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    1. Author Response:

      Reviewer #3 (Public Review):

      About 30 million years ago the ancestors of Old World primates lost the ability to produce the glycan a-gal due to the fixation of several loss-of-function mutations in the GGTA1 gene. The evolutionary advantage of such loss remains elusive. The current study builds upon previous work by the authors showing (i) that the presence of a-gal expressing bacteria in ggta1 deficient mice led to production of antibodies capable of clearance of malaria-causing plasmodia carrying a-gal (Yilmaz et al., 2014), and (ii) that ggta1 deficiency is associated with increased resistance to sepsis via the enhancement of IgG effector function (Sigh et al., 2021). Here they expand on these findings to show that ggta1 deletion in mice is associated with altered composition of the gut microbiome due to the action of IgA targeting of a-Gal expressing bacteria. In addition, they show that the absence of a-gal results in a microbiome that is less pathogenic (i.e., less likely to induce sepsis in their experimental model). Although some aspects of the work are not very novel (e.g., the fact that ggta1 is associated with a remodeled microbiome had already been shown in their previous publications) the work does provide additional insights into the pleiotropic role of ggta1 in immune function, susceptibility to sepsis, and eventual fitness advantage. The work is extremely well done and all conclusions are supported by solid data. Indeed, I felt that the authors were reading my mind every step of the way. Each time I questioned one of the conclusions the next paragraph would address that exact concern. There are, however, a few points that I think would deserve additional clarification.

      1 - I was a little surprised that they found no difference in the microbiome of F2 mice between a-gal deficient and wild-type mice. Although I understand that this might be due to antibodies received by the mom, the fact that the divergence in only seen in F3 to F5 would also be compatible with drift and not necessarily a genotype-driven phenotype. Are the microbiome differences detected in F3-F5 overlapping to those observed at F0? If the original differences were controlled by host genetics - the hypothesis being tested - we would expect to see some convergent (at least at the level of specific taxa)

      We agree essentially with the comment: “… would also be compatible with drift and not necessarily a genotype-driven phenotype” and have addressed this issue by adding the following statement in the Discussion section:

      “On the basis of this observation alone (Figure 1), one cannot exclude the observed divergence in the microbiota bacterial population frequencies of wild type vs. Ggta1-deleted mice (Figure 1) from being a stochastic event. However, the observation that these changes occur via an Ig-dependent mechanism that differs in wild type vs. Ggta1- deleted mice (Figure 3) does support that loss of αGal contributes critically to shape the microbiota composition of Ggta1- deficient mice.”

      We have previously shown that homogenization of the microbiota occurs between the littermates in the F2 generation (Singh et al., 2021). Having confirmed this finding in this manuscript (Figure 1C, Figure 3-figure supplement 7A-B), we find that the effect of the genotype and Ig is seen only from the F3 generation onwards (Figure 1D-F, Figure 3). Presumably, the inability of F1 Ggta1+/- mothers to produce anti-αGal antibodies accounts for the absence of overt shaping of the F2 microbiota. In these experiments, anti-αGal antibodies can only be generated from αGal-deficient F2 Ggta1-/- mice, being vertically transferred and shaping the microbiota from F3 Ggta1-/- mice onwards. We propose that the differences in the microbiota composition of the two F3 genotypes onwards are driven by a cumulative effect of maternal anti-αGal antibodies over the offspring microbiota composition.

      2 - I was really surprised that ggta1 deficient mice lacking a functional adaptive immune system (Figure S8) were equally resistant to systemic infection with the cecal inoculum isolated from ggta1 deficient mice. In the previous work they show that the increases resistance to sepsis comes from increases effector function of IgG. If that is the case, how come mice not having an adaptive system (hence no IgG) are equally protected? Is the pathogenicity of the microbiome of ggta1 deficient mice that reduced? It seems unlikely. More generally, I would like to have seen a better discussion about how these new findings connect to their past work. In the context of increased resistance to sepsis what seems to be more important - the remodeling of the microbiome by IgA or the increased effector function of IgG?

      The data reported in our manuscript does indeed support the conclusion that shaping of the microbiota composition of Ggta1-deficient mice is associated with an overall reduction of the microbiome pathogenicity. This finding is in keeping with host-microbe commensal interactions not being hard- wired but instead oscillating from pathogenic to symbiotic (Ayres, 2016; Vonaesch et al., 2018). Our findings suggest that the loss of Ggta1 function can modify the nature of host-microbiota interactions, through a mechanism whereby the absence of host αGal and the emergence of antibodies targeting this glycan in microbes, shapes and reduces the microbiome pathogenicity.

      We have shown that loss of αGal can enhance resistance to bacterial sepsis via a mechanism that increases IgG effector function (Singh et al., 2021). This was demonstrated by systemically infecting Ggta1-deficient mice with a “non-shaped” microbiota inoculum, isolated from Ggta1-deficient mice lacking adaptive immunity (Rag2-/-Ggta1-/- mice). As discussed in the manuscript “the gut microbiota of Rag2-/-Ggta1-/- mice, lacking adaptive immunity, is highly enriched in pathobionts such as Proteobacteria, including Helicobacter (Singh et al., 2021)”. Under these experimental conditions, resistance to infection is IgG dependent, explaining why modulation of IgG effector function by αGal impacts on the outcome of sepsis.

      In the current manuscript we describe another survival advantage against bacterial sepsis associated with Ggta1 deletion in mice. Namely, antibodies generated by Ggta1-deficient mice can shape and reduce the microbiota pathogenicity. This was demonstrated by infecting systemically Ggta1-deficient mice lacking adaptive immunity (Rag2-/-Ggta1-/- mice) with a “shaped- microbiota” inoculum isolated from Ggta1-deficient mice. While the mechanism underlying microbiota shaping is antibody-dependent, the effector mechanism conferring resistance against the shaped microbiota acts irrespectively of adaptive immunity, including IgG. This conclusion is supported by the observation that systemic infection by the shaped microbiota (isolated from Ggta1-deficient mice) failed to induce sepsis in Rag2-/-Ggta1-/- mice, which was not the case upon systemic infection with a non-shaped microbiota (isolated from Rag2-/-Ggta1-/- mice). We conclude that Ggta1 deletion in mice increases resistance to bacterial sepsis via two interrelated antibody-dependent mechanisms: i) Increased IgG effector function (Singh et al., 2021) and ii) Antibody shaping and reduction of microbiota pathogenicity (current manuscript). To what extent these two traits are related remains to be established.

      It is possible that similarly to what was demonstrated for IgG (Singh et al., 2021), the absence of αGal from glycan structures in other Ig isotypes, including IgA, might modify their effector function. We do not yet know if this is the case, as in our manuscript, what we find is an altered antibody response targeting immunogenic bacteria in the microbiota of Ggta1-deficient mice. This is associated with modulation of the microbiota bacterial composition, i.e. antibody shaping of the microbiota, and with a reduction of the microbiome pathogenicity. The latter explains why the Ggta1-deficient mice do not rely on circulating antibodies to prevent the development of sepsis upon systemic infection by bacteria emanating for their own “shaped” microbiota.

    2. Reviewer #3 (Public Review):

      About 30 million years ago the ancestors of Old World primates lost the ability to produce the glycan a-gal due to the fixation of several loss-of-function mutations in the GGTA1 gene. The evolutionary advantage of such loss remains elusive. The current study builds upon previous work by the authors showing (i) that the presence of a-gal expressing bacteria in ggta1 deficient mice led to production of antibodies capable of clearance of malaria-causing plasmodia carrying a-gal (Yilmaz et al., 2014), and (ii) that ggta1 deficiency is associated with increased resistance to sepsis via the enhancement of IgG effector function (Sigh et al., 2021). Here they expand on these findings to show that ggta1 deletion in mice is associated with altered composition of the gut microbiome due to the action of IgA targeting of a-Gal expressing bacteria. In addition, they show that the absence of a-gal results in a microbiome that is less pathogenic (i.e., less likely to induce sepsis in their experimental model). Although some aspects of the work are not very novel (e.g., the fact that ggta1 is associated with a remodeled microbiome had already been shown in their previous publications) the work does provide additional insights into the pleiotropic role of ggta1 in immune function, susceptibility to sepsis, and eventual fitness advantage. The work is extremely well done and all conclusions are supported by solid data. Indeed, I felt that the authors were reading my mind every step of the way. Each time I questioned one of the conclusions the next paragraph would address that exact concern. There are, however, a few points that I think would deserve additional clarification.

      1 - I was a little surprised that they found no difference in the microbiome of F2 mice between a-gal deficient and wild-type mice. Although I understand that this might be due to antibodies received by the mom, the fact that the divergence in only seen in F3 to F5 would also be compatible with drift and not necessarily a genotype-driven phenotype. Are the microbiome differences detected in F3-F5 overlapping to those observed at F0? If the original differences were controlled by host genetics - the hypothesis being tested - we would expect to see some convergent (at least at the level of specific taxa)

      2 - I was really surprised that ggta1 deficient mice lacking a functional adaptive immune system (Figure S8) were equally resistant to systemic infection with the cecal inoculum isolated from ggta1 deficient mice. In the previous work they show that the increases resistance to sepsis comes from increases effector function of IgG. If that is the case, how come mice not having an adaptive system (hence no IgG) are equally protected? Is the pathogenicity of the microbiome of ggta1 deficient mice that reduced? It seems unlikely. More generally, I would like to have seen a better discussion about how these new findings connect to their past work. In the context of increased resistance to sepsis what seems to be more important - the remodeling of the microbiome by IgA or the increased effector function of IgG?

    3. Reviewer #2 (Public Review):

      The authors aimed to examine the impact of GGTA1 deletion on host-microbial interactions using a mouse model of a primate-specific mutation. This is a very informative model system that provided interesting insights into the consequences of aGal elimination from host glycoproteins, with subsequent 'release' of immune tolerance breaks and generation of antibody responses agains bacterial aGal epitopes.

      The study is well executed and and the conclusions are well supported by the provided evidence. The findings are interesting for a broad audience of biologists.

      The identity of IgA targeted bacteria in GGTA1 vs WT mice would be interesting to investigate in the future studies.

    4. Reviewer #1 (Public Review):

      This work is a powerful example of thinking across silos. It combines much knowledge of innate and adaptive immunity, with primate evolution of certain antigens lost only in certain primate lineages and tests an important idea about host-mediated, antibody dependent shaping of gut microbiota using laboratory mice with different engineered genetic alterations. Gut microbiota are all the rage these days, but is often forgotten that these microbial communities represent formidable danger that is really too close (one epithelial layer away) for comfort. The authors demonstrate in laboratory mice, how antibodies against non-self sugar molecules present on bacteria can shape the microbiome. Claims and conclusions seem justified by the data presented.

    5. Evaluation Summary:

      30 million years ago the ancestors of Old World primates lost the ability to produce alpha-gal due to the fixation of several loss-of-function mutations in the GGTA1 gene. The evolutionary advantage of such loss remains elusive. Here, the authors provide additional insights into the pleiotropic role of ggta1 in shaping the gut microbiota, immune function, susceptibility to sepsis, and eventual fitness advantage.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1, Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

    1. Author Response:

      Reviewer #1 (Public Review):

      Facial muscles control the execution of essential tasks like eating, drinking, breathing and (in most mammals) tactile exploration. The activity of motor neurons targeting different muscles are coordinated by premotor regions distributed throughout brainstem. The precise identity of these cells and regions in adults is presently unclear, largely due to technical challenges. In the current work, Takaoh and colleagues develop an elegant strategy to label premotor neurons that target select muscles and register these cells on a common digital atlas. Their work confirms and also extends previous studies in neonates and provides a useful resource for the field.

      We thank Reviewer 1 for the positive evaluation.

      Reviewer #2 (Public Review):

      The authors describe a variant of retrograde monosynaptic rabies tracing from skeletal muscle. They make use of AAV2-retro-Cre to infect brainstem motoneurons projecting to muscles involved in regulation of orofacial movements (whisking, genioglossus, masseter motoneurons). The strategy that worked most efficiently and with specificity was to inject AAV2-retro-Cre intramuscularly at P17, followed 3 weeks thereafter by central injection of Cre-dependent AAVs expressing TVA and oG, and 2 weeks thereafter followed by central injection of EnvA(M21)-ΔG-RV-GFP. Five days after this final injection, experiments were terminated to analyse the distribution of premotor neurons. This allowed the authors to reconstruct and compare the distribution of premotor neurons to the whisking (lateral 7N), tongue protruding genioglossus (12N), and jaw-closing masseter (5N) motoneurons. To do so, they used the Allen Brain Atlas as a reference for 3D reconstruction, into which they integrated all data. Notably, the authors found that for all three injection types, the highest density of neurons was found in the IRt and PCRt, but the precise peak of highest density was consistently distinct for the three different injection types. The peak for whisker premotor neurons was most caudal-ventral, for masseter premotor neurons most rostro-dorsal, and jaw-closing genioglossal premotor neurons in between these. The authors also make use of the strong expression of fluorescent proteins through rabies virus to analyse collateralization to other motor nuclei. Interestingly, they found cross-talk to other motor nuclei in selective patterns, supporting a model whereby some premotor neurons to one brainstem motor pool also interact with other output circuits, perhaps to coordinate orofacial behaviors. Using a split-Cre retrograde approach from motor nuclei, dual-projecting premotor neurons were identified to be located in dorsal IRt and SupV.

      This is a high-quality study making use of several methods not previously brought together in one study. Particularly interesting is the 3-way virus strategy in wild-type mice allowing visualization of premotor neurons in the adult. Second, alignment in a common reference brain is also very useful. And finally, the beginning of understanding dynamics of premotor circuit distribution between development and adult is also a value of this paper. Overall, the study is very interesting for the field.

      We thank Reviewer 2 for the positive evaluation.

      Reviewer #3 (Public Review):

      Orofacial actions show exquisite coordination among many muscles, yet the pools of motor neurons exciting each of these muscles is specific to that muscle. The coordination of activity across muscles therefore relies on circuits of premotor neurons that excite the motor neurons. Work by the authors and others has produced major progress in delineating these complex premotor circuits. Recent work using transsynaptic viral tracing has overcome limitations associated with traditional retrograde tracing methods, such as a lack of adequate specificity. However, these transsynaptic viral methods have been unsuccessful in animals older than approximately postnatal day 8 (P8). This is a problem because circuits continue to develop far beyond P8 in mice. Here, the authors overcome this limitation by introducing a novel viral transsynaptic tracing method that can be applied in adult mice.

      The authors apply their method to trace premotor circuits for whisking, licking, and jaw movements. They align their anatomical data to the Allen Mouse Brain Common Coordinate Framework and make it available with the manuscript, greatly facilitating its quantitative use by other laboratories. The authors find premotor circuits in adult mice that are almost entirely consistent with results from younger mice, with some important exceptions that they highlight and discuss. The authors quantify overlap of premotor circuits for whisking, licking and jaw movements and discuss the implications of interactions among these circuits.

      The experiments and analysis are carefully performed, and the results put into proper context. Overall, this is a straightforward and valuable contribution to our knowledge of the premotor circuits that coordinate orofacial behaviors. It will be of wide interest to neuroscientists.

      Suggestions:

      -The methods applied in neonatal mice (Takatoh et al. 2013; Stanek et al. 2014), while obviously different, are similar enough that it may be worth including discussion of any possible ways that differences between the neonatal and adult results could be due to methods, rather than age. I defer to the authors about whether such discussion is worthwhile, but readers may benefit from knowing what was considered.

      Now we added the technical considerations that may cause the difference in the tracing patterns: Line 505-517.

      -Spatial correlation in Figure 5C. To interpret this properly it's important to know the degree of smoothing. I could not find this in the relevant methods section describing the kernel density estimation or elsewhere.

      Same as the above: The cells detected in each mouse were first registered into the standard three-dimensional brain model. The (x, y, z) coordinates of each cell were then extracted, and the multivariate kernel smoothing density estimation was applied (bandwidth = 1). The resulting kernel density estimation was then vectorized, and the cosine similarity between any two of the mice were calculated to form the correlogram.

    2. Reviewer #3 (Public Review):

      Orofacial actions show exquisite coordination among many muscles, yet the pools of motor neurons exciting each of these muscles is specific to that muscle. The coordination of activity across muscles therefore relies on circuits of premotor neurons that excite the motor neurons. Work by the authors and others has produced major progress in delineating these complex premotor circuits. Recent work using transsynaptic viral tracing has overcome limitations associated with traditional retrograde tracing methods, such as a lack of adequate specificity. However, these transsynaptic viral methods have been unsuccessful in animals older than approximately postnatal day 8 (P8). This is a problem because circuits continue to develop far beyond P8 in mice. Here, the authors overcome this limitation by introducing a novel viral transsynaptic tracing method that can be applied in adult mice.

      The authors apply their method to trace premotor circuits for whisking, licking, and jaw movements. They align their anatomical data to the Allen Mouse Brain Common Coordinate Framework and make it available with the manuscript, greatly facilitating its quantitative use by other laboratories. The authors find premotor circuits in adult mice that are almost entirely consistent with results from younger mice, with some important exceptions that they highlight and discuss. The authors quantify overlap of premotor circuits for whisking, licking and jaw movements and discuss the implications of interactions among these circuits.

      The experiments and analysis are carefully performed, and the results put into proper context. Overall, this is a straightforward and valuable contribution to our knowledge of the premotor circuits that coordinate orofacial behaviors. It will be of wide interest to neuroscientists.

      Suggestions:

      -The methods applied in neonatal mice (Takatoh et al. 2013; Stanek et al. 2014), while obviously different, are similar enough that it may be worth including discussion of any possible ways that differences between the neonatal and adult results could be due to methods, rather than age. I defer to the authors about whether such discussion is worthwhile, but readers may benefit from knowing what was considered.

      -Spatial correlation in Figure 5C. To interpret this properly it's important to know the degree of smoothing. I could not find this in the relevant methods section describing the kernel density estimation or elsewhere.

    3. Reviewer #2 (Public Review):

      The authors describe a variant of retrograde monosynaptic rabies tracing from skeletal muscle. They make use of AAV2-retro-Cre to infect brainstem motoneurons projecting to muscles involved in regulation of orofacial movements (whisking, genioglossus, masseter motoneurons). The strategy that worked most efficiently and with specificity was to inject AAV2-retro-Cre intramuscularly at P17, followed 3 weeks thereafter by central injection of Cre-dependent AAVs expressing TVA and oG, and 2 weeks thereafter followed by central injection of EnvA(M21)-ΔG-RV-GFP. Five days after this final injection, experiments were terminated to analyse the distribution of premotor neurons. This allowed the authors to reconstruct and compare the distribution of premotor neurons to the whisking (lateral 7N), tongue protruding genioglossus (12N), and jaw-closing masseter (5N) motoneurons. To do so, they used the Allen Brain Atlas as a reference for 3D reconstruction, into which they integrated all data. Notably, the authors found that for all three injection types, the highest density of neurons was found in the IRt and PCRt, but the precise peak of highest density was consistently distinct for the three different injection types. The peak for whisker premotor neurons was most caudal-ventral, for masseter premotor neurons most rostro-dorsal, and jaw-closing genioglossal premotor neurons in between these. The authors also make use of the strong expression of fluorescent proteins through rabies virus to analyse collateralization to other motor nuclei. Interestingly, they found cross-talk to other motor nuclei in selective patterns, supporting a model whereby some premotor neurons to one brainstem motor pool also interact with other output circuits, perhaps to coordinate orofacial behaviors. Using a split-Cre retrograde approach from motor nuclei, dual-projecting premotor neurons were identified to be located in dorsal IRt and SupV.

      This is a high-quality study making use of several methods not previously brought together in one study. Particularly interesting is the 3-way virus strategy in wild-type mice allowing visualization of premotor neurons in the adult. Second, alignment in a common reference brain is also very useful. And finally, the beginning of understanding dynamics of premotor circuit distribution between development and adult is also a value of this paper. Overall, the study is very interesting for the field.

    4. Reviewer #1 (Public Review):

      Facial muscles control the execution of essential tasks like eating, drinking, breathing and (in most mammals) tactile exploration. The activity of motor neurons targeting different muscles are coordinated by premotor regions distributed throughout brainstem. The precise identity of these cells and regions in adults is presently unclear, largely due to technical challenges. In the current work, Takaoh and colleagues develop an elegant strategy to label premotor neurons that target select muscles and register these cells on a common digital atlas. Their work confirms and also extends previous studies in neonates and provides a useful resource for the field.

    1. Author Response:

      Reviewer #3 (Public Review):

      1) The authors seem to assume a somewhat random sample throughout Washington state. They state that given a low sampling proportion they do not expect to have captured infection pairs, which seems reasonable. However, they then go onto assume that their sample is primarily comprised of samples from long, successful transmission chains. This is a reasonable assumption if there is no major difference in accessibility of samples from long transmission chains and shorter ones (for example, decreased access to healthcare). Could this impact the assumption of sampling primarily from long transmission chains? It seems from the data collected in this outbreak that this was not the case for mumps in Washington but addressing this assumption clearly (and potential ways to interrogate it) could make their methodology more applicable to other pathogen studies.

      2) There are many examples of phylogenetic analyses that have led to conclusions about pathogen sources and sinks that were later shown to be wrong because of oversampling or other sampling biases. The authors address unequal sampling between clades, but additional contextualization of the problem and how this approach is different may help strengthen the methodology presented in the paper.

      We thank the reviewer for these important points. We have attempted to address these by including an additional paragraph about different types of sampling and their impacts on phylodynamic studies.

      We agree that this is a helpful addition, and have added a new paragraph devoted to a discussion of sampling bias to the discussion on lines 458-484. This paragraph reads:

      “Sampling bias presents a persistent problem for phylodynamic studies that can complicate inference of source-sink dynamics (De Maio et al., 2015; Dudas et al., 2018; Frost et al., 2015; Kühnert et al., 2011; Lemey et al., 2020; Stack et al., 2010). Sampling bias can arise from unequal case detection or from curating a dataset that poorly represents the underlying outbreak. Washington State uses a passive surveillance system for mumps detection and case acquisition, which is known to result in underreporting. Because the WA Department of Health did not perform active mumps surveillance, it is difficult to assess whether different epidemiologic groups have different likelihoods of being sampled. Marshallese individuals are less likely to seek healthcare (Towne et al., 2020), which may have resulted in particularly high rates of underreporting in this group. If the number of cases within the Marshallese community were in fact higher than reported, this would increase the magnitude of the patterns we describe, making our estimates conservative. Given a distribution of cases, composing a dataset for analysis also requires sampling decisions. Uniform sampling regimes in which sampling probability is equal across groups have been shown to perform well for source- sink inferences (Hall et al., 2016). By selecting sequences that matched the overall attributes of the outbreak, including a near 50:50 split between Marshallese and non- Marshallese cases, we adhere to this recommendation. We then specifically employed structured coalescent approaches which have been shown to be robust to sampling differences (Dudas et al., 2018; Müller et al., 2018; Vaughan et al., 2014), rather than using other common approaches that treat sampling intensity as informative of population size (Lemey et al., 2009). Within this framework, we further explore the possibility that unequal sampling within Washington clades could skew internal node reconstruction by forcing the sampling within each Washington clade to be equal between Marshallese and non-Marshallese tips. In doing so, differences within each clade must necessarily be driven by differences in transmission dynamics, rather than sampling. By combining careful sample selection with overlapping approaches to evaluate sampling bias, we were able to mitigate concerns that our source-sink reconstructions are driven by sampling artifacts.”

      3) The authors present compelling evidence that the mumps outbreak in Washington state was sustained by the Marshallese community, and state that mumps did not transmit efficiently among the general Washington populace. That said, there were several other mumps outbreaks in the United States in the same 2016-2017 time period. Was there something different about Washington state that prevented mumps transmission outside of the Marshallese community? Were there no other close-knit communities (universities, prisons, other cultural communities, etc.) affected? It just seems surprising that the Marshallese community was the only community sustaining transmission at a time where many different types of communities were affected across the United States.

      We thank the reviewers and editor for this comment, and agree that further contextualization would be helpful. We did not make it clear in the initial submission that in 2016/2017, the vast majority of mumps outbreaks in the US were associated with either universities or ethnic communities. We have re-organized a few paragraphs in the discussion section and added information about other 2016/2017 outbreaks. This new paragraph is on lines 499-519, and reads:

      “Our finding that most introductions sparked short transmission chains suggests that mumps did not transmit efficiently among the general Washington populace. We suspect that more diffuse contact patterns may help explain this. Mumps has historically caused outbreaks in communities with strong, interconnected contact patterns (Barskey et al., 2012; Fields et al., 2019; Nelson et al., 2013), and in dense housing environments (Snijders et al., 2012), highlighted most recently by outbreaks in US detention centers (Lo et al., 2021). In 2016, most outbreaks in the US were associated with university settings (Albertson et al., 2016; Bonwitt et al., 2017; Donahue et al., 2017; Golwalkar et al., 2018; Shah et al., 2018; Wohl et al., 2020), including a separate, smaller outbreak in Washington State associated with Greek housing (Bonwitt et al., 2017). Outside of university settings, other outbreaks in 2016 were reported within close-knit ethnic communities (Fields et al., 2019; Marx et al., 2018). We speculate that while waning immunity may promote outbreaks by increasing susceptibility among young adults, outbreaks in younger age groups may be possible in sufficiently high-contact settings. Provision of an outbreak dose of mumps-containing vaccine to high-risk groups may therefore be especially effective for limiting mumps transmission in future outbreaks. Others have reported success in using outbreak dose mumps vaccinations to reduce mumps transmission on college campuses (Cardemil et al., 2017; Shah et al., 2018) and in the US army (Arday et al., 1989; Eick et al., 2008; Green, 2006; Kelley et al., 1991), and the CDC currently recommends providing outbreak vaccine doses to individuals with increased risk due to an outbreak (Marlow et al., 2020). Future work to quantify the interplay between contact rates and vaccine-induced immunity among different age and risk groups should be used to guide updated vaccine recommendations.”

      We also amended lines 42-46 in the introduction to highlight that most other US outbreaks in 2016/2017 were university-associated:

      “Like with other recent mumps outbreaks, most Washington cases in 2016/17 were vaccinated. Unusually though, while most US outbreaks in 2016/2017 were associated with university settings (Albertson et al., 2016; Bonwitt et al., 2017; Donahue et al., 2017; Golwalkar et al., 2018; Shah et al., 2018; Wohl et al., 2020), incidence in Washington was highest among children aged 10-18 years, younger than expected given waning immunity.”

    1. Author Response:

      Reviewer #1 (Public Review):

      The manuscript by Schrieber et al., explores whether inbreeding affects floral attractiveness to pollinators with additional factors of sex and origin in play, in male and female plants of Silene latifolia. The authors use a combination of spatial sampling, floral volatiles, flower color, and floral rewards coupled with the response of a specialized pollinator to these traits. Their results show that females are more affected by inbreeding and in general inbreeding negatively impacts the "composite nature" of floral traits. The manuscript is well written, the experiments are detailed and quite elaborate. For example., the methodology for flower color estimation is the most detailed effort in this area that I can remember. All the experiments in the manuscript show meticulous planning, with extensive data collection addressing minute details, including the statistics used. However, I do have some concerns that need to be addressed.

      Core strengths: Detailed experimental design, elaborate data collection methods, well-defined methodology that is easy to follow. There is a logical flow for the experiments, and no details are missing in most of the experiemnts.

      Weaknesses: A recent study has addressed some of the questions detailed in the manuscript. So, introduction needs to be tweaked to reflect this.

      Thank you very much for bringing this excellent article to our attention! We adjusted the writing in the introduction and the discussion accordingly. Please consider that this article was first published at the 15th of January 21, while our manuscript was submitted at the 9th of January. Hence, we were not able to account for this study in the first submission. Introduction pp 4-5, ll 48-54: “Although in a few cases inbreeding has been shown to alter single components of flower attractiveness (Ivey and Carr, 2005; Ferrari et al., 2006; Haber et al., 2019), insight into syndrome-wide effects is restricted to a single study. Kariyat et al. (2021) demonstrated that inbred Solanum carolinense L. display reduced flower size, pollen and scent production and receive fewer visits from diurnal generalists. It is necessary to broaden such integrated methodological approaches to other plant-pollinator systems (e.g., nocturnal specialist pollinators) and further floral traits (i.e., flower colour).” Discussion p 19, ll 535-542: “In summary, our research on S. latifolia suggests that in addition to inbreeding disrupting interactions with herbivores by changing plant leaf chemistry (Schrieber et al., 2018) it affects plant interactions with pollinators by altering flower chemistry. Our observations are in line with studies on other plant species (Ivey and Carr, 2005; Kariyat et al., 2012, 2021) and highlight that inbreeding has the potential to reset the equilibrium of species interactions by altering functional traits that have developed in a long history of co-evolution. These threats to antagonistic and symbiotic plant-insect interactions may mutually magnify in reducing plant individual fitness and altering the dynamics of natural plant populations under global change.”

      Some details and controls are missing in floral scent estimation. Flower age, a pesticide treatment of plants that could affect chemistry..needs to be better refined.

      We clarified this issue at different occasions in the methods section. Previous studies (and our study) on S. latifolia have shown no clear differences in the quality of floral scent between sexes. However, one study found higher total emission of VOC in males, while others found no differences. Hence, females produce no specific VOC that are used as oviposition cues but may be differentiated from males by the total amount of emitted VOC and pronounced differences in spatial flower traits. We highlight this at p 6, ll 111-116: “Silene latifolia exhibits various sexual dimorphisms with male plants producing more and smaller flowers that excrete lower volumes of nectar with higher sugar concentrations as compared to females (Gehring et al., 2004; Delph et al., 2010). The quality of floral scent exhibits no clear sex-specific patterns, while male plants have been shown to emit higher or equal total amounts of VOC as compared to females in different studies (Dötterl & Jürgens 2005, Waelti et al. 2009)”.

      Both male and female moths show pronounced behavioural responses to lilac aldehyde isomers and other VOC in the floral scent of S. latifolia (Dötterl et al., 2006). We therefore treated these VOC as typical floral scent compounds. We clarified this at p 7, ll 125-126: “A substantial fraction of floral VOC produced by S. latifolia triggers antennal and behavioural responses in male and female H. bicruris moths (Dötterl et al., 2006).” and p 9, ll 2010-218:” For targeted statistical analyses, we focused on those VOC that evidently mediate communication with H. bicruris according to Dötterl et al. (2006). We analysed the Shannon diversity per plant (calculated with R-package: vegan v.2.5-5, Oksanen et al. 2019) for 20 floral VOC in our data set that were shown to elicit electrophysiological responses in the antennae of H. bicruris (Supplementary File 1). Moreover, we analysed the intensities of three lilac aldehyde isomers, which trigger oriented flight and landing behaviour in both male and female H. bicruris most efficiently when compared to other VOC in the floral scent of S. latifolia. Furthermore, H. bicruris is able to detect the slightest differences in the concentration of these three compounds at very low dosages (Dötterl et al. 2006).”

      We used biological pest control agents in a preventive manner because S. latifolia is often infested by thrips and aphids under greenhouse conditions. The writing in the previous manuscript version was not clear with this regard and we changed the text at p 8, ll 157-161: ” Plants received water and fertilisation (UniversolGelb 12-30-12, Everris-Headquarters, NL) when necessary for the entire experimental period and were prophylactically treated with biological pest control agents under greenhouse conditions to prevent thrips (agent Amblyseius barkeri and Amblyseius cucumeris) and aphid (agent Chrysoperla carnea) infestation (Katz Biotech GmbH, GE) .”

      Indeed, flower size and scent emission can be correlated. Although the question whether differences in scent emission were based on a difference in flower size is an interesting one, it seemed less relevant to us because it is unlikely that our pollinators correct their perception of a scent for the size of a flower (see also p 19, 520-526). We were rather interested in whether scent emission differs between the plant treatments and thus pollinators may chemically perceive such differences. Moreover, we found it problematic to correct our models for flower size by including it as a covariate, which is the reason why we have not assessed this trait during scent collection. In this case, we would have corrected our scent responses for the effects of inbreeding, sex and population origin (i.e., the predictors we are interested in) because all of them determine the size of a flower (Figure 2 c,d). Hence, the inbreeding, sex and origin effects on flower scent would likely vanish. However, it is highly unlikely that the set of genes contributing to sex-, breeding treatment- and origin-based variation in flower size is exactly the same one that determines variation in scent emission per flower, which is basically the assumption underlying the model that includes flower size as a covariate. We critically mentioned the trade-off relationships and our reasoning to not correct for flower size at 9p ll 208-210: “The intensities of VOC were not corrected for flower size because we wanted to capture all variation in scent emission that is relevant for the receiver i.e., the pollinator.”

      While the study is laser-focused on floral traits, as the authors are aware inbreeding affects the total phenotype of the plants including fitness and defense traits. For example, there are quite a few studies that have shown how inbreeding affects the plant defense phenotype. This could be addressed in the introduction and discussion.

      We agree that this aspect is important and therefore addressed it in further detail in the introduction at p 4 ll 34-38: “While it is well established that inbreeding can increase a plant’s susceptibility to herbivores by diminishing morphological and chemical defences (Campbell et al., 2013; Kariyat et al., 2012; Kalske et al., 2014), its effects on plant-pollinator interactions are less well understood. Inbreeding may reduce a plant’s attractiveness to pollinating insects by compromising the complex set of floral traits involved in interspecific communication.” Since other referees suggested to rather tone down than increase the discussion based on floral scent results, we stick to the general feedback relationship among of herbivory and pollination, rather than relating it specifically to volatiles in the discussion at p 19, ll 535-544: “In summary, our research on S. latifolia suggests that in addition to inbreeding disrupting interactions with herbivores by changing plant leaf chemistry (Schrieber et al., 2018) it affects plant interactions with pollinators by altering flower chemistry. Our observations are in line with studies on other plant species (Ivey and Carr, 2005; Kariyat et al., 2012, 2021) and highlight that inbreeding has the potential to reset the equilibrium of species interactions by altering functional traits that have developed in a long history of co-evolution. These threats to antagonistic and symbiotic plant-insect interactions may mutually magnify in reducing plant individual fitness and altering the dynamics of natural plant populations under global change. As such, our study adds to a growing body of literature supporting the need to maintain or restore sufficient genetic diversity in plant populations during conservation programs.”

      Reviewer #2 (Public Review):

      A summary of what the authors were trying to achieve. This interesting and data-rich paper reports the results of several detailed experiments on the pollination biology of the dioceus plant Silene latfolia. The authors uses multiple accessions from several European (native range) and North American (introduced range) populations of S. latifolia to generate an experimental common garden. After one generation of within-population crosses, each cross included either two (half-)siblings or two unrelated individuals, they compared the effects of one-generation of inbreeding on multiple plant traits (height, floral size, floral scent, floral color), controlling for population origin. Thereby, they set out to test the hypothesis that inbreeding reduces plant attractiveness. Furthermore, they ask if the effect is more pronounced in female than male plants, which may be predicted from sexual selection and sex-chromosome-specific expression, and if the effect of inbreeding larger in native European populations than in North American populations, that may have already undergone genetic purging during the bottleneck that inbreeding reduces plant attractiveness. Finally, the authors evaluate to what extent the inbreeding-related trait changes affect floral attractiveness (measured as visitation rates) in field-based bioassays.

      An account of the major strengths and weaknesses of the methods and results. The major strength of this paper is the ambitious and meticulous experimental setup and implementation that allows comparisons of the effect of multiple predictors (i.e. inbreeding treatment, plant origin, plant sex) on the intraspecific variation of floral traits. Previous work has shown direct effects of plant inbreeding on floral traits, but no previous study has taken this wholesale approach in a system where the pollination ecology is well known. In particular, very few studies, if any, has tested the effects of inbreeding on floral scent or color traits. Moreover, I particularly appreciate that the authors go the extra mile and evaluate the biological importance of the inbreeding-induced trait variation in a field bioassay. I also very much appreciate that the authors have taken into account the biological context by using a relevant vision model in the color analyses and by focusing on EAD-active compounds in the floral scent analyses.

      The results are very interesting and shows that the effects of inbreeding on trait variation is both origin- and sex-dependent, but that the strongest effects were not always consistent with the hypothesis that North American plants would have undergone genetic purging during a bottleneck that would make these plants less susceptible to inbreeding effects. The authors made a large collection effort, securing seeds from eight populations from each continent, but then only used population origin and seed family origin as random factors in the models, when testing the overall effect of inbreeding on floral traits. It would have been very interesting with an analysis that partition the variance both in the actual traits under study and in the response to inbreeding to determine whether to what extent there is variation among populations within continents. Not the least, because it is increasingly clear that the ecological outcome of species interactions (mutualistic/antagonistic) in nursery pollination systems often vary among populations (cf. Thompson 2005, The geographic mosaic of coevolution), and some results suggest that this is the case also in Hadena-Silene interactions (e.g. Kephardt et al. 2006, New Phytologist). Furthermore, some plants involved in nursery pollination systems both show evidence of distinct canalization across populations of floral traits of importance for the interaction (e.g. Svensson et al. 2005), whereas others show unexpected and fine-grained variation in floral traits among populations (e.g. Suinyuy et al. 2015, Proceedings B, Thompson et al. 2017 Am. Nat., Friberg et al. 2019, PNAS). Hence, it is possible that the local population history and local variation in the interactions between the plants and their pollinators may be more important predictors for explaining variation in floral trait responses to inbreeding, than the larger-scale continental analyses. Not the least, because North American S. latifolia probably has multiple origins, with subsequent opportunity for admixture in secondary contact.

      Yes, it is necessary to put populations from the same continent into one category, since native and invasive plant populations differ significantly in their evolutionary history (p 5, ll 74-81, http://onlinelibrary.wiley.com/doi/10.1111/j.1365-294X.2012.05751.x). Origin explained sufficient amounts of variation in several traits including flower number, corolla expansion, VOC diversity, lilac aldehyde A intensity, and pollinator visitation rates (see Figures 2-3; and Table 2) and some variation in in the magnitude of inbreeding effects (Figure 2e, f; Figure 3). Even if we would not be interested in differences among native and invasive populations, we would have to include origin as a fixed effect in our models because:

      i) populations within a distribution range are no independent samples,

      ii) origin explains sufficient variation in many responses,

      iii) origin cannot be fitted as a random factor, since it has only two levels (the minimum number of levels for random effect is 4). We agree that it would be very interesting to specifically assess differences in the magnitude of breeding and sex effects among populations within origins. We now discuss this as important future research direction at p 18, ll 500-507: “As such, the precise mechanisms underlying variation in inbreeding effects on different scent traits across population origins of S. latifolia can only be explored based on comprehensive genomic resources, which are currently not available. Future studies should also incorporate field-data on the abundance of specialist pollinators and extend the focus from variation in the magnitude of inbreeding effects among geographic origins to variation among populations within geographic origins and individuals within populations. This would allow a detailed quantification of geographic variation in inbreeding effects and elaborating on the causes and ecological consequences of such variation (Thompson, 2005; Schrieber and Lachmuth, 2017; Thompson et al., 2017)”.

      To empirically address within-origin variation of inbreeding effects with our data, we would have to i) fit correlated random intercepts and slopes for the interaction breeding-sex on the population random factor (models consume min. 22 DF); or ii) include population as a fixed effect in our models (models consume min. 67 DF). We have tried both of these approaches when preparing the revision, but unfortunately it turned out that our study is not designed to address this question. The models for both variants only partially converge (see R-script ll. 1568-1580), and even if they do this does not imply that one can draw solid inference from them. Approach i often results in multiple singular convergence warning messages implying that no variance is explained by population-specific reaction norms to the fixed effects specified in the random effects structure. Approach ii results in odd rank- deficient models (I was seriously worried about type I errors). We simply have too few replicates (5) per population-breeding treatment-sex combination for both approaches. For solid inference we would need 10approach i-40approach ii replicates = 640-2600 individuals. However, our experimental design is sufficient to address the hypothesis we have raised in the introduction as well as general differences in response variables among populations. We now provide information on variance partitioning for all models that include population as a random effect in S9. As you will see, population explains lower amounts of variation in our responses as the fixed effects in 9 out of 12 models. The random effects maternal and paternal genotype (mother&father) explain more variation than the random effect population in 6 of 12 cases. Thus, these data do not make a strong case for an extensive discussion of population-based differences in floral traits and this was also not a question or hypotheses we wanted to address with our study.

      I see no major weaknesses in the study, and but in my detailed response, I have made a few questions and suggestions about the floral scent analyses. In short, the authors have used a technique that is not the standard method used for making quantitative floral scent analyses, and I am curious about how it was made sure that the results obtained from the static headspace sampling using PDMS adsorbents could be used as a quantitative measure. I would suggest the authors to validate the use of this method more thoroughly in the manuscript, and have detailed this comment in my response to the authors.

      Also, and this may seem like a nit-picky comment, I am not convinced that the best way to describe the traits under study is "plant attractiveness", because in the experimental bioassays, most of the traits under study that are affected by the inbreeding treatment, did not result in a reduced pollinator visitation. Most (or all) of these traits may also be involved in other plant functions and important for other interactions, so I suggest potentially using a term like "floral traits" or "(putative) signalling traits".

      We now avoid the term floral attractiveness throughout the manuscript and instead refer to “floral traits”.

      An appraisal of whether the authors achieved their aims, and whether the results support their conclusions: By and large, the authors achieved the aims of this study, and drew conclusions based in these results. One interesting aspect of this work that I think could be discussed a bit deeper is the lack of congruence between the effects of inbreeding on floral traits and the variation in visitation pattern in the bioassay. In fact, the only large effect of inbreeding on a floral trait that may play a role as an explanatory factor is the reduction of emission of lilac aldehyde A in inbred female S. latifolia from North America, which correspond to a reduced visitation rate in this group in the pollinator visitation bioassay. I have made some specific suggestions in my comments to the authors.

      We agree that this aspect required deeper discussion and revised the section at p 19, ll 520-526 accordingly. We believe that the limited spatial vision of H. bicruris in combination with our experimental setup for pollinator observations increased the relative importance of floral scent for pollinator visitation rates (suggested by referee #3).

      A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community: I think that one important aspect of this work that may broaden the impact of this study further is the link between these experiment, and our expectations from the evolution of selfing. Selfing plant species most often conform to the selfing syndrome, presenting smaller, less scented flowers than outcrossing relatives. Traditionally, the selfing syndrome is explained by natural selection against individuals that invest energy into floral signalling, when attracting pollinators is no longer crucial for reproduction. Some studies (for example Andersson, 2012, Am. J. Bot), however, have shown that only one, or a few, generations of inbreeding may reduce floral size as much as quite strong selection for reduced signalling. Here, at least for some populations and sexes, similar results are obtained in this paper regarding several traits (including floral scent), and one way to put this paper in context is by discussing the results in the light of these previous papers.

      We now address this issue at p 16, ll 417-420: “However, our findings highlight that even weak degrees of biparental inbreeding (i.e., one generation sib-mating) can result in a severe reduction of spatial flower trait and scent trait values that is detectable against the background of natural variation among multiple plant populations from a broad geographic region. This observation indirectly supports that the selfing syndrome (i.e., smaller, less scented flowers observed in selfing relative to outcrossing populations of hermaphroditic plant species) may not merely be a result of natural selection against resource investment into floral traits, but also a direct negative consequence of inbreeding (Andersson, 2012).”

      Reviewer #3 (Public Review):

      Schrieber et al. studied the effects of biparental inbreeding in the dioecious plant Silene latifolia, focusing specifically on traits important for floral attractiveness and pollinator attraction. These traits are especially important for dioecious species with separate sexes as they are obligate outcrossers. The authors find that inbreeding mostly decreases floral attractiveness, but that this effect tended to be stronger in the female flowers, which the authors suspect to result from the trade-off with larger investment in the sexual functions in the female plants. The authors then go on to couple the changes in visual and olfactory floral traits to pollinator attraction which allows them to conclude or at least speculate that differences in pollinator behavior are mostly driven by the changes in olfactory traits. The study is robust in its broad and well-balanced sampling of populations, rigorous and in large part meticulously documented experimental designs and linking of the effects on mechanisms to ecological function. The hypothesis are clearly stated and the study is able to address them mostly convincingly. However, some of the aspects of the decisions the authors made and possible caveats need to be addressed and elaborated on.

      A major caveat, in my opinion, is that while the authors find stronger effects of inbreeding on pollinator visitation rates in the plants from the North American (Na) origin, these plants were tested in an environment that was foreign to them, which could have important consequences for the results of this study. This is specifically because the main pollinator Hadena bicruris moth is completely absent from the populations in Na, and yet, was the main pollinator observed in the pollinator attraction experiment. As this pollinator is also a seed predator, the Na populations are released from the selection pressure to avoid attracting the females of this species and thus risking the loss of seeds and fitness. In fact, some of the results suggest that the release from the specialist pollinator and seed predator in Na has led to increase in the attractiveness of the female flowers based on the higher number of flowers visited in the outcrossed females compared to outcrossed males in the plant from the Na origin and the similar, though not statistically significant, pattern in the olfactory cue. While ideally this pollinator attraction experiment should be repeated within the local range of the Na plants, this is of course is not feasible. Instead I suggest the problem should be addressed in the discussion explicitly and its consequences for the interpretation of the results should be considered.

      Indeed, North American populations are tested in their “away”- habitat only and the observed plant performance and pollinator visitation rates can thus provide no direct implications for their “home”-habitat. We state this now more clearly at pp 11-12, ll 283-285. However, our design is appropriate for investigating inbreeding effects on plant-pollinator interactions in multiple plant populations in a common environment. Given the close taxonomic relationship of H. bicruris (main pollinator in Europe) and H. ectypa (main pollinator in North America), the behavioural responses of the former species to variation in the quality of its host plant was considered to overlap sufficiently with responses of the latter species as outlined at pp 11-12, ll 285-291.

      The hypothesis that North American (NA) S. latifolia evolved higher attractiveness to female Hadena moths because H. ectypa is not able to oviposit on female plants in contrast to H. bicruris is indeed a highly interesting one. However, as you have outlined correctly, our study is not designed to elaborate on questions related to adaptive evolutionary differentiation among North American and European plants. Instead of addressing this hypothesis based on our data, we thus take reference to previous studies in the discussion p 17, ll 482-487: “As discussed in detail in previous studies, higher flower numbers in North American S. latifolia plants (Figure 1b) may result from changes in the selective regimes for numerous abiotic factors (Keller et al., 2009) or from the release of seed predation. As opposed to H. bicruris, H. ectypa pollinates North American S. latifolia without incurring costs for seed predation, which may result in the evolution of higher flower numbers, specifically in female plants (Elzinga and Bernasconi, 2009).”

      The incorporation of the VOC data in the actual manuscript was quite limited and I found the reasoning for picking only the three lilac aldehydes (in addition to the Shannon diversity index) for the univariate statistical tests insufficient. How much more efficient was the effect of the lilac aldehydes compared to the other 17 compounds deemed important in the previous study? While the data on this one aldehyde matches the pollinator attraction results, having one compound out of 70 (or out of 20 if only considering the ones identified important for the main pollinator) seems, perhaps, fortuitous lest there is a good reason for focusing on these particular compounds.

      We adapted the text to increase clarity but sticked to our previous choice for the analyses of VOC data.

      i) We now explain our choice of analysing lilac aldehydes with more detail p9, ll 210-218: “For targeted statistical analyses, we focused on those VOC that evidently mediate communication with H. bicruris according to Dötterl et al. (2006). We analysed the Shannon diversity per plant (calculated with R-package: vegan v.2.5-5, Oksanen et al. 2019) for 20 floral VOC in our data set that were shown to elicit electrophysiological responses in the antennae of H. bicruris (Supplementary File 1). Moreover, we analysed the intensities of three lilac aldehyde isomers, which trigger oriented flight and landing behaviour in both male and female H. bicruris most efficiently when compared to other VOC in the floral scent of S. latifolia. Furthermore, H. bicruris is able to detect the slightest differences in the concentration of these three compounds at very low dosages (Dötterl et al. 2006).”

      ii) If one analyses 20 compounds with zero-inflation models (actually two models in one) + 8 floral trait models + 2 pollinator visitation models (zi-models with two component models), one ends up with 52 models investigating complex fixed and random effect structures. To keep type-1 errors as low as possible (see also comment 2.12.b from Referee#2), we approached the more comprehensive VOC data sets with multivariate analyses or Shannon diversity.

      iii) We tested the effect of sexoriginbreeding treatment on the Shannon diversity of 20 active VOC as well as in the random forest analyses with the 20 VOC and 70 VOC dataset and transparently reported the results from all of these analyses in the manuscript. Hence, the incorporation of VOC data was not limited. However, we agree that we have taken too little reference to these results and now changed the text accordingly. Results section p 13 ll 351-354: ”Multivariate statistical analyses of 20 H. bicruris active VOC and all 70 VOC detected in S. latifolia revealed no clear separation of floral headspace VOC patterns for any of the treatments (Figure 2-figure supplement 2). In summary, the combined effects of breeding treatment, sex and range on floral scent were rather week.”

      Sampling time of VOCs is reported ambiguously. Was it from 21:00 to 17:00 the next day or in fact from 9pm to 5AM (instead of 5 pm as reported)? Please be more specific in the text as this is quite important. If sampling tubes were left in place during the daytime, some of the compounds could have evaporated due to heating of the tubes in the summer. It would also be important to mention whether all of the headspace VOCs were sampled on the same day and whether there could be variation in i.e. temperature.

      Thank you very much for identifying this typo! It is from 9 pm to 5 am (p 9, l 186).

      Considering the experimental setup for the pollinator attraction observations and the pooling of the data at the block level (which I think is the right choice) it seems possible the authors were more likely to get a result where pollinator behavior matches the long-distance cue, the VOCs. Short-distance cues such a subtle difference in flower size would perhaps not be distinguished with the current setup. I would be interested to know if the authors agree, and if so, mention this in the discussion.

      Thank you very much for this excellent suggestion! We agree and discuss this aspect in detail at p 19, ll 520-526. Indeed, one would need two different experimental setups to assess the contributions of long and short distance cues. Our setup (large distances among plots) is optimal for long distance cues, while a setup for short distance cues should have all plants in close spatial proximity. However, the latter approach does then not allow to address long-distance cues and to exclude competition/facilitation for pollinators among plants from different treatment groups.

    1. Author Response:

      Evaluation Summary:

      This manuscript will be of interest to a broad audience of immunologists especially those studying host-pathogen interactions, mucosal immunology, innate immunity and interferons. The study reveals a novel role for neutrophils in the regulation of pathological inflammation during viral infection of the genital mucosa. The main conclusions are well supported by a combination of precise technical approaches including neutrophil-specific gene targeting and antibody-mediated inhibition of selected pathways.

      We would like to thank the reviewers for taking the time to review our manuscript, would also like to thank the editors for handling our manuscript. We are grateful for the positive response to our work and the thoughtful suggestions.

      Reviewer #1 (Public Review):

      Overall this is a well-done study, but some additional controls and experiments are required, as discussed below. The authors have done a considerable amount of work, resulting in quite a lot of negative data, and so should be commended for persistence to eventually identify the link between neutrophils with IL-18, though type I IFN signaling.

      Thank you! We appreciate the feedback and suggestions for strengthening the study.

      Major Comments:

      -A major conclusion of this manuscript is prolonged type I IFN production following vaginal HSV-2 infection, but the data presented herein did not actually demonstrate this. At 2 days post infection, IFN beta was higher (although not significantly) in HSV-2 infection, but much higher in HSV-1 infection compared to uninfected controls. At 5 days post infection the authors show mRNA data, but not protein data. If the authors are relying on prolonged type I IFN production, then they should demonstrate increased IFN beta during HSV-2 infection at multiple days after infection including 5dpi and 7dpi.

      We apologize for not including the IFN protein data and have now have provided this information in new Figure 3 and Figure 3 - Supplement 3. This new addition shows measurement of secreted IFNb in vaginal lavages at 4, 5 and 7 d.p.i., as well as total IFNb levels in vaginal tissue at 7 d.p.i..

      -Does the CNS viral load or kinetics of viral entry into the CNS differ in mice depleted of neutrophils, IFNAR cKO mice, or mice treated with anti- IL-18? Do neutrophils and/or IL-18 participate at all in neuronal protection from infection?

      To maintain the focus of our study on the host factors that contribute specifically to genital disease, we have not included discussion on viral dissemination into the PNS or CNS, especially as viral invasion of

      the CNS seems to be an infrequent occurrence during genital herpes in humans. However, we have performed some preliminary exploration of this interesting question, and find that viral invasion of the nervous system is unaltered in the absence of neutrophils. This is in accordance with the lack of antiviral neutrophil activity we have described in the vagina after HSV-2 infection. These preliminary data are provided below as a Reviewer Figure 1. We have not yet begun to investigate whether IL-18 modulates neuroprotection, but agree this is an important question to address in future studies.

      RFigure 1. Viral burden in the nervous system is similar in the presence or absence of neutrophils. Graphs show viral genomes measured by qPCR from the DRG, lower half of of the spinal cord and the brainstem at the indicated days post- infection.

      -In Figure 3 the authors show that neutrophil "infection" clusters 2 and 5 express high levels of ISGs. Only 4 of these ISGs are shown in the accompanying figures. Please list which ISGs were increased in neutrophils after both HSV-2 and HSV-1 infection, perhaps in a table. Were there any ISGs specifically higher after HSV-2 infection alone, any after HSV-1 infection alone?

      These tables listing differentially-expressed neutrophils ISGs during HSV-1 and HSV-2 have now been provided in new Figure 3 - Supplement 1, with complete lists of DEGs provided as Source Files for the same figure.

      -The authors claim that HSV-1 infection recruits non-pathogenic neutrophils compared to the pathogenic neutrophils recruited during HSV-2 infection. Can the authors please discuss if these differences in inflammation or transcriptional differences between the neutrophils in these two different infections could be due to differences in host response to these two viruses rather than differences in inflammation? Please elaborate on why HSV-1 used as opposed to a less inflammatory strain of HSV-2. Furthermore, does HSV-1 infection induce vaginal IL-18 production in a neutrophil-dependent fashion as well?

      These are excellent questions, and we have emphasized that differences in host responses against HSV-1 and HSV-2 likely lead to distinct inflammatory milieus that differentially affect neutrophil responses in lines 374-375 and 409-419. We completely agree that differences in neutrophil responses are likely due to distinct host responses against HSV-1 and HSV-2 and apologize for not making that clear. We have previously described some of the other differences in the immunological response against these two viruses (Lee et al, JCI Insight 2020). We would suggest that differences in the host response against these two viruses would naturally result in differences in the local inflammatory milieu, which then modulates neutrophil responses. Whether the transcriptomes of neutrophils beyond the immediate site of infection (outside the vagina) are different between HSV-1 and HSV-2 is currently an open question.

      As for why we used HSV-1 instead of a less inflammatory strain of HSV-2, we had originally been interested in trying to model the distinct disease outcomes that have previously been described during HSV-1 vs HSV-2 genital herpes in humans and thought this would be a relevant comparison. We have not yet examined infection with less inflammatory HSV-2 strains, but agree that this is a great idea. We have also not yet examined neutrophil-dependent IL-18 production in the context of HSV-1.

      Reviewer #2 (Public Review):

      This manuscript will be of interest to a broad audience of immunologists especially those studying host-pathogen interactions, mucosal immunology, innate immunity and interferons. The study reveals a novel role for neutrophils in the regulation of pathological inflammation during viral infection of the genital mucosa. The main conclusions are well supported by a combination of precise technical approaches including neutrophil-specific gene targeting and antibody-mediated inhibition of selected pathways.

      In this study by Lebratti, et al the authors examined the impact of neutrophil depletion on disease progression, inflammation and viral control during a genital infection with HSV-2. They find that removal of neutrophils prior to HSV-2 infection resulted in ameliorated disease as assessed by inflammatory score measurements. Importantly, they show that neutrophil depletion had no significant impact on viral burden nor did it affect the recruitment of other immune cells thus suggesting that the observed improvement on inflammation was a direct effect of neutrophils. The role of neutrophils in promoting inflammation appears to be specific to HSV-2 since the authors show that HSV-1 infection resulted in comparable numbers of neutrophils being recruited to the vagina yet HSV-1 infection was less inflammatory. This observation thus suggests that there might be functional differences in neutrophils in the context of HSV-2 versus HSV-1 infection that could underlie the distinct inflammatory outcomes observed in each infection. In ordered to uncover potential mechanisms by which neutrophils affect inflammation the authors examined the contributions of classical neutrophil effector functions such as NETosis (by studying neutrophil-specific PAD4 deficient mice), reactive oxygen species (using mice global defect in NADH oxidase function) and cytokine/phagocytosis (by studying neutrophil-specific STIM-1/STIM-2 deficient mice). The data shown convincingly ruled out a contribution by the neutrophil factors examined. The authors thus performed an unbiased single cell transcriptomic analysis of vaginal tissue during HSV-1 and HSV-2 infection in search for potentially novel factors that differentially regulate inflammation in these two infections. tSNE analysis of the data revealed the presence of three distinct clusters of neutrophils in vaginal tissue in mock infected mice, the same three clusters remained after HSV-1 infection but in response to HSV-2 only two of the clusters remained and showed a sustained interferon signature primarily driven by type I interferons (IFNs). In order to directly interrogate the impact of type I IFN on the regulation of inflammation the authors blocked type I IFN signaling (using anti IFNAR antibodies) at early or late times after infection and showed that late (day 4) IFN signaling was promoting inflammation while early (before infection) IFN was required for antiviral defense as expected. Importantly, the authors examined the impact of neutrophil-intrinsic IFN signaling on HSV-2 infection using neutrophil-specific IFNAR1 knockout mice (IFNAR1 CKO). The genetic ablation of IFNAR1 on neutrophils resulted in reduced inflammation in response to HSV-2 infection but no impact on viral titers; findings that are consistent with observations shown for neutrophil-depleted mice. The use of IFNAR1 CKO mice strongly support the importance of type I IFN signaling on neutrophils as direct regulators of neutrophil inflammatory activity in this model. Since type I IFNs induce the expression of multiple genes that could affect neutrophils and inflammation in various ways the authors set out to identify specific downstream effectors responsible for the observed inflammatory phenotype. This search lead them to IL-18 as possible mediator. They showed that IL-18 levels in the vagina during HSV-2 infection were reduced in neutrophil-depleted mice, in mice with "late" IFNAR blockade and in IFNAR1 CKO mice. Furthermore, they showed that antibody-mediated neutralization of IL-18 ameliorated the inflammatory response of HSV-2 infected mice albeit to a lesser extent that what was seen in IFNAR1 CKO. Altogether, the study presents intriguing data to support a new role for neutrophils as regulators of inflammation during viral infection via an IFN-IL-18 axis.

      In aggregate, the data shown support the author's main conclusions, but some of the technical approaches need clarification and in some cases further validation that they are working as intended.

      Thank you! We appreciate the enthusiasm for our work as well as the suggestions for improving our study.

      1) The use of anti-Ly6G antibodies (clone 1A8) to target neutrophil depletion in mice has been shown to be more specific than anti-Gr1 antibodies (which targets both monocytes and neutrophils) thus anti-Ly6G antibodies are a good technical choice for the study. Neutrophils are notoriously difficult to deplete efficiently in vivo due at least in part to their rapid regeneration in the bone marrow. In order to sustain depletion, previous reports indicate the need for daily injection of antibodies. In the current study the authors report the use of only one, intra-peritoneal injection (500 mg) of 1A8 antibodies and that this single treatment resulted in diminished neutrophil numbers in the vagina at day 5 after viral infection (Fig 1A). Data shown in figure 2B suggests that there are neutrophils present in the vagina of uninfected mice, that there is a significant increase in their numbers at day 2 and that their numbers remain fairly steady from days 2 to 5 after infection. In order to better understand the impact antibody-mediated depletion in this model the authors should have examined the kinetics of depletion from day 0 through 5 in the vaginal tissue after 1A8 injection as compared to the effect of antibodies in the periphery. These additional data sets would allow for a deeper understanding of neutrophil responses in the vagina as compared to what has been published in other models of infection at other mucosal sites.

      We agree and apologize for not providing this information in the original submission. Neutrophil depletion kinetics from the vagina have been shown in new Figure 1A, while depletion from the blood is shown in new Figure 1 - Supplement 1.

      2) The authors used antibody-mediated blockade as a means to interrogate the impact of type I IFNs and IL-18 in their model. The kinetics of IFNAR blockade were nicely explained and supported by data shown in supplementary figure 4. IFNAR blockade was done by intra-peritoneal delivery of antibodies at one day before infection or at day 4 after infection. When testing the role of IL-18 the authors delivered the blocking antibody intra-vaginally at 3 days post infection. The authors do not provide a rationale for changing delivery method and timing of antibody administration to target IL-18 relative to IFNAR signaling. Since the model presented argues for an upstream role for IFNAR as inducer of IL-18 it is unclear why the time point used to target IL-18 is before the time used for IFNAR.

      We thank Reviewer #2 for raising this point and apologize for not providing an explanation for the differences in antibody treatment regimens for modulating IFNAR and IL-18. As the anti-IL-18 mAb is a cytokine neutralizing antibody, we hypothesized that administering the antibody vaginally would help to concentrate the antibody at the relevant site of cytokine production and increase the potency of neutralization. This is in contrast to systemic administration of the anti-IFNAR1 mAb that acts to block signaling in the 'receiving' cell. We expect the anti-IFNAR1 mAb (given in much higher doses) to bind both circulating cells that are recruited to the site of infection as well as cells that are already at the site of infection. Similarly, we started the anti-IL-18 antibody treatment one day earlier to allow a presumably sufficient amount antibody to accumulate in the vagina. Our rationale has been included in the revised manuscript (lines 351-353). We are pleased to report, however, that we have conducted preliminary studies in which mice were treated beginning at 4 d.p.i. rather than 3 d.p.i., and observe similar trends. This data is provided below as Reviewer Figure 3.

      RFigure 3. Mice treated with anti-IL-18 mAb starting at 4 d.p.i. exhibit reduced disease severity. Mice were infected with HSV-2 and treated ivag with 100ug of anti-IL-18 on 4, 5 and 6 d.p.i.. Mice were monitored for disease until 7 d.p.i.. Data was analyzed by repeated measured two-way ANOVA with Geisser-Greenhouse correction and Bonferroni's multiple comparisons test.

      3) An open question that remains is the potential mechanism by which IL-18 is acting as effector cytokine of epithelial damage. As acknowledged by the authors the rescue seen in IFNAR1 CKO mice (Fig 5C) is more dramatic that targeting IL-18 (Fig 6D). It is thus very likely that IFNAR signaling on neutrophils is affecting other pathways. It would have been greatly insightful to perform a single cell RNA seq experiment with IFNAR CKO mice as done for WT mice in Fig 3. Such an analysis might would have provided a more thorough understanding of neutrophil-mediated inflammatory pathways that operate outside of classical neutrophil functions.

      We agree that the proposed scRNA-seq experiment comparing vaginal cells from IFNAR CKO and WT mice would be very interesting and insightful. Although a bit beyond the scope of the current manuscript, we are currently planning on performing these types of studies to better understand IFN-mediated regulation of inflammatory neutrophil functions.

      4) The inflammatory score scale used is nicely described in the methods and it took into consideration external signs of vaginal inflammation by visual observation. It would have been helpful to mention whether the inflammation scoring was done by individuals blinded to the experimental groups.

      This is an important point and we apologize for not making this clear. We have now provided this information in the methods section of the revised manuscript (lines 778).

      5) The presence of distinct clusters of neutrophils in the scRNA-seq data analysis is a fascinating observation that might suggest more diversity in neutrophils than what is currently appreciated. In this study, the authors do not provide a list of the genes expressed in each cluster within the data shown in the paper. Although the entire data set is deposited and publicly available, having the gene lists within the paper would have been helpful to provide a deeper understanding of the current study.

      The heterogeneity of the vaginal neutrophil population after HSV infection is indeed an unexpected finding. To provide a deeper understanding of these transcriptionally distinct clusters, we have now included complete lists of DEGs between the different clusters as Source Files for Figure 3.

      Reviewer #3 (Public Review):

      This paper examines the role of neutrophils, inflammatory immune cells, in disease caused by genital herpes virus infection. The experiments describe a role for type I interferon stimulation of neutrophils later in the infection that drives inflammation. Blockade of interferon, and to a lesser degree, IL-18 ameliorated disease. This study should be of interest to immunologists and virologists.

      This study sought to examine the role of neutrophils in pathology during mucosal HSV-2 infection in a mouse model. The data presented in this manuscript suggest that late or sustained IFN-I signals act on neutrophils to drive inflammation and pathology in genital herpes infection. The authors show that while depletion of neutrophils from mice does not impact viral clearance or recruitment of other immune cells to the infected tissue, it did reduce inflammation in the mucosa and genital skin. Single cell sequencing of immune cells from the infected mucosa revealed increased expression of interferon stimulated genes (ISGs) in neutrophils and myeloid cells in HSV-2 infected mice. Treatment of anti-IFNAR antibodies or neutrophil-specific IFNAR1 conditional knockout mice decreased disease and IL-18 levels. Blocking IL-18 also reduced disease, although these data show that other signals are likely to also be involved. It is interesting that viral titers and anti-viral immune responses were unaffected by IFNAR or IL-18 blockade when this treatment was started 3-4 days after infection, because data shown here (for IFN-I) and by others in published studies (for IFN-I or IL-18) have shown that loss of IFN-I or IL-18 prior to infection is detrimental.

      These data are interesting and show pathways (namely IFN-I and IL-18) that could be blocked to limit disease. While this suggests that IL-18 blockade might be an effective treatment for genital inflammation caused by HSV-2 infection, the utility of IL-18 blockade is still unclear, because the magnitude of the effect in this mouse model was less than IFNAR blockade. Additionally, further experiments, such as conditional loss of IL-18 in neutrophils, would be required to better define the role and source(s) of IL-18 that drive disease in this model.

      We thank the reviewer for the positive response and agree that additional studies would likely be necessary to fully understand the role of IL-18 during HSV-2 infection.

    1. Author Response:

      Reviewer #1 (Public Review):

      The study by Diboun et al. aims to investigate methylation profiles in Paget's disease of bone patients. Many of the genes identified near areas of differentially methylated sites were known to be involved in osteoclast differentiation, viral infection and mechanical loading. These gene pathways are known to play a role in the pathogenesis of PDB. The strength of this study is that it is the first study to look at changes in methylation profiles in Paget's disease of bone patients. Additionally, the genes identified as having differentially methylated sites suggest that environmental factors such as host immune responses may be altered and play a role in the pathogenesis of PBD. The main weakness of this study is that the cells that were analyzed for changes in methylation sites were not osteoclasts the cells of interest in PBD. While many of the genes identified have been shown to play a role in regulation of the skeletal system, results should be interpreted with caution until they are validated in bone tissue.

      We thank the reviewers and the editors for this thoughtful comment. Ebrahimi et al (EPIGENETICS; 2021, 16(1): 92–105) investigated correlation in methylation profiles between blood and bone tissue in 12 subjects using Illumina MethylationEPIC BeadChip array. Bone samples were taken from the exposed proximal femur after removal of the femoral head from osteoarthritis patients. After quality control, Ebrahimi et al focused the correlation analysis on 64,349 probes that fit their analysis criteria (to define the most highly correlated positions), of which 30,607 sites showed significant (FDR < 0.05) high correlation (r2 > 0.74) between bone and blood.

      Additional filter was applied to these sites to include those with at least 80% similar methylation profile between bone and blood (n = 28,549) which were reported as supplementary table in their paper. We assessed if CpG sites annotated to genes identified from our DMS and DMR analyses (Table 2 and 3) showed high correlation between bone and blood as reported by Ebrahimi et al. Results showed that CpGs annotated to 8 out of the 14 genes from our DMS analysis were among the highly correlated sites between blood and bone (r2 > 0.74; FDR <0.05; Supplementary File 6). For DMRs, out of the 10 genes reported in our study (Table 3), 6 had at least one CpG with high correlation between blood and bone (Supplementary File 6). It is important to note that, in the study by Ebrahimi et al, only 64,349 CpG sites were tested for correlation, owing to the stringent criteria adopted by the authors to identify the list of highly concordant sites. Therefore, our DMS/DMR sites that did not feature in the list are not necessarily uncorrelated. Unfortunately, these sites cannot be investigated further since Ebrahimi et al did not make their entire dataset available in public domain. To address this point, A table has been added to the manuscript (Supplementary File 6) listing the sites with high correlation and the text has been modified to include and discuss these results.

      Reviewer #2 (Public Review):

      This unique study has shown that epigenetic (therefore, potentially environment-driven) factors contribute to the pathogenesis of Paget's Disease of Bone (PDB). Although PDB is not very rare condition, its early diagnosis is problematic. The bone tissue is not easily accessible, thus many cases are not diagnosed till later in life. Thus, having diagnostic markers measured in blood, normalized to cell type count, might be of use for possible diagnostic applications.

      The PRISM trial's sample, comprising 232 cases and 260 controls from UK, was divided in two - discovery and replication sets - based on power calculations for EWAS. Meta-analysis of data from the discovery and replication sets revealed significant differences in DNA methylation. Among gene-body regions/loci, many associated with functions related to osteoclast differentiation, mechanical loading, immune function, etc. two loci were suggested as functional through expression quantitative trait methylation (eQTM) analysis. Further, there was some value in assessing the risk of developing PDB. The AUC of 82.5%, based on the 95 discriminatory sites from the "best subset" analysis, is promising for clinical applicability. If confirmed in independent samples and further studies, chromosomal loci found in this study may offer diagnostic markers for prediction of the disease.

      We would like to draw the reviewer’s attention to the fact that the original cohort comprised of 232 PDB cases and 260 controls (that is 116 cases and 130 controls in each of the discovery and cross validation set). The abstract has been slightly modified to make the text clearer.

      Reviewer #3 (Public Review):

      Diboun et al used a case-control study design to identify DNA methylation sites and regions that differ between individuals with Paget's Disease of Bone (PDB) and controls. Cases were identified from an ongoing PDB clinical trial. Spouses of cases were used as controls. Candidate methylation sites were identified in a discovery set and then tested in a validation set to confirm association with PDB. Meta-analysis was used to combine effects from the discovery and validation sets. A machine learning approach was then used to prioritize candidates and build a prediction model capable of differentiating PDB cases from controls. The model was associated with high level of accuracy (AUC >0.90) in the discovery and validation sets.

      A major strength of the study is the collection of a large population of individuals with a rare bone disease. Epigenetic features are appealing for building prediction models as they may represent interplay between genetics and environment. Using this approach, the authors built a prediction model with a high level of accuracy. The results advance our understanding of the etiology of PDB.

      Overall, the primary conclusions are generally well supported. However, there are several aspects of the paper that will require additional clarification.

      I commend the authors for using a split sample cross validation approach to maximize experimental rigor. However, this approach is distinct from a true external replication. Given that the 'training' and the 'test' sets come from the same overall population, we expect the 'replication' results to be optimistic relative to results from a true, external replication population. Given the absence of a suitable external replication population due the unique nature of the disease, this limitation is acceptable. However, I expect the authors to discuss the potential limitations of this approach in their discussion section and I encourage the authors to refer to the 'replication' set as a 'cross-validation' set to more appropriately convey their experimental approach to the broader scientific community.

      We have referred to the replication set as “cross-validation” as suggested by the reviewer. However, the study subjects were recruited from over 27 medical centres across the United Kingdom (UK) representing most major cities. We have also added text to discuss this point.

      The authors look for functional validation using the BIOS qTL database. This reference provides valuable information about functional role of methylation in gene expression in whole blood (eQTM). We know that eQTMs are tissue specific. Do the authors have any evidence whether the methylation plays a similar role in bone tissue?

      We agree that eQTMs tend to be tissue specific and although we were able to gather some confidence about concordance in methylation levels between blood and bone tissue samples using the Ebrahimi study, it is rather difficult to speculate about the concordance in the effect on gene expression. We therefore raise this issue in the study limitation section of the paper.

      The authors report the markers from their 'best set' for prediction have potential functional relevance. The potential clinical relevance, however, requires additional context. The data were obtained after onset of PDB. The potential for reverse causation cannot be overlooked. Do the authors have any evidence that the methylation markers precede clinical diagnosis? Appropriate temporality is an essential requisite for an effective clinical prediction model.

      We agree with the reviewers that this is an issue with most EWAS studies. The observed methylation changes reported in the study may exist as a consequence of the disease. We therefore updated our discussion of study limitations to reflect the potential issue of reverse causation (page 11). We also discussed the design of future experiments when the predictive value of our best subset set could be properly validated with appropriate temporality. Specifically, how individuals with a genetic predisposition or/and family history of PDB could be measured routinely for changes in the methylation patterns of the best subset identified in this study in an attempt to draw possible associations with future disease onset.

    2. Reviewer #3 (Public Review):

      Diboun et al used a case-control study design to identify DNA methylation sites and regions that differ between individuals with Paget's Disease of Bone (PDB) and controls. Cases were identified from an ongoing PDB clinical trial. Spouses of cases were used as controls. Candidate methylation sites were identified in a discovery set and then tested in a validation set to confirm association with PDB. Meta-analysis was used to combine effects from the discovery and validation sets. A machine learning approach was then used to prioritize candidates and build a prediction model capable of differentiating PDB cases from controls. The model was associated with high level of accuracy (AUC >0.90) in the discovery and validation sets.

      A major strength of the study is the collection of a large population of individuals with a rare bone disease. Epigenetic features are appealing for building prediction models as they may represent interplay between genetics and environment. Using this approach, the authors built a prediction model with a high level of accuracy. The results advance our understanding of the etiology of PDB.

      Overall, the primary conclusions are generally well supported. However, there are several aspects of the paper that will require additional clarification.

      I commend the authors for using a split sample cross validation approach to maximize experimental rigor. However, this approach is distinct from a true external replication. Given that the 'training' and the 'test' sets come from the same overall population, we expect the 'replication' results to be optimistic relative to results from a true, external replication population. Given the absence of a suitable external replication population due the unique nature of the disease, this limitation is acceptable. However, I expect the authors to discuss the potential limitations of this approach in their discussion section and I encourage the authors to refer to the 'replication' set as a 'cross-validation' set to more appropriately convey their experimental approach to the broader scientific community.

      The authors look for functional validation using the BIOS qTL database. This reference provides valuable information about functional role of methylation in gene expression in whole blood (eQTM). We know that eQTMs are tissue specific. Do the authors have any evidence whether the methylation plays a similar role in bone tissue?

      The authors report the markers from their 'best set' for prediction have potential functional relevance. The potential clinical relevance, however, requires additional context. The data were obtained after onset of PDB. The potential for reverse causation cannot be overlooked. Do the authors have any evidence that the methylation markers precede clinical diagnosis? Appropriate temporality is an essential requisite for an effective clinical prediction model.

    3. Reviewer #2 (Public Review):

      This unique study has shown that epigenetic (therefore, potentially environment-driven) factors contribute to the pathogenesis of Paget's Disease of Bone (PDB). Although PDB is not very rare condition, its early diagnosis is problematic. The bone tissue is not easily accessible, thus many cases are not diagnosed till later in life. Thus, having diagnostic markers measured in blood, normalized to cell type count, might be of use for possible diagnostic applications.

      The PRISM trial's sample, comprising 232 cases and 260 controls from UK, was divided in two - discovery and replication sets - based on power calculations for EWAS. Meta-analysis of data from the discovery and replication sets revealed significant differences in DNA methylation. Among gene-body regions/loci, many associated with functions related to osteoclast differentiation, mechanical loading, immune function, etc. two loci were suggested as functional through expression quantitative trait methylation (eQTM) analysis. Further, there was some value in assessing the risk of developing PDB. The AUC of 82.5%, based on the 95 discriminatory sites from the "best subset" analysis, is promising for clinical applicability. If confirmed in independent samples and further studies, chromosomal loci found in this study may offer diagnostic markers for prediction of the disease.

    4. Reviewer #1 (Public Review):

      The study by Diboun et al. aims to investigate methylation profiles in Paget's disease of bone patients. Many of the genes identified near areas of differentially methylated sites were known to be involved in osteoclast differentiation, viral infection and mechanical loading. These gene pathways are known to play a role in the pathogenesis of PDB. The strength of this study is that it is the first study to look at changes in methylation profiles in Paget's disease of bone patients. Additionally, the genes identified as having differentially methylated sites suggest that environmental factors such as host immune responses may be altered and play a role in the pathogenesis of PBD. The main weakness of this study is that the cells that were analyzed for changes in methylation sites were not osteoclasts the cells of interest in PBD. While many of the genes identified have been shown to play a role in regulation of the skeletal system, results should be interpreted with caution until they are validated in bone tissue.

    5. Evaluation Summary:

      Paget disease of bone (PDB) results in focal areas of disorganized bone, leading to bone deformities and fragility. There is substantial interest in finding circulating biomarkers that might be of use for possible diagnostic applications and towards this end, these authors identified novel DNA methylation patterns in peripheral blood mononuclear cells that are able to differentiate PDB cases from controls with a high level of accuracy. This prediction model has functional relevance as these candidate methylation sites and regions are associated with osteological and immunologic processes and in the longer term, has future clinical potential.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    1. Reviewer #2 (Public Review):

      The authors sought to understand the mechanisms determining whether the kinase RIPK3 induces apoptosis or necroptosis and the physiological significance of this dual function. They identified a new phosphorylation event on RIPK3 (S164/T165) that appears to inhibit its capacity to induce necroptosis and make it a potent inducer or apoptosis. Low levels of the chaperone HSP90/CDC37 seem to favor S164/T165 RIPK3 phosphorylation, which is suggested to be important for luteal regression by inducing apoptosis in luteal granulosa cells in the ovaries of female mice.

      The results presented expand on previous studies showing that whereas RIPK3 induces necroptosis by phosphorylating MLKL, inhibition of RIPK3 kinase activity by small molecules or by D160N mutation caused apoptosis and embryonic lethality. The authors provide experimental evidence supporting that phosphorylation on S164/T165 promotes apoptosis in vitro and in vivo, however the mechanisms regulating this transition remain poorly understood. The data on HSP90/CDC37 is supportive but largely correlative. The authors speculate that association with this chaperone is necessary for proper folding of RIPK3 into a configuration that can only be activated by upstream necroptosis inducers, while at low HSP90/CDC37 levels RIPK3 is not correctly folded and likely auto-phosphorylates on S164/T165, however this remains to be demonstrated. The authors propose that this process is particularly important in luteal granulosa cells and provide some evidence suggesting that RIPK3 phosphorylation on S164/T165 occurs in the ovaries of older mice. This seems counterintuitive given that corpus luteum involution occurs as part of the ovulation cycle and should therefore be especially relevant in young, sexually mature mice. Most importantly, there is no evidence that RIPK3 phosphorylation at these sites is important for female reproductive function, questioning its physiological significance. It would be important to know whether RIPK3 deficient or S165a/T166A mutant mice show any reproductive defects that would be expected by the lack of the proposed RIPK3-mediated apoptosis program in luteal granulosa cells.

      The in vivo data in the knock-in mouse models clearly show that phosphomimetic mutations (RIPK3S165D/T166E) on RIPK3 cause severe pathology in multiple organs associated with increased numbers of dying cells. However, rescue experiments, for example by crossing to caspase-8 knockout mice, to prove that the pathology is indeed induced by apoptosis are lacking. It is also interesting that heterozygous expression of the phosphomimetic mutants does not cause any pathology in vivo. The authors speculate that a threshold of expression is required for activation of this mutant, however an alternative explanation could be that the presence of the wild type protein prevents its activation, e.g. by trans-autophosphorylation on S227. Introducing a RIPK3 null allele to generate heterozygous RIPK3S165D/T166E mice that do not express wild type RIPK3 could help resolve this question, as in that case the phosphomimetic mutant will be expressed at the same level but in the absence of the wild type protein.

      Finally, most of the in vitro mechanistic studies rely on overexpression of the different mutants in cell lines. Using cells from the knock-in mice expressing the mutated proteins at endogenous levels would be a more appropriate experimental system to explore the mechanistic underpinnings such as the interaction with HSP90/CDC37.

    2. Reviewer #1 (Public Review):

      The protein kinase RIPK3 was widely known to promote a form of lytic cell death termed necroptosis. However, RIPK3 could also promote apoptotic cell death under certain conditions. However, the mechanism by which RIPK3 promotes apoptosis and the physiological relevance of this apoptotic activity were not understood. In this study, the authors provided answers to these two questions.

      Strengths:

      The authors found that a specific phosphorylation on RIPK3 plays a critical role in the switch of RIPK3 into an apoptosis-inducing protein. The authors provided strong evidence to support their conclusion using mouse genetics and demonstrated a role for this RIPK3 activity in reproductive physiology.

      Weaknesses:

      Although the authors succeeded in finding the protein phosphorylation that controls the form of cell death mediated by RIPK3, key questions remained as to how this modification prevents RIPK3 from promoting necroptosis. Also, the authors implied that the kinase activity of RIPK3 is critical in this switch to apoptosis. However, the phenotypes of mice that lack RIPK3 kinase activity do not match that of the mice that harbor mutations that mimic this phosphorylation.

      Overall, this work should provide useful information for future studies to further examine the mechanism by which RIPK3 controls different types of cell death in normal and pathophysiology.

    3. Evaluation Summary:

      This manuscript is of potential interest to the field of cell death research in terms of understanding basic mechanisms and in the context of disease. The authors have used a broad range of methodologies and identified key phosphorylation sites on the protein kinase RIPK3 that determine whether cells undergo necroptotic or apoptotic cell death. The authors examine this phosphorylation event in the context of corpus luteum regression.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    1. Reviewer #3 (Public Review):

      Jenny I. Aguilar et. al. present a manuscript that methodically investigates the behavioral, structural, functional, and physiological consequences of a Cys substitution at R445 in the human dopamine transporter. Parkinson's disease is a common progressive neurodegenerative disorder that affects millions of people worldwide. In most patients, the underlying cause their disease is unknown, but some genetic forms of Parkinsonism have been identified. In this manuscript, the authors investigate the effect of a mutation in the gene that encodes the dopamine transporter that was identified in a patient with infantile Parkinsonism-Dystonia. Using a Drosophila model and an abundance of tools, the data show that the mutation produces: 1) a reduction in spontaneous motor activity, movement vigor, compromised flight initiation, and impaired coordinated movements; 2) a decrease in dopamine content and the number of tyrosine hydroxylase containing neurons in fly brain; 3) a decrease in amphetamine-induced dopamine efflux and dopamine uptake; 4) altered dopamine transporter structure leading to increased probability of open conformations on both sides of the transporter; 5) a reduction in dopamine transporter surface expression and transport capacity. Chloroquine, used as means to limit dopamine transporter lysosomal degradation, increased the ratio of mature to immature dopamine transporter and improved flight initiation. So why does a decrease in dopamine reuptake promote a dopamine-deficient Parkinson phenotype? The Authors conclude that an overall reduction in dopamine transporter would deplete dopamine stores by promoting excessive extracellular dopamine. The decrease in vesicular release would be further exacerbated by DA stimulation of presynaptic dopamine-D2 receptors on dopamine axons. This rather novel counterintuitive hypothesis appears to be supported by the outcome of this investigation. Overall, the study may highlight the mechanism underlying a rare type of Parkinsonism that can affect children as well as adults.

    2. Reviewer #2 (Public Review):

      I have reviewed Psychomotor Impairments and Therapeutic Implications Revealed by a Mutation Associated with Infantile Parkinsonism-Dystonia by Aguilar et al. The authors first express hDAT in the dDAT loss of function background to explore in vivo effects. The comparison of hDAT rescue flies to wild type flies and the DAT mutant provide a nice control for the functionality of the hDAT transgene. A better control might have been rescue using dDAT with the same driver but this is a very minor concern since the wild type flies and the hDAT rescue look so similar. They then show that the R445C mutant decreases "movement vigor" and flight initiation. They use HPLC and immunolabeling to convincingly show deficits in both total tissue DA and a decrease in the number of detectable DA cells and use amperometry in the fly brain to quantify defects in efflux. Amperometry in the fly brain is technically impressive since few other labs have accomplished this without fouling the carbon electrode. In the second section of the paper, the authors perform a structural analysis, using LeuT to model DAT. The combination of Rosetta modeling, X-ray crystallography and EPR spectroscopy further adds to the technical strength of the paper. They show that substitution at the position in LeuT R375 analogous to DAT R445 disrupts a previously identified salt bridge and the IC vestibule. They then generate X-ray crystal structures of LeuT WT, LeuT R375A and LeuT R375D at resolutions of 2.1-2.6 Å. Their analysis confirms that substitution at LeuT-R375 disrupt salt bridge formation consistent with Rosetta modeling. They further conform the disruption of the interaction between R375 and its partner using a variant of EPR and show that substitutions at this site bias toward open conformations. In the final figure of the paper they heterologously express the DAT mutants in cell culture and show that cell surface expression, transport and efflux are compromised, similar to previously published findings from another lab. Finally, they show that chloroquine can rescue some of the behavioral deficits in the fly.

      The authors present a remarkably comprehensive and technically sophisticated analysis of the structure, function and behavioral sequelae of a mutation in the DAT (hDAT R445C). The analysis is translationally relevant since the mutation was identified in a patient suffering from a rare movement disorder relevant to Parkinson's disease. The combination of behavioral and biochemical analysis in a transgenic animal with X-ray crystallography and modeling is extremely unusual and from a technical standpoint the paper is unusually strong. The insight gained from comparing the structural and functional halves of the paper is also useful. The partial pharmacologic rescue of the behavioral deficits further elevates this work.

      Concerns It might be argued that the insights obtained from comparing the various data on modeling, structural analysis, biochemical assays and the behavior of the R445 mutant may not always be consistent with one another, making it difficult to determine the physiological relevance of each effect. This concern is balanced by the idea that we cannot know which aspects of any given mutant will or will not conform to expectations without the comprehensive analysis used here. As such, the paper provides an important example of examining a risk allele in a variety of different ways to determine which molecular deficits may be relevant to the observed phenotype and to the function of the transporter. That said, the authors should add text to acknowledge that some of the molecular defects they observe may be overshadowed by others and/or may not be as relevant to the in vivo defects in activity. For example, the idea that efflux may play a role in the R445 phenotype similar to other mutants and neuropsychiatric illness in general is provocative, but seems difficult to reconcile with the observation that relatively low levels of protein are present at the cell surface.

      The behavioral analysis is elegant and takes advantage of high-speed video recording to determine subtle defects in movement. The specificity of the defect is also interesting since grooming is not affected. However, it is difficult to determine whether the data represent a true deficit in movement versus wakefulness or overall activity of the animal. Dopamine is well known to be required for sleep in the fly and it is unclear whether the "deficits in movement vigor" are caused by the flies being "sleepy". Alternatively, higher order decision making processes rather than movement per se might be compromised. These explanations for the observed deficits would not take away from the importance of the findings. Indeed, as the authors acknowledge, the non-motor symptoms of PD are just as important as the motor symptoms. However, it seemed at times that authors felt compelled to fit their data into a motor paradigm rather than taking a more general view on the relationship of the observed defects to other problems that accompany PD. The authors should address these issues with additional text. Additional experiments to address this issue are likely beyond the scope of the current manuscript which is already quite lengthy.

      Minor points:

      The authors discuss a model in which loss or DAT reuptake and an increase in extracellular DA could down regulate TH. Since they use TH labeling to count DA cells they should acknowledge the possibility the cells are not absent in the mutant (even if they are functionally compromised) but are simply not detectable.

      It is unclear why (Brand and 147 Perrimon, 1993); are cited on line 146.

      Typo in "Initiate" on Y axis of Fig 3B.

      State somewhere in the text or in the Fig 3 legend that HPLC was used to measure tissue concentrations of DA to make it more obvious that amperometry was not used

    3. Reviewer #1 (Public Review):

      The authors generated new transgenic fly lines with the human dopamine transporter (hDAT-WT) and the hDAT with the R445C mutation (hDAT-R445C). Studies in the hDAT-R445C flies show a decrease of tissue DA content and a loss of TH+ PPL1 neurons indicating an effect of the DAT mutation on dopamine neuron phenotype or cell survival rather than general DA levels per se. The motor phenotypes observed in the fly include a decrease in the time to initiate flight and in the velocity of locomotion (vigor) but not in the velocity of locomotion initiation or grooming behavior. These behaviors are consistent with the bradykinesia observed in patients. This model system could potentially be used to assay for specific modulators of the mutant to restore surface expression, TH expression and motor behavior.

      In the recombinant cell culture system (HEK Cells), the major consequence of the mutation is a decrease in cell surface expression (there is a decrease in conversion to the mature form). A change in the Km is difficult to ascertain with such a dramatic change in the cell surface expression level but looks to be dramatically decreased (higher affinity). These data differ somewhat from those reported in the study by Ng et al, 2014 where the Bmax for CFT was slightly reduced and the affinity was significantly decreased (Km was ~8 fold higher) as was the Ki for DA inhibition of CFT. It should be noted that the decrease in cell surface expression of R445C reported by Ng et al was also not as dramatic as what the same group demonstrated for the other mutation, R87L, that was compound heterozygous in this family. Differences in the transport properties between the two studies should be discussed.

      X-ray crystallography and molecular modeling provide novel insights into how the mutation (and other substitutions at this site) affects structure-function relationships of the transporter with respect to gating, uptake and efflux. This information could be used to design modulators of the transporter mutants to rescue cell surface expression or function.

      The behavioral effect of CQ on the mutant flies was on the time to flight initiation, which decreased. Locomotion was not tested.

      The value of the study is the creation of the flies for screening and the crystallography and molecular modeling studies which examined the impact of this residue on function in detail. The weakness of the study was the limited characterization of the transport properties and cell surface expression in the flies. Being able to tie together the different studies into a cohesive understanding of what happens in patients and thus what needs to be corrected in patients is an important goal of the study. Some of the key questions needed to achieve this understanding were not fully addressed.

    4. Evaluation Summary:

      Infantile parkinsonism-dystonia is a rare but devastating condition that leads to early mortality. Mutations in the dopamine transporter that decrease its transport activity or cell surface expression have been identified as potential causes of this disease. Here, Aguilar et al perform a series of experiments to examine the effect of one of the mutations, R445C, on properties of the transporter in cell culture and on motor function in newly generated transgenic flies. They also explore structure function relationships of the mutation using X-ray crystallography of LeuT, a bacterial homolog, and molecular modeling. Lastly, they show blocking lysosomal degradation rescues a motor deficit in the flies. Insights from the work could lead to new approaches to specifically modulate the transporter structure to restore surface expression and function of the mutant dopamine transporter in this disorder. This elegant and technically sophisticated analysis is of interest to readers in the fields of neurobiology, behavior, and movement disorders, as the work provides an excellent example of using a variety of different approaches to determine the relationship between transporter structure and activity and potentially underlying pathology in human disease.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

    1. Author Response:

      We would like to thank the reviewers for their thoughtful and thorough critique of our manuscript. In our revised preprint, we added important additional data and restructured our manuscript to reflect as many of the recommendations as possible. Additionally, we have added experiments to define the cellular mechanisms underlying observed damage following mechanical injury. The most significant additions of new data include:

      • Further experiments demonstrating block of glutamate clearance exacerbates stimulus-induced hair-cell synapse loss.
      • Analysis of neuromast disruption in lhfpl5b mutant null larvae showing mechanical displacement. Lhfpl5b mediates mechanosensitivity in lateral-line hair cells, allowing us to determine whether mechanotransduction is required for mechanical disruption of neuromasts.
      • Testing the vibratory stimulus at various frequencies to confirm the optimal frequency to induce acute, generally sub-lethal damage to lateral-line hair cells is 60 Hz.
      • Assessment of neuromast supporting cell and hair cell proliferation following mechanical overstimulation.
      • Quantitative analysis of kinocilia SEM and confocal images of hair bundles in control and stimulus exposed fish. Individual comments are addressed as outlined below.

      Reviewer #1:

      1) The authors use a vertically-oriented Brüel+Kjær LDS Vibrator to deliver a 60 Hz vibratory stimulus to damage lateral line hair cells. It is not made clear on why this frequency was selected. Did the authors choose this frequency because they screened a number of frequencies and this is the one that did the most damage to hair cells or was it chosen for another reason? Or, do all frequencies do the same amount of damage? The authors should screen a number of frequencies and choose the stimulus that does the most damage to hair cells. This would set the field in the best direction, should members of the community attempt this new technique. It is not necessary to repeat all of the experiments, but the authors should show which frequencies are best for inducing damage.

      The frequency selected for mechanical overexposure of lateral-line organs was based on previous studies showing 60 Hz to be within the optimal upper frequency range of mechanical sensitivity of superficial posterior lateral-line neuromasts, with maximal response between 10-60 Hz, but a suboptimal frequency for hair cells of the anterior macula in the ear (Weeg and Bass 2002, Trapani et al, 2009, Levi et al, 2015). To confirm that 60 Hz was the optimal frequency to induce damage, we tested 45, 60, and 75 Hz at comparable intensities. We observed at 75 Hz no apparent damage to lateral line neuromasts while 45 Hz at a comparable intensity proved toxic i.e. it was lethal to the fish. We have updated the Results and Method Details to include our rationale for choosing 60 Hz.

      2) The SEM images of the hair bundle are beautiful and do show damage to the hair bundle, but historically speaking older studies in mammals have shown that the actin core of the stereocilia is damaged. It would be critical to know if this was the case. Showing damage to the kinocilium and stereocilia splaying is a start, but readers would need to know if the actin cores are damaged. So, TEM should be used to find damage to the actin cores of stereocilia.

      Our main goal of this initial manuscript was to survey morphological and functional changes in mechanically injured lateral line organs with an emphasis on inflammation and synapse loss. We agree TEM studies showing damage to the actin core of the stereocilia will be important to determine whether mechanical damage to neuromast hair bundles fully mimics mammalian stereocilia damage, but these experiments will require significant time to perform and optimize. We have expanded our analysis of hair-bundle morphology in this study and intend to pursue deeper analysis of hair bundle damage, i.e. examination of the stereocilia actin core, in future follow-up studies.

      3) I think the use of "Noise-exposed lateral line" as a term for mechanically overstimulated lateral line hair cells is not correct and could be misleading. The lateral line senses water motion not sound as the word noise would imply. Calling the stimulus "noise" should be removed throughout.

      We have removed the term “noise” throughout the manuscript and replaced it with either “strong water current stimulus” or “mechanical overstimulation” where appropriate.

      4) Decreases in mechanotransduction are shown by dye entry. These results should be strengthened using microphonic potentials to determine the extent of damage. This experiment is not necessary but would improve the quality of the document.

      While we agree that microphonic recordings would provide further support for reduced mechanotransduction, quantitative FM1-43 uptake in zebrafish lateral line hair cells is a well-established proxy for microphonic measurements. In a previous study using the same protocol utilized in our manuscript, FM1-43 labeling intensity was shown to directly correspond with microphonic amplitude (Toro et al, 2015). Moreover, the fixable analogue of FM1-43 (FM1-43FX) gave us comparable relative measurements of uptake as live FM1-43 and provided the additional advantage of high temporal resolution and the ability to simultaneously assay entire cohorts of control and overstimulated fish (which is not possible with microphonic measurements or live FM1-43 imaging), as we could expose groups of fish briefly to the dye at determined time intervals following overstimulation, then immediately place in fixative.

      5) In figure 2, PSD labeling is not clear.

      We assume the reviewer meant PSD labeling in Figure 4 and we agree it is difficult to discern. We have changed the hair-cell label from gray to blue in the images so that the green PSD labeling is clear.

      Reviewer #2:

      1) While the findings are carefully measured and described, the effects of insult on hair cells are relatively minor, with a change in hair cell number, extent of innervation or synapses per hair cell (Figs 3 and 4) in the range of 10% reduction compared to control. One potential value of the model would be to use it to discover underlying pathways of damage or screen for potential therapeutics. However with these modest changes it is not clear that there will be enough power to determine effects of potential interventions.

      One advantage of the zebrafish model is the ability to overstimulate large cohorts of larvae, thereby providing enough power to uncover modest but significant changes resulting from moderate damage to hair cells. While not as well suited for unbiased large-scale screens of therapeutics, our overexposure protocol provides the opportunity to determine the role of specific cellular pathways (e.g. metabolic stress, inflammation, and glutamate excitotoxicity) in hair-cell damage and synapse loss following mechanically-induced damage via genetic or pharmacological manipulation of these pathways. Additionally, as the hair cell synapses fully repair following stimulus-induced loss, the zebrafish model has the potential for identifying novel pathways for repair through transcriptomic profiling (for an example, see Mattern et al, Front. Cell Dev. Biol., 2018). Cumulatively, these future experimental directions will provide important mechanistic information that could be used toward the development of targeted therapeutic interventions.

      2) The most dramatic phenotype after shaking is a physical displacement of hair cells, described as disrupted morphology. However it is not clear what the underlying cause of this change. Are only posterior neuromasts damaged in this way? Is it a wounding response as animals are exposed to an air interface during shaking? It is also not clear to what extent this displacement reveals more general principles of the effects of noise on hair cells. Additional discussion of underlying causes would be welcome.

      We agree that the underlying causes of the physical displacement of posterior lateral-line neuromasts warranted further investigation and we have expanded appropriate sections of the results. To determine if excessive hair-cell activity plays a role in the displacement of neuromasts we have exposed lhfpl5b mutant—fish that have intact hair cell function in the ear, but no mechanotransduction in hair cells of the lateral line—to mechanical overstimulation. We observed comparable disruption of neuromasts lacking mechanotransduction, supporting that displacement of lateral-line hair cells is due to mechanical damage and does not require intact mechnotransduction. Further, when examining the adjacent supporting cells in disrupted neuromasts, we observed they are similarly displaced and elongated. We conclude that observed disruption of hair cells is a consequence of mechanical displacement of the entire neuromast organ. We have added additional discussion of this phenomenon to the Results and Discussion sections of the manuscript.

      3) Because afferent neurons innervate more than one neuromast and more than one hair cell per neuromast, measurements of innervation of neuromasts (Figure 3) or synapses per hair cell (Fig 4) cannot be assumed to be independent events. That is, changes in a single postsynaptic neuron may be reflected across multiple synapses, hair cells, and even neuromasts. This needs to be accounted for in experimental design for statistical analysis.

      We agree that changes in single postsynaptic neurons, which innervate groups of hair cells of the same polarity within a neuromast, could be reflected across multiple synapses. Additionally, it is plausable that excitotoxic events at the postsynapse, while not contributing to apparent neurite retraction, could be contributing to synapse loss across multiple innervated hair cells. We have updated the manuscript to reflect the potential contribution of postsynaptic signaling to synapse loss and added experiments pharmacologically blocking glutamate uptake.

      4) The SEM analysis provides compelling snapshots of apical damage, but could be supplemented by quantitative analysis with antibody staining or transgenic lines where kinocilia are labeled. The amount of reduced FM1-43 labeling is one of the more dramatic effects of the shaking insult, suggesting widespread disruption to mechanotransduction that could be related to this apical damage. Further examination of the recovery of mechanotransduction would be interesting.

      To supplement the SEM snapshots of severe apical damage, we have expanded the SEM image analysis with quantitative data on kinocilia morphology. We have also added confocal images of hair bundles using antibody labeling of acetylated tubulin in a transgenic line expressing β-actin-GFP in hair cells. We agree that correlative studies of mechanotransduction recovery relative to hair-bundle morphology would be interesting, and we intend to examine this question in a future follow-up study.

      5) A previous publication by Uribe et al.2018 describes a somewhat similar shaking protocol with somewhat different results - more long-lasting changes in hair cell number, presynaptic changes in synapses, etc. It would be worth discussing potential differences across the two studies.

      We agree we did not adequately address the considerable differences between our mechanical damage protocol for the zebrafish lateral line and the damage protocol described by Uribe et al, 2018. We have provided a more direct comparison in the Results section and addressed the differences in our protocols in-depth in the Discussion section.

      Our damage protocol uses a stimulus within the known frequency range of lateral-line hair cells (60 Hz) that is applied to free-swimming larvae and evokes a behaviorally relevant response (fast start response). The damage is observable immediately following noise exposure, is specific to posterior lateral-line neuromasts, and appears to be rapidly repaired. Some features of the damage we observe—reduced mechanotransduction and hair-cell synapse loss—may correspond to mechanically induced damage of hair cell organs in other species. Notably, hair cell synapse loss in seemingly intact neuromasts is exacerbated by pharmacologically blocking synaptic glutamate clearance, supporting that the 60 Hz frequency stimulus is overstimulating neuromast hair cells directly and suggesting that the mechanism of synapse loss may be similar to inner hair cell synapse loss reported in mice following moderate noise exposures.

      By contrast, the damage protocol published by Uribe et al used ultrasonic transducers (40-kHz) to generate small, localized shock waves rather than directly stimulate neuromast hair cells. The damaged they reported—delayed hair-cell death and modest synapse loss with no effect on hair-cell mechanotransduction—was not apparent until 48 hours following exposure and not specific to the lateral-line organ. Some of the features of the damage they observed—delayed onset apoptosis and hair-cell death—may correspond to damage reported in mice following blast injuries.

      Reviewer #3:

      1) As the authors point out, zebrafish hair cells can be regenerated. With that in mind, and to make the relevance for mammalian hair cell repair clear, a clear distinction between mechanisms mediated by "repair" or "regeneration" needs to be made. The authors discuss that proliferative hair cell generation can be excluded based on the short time period, but suggest that transdifferentiation might be involved. Recovery of NM hair cell number occurs within the same 2 hour period in which NM morphology and hair cell function improved, making it difficult to determine the extent to which "regeneration" contributed to the recovery. The amount of transdifferentiation has to be shown experimentally (lineage tracing?).

      We agree that the distinction between "repair" and "regeneration" needs to be made when discussing this model of mechanical damage to zebrafish hair cell organs. We have tried to clarify that most of what we observe regarding recovery—restoration of neuromast shape, mechanostransduction, afferent contacts, and synapse number —reflect mechanisms of repair following mechanical damage (and, in the case of synapse loss, overstimulation) rather than regeneration. However, one feature of damage that may reflect rapid regeneration is restoration of hair cells number following mechanical injury. To experimentally determine whether proliferation contributed to hair cell generation, we assessed the incorporation of the thymidine analog EdU during a 4 hour recovery following mechanical overexposure in a transgenic line expressing GFP in neuromast supporting cells and observe a modest but not statistically significant increase in the number of proliferating supporting cells in neuromasts exposed to strong current stimulus, suggesting recovery of lost hair cells is not primarily due to renewed proliferation.

      The number of hair cells that are lost and recover within several hours are low, i.e., typically ~1 hair cell/neuromast. We observed this consistently in all of our experiments, but the mechanisms responsible are not clear. Based on previous studies of hair cell regeneration in the lateral line, the recovery time appears too rapid to be caused by renewed proliferation, a notion that is further supported by our Edu studies. On the other hand, it is possible that a few supporting cells may undergo the initial phases of phenotypic change into hair cells during this short time period, and we speculate that such transdifferentiation may be responsible for the observed recovery. We should emphasize that this is a new observation and, at present, we do not fully understand the underlying mechanism. However, the focus of the present study is on mechanical damage, synaptic loss, and subsequent repair. We believe that it is important to report our consistent findings of low level hair cell loss and recovery, but a detailed characterization of the mechanism would require considerable effort and would best be the topic of a future study.

      2) The classification of "normal" vs "disrupted" is vague and not quantitative. The examples shown in the paper seem to be quite clear-cut, but this reviewer doubts that was the case throughout all analyzed samples. Formulate clear benchmarks and criteria for the disrupted phenotype (even when blind analysis is performed).

      We have defined measurable criteria for "normal" vs "disrupted" neuromasts that we have added to the Method Details section: “We defined exposed neuromast morphology as “normal” when hair cells appeared radially organized with a relatively uniform shape and size, with ≤7 μm difference observed when comparing the lengths from apex to base of an opposing pair of anterior/posterior hair cells. Length was measured from a fixed point at the center of the hair bundle to the basolateral end of each opposing hair cell. We defined neuromasts as “disrupted” when hair cells appeared elongated and displaced to one side, with >7 μm difference observed when comparing the lengths of an opposing pair of anterior/posterior hair cells. Generally, the apical ends of the hair cells were displaced posteriorly, with the basolateral ends oriented anteriorly.”

      3) Sustained and periodic exposure: These two exposure protocols not only differ with respect to sustained vs periodic, they also differ in total exposure time (Fig 2B). This complicates the interpretation, especially considering the authors own finding that a pre-exposure is protective.

      To clarify—pre-exposure was not protective to hair-cell survival. Rather, in preliminary experiments, pre-exposure appeared to reduce larval mortality, and we have clarified that observation in the text of the Results and the Methods Details sections. We agree with the reviewer that comparing the two protocols based on differences in time distribution is complicated in that they also differ in total exposure time. For the purpose of clarity, we now focus on the sustained exposure in the main figures and created supplemental figures for the reduced damage still observed using periodic exposure, specifying that reduced damage may be the result of periodic time distribution of stimulus and/or less cumulative time exposed to the stimulus.

      4) The data on the mitochondrial ROS aspect seems not well integrated into the overall story.

      We agree that the ROS story was not well integrated and incomplete. We have removed the data describing mpv17-/- mutants and mitochondrial disfunction from this manuscript. A more comprehensive report of mpv17-/- mutant mitochondrial function and morphological analysis of neuromasts following noise exposure is now described in a follow-up manuscript (“Influence of Mpv17 on hair-cell mitochondrial homeostasis, synapse integrity, and vulnerability to damage in the zebrafish lateral line”).

      5) It is surprising that the hair bundle morphology was not assessed after recovery. This is crucial. Overall, it would be good to see some quantification of the SEM data, e.g. kinocilia length and number of splayed bundles.

      We have expanded the SEM image analysis to quantitatively access kinocilia morphology following exposure. We agree that assessment of recovery using live imaging of hair bundles paired with subsequent SEM analysis will be informative, and we intend to perform those experiments in a future study.

      6) Behavioral recovery (measured as number of "fast start" responses) was also not assessed. This is essential for determining the functional relevance of the recovery.

      We attempted to measure behavior recovery of lateral-line function by measuring “fast-start” responses immediately and several hours after recovery, and discovered that i) strong water current provided stimulation that was too intense to reveal subtle behavioral changes following lateral-line damage and recovery, and ii) when testing larvae immediately following sustained strong current exposures, it was difficult to discern if fewer “fast-start” responses were due to lateral-line organ damage or larval fatigue. We agree that behavioral recovery is important to assay but acknowledge assessing lateral-line mediated behavior following mechanical damage will require a more sensitive testing paradigm that stimulates the lateral-line sensory organ with a relatively gentile, calibrated water flow stimulus. We are currently performing a follow-up study to this paper using a testing paradigm developed by a postdoctoral associate in our lab that analyses subtle changes in larval orientation to water flow (rheotaxis) mediated by the lateral-line organ. Using this behavior paradigm, we will directly correlate morphological and functional recovery over time.

      7) This reviewer is not yet convinced that this damage model displays enough commonalities to mammalian noise damage to justify the ubiquitous use of the term "noise" throughout the manuscript. It would be more prudent to use a more careful term along the lines of "mechanical overstimulation-induced damage".

      We have removed the term “noise” throughout the manuscript and replaced it with either “strong water current stimulus” or “mechanical overstimulation” where appropriate.

      8) Overall, there was a lack of experimental and analysis detail in the results section. For example, how was afferent innervation quantified? Just counting GFP labeled contacts to hair cells?

      Innervation of neuromast hair cells was quantified during blinded analysis by scrolling through confocal z-stacks of each neuromast (step size 0.3 μm) containing hair cell and afferent labeling and identifying hair cells that were not directly contacted by an afferent neuron i.e. no discernable space between the hair cell and the neurite. Hair cells that were identified as no longer innervated showed measurable neurite retraction; there was generally >0.5 μm distance between a retracted neurite and hair cell. We have added this information to the Methods Detail section.

      There was also inconsistency in the use of two variations of the mechanical damage protocol, the time points at which repair was assessed, and whether the damage was quantified in all neuromasts or in normal vs. disrupted neuromasts separately, making the data difficult to interpret.

      We have revised our figure legends to clearly indicate when we are assessing damage in all exposed neuromasts (pooled) to control vs. comparative analysis of normal vs. disrupted neuromasts relative to control. In addition, we now focus on the sustained exposure in the main figures, which was the exposure protocol used for the time points in which repair and recovery were assessed.

    1. Reviewer #3 (Public Review):

      Evolution is a historical phenomenon that plays out over time through the complex interaction of the stochastic processes of mutation and genetic drift and the deterministic process of natural selection. Biology has seen a vibrant debate over the last few decades over what this means for the repeatability of evolution, and to what degree evolutionary outcomes are shaped by the combination of necessity, chance, and historical contingency. This debate has led to intense empirical study of these factors in evolution. Reconstruction and examination of functional protein evolution has been one of the cleverest and most interesting systems used in this study. Here, the authors seek to examine the roles of chance, contingency, and necessity in the evolution of protein-protein interactions (PPI) between BCL-2 family proteins and their coregulators. They specifically look at the evolution of specific interaction between BCL-2 and BID and the more generalized interaction between MCL-1 and coregulators BID and NOXA. They authors reconstructed the last common ancestor protein of BCL-2 and MCL-1 and a series of intermediates along their respective lines of descent. They then used a very clever Phage Assisted Continuous Evolution (PACE) system to subject replicates from each time point to selection for different PPIs and examined variation in sequence variation. By looking at evolution in replicates from different time points, they were able to disentangle the effects of chance, contingency, and necessity. They found that necessity played little role in protein evolution, with little predictability between replicates of single time points and among those from multiple time points, indicating that there was no single pathway through sequence space to the selected function. They did, however, find strong and synergistic effects of chance and contingency. They did tests to demonstrate that the effects of contingency were due to epistatic interactions that affect the viability of particular historical paths. Chance, meanwhile, had effects because multiple mutations could lead down paths to the selected function. The authors conclude that history and chance must be considered when attempting to understand protein function evolution, and that the sequences of proteins with given functions reflect do not reflect necessary pathways or constrained endpoints, but particular and idiosyncratic histories. Moreover, they suggest that contingency may need to be considered as a fundamental aspect of the evolutionary process, along with mutation, drift, and selection.

      Altogether, this is a wonderful and interesting manuscript that makes a substantial and material contribution to our understanding of how history and chance affect evolution. It even speaks to the nature of more fine-grained protein sequence evolution relative to neutral and adaptationist theories. The amount of work and thought that went into the research is nothing less than astonishing. Every time I found myself wondering, "but did they check this...", I found that they, in fact, did in the next section. The work is solid, and the results are robust. I do not see anything that concerns me in the nitty gritty of the actual scientific work. I do, however, think that the authors should engage the work that philosophers of science have done in the last decade or so to better develop our conceptual understanding of contingency and reconsider the meaning of their findings in light of that work.

    2. Reviewer #2 (Public Review):

      The extensive description of mutational paths using high-throughput phenotyping combined with sequencing provides a rich and useful data set. However, the experimental setup has some serious limitations.

      First, the authors want to address the evolution of protein-protein interactions, but they actually do so comparing the interaction of actual and ancestral proteins with actual human BID and NOXA proteins. The analysis would have been stronger with reconstruction of ancestral sequences also for the BID and NOXA proteins, to test interaction of two proteins at the same evolutionary node. Actually, characterization of protein-protein interactions between proteins from Trichoplax, for example, suggest that the results may be different (Popgeorgiev et al., Science Advances 2021).

      Second, the specificity of the binding of NOXA to MCL-1 and not to BCL-2 seems to be an artifact due to the use of peptides instead of full-length protein during interaction assays. This is explicitly indicated in one of the reviews the authors cite in their introduction (Kale et al., 2018, p67). This review mentions a JBC paper clearly demonstrating that BCL-2/NOXA interaction do occur even in human cells: Smith AJ, Dai H, Correia C, Takahashi R, Lee SH, Schmitz I et al. Noxa/Bcl-2 protein interactions contribute to bortezomib resistance in human lymphoid cells. J Biol Chem 2011; 286: 17682-17692.

      Third, the same review also stresses that these proteins are partially membrane-bound in vivo. So testing their interactions in soluble protein bioassays is far from physiological relevance. Actually, such a warning appears already in one of the bullet points from the Kale review:

      "The majority of studies examining the interactions between BCL-2 family proteins use truncated proteins or peptides of the BH3 region at physiologically irrelevant concentrations or in the absence of membranes leading to confusion in defining the core mechanisms of the BCL-2 family proteins."

    3. Reviewer #1 (Public Review):

      This manuscript reports a novel and original approach to examine the possible mutational paths underlying directed protein evolution.

      The authors conclude from their experiments that "Necessity was almost entirely absent" (line 209). Indeed, the vast majority of states evolved in just one replicate from one starting point. But this is the problem of a half-full glass: is it half full or half empty? If I understand Fig.4F correctly, one can still detect amino acid changes that recapitulate historical substitutions, and others that revert to the historical state, so it does not seem that necessity is "almost entirely absent". Furthermore, several of the amino acid changes that were detected may not have any effect on NOXA or BID biding, maybe they occurred because of mutational bias, drift or hitchhiking. If this is the case, then one cannot compare all acquired states in each trajectory and conclude about the importance of chance, as in this sentence for example: "Pairs of trajectories launched from the same starting point differed, on average, at 78% of their acquired states, indicating a strong role for chance" (line 219). There are causal mutations that arose repeatedly during PACE replicates from each starting genotype and these mutations do indeed confer the selected-for specificity in their "native" background (as is nicely shown in Figure 6A-B). So this, to me, is evidence for necessity.

      Loosing a binding property can probably occur via multiple ways, which are likely to be more numerous than gaining binding for a given protein. It would be nice to discuss this point in more detail.

      The experiments presented are limited to one protein family and to the binding properties to two different proteins. In living organisms, each protein is likely to exhibit particular properties such that it can bind or not bind to hundreds of different proteins, and not just two as tested here. So the constraints present in living organisms may be much larger than the ones present within this experimental evolution set up. Furthermore, the tested proteins probably encounter other constraints in their native environment besides affinity for other proteins, and it is yet unclear whether the variant forms obtained here via experimental evolution would be fine to replace the endogenous proteins in living organisms. It is therefore difficult to generalize from the obtained results to all types of evolutionary changes. In general, the conclusions should be toned down and focused on this particular example.

    4. Evaluation Summary:

      This manuscript, which will be of interest to students of evolution and anybody interested in protein function, uses an original, clever, high throughput, and rapid experimental protein evolution method to assess the roles and contributions of contingency, chance, and necessity in the evolution of protein-protein interactions. The authors focus on the animal BCL-2 protein family and on the evolution of their binding properties to two proteins, NOXA and BID. Using several replicates and several starting points, they found little predictability between replicates of single starting points and among those from multiple starting points, indicating that there is no single pathway through sequence space to the selected function, and that historical contingency is the primary cause of protein evolution here. The presented results convincingly illustrate the potential of this novel technology for future work in directed protein evolution.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #3 agreed to share their names with the authors.)

    1. Reviewer #4 (Public Review):

      The authors analysed flavinylation across different species. They analysed impressive number of 31.910 prokaryotic genomes. They mined flavinylation associated gene clusters using a bioinformatic approach. They define five different protein classes responsible for transmembrane electron transfer. Moreover, they predicted and validated flavinylation of two domains with unknown functions (by ApbE). Unfortunately, the vast majority of predictions made in this study were not experimentally validated. It is therefore very difficult to judge the reliability of predictions, proposals and claims made in the manuscript.

    2. Reviewer #3 (Public Review):

      Summary

      The authors have applied a comprehensive bioinformatics analysis to 31,910 prokaryotic genomes and found evidence for extracytosolic flavin transferases ("ApbE") in approximately 50% of the genomes. Moreover, they have analyzed associated gene clusters resulting in the hypothesis that five protein classes are involved in transmembrane electron transfer. Furthermore, the authors postulate that these protein classes are subject to flavinylation by ApbEs. Although the exact biochemical role of these five classes of protein remains unknown, the authors hypothesize that they might be involved in iron assimilation and respiration, at least in some cases. In this context, the authors also identified multi-flavinylated proteins and propose that these might exert a similar role as multi-heme cytochromes, for example under iron depletion; in other words, multi-flavinylated systems might replace multi-heme cytochromes if iron is limiting.

      Strength & weaknesses

      As is evident from the summary, the basis of the article is the bioinformatic analysis of prokaryotic genomes leading to a number of interesting hypotheses with regard to transmembrane electron transport of hitherto uncharacterized protein complexes. Thus, the proposed functions of the potentially flavinylated membrane complexes will stimulate biochemical studies to characterize the suggested involvement of flavinylated protein complexes in prokaryotes. I would consider this as the main strength of the paper that it has generated multiple challenging hypotheses to follow up experimentally.

      As mentioned by the authors, about 50% of the prokaryotic genomes analyzed harbor targets for flavinylation/and the FMN transferase. However, no discussion and not even a hint is provided what these 50% of prokaryotes have in common and what distinguishes this group from the other (50%) prokaryotes. Is it lifestyle (environment), energy production, ...?

      On the other hand, the presented study leaves many issues unmentioned creating the (false) impression that all it takes to transport electrons across the membrane is a series of hemes and/or flavins along the way. For example, in the discussion of the very interesting hypothesis that flavinylation might replace multi-heme cytochromes under iron deficiency, discussed on page 20 (last para), the authors mention that "flavins possess two-electron transferring properties (ref. 46)" in contrast to the heme system. If this were true than the switch from heme to flavin would also imply that the electron transport itself would have to change from one- electron to two-electron transport. It is unclear that this would be compatible with all other components of the electron transport system. On the other hand, flavins can also - under certain circumstances and in certain environments - carry out one-electron transfer processes, e. g. DNA-photolyases, flavodoxins, etc. Thus, it is conceivable that the flavins operating in the suggested systems in prokaryotes also perform one-electron transport, similar to the operating mode of heme cytochromes. It is clear that we currently lack the biochemical/physical information to know what is really going on, but at least it should be discussed more thoroughly. Equally, several other aspects of the (multi-)flavinylation should be addressed:

      • What is known about the environment of the flavin(s)? - Is the flavin embedded in a protein matrix or freely accessible, in other words does it "behave" like a "free" flavin?

      • How does the binding of the flavin affect the redox potential (this is very important in order to understand the direction of electron transport).

      • In contrast to other covalent flavin attachments, the flavinylation addressed in the current work is reversible. Is anything known about the removal of flavins from the protein complexes in question?

      • Are there any enzymes that carry out de-flavinylation? If so, how are they regulated?

      • Connected to the last bullet point: Is the reversibility of flavinylation used for the overall regulation of electron transport?

      I assume that most of the questions cannot be satisfactorily answered yet, but I think these issues should at least be addressed in the discussion in order to stress the need for further in depths biochemical studies that target the obvious complexity of these systems.

    3. Reviewer #2 (Public Review):

      Interesting bioinformatics. The strength of this article lies in the extensive search for flavinylated domains in prokaryotic genomes. This has resulted in several new ideas about the functions of these domains in transmembrane electron transport. The comparison with (multi-heme) cytochromes and thioredoxins is interesting, and needs experimental validation in future work.

      Some weaknesses: In the introduction, I miss a clear explanation about the mode of flavinylation of the FMN-binding proteins and how this relates to other covalent flavinylation systems (where an increase in redox potential of the flavin is a prominent effect of covalent binding). It is also not clearly explained whether the predicted flavinylation of the phosphate moiety of FMN is reversible.

      Results and Discussion: The electron transfer properties of flavoproteins are not well explained. Quite some flavoproteins (e.g. flavodoxins) mediate one-electron transfer processes, and this is most likely the preferred way in the discussed transmembrane electron transport systems.

      I was wondering if there is any protein structural information about this mode of flavinylation, for instance is the flavin hidden in the protein or accessible? Can the authors tell us more whether the amino acid sequence results explain in more general terms the site(s) of flavinylation?

      I would also like to know how sure the authors are that the conserved motif always represents covalent flavinylation.

      Along similar lines, regarding the reversibility of the covalent flavinylation, I am curious how sure the authors are that the flavin is always covalently bound and what would be the consequence if this is not the case. For example, might there be next to iron limitation, also flavin limitation?

      Finally, I am wondering whether more could be said about the comparison with thioredoxins and cytochromes when we look at the 50% of bacteria that do not contain the flavinylation domains.

    4. Reviewer #1 (Public Review):

      The manuscript by MeHeust reports identification of flavinylation proteins that can potentially function as cellular redox mediators related to electron transfer systems in prokaryotes.

      The work is useful and informative. The authors used bioinformatic approach to illustrate wide distribution of these proteins in a variety of prokaryotes. Although exact functions of these proteins are not known, this work should inspire further investigation by researchers in the fields of redox enzymology and bioenergetics.

    5. Evaluation Summary:

      Light and coworkers provide evidence from mining 31,910 prokaryotic genomes for the widespread occurrence of extracytosolic flavinylated FMN-binding domains in bacteria. They discovered extracytosolic flavinylation of five protein classes potentially involved in transmembrane electron transfer. The study also proposes new connections between respiration and iron assimilation and identifies two novel substrates of ApbE enzymes. This work should inspire further work in the fields of redox enzymology and bioenergetics to characterize the suggested involvement of flavinylated protein complexes in prokaryotes.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1, Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

  2. Apr 2021
    1. Reviewer #3 (Public Review):

      The regulation of the calcium pump SERCA by phospholamban has been studied extensively over many years as this system has become a focus of many biophysical approaches to study the interplay between protein dynamics, the biological function of calcium transport, and its regulation via protein-protein interactions, all of which are occurring within the environment of the sarcoplasmic membrane of heart muscle.

      The authors themselves have a long track record with working on this system and the specific focus here is on the detailed mechanism of how phosphorylation of phospholamban leads to a release of its inhibitory function when bound to SERCA. Much effort has been spent on this question in the past, and the field has progressed over the years by deriving increasingly detailed structural models for SERCA-phospholamban interactions. There is now a structure from crystallography showing the interaction of the phospholamban TM domain with the SERCA TM helices and there is additional data from various biophysical methods that partially describe the conformational ensemble of the extramembrane N-terminal region of phospholamban and its interaction with SERCA. Some of that insight has distinguished between phosphorylated and unphosphorylated phospholamban, but despite much data and many simulation efforts, the exact mechanism for how phosphorylation of phospholamban alters its interaction with SERCA and thereby modulates its inhibitory functions has so far not been clearly described. This is the main goal of the present work.

      There is new experimental data presented here from oriented-sample solid-state NMR experiments with the main finding of orientational shifts of the phospholamban TM helix upon binding to SERCA and upon phosphorylation. Taking advantage of this data, the main part of the study is concerned with results from computer simulations that were restrained by experimental data to develop conformational ensembles of the SERCA-phospholamban complex with and without phosphorylated phospholamban. From that, new mechanistic hypotheses are developed. While the direction of the work proposed here is promising, there are concerns about the overall approach and - as a consequence - the significance of the reported findings:

      1) A main concern is the treatment of the extramembrane portion of phospholamban, which includes the serine that is being phosphorylated to relieve the inhibitory effect. Previous studies have described a helical conformation for the N-terminal segment that may be in equilibrium with a less-ordered/less-helical structure upon binding to SERCA. It is largely still not clear, however, how exactly that part of phospholamban would interact with SERCA. The idea put forth here is that a largely disordered conformation would interact with SERCA. That may be so, but it is unclear how much of that is a direct result of experimental constraints and how much could simply be a consequence of inadequate sampling. It seems that helical conformations for the N-terminal segment of phospholamban were not considered, while there is not enough discussion of why such conformations would be ruled out based on the experimental data.

      2) The simulations are probably too short to fully explore the full conformational landscape of a (partially) disordered N-terminal phospholamban and it is unclear how much the experimental constraints are really limiting the conformational space in that region.

      3) It is not completely clear how the present work relates to the crystal structure of the SERCA-phospholamban complex. Why were the starting structures for the SERCA-phospholamban complex initially taken from the available crystal structure (at least with respect to the TM domain of phospholamban) but then subsequently refined using much lower-resolution cross-linking data before initiating the simualtions? Is the crystal structure in significant disagreement with other experimental data considered here? More discussion and explanation is needed.

      4) The main focus of the analysis of the simulation results is on the impact of phosphorylated phospholamban on the conformational sampling of SERCA. That is the key step for developing new mechanistic hypotheses. However, given that the SERCA-phospholamban complex is very large and flexible and based on the results presented, it appears that the length of the simulations may not be sufficient to fully characterize the shift in the conformational ensemble of SERCA as a function of phospholamban phosphorylation. At the minimum, some time of convergence analysis is needed to establish confidence that the difference in conformational ensembles shown most prominently in Figure 2 are indeed significant. Moreover, related to Figure 2, it is unclear whether the projection of the conformational sampling onto just two principal coordinates is sufficient for a full characterization of the conformational dynamics. It is also unclear whether the principal coordinates are the same when projecting the sampling for PLN and pPLN, if not, the comparison between the two would be further complicated.

    2. Reviewer #2 (Public Review):

      In this paper, the authors present an extensive ssNMR study on the mini-membrane protein phospholamban (PLN), which regulates the Ca2+ ATPase SERCA. PLN stabilizes the low-affinity Ca2+ state of SERCA, which can be reversed by phosphorylation or increase in [Ca2+]. Despite extensive, studies this mechanism is still unknown: Although interaction sites within the membrane have been identified, not structural changes within PLN have been detected. In the paper, the authors address this question by oriented ssNMR, an approach which is highly suited to map topological changes of membrane embedded peptides and proteins. While oriented ssNMR is conceptionally very appealing, it has been hampered by sample preparation restrictions preventing its widespread use on more complex samples. A breakthrough has been magnetic alignment of membrane proteins embedded in bicelles as demonstrated here. The presented spectra represent in principle a projection of labelled transmembrane helices onto a spectroscopic plane by which re-orientations of these helices can be elegantly visualized. Based on high quality data, the authors are able to convincingly demonstrate that PLN is in a topological equilibrium, which shifts upon phosphorylation at Ser60. In complex with SERCA, phosphorylation or Ca2+ binding triggers a topological change of the whole PLN transmembrane domain, which then act as a 'switch' on SERCA.

      All presented data are of high quality and data interpretation is convincing. The paper addresses a complex and relevant biomolecular question by very advanced methodology.

      The authors have identified a topological allostery for PLN connecting a posttranslational modification at the cytoplasmic site with signal transduction across the membrane. They argue that the underlying mechanism might be of general relevance for the regulatory role fulfilled by miniproteins.

    3. Reviewer #1 (Public Review):

      The regulation of highly dynamic interactions is for many biological processes of great importance. The authors study the regulatory interaction of the single transmembrane helix protein Phospholamban with the P-type ATPase SERCA which is responsible for removing calcium ions from the sarcoplasm and restoring its concentrations in the sarcoplasmic reticulum. The inhibitory interaction between both proteins is relieved by phosphorylation of a single residue in the cytoplasmic domain of Phospholamban. The authors show by a combination of solid state NMR as well as MD simulations that phosphorylation results in a order to disorder transition in the cytoplasmic part which leads to an re-arrangement of electrostatic networks which is propagated into weakened hydrophobic interactions between the transmembrane parts, thus activating SERCA. Phospholamban has been studied extensively by solid state NMR, liquid state NMR or hybrid methods. For example the phosphorylated form was studied previously, showing that it interacts differently with lipids (doi: 10.1021/bi0614028) and that Ser-16 phosphorylation alters the structural properties of the cytoplasmic domain with respect to the lipid bilayers (doi:10.1016/j.bbamem.2009.12.020). There have also been EPR and other studies, in principle showing the same effect. The current paper adds to this a new solid state method that shows additional details that could not be investigated previously. The work confirms less well determined previous models. The major new aspect is an MD simulation that provides a more detailed view than what was previously possible.

    4. Evaluation Summary:

      There are many of membrane-embedded mini-proteins, which fulfill a large range of regulatory functions. One of them is phospholamban, a single transmembrane helix protein that regulates the sarcoplasmic reticulum Ca2+-ATPase by binding in the membrane. The work presented here combines new experiments with computer simulations with the aim of arriving at a more definitive answer to the long-standing mechanistic question of how exactly phosphorylation of phospholamban modulates its regulatory behavior. In this manuscript, an allosteric mechanism is presented, which could be of general importance for the whole family of these mini-proteins.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

    1. Reviewer #3 (Public Review):

      Using a combination of powerful approaches authors demonstrate large variability in the number of release sites at hippocampal excitatory synapses onto fast spiking interneurons in slices. High resolution studies of individual synapses showed highly variable amounts of Munc13-1within the AZs that have the same number of release sites. The authors further revealed a synapse size-independent variability in the number of Munc13-1 clusters per AZ and in the Munc13-1 content of individual clusters. There results support the presence of multiple independent release sites and provide insight into molecular heterogeneity of release sites.

      This is a high quality study using most advanced techniques available to study molecular determinants of AZ organization. In addition to some technical issues, my main concern is conceptual: this work, although of very good quality overall, is rather incremental because it largely confirms several previous studies showing a large variability in the number of release sites per AZ in small central synapses, the association between Munc-13 and release site properties, and variability in Munc-13 content. Surprisingly only one of the three of these previous studies have been cited or discussed. My second concern is that the paper could be written more clearly - there are multiple terms used to refer to the same concepts making it difficult to follow and there is some conceptual logical fallacy in the way the results are discussed.

    2. Reviewer #2 (Public Review):

      Karlocai et al addresses a prevailing concept of synapse diversity, asking whether diversity of release probability is caused by varying number of release sites and/or the properties of individual release sites. In other words, are there functionally uniform release sites (RS) that scale in numbers with the size of the AZ and thus regulate release probability (Pv), or are, in addition, RS may be heterogeneous in composition and function. Performing quantal analysis 2.0 by combining ephys from pyramidal-to-parv interneurons in hippocampus with quantitative anatomy of a presynaptic key transducer, Munc13, they define N, Pv and Q and compare it to the numbers of munc13 clusters and densities. As expected from previous studies, RS numbers covary with the size of the AZ, but the amounts of Munc13-1 are highly variable at individual RSs, providing a possible additional source of Pv variability.

      Overall the quality of data is just superb, and the conclusion are well supported by the data as sufficient electrophysiological experiments were performed, and importantly also correlated with multiple, highly quantitative microscopy techniques. Only very few labs can do this at this level.

      The findings carry enough impact as they negate the hypothesis that RS are made out of predefined release sites. Also, the finding that the post synapse as defined by PSD95 labeling was much less variable, indicates that pre- and postsynaptic makes do not necessarily correlate, arguing somewhat against the transsynaptic nano column concept as a main organizing principles. Thus, pre- and post-synapses are only loosely linked in their composition and function.

    3. Reviewer #1 (Public Review):

      The authors address the broad question of what is responsible for the large diversity of presynaptic function at synapses arising from a single type of neuron. They use a variety of sophisticated and complementary approaches to address the functional and molecular heterogeneity of hippocampal pyramidal cell to fast-spiking interneuron synapses. The rigorous functional and molecular analysis is clearly described and compelling. The conclusions are consistent with the current view that each presynaptic active zone contains a variable number of release sites, and this variability makes a substantial contribution to the heterogeneity in postsynaptic response amplitude at unitary synaptic connection. Using state-of-the-art imaging approaches, the authors report variability in the content of Munc13-1, a core component of release sites, between release sites. Although these results and conclusions are well-supported, the functional significance of Munc13-1 variability at release sites is unclear.

    4. Evaluation Summary:

      The authors study how individual synapses can compute information by tuning the properties of the individual components that drive synaptic communication between neurons. Using cutting edge physiology and morphology they show that the reliability of synaptic communication depends not only on how many units drive synaptic communication, but also the authors suggest that individual units vary in their quantitative molecular composition.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

    1. Reviewer #2 (Public Review):

      In this manuscript, the authors characterise a GluA4-knockout mouse with respect to changes of cerebellar cortical circuit properties and behaviours.

      They demonstrate a clear reduction in the component of mossy fibre--granule cell synaptic transmission mediated by AMPA receptors, as expected. They also show two parallel changes in granule cells that could be considered partially compensatory: tonic inhibition of granule cells is reduced and the NMDAR-mediated component of the mossy fibre input is upregulated. The overall effect of the mutation is nevertheless to reduce the efficacy of the mossy fibre input; spike emission is therefore reduced in frequency, delayed, and has less precise timing.

      Two other key synapses in the mossy fibre pathway are shown to be apparently unaffected in the knockout mouse, namely mossy fibre to Golgi cell transmission and also granule cell to Purkinje cell transmission.

      The authors then model representation in the granule cell layer and downstream learning by the Purkinje cell, focusing on a reduction of the effective coding space available in the expansion performed by the granule cell layer and the downstream reduction of learning speed in the Purkinje cell.

      In a final, behavioural, section, the authors show that locomotion is little affected but that eyelid conditioning is essentially abolished, with two different conditioned stimuli.

      Overall, the experiments, analysis and presentation are of excellent quality.

      However, the conceptual framework and broader interpretation of the work is quite ambitious and I believe that it requires more nuanced presentation.

      A first and reasonably straightforward issue is the fact that the authors are, as they are well aware, working with a systemic knockout. Logically, therefore, the behavioural effects on eyeblink conditioning could reflect interference with any part of the input-output loop. Within the cerebellar circuit, the authors address this reasonably comprehensively, by confirming that mossy fibre to Golgi cell and granule cell to Purkinje cell transmission are unaffected. Nevertheless, one quickly wonders whether the activity of interneurones, climbing fibres or cerebellar nuclei might somehow be altered. The authors address possible extracellular effects of the knockout by showing that eyeblink conditioning is essentially abolished with two different modalities of conditioned stimulus. Again, it remains logically possible that both inputs or the common output could be altered.

      Experimentally verifiying all possible stages of the behavioural input-output loop is not feasible, while the ideal experiment of a granule-cell-specific knockout would amount to redoing the whole project, which is obviously out of scope. Nevertheless, I believe the issue does require slightly more open and detailed discussion; maybe the developmental down-regulation of GluA4 in relevant tissues could be substantiated better with reference, for instance, to expression atlases of the Allen Brain Institute. Ultimately, if the locus of action is not completely certain, that should be reflected in the conclusions.

      Finally, I'm a little uncomfortable with the ambitious conclusion that learning and behaviour have been constrained by the reduced coding expansion by the granule cell layer. Although the changes observed are indeed almost certain to reduce coding expansion as defined, I feel that the failure of learning could also be understood in more prosaic terms. In particular, the inputs to the Purkinje cell may simply be too weak, too delayed or too unreliable to be an effective plasticity substrate for rapidly developing a conditioned response before the air puff. To a large extent the lower-level modifications will correlate with the higher-level coding expansion, so the concepts are more or less synonymous. Yet, it feels different to conclude that patterns can't be separated because they produced no granule cell activity (to consider a logical extreme) and to conclude that their separation is too difficult because of output similarity and saturation of learning.

      Furthermore, there are ways to view coding expansion that wouldn't necessarily align with the authors' conclusion. Specifically, the combinatorial pattern separation analysed in the original Marr paper would, I believe, increase as the ratio of mossy fibre input strength to granule cell threshold decreases. In other words, for given overlapping mossy fibre inputs, the overlap between granule cells outputs could decrease as the input/threshold ratio decreases.

      Addressing these issues experimentally is certainly unfeasible. However, it might be possible to explore correlations/overlaps between input and output patterns in the modelling. The discussion could be made a little less assertive on these issues, and the question of input delay should be addressed.

    2. Reviewer #1 (Public Review):

      This study focuses on the consequences of deleting the GluA4 subunit of AMPA receptors for cerebellar synaptic transmission and cerebellar-dependent behaviors. The manuscript is well organized and the information is clearly presented. The first aim of the study is to investigate the effect of the deletion at the level of synaptic function. This is well achieved by a combination of patch-clamp recordings from cerebellar slices and modeling. It is found that deletion of the GluA4 subunit results in a strong decrease in synaptic currents from mossy fibers (MF) to granule cells (GC) as well as in two «compensatory» changes pertaining to NMDA Rs and tonic inhibition. As a consequence, MF-GC transfer is strongly reduced at high frequencies but less affected al low frequencies. The second part of the work investigates the effect of the GluA4 deletion on cerebellar-dependent behaviors. GluA4 knock-out mice are found to have no deficits in locomotion but exhibit a total absence of associative learning in an eye-blink conditioning paradigm. Both, at the slice level and at the behavioral level the strength of this work resides on the quality of the data and the rigorous analysis. A shortcoming of the work stems from the «compensatory» changes which complicate interpretation. However modeling strategies are implemented incorporating those changes and they are able to well predict the observed alterations in GC firing pattern, thus limiting the negative impact.

    3. Evaluation Summary:

      This work explores the cellular and behavioural effects of a genetically induced reduction of the expression of a glutamate (excitatory) receptor (GluA4), focusing on the cerebellum , a structure involved in the acquisition of arbitrary, complex motor reflexes. The authors show that synaptic transmission at the input layer to the cerebellar cortex is reduced, despite some compensation by other mechanisms, which are characterised. Locomotion is little affected while acquisition of a "conditioned eyeblink" is abolished. The authors try to link the cellular and behavioural phenomena via modelling of the cerebellar computation, although this is not definitive. The work is of high quality, of interest to cerebellar physicists and neurocomputational modellers in particular.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

    1. Joint Public Review:

      The way homologous chromosomes identify one another and become paired is an intriguing phenomenon that has a long history of study, yet the molecular mechanism remains unclear. Recent studies have led to a phenomenological button model for homolog pairing, which hypothesizes that pairing is initiated at discrete sites along the length of each chromosome. The authors aimed to rigorously investigate this idea using biophysical modeling and live imaging. They first constructed a simple polymer model with buttons distributed along the chain that possess locus-specific interactions, and thoroughly investigated its property via stochastic simulation in 3D. Their study confirms that homolog-specific interactions are necessary for homolog pairing. They also tested the effect of time, interaction strength, initial inter-homolog distance, and button density. The authors went on to perform live imaging of pairing dynamics at two selected loci, using the fluorescent signal from nascent mRNA at the corresponding locus. They fitted the model to the experimentally quantified pairing probability of the selected loci over a 6-hour developmental window, and used the constrained model to predict the individual pairing dynamics. The predicted inter-homolog distance post pairing agrees very well with experimental observation.

      Their study supports a button mechanism for homolog pairing, where stable pairing is initiated by reversible random encounters that are propagated chromosome-wide. This work suggests that active processes are not necessary to explain pairing and paves the way for further investigating the molecular mechanism of such a pairing phenomenon.

    2. Evaluation Summary:

      This manuscript considers an important open problem in molecular biology, that is how distal chromosomes can recognise each other at a distance and become paired, as happens for example in homolog paring in Drosophila. To address this question, the authors combine theoretical models and experiments, which return valuable insights. However, a final proof of the envisaged mechanisms remains to be determined.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

    1. Reviewer #3 (Public Review):

      The manuscript entitled "Biosynthesis system of Synechan, a sulfated exopolysaccharide, in the model cyanobacterium Synechocystis sp. PCC 6803" is a scientifically sound manuscript and is of interest for a broad scientific audience. It provides interesting and valuable new insights and many experiments were performed. However, there are some points which must be addressed to make the manuscript more consistent and easier to grasp.

      • Title: I would suggest to change the title, since Biosynthesis system is not a common term.
      • Abstract: Cyanbobacteria are not unique in having sulphated polysaccharides. What is about Carrageenan's and also exopolysaccharides from Porphyridium strains (see current publications on that). If it means that amongst bacteria the cyanobacteria are the only ones, this should be clearly stated.
      • Would avoid to use may utilize the polysaccharides... Please be more specific or delete this.
      • Lane 32: Can really every bacterium produce several EPS? This should be carefully evaluated.
      • Lane 34: The applications named are very broad and not specific, what are the real applications there?
      • Lane 49: again uniquely?
      • Lane 56: the sulphated polysaccharides are used for colony and biofilm... This sentence must be rephrased and corrected.
      • Lane 84: bubbling culture etc. I can´t find any detailed explanation on the cultivation systems, what is essential for the methods part. Please add volume, light source and principle of illumination (inside outside etc.). Please rephrase the sentence that the light was generated by fluorescent lamps.... They were used for illumination.
      • Lane 97: GTs can not be screened by disruption, it is their function what is screened.
      • Figure: would suggest to use A) instead of A,
      • Table S1: What does Importance mean in the table, would suggest to change that towards a more specific value/information
      • Lane 232: to see the transcriptome... this should be rephrased
      • The description of the different EPS is a bit confusing, since it is only described that the WT contains several sugars, which are then given in table 1. The deletion strain shows a different composition. This should be explained a bit straighter. Why is ribose given in table 1, if there is no ribose observed? In general, the whole manuscript needs correction of the English language to make it clearer in some aspects. Also, the structure of the manuscript might be reworked a bit, since it is a bit confusing in some parts. Especially the effect of the different deletions should be given clearly and straight. Also, the complexity of the manuscript will be easier to grasp by some rearrangements of the results. The current complexity might come from having all supplement figures already in the manuscript, but it also comes from sometimes complex sentences, as well as jumping a bit in between the topics. But finally, this is a really valuable and interesting study.
    2. Reviewer #2 (Public Review):

      The paper does a very thorough job of identifying genes important for the production and export of a sulfated exopolysaccharide in Synechocystis, leading to a clear and well-justified model for EPS production and its regulation. The authors also make a convincing case for the importance of EPS production for the formation of floating multicellular aggregrates or "blooms". However, the relationship between EPS production and bloom formation is not quantitative (some mutants show markedly reduced EPS production without any discernible effect on bloom formation) which indicates that bloom formation must involve additional factors which are not currently discussed.

    3. Reviewer #1 (Public Review):

      The authors have identified an entire set of genes for the synthesis of sulfated exopolysaccharides (EPS) in the cyanobacterial model Synechocystis 6803. They show convincingly that the respective gene products are involved in the production of these compounds and they have extensively characterized the regulation of these genes. Among the regulators they found a STAND protein. STAND proteins include animal and plant regulators of programmed cell death but were rarely characterized in bacteria. Last but not least they come up with an entirely new model for the buoyancy regulation of cyanobacteria (as light-dependent aquatic organisms it is important for cyanobacteria where they are in the water column). The authors suggest a mechanism in which EPS-entrapped cells together with extracellular gas bubbles derived from photosynthesis form multicellular complexes that float at certain depths. This would be a very important function and explain the extensive regulatory and signaling apparatus in controlling the synthesis of these sulfated EPS.

    4. Evaluation Summary:

      The authors have elucidated the biochemical and regulatory apparatus for the biosynthesis of sulfated exopolysaccharides, an entire class of molecules not previously studied in cyanobacteria. The work has broad implications for the microbiology and ecology of these organisms and also opens the possibility to use these compounds in biotechnology and modify their structures by combinatorial synthesis.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1, Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

    1. Reviewer #2 (Public Review):

      The authors aimed to address the lack of therapeutic treatments for the Rett Syndrome by (a) identifying novel functional partners of MECP2 (mutations in which underlie Rett Syndrome), and (b) demonstrating the druggability of the partners using in-use drugs. The authors accomplish this by performing phylogenetic profiling across more than thousand species to identify genes that coevolved with MECP2. Using drugs that target three of their top hit genes in RTT models, they demonstrate the potential efficacy of these drugs against RTT and validate their new molecular targets.

      Strengths:

      Overall, the manuscript is very well written and easy to follow even for people outside the fields, and provides insights into an important biological process and identifying much needed therapeutic targets. The authors reproduced various RTT phenotypes in human neural cells with reduces MECP2 expression and demonstrated the ability of the three drugs to rescue the phenotypic profiles. In doing so, the authors were able to shed light on some of the potential mechanisms of action through which these drugs operate. Given that all three drugs have approved safety profiles, with further pre-clinical investigation, these drugs could serve as potential therapeutic agents for Rett Syndrome.

      Weakness:

      The biggest weakness of the paper is the lack of a strong link between comparative phylogenetic profiling and the identification of potential therapeutic agents. The paper is currently framed as a 'comparative genomic pipeline' to identify novel drug targets, yet the authors didn't demonstrate the robustness of the pipeline using appropriate positive and negative controls. Basic network analyses weren't performed to demonstrate a wide usability of the methodology beyond RTT.

      While the authors do a good job of demonstrating the RTT phenotype-rescuing abilities of the three drugs, they don't exhaustively demonstrate how their comparative evolutionary pipeline was essential for identifying the three drugs. MECP2 forms a complex with HDACs and all three of the drugs selected here have known direct/indirect effects on HDAC activity. It is therefore plausible that the drugs are mediating their effects through HDACs, in which case the comparative genomic pipeline was not required to select these drugs.

    2. Reviewer #1 (Public Review):

      Major Comments/Concerns

      On line 101 - The use of only the longest transcript for each gene could miss important functional sections of the genome. This could create bias against genes with many isoforms and miss exons that do not happen to lie in the longest transcript. How different would the resulting profiles of conservations be if all coding regions or exons of every gene were used?

      On line 106 - Does this approach create good specificity to our gene of interest rather than just broad functional similarity? For example, with this approach, are there any major neuronal function genes that have NPP very different from MeCP2? Could authors provide a more objective evaluation to baseline/null?

      Minor Comments/Concerns

      On line 132 - It seems fair to examine this set of genes first, but I am not sure this approach to filtering in particular moves us further towards finding a therapeutic for Rett. These genes could be all good potential targets, and your subset of focus are just the best ones for current validation.

      Figure 2C could be made with all 390 co-evolved genes to strengthen the argument that chr19p13.2 is an important region for MeCP2s role.

      Figure 3, 4, 5, 6 - Dynamite plots. While the stats tests are great for understanding the impact of different treatments, box plots or jittered dots would be even more clear.

    3. Evaluation Summary:

      The manuscript has the potential to be of broad interest to neuroscientists who are aiming to leverage concepts and tools of evolutionary biology to identify novel gene targets and much-needed therapeutic interventions. The follow up experiments are detailed, well thought out, and do a good job of proving the potential of the identified drugs in alleviating molecular signatures in in vitro disease models. However, the link between comparative genomic analysis and identification of specific drugs is not yet sufficiently established and doesn't convincingly demonstrate the usability of the evolutionary pipeline in identifying novel therapeutics.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    1. Author Response:

      Reviewer #2 (Public Review):

      In their study, Lutes et al examine the fate of thymocytes expressing T cell receptors (TCR) with distinct strengths of self-reactivity, tracking them from the pre-selection double positive (DP) stage until they become mature single positive (SP) CD8+ T cells. Their data suggest that self-reactivity is an important variable in the time it takes to complete positive selection, and they propose that it thus accounts for differences in timescales among distinct TCR-bearing thymocytes to reach maturity. They make use of three MHC-I restricted T cell receptor transgenics, TG6, F5, and OT1, and follow their thymic development using in vitro and in vivo approaches, combining measures at the individual cell-level (calcium flux and migratory behaviour) with population-level positive selection outcomes in neonates and adults. By RNA-sequencing of the 3 TCR transgenics during thymic development, Lutes et al make the additional observation that cells with low self-reactivity have greater expression of ion channel genes, which also vary through stages of thymic maturation, raising the possibility that ion channels may play a role in TCR signal strength tuning.

      This is a well-written manuscript that describes a set of elegant experiments. However, in some instances there are concerns with how analyses are done (especially in the summaries of individual cell data in Fig 2 and 3), how the data is interpreted, and the conclusions from the RNA-seq with regard to the ion channel gene patterns are overstated given the absence of any functional data on their role in T cell TCR tuning. As such the abstract is currently not an accurate reflection of the study, and the discussion also focuses disproportionately on the data in the final figure, which forms the most speculative part of this paper.

      (1) As the authors themselves point out (discussion), one of the strengths of this study is the tracking of individual cells, their migratory behaviour and calcium flux frequency and duration over time. However, the single-cell experiments presented (Figure 2 and 3) do not make use of the availability of single-cell read-outs, but focus instead on averaging across populations. For instance, Figure 3a,b provides only 2 sets of examples, but there is no summary of the data providing a comparison between the two transgenics across all events imaged. In Figure 3c, the question that is being asked, which is to test for between-transgenic differences is ultimately not the question that is being answered: the comparison that is made is between signaling and non-signaling events within transgenics. However, this latter question is less interesting as it was already shown previously that thymocytes pause in their motion during Ca flux events (as do mature T cells). Moreover, the average speed of tracks is probably not the best measure here in reading out self-reactivity differences between TCR transgenic groups.

      We regret any lack of clarity in how we presented our analyses of the calcium imaging data. In the original submission, we did provide analyses of individual cells (Fig 2b, Fig 3c (Fig 2e in the revised manuscript), Suppl Tables 1 and 2, and supplemental videos S1 and S2). In the revised manuscript, we have added an additional analyses of individual cells (Figure 2—figure supplement 1a). In addition, Fig 3a and b (Fig 2c and d in the revised manuscript) provided information about the average behavior of thymocytes during signaling events by identifying numerous examples of individual signaling cells (23-37 individual cell signaling events per condition), aligning these multiple examples based on the start of their signaling events, and displaying the average changes in calcium and speed over time. Thus this data does take advantage of the single-cell measurements by providing information about the average behavior of signaling events, which could not be inferred from bulk measurements. Regarding Fig. 3c (Fig. 2e in the revised manuscript), we agree that a more direct comparison of pausing between TCR transgenic models was needed. To address this point, we have added a new panel (Fig 2f in the revised manuscript) that uses the difference in speed between the signaling and nonsignaling portions of the same track to define a “pause index” for each cell. The difference in pause index between the transgenic models is highly significant at both 3 and 6 hours into positive selection. In the revised manuscript we have added additional text to detail more precisely how we performed the analyses, and to make it clearer that individual tracks are being analyzed. We have also included a graph of the Calcium Ratio and the Average Speed for the individual cells shown in the supplemental videos.

      (2) The authors conclude from their data that the self-reactivity of thymocytes correlates with the time to complete positive selection. However the definition of what this includes is blurry. It could be that while an individual cell takes the same amount of time to complete positive selection (ie, the duration from the upregulation of CD69 until transition to the SP stage is the same), but the initial 'search' phase for sufficient signaling events differs (eg. because of lower availability of selecting ligands for TG6 than for OT1), in which case at the population level positive selection would appear to take longer. Given that from Fig 2/3 it appears that both the frequency of events and their duration differ along the self-reactivity spectrum, this needs to be clarified. Moreover, whether the positive selection rate and positive selection efficiency can be considered independently is not explained. It appears that the F5 transgenic in particular has very low positive selection efficiency (substantially lower %CD69+ and of %CXCR4-CCR7+ cells than the OT1 and TG6) and how this relates to the duration of positive selection, or is a function of ligand availability is unclear.

      (3) While the question of time to appearance of SP thymocytes of distinct self-reactivities during neonatal development presented (Figure 5) is interesting, it is difficult to understand the stark contrast in time-scales seen here compared with their in vitro thymic slice (Figure 4) and in vivo EdU-labelling data (Figure 6), where differences in positive selection time was estimated to be ~1-2 days between TCR transgenics of high versus low affinity. This would suggest that there may be other important changes in the development of neonates to adults not being considered, such as the availability of the selecting self-antigens.

      Since, Reviewer #2’s comments 2 and 3 are related, we will discuss them together. In this study, we have used 3 independent approaches (the thymic slice system, the EdU labeling study, and analyses of neonatal transgenic mice) to estimate the relative time for thymocytes bearing different TCRs to complete positive selection, and all three confirm that OT1 is the most rapid and TG6 the slowest of the 3 transgenic models examined here. However, each approach relies on different start times and different read outs, so they are not directly comparable to each other. The thymic slice system tracks a cohort of preselection thymocytes over time. However, given the 4 day limit for this system, it is not possible to reach the theoretical maximum number of CD8SP. Thus, our estimates of the delay in positive selection are based on the timing of multiple phenotypic changes (CD69 induction, chemokine receptor switch, and CD8SP appearance) in this system. The EdU study (Fig 5 in the revised manuscript) allows us to track a cohort of thymocytes that have recently completed TCRb selection and follows them over a longer time period (up to 9 days). Because the number of OT1 and F5 CD8 SP thymocytes reached a clear plateau, this allows us to estimate the average time between the burst of cell division after TCRb selection and the downregulation of CD4 (3.5 days for OT1 and 4.5 days for F5). However, at 9 days the number of TG6 thymocytes is still increasing, and thus we have only a lower estimate (>6 days) of the average time after TCRb selection to the appearance of CD8SP thymocytes with this TCR. When we track the appearance of mature CD8SP after birth (Figure 4 in the revised manuscript), we are not tracking a synchronized cohort of positively selecting cells, but rather we are measuring the amount of time it takes for single positive cells to accumulate into a population size similar to what is seen in an adult. Thus, these experiments do not provide a direct measure of the time to complete positive selection, but rather provide an indirect measure of the number of cells that have successfully completed positive selection at the given timepoints post birth. The observation that OT-1 CD8 SP thymocytes reach their adult steady state numbers at one week whereas TG6 CD8 SP thymocytes are well below adult levels at 21 days is likely a reflection of lengthy positive selection of TG6, resulting in a much longer time to fill the adult niche for CD8SP thymocytes. We agree with the reviewer that there could be additional important differences in positive selection between neonatal vs adult. We explore this topic and relate our data to recent published in the discussion (line 574) of the manuscript.

      With regard to point (2), our data suggest that the longer time for positive selection is a result of both a longer search phase and a longer progression phase. Specifically, the % of CD69+ cells (Fig 3b and Figure 3—figure supplement 2a) peaks at 24 hours for OT1 and F5, but is delayed until 48 hours for TG6, consistent with a 1-2 day delay in the “search phase” for TG6. However, if this initial search phase was the only factor contributing to delayed TG6 development then we might expect to see a 1-2 day lag in TG6 development compared to OT-1. However, as discussed above, the EdU data indicates a > 3 day lag in the appearance of TG6 CD8SP compared to OT1. Thus, there is evidence that both the search phase and the progression phase of positive selection are longer in thymocytes with low self reactivity.

      (4) The conclusion that "ion channel activity may be an important component of T cell tuning during both early and late stages of T cell development" is not supported by any data provided. The authors have shown an interesting association between levels of expression of ion channels, their self-affinity and the thymus selection stage. However, some functional data on their expression playing a role in either the strength of TCR signaling or progression through the thymus (for instance using thymic slices and the level of CD69 expression over time), would be needed to make this assertion. Moreover, from how the data is presented it is difficult to follow the conclusion that a 'preselection signature' is retained by the low but not the high self-reactivity thymocytes.

      We agree that a role for ion channel activity in T cell tuning is speculative at this point, and we have tempered our conclusions in the revised manuscript. With regard to the evidence that a preselection signature is retained by thymocytes with low self reactivity, this conclusion is based on 2 separate lines of evidence presented in Figure 6 (previously Figure 7 in the original submission) and Figure 6—figure supplement 2. To summarize: 1). We defined a “preselection” gene signature based on preselection (CD69-DP) wild type thymocytes from the ImmGen microarray data, and show that this set of genes is also tends to be more highly expressed in thymocytes of low vs high self reactivity (TG6>F5>OT1) at equivalent stages of development (Fig 6d). 2). We identify a set of ion channel genes (cluster 2a from Fig 6c) that are more highly expressed in thymocytes of low vs high self reactivity (TG6>F5>OT1), and are also more highly expressed in earlier stages of positive selection for each TCR. This trend can also be seen in Figure 6— figure supplement 2c when comparing the expression of all cluster 2 ion channel genes across the wild type thymocyte subsets from ImmGen microarray data. Again, expression of this gene set peaks in the DP CD69- (preselection) population compared to other stages, including the preceding (DN4) and following (DP CD69+) stages of thymocyte development. We have edited this part of the results section in the revised manuscript to improve clarity.

    1. Reviewer #3:

      The prevalent treatment options for LSCC are limited in efficacy. Through genetic inactivation of Usp28 in a novel lung cancer mouse model, and chemical inhibition of Usp28 in induced LSCC in mice and human LSCC xenograft tumors, the authors demonstrated the specific dependency of LSCC (but not LADC) on the protein deubiquitinase Usp28. The authors also showed that loss of Usp28 by either means leads to depletion of the oncoproteins c-Myc, p63 and c-Jun in LSCC. Finally, the authors described a novel small molecule that is specific for Usp25/28 among a group of assessed deubiquitinases. Based on these results, the authors suggested chemically targeting USP28 as a potential therapeutic option for human LSCC patients.

      Strengths: The presentation of the work is clear, concise and easily readable. The data presented largely supports the authors' conclusions on the role of USP28 in LSCC tumorigenesis and that inhibition of USP28 is a viable therapeutic option for LSCC treatment. The generation of the KFCU mice model that can give rise to both LADC and LSCC concurrently is interesting and presents a valuable tool for the wider cancer community.

      Weakness: The manuscript can benefit from a deeper analysis of the relationship between FBW7 and USP28 in patient cohorts. A comparison of the activity/efficacy of FT206 to existing USP28 inhibitors will also be helpful.

    2. Reviewer #2:

      In this work Ruiz et al, use a couple of elegant mouse genetic models - KFCU (Fbxw7 deletion and mutant Ras over-expression) and KPCU (p53 deletion and mutant Ras over-expression) - to generate both LADC and LSCC tumors. Using this system, the authors show that deletion of USP28 resulted in less LSCC but not LADC tumor formation. However, both tumor types showed an overall decrease in tumor size (in KFCU; data are not shown in KPCU). These results are the genetic proof of concept that USP28 inhibition will be particularly detrimental in the context of LSCC tumors. They further test a compound (FT206) that was previously found to target USP28 and show that indeed this compound is specific for USP28 binding among USPs and can reduce the tumor numbers and size only in LSCC tumors and not LADC in the KF model and in three separate LSCC cell line xenograft models. Altogether, they make the argument that targeting LSCC tumors with chemical inhibitors of USP28 is a promising clinical strategy for LSCC cancers. Overall this paper is interesting and the results provided in vivo are strong and nicely demonstrate an on-target effect of FT206 and its specificity in LSCC tumors. The work is very similar to a recent publication of (Prieto-Garcia EMBO Mol Med 2020) describing very similar results for USP28 dependency in LSCC tumors and previous findings regarding the chemical matter used in this paper (FT206).

      The major strengths of this paper is that the authors use several very elegant mouse models to establish that Usp28 is a good candidate target for potential therapeutic development designated for LSCC patients. They also show the proof of concept using a compound that is described as a Usp28 inhibitor (FT206). It should be noted that much of the genetic data, showing the importance of Usp28 in LSCC was previously described (Prieto-Garcia EMBO Mol Med 2020) including the potential benefit of chemical inhibition of USP28 . A potential weakness is that there is no rigorous characterizing of Usp28 substrate ubiquitination and degradation following FT206 treatment. This work will likely motivate the development of the USP28 inhibitor(s) for further preclinical assessment in Usp28 dependent tumors such as LSCC.

    3. Reviewer #1:

      The authors investigate a role for a candidate new inhibitor of USP28 in destabilizing c-MYC to reduce the growth of lung squamous carcinomas. They demonstrate that c-MYC levels are higher in lung squamous cell carcinomas (LSCC) versus lung adenocarcinomas (LADC), and depletion of c-MYC reduces LSCC cell growth. The deubiquitinase USP28 is known to stabilize c-MYC; the authors show that depletion of USP28 also decreases c-MYC protein levels. USP28 action opposes that of a ubiquitin complex targeted by the FBXW7 tumor suppressor. The authors create a new mouse model in which FLP recombinase initially causes deletion of FBXW7 and activation of KRAS to cause tumorigenesis with LSCC and LADC, followed by tamoxifen-dependent CRE recombinase deletion of USP28. Loss of USP28 in this model reduced numbers of LSCC but not LADC, and led to decreased expression of c-MYC and other short-lived proteins such as c-JUN and deltap63. A limitation of the data shown is that tumor number calculations are shown for a relatively small number of mice. Deletion of USP28 also did not restrict LADC growth in a second mouse model, with tumors forming based on activation of KRAS and loss of TP53. The authors then describe a compound, FT206, which they show is a specific inhibitor of USP28 among other ubiquitinases. They demonstrate that this compound reduces expression of c-MYC, c-JUN, and deltap63, but do not demonstrate this effect is directly mediated through USP28. They also show FT206 reduces growth of LSCC but not LADC in the KRAS/FBXW7 tumor model, and in human LSCC xenografts. These latter data suggest the compound FT206 may be useful as a lead compound. However, the current data are not sufficient to demonstrate FT206 binding and biological effect is specific for USP28, as the compound may also bind and regulate other non-deubiquitinase proteins.

    4. Summary:

      This paper is of general interest to cancer biologists focusing on identifying new targets for cancer therapy particularly in the context of squamous cell lung carcinoma. The authors demonstrate that genetic ablation of the deubiquitinase USP28 reduces the growth of lung squamous cell carcinomas but not lung adenocarcinomas in a mouse model of lung cancer, and that that this restriction of growth is accompanied by loss of expression of several USP28 targets. They also describe activity of a new small molecule compound in controlling the growth of lung squamous cell carcinomas in mouse genetic and xenograft models, and reducing expression of USP28 targets. They demonstrate that USP28 is one target of the newly identified compound, but they do not establish whether it is the only and biologically relevant target of this compound.

      Reviewer #3 opted to reveal their name to the authors in the decision letter after review.

    1. Reviewer #3 (Public Review):

      In this manuscript, authors seek to resolve conflicting models for corepressor function using the elegant synthetic auxin response system. Auxin signaling is governed by a de-repression paradigm and is ideally suited to interrogate co-repressor function - in this case, the TOPLESS (TPL) co-repressor. Several contradicting models have been put forward for the mechanism of TPL-mediated gene repression, ranging from a requirement for protein oligomerization for activity, interaction with distinct partners, and even which regions of the protein are required for repressive activity. Leydon et al use the yeast-based synthetic auxin response system to interrogate these models using a single reporter locus, allowing for straight-forward examination of TPL function.

    2. Reviewer #2 (Public Review):

      In this manuscript, the authors studied the specific domains of the plant A. thaliana TPL corepressor using a synthetic auxin response circuit (ARC) in the yeast S. cerevisiae that allows to monitor the repression and response to auxin of the reporter expression. Two domains of TPL corepressor that independently contribute to repression in this system were identified. Moreover, one of these domains interacts with Med21 and Med10 Mediator subunits. The authors show that this interaction is required for TPL-mediated auxin-responsive repression in plants. On the contrary to some repression models, they propose that multimerization of TPL is not required for repression mechanisms. Taken together, the work provides important information on auxin-responsive repression mechanisms involving TLP corepressor and the Mediator complex.

      A lot of work was done to analyze the TPL domains and critical residues involved in repression using ARC system, TPL interaction with Mediator using yeast cytoSUS and two-hybrid assays, completed by CoIP experiments with yeast and plant extracts. Point mutations, small deletions or Anchor Away-mediated depleted strains were used to analyze their consequences on TPL-Mediator interactions and auxin-responsive repression in artificial system in yeast and directly in plants.

      The mechanism of how TPL-Mediator interaction is involved in auxin-responsive repression remains to be determine. No results were provided in the manuscript on the composition of Mediator upon auxin induction and a discussion sentence that "as supported by our synthetic system, auxin-induced removal of TPL is sufficient to induce changes in the composition of the Mediator complex" is not supported by the results. In general, the transition between transcriptionally repressed and active states was not analyzed. The authors have made considerable efforts to answer the reviewers' criticism and to include a number of new experiments and approaches. However, several points and conclusions need to be further developed and specified. In particular, CoIP experiments in plant extracts lack a negative IP control to conclude on the specificity of CoIP signal. Moreover, the relevance of ChIP experiments on yeast plasmid remains questionable and appropriate control regions (chromosomal ACT1 gene body is completely inappropriate as a background for Pol II ChIP), regulatory, core promoter and transcribed regions, as well as experiments with untagged control strains should be added. The ChIP occupancy was analyzed only in transcriptionally repressed state and essentially on a plasmid and no results are provided for transition to the active state.

      Many problems with inappropriate citations for Figures or Figure panels did not facilitate the reading of the manuscript.

    3. Reviewer #1 (Public Review):

      In this study, Leydon et al., use an elegant multi-component genetic system to address the mechanisms of repression by the Arabadopsis TOPLESS (Tpl) protein. Taking advantage of the genetic tools and knowledge of the structure of the Tpl protein the authors determine two short alpha helical regions that act as independent repression domains. They provide evidence that the target of one of these domains is the N-terminal region of the Med21 subunit of the mediator complex. Chromatin immunoprecipitation experiments, anchor-away loss of function and co-immunoprecipitation assays indicate that Tpl mediated repression involves formation of a promoter complex comprising the mediator complex along with several general transcription factors, but lacking RNA polymerase II. The authors also show that Tpl-Med21 interactions are involved in Tpl mediated repression in plants.

    4. Evaluation Summary:

      In this study, Leydon et al. use an elegant multi-component genetic system to address the mechanisms of repression by the Arabadopsis TOPLESS (Tpl) protein. Taking advantage of the genetic tools and knowledge of the structure of the Tpl protein, the authors determine two short alpha helical regions that act as independent repression domains, with the target of one of these domains being the N-terminal region of the Med21 subunit of the mediator complex. Experiments are presented that indicate that Tpl mediated repression involves formation of a promoter complex comprising the mediator complex along with several general transcription factors, but lacking RNA polymerase II. The experimental data comes from both heterologous experimental systems in yeast and the native plant setting and involves diverse but complementary experimental approaches that converge towards a model for gene repression. This paper will be of interest to researchers investigating the mechanisms regulating gene expression, in particular how specific protein-protein interactions repress gene expression.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #3 agreed to share their names with the authors.)

    1. Reviewer #3 (Public Review):

      In this manuscript, the authors aim to elucidate the evolutionary history of the paired NLRs Pik-1/Pik-2 in rice. They ask two primary questions:

      (1) When (in evolutionary history) did the paired Pik-1/Pik-2 locus arise and when was the integrated domain integrated into the locus?

      (2) Has the binding affinity of the integrated domain changed over evolutionary time?

      The authors convincingly demonstrate that the integrated domain is undergoing positive selection, that its integration is ancient (~15MYA) and that inferred ancient alleles bind modern AVR-PikD with poor affinity. The subsequent biochemistry experiments and structural analyses identify which residues are important for interactions with AVR-PikD and which allelic combinations induce autoimmunity.

      The biochemical work, while interesting in and of itself for identifying the interacting residues and interactions between domains, was less informative about the evolution of the NLR-effector interaction, and most of the work did not advance our understanding of the questions listed above. The most emphasized biochemistry finding was that of reduced binding affinity of ancestral Pik-1 integrated domain. Specifically, the authors demonstrate that modern AVR-PikD has poor affinity with the ancient Pik-1 integrated domain. From this result the authors infer that ancestral Pik-1 likely bound a different effector. But it was not clear how the authors ruled out binding to an ancient AVR-PikD? I was confused as to why the authors excluded this possibility. Perhaps the authors contend that the absence of the Avr-PikD in other modern blast lineages indicates Avr-PikD is unique to modern rice-infecting M. oryzae. But this modern absence does not preclude Avr-PikD in the ancestral population. Furthermore, changes in binding over time would be the effective null hypothesis in the scenario of coevolving NLR and effector. Their finding seems consistent with expectations of coevolution, a phenomenon that has been widely reported in interactions between NLRs and effectors. The novelty in this manuscript stems from the synthesis of molecular evolution analysis with ancestral state reconstruction and testing.

      Overall this manuscript is exemplary in its integration of biochemical and evolutionary analyses to study plant-pathogen coevolution. While the findings are unsurprising, future emulation of this type of data integration will likely lead to significant insight into the coevolution of plants and their pathogens.

    2. Reviewer #2 (Public Review):

      In this study, Bialas et al. aimed at understanding the evolution of the diversity of Pik-1 immune receptors. First, using phylogenetic and selection analyses they determined that the Pik family of immune receptors is present in multiple grass species, with both Pik-1 and Pik-2 evolving before the radiation of the PACMAD and BOP clades. The author dated the insertion of an HMA domain in a Pik-1 subclade before the radiation of the Oryzinae and detected signs of positive selection on this domain. Using a combination of ancestral sequence reconstructions and biochemistry they determined that two of the extant Pik-1 haplotypes (Pikp-1 and Pikm-1) evolved independently the ability to associate at high affinity with the AVR-PikD effector following two different evolutionary paths. The authors determined that the increased binding correlates at least in one case with the improved ability to induce cell death when co-expressed in tobacco leaves with Pik-2 and AVR-PikD.

      Main strengths:

      The study combines a large diversity of methods to comprehensively address an important question. Despite the large amount of presented data (including a large number of variant names) it was a pleasure to read this very well structured manuscript. The work conducted here by the authors on the ancestral sequence reconstruction, the chimera and the biochemical assays (on two haplotypes!) is impressive and supports a very exciting conclusion. The presentation of all the experimental replicates as supplementary figure is a model of transparency and strengthen the conclusions.

      Weaknesses:

      The conclusions reached by the authors are mostly supported by the presented data, although there are a few points that need to be clarified. The Pik-1 phylogeny (Fig 1A): From the phylogenetic tree presented in Figure 1A it seems that Pik-1 experienced a duplication before the radiation of the BOP and PACMAD clades, with varying patterns of gene retention/loss (for instance loss of both copies in Brachypodium, loss in one clade for maize) and expansion (massive in wheat for instance in the clade where the fusion with the HMA domain did not occur, not in the other). I did not find this point discussed in the manuscript, although this could have an important impact. This would support the hypothesis that the HMA integration occurred before the radiation of the PACMAD clade. A better resolved phylogeny is needed to further test this possibility. In that context, the nomenclature should restrict the Pik-1 name to the actual orthologs, changing the number of Pik-1 per species (in panel 1D for instance).

      In Figure 4C and S13 the Pikp-1 variant I-N11 seems to associate more significantly with AVR-PikD than all the other variants, including I-N2 that was selected for the swap experiments. The reason why I-N2 was selected over other options (including I-N11) should be better explained.

      The correlation between evolution of high-affinity binding to AVR-PikD and the ability to induce immune response should be tested in reconstructed ancestral Pikm-1 variants. The presented data demonstrate nicely the gain of high-affinity binding in Pikm-1, but the impact this may have on the actual immunity function was not tested. It would be important to know whether additional mutations were required or not to turn the ancestral Pik1 into a functional Pikm-1 given that it is the basis for the model proposed in Figure 9. Alternatively, as the result of this experiment would not contradict the model even in absence of immune abilities (it would just add one extra step from high-affinity binding to immune function) the authors could propose this second evolutionary scenario as a supplementary figure.

      The nomenclature used for the Pik variants is not consistent throughout the manuscript, please homogenize as it is not always easy to follow.

      I am not familiar with the besthr R library used for the statistical analyses of the cell death assays, and I am not an expert in biochemistry (SPR, cristal structure) and cannot properly evaluate these aspects of the work.

    3. Reviewer #1 (Public Review):

      This paper was a pleasure to read. It is a tour-de-force study that is well-written, clear, and transparent. The study recounts how the HMA domain became integrated into the Pik NLRs and how it evolved higher affinity binding to a pathogen effector. Strikingly the authors demonstrate adaptability of distinct regions of the HMA:effector interface on two Pik NLRs, driving the convergent evolution of high-affinity binding to the effector. The study furthermore provides a framework for understanding protein evolution in the context of host-microbe interactions. The breadth and depth of the experiments that support the authors conclusions is extraordinary in my view.

    4. Evaluation Summary:

      Convergent evolution is often observed in nature, but the molecular mechanisms allowing similar functions to independently emerge are rarely understood. This work determines how the high-affinity recognition of a pathogenic effector produced by the rice blast fungus, Avr-PikD, evolved in the immune receptor Pik-1. The integration of molecular evolution analyses with structure-function biochemical testing is novel to the field and the data quality is exceptional. In addition to advancing knowledge of host-microbe co-evolution, this work is exemplary in its transparency and the breadth of approaches utilized to understand protein evolution, and we expect that this study will provide a conceptual framework for similar studies in the future.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

    1. Reviewer #3 (Public Review):

      In this study, the authors present a high-resolution single-cell transcriptomic atlas of the pancreatic ductal tree. Using a DBA+ lectin sorting strategy murine pancreatic duct, intrapancreatic bile duct, and pancreatobiliary cells were isolated and subjected to scRNA-seq. Computational analysis of the datasets unveiled important heterogeneity within the pancreatic ductal tree and identified unique cellular states. Furthermore, the authors compared these clusters to previously reported mouse and human pancreatic duct populations and focused on the functional properties of selected duct genes, including Spp1, Anxa3 and Geminin. Overall, the results presented here suggest distinct functional roles for subpopulations of duct cells in maintenance of duct cell identity and implication in chronic pancreatic inflammation. Finally, such detailed analysis of the pancreatic duct tree is relevant also in the context of cancer biology and might help elucidating the transition from pancreatitis to pancreatic cancer and/or different predisposition to cancer.

      The study is very well done, with careful controls and well-designed experiments.

    2. Reviewer #2 (Public Review):

      In this study the authors address the heterogeneity of the mouse ductal cell at the single cell level and conduct functional studies for selected marker genes. They isolated duct cells using the DBA lectin as a molecular surface marker. This is an noteworthy approach as it does not rely on the specificity and expression levels of reporter lines. Isolated cells contained a majority of non-duct cells that were identified by their transcriptomic profile and excluded from further analysis. The transcriptomic profiles of bona fide duct cells were then subjected to standard analyses for differentially expressed genes, activated pathways and lineage relationships. Of particular interest is the comparison of these data with human data from a recently published study that used a different sorting strategy for duct cells. As more studies at the single cell level are conducted, these types of comparisons need to become part of them in order to derive commonalities and identify deficits due to methodological or technological limitations. The study was by necessity descriptive up to this point and the authors addressed this with functional studies on SPP1 and GMNN which suggested that SPP1 is necessary for the maintenance of the ductal differentiated phenotype whereas GMNN protects cells against DNA damage during increased proliferation triggered by chronic pancreatitis.

      It is an interesting study, but there are caveats, particularly concerning the functional studies. The functional analysis of SPP1 needs to be strengthened and some findings on the the analysis of GMNN clarified. There is also an over reliance on the outcome of pathways analyses and upstream regulators which are often treated as actual findings rather than possibilities to be explored in this or future studies. The single cell RNA Seq analysis would benefit from reducing speculation and restrict descriptions to the essential features of each cluster. Main figures for this analysis could also be simplified along the same lines.

    3. Reviewer #1 (Public Review):

      The study by Hendley et al takes advantage of duct-specific DBA-lectin expression to purify pancreatic ductal populations that were then subjected to scRNA-seq analysis. The ability to enrich for this relatively low abundant pancreatic cell population resulted in a more robust dataset that had been generated previously from whole pancreas analyses. The manuscript catalogs several different gene clusters that delineate heterogeneous subpopulations of three different pancreatic ductal subpopulations in mice: mouse pancreatic ductal cells, pancreatobiliary cells, and intra pancreatic bile duct cells. Additional comparisons of the resulting data sets with published embryonic and adult datasets is a strength of the study and allows the authors to subclassify the different ductal cell populations and facilitates the identification of potentially novel subpopulations. Pseudotime analysis also identified gene programs that led the authors to speculate the existence of an EMT axis in pancreatic ducts. Overall, the data analyses is strong, but the authors tend to draw conclusions that are not fully supported by the presented data.

      The second half of this study focuses on three candidate proteins that were identified in the transcriptome analysis - Anxa3, SPP1 and Geminin. Crispr-Cas9 was used to delete each gene in an immortalized human duct cell line (HPDE). Deletion of each gene resulted in increased proliferation; SPP1 mutant cells also displayed abnormal morphology. Additional functional studies of the cell lines or in mouse models suggested a role for SPP1 in maintaining the ductal phenotype and Geminin in protecting ductal cells from DNA damage, respectively. Although the provided phenotypic analysis suggest important functional roles for these proteins, follow up studies will be required to fully understand the role of these genes in homeostatic or cancer conditions.

      Strengths:

      1) Enrichment of pancreatic ductal populations enhanced the robustness of the scRNA-Seq dataset

      2) Quality of the sequencing data and extensive computational analysis is extremely good and more comprehensive than previously published datasets

      3) Comparative analysis with existing mouse and human data sets

      4) Use of human ductal cell lines and mouse models to begin to explore the function of candidate ductal genes.

      Weaknesses:

      1) There are many suppositions based on gene expression changes that are somewhat overstated.

      2) The conclusion that there is an EMT axis in pancreatic ducts is not fully supported by the gene expression and immunofluorescence data

      3) A good rationale for choosing Anxa3, SPP1 and Geminin for additional functional analysis is not provided. In addition, it isn't clear why Anxa3 function isn't pursued further.

      4) Although extensive models (transplanted cells for SPP1 and mouse conditional KOs for Geminin) were generated, the functional analysis for each gene is preliminary; additional longer term studies will be necessary to fully understand the role of these proteins in pancreatic duct development and cancer.

    4. Evaluation Summary:

      In this study, the authors present a high-resolution single-cell transcriptomic atlas of the pancreatic ductal tree. Their analysis unveiled important heterogeneity within the pancreatic ductal tree and identified unique cellular states. Overall, the results presented here suggest distinct functional roles for subpopulations of duct cells in maintenance of duct cell identity and implication in chronic pancreatic inflammation. Finally, such detailed analysis of the pancreatic duct tree is relevant also in the context of cancer biology and might help elucidating the transition from pancreatitis to pancreatic cancer and/or different predisposition to cancer.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    1. Reviewer #3 (Public Review):

      The new models proposed here provide some potentially useful alternatives to estimating the generation time, serial interval, and the relative infectiousness of pre-symptomatic infections. The framing of the paper seems very focused on improving fits to the transmission pair data, however, and I think it would be more impactful to consider the implications of poor estimation of pre-symptomatic transmission and the generation time. I think this shift in focus could also help strengthen the narrative of the paper, which wavers between focusing on model fitting and the importance of implications for contact tracing.

      I was a bit lost in the application of the models to the contact tracing example. The definition of the contact elicitation window (lines 142-144), where identification of contacts would occur up to x days prior to contact symptom onset, makes sense theoretically in this model comparison setting, but it is hard to translate these findings to real-world application. Are there any implications that could be useful for informing contact elicitation strategy (e.g., for how many days after time of infection or symptom onset could contact tracing have a measurable benefit in preventing onward transmissions?)

      Lines 147-151: Given that the impact on onward transmission events is so dependent on the contact tracing assumptions, I would recommend stating the assumptions explicitly here, reporting the results in relative terms as compared to a single model, or both.

      How different are the variable infectiousness model results from parameter estimates from the original studies that reported the transmission pairs data?

      Can the authors comment on the plausibility of the infectiousness distribution in their new proposed models? While better model fitting certainly provides a measurable improvement to leveraging existing data, I'm not aware of studies that support the discontinuous assumptions about infectiousness made here.

      Assuming alpha means the same thing across the models, why is the 95% credible interval so large for the Feretti model? In general, the model parameters should be more clearly explained for this model.

    2. Reviewer #2 (Public Review):

      In this analysis, the authors consider the impact of the duration of infectiousness of a person infected with COVID-19 prior to the appearance of clinical signs. This is an important problem, as identification of disease status often relies on a self-reporting, i.e. from people experiencing clinical signs, and in the case of COVID-19 in the UK, where they have then gone on to test positive (typically with a PCR test). The greater the proportion of transmission that occurs before clinical signs appear then, the less likely that methods based on self-reporting will be sufficient to contain epidemic spread.

      The general problem is well known, with examples of previous analyses including for livestock diseases such as foot-and-mouth disease (see for example, Haydon et al. 1997 https://doi.org/10.1093/imammb/14.1.1 and the very many papers on the 2001 FMD epidemic), and most importantly the seminal paper by Fraser et al. on the SARS-CoV-1 pandemic which laid out the problem in extensive detail https://doi.org/10.1073/pnas.0307506101. In the analyses of the current SARS-CoV-2 dynamics, the authors refer to the paper by Feretti et al. (https://doi.org/10.1126/science.abb6936) which at this point represents the most prominent analysis of this type that is directly relevant to the current pandemic. More broadly, issues with exponential distributions and the impact that their use has on analyses of infection dynamics and epidemic behaviour have been well studies in other systems such as measles (e.g. Lloyd 2001 https://doi.org/10.1006/tpbi.2001.1525, and Conlan et al. 2009 https://doi.org/10.1098/rsif.2009.0284). It would be helpful for the paper to refer to this broader literature in order to contextualise the analysis though this does not of course detract from the relevance to the current COVID-19 pandemic.

      In this analysis the authors show that, by choosing a pre-infectious period that is explicitly excludes any probability of infection, they achieve a better fit to the distribution of serial interval for a large number of known transmission pairs (previously analysed in the Ferretti paper). This is an entirely sensible result and a good use of a better mechanistically informed idea of the infection process (in essence, here incorporating explicitly the inevitable delay between virus entering the body, and a person becoming infectious).

      By examining the proportion of infections that would be captured by contact tracing when considering a two-day window prior to symptom onset, they show a substantially greater efficacy for contact tracing, compared to a more standard compartmental modelling approach (where the duration of each consecutive period is independently determined).

      While the analysis itself is detailed and thoroughly explained I have some questions regarding the utility of the result when making the comparison to other models. As noted earlier, the fundamental problem is already well known, and the application to COVID-19, while useful, is better than poorer models, but only marginally better performing than the Ferretti model. The serial interval estimates are only slightly better (figure 2), there are 84% of contacts when considering tracing two days prior to symptoms, compared to what looks like about 80% for the alternative in figure 4 and by the looks of the violin plots from figure 3, quite a bit of overlap if one considers credible intervals.

      As such, while the analysis is a solid, useful addition to the literature, it could use a better exposition on how it advances scientific insight (the fundamental issues regarding exponential distributions having been identified previously), methodologically (given the thorough analysis by Fraser et al in 2004) or in terms of impact (given the limited improvement over the Ferretti model).

    3. Reviewer #1 (Public Review):

      The authors develop a mechanistic model for inferring infectiousness profile from data on times of symptom onset in pairs of infector-infectee. The novelty of their approach lies in assuming that infectiousness of an infected individual depends also on the whether or not they have symptoms. The authors fit a data set of time of symptom onset in 191 transmission pairs to a model that assumes that infectiousness varies along the incubation period. They compare the model fit to fits from models and find that their model of differential infectiousness explains the data better than the other models considered.

      This is a carefully constructed study, and the conclusions are well supported by the analysis carried out. My only concern is that the data used were obtained during the early stage of the pandemic (January to February 2020). As the pandemic was growing in most countries during this time, we are more likely to have observed shorter serial intervals. Similarly, as isolation of infected individuals would prevent them from transmitting further, longer serial intervals are likely to be under-represented in the data. Indeed, the longest serial interval in the data used was 5 days. It would be interesting to understand whether the conclusions about the proportion of onward transmissions averted by contact tracing and subsequent isolation still hold as the pandemic progresses, and we continue to observe longer serial intervals. If the authors are unable to find more recent data, this caveat should be clearly discussed.

    4. Evaluation Summary:

      The manuscript uses a new approach to model the infectiousness profile of COVID-19 infected individuals. The work suggests a higher proportion of pre-symptomatic infectiousness in COVID-19 than the current evidence. The findings are of great interest to public health policy makers. The methodology is of general interest to modellers working on COVID-19.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

  3. Mar 2021
    1. Reviewer #2 (Public Review):

      Summary:

      Frey et al develop an automated decoding method, based on convolutional neural networks, for wideband neural activity recordings. This allows the entire neural signal (across all frequency bands) to be used as decoding inputs, as opposed to spike sorting or using specific LFP frequency bands. They show improved decoding accuracy relative to standard Bayesian decoder, and then demonstrate how their method can find the frequency bands that are important for decoding a given variable. This can help researchers to determine what aspects of the neural signal relate to given variables.

      Impact:

      I think this is a tool that has the potential to be widely useful for neuroscientists as part of their data analysis pipelines. The authors have publicly available code on github and Colab notebooks that make it easy to get started using their method.

      Relation to other methods:

      This paper takes the following 3 methods used in machine learning and signal processing, and combines them in a very useful way. 1) Frequency-based representations based on spectrograms or wavelet decompositions (e.g. Golshan et al, Journal of Neuroscience Methods, 2020; Vilamala et al, 2017 IEEE international workshop on on machine learning for signal processing). This is used for preprocessing the neural data; 2) Convolutional neural networks (many examples in Livezey and Glaser, Briefings in Bioinformatics, 2020). This is used to predict the decoding output; 3) Permutation feature importance, aka a shuffle analysis (https://scikit-learn.org/stable/modules/permutation_importance.htmlhttps://compstat-lmu.github.io/iml_methods_limitations/pfi.html). This is used to determine which input features are important. I think the authors could slightly improve their discussion/referencing of the connection to the related literature.

      Overall, I think this paper is a very useful contribution, but I do have a few concerns, as described below.

      Concerns:

      1) The interpretability of the method is not validated in simulations. To trust that this method uncovers the true frequency bands that matter for decoding a variable, I feel it's important to show the method discovers the truth when it is actually known (unlike in neural data). As a simple suggestion, you could take an actual wavelet decomposition, and create a simple linear mapping from a couple of the frequency bands to an imaginary variable; then, see whether your method determines these frequencies are the important ones. Even if the model does not recover the ground truth frequency bands perfectly (e.g. if it says correlated frequency bands matter, which is often a limitation of permutation feature importance), this would be very valuable for readers to be aware of.

      2) It's unclear how much data is needed to accurately recover the frequency bands that matter for decoding, which may be an important consideration for someone wanting to use your method. This could be tested in simulations as described above, and by subsampling from your CA1 recordings to see how the relative influence plots change.

      3)

      a) It is not clear why your method leads to an increase in decoding accuracy (Fig. 1)? Is this simply because of the preprocessing you are using (using the Wavelet coefficients as inputs), or because of your convolutional neural network. Having a control where you provide the wavelet coefficients as inputs into a feedforward neural network would be useful, and a more meaningful comparison than the SVM. Side note - please provide more information on the SVM you are using for comparison (what is the kernel function, are you using regularization?).

      b) Relatedly, because the reason for the increase in decoding accuracy is not clear, I don't think you can make the claim that "The high accuracy and efficiency of the model suggest that our model utilizes additional information contained in the LFP as well as from sub-threshold spikes and those that were not successfully clustered." (line 122). Based on the shown evidence, it seems to me that all of the benefits vs. the Bayesian decoder could just be due to the nonlinearities of the convolutional neural network.

    2. Reviewer #1 (Public Review):

      In the current manuscript, Frey et al. describe a convolutional neural network capable of extracting behavioral correlates from wide-band LFP recordings or even lower-frequency imaging data. Other publications (referenced by the authors) have employed similar ideas previously, but to my knowledge, the current implementation is novel. In my opinion, the real value of this method, as the authors state in their final paragraph, is that it represents a rapid, "first-pass" analysis of large-scale electrophysiological recordings to quickly identify relevant neural features which can then become the focus of more in-depth analyses. As such, I think the analysis program described by the authors is of real value to the community, particularly as it becomes more commonplace for labs to acquire multi-site in vivo recordings. However, to maximize its utility to the community, several aspects of the analysis need clarification.

    3. Evaluation Summary:

      Frey et al. describe a convolutional neural network capable of extracting behavioral correlates from wide-band LFP recordings or even lower-frequency imaging data. The analysis program described by the authors provides a rapid "first pass" analysis using raw, unprocessed data to generate hypotheses that can be tested later with conventional in-depth analyses. This approach is of real value to the community, particularly as it becomes more commonplace for labs to acquire multi-site in vivo recordings.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    1. Reviewer #3 (Public Review):

      In this manuscript, the authors use high-resolution live imaging to investigate how progenitor cells travel through an embryo to a distant site for differentiation and organ formation. The test case is the movement of dorsal forerunner cells (DFCs) in the zebrafish embryo, which give rise to a transient organ called Kupffer's vesicle that functions to establish the left-right body axis. DFCs are derived from enveloping layer (EVL) cells ~5 hours post-fertilization (hpf) and then move towards the vegetal pole of the embryo. They ultimately end up in the tailbud where they differentiate into epithelial cells to form Kupffer's vesicle between 10-11 hpf. Live imaging convincingly shows that EVL cells undergo apical constriction and delaminate from the EVL layer to form DFCs. Some DFCs remain connected to the EVL via ZO-1 enriched tight junction-like apical attachments. The authors propose that spreading of the EVL layer 'drags' the underlying DFCs towards the vegetal pole via these apical attachments. Supporting this model, EVL and DFCs co-migrate with the same speed and directionality, and perturbation of an actomyosin ring network in the yolk syncytial layer (YSL) disrupts movement of both EVL and DFCs. Between 8-9 hpf DFCs detach and are uncoupled from the EVL. The authors show that E-cadherin is necessary for DFC-DFC adhesion, and additional imaging experiments show that DFCs can extend long protrusions that 'capture' detached DFCs to facilitate clustering. Taken together, these data suggest an interesting drag mechanism for guiding progenitor cell movements, however the results presented do not fully demonstrate this mechanism, and alternative mechanisms were not thoroughly tested.

    2. Reviewer #2 (Public Review):

      This work analyses the movement of the dorsal forerunner cells (DFCs) and its interaction with the extra-embryonic enveloping layer (EVL). By doing high-resolution time lapse microscopy the authors characterize the movement of the DFCc showing that they delaminate from the epithelium by apical constriction but they remain attached to the superficial EVL. By doing laser ablations they show that the movement of the DFCc depends on the attachment and vegetal displacement of the EVL. However, they show that with some frequencies some DFCc are detached from the rest of the cluster, leading to some random movement or even being left behind and differentiating into other cell types. Importantly, they investigate an additional mechanism to explain the movement of the DFCc detached cells. They show that single cells generate protrusions that connect them with the DFCc cluster forming an E-cadherin junction. This paper makes an important contribution by adding some new mode of migrations during development. Most of the conclusion are supported by the experiments.

    3. Reviewer #1 (Public Review):

      Pulgar et al. describe an interesting mechanism explaining how directed motion of group of cells maintain their migratory path as a group of cells. Incomplete delamination allows here to maintain coordinated cell movements amongst the DFC. The story is self-contained, logical, well-written and just in general very nice. The mechanism described belongs to the so-called mechanical drag which is a new type of multicellular locomotion and may be a general feature involved in many morphogenetic systems.

      The major strength of the study is the extensive use of live imaging and analysis of dynamic events. The study provides a nice cellular mechanism in the process they described. The molecular mechanism would be the only weakness of the study.

      An overall very exciting study.

    4. Evaluation Summary:

      In this study, Pulgar et al. describe an interesting phenomenon addressing organ integrity in a unique example of collective cell migration. The group focused on the migration of the dorsal forunner cells (DFC), which will constitute the left-right organizer of the zebrafish. The authors show that DFCs retain apical contacts stemming from incomplete delamination and drag detached DFCs to their final destination. The study opens a number of exciting new questions related to the mechanism underlying the 'safeguards' process and the mechanism of coordination between migration and regulation of attachment.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    1. Reviewer #3 (Public Review):

      The authors study the leaf transcriptomes of males and females in 10 species of Leucadendron and infer genes expressed significantly differently between males and females (sex-biased genes, hereafter SBGs). Most SBGs in Leucadendron leaves evolved recently, suggesting that SBGs turnover (evolution and reversion) is very high because the genus is ancestrally dioecious since >10My. Using species in which the genes orthologous to SBGs are not sex-biased, the authors show that SBGs have high rates of expression evolution already before becoming SBGs. This suggests that most SBGs evolved under drift and the majority of SBGE (sex-biased gene expression) is evolving neutrally. This is confirmed by the estimated small proportion of SBGs evolving under adaptation (about 20% of SBGs have 5 fold higher expression divergence compared to polymorphism divergence, a mark of relatively recent adaptation). Also, SBGs are more tissue specific (less pleiotropic). Finally, the percentage of SBG is not correlated to the intensity of morphological dimorphism. All these findings go against the classical view that SBGE is driven by sex-specific selection for sexual dimorphism.

      The analyses are very cautious with well designed controls and randomizations.

      The results support well the conclusions.

      This study puts forward the role of drift in sex-biased gene expression, offering a new interpretation of this common evolutionary phenomenon.

    2. Reviewer #2 (Public Review):

      Scharmann et al. present a study of sex-biased gene expression as a function of sexual dimorphism in leaf tissue in the genus Leucadendron. Comparative studies of sex-biased expression across clades are still relatively rare, and this analysis tests some core findings of a recent paper (Harrison et al. 2015). Overall, I like the analysis and think it could be a valuable addition to the literature on sex-biased genes. This is particularly true given the difficulty of cross-species expression comparisons and the paucity of them in plants.

      However, there are some critical differences between the Harrison paper and the one here, and I think it would be helpful if the authors present them early in the text. Specifically, Harrison et al. (2015) was primarily focused on gonad tissue, which in animals is the site of the vast majority of sex-biased genes. In contrast, the authors here focus on vegetative (leaf) tissue, which is analogous to animal somatic tissue. None of the patterns that Harrison et al. (2015) observed and report from the gonad were evidence in the somatic tissue they assessed. Also, by looking at gonadal tissue, Harrison et al. (2015) focused on the tissue that produces gametes, which are thought to be subject to some of the strongest sexual selection pressures. The fairest comparison would be flower tissue in plants, so I am unsure how much of the Harrison results would be expected to hold up in leaf samples. This doesn't mean the authors should do the analyses they present, just that they should be a little more upfront about what they might reasonably expect to find.

      There is also a conflation at times in the paper between sexual dimorphism, which the authors can quantify in their leaf samples, and sexual selection. I explain this in more detail below, but to summarize here, I think the expectations for the relationship between sex-biased gene expression and sexual selection versus sexual dimorphism are somewhat distinct.

      Finally, I am a little concerned that the low numbers of sex-biased genes, expected from leaf tissue, offer limited power for some of the tests the authors want to do. Harrison et al. (2015) had hundreds of sex-biased genes from the gonad, and this power made it possible to detect subtle patterns. The authors have a few dozen sex-biased genes, and this makes it difficult to know whether their negative results are the result of low statistical power. That they find clear associations between pre-sex-biased genes and rates of evolution is quite impressive given this low power.

    3. Reviewer #1 (Public Review):

      *A summary of what the authors were trying to achieve.

      The study takes advantage of the interesting plant genus Leucadendron to compare gene expression between male vs. female in species with more or less sexual dimorphism. This question was addressed in a somewhat comparable manner in only one previous paper by Harrison et al. 2015 across six bird species. The overarching question is the role of natural selection in sexual dimorphism.

      *An account of the major strengths and weaknesses of the methods and results.

      -Beside the genus-wide comparison of whole transcriptomes across related species, which makes in itself a strong dataset, the major strength of the analysis is the phylogenetic framework that allows the authors to track the evolution of sex bias through several tens of million years of evolutionary history. Despite ancestral dioecy in the genus, very few genes show consistent sex bias across several species, with sex-bias being mostly species-specific. Two striking negative results will be of special interest to the community : 1) species with more pronounced sexual dimorphism at the morphological level do not tend to exhibit more pronounced sex-biased gene expression 2) the few genes that do show sex-biased expression were apparently recruited among those with the highest expression variance to begin with, strongly suggesting that sexual selection has not been the main force driving their expression divergence.

      -In my view, the main limitation of the work is the use of leaf rather than reproductive tissues, making the comparison to other studies less straightforward to interpret. It is especially important that the expectations for somatic vs gonadic tissues be made a lot clearer in the text. Also, the fact that a single leaf phenotype is measured (specific leaf area) seems arbitrary : one could imagine sexual dimorphism on many other characteristics, yet they are not considered here. The text on p.324 mentions "striking convergence in aspects of morphological dimorphism across the genus", but there is no way for the reader to appreciate the extent of this convergence. Finally, it would be useful to at least make some mention of the sex-determination system in these species, since the expectations would differ if some of the sex-biased genes were linked to sex chromosomes.

      *An appraisal of whether the authors achieved their aims, and whether the results support their conclusions.

      The analysis is mostly sound, but I am a bit concerned by the arbitrary threshold used to define SBGE. The text on p.305 says that "This result is extremely robust to the choice of threshold", but 1) the results are not reported so it is impossible for the readers to evaluate the basis of this assertion and 2) it is not clear whether robustness of the other results has been evaluated at all. This aspect clearly deserves more attention.

      *A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community.

      This work will be of interest to the community, as rapid rates of expression evolution would generally be interpreted as the consequence of sex bias, whereas the phylogenetic analysis presented here instead supports the idea that the expression of genes that end up being sex biased were instead intrinsically less constrained to begin with.

    4. Evaluation Summary:

      This is one of the first studies to investigate sex-biased gene expresion in a broad phylogenetic context, and the first in a plant genus. The findings go against the classical view that sex-biased gene expression is driven by sex-specific selection for sexual dimorphism, and instead suggests that sex-bias preverentially evolved in genes that already had the highest expression variance to begin with. It will broadly appeal to researchers interested in the evolution of sexual dimorphism.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    1. Reviewer #4 (Public Review):

      The goal of the manuscript was to add to the research on the rates of success of African American/Black PI in their pursuit of NIH funding. The authors specifically addressed variability in funding levels of NIH Institutes and Centers(ICs). The authors were successful in identifying that there are differentials rates of award rates by IC. The authors describe that topic choice was not associated with funding after accounting for IC assignment which vary in their funding rates.

    2. Reviewer #3 (Public Review):

      This analysis focuses on funding success for a set of NIH R01-mechanism grant applications submitted between 2011 and 2015, with a focus only on those which had white and Black Principal Investigators (PIs). It is presented as a follow-up to the previously published paper from Hoppe and colleagues in 2019, uses the same population of applications and relies on the same analysis of application text to cluster these applications by topic. The authors set out to determine how success rates associated with the application's proposed topic may be determined by the success rates associated with the Institute or Center within the NIH to which the application had been assigned for potential funding. This is a critical and important investigation that is of high potential impact. The scholarship of the Introduction and Discussion, however, fails to convey this to the reader. There are many recent publications in the academic literature that address why a disparity of funding to AA/B investigators, and a disparity of funding of topics that are of interest to AA/B investigators, are such critical matters for the NIH to identify and redress. Similarly, the Discussion and Conclusions sections do not suggest any specific actions that may be recommended by these findings, which is an unfortunate oversight that limits the likely impact of this work.

      The significance of this work is limited by a number of methodological choices that are unexplained or have not been justified and therefore appear to be somewhat arbitrary. While it can be necessary to draw category lines in an investigation of this type, it is necessary to provide some indication of what would happen to the support for the central conclusions if other choices had been made. This includes the exclusion of multi-PI applications if the Black PI was not the contact PI, the definition of AA/B-preferred ICs as the top quartile (particularly given the distribution of success rates within this quartile), the definition of AA/B-preferred topics as the 15 word clusters that accounted for only half of the AA/B applications, and the ensuing inclusion of only 27% of the AA/B applications. Arbitrary choices to use only a subset of the data raise questions about what the conclusions would be if the entire dataset of grants assigned across all of the ICs, and on all of the topics, was used.

      A fundamental limitation to this manuscript is that the authors are relying on an indirect logic of analysis instead of simply reporting the success rates for applications with AA/B and white PIs within each IC. The primary outcome deployed in support of the central conclusion is a reduction of the regression coefficient for the contribution of PI race to award success and an elimination of statistically significant contribution of research topic preferred by AA/B applicants to the award success once IC success was partialed out. The former analysis is interpreted in imprecise terms instead of simply reporting what magnitude of effect on the white/Black success rate gap is being described. And the latter analysis appears to show a continued significant effect of PI race on award success even when the IC success rate is included. The much more intuitive question of whether award rates for white and AA/B applicants differ within each IC has not been addressed with direct data but the probit model outcome suggests it is still significantly different. This gives the impression that the authors have conducted an unnecessarily complex analysis and thereby missed the forest for the trees- i.e. even when accounting for IC award rates there is still a significant influence of PI race.

      The manuscript is further limited by atheism omission of any discussion of how and why a given grant is assigned to a particular IC (this is exacerbated by incorrect phrasing suggesting the applicant "submits an application to" a specific IC) and any discussion of the amount of the NIH budget that is assigned to a given IC and how that impacts the success rate. This is, at the least, necessary explanatory context for the investigation.

    3. Reviewer #2 (Public Review):

      The paper by Lauer et al provides further insight into the factors that might determine why RO1 applications from AAB (African American Black) principal investigators appear to fare worse than their white counterparts. Their work is derived from an earlier analysis published by Hoppe et al that found 3 factors determined funding success among AAB PIs. These included decision to discuss at study section, impact score, and topic choice. The latter, topic choice (community and population studies) appeared to represent more than 20% of the variability in funding gaps. This raised the question of whether there was reviewer bias at study sections. In the Lauer paper, after controlling for several of these variables, the authors found that the topic choice of AABs (ie. preferred topics) were indeed important in respect to funding, but they uncovered the fact that the topic choices occurred more frequent in ICs that had lower funding rates. Thus the authors conclude that the disparity between AAB and white investigator RO1s is very dependent on topic choice which ultimately ends up in larger ICs with lower funding percentiles.

      Overall the paper is relatively straightforward and could be important as It provides some additional data to consider. It is in fact basically a re-analysis of the Hoppe paper, but that is reasonable since that paper left many unanswered questions. Its implications however are less clear, and these raise additional questions of importance to the extramural scientific community as well as IC leadership.

      Overall the reader is left with the unsettling question: Can we just wish away these disparities based on IC funding rates? (Figure 1).

      1) Why would topic choice of community engagement or population studies fare worse at an Institute such as AI rather than at GM if both have the relatively same proportion of preferred topics, and both have relatively high budgets compared to other institutes. Is there one or more ICs that drive the correlations between IC funding and preferred topics or PIs?

      2) Since only 2% of all PIs are AAB does that represents another issue of low frequency relative to the larger cohort?

      3) It would be valuable to know if community engagement or population studies in total do worse than mechanistic studies. The authors do admit that preferred topics of AABs in general fare worse(Figure 2, Panel B).

      4) Another concern is that the data are up to 2015; it has now been five years and things have changed dramatically at NIH and in society. There are now many more multiple PI applications including AABs that may not be the contact PI yet are likely to be in a preferred topic area.

      5) There is nothing in the discussion about potential resolutions to this very timely issue; In other words we now know that the disparity in funding is such that AAB RO1s do worse than white PIs because they are selecting topics that end up at institutes with lower funding rates. Should the institutes devote a set aside for these topic choices to balance the portfolio of the IC and equal the playing field for AABs? Are there other alternative approaches?

    4. Reviewer #1 (Public Review):

      This manuscript by Lauer et al follows up on previous articles that ask the question whether there are funding disparities at the National Institutes of Health for African American or Black (AAB) investigators. The investigators breakdown the analysis by race, topic of proposal, and NIH institute-Center (IC) to which an application was assigned. They conclude that the most important factor in determining funding is the Institute assignment with lower funding rates related to the funding capacity of a particular Institute (e.g National Eye Institute vs Minority Health and Health Disparities). The present study is a welcome addition to this debate since if biases do exist, NIH needs to address these. The strengths of this manuscript are the detailed breakdown of the available data in order to evaluate for biases, the availability of data for multiple years (2011-2015) and the consideration of alternate explanations (e.g new applications vs resubmissions; single vs multi PI, etc). A weakness of the data is that if their conclusion is that Institute assignment was the main determinant of funding rates, why wasn't the approach for Institute assignment discussed? Are there possible biases in this assignment besides keyword searches? There is also the question of whether there is circular logic operating here. The Minority Health and Health Disparities received the most AAB applications but had one of the lowest funding rates. Wouldn't this Institute be expected to be one in which AAB applicants would try to direct their application to? This manuscript is sure to generate additional discussion on this topic which is an important step in trying to address the issue of potential funding disparities. However as the authors point out the fact that only 2% of the applications submitted to the NIH were from AAB investigators is of concern.

    5. Evaluation Summary:

      This paper provides the basis for further discussion about the perceived inequities in NIH funding based on race. The strengths of this manuscript are the detailed breakdown of the available data in order to evaluate for biases, the availability of data for multiple years (2011-2015) and the consideration of alternate explanations (e.g. new applications vs resubmissions; single vs multi PI). With that said, given their conclusion that Institute (IC) assignment was the main determinant of funding rates, the approach for IC assignment should have been discussed. Other issues relate to the complexity of statistical analyses and a lack of clarity on confounding issues towards firm conclusions.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #2 agreed to share their name with the authors.)

    1. Reviewer #2 (Public Review):

      Halliday et al. sampled plant communities and foliar fungal diseases along an elevation gradient in Swiss Alps, to test the potential relationship between environment, plant communities and diseases in the context of climate change. The authors confirmed that elevation can affect diseases by both abiotic and biotic factors, and, host community pace-of-life was the main driver for diseases along elevation. The topic is important and new, the study is well-designed, and the analysis is reasonable.

    2. Reviewer #1 (Public Review):

      Halliday et al. developed a framework to disentangle the total effect of environment on disease into a direct effect and indirect effects by environment-induced change of host community and by modifying the relationships between host community and disease.

      Applying this framework, the authors studied the direct and indirect effects of elevation on plant leaf disease in the Swiss Alps. They focused on host community structures as mediator of indirect effects. Host community structures were measured by host species richness, phylogenetic diversity, and community pace of life. One important finding is that the positive effect of host community pace-of-life on disease weakened as elevation increased, suggesting an important, but less appreciated, mechanism on how elevation can indirectly influence plant disease. However, since the major findings were based on the analyses with elevation but not specific environmental variables, it does not have that strong implications about the influence of global climate change on disease as the authors stated.

      The developed framework on environmental effects on disease, the well-designed filed study and the large-scale dataset would all make this paper an important contribution to the field.

      Overall, the statistical analyses were reasonable. However, accurate interpretations of some results would require more clarifications on the analyses.

    3. Evaluation Summary:

      This paper provides a framework for disentangling the direct vs. indirect effects of environment on disease, which should be of broad interest across domains of ecology, epidemiology and plant biology. The authors validate this framework with a well-designed field study of plant leaf disease across a large elevational gradient. Overall, the data analyses are appropriate, but a few aspects of interpretations could be improved.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

    1. Reviewer #2 (Public Review):

      In this manuscript, Lamers et al wanted to characterise the previously reported adaptation of SARS-CoV-2 to non-human (Vero) cells. Vero cells are commonly used by laboratories to grow experimental stocks of some viruses as these cells permit high titres of many viruses, they lack the ability to produce type I interferons (cytokines which could interfere with downstream assays), and their non-human nature means soluble factors in virus stocks are less likely to impact experiments in human cells. However, a number of reports have recently been published describing that growth of SARS-CoV-2 in Vero cells leads to loss of the SARS-CoV-2 Spike protein multibasic cleavage site (MBCS). This apparent adaptation to the Vero cell-line leads to a virus compromised in its ability to enter, and therefore replicate in, human cells, meaning that experimental results obtained in human cells using the Vero-adapted SARS-CoV-2 may not fully reflect the situation occurring with authentic SARS-CoV-2. It is therefore important for the research community to understand SARS-CoV-2 adaptation to laboratory cell-lines/conditions and to have propagation methods that are suitable for maintaining the authenticity of clinical virus isolates.

      The major finding of Lamers et al in this manuscript is that human cell-lines (e.g. Calu-3) and primary human organoid systems can be used to propagate clinical isolates of SARS-CoV-2 to high titres without the acquisition of 'laboratory adaptations'. To get to this finding, the authors carefully study the adaptation of a representative SARS-CoV-2 isolate in Vero cells, monitoring plaque size phenotypes and performing whole-genome deep sequencing to identify adaptive variants that appear in the viral Spike gene. These variants (including newly-described substitutions as well as deletions around the MBCS) are validated for their impact on viral infectivity in human and Vero cells using pseudovirus assays, fusion assays, and western blot assays, and their role in affecting the entry route of SARS-CoV-2 is dissected using pathway-specific inhibitors (such as camostat and E64D) and cell-lines with/without TMPRSS2 (an important protease for Spike cleavage). Importantly, using these assays and tools, the authors can make solid and well-reasoned arguments as to why SARS-CoV-2 adapts to Vero cells, and thus why certain culture conditions and cell substrates lead to a loss of SARS-CoV-2 genetic stability. Using similar tools, this also allows the authors to carefully study whether any adaptations occur when SARS-CoV-2 stocks are passaged in human cell substrates (such as Calu-3 or primary human organoids), and study culture conditions in Veros (such as expression of TMPRSS2) that prevent changes in SARS-CoV-2.

      The data in this manuscript are thorough and well-presented. Importantly, the conclusions are strongly supported by the data, particularly the overall take-home message that human cell substrates can be used to efficiently propagate SARS-CoV-2 isolates without introducing cell culture adaptations. However, beyond this simple message, the manuscript also provides new mechanistic insights into the reasons for such viral adaptations in the Vero cell system, and identifies previously undescribed adaptations in the MBCS region that will be valuable for other researchers to take note of. The authors also describe a methodological workflow to produce SARS-CoV-2 in human cells that highlights a buffer-exchange step to remove potentially interfering human cytokines/debris, and which will be useful for other researchers.

      Overall, the manuscript makes a clear and important contribution to the SARS-CoV-2 field and will be of interest to active researchers who are studying this virus experimentally.

    2. Reviewer #1 (Public Review):

      This manuscript, which follows on from a recent eLife paper documenting the relevance of the multi-basic cleavage site (MBCS) in the spike (S) protein of SARS-CoV-2, shows that growing SARS-CoV-2 on relevant epithelial cell lines or differentiated stem cell-derived culture systems prevents the emergence of MBCS mutations than impact on properties of S that contribute to cell tropism and the viral entry mechanism.

      The paper builds on the authors previous work and that of others, and in some respects the results are not surprising. Nevertheless, the paper sets out a number of important findings. 1) That SARS-CoV-2 grown in Vero cells rapidly acquire MBCS mutations, where as virus grown in airway epithelial cells or Vero-TMPRSSR2 cells do not; 2) that deep sequencing is necessary to see mutations that are not apparent in consensus sequence reads, 3) that factors such as the addition of fetal calf serum can influence the selection of mutant phenotypes and 4) that cultures derived from differentiated stem cells can provide reproducible systems for virus culture. Together, the work sets out clear guidelines for the production of SARS-CoV-2, and potentially other viruses, avoiding the pitfalls that can arise from growing viruses in permissive transformed cell lines.

      The data and manuscript are clearly presented, and my concerns are minimal. Overall, the paper will make a useful addition to the SARS-CoV-2 literature and will be of value to researchers working not just of SARS-CoV-2 but on many other viruses.

    3. Evaluation Summary:

      This manuscript follows up on work documenting the relevance of the multi-basic cleavage site (MBCS) in the spike (S) protein of SARS-CoV-2 for determining cell tropism and mode of cell entry. The paper describes a number of important findings: 1) That SARS-CoV-2 grown in Vero cells rapidly acquires MBCS mutations, where as virus grown in airway epithelial cells or Vero-TMPRSSR2 cells do not; 2) that deep sequencing is necessary to see mutations emerging that are not apparent in consensus sequence reads; 3) that factors such as fetal calf serum can influence the selection of mutant phenotypes, and 4) that cultures derived from differentiated stem cells can provide reproducible systems for virus culture. Together, the work sets out clear guidelines for the propagation of SARS-CoV-2 to avoid adaptations to laboratory cell-lines/conditions and maintain the authenticity of clinical isolates. The work has relevance to other viruses and the use of permissive transformed cell lines.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

    1. Reviewer #3 (Public Review):

      In some species, supporting cells (SCs) of the cochlea can replace hair cells and thus restore hearing. In the mouse, neonatal SCs can also produce hair cells; however, this property is lost during early postnatal life. This study sought to test whether forced expression of two transcription factors normally associated with OHC development, Atoh1 and Ifzh2, can induce adult mammalian supporting cells to take OHC-like properties. Using Cre-dependent expression in mice, the authors showed that co-expression of Atoh1 and Izfh2 could induce a small number of adult SCs to express the OHC-specific gene, Prestin. This conversion was significantly enhanced when existing OHCs were ablated, in this case using a Prestin-DTR mouse model generated by the authors. A detailed phenotypic analysis combined with single cell RNA-sequencing (scRNA-seq) supports the idea that Atoh1/Izfh2 can partially convert adult SCs into OHC-like cells. However, the conversion is not complete, with immature bundles and a gene signature that resembles P1 OHCs (and sometimes E16 OHCs) more than P7/P30 OHCs or P60 SCs. Accordingly, the new OHCs are not sufficient to restore hearing in the Prestin-DTR mouse model. Together, these data encourage optimism that adult SCs can be steered along the OHC path, though clearly more manipulations will be needed to produce mature, functional OHCs.

      The main weakness of the study is the scRNA-seq analysis, which depends on very small sample sizes. Suggestions to improve upon the analysis are listed under Specific Recommendations.

    2. Reviewer #2 (Public Review):

      The goal of this study is to devise a means of promoting adult mouse auditory sensory cell development from supporting cells (SCs), as occurs naturally in birds and fish following sensory cell death. Previous studies indicated that activating Atoh1, an early acting transcription factor that specifies sensory cell fate during embryogenesis, was not sufficient for such regeneration. The authors hypothesized that adding a second transcription factor, Ikzf2, which maintains outer hair cell (OHC) fate, would synergize with Atoh1 and push adult SCs to differentiate as OHCs. They tested this hypothesis by over-expressing both Atoh1 and Ikzf2 in supporting cells after killing the endogenous OHCs in adult cochleae. The authors showed that the induced cells first express the general HC marker, Myo6, and only later become Prestin-positive, much as occurs during normal development. Unfortunately, these induced OHC-like cells had abnormal stereocilia and did not restore auditory (ABR) thresholds. Moreover, there was a loss of IHCs (the primary auditory receptors) suggesting that much more is needed to induce a real OHC and to protect IHCs than simply inducing the two selected transcription factors. Single-cell RNAseq (scRNA-seq) results showed that the induced OHC-like cells are enriched for HC genes and depleted for SC genes, but overall are most similar to neonatal HCs as defined in published scRNA-seq data from other groups. Overall, the scRNA-seq data did not offer a clear path forward, other than to identify and test additional transcription factors that might push the induced cells to the next stage. Nevertheless, the extent of SC transformation is impressive and has not been seen in previous approaches. This is an important contribution to our understanding of the control of OHC gene expression and differentiation contributed by two important transcription factors.

    3. Reviewer #1 (Public Review):

      Mature mammalian hair cells in the cochlea do not regenerate after damage. The outer hair cells of the cochlea, which function to amplify sound, are particularly susceptible to damage. Ectopic activation of two key transcription factors for outer hair cell formation, Atoh1 and Ikzf2, in damaged adult cochlea is sufficient to convert supporting cells into hair cells expressing Prestin, which is an essential protein mediating outer hair cell functions. Although there is no functional recovery in these transgenic mice based on auditory brainstem response, this study paves the way for future design of models for hearing recovery. The main concern is the identity of the OHC-like cells drawn from the small sample size in the scRNA-seq experiments.

    4. Evaluation Summary:

      This manuscript demonstrated the effectiveness of combined activation of Atoh1 and Ikzf2 in converting adult supporting cells to outer hair cell (OHC)-like cells in a mouse model, in which the OHCs were selectively ablated with diphtheria toxin. The authors showed that while the number of regenerated hair cells was low and there was no functional recovery based on ABR, these OHC-like cells do express Prestin and exhibit a genetic profile that resembles nascent hair cells. This paper will be of great interest to researchers interested in hearing restoration, as well as regenerative biology.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

    1. Author Response:

      Evaluation Summary:

      This paper will be of considerable interest to anybody focusing on highly sensitive T cell antigen recognition. It uses an extended experimental protocol and analytical methods to assess very low T cell receptor binding affinities, and to determine how T cells discriminate between self- and non-self antigens. The main conclusions are well supported by the presented analysis and provide a novel view on a previously considered concept.

      Reviewer #1 (Public Review):

      The presented manuscript takes a comprehensive and elaborated look at how T cell receptors (TCR) discriminate between self and non-self antigens. By extending a previous experimental protocol for measuring T cell receptor binding affinities against peptide MHC complexes (pMHC), they are able to determine very low TCR-pMHC binding affinities and, thereby, show that the discriminatory power of the TCR seems to be imperfect. Instead of a previously considered sharp threshold in discriminating between self and non-self antigen, the TCR can respond to very low binding affinities leading to a more transient affinity threshold. However, the analysis still indicates an improved discrimination ability for TCR compared to other cell surface receptors. These findings could impact the way how T cell mediated autoimmunity is studied.

      The authors follow a comprehensive and elaborated approach, combining in vitro experiments with analytical methods to estimate binding affinities. They also show that the general concept of kinetic proofreading fits their data with providing estimates on the number of proofreading steps and the corresponding rates. The statistical and analytical methods are well explained and outlined in detail within the Supplemental Material. The source of all data, and especially how the data to analyze other cell surface receptor binding affinities was extracted, are given in detail as well. Besides being able to quantify TCR-pMHC interactions for very low binding affinities, their findings will improve the ability to assess how autoimmune reactions are potentially triggered, and how potent anti-tumour T cell therapies can be generated.

      In summary, the study represents an elaborated and concise analysis of TCR-pMHC affinities and the ability of TCR to discriminate between self and non-self antigens. All conclusions are well supported by the presented data and analyses without major caveats.

      Reviewer #2 (Public Review):

      The paper revisits the question of ligand discrimination ability of TCRs of T cells. The authors find that the commonly held notion of very sharp discrimination between strongly and weakly binding peptides does not hold when the affinities of the weak peptides are re-measured more accurately, using their own new method of calibration of SPR measurements. They are able to phenomenologically fit their results with a ~2 step Kinetic Proofreading model.

      It is a very carefully researched and thorough paper. The conclusions seem to be supported by the data and fundamental for our understanding of the T cell immune response with potentially very high impact in many scientific and applied fields. The calibration method could be of potential use in other cases where low affinities are an issue.

      As a non-expert in the details of experimental technique, it is somewhat difficult to understand in detail the Ab calibration of the SPR curve - which is a central piece of the paper. The main question is - what are the grounds (theoretical and/or empirical) to expect that the B_max of the TCR dose response curve will continue to be proportional to the plateau level of the Ab. Figure 1D does suggest that, but it would be hard to predict what proportionality shape the curve will take for lower affinity peptides. Given that essentially all the paper claims rest on this assumption, this should explained/reasoned/supported more clearly.

      We have revised the relevant Results and Methods sections to provide additional information. This information should clarify the expected relationship between Bmax and W6/32 binding. We emphasise that we have only interpolated within the curve and therefore, have not relied on any assumptions about the relationship between these two values outside of the empirical curve that we have generated.

      On the theoretical side - I think the scaling alpha\simeq 2 in Figure 2 is indeed consistent with a two-step KPR amplification. However, there are some questions regarding the fitting of the full model to the P_15 of the CD69 response. As explained in the Supplementary Material the authors use 3 global and 2 local parameters resulting in 37 (or 27) parameters for 32 data points. To a naive reader this might look excessive and prone to overfitting. On the other hand, looking at Figure S8 shows the value ranges of lambda and k_p are quite tight. This is in contrast to gamma and dellta that look completely unconstrained.

      We have revised the relevant Results section to explicitly indicate that the number of data points ex- ceeds the number of free parameters, which together with the ABC-SMC results, should provide additional confidence that we are not over-fitting.

      Finally, one of the stated advantages of the adaptive proof-reading model is that it is capable of explaining antagonism. It is hard to see how a 'vanilla" KPR model is capable of explaining antagonism.

      We have added a discussion paragraph to discuss antagonism, which cannot be explained by the basic KP model that we found is sufficient to explain our data on antigen discrimination in the presence of self pMHCs on autologous APCs. We describe how the methods we have employed can be used to study antagonism.

      Reviewer #3 (Public Review):

      Pettmann et al. aimed at significantly improving the accuracy of SPR-based measurements of low affinity TCR-pMHC interactions by including a 100% binding control (injecting of a conformation-specific HLA-antibody) in the surface plasmon resonance protocol. Interpolating with the information of saturated pMHC binding on the chip The authors arrive at KDs for low affinity binders that are significantly higher than the previously reported constants. If correct, this has considerable ramifications for the interpretations of the results obtained from functional assays measuring the T cell response towards pMHCs featured in a titrated fashion. Unlike what was put forward by earlier reports, the authors conclude that the discriminatory power of TCRs is far from perfect, as T cells still respond to low affinity pMHC-ligands without a sharp affinity threshold. This is also because they managed to detect T cells responding to even ultra-low affinity ligands if provided in sufficient numbers.

      The body of work convinces in several regards:

      (i) It is exceedingly well thought out and introduces a quality of analytical strength that is absent in most of the literature published thus far on this topic.

      (ii) At the same time theoretical arguments are bolstered by a large body of experimental "wet" work, which combines a synthetic approach with cellular immunology and which appears overall well executed.

      (iii) The data lead to hypotheses in the field of T cell antigen recognition in general and in the theatre of autoimmunity, cancer and infectious diseases.

      There are a few aspects that may limit the impact of the study. I have listed them below:

      (i) The study does not provide kinetic data for the low affinity ligand-TCR binding but rather argues from the position of affinities as determined via Bmax. This limits somewhat the robustness of the statements made with regard to kinetic proofreading.

      We agree with this statement and are hoping to directly measure off-rates in the future. We note that in the published literature, including our own work, point mutations to the peptide generally modify the off-rate with only minor impact on the on-rate. An example of this can be found in Lever et al (2016) PNAS where point mutations led to 100,000-fold change in the off-rate but only a 10-fold change in the on-rate. This likely explains why antigen potency is often well-correlated with affinity when using point mutations to the peptide.

      (ii) Thresholds for readouts were arbitrarily chosen (e.g. 15% activation). It appears such choices were based on system behavior (with the largest differences observed among the groups) but may have implications for the drawn conclusions.

      We have chosen 15% in order to capture the ultra-low affinity pMHCs in our potency plots and have now added a sentence for why we have chosen this particular threshold. We did explore different thresholds but found that they produced similar values of α. The precise threshold could change the estimate of α if the shape of dose-response curves was dependent on antigen affinity but we did not find any evidence for this within our data.

      In summary, the work presented contributes to demystifying the link between TCR-engagement and (membrane proximal) signaling. It also provides a fresh perspective on the potential of TCR-cossreactivity.

    2. Reviewer #3 (Public Review):

      Pettmann et al. aimed at significantly improving the accuracy of SPR-based measurements of low affinity TCR-pMHC interactions by including a 100% binding control (injecting of a conformation-specific HLA-antibody) in the surface plasmon resonance protocol. Interpolating with the information of saturated pMHC binding on the chip The authors arrive at KDs for low affinity binders that are significantly higher than the previously reported constants. If correct, this has considerable ramifications for the interpretations of the results obtained from functional assays measuring the T cell response towards pMHCs featured in a titrated fashion. Unlike what was put forward by earlier reports, the authors conclude that the discriminatory power of TCRs is far from perfect, as T cells still respond to low affinity pMHC-ligands without a sharp affinity threshold. This is also because they managed to detect T cells responding to even ultra-low affinity ligands if provided in sufficient numbers.

      The body of work convinces in several regards:

      (i) It is exceedingly well thought out and introduces a quality of analytical strength that is absent in most of the literature published thus far on this topic.

      (ii) At the same time theoretical arguments are bolstered by a large body of experimental "wet" work, which combines a synthetic approach with cellular immunology and which appears overall well executed.

      (iii) The data lead to hypotheses in the field of T cell antigen recognition in general and in the theatre of autoimmunity, cancer and infectious diseases.

      There are a few aspects that may limit the impact of the study. I have listed them below:

      (i) The study does not provide kinetic data for the low affinity ligand-TCR binding but rather argues from the position of affinities as determined via Bmax. This limits somewhat the robustness of the statements made with regard to kinetic proofreading.

      (ii) Thresholds for readouts were arbitrarily chosen (e.g. 15% activation). It appears such choices were based on system behavior (with the largest differences observed among the groups) but may have implications for the drawn conclusions.

      In summary, the work presented contributes to demystifying the link between TCR-engagement and (membrane proximal) signaling. It also provides a fresh perspective on the potential of TCR-cossreactivity.

    3. Reviewer #2 (Public Review):

      The paper revisits the question of ligand discrimination ability of TCRs of T cells. The authors find that the commonly held notion of very sharp discrimination between strongly and weakly binding peptides does not hold when the affinities of the weak peptides are re-measured more accurately, using their own new method of calibration of SPR measurements. They are able to phenomenologically fit their results with a ~2 step Kinetic Proofreading model.

      It is a very carefully researched and thorough paper. The conclusions seem to be supported by the data and fundamental for our understanding of the T cell immune response with potentially very high impact in many scientific and applied fields. The calibration method could be of potential use in other cases where low affinities are an issue.

      As a non-expert in the details of experimental technique, it is somewhat difficult to understand in detail the Ab calibration of the SPR curve - which is a central piece of the paper. The main question is - what are the grounds (theoretical and/or empirical) to expect that the B_max of the TCR dose response curve will continue to be proportional to the plateau level of the Ab. Figure 1D does suggest that, but it would be hard to predict what proportionality shape the curve will take for lower affinity peptides. Given that essentially all the paper claims rest on this assumption, this should explained/reasoned/supported more clearly.

      On the theoretical side - I think the scaling alpha\simeq 2 in Figure 2 is indeed consistent with a two-step KPR amplification. However, there are some questions regarding the fitting of the full model to the P_15 of the CD69 response. As explained in the Supplementary Material the authors use 3 global and 2 local parameters resulting in 37 (or 27) parameters for 32 data points. To a naive reader this might look excessive and prone to overfitting. On the other hand, looking at Figure S8 shows the value ranges of lambda and k_p are quite tight. This is in contrast to gamma and dellta that look completely unconstrained.

      Finally, one of the stated advantages of the adaptive proof-reading model is that it is capable of explaining antagonism. It is hard to see how a 'vanilla" KPR model is capable of explaining antagonism.

    4. Reviewer #1 (Public Review):

      The presented manuscript takes a comprehensive and elaborated look at how T cell receptors (TCR) discriminate between self and non-self antigens. By extending a previous experimental protocol for measuring T cell receptor binding affinities against peptide MHC complexes (pMHC), they are able to determine very low TCR-pMHC binding affinities and, thereby, show that the discriminatory power of the TCR seems to be imperfect. Instead of a previously considered sharp threshold in discriminating between self and non-self antigen, the TCR can respond to very low binding affinities leading to a more transient affinity threshold. However, the analysis still indicates an improved discrimination ability for TCR compared to other cell surface receptors. These findings could impact the way how T cell mediated autoimmunity is studied.

      The authors follow a comprehensive and elaborated approach, combining in vitro experiments with analytical methods to estimate binding affinities. They also show that the general concept of kinetic proofreading fits their data with providing estimates on the number of proofreading steps and the corresponding rates. The statistical and analytical methods are well explained and outlined in detail within the Supplemental Material. The source of all data, and especially how the data to analyze other cell surface receptor binding affinities was extracted, are given in detail as well. Besides being able to quantify TCR-pMHC interactions for very low binding affinities, their findings will improve the ability to assess how autoimmune reactions are potentially triggered, and how potent anti-tumour T cell therapies can be generated.

      In summary, the study represents an elaborated and concise analysis of TCR-pMHC affinities and the ability of TCR to discriminate between self and non-self antigens. All conclusions are well supported by the presented data and analyses without major caveats.

    5. Evaluation Summary:

      This paper will be of considerable interest to anybody focusing on highly sensitive T cell antigen recognition. It uses an extended experimental protocol and analytical methods to assess very low T cell receptor binding affinities, and to determine how T cells discriminate between self- and non-self antigens. The main conclusions are well supported by the presented analysis and provide a novel view on a previously considered concept.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors)

    1. Reviewer #3 (Public Review):

      Sun et al have assembled, modified, and applied a series of existing gene editing tools to tissue-derived human fetal lung organoids in a workflow they have termed "Organoid Easytag". Using approaches that have previously been applied in iPSCs and other cell models in some cases including organoids, the authors demonstrate: 1) endogenous loci can be targeted with fluorochromes to generate reporter lines; 2) the same approach can be applied to genes not expressed at baseline in combination with an excisable, constitutively active promoter to simplify identification of targeted clones; 3) that a gene of interest could be knocked-out by replacing the coding sequence with a fluorescent reporter; 4) that knockdown or overexpression can be achieved via inducible CRISPR interference (CRISPRi) or activation (CRISPRa). In the case of CRISPRi, the authors alter existing technology to lessen unwanted leaky expression of dCas9-KRAB. While these tools have previously been applied in other models, their assembly and demonstrated application to tissue-derived organoids here could facilitate their use in tissue-derived organoids by other groups.

      Limitations of the study include:

      1) is demonstrated application of these technologies to a limited set of gene targets;

      2) a lack of detail demonstrating the efficiency and/or kinetics of the approaches demonstrated.

      While access to human fetal lung organoids is likely not available to many or most researchers, it is probable that the principles applied here could carry over to other organoid models.

    2. Reviewer #2 (Public Review):

      There is now a considerable body of knowledge about the genetic and cellular mechanisms driving the growth, morphogenesis and differentiation of organs in experimental organisms such as mouse and zebrafish. However, much less is known about the corresponding processes in developing human organ systems. One powerful strategy to achieve this important goal is to use organoids derived from self-renewing, bona fide progenitor cells present in the fetal organ. The Rawlins' lab has pioneered the long-term culture of organoids derived from multipotent epithelial progenitors located in the distal tips of the early human lung. They have shown that clonal cell "lines" can be derived from the organoids and that they capable of not only long-term self-renewal but also limited differentiation in vitro or after grafting under the kidney capsule of mice. Here, they now report a strategy to efficiently test the function of genes in the embryonic human lung, regardless of whether the genes are actively transcribed in the progenitor cells. The strengths of the paper are that the authors describe a number of different protocols (work-flows), based on Crisper/Cas9 and homology directed repair, for making fluorescent reporter alleles (suitable for cell selection) and for inducible over-expression or knockout of specific genes. The so-called "Easytag" protocols and results are carefully described, with controls. The work will be of significant interest to scientists using organoids as models of many human organ systems, not just the lung. The weaknesses are that they authors do not show that their lines can undergo differentiation after genetic manipulation, and therefore do not provide proof of principle that they can determine the function in human lung development of genes known to control mouse lung epithelial differentiation. It would also be of general interest to know whether their methods based on homologous recombination are more accurate (fewer incorrect targeting events or off target effects) than methods recently described for organoid gene targeting using non homologous repair.

    3. Reviewer #1 (Public Review):

      The authors demonstrate applications including fluorescent marking of membranes with GFP or monomeric RFP, reporter alleles for convenient assessment of differentiation status based on fluorescence, and targeted gene knockout. They also demonstrate conditional gene knockdown and induction with tight control achieved by engineering a protein destabilizing domain. The design of the constructs is clever and imparts the ability to leverage iterative FACS to enrich successfully targeted cells, particularly useful when targeting alleles that are not actively expressed by the progenitors. The work is well done and clearly presented.

    4. Evaluation Summary:

      In this paper Sun and colleagues aimed to demonstrate the feasibility of using CRISPR-based gene editing techniques applied to tissue-derived human fetal lung organoids. While previous studies have used CRISPR-Cas9 to perform knock-in or knock-out studies in organoids (such as intestinal, hepatic or tumor organoids), this is the first report to apply these tools to a tissue-derived lung organoid model. A major strength of this report is the additional use of CRISPRi and CRISPRa technologies. The work is well done, clearly presented and makes an important contribution to the literature.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #3 agreed to share their name with the authors.)

    1. Author Response:

      Reviewer #1:

      This manuscript reports the results of two timing experiments. The experimental paradigm asks participants to judge the time of target items in an unfilled interval between two landmark stimuli. In experiment 1, there is one item that must be judged. In experiment 2, there are two items to be judged. The basic empirical result is that relative order judgments in experiment 2 are more accurate than one might expect from the absolute timing judgments of experiment 1. A model is presented.

      My overall reaction is that this paper does not present a sufficiently noteworthy empirical result. I can't imagine that there is a cognitive psychologist studying memory who would be surprised by the finding that relative order judgments in the second experiment are more accurate than one might expect from the absolute judgments in experiment 1. On the encoding side, in these really short lists (with no secondary task), there is nothing preventing the participant from noting and encoding the order as the items are presented (not unlike the recursive reminding). On the retrieval side, we've known for a very long time that judgments of serial position use temporal landmarks (see for instance a series of remarkable studies by Hintzman and colleagues circa 1970).

      Methodologically, this paper falls short of the standards one would expect for a cognitive psychology paper. There are basically no statistics or description of the distribution of the effect across participants. Although I'm pretty well-convinced that the basic finding (distributions in experiment 2 are different from experiment 1), I could not begin to guess at an effect size. The model is not seriously evaluated. The bimodal distributions are a large qualitative discrepancy that is not really discussed.

      Although the title of the paper invites us to understand these results as telling us something about episodic memory, the empirical burden of this claim is not carried. Amnesia patients (and animals with hippocampal lesions) show relatively subtle differences in timing tasks. There is no evidence presented here, nor literature review, to convince the reader of this point.

      Reviewer #1

      We regret that the reviewer did not focus on the main results of the paper, and limited their remarks to just one analysis comparing the relative order precision to the one predicted from the naive assumption on independent absolute time judgements for each item. This analysis was done to confirm that relative order is quite precisely remembered for short lists that is indeed not surprising, but we still did it in order to get a quantitative estimate of ordering mistakes that we needed for our Bayesian experiments. Another purpose was to filter out the participants that don’t pay attention to the task (a common problem when performing experiments over the internet).

      Regarding the title of the paper, we are not aware of similar experiments as ours done with amnesic patients. However we take the reviewer's point that the relation of our experiments to episodic memory as usually understood is not direct, so we took the word 'episodic' off the title in the revised version. We also added statistical analysis of the results.

      Reviewer #2:

      In this manuscript, the authors set out to measure participant's decisions about when an item occurred in a short list of 3 or 4 items, where the first and last items were always at the beginning and end, respectively. They report two behavioral studies that examine time judgments to items in the intermediate positions. They show that time judgments (when did you see X item using a continuous line scale) are always a little off but, more importantly, they tend to be anchored to other items presented. The results are interesting and add to our knowledge of the representation of time in the brain mainly by introducing a new paradigm with which to study time. Within the broader context of research on timing capacities, it should not be surprising that participants do not have a continuous representation of time that lasts beyond traditional time interval training of a few hundred milliseconds to a few seconds. Furthermore, research has also shown that 'events' that require attentional resources do morph our perception and memory for time. So while the paradigm is worth expanding on, the behavioral results are not surprising given this past literature. I do feel however that this work is an important first step in developing a more firm model of memory for time.

      Reviewer #2

      Indeed, as mentioned above in response to Reviewer #1, we are not surprised that subjects don't remember well the absolute presentation time, especially when several items were involved. Exactly what they remember is the main point of this study, and the model is quite crucial in understanding what we believe is our novel result about how ordinal and absolute time representations interact in memory. The reviewer did not seem to appreciate this; rather they re-formulated our results as time judgments (when did you see X item using a continuous line scale) being 'anchored' to other items presented. We are not sure what this exactly means, probably that on average the difference between reported times of different items stayed almost constant for each presentation conditions. However our study not only presented this result but showed how it follows from the Bayesian theory.

    1. Reviewer #3:

      The authors hypothesized lower GABA levels in older adults would influence cortico-cortical phase relationships more than cortico-muscular phase relationships during performance of a bimanual motor task. To this end, they evaluated the mediating role of endogenous bilateral sensorimotor cortex GABA content in relation to behavioral performance and patterns of interhemispheric and cortico-muscular electrophysiological phase coherence during a bimanual motor control task. The central finding was that the mediating influence of right M1 GABA on the relationship between cortico-cortical electrophysiology and behavior diverged between the younger and older groups, with lower endogenous GABA concentrations potentially benefitting bimanual motor performance in young adults and hindering performance in older adults. The result was specific to right M1 GABA, raising questions about hemispheric asymmetry, and behavioral performance differed substantially between groups, possibly influencing the sensitivity of the analyses of the electrophysiological phase relationships. Moreover, several earlier studies suggest endogenous M1 GABA content relates to cortico-muscular excitability measurements, other than phase synchrony, and it is unclear what distinguishes phase synchrony from these other measurements. The behavioral, MRS, and electrophysiological methods employed are fairly well-established and are combined in a novel manner. The Bayesian moderated mediation analysis represents a new approach to evaluating relationships between these measures under the moderating influence of age. The central questions concerning the roles of cortical endogenous GABA in bimanual control, and in age-related changes in motor control more generally, are important for determining the neural computations underlying flexible and precise behavior.

      1) The total number of finger taps within the 2000 ms transition epoch likely differed between groups and could influence the ISPC measures. It would be helpful to rule out this possibility by examining relationships between ISPC measures and the total number of taps.

      2) The differences between right and left M1 are somewhat surprising and merit further attention, particularly given the cortico-cortical ISPC results. The interpretation provided in the discussion (lines 607-618) is not particularly satisfying since this asymmetry is a critical feature of a key result. Can the authors leverage their own data to provide further insight into why RM1 GABA+ may be more likely to exhibit a relationship than LM1 GABA+? Would analyzing the behavioral data separately for the left and right hands provide further insight? Does the non-dominant hand lag behind the dominant hand, and/or is it more susceptible to errors?

      3) There were some general issues concerning the GABA+ data:

      a. Figure 2a suggests an interaction in the pattern of variance in the GABA+ data between the Young and Older groups for the LM1 and RM1 voxels. Is this interaction in variance significant, and if so, what might this mean for the M1 GABA+ results? Specifically, Young show greater variance for LM1, and Older show greater variance for RM1. Also, Young appear to show considerably lower variance for RM1 than LM1. However, the data in Figure 2 supplement 2 suggest that variance in the Young is similar between LM1 and RM1. Do these numbers accurately reflect the data depicted in Figure 2a?

      b. It would be helpful to show the difference spectra in Figure 2 supplement 1b with separate plots for Young and Older.

      c. Figure 2, supplement 1a: Was the LM1 voxel more dorsal and medial than the RM1 voxel?

      4) The authors interpret the decrease in failure and increase in error rate across the task in the Older group as an indication of a loss of precision over time. Alternatively, might this pattern also arise because these participants are becoming faster at correcting their errors (i.e. within 2000 ms), avoiding trials from being categorized as a failure? More generally speaking, it would be helpful if the authors provided additional information about the cumulative error rate trials and what behavior looked like on these trials.

      5) The authors should provide further justification for the assignment of age as the moderator and GABA+ as the mediator in their statistical model. Conceptually, it seems these factors could be reversed.

      6) Several studies have established relationships between transcranial magnetic stimulation measures of cortico-muscular excitability and endogenous GABA+ content in the dominant M1. The manuscript would benefit from further discussion of the relationship of the phase connectivity measurements used here in comparison to these other previous studies.

      7) It is not clear that data or analysis code are available.

    2. Reviewer #2:

      I like this type of multimodal study, and I think that the rationale for the study is good. I am not, however, convinced about the results/conclusions provided. Here are my main points:

      I don't agree with your conclusion that the mediating role of GABA changes in aging. This requires longitudinal data, the cross-sectional approach in this study can only conclude differences between groups since only 1 time point is available.

      No age interaction, this is surprising to me since there are age differences?

      Compensatory explanation: Is there a correlation with performance? If there isn't, the proposal of compensatory mechanisms is unclear since it is then not obvious what the compensation is for?

    3. Reviewer #1:

      The authors have acquired a substantial multimodal dataset and have used careful statistical approaches throughout. The data are acquired and analysed using appropriate methods.

      Overall, this is an impressive body of work that aims to answer an interesting question. However, a number of questions over the methods and interpretation make the authors' conclusions difficult to justify.

      When comparing between older and younger adults it would also be helpful to know the amount of grey matter in the voxels of interest. It might be expected that older adults might have more atrophy and therefore lower GABA+, than younger adults and this should be controlled for in the statistical models. The authors have put assumptions into their quantification, which are reasonable but are still assumptions. It would be helpful to directly test for a difference in grey matter fraction in the voxel between the two groups, and include this in the model if necessary.

      The authors then look at behaviour, where they use a previously described task which consists of bimanual tapping, with switching between two patterns. The results are complex as there are a number of behavioural metrics, and no clear pattern emerges. While older adults produced more errors in continuation, they also produced more fully correct switching transitions. Older subjects were slower than younger adults in all trials. While this task produces a very rich dataset, which is helpful for analysing complex behaviour, it is not clear how each metric should be interpreted in terms of the underlying neural mechanisms, and how they can be usefully combined, could be given.

      In terms of connectivity, the authors found no significant group X task difference between in-phase and anti-phase conditions. They therefore look at the groups and tasks separately. They show different changes in connectivity between age groups in different frequency bands, for example between left and right M1 in the alpha/mu and beta, between EMG and left M1 in the theta band. I am not sure that describing EEG-EMG connectivity as cortico-spinal is strictly accurate - there may be a number of other factors in this -corticomuscular would seem to be more precise. The frequency bands used are not typical, and it would be helpful to have an a priori explanation of which are being tested and why - as well as details about correction for multiple comparisons across these bands.

      Finally, the authors bring their GABA, behaviour and connectivity metrics together in a number of mediation analyses. They demonstrate a relationship between cortico-cortical connectivity and behaviour, which is mediated by age.

      The authors describe their finding of higher GABA+ in the occipital cortex as a posterior-anterior gradient, which I think is not justified by the results - there could be a number of other reasons for this, for example that different functional networks have different GABA+ levels, which is not related to their anatomical position. With only three voxels it is difficult to make a general claim such as this, and this should probably be reworded.

      The authors state that higher GABA+ indicated neural system integrity and better functioning in the older group. This seems to be rather over-interpreting their results - there are many other metrics of integrity and functioning that have not been assessed here. I would suggest rewording.

    1. Author Response:

      Reviewer #1 (Public Review):

      This paper presents the exciting statement that increasing viral loads within a community can be used as an epidemiological early-warning indicator preceeding increased positivity. It would be interesting to support this claim to present both Ct and positivity on the same graph to demonstrate that indeed, declining Ct can be used as an early marker of a COVID-19 epidemic wave. Percentage of positive test data should not only include the ones obtained in the present study but should be compared with "national data" as the present study design includes a bias in patients selection that might not reflect the "true" situation at the time. Only with this comparison, we could claim that the present study design could predict COVID-19 epidemic waves. A correlation of Ct with clinical evidence to rank the confidence of positive results is also included and further support the high specificity of the RT-PCR for detecting SARS-CoV-2 (99.995%).

      In a serological investigation, it was observed that some of these RT-PCR-positive cases do not appear to seroconvert and that possible re-infections might occures despite the presence of anti-spike antibodies. Although, reported on few individuals and therefore to be taken with extreme caution, this add some piece of information to the current unknown of the serological response of COVID-19 patient and would be of uttermost importance in the context of the current vaccination campaign.

      We do not agree that this study is biased in terms of patient selection – it invites randomly selected households to join the survey and is in fact the major source of unbiased surveillance data in the 4 nations of the United Kingdom.

    2. Reviewer #1 (Public Review):

      This paper presents the exciting statement that increasing viral loads within a community can be used as an epidemiological early-warning indicator preceding increased positivity. It would be interesting to support this claim to present both Ct and positivity on the same graph to demonstrate that indeed, declining Ct can be used as an early marker of a COVID-19 epidemic wave. Percentage of positive test data should not only include the ones obtained in the present study but should be compared with "national data" as the present study design includes a bias in patients selection that might not reflect the "true" situation at the time. Only with this comparison, we could claim that the present study design could predict COVID-19 epidemic waves. A correlation of Ct with clinical evidence to rank the confidence of positive results is also included and further support the high specificity of the RT-PCR for detecting SARS-CoV-2 (99.995%).

      In a serological investigation, it was observed that some of these RT-PCR-positive cases do not appear to seroconvert and that possible re-infections might occur despite the presence of anti-spike antibodies. Although, reported on few individuals and therefore to be taken with extreme caution, this add some piece of information to the current unknown of the serological response of COVID-19 patient and would be of uttermost importance in the context of the current vaccination campaign.

    3. Evaluation Summary:

      The authors present a systematic and complete study of Ct (cycle threshold) values in RT-PCR tests and gene-specific positivity for the UK ONS infection survey. There are very few datasets like this for any viral pathogen, regardless of pandemics. The patterns are fascinating and thought-provoking.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    1. Reviewer #3 (Public Review):

      In this revised manuscript (Oon and Prehoda), the authors performed additional live-imaging experiments and recorded aPKC and actin dynamics simultaneously in larval neuroblasts. They also provide evidence that aPKC polarization is lost upon F-actin disruption by Latrunculin A treatment. These are great improvements. The pulsatile dynamics of actin and myosin II showed in the manuscript are compelling. Images presented in this manuscript are of high-quality and impressive.

      However, the pulsatile apical myosin network in delaminating neuroblasts in Drosophila embryos was reported previously (An Y. et al., Development, 2017). This important and relevant paper should be cited in the introduction of the current manuscript. Therefore, the finding on the pulsatile actomyosin in larval brain neuroblasts reported in this manuscript is not a total novel discovery. Another major concern is that Lat-A did not specifically disrupt actomyosin pulsatile movements, as it generally disrupts the F-actin network. So these experiments only strengthened the link between the F-actin network and Par polarity (which was already demonstrated in Kono et al., 2019; Oon 22 and Prehoda, 2019). Low doses of Cytochalasin D are known to disrupt myosin pulses still allowing the assembly of the actomyosin network (Mason et al., Nature Cell Biology 2014). The author should treat neuroblasts with low doses of CytoD to only disrupt actomyosin pulses, not the entire F-actin network, and examine the effect on Par polarity. It is also worthwhile to knockdown sqh to disrupt apical pulsatile actin dynamics. Besides, most of the concerns previously raised by the reviewer were not addressed in the revised manuscript.

    2. Reviewer #2 (Public Review):

      Previously, Oon and Prehoda showed apically directed movement of aPKC clusters during polarization of the neuroblast prior to asymmetric cell division. They found that these movements required F-actin, but the distribution of F-actin has only been reported for later stages of neuroblast polarization and division. Here, the authors report pulses of cortical F-actin during interphase, followed by an apically directed flow at the onset of mitosis, a strong apical accumulation of F-actin at metaphase and anaphase, followed by fragmentation and basally directed flow of the fragments. aPKC clusters are shown to colocalize with the F-actin networks as they flow apically. The F-actin networks are also shown have partial colocalization with non-muscle myosin II, suggesting a possible mechanism for their movement. Finally, the authors solidify the results of actin inhibitor studies from their 2019 study by showing that reported effects on aPKC localization are preceded by F-actin loss as would be expected but was not previously shown. Overall, the Research Advance extends the past study by more directly showing the involvement of F-actin and myosin in the apical localization mechanism of aPKC, and by describing F-actin and myosin dynamics prior to this transition. The following concerns should be addressed.

      1) The pulsatile nature of broad F-actin networks is evident during interphase, but these pulsations substantially subside upon entry into mitosis, and at this stage an apically directed flow of F-actin is the main behavior evident. This transition from pulses to flow is evident in both the movies and the kymographs of the F-actin probe. However, the authors state that the pulsations continue at the onset of mitosis and as the apical cap of aPKC matures. It is unclear whether the apical flow of aPKC and F-actin is associated with small-scale defined F-actin pulses, or small-scale random fluctuations of F-actin. The F-actin flow alone is an informative finding. The authors should consider revising their descriptions of these data (including in the manuscript title), or provide clearer examples of defined F-actin pulsations during the stage when aPKC polarizes.

      2) I checked the main text, methods, figures and figure legends, but could not find listings of sample sizes. Thus, the reproducibility of the findings has not been reported.

    3. Reviewer #1 (Public Review):

      Oon and Prehoda report pulsatile contraction of apical membrane in the process of Par protein polarization in Drosophila neuroblasts. This explains how/why actin filament was required to localize/polarize Par complex. Specifically, using spinning disc confocal microscopy with high temporal resolution, they found the directed actin movement toward the apical pole, which nicely correlates with concentration of aPKC. They also show that myosin II is involved in this pulsatile movement of actin filament. This very much resembles the observation in C. elegans embryos, and nicely unifies observations across systems. Although descriptive in nature, I think this is an important observation and indicates a universal mechanism by which cells are polarized. I think this is a well executed study.

    4. Evaluation Summary:

      Oon and Prehoda report pulsatile contraction of apical membrane in the process of Par protein polarization in Drosophila neuroblasts. This explains how/why actin filament was required to localize/polarize Par complex. This very much resembles the observation in C. elegans embryos, and nicely unifies observations across systems.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

    1. Reviewer #2:

      This study reports a new cell line model for Dyskeratosis congenita, generated by introducing a disease-causing mutation, DKC1 A386T, into human iPS-derived type II alveolar epithelial cells (iAT2). The authors found that the mutant cells failed to form organoids after serial passaging and displayed hallmarks of cellular senescence and telomere shortening. Transcriptomics analysis for the mutant cells unveiled defects in Wnt signaling and down-regulation of the downstream shelterin complex components. Finally, treating the mutant cells with a Wnt agonist, a GSK3 inhibitor CHIR99021 can rescue these defects and enhance telomerase activity. Overall, the study is well designed and executed. Data presented are generally clear and convincing. The new model presented here can be of great interests in the field to study the effects of DC disease causing mutants in diverse cell types.

    2. Reviewer #1:

      In this manuscript, Fernandez et al examine the impact of defective telomere length maintenance on type II alveolar epithelial cells, which are thought to be central to the pathogenesis of pulmonary fibrosis in dyskeratosis congenita (DC) and related telomere biology disorders. Murine models have been used to address how telomere dysfunction in AT2 cells drives pulmonary fibrosis however these models have limitations. Therefore, the investigators' study of human AT2 cells/organoids derived from induced pluripotent stem cells (iAT2 cells) in the presence and absence of a known DC pathogenic variant provides an exceptional model. In addition, the investigators use expression profiling to uncover decreased canonical WNT signaling in iAT2 cells with telomere dysfunction and then demonstrate rescue of telomere dysfunction and iAT2 cell growth with the addition of a GSK3 inhibitor, a canonical WNT agonist. The data appear to be of high quality, the approaches and interpretation appropriate, with some noted exceptions below. Given the importance of the problem (dysfunctional telomere-induced pulmonary fibrosis) and the apparent benefit of GSK3 inhibition of iAT2 cell growth and telomere dysfunction, which extends the work published by this group previously on intestinal organoids (might enhanced canonical WNT signaling more broadly affect other tissues with telomere-induced senescence?), this work is significant.

      A few aspects of the studies dampen the ability to draw certain conclusions. For example, the authors use iPSCs that are 5 vs 25 passages after introduction (or not) of the DKC1 A386T mutation for the generation of iAT2 cells. They then show iAT2 DKC1 mutant organoids generated from the later passage iPSCs have an apparent growth defect as early as Day 50 but that those generated from the earlier passage iPSCs do not at Day 70 [with caveats the images are of different quality (comparing Fig. 1B and Fig. S3D) and quantitative data (similar to Fig. 1C) are lacking for the iAT2 organoids generated from the early passage iPSCs]. They argue that progressive telomere shortening is the cause of the growth defects. If this is the case, then the iAT2 cells generated from the earlier passages should eventually show growth defects with progressive telomeres shortening, which was not shown.

      The telomere length analysis of the iAT2 cells at Day 50 and Day 70 are not markedly different, and neither the % p21 + nor TIF+ cells is shown for Day 50. Therefore, the conclusion that it is the accumulation of short uncapped telomeres in the DKC1 mutant iAT2 cells that alters gene expression and induces senescence at Day 70 ignores the extent of these changes at Day 50.

      The statement that CHIR99021 (when present in the medium from Day 49-70) rescued the growth defect seems generous; the effect is partial and the assay is for organoid formation efficiency only. Moreover, it is most likely prohibiting the further accumulation of senescent cells rather than rescuing cells that were not previously growing.

      It is striking that prolonged CHIR99021 treatment (ie, through to Day 70) resulted in increased telomerase activity, and more so in mutant compared to wild type cells. First, how reproducible was this effect? I appreciate that the authors have not explored this for this manuscript, however, TERT expression does not rescue DKC1 mutants but TERC does. Were TERC levels increased? Also, given this robust increase, it is striking that no difference is detected in TeSLA assays given the proportion of very short detected telomeres that would presumably be substrates for telomerase. It is noteworthy that, in the protocol to derive iAT2 cells, CHIR99021 is present in the media prior to Day 28. This raises the question of whether there is rescue of telomerase in the cells exposed to CHIR99021 in the interval of iAT2 specification?

    3. Summary: The investigators' study of human AT2 cells-derived from induced pluripotent stem cells (iAT2 cells) in the presence and absence of a known dyskeratosis congenita (DC) pathogenic variant provides an exceptional model for understanding pathogenesis of pulmonary fibrosis in dyskeratosis congenita (DC) and related telomere biology disorders (TBDs). They provide convincing data demonstrating altered WNT signaling in iAT2 cells with short, dysfunctional telomeres and improved growth of iAT2 cells by GSK3 inhibition but fall short of convincingly showing the latter is due restored telomere end protection. The work should be of interest to those in the fields of telomere biology and the TBDs, lung physiology, WNT signaling and stem cell biology.

    1. Reviewer #3 (Public Review):

      The authors sought to show how the segments of influenza viruses co-evolve in different lineages. They use phylogenetic analysis of a subset of the complete genomes of H3N2 or the two H1N1 lineages (pre and post 2009), and use a method - Robinson-Foulds distance analysis - to determine the relationships between the evolutionary patterns of each segment, and find some that are non-random.

      1) The phylogenetic analysis used leaves out sequences that do not resolve well in the phylogenic analysis, with the goal of achieving higher bootstrap values. It is difficult to understand how that gives the most accurate picture of the associations - those sequences represent real evolutionary intermediates, and their inclusion should not alter the relationships between the more distantly related sequences. It seems that this creates an incomplete picture that artificially emphasizes differences among the clades for each segment analyzed?

      2) It is not clear what the significance is of finding that sequences that share branching patterns in the phylogeny, and how that informs our understanding of the likelihood of genetic segments having some functional connection. What mechanism is being suggested - is this a proxy for the gene segments having been present in the same viruses - thereby revealing the favored gene segment combinations? Is there some association suggested between the RNA sequences of the different segments? The frequently evoked HA:NA associations may not be a directly relevant model as those are thought to relate to the balance of sialic acid binding and cleavage associated with mutations focused around the receptor binding site and active site, length of NA stalk, and the HA stalk - does that show up in the overall phylogeny of the HA and NA segments? Is there co-evolution of the polymerase gene segments, or has that been revealed in previous studies, as is suggested?

      The mechanisms underlying the genomic segment associations described here are not clear. By definition they would be related to the evolution of the entire RNA segment sequence, since that is being analyzed - (1) is this because of a shared function (seems unlikely but perhaps pointing to a new activity), or is it (2) because of some RNA sequence-associated function (inter-segment hybridization, common association of RNA with some cellular or viral protein)? (3) Related to specific functions in RNA packaging - please tell us whether the current RNA packaging models inform about a possible process. Is there a known packaging assembly process based on RNA sequences, where the association leads to co-transport and packaging - in that case the co-evolution should be more strongly seen in the region involved in that function and not elsewhere? The apparent increased association in the cytoplasm of the subset of genes examined for the single virus looks mainly in the cytoplasm close to the nucleus - suggesting function (2) and/or (3)?.

      It is difficult to figure out how the data found correlates with the known data on reassortment efficiency or mechanisms of systems for RNA segment selection for packaging or transport - if that is not obvious, maybe you can suggest processes that might be involved.

    2. Reviewer #2 (Public Review):

      The influenza A genome is made up of eight viral RNAs. Despite being segmented, many of these RNAs are known to evolve in parallel, presumably due to similar selection pressures, and influence each other's evolution. The viral protein-protein interactions have been found to be the mechanism driving the genomic evolution. Employing a range of phylogenetic and molecular methods, Jones et al. investigated the evolution of the seasonal Influenza A virus genomic segments. They found the evolutionary relationships between different RNAs varied between two subtypes, namely H1N1 and H3N2. The evolutionary relationships in case of H1N1 were also temporally more diverse than H3N2. They also reported molecular evidence that indicated the presence of RNA-RNA interaction driving the genomic coevolution, in addition to the protein interactions. These results do not only provide additional support for presence of parallel evolution and genetic interactions in Influenza A genome and but also advances the current knowledge of the field by providing novel evidence in support of RNA-RNA interactions as a driver of the genomic evolution. This work is an excellent example of hypothesis-driven scientific investigation.

      The communication of the science could be improved, particularly for viral evolutionary biologists who study emergent evolutionary patterns but do not specialise in the underlying molecular mechanisms. The improvement can be easily achieved by explaining jargon (e.g., deconvolution) and methodological logics that are not immediately clear to a non-specialist.

      The introduction section could be better structured. The crux of this study is the parallel molecular evolution in influenza genome segments and interactions (epistasis). The authors spent the majority of the introduction section leading to those two topics and then treated them summarily. This structure, in my opinion, is diluting the story. Instead, introducing the two topics in detail at the beginning (right after introducing the system) then discussing their links to reassortments, viral emergence etc. could be a more informative, easily understandable and focused structure. The authors also failed to clearly state all the hypotheses and predictions (e.g., regarding intracellular colocalisation) near the end of the introduction.

      The authors used Robinson-Foulds (RF) metric to quantify topological distance between phylogenetic trees-a key variable of the study. But they did not justify using the metric despite its well-known drawbacks including lack of biological rational and lack of robustness, and particularly when more robust measures, such as generalised RF, are available.

      Figure 1 of the paper is extremely helpful to understand the large number of methods and links between them. But it could be more useful if the authors could clearly state the goal of each step and also included the molecular methods in it. That would have connected all the hypotheses in the introduction to all the results neatly. I found a good example of such a schematic in a paper that the authors have cited (Fig. 1 of Escalera-Zamudio et al. 2020, Nature communications). Also this methodological scheme needs to be cited in the methods section.

      Finally, I found the methods section to be difficult to navigate, not because it lacked any detail. The authors have been excellent in providing a considerable amount of methodological details. The difficulty arose due to the lack of a chronological structure. Ideally, the methods should be grouped under research aims (for example, Data mining and subsampling, analysis of phylogenetic concordance between genomic segments, identifying RNA-RNA interactions etc.), which will clearly link methods to specific results in one hand and the hypotheses, in the other. This structure would make the article more accessible, for a general audience in particular. The results section appeared to achieve this goal and thus often repeat or explain methodological detail, which ideally should have been restricted to the methods section.

    3. Reviewer #1 (Public Review):

      In this paper, authors did a fine job of combining phylogenetics and molecular methods to demonstrate the parallel evolution across vRNA segments in two seasonal influenza A virus subtypes. They first estimated phylogenetic relationships between vRNA segments using Robinson-Foulds distance and identified the possibility of parallel evolution of RNA-RNA interactions driving the genomic assembly. This is indeed an interesting mechanism in addition to the traditional role for proteins for the same. Subsequently, they used molecular biology to validate such RNA-RNA driven interaction by demonstrating co-localization of vRNA segments in infected cells. They also showed that the parallel evolution between vRNA segments might vary across subtypes and virus lineages isolated from distinct host origins. Overall, I find this to be excellent work with major implications for genome evolution of infectious viruses; emergence of new strains with altered genome combination.

      Comments:

      I am wondering if leaving out sequences (not resolving well) in the phylogenic analysis interferes with the true picture of the proposed associations. What if they reflect the evolutionary intermediates, with important implications for the pathogen evolution which is lost in the analyses?

      Lines 50-51: Can you please elaborate? I think this might be useful for the reader to better understand the context. Also, a brief description on functional association between different known fragments might instigate curiosity among the readers from the very beginning. At present, it largely caters to people already familiar with the biology of influenza virus.

      Lines 95-96 Were these strains all swine-origin? More details on these lineages will be useful for the readers.

      Lines 128-132: I think it will be nice to talk about these hypotheses well in advance, may be in the Introduction, with more functional details of viral segments.

      Lines 134-136: Please rephrase this sentence to make it more direct and explain the why. E.g. "... parallel evolution between PB1 and HA is likely to be weaker than that of PB1 and PA" .

      Lines 222-223: Please include a set of hypotheses to explain you results? Please add a perspective in the discussion on how this contribute might to the pandemic potential of H1N!?.

      Lines 287-288: I am wondering how likely is this to be true for H1N1.

    4. Evaluation Summary:

      The manuscript reports phylogenetic and molecular evidence of novel RNA-RNA interactions driving the genomic coevolution of Influenza virus subtypes, in addition to protein interactions. With a few minor changes, this study could reveal how the likelihood of certain genetic combinations might lead to new viral variants emerging with the possibility of new antigenic properties and implications in disease spread.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

    1. Reviewer #3 (Public Review):

      This is a well-executed study with interesting and novel findings. The main strength is the combined use of well-executed flow cytometry studies in human patients with MI and in vitro experiments to suggest a role for immature neutrophils in infarction. The main weakness is the descriptive/associative nature of the data. What is lacking is in vivo experimentation documenting the proposed pro-inflammatory role of immature neutrophils. This limits the conclusions. The following specific concerns are raised:

      Major:

      1.In some cases, conclusions are not supported by robust data. For example, the authors conclude that CD14+HLA-DRneg/lo monocytes play a crucial role in post-infarction inflammation based exclusively on in vitro experiments. Moreover, conclusions regarding the pro-inflammatory role of immature neutrophils are based on in vitro data and associative studies.

      2.Immature neutrophils have a short lifespan. Information on the fate of immature neutrophils in the infarct is lacking. The in vivo mouse model may be ideal to address whether immature neutrophils undergo apoptosis or mature within the infarct environment

      3.The rationale for selective assessment of specific genes and for the specific neutrophil-lymphocyte co-culture system is unclear. In neutrophils, the basis for selective assessment of some specific genes (MMP9, IL1R1, IL1R2, STAT3 etc), vs. other inflammatory genes known to be expressed at high levels by neutrophils is not explained. Similarly, the rationale for the experiment examining interactions of CD10neg neutrophils with T cells is not clear. Considering the effects of neutrophils on macrophage phenotype and on cardiomyocytes, study of interactions with other cell types may have made more sense.

      4.The concept of CMV seropositivity is suddenly introduced without a clear rationale. The data show infiltration of the infarcted heart with immature neutrophils and CD14+HLA-DRneg monocytes. One would have anticipated more experiments investigating the (proposed) role of these cells in the post-infarction inflammatory response, rather than comparison of CMV+ vs negative patients.

    2. Reviewer #2 (Public Review):

      In this study, Fraccarollo and colleagues describe the existence and higher prevalence of subpopulations of immature monocytes and neutrophils with pro-inflammatory responses in patients with acute myocardial infarction. CD14+HLA-DRneg/low monocytes and CD16+CD66b+CD10neg neutrophils correlate with markers of systemic inflammation and parameters of cardiac damage. In particular in patients positive for cytomegalovirus and elevated levels of CD4+CD28null T cells, the expansion of immature neutrophils associates with increased levels of circulating IFNg. Mechanistically, immature neutrophils regulate T-cell responses by inducing IFN release through IL-12 production in a contact-independent manner. Besides, CD14+HLA-DRneg/low monocytes differentiate into macrophages with a potent pro-inflammatory phenotype characterized by the release of pro-inflammatory cytokines upon IFNg stimulation.

      This very interesting study provides new insights into the diversity and complexity of myeloid populations and responses in the context of cardiac ischemia. It is technically well performed and the results sufficiently support the conclusions of the study.

      Strengths

      The authors provide a detailed analysis of the phenotype and function of two subpopulations of CD14+HLA-DRneg/low monocytes and CD16+CD66b+CD10neg neutrophils in the context of acute myocardial infarction (AMI). Extensive phenotyping of these immune populations at different time-points after the onset of the disease provides strong correlations with multiple parameters of inflammation and severity of the disease. Hence, these subpopulations emerge as biomarkers of heart ischemic diseases with predictive potential. Using in vitro approaches, the authors support these correlations with mechanistic analyses of the inflammatory and immunomodulatory function of these populations. Finally, the authors use mouse models of ischemia-reperfusion injury to mimic the conditions observed in the AMI patients and supporting the pro-inflammatory role of immature neutrophils in this disease.

      Weaknesses

      The associations between immature neutrophils, IFNg, and CD4+CD28null T cells found in AMI patients positive for cytomegalovirus are not well supported by the mechanistic findings observed in vitro. Here, the induction of IFNg production by immature neutrophils is restricted to CD4+CD28+ T cells but not CD4+CD28null T cells.

      The experimental data obtained from mouse models of AMI to support their findings in humans would require a more extensive study. Causality between the expansion of these immature populations and the course of the disease is missing. Also, although expected, substantial differences are found between equivalent subpopulations in mice and humans thus limiting the relevance of the mouse data.

    3. Reviewer #1 (Public Review):

      In this paper, the authors tried to investigate complex roles of immune cells during acute myocardial infarction (AMI) by examining immune cells in blood samples from acute coronary syndrome (ACS) patients. They found an increase in the circulating levels of CD14+HLA-DRneg/low monocytes and CD16+CD66b+CD10neg neutrophils in the blood of ACS patients compared to healthy people, all of which were correlated with elevated levels of inflammatory markers in serum. Those findings were then further explored at a mechanistic level by using in vitro and in vivo experiments. Interestingly, the researchers also found that high cytomegalovirus (CMV) antibody titers could affect the immunoregulatory mechanisms in AMI patients. Taken together, the findings of the researchers could potentially contribute to the development of a more effective strategy to prevent cardiac deterioration and cardiovascular adverse events after AMI.

      Strengths:

      This paper contains novel insight regarding role of neutrophil and monocyte subset in pathophysiology of AMI. Although the increased level of CD10neg subsets of neutrophils in AMI patients has recently been reported (Marechal, P., et al. 2020. Neutrophil phenotypes in coronary artery disease. Journal of Clinical Medicine), the current paper aptly complemented the previous findings obtained by using its in vitro and in vivo mice model. This study also has robust methods to support their conclusion.

      Weakness:

      To further improve the strength of their conclusion, the experiments investigating the effects of immunoregulatory function of immature neutrophils and HLA-DRneg/low monocytes subsets would be advised.

    4. Evaluation Summary:

      This paper will be of broad interest to cardiologist and scientists studying acute myocardial infarction (AMI), especially to those focussing on the immune responses during AMI. Using combination of in vivo and in vitro model, as well as tissue from patients, the authors reveal new insights regarding the immune mechanisms during AMI, highlighting the importance of neutrophils and monocytes during the early days of its process. The findings in this paper add to the understanding of how immune mechanisms may contribute to subsequent adverse events after AMI.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors)

    1. Reviewer #3 (Public Review):

      The differences in signaling and responses in the three different T cell receptor transgenics are shown by several different means. These include Nur77 and CD5 expression as markers for the strength of signaling, the frequency of calcium fluxes and length of signaling-induced pauses in movement, using 2 photon microscopy of thymic slices (comparing selecting and non-selecting thymus), time course of induction of markers of positive selection signaling, the time course of "arrival" of CD8 single positive cells and CCR7+ cells in the post-natal thymus, and a time course of development of SP thymocytes after injection of EdU. Each of these methods is fairly convincing on its own, but added up, they are very convincing.

      The only issues that I could take issue with are about how we define self-reactivity. Because it is not feasible to measure the affinities for self peptides on MHC (due to low affinity and the fact that we mostly don't know what they are), the authors have to rely on surrogate markers, the upregulation of CD69 and of Nur77. These are widely accepted in the field, so they are as good a surrogate as is possible at this time.

      Similarly, 3 transgenic strains are taken as examples of high, medium and low self-reactivity. Two of the strains are positively selected on H2Kb, one on Db, one on Ld. Therefore, the experiments cannot be genetically controlled in the same manner. On balance, I accept that there aren't too many other ways to do the experiment, and that all the main points are supported by other types of experiment.

      The most interesting aspect of the work consists of analysis of gene expression by RNASeq from cells from each of the three TCR transgenic mice from early positive selection, late positive selection, and mature CD8 SP. Perhaps unsurprisingly, the more strongly self-reactive cells showed increased expression of genes involved in protein translation, RNA processing, etc. However, genes associated with lower self-reactivity were enriched for lots of different ion channels. These included calcium, potassium, sodium and chloride channels. One of these was Scn4b, part of a voltage gated sodium channel previously shown by Paul Allen's lab to be involved in positive selection. These types of genes were associated with the stage of development before selection, and were retained through selection in the weakly self-reactive thymocytes. Other ion channel genes that typically came on at the end of selection were also upregulated earlier in the lower self-reactivity cells, and may be involved in allowing long-term signaling for these cells to undergo the whole positive selection program.

    2. Reviewer #2 (Public Review):

      In their study, Lutes et al examine the fate of thymocytes expressing T cell receptors (TCR) with distinct strengths of self-reactivity, tracking them from the pre-selection double positive (DP) stage until they become mature single positive (SP) CD8+ T cells. Their data suggest that self-reactivity is an important variable in the time it takes to complete positive selection, and they propose that it thus accounts for differences in timescales among distinct TCR-bearing thymocytes to reach maturity. They make use of three MHC-I restricted T cell receptor transgenics, TG6, F5, and OT1, and follow their thymic development using in vitro and in vivo approaches, combining measures at the individual cell-level (calcium flux and migratory behaviour) with population-level positive selection outcomes in neonates and adults. By RNA-sequencing of the 3 TCR transgenics during thymic development, Lutes et al make the additional observation that cells with low self-reactivity have greater expression of ion channel genes, which also vary through stages of thymic maturation, raising the possibility that ion channels may play a role in TCR signal strength tuning.

      This is a well-written manuscript that describes a set of elegant experiments. However, in some instances there are concerns with how analyses are done (especially in the summaries of individual cell data in Fig 2 and 3), how the data is interpreted, and the conclusions from the RNA-seq with regard to the ion channel gene patterns are overstated given the absence of any functional data on their role in T cell TCR tuning. As such the abstract is currently not an accurate reflection of the study, and the discussion also focuses disproportionately on the data in the final figure, which forms the most speculative part of this paper.

      (1) As the authors themselves point out (discussion), one of the strengths of this study is the tracking of individual cells, their migratory behaviour and calcium flux frequency and duration over time. However, the single-cell experiments presented (Figure 2 and 3) do not make use of the availability of single-cell read-outs, but focus instead on averaging across populations. For instance, Figure 3a,b provides only 2 sets of examples, but there is no summary of the data providing a comparison between the two transgenics across all events imaged. In Figure 3c, the question that is being asked, which is to test for between-transgenic differences is ultimately not the question that is being answered: the comparison that is made is between signaling and non-signaling events within transgenics. However, this latter question is less interesting as it was already shown previously that thymocytes pause in their motion during Ca flux events (as do mature T cells). Moreover, the average speed of tracks is probably not the best measure here in reading out self-reactivity differences between TCR transgenic groups.

      (2) The authors conclude from their data that the self-reactivity of thymocytes correlates with the time to complete positive selection. However the definition of what this includes is blurry. It could be that while an individual cell takes the same amount of time to complete positive selection (ie, the duration from the upregulation of CD69 until transition to the SP stage is the same), but the initial 'search' phase for sufficient signaling events differs (eg. because of lower availability of selecting ligands for TG6 than for OT1), in which case at the population level positive selection would appear to take longer. Given that from Fig 2/3 it appears that both the frequency of events and their duration differ along the self-reactivity spectrum, this needs to be clarified. Moreover, whether the positive selection rate and positive selection efficiency can be considered independently is not explained. It appears that the F5 transgenic in particular has very low positive selection efficiency (substantially lower %CD69+ and of %CXCR4-CCR7+ cells than the OT1 and TG6) and how this relates to the duration of positive selection, or is a function of ligand availability is unclear.

      (3) While the question of time to appearance of SP thymocytes of distinct self-reactivities during neonatal development presented (Figure 5) is interesting, it is difficult to understand the stark contrast in time-scales seen here compared with their in vitro thymic slice (Figure 4) and in vivo EdU-labelling data (Figure 6), where differences in positive selection time was estimated to be ~1-2 days between TCR transgenics of high versus low affinity. This would suggest that there may be other important changes in the development of neonates to adults not being considered, such as the availability of the selecting self-antigens.

      (4) The conclusion that "ion channel activity may be an important component of T cell tuning during both early and late stages of T cell development" is not supported by any data provided. The authors have shown an interesting association between levels of expression of ion channels, their self-affinity and the thymus selection stage. However, some functional data on their expression playing a role in either the strength of TCR signaling or progression through the thymus (for instance using thymic slices and the level of CD69 expression over time), would be needed to make this assertion. Moreover, from how the data is presented it is difficult to follow the conclusion that a 'preselection signature' is retained by the low but not the high self-reactivity thymocytes.

    3. Reviewer #1 (Public Review):

      The work by Lutes et al. addresses how thymocytes undergo positive selection during their differentiation into mature T cells. The authors make use of several in vitro and in vivo model systems to the test whether developing thymocytes at the critical preselection CD4+CD8+ stage, expressing T cell receptors (TCRs) with different levels of putative self-reactivity, undergo different or similar differentiation events, in terms of migration, thymic epithelial cell engagement and temporal kinetics, and gene expression changes.

      The authors selected three TCR-transgenes, which have increasing levels of self-reactivity, TG6, F5 and OT1, respectively, to test their hypothesis, that TCR signals during positive selection are not only sensed differently but lead to different outcomes that then define the functional status of mature T cells. The author's conclusions that thymocytes with low self-reactivity differentiate with distinct kinetics (migration, engagement and temporal) and express a different suite of genes than thymocytes that experience high self-reactivity is well supported by several elegant approaches, and convincing findings.

      The authors clearly established that low to high TCR signaling outcomes affect the timing of positive selection, which is beautifully illustrated in Figures 3-6, and extend that work to non-TCR transgenic mice as well. Lastly, their findings from RNA-seq analyses shed light into the different genetic programs experienced by high-reactivity fast differentiating CD8 T cells as compared to low-reactivity slower differentiating cells, which appear to retain the expression a unique set of ion channels during later stages of their differentiation process.

      However, what the expression of these ion channels means in terms of either supporting the slow progression or perhaps responsible for the slow progression is not directly addressed, and likely beyond the scope. Nevertheless, the authors posit as to the potential role(s) for the differently expressed gene subsets. Overall, the work is crisply executed, and the findings reveal new aspects as to how positive selection can be achieved by thymocytes expressing very different TCR reactivities.

    4. Evaluation Summary:

      This study is of interest to immunologists as it fills a key knowledge gap in understanding factors involved in determining the duration of intrathymic positive selection of T cells. The findings come from a series of both in vitro and in vivo experiments implicating the self-reactivity of thymocytes in the time to completion of positive selection. An RNA-sequencing analysis suggests that gene expression differences from the pre-selection to the single-positive thymocyte stage is self-reactivity dependent, correlating in particular the level of ion channel expression with positive selection completion rates.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #3 agreed to share their names with the authors.)

    1. Reviewer #3 (Public Review):

      Miskolci et al have investigated if it is possible to measure the natural fluorescence of two important co-enzymes (NADH/NADPH and FAD) in living cells to determine their metabolic status. This tests the hypothesis that changes to the relative ratio of NADH/NADPH to FAD+ reflect a shift between glycolytic and oxidative phosphorylation in living macrophages. To investigate this they have used 2-photon FLIM to measure intensity and fluorescence lifetime of NAD/NADPH and FAD+ in mouse macrophages in vitro and zebrafish macrophages in vivo in a tail injury model. By comparing their measures of NAD(P)H and FAD+ from macrophages responding to different injury or infection cues and comparing this to a maRker of inflammation (TNF-alpha) they argue that there is a reduced redox state indicative of glycolytic metabolism in pro-inflammatory macrophages.

      The adoption of label free imaging techniques to measure metabolic processes in cells in vivo is a valuable and important development that, although not novel to this work, will help researchers to probe cell biology in situ. FLIM using time correlated single photon counting (TCSPC) allows an accurate and robust measure of the relative state of a molecule that shows changes in its fluorescent lifetime as a consequence of changing chemical state. Although Stringari et al (doi.org/10.1038/s41598-017-03359-8) were the first to describe the utility of wavelength mixing FLIM for measuring NAD(P)H and FAD+ levels in zebrafish, they did not focus on macrophages which is the focus of this work.

      The results from this work are interesting, as they argue that it is possible to determine cell metabolism in cells within living animals without a need to use a genetically encoded sensor and they argue that pro-inflammatory macrophages in zebrafish appear to have a lower redox state, which may reflect a more glycolytic metabolism. This assumption is not tested but rather inferred based on the measures of fluorescence intensity and lifetime of endogenous NADH/NADPH and FAD coupled with a small metabolic sampling of injured tissue. This lack of corroboration for a the supposed difference in metabolism between pro-inflammatory and non-inflammatory macrophages is a weakness of the paper and makes it hard to accept the conclusion that the redox state may reflect different metabolic profiles. A biosensor for NADH/NADPH (iNap) has been demonstrated to be a sensitive tool for measuring NADPH concentration in vivo in zebrafish during the injury response (Tao et al (doi: 10.1038/nmeth.4306) and it would be intriguing to know how similar the response is of this biosensor to the label free measurements described using FLIM. This is additionally relevant as the authors also note that in mouse macrophages cultured in vitro, they observe an opposite redox response which is well supported by the literature and a variety of different methods. Why the zebrafish macrophages should show a different redox state to mouse macrophages is not clear and an alternative explanation is that the measures used do not directly reflect the metabolic profile of the cells. One further caveat to the chosen method of using fluorescence lifetime to measure the redox state of NADH/NADPH is that lifetime of NADH is affected by which proteins it is bound to. This is not accounted for in the method used for calculating the redox ratio used for defining the redox state and could potentially alter the interpretations of relative NADH/NADPH levels in a cell. The authors acknowledge this, but do not consider whether this would affect the conclusions they arrive at from their measures of NAD(P)H intensity and fluorescence lifetime in macrophages.

    2. Reviewer #2 (Public Review):

      • The aim of this paper was to demonstrate whether FLIM-based imaging of optical redox ratio can be used to monitor metabolic states of immune cells in vivo during the course of inflammatory responses.

      • The study is rigorous and well-presented and the findings are interesting and novel. The main strength is in the in vivo data, where the authors used a variety of inflammatory challenges and perturbations and were able to detect previously unreported trends in metabolic states of macrophages.

      • The authors have demonstrated the potential of the technique to be used in vivo. Their initial findings are intriguing and can be followed up by more mechanistic studies.

      • The work is timely, because of growing interest in the role of metabolism in immune cell signalling and functions. Relevant microscopy-based assays in vivo are limited, so this innovation is important and can form the basis of further technology developments.

    3. Reviewer #1 (Public Review):

      The zebrafish has a rich history of enabling innovative microscopy techniques, and is also a well established system to model inflammation and infection by human pathogens. Consistent with this, Miskolci et al use zebrafish to test a novel imaging approach that has great potential to significantly impact the field of immunometabolism. Fluorescence lifetime is a label-free, non-invasive imaging approach to detect metabolic changes in situ, at the level of the single cell. In this report, Miskolci et al use fluorescence lifetime imaging of NAD(P)H and FAD to detect metabolic changes in zebrafish macrophages (with temporal and spatial resolution) in response to inflammatory and infectious cues.

      Miskolci et al (eLife 2019) have previously characterized inflammatory and wound healing responses to distinct caudal fin injuries (tail wound, infection and tail wound, thermal injury). In this report, authors use these different injury models to show that fluorescence lifetime imaging can detect variations in macrophage metabolism. Although many interesting results are presented and future directions are proposed, the study in its current state is descriptive and lacks validation across different modalities. As a result, the reliability of fluorescence lifetime imaging in zebrafish macrophages is not yet convincing. Moreover, any metabolomic changes in macrophages are not functionally linked to zebrafish phenotypes (eg inflammation, bacterial burden, caudal fin regeneration).

    4. Evaluation Summary:

      Immunometabolism is an emerging field, and to understand immune cell metabolism during inflammation and infection is of great interest. In this report, cutting edge (label free) microscopy techniques and innovative zebrafish models are used to characterize the metabolism of macrophages in situ. In the future, fluorescence microscopy approaches pioneered using zebrafish may illuminate strategies to therapeutically manipulate metabolism in human immune cells.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to share their name with the authors.)

    1. Reviewer #3 (Public Review):

      This analysis is enormous in scope. That said, approximately half the glomeruli were either truncated or had very fragmented ALRNs. The authors may wish to reserve use of the term "full" in the title ("....a full olfactory connectome") until a subsequent paper.

      ALRN-ALRN connectivity seems very interesting. It would be helpful to provide more information about this in the text (line 148 or so). The information in Fig. 3D is hard for non-specialists to interpret. Does the connectivity show any patterns? Is it stereotyped? Do the connections make functional sense?

      One intriguing finding is the "shortcuts" between the olfactory and motor systems that could be used for behaviors that are hard-wired or require fast responses. These may be particularly relevant to thermosensory and hygrosensory input, but can the authors say anything about what kind of olfactory information flows through these shortcuts? For example, the ALRNs that respond to wasp odorants have been identified. Please note that most readers do not know what kind of odorants project to individual glomeruli, e.g. "DC4" .

      Fig. 8C It's hard to know how confident to be of the neurotransmitter assignments here. It would be helpful to provide in the text a statement about what assumptions these assignments are based on. In the same vein, line 380 refers to "a neurotransmitter prediction pipeline". Some kind of reference should be provided here.

      line 522 "This suggests that thermo/hygrosensation might employ labeled lines whereas olfaction uses population coding to affect motor output." This brings up the question of whether very narrowly tuned ORNs such as the one signaling geosmin show any differences in connectivity from broadly tuned ORNs.

      lines 94-96 Graph traversal model. Some more discussion of this model and its underlying assumptions would be helpful. Are the results influenced by the lack of some of the glomeruli from the dataset?

      Fig. 7D Can the authors provide more discussion of the possible functional significance of the two uPN types?

    2. Reviewer #2 (Public Review):

      Here are three notable examples (among a long list of new discoveries). (1) The authors provided a comprehensive description of the antennal lobe local interneuron (LN) network for the first time, providing a "final" counts of neuronal number and type of LNs as well as the preference for the input and output partners of each LN type. (2) They introduced "layer" as a quantitative parameter to describe how many synapses away on average a particular neuron or neuron type is from the sensory world. A few interesting new discoveries from this analysis include that on average, multi-glomerular antennal lobe projection neurons (PNs) are further away from the sensory world than uniglomerular PNs; inhibitory lateral horn neurons are closer to the sensory world than excitatory lateral horn neurons. (3) By leveraging previous analyses they performed on another EM volume (FAFB) and comparing n = 3 (bilateral FAFB, unilateral hemibrain) samples, they analyzed stereotypy and variability of neurons and connections, something rarely done in serial EM reconstruction studies but is very important.

      Overall, the text is clearly written, figures well illustrated, and quantitative analysis expertly performed. I have no doubt that this work will have long-lasting values for anyone who study the fly olfactory system, and for the connectomics field in general.

    3. Reviewer #1 (Public Review):

      The manuscript presents a very nice and very detailed approach to illustrate the anatomical hierarchies and also some differences of signal transmission in the olfactory vs. thermosensory-/hygrosensory systems.

      The authors first provide a complete description of the Drosophila olfactory system, from first, second and third-order neurons in the lateral horn. Using a generally applicable analysis methods, they extract information flow and layered organisation betw