, in addition to reduced expression patterns. We however do wish to clarify that our model implicitly equates antigen loss and the progression of a subpopulation currently adapted to evade immune targeting – either by direct pruning of the fittest subclone or by stochastic emergence and subsequent growth of a new one lacking the targeted antigens – as equivalent.

Because we for foundational understanding studied the case where a single clonal signature was tracked in time, we under-explained the implementation of such a model in more complicated cases. As mentioned previously, the next most complicated scenario involves a heterogeneous population of cancer cells with disjoint neoantigen profiles. In this case, a parallel process can be studied wherein the effects of recognition in one environment are decoupled from the other (relevant to, for example, spatially distinct sub-populations). This description however misses the case where such disparate populations evolve to express shared antigens, or in the case where there are both clonal and subclonal antigen targets. Here, our model can still be applied in parallel to study distinct clones but requires additional structure. Namely, in this case we would need to incorporate non-trivial coupling between the possible recognition/selection against certain antigens shared across clones. For example, control of a population with clonal antigens {a,b} but having unique subclones having either antigens {w,x} or {y,z} could be considered by studying the process in parallel, and control in the next periods would require recognition/selection against either 1) at least one of {w,x} and at least one of {y,z}, or 2) at least one of {a,b}. In this more general framework, the arrival of new subclones with distinct features from the parent clone in question could also be incorporated and studied across time periods. This strategy of subdividing more complicated evolutionary structures has now been further elaborated on in the Methods section, and we have expounded these points in the discussion (see additions given under Editor Comment 2).

2) Time scales

Section 2, p2: "We assume henceforth that the recognition-evasion pair consists of the T cell repertoire of the adaptive immune system and a cancer cell population, recognizable by a minimal collection of s_n TAAs present on the surface of cancer cells in sufficient abundance for recognition to occur over some time interval n.".

How do the results depend on the duration of interval n? The duration should be long enough to allow for recognition and, up to some limiting duration, proportional to the TAA recognition probability q. However, it should not be so long that the state of the system can change significantly. A clarification on this point is needed.

We agree with the reviewer that these points should be elaborated upon when discussing the time interval. Very briefly, we opted for a discrete-time model tracking a cancer population under selective immune pressure. In order for 𝒒 to represent the total recognition probability of an immune system against a particular TAA, the time interval 𝚫𝒏 in question is a coarse-grained feature representing the time between the earliest chance that the adaptive immune system may identify a cancer clone and the latest point after which such a recognition event would no longer be able to prevent cancer escape. This time period may vary substantially across cancer subtypes and depends on the cancer per-cell division rate, for example (George, Levine. Can Res 2020). As the reviewer pointed out, in implementing such a model there is an asymmetric risk to considering 𝚫𝒏 too large, as the future state of the system may not be well-reflected by the simple loss and addition of new TAAs. On the other hand, considering small time intervals 𝚫𝒏, while possible, would require the incorporation of additional intermediate states ending in neither cancer elimination nor cancer escape.

We have clarified the points that the reviewer has brought up by adding them to the discussion section: In this discrete-time evolutionary model, the intertemporal period considered represents the time period between the earliest moment that the adaptive immune system may identify a cancer clone and the latest point after which such a recognition event would no longer be able to prevent cancer escape (George, Levine. Can Res 2020). This effectively gives 𝒒 a probabilistic representation for the total rate of opportunity to recognize a given TAA during cancer progression. Implementing this model in cancer subtype-specific contexts thus requires a consideration of the per-cell division rates, for example.

Reviewer #3 (Public Review):

Cancer cell populations co-evolve under the pressure exerted by the recognition of tumor-associated antigens by the adaptive immune system. Here, George and Levine analyze how cancers could dynamically adapt the rate of tumor-associated antigen loss to optimize their probability of escape. This is an interesting hypothesis that if confirmed experimentally could potentially inform treatments. The authors analyze mathematically how such optimally adapting tumors gain and lose tumorassociated antigens over time. By simplifying the complex interplay of immune recognition and tumor evolution in a toy model, the authors are able to study questions of practical interest analytically or through stochastic simulations. They show how different model parameters relating to the tumor microenvironment and immune surveillance lead to different dynamics of tumor immunogenicity, and more immunologically hot or cold tumors.

Simple models are important because they allow an exhaustive study of dynamical regimes for different parameters, such as has been done elegantly in this study. However, in this quest for simplification, the authors have not considered biological features that are likely to be of importance for understanding the process of cancer immune co-evolution in generality: tumor heterogeneity and immune recognition that only stochastically results in cancer elimination. In this sense, this paper might be seen as the opening act in a series of more sophisticated models, and the authors discuss avenues towards such further developments.

We share the reviewer’s credence in foundational modeling for comprehensive predictions on available dynamical behavior for the important problem at hand. The reviewer also correctly points out that that future model refinement will be needed to further develop the foundational model developed in this work. In an attempt to illustrate one of the more reasonable generalizations, which is to include nontrivial sub-clonal heterogeneity in tumor antigens, we now describe how one would go about enhancing the existing model to address this, which has been added to the Methods and Discussion sections (see additions given under Editor Comment 2).

Author Response

Reviewer #1 (Public Review):

N1-methyladenosine (m1A) is a rather intriguing RNA modification that can affect gene expression and RNA stability etc. The manuscript presented the exploration of RNAs m1A modification in normal and OGD/R-treated neurons and the effects of m1A on diverse RNAs. The authors showed that m1 modification can mediate circRNA/LncRNA-miRNA-mRNA mechanism and 3'UTR methylation of mRNAs can disturb miRNA-mRNA binding.

The manuscript provides evidence for the following,

1) The OGD/R can have impacts on various functions of m1A mRNAs and neuron fates.

2) The m1A methylation of mRNA 3'UTRs disturbs the miRNA-mRNA binding.

3) The authors identified three possible patterns of m1A modification regulation in neurons.

The main merit of the manuscript is that the authors identified some critical features and patterns of m1A modification and in neurons and OGD/R-treated neurons. Moreover, the authors identified m1A modifications on different RNAs and explored the possible effects of m1A modification on the functions of different RNAs and the overall posttranscriptional regulation mechanism via an integrated approach of omics and bioinformatics. The major weakness of the manuscript is that technique details for many results are missing. Moreover, language inconsistences can be found throughout the manuscript. My general feeling about the manuscript is that some conclusions are rather superficial and therefore require validation and discussion.

We appreciate your endorsement and constructive opinion concerning our work. Our study provides a comprehensive exploration of the characteristics of m1A modifications in neurons. According to your suggestions, we have specified the technique details in the revised manuscript have included our perspectives on some of the conclusions in the Discussion section. In addition, we have made changes to language inconsistences throughout the manuscript. We hope that the revisions made are acceptable and meet your requirements.

Reviewer #2 (Public Review):

In this manuscript, investigators explore the m1A modification, an important post-transcriptional regulatory mechanism, in primary normal neuron and OGD/R treated neuron. As far as I know, the regulatory m1A modification remains poorly characterized in neuron. This is an interesting topic in the context of epitranscriptomics. This paper not only provided us with a landscape of m1A modifications in neuron, but also explored the impact of m1A modifications on the biological functions of different RNA (mRNA, lncRNA, circRNA). In addition, the argument that m1A modification affects miRNA binding to other RNAs is of interest to reader, and the authors have performed a dual luciferase validation here to add feasibility to this conclusion.

Thank you for your careful review of our study, and thank you for your appreciation on our work. The aim of this work was to explore the characteristics of m1A modification in neurons. We believe that incorporating your advice into the revised manuscript has enhanced the quality of our article.

Reviewer #3 (Public Review):

Overall, this is an interesting and well performed study that described a comprehensive landscape of m1A modification in primary neuron and investigated the role of m1A in the circRNA/lncRNA‒miRNA-mRNA regulatory network following OGD/R. The focus on the two different complex regulatory networks for differential expression and differential methylation is important and it will be a valuable resource for the research community that focuses on epitranscriptomics and central nerve system diseases. Collectively, the authors present an exciting piece of work that certainly adds to the literature regarding epitranscriptomic features in neuron. While interesting results obtained and the paper is nicely written, I have the following suggestions for minor revisions to improve the paper.

We are grateful for your many positive comments and recognition of the potential of our work. Due to your suggestion, we found some shortcomings in our current manuscript. These suggestions were introduced and added value to our article. Our future research will continue to explore some conclusions obtained from this work. And we will continue to contribute our research outcomes in this field. Thank you again for your excellent suggestions!

1) The authors have explored the role of m1A modification in neuron, but it would have been helpful if the authors described the significance of these findings in depth in some sections (Figure 5 and Figure 6) to enhance the value of the article.

Thank you for your insightful suggestion. We agree to the comment that the significance of these findings should be described in detail. As such, we have added corresponding content to the Results (line 407-424) and Discussion (line 532-550) sections respectively.

2) The authors should describe in detail the current research state of m1A modification and the significance of this study to the field of epitranscriptomics in the introduction and Discussion section.

Thank you for your insightful suggestion. There is relatively little knowledge in the m1A modification area. It is really important to summarize the existing knowledge and research progress in a comprehensive and detailed manner. We conducted a comprehensive latest literature search and added corresponding content to the Introduction (line 78-83) and Discussion section (line 505-511, line 532-562) as you suggested.

Author Response

Reviewer 1 (Public Review):

Protein oligomerization is essential to their in vivo function, and it is generally challenging to determine the distribution of oligomeric states and the corresponding conformational ensembles. By combining coarse-grained molecular dynamics simulations and experimental small-angle X-ray scattering profiles at different protein concentrations, the authors have established a robust approach to self-consistently determine the oligomeric state(s) and the conformational ensemble. The approach has been applied specifically to the speckle-type POZ protein (SPOP) and generated new insights into the conformational ensemble and structural features that determine the ensemble. The model was further tested by the analysis of several relevant mutants as well as models with different types of structural restraints. The results also support the isodesmic selfassociation model, with KD values comparable to those measured from independent experiments in the literature. The approach is potentially applicable to a broad set of systems.

We thank the reviewer for taking the time to assess our work.

Reviewer 2 (Public Review):

This manuscript applied the SAXS data analysis of protein selfassembly by implementing the simultaneous fitting of intra- and intermolecular motions/conformations against SAXS data at a series of oligomerization states/concentrations. Despite several major assumptions hinted, a diverse pool of conformational and oligomeric candidates was generated from CG simulations, and more importantly, these candidates were fitted into these SAXS data to reach a reasonable agreement, suggesting a somewhat convergence (even if the ensemble-fitting could well be at a local minimal). This is considered a technical advance, given the fairly large numbers of both the oligomer fraction phi_i (i=1, ..., N) and the conformational weight w_k (k=1, ..., n), where N is the number of oligomers and n is the number of internal conformational states.

We thank Prof. Yang for taking the time to assess our work.

Central is optimizing phi_i and w_k, simultaneously. The former has been illustrated in Fig. 4 and SI-Fig. 7 for the total number of 60mers. The latter relies on an overfitting-preventing strategy, as shown in SI_Fig. 1, where an effective fraction cutoff was used from 0.1 to 1.0, as opposed to the number of conformational states. What are the numbers of conformational states for these oligomers? This should be quantifiable, e.g., defining the conformational differences by chi_2.

The reviewer is correct that the entropy-based term for preventing overfitting is a key aspect of the method. In contrast to some of the other methods to combine experiments with simulations, our approach does, however, not require us to define individual conformational states. Instead, the weights in the entropy term refer to individual configurations rather than states, and we can thus integrate the SAXS experiments and simulations without, for example, clustering the conformations. Indeed, for most of the collective variables that we have calculated from the ensembles, such as the radii of gyration, end-to-end distances, and MATH-MATH distances, we observe continuous monomodal probability distributions, which suggests that it might be difficult to define a few distinct conformational states. For the MATH-BTB/BACK distance, we observe a trimodal distribution, and these distinct conformational states are shown as overlaid structures in Fig. 4i. Thus, while these “states” change populations during reweighting, this is the result from changing weights of the individual configurations.

Reviewer 3 (Public Review):

Molecular-level interpretations of SAXS data are challenging, especially for oligomeric systems of variable length with intrinsic flexibility and the possibility of multiple association interfaces. In order to make this challenge tractable, a number of assumptions are made here: 1) There is a single pathway by which individual domains associate first into homodimers and then into longer oligomers; 2) the association kinetics is isodesmic, which allows the direct calculation of oligomer distributions based on the given value of a single dissociation constant; 3) the internal dynamics within dimers is restricted essentially to relative domain-domain motions, that are sampled comprehensively via MD simulations. As a result, excellent fits to the SAXS data are obtained and the underlying conformational ensembles are highly plausible. The resulting models are useful to further understand SPOP function, especially in the context of liquidliquid phase separation.

We thank the reviewer for taking time to read our work and for their various suggestions.

Author Response

Reviewer #1 (Public Review):

This work provides a new general framework for estimating missing data on cervical cancer epidemiology, including sexual behavior, HPV prevalence, and cervical cancer incidence. These data are useful to determine impact projections of cervical cancer prevention. The authors suggest a three-step approach: 1) a clustering method applied on registries with an intermediate level of data availability to cluster cervical cancer incidence based on a Poisson-regression-based CEM algorithm, 2) a classification method applied on registries with a low level of data availability to classify cervical cancer incidence based on a Random Forest, 3) a projection method applied on missing data based on the mean of available data. The authors use India as a case study to implement this new methodology. Results indicate that two patterns of cervical cancer incidence are identified in India (high and low incidence), classifying all Indian states with missing data to a low incidence. From this classification, missing data is approximated using the mean of the available data within each cluster.

A strength of this approach is that this methodology can be applied to regions with missing data, although a minimum set of information is needed. This makes it possible to have individual data for each unit in the region.

One of the weaknesses of this methodology is the need for a minimum set of epidemiological data to enable impact projections. It is true that when epidemiological cervical cancer data is not available, authors mentioned that general indicators (e.g., human development index, geography) can be used but projections will be probably less realistic. As observed with other techniques, countries with fewer resources have less data available and cannot benefit from these types of techniques to have more adequate guidelines.

Imputation of missing data is always a challenging issue. The technique proposed in this manuscript is an interesting new approach to missing data imputation that could be applied with a minimum set of available data. However, we must focus on obtaining reliable data from each region of the world to help local health authorities implement better preventive measures for the local population.

We thank the reviewer for the considerate comments and suggestions and have tried to incorporate them as much as possible in the revised manuscript.

As the reviewer has pointed out, the applicability of the proposed methodology depends on the available data. In our opinion, it is a general challenge for approximating missing data, rather than a weakness particular to our methodology. In fact, we believe that our framework is flexible to address missing data in many situations. To clarify this point, we have included the following sentences in the Discussion (lines 363-376, page 18): “It is important to note that, in general, the applicability the proposed framework depend on the actual amount of data available. However, in our opinion, it is a general challenge for approximating missing data, rather than a weakness particular to our methodology. By allowing possible adaptations, we believe that our framework is sufficient flexible to address missing data in many situations.”

Finally, we fully agree with the reviewer that we should continue our effort to collect more data for countries where these are not available. The proposed framework should be considered as a solution to the situation in which collection of additional data is not or not yet possible.

Reviewer #2 (Public Review):

The burden of cervical cancer worldwide is well recognized. While prevention strategies, including vaccination against human papillomavirus (HPV), cervical cancer screening, and pre-cancer treatment, can reduce the burden of cervical cancer, access to these measures is still limited, especially in low- and middle-income countries. Since the impact of prevention strategies is heavily dependent on the disease's burden on a particular population, we need to know the latter to assess the impact of these context-specific prevention strategies.

However, epidemiological data on cervical cancer are not always available for all geographical areas. This paper uses India as a case study to propose a framework called "Footprinting" to comprehensively evaluate the burden of cervical cancer. The authors applied a three-step analytical strategy to impute cervical cancer epidemiological data in states where this information was unavailable using data from cervical cancer incidence, HPV prevalence, and sexual behaviour from other regions. The findings suggest a high and low incidence of cervical cancer incidence in different parts of India; all Indian states with missing data were classified as low incidence.

The proposed analytical strategy presents an important solution for imputing data from geographic areas of a country where data are missing.

We thank the reviewer for the considerate comments and suggestions and have tried to incorporate them as much as possible in the revised manuscript.

One conceptual limitation of this work is the lack of explanation or evidence that sexual behaviour can be used to approximate cervical cancer and/or HPV rates.

A similar comment was raised by Reviewer #1. It is well established that sexual contact is the only transmission route of carcinogenic HPV infection, and hence necessary for the occurrence of cervical cancer [ref #26 Vaccerella 2006, Muñoz 1992 Int J Cancer 52, 743-749].

We have included sexual behaviour variables that have previously been shown to be risk factors of HPV infection and cervical cancer risk, e.g., age of sexual debut and number of sexual partners [ref #26 Vaccerella 2006, ref #27 Schulte-Frohlinde 2021]. Furthermore, we used variables that are commonly available so that the analyses can be easily applied to other settings.

As far as we know, there is no established set of sexual behaviour variables for predicting the patterns of HPV prevalence and cervical cancer incidence. The good prediction performance in the India case study shows that using the selected set is sufficient. As sexual behaviour variables are highly correlated, including more variables might even risk overfitting.

To clarify these points we have included the following paragraph in the Discussion (lines 319-325, page 16): “In our analysis of classifying clusters of cervical cancer incidence, we only included some of the sexual behaviour variables available in the NACO report [15]. We selected variables that were previously shown to be risk factors of HPV infection and cervical cancer risk and that are commonly available so that the analyses can be easily applied to other settings, e.g., age of sexual debut and number of sexual partners [26, 27]. As far as we know, there is no established set of sexual behaviour variables for predicting the patterns of HPV prevalence and cervical cancer incidence. The good prediction performance shows that using the selected set is sufficient. As sexual behaviour variables are highly correlated, including more variables might even risk overfitting.”

Also, full information on the three main indicators is only available in two states. This is used to impute the values for the other states.

Indeed, HPV prevalence data were only available for two states. While we acknowledge that this affects the certainty in the imputed HPV prevalence, we considered the imputed results to be satisfactory based on the good accordance with the cervical cancer incidence data we found in the validation step (lines 286-23, page 14). We verified that the ratio of HPV prevalence between the high-and low-incidence cluster (1.7-fold) was very similar to the ratio of age-standardized cervical cancer incidence (1.9-fold).

Furthermore, we note that previous modelling works on India relied on even less data, namely one source of HPV prevalence and cervical cancer incidence data [ref #29 Brisson 2020, Diaz 2008 Br J Cancer].

Moreover, the available data used in this study also present some limitations; for example, cervical cancer incidence data were from 2012 to 2016, while sex behaviour data were from 2006. This large gap is likely to have a significant cohort effect, especially given changes in sexual norms in Western countries over the last few decades, which may have gradually influenced other countries, especially in this age of the internet and social media.

In our opinion, for the purpose of modelling the natural history of cervical cancer, it is not necessarily more adequate to use the most recent data of sexual behaviour data. Arguably, as sexual behaviour is the “exposure” for the “outcome” cervical cancer, calibration of HPV transmission and cervical cancer model is best done with data of sexual behaviour and cervical from the same cohorts, hence, sexual behaviour data from an earlier period than the cervical cancer data.

In addition, if changes of sexual behaviour occur across the country, it should not affect the clustering much.

Finally, due to delay in reporting, cervical cancer incidence from the period 2012-2016 is the most recent edition at the moment of writing. Regarding sexual behaviour data, there is at the moment no later edition of the NACO report published after that of year 2006.

Finally, it would be interesting to validate this methodology to confirm its utility.

We agree that it would be very interesting to validate this proposed methodology in other regions. Unfortunately, it was beyond the scope of this work. Currently, we are working on a project in which we try to apply footprinting to a collection of low- and middle-income countries.

The proposed framework's strength is difficult to evaluate because the steps and justification for the model variables were not clearly presented, nor were the models validated.

We acknowledge that the framework could be more clearly presented and have added additional explanation in the following places to do so:

• Concerning the framework steps, in Method (144-163, pages 7-8): “For convenience of explanation, we assumed earlier that data availability occurs hierarchically. However, the framework can also be applied with less stringent data requirements. First, the source of Footprint data needs not necessarily cover all geographical units. It is still possible to train a classifier in the classification step with Footprint data available for only a part of clustered geographical units. Second, if none of the key cervical cancer epidemiological data (sexual behavior, HPV prevalence, and cervical cancer incidence data) have large enough coverage to serve as Footprint data, alternatives indicators of similarity, such as human development index and geographical distance, could also be used as substitute. However, the resulting classification performance might be suboptimal, as we expect these indicators to correlate less well with cervical cancer risk. Third, for the projection step, data of cervical cancer incidence, sexual behavior, and HPV prevalence needed for calibration of projection models need not necessarily belong to the same geographical unit. Calibration can be performed as long as the three types of data are available within each cluster.

With these less stringent data requirements, the proposed framework should sufficient flexible to be applied to many situations. However, one should still be cautious in applying the framework when there are little data. This means that, in some cases, we might need to exclude from the analysis some geographical units with too little data or redefine bigger geographical units if the data are not granular enough. Furthermore, we should assess the goodness-of-fit of the obtained clustering, performance of classification, correlation of data within different clusters, and calibration fits to ensure the validity of the final impact projections.”

• Concerning selection of model variables (lines 319-325, page 16): “In our analysis of classifying clusters of cervical cancer incidence, we only included some of the sexual behaviour variables available in the NACO report [15]. We selected variables that were previously shown to be risk factors of HPV infection and cervical cancer risk and that are commonly available (e.g., age of sexual debut and number of sexual partners) so that the analyses can be easily applied to other settings [26, 27]. In the India case study, the good classification performance shows that using the selected set is sufficient. As sexual behaviour variables are highly correlated, including more variables might even risk overfitting.”

Based on the authors' interpretation of the framework findings, this framework may help extrapolate data from one country to another. I'm curious as to whether this framework could be applied across states and countries.

We thank the reviewer for this comment. Currently, we are working on a multi-year projects in which we try to apply the framework to all low- and middle-income countries.

Author Response:

eLife assessment

This work is an attempt to establish conditions that accurately and efficiently mimic a drought response in Arabidopsis grown on defined agar-solidified media - an admirable goal as a reliable experimental system is key to conducting successful low water potential experiments and would enable high-throughput genetic screening (and GWAS) to assess the impacts of environmental perturbations on various genetic backgrounds. The authors compare transcriptome patterns of plant subjected to water limitation imposed using different experimental systems. The work is valuable in that it lays out the challenges of such an endeavor and points out shortcomings of previous attempts. However, a lack of water relations measurements, incomplete experimental design, and lack of critical evaluation of these methods in light of previous results render the proposed new methodology inadequate.

We thank eLife for the initial assessment and comments to our work. In our revised manuscript we plan to address the main concerns raised by reviewers. Specifically, we plan to perform water relations measurements for all our treatment assays, as well as explore the separate effects agar hardening and nutrient concentration have in our low-water agar assay. We will also provide a more in depth critical review of our results compared to previously published results.

Reviewer #1 (Public Review):

High-throughput genetic screening is a powerful approach to elucidate genes and gene networks involved in a variety of biological events. Such screens are well established in single-celled organisms (i.e. CRISPR-based K/O in tissue culture or unicellular organisms; screens of natural variants in response to drugs). It is desirable to extend such methodology, for example to Arabidopsis where more than 1000 ecotypes from around the Northern hemisphere are available for study. These ecotypes may be locally adapted and are fully sequenced, so the system is set up for powerful exploration of GxE. But to do so, establishing consistent "in vitro" conditions that mimic ecologically relevant conditions like drought is essential. 

The authors note that previous attempts to mimic drought response have shortcomings, many of which are revealed by 'omics type analysis. For example, three treatments thought to induce osmotic stress; the addition of PEG, mannitol, or NaCl, fail to elicit a transcriptional response that is comparable to that of bonafide drought. As an alternative, the authors suggest using a low water-agar assay, which in the things they measure, does a better job of mimicking osmotic stress responses. The major issues with this assay are, however, that it introduces another set of issues, for example, changing agar concentration can lead to mechanical effects, as illustrated nicely in the work of Olivier Hamant's group.

We thank the reviewer for their comments. We hypothesize that our low-water agar assay is able to replicate drought gene expression patterns through a combination of hardened agar and higher nutrient concentration. However, we did not explore the separate effects each of these factors may play in eliciting such responses. Thus, in our revised manuscript, we will explore what role the mechanical effects of changing agar concentration has on root gene expression. However, we suspect that the mechanical effects introduced by hard agar does not introduce another issue per se, but in fact may help with replicating the transcriptional effects seen under drought.

Reviewer #2 (Public Review):

[…] The authors have not always considered literature that would be relevant to their topic. For example, there is a number of studies that have reported (and deposited in the public database) transcriptome analysis of plants on PEG-plates or plants exposed to well-controlled, moderate severity soil drying assays (for the latter, check the paper of Des Marais et al. and others, for the former, Verslues and colleagues have published a series of studies using PEG-agar plates). They also overlook studies that have recorded growth responses of wild type and a range of mutants on properly prepared PEG plates and found that those results agree well with results when plants are exposed to a controlled, partial soil drying to impose a similar low water potential stress. In short, the authors need to make such comparisons to other data and think more about what may be wrong with their own experimental designs before making any sweeping conclusions about what is suitable or not suitable for imposing low water potential stress. 

To solve the problem of using these other systems to impose low water potential stress, the authors propose the seemingly logical (but overly simplistic) idea of adding less water to the same mix of nutrients and agar. Because the increased agar concentration does not substantially influence water potential (the agar polymerizes and thus is not osmotically active), what they are essentially doing is using a concentrated solution of macronutrients in the growth media to impose stress. This is a rediscovery of an old proposal that concentrated macronutrient solutions could be used to study the osmotic component of salt stress (see older papers of Rana Munns). There are also effects of using very hard agar that is of unclear relationship to actual drought stress and low water potential. Thus, I see no reason to think that this would be a better method to impose low water potential. 

We thank the reviewer for their comments. In our revised manuscript, we will address points regarding plant and soil water potential; similar concerns were also raised by Reviewer 1 and 3. We note that we report vermiculite water content in Supplementary Table 4.

We would like to clarify that both the PEG media and overlay solution were buffered - we did not include this within the written description in the methods, but will do in our revised manuscript.

We agree with the reviewer’s concern that it may be problematic to compare the transcriptomic profiles of seedling and mature plants. In light of this, we plan to explore what effects our treatment media has on mature rosettes.

We note that we do not claim that PEG is unable to produce low-water potential responses similar to partial soil drying. Indeed, we indicate that it is a good technique for eliciting phenotypes comparable to drought at the physiological level (line 48). Rather, we claim that PEG is unable to produce gene expression responses that are sufficiently similar to partial vermiculite drying.

Reviewer #3 (Public Review):

[…] The authors observed that gene expression responses of roots in their 'low-water agar' assay resembled more closely the water deficit in pots compared to the PEG, mannitol, and salt treatments (all at the highest dose). In particular, 28 % of PEG led to the down-regulation of many genes that were up-regulated under drought in pots. Through GO term analysis, it was pointed out that this may be due to the negative effect of PEG on oxygen solubility since downregulated genes were over-represented in oxygen-related categories. The data also shows that the treatment with abscisic acid on plates was very good at simulating drought in roots. Gene expression changes in shoots showed generally a high concordance between all treatments at the highest dose and water deficit in pots, with mannitol being the closest match. This is surprising, since plants grow in plates under non-transpiring conditions, while a mismatch between water loss by transpiration on water supply via the roots leads to drought symptoms such as wilting in pot and field-grown plants. The authors concluded that their 'low-water agar' assay provides a better alternative to simulate drought on plates. 

Strengths: 

The development of a more robust assay to simulate drought on plates to allow for high-throughput screening is certainly an important goal since many phenotypes that are discovered on plates cannot be recapitulated on the soil. Adding less water to the media mix and thereby increasing agar strength and nutrient concentration appears to be a good approach since nutrients are also concentrated in soils during water deficit, as pointed out by the authors. To my knowledge, this approach has not specifically been used to simulate drought on plates previously. Comparing their new 'low-water agar' assay to popular treatments with PEG, mannitol, salt, and abscisic acid, as well as plants grown in pots on vermiculite led to a comprehensive overview of how these treatments affect gene expression changes that surpass previous studies. It is promising that the impact of 'low-water agar' on the shoot size of 20 diverse Arabidopsis accessions shows some association with plant fitness under drought in the field. Their methodology could be powerful in identifying a better substitute for plate-based high-throughput drought assays that have an emphasis on gene expression changes. 

Weaknesses: 

While the authors use a good methodological framework to compare the different drought treatments, gene expression changes were only compared between the highest dose of each stress assay (Fig. 2B, 3B). From Fig. 1F it appears that gene expression changes depend significantly on the level of stress that is imposed. Therefore, their conclusion that the 'low-water agar' assay is better at simulating drought is only valid when comparing the highest dose of each treatment and only for gene expression changes in roots. Considering how comparable different levels of stress were in this study leads to another weakness. The authors correctly point out that PEG, mannitol, and salt are used due to their ability to lower the water potential through an increase in osmotic strength (L. 45/46). In soils, water deficit leads to lower water potential, due to the concentration of nutrients (as pointed out in L. 171), as well as higher adhesion forces of water molecules to soil particles and a decline in soil hydraulic conductivity for water, which causes an imbalance between supply and demand (see Juenger and Verslues, The Plant Cell 2022 for a recent review). While the authors selected three different doses for each treatment that are commonly used in the literature, these are not necessarily comparable on a physiological level. For example, 200 mM mannitol has an approximate osmotic potential of around -5 bar (Michel et al. Plant Physiol. 1983) whereas 28 % PEG has an osmotic potential closer to -10 bar (Michel et al. Plant Physiol. 1973). It also remains unclear how the increase in agar concentration versus the increase in nutrient concentration in the 'low-water agar' affect water potentials. For these reasons it cannot be known whether a better match of the 'low-water agar' at the 28% dose to water deficit in pots for roots in comparison to the other treatments is due to a good match in stress levels with the 'low-water agar' or adverse side-effect of PEG, mannitol, or and salt on gene regulation. Lastly, since only two biological replicates for RNA sequencing were collected per treatment, it is not possible to know how much variance exists and if this variance is greater than the treatments themselves. 

We thank the reviewer for their comments. In our statistical analyses, we found that dose-responsive genes (as fit by a linear model) were very similar to those genes found differentially expressed at the highest dose. Thus, for clarity, we decided to simply present the genes differentially expressed at the highest dose. We see now that this might have been an oversimplification. In our revised manuscript, we will present genes that are dose responsive across the range of treatment doses, thus providing more evidence that lower doses of low-water agar are also capable of simulating drought (as is suggested by overlap analysis of Figure 2A).

Additionally, we will also explore the osmotic potential of each of our different assays to provide a better benchmark of how comparable each of our treatments are (as similarly requested by Reviewer 1 and 2). Lastly, to address concerns regarding the size of variance in gene expression, we will sequence a 3rd replicate of RNA.

Author Response

Reviewer #2 (Public Review):

1) Although the images and videos were of great quality, the results derived from them provided little new knowledge and few conceptual insights into male reproductive tract biology and basically confirmed what has been published using traditional methods. For example, the high intensity of the vascular network in the initial segment was previously reported by Abe in 1984 and Suzuki in 1982; the pattern of the major lymphatic vessel and drainage was beautifully depicted by Perez-Clavier, 1982.

We thank the reviewer for his/her appreciative comments regarding the quality of the images/videos we provide in this study. We do not fully agree with his/her assessment of the lack of novelty. Our work confirms earlier reports that are now dated (1980s), which in itself is worth mentioning for the interested community, especially when the confirmation uses the most advanced technologies available today. We have never said that nothing was done in the past, and we have acknowledged all past contributors (including those mentioned by the reviewer) by pointing out the limitations of the technical tools that were available at the time. In addition, our current work provides a more comprehensive and global view by extending our approach to the entire mouse epididymis, whereas previous work was much more limited.

2) The authors were very cautious when interpreting the results of marker immunostaining however these markers were not specific for a definite cell type. For example, as the authors stated, VEGFR3 marks both lymphatic vessels and fenestrated blood vessels. how could the authors claim the VEGFR3+ network was lymphatic? The authors claimed that they used three markers for the lymphatic vessel. But staining results of the networks were very different. How could the author make conclusions about the network of lymphatic vessels in the epididymis?

We broadly agree with the reviewer and have made it clear that one cannot be 100% sure that all the VEGFR3+ structures we present are lymphatic. However, in total, we used 4 documented lymphatic markers (not 3 as mentioned by the reviewer) which are (VEGFR3, LYVE1, PROX1 and PDPN). Three of them give very similar profiles, while only PDPN shows some differences. We are currently studying in more detail the expression of PDPN in the mouse epididymis because we speculate that this marker may target a population of pluripotent cells in this tissue. Therefore, with the 3 similar profiles and with the subtraction of PVLAP+ structures, we are pretty confident that what we show corresponds to the different lymphatic structures.

3) To understand the vascular network development in the epididymis, would the authors please look at the fetal stage when the vascular network is established in the first place? Wolffian duct tissues are much smaller and thinner and would be amenable for 3D imaging probably even without clearing.

We generally agree with the reviewer that this could be an interesting addition. However, it represents a significant amount of additional work. Organ clearing will certainly be required because it is unlikely that Wolffian duct will be sufficiently transparent to allow lightsheet microscopy. In the literature, the study of Wolffian duct relies primarily on whole mounts, inclusions, and cryosections. Besides the fact that this represents a lot of extra work, we are not totally convinced that this would be of much use. A key reason is that the epididymis is an organ that differentiates completely after birth (Robaire and Hinton, 2015). It is reported that differentiation of mouse caput segment 1 occurs around 19DPN (Xu et al., 2016) and is intimately related to the development of the vasculature (Lebarr et al., 1986). Regarding the lymphatic network, Swingen et al, (2012) reports that lymphangiogenesis in the mouse testis and epididymis is initiated late in gestation after 15DPC. Videos showing the external lymphatic vessels of the testis and epididymis at 17.5DPC can be seen at https://doi.org/10.1371/journal.pone.0052620.s002. The authors indicate that lymphangiogenesis occurs via sprouting from the adjacent mesonephros. We hypothesize that the more internal lymphatics evolve between birth and 10DPN, which corresponds to the time when we observed LEPC Lyve1pos cells.

4) Immunofluorescence staining of VEGF factors was not convincing. As a secreted factor, VEGF will be secreted out of the cells, would it be detected more in the interstitium? I am always skeptical about the results of immunostaining secreted growth factors. Would it be possible to perform in situ or RNAscope to confirm the spatial expression pattern of VEGFs?

Well, active VEGF factors result from alternative mRNA splicing events and posttranslational proteolytic cleavage. Therefore, in our opinion, the study of VEGF mRNA by in situ hybridization or RNAscope analysis will not be very informative about the actual presence of active forms of VEGF in the epididymis. If necessary, we can provide as supplementary material immunohistochemistry data showing the presence of VEFG-A in the epididymal principal cells. Our major objective with these data was to show that VEGF factors and their respective receptors were present in the epididymis. Nevertheless, in an attempt to convince the reviewer, we provide as accompanying data to this rebuttal letter new sets of figures (Figures VEGF-A-response editor & VEGFC /VEGF-D-response editor) that we believe can improve the perception of our data. If the editorial office feels it is necessary, these figures could be added to the supplementary figure set (as Figure 6figure supplement 1 and Figure 6-figure supplement 2). For VEGF-A the data exists already in the literature as we have indicated (Korpelainen, 1998). In fine, our goal was not to show which cell types of the epididymis epithelium produce VEGFs but rather than VEGF factors and their receptors where there in order to support angiogenesis or lymphangiogenic activity in the tissue. In addition, we hypothesize that because septa have been reported to constitute barriers between segments restricting passive diffusion of molecules (Turner et al., 2003; Stammler et al., 2015), the VEGF factors are expected to be produced locally.

Figure VEGF-A - response editor : Immunofluorescence of the angiogenic ligand VEGF-A in the epididymis. Figure 6 shows that this ligand is mainly found in the caput and more precisely in S1.It is very strongly expressed in the peritubular microvascularization of the SI which expresses the VEGFR3:YFP transgene whereas it is less expressed by intertubular blood vessels (asterisk). This seems to indicate that it is the peritubular vessels that are in the majority responsible for the angiogenic activity measured in our study. Furthermore, it is expressed by the epithelium as secretory vesicles (IS, and S3 and enlargement) which is in agreement with in situ hybridization work performed by Korpelainene E.I et al J.Cell.biol 1998). The enlargement shown in S3_Z shows the sagital plane of the tubule where one can distinguish VEGFR:YFP positive cells that strongly express are also VEGF-A positive indicating that the same cells of the epithelium express both the receptor and the ligand. Here the transgene is detected directly without the use of an anti-GFP which allows to enhance the signal.

Figure VEGF-C / VEGF-D - response editor : Immunofluorescence of VEGF-C and VEGF-D lymphangiogenic ligands in the epididymis. This figure shows that these ligands are mainly found in the interstitial tissue throughout the organ with a higher proportion in the caudal part. This expression may be largely driven by fibroblasts, which are widely represented in the interstitium, or by endothelial cells, since these two ligands are expressed by these cell types. However, as shown in the figures and in the enlargement of panel A, VEGF-C is also produced by epithelial cells within what may appear as secretory vesicles. In contrast, for VEGF-D, we observe only few weakly positive epithelial cells (panel B). These ligands are also detected in the lumen of epididymal tubules (visible for VEGF-C Panel A S2). This presence may be explained by lumicrine transfer from the testis, in addition to secretion from epithelial cells. Here the transgene is detected directly without the use of an anti-GFP which allows to enhance the signal.

5) The study is descriptive and does not provide functional and mechanistic insights. Maybe, the combination of 3D imaging with lineage tracing of endothelium cells or ligation study (removal/ligation of the certain vessel) would help better understand how the vascular network is established and their functional significance.

The technical approaches suggested by the reviewer could certainly improve our understanding of the rather complex epididymal vascular network. Taken together, they represent the body of a comprehensive follow-up study that is worth undertaking.

6) Immune response is among many physiological processes in which vascular networks play significant roles. Discussion would be needed in other physiological processes, such as tissue metabolism and stem/progenitor cell niche microenvironment.

We agree with the reviewer that the mammalian vasculature is involved in other physiological processes beyond immune/inflammatory responses. We have deliberately chosen to focus our discussion on the inflammatory and immune context of the epididymis, as we believe this is the most relevant aspect. It is also in full agreement with the research that our team has been conducting for 15 years to try to understand the complex orchestration of tolerance versus immune surveillance in this territory. This is a finely tuned process that, if properly understood, can help to understand and appropriately treat clinical situations of infertility and/or urological problems. As our discussion section is already quite long, we feel that it was not justified to extend it further on other aspects. However, in response to the reviewer's suggestion, we now mention at the end of the first paragraph of the discussion that the epididymal vascular network is likely to serve different processes in this tissue (page 9, lines 299 to 303).

7) How could the author determine the Cd-A labeled vessel in Fig 1 was an artery, not a vein? This leads to another critical question. Would it be possible to stain with artery and vein markers to help illustrate the blood flow directions of the vessel?

The reviewer is right on the fact that we arbitrarily called the Cd-A vessel in Figure 1 an artery. Cd-A is not an acronym we use anymore. What we have done is to use the acronym SEA (superior epididymal artery) to indicate what we firmly believe to be an artery, as also suggested by previous literature (e.g., Suzuki, 1982; Abe et al, 1982) in which this same structure has been consistently referred to as an artery. For other blood vessels, we now have used the acronym "Cd-BV" because we do not know whether we are dealing with a vein or an artery as rightfully pointed out by the reviewer. This is clearly stated in the legend of Figure 1.

Author Resposnse

Reviewer #2 (Public Review):

This manuscript reassesses the strength of evidence for rapid human germline mutation spectrum evolution, using high coverage whole genome sequencing data and paying particular attention to the potential impact of confounders like biased gene conversion. The authors also refute some recently published arguments that historical changes in the age of reproduction might explain the existence of such mutation spectrum changes. My overall impression is that the paper presents a useful new angle for studying mutation spectrum evolution, and the analysis is nicely suited to addressing whether a particular model such as the parental age model can explain a set of observed polymorphism data. My main criticism is that the paper overstates certain weaknesses of previously published papers on mutation spectrum evolution as well as the generation time hypothesis; correcting these oversimplifications would more accurately capture what the paper's new analyses add to the state of knowledge in these areas.

As part of the motivation for the current study, the introduction states in lines 97-99 that "it thus remains unclear if the numerous observed [mutation spectrum] differences across human populations stem from rapid evolution of the mutation process itself, other evolutionary processes, or technical factors." This seems to overstate the uncertainty that existed prior to this study, given that Speidel, et al. 2021 found elevated TCC>TTC fractions in ancient genomes from a specific ancient European population, which seems like pretty airtight evidence that this historical mutation rate increase really happened. In addition, earlier papers (Harris 2015, Mathieson & Reich 2016, Harris & Pritchard 2017) already presented analyses rejecting the hypothesis that biased gene conversion or genetic drift could explain the reported patterns-in fact, the Mathieson & Reich paper reports one mutation spectrum difference between populations that they conclude is an artifact caused by the Native American population bottleneck, but they conclude that other mutation spectrum differences appear more robust.

We completely agree with the reviewer that there has been compelling evidence from multiple independent groups supporting transient elevation of TCC>TTC mutation rate in Europeans. Beyond the TCC signal, however, the mechanisms underlying the observed differences in mutation spectrum across populations remain unclear. In particular, several biological and technical factors impact the mutation spectrum and none of the previous studies have investigated their effects, independently or altogether. Thus, it remains unclear if the mutation rate is evolving rapidly across populations, or if one or more factors (like biased gene conversion) differ across groups or over evolutionary time. Our analysis framework attempts to control these effects together to more reliably investigate the effects of various factors and examine when and how often there has been evolution of mutation rate over the course of human evolution.

As the authors acknowledge in the discussion of their own results, biased gene conversion and non-equilibrium demography are difficult confounders to deal with, and neither previous papers nor the current paper are able to do this in a way that is 100% foolproof. The current manuscript makes a valuable contribution by presenting new ways of dealing with these issues, particularly since previous papers' work on this topic was often confined to supplementary material, but it seems appropriate to acknowledge that earlier papers discussed the potential impacts of biased gene conversion and demographic complexity and presented their own analyses arguing that these phenomena were poor explanations for the existence of mutation spectrum differences between populations.

For the most part, I found the paper's introduction to be a useful summary of previous work, but there are a few additional places where the limitations of previous work could be described more clearly. I'd suggest noting that the data artifacts discovered by Anderson-Trocmé, et al. were restricted to a few old samples and that the large differences the current manuscript focuses on were never implicated as potential cell line artifacts. In addition, when the authors mention that their new approach includes "minimiz[ing] confounding effects of selection by removing constrained regions and known targets of selection" (lines 106-107), they should note that earlier papers like Harris & Pritchard 2017 also excluded conserved regions and exons.

We agree with the reviewer that some of the previous work also attempted to account for the contributions of selection or other factors in post hoc ways; we now acknowledge this in the Results section more explicitly. However, we note that our contribution is in introducing a framework to account for these effects a priori and then assess if there are differences in mutation spectrum across populations and over the course of human evolution. In particular, an innovation of our framework is to better control for the effect of gBGC, which has not been done in previous studies.

One innovative aspect of the current paper's approach is the use of allele ages inferred by Relate, which certainly has advantages over using allele frequencies as a proxy for allele age. Though the authors of Relate previously used this approach to study mutation spectrum evolution, they did not perform such a thorough investigation of ancient alleles and collapsed mutation type ratios. I like the authors' approach of building uncertainty into the use of Relate's age estimates, but I wonder about the validity of assuming that the allele age posterior probability is distributed uniformly between the upper and lower confidence bounds. Can the authors address why this is more appropriate than some kind of peaked distribution like a beta distribution?

The lower and upper bounds of the allele age reported by Relate reflect the start and end points of the branch that the mutation falls on in the reconstructed genealogical tree. If Relate does a perfect job in reconstructing the tree and estimating the branch lengths, the mutation age should be uniformly distributed in the inferred interval. It is unrealistic that Relate can perform perfectly in tree building, and there is likely considerable uncertainty and even bias in the time to endpoints of the branch. Unfortunately, Relate does not report the uncertainty in the lower and upper bounds of the mutation age, so we were not able to model the posterior distribution of the allele age properly. However, assuming a uniform distribution of the mutation age between the upper and lower confidence bounds should be valid to first approximation.

I would also argue that the statement on line 104 about Relate's reliability is not yet supported by data-there is certainly value in using Relate ages to investigate mutation spectrum change over time and compare this to what has been seen using allele frequencies, but I don't think we know enough yet to say that the Relate ages are definitely more reliable. Relate's estimates might be biased by the same processes like selection and demography that make allele frequencies challenging to interpret. The paper's statements about the limitations of allele frequencies are fair, but there is always a tradeoff between the clear drawbacks of simple summary statistics and the more cryptic possible blind spots of complicated "black box" algorithms (in the case of Relate, an MCMC that needs to converge properly). DeWitt, et al. 2021 noted that the demographic history inferred by Relate doesn't accurately predict the underlying data's site frequency spectrum, indicating that the associated allele ages might have some problems that need to be better characterized. While testing Relate for biases is beyond the scope of this work, the introduction should acknowledge that the accuracy and precision of its time estimates are still somewhat uncertain.

We agree with the reviewer and have now added a paragraph in the Discussion highlighting some issues of Relate regarding mutation age estimation and ancestral allele polarization.

The paper's results on C>T mutations in Europeans versus Africans are a nice confirmation of previous results, including the observation from Mathieson & Reich that neither SBS7 nor SBS11 is a good match for the mutational signature at play. More novel is the ancient mutational signature enriched in Africa and the interrogation of the ability of parental age to explain the observed patterns. I just have a few minor suggestions regarding these analyses:

1) I like the idea of using maternal age C>G hotspots to test the plausibility of the maternal age as an explanatory factor, but I think this would be more convincing with the addition of a power analysis. Given two populations that have average maternal ages of 20 and 40, and the same population sample sizes available from 1000 Genomes, can the authors calculate whether the results they'd predict are any different from what is observed (i.e. no significant differences within the maternal hotspots and significant differences outside of these regions)?

We thank the review for this suggestion. We performed simulations to estimate the power of observing significant inter-population differences within and outside the maternal C>G mutation hotspots, under the assumption that all differences in the mutation spectrum between the two populations are related to the parental age (i.e., generation time). We found that, because of the extraordinarily strong maternal age effects in the maternal mutation hotspots, the power for detecting variation in C>G/T>A ratio due to change in generation age is much greater within maternal hotspots than outside, despite the smaller total size of the maternal hotspot regions (and hence fewer SNPs; Figure 3 – figure supplement 4). For example, even with an age difference of five years, there is nearly 100% power to detect significant differences in the maternal hotspots, compared to <12% for regions outside the maternal hotspots. In other words, if inter-population differences in the mutation spectrum are driven by differences in maternal age across populations, we should have enough power to observe a signal in the maternal hotspot regions alone, the lack of which (Figure 2C) strongly suggests that maternal age is not driving these signals.

2) Is it possible that the T>C/T>G ratio is elevated in all variants above a certain age but shows up as an African-specific signal because the African population retains more segregating variation in this age range, whereas non-African populations have fixed or lost more of this variation? Since Durvasula & Sankararaman identified putative tracts of super-archaic introgression within Africans, is it possible to test whether the mutation spectrum signal is enriched within those tracts?

The observation that the T>C / T>G signal is driven by TpG>CpG mutations (which might be mis-polarized CpG transitions) casts a doubt on the signal. Given the unresolved technical issue, we have now removed any discussion of the biological explanations behind the signal and instead focus on describing the challenges with ancestral allele polarization under context-dependent mutation rate variation.

3) Although Coll Macià, et al. argued that generation time is capable of explaining all mutation spectrum differences between populations, including the excess of TCC>TTC in Europeans, Wang et al. argue something slightly different. They exclude TCC>TTC and the other major components of the European signature from their analysis and then argue that parental age can explain the rest of the differences between populations. I think the analysis in this paper convincingly refutes the Coll Macià, et al. argument, but refuting the Wang, et al. version would require excluding the same mutation types that are excluded in that paper.

Although we did not present an analysis that explicitly excludes TCC>TTC mutations, our analysis still shows that generation time alone cannot explain the remaining variations in the mutation spectrum observed (Figure 4). Specifically, the temporal trend of T>C/T>G ratio would suggest a decreasing generation time of Europeans with time, whereas the C>G/T>A ratio suggests the opposite. In addition, the power analysis for C>G maternal hotspots (suggested by the reviewer) further supports that the inter-population differences observed cannot be entirely driven by differences in parental ages. These observations, which do not involve TCC>TTC mutations, strongly suggest that generation time is not the sole or primary driver of differences in mutation spectrum across populations. Further, our analysis shows that several technical issues and biological processes, in addition to changes in life history traits can lead to changes in the mutation spectrum of polymorphisms. Therefore, inferring generation time using changes in mutation spectrum is not straightforward as Wang et al. proposed, because generation time is not the only or dominant factor impacting mutation spectrum.

Author Response

Reviewer #1 (Public Review):

This is an awesome comprehensive manuscript. Authors start by sorting putative stromal cellcontaining BM non-hematopoietic (CD235a-/CD45-) plus additional CD271+/CD235a/CD45- populations to identify nine individual stromal identities by scRNA-seq. The dual sorting strategy is a clever trick as it enriches for rare stromal (progenitor) cell signals but may suffer a certain bias towards CD271+ stromal progenitors. The lack of readable signatures already among CD45-/CD45- sorts might argue against this fear. This reviewer would appreciate a brief discussion on number & phenotype of putative additional MSSC phenotypes in light of the fact that the majority of 'blood lineage(s)'-negative scRNA-seq signatures identified blood cell progenitor identities (glycophorin A-negative & leukocyte common antigen-negative). The nine stromal cell entities share the CXCL12, VCAN, LEPR main signature. Perhaps the authors could speculate if future studies using VCAN or LEPRbased sort strategies could identify additional stromal progenitor identities?

We would like to thank the reviewer for critically evaluating our work and for the generally positive evaluation of the paper. We apologize for delayed resubmission as it took a long time for a specific antibody to arrive to complete the confocal microscopy analyses.

The reviewer asks for a brief discussion on the cell numbers and phenotypes of MSSC phenotypes. The cell numbers and percentages of MSSC in sorted CD45low/-CD235a- and CD45low/-CD235a-CD271+ cells can be found in Supplementary File 3 and we have added a summary of the phenotypes of MSSC in the new Supplementary File 7.

Due to the extremely low frequency of stromal cells in human bone marrow, we chose a sorting strategy that also included CD45low cells (Fig 1A) to ensure that no stromal cells were excluded from the analysis. Although stromal elements are certainly enriched using this approach, the CD45low population contains several different hematopoietic cell types. These include CD34+ HSPCs which are characterized by low CD45 expression2, as well as the CD45low-expressing fractions of other hematopoietic cell populations such as B cells, T cells, NK cells, megakaryocytes, monocytes, dendritic cells, and granulocytes. Furthermore, CD235a- late-stage erythroid progenitors, which are negative for CD45, are represented as well. Of note, our data are consistent with previously reported murine studies showing the presence of a number of hematopoietic populations in CD45- cells, which accounted for the majority of CD45-Ter119-CD31- murine BM cells3,4. However, despite a certain enrichment of stromal elements in the CD45low cell fraction, frequencies were still too low to allow for a detailed analysis of this important bone marrow compartment. This prompted us to adopt the stromal cell-enrichment strategy as described in the manuscript to achieve a better resolution of the stromal compartment. In fact, sorting based on CD45low/-CD235a-CD271+ allowed us to sufficiently enrich bone marrow stromal cells to be clearly detectable in scRNAseq analysis. According to the reviewer’s suggestion, a brief discussion on this issue is now included in the Discussion (page 28, lines 10-15).

The reviewer also suggested using VCAN or LEPR-based sorting strategy to identify additional stromal identities in future studies.

However, as an extracellular matrix protein, FACS analysis of cellular VCAN expression can only be achieved based on its intracellular expression after fixation and permeabilization5,6. Additionally, while VCAN is highly and ubiquitously expressed by stromal clusters, VCAN is also expressed by monocytes (cluster 36). Therefore, VCAN is not an optimal marker to isolate viable stromal cells.

LEPR is the marker that was reported to identify the majority of colony-forming cells in adult murine bone marrow7. We have previously reported that the majority of human adult bone marrow CFU-Fs is contained in the LEPR+ fraction 8. In our current scRNAseq surface marker profiling analysis, group A cells showed high expression of several canonical stromal markers including VCAM1, PDGFRB, ENG (CD73), as well as LEPR (Fig. 4A). However, the four stromal clusters in Group A could not be separated based on the expression of LEPR. Therefore, we chose not to use LEPR as a marker to prospectively isolate the different stromal cell types.

The authors furthermore localized CD271+, CD81+ and NCAM/CD56+ cells in BM sections in situ. Finally, referring to the strong background of the group in HSC research, in silico prediction by CellPhoneDB identified a wide range of interactions between stromal cells and hematopoietic cells. Evidence for functional interdependence of FCU-F forming cells is completing the novel and more clear bone marrow stromal cell picture.

We thank the reviewer for the positive comments.

An illustrative abstract naming the top9 stromal identities in their top4 clusters by their "top10 markers" + functions would be highly appreciated.

We thank the reviewer for the suggestion. A summary of the characteristics of stromal clusters is now shown in the new Supplementary File 7, which we hope matches the reviewer’s expectations.

Reviewer #2 (Public Review):

Knowledge about composition and function of the different subpopulations of the hematopoietic niche of the BM is limited. Although such knowledge about the mouse BM has been accumulating in recent years, a thorough study of the human BM still needs to be performed. The present manuscript of Li and coworkers fills this gap by performing single cell RNA sequencing (scRNAseq) on control BM as well as CD271+ BM cells enriched for non-hematopoietic niche cells.

We apologize for delayed resubmission as it took a long time for a specific antibody to arrive to complete the confocal microscopy analyses. We thank the reviewer for the critical expert review and overall positive comments.

Based on their scRNAseq, the authors propose 41 different BM cell populations, ten of which represented non-hematopoietic cells, including one endothelial cell cluster. The nine remaining skeletal subpopulations were subdivided into multipotent stromal stem cells (MSSC), four distinct populations of osteoprogenitors, one cluster of osteoblasts and three clusters of pre-fibroblasts. Using bioinformatic tools, the authors then compare their results and divisions of subpopulations to some previously published work from others and attempt to delineate lineage relationships using RNA velocity analyses. From these, they propose different paths from which MSSC enter the progenitor stages, and might differentiate into pre-osteoblasts and -fibroblasts.

It is of interest to note, that apparently adipo-primed cells may also differentiate into osteolineage cells, something that should be further explored or validated. Furthermore, although this analysis yields a large adipo-primed populations, pre-adipocytes and mature adipocytes appear not to be included in the data set the authors used, which should also be explained.

We thank the reviewer for this comment. We chose to annotate Cluster 5 as adipoprimed cluster based on the higher expression of adipogenic differentiation markers as well as a group of stress-related transcription factors (FOS, FOSB, JUNB, EGR1) (Fig. 2B-C, Figure 2-figure supplement 1C) some of which had been shown to mark bone marrow adipogenic progenitors1. Although at considerably lower levels compared to adipogenic genes, osteogenic genes were also expressed in cluster 5 cells (Fig. 2B and D), indicating the multi-potent potential of this cluster. Therefore, our initial annotation of these cells as adipoprimed progenitors was too narrow as it did not include the possible osteogenic differentiation potential. We apologize for the confusion caused by the inappropriate annotation and, in order to avoid any further confusion, cluster 5 has now been re-annotated as ‘highly adipocytic gene-expressing progenitors (HAGEPs), which we believe is a better representation of the cells. We furthermore agree with the reviewer that in-vivo differentiation needs to be performed to address potential differentiation capacities in future studies.

With regard to the lack of adipocytes in our data set, we described in the Materials and Methods section that human bone marrow cells were isolated based on density gradient centrifugation. After centrifugation, the mononuclear cell-containing monolayers were harvested for further analysis. However, the resulting supernatant containing mature adipocytic cells was discarded14. Therefore, adipocyte clusters were not identified in our dataset. We have amended the manuscript accordingly (page 5, line 7).

Regarding the pre-adipocytes, we are not aware of any specific markers for pre-adipocytes in the bone marrow. We examined the only known markers (ICAM1, PPARG, FABP4) that have been shown to mark committed pre-adipocytes in human adipose tissue15. As illustrated in Fig. R1 (below), low expression of all three markers was not restricted to a single distinct cluster but could be found in almost all stromal clusters. These data thus allow us to neither confirm nor exclude the presence of pre-adipocytes in the dataset. Due to the lack of specific markers for pre-adipocytes and the absence of mature adipocytes in the current dataset, it is therefore difficult to identify a well-defined pre-adipocytes cluster.

Figure R1. UMAP illustration of the normalized expression of the markers for pre-adipocytes in stromal clusters.

In addition, based on a separate analysis of surface molecules, the authors propose new markers that could be used to prospectively isolate different human subpopulations of BM niche cells by using CD52, CD81 and NCAM1 (=CD56). Indeed, these analyses yield six different populations with differential abilities to form fibroblast-like colonies and differentiate into adipo-, osteo-, and chondrogenic lineages. To explore how the scRNAseq data may help to understand regulatory processes within the BM, the authors predict possible interactions between hematopoietic and non-hematopoietic subpopulations in the BM. These should be further validated, to support statements as the suggestion in the abstract that separate CXCL12- and SPP1-regulated BM niches might exist.

We agree with the reviewer that functional validation of the CellPhoneDB results using for example in vivo humanized mouse models would be needed to demonstrate the presence of different niches in the bone marrow. At this point of time we only put forward the hypothesis that different niche types exist while we will work on providing experimental proof in our future studies.

The scRNAseq analysis is indeed a strong and important resource, also for later studies meant to increase knowledge about the hematopoietic niche of the BM. Although the analyses using different bioinformatic tools is very helpful, they remain mostly speculative, since validatory experiments, as already mentioned, are missing. As such, I feel the authors did not succeed in achieving their goals of understanding how non-hematopoietic cells of the BM regulate the different hematopoietic processes within the BM. Nevertheless, they have created valuable resources, both in the scRNAseq data they generated, as well as the different predictions about different cell populations, their lineage relationships, and how they might interact with hematopoietic cells.

We thank the reviewer for the appreciation of the value of this dataset. We agree with the reviewer that it is of great importance to validate the contribution of potential driver genes for stromal cell differentiation and verify the in vitro data and in-silico prediction using in-vivo models. As the main goal of the current study was to formulate hypotheses based on the scRNAseq data for future studies, we believe that in vivo validation experiments using engineered human bone marrow models or humanized bone marrow ossicles are out of the scope of the current study, but certainly need to be performed in the future.

The impact of this work is difficult to envision, since validations still need to be performed. Also, it has the born in mind that humans are not mice, which can be studied in neat homogeneous inbred populations. Human populations on the other hand, are quite diverse, so that the data generated in this manuscript and others will probably have to be combined to extrapolate data relevant to the whole of the human population. However, as it is equally difficult to generate reliable scRNAseq data from human BM, it seems likely that the data will indeed an important resource, when more data from different donors become available.

We thank the reviewer for the generally positive evaluation of this study.

Taken at point value, the authors provide evidence that human counterparts exist to several BM populations described in mice. In my opinion, the lineage relationships predicted using the RNA velocity analyses need more substance, as it seems the differentiation-paths may diverge from what is known from mice. If so, this issue should be studied more stringently. Similarly, the paper would have been strengthened considerably if a relevant experimental validation would have been attempted, perhaps by using genetically modified (knockdown) MSSC, similar to Battula et al. (doi: 10.1182/blood-2012-06-437988).

In the study from Welner’s group, stromal differentiation trajectory was inferred based on scRNAseq analysis of murine bone marrow cells using Velocyto16. Velocyto identified MSCs as the ‘source’ cell state with pre-adipocytes, pro-osteoblasts, and prochondrocytes being end states. In our study, the MSSC population was predicted to be at the apex of the trajectory and the pre-osteoblast cluster was placed close to the terminal state of differentiation, which is consistent with the murine study. However, different stromal cell types were identified in mice compared with humans. For example, we have identified prefibroblasts in our dataset which are absent in the murine study, while a well-defined murine pre-adipocyte population was not identified in our human dataset. Therefore, it is not surprising to find some discrepancies between human and murine stromal differentiation trajectories. Of course and as mentioned before, critical in-vivo functional validations need to be carried out to address these important issues in the future.

In summary, this is a very interesting but also descriptive paper with highly important resources. However, to prospectively identify or isolate human non-hematopoietic/nonendothelial niche populations, more stringent validations should have been performed to strengthen the validity of the different analyses that have been performed. As such, it remains an open question which niche subpopulations has the most impact on the different hematopoietic processes important for normal and stress hematopoiesis, as well as malignancies.

Thank you for this comment. We completely agree that more stringent validations are necessary but are outside of the aim of our current hypothesis-generating study. Accordingly, we are planning functional verification studies using genetically manipulated stromal cells in combination with in-vivo humanized ossicles. Furthermore, other groups will hopefully use our database and contribute with functional studies in model systems that are currently not available to us, e.g. iPS-derived bone marrow in-vitro proxies.

Specific remarks

• Since CD45, CD235a, and CD271 are used as distinguishing markers in the sample preparation of the scRNAseq, it would be helpful to highlight these markers in the different analyses (Figures 1D, 2B, 2C-F, and 4A), and restrict the analyses to those cells that also not express CD45, CD235a (why use CD71?) and highly express CD271.

Thank you for this comment. As shown in Fig. R2, we have modified figures Fig. 1D, 2B, and 4A showing now also the expression of PTPRC (CD45), GYPA (CD235a), and NGFR (CD271) on the top (Fig. 1D and 2B) or right (Fig. 4A) panel of the figures. To complement Fig. 2C-F, we have generated new stacked violin plots showing the expression level of three markers by all 9 stromal clusters (Fig. R2B). As we believe that including these three markers in the figures does not provide a better strategy to improve the analyses, we decided to leave the original figures unchanged in this respect.

Figure R2. (A) Modified Fig. 1D, 2B and 4A with PTPRC (CD45), GYPA (CD235a) and NGFR (CD271) expression. (B) Stacked violin plots of PTPRC, GYPA and NGFR expressed by stromal clusters to complement Fig. 2C-F.

With regard to cell exclusion based on CD45, as shown in the modified Figure corresponding to Fig 1A in the manuscript (Fig R2A), CD45 gene expression is observed also in the endothelial cluster, basal cluster, and neuronal cluster (Fig. R2A). These clusters represent non-hematopoietic clusters that we would like to keep in our dataset for further analysis, such as cell-cell interaction. Therefore, we choose to not restrict the analysis to solely CD45 nonexpressing cells.

With regard to CD235a (GYPA), expression of CD235a is not detected in any of the nonhematopoietic clusters. Thus, CD235a-expressing cell exclusion is not necessary.

For CD271, according to our previous results (own unpublished data, belonging to a dataset of which only significantly expressed genes were reported in Li et al.8), protein expression of CD271 is not necessarily reflected by gene expression. In the other words, stromal cells with CD271 protein expression do not always have high mRNA expression. A significant fraction of stromal cells would be excluded if we restrict the analyses only to those cells that show high CD271 gene expression, which would not reflect the real cellular composition of human bone marrow stroma. In order to not risk losing stromal cells, we therefore kept our previous analyses which included stromal cells with various CD271 expression levels.

With regard to using CD71 as an exclusion marker, please see also the comments to reviewer 1. Briefly, according to our data, CD71 (TFRC)-expressing erythroid precursors could still be found after excluding CD45 and CD235a positive cells (Figure 1-figure supplement 1B and R3). As furthermore shown in Figure 1-figure supplement 1G and R2, CD71 expression in the stromal clusters is negligible. Therefore, we believe that this justifies the use of CD71 as an additional marker to exclude erythroid cells. We have amended the discussion to address this issue (page 19, lines 7-8).

Figure R3. FACS plots illustrating the expression of (A) CD71 (TFRC) vs CD271 in CD45- CD235a- cells and (B) FSC-A vs CD81 in CD45-CD235a-CD271+CD71+ cells following exclusion of doublets and dead cells.

• Despite a distinct neuronal cluster (39), there does not seem to be a distinctive marker for these cells. Is this true?

Yes, the reviewer is correct that there is no significantly-expressed distinctive marker for neuronal cells. Multiple markers indicating the presence of different cell types were identified in cluster 39 (Supplementary File 4). Among them, several neuronal markers (NEUROD1, CHGB, ELAVL2, ELAVL3, ELAVL4, STMN2, INSM1, ZIC2, NNAT) were found to be enriched in this cluster (Supplementary File 4 and Fig. 1D) with higher fold changes compared to other identified genes. However, the expression of these genes was not statistically significant, which is mainly due to the heterogeneity of the cluster and thus does not allow us to draw any firm conclusions.

Several genes including MALAT1, HNRNPH1, AC010970.1, and AD000090.1 were identified to be statistically highly expressed by cluster 39 (Supplementary File 4). The expression of these genes is not restricted to any specific cell type. It is therefore impossible to annotate the cluster based on this and our data thus indicated that cluster 39 is a heterogeneous population containing multiple cell types. Based on the expression of neuronal markers, we nevertheless chose to annotate Cluster 39 as “neuronal” as the prominent expression of neuronal markers indicated the presence of neurons in this cluster. To be more accurate, the annotation of cluster 39 has been changed to ‘neuronal cell-containing cluster’ to correctly reflect the presence of non-neuronal gene expressing cells as well (page 29, lines 3-8).

• Since based on 2C and 2D, the authors are unable to distinguish adipo- from osteogenic cells, would the authors use the same molecules to distinguish different populations of 2C-D, or would they use other markers, if so which and why.

We agree with the reviewer that at the first glance adipo-primed (cluster 5, now annotated as “highly adipocytic gene-expressing progenitors”, HAGEPs), balanced progenitors (cluster 16), and pre-osteoblasts (cluster 38) shared a similar expression pattern according to the violin plots in Fig. 2C and 2D. However, as illustrated in the heatmap (Fig. 2B), the expression patterns of adipo-primed (HAGEP) and balanced progenitors were quite different in terms of their expression of adipogenic and osteogenic markers. Both adipogenic and osteogenic marker expression was detected in HAGEPs, balanced progenitors, and preosteoblasts. Thus, as violin plots are summarizing the overall expression levels of a certain marker in a certain cluster, these plots tend to make it more difficult to detect differential expression patterns between different clusters. In this case, the heatmap shown in Fig. 2B is a good complement to the violin plots as it is demonstrating the different expression patterns of every cell in the different stromal clusters.

Additionally, cluster 5 showed the expression of a group of stress-related transcription factors (FOS, FOSB, JUNB, EGR1) (Fig. 2B and Figure 2-figure supplement 1C), some of which had been shown to mark bone marrow adipogenic progenitors1. The expression of the abovementioned stress-related transcription factors (putative adipogenic progenitor markers) was generally lower in cluster 38 compared to cluster 5, further demonstrating that clusters were different.

Furthermore, there was a gradual upregulation of more mature osteogenic markers such as RUNX1, CDH11, EBF1, and EBF3 from cluster 5 to cluster 16 and finally cluster 38. As shown in Fig. 2D, the expression of these markers was higher in cluster 38 compared to cluster 5. Therefore, cluster 38 was annotated as pre-osteoblasts.

Most of the stromal clusters form a continuum (Fig. 2A), which correlates very well with the gradual transition of different cellular states during stromal cell development. It is highly unlikely that abrupt and dramatic gene expression changes would occur during the cellular state transition of cells of the same lineage. Therefore, it is not surprising to find the differences in gene expression profiles between stromal clusters share a certain level of similarities.

In summary, we rely on several factors to distinguish different stromal clusters, which include canonical adipo-, osteo- and chondrogenic markers, stress markers, heatmap, violin plots, and the gradual up-regulation of certain lineage-specific markers.

To directly answer the reviewer’s question, we believe that we are able to distinguish different stromal clusters based on our data.

• In de Jong et al., an inflammatory MSC population (iMSC) is defined. Since the Schneider group showed that inflammatory S100A8 and A9 are expressed by inflamed MSC, is it possible that the some of the designated pre-fibroblasts actually correspond to these S100A8/A9-expressing iMSC?

We thank the reviewer for raising this interesting question.

First of all, we would like to point out that scRNAseq was performed using viably frozen bone marrow aspirates in de Jong’s study while freshly isolated bone marrows were used in our study. There might be discrepancies between frozen and fresh bone marrow samples in terms of cellular composition including stromal composition and, importantly, processinginduced stress-related gene expression profiles.

To investigate if designated pre-fibroblasts actually correspond to iMSCs as suggested by the reviewer, we have re-examined the expression of some of the key iMSC genes as reported by de Jong et al 17. As shown in Fig. R6, the markers that can distinguish iMSC from other MSC clusters in de Jong et al. study were not exclusively expressed by pre-fibroblasts, but also by other stromal cell types including HAGEPs, balanced progenitors, and pre-osteoblasts.

In the study by R. Schneider’s group18, significant upregulation of S100A8/S100A9 was observed in stromal cells from patients with myelofibrosis. Furthermore, base-line expression of S100A8/A9 was also observed in the fibroblast clusters in the control group, which correlates very well with our data of S100A8/9 expression in pre-fibroblasts in normal donors (Fig. 2F). Our data thus indicate – in line with Schneider’s findings - that there is a baseline level expression of S100A8/9 in fibroblasts in hematologically normal samples and that the expression of S100A8/9 is not restricted to inflamed MSC.

In summary, the gene expression profiles observed in our study do not indicate the presence of iMSC in the healthy bone marrow.

• Figure 3A: Do human adipo-primed cells (cluster 5) indeed differentiate into osteogenic cells (clusters 6, 38, and 39). This would be highly unexpected. Can the authors substantiate this "reliable outcome of the RNA velocity analysis"?

Please refer to our previous responses regarding this topic. Briefly, as shown in Fig. 2B and D, both osteogenic and adipogenic genes are expressed in cluster 5, indicating the multi-potent potentials of this cluster. Although the cluster was initially annotated as adipo-primed progenitors, this was not intended to exclude the osteogenic differentiation potential of these progenitors. Nevertheless, this annotation did not correctly reflect the differentiation potential and might thus have caused confusion, for which we apologize. In order to more correctly describe the characteristics of these cells, cluster 5 has now been reannotated as ‘highly adipocytic gene-expressing progenitors (HAGEPs)’.

In general, the outcome of the RNA velocity analysis needs to be corroborated by in-vivo differentiation experiments. But we believe that functional verification, which would be extensive, is out of the scope of the current study and we will address these questions in future studies.

• How statistically certain are the authors, that the populations in Figure 4B as defined by flow cytometry, correspond to MSSC, adipo-primed cells, osteoprogenitors, etc., as defined by scRNAseq?

To address this question, we sorted the A1-A4 populations and performed RT- PCR to examine the CD81 expression level in each cluster. As shown in Figure 4-figure supplement 1B, CD81 expression levels were higher in A1 and A2 compared with A3 and A4, which is consistent with the scRNAseq data that showed the highest CD81 expression in MSSCs compared to other clusters (Supplementary File 4).

The phenotypes defined in this study allowed us to isolate different stromal cell types which demonstrated significant functional differences as described in the manuscript (page 19, lines 17-25; page 20, lines 1-11). These results, in combination with the quantitative real-time PCR results (Figure 4-figure supplement 1B), demonstrated that the A1-A4 subsets in FACS are functionally distinct populations and are likely to be – at least in large parts – identical or equivalent to the transcriptionally identified clusters in group A stromal cells. However, at this point, we do not have performed the required experiments (scRNAseq of sorted cells) that would provide sufficient proof to confirm this statement statistically.

• The immunohistochemistry results shown do not allow distinct conclusions as the colors give unequivocal mix-colors, and surface expression cannot be distinguished from intracellular expression. Please use a 3D (confocal) method for such statements.

We thank the reviewer for the suggestion and we have performed additional confocal microscopy analysis of human bone marrow biopsies as suggested by the reviewer. Representative confocal images are now presented in the middle and right panel of Fig. 6E. We also include a separate file (Supplemental confocal image file). Here, confocal scans of all maker combinations are shown as ortho views in addition to detailed intensity profile analyses of the cells of interest clearly distinguishing surface staining from intracellular staining.

Confocal analysis of bone marrow biopsies confirmed our findings presented in the manuscript. As observed in the scanning images, CD271-expressing cells were negative for CD45 and were located in perivascular, endosteal, and peri-adipocytic regions. CD271/CD81double positive cells could be found either in the peri-adipocytic regions or perivascular regions while CD271/NCAM1 double-positive cells were exclusively situated at the bone-lining endosteal regions. The results of the confocal analysis have been added to the revised manuscript (page 21, lines 15-17).

• Figure 5A: as all cells seem to interact with all other cells, this figure does not convey relevant information about BM regions using for instance CXCL12 or SPP1. Please reanalyze to show specificity of the interactions of the single clusters. Also, since it is unlikely the CellPhoneDB2-predicted interactions are restricted to hematopoietic responders, please also describe the possible interactions between non-hematopoietic cells.

Fig. 5A was used to demonstrate the complexity of the interactions between hematopoietic cells and stromal cells.

To gain a more detailed understanding of the interactions, we also performed an analysis with the top-listed ligand-receptor pairs as shown in Fig. 5B-C and Figure 5-figure supplement 1B. Here, each dot represents the interaction of a specific ligand-receptor pair listed on the x-axis between the two individual clusters indicated in the y-axis, which we believe shows what the reviewer is asking for.

The specificity of the interactions between single clusters were shown in Fig. 5B-C and Figure 5-figure supplement 1B. The CXCL12- and SPP1-mediated interactions between MSSC/OC and hematopoietic clusters clearly suggested stromal cell type-specific interactions.

Regarding non-hematopoietic cells, both inter- and intra-stromal interactions were identified to be operative between different stromal subsets as well as within the same stromal cell population as shown in Figure 5-figure supplement 3B. In addition, we have also analyzed the interaction pattern between endothelial cells and hematopoietic cells as shown in Fig. 7A, and thus we believe that we have sufficiently described these interactions as requested by the reviewer.

Author Response

Reviewer #2 (Public Review):

This study identifies the neural circuits inhibited by activation of opioid receptors using complex experimental approaches such as electrophysiology, pharmacology, and optogenetics and combined them with retrograde and anterograde tracings. The authors characterize two key regions of the brainstem, the preBötzinger Complex, and the Kolliker-Fuse, and how these neuronal populations interact. Understanding the interactions of these circuits substantially increases our understanding of the neural circuits sensitive to opioid drugs which are critical to understand how opioids act on breathing and potentially design new therapies.

Major strengths.

This study maps the excitatory projections from the Kolliker-Fuse to the preBötzinger Complex and rostral ventral respiratory group and shows that these projections are inhibited by opioid drugs. These Kolliker-Fuse neurons express FoxP2, but not the calcitonin gene-related peptide, which distinguishes them from parabrachial neurons. In addition, the preBötzinger Complex is also hyperpolarized by opioid drugs. The experiments performed by the authors are challenging, complex, and the most appropriate types of approaches to understanding pre- and post-synaptic mechanisms, which cannot be studied in vivo. These experiments also used complex tracing methods using adenoassociated virus and cre-lox recombinase approaches.

Limitations.

(1) The roles of the mechanisms identified in this study have not been established in models recording opioid-induced respiratory depression or respiratory activity. This study does not record, modulate, or assess respiratory activity in-vitro or in-vivo, without or with opioid drugs such as fentanyl or morphine.

(2) Experiments are performed in-vitro which do not mimic the effects of opioids observed in-vivo or in freely-moving animals. However, identification of pre- and post- synaptic mechanisms, as well as projections, cannot be performed in-vivo, so the authors use the right approaches for their experiments.

We agree with both of these points. We hope this study lays the groundwork for future studies assessing the impact of these projections on respiratory activity in vitro and in vivo.

(3) The type of neurons projecting from KP to preBötzinger Complex or ventral respiratory group have not been identified. Although some of these cells are glutamatergic, optogenetic experiments could have been performed in other cre-expressing cell populations, such as neurokinin-1 receptors.

There are indeed many different cell populations that could be interrogated. In addition to the optogenetic identification of glutamatergic projections, we identified immunohistochemically that at least some opioid receptor-expressing, medullary-projecting KF neurons express FoxP2, and not CGRP. Further dissection of other cell populations, such as Lmx1b and Phox2b, are excellent future directions.

Reviewer #3 (Public Review):

This manuscript reveals opioid suppression of breathing could occur via multiple mechanisms and at multiple sites in the pontomedullary respiratory network. The authors show that opioids inhibit an excitatory pontomedullary respiratory circuit via three mechanisms: 1) postsynaptic MOR-mediated hyperpolarization of KF neurons that project to the ventrolateral medulla, 2) presynaptic MOR mediated inhibition of glutamate release from dorsolateral pontine terminals onto excitatory preBötC and rVRG neurons, and 3) postsynaptic MOR-mediated hyperpolarization of the preBötC and rVRG neurons that receive pontine glutamatergic input.

This manuscript describes in detail a useful method for dissecting the relationship between the dorsolateral pons and the rostral medulla, which will be useful for various researchers. It's also great to see how many different methods have been applied to improve the accuracy of the results.

1. Relationship between the dorsolateral pons and rostral ventrolateral medulla.

The method of this paper is a good paper to show a very precise relationship between the presence of opioid receptors and the dorsolateral pons and rostral ventrolateral medulla, and for opioid receptors, based on the expression of Oprm1, the use of genetically modified mice with anterograde or retrograde viruses with additional fluorescent colors showed both anterograde and retrograde projections, revealing a relationship between the dorsolateral pons and rostral ventrolateral medulla.

For example, to visualize dorsal pontine neurons expressing Oprm1, Oprm1Cre/Cre mice were crossed with Ai9tdTomato Cre reporter mice to generate Ai9tdT/+ oprm1Cre/+ mice (Oprm1Cre/tdT mice) expressing tdTomato on neurons that also express MOR at any point during development, and the retrograde virus encoding Cre-dependent expression of GFP (retrograde AAV-hSIN-DIO-eGFP was injected into the respiratory center of Oprm1Cre/+ mice and into the ventral respiratory neuron group, showing that KF neurons expressing Oprm1 project to the respiration-related nucleus of the ventrolateral medulla.

However, although the authors have also corrected it, the virus may spread to other places as well as where they thought it would be injected, and it is important to note that it is injected accordingly to mark the injection site with an anterograde virus encoding a different fluorescent color mCherry, and the extent of the injection is quantified, which is excellent as a control experiment.

In addition, the respiratory center seems to be related not only to preBötC but also to pFRG recently, so if the relation with it is described, it is important from the viewpoint of the effect on the respiratory center and the effect on the rhythm.

Our injections centered in preBotC, rVRG or BötC did not spread extensively to slices containing 7N/pFRG (Figure 2C and Figure 2-supplement 1D, Bregma -6.0 to -6.4, shaded region labeled 7N).

Author Response:

eLife assessment

This manuscript analyzes large-scale Neuropixels recordings from visual areas and hippocampus of mice passively viewing repeated clips of a movie and reports that neurons respond with elevated firing activities to specific, continuous sequences of movie frames. The important results support a role of rodent hippocampal neurons in general episode encoding and advance understanding of visual information processing across different brain regions. The strength of evidence for the primary conclusion is solid, but some technical limitations of the study were identified that merit further analyses.

We thank the editors and reviews for the assessment and reviews. We have provided clarifications and updated the manuscripts to address the seeming technical limitations that are perhaps due to some misunderstanding, please see below. We provide additional results that isolate the contribution of pupil diameter, sharpwave ripple and theta power to show that movie tuning cannot be explained by these nonspecific effects. Nor are these mere time cells or some other internally generated patterns due to many differences highlighted below.

Reviewer #1 (Public Review):

Taking advantage of a publicly available dataset, neuronal responses in both the visual and hippocampal areas to passive presentation of a movie are analyzed in this manuscript. Since the visual responses have been described in a number of previous studies (e.g., see Refs. 11-13), the value of this manuscript lies mostly on the hippocampal responses, especially in the context of how hippocampal neurons encode episodic memories. Previous human studies show that hippocampal neurons display selective responses to short (5 s) video clips (e.g. see Gelbard-Sagiv et al, Science 322: 96-101, 2008). The hippocampal responses in head-fixed mice to a longer (30 s) movie as studied in this manuscript could potentially offer important evidence that the rodent hippocampus encodes visual episodes.

We have now included citations to Gelbard-Sagiv et al. Science 2008 paper and many other references too, thank you for pointing that out. There are major differences between that study and ours.

• The movies used in previous study contained very familiar, famous people and famous events, and the experiment was about the patient’s ability to recall those famous movie episodes. In our case the mice had seen this movie clip only twice before.

• They did not look at the fine structure of neural responses below half a second whereas we looked at the mega-scale representations from 30ms to 30s.

• The movie clips in that study were in full color with audio, we used an isoluminant, black-and-white, silent movie clip.

• Their movie clips contained humans and was observed by humans, whereas our study mice observed a movie clip with humans and no mice or other animals.

The analysis strategy is mostly well designed and executed. A number of factors and controls, including baseline firing, locomotion, frame-to-frame visual content variation, are carefully considered. The inclusion of neuronal responses to scrambled movie frames in the analysis is a powerful method to reveal the modulation of a key element in episodic events, temporal continuity, on the hippocampal activity. The properties of movie fields are comprehensively characterized in the manuscript.

Thank you.

Although the hippocampal movie fields appear to be weaker than the visual ones (Fig. 2g, Ext. Fig. 6b), the existence of consistent hippocampal responses to movie frames is supported by the data shown. Interestingly, in my opinion, a strong piece of evidence for this is a "negative" result presented in Ext. Fig. 13c, which shows higher than chance-level correlations in hippocampal responses to same scrambled frames between even and odd trials (and higher than correlations with neighboring scrambled frames). The conclusion that hippocampal movie fields depend on continuous movie frames, rather than a pure visual response to visual contents in individual frames, is supported to some degree by their changed properties after the frame scrambling (Fig. 4).

Yes, hippocampal selectivity is not entirely abolished with scrambled movie, as we show in several figures (Fig 4d,g and Extended Data Fig. 16), but it is greatly reduced, far more than in the afferent visual cortices. The fraction of tuned cells for scrambled movies dropped to 4.5% in hippocampus, which is close to the chance level of 3%. In contrast, in visual areas selectivity was still above 80%.

Significant overlap between even and odd trials is to be expected for the tuned cells. Without a significant overlap, i.e. a stable representation, they will not be tuned. Despite this, the correlation between even and odd trials for the (only 4.5% of) tuned cells in the hippocampus was more than 2-fold smaller than (more than 80% of) cells in visual cortices. This strongly supports our hypothesis that unlike visual cortices, hippocampal subfields depended very strongly on the continuity of visual information. We will clarify this in the main text.

However, there are two potential issues that could complicate this main conclusion.

One issue is related to the effect of behavioral variation or brain state. First, although the authors show that the movie fields are still present during low-speed stationary periods, there is a large drop in the movie tuning score (Z), especially in the hippocampal areas, as shown in Ext. Fig. 3b (compared to Ext. Fig. 2d). This result suggests a potentially significant enhancement by active behavior.

There seems to be some misunderstanding here. There was no major reduction in movie tuning during immobility or active running. As we wrote in the manuscript, the drop in selectivity during purely immobile epochs is because of reduction in the amount of data, not reduction in selectivity per se. Specifically, as the amount data reduces, the statistical strength of tuning (z-scored sparsity) reduces. For example, if we split the total of 60 trials worth of data into two parts, the amount of data reduces to about half in each part, leading to a seeming reduction in selectivity in both halves. Extended figure 2B shows nearly identical tuning in all brain regions during immobility and equivalent subsamples chosen randomly from the entire data, including mobility and immobility. We will include additional data in the revised manuscript to demonstrate this more clearly. Please see below for more details.

Second, a general, hard-to-tackle concern is that neuronal responses could be greatly affected by changes in arousal or brain state (including drowsy or occasional brief slow-wave sleep state) in head-fixed animals without a task. Without the analysis of pupil size or local field potentials (LFPs), the arousal states during the experiment are difficult to know.

In the revised manuscript we will that the behavioral state effects cannot explain movie tuning. Specifically:

• We compare sessions in which the mouse was mostly immobile versus sessions in which the mouse was mostly running. Movie tuned cells were found in both these cases (Extended Data Fig. 7).

• b. We detect and remove all data around sharp-wave ripples (SWR). Movie tuning was unchanged in the remaining data.

• c. As a further control, we quantified arousal by two standard metrics. First within a session, we split the data into two groups, segments with high theta power and segments with low theta power. Significant movie tuning persisted in both.

• d. Finally, pupil dilation is another common method to estimate arousal, so data within a session were split into two parts: those with pupil dilation versus constriction. Movie tuning remained significant in both parts. See the new Extended Data Fig. 7.

Many example movie fields in the presented raw data (e.g., Fig. 1c, Ext. Fig. 4) are broad with low-quality tuning, which could be due to broad changes in brain states. This concern is especially important for hippocampal responses, since the hippocampus can enter an offline mode indicated by the occurrence of LFP sharp-wave ripples (SWRs) while animals simply stay immobile. It is believed that the ripple-associated hippocampal activity is driven mainly by internal processing, not a direct response to external input (e.g., Foster and Wilson, Nature 440: 680, 2006). The "actual" hippocampal movie fields during a true active hippocampal network state, after the removal of SWR time periods, could have different quantifications that impact the main conclusion in the manuscript.

We included the broadly tuned hippocampal neurons to demonstrate the movie-field broadening compared to those in visual areas. We will include more examples with sharp movie fields in the hippocampal regions (Main figure 1a-d right column, 2d and h, Extended Data Fig 5 and 8). Further, as stated above, we detected sharp-wave ripples and removed one second of data around SWR. Move tuning was unchanged in the remaining data. Thus, movie tuning is not generated internally via SWR (Extended Data Fig. 6). See also Extended Data 7 and 8 and the response above.

Another issue is related to the relative contribution of direct visual response versus the response to temporal continuity in movie fields. First, the data in Ext. Fig. 8 show that rapid frame-to-frame changes in visual contents contribute largely to hippocampal movie fields (similarly to visual movie fields).

There seems to be some misunderstanding here. That figure showed that the frame-toframe changes in the visual content had the highest effect on visual areas MSUA and much weaker in hippocampus (Extended Data Fig. 8, as per previous version). For example, the depth of modulation (max – min) / (max + min) for MSUA was 21% and 24% for V1 but below 6% for hippocampal regions. Similarly, the MSUA was more strongly (negatively) correlated with F2F correlation for visual areas (r=0.48 to 0.56) than hippocampal (0.07 to 0.3). Similarly, comparing the number of peaks or their median widths, visual regions showed stronger correlation with F2F, and largest depth of modulation than hippocampal regions, barring handful exceptions (like CA3 correlation between F2F and median peak duration). This strongly supports our claim that visual regions generated far greater response of the frame-to-frame changes in the movie than hippocampal regions.

Interestingly, the data show that movie-field responses are correlated across all brain areas including the hippocampal ones.

The changes in multiunit activity are strongly correlated only between visual areas and some of the hippocampal region pairs. The correlation is much weaker for hippocampal areas, or hippocampal-visual area pairs. This will be quantified explicitly in the revised text Extended Data Fig. 11 with an additional correlation matrix. Further, in Fig 3c we compared the MSUA responses with normalization between brain regions. Amongst the 21 possible brain region pairs, 5 were uncorrelated, 7 were significantly negatively correlated and 9 were significantly correlated.

This could be due to heightened behavioral arousal caused by the changing frames as mentioned above, or due to enhanced neuronal responses to visual transients, which supports a component of direct visual response in hippocampal movie fields.

As shown in Extended data 7 and 8 and described above, the effect of arousal as quantified by theta power of pupil diameter cannot explain the results in hippocampal areas and the correlations in multiunit responses are unrelated across many brain areas.

Second, the data in Ext. Fig. 13c show a significant correlation in hippocampal responses to same scrambled frames between even and odd trials, which also suggests a significant component of direct visual response.

This is plausible. The fraction of hippocampal cells which were significantly tuned for the scrambled presentation (4.5%) was close to chance level (3%), and this small subset of cells was used to compute the population overlap between even and odd trials in Ext Fig. 13 (old numbering). As described above, this significant but small amount of tuning could generate significant population overlap, which is to be expected by construction.

Is there a significant component purely due to the temporal continuity of movie frames in hippocampal movie fields? To support that this is indeed the case, the authors have presented data that hippocampal movie fields largely disappear after movie frames are scrambled. However, this could be caused by the movie-field detection method (it is unclear whether single-frame field could be detected).

As described in the methods section, the movie-field detection algorithm had a resolution of 3.3ms resolution, which ensured that we could detect single frame fields. As reported, we did find such short movie fields in several cells in the visual areas. The sparsity metric used is agnostic to the ordering of the responses, and hence single frame field, and the resultant significant movie-tuning, if present, can be detected by our methods.

Another concern in the analysis is that movie-fields are not analyzed on re-arranged neural responses to scrambled movie frames. The raw data in Fig. 4e seem quite convincing. Unfortunately, the quantifications of movie fields in this case are not compared to those with the original movie.

We saw very few (3.6-4.9%) cells with significant movie tuning for scrambled presentation in the hippocampus. Hence, we did not quantify this earlier. This is now provided in new Extended Data Fig. 16. The amount of movie tuning for the scrambled presentation taken as-is, or after rearranging the frames is below 5% for all hippocampal brain regions.

Reviewer #2 (Public Review):

[…] The authors have concluded that the neurons in the thalamo-cortical visual areas and the hippocampus commonly encode continuous visual stimuli with their firing fields spanning the mega-scale, but they respond to different aspects of the visual stimuli (i.e., visual contents of the image versus a sequence of the images). The conclusion of the study is fairly supported by the data, but some remaining concerns should be addressed.

1) Care should be taken in interpreting the results since the animal's behavior was not controlled during the physiological recording.

This was done intentionally since plenty of research shows that task demand (e.g., Aronov and Tank, Nature 2017) can not only modulate hippocampal responses but also dramatically alter them. We have now provided additional figures (Extended Data Fig. 6 and 7) where we quantified the effects of the behavioral states (sharp wave ripples, theta power and pupil diameter), as well as the effect of locomotion (Extended Data Fig. 4). Movie tuning remained unaffected with these manipulations. Thus, movie tuning cannot be attributed to behavioral effects.

It has been reported that some hippocampal neuronal activities are modulated by locomotion, which may still contribute to some of the results in the current study. Although the authors claimed that the animal's locomotion did not influence the movie-tuning by showing the unaltered proportion of movie-tuned cells with stationary epochs only, the effects of locomotion should be tested in a more specific way (e.g., comparing changes in the strength of movie-tuning under certain locomotion conditions at the single-cell level).

Single cell analysis of the effect of locomotion and visual stimulation is underway, and beyond the scope of the current work. As detailed in the (Extended Data Fig. 4), we have ensured that in spite of the removal of running or stationary epochs, as well as removal of sharp wave ripple events (Extended Data Fig. 6) movie tuning persists. Further, we will provide examples of strongly tuned cells from sessions with predominantly running or predominantly stationary behavior (Extended Data Fig. 7).

2) The mega-scale spanning of movie-fields needs to be further examined with a more controlled stimulus for reasonable comparison with the traditional place fields. This is because the movie used in the current study consists of a fast-changing first half and a slow-changing second half, and such varying and ununified composition of the movie might have largely affected the formation of movie-fields. According to Fig. 3, the mega-scale spanning appears to be driven by the changes in frame-to-frame correlation within the movie. That is, visual stimuli changing quickly induced several short fields while persisting stimuli with fewer changes elongated the fields.

Please note that a strong correlation between the speed at which the movie scene changed across frames was correlated with movie-field width in the visual areas, but that correlation was much weaker in the hippocampal areas (see above). Please see Extended Data Fig. 11 and the quantification of correlation between frame-to-frame changes in the movie and the properties of movie fields.

The presentation of persisting visual input for a long time is thought to be similar to staying in one place for a long time, and the hippocampal activities have been reported to manifest in different ways between running and standing still (i.e., theta-modulated vs. sharp wave ripple-based). Therefore, it should be further examined whether the broad movie-fields are broadly tuned to the continuous visual inputs or caused by other brain states.

As shown in Extended Data Fig. 6, movie field properties are largely unchanged when SWR are removed from the data, or when the effect of pupil diameter or theta power were factored for (Extended Data Fig.7).

3) The population activities of the hippocampal movie-tuned cells in Fig. 3a-b look like those of time cells, tiling the movie playback period. It needs to be clarified whether the hippocampal cells are actively coding the visual inputs or just filling the duration.

Tiling patterns would be observed when the maximal are sorted in any data, even for random numbers. This alone does not make them time cells. The following observations suggest that movie fields cannot be explained as being time cells.

• a. Time cells mostly cluster at the beginning of a running epoch (Pastalkova et al. Science 2008, MacDonald et al. Neuron 2011) and they taper off towards the end. Such large clustering is not visible in these tiling plots for movie tuned cells.

• b. Time fields become wider as the temporal duration progresses (Pastalkova et al. Science 2008, MacDonald et al. Neuron 2011) as the encoded temporal duration increases. This is not evident in any movie fields.

• c. Widths of movie fields in visual areas, and to a smaller extent in the hippocampal areas, were clearly modulated by the visual content, like the change from one frame to the next (F2F correlation, Extended Data Fig. 11).

• d. Tiling pattern of movie fields was found in visual areas too, with qualitatively similar pattern as hippocampus. Clearly, visual area responses are not time cells, as shown by the scrambled stimulus experiment. Here, neural selectivity could be recovered by rearranging them based on the visual content of the continuous movie, and not the passage of time.

The scrambled condition in which the sequence of the images was randomly permutated made the hippocampal neurons totally lose their selective responses, failing to reconstruct the neural responses to the original sequence by rearrangement of the scrambled sequence. This result indirectly addressed that the substantial portion of the hippocampal cells did not just fill the duration but represented the contents and temporal order of the images. However, it should be directly confirmed whether the tiling pattern disappeared with the population activities in the scrambled condition (as shown in Extended Data Fig. 11, but data were not shown for the hippocampus).

As stated above for the continuous movie, tiling pattern alone does not mean those are time cells. Further, tuning, and tiling pattern remained intact with scrambled movie in the visual cortices but not in hippocampus.

Reviewer #3 (Public Review):

[…] The paper is conceptually novel since it specifically aims to remove any behavioral or task engagement whatsoever in the head-fixed mice, a setup typically used as an open-loop control condition in virtual reality-based navigational or decision making tasks (e.g. Harvey et al., 2012). Because the study specifically addresses this aspect of encoding (i.e. exploring effects of pure visual content rather than something task-related), and because of the widespread use of video-based virtual reality paradigms in different sub-fields, the paper should be of interest to those studying visual processing as well as those studying visual and spatial coding in the hippocampal system. However, the task-free approach of the experiments (including closely controlling for movement-related effects) presents a Catch-22, since there is no way that the animal subjects can report actually recognizing or remembering any of the visual content we are to believe they do.

Our claim is that these are movie scene evoked responses. We make no claims about the animal’s ability to recognize or remember the movie content. That would require entirely different set of experiments. Meanwhile, we have shown that these results are not an artifact of brain states such as sharp wave ripples, theta power or pupil diameter (Extended Data Fig. 6 and 7) or running behavior (Extended Data Fig. 4). Please see above for a detailed response.

We must rely on above-chance-level decoding of movie segments, and the requirement that the movie is played in order rather than scrambled, to indicate that the hippocampal system encodes episodic content of the movie. So the study represents an interesting conceptual advance, and the analyses appear solid and support the conclusion, but there are methodological limitations.

It is important to emphasize that these responses could constitute episodic responses but does not prove episodic memory, just as place cell responses constitute spatial responses but that does not prove spatial memory. The link between place cells and place memory is not entirely clear. For example, mice lacking NMDA receptors have intact place cells, but are impaired in spatial memory task (McHugh et al. Cell 1996), whereas spatial tuning was virtually destroyed in mice lacking GluR1 receptors, but they could still do various spatial memory tasks (Resnik et al. J. Neuro 2012). The experiments about episodic memory would require an entirely different set of experiments that involve task demand and behavioral response, which in turn would modify hippocampal responses substantially, as shown by many studies. Our hypothesis here, is that just like place cells, these episodic responses without task demand would play a role, to be determined, in episodic memory. We will emphasize this point in the main text (Ln 432-436 in the revised manuscript).

Major concerns:

1) A lot hinges on hinges on the cells having a z-scored sparsity >2, the cutoff for a cell to be counted as significantly modulated by the movie. What is the justification of this criterion?

The z-scored sparsity (z>2) corresponds to p<0.03. This would mean that 3% of the results could appear by chance. Hence, z>2 is a standard method used in many publications. Another advantage of z-scored sparsity is that it is relatively insensitive to the number of spikes generated by a neuron (i.e. the mean firing rate of the neuron and the duration of the experiment). In contrast, sparsity is strongly dependent on the number of spikes which makes it difficult to compare across neurons, brain regions and conditions (See Supplement S5 Acharya et al. Cell 2016). To further address this point, we compared our z-scored sparsity measure with 2 other commonly used metrics to quantify neural selectivity, depth of modulation and mutual information (Extended Data Fig. 3). Comparable movie tuning was obtained from all 3 metrics, upon z-scoring in an identical fashion.

It should be stated in the Results. Relatedly, it appears the formula used for calculating sparseness in the present study is not the same as that used to calculate lifetime sparseness in de Vries et al. 2020 quoted in the results (see the formula in the Methods of the de Vries 2020 paper immediately under the sentence: "Lifetime sparseness was computed using the definition in Vinje and Gallant").

The definition of sparsity we used is used commonly by most hippocampal scientists (Treves and Rolls 1991, Skaggs et al. 1996, Ravassard et al. 2013). Lifetime sparseness equation used by de Vries et al. 2020, differs from us by just one constant factor (1-1/N) where N=900 is the number of frames in the movie. This constant factor equals (1- 1/900)=0.999. Hence, there is no difference between the sparsity obtained by these two methods. Further, z-scored sparsity is entirely unaffected by such constant factors. We will clarify this in the methods of the revised manuscript.

To rule out systematic differences between studies beyond differences in neural sampling (single units vs. calcium imaging), it would be nice to see whether calculating lifetime sparseness per de Vries et al. changed the fraction "movie" cells in the visual and hippocampal systems.

As stated above, the two definitions of sparsity are virtually identical and we obtained similar results using two other commonly used metrics, which are detailed in Extended Data Fig. 3.

2) In Figures 1, 2 and the supplementary figures-the sparseness scores should be reported along with the raw data for each cell, so the readers can be apprised of what types of firing selectivity are associated with which sparseness scores-as would be shown for metrics like gridness or Raleigh vector lengths for head direction cells. It would be helpful to include this wherever there are plots showing spike rasters arranged by frame number & the trial-averaged mean rate.

As shown in several papers (Aghajan et al Nature Neuroscience 2015, Acharya et al., Cell 2016) raw sparsity (or information content) are strongly dependent on the number of spikes of a neuron. This makes the raw values of these numbers impossible to compare across cells, brain regions and conditions. (Please see Supplement S5 from Acharya et al., Cell 2016 for details). Including the data of sparsity would thus cause undue confusion. Hence, we provide z-scored sparsity. This metric is comparable across cells and brain regions, and now provided above each example cell in Figure 1 and Extended Data Fig. 2.

3) The examples shown on the right in Figures 1b and c are not especially compelling examples of movie-specific tuning; it would be helpful in making the case for "movie" cells if cleaner / more robust cells are shown (like the examples on the left in 1b and c).

We did not put the most strongly tuned hippocampal neurons in the main figures so that these cells are representative of the ensemble and not the best possible ones, so as to include examples with broad tuning responses. We have clarified in the legend that these cells are some of the best tuned cells. Although not the cleanest looking, the z-scored sparsity mentioned above the panels now indicates how strongly they are modulated compared to chance levels. Additional examples, including those with sharply tuned responses are shown in Extended Data Fig. 5 and 8.

4) The scrambled movie condition is an essential control which, along with the stability checks in Supplementary Figure 7, provide the most persuasive evidence that the movie fields reflect more than a passive readout of visual images on a screen. However, in reference to Figure 4c, can the authors offer an explanation as to why V1 is substantially less affected by the movie scrambling than it's main input (LGN) and the cortical areas immediately downstream of it? This seems to defy the interpretation that "movie coding" follows the visual processing hierarchy.

This is an important point, one that we find very surprising as well. Perhaps this is related to other surprising observations in our manuscript, such as more neurons appeared to be tuned to the movie than the classic stimuli. A direct comparison between movie responses versus fixed images is not possible at this point due to several additional differences such as the duration of image presentations and their temporal history. The latency required to rearrange the scrambled responses (60ms for LGN, 74ms for V1, 91ms for AM/PM) supports the anatomical hierarchy. The pattern of movie tuning properties was also broadly consistent between V1 and AM/PM (Fig 2). However, all metrics of movie selectivity (Fig 2) to the continuous movie showed a consistent pattern that was the exact opposite pattern of the simple anatomical hierarchy: V1 had stronger movie tuning, higher number of movie fields per cell, narrower movie-field widths, larger mega-scale structure, and better decoding than LGN. V1 was also more robust to the scrambled sequence than LGN. One possible explanation is that there are other sources of inputs to V1, beyond LGN, that contribute significantly to movie tuning. This is an important insight and we will modify the discussion to highlight this.

Relatedly, the hippocampal data do not quite fit with visual hierarchical ordering either, with CA3 being less sensitive to scrambling than DG. Since the data (especially in V1) seem to defy hierarchical visual processing, why not drop that interpretation? It is not particularly convincing as is.

The anatomical organization is well established and an important factor. Even when observations do not fit the anatomical hierarchy, it provides important insights about the mechanisms. All properties of movie tuning (Fig 2) –the strength of tuning, number of movie peaks, their width and decoding accuracy firmly put visual areas upstream of hippocampal regions. But, just like visual cortex there are consistent patterns that do not support a simple feed-forward anatomical hierarchy. We have pointed out these patterns so that future work can build upon it.

5) In the Discussion, the authors argue that the mice encode episodic content from the movie clip as a human or monkey would. This is supported by the (crucial) data from the scrambled movie condition, but is nevertheless difficult to prove empirically since the animals cannot give a behavioral report of recognition and, without some kind of reinforcement, why should a segment from a movie mean anything to a head-fixed, passively viewing mouse?

We emphasize once again that our claim is about the nature of encoding of the movie across these neurons. We make no claims about whether this forms a memory or whether the mouse is able to recognize the content or remember it. Despite decades of research, similar claims are difficult to prove for place cells, with plenty of counter examples (See the points above). The important point here is that despite any cognitive component, we see remarkably tuned responses in these brain areas. Their role in cognition would take a lot more effort and is beyond the scope of the current work.

Would the authors also argue that hippocampal cells would exhibit "song" fields if segments of a radio song-equally arbitrary for a mouse-were presented repeatedly? (reminiscent of the study by Aronov et al. 2017, but if sound were presented outside the context of a task). How can one distinguish between mere sequence coding vs. encoding of episodically meaningful content? One or a few sentences on this should be added in the Discussion.

Aronov et al 2017, found the encoding of an audio sweep in hippocampus when the animals were doing a task (release the lever at a specific frequency to obtain a reward). However, without a task demand they found that hippocampal neurons did not encode the audio sequence beyond chance levels. This is at odds with our findings with the movie where we see strong tuning despite any task demand or reward. These results are consistent with but go far beyond our recent findings that hippocampal (CA1) neurons can encode the position and direction of motion of a revolving bar of light (Purandare et al. Nature 2022). Please see Ln 414-420 for related discussion.

These responses are unlikely to be mere sequence responses since the scrambled sequence was also fixed sequence that was presented many times and it elicited reliable responses in visual areas, but not in hippocampus. Hence, we hypothesize that hippocampal areas encode temporally related information, i.e. episodic content. We will modify the discussion to address these points.

Author Response:

We thank the eLife editorial board and the reviewers for the assessment of our article. We look forward to thoroughly addressing their comments and concerns. We would like to correct one factual error in the consensus public review:

“Importantly, the authors do not present evidence that value itself is stably encoded across days, despite the paper's title. The more conservative in its claims in the Discussion seems more appropriate: "these results demonstrate a lack of regional specialization in value coding and the stability of cue and lick [(not value)] codes in PFC."

The imaging sessions in which we identify value coding cells were in fact performed on separate days: Experimental Days 6 and 7 (see Figure 1b), which is evidence of the stability of value coding across consecutive days. Days 6 and 7 correspond to the third day of Odor Set 1 and the third day of Odor Set 2, respectively, which is why we referred to them both as “Day 3” in the manuscript, and this may have led to the confusion about the temporal relationship between these sessions. We will clarify this terminology in the revised manuscript.

Author Response

Reviewer #1 (Public Review):

In this well-written manuscript, Afshar et al demonstrated the significant transcriptional and proteomic differences between cultured human umbilical vein endothelial cells (HUVECs) and those freshly isolated from the cords. They showed that TGFbeta and BMP signaling target genes were enriched in cord cells compared to those in culture. Extracellular matrix (ECM) and cell cycle-related genes were also different between the two conditions. Because master regulators of EC shear stress response genes, KLF2 and KLF4, were downregulated in culture, the authors sought to restore the in vivo transcriptional profile with the application of shear stress in an orbital shaker and dextran-containing media for various time periods. They showed that after 48 hours of shear stress the transcriptional profile of sheared cells correlated with in vivo transcriptional profile more significantly than static cultures. They also showed, using single cell RNAseq, that EC-smooth muscle cell cocultures resulted in changes in TGFbeta and NOTCH signaling pathways and rescued 9% of the in vivo transcriptional signatures.

This is an important study that was elegantly executed. The authors should also be commended for making their data public; thereby, creating a valuable resource for vascular biologists.

We much appreciate the comments and thank the reviewer for the time and effort evaluating the study.

Reviewer #2 (Public Review):

The authors profiled the transcriptome and proteome of human umbilical vein endothelial cells freshly isolated from in vivo and compared that with the same cells exposed to in vitro culture under different conditions, including static culture, flow, and co-culture with smooth muscle cells. The experiments were properly designed and performed. The authors also provided a reasonable and sound interpretation of their findings. This study provides valuable insights into how the culturing conditions impact on gene expression, encouraging the field to select their in vitro work setting appropriately. Overall, the manuscript is well-written and easy to follow.

Several notable strengths include:

1. Parallel transcriptome- and proteome-wide profiling of endothelial cells enabling the unbiased interrogation of gene expression and a genome-wide view of the impact of in vitro culture on endothelial transcriptome.

2. The innovative experimental design and comparisons were done with genetically identical ECs (from the same donors) in vivo and in vitro.

3. The analyses were robust and provided novel information on flow-dependent and cell context-dependent gene regulation, with the native freshly isolated cells as a baseline.

4. The donor samples used in this study were diverse including Asian, White, Black, Latino, and American Indian samples which reduce racial background bias.

Some points that can strengthen the study:

A clear description of experimental and analytical details (e.g. how the comparisons were made) and more in-depth interpretation and discussion of the results, e.g. the complete genes that are rescued by flow and co-culture and potential synergy of these factors.

We thank the reviewer for highlighting the strengths and appreciate the comments on experimental and analytical details which have been now addressed in this revised manuscript. Specifically, we have expanded the discussion and included synergy and additional comments on the rescued genes. A clear description of experimental and analytical details (e.g. how the comparisons were made) and more in-depth interpretation and discussion of the results, e.g. the complete genes that are rescued by flow and co-culture and potential synergy of these factors are now included.

Reviewer #3 (Public Review):

Afshar et al. performed RNA-seq and LC-MS of in vivo and in vitro HUVECs to identify the role of culture conditions on gene expression. Given the widespread use of HUVECs to study EC biology, these findings are interesting and can help design better in vitro experiments. There have been previous papers that compared in vivo and in vitro HUVECs, however, the depth of sequencing and analysis in this manuscript identifies some novel effects which should be accounted for in future in vitro experiments using ECs.

Strengths:

1. Major findings of distinct pathways affected by cell culture are novel and interesting. The authors identify major effects on TGFb and ECM gene expression. They also corroborate previous findings of flow response pathways, namely KLF2/4 and Notch pathway regulation.

2. Use of multiple genomic methods to profile effects of culture conditions. The LC-MS data showed a significant correlation with RNA-seq, however, the data were not as strong so not used for subsequent analyses.

3. Use of scRNA-seq to show the dynamic effects of co-culture and shear stress on ECs is very novel. However, the heterogeneity in the EC populations is not discussed in this manuscript.

We would like to thank the reviewer for the in-depth analysis of our study and for highlighting the novelty and strength of the data. Note that we included comments in relation to EC heterogeneity as part of the limitations of this study (in the Discussion).

Weaknesses:

1. The physiological relevance of these changes in gene expression is not demonstrated in the manuscript. The authors claim the significance of their data is to improve in vitro culture to better represent in vivo biology. Is this the case with orbital shear stress? Do they rescue some functional effects in ECs with long-term shear stress? An angiogenesis, barrier function, or migration assay for HUVECs exposed to different conditions would help answer this question. A similar assay for cells after EC-VSMC co-culture would validate the importance of these stimuli.

The reviewer is correct, our manuscript did not expand into physiological read outs, we have now clearly acknowledged this as part of the limitations of the study. Notably, there is already extensive literature on the effects of different types of flow on several physiological parameters. For example, others have shown that laminar shear stress (by orbital or other means) reduces proliferation and migration (PMID: 31831023; PMID: 22012789, PMID: 12857765, PMID: 21312062, PMID: 15886673; PMID: 17323381), reduces inflammation (PMID: 34747636; PMID: 32951280), and improves barrier function (PMID: 20543206; PMID: 32457386 ; PMID: 12577139, PMID: 27246807; PMID: 31500313 ).

From the onset, our objective was to bring granularity to transcriptional changes associated with the transition from in vivo to in vitro. Further, it was our goal to identify the cohorts of transcripts that could and those that could not be rescued by altering culture conditions. Because we had transcriptional information from the identical samples at a time that they were in the vessel, we have been able to fulfill our goal. We feel this is important, and currently missing data, that will be of value to many investigators.

1. One explanation for the increased expression of ECM genes in vivo is that these cells are contaminated with VSMCs/fibroblasts. This could be very likely given that cells were not sorted or purified upon isolation. Expression of other VSMC or fibroblast-specific markers (i.e. CNN1, MYH11, SMTN, DCN, FBLN1) would help determine if there is some level of non-EC contamination.

We thank the reviewer for this comment and prompted by this, we have included a new figure (Supplemental Figure 1 and new panels in Supplemental Figure 5) that directly address this concern.

Amongst the several pieces of data, we included scRNAseq from cells that were immediately obtained from umbilical vein – three independent experiments sequenced together and showed in one UMAP (Supplemental Figure 1C). As can be appreciated, the very large majority of cells are endothelial and the only other cell types present were blood cells (erythrocytes and CD45+ cells). No smooth muscle cells or fibroblasts were detected. These three examples are indeed representative of a large number of scRNAseq datasets (35 from cords and cultures for this and other projects). Furthermore, our cultures are also routinely evaluated by FACS (one example has been provided in Supplemental Figure 1E). We do not find, as illustrated in that example, cells that are not positive for CD31 and VE-Cadherin.

We hope this information reveals the rigor of our studies and convinces the reviewer that the transcriptional changes observed are from endothelial cells.

1. The use of scRNA-seq in Figure 4 is interesting. There appear to be 2 distinct EC populations in the co-cultured ECs. What are the marker genes for the 2 populations?

Indeed, we and others (Kalluri et al., 2019) have noticed two distinct populations in the in vivo and also in cultured ECs, as pointed by the reviewer. Evaluation as to these two subpopulations reflect two transcriptionally distinct groups or different states of cyclic expression patterns, requires more thorough analysis and lineage tracing studies and distinct from the focus of this manuscript. Nonetheless, we have made a point in the revised manuscript to highlight these possibilities.

Reference: Kalluri, AS, Vellarikkal, SK, Edelman, ER, Nguyen, L, Subramanian, A, Ellinor PT, Regev, A, Kathiresan, S, Gupta, RM. Single Cell Analysis of the Normal Mouse Aorta Reveals Functionally Distinct Endothelial Cell Populations. Circulation, 2019. 140:147-163.

1. The modest shifts in gene expression with shear stress and co-culture could be attributed to the batch effect. The authors describe 1 batch correction method (ComBat) in the bulk RNA-seq, but no mention of batch correction was noted in the scRNA-seq methods. The authors should ensure that batch effect correction in all data is adequate, and these results should be added to the manuscript.

We thank the reviewer for this comment. Indeed, batch effects are a particularly important consideration when samples are prepared separately and/or sequenced at distinct times, note this was not the case in this study.

For the scRNA-seq analysis, we removed the low-quality cells, but did not use batch-effect correction methods because the samples were prepared and run at the same time. Meaning, isolation was performed in parallel, generation of cDNA libraries was done concurrently, and sequencing was run in the same gel. The quality of the data (and lack of batch effect) was subsequently verified when the two mono-culture biological replicates were evaluated by Seurat and were found to overlap on the UMAP (Figure 4), the same applies to the two co-culture biological replicates. These results clearly indicate that there’s no batch effect (as the samples were not process in distinct batches) among these samples.

1. Table 1 shows ATAC-seq was done, however, no data from these experiments are provided in the manuscript.

As mentioned (reviewer 2), we had performed ATACseq but decided to remove from the manuscript for several reasons and apologize for missing reference to Table 1. We have now corrected this error.

1. Shear stress was achieved with an orbital shaker, which the accompanying citation states introduces significant heterogeneity in the ECs. This is based on the location of the culture dish. Was this heterogeneity seen in the scRNA-seq data?

Correct. We only use the 2/3 peripheral area of the plates and discard the central aspect of the plate. We have added clarifying language to the Methods > Shear stress application to reflect this: “Orbital shear stress (130 rpm) was applied to confluent cell cultures by using an orbital shaker positioned inside the incubator as previously discussed (32). The shear stress within the cell culture well corresponds to arterial magnitudes (11.5 dynes/cm2) of shear stress. To reduce issues associated with uniformity of shear stress, the endothelial cell monolayers in 6-well plates were lysed after removing center region using cell scraper (BD Falcon #35-3085) and washing with 1X HBSS (Corning #21-022-CV). The 1.8cm blade was circumferentially used in the center of the 6-well plate to remove the center of the monolayer that did not see the higher shear stress.”

1. It would be important to know whether the authors reproduce the findings from other papers that CD34 expression is reduced in cultured HUVECs:

Muller AM, Cronen C, Muller KM, Kirkpatrick CJ: Comparative analysis of the reactivity of human umbilical vein endothelial cells in organ and monolayer culture. Pathobiology 1999;67:99-107. Delia D, Lampugnani MG, Resnati M, Dejana E, Aiello A, Fontanella E, Soligo D, Pierotti MA, Greaves MF: Cd34 expression is regulated reciprocally with adhesion molecules in vascular endothelial cells in vitro. Blood 1993;81:1001-1008.

Thank you for this suggestion. Supplemental Excel 4 allows the reader to review single genes that are modulated by condition and in fact, consistent with all previous literature, CD34 expression is one of the most significantly decreased genes in cultured HUVECs (0.9, p=1E-5).

Author Response

Reviewer #1 (Public Review):

1) I was confused about the nature of the short-term plasticity mechanism being modeled. In the Introduction, the contrast drawn is between synaptic rewiring and various plasticity mechanisms at existing synapses, including long-term potentiation/depression, and shorter-term facilitation and depression. And the synaptic modulation mechanism introduced is modeled on STDP (which is a natural fit for an associative/Hebbian rule, especially given that short-term plasticity mechanisms are more often non-Hebbian).

Indeed, because of its associative nature, the modulation mechanism was envisioned to be STDP-like, i.e. on faster time scales than the complete rewiring of the network (via backpropagation) but slower time scales than things like STSP which, as the reviewer points out, are usually not considered associative. One thing we do want to highlight is that backpropagation and the modulation mechanism are certainly not independent of one another. During training, the network’s weights that are being adjusted by backpropagation are experiencing modulations, and said modulations certainly factor into the gradient calculation.

We have edited the abstract and introduction to try to make the distinction of what we are trying to model clearer.

1) cont: On the other hand, in the network models the weights being altered by backpropagation are changes in strength (since the network layers are all-to-all), corresponding more closely to LTP/LTD. And in general, standard supervised artificial neural network training more closely resembles LTP/LTD than changing which neurons are connected to which (and even if there is rewiring, these networks primarily rely on persistent weight changes at existing synapses).

Although we did not highlight this particular biological mechanism because we wanted to keep the updates as general as possible, one could view the early versus late LTP. We have added an additional discussion of how the associative modulation mechanisms and backpropagation might biologically map into this mechanism in the discussion section.

1) cont: Moreover, given the timescales of typical systems neuroscience tasks with input coming in on the 100s of ms timescale, the need for multiple repetitions to induce long-term plasticity, and the transient nature/short decay times of the synaptic modulations in the SM matrix, the SM matrix seems to be changing on a timescale faster than LTP/LTD and closer to STP mechanisms like facilitation/depression. So it was not clear to me what mechanism this was supposed to correspond to.

We note that although the structure of the tasks certainly resembles known neuroscience experiments that happen on shorter time scales (and with the introduction of the 19 new NeuroGym tasks, even more so), we did not have a particular time scale for task effects in mind. So each piece of “evidence” in the integration tasks may indeed occur over significantly slower time scales and could abstractly represent multiple repetitions in order to induce (say) early phase LTP.

Given that the separation between the two plasticity mechanisms may be clearer for STSP, and indeed many of the tasks we investigate may more naturally be mapped to tasks that occur on time scales more relevant to STSP, we have introduced a second modulation rule that is only dependent upon the presynaptic firing rates. See our response to the Essential Revisions above for additional details on these new results.

2) A number of studies have explored using short-term plasticity mechanisms to store information over time and have found that these mechanisms are useful for general information integration over time. While many of these are briefly cited, I think they need to be further discussed and the current work situated in the context of these prior studies. In particular, it was not clear to me when and how the authors' assumptions differed from those in previous studies, which specific conclusions were novel to this study, and which conclusions are true for this specific mechanism as opposed to being generally true when using STP mechanisms for integration tasks.

We have added additional works to the related works sections and expanded the introduction to try to better convey the differences with our work and previous studies. Briefly, mostly our assumptions differed from previous studies in that we considered a network that relied only on synaptic modulations to do computations, rather than a network with both recurrence and synaptic modulations. This allowed us to isolate the computational power and behavior of computing using synaptic modulations alone.

It is hard to say which of the conclusions are generally true when using STP mechanisms for integration tasks without a comprehensive comparison of the various models of STP on the same tasks we investigated here. That being said, we believe we have presented in this work conclusions that are not present in other works (as far as we are aware) including: (1) a demonstration of the strength of computing with synaptic connection on a large variety of sequential tasks, (2) an investigation into the dynamics of such computations how they might manifest in neuronal recordings, and (3) a brief look at how these different dynamics might be computational beneficial in neuroscience-relevant areas. We also note that one reason for the simplicity of our mechanism is that we believe it captures many effects of synaptic modulations (e.g. gradual increase/decrease of synaptic strength that eventually saturates) with a relatively simple expression, and so we believe other STP mechanisms would yield qualitatively similar results. We have edited the text to try to clarify when conclusions are novel to this study and when we are referencing results from other works.

Reviewer #2 (Public Review):

On the other hand, the general principle appears (perhaps naively) very general: any stimulus-dependent, sufficiently long-lived change in neuronal/synaptic properties is a potential memory buffer. For instance, one might wonder whether some non-associative form of synaptic plasticity (unlike the Hebbian-like form studied in the paper), such as short-term synaptic plasticity which depends only on the pre-synaptic activity (and is better motivated experimentally), would be equally effective. Or, for that matter, one might wonder whether just neuronal adaptation, in the hidden layer, for instance, would be sufficient. In this sense, a weakness of this work is that there is little attempt at understanding when and how the proposed mechanism fails.

We have tried to address if the simplicity of the tasks considered in this work may be a reason for the MPN’s success by training it on 19 additional neuroscience tasks (see response to Essential Revisions above). Across all these additional tasks, we found the MPN performs comparable to its RNN counterparts.

To address whether associativity is necessary in our setup we have introduced a version of the MPN that has modulation updates that are only presynaptic dependent. We call this the “MPNpre” and have added several results across the paper addressing its computational abilities (again, additional details are provided above in Essential Revisions). We find the MPNpre has dynamics that are qualitatively the same as its MPN counterpart and has very comparable computational capabilities.

Certainly, some of the tasks we consider may also be solvable by introducing other forms of computation such as neuronal adaptation. Indeed, we believe the ability of the brain to solve tasks in so many different ways is one of the things that makes it so difficult to study. Our work here has attempted to highlight one particular way of doing computations (via synapse dynamics) and compared it to one particular other form (recurrent connections). Extending this work to even more forms of computation, including neuronal dynamics, would be very interesting and further help distinguish these different computational methods from one another.

Reviewer #3 (Public Review):

Because the MPN is essentially a low-pass filter of the activity, and the activity is the input - it seems that integration is almost automatically satisfied by the dynamics. Are these networks able to perform non-integration tasks? Decision-making (which involves saddle points), for instance, is often studied with RNNs.

We have tested the MPN on 19 additional supervised learning tasks found in the NeuroGym package (Molano-Mazon et. al., 2022), which consists of several decision-making-based tasks and added these results to the main text (see response to Essential Revisions above, and also Figs. 7i & 7j). Across all tasks we investigated, we found the MPN performs at comparable levels to its RNN counterparts.

Manuel Molano-Mazon, Joao Barbosa, Jordi Pastor-Ciurana, Marta Fradera, Ru-Yuan Zhang, Jeremy Forest, Jorge del Pozo Lerida, Li Ji-An, Christopher J Cueva, Jaime de la Rocha, et al. “NeuroGym: An open resource for developing and sharing neuroscience tasks”. (2022).

The current work has some resemblance to reservoir computing models. Because the M matrix decays to zero eventually, this is reminiscent of the fading memory property of reservoir models. Specifically, the dynamic variables encode a decaying memory of the input, and - given large enough networks - almost any function of the input can be simply read out. Within this context, there were works that studied how introducing different time scales changes performance (e.g., Schrauwen et al 2007).

Thank you for pointing out this resemblance and work. In our setup, the fact that lamba is the same for the entire network means all elements of M decrease uniformly (though the learned modulation updates may allow for the growth of M to be non-uniform). One modification that we think would be very interesting to explore is the effects on the dynamics of non-uniform learning rates or decays across synapses. In this setting, the M matrix could have significantly different time scales and may even further resemble reservoir computing setups. We have added a sentence to the discussion section discussing this possibility.

Another point is the interaction of the proposed plasticity rule with hidden-unit dynamics. What will happen for RNNs with these plasticity rules? I see why introducing short-term plasticity in a "clean" setting can help understand it, but it would be nice to see that nothing breaks when moving to a complete setting. Here, too, there are existing works that tackle this issue (e.g., Orhan & Ma, Ballintyn et al, Rodriguez et al).

Thank you for pointing out these additional works, they are indeed very relevant and we have added them all to the text where relevant.

Here we believe we have shown that either recurrent connections or synaptic dynamics alone can be used to solve a wide variety of neuroscience tasks. We don’t believe a hybrid setting with both synaptic dynamics and recurrence (e.g. a Vanilla RNN with synaptic dynamics) would “break” any part of this setup. Since each of the computational mechanisms could be learned to be suppressed the network could simply solve the task by relying on only one of the two mechanisms. For example, it could use a strictly non-synaptic solution by driving eta (the learning rate of the modulations) to zero or it could use a non-recurrent solution by driving the influence of recurrent connections to be very small. Orhan & Ma mention they have a hard time training a Vanilla RNN with Hebbian modulations on the recurrent weights for any modulation effect that goes back more than one time step, but unlike our work they rely on a fixed modulation strength.

Indeed, we think how networks with multiple computational mechanisms will solve tasks is a very interesting question to be further investigated, and a hybrid solution may be likely. We believe our work is valuable in that it illuminates one end of the spectrum that is relatively unexplored: how such tasks could be solved using just synaptic dynamics. However, what type of solution a complete setup ultimately lands on is likely largely dependent upon both the initialization and the training procedure, so we felt exploring the dynamics of such networks was outside the scope of this work.

One point regarding biological plausibility - although the model is abstract, the fact that the MPN increases without bounds are hard to reconcile with physical processes.

Note although the MPN expression does not have explicit bounds, in practice the exponential decay eventually does balance with the SM matrix updates, and so we observe a saturation in its size (Fig. 4c, except for the case of lamba=1.0, which is not considered elsewhere in the text). However, we explicitly added modulation bounds to the M matrix update expression and did not find it significantly changed the results (see comments on Essential Revisions above for details).

Author Response

Reviewer #2 (Public Review):

Here I will mainly comment on the biology of adipocytes, which is my specialty.

In this manuscript, it has been very convincingly shown that O-GlcNAc acts as an important regulator of MSC differentiation in mice, and given previous studies in which O-GlcNAc is regulated by aging and nutritional status, it makes sense that this PTM determines differentiation and BM niche.

The point that O-GlcNAc regulates adipocyte differentiation is convincing, but there are already previous studies using 3T3-L1 (e.g., Biochemical and Biophysical Research Communications 417 (2012) 1158-1163), and a more step-by-step demonstration of the molecular mechanism would make this an excellent paper that can be extended to adipocyte research in general, not just BM.

While O-GlcNAc has been demonstrated in regulating many aspects of metabolic physiology, our understanding of its role in adipogenesis has been limited so far. As the reviewer pointed out, there was an in vitro report on its inhibition of adipogenesis in 3T3-L1 cells (Ji et al., 2012). Two recent publications from Dr. Xiaoyong Yang’s group revealed the profound role of mature white adipocytes OGT in regulating lipolysis and obesity (Li et al., 2018; Yang et al., 2020). To my knowledge, our manuscript is the first attempt to address the regulation of adipogenesis by O-GlcNAc in vivo. While using the BMSCs as a non-conventional model, we speculate our molecular mechanisms (i.e., O-GlcNAc inhibition of C/EBPβ) could be conserved in peripheral adipose organs, including white and brown adipose tissues. Future experiments are warranted in the lab to extend the current knowledge to these adipocyte progenitors. Nonetheless, I would also like to point out that, due to the broad actions of OGT and the current lack of adipocyte progenitor specific Cre animal tools, such efforts might be futile as results can be confounded by defects in other organs/cells.

It is somewhat unclear whether or not the authors' in vitro experiments using 10T1/2 cells accurately reflect what is happening in vivo in knockout mice. The PDGFRa+VCAM1+ population of adipocyte progenitors shown by the authors is upregulated by about 30% by knockout of Ogt (Figure 4C). How significant is this difference? Rather, might the expression of Pparg, which indicates lineage commitment, be the underlying mechanism? In any case, this manuscript is highly impactful in the sense that the differentiation of adipocytes forming the BM niche can be controlled using tissue-specific knockouts of the Ogt gene.

We agree with the reviewer that the role of OGT in BMSC fate determination and adipogenesis might be multifaceted. The 30% increase in PDGFRa+VCAM1+ BM adipose progenitors cannot fully explain the massive adipogenesis observed in OgtΔOsx animals (Fig. 4A). Indeed, we provided in vitro evidence that genetic deletion or chemical inhibition of OGT activates adipogenesis (Fig. 4D-I). Mechanistically, we found the O-GlcNAcylation of C/EBPβ protein (but not PPARγ) is responsible in the inhibition, which leads to reduced expression of adipogenic genes, including Pparg (Fig. 4H).

Author Response

Reviewer #1 (Public Review):

The paper states that they observed a combined total of 77,017 single-nucleotide variants (SNVs) and 12,031 insertion/deletions (In/Dels) across all tissue, age, and intervention groups. Collectively, these data represent the largest collection of somatic mtDNA mutations obtained in a single study to date. However, A study with more somatic mtDNA mutations by the LostArc method (PMID 32943091) revealed 35 million deletions (~ 470,000 unique spans) in skeletal muscle from 22 individuals with and 19 individuals without pathogenic variants in POLG. Thus, the authors should reword this part to say that this study represents the largest collections of mouse mtDNA point mutations detected, but not the largest amount of mutations (deletions exceed this number).

Thank you for pointing this out. When we wrote that sentence, we were more referring to small polymerase-based errors, as opposed to larger structural variants that likely arise from a different mechanism. However, the distinction between these two event classes is poorly defined. We have amended our statement and have added a citation to Lujan et al. Our statement now reads “We observed a combined total of 77,017 single-nucleotide variants (SNVs) and 12,031 small insertion/deletions (In/Dels) (≲15bp in size) across all tissue, age, and intervention groups. Collectively, these data represent the largest collection of somatic mtDNA point mutations obtained in a single study to date and is second only to Lujan et al. in terms overall In/Del counts (Lujan et al., 2012).” (Lines 252-256)

What is the theoretical limit of pt mutations in the mitochondrial genome, assuming only one pt mutation per genome? Doesn't 77000 detected independent pt mutations approach that limit? Can the authors estimate how many molecules contained two or more pt mutations? Did the analysis reveal any un-mutated regions implying an essential function? For example, on p.9 can the authors provide an explanation of why OriL and other G/C-rich regions were not uniformly covered as compared to the rest of the genome?

This is an interesting question and one we’ve given some thought to. In fact, this basic question was the inspiration for our recent Nucleic Acids Research paper (PMC8565317) where we asked how mutations were distributed in the genome. The short answer is that we likely exceed the limit for only dG site mutations (and only for G>A mutations, at that), but not the other reference sites. The reason is that there are only 2013 dG sites and the mutation spectrum is heavily skewed toward G>X (there are 47,680 dG site mutations, 42,924 of which are G>A). In comparison, we observe only 4,421 A>X, 9,277 T>X, and 15,632 C>X mutations, but with 5,629, 4,681, and 3,976 dA, dT, and dC genomic sites, respectively. Assuming the mutations are uniformly distributed along the genome (which they are not; see our NAR paper), then random binomial sampling would require a fair amount more mutations in order to reach saturation for the other genomic sites. The uneven distribution increases this number further.

With regard to the second question, we can’t actually do this estimation with this data set. The reason is because the ~77,000 mutations aren’t found in a single sample, but are distributed across may independent or semi-independent (i.e. different organs within a mouse), which means that most, if not all, of the mutations are necessarily on different mtDNA molecules.

With regard to the OriL and G/C rich regions, these presumably have some sort of secondary structure that prevents the sequencer from obtaining any useful information. However, this is all speculative and we don’t know why. Interestingly, human mtDNA doesn’t show this dip at the OriL, despite a similar function and location in the mtDNA.

Given that mitochondrial disease usually doesn't present until >60% of the genomes are affected, the very low level of detected pt mutations observed in the mouse (and presumably similar to human) would mean that they are well below a physiological level. Thus, these low-level pt mutations are well tolerated. Can the authors estimate a theoretical age of the mouse (well beyond their life span) where over 50% of the genomes carry at least one pt mutation?

The reviewer brings up a frequent noted point in mitochondrial biology that is very much worth addressing in this manuscript. The often-cited statistic that mitochondrial disease doesn’t present until ~60% of genomes are affected is, while true, only pertinent to overt mitochondrial diseases, such as LHON, MERRF, etc, where all or nearly all cells in an individual are affected by the mutation. However, the impact of mtDNA mutations is not only contingent on how many cells have the mutation, but also the fraction of mtDNA molecules within a cell that harbor the variant. Because the deleterious effects of a mtDNA mutation act at the level of individual cells, it is important to know both how many cells harbor a mutation as well as what the heteroplasmic level is within the cell before making claims on their pathological impact.

To date, nearly all studies on mtDNA mutations rely on bulk DNA analysis from thousands to millions of cells, which necessarily decouples variant phasing information between any two reads, resulting in a loss of important biological information such as the heteroplasmic level within any given cell. As such, with bulk sequencing it is impossible to tell the difference between a homoplasmic mutation in a small subset of cells and heteroplasmic mutation in all cells. In the first case, the cells harboring this mutation would be negatively impacted, whereas in the second example, it is unlikely. One can imagine a scenario where every cell contains a different homoplasmic pathogenic mutation which would negatively affect cellular function for every cell. In this case, mutations would be highly prevalent (100% of cells), yet individually rare. However, bulk sequencing would give the appearance that no mutation comes close to exceeding the phenotypic threshold. We highlight this issue in a recent review (Sanchez-Contreras and Kennedy, 2022; PMC8896747).

The point that the review brings up is extremely important, so we have added a section in the discussion related to heteroplasmy versus clones.

Also, the problem with this low level of pt mutations is that they are not physiological, the effect of the drug treatment causing a reduction in ROS-mediated transversions would not be expected to have a detectable effect on mitochondria. The improvement on mitochondrial seen by others is most likely independent of the mutations in the genome. There needs to be a cause and effect here and I don't see one.

It is important to note that we do not make the claim (no do we want to imply) that the reduction of mutations is the reason behind the improvements in mitochondrial function by these interventions. Instead, we believe that loss of ROS-linked mutations is a consequence of the mechanism by which these interventions work. We do hypothesize that the reduction in ROS-linked mutations suggests that “there is tissue specificity in how cells repair and/or destroy oxidatively damaged mitochondria and/or mtDNA resulting in a steady-state of ROS-linked mutations.” (Lines 551-553) and that “We propose that rather than the incidence and impact of ROS damage on mtDNA being minimal, recognition and removal of ROS-linked mutations are maintained at a steady state during aging.” (Lines 572-574).

In addition, as noted above, how “low level” these mutations are and their impact on cellular function is not easily determined in bulk sequencing studies, so a strong link between cause and effect is not an answerable relationship with this data set.

There's no mention in this paper and methodology about how point mutations in nuclear-encoded mtDNA (NUMTs) are excluded from the reads and I'm worried that these errors are being read as rare errors in the mtDNA genome. While NUMTs have been documented for decades, a recent report in Science (PMID: 36198798) documents how frequently and fluidly NUMTs occur. Can the authors provide a clear explanation of how mutations in NUMTs are excluded?

The reviewer is absolutely correct to call attention to this important aspect of mitochondrial biology. We don’t believe NUMTs are an important confounder in our data set for several reasons.

1) We used isogenic inbred C57Blk6/J which, frequently, were litter mates (siblings). Therefore, any mutations from NUMTS that are there would be expected to be uniform across samples, especially between tissues from a single sample animal. Unknown and variations of NUMTS would certainly be a potentially strong confounder in an outbred population, but the use of one isogenic inbred line for this study likely eliminates this confounder.

2) We used the mm10 reference genome which is based on the C57Blk6/J strain so any NUMTS derived variants present in our mtDNA data should preferentially align against the NUMT. Therefore, we perform a BLAST step of all reads containing at least one variant against the mm10. BLAST is much more sensitive to sequence variation compared to bwa but is far slower, so it is impractical to run as the initial aligner. We then reassign the read based to whatever genomic location has the lower e-score. The result is typically around a dozen reads are removed, demonstrating that NUMTS are not likely a major source of false mutations.

3) Because NUMTS are inherited, then any variants would be found across all the tissues and animals we used in this study. As part of our processing, we mark and remove variants shared between multiple individual samples.

We have made edits to the Methods section (Lines 198-206) to more explicitly highlight the filtering steps and the logic behind them. In addition, we have added a paragraph in the discussion that addresses NUMTs (Starting on line 642).

Reviewer #2 (Public Review):

A common problem in mutation analysis is that DNA damage (present in one strand) is difficult to separate from real mutations (present in both strands). One of the approaches to solve this problem based on independent tagging of the two strands by different unique molecular identifiers was developed by the authors about 10 years ago. This study summarizes the application of this method to a wide range of mouse tissues, ages, and drug treatment regimes. Much of the results confirm previous conclusions from this laboratory. This involves overall mutational levels of somatic mtDNA mutations (~10-6-10-5), their accumulation with age, the prevalence of GA/CT transitions, and their clonality. Although these results were not new, it is important that these were confirmed in a single study with high confidence in a huge number of independent mutations.

We thank the reviewer for the comment and really hope this data set will be of significant use to other researchers given its breadth of sample types and large number of mutations.

What really sets this study apart from other studies is the detection of a large proportion of transversion mutations, primarily of the C>A/G>T and C>G/G>C types. Transversions are traditionally considered 'persona non grata' in mtDNA mutational spectra and are typically associated with errors of mutational analysis (which they in fact are). The presence of these mutations in both strands of the duplex makes a good case that these mutations are real, rather than converted damage. However, because this is such a novel discovery and because regular controls do not work (I mean, for example, that these mutations never clonally expand. If there is a clonal expansion, then the mutation is real, only real mutation can expand. But in the case of non-expandable C>A/G>T and C>G/G>C this control does not help to validate these mutations), it would be nice to provide extra assurances that this is not some kind of artifact that somehow slipped through the ds sequencing procedure. I would recommend including in the supplement the data on the abundance of single-stranded base changes as detected by ds sequencing (i.e., changes confirmed in one and not in the other strand of a given molecule). An unusually high presence of such single-stranded changes of the C>A/G>T and C>G/G>C type would be a red flag for me. If ratios of single and double-stranded mutations were similar for transitions and transversions - that would reassure me and hopefully the reader.

Furthermore, a similar excess of C>A/G>T and C>G/G>C has been observed in a recent paper by Abascal 2021 (cited in the manuscript). In that paper, a UMI- free, but otherwise very similar ds sequencing approach in nuclear DNA (BotSeqS) was demonstrated to suffer from an artifact causing (among other effects) an excess of C>A/G>T and C>G/G>C transversions. This artifact is related to end repair and nick-translation of DNA fragments during library preparation. Because BotSeqS is very similar to ds sequencing, we expect that same artifact may be taking place in the study under review. We recommend running checks similar to those undertaken by Abascal et al (which include, at the very minimum, checking the distribution of the C>A/G>T and C>G/G>C transversions within the reads (artifacts tend to be concentrated towards the ends of the reads).

The reviewer is absolutely correct to bring up this extremely important point. We have addressed these concerns in two ways that are addressed on Lines 332-361. 1) by performing an analysis of the single-stranded consensus data, which is a measure of PCR artifacts that frequently arise as a function of DNA damage, across all the tissues of the aged cohort. We noted no differences between tissues, which indicates that the amount of ROS-induced PCR artifacts is no different between the tissues. Thus, it would require a different rate at which ROS artifacts lead to false “Duplex consensus” variants that is tissue specific. The analysis is presented in Figure 3-figure supplement 2. 2) we have included an experiment in which we show that treatment of post-fragmented DNA with FPG, a glycosylase that targets Fapy-dG and 8-oxo-dG, does not differ from untreated control DNA. Because Duplex-Seq requires that both strands of a parent DNA molecule be present to form a final Duplex Consensus Sequence, the scission of one strand by the lyase activity of FPG would prevent the formation of this final consensus and prevent this sort of error from “bleeding through”. This analyses can now be found in a Figure 3-figure supplement 3.

Of note, even if transversions detected in this study prove to be artifacts of the Abascal type (likely) they still may reflect real ss damage in mtDNA (not instrumental artifacts, like sequencing errors or in vitro DNA damage). This is supported by the strong variation in the levels of transversions across tissues and as a result of the ameliorating drug intervention. Artifacts, in contrast, would be expected to be at a constant level. This logic, however, does not differentiate between real ds mutations and ss damage. So UMI-based ds sequencing evidence remains the only (though very strong) independent proof. So, in my view, whereas the jury may be still out on whether the observed transversions are true ds mutations or some kind of single-stranded damage, this is a critically important observation. The evidence of ss damage greatly varied between tissues and detected with such precision on a single molecule level is a very important finding as well.

Out of caution, I would recommend mentioning the above-stated uncertainty and noting that more research is needed to fully confirm that C>A/G>T and C>G/G>C changes detected in this study are indeed double-stranded mutations.

We agree. Together with comments from Reviewer #1 regarding NUMTs (Comment #5), we have added a paragraph in the Discussion about potential alternative explanations for our observations.

Author Response

Reviewer #1 (Public Review):

In this manuscript, May et al use H2B overexpression driven by Keratin14 Cre-mediated excision of a loxPstop cassette to quantify bulk chromatin dynamics in the live epidermis. They observe heterogeneity of H2B distribution within the basal stem cell layer and a change in distribution when the stem cells delaminate into the suprabasal layers. They further show that these chromatin rearrangements precede cell fate commitment, as detected by adding another Cre-mediated transgene on top (tetO-Cre mediated Keratin10 reporter). Finally, they generate an MST stem-loop transgene for the keratin 10 transcript and observe transcriptional bursting.

We would like to clarify for the reviewer that the H2B system used is a transgenic allele of histone-2B-GFP that is driven directly by the Keratin-14 promoter (Kanda et al., 1998; Tumbar et al., 2004). This system does not rely on any Cre-mediated excision of the LoxP-stop cassette, and these mice do not carry Cre alleles. We will touch on this point below when addressing the comment on Cre expression in cells and the raised question on whether it influences the quantifications of chromatin compaction.

The manuscript uses elegant in vivo imaging approaches to describe a set of observations that are logically based on a panel of studies that have used genetic approaches to dissect the role of heterochromatin and histone/DNA modifications in epidermal state transitions. In addition, the MST stem-loop analysis is a nice technical advance, confirming transcriptional bursting as a general phenomenon of how transcription is regulated in cells (see work from Daniel Larsson, Jonathan Chubb, Arjun Raj, and others).

We thank the reviewer for their recognition of our contribution to the transcription field. To deepen the connection between our data and previous characterizations of transcriptional dynamics in other systems, we have added new analyses of K10MS2 transcriptional bursting on a finer temporal scale (Fig 5G-K). We find pervasive “transcriptional bursting,” consistent with findings in vitro and in other model organisms, and a surprising variation of burst durations. We believe these additional analyses significantly strengthen our conclusions and the relevance of our study to the overall transcription field.

The value of the study in my view is recapitulating these known phenomena in a live tissue setting with high-quality imaging and careful quantification. Overall, the analyses appear thorough, although the overall changes appear relatively minor, which is perhaps to be expected from imaging bulk H2B distribution as a proxy for chromatin states.

There is one major technical concern that might impact the interpretation of the data. The authors combine Cre lines for their key conclusions (Krt10 reporter and SRF KO) and analyze single cells that thus express very high levels of Cre. Knowing that Cre will target non-loxP sites and is genotoxic, it is possible that the effect of chromatin is due to high levels of Cre expression in single cells rather than specific effects due to cell state transitions. I would encourage the authors to carefully quantify the dose-dependent effects of the Cre protein (independent of the LoxP sites) on chromatin organization. Along these lines, is the phenotype of the SRF KO similar in the presence of two Cre alleles versus just one?

Thank you for these kind words. This is an important potential caveat to consider. We believe that Cre activity does not significantly affect the chromatin compaction profiles for several reasons. First, we interrogated Cre activity. The quantifications in Figure 1A-E and Figure 2B-C are from mice containing K14H2B-GFP allele alone and do not carry any Cre allele. When these data were compared to those from mice that had been treated with a high dose of tamoxifen to induce Cre-mediated recombination in the vast majority of cells, the chromatin compaction profiles were not significantly different (Supp Fig 3C). We have added this comparison to Supplemental Figure 3 and addressed this point in the text (page 9). To further determine whether Cremediated recombination affects our measurement of chromatin compaction, we also analyzed adjacent basal cells with and without Cre activity in the same animal. K14H2BGFP; K14CreER; tdTomato mice were induced with a low dose of tamoxifen such that roughly 65% of epidermal cells underwent Cre recombination as demonstrated by expression of the tdTomato fluorescent reporter (Gallini et al., 2022). They also received a punch biopsy performed on the unimaged ear. Three days post injury and six days after Cre induction, the chromatin compaction profiles of cells positive and negative for Cre-mediated recombination were also not significantly different (Rebuttal Figure 1). Together, these direct comparisons between cells exposed to Cre activity and cells not exposed to Cre activity indicate that Cre activity at levels comparable to those used in our experiments has no measurable effect on our measurements of chromatin compaction.

Rebuttal Figure 1: Effect of Cre expression on chromatin compaction profiles

The second issue is the conclusion of "chromatin spinning". Concluding that chromatin is spinning would in my view require that the authors demonstrate that the nuclear envelope is not moving or is moving less than the chromatin. To support this conclusion the authors should do double imaging for example with LINC complex proteins, an ER/outer nuclear membrane marker, or equivalent.

This is an excellent point. While we expect that the entire nucleus is spinning based on observations others have made in in vitro fibroblasts systems, we describe our observation as “chromatin spinning” instead of “nuclear spinning” because the K14H2B-GFP allele only allows us to directly visualize chromatin itself (Kumar et al., 2014; Zhu et al., 2018).

Unfortunately, LINC complex proteins and nuclear membrane proteins have not been fluorescently tagged in mice, which prevents us from visualizing their dynamics in vivo. To establish these new tools and perform experiments would take more than a year, making it therefore beyond the scope of this current paper. Additionally, their relatively uniform distribution across the nuclear membrane would not allow us to visualize potential spinning of these components. We have made efforts towards the reviewer’s question by asking whether other compartments within the cell also spin in delaminating cells. To do this, we leveraged a mouse line developed by Claudio Franco’s lab (Barbacena et al., 2019), which fluorescently labels both the chromatin (H2B-GFP) and the Golgi (GTS-mCherry). As expected, this model showed a perinuclear and polarized Golgi in skin fibroblasts (Rebuttal Figure 2). However, this tool is incompatible with our questions in epidermal cells for a few reasons. First, the system is toxic to epithelial cells in vivo, resulting in apoptosis, nuclear fragmentation, and binucleate cells. Second, the Golgi is not discretely polarized (or even perinuclear) in epithelial cells (Rebuttal Figure 2). As such, although we observe chromatin spinning in delaminating basal cells, we are uncertain as to whether the whole nucleus or any other cellular compartments are spinning in these cells.

Rebuttal Figure 2: Interrogation of intracellular spinning

Given the above reasoning and efforts, we have altered the text and specified that we only have the capacity to visualize chromatin through the H2B-GFP allele and that we hypothesize the entire nucleus is spinning (page 11).

Reviewer #2 (Public Review):

In this work entitled "Live imaging reveals chromatin compaction transitions and dynamic transcriptional bursting during stem cell differentiation in vivo" the authors use a combination of genetic and imaging tools to characterize dynamic changes in chromatin compaction of cells undergoing epidermal stem cell differentiation and to relate chromatin compaction to transcriptional regulation in vivo. They track this phenomenon by imaging the epithelium at the ear of live mice, thus in a physiological context. By following individual nuclei expressing H2B-GFP along time ranges of hours and up to 3 days, they develop a strategy to quantify the profile of chromatin compaction across different epidermal layers based on normalized intensity profiles of H2B-GFP. They observe that cells belonging to the basal stem cell layer display a considerable level of internuclear variability in chromatin compaction that is cell-cycle independent. Instead, intercellular variability in chromatin compaction appears more related to the differentiation status of the cells as it is stable in the hours range but dynamic in the days range. The authors show that differentiated nuclei in the spinous layer exhibit higher chromatin compaction. They also identified a subset of cells in the basal stem layer with an intermediate profile of chromatin compaction and with the dynamic expression of the early differentiation marker keratin 10. Lastly, they show that the expression of keratin-10 precedes the chromatin compaction establishing relevant temporal relationships in the process of epidermal differentiation.

This work includes a number of challenging approaches and techniques since it is carried out in living mice. Also, it provides nice tools and methods to study chromatin structure in vivo during multiple days and within a differentiation physiological system. On the other hand, the results are descriptive and, in some respect, expected in line with previous observations.

Thank you very much for this great summary, kind words, and the recommendations listed below. We will address each of them specifically. We have also deepened the analysis of transcriptional dynamics in ways that are more comparable with how other groups have studied transcription and included those results in Figure 5.

References

Kanda, T., Sullivan, K.F., and Wahl, G.M. (1998). Histone–GFP fusion protein enables sensitive analysis of chromosome dynamics in living mammalian cells. Current Biology 8, 377–385. 10.1016/S09609822(98)70156-3.

Tumbar, T., Guasch, G., Greco, V., Blanpain, C., Lowry, W.E., Rendl, M., and Fuchs, E. (2004). Defining the epithelial stem cell niche in skin. Science 303, 359–363. 10.1126/science.1092436.

Kumar, A., Maitra, A., Sumit, M., Ramaswamy, S., and Shivashankar, G.V. (2014). Actomyosin contractility rotates the cell nucleus. Sci Rep 4, 3781. 10.1038/srep03781.

Zhu, R., Liu, C., and Gundersen, G.G. (2018). Nuclear positioning in migrating fibroblasts. Seminars in Cell & Developmental Biology 82, 41–50. 10.1016/j.semcdb.2017.11.006.

Sara Gallini, Nur-Taz Rahman, Karl Annusver, David G. Gonzalez, Sangwon Yun, Catherine Matte-Martone, Tianchi Xin, Elizabeth Lathrop, Kathleen C. Suozzi, Maria Kasper, Valentina Greco . Injury suppresses Ras cell competitive advantage through enhanced wild-type cell proliferation.<br /> bioRxiv 2022.01.05.475078; doi: https://doi.org/10.1101/2022.01.05.475078

Pedro Barbacena, Marie Ouarné, Jody J Haigh, Francisca F Vasconcelos, Anna Pezzarossa, Claudio A Franco. GNrep mouse: A reporter mouse for front-rear cell polarity. Genesis 2019 Jun. DOI: 10.1002/dvg.23299

Cristiana M Pineda, Sangbum Park, Kailin R Mesa, Markus Wolfel, David G Gonzalez, Ann M Haberman, Panteleimon Rompolas, Valentina Greco. Intravital imaging of hair follicle regeneration in the mouse. Nature Protocols 2015 July. DOI: 10.1038/nprot.2015.070

Author Response

Reviewer #1 (Public Review):

Reviewer 1 confirmed the view that your paper provides new insight into YTHDC1 function in regulating SC activation/proliferation but added that some of the data could be improved to fully support the conclusions. Specifically:

The title "Nuclear m6A Reader YTHDC1 Promotes Muscle Stem Cell Activation/Proliferation by Regulating mRNA Splicing and Nuclear Export" seems a bit overstated. Their data are not sufficient to show YTHDC1 regulating nuclear export. From figure 6 we could see some mRNAs export was inhibited upon YTHDC1 loss but intron retention also occurs on these mRNAs, for example, Dnajc14. Since intron retention could lead to mRNA nuclear retention, the mRNA export inhibition may be caused by splicing deficiency. From the data they provided we could not draw the conclusion that YTHDC1 directly affects mRNA export. I think they could not emphasize this point in the title.

Thanks for the suggestion. It is true that in our initial submission, we had more data to support YTHDC1 regulation of mRNA splicing but not enough on nuclear export. It will take substantial amount of time and efforts to have thorough dissection on both mechanisms. Nevertheless, we argue that our data does provide evidence on YTHDC1 regulation of nuclear export. For example, in Figures 6 C, H, and M, only ~20% of the target mRNAs (such as Dnaj14) showed alteration in both splicing and export upon YTHDC1 loss while the majority of the export targets showed no splicing deficiency. For example, Btbd7 and Tiparp in Figure 6 N showed no intron retention. In addition, we have now performed Co-IP experiments to validate the interaction between YTHDC1 and THOC7 (new result added in Figure 7L), which provides extra evidence to support YTHDC1 function in regulating mRNA nuclear export. We thus would like to keep the original title in order to reflect the multifaceted function of YTHDC1 in muscle stem cells.

The mechanism of YTHDC1 promoting muscle stem cell activation/proliferation is not solidified. The authors could strengthen their evidence through bioinformatics analysis or give more discussion. Besides, the previous work done by Zhao and colleagues (Zhao et al,. Nature 542, 475-478 (2017).) reported another m6A reader Ythdf2 promotes m6A-dependent maternal mRNA clearance to facilitate zebrafish maternal-to-zygotic transition. Does YTHDC1 regulate mRNA clearance during SC activation/proliferation? The authors should explore this possibility by deep-seq data analysis and give some discussion.

Thanks for the critical comment. For the first concern, we think YTHDC1 promotes muscle stem cell activation/proliferation through the multi-level gene regulatory capabilities of YTHDC1 on both transcriptional and post-transcriptional processes and the myriads of targets regulated by YTHDC1. In addition, with the newly added data, we believe that YTHDC1’s function is largely dependent on its synergism with hnRNPG (Figure 7 K). We have added the discussion in lines 421-427 of the revised text. For the second question, our data showed that YTHDC1 predominantly localizes in the nucleus of SCs and myoblasts (Figure 1 F&G), thus it may not have a role in regulating mRNA clearance in the cytoplasm like YTHDF2. Nevertheless, there are a few existing reports1, 2 suggesting its possible role in mRNA degradation and stability which may arise from its transient shuttling to cytoplasm of cells. We have now added this point in lines 469-472 of the revised text.

Reviewer #2 (Public Review):

Reviewer 2 was similarly positive stating that several tour-de-force techniques were used to examine m6A and the biological consequence in satellite cells and that there was a large amount of data supporting the conclusions with only a few minor weaknesses.

General points: The main body is lengthy, and some content can be reduced or condensed. For example, RNA-seq was used to determine gene expression in WT and cKO cells, but the purpose of this is not well justified given that YTHDC1 mainly functions to regulate splicing and nuclear expert of mRNA rather than controlling their expression levels. Does the RNA-seq data suggest that YTHDC1 may also regulate gene expression independent of m6A reader function?

Thanks for the comment. We have now revised the entire text to condense the content. Nevertheless, we must point out that the purpose of the RNA-seq is to provide extra evidence for the proliferation defect of the YTHDC1 KO cells but not to search for the underlying mechanism. We have now revised in lines 159-160 to clarify this.

Reference:

1. Shima, H., Matsumoto, M., Ishigami, Y., Ebina, M., Muto, A., Sato, Y., Kumagai, S., Ochiai, K., Suzuki, T. & Igarashi, K. S-Adenosylmethionine Synthesis Is Regulated by Selective N(6)-Adenosine Methylation and mRNA Degradation Involving METTL16 and YTHDC1. Cell Rep 21, 3354-3363 (2017).
2. Zhang, Z., Wang, Q., Zhao, X., Shao, L., Liu, G., Zheng, X., Xie, L., Zhang, Y., Sun, C. & Xu, R. YTHDC1 mitigates ischemic stroke by promoting Akt phosphorylation through destabilizing PTEN mRNA. Cell Death Dis 11, 977 (2020).
3. He, P.C. & He, C. m(6) A RNA methylation: from mechanisms to therapeutic potential. EMBO J 40, e105977 (2021).
4. Widagdo, J., Anggono, V. & Wong, J.J. The multifaceted effects of YTHDC1-mediated nuclear m(6)A recognition. Trends Genet 38, 325-332 (2022).
5. Sheng, Y., Wei, J., Yu, F., Xu, H., Yu, C., Wu, Q., Liu, Y., Li, L., Cui, X.L., Gu, X., Shen, B., Li, W., Huang, Y., Bhaduri-Mcintosh, S., He, C. & Qian, Z. A Critical Role of Nuclear m6A Reader YTHDC1 in Leukemogenesis by Regulating MCM Complex-Mediated DNA Replication. Blood (2021).
6. Cheng, Y., Xie, W., Pickering, B.F., Chu, K.L., Savino, A.M., Yang, X., Luo, H., Nguyen, D.T., Mo, S., Barin, E., Velleca, A., Rohwetter, T.M., Patel, D.J., Jaffrey, S.R. & Kharas, M.G. N(6)-Methyladenosine on mRNA facilitates a phase-separated nuclear body that suppresses myeloid leukemic differentiation. Cancer Cell 39, 958-972 e958 (2021).
7. Chen, C., Liu, W., Guo, J., Liu, Y., Liu, X., Liu, J., Dou, X., Le, R., Huang, Y., Li, C., Yang, L., Kou, X., Zhao, Y., Wu, Y., Chen, J., Wang, H., Shen, B., Gao, Y. & Gao, S. Nuclear m(6)A reader YTHDC1 regulates the scaffold function of LINE1 RNA in mouse ESCs and early embryos. Protein Cell 12, 455-474 (2021).
8. Xiao, W., Adhikari, S., Dahal, U., Chen, Y.S., Hao, Y.J., Sun, B.F., Sun, H.Y., Li, A., Ping, X.L., Lai, W.Y., Wang, X., Ma, H.L., Huang, C.M., Yang, Y., Huang, N., Jiang, G.B., Wang, H.L., Zhou, Q., Wang, X.J., Zhao, Y.L. & Yang, Y.G. Nuclear m(6)A Reader YTHDC1 Regulates mRNA Splicing. Mol Cell 61, 507-519 (2016).
9. Webster, M.T., Manor, U., Lippincott-Schwartz, J. & Fan, C.M. Intravital Imaging Reveals Ghost Fibers as Architectural Units Guiding Myogenic Progenitors during Regeneration. Cell Stem Cell 18, 243-252 (2016).
10. Yankova, E., Blackaby, W., Albertella, M., Rak, J., De Braekeleer, E., Tsagkogeorga, G., Pilka, E.S., Aspris, D., Leggate, D., Hendrick, A.G., Webster, N.A., Andrews, B., Fosbeary, R., Guest, P., Irigoyen, N., Eleftheriou, M., Gozdecka, M., Dias, J.M.L., Bannister, A.J., Vick, B., Jeremias, I., Vassiliou, G.S., Rausch, O., Tzelepis, K. & Kouzarides, T. Small-molecule inhibition of METTL3 as a strategy against myeloid leukaemia. Nature 593, 597-601 (2021).
11. Otto, A., Schmidt, C., Luke, G., Allen, S., Valasek, P., Muntoni, F., Lawrence-Watt, D. & Patel, K. Canonical Wnt signalling induces satellite-cell proliferation during adult skeletal muscle regeneration. J Cell Sci 121, 2939-2950 (2008).
12. Liu, J., Gao, M., He, J., Wu, K., Lin, S., Jin, L., Chen, Y., Liu, H., Shi, J., Wang, X., Chang, L., Lin, Y., Zhao, Y.L., Zhang, X., Zhang, M., Luo, G.Z., Wu, G., Pei, D., Wang, J., Bao, X. & Chen, J. The RNA m(6)A reader YTHDC1 silences retrotransposons and guards ES cell identity. Nature 591, 322-326 (2021).
13. Xu, W., Li, J., He, C., Wen, J., Ma, H., Rong, B., Diao, J., Wang, L., Wang, J., Wu, F., Tan, L., Shi, Y.G., Shi, Y. & Shen, H. METTL3 regulates heterochromatin in mouse embryonic stem cells. Nature 591, 317-321 (2021).
14. Roberson, P.A., Romero, M.A., Osburn, S.C., Mumford, P.W., Vann, C.G., Fox, C.D., McCullough, D.J., Brown, M.D. & Roberts, M.D. Skeletal muscle LINE-1 ORF1 mRNA is higher in older humans but decreases with endurance exercise and is negatively associated with higher physical activity. J Appl Physiol (1985) 127, 895-904 (2019).
15. Mumford, P.W., Romero, M.A., Osburn, S.C., Roberson, P.A., Vann, C.G., Mobley, C.B., Brown, M.D., Kavazis, A.N., Young, K.C. & Roberts, M.D. Skeletal muscle LINE-1 retrotransposon activity is upregulated in older versus younger rats. Am J Physiol Regul Integr Comp Physiol 317, R397-R406 (2019).