Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Recommendations For The Authors):

Summary:

In this manuscript, the molecular mechanism of interaction of daptomycin (DAP) with bacterial membrane phospholipids has been explored by fluorescence and CD spectroscopy, mass spectrometry, and RP-HPLC. The mechanism of binding was found to be a two-step process. A fast reversible step of binding to the surface and a slow irreversible step of membrane insertion. Fluorescence-based titrations were performed and analysed to infer that daptomycin bound simultaneously two molecules of PG with nanomolar affinity in the presence of calcium. Conformational change but not membrane insertion was observed for DAP in the presence of cardiolipin and calcium.

Strengths:

The strength of the study is skillful execution of biophysical experiments, especially stoppedflow kinetics that capture the first surface binding event, and careful delineation of the stoichiometry.

Weaknesses:

The weakness of the study is that it does not add substantially to the previously known information and fails to provide additional molecular details. The current study provides incremental information on DAP-PG-calcium association but fails to capture the complex in mass spectrometry. The ITC and NMR studies with G3P are inconclusive. There are no structural models presented. Another aspect missing from the study is the reconciliation between PG in the monomer, micellar, and membrane forms.

Besides the two-stage process, another important finding in the current work is the stable complex that plays a critical role in the drug uptake both in vitro and in B. subtilis. This complex has been shown to be a stable species in HPLC and its binding stoichiometry and affinity have been quantitatively characterized. The complex may not be stable enough in gas phase to be detected in the MS analysis, which was designed to detect the phospholipid and Dap components, not the complex itself. The structural model of this complex is clearly proposed and presented in Figure 6. 

The NMR and ITC studies have a very clear conclusion that Dap has a weak interaction with the PG headgroup alone, which is unable to account for the Dap-PG interaction observed in the fluorescence studies. Thus, the whole PG molecule has to be involved in the interaction, leading to the discovery of the stable complex.  

Reviewer #2 (Recommendations For The Authors):

(1) I appreciate and agree with the comment that there are stages of daptomycin insertion, and these might involve the formation of different complexes with different binding partners (e.g. pre-insertion vs quaternary vs bactericidal). However, it seems like lipid II is an apparent participant in daptomycin membrane dynamics (Grein et al. Nature Communications 2020). It's not clear why this was excluded from analysis by the authors, or what basis there is for the discussion statement that the quaternary complex can shift into the bactericidal complex by exchanging 1 PG for lipid II. 

We agree that lipid II and other isoprenyl lipids may be involved in the uptake and insertion of daptomycin into membrane according to the results of the Nat. Comm. paper. However, these isoprenyl lipids are very small components of the membrane in comparison to PG and their contribution to the drug uptake is thus expected to be much less significant. Nonetheless, we included farnesyl pyrophosphate (FPP) as an analog of bactoprenol pyrophosphate (C55PP), which was reported to have the same promoting effect as lipid II in the previous study, in our study but found no promoting effect in the fluorescence assay (Fig. 2B). In addition, no complex was formed when FPP replaced PG in our preparation and analysis of the drug-lipid complex. In consideration of these negative results and the expected small contribution, other isoprenyl lipids or their analogs were not included in the study.

The statement of forming the proposed bactericidal complex from the identified complex is a speculation that is possible only when lipid II has a higher affinity for Dap than a PG ligand. To avoid confusion, we deleted the sentence’ in the revision. 

(2) The detailed examination of daptomycin dynamics, particularly on the millisecond scale, in this paper is ideal for characterizing the effect of lipid II on daptomycin insertion. It would be helpful to either include lipid II in some analyses (micelle binding, fluorescence shifts, CD) or at least address why it was excluded from the scope of this work.

As mentioned in the response to the first comment, we did not exclude isoprenyl lipids in our study but used some of their analogs in the fluorescence assay. Besides FPP mentioned above, we also tested geranyl pyrophosphate and geranyl monophosphate but obtained the same negative results. Lipid II was not directly used because it is one of the three isoprenyl lipids reported to have the same promoting effects in the Nat. Comm. paper and also because its preparation is not easy. Even if lipid II were different from other isoprenyl lipids in promoting membrane binding, its contribution is likely negligible at the reversible stage compared to the phospholipids because of its minuscule content in bacterial membrane. This is the main reason we did not use the isoprenyl lipids in the fast kinetic study (this stage only involves reversible binding, not insertion). 

(3) Grein et al. 2020 saw that PG did not have a strong effect on daptomycin interaction with membranes. I believe this discrepancy is more likely due to the complex physical parameters of supported bilayers versus micelles/vesicles or some other methodological variable, but if the authors have more insight on this, it would be valuable commentary in the discussion.

We totally agree that the discrepancy is likely due to the different conditions in the assays. It is hard to tell exactly what causes the difference. Thus, we did not attempt to comment on the cause of this difference in the discussion.

(4) Isolation of the daptomycin complex from B. subtilis cells clearly had different traces from the in vitro complex; is it possible that lipid II is present in the B. subtilis complex? If not, a time-course extraction could be useful to support the model that different complexes have different activities. Isolates from early-stage incubation with daptomycin may lack lipid II but isolates from longer incubations may have lipid II present as the complex shifts from insertion to bactericidal.

From the day we isolated the complex from B. subtilis, we have been looking for evidence for the previously proposed lipid complexes containing lipid II or other isoprenyl lipids but have not been successful. We did not see any sign of lipid II or other isoprenyl lipids in the MALDI or ESI mass spectroscopic data. The minute peaks in the HPLC traces are not the expected complexes in separate LC-MS analysis. However, this does not mean that such complexes are not present in the isolated PG-containing complex because: (1) the amount of such complexes may be too small to be detected due to the low content of the isoprenyl lipids; (2) the isoprenyl lipids, particularly lipid II, are not easily ionizable due to their size and unique structure for detection in mass spectrometry. 

We don’t think the drug treatment time is the reason for the failure in detecting lipid II or other isoprenyl lipids. In our reported experiment, the cells were treated with a very high dose of Dap for 2 hours before extraction. In a separate experiment done recently, we treated B. subtilis at 1/3 of the used dose under the same condition and found all treated cells were dead after 1 hour in a titration assay, consistent with the results from reported time-killing assays in the literature. From this result, the proposed bactericidal lipid-containing complex should have been formed in the treated cells used in our extraction and isolated along with the PG-containing complex. It was not detected likely due to the reasons discussed above. To avoid the interference of the PG-containing complex, a large amount of bacterial cells might have to be treated at a low dose to isolate enough amount of the lipid II-containing complex for identification. However, isolation or identification of the lipid II-containing complex is outside the scope of the current investigation and is therefore not pursued. 

(5) Part of the daptomycin mechanism of interacting with bacterial membranes involves the flipping of daptomycin from one leaflet to another. There was some mentioned work on the consistency of results between micelles and vesicles, but the dynamics or existence of a flipping complex in the bilayer system wasn't addressed at all in this paper.

The current investigation makes no attempt to solve all problems in the daptomycin mode of action and is limited to the uptake of the drug, up to the point when Dap is inserted into the membrane. Within this scope, flipping of the complex is not yet involved and is thus irrelevant to the study. How the complex is flipped and used to kill the bacteria is what should be investigated next.  

(6) The authors mention data with phosphatidylethanolamine in the text, but I could not find the data in the main or supplemental figures. I recommend including it in at least one of the figures.

It is much appreciated that this error is identified. The POPE data was lost when the graphic (Fig. 2B) was assembled in Adobe to create Figure 2. We re-draw the graphic and reassemble the figure to solve this problem. Fig. 2B has also been modified to use micromolar for the concentration of the lipids.

(7) Readability point: I'd suggest some consistency in the concentrations mentioned. Making the concentrations either all molar-based or all percentage-based would make comparison across figures easier.

As suggested, we have changed the % into micromolar concentrations in Fig. 2B and also in Fig. 3A. 

(8) The model figure is quite difficult to interpret, particularly the final stage of the tail unfolding. I recommend the authors use a zoomed-in inset for this stage, or at least simplify the diagram by removing the non-participating lipid structures. The figure legend for the model figure should also have a brief description of the events and what the arrows mean, particularly the POPS PG arrow in the final panel of the figure. I am assuming here the authors are implying that daptomycin can transiently interact with one lipid species and move to another, but the arrow here suggests that daptomycin is moving through the lipid headgroup space.

We really appreciate the suggestions. As suggested, we put an inset to show the preinsertion complex more clearly. In addition, we have removed the green arrows originally intended to show the re-organization/movement of the phospholipids. Moreover, the legend is changed to ‘Proposed mechanism for the two-phased uptake of Dap into bacterial membrane. In the first phase, Dap reversibly binds to negative phospholipids with a hidden tail in the headgroup region, where it combines with two PG molecules to form a pre-insertion complex. In the second phase, the hidden tail unfolds and irreversibly inserts into the membrane. The inset shows the headgroup of the pre-insertion complex with the broad arrow showing the direction for the unfolding of the hidden tail. The red dots denote Ca2+.’  

(9) The authors listed the Kd for daptomycin and 2 PG as 7.2 x 10-15 M2. Is this correct? This is an affinity in the femtomolar range.

Please note that this Kd is for the simultaneous binding of two PG molecules, not for the binding of a single ligand that we usually refer to. Assuming that each PG contributes equally to this interaction, the binding affinity for each ligand is then the squared root of 7.2 x 10-15 M2, which equals to 8.5 x 10-8 M. This is equivalent to a nanomolar affinity for PG and is a reasonably high affinity.

Reviewer #3 (Recommendations For The Authors):

(1) The authors reported an increase in daptomycin intensity with the increasing amount of negatively charged DMPG. A similar observation has been reported for GUVs, however, the authors did not refer to this paper in their manuscript: E. Krok, M. Stephan, R. Dimova, L. Piatkowski, Tunable biomimetic bacterial membranes from binary and ternary lipid mixtures and their application in antimicrobial testing, Biochim. Biophys. Acta - Biomembr. 1865 (2023) [1]. This paper is also consistent with the authors' observation that there is negligible fluorescence detected for the membranes composed of PC lipids upon exposure to the Dap treatment.

As suggested, this paper is cited as ref. 29 in the revision by adding the following sentence at the end of the section ‘Dependence of Dap uptake on phosphatidylglycerol.’: ‘PG-dependent increase of the steady-state fluorescence was also observed in giant unilamellar vesicles (GUVs).29’. The numbering is changed accordingly for the remaining references.  

(2) Please include the plot of the steady-state Kyn fluorescence vs the content of POPA (Figure 2C shows traces for DMPG, CL, and POPS). Both POPA and POPS lipids are negatively charged, however, POPS seems to interact with Dap, while POPA does not. In my opinion, this observation is really interesting and might deserve a more thorough discussion. The authors might want to describe what could be the mechanism behind this lipid-specific mode of binding.

As suggested, a plot is now added for POPA in Fig. 2C, which is basically a flat line without significant increase for the Kyn fluorescence. Indeed, the different effect of the negative phospholipids is very interesting, indicating that the reversible binding of Dap to the lipid surface is dependent not only on the Ca2+-mediated ionic interaction but also the structure of the headgroup. In other words, Dap recognizes the phospholipids at the surface binding stage. Considering this headgroup specificity, the last sentence in the second paragraph in “Discussion’ is changed from ‘In addition, due to the low lipid specificity, this reversible binding likely involves Ca2+-mediated ionic interaction between Dap and the phosphoryl moiety of the headgroups.’ to ‘In addition, due to the specificity for negative phospholipids (Fig. 2B and 2C), this reversible binding of Dap likely involves both a nonspecific Ca2+-mediated ionic interaction and a specific interaction with the remaining part of the headgroups.’

(3) The authors write that they propose a novel mechanism for the Ca2+-dependent insertion of Dap to the bacterial membrane, however, they rather ignored the already published findings and hypotheses regarding this process. In fact the role of Ca2+, as well as the proposed conformational changes of Dap, which allow its deeper insertion into the membrane are well known:

The role of Ca2+ ions in the mechanism of binding is actually three-fold: (i) neutralization of daptomycin charge [2], (iii) creating the connection between lipids and daptomycin and (iii) inducing two daptomycin conformational changes. It should be noted that the interactions between calcium ions and daptomycin are 2-3 orders of magnitude stronger than between daptomycin and PG lipids [3,4]. Thus, upon the addition of CaCl2 to the solution, the divalent cations of calcium bind preferentially to the daptomycin, rather than to the negatively charged PG lipids, which results in the decrease of daptomycin net negative charge but also leads to its first conformational change [4]. Upon binding between calcium ions and two aspartate residues, the area of the hydrophobic surface increases, which allows the daptomycin to interact with the negatively charged membrane. In the next step, Ca2+ acts as a bridge connecting daptomycin with the anionic lipids. This event leads to the second conformational change, which enables deeper insertion of daptomycin into the lipid membrane and enables its fluorescence [4]. The overall mechanism has a sequential character, where the binding of daptomycin-Ca2+ complex to the negatively charged PG (or CA) occurs at the end.

The authors should focus on emphasizing the novelty of their manuscript, keeping in mind the already published paper.

We agree with the comments on the three general roles of calcium ion in the Dap interaction with membrane. The current investigation does not ignore the previous findings, which involve many more works than mentioned above, but takes these findings as common knowledge. Actually, the role of calcium ion is not the focus of current work. Instead, the current work focuses on how the drug is taken up and inserted into the membrane in the presence of the ion and how its structure changes in this process. With the known roles of calcium ion in mind, we propose an uptake mechanism (Fig. 6) that shows no conflict with the common knowledge.

We would like to point out that the ‘deeper insertion into the membrane’ in the comment is different from the membrane insertion referred to in our manuscript. This ‘deeper insertion’ still remains in the reversible stage of binding to the membrane surface because all negative phospholipids can do this (causing a conformational change and fluorescence increase, as quantified in Fig.2C) but now we know that only PG can enable irreversible membrane insertion because of our work. In addition, the comment that calcium binding to daptomycin causes first conformational change is not supported by our finding that no conformational change is found for Dap in the presence of calcium in a lipid-free environment (Fig. 3B). One important aspect of novelty and contribution of our work is to clear up some of these ambiguities in the literature. Another contribution of our work is to demonstrate the formation of a stable complex between Dap and PG with a defined stoichiometry and its crucial role in the drug uptake. 

(4) One paragraph in the section "Ca2+- dependent interaction between Dap and DMPG" is devoted to a discussion of the formation of precipitate upon extraction of DMPG-containing micelles, exposed to Dap in the calcium-rich environment. Contrary, in the absence of Dap, no precipitate was detected. The authors did not provide any visual proof for their statement. Please include proper photographs in the supplementary information.

The precipitate formed upon extraction of the DMPG-containing micelles was too little to be visually identifiable but could be collected by centrifugation and detected by fluorescence or HPLC after dissolving in DMSO. For visualization, we show below the precipitate formed using higher amount of Dap and DMPG. The Dap-DMPG-Ca2+ complex (left tube) was formed by mixing 1 mM Dap, 2 mM DMPG and 1 mM Ca2+ and the control (right tube) was a mixture of 2 mM DMPG and 1 mM Ca2+. This is now added as Fig. S7 in the supplementary information (the index is modified accordingly) and cited in the main text.

(5) The authors wrote that it is not clear how many calcium ions are bound to Dap-2PG complex (page 11, Discussion section). There are already reports discussing this issue. I recommend citing the paper discussing that exactly two Ca2+ ions bind to a single Dap molecule: R. Taylor, K. Butt, B. Scott, T. Zhang, J.K. Muraih, E. Mintzer, S. Taylor, M. Palmer, Two successive calcium-dependent transitions mediate membrane binding and oligomerization of daptomycin and the related antibiotic A54145, Biochim. Biophys. Acta - Biomembr. 1858, (2016) 1999-2005 [5]

We were aware of the cited work that shows binding of two Ca2+ but also noted that there are more works showing one Ca2+ in the binding, such as the paper in [Ho, S. W., Jung, D., Calhoun, J. R., Lear, J. D., Okon, M., Scott, W. R. P., Hancock, R. E. W., & Straus, S. K. (2008), Effect of divalent cations on the structure of the antibiotic daptomycin. European Biophysics Journal, 37(4), 421–433.]. That was the reason we said ‘it is not clear how many calcium ions are bound to Dap-2PG complex’. Now, both papers are cited (as Ref. #33, 34) to support this statement.

(6) The authors wrote two contradictory statements:

-  PG cannot be found in mammalian cell membranes:

"Moreover, the complete dependence of the membrane insertion on PG also explains why Dap selectively attacks Gram-positive bacteria without affecting mammalian cells, because PG is present only in bacterial membrane but not in mammalian membrane. " (Page 10, Discussion section, last sentence of the first paragraph)

"However, Dap absorbed on bacterial surface is continuously inserted into the acyl layer via formation of complex with PG in a time scale of minutes, whereas no irreversible insertion of Dap occurs on mammalian membrane due to the absence of PG while the bound Dap is continuously released to the circulation as the drug is depleted by the bacteria." (Page 13, Discussion section)

-  PG in trace amounts is present in mammalian membranes:

"The proposed requirement of the pre-insertion quaternary complex increases the threshold of PG content for the membrane insertion to happen and thus makes it impossible on the surface of mammalian cells even if their plasma membrane contains a trace amount of PG." (Page 13, Discussion section).

In fact, phosphatidylglycerol comprises 1-2 mol% of the mammalian cell membranes. Please, correct this information, which in this form is misleading to the readers.

We appreciate the comments about the PG content in mammalian cells. Changes are made as listed below:

(1) p10, the sentence is changed to ‘Moreover, the complete dependence of the membrane insertion on PG also explains why Dap selectively attacks Gram-positive bacteria without affecting mammalian cells, because PG is a major phospholipid in bacterial membrane but is a minor component in mammalian membrane.’ 

(2) p13, the sentence is changed to ‘However, Dap absorbed on bacterial surface is continuously inserted into the acyl layer via formation of complex with PG in a time scale of minutes, whereas little irreversible insertion of Dap occurs on mammalian membrane due to the low content of PG while the bound Dap is continuously released to the circulation as the drug is depleted by the bacteria.’

(3) p13, another sentence is modified to ‘The proposed requirement of the pre-insertion quaternary complex increases the threshold of PG content for the membrane insertion to happen and thus makes it less likely on the surface of mammalian cells that contain PG at a low level in the membrane.’ 

(7) Please include information that Dap is effective only against Gram-positive bacteria and does not show antimicrobial properties against Gram-negative strains. The authors focused on emphasizing that Dap does not affect mammalian membranes, most likely due to the low PG content, however even membranes of Gram-negative bacteria are not susceptible to the Dap, despite the relatively high content of negatively charged PG in the inner membrane (e.g. inner cell membrane of E. coli has ~20% PG).

The requested information is already included in ‘Introduction’. In this part, Dap is introduced to be only active against Gram-positive bacteria, implicating that it is not active against Gram-negative bacteria. The reason Dap is inactive against E. coli or other Gramnegative bacteria is because the outer membrane prevents the antibiotic from accessing the PG in the inner membrane to cause any harm. When the outer membrane is removed, Dap will also attack the plasma membrane of Gram-negative bacteria. 

Literature cited in the comments:

(1) E. Krok, M. Stephan, R. Dimova, L. Piatkowski, Tunable biomimetic bacterial membranes from binary and ternary lipid mixtures and their application in antimicrobial testing, Biochim. Biophys. Acta - Biomembr. 1865 (2023). https://doi.org/10.1101/2023.02.12.528174.

(2) S.W. Ho, D. Jung, J.R. Calhoun, J.D. Lear, M. Okon, W.R.P. Scott, R.E.W. Hancock, S.K. Straus, Effect of divalent cations on the structure of the antibiotic daptomycin, Eur. Biophys. J. 37 (2008) 421-433. https://doi.org/10.1007/S00249-007-0227-2/METRICS.

(3) A. Pokorny, P.F. Almeida, The Antibiotic Peptide Daptomycin Functions by Reorganizing the Membrane, J. Membr. Biol. 254 (2021) 97-108. https://doi.org/10.1007/s00232-02100175-0.

(4) L. Robbel, M.A. Marahiel, Daptomycin, a bacterial lipopeptide synthesized by a nonribosomal machinery, J. Biol. Chem. 285 (2010) 2750127508. https://doi.org/10.1074/JBC.R110.128181.

(5) R. Taylor, K. Butt, B. Scott, T. Zhang, J.K. Muraih, E. Mintzer, S. Taylor, M. Palmer, Two successive calcium-dependent transitions mediate membrane binding and oligomerization of daptomycin and the related antibiotic A54145, Biochim. Biophys. Acta - Biomembr. 1858 (2016) 1999-2005. https://doi.org/10.1016/J.BBAMEM.2016.05.020.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This work used a comprehensive dataset to compare the effects of species diversity and genetic diversity within each trophic level and across three trophic levels. The results showed that species diversity had negative effects on ecosystem functions, while genetic diversity had positive effects. These effects were observed only within each trophic level and not across the three trophic levels studied. Although the effects of biodiversity, especially genetic diversity across multi-trophic levels, have been shown to be important, there are still very few empirical studies on this topic due to the complex relationships and difficulty in obtaining data. This study collected an excellent dataset to address this question, enhancing our understanding of genetic diversity effects in aquatic ecosystems.

Strengths:

The study collected an extensive dataset that includes species diversity of primary producers (riparian trees), primary consumers (macroinvertebrate shredders), and secondary consumers (fish). It also includes the genetic diversity of the dominant species at each trophic level, biomass production, decomposition rates, and environmental data.

The conclusions of this paper are mostly well supported by the data and the writing is logical and easy to follow.

Weaknesses:

(1) While the dataset is impressive, the authors conducted analyses more akin to a "meta-analysis," leaving out important basic information about the raw data in the manuscript. Given the complexity of the relationships between different trophic levels and ecosystem functions, it would be beneficial for the authors to show the results of each SEM (structural equation model).

We understand the point raised by the reviewer. We now provide individual SEMs (Figure 3), although we limit causal relationships to those for which the p-value was below 0.2 for the sake of graphical clarity. We also provide the percentage of explained variance for each ecosystem function. We detail the graph in the Results section (see l. 317-328) and discuss them (see l. 387-398). Note that we do not detail each function separately as this would (in our opinion) result in a long descriptive paragraph from which it might be difficult to get some key information. Rather, we summarize the percentage of explained variance for each function and discuss the strength of environmental vs biodiversity effects for some examples. In the Discussion, we explain why environmental effects (on functions and biodiversity) are relatively weak. We mainly attribute this to the sampling scheme that follows an East-West gradient (weak altitudinal range) rather than an upstream-downstream gradient as it is traditionally done in rivers. The reasoning behind this sampling scheme is explained in our companion paper (Fargeot et al. Oikos 2023) to which we now refer more explicitly in the MS. Briefly, using an upstream-downstream gradient would have certainly push up the effects of the environment, but this would have made extremely complex the inference of biodiversity effects due to strong collinearity among environmental and biodiversity parameters.

(2) The main results presented in the manuscript are derived from a "metadata" analysis of effect sizes. However, the methods used to obtain these effect sizes are not sufficiently clarified. By analyzing the effect sizes of species diversity and genetic diversity on these ecosystem functions, the results showed that species diversity had negative effects, while genetic diversity had positive effects on ecosystem functions. The negative effects of species diversity contradict many studies conducted in biodiversity experiments. The authors argue that their study is more relevant because it is based on a natural system, which is closer to reality, but they also acknowledge that natural systems make it harder to detect underlying mechanisms. Providing more results based on the raw data and offering more explanations of the possible mechanisms in the introduction and discussion might help readers understand why and in what context species diversity could have negative effects.

(We now provide more details. However, we are unfortunately not sure that this helped reaching some stronger explanation regarding underlying mechanisms. To be frank, we did not succeed in improving mechanistic inferences based on the outputs of the SEM models. We explored visually some additional relationships (e.g. relationships between the biomass of the focal species and that of other species in the assemblage) that we now discuss a bit more, but again, this did not really help in better understanding processes. We realize this is a limitation of our study and that this can be frustrating for readers. Nonetheless, as said in the Discussion, field-based study must be taken for what they are; observational studies forming the basis for future mechanistic studies. Although we failed to explain mechanisms, we still think that we provide important field-base evidence for the importance of biodiversity (as a whole) for ecosystem functions.

3) Environmental variation was included in the analyses to test if the environment would modulate the effects of biodiversity on ecosystem functions. However, the main results and conclusions did not sufficiently address this aspect.

This is now addressed, see our response to your first comment. We now explain (result section) and discuss environmental effects. As explained in the MS, environmental effects are similar in strength to those of biodiversity and are not that high, which is partly explained by the sampling scheme (see Fargeot et al. 2023). This is a choice we’ve made at the onset of the experiment, as we wanted to focus on biodiversity effects and avoid strong collinearity as it is generally the case in rivers (which impedes any proper and strong statistical inferences).

Reviewer #2 (Public review):

Summary:

Fargeot et al. investigated the relative importance of genetic and species diversity on ecosystem function and examined whether this relationship varies within or between trophic-level responses. To do so, they conducted a well-designed field survey measuring species diversity at 3 trophic levels (primary producers [trees], primary consumers [macroinvertebrate shredders], and secondary consumers [fishes]), genetic diversity in a dominant species within each of these 3 trophic levels and 7 ecosystem functions across 52 riverine sites in southern France. They show that the effect of genetic and species diversity on ecosystem functions are similar in magnitude, but when examining within-trophic level responses, operate in different directions: genetic diversity having a positive effect and species diversity a negative one. This data adds to growing evidence from manipulated experiments that both species and genetic diversity can impact ecosystem function and builds upon this by showing these effects can be observed in nature.

Strengths:

The study design has resulted in a robust dataset to ask questions about the relative importance of genetic and species diversity of ecosystem function across and within trophic levels.

Overall, their data supports their conclusions - at least within the system that they are studying - but as mentioned below, it is unclear from this study how general these conclusions would be.

Weaknesses:

(4) While a robust dataset, the authors only show the data output from the SEM (i.e., effect size for each individual diversity type per trophic level (6) on each ecosystem function (7)), instead of showing much of the individual data. Although the summary SEM results are interesting and informative, I find that a weakness of this approach is that it is unclear how environmental factors (which were included but not discussed in the results) nor levels of diversity were correlated across sites. As species and genetic diversity are often correlated but also can have reciprocal feedbacks on each other (e.g., Vellend 2005), there may be constraints that underpin why the authors observed positive effects of one type of diversity (genetic) when negative effects of the other (species). It may have also been informative to run SEM with links between levels of diversity. By focusing only on the summary of SEM data, the authors may be reducing the strength of their field dataset and ability to draw inferences from multiple questions and understand specific study-system responses.

We have addressed this remark and we ask the reviewers and the readers to refer to our response to comment 1 from reviewer 1. Regarding co-variation among biodiversity estimates (SGDCs according to Vellend’s framework), we have addressed these issues in a companion paper that we now cite and expand further in the MS (Fargeot et al. Oikos, 2023). Given the size of the dataset and its complexity (and associated analyses), we have decided to focus on patterns of species and genetic biodiversity in a first paper (Oikos paper) and then on the link between biodiversity and functions (this paper). As it can be read in the Oikos’s paper, there are no co-variation in term of biodiversity estimates; species diversity is not correlated to genetic diversity, and within facet, there are not co-variation among species. In addition, environmental predictors are highly estimate-specific (i.e. environmental predictors sustaining species and genetic estimates are idiosyncratic). As a result (see the new Figure 3), environmental effects are relatively weak (the same intensity that those of biodiversity) and collinearity among parameters is relatively weak. The second point is important, as this permit to better infer parameters from models, and this allows to discuss direct relationships (as observed in Figure 3, indirect environmental effects are relatively rare). We provide in the Discussion a bit more explanation about the absence of co-variation among biodiversity estimates (see l. 433-440).

(5) My understanding of SEM is it gives outputs of the strength/significance of each pathway/relationship and if so, it isn't clear why this wasn't used and instead, confidence intervals of Z scores to determine which individual BEFs were significant. In addition, an inclusion of the 7 SEM pathway outputs would have been useful to include in an appendix.

We now provide p-values (Table S2) and the seven models (Figure 3).

(6) I don't fully agree with the authors calling this a meta-analysis as it is this a single study of multiple sites within a single region and a specific time point, and not a collection of multiple studies or ecosystems conducted by multiple authors. Moreso, the authors are using meta-analysis summary metrics to evaluate their data. The authors tend to focus on these patterns as general trends, but as the data is all from this riverine system this study could have benefited from focusing on what was going on in this system to underpin these patterns. I'd argue more data is needed to know whether across sites and ecosystems, species diversity and genetic diversity have opposite effects on ecosystem function within trophic levels.

We agree. “Meta-regression” would perhaps be more adequate than “meta-analyses”. We changed the formulation.

Reviewer #3 (Public review):

The manuscript by Fargeot and colleagues assesses the relative effects of species and genetic diversity on ecosystem functioning. This study is very well written and examines the interesting question of whether within-species or among-species diversity correlates with ecosystem functioning, and whether these effects are consistent across trophic levels. The main findings are that genetic diversity appears to have a stronger positive effect on function than species diversity (which appears negative). These results are interesting and have value.

However, I do have some concerns that could influence the interpretation.

(7) Scale: the different measures of diversity and function for the different trophic levels are measured over very different spatial scales, for example, trees along 200 m transects and 15 cm traps. It is not clear whether trees 200 m away are having an effect on small-scale function.

Trees identification and invertebrate (and fish) sampling are done on the same scale. Trees are spread along the river so that their leaves fall directly in the river. Traps have been installed all along the same transect in various micro-habitats. Diversity have been measured at the exact same scale for all organisms. We have modified the MS to make this clear.

(8) Size of diversity gradients: More information is needed on the actual diversity gradients. One of the issues with surveys of natural systems is that they are of species that have already gone through selection filters from a regional pool, and theoretically, if the environments are similar, you should get similar sets of species, without monocultures. So, if the species diversity gradients range from say, 6 to 8 species, but genetic diversity gradients span an order of magnitude more, you can explain much more variance with genetic diversity. Related to this, species diversity effects on function are often asymptotic at high diversity and so if you are only sampling at the high diversity range, we should expect a strong effect.

Fish species number varies from 1 to 11, invertebrate family number varies from 15 to 42 and the tree species number varies from 7 to 20 (see Fargeot et al. 2023 for details). We have added this information in the M&M. The gradients are hence relatively large and do not cover a restricted set of values. There is a variance in species number among sites, even if sites are collected along a relatively weak altitudinal gradient. This is obviously complex to compare to SNP (genomic) diversity. Genetic and species effects are similar in effect sizes (percentage of explained variance), so it does not seem we have biased one of the two gradients of biodiversity.

(9) Ecosystem functions: The functions are largely biomass estimates (expect decomposition), and I fail to see how the biomass of a single species can be construed as an ecosystem function. Aren't you just estimating a selection effect in this case?

The biomass estimated for a certain area represents an estimate of productivity, whatever the number of species being considered. Obviously, productivity of a species can be due to environmental constraints; the biomass is expected to be lower at the niche margin (selection effect). But if these environmental effects are taken into account (which is the case in the SEMs), then the residual variation can be explained by biodiversity effects. We provide an explanation (l. 217-219).

(10) Note that the article claims to be one of the only studies to look at function across trophic levels, but there are several others out there, for example:

Thanks, we now cite some of these studies (Li et al 2020, Moi et al. 2021, Seibold et al. 2018).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Introduction:

The introduction of the manuscript is generally well-structured, and the scientific questions are clearly presented. However, in each paragraph where specific aspects are introduced, the authors do not focus sufficiently on the given points. The current introduction discusses the weaknesses of previous studies extensively but lacks detailed explanations of mechanisms and a clear anticipation of this study's contributions.

For example:

L72-77: The authors mention that "genetic diversity may functionally compensate for a species loss," but this point is not highly relevant to the main analyses of this study, which focus on comparing the relative effects of species diversity and genetic diversity.

Yes true, we understand the point made by the reviewers. We deleted this part of the sentence.

L87-95: As previously noted, "whether environmental variation decreases or enhances the relative influence of genetic and species diversity on ecosystem functions" was not addressed in this study. Additionally, the last sentence seems unnecessary here, as it does not relate to "environmental variation." The phrase "generate insightful knowledge for future mechanistic models" is vague. It would be helpful to specify what kind of knowledge and what types of future mechanistic models are being referred to.

We modified these two sentences. We now posit the prediction that what has been observed under controlled conditions (that genetic and species have effects of similar magnitude) might not be the norm under fluctuating environments (because it has been shown that environmental variation modulates the strength of interspecific BEFS and create huge variance).

L96-116: The use of "for instance" three times in this paragraph makes the structure seem scattered, as only examples are provided. Improving the transition words can help the text focus better on the main point.

We have modified some parts of this section to better reflect predictions

L115-116: Again, it would be beneficial to specify what kind of insightful information can be provided.

We have modified this sentence by making more explicit some of the information that may be gained.

L117-134: Stating clear expectations can help the introduction focus on the mechanisms and assist readers in following the results.

We now provide some predictions. We were reluctant to make predictions in the first version of the MS as we have the feeling that predictions can go on very different direction depending on how we set the scene. We therefore stick to predictions that we think are the most logical (the simplest ones). This illustrates the lack of theoretical papers on these issues.

Methods:

L287-293: The method for estimating the standard effect size is unclear. I assume it was derived from the SEM models? This needs further clarification.

Yes, it is derived from the standardized estimate from each pSEM. This is now explained in the MS.

Results:

As mentioned in the public review, it is very important to show the results of analyzing raw data.

Done, see Figure 3 and Results section.

Table 1: The font and format of the PCA table are different from other tables and appear vague, resembling a picture rather than a table.

Changed.

Table 2 (and supplementary table): "D.f." is not explained in the table legend. Is 1 the numerator df and 30 the denominator df? Is the denominator the residual? Additionally, the table legend mentions "magnitude and direction." ANOVA only tests if the biodiversity effects are significantly different between species or genetic diversity, but not the magnitude. For example, -0.5 and 0.5 are very different, but their effect magnitudes are the same.

This is a mistake; sorry the format of the Table was from a previous version of the MS in which we used linear models rather that linear mixed models (both lead to the same results). The ANOVA used to test the significance of fixed terms in linear mixed model are based on Wald chi-sqare tests, and it should have been read “Chi-value” rather than “F-value” in both tables and the only degree of freedom in this test is the one at the numerator. This has been changed. We have changed the caption of the Table (“ANOVA table for the linear mixed model testing whether the relationships between biodiversity and ecosystem functions measured in a riverine trophic chain differ between the biodiversity facets (species or genetic diversity) and the types of BEF (within- or between-trophic levels)”)

Minor:

There should always be a space between a number and a unit. In the manuscript, spaces are inconsistently used between numbers and units.

Corrected

Reviewer #2 (Recommendations for the authors):

(1) In the introduction, the authors could focus more and build out what they predicted/hypothesized as well as what has been found in the manipulated experiments that examined the role of species and genetic diversity. That would enhance the background information for a more general audience, and highlight expected results and why.

We modified the Introduction according to comments made by reviewer 1 and clarified the predictions as best as we can.

(2) Similarly, the discussion is fairly big picture, but this dataset focused exclusively on this 3-trophic interaction in a riverine system. It could be beneficial to dig into the ecology to find out why the opposite effects of species and genetic diversity are seen within trophic levels in this system.

We have added some explanations based on the specific pSEM (see our responses to the public reviews for details). But as said in the responses to the public reviews, even with mode detailed models, it is hard to tease apart mechanisms. One important point is that genetic and species diversity do not correlate one to each other (they do not co-vary over space), which means the effect of one facet is independent from the other. However, apart from that, we can’t really tell more without more mechanistic approaches. We understand this is frustrating, but this is the nature of field-based data. This does not mean they are useless. On the contrary, they confirm and expand patterns found under controlled conditions (which for ecologists is quite important as nature is our playground), but they are limited in inferences of mechanisms.

(3) It would also be informative if the authors specified what positive and negative Z scores mean. It seems counterintuitive in Figure 3. For example, in the upper left, it's denoted as a larger intraspecific effect - which I'd assume is higher genetic (within species) diversity - but is this not where species diversity effects are higher? In theory this figure could be similar to Figure 1 from Des Roches et al. 2018 - where showing the 1:1 line of where species and genetic diversity effects are similar and then how some are more impacted by SD or GD as that links to the overall question, right?

For example: Figure 3 makes it seem that GD effects are stronger (more positive) for within trophic responses (which is reflected in the text), but in that quadrant, it states that the interspecific effect is larger?

yes, you’re true Figure 3 (now Figure 4) is not ideal. We added an explicit explanation for interpreting Zr in the main text. In addition, we modified the text in the quadrat as this was not correct. Note that it cannot be directly be compared to that of DesRoches et al. In DesRoches et al., there is a single effect size (ES) per situation (which is roughly expressed as “ES = effect of species - effect of genotypes”). Here, there are two ES per situation, one for the species effect, the other for the genetic effect, which makes the biplot more complex (as species and genetic can be similar in magnitude, but opposite in direction, e.g., 0.5 and -0.5). We may have done as DesRoches et al. (“ES = effect of species - effect of genotypes”), but as we don’t have absolute ES (as in DesRoches) the resulting signs of the ES are non sensical…Not easy for us to find a clever solution (or said differently, we were not clever enough to find an easy solution).  Nonetheless, we tried another visualization by including “sub-quadrats” into the four main quadrats. We hope this will be clearer

(4) It's unclear why authors included both a simplified linear mixed model with diversity type and biodiversity facet as fixed factors, and then a second linear model that included trophic level (with those other 2 factors and interactions), but only showed results of trophic level from that more complex model. It is unclear why they include two models when the more complex one would have evaluated all aspects of their research question and shown the same patterns.

You’re true, the more complex model evaluates both aspects. Nonetheless, as the hypotheses were strictly separated, we thought it is simpler to associate one model to one hypothesis. We agree that this duplicates information, but we would like to keep the two models to make the text more gradual.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Manuscript number: RC-2024-02545

Corresponding author(s): Woo Jae, Kim

1. General Statements

We sincerely appreciate the positive and constructive feedback provided by all three reviewers. Their insightful comments have been invaluable in guiding our revisions. In response, we have made every effort to address their suggestions through additional experiments and by restructuring our manuscript to improve clarity and coherence.

In this revision, we have streamlined the presentation of our data to enhance the narrative flow, ensuring that it is more accessible to a general readership. We believe that these changes not only strengthen our manuscript but also align with the reviewers' recommendations for improvement.

We are hopeful that the revisions we have implemented meet the expectations of the reviewers and contribute to a clearer understanding of our findings. Thank you once again for your thoughtful critiques, which have greatly aided us in refining our work.

2. Point-by-point description of the revisions

Reviewer #1

General comment: This manuscript by Song et al. investigates the molecular mechanisms underlying changes in mating duration in Drosophila induced by previous experience. As they have shown previously, they find that male flies reared in isolation have shorter mating duration than those reared in groups, and also that male flies with previous mating experience have shorter mating duration than sexually naïve males. They have conducted a myriad of experiments to demonstrate that the neuropeptide SIFa is required for these changes in mating duration. They have further provided evidence that SIFa-expressing neurons undergo changes in synaptic connectivity and neuronal firing as a result of previous mating experience. Finally, they argue that SIFa neurons form reciprocal connections with sNPF-expressing neurons, and that communication within the SIFa-sNPF circuit is required for experience-dependent changes in mating duration. These results are used to assert that SIFa neurons track the internal state of the flies to modulate behavioral choice.

   __Answer:__ We appreciate the reviewer's thoughtful comments and commendations regarding our manuscript. The recognition of our investigation into the molecular mechanisms influencing mating duration in *Drosophila* is greatly valued. In particular, we are grateful for the reviewer's positive remarks about our comprehensive experimental approach to demonstrate the role of the neuropeptide SIFa in these changes. The evidence we provided indicating that SIFa-expressing neurons undergo alterations in synaptic connectivity and neuronal firing due to previous social experiences is crucial for elucidating the underlying neural circuitry involved in experience-dependent behaviors. Finally, we are thankful for the recognition of our assertion that SIFa neurons form reciprocal connections with sNPF-expressing neurons, emphasizing the importance of this circuit in modulating behavioral choices based on internal states. To provide stronger evidence for the interactions between SIFa and sNPF, we conducted detailed GCaMP experiments, which revealed intriguing neural connections between these two neuropeptides. We have included this new data in our main figure. We believe these insights contribute significantly to the existing literature on neuropeptidergic signaling and its implications for understanding complex behaviors in *Drosophila*. We look forward to addressing any further comments and enhancing our manuscript based on your invaluable feedback. Thank you once again for your constructive critique and support.

Major concerns:

Comment 1. The authors are to be commended for the sheer quantity of data they have generated, but I was often overwhelmed by the figures, which try to pack too much into the space provided. As a result, it is often unclear what components belong to each panel. Providing more space between each panel would really help.

   __Answer:__ We sincerely appreciate the reviewer’s commendation regarding the extensive data we have generated in our study. It is gratifying to know that our efforts to provide a comprehensive analysis of the molecular mechanisms underlying changes in mating duration have been recognized. We understand the concern regarding the density of information presented in our figures. We aimed to convey a wealth of data to support our findings, but we acknowledge that this may have led to some confusion regarding the organization and clarity of the panels. We are grateful for your constructive feedback on this matter. In response, we have significantly reduced the density of the main figures and decreased the size of the graphs to improve clarity. We have also increased the spacing between panels to ensure that each component is more easily distinguishable. Further details will be provided in our responses to each comment below.

• *

Comment 2. This is a rare instance where I would recommend paring down the paper to focus on the more novel, clear and relevant results. For example, all of Figure 2 shows the projection pattern of SIFa+ neuron dendrites and axons, which have been reported by multiple previous papers. Figure 7G and J show trans-tango data and SIFaR-GAL4 expression patterns, which were previously reported by Dreyer et al., 2019. These parts could be removed to supplemental figures. Figure 5 details experiments that knock down expression of different neurotransmitter receptors within the SIFa-expressing cells. The results here are less definitive than the SIFa knockdown results, and the SCope data supporting the idea that these receptors are expressed in SIFa-expressing neurons is equivocal. I would recommend removing these data (perhaps they could serve as the basis for another manuscript) or focusing solely on the CCHa1R results, which is the only manipulation that affects both LMD and SMD.

   __Answer:__ We sincerely appreciate the reviewer’s positive feedback regarding the extensive data generated in our study. We also fully agree with the reviewer that the sheer volume of our data made it challenging to support our hypothesis that SIFa neurons serve as a hub for integrating multiple neuropeptide inputs and orchestrating various behaviors related to energy balance, as highlighted in our new Figure 5N.

   In response to the reviewer's suggestions, we have streamlined our manuscript by removing excessive and redundant data to enhance clarity and simplicity. First, we have moved Figure 2 to the supplementary materials as the reviewer noted that the branching patterns of SIFa neurons are well-documented in previous literature. Second, we relocated the trans-tango data from Figure 7G to Figure S7, since this information is also well-established. We retained this data in the supplementary section to illustrate the connection of SIFa to our recent findings regarding SIFaR24F06 neuron connections. Additionally, we have completely removed the neuropeptide receptor input screening data previously included in Figure 5, as well as Figure S8, which presented fly SCope tSNE data. As suggested by the reviewer, we plan to utilize these data for a future paper focused on investigating the underlying mechanisms of SIFa inputs that modulate SIFa activity. Thanks to the reviewer’s constructive suggestions, we believe our manuscript is now more convincing and clearer for readers.

Comment 3. Finally, I would like the authors to spend more time explaining how they think the results tie together. For example, how do the authors think the changes in branching and activity in SIFa-expressing neurons tie to the change in mating duration provoked by previous experience? It would benefit the manuscript to simplify and clarify the message about what the authors think is happening at the mechanistic level. The various schematics (eg. Fig 7N) describe the results but the different parts feel like separate findings rather than a single narrative. (MECHANISMS diagram and explanation)

   __Answer:__ We appreciate the reviewer’s constructive comments, which have significantly improved our manuscript and conclusions for our readers. As the reviewer will see, we have made substantial revisions in line with the suggestions provided. We dedicated additional time to clarify the electrical activities and synaptic plasticity of SIFa neurons in relation to internal states that orchestrate various behaviors. We have summarized our hypothesis regarding the mechanistic role of SIFa neurons in Figure 5N. In brief, we propose that SIFa neurons function as a hub that receives diverse neuropeptidergic signals, which subsequently alters their electrical activity and synaptic branching. This, in turn, leads to different internal states. The internal states of SIFa neurons can then be interpreted by SIFaR-expressing cells, which help orchestrate various behaviors and physiological responses. We aim to address these aspects further in another manuscript that has been co-submitted alongside this one [1].

Comment 4. Most of the experiments lack traditional controls. For example, in experiments in Fig 1C-K, one would typically include genetic controls that contain either the GAL4 or UAS elements alone. The authors should explain their decision to omit these control experiments and provide an argument for why they are not necessary to correctly interpret the data. In this vein, the authors have stated in the methods that stocks were outcrossed at least 3x to Canton-S background, but 3 outcrosses is insufficient to fully control for genetic background.

   __Answer:__ We sincerely thank the reviewer for insightful comments regarding the absence of traditional genetic controls in our study of LMD and SMD behaviors. We acknowledge the importance of such controls and wish to clarify our rationale for not including them in the current investigation. The primary reason for not incorporating all genetic control lines is that we have previously assessed the LMD and SMD behaviors of GAL4/+ and UAS/+ strains in our earlier studies. Our past experiences have consistently shown that 100% of the genetic control flies for both GAL4 and UAS exhibit normal LMD and SMD behaviors. Given these findings, we deemed the inclusion of additional genetic controls to be non-essential for the present study, particularly in the context of extensive screening efforts. We understand the value of providing a clear rationale for our methodology choices. To this end, we have added a detailed explanation in the "MATERIALS AND METHODS" section and the figure legends of Figure 1. This clarification aims to assist readers in understanding our decision to omit traditional controls, as outlined below.

"Mating Duration Assays for Successful Copulation

The mating duration assay in this study has been reported[33,73,93]. To enhance the efficiency of the mating duration assay, we utilized the Df (1)Exel6234 (DF here after) genetic modified fly line in this study, which harbors a deletion of a specific genomic region that includes the sex peptide receptor (SPR)[94,95]. Previous studies have demonstrated that virgin females of this line exhibit increased receptivity to males[95]. We conducted a comparative analysis between the virgin females of this line and the CS virgin females and found that both groups induced SMD. Consequently, we have elected to employ virgin females from this modified line in all subsequent studies. For naïve males, 40 males from the same strain were placed into a vial with food for 5 days. For single reared males, males of the same strain were collected individually and placed into vials with food for 5 days. For experienced males, 40 males from the same strain were placed into a vial with food for 4 days then 80 DF virgin females were introduced into vials for last 1 day before assay. 40 DF virgin females were collected from bottles and placed into a vial for 5 days. These females provide both sexually experienced partners and mating partners for mating duration assays. At the fifth day after eclosion, males of the appropriate strain and DF virgin females were mildly anaesthetized by CO2. After placing a single female in to the mating chamber, we inserted a transparent film then placed a single male to the other side of the film in each chamber. After allowing for 1 h of recovery in the mating chamber in 25℃ incubators, we removed the transparent film and recorded the mating activities. Only those males that succeeded to mate within 1 h were included for analyses. Initiation and completion of copulation were recorded with an accuracy of 10 sec, and total mating duration was calculated for each couple. All assays were performed from noon to 4pm. Genetic controls with GAL4/+ or UAS/+ lines were omitted from supplementary figures, as prior data confirm their consistent exhibition of normal LMD and SMD behaviors [33,73,93,96,97]. Hence, genetic controls for LMD and SMD behaviors were incorporated exclusively when assessing novel fly strains that had not previously been examined. In essence, internal controls were predominantly employed in the experiments, as LMD and SMD behaviors exhibit enhanced statistical significance when internally controlled. Within the LMD assay, both group and single conditions function reciprocally as internal controls. A significant distinction between the naïve and single conditions implies that the experimental manipulation does not affect LMD. Conversely, the lack of a significant discrepancy suggests that the manipulation does influence LMD. In the context of SMD experiments, the naïve condition (equivalent to the group condition in the LMD assay) and sexually experienced males act as mutual internal controls for one another. A statistically significant divergence between naïve and experienced males indicates that the experimental procedure does not alter SMD. Conversely, the absence of a statistically significant difference suggests that the manipulation does impact SMD. Hence, we incorporated supplementary genetic control experiments solely if they deemed indispensable for testing. All assays were performed from noon to 4 PM. We conducted blinded studies for every test[98,99] .

   While we have previously addressed this type of reviewer feedback in our published manuscript [2–7], we appreciate the reviewer’s suggestion to include traditional genetic control experiments. In response, we conducted all feasible combinations of genetic control experiments for LMD/SMD during the revision period. The results are presented in the supplementary figures and are described in the main text.

   We appreciate the reviewer's inquiry regarding the genetic background of our experimental lines. In response to the comments, we would like to clarify the following. All of our GAL4, UAS, or RNAi lines, which were utilized as the virgin female stock for outcrosses, have been backcrossed to the Canton-S (CS) genetic background for over ten generations. The majority of these lines, particularly those employed in LMD assays, have been maintained in a CS backcrossed status for several years, ensuring a consistent genetic background across multiple generations. Our experience has indicated that the genetic background, particularly that of the X chromosome inherited from the female parent, plays a pivotal role in the expression of certain behavioral traits. Therefore, we have consistently employed these fully outcrossed females as virgins for conducting experiments related to LMD and SMD behaviors. It is noteworthy that, in contrast to the significance of genetic background for LMD behaviors, we have previously established in our work [6] that the genetic background does not significantly influence SMD behaviors. This distinction is important for the interpretation of our findings. To provide a comprehensive understanding of our experimental design, we have detailed the genetic background considerations in the __"Materials and Methods"__ section, specifically in the subsection __"Fly Stocks and Husbandry"__ as outlined below.

"To reduce the variation from genetic background, all flies were backcrossed for at least 3 generations to CS strain. For the generation of outcrosses, all GAL4, UAS, and RNAi lines employed as the virgin female stock were backcrossed to the CS genetic background for a minimum of ten generations. Notably, the majority of these lines, which were utilized for LMD assays, have been maintained in a CS backcrossed state for long-term generations subsequent to the initial outcrossing process, exceeding ten backcrosses. Based on our experimental observations, the genetic background of primary significance is that of the X chromosome inherited from the female parent. Consequently, we consistently utilized these fully outcrossed females as virgins for the execution of experiments pertaining to LMD and SMD behaviors. Contrary to the influence on LMD behaviors, we have previously demonstrated that the genetic background exerts negligible influence on SMD behaviors, as reported in our prior publication [6]. All mutants and transgenic lines used here have been described previously."

Comment 5. Throughout the manuscript, the authors appear to use a single control condition (sexually naïve flies raised in groups) to compare to both males raised singly and males with previous sexual experience. These control conditions are duplicated in two separate graphs, one for long mating duration and one for short mating duration, but they are given different names (group vs naïve) depending on the graph. If these are actually the same flies, then this should be made clear, and they should be given a consistent name across the different "experiments".

   __Answer:__ We are grateful to the reviewer for highlighting the potential for confusion among readers regarding the visualization methods used in our figures. In response to this valuable feedback, we have now included a more detailed explanation of the graph visualization techniques in the legends of Figure 1, as detailed below. This additional information should enhance the clarity and understanding of the figure for all readers.

In the mating duration (MD) assays, light grey data points denote males that were group-reared (or sexually naïve), whereas blue (or pink) data points signify males that were singly reared (or sexually experienced). The dot plots represent the MD of each male fly. The mean value and standard error are labeled within the dot plot (black lines). Asterisks represent significant differences, as revealed by the unpaired Student’s t test, and ns represents non-significant differences M.D represent mating duration. DBMs represent the 'difference between means' for the evaluation of estimation statistics (See MATERIALS AND METHODS). Asterisks represent significant differences, as revealed by the Student’s t test (* p

Comment 6. The authors use SCope data to provide evidence for co-expression of SIFa and other neurotransmitters or neuropeptide receptors. The graphs they show are hard to read and it is not clear to what extent the gene expression is actually overlapping. It would be more definitive to show graphs that indicate which percentage of SIFa-expressing cells co-express other neurotransmitter components, and what the actual level of expression of the genes is. The authors should also provide more information on how they identified the SIFa+ cells in the fly atlas dataset. These are important pieces of information to be able to interpret the effects of manipulation of these other neurotransmitter systems within SIFa-expressing cells on mating duration.

__ Answer: We appreciate the reviewer for pointing out the potential for confusion among readers regarding the visualization methods used in our figures, particularly concerning the tSNE plots of scRNA-seq data. As mentioned in our previous response, we have removed most of the tSNE plots related to co-expression data with SIFa and NPRs, which we believe will reduce any confusion for readers interpreting these plots. However, we have retained a few tSNE plots, specifically Figures 2N-O, to confirm the potential co-expression of the ple and Vglut genes in SIFa cells. We understand the reviewer’s concerns about the clarity of the presented data and the necessity for more detailed information regarding the extent of co-expression and the identification of SIFa-expressing cells. To address these concerns, we have included a comprehensive description of our methods in the __MATERIALS AND METHODS section below.

"Single-nucleus RNA-sequencing analyses

The snRNAseq dataset analyzed in this paper is published in [112] and available at the Nextflow pipelines (VSN, https://github.com/vib-singlecell-nf), the availability of raw and processed datasets for users to explore, and the development of a crowd-annotation platform with voting, comments, and references through SCope (https://flycellatlas.org/scope), linked to an online analysis platform in ASAP (https://asap.epfl.ch/fca). For the generation of the tSNE plots, we utilized the Fly SCope website (https://scope.aertslab.org/#/FlyCellAtlas/*/welcome). Within the session interface, we selected the appropriate tissues and configured the parameters as follows: 'Log transform' enabled, 'CPM normalize' enabled, 'Expression-based plotting' enabled, 'Show labels' enabled, 'Dissociate viewers' enabled, and both 'Point size' and 'Point alpha level' set to maximum. For all tissues, we referred to the individual tissue sessions within the '10X Cross-tissue' RNAseq dataset. Each tSNE visualization depicts the coexpression patterns of genes, with each color corresponding to the genes listed on the left, right, and bottom of the plot. The tissue name, as referenced on the Fly SCope website is indicated in the upper left corner of the tSNE plot. Dashed lines denote the significant overlap of cell populations annotated by the respective genes. Coexpression between genes or annotated tissues is visually represented by differentially colored cell populations. For instance, yellow cells indicate the coexpression of a gene (or annotated tissue) with red color and another gene (or annotated tissue) with green color. Cyan cells signify coexpression between green and blue, purple cells for red and blue, and white cells for the coexpression of all three colors (red, green, and blue). Consistency in the tSNE plot visualization is preserved across all figures.

Single-cell RNA sequencing (scRNA-seq) data from the Drosophila melanogaster were obtained from the Fly Cell Atlas website (https://doi.org/10.1126/science.abk2432). Oenocytes gene expression analysis employed UMI (Unique Molecular Identifier) data extracted from the 10x VSN oenocyte (Stringent) loom and h5ad file, encompassing a total of 506,660 cells. The Seurat (v4.2.2) package (https://doi.org/10.1016/j.cell.2021.04.048) was utilized for data analysis. Violin plots were generated using the “Vlnplot” function, the cell types are split by FCA.

   We have also included detailed descriptions in the figure legends for the initial tSNE plot presented below to help readers clearly understand the significance of this visualization.

"Each tSNE visualization depicts the coexpression patterns of genes, with each color corresponding to the genes listed on the left, right, and/or bottom of the plot. The tissue name, as referenced on the Fly SCope website is indicated in the upper left corner of the tSNE plot. Consistency in the tSNE plot visualization is preserved across all figures."

Comment 7. I would like to see more information on how the thresholding and normalization was done for immunohistochemistry experiments. Was thresholding applied equally across all datasets? Furthermore, "overlap" of Denmark and Syt-eGFP is taken as evidence for synaptic connectivity, but the latter requires more than just overlap in the location of the axon terminal and dendrite regions of the neuron.

__ Answer: Thank you for your continued engagement with our manuscript and for highlighting the need for further clarification on our methods. Your attention to the details of our immunohistochemistry experiments is commendable, and we agree that providing a clear explanation of our thresholding and normalization procedures is essential for the transparency and reproducibility of our results. We concur that the intensity of these signals is indeed correlated with the area measurements, which is a critical factor to consider. In response to the reviewer's valuable suggestion, we have revised our approach and now present our data based on intensity measurements. Additionally, we have updated the labeling of our Y-axis to "Norm. GFP Int.", which stands for "normalized GFP intensity". This change ensures clarity and consistency in the presentation of our data. We primarily adhered to the established methods outlined by Kayser et al. [8]. To address your first point, we have now included a more detailed description of our thresholding and normalization procedures in the __MATERIALS AND METHODS section as below.

"Quantitative analysis of fluorescence intensity

To ascertain calcium levels and synaptic intensity from microscopic images, we dissected and imaged five-day-old flies of various social conditions and genotypes under uniform conditions. The GFP signal in the brains and VNCs was amplified through immunostaining with chicken anti-GFP primary antibody. Image analysis was conducted using ImageJ software. For the quantification of fluorescence intensities, an investigator, blinded to the fly's genotype, thresholded the sum of all pixel intensities within a sub-stack to optimize the signal-to-noise ratio, following established methods [93]. The total fluorescent area or region of interest (ROI) was then quantified using ImageJ, as previously reported. For CaLexA or TRIC signal quantification, we adhered to protocols detailed by Kayser et al. [94], which involve measuring the ROI's GFP-labeled area by summing pixel values across the image stack. This method assumes that changes in the GFP-labeled area and intensity are indicative of alterations in the CaLexA and TRIC signal, reflecting synaptic activity. ROI intensities were background-corrected by measuring and subtracting the fluorescent intensity from a non-specific adjacent area, as per Kayser et al. [94]. For normalization, nc82 fluorescence is utilized for CaLexA, while RFP signal is employed for TRIC experiments, as the RFP signal from the TRIC reporter is independent of calcium signaling [76]. For the analysis of GRASP or tGRASP signals, a sub-stack encompassing all synaptic puncta was thresholded by a genotype-blinded investigator to achieve the optimal signal-to-noise ratio. The fluorescence area or ROI for each region was quantified using ImageJ, employing a similar approach to that used for CaLexA or TRIC quantification [93]. 'Norm. GFP Int.' refers to the normalized GFP intensity relative to the RFP signal."

Comment 8. None of the RNAi experiments have been validated to demonstrate effective knockdown. In many cases, this would be difficult to do because of a lack of an antibody to quantify in a cell-specific manner; however, this fact should be acknowledged, especially in cases where there was found to be a lack of phenotype, which could result from lack of knockdown. The authors could also look for evidence in the literature of cases where RNAi lines they have used have been previously validated. For SIFa, knockdown can be easily confirmed with the SIFa antibody the authors have used elsewhere in the manuscript.

__ Answer:__ We appreciate the reviewer’s constructive and critical comments regarding the validation of our RNAi experiments through effective knockdown. We understand the reviewer’s concerns about achieving effective knockdown with RNAi; however, we have demonstrated in our unpublished preprint that the neuronal knockdown using independent SIFa-RNAi lines aligns with the SIFa mutant phenotype, which is consistent with our current findings on SIFa knockdown (Wong 2019). In most cases involving RNAi experiments, we have utilized independent RNAi strains to confirm consistent phenotypes and have compared these results with those from mutant phenotypes [1,9]. Therefore, we are confident that our observed SIFa phenotype results from effective RNAi knockdown. Nevertheless, we respect the reviewer’s comments and have conducted additional SIFa knockdown experiments using various GAL4 drivers, followed by immunostaining with SIFa antibodies. As shown in Figure S1B, both neuronal GAL4 drivers and SIFa-GAL4 effectively reduced SIFa immunoreactivity. We believe this indicates that our SIFa knockdown efficiently phenocopies the SIFa mutant phenotype. We also described this result in manuscript as below:

"Using the GAL4SIFa.PT driver and the elavc155 driver, we observed a significant decrease in SIFa immunoreactivity following SIFa-RNAi treatment, thereby confirming the effective knockdown of SIFa in these cells. In contrast, when SIFa-RNAi was expressed under the control of the repo-GAL4 driver, no significant change in SIFa immunoreactivity was detected (Fig. S1B). This control experiment highlights the specificity of the SIFa-RNAi effect and supports the conclusion that the behavioral changes observed in SMD and LMD are likely attributable to the targeted reduction of SIFa in the intended neuronal populations."

Minor comments:

Comment 1. There are quite a lot of citations to preprints, including preprints of the manuscripts under review. It seems inappropriate to cite a preprint of the manuscript you are submitting because it gives a false sense of strengthening the assertions being made in the manuscript.

   __Answer:__ We agree with the reviewer and have omitted all preprints that are currently under review, except for those that are deemed necessary, such as the Zhang et al. 2024 preprint, which is being submitted alongside this manuscript.

Comment 2. It seems that labels are incorrect on a number of the immunohistochemistry figures. For example, in Fig 2N, it labels dendrites as green, but this is sytEGFP, which is the presynaptic terminal.

__ Answer:__ We thoroughly reviewed and corrected the errors in the labels.

Comment ____3. Fig 4N shows grasp between SIFa-LexA and sNPF-R-GAL4, but the authors have argued that these two components should both be expressed in SIFa-expressing cells. This would make grasp signal misleading, because it would appear in the SIFa-expressing cells even without synaptic contacts due to both split GFP molecules being expressed in these cells.

   __Answer:__ We appreciate the reviewer’s critical comments regarding the interpretation of our GRASP experiments. As the reviewer noted, we acknowledge that the GRASP results also indicate synaptic contacts between SIFa cells. We have elaborated on these results in the following sections.

"This indicates that the synapses between SIFa cells expressing sNPF-R become stronger (S5K to S5M Fig)."

   However, we understand that readers may find the interpretation of this GRASP data confusing, so we have included additional explanations below to clarify.

This indicates that the synapses between SIFa cells expressing sNPF-R become stronger (S5K to S5M Fig) since we have found that SIFa cells express sNPF-R (Fig 3M, S5E and S5G)

Comment 4. For quantifying TRIC and CaLexA experiments (eg. Figure 6A-E), intensity of signal should be measured in addition to the area covered by the signal.

__ Answer:__ We concur with the reviewer. Since all of our analyses indicated that the area covered by the signal correlates with the signal intensity, we opted to use normalized intensity rather than area coverage.

Conclusive Comments: This study will be most relevant to researchers interested in understanding neuronal control of behavior. It has provided novel information about the mechanisms underlying mating duration in flies, which is used to delineate how internal state influences behavioral outcomes. This represents a conceptual advance, particularly in identifying a cell type and molecule that influences mating duration decisions. The strength of the manuscript is the number of different assays used to investigate the central question from a number of angles. The limitation is that there is a lack of a big picture tying the different components of the manuscript together. Too much data is presented without providing a framework to understand how the data points fit together.

• Answer: We sincerely appreciate the reviewer’s positive feedback regarding our study and the recognition of its relevance to researchers interested in understanding the neuronal control of behavior. We are grateful for the acknowledgment of our novel insights into the mechanisms underlying mating duration in Drosophila*, particularly in how internal states influence behavioral outcomes. The identification of specific cell types and molecules that affect mating duration decisions indeed represents a significant conceptual advance. We also appreciate the reviewer’s commendation of the diverse array of assays employed in our investigation, which allowed us to approach our central question from multiple perspectives.

  In response to the reviewer’s constructive criticism regarding the lack of a cohesive framework tying the various components of our manuscript together, we have completely restructured our manuscript. We removed redundant data and incorporated additional convincing experiments, such as GCaMP analyses, to enhance clarity and coherence. Furthermore, we have provided a simplified yet comprehensive overview that describes the role of SIFa as a hub for neuropeptidergic signaling. This framework illustrates how SIFa orchestrates multiple behaviors related to energy balance through calcium signaling and synaptic plasticity via SIFaR-expressing cells.

  We believe these revisions address the reviewer’s concerns and provide a clearer understanding of how the different elements of our study fit together, ultimately strengthening the overall impact of our manuscript. Thank you for your valuable feedback, which has guided us in improving our work.

Reviewer #2

General Comments:* In the present study, the authors employ mating behavior in male fruit flies, Drosophila melanogaster, to investigate the behavioral roles of the neuropeptide SIFamide. The duration of mating behavior in these animals varies depending on context, previous experience, and internal metabolic state. The authors use this variability to explore the neuronal mechanisms that control these influences. In an abstraction step, they compare the different mating durations to concepts of neuronal interval timing.

The behavioral functions of the neuropeptide SIFamide have been thoroughly characterized in several studies, particularly in the contexts of circadian rhythm and sleep, courtship behavior, and food uptake. This study adds new data, demonstrating that SIFamide is essential for the proper control of mating behavior, highlighting the interconnection of various state- and motivation-dependent behaviors at the neuronal level. However, the hypothesis that mating behavior is related to interval timing is not convincingly supported.

Experimentally, the authors show that RNAi-mediated downregulation of SIFamide affects mating duration in male flies. They use combinations of RNAi lines under the control of various Gal4 lines to identify additional neurotransmitters, neuropeptides, and receptors involved in this process. This approach is complemented by neuroanatomical staining and single-cell RNA sequencing.*

* Overall, the study advances our knowledge about the behavioral roles of SIFamide, which is certainly important, interesting, and worthy of being reported. However, the manuscript also raises several serious caveats and includes points that remain speculative, are less convincing, or are simply incorrect.*

• Answer: We would like to thank the reviewer for their thoughtful and constructive comments regarding our study. We appreciate the recognition of our investigation into the behavioral roles of the neuropeptide SIFamide in male Drosophila melanogaster*, particularly how we explored the variability in mating duration influenced by context, previous experience, and internal metabolic state. We are grateful for the acknowledgment that our study adds valuable data demonstrating the essential role of SIFamide in regulating mating behavior, highlighting the interconnectedness of various state- and motivation-dependent behaviors at the neuronal level.

  We also appreciate the reviewer's recognition of our experimental approach, which includes RNAi-mediated downregulation of SIFamide, the use of various Gal4 lines to identify additional neurotransmitters, neuropeptides, and receptors involved in this process, as well as our incorporation of neuroanatomical staining and single-cell RNA sequencing.

  In response to the reviewer’s concerns regarding the hypothesis that mating behavior is related to interval timing, we acknowledge that this aspect requires further clarification and support. We have revisited this hypothesis in our manuscript to strengthen its foundation and address any speculative elements. We aim to provide more robust evidence and clearer connections between mating behavior and neuronal interval timing.

  Furthermore, we have taken care to address any points that may have been perceived as less convincing or incorrect. We are committed to refining our manuscript to ensure that all claims are well-supported by our data. Thank you once again for your valuable feedback. We believe that these revisions will enhance the clarity and impact of our study while addressing the concerns raised.

Major concerns:

Comment 1. The authors conclude from their mating experiments that SIFamide controls interval timing. This conclusion is not supported by the data, which only indicate that SIFamide is required for normal mating duration and modulates the motivation-dependent component of this behavior. There is no clear evidence linking this to interval timing.

__ Answer: __We appreciate the reviewer’s insightful comments regarding our conclusion linking SIFamide to interval timing in mating behavior. We acknowledge that our data primarily demonstrate that SIFamide is required for normal mating duration and modulates the motivation-dependent aspects of this behavior, and we recognize the need for clearer evidence connecting these observations to interval timing. Current research by Crickmore et al. has shed light on how mating duration in Drosophila serves as a powerful model for exploring changes in motivation over time as behavioral goals are achieved. For instance, at approximately six minutes into mating, sperm transfer occurs, leading to a significant shift in the male's nervous system: he no longer prioritizes sustaining the mating at the expense of his own survival. This change is driven by the output of four male-specific neurons that produce the neuropeptide Corazonin (Crz). When these Crz neurons are inhibited, sperm transfer does not occur, and the male fails to downregulate his motivation, resulting in matings that can last for hours instead of the typical ~23 minutes [10].

   Recent research by Crickmore et al. has received NIH R01 funding (Mechanisms of Interval Timing, 1R01GM134222-01) to explore mating duration in *Drosophila* as a genetic model for interval timing. Their work highlights how changes in motivation over time can influence mating behavior, particularly noting that significant behavioral shifts occur during mating, such as the transfer of sperm at approximately six minutes, which correlates with a decrease in the male's motivation to continue mating [10]. These findings suggest that mating duration is not only a behavioral endpoint but may also reflect underlying mechanisms related to interval timing.

   We believe that by leveraging the robustness and experimental tractability of these findings, along with our own work on SIFamide's role in mating behavior, we can gain deeper insights into the molecular and circuit mechanisms underlying interval timing. We will revise our manuscript to clarify this relationship and emphasize how SIFamide may interact with other neuropeptides and neuronal circuits involved in motivation and timing.

   In addition to the efforts of Crickmore's group to connect mating duration with a straightforward genetic model for interval timing, we have previously published several papers demonstrating that LMD and SMD can serve as effective genetic models for interval timing within the fly research community. For instance, we have successfully connected SMD to an interval timing model in a recently published paper [6], as detailed below:

"We hypothesize that SMD can serve as a straightforward genetic model system through which we can investigate "interval timing," the capacity of animals to distinguish between periods ranging from minutes to hours in duration.....

In summary, we report a novel sensory pathway that controls mating investment related to sexual experiences in Drosophila. Since both LMD and SMD behaviors are involved in controlling male investment by varying the interval of mating, these two behavioral paradigms will provide a new avenue to study how the brain computes the ‘interval timing’ that allows an animal to subjectively experience the passage of physical time [11–16]."

   Lee, S. G., Sun, D., Miao, H., Wu, Z., Kang, C., Saad, B., ... & Kim, W. J. (2023). Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster. *PLoS Genetics*, *19*(5), e1010753.

   We have also successfully linked LMD behavior to an interval timing model and have published several papers on this topic recently [4,5,7].

   Sun, Y., Zhang, X., Wu, Z., Li, W., & Kim, W. J. (2024). Genetic Screening Reveals Cone Cell-Specific Factors as Common Genetic Targets Modulating Rival-Induced Prolonged Mating in male Drosophila melanogaster. *G3: Genes, Genomes, Genetics*, jkae255.

   Zhang, T., Zhang, X., Sun, D., & Kim, W. J. (2024). Exploring the Asymmetric Body’s Influence on Interval Timing Behaviors of Drosophila melanogaster. *Behavior Genetics*, *54*(5), 416-425.

   Huang, Y., Kwan, A., & Kim, W. J. (2024). Y chromosome genes interplay with interval timing in regulating mating duration of male Drosophila melanogaster. *Gene Reports*, *36*, 101999.

   Finally, in this context, we have outlined in our INTRODUCTION section below how our LMD and SMD models are related to interval timing, aiming to persuade readers of their relevance. We hope that the reviewer and readers are convinced that mating duration and its associated motivational changes such as LMD and SMD provide a compelling model for studying the genetic basis of interval timing in *Drosophila*.

"The mating duration of male fruit flies is a suitable model for studying interval timing and it could change based on internal states and environmental context. Previous studies by our group[27–30] and others[31,32] have established several frameworks for investigating the mating duration using sophisticated genetic techniques that can analyze and uncover the neural circuits’ principles governing interval timing. In particular, males exhibit LMD behavior when they are exposed to an environment with rivals, which means they prolong their mating duration. Conversely, they display SMD behavior when they are in a sexually saturated condition, meaning they reduce their mating duration[33,34]."

Comment 2. On line 160, the authors state, "The connection between the dendrites and axons of the SIFamide neuronal processes is unknown." This is not entirely correct. State-of-the-art connectome analyses can determine synaptic connectivities between SIFamidergic neurons and pre-/postsynaptic neurons. The authors also overlook the thorough connectivity analysis by Martelli et al. (2017), which includes functional analyses and detailed anatomical descriptions that the current study confirms.

__ Answer:__ We appreciate the reviewer for acknowledging the efforts of Martelli et al. in elucidating the neuronal architecture of SIFa neurons. We recognize that it was an oversight on our part to state that "the connection between the dendrites and axons of SIFa neurons is unknown." This error arose because our manuscript has been in preparation for over ten years, predating the publication of Martelli et al.'s work. That statement likely reflects an outdated section of the manuscript.

We fully acknowledge the findings from previous publications and have removed that sentence entirely from our manuscript. In its place, we have added the following statement:

"The established connections and architecture of SIFa neurons has been described by Martelli et al., which enhances our understanding of their functional roles within the neuronal circuitry [51]. To identify the dendritic and axonal components of SIFa-neuronal processes, we employed a similar approach to that reported by Martelli [51]."

Thank you for your valuable feedback, which has helped us improve the clarity and accuracy of our manuscript.

Comment 3. The mating experiments are overall okay, with sufficiently high sample sizes and appropriate statistical tests. However, many experiments lack genetic controls for the heterozygous parental strains, such as Gal4-ines AND UAS-lines. This is of course of importance and common standard.

__ Answer: __While we have previously addressed this type of reviewer feedback in our published manuscript [2–7] as well as this manuscript by Reviewer #1, we appreciate the reviewer’s suggestion to include traditional genetic control experiments. In response, we conducted all feasible combinations of genetic control experiments for LMD/SMD during the revision period. The results are presented in the supplementary figures and are described in the main text.

Comment 4. *Using a battery of RNAi lines, the authors aim to uncover which neurotransmitters might be co-released from SIFamide neurons to influence mating behavior. However, a behavioral effect of an RNAi construct expressed in SIFamidergic neurons does not demonstrate that the respective transmitter is actually released from these neurons. Alternative methods are needed to show whether glutamate, dopamine, serotonin, octopamine, etc., are present and released from SIFamide neurons. It is particularly challenging to prove that a certain substance acts as a transmitter released by a specific neuron. For example, anti-Tdc2 staining does not actually cover SIFamide neurons, and dopamine has not been described as present in SIFamide neurons. *

__ Answer:__ We appreciate the reviewer’s constructive comments regarding the need to demonstrate the presence of the responsible neurotransmitters in SIFa neurons. While many studies utilize neurotransmitter-synthesizing enzymes such as TH, VGlut, Gad1, and Trhn to assess neurotransmitter effects, we recognize the importance of conclusively establishing that glutamate and dopamine play significant roles in modulating energy balance within SIFa neurons.

   First, the enrichment of tyramine (TA), octopamine (OA), and dopamine (DA) in SIFa neurons was suggested in the study by Croset et al. (2018) [17]. Although we tested Tdc2-RNAi and observed interesting phenotypes, we chose not to publish these findings, as our data on glutamate and dopamine provide a more compelling explanation for how SIFa cotransmission with these neurotransmitters can independently influence various behaviors, including sleep and mating duration.

   To confirm the expression of DA in SIFa neurons, we employed a well-established genetic toolkit for dissecting dopamine circuit function in *Drosophila* [18]. Our findings indicate that TH-C-GAL4 specifically labels SIFa neurons, which have been confirmed as dopaminergic (S4M Fig). Our genetic intersection data, along with Xie et al.'s findings from 2018, confirm that a subset of SIFa neurons is indeed dopaminergic. We have described these new results in the main text as follows:

To further verify the presence of DA neurons within the SIFa neuron population, we utilized a well-established genetic toolkit for dissecting DA circuits and confirmed part of SIFa neurons are dopaminergic (S4M Fig) [58].

    To confirm the glutamatergic characteristics of SIFa neurons, we conducted several experiments that established glutamate as the most critical neurotransmitter for generating interval timing in both SIFa and SIFaR neurons. First, to demonstrate the presence of glutamatergic synaptic vesicles in SIFa neurons, we utilized a conditional glutamatergic synaptic vesicle marker for *Drosophila*, developed by Certel et al. [19]. Our results confirmed that SIFa neurons exhibit strong expression of glutamatergic synaptic vesicles (Fig. 2P and Fig. S4N as a genetic control). We have described these new results in the main text as follows:

“To further verify the presence of DA neurons within the SIFa neuron population, we utilized a well-established genetic toolkit for dissecting DA circuits and confirmed part of SIFa neurons are dopaminergic (S4M Fig) [58]. We also employed a conditional glutamatergic synaptic vesicle marker to confirm the presence of glutamatergic SIFa neurons (Fig 2P and Fig S4N) [59].”

   To further confirm that glutamate release from SIFa neurons influences the function of SIFaR neurons, we tested several RNAi strains targeting glutamate receptors. Our results showed that the knockdown of glutamate receptors in SIFaR-expressing neurons produced phenotypes similar to those observed with VGlut-RNAi knockdown in SIFa neurons (Fig. G-L). We believe that this series of experiments demonstrates that glutamate and dopamine work in conjunction with SIFa to modulate interval timing and other behaviors related to energy balance. We have described these new results in the main text as follows:

"To further substantiate the role of glutamate in SIFa-mediated behaviors. we targeted knockdown of VGlut receptors in SIFaR-expressing neurons. Strikingly, the knockdown of VGlut receptors in these neurons also disrupted SMD behavior, mirroring the phenotype observed upon direct suppression of glutamatergic signaling in SIFa neurons (S4G to S4L Fig). This suggests that glutamate is an essential neurotransmitter for modulating interval timing in SIFa neurons.”

Comment 5. Single-cell RNA sequencing data alone is insufficient to claim multiple transmitter co-release from SIFamide neurons. Figures illustrating single-cell RNA sequencing, such as Figure 3P-R, are not intuitively understandable, and the figure legends lack sufficient information to clarify these panels. As a side note, Tdc2 is not only present in octopaminergic neurons, but also in tyraminergic neurons.

__ Answer:__ We agree with the reviewer that scRNA-seq data alone is insufficient to support claims of multiple transmitter co-release in SIFa neurons. We also appreciate the reviewer for highlighting the potential for confusion among readers regarding the visualization methods used in our figures, particularly the tSNE plots of the scRNA-seq data. As noted in our previous response to Reviewer #1, we have removed most of the tSNE plots related to co-expression data involving SIFa and NPRs, which we believe will help clarify the interpretation for readers. However, we have retained a few tSNE plots, specifically Figures 2N-O, to illustrate the potential co-expression of the ple and Vglut genes in SIFa cells.

   We understand the reviewer’s concerns regarding the clarity of the presented data and the need for more detailed information about the extent of co-expression and the identification of SIFa-expressing cells. To address these concerns, we have provided a comprehensive description of our methods in the __MATERIALS AND METHODS__ section below.

"Single-nucleus RNA-sequencing analyses

The snRNAseq dataset analyzed in this paper is published in [20]and available at the Nextflow pipelines (VSN, https://github.com/vib-singlecell-nf), the availability of raw and processed datasets for users to explore, and the development of a crowd-annotation platform with voting, comments, and references through SCope (https://flycellatlas.org/scope), linked to an online analysis platform in ASAP (https://asap.epfl.ch/fca). For the generation of the tSNE plots, we utilized the Fly SCope website (https://scope.aertslab.org/#/FlyCellAtlas/*/welcome). Within the session interface, we selected the appropriate tissues and configured the parameters as follows: 'Log transform' enabled, 'CPM normalize' enabled, 'Expression-based plotting' enabled, 'Show labels' enabled, 'Dissociate viewers' enabled, and both 'Point size' and 'Point alpha level' set to maximum. For all tissues, we referred to the individual tissue sessions within the '10X Cross-tissue' RNAseq dataset. Each tSNE visualization depicts the coexpression patterns of genes, with each color corresponding to the genes listed on the left, right, and bottom of the plot. The tissue name, as referenced on the Fly SCope website is indicated in the upper left corner of the tSNE plot. Dashed lines denote the significant overlap of cell populations annotated by the respective genes. Coexpression between genes or annotated tissues is visually represented by differentially colored cell populations. For instance, yellow cells indicate the coexpression of a gene (or annotated tissue) with red color and another gene (or annotated tissue) with green color. Cyan cells signify coexpression between green and blue, purple cells for red and blue, and white cells for the coexpression of all three colors (red, green, and blue). Consistency in the tSNE plot visualization is preserved across all figures.

Single-cell RNA sequencing (scRNA-seq) data from the Drosophila melanogaster were obtained from the Fly Cell Atlas website (https://doi.org/10.1126/science.abk2432). Oenocytes gene expression analysis employed UMI (Unique Molecular Identifier) data extracted from the 10x VSN oenocyte (Stringent) loom and h5ad file, encompassing a total of 506,660 cells. The Seurat (v4.2.2) package (https://doi.org/10.1016/j.cell.2021.04.048) was utilized for data analysis. Violin plots were generated using the “Vlnplot” function, the cell types are split by FCA."

   We have also included detailed descriptions in the figure legends for the initial tSNE plot presented below to help readers clearly understand the significance of this visualization.

"Each tSNE visualization depicts the coexpression patterns of genes, with each color corresponding to the genes listed on the left, right, and/or bottom of the plot. The tissue name, as referenced on the Fly SCope website is indicated in the upper left corner of the tSNE plot. Consistency in the tSNE plot visualization is preserved across all figures."

   We appreciate the reviewer for acknowledging that Tdc2 is present in both TA and OA neurons. As we mentioned earlier, we have completely removed the Tdc2-related results from this manuscript, as we believe that more detailed experiments are necessary to confirm the roles of TA and OA in SIFa neurons.

Comment 6. The same argument applies to the expression of sNPF receptors in SIFamide neurons. The rather small anatomical stainings shown in figure 4M do not convincingly and unambiguously show that actually sNPF receptors are located on SIFamide neurons.

__ Answer:__ We appreciate the reviewer for pointing out that the co-expression of sNPF-R and SIFa needs further verification, and we agree with this assessment. To confirm the co-expression of SIFa with sNPF-R, we conducted a mini-screen of various sNPF-R driver lines and found that the chemoconnectome (CCT) sNPF-R2A driver which represent the physiological expression patterns of sNPF-R, consistently labels SIFa neurons [21].

   To further establish the functional connection between the SIFa and sNPF systems, we performed GCaMP experiments using SIFa-driven GCaMP in conjunction with sNPF-R neurons expressing P2X2, which can be activated by ATP treatment. As shown in Figures 3N-P, we demonstrated that activation of sNPF-R neurons by ATP significantly increases calcium levels in SIFa neurons. Our results strongly suggest that the sNPF-sNPF-R/SIFa system is functionally present and plays a role in modulating interval timing behaviors.

Comment 7. The authors use the GRASP technique (figure 4N) to determine whether synaptic connections are subject to modulation as a result from the animals' individual experience. The overall extremely bright fluorescence at the dorsal areas of both brain hemispheres (figure 4 N, middle panel) raises doubts whether this signal is actually a specific GRASP fluorescence between two small populations of neurons.

Answer: We appreciate the reviewer for critically highlighting the inadequacies in our presentation of the GRASP data. We agree that one of our previous panels contained excessive background noise, making it difficult for reviewers and readers to discern the different neuronal connections. To address this issue, we have replaced it with a more representative image that clearly illustrates the strengthening of synaptic connections from SIF to sNPF-R in several neurons, including SIFa cells (Fig. S5J). We hope that this updated image will help convince both the reviewer and readers of the validity of our GRASP data.

Comment 8. The authors cite Martelli et al. (2017) with the hypothesis that sNPF-releasing neurons provide input signals to SIFamide neurons to modulate feeding behavior. However, the cited manuscript does not contain such a hypothesis. The authors should review the reference in more detail.

__ Answer:__ We appreciate reviewer to correctly point our misunderstanding of references. We agree with reviewer that Martelli et al.'s paper didn't mention about sNPF signaling transmits hunger and satiety information to SIFa neurons. We removed this sentence and replaced it as below correctly mentioning that sNPF signaling is related to feeding behavior however it's connection to SIFa neurons are not known. We greatly appreciate the reviewer for acknowledging our efforts to accurately cite previous articles that support our rationale and ideas.

" Short neuropeptide F (sNPF) signaling plays a crucial role in regulating feeding behavior in Drosophila melanogaster, influencing food intake and body size [60,66,67]. However, there is currently no direct evidence reported linking sNPF signaling to SIFa neurons."

Comment ____9. In lines 281 ff., the authors state that SIFamide neurons receive inputs from peptidergic neurons but simultaneously claim that "this speculation is based on morphological observations." This is incorrect. The functional co-activation/imaging analyses provided in Martelli et al. (2017) should not be ignored.

* Answer: We fully agree with the reviewer that we misinterpreted Martelli et al.'s analysis. We have removed "this speculation is based on morphological observations." from* the following sentence and finalize as below:

"The SIFa neurons receive inputs from many peptidergic pathways including Crz, dilp2, Dsk, sNPF, MIP, and hugin"

Comment 10. Figure 6: A transcriptional calcium sensor (TRIC) was used to quantify the accumulation GFP induced by calcium influx in SIFamide neurons. However, I could not find any description of the method in the materials and methods section, nor any explanation how the data were acquired or analyzed. What is the RFP expression good for? How exactly are thresholds determined, and why are areas rather than fluorescence intensities quantified? Overall, this part of the manuscript is rather confusing and needs more explanation.

__ Answer: Thank you for your continued engagement with our manuscript and for highlighting the need for further clarification on our methods. Your attention to the details of our immunohistochemistry experiments is commendable, and we agree that providing a clear explanation of our thresholding and normalization procedures is essential for the transparency and reproducibility of our results. We primarily adhered to the established methods outlined by Kayser et al. [8]. To address your first point, we have now included a more detailed description of our thresholding and normalization procedures in the __MATERIALS AND METHODS section as below.

"Quantitative analysis of fluorescence intensity

To ascertain calcium levels and synaptic intensity from microscopic images, we dissected and imaged five-day-old flies of various social conditions and genotypes under uniform conditions. The GFP signal in the brains and VNCs was amplified through immunostaining with chicken anti-GFP, rabbit anti-DsRed, and mouse anti-nc82 primary antibodies. Image analysis was conducted using ImageJ software. For the quantification of fluorescence intensities, an investigator, blinded to the fly's genotype, thresholded the sum of all pixel intensities within a sub-stack to optimize the signal-to-noise ratio, following established methods [100]. The total fluorescent area or region of interest (ROI) was then quantified using ImageJ, as previously reported. For CaLexA or TRIC signal quantification, we adhered to protocols detailed by Kayser et al. [101], which involve measuring the ROI's GFP-labeled area by summing pixel values across the image stack. This method assumes that changes in the GFP-labeled area and intensity are indicative of alterations in the CaLexA and TRIC signal, reflecting synaptic activity. ROI intensities were background-corrected by measuring and subtracting the fluorescent intensity from a non-specific adjacent area, as per Kayser et al. [101]. For normalization, nc82 fluorescence is utilized for CaLexA, while RFP signal is employed for TRIC experiments, as the RFP signal from the TRIC reporter is independent of calcium signaling [72] . For the analysis of GRASP or tGRASP signals, a sub-stack encompassing all synaptic puncta was thresholded by a genotype-blinded investigator to achieve the optimal signal-to-noise ratio. The fluorescence area or ROI for each region was quantified using ImageJ, employing a similar approach to that used for CaLexA or TRIC quantification [100]. 'Norm. GFP Int.' refers to the normalized GFP intensity relative to the RFP signal.

• *

__Comment 11. __Similarly, it remains unclear how exactly syteGFP fluorescence and DenMark fluorescence were quantified. Why are areas indicated and not fluorescence intensity values? In fact, it appears worrisome that isolation of males should lead to a drastic decline in synaptic terminals (as measure through a vesicle-associated protein) by ~ 30%, or, conversely, keeping animals in groups lead to an respective increase (figure 7D). The technical information how exactly this was quantified is not sufficient.

__ Answer: __Thank you for your ongoing engagement with our manuscript and for emphasizing the need for clarification on our methods. We appreciate your attention to the details of our immunohistochemistry experiments and agree that a clear explanation of our thresholding and normalization procedures is vital for transparency and reproducibility. We acknowledge that signal intensity correlates with area measurements, which is an important consideration. In response to your valuable suggestion, we have revised our approach to present data based on intensity measurements and updated the Y-axis labeling to "Norm. GFP Int." (normalized GFP intensity) for clarity. We primarily followed the established methods from Kayser et al. (2014) [8]. Additionally, we have included a more detailed description of our thresholding and normalization procedures in the "Quantitative analysis of fluorescence intensity" in __MATERIALS AND METHODS __section as we quoted above.

• *

Minor concerns:

Comment 1. Reference 29 and reference 33 are the same.

   __Answer:__ We removed reference 29.

Comment 2. In figure legends, abbreviations should be explained when used first (e.g., figure 1 A "MD", is explained below for panel C-F), or "CS males". __ __

__Answer: __We have ensured that abbreviations are explained only when they are first used in the figure legends.

Comment 3. Indications for statistical significance must be shown in all figure legends at the end of each figure legend, not only in figure 1. __ __

__ Answer:__ We appreciate the reviewer’s advice. However, we have published all our other manuscripts using the same format for mating duration, stating, "The same notations for statistical significance are used in other figures," in the first figure where we describe our statistical significances. We intend to continue with this approach initially and will then adhere to the journal's policy.

Comment 4. The figures appear overloaded. For example why do you need two different axis designations (mating duration and differences between means)? __ __

__ Answer: __We appreciate the reviewer's suggestion to refine our figures, and we have indeed reformatted them to provide clearer presentation and improved readability. Our decision is based on the fact that our analysis encompasses not only traditional t-tests but also incorporates estimation statistics, which have been demonstrated to be effective for biological data analysis [22]. The inclusion of DBMs is essential for the accurate interpretation of these estimation statistics, ensuring a comprehensive representation of our findings. This is the primary area where we present two different axis designations.

Comment 5. Line: 1154: Typo: gluttaminergic should be glutamatergic.

   __Answer:__ We fixed all.

Comment 6. The authors frequently write "system" when referring to transmitter types, e.g., "glutaminergic system", "octopaminergic system", etc. It I not clear what the term "system" actually refers to. If the authors claim that SIFamide neurons release these transmitters in addition to SIFamide, they should state that precisely and then add experiments to show that this is the case.

   __Answer:__ We agree with reviewer and removed the word 'system' after the name of neurotransmitter's name.

Comment 7. Figure S6: It is not explained in the figure legend what fly strain "UAS-ctrl" actually is. Does "ctrl" mean control? And what genotype is hat control? __ __

__Answer: __It was wild-type strain. We fixed it as "+".

Comment 8. Figure legend S6, line 1371: The authors indicate experiments using UAS-OrkDeltaC. I could not find these data in the figure. __ __

__Answer: __It's now in Fig.S6U-W.

Comment 9. Line 470: "...reduced branching of SIFa axons at the postsynaptic level" should perhaps be "presynaptic level"?

Answer: Reviewer is correct. We fixed it.

Conclusive Comments:* Overall, the study advances our knowledge about the behavioral roles of SIFamide, which is certainly important, interesting, and worthy of being reported. However, the manuscript also raises several serious caveats and includes points that remain speculative and are less convincing.

Overall, the neuronal basis of action selection based on motivational factors (metabolic state, mating experience, sleep/wake status, etc.) is not well understood. The analysis of SIFamide function in insects might provide a way to address the question how different motivational signals are integrated to orchestrate behavior.*

• *Answer: Thank you for your thoughtful review and for recognizing the significance of our study in advancing knowledge about the behavioral roles of SIFamide. We appreciate your acknowledgment that our work is important, interesting, and worthy of publication.

We understand your concerns regarding the caveats and speculative points raised in the manuscript. We agree that the neuronal basis of action selection influenced by motivational factors—such as metabolic state, mating experience, and sleep/wake status—remains poorly understood. We believe that our analysis of SIFamide function in insects offers valuable insights into how various motivational signals are integrated to orchestrate behavior.

In response to your comments, we have made revisions to clarify our findings and address the concerns raised. We aim to strengthen the arguments presented in the manuscript and provide a more robust discussion of the implications of our results. Thank you once again for your constructive feedback, which has been instrumental in improving the clarity and impact of our work.

• *

* *

Reviewer #3

General Comments:* The Manuscript Peptidergic neurons with extensive branching orchestrate the internal states and energy balance of male Drosophila melanogaster by Yuton Song and colleagues addresses the question how SIFamidergic neurons coordinate behavioral responses in a context-dependent manner. In this context the authors investigate how SIFa neurons receive information about the physiological state of the animal and integrate this information into the processing of external stimuli. The authors show that SIFamidergic neurons and sNPPF expressing neurons form a feedback loop in the ventral nerve cord that modulate long mating (LMD) and shorter mating duration (SMD).

The manuscript is well written and very detailed and provides an enormous amount of data corroborating the claims of the authors. However, before publication the authors may want to address some points of concern that warrant some deeper explanation.*

• *__Answer: __Thank you for your positive feedback on our manuscript. We appreciate your recognition of the importance of our study in investigating how SIFa neurons integrate information about the physiological state of the animal with external stimuli, as well as your acknowledgment of the substantial data we provide to support our claims. We understand your concerns regarding certain points that require deeper explanation, and we are committed to addressing these issues to enhance the clarity and robustness of our findings. Your insights into the neuronal basis of action selection influenced by motivational factors are invaluable, and we believe that our exploration of SIFamide function in insects contributes significantly to understanding how various motivational signals orchestrate behavior. Thank you once again for your constructive comments, which will help us improve our manuscript before publication.

Major concerns:

Comment 1. On page 6 line 110 the authors describe that knocking-down SIFamide in glia cell does not change LMD or SMD and say that SIFa expression in glia does not contribute to interval timing behavior. However, the authors do not provide any information why they investigate the role of SIFa expression in glia. Is there any SIFa-expression in glia? The authors should somehow demonstrate using antibody labelling against SIFamide whether any glia specific expression of this peptide is to be expected. If they cannot provide this data - the take home message of the experiment cannot be that glia knockdown of SIFamide does not affect the behavior because you cannot knockdown anything that is not there.

• *

• In the latter case the experiment could be considered as a nice negative control for the elav-Gal4 pan-neuronal knockdown of SIFamide. The authors provide some Figure supplement where they use repo-Gal80 to partially answer this question. However, the authors should keep in mind that Gal4-drivers are not always complete in the expression pattern. Accordingly, the result should be corroborated with immune-labelling against SIFamide directly.*

__ Answer: __We appreciate the reviewer's constructive and critical comments regarding the use of our glial cell drivers. As the reviewer rightly pointed out, we believe that glial control is not essential for our manuscript, given that the expression of SIFa is well established in only four neurons. Therefore, we have removed the data related to glial drivers from this manuscript.

Comment 2. At this point I would like to directly comment on the figure quality. The figures are so crowded that the described anatomical details are hardly visible. In my opinion the manuscript would profit from less data in the main part and more stringent description of the core of the biological problem the authors want to address. The authors may want to reduce data from the main text and provide additional data that are not directly related to the main story as supplementary information.

__ Answer: __We agree with the reviewer. As another reviewer also suggested that we streamline our figures and data, we have completely restructured our figures and their presentation. In response, we have significantly reduced the density of the main figures and decreased the size of the graphs to enhance clarity. Additionally, we have increased the spacing between panels to ensure that each component is more easily distinguishable. Further details will be provided in our responses to each comment below.

• *

Comment 3. On page 8 starting with line 140 the authors describe the architecture of SIFamidergic neurons using several anatomical markers e.g., Denmark and further state that they have discovered that the dendrites of SIFa neurons span just the central brain area. Seeing that these data have been published in Martelli et al., 2017 the authors should tune down the claim that this was discovered in their work but rather corroborated earlier results.

__ Answer: __We acknowledge this error, as another reviewer also raised this issue. We have corrected our manuscript as follows:

"The established connections and architecture of SIFa neurons has been described by Martelli et al., which enhances our understanding of their functional roles within the neuronal circuitry [51]. To identify the dendritic and axonal components of SIFa-neuronal processes, we employed a similar approach to that reported by Martelli [51]."

Comment 4. In the next chapter, the authors aim at identifying the presynaptic inputs from SIFa positive neurons that may influence interval timing behavior and make a broad RNAi knock-down screen targeting a majority of neuromodulators. The authors claim that glutaminergic and dopaminergic signaling is necessary for interval timing behavior. I guess the authors mean "glutamatergic" instead of "glutaminergic" as glutamine is the precursor but not the neurotransmitter.

__ Answer: __The reviewer is correct. We have corrected this error and changed all instances to "glutamatergic."

Comment 5____. Furthermore, the authors show that the knock down of Tdc2 with RNAi has comparable effects on SMD than Glutamate and dopamine but appear to not further discuss this in the main text. To me it is not clear why the authors exclude Tdc2 from their resume. The authors should explain this in detail.

   __Answer:__ We appreciate the reviewer’s constructive comments regarding the need for a more detailed demonstration of the role of Tdc2 data. While we did test Tdc2-RNAi and observed interesting phenotypes, we decided not to include these findings in our publication, as our data on glutamate and dopamine offer a more compelling explanation for how SIFa cotransmission with these neurotransmitters can independently influence various behaviors, such as sleep and mating duration. Consequently, we have removed all data related to Tdc2. We believe that further evaluation is necessary to better understand the roles of the tyramine and octopamine systems in SIFa neurons.

Comment 6. The authors base their assumptions that the tested neurotransmitters are expressed in SIFamidergic neurons on Scope database analysis. But a transcript does not necessarily mean that it will be translated too. To my knowledge there is no available data in the literature showing that tyrosine hydroxylase is expressed in SIFamidergic neurons (see e.g., Mao and Davis, 2010). To show that ple or Tdc2 are indeed expressed and translated into functional enzymes in SIFamidergic neurons the authors should provide the according antibody labelling corroborating the result from the transcriptome analysis.

__ Answer:__ We appreciate the reviewer’s constructive comments regarding the role of neurotransmitters in conjunction with SIFa in modulating interval timing behaviors. To confirm the expression of dopamine (DA) in SIFa neurons, we utilized a well-established genetic toolkit for dissecting dopamine circuit function in Drosophila [18]. Our findings demonstrate that TH-C-GAL4 specifically labels SIFa neurons, which have been confirmed to be dopaminergic (Fig. S4M). This aligns with the genetic intersection data and the findings from Xie et al. (2018), confirming that a subset of SIFa neurons is indeed dopaminergic. We have included these new results in the main text as follows:

" To further verify the presence of DA neurons within the SIFa neuron population, we utilized a well-established genetic toolkit for dissecting DA circuits and confirmed part of SIFa neurons are dopaminergic (S4M Fig) [58]."

   To confirm the glutamatergic characteristics of SIFa neurons, we conducted several experiments that established glutamate as the most critical neurotransmitter for generating interval timing in both SIFa and SIFaR neurons. First, to demonstrate the presence of glutamatergic synaptic vesicles in SIFa neurons, we utilized a conditional glutamatergic synaptic vesicle marker for *Drosophila*, developed by Certel et al. [19]. Our results confirmed that SIFa neurons exhibit strong expression of glutamatergic synaptic vesicles (Fig. 2P and Fig. S4N as a genetic control). We have described these new results in the main text as follows:

"To further substantiate the role of glutamate in SIFa-mediated behaviors. we targeted the expression of VGlut receptor in neurons that carry the SIFaR. Strikingly, the knockdown of VGlut receptor in these neurons also disrupted SMD behavior, mirroring the phenotype observed upon direct suppression of glutamatergic signaling in SIFa neurons (S4O-L Fig)."

   To further confirm that glutamate release from SIFa neurons influences the function of SIFaR neurons, we tested several RNAi strains targeting glutamate receptors. Our results showed that the knockdown of glutamate receptors in SIFaR-expressing neurons produced phenotypes similar to those observed with VGlut-RNAi knockdown in SIFa neurons (Fig. S4I-N). We believe that this series of experiments demonstrates that glutamate and dopamine work in conjunction with SIFa to modulate interval timing and other behaviors related to energy balance. We have described these new results in the main text as follows:

"We also further verified that the knockdown of glutamate receptors in SIFaR-expressing neurons produces phenotypes similar to those resulting from VGlut knockdown in SIFa neurons (S4G to S4L Fig). This suggests that glutamate is an essential neurotransmitter for modulating interval timing in SIFa neurons."

Comment 7. The authors compare the LMD and SMD behavior of the animals with reduced expression with "heterozygous control animals" the authors should describe in detail what these are - are these controls the driver lines or the effector lines or a mix of both? The authors should provide the data for heterozygous driver line controls as well as heterozygous effector line controls to exclude any genetic background influence on the measured behavior. Accordingly, the authors should provide the data for the same controls for the sleep experiment in figure 3O and all the other behavioral experiments in the following parts of the manuscript.

__ Answer: __We sincerely thank the reviewer for insightful comments regarding the absence of traditional genetic controls in our study of LMD and SMD behaviors. We acknowledge the importance of such controls and wish to clarify our rationale for not including them in the current investigation. The primary reason for not incorporating all genetic control lines is that we have previously assessed the LMD and SMD behaviors of GAL4/+ and UAS/+ strains in our earlier studies. Our past experiences have consistently shown that 100% of the genetic control flies for both GAL4 and UAS exhibit normal LMD and SMD behaviors. Given these findings, we deemed the inclusion of additional genetic controls to be non-essential for the present study, particularly in the context of extensive screening efforts. We understand the value of providing a clear rationale for our methodology choices. To this end, we have added a detailed explanation in the "MATERIALS AND METHODS" section and the figure legends of Figure 1. This clarification aims to assist readers in understanding our decision to omit traditional controls, as outlined below.

"Mating Duration Assays for Successful Copulation

The mating duration assay in this study has been reported [33,73,93]. To enhance the efficiency of the mating duration assay, we utilized the Df (1) Exel6234 (DF here after) genetic modified fly line in this study, which harbors a deletion of a specific genomic region that includes the sex peptide receptor (SPR)[94,95]. Previous studies have demonstrated that virgin females of this line exhibit increased receptivity to males [95]. We conducted a comparative analysis between the virgin females of this line and the CS virgin females and found that both groups induced SMD. Consequently, we have elected to employ virgin females from this modified line in all subsequent studies. For naïve males, 40 males from the same strain were placed into a vial with food for 5 days. For single reared males, males of the same strain were collected individually and placed into vials with food for 5 days. For experienced males, 40 males from the same strain were placed into a vial with food for 4 days then 80 DF virgin females were introduced into vials for last 1 day before assay. 40 DF virgin females were collected from bottles and placed into a vial for 5 days. These females provide both sexually experienced partners and mating partners for mating duration assays. At the fifth day after eclosion, males of the appropriate strain and DF virgin females were mildly anaesthetized by CO2. After placing a single female in to the mating chamber, we inserted a transparent film then placed a single male to the other side of the film in each chamber. After allowing for 1 h of recovery in the mating chamber in 25℃ incubators, we removed the transparent film and recorded the mating activities. Only those males that succeeded to mate within 1 h were included for analyses. Initiation and completion of copulation were recorded with an accuracy of 10 sec, and total mating duration was calculated for each couple. All assays were performed from noon to 4pm. Genetic controls with GAL4/+ or UAS/+ lines were omitted from supplementary figures, as prior data confirm their consistent exhibition of normal LMD and SMD behaviors [33,73,93,96,97]. Hence, genetic controls for LMD and SMD behaviors were incorporated exclusively when assessing novel fly strains that had not previously been examined. In essence, internal controls were predominantly employed in the experiments, as LMD and SMD behaviors exhibit enhanced statistical significance when internally controlled. Within the LMD assay, both group and single conditions function reciprocally as internal controls. A significant distinction between the naïve and single conditions implies that the experimental manipulation does not affect LMD. Conversely, the lack of a significant discrepancy suggests that the manipulation does influence LMD. In the context of SMD experiments, the naïve condition (equivalent to the group condition in the LMD assay) and sexually experienced males act as mutual internal controls for one another. A statistically significant divergence between naïve and experienced males indicates that the experimental procedure does not alter SMD. Conversely, the absence of a statistically significant difference suggests that the manipulation does impact SMD. Hence, we incorporated supplementary genetic control experiments solely if they deemed indispensable for testing. All assays were performed from noon to 4 PM. We conducted blinded studies for every test[98,99] .

   While we have previously addressed this type of reviewer feedback in our published manuscript [2–7], we appreciate the reviewer’s suggestion to include traditional genetic control experiments. In response, we conducted all feasible combinations of genetic control experiments for LMD/SMD during the revision period. The results are presented in the supplementary figures and are described in the main text.

__Comment 8. __On page 11 line 231 to page 12 line 233 the authors claim that "sNPF signaling transmits hunger and satiety information to SIFa neurons in order to control food search and feeding" and cite Martelli et al., 2017. Could the authors explain more in detail how the Martelli paper somehow proposes this idea? I do not find the link between sNPF signaling hunger and SIFamide in this precise paper.

__ Answer:__ We appreciate the reviewer for accurately pointing out our misunderstanding of the references. We agree that Martelli et al.'s paper does not mention that sNPF signaling transmits hunger and satiety information to SIFa neurons. Consequently, we have removed the relevant sentence and replaced it with a statement correctly indicating that while sNPF signaling is related to feeding behavior, its connection to SIFa neurons remains unknown. We are grateful to the reviewer for acknowledging our efforts to accurately cite previous articles that support our rationale and ideas.

" Short neuropeptide F (sNPF) signaling plays a crucial role in regulating feeding behavior in Drosophila melanogaster, influencing food intake and body size [60,66,67] . However, there is currently no direct evidence reported linking sNPF signaling to SIFa neurons."

Comment 9. On page 15 line 302 - 303 the authors write that "except for PK2-R2, all other genes coexpress with SIFa in SCope data, indicating that hugin inputs to SIFa may not be transmitted through peptidergic signaling" - if SIFamidergic neurons do not express hugin-receptors how do the authors explain the inverted effect of PK2-R2-RNAi on single housed male courtship index when compared to heterozygous SIFaPT Gal4 control that show a reduction under comparable conditions.

__ Answer:__ We appreciate the reviewer’s constructive comments. In line with another reviewer’s suggestion, we have completely removed results of other neuropeptidergic inputs, focusing instead on how sNPF inputs modulate SIFa-mediated behavioral modulation using more advanced techniques such as GCaMP (Fig 3N). Consequently, the phenotypes resulting from various knockdowns of neuropeptide receptors are currently under investigation for a separate manuscript that we are preparing. We hope to successfully address how different neuropeptidergic inputs regulate SIFa neuron activity through various strategies.

Comment 10. On page 17 line 350 - 351 the authors write that "Stimulation of SIFa neurons resulted in an elevation in food consumption. Further, the authors write that "deactivation of SIFa neurons leads to a decrease in food consumption in male flies". From the way this is formulated it is not visible that the role of SIFamide in feeding control was published by Martelli and colleagues before. As the authors do not discuss the finding further in their discussion but cite the concerned paper in other aspects it appears as the authors intentionally want to omit this information to the reader. The authors may add a note that this has been shown before for female flies by Martelli and colleagues.

__ Answer:__ We appreciate reviewer's concern for properly mention previous Martelli et al.'s results about female feeding behavior modulated by SIFa neurons' activity. We agree with reviewer and added sentence as below in main text.

"Nevertheless, the temporary deactivation of SIFa neurons leads to a decrease in food consumption in male flies (Fig 4N and S6F to S6H) as previously described by Martelli et al.'s report in female flies [43]."

Comment 11. SIFamide receptor and GnIHR are discussed as descendants from a common ancestor and the authors nicely demonstrate that SIFamide does not only control homeostatic behavior as shown by Martelli and colleagues but also controls reproductive behavior. The evolution of such behavior control mechanisms may be integrated in the discussion too.

Answer: We appreciate the reviewer’s constructive comments, which enhance the evolutionary significance of our study. We agree with the reviewer and have added the following paragraph to the DISCUSSION section:

"The relationship between SIFamide receptors (SIFaR) and gonadotropin inhibitory hormone receptors (GnIHR) [89] highlights an intriguing evolutionary connection, as both are believed to have descended from a common ancestor [90,91]. This study expands on previous findings by Martelli et al., demonstrating that SIFamide not only regulates homeostatic behaviors but also plays a significant role in reproductive behavior [43]. GnIHR regulates food intake and reproductive behavior in opposing directions, thereby prioritizing feeding behavior over other behavioral tasks during times of metabolic need [92]. The evolution of these behavioral control mechanisms suggests a complex interplay between neuropeptides that modulate both physiological states and reproductive strategies. As SIFamide influences various behaviors, including feeding and sexual activity, it may be integral to understanding how organisms adapt their reproductive strategies in response to environmental and internal cues. This integration of behavioral modulation underscores the evolutionary significance of SIFamide signaling in coordinating essential life functions in Drosophila melanogaster and potentially other species, revealing pathways through which neuropeptides can shape behavior across different contexts."

Conclusive Comments: The manuscript by Song and colleagues is very interesting and may attract a broad readership. However, the authors miss to make clear what was already known and published on the role of SIFamide in homeostatic behavior control before their own study. Seen that the receptors for SIFamide and GnRHI derive from a common ancestor and apparently both GnRHI and SIFamide share similar roles in behavioral control this might indeed suggests that the basic function of this SIFaR/GnIHR-signaling pathway is conserved. This more broad evolutionary aspect is missing in the discussion of the manuscript.

• *Answer: We wholeheartedly agree with the reviewer regarding the evolutionary significance of SIFaR's function in relation to GnIHR, and we have expanded the DISCUSSION section to emphasize this important aspect.

"The relationship between SIFamide receptors (SIFaR) and gonadotropin inhibitory hormone receptors (GnIHR) [89] highlights an intriguing evolutionary connection, as both are believed to have descended from a common ancestor [90,91]. This study expands on previous findings by Martelli et al., demonstrating that SIFamide not only regulates homeostatic behaviors but also plays a significant role in reproductive behavior [43]. GnIHR regulates food intake and reproductive behavior in opposing directions, thereby prioritizing feeding behavior over other behavioral tasks during times of metabolic need [92]. The evolution of these behavioral control mechanisms suggests a complex interplay between neuropeptides that modulate both physiological states and reproductive strategies. As SIFamide influences various behaviors, including feeding and sexual activity, it may be integral to understanding how organisms adapt their reproductive strategies in response to environmental and internal cues. This integration of behavioral modulation underscores the evolutionary significance of SIFamide signaling in coordinating essential life functions in Drosophila melanogaster and potentially other species, revealing pathways through which neuropeptides can shape behavior across different contexts."

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Flag - suggested answer (don't read if don't want to see a (possibly incorrect) attempt:

Grateful for comments here as I am not very certain on the situations that the MLE approach is better vs situations where Bayesian approach is better

Suggested answer:

c(i) Is frequentist approach where we have one parameter estimate (the MLE) c(ii) bayesian approach - distribution over parameters and we update our prior belief based on observations If we have no prior belief - c(i) may be a better estimate (i.e. in (my version of) c(ii) we are constraining the parameters to be 0.7 or 0.2 and updating our relative convictions about these - which is a strong prior asssumption (we can never have 0.5 for instance) If we do have prior belief and also want to incorporate uncertainty estimations in our parameters, I think c(ii) is better If the MLE is 0.7 then we will have c(i) giving 0.7 and c(ii) giving 0.7 with a very high probability and 0/2 with a very low probability to the methods will perform similarly

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review): 

In the presented manuscript, the authors investigate how neural networks can learn to replay presented sequences of activity. Their focus lies on the stochastic replay according to learned transition probabilities. They show that based on error-based excitatory and balance-based inhibitory plasticity networks can selforganize towards this goal. Finally, they demonstrate that these learning rules can recover experimental observations from song-bird song learning experiments. 

Overall, the study appears well-executed and coherent, and the presentation is very clear and helpful. However, it remains somewhat vague regarding the novelty. The authors could elaborate on the experimental and theoretical impact of the study, and also discuss how their results relate to those of Kappel et al, and others (e.g., Kappel et al (doi.org/10.1371/journal.pcbi.1003511))). 

We agree with the reviewer that our previous manuscript lacked comparison with previously published similar works. While Kappel et al. demonstrated that STDP in winner-take-all circuits can approximate online learning of hidden Markov models (HMMs), a key distinction from our model is that their neural representations acquire deterministic sequential activations, rather than exhibiting stochastic transitions governing Markovian dynamics. Specifically, in their model, the neural representation of state B would be different in the sequences ABC and CBA, resulting in distinct deterministic representations like ABC and C'B'A', where ‘A’ and ‘A'’ are represented by different neural states (e.g., activations of different cell assemblies). In contrast, our network learns to generate stochastically transitioning cell assemblies which replay Markovian trajectories of spontaneous activity obeying the learned transition probabilities between neural representations of states. For example, starting from reactivation from assembly ‘A’, there may be an 80% probability to transition to assembly ‘B’ and 20% to ‘C’. Although Kappel et al.'s model successfully solves HMMs, their neural representations do not themselves stochastically transition between states according to the learned model. Similar to the Kappel et al.'s model, while the models proposed in Barber (2002) and Barber and Agakov (2002) learn the Markovian statistics, these models learned a static spatiotemporal input patterns only and how assemblies of neurons show stochastic transition in spontaneous activity has been still unclear. In contrast with these models, our model captures the probabilistic neural state trajectories, allowing spontaneous replay of experienced sequences with stochastic dynamics matching the learned environmental statistics.

We have included new sentences for explain these in ll. 509-533 in the revised manuscript.

Overall, the work could benefit if there was either (A) a formal analysis or derivation of the plasticity rules involved and a formal justification of the usefulness of the resulting (learned) neural dynamics; 

We have included a derivation of our plasticity rules in ll. 630-670 in the revised manuscript. Consistent with our claim that excitatory plasticity updates the excitatory synapse to predict output firing rates, we have shown that the corresponding cost function measures the discrepancy between the recurrent prediction and the output firing rate. Similarly, for inhibitory plasticity, we defined the cost function that evaluates the difference between the excitatory and inhibitory potential within each neuron. We showed that the resulting inhibitory plasticity rule updates the inhibitory synapses to maintain the excitation-inhibition balance.

and/or (B) a clear connection of the employed plasticity rules to biological plasticity and clear testable experimental predictions. Thus, overall, this is a good work with some room for improvement. 

Our proposed plasticity mechanism could be implemented through somatodendritic interactions. Analogous to previous computational works (Urbanczik and Senn., 2014; Asabuki and Fukai., 2020; Asabuki et al., 2022), our model suggests that somatic responses may encode the stimulus-evoked neural activity states, while dendrites encode predictions based on recurrent dynamics that aim to minimize the discrepancy between somatic and dendritic activity. To directly test this hypothesis, future experimental studies could simultaneously record from both somatic and dendritic compartments to investigate how they encode evoked responses and predictive signals during learning (Francioni et al., 2022).

We have included new sentences for explain these in ll. 476-484 in the revised manuscript.

Reviewer #2 (Public Review): 

Summary: 

This work proposes a synaptic plasticity rule that explains the generation of learned stochastic dynamics during spontaneous activity. The proposed plasticity rule assumes that excitatory synapses seek to minimize the difference between the internal predicted activity and stimulus-evoked activity, and inhibitory synapses try to maintain the E-I balance by matching the excitatory activity. By implementing this plasticity rule in a spiking recurrent neural network, the authors show that the state-transition statistics of spontaneous excitatory activity agree with that of the learned stimulus patterns, which are reflected in the learned excitatory synaptic weights. The authors further demonstrate that inhibitory connections contribute to well-defined state transitions matching the transition patterns evoked by the stimulus. Finally, they show that this mechanism can be expanded to more complex state-transition structures including songbird neural data. 

Strengths: 

This study makes an important contribution to computational neuroscience, by proposing a possible synaptic plasticity mechanism underlying spontaneous generations of learned stochastic state-switching dynamics that are experimentally observed in the visual cortex and hippocampus. This work is also very clearly presented and well-written, and the authors conducted comprehensive simulations testing multiple hypotheses. Overall, I believe this is a well-conducted study providing interesting and novel aspects of the capacity of recurrent spiking neural networks with local synaptic plasticity. 

Weaknesses: 

This study is very well-thought-out and theoretically valuable to the neuroscience community, and I think the main weaknesses are in regard to how much biological realism is taken into account. For example, the proposed model assumes that only synapses targeting excitatory neurons are plastic, and uses an equal number of excitatory and inhibitory neurons. 

We agree with the reviewer. The network shown in the previous manuscript consists of an equal number of excitatory and inhibitory neurons, which seems to lack biological plausibility. Therefore, we first tested whether a biologically plausible scenario would affect learning performance by setting the ratio of excitatory to inhibitory neurons to 80% and 20% (Supplementary Figure 7a; left). Even in such a scenario, the network still showed structured spontaneous activity (Supplementary Figure 7a; center), with transition statistics of replayed events matching the true transition probabilities (Supplementary Figure 7a; right). We then asked whether the model with our plasticity rule applied to all synapses would reproduce the corresponding stochastic transitions. We found that the network can learn transition statistics but only under certain conditions. The network showed only weak replay and failed to reproduce the appropriate transition (Supplementary Fig. 7b) if the inhibitory neurons were no longer driven by the synaptic currents reflecting the stimulus, due to a tight balance of excitatory and inhibitory currents on the inhibitory neurons. We then tested whether the network with all synapses plastic can learn transition statistics if the external inputs project to the inhibitory neurons as well. We found that, when each stimulus pattern activates a non-overlapping subset of neurons, the network does not exhibit the correct stochastic transition of assembly reactivation (Supplementary Fig. 7c). Interestingly, when each neuron's activity is triggered by multiple stimuli and has mixed selectivity, the reactivation reproduced the appropriate stochastic transitions (Supplementary Fig. 7d).

We have included these new results as new Supplementary Figure 7 and they are explained in ll.215-230 in the revised manuscript.

The model also assumes Markovian state dynamics while biological systems can depend more on history. This limitation, however, is acknowledged in the Discussion. 

We have included the following sentence to provide a possible solution to this limitation: “Therefore, to learn higher-order stochastic transitions, recurrent neural networks like ours may need to integrate higher-order inputs with longer time scales.” in ll.557-559 in the revised manuscript. 

Finally, to simulate spontaneous activity, the authors use a constant input of 0.3 throughout the study. Different amplitudes of constant input may correspond to different internal states, so it will be more convincing if the authors test the model with varying amplitudes of constant inputs. 

We thank the reviewer for pointing this out. In the revised manuscript, we have tested constant input with three different strengths. If the strength is moderate, the network showed accurate encoding of transition statistics in the spontaneous activity as we have seen in Fig.2. We have additionally shown that the weaker background input causes spontaneous activity with lower replay rate, which in turn leads to high variance of encoded transition, while stronger inputs make assembly replay transitions more uniform. We have included these new results as new Supplementary Figure 6 and they are explained in ll.211214 in the revised manuscript.

Reviewer #3 (Public Review): 

Summary: 

Asabuki and Clopath study stochastic sequence learning in recurrent networks of Poisson spiking neurons that obey Dale's law. Inspired by previous modeling studies, they introduce two distinct learning rules, to adapt excitatory-to-excitatory and inhibitory-to-excitatory synaptic connections. Through a series of computer experiments, the authors demonstrate that their networks can learn to generate stochastic sequential patterns, where states correspond to non-overlapping sets of neurons (cell assemblies) and the state-transition conditional probabilities are first-order Markov, i.e., the transition to a given next state only depends on the current state. Finally, the authors use their model to reproduce certain experimental songbird data involving highly-predictable and highly-uncertain transitions between song syllables. 

Strengths: 

This is an easy-to-follow, well-written paper, whose results are likely easy to reproduce. The experiments are clear and well-explained. The study of songbird experimental data is a good feature of this paper; finches are classical model animals for understanding sequence learning in the brain. I also liked the study of rapid task-switching, it's a good-to-know type of result that is not very common in sequence learning papers. 

Weaknesses: 

While the general subject of this paper is very interesting, I missed a clear main result. The paper focuses on a simple family of sequence learning problems that are well-understood, namely first-order Markov sequences and fully visible (nohidden-neuron) networks, studied extensively in prior work, including with spiking neurons. Thus, because the main results can be roughly summarized as examples of success, it is not entirely clear what the main point of the authors is. 

We apologize the reviewer that our main claim was not clear. While various computational studies have suggested possible plasticity mechanisms for embedding evoked activity patterns or their probability structures into spontaneous activity (Litwin-Kumar et al., Nat. Commun. 2014, Asabuki and Fukai., Biorxiv 2023), how transition statistics of the environment are learned in spontaneous activity is still elusive and poorly understood. Furthermore, while several network models have been proposed to learn Markovian dynamics via synaptic plasticity (Brea, et al. (2013); Pfister et al. (2004); Kappel et al. (2014)), they have been limited in a sense that the learned network does not show stochastic transition in a neural state space. For instance, while Kappel et al. demonstrated that STDP in winner-take-all circuits can approximate online learning of hidden Markov models (HMMs), a key distinction from our model is that their neural representations acquire deterministic sequential activations, rather than exhibiting stochastic transitions governing Markovian dynamics. Specifically, in their model, the neural representation of state B would be different in the sequences ABC and CBA, resulting in distinct deterministic representations like ABC and C'B'A', where ‘A’ and ‘A'’ are represented by different neural states (e.g., activations of different cell assemblies). In contrast, our network learns to generate stochastically transitioning cell assemblies that replay Markovian trajectories of spontaneous activity obeying the learned transition probabilities between neural representations of states. For example, starting from reactivation from assembly ‘A’, there may be an 80% probability to transition to assembly ‘B’ and 20% to ‘C’. Although Kappel et al.'s model successfully solves HMMs, their neural representations do not themselves stochastically transition between states according to the learned model. Similar to the Kappel et al.'s model, while the models proposed in Barber (2002) and Barber and Agakov (2002) learn the Markovian statistics, these models learned a static spatiotemporal input patterns only and how assemblies of neurons show stochastic transition in spontaneous activity has been still unclear. In contrast with these models, our model captures the probabilistic neural state trajectories, allowing spontaneous replay of experienced sequences with stochastic dynamics matching the learned environmental statistics.

We have explained this point in ll.509-533 in the revised manuscript.

Going into more detail, the first major weakness I see in this paper is the heuristic choice of learning rules. The paper studies Poisson spiking neurons (I return to this point below), for which learning rules can be derived from a statistical objective, typically maximum likelihood. For fully-visible networks, these rules take a simple form, similar in many ways to the E-to-E rule introduced by the authors. This more principled route provides quite a lot of additional understanding on what is to be expected from the learning process. 

We thank the reviewer for pointing this out. To better demonstrate the function of our plasticity rules, we have included the derivation of the rules of synaptic plasticity in ll. 630-670 in the revised manuscript. Consistent with our claim that excitatory plasticity updates the excitatory synapse to predict output firing rates, we have shown that the corresponding cost function measures the discrepancy between the recurrent prediction and the output firing rate. Similarly, for inhibitory plasticity, we defined the cost function that evaluates the difference between the excitatory and inhibitory potential within each neuron. We showed that the resulting inhibitory plasticity rule updates the inhibitory synapses to maintain the excitation-inhibition balance.

For instance, should maximum likelihood learning succeed, it is not surprising that the statistics of the training sequence distribution are reproduced. Moreover, given that the networks are fully visible, I think that the maximum likelihood objective is a convex function of the weights, which then gives hope that the learning rule does succeed. And so on. This sort of learning rule has been studied in a series of papers by David Barber and colleagues [refs. 1, 2 below], who applied them to essentially the same problem of reproducing sequence statistics in recurrent fully-visible nets. It seems to me that one key difference is that the authors consider separate E and I populations, and find the need to introduce a balancing I-to-E learning rule. 

The reviewer’s understanding that inhibitory plasticity to maintain EI balance is one of a critical difference from previous works is correct. However, we believe that the most striking point of our study is that we have shown numerically that predictive plasticity rules enable recurrent networks to learn and replay the assembly activations whose transition statistics match those of the evoked activity. Please see our reply above.

Because the rules here are heuristic, a number of questions come to mind. Why these rules and not others - especially, as the authors do not discuss in detail how they could be implemented through biophysical mechanisms? When does learning succeed or fail? What is the main point being conveyed, and what is the contribution on top of the work of e.g. Barber, Brea, et al. (2013), or Pfister et al. (2004)? 

Our proposed plasticity mechanism could be implemented through somatodendritic interactions. Analogous to previous computational works (Senn, Asabuki), our model suggests that somatic responses may encode the stimulusevoked neural activity states, while dendrites encode predictions based on recurrent dynamics that aim to minimize the discrepancy between somatic and dendritic activity. To directly test this hypothesis, future experimental studies could simultaneously record from both somatic and dendritic compartments to investigate how they encode evoked responses and predictive signals during learning.

To address the point of the reviewer, we conducted addionnal simulations to test where the model fails. We found that the model with our plasticity rule applied to all synapses only showed faint replays and failed to replay the appropriate transition (Supplementary Fig. 7b). This result is reasonable because the inhibitory neurons were no longer driven by the synaptic currents reflecting the stimulus, due to a tight balance of excitatory and inhibitory currents on the inhibitory neurons. Our model predicts that mixed selectivity in the inhibitory population is crucial to learn an appropriate transition statistics (Supplementary Fig. 7d). Future work should clarify the role of synaptic plasticity on inhibitory neurons, especially plasticity at I to I synapses. We have explained this result as new supplementary Figure7 in the revised manuscript.

The use of a Poisson spiking neuron model is the second major weakness of the study. A chief challenge in much of the cited work is to generate stochastic transitions from recurrent networks of deterministic neurons. The task the authors set out to do is much easier with stochastic neurons; it is reasonable that the network succeeds in reproducing Markovian sequences, given an appropriate learning rule. I believe that the main point comes from mapping abstract Markov states to assemblies of neurons. If I am right, I missed more analyses on this point, for instance on the impact that varying cell assembly size would have on the findings reported by the authors.

The reviewer’s understanding is correct. Our main point comes from mapping Markov statistics to replays of cell assemblies. In the revised manuscript, we performed additional simulations to ask whether varying the size of the cell assemblies would affect learning. We ran simulations with two different configurations in the task shown in Figure 2. The first configuration used three assemblies with a size ratio of 1:1.5:2. After training, these assemblies exhibited transition statistics that closely matched those of the evoked activity (Supplementary Fig.4a,b). In contrast, the second configuration, which used a size ratio of 1:2:3, showed worse performance compared to the 1:1.5:2 case (Supplementary Fig.4c,d). These results suggest that the model can learn appropriate transition statistics as long as the size ratio of the assemblies is not drastically varied.

Finally, it was not entirely clear to me what the main fundamental point in the HVC data section was. Can the findings be roughly explained as follows: if we map syllables to cell assemblies, for high-uncertainty syllable-to-syllable transitions, it becomes harder to predict future neural activity? In other words, is the main point that the HVC encodes syllables by cell assemblies? 

The reviewer's understanding is correct. We wanted to show that if the HVC learns transition statistics as a replay of cell assemblies, a high-uncertainty syllable-to-syllable transition would make predicting future reactivations more difficult, since trial-averaged activities (i.e., poststimulus activities; PSAs) marginalized all possible transitions in the transition diagram.

(1) Learning in Spiking Neural Assemblies, David Barber, 2002. URL: https://proceedings.neurips.cc/paper/2002/file/619205da514e83f869515c782a328d3c-Paper.pdf  

(2) Correlated sequence learning in a network of spiking neurons usingmaximum likelihood, David Barber, Felix Agakov, 2002. URL: http://web4.cs.ucl.ac.uk/staff/D.Barber/publications/barber-agakovTR0149.pdf  

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

In more detail: 

A) Theoretical analysis 

The plasticity rules in the study are introduced with a vague reference to previous theoretical studies of others. Doing this, one does not provide any formal insight as to why these plasticity rules should enable one to learn to solve the intended task, and whether they are optimal in some respect. This becomes noticeable, especially in the discussion of the importance of inhibitory balance, which does not go into any detail, but rather only states that its required, both in the results and discussion sections. Another unclarity appears when error-based learning is discussed and compared to Hebbian plasticity, which, as you state, "alone is insufficient to learn transition probabilities". It is not evident how this claim is warranted, nor why error-based plasticity in comparison should be able to perform this (other than referring to the simulation results). Please either clarify formally (or at least intuitively) how plasticity rules result in the mentioned behavior, or alternatively acknowledge explicitly the (current) lack of intuition. 

The lack of formal discussion is a relevant shortcoming compared to previous research that showed very similar results with formally more rigorous and principled approaches. In particular, Kappel et al derived explicitly how neural networks can learn to sample from HMMs using STDP and winner-take-all dynamics. Even though this study has limitations, the relation with respect to that work should be made very clear; potentially the claims of novelty of some results (sampling) should be adjusted accordingly. See also Yanping Huang, Rajesh PN Rao (NIPS 2014), and possibly other publications. While it might be difficult to formally justify the learning rules post-hoc, it would be very helpful to the field if you very clearly related your work to that of others, where learning rules have been formally justified, and elaborate on the intuition of how the employed rules operate and interact (especially for inhibition). 

Lastly, while the importance of sampling learned transition probabilities is discussed, the discussion again remains on a vague level, characterized by the lack of references in the relevant paragraphs. Ideally, there should be a proof of concept or a formal understanding of how the learned behaviour enables to solve a problem that is not solved by deterministic networks. Please incorporate also the relation to the literature on neural sampling/planning/RL etc. and substantiate the claims with citations. 

We have included sentences in ll. 691-696 in the revised manuscript to explain that for Poisson spiking neurons, the derived learning rule is equivalent to the one that minimizes the Kullback-Leibler divergence between the distributions of output firing and the dendritic prediction, in our case, the recurrent prediction (Asabuki and Fukai; 2020). Thus, the rule suggests that the recurrent prediction learns the statistical model of the evoked activity, which in turn allows the network to reproduce the learned transition statistics.

We have also added a paragraph to discuss the differences between previously published similar models (e.g., Kappel et al.). Please see our response above.

B) Connection to biology 

The plasticity rules in the study are introduced with a vague reference to previous theoretical studies of others. Please discuss in more detail if these rules (especially the error-based learning rule) could be implemented biologically and how this could be achieved. Are there connections to biologically observed plasticity? E.g. for error-based plasticity has been discussed in the original publication by Urbanzcik and Senn, or more recently by Mikulasch et al (TINS 2023). The biological plausibility of inhibitory balance has been discussed many times before, e.g. by Vogels and others, and a citation would acknowledge that earlier work. This also leaves the question of how neurons in the songbird experiment could adapt and if the model does capture this well (i.e., do they exhibit E-I balance? etc), which might be discussed as well. 

Last, please provide some testable experimental predictions. By proposing an interesting experimental prediction, the model could become considerably more relevant to experimentalists. Also, are there potentially alternative models of stochastic sequence learning (e.g., Kappel et al)? How could they be distinguished? (especially, again, why not Hebbian/STDP learning?) 

We have cited the Vogels paper to acknowledge the earlier work. We have also included additional paragraphs to discuss a possible biologically plausible implementation of our model and how our model differs from similar models proposed previously (e.g., Kappel et al.). Please see our response above.

Other comments 

As mentioned, a derivation of recurrent plasticity rules is missing, and parameters are chosen ad-hoc. This leaves the question of how much the results rely on the specific choice of parameters, and how robust they are to perturbations. As a robustness check, please clarify how the duration of the Markov states influences performance. It can be expected that this interacts with the timescale of recurrent connections, so having longer or shorter Markov states, as it would be in reality, should make a difference in learning that should be tested and discussed.

We thank the reviewer for pointing this out. To address this point, we performed new simulations and asked to what extent the duration of Markov states affect performance. Interestingly, even when the network was trained with input states of half the duration, the distributions of the durations of assembly reactivations remain almost identical to those in the original case (Supplementary Figure 3a). Furthermore, the transition probabilities in the replay were still consistent with the true transition probabilities (Supplementary Figure 3b). We have also included the derivation of our plasticity rule in ll. 630-670 in the revised manuscript. 

Similarly, inhibitory plasticity operates with the same plasticity timescale parameter as excitatory plasticity, but, as the authors discuss, lags behind excitatory plasticity in simulation as in experiment. Is this required or was the parameter chosen such that this behaviour emerges? Please clarify this in the methods section; moreover, it would be good to test if the same results appear with fast inhibitory plasticity. 

We have performed a new simulation and showed that even when the learning rate of inhibitory plasticity was larger than that of excitatory plasticity, inhibitory plasticity still occurred on a slower timescale than excitatory plasticity. We have included this result in a new Supplementary Figure 2 in the revised manuscript.

What is the justification (biologically and theoretically) for the memory trace h and its impact on neural spiking? Is it required for the results or can it be left away? Since this seems to be an important and unconventional component of the model, please discuss it in more detail. 

In the model, it is assumed that each stimulus presentation drives a specific subset of network neurons with a fixed input strength, which avoids convergence to trivial solutions. Nevertheless, we choose to add this dynamic sigmoid function to facilitate stable replay by regulating neuron activity to prevent saturation. We have explained this point in ll.605-611 in the revised manuscript.

Reviewer #2 (Recommendations For The Authors): 

I noticed a couple of minor typos: 

Page 3 "underly"->"underlie" 

Page 7 "assemblies decreased settled"->"assemblies decreased and settled"

We have modified the text. We thank the reviewer for their careful review.

I think Figure 1C is rather confusing and not intuitive. 

We apologize that the Figure 1C was confusing. In the revised figure, we have emphasized the flow of excitatory and inhibitory error for updating synapses.

Reviewer #3 (Recommendations For The Authors): 

One possible path to improve the paper would be to establish a relationship between the proposed learning rules and e.g. the ones derived by Barber. 

When reading the paper, I was left with a number of more detailed questions I omitted from the public review: 

(1) The authors introduce a dynamic sigmoidal function for excitatory neurons, Eq. 3. This point requires more discussion and analysis. How does this impact the results? 

In the model, it is assumed that each stimulus presentation drives a specific subset of network neurons with a fixed input strength, which avoids convergence to trivial solutions. Nevertheless, we choose to add this dynamic sigmoid function to facilitate stable replay by regulating neuron activity to prevent saturation. We have explained this point in ll.605-611 in the revised manuscript.

(2) For Poisson spiking neurons, it would be great to understand what cell assemblies bring (apart from biological realism, i.e., reproducing data where assemblies can be found), compared to self-connected single neurons. For example, how do the results shown in Figure 2 depend on assembly size? 

We have changed the cell assembly size ratio and how it affects learning performance in a new Supplementary Figure 4. Please see our reply above.

(3) The authors focus on modeling spontaneous transitions, corresponding to a highly stochastic generative model (with most transition probabilities far from 1). A complementary question is that of learning to produce a set of stereotypical sequences, with probabilities close to 1. I wondered whether the learning rules and architecture of the model (in particular under the I-to-E rule) would also work in such a scenario. 

We thank the reviewer for pointing this out. In fact, we had the same question, so we considered a situation in which the setting in Figure 2 includes both cases where the transition matrix is very stochastic (prob=0.5) and near deterministic (prob=0.9).

(4) An analysis of what controls the time so that the network stays in a certain state would be welcome. 

We trained the network model in two cases, one with a fast speed of plasticity and one with a slow speed of plasticity. As a result, we found that the duration of assembly becomes longer in the slow learning case than in the fast case. We have included these results as Supplementary Figure 5 in the revised manuscript.

Regarding the presentation, given that this is a computational modeling paper, I wonder whether *all* the formulas belong in the Methods section. I found myself skipping back and forth to understand what the main text meant, mainly because I missed a few key equations. I understand that this is a style issue that is very much community-dependent, but I think readability would improve drastically if the main model and learning rule equations could be introduced in the main text, as they start being discussed. 

We thank the reviewer for the suggestion. To cater to a wider audience, we try to explain the principle of the paper without using mathematical formulas as much as possible in the main text.

Author response:

In this manuscript, we have addressed one of the possible modes of recruitment of Swi6 to the putative heterochromatin loci.

Our investigation was guided by earlier work showing ability of HP1 a to bind to a class of RNAs and the role of this binding in recruitment of HP1a to heterochromatin loci in mouse cells (Muchardt et al). While there has been no clarity about the mechanism of Swi6 recruitment given the multiple pathways being involved, the issue is compounded by the overall lack of understanding as to how Swi6 recruitment occurs only at the repeat regions. At the same time, various observations suggested a causal role of RNAi in Swi6 recruitment.

Thus, guided by the work of Muchardt et al we developed a heuristic approach to explore a possibly direct link between Swi6 and heterochromatin through RNAi pathway. Interestingly, we found that the lysine triplet found in the hinge domain in HP1, which influences its recruitment to heterochromatin in mouse cells, is also present in the hinge domain of Swi6, although we were cautious, keeping in mind the findings of Keller et al showing another role of Swi6 in binding to RNAs and channeling them to the exosome pathway. 

Accordingly, we envisaged that a mode of recruitment of Swi6 through binding to siRNAs to cognate sites in the dg-dh repeats shared among mating type, centromere and telomere loci could explain specific recruitment as well as inheritance following DNA replication. In accordance we framed the main questions as follows: i) Whether Swi6 binds specifically and with high affinity to the siRNAs and the cognate siRNA-DNA hybrids and whether the Swi63K-3A mutant is defective in this binding, ii) whether this lack of binding of Swi63K-3A affects its localization to heterochromatin, iii) whether the this specificity is validated by binding of Swi6 but not Swi63K-3A  to siRNAs and siRNA-DNA hybrids in vivo and iv) whether the binding mode was qualitatively and quantitatively different from that of Cen100 RNA or random RNAs, like GFP RNA.

We think that our data provides answers to these lines of inquiry to support a model wherein the Swi6-siRNA mediated recruitment can explain a cis-controlled nucleation of heterochromatin at the cognate sites in the genome. We have also partially addressed the points raised by the study by Keller et al by invoking a dynamic balance between different modes of binding of Swi6 to different classes of RNA to exercise heterochromatin formation by Swi6 under normal conditions and RNA degradation under other conditions.

While we aver about our hypothesis, we do acknowledge the need for more detailed investigation both to buttress our hypothesis and address the dynamics of siRNA binding and recruitment of Swi6  and how Swi6 functions fit in the context of other components of heterochromatin assembly, like the HDACs and Clr4 on one hand and exosome pathway on the other. Our future studies will attempt to address these issues.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript explores the RNA binding activities of the fission yeast Swi6 (HP1) protein and proposes a new role for Swi6 in RNAi-mediated heterochromatin establishment. The authors claim that Swi6 has a specific and high affinity for short interfering RNAs (siRNAs) and recruits the Clr4 (Suv39h) H3K9 methyltransferases to siRNA-DNA hybrids to initiate heterochromatin formation. These claims are not in any way supported by the incomplete and preliminary RNA binding or the in vivo experiments that the authors present. The proposed model also lacks any mechanistic basis as it remains unclear (and unexplored) how Swi6 might bind to specific small RNA sequences or RNA-DNA hybrids. Work by several other groups in the field has led to a model in which siRNAs produced by the RNAi pathway load onto the Ago1-containing RITS complex, which then binds to nascent transcripts at pericentromeric DNA repeats and recruits Clr4 to initiate heterochromatin formation. Swi6 facilitates this process by promoting the recruitment of the RNA-dependent RNA polymerase leading to siRNA amplification.

Weaknesses:

(1) a) The claims that Swi6 binds to specific small RNAs or to RNA-DNA hybrids are not supported by the evidence that the authors present. Their experiments do not rule out non-specific charged-based interactions.

We disagree. We have used synthetic siRNAs of 20-22 nt length to do EMSA assay, as mentioned in the manuscript. Further, we have sequenced the small RNAs obtained after RIP experiments to validate the enrichment of siRNA in Swi6 bound fraction as compared to the mutant Swi6-bound fraction. These results are internally consistent regardless of the mode of binding. In any case the binding occurs primarily through the chromodomain although it is influenced by the hinge domain (see below).

Furthermore, we have carried out EMSA experiments using Swi6 mutants carrying all three possible double mutations of the K residues in the KKK triplet and found that there was no difference in the binding pattern as compared to the wt Swi6: only the triple mutant “3K-3A” showed the effect. These results suggest that that the bdining is not completely dependent on the basic residues. These results will be included in the revised version.

We also have some preliminary data from SAXS study showing that the CD of wt Swi6 shows a change in its structure upon binding to the siRNA, while the “3K-3A” mutant of Swi6 has a compact, folded structure that occludes the binding site of Swi6 in the chromodomain.” We propose to mention this preliminary finding in the revised version as unpublished data.

b) Claims about different affinities of Swi6 for RNAs of different sizes are based on a comparison of KD values derived by the authors for a handful of S. pombe siRNAs with previous studies from the Buhler lab on Swi6 RNA binding. The authors need to compare binding affinities under identical conditions in their assays.

Thus, the EMSA data do suggest sequence specificity in binding of Swi6 to specific siRNA sequences (Figure S5) and implies specific residues in Swi6 being responsible for that. Thus, Identification of the residues in Swi6 involved in siRNA binding in the CD would definitely be interesting, as also the experimental confirmation of the consensus siRNA sequence. It may however be noted that as against the binding of Swi6 to siRNAs occurs through CD, that of Cen100 or GFP RNA was shown be through the hinge domain by Keller et al.

The estimation of Kd by the Buhler group was based on NMR study, which we are not in a position to perform in the near future. Nonetheless, we did carry out EMSA study using the ‘Cen100’ RNA, same as the one used by the Keller et al study. Surprisingly, in contrast with the result of EMSA in agarose gel showing binding of Swi6 to “Cen100” RNA as reported by Keller et al, we fail to observe any binding in EMSA done in acrylamide gel. (The same is true of the RevCen 100). While this raises issues of why the Keller et al chose to do EMSA in agarose gel instead of the conventional approach of using acrylamide gel, it does lend support to our claim of stronger binding of Swi6 to siRNAs. Another relevant observation of binding of Swi6 to the “RevCen” RNA precursor RNAs but a detectable binding to siRNAs denoted as VI-IX (as measured by competition experiments, that are derived from RevCen RNA; Figure S4 and S7), which are derived by Dcr1 cleavage of the ‘’RevCen’’ RNA.

We also disagree that we carried out EMSA with a small bunch of siRNAs. As indicated in Figure 1 and S1, we synthesized nearly 12 siRNAs representing the dg-dh repeats at Cen, mat and tel loci and measured their specificity of binding to Swi6 using EMSA assay by labeling the ones labelled “D”, “E” and “V” directly and those of the remaining ones by the latter’s ability to compete against the binding (Figure 1, S4). These results point to presence of a consensus sequence in siRNAs that shows highly specific and strong binding to Swi6 in the low micromolar range.

Further, our claim of binding of Swi6 and not Swi63K>3A to siRNA in vivo is validated by RIP experiments, as shown in Fig 2 and S9.

c) The regions of Swi6 that bind to siRNAs need to be identified and evidence must be provided that Swi6 binds to RNAs of a specific length, 20-22 mers, to support the claim that Swi6 binds to siRNAs. This is critical for all the subsequent experiments and claims in the study.

We have provided both in vitro data, which is va;idiated in vivo by RIP experiments, as mentioned above. However, we agree that it wpuld be very interesting to identify the residues in Swi6 chromdomain responsible for binding to siRNA. However, such an investigation is beyond the scope of the present study.

(2) a) The in vivo results do not validate Swi6 binding to specific RNAs, as stated by the authors. Swi6 pulldowns have been shown to be enriched for all heterochromatic proteins including the RITS complex. The sRNA binding observed by the authors is therefore likely to be mediated by Ago1/RITS.

We disagree with the first comment. Our RIP experiments do validate the in vitro results (Fig 1, 2, S4 and S9), as argued above. The observation alluded to by the reviewer “Swi6 pulldowns have been shown to be enriched for all heterochromatic proteins including the RITS complex” is not inconsistent with our observation; it is possible that the siRNA may be released from the RITS complex and transferred to Swi6, possibly due to its higher affinity.

Thus, we would like to suggest that the role of Swi6 is likely to be coincidental or subsequent to that of Ago1/RITS (see below). We think that the binding by Swi6 to the siRNA and siRNA-DNA hybrid and could be also carried out in cis at the level of siRNA-DNA hybrids.

This point needs to be addressed in future studies.

b) Most of the binding in Figure S8C seems to be non-specific.

We would like to point out that the result in Figure S8C needs to be examined together with the Figure S8B, which shows RNA bound by Swi6 but not Swi63K-3A to hybridize with dg, dh and dh-k probes.

c) In Figure S8D, the authors' data shows that Swi6 deletion does not derepress the rev dh transcript while dcr1 delete cells do, which is consistent with previous reports but does not relate to the authors' conclusions.

The purpose of results shown in Figure S8D is just to compare the results of Swi6 with that of Swi63K-3A.

d) Previous results have shown that swi6 delete cells have 20-fold fewer dg and dh siRNAs than swi6+ cells due to decreased RNA-dependent RNA polymerase complex recruitment and reduced siRNA amplification.

This result is consistent with our results invoking a role of Swi6 in binding to, protecting and recruiting siRNAs to homologous sites.

To find if the overall production of siRNA is compromised in swi6 3K->3A mutant, we i) calculated the RIP-Seq read counts for swi6 3K->3A , swi6+ and vector control in 200 bp genomic bins , ii) divided the Swi6 3K->3A and swi6+ signals by that of control, iii) removed the background using the criteria of signal value < 25% of max signal, and iv) counted the total reads (in excess to control) in all peak regions in both samples.  This revealed a total count of 10878 and 8994 respectively for Swi6 3K->3A  and swi6+ samples, possibly implying that the overall siRNA production is not compromised in the Swi6 3K->3A mutant.

(3) a) The RIP-seq data are difficult to interpret as presented. The size distribution of bound small RNAs, and where they map along the genome should be shown as for example presented in previous Ago1 sRNA-seq experiments.

Please see the response to 2(d).

b) It is also unclear whether the defects in sRNA binding observed by the authors represent direct sRNA binding to Swi6 or co-precipitation of Ago1-bound sRNAs.

The correspondence between our in vivo and in vitro results suggests that the binding to Swi6 would be direct. We do not observe a complete correspondence between the Swi6- and Ago-bound siRNAs. We think Swi6 binding may be coincident with or following RITS complex formation.

This point will be discussed in the Revision.

The authors should also sequence total sRNAs to test whether Swi6-3A affects sRNA synthesis, as is the case in swi6 delete cells.

Please see response to 2(d) above.

(4) The authors examine the effects of Swi6-3A mutant by overexpression from the strong nmt1 promoter. Heterochromatin formation is sensitive to the dosage of Swi6. These experiments should be performed by introducing the 3A mutations at the endogenous Swi6 locus and effects on Swi6 protein levels should be tested.

Although we agree, we think that the heterochromatin formation is occurring in presence of nmt1-driven Swi6 but not Swi63K>3A, as indicated by the phenotype and Swi6 enrichment at otr1R::ade6, imr1::ura4 and his3-telo (Figure 3) and mating type (Fig. S10). Furthermore, the both GFP-Swi6 and GFPSwi63K>3A are expressed at similar level (Fig. S8A).

(5) The authors' data indicate an impairment of silencing in Swi6-3A mutant cells but whether this is due to a general lower affinity for nucleosomes, DNA, RNA, or as claimed by the authors, siRNAs is unclear. These experiments are consistent with previous findings suggesting an important role for basic residues in the HP1 hinge region in gene silencing but do not reveal how the hinge region enhances silencing.

Our study aims to correlate the binding of Swi6 but not Swi63K-3A to siRNA with its localization to heterochromatin. A similar difference in binding of Swi6 but not Swi63K-3A to siRNA-DNA hybrid, together with sensitivity of silencing and Swi6 localization to heterochromatin to RNaseH support the above correlations as being causally connected.

In terms of mechanism of binding, we need to clarify that the primary mode of binding is through the CD and not the hinge domain, although the hinge domain does influence this binding. This result is different from those of Keller et al.

We have some structural data based on preliminary SAXS experiment supporting binding of siRNA to the CD and influence of the hinge domain on this binding. However, this line of investigation need to be extended and will be subject of future investigations.

(6) RNase H1 overexpression may affect Swi6 localization and silencing indirectly as it would lead to a general reduction in R loops and RNA-DNA hybrids across the genome. RNaseH1 OE may also release chromatin-bound RNAs that act as scaffolds for siRNA-Ag1/RITS complexes that recruit Clr4 and ultimately Swi6.

These are formal possibilities. However, the correlation between swi6 binding to siRNA-DNA hybrid and delocalization upon RNase H1 treatment argues for a more direct link.

(7) Examples of inaccurate presentation of the literature.

a) The authors state that "RNA binding by the murine HP1 through its hinge domains is required for heterochromatin assembly (Muchardt et al, 2002). The cited reference provides no evidence that HP1 RNA binding is required for heterochromatin assembly. Only the hinge region of bacterially produced HP1 contributes to its localization to DAPI-stained heterochromatic regions in fixed NIH 3T3 cells.

Noted. Statement will be corrected.

b) "... This scenario is consistent with the loss of heterochromatin recruitment of Swi6 as well as siRNA generation in rnai mutants (Volpe et al, 2002)." Volpe et al. did not examine changes in siRNA levels in swi6 mutant cells. In fact, no siRNA analysis of any kind was reported in Volpe et al., 2002.

Correct.  We only say that Swi6 recruitment is reduced in rnai mutants and correlate it with ability of SWi6 to bind to siRNA generated by RNAi and subsequently to siRNA-DNA hybrid.

Reviewer #2 (Public review):

The aim of this study is to investigate the role of Swi6 binding to RNA in heterochromatin assembly in fission yeast. Using in vitro protein-RNA binding assays (EMSA) they showed that Swi6/HP1 binds centromere-derived siRNA (identified by Reinhardt and Bartel in 2002) via the chromodomain and hinge domains. They demonstrate that this binding is regulated by a lysine triplet in the conserved region of the Swi6 hinge domain and that wild-type Swi6 favours binding to DNA-RNA hybrids and siRNA, which then facilitates, rather than competes with, binding to H3K9me2 and to a lesser extent H3K9me3.

However, the majority of the experiments are carried out in swi6 null cells overexpressing wild-type Swi6 or Swi63K-3A mutant from a very strong promoter (nmt1). Both swi6 null cells and overexpression of Swi6 are well known to exhibit phenotypes, some of which interfere with heterochromatin assembly. This is not made clear in the text.

We think that the argument is not valid as we show that swi6 but not Swi63K-3A could restore silencing at imr1::ura4, otr1::ade6 and his3-telo (Fig 3) and mating type (Fig. S10), when transformed into a swi6D strain.

Whilst the RNA binding experiments show that Swi6 can indeed bind RNA and that binding is decreased by Swi63K-3A mutation in vitro (confusingly, they only much later in the text explained that these 3 bands represent differential binding and that II is likely an isotherm). The gels showing these data are of poor quality and it is unclear which bands are used to calculate the Kd.

We disagree with the comment about the quality of EMSA data. We think it is of similar quality or better than that of Keller et al, except in some cases, like Fig 1D, a shorter exposure shown to distinguish the slowest shifted band has caused the remaining bands to look fainter.

RNA-seq data shows that overall fewer siRNAs are produced from regions of heterochromatin in the Swi63K-3A mutant so it is unsurprising that analysis of siRNA-associated motifs also shows lower enrichment (or indeed that they share some similarities, given that they originate from repeat regions).

Please see response to comment 2(d) of the first reviewer above.

It is not clear which bands are being alluded to. However, we‘ll rectify any gaps in information in the revision.

The experiments are seemingly linked yet fail to substantiate their overall conclusions. For instance, the authors show that the Swi63K-3A mutant displays reduced siRNA binding in vitro (Figure 1D) and that H3K9me2 levels at heterochromatin loci are reduced in vivo (Figure 3C-D). They conclude that Swi6 siRNA binding is important for Swi6 heterochromatin localization, whilst it remains entirely possible that heterochromatin integrity is impaired by the Swi63K-3A mutation and hence fewer siRNAs are produced and available to bind. Their interpretation of the data is really confusing.

Our argument is that the lack of binding by Swi63K>3A to siRNA can explain the loss of recruitment to heterochromatin loci and thus affect the integrity of heterochroamtin; the recruitment of Swi6 can occur possibly by binding initially to siRNA and thereafter as siRNA-DNA hybrid. However, the overall level of siRNAs is not affected, as in 2(D) above. This interpretation is supported by results of ChIP assay and confocal experiments, as also by the effect of RNaseH1 in the recruitment of Swi6.

The authors go on to show that Swi63K-3A cells have impaired silencing at all regions tested and the mutant protein itself has less association with regions of heterochromatin. They perform DNA-RNA hybrid IPs and show that Swi63K-3A cells which also overexpress RNAseH/rnh1 have reduced levels of dh DNA-RNA hybrids than wild-type Swi6 cells. They interpret this to mean that Swi6 binds and protects DNA-RNA hybrids, presumably to facilitate binding to H3K9me2. The final piece of data is an EMSA assay showing that "high-affinity binding of Swi6 to a dg-dh specific RNA/DNA hybrid facilitates the binding to Me2-K9-H3 rather than competing against it." This EMSA gel shown is of very poor quality, and this casts doubt on their overall conclusion.

We do agree with the reviewer about the quality of EMSA (Fig. 5B). However, as may be noticed in the EMSA for siRNA-DNA hybrid binding  (Fig 4A), the bands of Swi6-bound siRNA-DNA hybrid are extremely retarded. Hence the EMSA for subsequent binding by H3-K9-Me peptides required a longer electrophoretic run, which led to reduction in the sharpness of the bands. Nevertheless, the data does indicate binding efficiency in the order H3K9-Me2> H3-K9-Me3 > H3-K9-Me0. Having said that, we plan to repeat the EMSA or address the question by other methods, like SPR.

Unfortunately, the manuscript is generally poorly written and difficult to comprehend. The experimental setups and interpretations of the data are not fully explained, or, are explained in the wrong order leading to a lack of clarity. An example of this is the reasoning behind the use of the cid14 mutant which is not explained until the discussion of Figure 5C, but it is utilised at the outset in Figure 5A.

We tend to agree somewhat and will attempt to submit a revised version with greater clarity, as also the explanation of experiment with cid14D strain.

Another example of this lack of clarity/confusion is that the abstract states "Here we provide evidence in support of RNAi-independent recruitment of Swi6". Yet it then states "We show that...Swi6/HP1 displays a hierarchy of increasing binding affinity through its chromodomain to the siRNAs corresponding to specific dg-dh repeats, and even stronger binding to the cognate siRNA-DNA hybrids than to the siRNA precursors or general RNAs." RNAi is required to produce siRNAs, so their message is very unclear. Moreover, an entire section is titled "Heterochromatin recruitment of Swi6-HP1 depends on siRNA generation" so what is the author's message?

The reviewer has correctly pointed out the error. Indeed, our results actually indicate an RNAi-dependent rather than independent mode of recruitment. Rather, we would like to suggest an H3-K9-Me2-indpendnet recruitment of Swi6. We will rectify this error in our revised manuscript.

The data presented, whilst sound in some parts is generally overinterpreted and does not fully support the author's confusing conclusions. The authors essentially characterise an overexpressed Swi6 mutant protein with a few other experiments on the side, that do not entirely support their conclusions. They make the point several times that the KD for their binding experiments is far higher than that previously reported (Keller et al Mol Cell 2012) but unfortunately the data provided here are of an inferior quality and thus their conclusions are neither fully supported nor convincing.

We have used the method of Heffler et al (2012) to compute the Kd from EMSA data.

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Reply to the reviewers

Reviewer #1

Drawbacks: -While the population-specific approach is a strength, it also limits the direct applicability of findings to other populations.

We thank the Reviewer for highlighting this important question. While we acknowledge the mentioned limitation, we would like to emphasize the benefits of adopting a population-specific approach, especially given that human gut microbiome diversity remains underexplored in many populations worldwide. Researching the Estonian population microbiome, we contribute to the broader global collection of gut microbial species, helping to address this gap.

Moreover, new microbial species and strains identified in the Estonian population may be relevant for populations with similar environmental and lifestyle factors, such as the Finnish, Baltic, and Nordic populations. These findings can enhance understanding of regionally relevant microbiome characteristics and may serve as a useful reference for studies in these related populations. As more population-based microbiome research is published, it will build a valuable resource for cross-population comparative studies, shedding light on global microbiome diversity and its implications for health.

Lastly, as part of the Estonian Biobank, our primary objective is to advance personalized medicine for the Estonian population. This requires a highly accurate reference for our specific population. We believe our approach not only benefits Estonian healthcare but also provides insights and methodologies that other population biobanks may find valuable as they embark on similar paths toward personalized medicine.

-The study primarily focuses on taxonomic composition at the genus or species level, but a more in-depth functional analysis of the novel species could provide additional insights.

We thank the Reviewer for this valuable addition. Functional analysis plays a crucial role in understanding the mechanisms that link the microbiome to human health, making it an essential. This becomes even more critical when studying newly discovered species. However, before embarking on functional analysis, we believe it is important to emphasize that, while high-quality metagenome-assembled genomes (MAGs) provide valuable insights, they do not fully represent the genomic completeness and accuracy of genomes reconstructed from pure bacterial cultures. Acknowledging this distinction was one of the reasons we decided not to include functional analysis in the original article. With these considerations in mind, we research a strain structure of four known species of Butyricimonas genus. While the primary interest lies in species associated with diseases, this particular species lacks a substantial number of high-quality MAGs. To gain deeper insights, we prioritized including a new species within the analyzed genus to perform a comparative analysis between the new species and a well-defined strain of a known species, creating a more comprehensive understanding. Among the 758 different genera present in our MAG collection, we selected the Butyricimonas genus for the following reasons: (1) it is a well-described genus of gut bacteria, represented by 300 high-quality MAGs in our dataset (2) it contains four known species along with two newly identified species clusters, and (3) the newly discovered species were shown to be prevalent in the human gut microbiome, being detected in more than 50% of samples through mapping.

The following section was integrated in the new paragraph “Genome level analysis of species of interest” on page 6 in the revised version of the manuscript:

“Species-level association studies can help identify candidates for genome-level analysis by exploring strain structure and functional differences. However, such analyses require a large number of high-quality MAGs from the same species, which is only feasible within large cohorts with deep sequencing data. While we currently need more samples to obtain sufficient MAGs for the new disease-associated species, we perform an analysis with the Butyricimonas genus species as an example. We show that the assembled MAGs of Butyricimonas species such as B. faeciominis, B. virosa, B. paravirosa and B. faecalis make up different strains (Figure 4a, Figure 4b, Supplementary results, Supplementary Table S5). After selecting a strain representative, we conducted a pan-genome analysis of species and strain-representative MAGs, including the two new species. The analysis revealed unique gene clusters consistently present in the new species but absent in all other analyzed species and strains (Figure 4c, Supplementary results, Supplementary Table S6).

Figure 4. Strain-level structure of the Butyricimonas genus and comparative functional analysis of new species and known species strain. a. The strain structure of known Butyricimonas species assembled in the Estonian population - B. paravirosa, B. faecalis, B. virosa, and B. faecihominis (based on ANI index comparison). __b. __Butyricimonas genus structure. Comparisons include all known species from Butyricimonas genus (species assembled in Estonian population and publically available species) and all 4 newly assembled MAGs belonged to a new species. Publicly available Butyricimonas species - B. synergistica, "Candidatus B. faecavium", "Candidatus B. hominis", "Candidatus B. phoceensis", and "Candidatus B. vaginalis"—are each represented by a single genome of the type strain (the strain defining the species according to ISCP). Species assembled from our data are represented by both the type strain and all strain-representative MAGs. ANI values less than 95% (represent that MAGs belonged to different species) are not coloured, 95–100% ANI colored in different colors with 1% step. c. Pan-genome analysis of Butyricimonas genus. The analysis included the same genomes and MAGs as the analysis of the Butyricimonas genus structure and showed a core gene, as well as specific gene, set for the species. The two new species clusters (highlighted in green) also exhibit unique species-specific gene sets.

We have also added Supplementary Results to our paper, providing a more detailed description of the strain structure analysis of Butyricimonas species and the functional analysis of both known and new species. We chose not to include this in the main text to avoid shifting the focus of the paper.

Supplementary results

Butyricimonas genus species strain-level and functional analysis

Beyond taxonomic characterisation, it is crucial to understand the functional differences of newly detected species, as this insight is key to fully understanding the mechanisms that link the microbiome to human health. Reconstructing MAGs from a large cohort provides multiple genomes of the same species, particularly for prevalent species. During our study, we assembled MAGs from 758 different genera, including 358 genera with more than 10 extracted MAGs. Conducting a detailed in-depth strain-level and functional analysis of all these genera requires substantial effort. Therefore, we conduct an in-depth strain-level and functional analysis using the genus Butyricimonas as an example, because. The genus Butyricimonas was chosen for the following reasons: (1) it is a well-characterized genus of gut bacteria, represented by 300 high-quality MAGs in our dataset (2) it included four known species and two newly identified species clusters, and (3) the new discovered species have been shown to be prevalent in the human gut microbiome.

*Known Butyricimonas species exhibit a clear strain-level structure based on pairwise ANI comparisons (ANI > 99.0), as calculated using ANIclustermap19 (Figure 4a). From a total of 300 high-quality MAGs selected for strain and functional analysis within the Butyricimonas genus, the species Butyricimonas paravirosa is represented by 23 MAGs and forms 5 distinct strain clusters. While one big cluster (cluster_id: B30) includes 7 highly similar genomes with ANI values close to 100%, other clusters (B31, B32, B34) exhibit more genomic diversity, with genomes showing ANI values greater between 99.0% and 99.6%. The final cluster (B33) contains a single MAG, suggesting unique genomic variation. Butyricimonas faecihominis is represented by 65 MAGs and forms 8 distinct strain clusters, exhibiting high genome similarity within each cluster. Butyricimonas virosa is represented by 67 MAGs and forms 14 distinct strain clusters. These strain clusters can be divided into two strain cluster groups, with low similarity between the groups (ANI values between strain cluster groups ranging from 95.0% to 96% and approaching the species boundary). Within each group, the strain clusters also exhibit genomic diversity, indicating a substantial level of variation even within closely related strains. Finally, Butyricimonas faecalis has the highest number of MAGs within its species 141 MAGs and shows a clean picture of 5 strain clusters with high similarity within the strain cluster (Figure SR1). *

Figure SR1. The strain structure of known Butyricimonas species assembled in the Estonian population - B. paravirosa, B. faecalis, B. virosa, and B. faecihominis (ANI index comparison histogram).

In addition to the four known species, we assembled two new species within the Butyricimonas genus. The first new species cluster (id: Bn1) is represented by a single MAG (H0366_Butyricimonas_undS), which serves as the representative genome for this species. The second new species cluster (id: Bn2) comprises three MAGs, with H1068_Butyricimonas_undS designated as the representative genome, selected using dRep. To determine the placement of these new species within the genus, we conducted genome pairwise comparisons based on the Average Nucleotide Identity (ANI) index between the MAGs of the new species and other species within the Butyricimonas genus. For the known species identified in our population, we selected representative genomes for each strain. These comparisons were made between the all new species MAGs, strain-level representative MAGs of four known species, and type strain genomes (the strain that defines the species according to ISCP) from other species of the Butyricimonas genus that were not present in our cohort,, such as Butyricimonas synergistica, "Candidatus Butyricimonas faecavium", "Candidatus Butyricimonas hominis", "Candidatus Butyricimonas phoceensis", and "Candidatus Butyricimonas vaginalis" (Figure 4b). The MAGs from the second new species cluster (Bn2) form a distinct and cohesive group, showing a closer relationship to Butyricimonas paravirosa and Butyricimonas faecihominis. In contrast, the first new species (Bn1), represented by a single MAG, is positioned closer to Butyricimonas virosa. Interestingly, while the ANI index between the type strain of Butyricimonas virosa and the Bn1 MAG is less than 95%, certain strains of B. virosa (e.g., strains 3, 6, 7, 9, 10, and 12) show ANI values slightly above 95%, which technically classifies them as the same species.

To explore functional differences between new species clusters and other known species we perform pangenomic analysis using the analysis and visualization platform for ‘omics data (Anvi’o) workflow for microbial pangenomics20__. As the first new species cluster (id:Bn1) is represented by a single MAG, despite it containing unique genes not found in any other analyzed genomes, it is challenging to draw definitive conclusions. Another new species cluster (id:Bn2) consisting of three MAGs provides clearer insights. All three MAGs within this new species cluster share 183 unique genes that are consistently present across the species cluster but absent in all other analyzed species and strains. (Figure 4c). The majority of these genes (142 genes, 73.96%) have unknown functions. Among the genes with defined functions, the functions are distributed across various COG categories (__Suppl. Table S5,____Suppl. Figure SR2), with the top three categories being “Cell wall/membrane/envelope biogenesis”, “General function prediction only”, and “Posttranslational modification, protein turnover, and chaperones”.

Figure SR2. COG categories for 183 unique genes that are consistently present across the new species MAGs from Butyricimonas genus (cluster id:Bn2) but absent in all other analyzed species and strains.

Undoubtedly, further research is needed to understand the role of newly identified species in the human microbiome and to determine whether strain-level differences influence bacterial interactions with the gut and their overall impact. However, our current analysis has already significantly expanded our knowledge of the diversity within this genus. It has added two new species to the ten previously described and revealed the strain structure of known species within the Estonian population.

-Is it possible for this large dataset to distill information and have plots for strain diversity of abundant and prevalent species, including low abundance species per donor or between donors? Can authors add such a plot or discuss this?

We thank the Reviewer for this insightful question. Strain-level analysis holds significant potential and is one of the key reasons to use the genome assembly approach, rather than relying on microbiome community profiling using existing human gut species databases. To demonstrate how this can be applied in large datasets like ours, we focused on the same Butyricimonas genus selected for functional analysis. We believe that combining both strain-level and functional analyses provides a more comprehensive understanding when used together.

The following section has been incorporated into a new paragraph, “Genome-Level Analysis of Species of Interest,” on page 6 of the revised manuscript, and in-depth analysis has been included in the Supplementary Results. As this section has already been cited in a previous response (due to its logical connection with the functional analysis of the new species), we will not cite it again here. Please refer to the previous answer for further details.

-While associations between microbes and diseases were found, the study design cannot establish causal relationships. Are the authors planning to test some of the associations experimentally and see whether these observations work in vitro or in vivo?

We agree that elaboration of causal relationships is crucial. However, this was beyond the scope of the current study, which is intended as a foundational step for future investigations. However, the samples are stored in the Estonian Biobank in a way that allows culturomic studies and follow-up experiments as done by Krigul et al [1].

Krigul KL, Feeney RH, Wongkuna S, Aasmets O, Holmberg SM, Andreson R, Puértolas-Balint F, Pantiukh K, Sootak L, Org T, Tenson T, Org E, Schroeder BO. A history of repeated antibiotic usage leads to microbiota-dependent mucus defects. Gut Microbes. 2024 Jan-Dec;16(1):2377570. doi: 10.1080/19490976.2024.2377570.

Minor comments:

• The authors could provide more context on how their findings compare to similar studies in other populations. What are the differences and similarities, and how does this work at the next level and set new directions?

We thank the Reviewer for this suggestion. We provided a summary of other population cohorts in the Introduction (Lines 79–90). Since MAG recovery from large cohorts is a relatively new approach, there are limited opportunities for direct comparison. However, we did note a decreasing number of newly recovered species in our study compared to previous studies (Lines 274–290).

• Figures' quality and readability can be improved easily; all of them are low resolution, and the axes are hardly visible, particularly Figure 2, which could benefit from additional labeling or explanations in the legend to improve clarity.

We apologize for the quality issues with the figures. We completely revised Figure 2 to improve clarity and placed a new higher-resolution version of Figure 2 to improve readability, ensuring that axes and details are clearly visible.

Summary of performed changes: (1) we introduced a new Figure 2a to showcase the phylogenetic diversity of the recovered species and highlight the position of the newly assembled species identified for the first time in this study (2) We have updated Figure 2b. In the initial figure, a single line was presented. However, to enhance the visualization and emphasize the trend, five lines were subsequently plotted by altering the order of the samples. Since the order of the samples is not significant, this modification allows for a clearer representation of the overall trend of accumulation of the new species (3) we added new Figure 2c, to address the question about the range of diversity of detected species (4) we moved Figure 2a and 2d to Supplementary Figures to enhance clarity and relevance (Figure S4 and Figure S6 respectively).

“Figure 2. Overview of species from the EstMB MAG collection a. Phylogenetic tree of the Estonian species representative MAGs. The inner circle displays a phylogenetic tree of species cluster representative MAGs, with branches colored according to their assigned phylum in the Genome Taxonomy Database (GTDB) (see color text). The surrounding ring highlights MAGs that represent novel species assembled in the current study, using the same colors as in the inner circle to indicate the phylum to which each new species belongs (see color text). b. The relationship between the number of samples analyzed and the cumulative number of new species identified c. Distribution of number of species detected by mapping per sample “species hits” (yellow color violinplot) and number of recovered MAGs per sample (blue color violinplot) from Estonian representative MAGs number. d. Number of recovered species (blue color dots) and species detected by mapping the reads against the EstMB MAG collection (yellow color dots) for each sample. Samples are sorted from those with the highest to the lowest number of recovered MAGs e. __The prevalence and number of recovered MAGs per species. The top 10 species with the highest number of recovered MAGs are shown. Blue bars represent the number of samples where MAG of the species were recovered, while gray bars show the species prevalence in EstMB __f. The prevalence and number of recovered MAGs per new species. The top 10 new species with the highest number of recovered MAGs are shown. Green bars represent the number of samples where MAG of the new species were recovered, while gray bars show the new species prevalence.”

-A brief discussion on the potential clinical implications of the new species-disease associations would enhance the relevance. Why discovering new species are in testing and relevant for the microbiome field? Can authors add this somewhere, discussion?

We thank the Reviewer for this suggestion. As such, the following section was integrated in the Discussion on page 8 in the revised version of the manuscript:

“Reconstruction of a new species and new strain is critical for many aspects of personal medicine. We can identify three primary applications of the microbiome in personalized medicine: disease risk assessment and prevention, disease diagnosis, and disease treatment. The latter includes approaches such as microbial supplementation, suppression, or metabolite modulation [Karina Ratiner, 2024]. Both disease prevention and diagnosis rely on identifying bacterial biomarkers associated with prevalent or incident disease cases. In our study, an average of 4% of reads belonged to the newly identified species, with a maximum of 34.76%, demonstrating that excluding this species would lead to a significant loss of community diversity. This omission could potentially exclude biomarkers critical for disease prediction and diagnosis. Notably, one-third of the associations between bacterial species and diseases in our analysis involved the newly identified species, further emphasizing its potential importance as a biomarker. For disease treatment, it is crucial to understand the complete microbial diversity to distinguish between beneficial and harmful species. Equally important is knowing the genomic structure of species and strains to develop effective strategies for microbiome modulation. Without genome assembly, we are limited to assumptions based on previously described genomes of related bacteria. However, given the substantial genomic diversity within species, such assumptions may be highly inaccurate, underscoring the importance of genome assembly in advancing microbiome-based interventions.”

• In lines 265-266, the authors discuss detected species per sample, on average, 389 species. Can the authors guide which plot is linked to it and whether it is possible to show the disturbing median number of species per sample to get an overall idea about the range of diversity this type of analysis can capture now? Maybe this will improve in the future; it is worth mentioning here.

We thank the Reviewer for highlighting the need for the clarification. Original Figure 2c displayed the number of species detected through mapping (species hits) and the number of assembled MAGs for each individual sample. To provide a broader characterization of the distribution, we calculated the minimum, mean, median, and maximum values across all samples. As such, the __new Figure 2c __and the following section was integrated in the paragraph “Estimation of species prevalence using population-specific reference” on page 5 in the revised version of the manuscript:

“Distribution of the number of species detected by mapping per sample exhibits a wide range of values, with a maximum of 842 and a minimum of 7, while the mean and median are 399 and 405, respectively. The distribution of numbers of recovered MAGs per sample shows a narrower range, with a maximum of 155 and a minimum of 1, alongside a mean of 45 and a median of 41 (Figure 2c).”

Figure 2c.* Distribution of number of species detected by mapping per sample “species hits” (yellow color violinplot) and number of recovered MAGs per sample (blue color violinplot). *

Other comments:

-The key conclusions are generally convincing. The authors have successfully assembled a large number of MAGs from the Estonian population, identified potentially novel species, and established associations between microbial abundance and diseases.

We appreciate the Reviewer's positive feedback on our findings. We are pleased that the significance of our MAG assembly, novel species identification, and disease associations is well-received.

-The data presented appear to support the claims well. However, the authors should emphasize and clarify that the disease associations are correlational, not causal, and further validation is required.

We agree that this is an important point to emphasize. We revised the manuscript to clarify that the disease associations are correlational and emphasize the need for further validation by adding the following section in Discussion on page 8 in the revised version of the manuscript:

“While association does not imply causation, analyzing the association between bacterial species and diseases is a crucial first step in identifying potential biomarkers. This can be followed by meta-analyses across different cohorts and laboratory experiments to validate and confirm the observed effects.”

-Even though I am not an expert in metagenomics analysis, the current experimental design and analysis are sound to support the main claims.

We thank the Reviewer for recognizing the robustness of our experimental design and analysis.

-The methods section can be improved by providing more details about how samples were collected and stored and how long after storage gDNA was extracted and processed for sequencing, allowing for reproducibility. The authors provide information on the bioinformatics pipelines, including software versions and parameters, but this can again be improved by adding details about the steps between sample processing and raw data processing.

We thank the Reviewer for this suggestion and we agree that this is important information. All these details were thoroughly described in our previous paper, which focuses on our cohort description (Aasmets, O., Krigul, K.L., Lüll, K., Metspalu, A., and Org, E. (2022). Gut metagenome associations with extensive digital health data in a volunteer-based Estonian microbiome cohort. Nat. Commun. 13, 869.

https://doi.org/10.1038/s41467-022-28464-9).

However, to improve accessibility of this information, the following paragraph was integrated in the Methods on page 17 in the revised version of the manuscript:

“Microbiome sample collection and DNA extraction

The participants collected a fresh stool sample immediately after defecation with a sterile Pasteur pipette and placed it inside a polypropylene conical 15 mL tube. The participants were instructed to time their sample collection as close as possible to the visiting time in the study centre The samples were stored at −80 °C until DNA extraction. The median time between sampling and arrival at the freezer in the core facility was 3 h 25 min (mean 4 h 34 min) and the transport time wasn’t significantly associated with alpha (Spearman correlation, p-value 0.949 for observed richness and 0.464 for Shannon index) nor beta diversity (p-value 0.061, R-squared 0.0005). Microbial DNA extraction was performed after all samples were collected using a QIAamp DNA Stool Mini Kit (Qiagen, Germany). For the extraction, approximately 200 mg of stool was used as a starting material for the DNA extraction kit, according to the manufacturer’s instructions. DNA was quantified from all samples using a Qubit 2.0 Fluorometer with a dsDNA Assay Kit (Thermo Fisher Scientific).”

-The study includes a large cohort (1,878 samples), which provides statistical power. The statistical analyses, including linear regression models adjusted for BMI, gender, and age, seem appropriate for the type of data presented. I suggest adding a separate paragraph about how the data is processed and statistically analyzed.

Authors should include:

• Appropriateness of the statistical tests used for the data types and experimental designs

• Adequate description and justification of the statistical models and test and assumptions

• Proper handling of replicates, controls, and data normalization

• Reporting of effect sizes, sample size, confidence intervals, and statistical power

• Data processing and analysis workflows.

We thank the Reviewer for this recommendation. To highlight the statistical analysis carried out, we have made a separate paragraph for statistical analysis under the Methods section (lines 617-628). We note that we have previously described data processing and normalization. This study has an exploratory nature. Hence, the power calculations are not applicable, but this study can be an input for the power calculations of future studies testing statistical hypotheses. However, we agree that the sample sizes for each phenotype and beta estimation would support our results. We have now added them to __Table 1_. _ __

Reviewer #1 (Significance (Required)):

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-This study represents an advance in the context of population-specific studies. Creating a comprehensive Estonian population-specific MAG reference and identifying new species contribute to our understanding of microbiome diversity.

-The work builds upon previous large-scale microbiome projects, such as those that established the Unified Human Gastrointestinal Genome (UHGG) collection but focuses on a specific population.

-The associations between microbial species (including novel ones) and common diseases provide potential avenues for future research into microbiome-based diagnostics or therapeutics.

-The findings would interest microbiome researchers, bioinformaticians, and clinicians interested in the role of the gut microbiome in health and disease.

We thank the Reviewer for the thoughtful feedback and recognition of our study's contributions to microbiome research. By creating an Estonian population-specific MAG reference and identifying new species, we advance population-specific studies and enhance global microbiome diversity. Building on projects like UHGG, we integrate local data into the global context and highlight potential applications in microbiome-based diagnostics and therapeutics. To address your suggestions, we expanded the results section with an example from the Butyricimonas genus. We hope our publicly available data will support future research and further advance understanding of the gut microbiome in health and disease.

__ Reviewer #2 (Evidence, reproducibility and clarity (Required)):__

The manuscript by Pantiukh et al. presents the collection of MAGs assembled from the Estonian Biobank, with a specific focus on the novel species clusters the authors defined and found associations with some of the diseases as collected among the samples available in their biobank. The manuscript is well organized. However, it lacks a bit in terms of novelty and also some statements that can mislead the readers to overinterpret some parts.

Majors

• The last paragraph of the introduction (lines 91-98) anticipates some results but lacks some methodological details. Please consider whether to move it to the results section or add very brief specifications, like (1) "sequence with deep coverage" is vague, how deep is deep? (2) "84,762 MAGs representing 2,257 species" are the 84k MAGs already quality-controlled? (3) "353 MAGs (15,6%) of the EstMB MAGs collection to represent potentially novel species." 353 are MAGs or species? As species clusters are defined later at 95% ANI, are all these 353 defining their own species clusters?

We thank the Reviewer for insightful questions and suggestions. To address these points, we have added the following clarifications to the text:

We specified the depth of coverage for sequences, providing an average reads number per sample - 56 mln reads. (Lines 92). We clarified that among 84,762 assembled MAGs, 42,049 MAGs (49.60 %) were high-quality (HQ) MAGs. (Lines 93-94). We revised the statement about the 353 MAGs, explicitly noting that they represent potentially novel species. Additionally, we clarified that all 2,257 representative MAGs, including these 353 new species MAGs represent separate species clusters based on the 95% ANI threshold mentioned later in the text. (Lines 94-98).

In the paper, we included only the figure showing the quality group distribution for species cluster representative MAGs to avoid potential confusion between two similar figures: one for all assembled MAGs (n=84,762) and another for cluster representative MAGs (n=2,257). However, in response to this query, we have added a new __Supplementary Figure S1__that illustrates the quality group distribution for all assembled MAGs to provide a more comprehensive view.

Figure S1. Quality estimation for the assembled MAGs (n=84,762). High-quality MAGs (HQ) – 42,049; Medium-quality MAGs (MQ) – 26,806; Low-quality MAGs (LQ) – 15,907.

• lines 109 and 265, "11.73 +/- 3.9 Gb data per sample and 56.13 +/- 19.37 million reads per sample", numbers don't match... 11.73 Gbp is about 78M reads at 150nt read length, plus later the average depth is not 56.13 but 53.04, please double check these numbers

We apologize for any misunderstanding. The numbers mentioned in the paper refer to the number of reads and the file size of each compressed *.fasta.gz file. This file size does not directly represent the total base pairs (Gb) for the current metagenome. Instead, it reflects the disk space occupied by the compressed sequencing data, including additional information such as sequence headers. We selected this parameter to provide an easy point of comparison with file sizes from other metagenome sequencing datasets, as *.fasta.gz is a commonly used format for storing sequence data. To clarify further, here is an example of the relationship between these parameters for one sample:

Sample XX

Value

Meaning

Program

Compressed file size

4.2 GB

Represents disk space occupied by the compressed sequencing data. This applies to forward reads only; for a rough estimation of the disk space for both forward and reverse reads, it should be multiplied by 2 or calculated separately for both files.

du -sh V00HXZ.fq1.gz

The total number of reads

41,062,933 reads

(avg. read len = 147.7 bp)

Represents number of forward reads. This applies to forward reads only; for a rough estimation of both forward and reverse reads, it should be multiplied by 2 or calculated separately for both files.

seqkit stats V00HXZ.fq1.gz -a -T

Total base pairs (Gb)

6,066,493,002 bp (6.07 Gb)

Represents total base pairs (Gb) for the current sample. This applies to forward reads only; for a rough estimation of both forward and reverse reads, it should be multiplied by 2 or calculated separately for both files.

seqkit stats V00HXZ.fq1.gz -a -T

We now realize this may have caused confusion. To address this, we have calculated the total base pairs (Gb) parameter for both forward and reverse reads and exchanged the __Compressed file size __number to __Total base pairs__with following section in the paragraph “Cohort overview and study design” on page 3 in the revised version of the manuscript:

“The EstMB-deep samples were resequenced at deep coverage, generating an average of 16.49 ± 6.2 Gb of total base pairs per sample, or 56.13 ± 19.37 million paired reads per sample, with an average forward read length of 146.85 bp and an average reverse read length of 147.01 bp.”

• line 118, "completeness > 90% and contamination We thank Reviewer for this comment, we use CheckM v2 for evaluation MAG completeness and contamination. We have incorporated the requested information into the manuscript. (Lines 128).

• line 120, "84,762 MAGs were clustered at the species level with an average nucleotide identity (ANI) threshold of 95%.", as for my previous comment, either specify the Methods or quickly mention the tool used for the ANI analysis.

We use dRep with default parameters for clustering. We have incorporated the requested information into the manuscript. (Lines 130).

• lines 135-138, "The bacterial species most represented in our MAGs collection were Odoribacter splanchnicus (MAG recovered from 70.93% samples), Barnesiella intestinihominis (62.83%), Parabacteroides distasonis (60,38%), Alistipes putredinis (54,53%) and Agathobacter rectalis (51.92%) (Figure S2, Table S2).", it will be interesting to compare (some of) these speceis with other populations, to see if these species are globally prevalent in the human gut microbiome or specific to the Estonian population.

We thank the Reviewer for this question. As highlighted in Figures 4e and 2d, the number of MAGs recovered for a given species often differs significantly from its prevalence in the population. Due to the complexities of MAG assembly, species prevalence is generally much higher, and these values do not correlate linearly, as shown in Supplementary Figure S5. Keeping in mind that species with the higher number of assembled MAGs are not the same as species with the higher prevalence, we compared our top assembled species with the most comprehensive up to date USGG collection of gut bacteria and integrated the following section in the paragraph “Population-specific Metagenome-Assembled Genomes (MAGs) reference” on page 4 in the revised version of the manuscript:

“... All these species are also well-represented in other cohorts. For example, Parabacteroides distasonis, Alistipes putredinis, and Agathobacter rectalis rank among the top 6 species in the USGG by the number of genomes. Additionally, Barnesiella intestinihominis and Odoribacter splanchnicus rank among the top 40 species out of a total of 4,644 species in the USGG database.”

• lines 143-144, "MAGs, 353 MAGs (15,64%) represent a new species according to the GTDB criteria.", these 353 MAGs might define fewer species clusters, I think the 'species' word in this sentence is misleading and can lead to an overinterpretation of the diversity, it will be more correct to report how many species clusters these MAGs defined.

We apologize for not providing sufficient clarification. In our case each cluster represented a new distinct species. We added clarification in lines 152-153.

• lines 163-168, the paragraph could be an overinterpretation, as it is unlikely that there is 'infinite' diversity, so it could be that by doubling the samples, there is already a plateau in terms of novel species clusters identified. I think this paragraph should be reconsidered.

We thank the Reviewer for this question. We have updated Figure 2b. Instead of presenting a single version of the cumulative sum of new species discoveries, we reordered the samples five times to provide a more accurate approximation of new species accumulation as the number of samples increases. Additionally, we integrated the following section in the paragraph “Novel species and comparison of the population-specific reference with global reference UHGG” on page 4 in the revised version of the manuscript:

“Our analysis so far shows a clear linear trend without indication of a plateau (although we can not exclude that plateau had been reached exactly at current sample size, which may not yet be evident).”

__Figure 4b. __The relationship between the number of samples analyzed and the cumulative number of new species identified.

• lines 182-184, "Even species which have been recovered from a large number of samples can be found in significantly more samples after mapping (Figure 2e, Table S2).", this is not novel as assembly requires higher coverage than calling a species present via mapping, please, rephrase this part.

We thank the Reviewer for this thoughtful suggestion. We included this point in the article not because of its novelty but to emphasize that even a small number of recovered MAGs per sample can still hold significant value. This is because despite a small number of assembled genomes, the same species prevalence, as detected through mapping, can still be substantial which makes it possible to use them for, for example, association study. We added this perspective based on our personal experience of initial disappointment with the small number of MAGs recovered for many new species clusters. Our intention is to prevent similar discouragement among other researchers who may begin recovering MAGs from their large population cohorts.

• lines 185-188, "which are usually extracted from a small number of samples, 185 show a prevalence exceeding 80% for some species. For example, Bacteroides faecalis has a prevalence of 97.23%, although only 1 MAG was assembled, and Bacteroides intestinigallinarum has a prevalence of 95.85% although only 2 MAGs were assembled.", this should be much better contextualized and discussed in terms of relative abundance and not only on the ability to reconstruct (which is highly impacted by coverage, which is a proxy for abundance) with its prevalence, it is known in the field that there are very highly prevalent species at very low abundance values, which are not that often reconstructed via metagenomic assembly.

We agree that understanding the causes of assembly complications is important in the field, with abundance playing a key role. Moreover, other factors such as the presence of closely related species with similar genomes or multiple strains of the same species within a sample can significantly impact assembly, even for species with high abundance. However, since this paper focuses on the potential applications of MAG assembly in large population cohorts rather than the technical aspects of assembly, our main goal was to emphasize that MAGs assembled from the samples should not be used to estimate species prevalence.

• Data availability, it appears that the provided accession number does not exist, please double-check this.

We apologies about that issue, data now available with provided accession number PRJEB76860:

Minors

• line 106, "includes 1,308 women (69.64 %) and 570 men (30.35 %)", these sums up to 99.99%, the ratio for women is 1308/1878=0.69648, so can be rounded up to 69.65%.

We thank the Reviewer for this correction. We correct numbers from 69.64% to 69.65% (Lines 114).

• line 293, "ones[Philip Hugenholtz, 2008].", citation to fix.

Thank you for the correction. We corrected the links. (Lines 414).

• Fig. 1g, why completeness is up to 25%, from the text it seemed the MAGs were screened for completeness We apologize for not providing sufficient clarification. Indeed, as noted in Lines 124-126, *"We successfully reconstructed 84,762 metagenome-assembled genomes (MAGs), an average of 45 MAGs per sample. Among these, 42,048 according to CheckM, MAGs (49.6%) have completeness > 90% and contamination 90% and contamination 50% and contamination (Lines 131-132).

• Fig. 2f says "Blue bars represent", but I believe it should be green instead of blue.

Thank you for the correction. We corrected the color.

(Lines 520).

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

Summary:

This paper explores how diverse forms of inhibition impact firing rates in models for cortical circuits. In particular, the paper studies how the network operating point affects the balance of direct inhibition from SOM inhibitory neurons to pyramidal cells, and disinhibition from SOM inhibitory input to PV inhibitory neurons. This is an important issue as these two inhibitory pathways have largely been studies in isolation. Support for the main conclusions is generally solid, but could be strengthened by additional analyses.

Strengths:

A major strength of the paper is the systematic exploration of how circuit architecture effects the impact of inhibition. This includes scans across parameter space to determine how firing rates and stability depend on effective connectivity. This is done through linearization of the circuit about an effective operating point, and then the study of how perturbations in input effect this linear approximation.

Weaknesses:

The linearization approach means that the conclusions of the paper are valid only on the linear regime of network behavior. The paper would be substantially strengthened with a test of whether the conclusions from the linearized circuit hold over a large range of network activity. Is it possible to simulate the full network and do some targeted tests of the conclusions from linearization? Those tests could be guided by the linearization to focus on specific parameter ranges of interest.

We agree with the reviewer that it would be interesting to test if our results hold in a nonlinear regime of network behaviour (i.e. the chaotic regime, see also comment 1 by reviewer 2). As mentioned above, this requires a different type of model (either rate-based or spiking model with multiple neurons instead of modelling the mean population rate dynamics) which, in our opinion, exceeds the scope of this manuscript. Furthermore, the core measures of our study, network gain, and stability require linearization. In a chaotic regime where the linearization approach is impossible, we would need to consider/define new measures to characterize network response/activity. Therefore, while certainly being an interesting question to study, the broad scope of the studying networks in a nonlinear regime is better tackled in a separate study. We now acknowledge in the discussion of our manuscript that the linearization approach is a limitation in our study and that it would be an interesting future direction to investigate chaotic dynamics.

The results illustrated in the figures are generally well described but there is very little intuition provided for them. Are there simplified examples or explanations that could be given to help the results make sense? Here are some places such intuition would be particularly helpful:

page 6, paragraph starting ”In sum ...”

Page 8, last paragraph

Page 10, paragraph starting ”In summary ...”

Page 11, sentence starting ”In sum ...”

We agree with the reviewer that we didn’t provide enough intuition to our results. We now extended the paragraphs listed by the reviewer with additional information, providing a more intuitive understanding of the results presented in the respective chapter.

Reviewer #2 (Public Review):

Summary:

Bos and colleagues address the important question of how two major inhibitory interneuron classes in the neocortex differentially affect cortical dynamics. They address this question by studying Wilson-Cowan-type mathematical models. Using a linearized fixed point approach, they provide convincing evidence that the existence of multiple interneuron classes can explain the counterintuitive finding that inhibitory modulation can increase the gain of the excitatory cell population while also increasing the stability of the circuit’s state to minor perturbations. This effect depends on the connection strengths within their circuit model, providing valuable guidance as to when and why it arises.

Overall, I find this study to have substantial merit. I have some suggestions on how to improve the clarity and completeness of the paper.

Strengths:

(1) The thorough investigation of how changes in the connectivity structure affect the gain-stability relationship is a major strength of this work. It provides an opportunity to understand when and why gain and stability will or will not both increase together. It also provides a nice bridge to the experimental literature, where different gain-stability relationships are reported from different studies.

(2) The simplified and abstracted mathematical model has the benefit of facilitating our understanding of this puzzling phenomenon. (I have some suggestions for how the authors could push this understanding further.) It is not easy to find the right balance between biologically detailed models vs simple but mathematically tractable ones, and I think the authors struck an excellent balance in this study.

Weaknesses:

(1) The fixed-point analysis has potentially substantial limitations for understanding cortical computations away from the steady-state. I think the authors should have emphasized this limitation more strongly and possibly included some additional analyses to show that their conclusions extend to the chaotic dynamical regimes in which cortical circuits often live.

We agree with the reviewer that it would be interesting to test if our results hold in a chaotic regime of network behaviour (see also comment by reviewer 1). As mentioned above, this requires a different type of model (either rate-based or spiking model with multiple neurons instead of modelling the mean population rate dynamics) which, in our opinion, exceeds the scope of this manuscript. Furthermore, the core measures of our study, network gain, and stability require linearization. In a chaotic regime where the linearization approach is impossible, we would need to consider/define new measures to characterize network response/activity. Therefore, while certainly being an interesting question to study, the broad scope of the studying networks in a nonlinear regime is better tackled in a separate study. We now acknowledge in the discussion of our manuscript that the linearization approach is a limitation in our study and that it would be an interesting future direction to investigate chaotic dynamics.

(2) The authors could have discussed – even somewhat speculatively – how SST interneurons fit into this picture. Their absence from this modelling framework stands out as a missed opportunity.

We believe that the reviewer wanted us to speculate about VIP interneurons (and not SST interneurons, which we already do extensively in the manuscript). Previous models have included VIP neurons in the circuit (e.g. del Molino et al., 2017; Palmigiano et al., 2023; Waitzmann et al., 2024). While we do not model VIP cells explicitly, we implicitly assume that a possible source of modulation of SOM neurons comes from VIP cells. We have now added a short discussion on VIP cells in the last paragraph in our discussion section.

(3) The analysis is limited to paths within this simple E,PV,SOM circuit. This misses more extended paths (like thalamocortical loops) that involve interactions between multiple brain areas. Including those paths in the expansion in Eqs. 11-14 (Fig. 1C) may be an important consideration.

We agree with the reviewer that our framework can be extended to study many other different paths, like thalamocortical loops, cortical layer-specific connectivity motifs, or circuits with VIP or L1 inhibitory neurons. Studying these questions, however, are beyond the scope of our work. In our discussion, we now mention the possibility of using our framework to study those questions.

Reviewer #3 (Public Review):

Summary:

Bos et al study a computational model of cortical circuits with excitatory (E) and two subtypes of inhibition parvalbumin (PV) and somatostatin (SOM) expressing interneurons. They perform stability and gain analysis of simplified models with nonlinear transfer functions when SOM neurons are perturbed. Their analysis suggests that in a specific setup of connectivity, instability and gain can be untangled, such that SOM modulation leads to both increases in stability and gain. This is in contrast with the typical direction in neuronal networks where increased gain results in decreased stability.

Strengths:

- Analysis of the canonical circuit in response to SOM perturbations. Through numerical simulations and mathematical analysis, the authors have provided a rather comprehensive picture of how SOM modulation may affect response changes.

- Shedding light on two opposing circuit motifs involved in the canonical E-PV-SOM circuitry - namely, direct inhibition (SOM → E) vs disinhibition (SOM → PV → E). These two pathways can lead to opposing effects, and it is often difficult to predict which one results from modulating SOM neurons. In simplified circuits, the authors show how these two motifs can emerge and depend on parameters like connection weights.

- Suggesting potentially interesting consequences for cortical computation. The authors suggest that certain regimes of connectivity may lead to untangling of stability and gain, such that increases in network gain are not compromised by decreasing stability. They also link SOM modulation in different connectivity regimes to versatile computations in visual processing in simple models.

Weaknesses:

The computational analysis is not novel per se, and the link to biology is not direct/clear.

Computationally, the analysis is solid, but it’s very similar to previous studies (del Molino et al, 2017). Many studies in the past few years have done the perturbation analysis of a similar circuitry with or without nonlinear transfer functions (some of them listed in the references). This study applies the same framework to SOM perturbations, which is a useful and interesting computational exercise, in view of the complexity of the high-dimensional parameter space. But the mathematical framework is not novel per se, undermining the claim of providing a new framework (or ”circuit theory”).

In the introduction we acknowledge that our analysis method is not novel but is rather based on previous studies (del Molino et al., 2017; Kuchibhotla et al., 2017; Kumar et al., 2023, Litwin-Kumar et al., 2016; Mahrach et al., 2020; Palmigiano et al., 2023; Veit et al., 2023; Waitzmann et al., 2024). We now rewrote parts of the introduction to make sure that it does not sound like the computational analysis has been developed by us, but that we rather use those previously developed frameworks to dissect stability and gain via SOM modulation.

Link to biology: the most interesting result of the paper with regard to biology is the suggestion of a regime in which gain and stability can be modulated in an unconventional way - however, it is difficult to link the results to biological networks: - A general weakness of the paper is a lack of direct comparison to biological parameters or experiments. How different experiments can be reconciled by the results obtained here, and what new circuit mechanisms can be revealed? In its current form, the paper reads as a general suggestion that different combinations of gain modulation and stability can be achieved in a circuit model equipped with many parameters (12 parameters). This is potentially interesting but not surprising, given the high dimensional space of possible dynamical properties. A more interesting result would have been to relate this to biology, by providing reasoning why it might be relevant to certain circuits (and not others), or to provide some predictions or postdictions, which are currently missing in the manuscript.

- For instance, a nice motivation for the paper at the beginning of the Results section is the different results of SOM modulation in different experiments - especially between L23 (inhibition) and L4 (disinhibition). But no further explanation is provided for why such a difference should exist, in view of their results and the insights obtained from their suggested circuit mechanisms. How the parameters identified for the two regimes correspond to different properties of different layers?

As pointed out by the reviewer, the main goal of our manuscript is to provide a general understanding of how gain and stability depend on different circuit motifs (ie different connectivity parameters), and how circuit modulations via SOM neurons affect those measures. However, we agree with the reviewer that it would be useful to provide some concrete predictions or postdictions following from our study.

An interesting example of a postdiction of our model is that the firing rate change of excitatory neurons in response to a change in the stimulus (which we define as network gain, Eq. 2) depends on firing rates of the excitatory, PV, and SOM neurons at the moment of stimulus presentation (Fig. 3ii; Fig. 4Aii,Bii,Cii; Fig. 5Aii, Bii, Cii). Hence any change in input to the circuit can affect the response gain to a stimulus presentation, in line with experimental evidence which suggests that changes in inhibitory firing rates and changes in the behavioral state of the animal lead to gain modifications (Ferguson and Cardin 2020).

Another recent concrete example is the study of Tobin et al., 2023, in which the authors show that optogenetically activating SOM cells in the mouse primary auditory cortex (A1) decreases the excitatory responses to auditory stimuli. In our framework, this corresponds to the case of decreases in network gain (gE) for positive SOM modulation, as seen in the circuit with PV to SOM feedback connectivity (Suppl. Fig. S1).

Another example is the study by Phillips and Hasenstaub 2016, in which the authors study the effect of optogenetic perturbations of SOM (and PV) cells on tuning curves of pyramidal cells in mouse A1. While they find large heterogeneity in additive/subtractive or multiplicative/divisive tuning curve changes following SOM inactivation, most cells have a purely multiplicative or purely additive component (and none of the cells have a divisive component). In our study, we see that large multiplicative responses of the excitatory population follow from circuits with strong E to SOM feedback connectivity.

We note that in future computational studies, it would be useful to apply our framework with a focus on a specific brain region and add all relevant cell types (at a minimum E, PV, SOM, and VIP) plus a dendritic compartment, in order to formulate much more precise experimental predictions.

We have now added additional information to the discussion section.

- Another caveat is the range of parameters needed to obtain the unintuitive untangling as a result of SOM modulation. From Figure 4, it appears that the ”interesting” regime (with increases in both gain and stability) is only feasible for a very narrow range of SOM firing rates (before 3 Hz). This can be a problem for the computational models if the sweet spot is a very narrow region (this analysis is by the way missing, so making it difficult to know how robust the result is in terms of parameter regions). In terms of biology, it is difficult to reconcile this with the realistic firing rates in the cortex: in the mouse cortex, for instance, we know that SOM neurons can be quite active (comparable to E neurons), especially in response to stimuli. It is therefore not clear if we should expect this mechanism to be a relevant one for cortical activity regimes.

We agree with the reviewer that it’s important to test the robustness of our results. As suggested by the reviewer, we now include a new supplementary figure (Suppl. Fig. S2) which measures the percentage of data points in the respective quadrant Q1-Q4 when changing the SOM firing rates (as done in Fig. 5). We see that the quadrants in which the network gain and stability change in the same direction (Q2 and Q3) remain high in the case for E to SOM feedback (Suppl. Fig. S2A) over SOM rates ranging over 0-10 Hz (and likely beyond).

- One of the key assumptions of the model is nonlinear transfer functions for all neuron types. In terms of modelling and computational analysis, a thorough analysis of how and when this is necessary is missing (an analysis similar to what has been attempted at in Figure 6 for synaptic weights, but for cellular gains). In terms of biology, the nonlinear transfer function has experimentally been reported for excitatory neurons, so it’s not clear to what extent this may hold for different inhibitory subtypes. A discussion of this, along with the former analysis to know which nonlinearities would be necessary for the results, is needed, but currently missing from the study. The nonlinearity is assumed for all subtypes because it seems to be needed to obtain the results, but it’s not clear how the model would behave in the presence or absence of them, and whether they are relevant to biological networks with inhibitory transfer functions.

It is true that the nonlinear transfer function is a key component in our model. We chose identical transfer functions for E, PV, and SOM (; Eq. 4) to simplify our analysis. If the transfer function of one of the neuron types would be linear (β \= 1), then the corresponding b terms (the slope of the nonlinearity at the steady state; b \= dfX/dqX; Fig. 1B; Eq. 4) would be equal to α. Therefore, if neurons had a linear transfer function in our model, there would not be a dependence of network gain on E and PV firing rate as studied in Fig. 3-5. This is because the relationship between PV rates and their gain would be constant (bP \= α) in Fig. 1B (bottom).

If all the transfer functions were linear, changes in firing rates would not have an impact on network gain or stability. Changing the nonlinear transfer function by changing the α or β terms in Eq. 4 would only scale the way a change in the rates affects the b terms and hence the results presented in Fig. 3-5. More interesting would be to study how different types of nonlinearities, like sigmoidal functions or sublinear nonlinearities (i.e. saturating nonlinearities), would change our results. However, we think that such an investigation is out of scope for this study. We now added a comment to the Methods section.

Experimentally, F-I curves have been measured also for PV and SOM neurons. For example, Romero-Sosa et al., 2021 measure the F-I curve of pyramidal, PV and SOM neurons in mouse cortical slices. They find that similar to pyramidal neurons, PV and SOM neurons show a nonlinear F-I curve. We now added the citation of Romero-Sosa et al., 2021 to our manuscript.

- Tuning curves are simulated for an individual orientation (same for all), not considering the heterogeneity of neuronal networks with multiple orientation selectivity (and other visual features) - making the model too simplistic.

The reviewer is correct that we only study changes in tuning curves in a simplistic model. In our model, the excitatory and PV populations are tuned to a single orientation (in the case of Fig. 7 to θ \= 90). While this is certainly an oversimplification, it allows us to understand how additive/subtractive and multiplicative/divisive changes in the tuning curves come about in networks with different connectivity motifs. To model heterogeneity of tuning responses within a network, it requires more complex models. A natural choice would be to extend a classical ring attractor model (Rubin et al., 2015) by splitting the inhibitory population into PV and SOM neurons, or study the tuning curve heterogeneity that occurs in balanced networks (Hansel and van Vreeswijk 2012). However, this model has many more parameters, like the spatial connectivity profiles from and onto PV and SOM neurons. While highly valuable, we believe that studying such models exceeds the scope of our current manuscript. We now added a paragraph in the discussion section, mentioning this as an interesting future direction.

Reviewer #1 (Recommendations For The Authors):

The last sentence of the abstract is hard to interpret before reading the rest of the paper - suggest replacing or rephrasing.

We rephrased the sentence to make more clear what we mean.

Page 3, last full paragraph: I think this assumes that phi is positive. What is the justification for that assumption? More generally, I think you could say a bit more about phi in the main text since it is a fairly complicated term.

The reviewer is correct, for a stable system phi is always positive. We now clarify this and explain phi in more detail in the main text.

Fig 1D: It would be helpful to identify when the stimulus comes on and be clearer about what the stimulus is. I assume it’s a step increase in S input at 0.05 s or so - but that should be immediately apparent looking at the figure.

We agree with the reviewer and we added a dashed line at the time of stimulus onset in Fig. 1D.

Page 5: ”To motivate our analysis we compare ... (Fig. 2A)” - Figure 2A does not show responses without modulation, so this sentence is confusing.

The dashed lines in Fig. 2A (and Fig. 2C) actually represents the rate change without modulation.

Page 6: sentence “The central goal of our study ...” seems out of place since this is pretty far into the results, and that goal should already be clear.

We agree with the reviewer, hence we updated the sentence.

Page 10, top: the green curve in panel Aii always has a negative slope - so I am confused by the statement that increasing wSE decreases both gain and stability.

We thank the reviewer for pointing out this mistake. We now fixed it in the text.

Figure 6: in general it is hard to see what is going on in this figure (the green and blue in particular are hard to distinguish). Some additional labels would be helpful, but I would also see if the color scheme can be improved.

We added a zoom-in to the panels which were hard to distinguish.

Reviewer #2 (Recommendations For The Authors):

Major recommendations:

(1) The authors should explain early on in the results section what the key factor(s) is that differentiates SOM from PV cells in their model. E.g., in Fig. 1A, the only obvious difference is that SOM cells don’t inhibit themselves. However, later on in the paper, the difference in external stimulus drive to these interneuron classes is more heavily emphasized. Given the importance of that difference (in external stim drive), I think this should be highlighted early on.

We now mention the key factors that differentiate PV and SOM neurons already when describing Fig. 1A.

(2) The result in Figs. 5,6 demonstrate that recurrent SOM connectivity is important for achieving increases in both gain and stability. This observation could benefit from some intuitive explanation. Perhaps the authors could find this explanation by looking at their series expansion (Eqs. 11-14, Fig. 1C) and determining which term(s) are most important for this effect. The corresponding paths through the circuit – the most important ones – could then be highlighted for the reader.

We agree with the reviewer that our results benefit from more intuitive explanations. This has also been pointed out by reviewer 1 in their public review. We now extended the concluding paragraphs in the context of Fig. 4-6 with additional information, providing a more intuitive understanding of the results presented in the respective chapter. While it is possible to gain an intuitive understanding of how the network gain depends on rate and weight parameters (Eq. 2), this understanding is unfortunately missing in the case of stability. The maximum eigenvalue of the system have a complex relationship with all the parameters, and often have nonlinear dependencies on changes of a parameter (e.g. as we show in Fig. 3iv or one can see in Fig. 6). We now discuss this difficulty at the end of the section “Influence of weight strength on network gain vs stability”.

(3) I think the authors should consider including some analyses that do not rely on the system being at or near a fixed point. I admit that such analysis could be difficult, and this could of course be done in a future study. Nevertheless, I want to reiterate that this addition could add a lot of value to this body of work.

As outlined above, we decided to not include additional analysis on network behaviour in nonlinear regimes but we now acknowledge in the discussion of our manuscript that the linearization approach is a limitation in our study and that it would be an interesting future direction to investigate chaotic dynamics.

Minor recommendations:

(1) At the top of P. 6, when the authors first discuss the stability criterion involving eigenvalues, they should address the question ”eigenvalues of what?”. I suggest introducing the idea of the Jacobian matrix, and explaining that the largest eigenvalue of that matrix determines how rapidly the system will return to the fixed point after a small perturbation.

We included an additional sentence in the respective paragraph explaining the link between stability and negative eigenvalues, and we also added a sentence in the Methods section stating the the largest real eigenvalue dominates the behavior of the dynamical system.

(2) The panel labelling in Fig. 3 is unnecessarily confusing. It would be simpler (and thus better) to simply label the panels A,B,C,D, or i,ii,iii,iv, instead of the current labelling: Ai, Aii, Aiii, Aiv. (There are currently no panels ”B” in Fig. 3).

We updated the figure accordingly.

Reviewer #3 (Recommendations For The Authors):

• Suggestions for improved or additional experiments, data or analyses.

Analysis of the effect of different nonlinear transfer functions is necessary.

Please see our detailed answer to the reviewer’s comment in the public review above.

Analysis of gain modulation in models with more realistic tuning properties.

Please see our detailed answer to the reviewer’s comment in the public review above.

Mathematical analysis of the conditions to obtain ”untangled” gain and stability:

One of the promises of the paper is that it is offering a computational framework or circuit theory for understanding the effect of SOM perturbation. However, the main result, namely the untangling of gain and stability, has only been reported in numerical simulations (e.g. Fig. 6). Different parameters have been changed and the results of simulations have been reported for different conditions. Given the simplified model, which allows for rigorous mathematical analysis, isn’t it possible to treat this phenomenon more analytically? What would be the conditions for the emergence of the untangled regime? This is currently missing from the analyses and results.

We agree with the reviewer that our results benefit from more intuitive explanations. This has also been pointed out by reviewer 1 in their public review. We now extended the concluding paragraphs in the context of Fig. 4-6 with additional information, providing a more intuitive understanding of the results presented in the respective chapter. While it is possible understand analytically of how the network gain depends on rate and weight parameters (Eq. 2), this understanding is unfortunately missing in the case of stability. The maximum eigenvalue of the system have a complex relationship with all the parameters, and often have nonlinear dependencies on changes of a parameter (e.g. as we show in Fig. 3iv or one can see in Fig. 6). This doesn’t allow for a a deep analytical understanding of the entangling of gain and stability. We now discuss this difficulty at the end of the section “Influence of weight strength on network gain vs stability”.

• Recommendations for improving the writing and presentation. The Results section is well written overall, but other parts, especially the Introduction and Discussion, would benefit from proof reading - there are many typos and problems with sentence structures and wording (some mentioned below).

We have gone through the manuscript again and improved the writing.

The presentation of the dependence on weight in Figure 6 can be improved. For instance, the authors talk about the optimal range of PV connectivity, but this is difficult to appreciate in the current illustration and with the current colour scheme.

We added a zoom in to the panels which were hard to distinguish.

• Minor corrections to the text and figures. Text:

We thank the reviewer for their thorough reading of our manuscript. We fixed all the issues from below in the manuscript.

Some examples of bad structure or wording:

From the Abstract:

”We show when E - PV networks recurrently connect with SOM neurons then an SOM mediated modulation that leads to increased neuronal gain can also yield increased network stability.” From Introduction:

Sentence starting with ”This new circuit reality ...”

”Inhibition is been long identified as a physiological or circuit basis for how cortical activity changes depending upon processing or cognitive needs ...”

Sentence starting with ”Cortical models with both ...”

”... allowing SOM neurons the freedom to ..”

From Results:

”... affects of SOM neurons on E ..”

”seem in opposition to one another, with SOM neuron activity providing either a source or a relief of E neuron suppression”. The sentence after is also difficult to read and needs to be simplified.

P. 7: ”We first remark that ...”

Difficult to read/understand - long and badly structured sentence.

P. 8: ”adding a recurrent connection onto SOM neurons from the E-PV subcircuit” It’s from E (and not PV) to be more precise (Fig. 5).

Discussion:

”Firstly, E neurons and PV neurons experience very similar synaptic environments.” What does it mean?

”Fortunately, PV neurons target both the cell bodies and proximal dendrites” Fortunately for whom or what? ”in line with arge heterogeneity”

Methods:

Matrix B is never defined - the diagonal matrix of b (power law exponents) I assume.

Some of the other notations too, e.g. bs, etc (it’s implicit, but should be explained).

Structure of sentence:

”Network gain is defined as ...” (p. 17)

Figure:

The schematics in Figure 4 can be tweaked to highlight the effect of input (rather than other components of the network, which are the same and repetitive), to highlight the main difference for the reader.

Author response:

Reviewer #1 (Public review):

Weaknesses:

(1) The assertion that membrane trafficking is impaired by this variant could be bolstered by additional data.

We agree with this comment and will perform additional analysis and experiments to support the assertion that membrane trafficking is impaired. As noted by the Reviewers, standard biochemical approaches to obtain such data may be challenging due to the fact that Kv3.1 is expressed in only a subset of cells and that we do not have a Kv3.1-A421V specific antibody.

(2) In some experiments details such as the age of the mice or cortical layer are emphasized, but in others, these details are omitted.

We appreciate that the Reviewer has noted this omission. We will include such details in the resubmission.

(3) The impairments in PV neuron AP firing are quite large. This could be expected to lead to changes in PV neuron activity outside of the hypersynchronous discharges that could be detected in the 2-photon imaging experiments, however, a lack of an effect on PV neuron activity is only loosely alluded to in the text. A more formal analysis is lacking. An important question in trying to understand mechanisms underlying channelopathies like KCNC1 is how changes in membrane excitability recorded at the whole cell level manifest during ongoing activity in vivo. Thus, the significance of this work would be greatly improved if it could address this question.

Yes, the impairments in neocortical PV-IN excitability are more marked than any other PV interneuronopathy that we have studied. We will include a more extensive analysis of the 2-photon imaging data in the resubmission. However, there are limitations to the inferences that can be made as to firing patterns based on 2-photon calcium imaging data, particularly for interneurons.

(4) Myoclonic jerks and other types of more subtle epileptiform activity have been observed in control mice, but there is no mention of littermate control analyzed by EEG.

We did not observe myoclonic jerks in control mice. This data will be included in the resubmission.

Reviewer #2 (Public review):

Weaknesses:

In some experiments, the age of the animal in each experiment is not clearly stated. For example, the experiments in Figure 2 demonstrate impaired K+ conductance and membrane localization, but it is not clear whether they correlated with the excitability and synaptic defects shown in subsequent figures. Similarly, it is unclear how old mice the authors conducted EEG recordings, and whether non-epileptic mice are younger than those with seizures.

We will include explicit information as to the age of the animals used for each experiment in the resubmission.

The trafficking defect of mutant Kv3.1 proposed in this study is based only on the fluorescence density analysis which showed a minor change in membrane/cytosol ratio. It is not very clear how the membrane component was determined (any control staining?). In addition to fluorescence imaging, an addition of biochemical analysis will make the conclusion more convincing (while it might be challenging if the Kv3.1 is expressed only in PV+ cells).

We will include additional information in the Methods section as to how the membrane component was determined in a revised version of the manuscript. We agree with Reviewer #2 regarding the limitations in the ability to further evaluate this.

While the study focused on the superficial layer because Kv3.1 is the major channel subunit, the PV+ cells in the deeper cortical layer also express Kv3.1 (Chow et al., 1999) and they may also contribute to the hyperexcitable phenotype via negative effect on Kv3.2; the mutant Kv3.1 may also block membrane trafficking of Kv3.1/Kv3.2 heteromers in the deeper layer PV cells and reduce their excitability. Such an additional effect on Kv3.2, if present, may explain why the heterozygous A421V KI mouse shows a more severe phenotype than the Kv3.1 KO mouse (and why they are more similar to Kv3.2 KO). Analyzing the membrane excitability differences in the deep-layer PV cells may address this possibility.

We will include recordings from PV-INs in deeper layers of the neocortex in the revised version of the manuscript, as requested.

In Table 1, the A421V PV+ cells show a depolarized resting membrane potential than WT by ~5 mV which seems a robust change and would influence the circuit excitability. The authors measured firing frequency after adjusting the membrane voltage to -65mV, but are the excitability differences less significant if the resting potential is not adjusted? It is also interesting that such a membrane potential difference is not detected in young adult mice (Table 2). This loss of potential compensation may be important for developmental changes in the circuit excitability. These issues can be more explicitly discussed.

We will include a more thorough discussion of this finding in the revised version of the manuscript. However, we do not completely understand this finding. It could be compensatory, as suggested by the Reviewer; however, it is transient and seems to be an isolated finding (i.e., there does not appear to be parallel “compensation” in other properties). Alternatively, it could be that impaired excitability of the Kcnc1-A421V/+ PV-INs may reflect impaired/delayed development, which itself is known to be activity-dependent.

Reviewer #3 (Public review):

Weaknesses:

The manuscript identifies a partial mechanism of disease that leaves several aspects unresolved including the possible role of the observed impairments in thalamic neurons in the seizure mechanism. Similarly, while the authors identify a reduction in potassium currents and a reduction in PV cell surface expression of Kv3.1 it is not clear why these impairments would lead to a more severe disease phenotype than other loss-of-function mutations which have been characterized previously. Lastly, additional analysis of video-EEG data would be helpful for interpreting the extent of the seizure burden and the nature of the seizure types caused by the mutation.

We agree with this comment. We studied neurons in the reticular thalamus as these cells are known to express Kv3.1 and are linked to epilepty pathogenesis. Yet, we focused on neocortical PV-INs over other Kv3.1-expressing neurons such as neurons of the reticular thalamus because we evaluated the impairments of intrinsic excitability to be more profound in neocortical PV-INs. Cross of Kcnc1-Flox(A421V)/+ mice to a cerebral cortex interneuron-specific driver that would avoid recombination in thalamus – such as Ppp1r2-Cre (RRID:IMSR_JAX:012686) – could assist in determining the relative contribution of thalamic reticular nucleus dysfunction to the overall phenotype, as performed by Makinson et al (2017) to address a similar question. There are of course other Kv3.1-expressing neurons in the brain, including in GABAergic interneurons in hippocampus and amygdala. We will include additional discussion in a revised version of the manuscript as to why we think there is more severe impairment in our Kcnc1-Flox(A421V)/+ mice relative to Kv3.1 and Kv3.2 knockout mice. We will include additional data on the epilepsy phenotype in the revised version of the manuscript, as requested.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors follow up on their published observation that providing a lower glucose parental nutrition (PN) reduces sepsis from a common pathogen [Staphylococcus epidermitis (SE)] in preterm piglets. Here they found that a higher dose of glucose could thread the needle and get the protective effects of low glucose without incurring significant hypoglycemia. They then investigate whether the change in low glucose PN impacts metabolism to confer this benefit. The finding that lower glucose reduces sepsis is important as sepsis is a major cause of morbidity and mortality in preterm infants, and adjusting PN composition is a feasible intervention.

Strengths:

(1) They address a highly significant problem of neonatal sepsis in preterm infants using a preterm piglet model.

(2) They have compelling data in this paper (and in a previous publication, ref 27) that low glucose PN confers a survival advantage. A downside of the low glucose PN is hypoglycemia which they mitigate in this paper by using a slightly high amount of glucose in the PN.

(3) The experiment where they change PN from high to low glucose after infection is very important to determine if this approach might be used clinically. Unfortunately, this did not show an ability to reduce sepsis risk with this approach. Perhaps this is due to the much lower mortality in the high glucose group (~20% vs 87% in the first figure).

(4) They produce an impressive multiomics data set from this model of preterm piglet sepsis which is likely to provide additional insights into the pathogenesis of preterm neonatal sepsis.

Weaknesses:

(1) The high glucose control gives very high blood glucose levels (Figure 1C). Is this the best control for typical PN and glucose control in preterm neonates? Is the finding that low glucose is protective or high glucose is a risk factor for sepsis?

This work is a follow-up from our previous work where we explored different PN glucose regimens. Taken together our experiments heavily imply that glucose provision is associated to severity in a seemingly linear manner. In the clinical setting, there is no fixed glucose provision, but guidelines specify ranges that are acceptable. However, these guidelines do not take possible infections into account and are designed to optimize growth outcomes. Increased provision of glucose to preterm neonates may therefore increase their infection risk, but parenteral glucose cannot be entirely avoided as it would lead to hypoglycaemia and associated brain damage. In the present paper the reduced glucose PN reflects the lowest end of the recommended PN glucose intake. More work is needed to figure out the best glucose provision to infected preterm newborns, balancing positive and negative factors.

(2) In Figure 1B, preterm piglets provided the high glucose PN have 13% survival while preterm piglets on the same nutrition in Figure 6B have ~80% survival. Were the conditions indeed the same? If so, this indicates a large amount of variation in the outcome of this model from experiment to experiment.

In the follow-up experiment outlined in Figure 6 we reduced the follow-up time to 12 hours in an effort to minimize the suffering of the animals. We did this because we could detect relevant differences in the immune response between High and low glucose infected pigs as 12 hours. If we had extended the follow-up experiment to 22 hours we would likely have seen a much increased mortality.

(3) Piglets on the low glucose PN had consistently lower density of SE (~1 log) across all time points. This may be due to changes in immune response leading to better clearance or it could be due to slower growth in a lower glucose environment.

We agree with this assessment and have adjusted our result section to reflect this.

(4) Many differences in the different omics (transcriptomics, metabolomics, proteomics) were identified in the SE-LOW vs SE-HIGH comparison. Since the bacterial load is very different between these conditions, could the changes be due to bacterial load rather than metabolic reprogramming from the low glucose PN?

We analyzed the relationship between bacterial burdens and mortality and found that it did not correlate within each of the treatment groups. We have now added this data to the results section as supplemental and report this fact in the section called “Reduced glucose supply increases hepatic OXPHOS and gluconeogenesis and attenuates inflammatory pathways”. This finding inspired us to further explore the relationship between bacterial burdens and infection responses in our model which has resulted in our recent preprint: Wu et at. Regulation of host metabolism and defense strategies to survive neonatal infection. BioRxiv 2024.02.23.581534; doi: https://doi.org/10.1101/2024.02.23.581534

Reviewer #2 (Public Review):

Summary:

The authors demonstrate that a low parenteral glucose regimen can lead to improved bacterial clearance and survival from Staph epi sepsis in newborn pigs without inducing hypoglycemia, as compared to a high glucose regimen. Using RNA-seq, metabolomic, and proteomic data, the authors conclude that this is primarily mediated by altered hepatic metabolism.

Strengths:

Well-defined controls for every time point, with multiple time points and biological replicates. The authors used different experimental strategies to arrive at the same conclusion, which lends credibility to their findings. The authors have published the negative findings associated with their study, including the inability to reverse sepsis-related mortality after switching from SE-high to SE-low at 3h or 6h and after administration of hIAIP.

Weaknesses:

(1) The authors mention, and it is well-known, that Staph epi is primarily involved in late-onset sepsis. The model of S. epi sepsis used in this study clearly replicates early-onset sepsis, but S. epi is extremely rare in this time period. How do the authors justify the clinical relevance of this model?

The distinction between early and late onset sepsis makes sense clinically because they are likely to be caused by different organisms and therefore require different empirical antibiotic regimes. Early onset sepsis is caused by organisms transferred perinatally often following chorioamnionitis or uro-gential maternal infections (Strep. agalacticae/E. coli) whereas Late onset sepsis is likely caused by organisms from indwelling catheters or mucosal surfaces, most often coagulase negative staphylococci. Timing of an infection after birth of course plays a role, but the virulence factors of the pathogen probably plays a large role in shaping the immune response. Therefore, even though the infection in our model is initiated on the first day after birth, the organism that we use, Staph epidermidids, makes it a better model for pathogenesis of late onset sepsis. However, it is also important to acknowledge that the pathophysiology of “sepsis” may be similar despite timing and pathogen and depends on the degree of immune activation and downstream effects on organs.

(2) The authors find that the neutrophil subset of the leukocyte population is diminished significantly in the SE-low and SE-high populations. However, they conclude on page 10 that "modulations of hepatic, but not circulating immune cell metabolism, by reduced glucose supply..." and this is possible because the authors have looked at the entire leukocyte transcriptome. I am curious about why the authors did not sequence the neutrophil-specific transcriptome.

We collected the whole blood transcript during the experiments, which reflect the transcription profile of all the circulating leucocytes. Since we did not do single cell RNA sequencing during the experiment there is no possibility of isolating the neutrophil transcriptome at this time. Your point however is valid and we will reconsider incorporating single cell transcriptomics in future experiments.

(3) The authors use high (30g/k/d) and low (7.2g/k/d) glucose regimens. These translate into a GIR of 21 and 5 mg/k/min respectively. A normal GIR for a preterm infant is usually 5-8, and sometimes up to 10. Do the authors have a "safe GIR" or a threshold they think we cannot cross? Maybe a point where the metabolism switch takes place? They do not comment on this, especially as GIR and glucose levels are continuous variables and not categorical.

Our reduced glucose PN was chosen as it corresponded with the low end of recommended guidelines for PN glucose intake. There likely is not a “safe GIR” as the clinical responses to glucose intake during infections do not seem binary but increase with glucose intake. It is also important to remember that the reduced glucose intervention still resulted in significant morbidity and a 25% mortality within 22 hours. There is therefore still vast room for improvement, but even though further reduction in PN glucose would probably provide further protection it would entail dangerous hypoglycaemia (as described in our previous paper). The findings in this current paper has prompted us to explore several strategies to replace parenteral glucose with alternative macronutrients. Thus, the optimal PN for infected newborns would probably differ from standard PN in all macronutrients and will require much more pre- and clinical research.

(4) In Figures 2B and C the authors show that SE-high and SE-low animals have differences in the oxphos, TCA, and glycolytic pathways. The authors themselves comment in the Supplementary Table S1B, E-F that these same metabolic pathways are also different in the Con-Low and Con-high animals, it is just the inflammatory pathways that are not different in the non-infected animals. How can they then justify that it is these metabolic pathways specifically which lead to altered inflammatory pathways, and not just the presence of infection along with some other unfound mechanism?

It is to be expected that the inflammatory pathways do not differ between the Con-Low and Con-High groups as there is no infection to induce these pathways. The identified metabolic pathways that differ between SE-High and SE-Low animals seem to us the best explanation of the differences in clinical phenotype.

(5) The authors mention in Figure 1F that SE-low animals had lower bacterial burdens than SE-high animals, but then go on to infer that the inflammatory cytokine differences are attributed to a rewiring of the immune response. However, they have not normalized the cytokine levels to the bacterial loads, as the differences in the cytokines might be attributed purely to a difference in bacterial proliferation/clearing.

Please see our response to reviewer #1

(6) The authors mention that switching from SE-high to SE-low at 3 or 6 h time points does not reduce mortality. Have the authors considered the reverse? Does hyperglycemia after euglycemia initially, worsen mortality? That would really conclude that there is some metabolic reprogramming happening at the very onset of sepsis and it is a lost battle after that.

A very good point that we have not explored yet, we have added this consideration to the discussion and slightly amended our conclusions of this follow-up experiment.

Reviewer #3 (Public Review):

Summary:

Baek and colleagues present important follow-up work on the role of serum glucose in the management of neonatal sepsis. The authors previously showed high glucose administration exacerbated neonatal sepsis, while strict glucose control improved outcomes but caused hypoglycemia. In the current report they examined the effect of a more tailored glucose management approach on outcomes and examined hepatic gene expression, plasma metabolome/proteome, blood transcriptome, as well as the the therapeutic impact of hIAIP. The authors leverage multiple powerful approaches to provide robust descriptive accounts of the physiologic changes that occur with this model of sepsis in these various conditions. Strengths:

(1) Use of preterm piglet model.

(2) Robust, multi-pronged approach to address both hepatic and systemic implications of sepsis and glucose management.

(3) Trial of therapeutic intervention - glucose management (Figure 6), hIAIP (Figure 7).

Weaknesses:

(1) The translational role of the model is in question. CONS is rarely if ever a cause of EOS in preterm neonates. The model. uses preterm pigs exposed at 2 hours of age. This model most likely replicates EOS.

Please see our response to Reviewer #2

(2) Throughout the manuscript it is difficult to tell from which animals the data are derived. Given the ~90% mortality in the experimental CONS group, and 25% mortality in the intervention group, how are the data from animals "at euthanasia" considered? Meaning - are data from survivors and those euthanized grouped together? This should be clarified as biologically these may be very different populations (ie, natural survivor vs death).

This is a very valid point. For all endpoints that are analyzed “at euthanasia” the age of the animal will vary. Some will have been euthanized early due to clinical deterioration and some will have survived all the way to the end of the experiment. This needs to be kept in mind when interpreting the results. We have further highlighted this point in the discussion and made it clear to the reader at what time-point each analysis was performed.

(3) With limited time points (at euthanasia ) for hepatic transcriptomics (Figure 2), plasma metabolite (Figure 3) blood transcriptome (Figure 4), and plasma proteome (Figure 5) it is difficult to make conclusions regarding mechanisms preceding euthanasia. Per methods, animals were euthanized with acidosis or clinical decompensation. Are the reported findings demonstrative of end-organ failure and deterioration leading to death, or reflective of events prior?

Yes, all organ specific endpoints are snapshots of the state of the animals at the time of euthanasia, pooling together animals that succumbed to sepsis and those that survived to 22 hours post infection. These results therefore reflect the end-state of the infection we cannot be sure when the differences between groups manifested themselves. However, given the stark differences in plasma lactate at 12 hours post infection it is likely that changes to metabolism occurred before most of animals succumbed to sepsis.

We agree this is a weakness in our model, but we have since published a pre-print where we have further explored how metabolic adaptations shape the fate of similarly infected preterm pigs: BioRxiv 2024.02.23.581534; doi: https://doi.org/10.1101/2024.02.23.581534

(4) Data are descriptive without corresponding "omics" from interventions (glucose management and/or hIAIP) or at least targeted assessment of key differences.

We only did in-depth analysis of the glucose intervention as this showed the most promising clinical effects that warranted further in-depth investigation. It is possible that further insights could be gained from in-depth analysis of the other interventions but given that there were no obvious clinical befits we refrained from that.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I am intrigued that mortality was not correlated to bacterial burden. Please provide the "data not shown" as this would help the reader understand better whether the difference in bacterial burden is driving the phenotypes and findings of the low glucose group.

We have added this data to supplementary figure 1.  

Reviewer #2 (Recommendations For The Authors):

(1) I would urge the authors to consider a neutrophil-specific transcriptomic analysis. I understand that this would add significantly to the resubmission process. If the authors wish to include that as a future direction instead, they need to specifically mention the limitations of whole blood transcriptomics and how different immune cell types react differently to bacterial antigens.

We agree with your considerations but we cannot include that data using the whole blood method applied in the experiment. We have added your consideration to the discussions.

(2) I urge the authors to remove any impression that this is a model of late-onset sepsis, which is implied from the introduction, lines 3 and 4.

Our intention was not to directly suggest that our model is a perfect reflection of late-onset sepsis but rather to highlight the relevance of using a pathogen commonly associated with LOS. We believe our model primarily captures the effects of intense pro-inflammatory immune activation, which may have parallels with various forms of sepsis, including LOS.

Reviewer #3 (Recommendations For The Authors):

Drawing on the robust nature of your "omics", identify key measures and test whether they are altered earlier in the development of clinical sepsis. Test whether these are altered by the intervention.

A very valid point, at the moment it is not possible for us to explore this within the confines of these experiments. But, building upon these findings and the ones in our recent preprint we are confident that shifts in hepatic ratio of Oxidative phosphorylation and gluconeogenesis vs glycolysis shape the immune response to infections in neonates. In our upcoming experiments we are planning to incorporate plasma metabolomics at earlier timepoints to monitor when shifts in metabolism occur. However, given the heterogeneity of pigs, as opposed to inbred rodent models, sacrificing animals at fixed timepoints to gauge their organ function will be hard to interpret as it is impossible to know what the end state of the particular animal would have been. Therefore longitudinal sampling of liver tissue, during the course of infection would be challenging.

Reviewer #2 (Public review):

Summary:

This is an inspired study that merges the concept of individuality with evolutionary processes to uncover a new strategy that diversifies individual behavior that is also potentially evolutionarily adaptive.

The authors use a time-resolved measurement of spontaneous, innate behavior, namely handedness or turn bias in individual, isogenic flies, across several genetic backgrounds.

They find that an individual's behavior changes over time, or drifts. This has been observed before, but what is interesting here is that by looking at multiple genotypes, the authors find the amount of drift is consistent within genotype i.e., genetically regulated, and thus not entirely stochastic. This is not in line with what is known about innate, spontaneous behaviors. Normally, fluctuations in behavior would be ascribed to a response to environmental noise. However, here, the authors go on to find what is the pattern or rule that determines the rate of change of the behavior over time within individuals. Using modeling of behavior and environment in the context of evolutionarily important timeframes such as lifespan or reproductive age, they could show when drift is favored over bet-hedging and that there is an evolutionary purpose to behavioral drift. Namely, drift diversifies behaviors across individuals of the same genotype within the timescale of lifespan, so that the genotype's chance for expressing beneficial behavior is optimally matched with potential variation of environment experienced prior to reproduction. This ultimately increases the fitness of the genotype. Because they find that behavioral drift is genetically variable, they argue it can also evolve.

Strengths:

Unlike most studies of individuality, in this study, the authors consider the impact of individuality on evolution. This is enabled by the use of multiple natural genetic backgrounds and an appropriately large number of individuals to come to the conclusions presented in the study. I thought it was really creative to study how individual behavior evolves over multiple timescales. And indeed this approach yielded interesting and important insight into individuality. Unlike most studies so far, this one highlights that behavioral individuality is not a static property of an individual, but it dynamically changes. Also, placing these findings in the evolutionary context was beneficial. The conclusion that individual drift and bet-hedging are differently favored over different timescales is, I think, a significant and exciting finding.

Overall, I think this study highlights how little we know about the fundamental, general concepts behind individuality and why behavioral individuality is an important trait. They also show that with simple but elegant behavioral experiments and appropriate modeling, we could uncover fundamental rules underlying the emergence of individual behavior. These rules may not at all be apparent using classical approaches to studying individuality, using individual variation within a single genotype or within a single timeframe.

Weaknesses:

I am unconvinced by the claim that serotonin neuron circuits regulate behavioral drift, especially because of its bidirectional effect and lack of relative results for other neuromodulators. Without testing other neuromodulators, it will remain unclear if serotonin intervention increases behavioral noise within individuals, or if any other pharmacological or genetic intervention would do the same. Another issue is that the amount of drugs that the individuals ingested was not tracked. Variable amounts can result in variable changes in behavior that are more consistent with the interpretation of environmental plasticity, rather than behavioral drift. With the current evidence presented, individual behavior may change upon serotonin perturbation, but this does not necessarily mean that it changes or regulates drift.

However, I think for the scope of this study, finding out whether serotonin regulates drift or not is less important. I understand that today there is a strong push to find molecular and circuit mechanisms of any behavior, and other peers may have asked for such experiments, perhaps even simply out of habit. Fortunately, the main conclusions derived from behavioral data across multiple genetic backgrounds and the modeling are anyway novel, interesting, and in fact more fundamental than showing if it is serotonin that does it or not.

To this point, one thing that was unclear from the methods section is whether genotypes that were tested were raised in replicate vials and how was replication accounted for in the analyses. This is a crucial point - the conclusion that genotypes have different amounts of behavioral drift cannot be drawn without showing that the difference in behavioral drift does not stem from differences in developmental environment.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In "Drift in Individual Behavioral Phenotype as a Strategy for Unpredictable Worlds," Maloy et al. (2024) investigate changes in individual responses over time, referred to as behavioral drift within the lifespan of an animal. Drift, as defined in the paper, complements stable behavioral variation (animal individuality/personality within a lifetime) over shorter timeframes, which the authors associate with an underlying bet-hedging strategy. The third timeframe of behavioral variability that the authors discuss occurs within seasons (across several generations of some insects), termed "adaptive tracking." This division of "adaptive" behavioral variability over different timeframes is intuitively logical and adds valuable depth to the theoretical framework concerning the ecological role of individual behavioral differences in animals.

Strengths:

While the theoretical foundations of the study are strong, the connection between the experimental data (Figure 1) and the modeling work (Figure 2-4) is less convincing.

Weaknesses:

In the experimental data (Figure 1), the authors describe the changes in behavioral preferences over time. While generally plausible, I identify three significant issues with the experiments:

(1) All of the subsequent theoretical/simulation data is based on changing environments, yet all the experiments are conducted in unchanging environments. While this may suffice to demonstrate the phenomenon of behavioral instability (drift) over time, it does not properly link to the theory-driven work in changing environments. An experiment conducted in a changing environment and its effects on behavioral drift would improve the manuscript's internal consistency and clarify some points related to (3) below.

In our framework, we posit that the amount of drift has been shaped by evolution to maximize fitness in the environments that the population has experienced, and this drift is observed independent of environment. While we agree that exploring the role of changing environments on the measure of drift would be interesting, we would anticipate the effects may be nuanced and beyond the scope of the current paper (and the scope of our theoretical work, which assumes that the individual phenotype is unaffected by change of environment except as mediated by death due to fitness effects). For example, it would be difficult to differentiate drift from idiosyncratic differences in learning (Smith et al., 2022), and non-adaptive plasticity to unrelated cues has been posited as a method of producing diverse phenotypes (Maxwell and Magwene, 2017), so “learning” to uncorrelated stimuli could conceivably be a mechanism for drift. Given the scope of the current study, we prioritized eliminating potential confounds for measuring drift, but remain interested in the interaction between learning and drift.

(2) The temporal aspect of behavioral instability. While the analysis demonstrates behavioral instability, the temporal dynamics remain unclear. It would be helpful for the authors to clarify (based on graphs and text) whether the behavioral changes occur randomly over time or follow a pattern (e.g., initially more right turns, then more left turns). A proper temporal analysis and clearer explanations are currently missing from the manuscript.

We agree it would be helpful to have more description of the dynamics over time aside from the power spectrum and autoregressive model fits. We hope to address this in more detail to provide more description of the changes over time in a revision.

(3) The temporal dimension leads directly into the third issue: distinguishing between drift and learning (e.g., line 56). In the neutral stimuli used in the experimental data, changes should either occur randomly (drift) or purposefully, as in a neutral environment, previous strategies do not yield a favorable outcome. For instance, the animal might initially employ strategy A, but if no improvement in the food situation occurs, it later adopts strategy B (learning). In changing environments, this distinction between drift and learning should be even more pronounced (e.g., if bananas are available, I prefer bananas; once they are gone, I either change my preference or face negative consequences). Alternatively, is my random choice of grapes the substrate for the learning process towards grapes in a changing environment? Further clarification is needed to resolve these potential conflicts.

As in our response to point 1, we believe this is a crucial distinction, and we intend to further highlight it in the discussion in the revision and further expand our discussion of how the two strategies may interact.

Reviewer #2 (Public review):

Summary:

This is an inspired study that merges the concept of individuality with evolutionary processes to uncover a new strategy that diversifies individual behavior that is also potentially evolutionarily adaptive.

The authors use a time-resolved measurement of spontaneous, innate behavior, namely handedness or turn bias in individual, isogenic flies, across several genetic backgrounds.

They find that an individual's behavior changes over time, or drifts. This has been observed before, but what is interesting here is that by looking at multiple genotypes, the authors find the amount of drift is consistent within genotype i.e., genetically regulated, and thus not entirely stochastic. This is not in line with what is known about innate, spontaneous behaviors. Normally, fluctuations in behavior would be ascribed to a response to environmental noise. However, here, the authors go on to find what is the pattern or rule that determines the rate of change of the behavior over time within individuals. Using modeling of behavior and environment in the context of evolutionarily important timeframes such as lifespan or reproductive age, they could show when drift is favored over bet-hedging and that there is an evolutionary purpose to behavioral drift. Namely, drift diversifies behaviors across individuals of the same genotype within the timescale of lifespan, so that the genotype's chance for expressing beneficial behavior is optimally matched with potential variation of environment experienced prior to reproduction. This ultimately increases the fitness of the genotype. Because they find that behavioral drift is genetically variable, they argue it can also evolve.

Strengths:

Unlike most studies of individuality, in this study, the authors consider the impact of individuality on evolution. This is enabled by the use of multiple natural genetic backgrounds and an appropriately large number of individuals to come to the conclusions presented in the study. I thought it was really creative to study how individual behavior evolves over multiple timescales. And indeed this approach yielded interesting and important insight into individuality. Unlike most studies so far, this one highlights that behavioral individuality is not a static property of an individual, but it dynamically changes. Also, placing these findings in the evolutionary context was beneficial. The conclusion that individual drift and bet-hedging are differently favored over different timescales is, I think, a significant and exciting finding.

Overall, I think this study highlights how little we know about the fundamental, general concepts behind individuality and why behavioral individuality is an important trait. They also show that with simple but elegant behavioral experiments and appropriate modeling, we could uncover fundamental rules underlying the emergence of individual behavior. These rules may not at all be apparent using classical approaches to studying individuality, using individual variation within a single genotype or within a single timeframe.

Weaknesses:

I am unconvinced by the claim that serotonin neuron circuits regulate behavioral drift, especially because of its bidirectional effect and lack of relative results for other neuromodulators. Without testing other neuromodulators, it will remain unclear if serotonin intervention increases behavioral noise within individuals, or if any other pharmacological or genetic intervention would do the same. Another issue is that the amount of drugs that the individuals ingested was not tracked. Variable amounts can result in variable changes in behavior that are more consistent with the interpretation of environmental plasticity, rather than behavioral drift. With the current evidence presented, individual behavior may change upon serotonin perturbation, but this does not necessarily mean that it changes or regulates drift.

However, I think for the scope of this study, finding out whether serotonin regulates drift or not is less important. I understand that today there is a strong push to find molecular and circuit mechanisms of any behavior, and other peers may have asked for such experiments, perhaps even simply out of habit. Fortunately, the main conclusions derived from behavioral data across multiple genetic backgrounds and the modeling are anyway novel, interesting, and in fact more fundamental than showing if it is serotonin that does it or not.

We agree that our data do not support a strong conclusion that serotonin plays a privileged role in regulating drift. Based on previous literature (e.g. Kain et al., 2014, where identical pharmacological manipulations had an effect on variability while dopaminergic and octopaminergic manipulations did not), we think it likely that large global perturbations in serotonin that we observe are likely to influence plasticity that might be involved in drift (and thus find the results we observe not particularly surprising). Nonetheless, we agree that the mechanism by which serotonin may affect drift could be indirect, and it is similarly plausible that many global perturbations could lead to some shift in the amount of drift. We intend to further discuss these issues in the revision.

To this point, one thing that was unclear from the methods section is whether genotypes that were tested were raised in replicate vials and how was replication accounted for in the analyses. This is a crucial point - the conclusion that genotypes have different amounts of behavioral drift cannot be drawn without showing that the difference in behavioral drift does not stem from differences in developmental environment.

While a cursory inspection suggests that batch effects between different replicates was small, we intend to clarify this and more explicitly address the effects of replicates in revision.

Reviewer #3 (Public review):

Summary:

The paper begins by analyzing the drift in individual behavior over time. Specifically, it quantifies the circling direction of freely walking flies in an arena. The main takeaway from this dataset is that while flies exhibit an individual turning bias (when averaged over time), their preferences fluctuate over slow timescales.

To understand whether genetic or neuromodulatory mechanisms influence the drift in individual preference, the authors test different fly strains concluding that both genetic background and the neuromodulator serotonin contribute to the degree of drift.

Finally, the authors use theoretical approaches to identify the range of environmental conditions under which drift in individual bias supports population growth.

Strengths:

The model provides a clear prediction of the environmental fluctuations under which a drift in bias should be beneficial for population growth.

The approach attempts to identify genetic and neurophysiological mechanisms underlying drift in bias.

Weaknesses:

Different behavioral assays are used and are differently analysed, with little discussion on how these behaviors and analyses compare to each other.

We intend to address this in a revision of the discussion.

Some of the model assumptions should be made more explicit to better understand which aspects of the behaviors are covered.

We will further clarify the assumptions of the model in revision.

The difference between what you work out using the Zettelkasten and the memory palace technique is that the memory palace is a pure memory technique. It uses meaningless connections [emphasis added] and the way the brain works to gain access to information. For example, if I mentally write the date Rome was founded with the mnemonic “BC 753 Rome came to be” as a number on an egg in the kitchen fridge, the only reason for this link between the egg in the kitchen fridge of my memory palace and the year Rome was founded is that I can remember this number.

Certainly not an attack against him, but I feel as if Sascha is making an analogistic reference to areas of mnemonics he's heard about, but hasn't actively practiced. As a result, some may come away with a misunderstanding of these practices. Even worse, they may be dissuaded from combining a more specific set of mnemonic practices with their zettelkasten practice which can provide them with even stronger memories of the ideas hiding within their zettelkasten.

There is a mistaken conflation of two different mnemonic techniques being described here. The memory palace portion associates information with well known locations which leverages our brains' ability to more easily remember places and things in them with relation to each other. There is nothing of meaningless connections here. The method works precisely because meaning is created and attributed to the association. It becomes a thing in a specific well known place to the user which provides the necessary association for our memory.

The second mnemonic technique at play is the separate, unmentioned, and misconstrued Major System (or possibly the related Person-Action-Object method) which associates the number with a visualizable object. While there is a seeming meaningless connection here, the underlying connection is all about meaning by design. The number is "translated" from something harder to remember into an object which is far easier to remember. This initial translation is more direct than one from a word in one language to another because it can be logically generated every time and thus gives a specific meaning to an otherwise more-difficult-to-remember number. As part of the practice this object is then given additional attributes (size, smell, taste, touch, etc., or ridiculous proportion or attributes like extreme violence or relationships to sex) which serve to make it even more memorable. Sascha seems break this more standard mnemonic practice by simply writing his number on the egg in the refrigerator rather than associate 753 with a more memorable object like a "golem" which might be incubating inside of my precious egg. As a result, the egg and 753 association IS meaningless to him, and I would posit will be incredibly more difficult for him to remember tomorrow much less next month. If we make the translation of 753 more visible in Sascha's process, we're more likely to see the meaning and the benefit of the mnemonic. (I can only guess that Sascha doesn't practice these techniques, so won't fault him for missing some steps, particularly given the ways in which the memory palace is viewed in the zeitgeist.)

To say that the number and the golem (here, the object which 753 was translated to—the Major System mnemonic portion) have no association is akin to saying that "zettlekasten" has no associated meaning to the words "slip box." In both translations the words/numbers are exactly the same thing. The second mnemonic is associating the golem to the egg in the refrigerator (the memory palace portion). I suspect that if you've been following along and imagining Andy Serkis gestating inside of an egg to become Golem who will go on to fight in the Roman Coliseum in your refrigerator, you're going to see Golem every time you reach for an egg in your refrigerator. Now if you've spent the ten minutes to learn the Major System to do the reverse translation, you'll think about the founding date of Rome every time you go to make an omelette. And if you haven't, then you'll just imagine the most pitiful gladiator loosing in the arena against a vicious tiger.

Naturally one can associate all their thoughts in their ZK to both the associated numbers and their home, work, or neighborhood environments so that they can mentally take their (analog or digital) zettlekasten with them anywhere they go. This is akin to what Thomas Aquinus and Raymond Llull were doing with their "knowledge management systems", though theirs may have had slightly simpler forms. Llull actually created a system which allowed him to more easily meditate on his stored memories and juxtapose them to create new ideas.

For the beginners in these areas who'd like to know more, I recommend the following as a good starting place: <br /> Kelly, Lynne. Memory Craft: Improve Your Memory Using the Most Powerful Methods from around the World. Pegasus Books, 2019.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Manuscript number: RC-2024-02535

Corresponding author(s): Modica, Maria Vittoria

1. General Statements [optional]

We are grateful to the reviewers for their detailed evaluation and insightful comments on our manuscript, which has led us to introduce several clarifications, expand a few issues initially underscored, and amend some incongruencies.

We have been able to incorporate changes to reflect most of the suggestions provided by the reviewers, as highlighted in the main text. Most of the additional analyses proposed by the reviewers were carried out, in some cases providing interesting insights that were included in the manuscript, while in others revealed not conclusive, as detailed below.

We believe that the congruence and readability of the manuscript has been overall improved, and we are confident that our responses align with the level of detail required by the reviewers

• *

2. Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

* Summary: The manuscript by Modica et al reports characterisation of the venom system in the white sea fan Eunicella singularis, a species of an octocorallian coral. E. singularis is common in the north-western Mediterranean sea. The authors used a proteo-transcriptomic approach followed by extensive bioinformatics analysis. Specifically, they generated a new E. singularis *transcriptome and characterised extracts from nematocysyts (venom-bearing structures) and whole body using tandem mass spectrometry. Toxins were identified by HMMER using Tox-prot and VenomZone databases as queries as well as ClanTox web server.

Major comments:

As far as I am aware, venom production by ectodermal gland cells has been reported only in sea anemones (Moran et al, 2011), therefore it is unclear whether it is the case in the octocorallian sea fan as well. Additionally, cnidarian toxin-like proteins might be produced by neurons (Sachkova et al, 2020) or involved in development (Surm et al 2024). Thus, it is probable that in E. singularis not all the toxin-like proteins found in the whole body proteome and missing from the nematocyst proteome are venom components. Thus, additional experiments would be required to localise those proteins to ectodermal gland cells. I suggest to mention this limitation and refer to such proteins as "toxin-like" or "putative toxins".

• *

We thank the Reviewer for this observation, which is indeed correct. We have modified the text according to this suggestion and we have added a cautionary statement to the analysis section.

In addition to submitting proteomics data to PRIDE, it would be helpful for readers/reviewers to provide a supplementary excel file with all the peptides and proteins identified by PEAKS Studio. I could not access the data on PRIDE as I think they still have not been assigned a PXD dataset identifier.

Excel files with both proteomes have now been provided as supplementary material (Suppl tab. 2 and 3).

* *Minor comments:

It would be helpful for readers to split the Results and Discussions into smaller subsections with headings, perhaps according to the identified toxin families. It would be also helpful to provide a summary figure with all the toxins identified and perhaps toxin expression levels. Especially showing cysteine patterns for new toxins would be very useful.

Wherever possible, Results and Discussions were split into subsections according to toxin families, following reviewer’s suggestion.

Figure 2.C summarizes the identified toxin families along with the number of validated sequences for each of them. We provided an excel file with the sequences and expression levels of the identified toxins as supplementary table 2. We have now added a column with cysteine patterns to better define and characterize these toxins

It is unclear why the Toxin annotation pipeline is hidden in the supplementary material. It would be also helpful to show it as a schematic pipeline in the main text.

We have prepared a figure describing the annotation pipeline that is now provided as Fig.1 in the main text.

The identification of proteolytic cleavage sites is not really described. It would be also helpful to mark them at the Figure 2.

We have adjusted the Methods section in the Supplementary Material to give a clearer explanation of the methods applied to identify putative cleavage sites. The figure (now Fig. 3) has been adjusted to include the protease recognition site.

"Other peptides present in E. singularis nematocysts and displaying protease inhibitory domains, but likely lacking a toxin function (Kazal-type, cystatines, antistasins, and macins)..." - why do they likely lack a toxin function? what is the rational behind this statement?

• *While we were referring to a strictly neurotoxic function, the statement is indeed misleading and was removed from the amended text and modified as follows “Other peptides present in E. singularis nematocysts displaying protease inhibitory domains (Kazal-type, cystatines, antistasins, and macins) were detected but did not present novelty elements. Their sequences are described in supplementary data.”

"cell- or tissue-specific differential maturation patterns" - I think the differential maturation needs to be confirmed by additional experiments to exclude a possibility of being an artifact due to low mass spectrometry sensitivity.

This is indeed true. Nonetheless, our proteomic analyses provided quite convincing evidence of this phenomenon. Figure 3 in the manuscript summarizes the output of our PEAKS studio analyses, but for clarity we reported as Suppl. Fig. 1 the original output for the identification of U-GRTX-Esi2a/b.In the figure, each blue line below the precursor sequence denotes a peptide that was confidently identified by LC-MS/MS. As visible, several peptides were identified for this protein in either proteome, but there is a clear pattern pointing toward the complete absence of the first domain in the NEM-P. The Reviewers have rightfully raised concerns that, given the ethanol extraction protocol employed, our NEM-P may be partial and/or contaminated by other extracted proteins. This is true, and in fact we have added cautionary statements throughout the text. It is reasonable to assume, though, that proteins with similar sequence and physicochemical features, like U-GRTX-ESI-2a and 2b, will respond similarly to the ethanol extraction procedure. If present, we believe the first domain (U-GRTX-ESI-2a) should have produced some detectable peptide also in the NEM-P. This seems even more reasonable if we consider that the WB-P contained a much higher number of proteins, which could have led to the loss of detection of some peptides due to instrument settings. With the due caution, we believe it is reasonable to leave our claim in the manuscript, supporting it by adding the Suppl. Fig.1.

"three consecutive ShK domains with peculiar characteristics (Suppl. Fig. 2)" - what are these characteristics?* *

This has been better clarified in the text which now reads “Only the C-terminal domain has the typical ShKT cysteine pattern, whereas the first two domains present an unusual shift of the C-terminal cysteine. None of the domains of U-GRTX-Esi4 presents the key Lys residue necessary for binding KV1.2 and KV1.3, while the subsequent Tyr residue, also important for binding KV1, is extremely conserved”. The reference figure is now Suppl. Fig. 3.

Fig. S1 legend: "Octocorallia (cyano bar) and Hexacorallia (blue bar)" - the bars look pink and cyan.* *

*The figure (now Suppl. Fig. 2) was modified in order to fix this issue. *

* *Referee cross-commenting

I agree with both reviewers that additional validation of the ethanol extraction method would be required to confirm its specificity and efficiency. Since ethanol is widely used for tissue fixation, I would guess that it is improbable that it leads to disruption of other coral cell types in addition to discharging nematocytes. However, to be 100% sure that would need to be confirmed experimentally. I think the suggestion to use Xenia single cell dataset to validate the nematocyst proteome reported in this paper is really worth trying. However, toxin-like genes in cnidarians might be recruited to non-venom cell types (Sachkova et al, 2020; Surm et al 2024) therefore if a gene is nematocyte-specific in one species it does not mean it would the same in another one, especially if they are distantly related. Thus, the best would be to run some additional experiments in Eunicella singularis, if the tissue is available.

We have received this concern and addressed it by rephrasing the text. We have also performed the requested check with Xenia nematocysts single cell data set. In detail, we recovered 243 high-confidence single-copy orthologs conserved between Xenia and E. singularis, which were described as belonging to cluster 11, associated to nematocytes by Hu and colleagues in their 2020 Nature article. We comparatively evaluated the abundance of the peptide fragments that could be mapped to the corresponding de novo assembled contigs in E. singularis whole-body and nematocyst proteomes, finding very little overlap, both with the whole-body, and with the nematocyst proteome. In detail, we found none of the sequences shared with Xenia cluster 11 in the NEM-P, while 16 sequences were retrieved in the WB-P. None of the latter corresponded to toxins, but rather possessed PFAM domains indicative of housekeeping functions.

We believe that these observations are not surprising, due to the following reasons:

(i) as we show in Figure 6, Xenia appears to display a highly divergent venom arsenal not just from Eunicella singularis, but also from all other Octocorallia. Consequently, we can hardly expect any of the main molecular components of the venom to display a 1:1 orthology between the two species. In addition, Xenia is a zooxanthellate species, obtaining most of its energy autotrophically and complementing with the absorption of particulated organic matter. Due to its trophic ecology, we do not expect this species to produce predatory venom.

(ii) although Xenia cluster 11 includes genes specifically expressed in the nematocysts, these do not necessarily encode venom components but also other cellular components from the nematocytes. In contrast, if successful, our approach would yield a fraction enriched in secretory products while other intracellular or membrane-bound proteins that are specifically expressed by nematocytes, are not expected to be particularly enriched in the NEM-P.

In addition, due to the remarkable divergence between these two species, not all Xenia nematocyte-specific transcripts are expected to retain the same specificity also in Eunicella.

Reviewer #1 (Significance (Required)):

This study reports venom composition of an octocoral for the first time. These data are very important for understanding biology and ecology of these animals as they rely on venom for feeding and deterring predators. This study is a significant advancement of the cnidarian venomics as most of the literature is limited to sea anemone and jellyfish venoms. This study will be interesting to the broad audience: venomics and coral ecology communities, evolutionary biologists and marine scientists. The main strength of this work is that it provides a comprehensive overview of the venom system in a widespread octocoral species with important ecological roles. The limitations of this study is that the toxicity and biological function of the identified venom components have not been confirmed experimentally. However, the localisation of the proteins to nematocysts is a very strong indication of being a venom component. My expertise: cnidarian venom (biochemistry, ecology and evolution).

*

*Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary: The authors of this work explore the venom repertoire of octocoral, a group of cnidarians whose venom has largely been ignored in the literature. As a first step into characterizing the venom of octocorals, the authors use a proteo-transcriptomic approach for Eunicella singularis, Specifically, they generated the transcriptome and proteome from whole-body as well as a more specific proteome of the nematocyst, a specialized sub-cellular structure found only in cnidarians and used to inject venom. The nematocyst proteome is a crucial dataset of the manuscript as it allows the authors to discriminate what is most likely a bona fide toxin compared to general physiological proteins.

* Major: However, I have some skepticism regarding the legitimacy of this nematocyst proteome. Specifically, the proteins from this are nematocyst-specific. The authors used an approach to soak the animal in ethanol, which theoretically should cause the nematocyst to fire, releasing the venom housed inside. This is a technique previously used in box jellyfish where they show that indeed the nematocyst have fired using histological approaches. However, this was not validated for Eunicella singularis*. I am hesitant to fully accept that the data from the nematocyst-proteome is specific. Other approaches, such as isolating nematocyst using a percoll gradient, will likely generate a more specific nematocyst proteome. This percoll gradient approach has been used to isolate nematocysts from different species of cnidarians ranging from hydra to sea anemones, however, I recognize that although this approach is robust for different cnidarians, acquiring enough material is challenging and maybe beyond the capacity for this octocoral. I would argue this would be the best approach, but if not feasible I can understand. However, other potential validation could be used to help improve the confidence that this is, at least mostly, nematocyst-specific. Furthermore, one could argue that this ethanol approach used in box jellyfish also specifically used tentacle, a tissue significantly enriched in nematocyst likely greatly improving the specificity in isolating nematocyst-specific proteins. whereas in this study they use a collection of whole polyps, therefore, anything that is extracted from the ethanol would precipitate. This is a much more complex collection of tissues which I would assume could interfere with isolating nematocyst-specific proteins

We thank the Reviewer for these comments. It is indeed true that there are cleaner procedures to extract venom from nematocysts. Preliminary attempts with electrical stimulation of colonies to milk the venom were also performed, but did not yield satisfactory peptide amounts for further analysis. We then decided to attempt ethanol extraction. As also noted by Reviewer #1, ethanol is routinely used for tissue fixation, and we think that it could have only limited effect on other cell types, therefore we assumed that most proteins in this extract had to come from nematocysts firing. While we cannot be sure that we fired all kind of nematocysts from E. singularis, the enrichment of the NEM-P in proteins with typical toxin features (i.e. signal peptide, small size, elaborate cysteines patterns), represented an indirect proof of this hypothesis. We believe this NEM-P may represent a good snapshot of venom components from E. singularis. On the other hand, it is true that the ethanol procedure may introduce some contamination. Indeed, we adopted a conservative approach and discussed in detail only the proteins with toxin-like features. At any rate, we have clearly stated the methodological limitations of our approach in the text and added cautionary statements through the manuscript.

* *A computational approach, that I think is essential, is to use the Xenia single-cell atlas. Xenia is also an octocoral with a nice single-cell atlas in which the cnidocytes form a distinct cluster. The authors can perform a reciprocal best-blast hit with the xenia genome and Eunicella singularis transcriptome and then see if gene-encoding proteins found in Eunicella nematocyst proteome have orthologs with genes found in the Xenia cnidocyte cluster. A statistical test could then be performed to show that there is a significant overlap between the nematocyst proteins from Eunicella and their orthologs in the Xenia cnidocyte cluster. This is still quite indirect but can give some insights. A better approach would be to perform proteomics from Xenia using the ethanol approach and mapping to see where the proteins captured are found in the atlas. This would massively elevate this work and provide proof that indeed this approach using ethanol is capable of precipitating nematocyst-specific proteins. I would strongly recommend trying to provide some evidence that this is indeed a nematocyst-specific protein, or at the least, is significantly enriched. Because this is unknown, many of the interpretations presented downstream are not well supported.

As previously stated in response to Reviewer #1, we have performed the requested check on Xenia nematocyte single cell data set. In detail, we followed the advice provided by the reviewer, extracting the protein sequences of the 432 Xenia genes included in cluster 11 from the work by Hu and colleagues, and recovered the nucleotide sequence of the assembled transcripts of 243 high-confidence 1:1 orthologs from E. singularis. In this process, we paid particular attention to excluding ambiguous matches, such as genes subjected to lineage-specific duplications, and therefore we exploited the availability of the annotated genome of the congeneric species E. verrucosa for the first step of orthology detection (performed through a reciprocal BLASTp approach). In the second step of the analysis, the corresponding assembled transcripts from E. singularis were identified with tBLASTn, assuming an inter-specific divergence This subset of putative nematocyst-specific sequences was subjected to an in-depth analysis, which comparatively evaluated the relative abundance of mapped peptide fragments in the whole-body and nematocyst proteomes. This process led to the identification of very little overlap between Xenia and E. singularis. We believe that these observations are not surprising, due to the following reasons:

(i) as we show in Figure 6, Xenia appears to display a highly divergent venom arsenal not just from Eunicella singularis, but also from all other Octocorallia. Consequently, we can hardly expect any of the main molecular components of the venom to display a 1:1 orthology between the two species. In addition, Xenia is a zooxanthellate species, obtaining most of its energy autotrophically and complementing with the absorption of particulated organic matter. Due to its trophic ecology, we do not expect this species to produce predatory venom.

(ii) although Xenia cluster 11 includes genes specifically expressed in the nematocysts, these do not necessarily encode venom components but also other cellular components from the nematocytes. In contrast, if successful, our approach would yield a fraction enriched in secretory products while other intracellular or membrane-bound proteins that are specifically expressed by nematocytes, are not expected to be particularly enriched in the NEM-P.

In addition, due to the remarkable divergence between these two species, not all Xenia nematocyte-specific transcripts are expected to retain the same specificity also in Eunicella.

Another major issue with the manuscript is the section referring to SCRiPs. First, the authors do not cite Jouiaei, Sunagar et al. (2015) which was the first publication to functionally characterize SCRiPs as toxins. Additionally, the majority of SCRiPs identified in this study and those found in Eunicella have a different cysteine framework. The authors acknowledge this online 245 but claim that, given the alphafold structure is similar, they are from the same gene family. First, I think this is very weak support as typically sharing a conserved cysteine framework is the bare minimum to categorize these toxins in a gene family. Although some cysteine frameworks are somewhat hard to resolve as the space between the cysteines can be variable, in this case, SCRiPs have a very distinct triple repeat of cysteines near the C terminal that is missing in these octocoral SCRiPs. These make me suspicious that these are indeed from the same gene family. Then relying on alphafold to predict the structure and claiming it's similar to Tau-AnmTx Ueq 12-1 from Urticina eques is also fairly weak support. Although I am not an expert in protein structures, I cannot tell from the images comparing the 2 structures in the supplementary figure s1 that these are similar. Perhaps you could align or overlap them, or give some readout of the similarity of these structures. Currently, I am skeptical of any of the SCRiPs described in this manuscript. Additionally, if the authors can show that indeed these are SCRiPs, again I would strongly advise the authors to check the Xenia scRNA-seq to see if these Xenia SCRiP-like sequences are expressed in cnidocytes.

Given the concerns raised by the Reviewer, throughout the text we now referred to octocoral SCRiPs as SCRIP-like proteins or octo-SCRiPs. Reference to Jouiaei, Sunagar et al. (2015) was added. However, we would like to point out that we do not associate them to hexacoral SCRiPs based on their predicted structure similarity: the Suppl. Fig. 2 presents the alignment of the sequences of these proteins with representative sequences from Hexacorallia, highlighting a sequence similarity up to 68%. Considering the high level of sequence divergence generally recognized within toxin families, this high similarity value contributes to support our claims. Despite the relevance of the cys framework in defining toxin families, a single amino acid shift is not necessarily indicative of a new structural family.

Concerning the structural comparison between SCRiPs and octo-SCRiPs, Suppl. Figure 2.B has been replaced with a superposition of the structure of AnmTx Ueq 12-1 with the model of U-GRTX-Esi1a. The structures were aligned with TM-align, resulting in a Cα RMSD for the aligned region of 1.86 Å, which confirms the strict similarity of the two proteins.

Unfortunately, we need to rely on available genome annotations for the evaluation of the Xenia scRNA-seq data. The only currently annotated Xenia gene showing significant homology with the SCRiP-like of E. singularis (Xe_002907) has a highly different organization, as it shows five consecutive cysteine-rich domains, and is therefore not orthologous to any of the three sequences we report in the present work. In the paper by Hu and colleagues, Xe_002907 is associated to cluster 2, which was unrelated with nematocysts.

* Minor:

*The ShK protein, U-GRTX-Esi4, strikes me as similar to NEP3 gene family identified in Nematostella, which also has 3 ShK domains (Columbus-Shenkar et al. 2018).

We have added reference to the NEP3 family in the text and discussed the similarities of U-GRTX-Esi4 with its members, highlighting that while in NEP3 the mature toxin corresponds only to the first ShK domain, U-GRTX-Esi4 is supported as a multidomain protein by our proteomic analyses.

Interestingly U-GRTX-Esi20 and 21 were found to be structurally similar to acrorhagin 1a but do not share a conserved cysteine framework ( 6 cysteines vs 8). One thing that the authors should be careful of, and perhaps point out that this is indeed not nematocyst-specific, is that an ortholog acrorhagin 1a was found to be expressed in the neurons in Nematostella (Sachkova et al. 2020). Perhaps ancestral acrorhagin 1 was found in the last common ancestor of Anthozoa but was a neuropeptide that got recruited to the venom in Actinia.

Because of the methodology employed, we expected the NEM-P to be a toxin-enriched subset of the WB-P. Indeed, some of the toxin-like proteins detected in the NEM-P were not observed in the WB-P, where they might have been below the LOD during proteomic analysis. On the other hand, being a whole-body proteome, we expect the WB-P to contain ALSO nematocyst specific proteins. At present, the detection of U-GRTX-Esi20 and 21 in the WB-P does not rule out that these may be nematocyst specific, whereas their presence in the NEM-P, in our view, confirms their occurrence in the venom. At any rate, given the current level of evidence, this Reviewer is right in considering all possibilities, such as their neuropeptide nature. These considerations have been added to the text.

* Also in general the authors refer to a lot of phylogenetics that I cannot see in the paper. For example, on line 339: "Our genomic survey indicates that these two toxins belong to two distinct monophyletic orthogroups within a very large superfamily of cysteine-rich peptides, encoded by ancestrally duplicated paralogous genes with intronless structures, that also include other members in E. singularis, not detected in the NEM-P." *What genomic survey are you referring to (where is this data)? What do you mean by "belong to two distinct monophyletic orthogroups".

In the attempt to keep the manuscript more concise, we concentrated comparative genomic analyses in the supplementary material. We now provide in the main text a detailed phylogenetic tree that displays the complex evolutionary relationships between U-GRTX-Esi20 and 21 and a number of other related sequences sharing significant sequence homology and predicted structural organization (Figure 6). In detail, the two Eunicella toxins belong to two groups of sequences, labeled as “type I” and “type VI” which are highly supported by robust bootstrap values (94 and 95, respectively) as monophyletic within Malacalcyonacea. Notably, we could identify four additional monophyletic groups, characterized by similar support values, that included sequences from both Eunicella and other Malacalcyonacea species (type II, III, IV and V). Nevertheless, these sequences were not identified as venom components by our proteomic analyses. Related proteins were also identified in species belonging to Scleralcyonacea, even though their precise relationships with those of Malacalcyonacea were often unclear.

Also, there is no visualization of the results when the authors refer to the genomic surveys, especially when referring to intron-exon boundaries. Please include which genomes include which sequences and their given intron-exon boundaries for a given gene family. I do not understand how the authors resolved figure 4. How do you know there was a loss not a gain of f exon 2 in the gene encoding for U-GRTX-Esi17. Providing the genomic loci for the toxin gene families would help. Maybe something like figure 5 from Koludarov et al. (2024) would be useful, but ideally including intron-exon boundaries.

The scenario we propose is far more parsimonious than the alternative hypothesis involving an intron gain, since this would have required an extremely complex combination of far less likely events, i.e. the independent acquisition of two partial colipase-like arrays in positions compatible with the generation of a complete colipase-like cysteine array. Despite being theoretically possible, we believe this scenario to be highly unlikely, also considering the well-established differences between the rates of intron gain and intron loss in eukaryotes, with the latter exceeding the former by several orders of magnitude (see Roy and Gilbert, 2005, https://doi.org/10.1073/pnas.0500383102).

We present a supplementary figure which schematically displays the architecture of the genes encoding novel putative venom components described in this manuscript. We need to remark the fact that, as mentioned in the main text, no genome assembly is presently available for E. singularis, and therefore such gene architectures have been inferred from the congeneric species E. verrucosa. Despite being certainly interesting, the approach proposed by the reviewer referring to figure 5 from Koludarov et al., which would basically involve a microsynteny analysis for all loci, would go far beyond the aims and scopes of the present work and require an unreasonable workload, with a very marginal increase in the quality of the data we report. First and foremost, no genome assembly is available for our target species. Moreover, just a very few genomes of Octocorallia are associated with publicly available gene annotations (in detail, no gene annotation tracks are available for R. reniformis, P. caledonicum, V. gustaviana, P. papillata, Chrysogorgia sp., H. coerulea, P. subtilis, Trachytela sp. and M. muricata). The lack of existing annotations does de facto prevent the possibility of retrieving flanking genes and providing evolutionary insights at the level requested by the reviewer. We believe that the manual annotation of the target genes of interest in all analyzed species fully meets the objectives of this study.

In the methods the author's mention:

"Whenever needed (i.e., U-GRTX-Esi20 and 21), a fine-scale classification of orthologous sequences was aided by Maximum Likelihood phylogenetic inference analyses, carried out with IQ-Tree [49] with 1000 ultrafast bootstrap replicates based on the best-fitting model of molecular evolution detected by ModelFinder [50]."

So please include this data as supplementary figures. The authors did plenty of analysis they refer to but do not include this in the paper. This lack of data makes it very hard to follow many of the phylogenetic and genomic insights from this manuscript.

The phylogenetic tree which concerns U-GRTX-Esi20 and 21 has been added in the main text as Figure 6. In pretty much all other cases where we referred to comparative genomics analyses, our inferences were simply based on the detection (or lack thereof) of orthologous genes. Considering the narrow taxonomic distribution of most target sequences, which prevents the possibility of identifying suitable outgroups for tree rooting purposes, and their usual presence as single-copy genes in E. singularis, we don’t think that adding phylogenetic trees would add useful information to the manuscript. Nevertheless, we have added the multiple sequence alignments of all relevant groups of orthologous sequences as supplementary figures.

• *Reviewer #2 (Significance (Required)):

* *This work is very can be very useful in extending our knowledge of venom in cnidarians and can help build better resolution of the evolutionary history of the ecologically essential proteins

* *Reviewer #3 (Evidence, reproducibility and clarity (Required)):

*

*SECTION A - Evidence, reproducibility and clarity

* =================================================

Summary: *Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

* This manuscript describes the proteotranscriptomic analysis of samples from the coral Eunicella singularis. A number of putative venom toxins are identified. In silico structural analyses are performed for select putative toxins and inferred activity/function is discussed. In my opinion the subject of the study is important. However, I have some important questions about the methodology (regarding "venom collection" and assignment of "venom components"), and given the preliminary nature of the study I found some of the conclusions (regarding activity) somewhat overstated. *Major comments:

• Are the key conclusions convincing?

* While some conclusions were justified, I felt unconvinced by others. Some of my pessimism stems from the technique used to extract the venom i.e. ethanol immersion. I'm not familiar with the use of this technique, however it strikes me as likely to be associated with some limitations. For example, while the nematocysts may indeed discharge their contents I would expect some contents e.g. larger proteins to be insoluble. Was this considered? This would have some major impacts on the conclusions drawn e.g. *(L418: "absence, in the NEM-P of E. singularis, of the common cnidarian cytolytic proteins." AND (L492): "conventional pore forming toxins (PFTs) of Cnidaria, including the aerolysin-like Δ-GRTX-Esi29 and the two actinoporins Δ-GRTX-Esi30 and 31 were not retrieved in the nematocysts' proteome."

Because of this observation, the authors concluded that these were not venom components in this species and speculated on other functions. However, I can't help wondering if these were simply excluded from analysis as a result of the ethanol extraction i.e. a false negative.

As anticipated in our response to Reviewer #1, we opted for ethanol extraction due to sample limitation and unsuccessful attempts with other venom collection protocols. The procedure we employed was first described by Jouiaei et al., 2015, to extract venom from the tentacles of Chironex fleckeri. Proteins and peptides extracted from the nematocysts were indeed precipitated from ethanol and subsequently resuspended for proteomic analysis. The original protocol by Jouiaei et al. used precipitation at -80°C to recover the proteins from ethanol. Albeit denaturing, this protocol should not imply sample losses. Large proteins that did precipitate were still resuspended and analyzed. We have introduced an evaporation/lyophilization step, which should not alter the outcome. In fact, we did detect higher molecular weight proteins in the NEM-P (mostly structural and enzymes). While denaturation and precipitation may functionally inactivate these proteins, these should all be detected by proteomics. The authors of the original paper presented a comparison between the venom obtained from ethanol extracted tentacles and the proteome of pressure disrupted purified nematocysts. In both cases, additional “non venom” and “structural” proteins were also detected (e.g. histones, filamin, ribosomal proteins, myosin, actin, collagen…). Given the prevalence of toxins or toxin-like proteins in our extract, we were reasonably convinced of the success of the extraction protocol. For sure, the method may present limitations: as also observed by Reviewer #1 and #3, contamination with non-nematocyst proteins is possible. This has also been considered. In fact, we adopted a conservative approach, choosing to discuss in detail only proteins with structural similarities with known toxins and/or typical toxin-like features. On the other hand, as noted by this Reviewer, our results may be partial, but, in our opinion, this would be most likely due to incomplete nematocysts firing rather than to sample loss. All these possibilities have now been better discussed and addressed in the text. At any rate, we are convinced that the protein diversification detected in the NEM-P is indicative of the presence of several venom components and provides a first indication of the existence of novel, octocoral-specific, venom protein families.

Comparisons were made to other tissue samples (whole bodies). Were these samples prepared in the same way i.e. ethanol extraction? If not, the power of any comparisons would be limited.

Following the described experimental approach, we expected the NEM-P to be a subset of the WB-P, for which no purification/enrichment of sort was performed. In fact, we reported both proteomes to confirm the enrichment of the NEM-P in venom components, highlighting the presence of putative toxins that might have been below the instrumental limit of detection in the more crowded whole body protein extract. At any rate, we have now modified the text, adding cautionary statements that may also explain our results.

• *It was unclear to me exactly how "venom components" (Fig. 1A) were defined. Why are "enzymes" , "structural" and "unknow" NOT considered venom components when they were identified in the "venom" extract?

The “structural” and “enzymes” categories were used to analyze the hits in the NEM-P. We decided to discuss only putative neurotoxins or cytolytic toxins based on the limited selectivity of the extraction protocol employed and on the lack of histological control. As structural components and enzymes, in the absence of a crude venom extract, may derive from other tissues, we preferred not to discuss them. We hope this is clearer in the amended version of the manuscript.

Furthermore, a large proportion of proteins detected are "structural" - doesn't this suggest that the "venom" extract included a large proportion of false positives i.e. non-toxin proteins? Is it possible that some of the proteins which are considered as "venom components" are also false positives?

• *As also noted by Reviewer #1, aside from contamination from other tissues, some of the toxin-like proteins we identified may have different functions (e.g, neuronal, developmental) and their toxin function is presumed on the basis of structural features. This issue is clearly addressed in the manuscript. Nonetheless, putative toxins are definitely enriched in the NEM-P compared to the WB-P, which leads us to believe that the NEM-P is a fraction enriched in nematocysts content. This is now more evident also in the PEAKS output files, provided as Supplementary Tables 2 and 3.

The nematocyst ethanol extract is referred to throughout the manuscript as "venom". Similarly, what I would consider putative toxins are referred to throughout the manuscript as "toxins". Given the preliminary nature of the study I suggest the authors consider rewording these.

This has been changed throughout the text.

In short, the evidence presented left me unconvinced that the nematocyst ethanol extract that was analysed represented the genuine "venom" of this species and that the "toxins" identified represent the genuine toxin repertoire. The authors should at least discuss potential limitations, defend my claims in this context and adjust conclusions accordingly.

We hope that the additional clarifications provided in the Results and Discussion section, and the amendments we made throughout the manuscript made our statements more convincing

Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? See comment above regarding venom collection and conclusions drawn.

We have introduced cautionary statements throughout the text.

* *Also, despite the absence of any experimental activity/functional data, there was a lot of inference about activity and function.

A few examples: L299 - "might have acquired peculiar biological activity."

L301 - "support their relevance for the predatory and/or defensive strategies…"

L326 - "abundance of this protein suggests a strong functional relevance…"

L358 - "the structure presented a SCRiP-like W-shaped fold, indicative of a potential neurotoxic function."

L427 - "suggestive of a peculiar chemical selectivity towards different lipids"

L506 - "the cytolytic activity seems to be ascribable mostly to the six saposins"

* *I suggest some removal or rewording throughout the Results/Discussion section to reflect the fact that most of this is purely speculative.

This has been modified according to the reviewer’s suggestions.

Regarding the following statement on L300 - "Notably, the transcripts for all these toxins had exceptionally high TPM values (1806, 569, 826 and 429, respectively for the U-GRTX-Esi14 to 17/18), which support their relevance for the predatory and/or defensive strategies of Eunicella singularis." These TPM values don't seem high to me e.g. 1806 TPM = 0.0018% of transcripts. How do these numbers compare to other "non-venom" components of the transcriptome? A graph illustrating this would be helpful.

We thank the Reviewer for this suggestion. The expression values we report in this work were calculated based on an RNA-seq library generated from a whole body sample. Consequently, considering the low relative abundance of nematocysts to total body weight, we expect that the contribution of this cell type to the total extracted RNA to be rather low. We exploited the available information from a previously published single-cell RNA-seq dataset obtained from another octocoral species (i.e. Xenia, see Hu et al., 2020, Nature) to identify the most likely candidate nematocyst-specific mRNAs venom components having a 1:1 orthology relationship with E. singularis. In detail, we were able to detect high-confidence 1:1 orthologs for 242 out of the 432 Xenia genes included in cluster 11 in the study by Hu and colleagues (i.e. the cluster associated with nematocysts). This allowed us to assess the expression of the orthologous sequences, expected to share a similar cell-specificity, in E. singularis. The 242 putative nematocyst-specific mRNAs displayed an average expression level of 16.65 TPM (median = 4.85 TPM) in the whole body sample, and just 8 out of these (i.e. about 3% of the total) had an expression level higher than 100 TPM. Based on these observations, we believe that our statement that “all these toxins had exceptionally high TPM values” holds true. Supplementary table 2 reports the sequences of the toxins identified in the NEM-P together with the TPM of the corresponding transcripts.

Regarding the following statement on L463 - "Our investigation unequivocally demonstrated that Octocorallia do produce venom" Was it not already known that Octocorallia have nematocysts and therefore are venomous (in which case this should be cited)? If this wasn't known, I don't think this study was really designed to test this hypothesis. Regardless, I don't think this is a meaningful claim to make here.

This observation is correct. We have rephrased the text accordingly.

Table S2: on what basis are the sequences highlighted in red considered "proteomics validated" e.g. confidence, coverage? Could a protein abundance column be included in this table (for NEM and WB tissues)?* *

Residues highlighted in red in Table S2 (now Suppl tab. 4) correspond to the tryptic peptides identified with good confidence by the LC-MS analysis. We have added supplementary files, as per request of Reviewer #1, with the summary of the PEAKS Studio outputs for the two proteomes, highlighting the confidence and coverage scores. In Suppl. Tab. 4, coverage has been recalculated considering the sequence of the predicted mature peptide (not the precursor identified by PEAKS Studio). Finally, as PEAKS Studio does not provide a quantitative measure of the identified peptides (i.e., counts), we have calculated and added to said tables the exponentially modified Protein Abundance Index (emPAI), which provides an approximate label free measure of each protein’s abundance. We have also added the relative emPAI, which normalizes each protein's emPAI value relative to the total emPAI of all proteins in the sample, providing a percentage abundance. It is noteworthy that all the proteins that have been identified as putative toxins have higher relative emPAI values in the NEM-P, thus providing yet an additional indirect proof of the validity of the ethanol extraction protocol (see Suppl. Tab. 2 and 3).

• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. *Additional experiments e.g. synthesis and activity assays would go a long way towards bolstering some of the conclusions. However, if some of the conclusions can be toned down a little (see comments above), I don't consider these to be essential.

In my opinion, the study would benefit from some additional analyses (described in the comments above).

See our answers to the specific comments above.

Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

N/A* * Are the data and the methods presented in such a way that they can be reproduced?

Yes. * Are the experiments adequately replicated and statistical analysis adequate? *No - I may be wrong, but as far as I can tell from the text, replicates were not collected. Three cDNA libraries were generated but were these replicates (please clarify this in the Methods)? It could be reasonably argued (and I would mostly agree) that replicates are not necessary for a general analysis of the composition of the samples. However in a couple of instances conclusions are drawn based on "differential expression". I suggest that in the absence of expression level replicates these conclusions should be withdrawn.

The statements about differential expression (more correctly differential maturation) are based on proteomics results and not on DEG analysis in the transcriptome (see also reply to reviewer #1). All the claims have been rephrased and the supplementary figure 1 has been added to support our statements.

Concerning the cDNA libraries, however, they were prepared as technical replicates to account for variations in venom expression among samples, and the resulting assemblies were pooled before assembly, as explained in the Methods section.

• *"Abundance" of proteins or toxins was mentioned on occasion, but no data on quantification or abundance of proteins is mentioned anywhere (although this is something that could be done with the LC-MS/MS data). In my opinion these data would be very useful and should be included, especially if mentioned in the text.

• *As previously discussed, we have calculated and added to the PEAKS output file the emPAI and the relative emPAI values. These data are now provided in the supplementary Tables 2 and 3.

Minor comments:

* *Specific experimental issues that are easily addressable.

Are there limitations to the ethanol extraction procedure (please add a paragraph in the Discussion)? Are there any previous studies using this procedure?

This has been done: the potential drawbacks of the ethanol extraction procedure are now addressed in the Results and Discussion section.

* *Are prior studies referenced appropriately?

Yes, for the most part (but see comment above).

* *Are the text and figures clear and accurate?

In general yes, although I found myself looking for actual data. Most of the current figures are summaries or cartoons. I would have liked to have seen pictures of the species in question (including a picture/diagram of the tissue from which the cDNA libraries and proteomes were derived); a picture of the nematocysts; the total ion chromatogram of the "venom"; Some type of figure to place the "toxin" expression level in the context of all transcripts; some more of the actual sequences identified including alignments (in the main text rather than the SI);

Various figures in the manuscript have been modified in accordance to the Reviewers’ suggestions. We have included a workflow of the extraction with a picture of E. singularis and modified Fig1 (now Fig 2) to include the TIC of the NEM-P.

Figure 4: could the motifs and termini for each be labelled please.

This has been done.

Do you have suggestions that would help the authors improve the presentation of their data and conclusions? See comments above. In my opinion, the work done was quite preliminary (i.e. analysis of a single species and does not include any activity/functional data) but still significant and useful to the field. I felt that some of the conclusions were unnecessarily over-reaching and could be toned down without detracting from the importance of the manuscript.

Several instances of hyperbole could be toned down e.g. use of the words: remarkable (L27); rich (L28); intricate (L38); significant (L189); peculiar (L299, 427); only (L191); exceptionally (L300); extremely (L316); strong (L326). Similarly, some wording is subjective e.g. "worthy of" (L33); "interestingly" (L220, 382, 426, 492, 535). Please amend.

We have toned down our statements through the manuscript.

"Homology" is used throughout when referring to similarity. Please change.

This has been done

Minor typos and similar:

2.5 cm (L97) - use 25 mm (cm is not a standard scientific measure).

30" (L97) - 30 min?

ml (L97) - mL is technically correct although some journals use ml, regardless should be consistent throughout. Reverse-phase (L127) – reversed-phase

30,000 (L141) – units?

Typos were corrected.

*

*Reviewer #3 (Significance (Required)):

*

*SECTION B – Significance

* ========================

*- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

* *Cnidarian venoms and toxins have been the subject of extensive study over the past several decades. However there has been very little work performed on corals. In this respect, this subject of this manuscript is significant.

* *- Place the work in the context of the existing literature (provide references, where appropriate).

* *The subject of this manuscript i.e. the characterisation of the venom composition of a coral is an interesting topic. The work is rather preliminary, but still represents an important addition to the literature (without requiring overinterpretation of the results-see comments above).

* *- State what audience might be interested in and influenced by the reported findings.

* *I would expect the manuscript to be of interest to others working in the toxinology field, particularly those working on Cnidarian venoms or toxins.

* *- Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

* *Venom; Toxins; Pep

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The study aimed to better understand the role of the H3 protein of the Monkeypox virus (MPXV) in host cell adhesion, identifying a crucial α-helical domain for interaction with heparan sulfate (HS). Using a combination of advanced computational simulations and experimental validations, the authors discovered that this domain is essential for viral adhesion and potentially a new target for developing antiviral therapies.

Strengths:

The study's main strengths include the use of cutting-edge computational tools such as AlphaFold2 and molecular dynamics simulations, combined with robust experimental techniques like single-molecule force spectroscopy and flow cytometry. These methods provided a detailed and reliable view of the interactions between the H3 protein and HS. The study also highlighted the importance of the α-helical domain's electric charge and the influence of the Mg(II) ion in stabilizing this interaction. The work's impact on the field is significant, offering new perspectives for developing antiviral treatments for MPXV and potentially other viruses with similar adhesion mechanisms. The provided methods and data are highly useful for researchers working with viral proteins and protein-polysaccharide interactions, offering a solid foundation for future investigations and therapeutic innovations.

Weaknesses:

However, some limitations are notable. Despite the robust use of computational methodologies, the limitations of this approach are not discussed, such as potential sources of error, standard deviation rates, and known controls for the H3 protein to justify the claims. Additionally, validations with methodologies like X-ray crystallography would further benefit the visualization of the H3 and HS interaction.

Thank you very much for the evaluation and appreciation of our work. In response to the identified weakness, we have conducted additional analyses to further assess the limitations of the computational methodologies used. Specifically, we predicted the MPXV H3 structure using two other AI-based protein structure prediction models, ESMFold and RoseTTAFold2. Both models also predicted an a-helical structure, which supports our conclusion. However, they yielded lower pLDDT scores (Figure S1A-C in the revised SI), indicating that some error may be present.

We agree with this reviewer, as well as the other reviewers, that X-ray crystallography data for the H3 structure would be highly valuable. Unfortunately, we lack the expertise in structural biology to obtain these results at this stage. To complement this, we performed molecular dynamics (MD) simulations, which suggest that the helical domain is connected to the main domain via a flexible linker. This flexibility may help explain the challenges in obtaining a high-resolution X-ray structure. In fact, to date, the only structural data available for H3 is from the VAVC, which excludes the helical domain (The helical domain part is cleaved for the X-ray studies). We have added this point to the discussion and hope that experts in structural biology will be able to resolve the structure of this domain in the future.

Reviewer #2 (Public Review):

Summary:

The manuscript presenting the discovery of a heparan-sulfate (HS) binding domain in monkeypox virus (MPXV) H3 protein as a new anti-poxviral drug target, presented by Bin Zhen and co-workers, is of interest, given that it offers a potentially broad antiviral substance to be used against poxviruses. Using new computational biology techniques, the authors identified a new alpha-helical domain in the H3 protein, which interacts with cell surface HS, and this domain seems to be crucial for H3-HS interaction. Given that this domain is conserved across orthopoxviruses, authors designed protein inhibitors. One of these inhibitors, AI-PoxBlock723, effectively disrupted the H3-HS interaction and inhibited infection with Monkeypox virus and Vaccinia virus. The presented data should be of interest to a diverse audience, given the possibility of an effective anti-poxviral drug.

Strengths:

In my opinion, the experiments done in this work were well-planned and executed. The authors put together several computational methods, to design poxvirus inhibitor molecules, and then they test these molecules for infection inhibition.

Weaknesses:

One thing that could be improved, is the presentation of results, to make them more easily understandable to readers, who may not be experts in protein modeling programs. For example, figures should be self-explanatory and understood on their own, without the need to revise text. Therefore, the figure legend should be more informative as to how the experiments were done.

Thank you very much for your appreciation of our work and your support. In response to the identified weakness, we have carefully reviewed all the figure legends to ensure they are more informative.

Reviewer #3 (Public Review):

Summary:

The article is an interesting approach to determining the MPOX receptor using "in silico" tools. The results show the presence of two regions of the H3 protein with a high probability of being involved in the interaction with the HS cell receptor. However, the α-helical region seems to be the most probable, since modifications in this region affect the virus binding to the HS receptor.

Strengths:

In my opinion, it is an informative article with interesting results, generated by a combination of "in silico" and wet science to test the theoretical results. This is a strong point of the article.

Weaknesses:

Has a crystal structure of the H3 protein been reported?

The following text is in line 104: "which may represent a novel binding site for HS". It is unclear whether this means this "new binding site" is an alternative site to an old one or whether it is the true binding site that had not been previously elucidated.

Thank you very much for your thoughtful evaluation and appreciation of our work.

We agree with this reviewer, as well as the other reviewers, that X-ray crystallography data for the H3 structure would be highly valuable. Unfortunately, we are not experts in structural biology, and we have not yet been able to obtain these structural results. To date, the only structure available for H3 is the one from VAVC, which does not include the helical domain. We have included this point in the discussion and hope that experts in structural biology will be able to resolve the structure of this domain in the future.

Regarding the "novel binding site," this term refers to "the true binding site that had not been previously elucidated." Previous research identified that H3 binds to heparan sulfate (HS), but the exact binding site had not been determined.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Validation of Results with Other Experimental Methods: While single-molecule force spectroscopy and flow cytometry provide valuable data, including complementary methods such as X-ray crystallography could offer additional insights into the H3-HS interaction and the effectiveness of the inhibitors.

Discussion of Computational Model Limitations: Although the use of AlphaFold2 and other advanced tools is a strength, it is important to discuss the limitations of these models in more detail, including potential sources of error and how they may impact the interpretation of the results.

During the manuscript evaluation, it is not clear the protein localization (transmembrane?) since the protein`s end is very close to the virus membrane surface. All experiments demonstrated the protein without being anchored to the membrane, letting the interaction site always be exposed. If the protein is linked to the membrane, how would the site be exposed due to the limited space between it and the virus structure?

Thank you for these insightful comments. As you pointed out, the H3 protein, particularly the helical domain at the C-terminal, is indeed located close to the membrane, which could limit the available space for H3 binding. To investigate this further, we modeled the full-length H3 protein in the context of the membrane and performed molecular dynamics (MD) simulations to assess the available space. Our results show that there is more than 1 nm of space between the helical domain and the membrane, which should be sufficient for potential heparan sulfate (HS) binding (see Figure 1E, and Figure S1D&E in the revised manuscript).

Minor corrections:

Line 31: "is an emerging zoonotic pathogen" should be revised to reflect that Mpox is a re-emerging virus, given its history of causing outbreaks, such as in 2003.

Line 71 and Line 75: Adding an explanation of "Mg binding sites" and "GAG motifs" would enhance reader understanding, as these represent important points in the study. The current positioning of Figure 1 causes some confusion for the reader.

Line 111: High score? What controls were used for the protein? Are there known inhibitors of H3? If so, why weren't they tested for structure comparison? Additionally, what about other molecules that H3 binds to, such as UDP-Glucose, as demonstrated in the base article for the Vaccinia virus H3 protein available in the PDB?

Figure 2B: Improve the legend, as the colors of the lines are not clear.

Thank you for your instructive comments. We have addressed most of them in the revised manuscript.

Regarding the "high score," AlphaFold2 provides a confidence score for its protein structure predictions, with a maximum score of 100. A score above 80 indicates a high level of confidence in the prediction.

There are known inhibitors (such as antibodies) of H3, and while the sequence is available, no structure has been reported so far. Previous s NMR titration measurements have shown that UDP-glucose binds to H3, but no structural data for the complex exist. To date, the only available crystal structure is of a truncated H3, which does not include the helical domain we identified from VAVC.

Reviewer #2 (Recommendations For The Authors):

The text described in the result section does not match the text presented in Figures. So, it is not easy to see what are the authors referring to when they mention the Figure. For example, the text referring to Figure S8 mentions the GB1 domain and the Cohesin module, but these are not mentioned in Figure S8.

I do not understand the results presented in Figure 5B. It is not clear to me, from the Figure legend nor after reading the Material and Methods, how this experiment was done. Specifically, what is plotted on X, is it the amount of inhibitor or the amount of protein? These things have to be checked through the manuscript.

It would be interesting to confirm if the inhibition of infection is based on the inhibition of viral binding to the cells. This should not be complicated to realize, and it could provide evidence for the mechanism of action.

Extensive use of terms like "this domain" is not good in this type of article, like in lines 207, and 211. It is not always clear to what domain are authors referring to, so it may be much better to mention the domain in question by the exact name.

Line 337, If I am not mistaken dilutions are serial not series.

Line 613, in methods. Please use g force instead of rpm, it is more informative. Even if it is just to pellet cells.

Thank you very much for your instructive comments. We have addressed most of them in the revised manuscript. For instance, the immobilization of the GB1 domain and the cohesin module is now mentioned in Figure S9. Additionally, in the previous Figure 5B, the "x" represents the concentration of the inhibitor. Serial and g force is updated.

Reviewer #3 (Recommendations For The Authors):

Line 190

Did you mutate all the amino acids at the same time? What was the impact of all these mutations on the structure of the helical region? Or if you modeled the protein again after replacing these 7 amino acids, did you find that there was no difference? Regardless of your answer, you must include a superposition of the mutated structure and the wt.

Thank you for the insightful comment. We have now also predicted the structure of the serine mutant using AlphaFold2 (AF2). As expected, the helical domain structure remains largely preserved with only minor differences. We have included these results in Figure S6, as suggested.

Figure 2D

In this graph, the authors should indicate the ΔG as a negative value. In fact, the graph does not match the text.

Thanks for the reminder, it is corrected in the graph

Figure 4B

Is the difference in binding force significantly different? 28.8 vs 33.7 pN

The absolute difference in binding force is not large (~5 pN). However, for a system with a relatively low binding force, this difference is significant. Specifically, the 5 pN difference accounts for approximately a 14% reduction in binding force. We have included this percentage in the revised manuscript.

Figure 5

If AI-PoxBlocks723 was the only peptide effective in inhibiting viral infection of MPOX and other related viruses but not with 100% effectiveness, do you think this could be a consequence of a low interaction efficiency or the existence of a different receptor? Or a secondary region of binding in the H3? Can you argue about this?

It has been proposed that there are other adhesion proteins for MPXV, such as D8, in addition to H3. We believe this accounts for the observed less-than-100% effectiveness.

The use of peptides as "inhibitory tools" could have an interesting effect in vitro, however, in vivo the immunological response against the peptide will reduce/eliminate it, how you may optimize the "drug" development with this system, as you state in line 387.

Thank you for your thoughtful comment. You are correct that the use of peptides as inhibitory tools could induce an immune response in vivo, which might limit their effectiveness over time. To optimize this approach for drug development, conjugate the peptides with carrier molecules, such as liposomes, nanoparticles, or dendrimers, which can protect the peptides from immune detection and improve their delivery to target cells. This could allow for more controlled and sustained release of the peptide in vivo, reducing the chances of immune clearance. We have added this discussion in the revised manuscript.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

Previous work has shown that the evolutionarily-conserved division-orienting protein LGN/Pins (vertebrates/flies) participates in division orientation across a variety of cell types, perhaps most importantly those that undergo asymmetric divisions. Micromere formation in echinoids relies on asymmetric cell division at the 16-cell stage, and these authors previously demonstrated a role for the LGN/Pins homolog AGS in that ACD process. Here they extend that work by investigating and exploiting the question of why echinoids but not other echinoderms form micromeres. Starting with a phylogenetics approach, they determine that much of the difference in ACD and micromere formation in echinoids can be attributed to differences in the AGS Cterminus, in particular a GoLoco domain (GL1) that is missing in most other echinoderms.

Thank you for the summary.

Strengths: 

There is a lot to like about this paper. It represents a superlative match of the problem with the model system and the findings it reports are a valuable addition to the literature. It is also an impressively thorough study; the authors should be commended for using a combination of experimental approaches (and consequently generating a mountain of data). 

Thank you.

Weaknesses: 

There is an intriguing finding described in Figure 1. AGS in sea cucumbers looks identical to AGS in the pencil urchin, at least at the C terminus (including the GL1 domain). Nevertheless, there are no micromeres in sea cucumbers. Therefore another mechanism besides GL motif organization has arisen to support micromere formation. It is a consequential finding and an important consideration in interpreting the data, but I could not find any mention of it in the text. That is a missed opportunity and should be remedied, ideally not only through discussion but also experimentation. Specifically: does sea cucumber AGS (SbAGS) ever localize to the vegetal cortex in sea cucumbers? Can it do so in echinoids? Will that support micromere formation? 

Thank you for pointing this out. 

To respond to the Reviewer’s request, we synthesized sea cucumber (Sb) AGS based on the sequence available in the database and tested it in the sea urchin (Sp) embryos, which is enclosed in Fig. S3. We performed this experiment to confirm that SbAGS localizes less at the vegetal cortex than SpAGS as a proof of principle. However, we hesitate to conduct further studies using the synthetic sequence in this study. Sea cucumbers are an emerging yet understudied model. This species is not readily available or established as a model system for embryology. Even for the two species (A. japonicus in Japan and P. parvimensis in the USA) that were previously used for embryonic studies, their gametes are typically available only for 12 months in a year. Since some echinoderm researchers are aiming to establish sea cucumbers as a model system in the near future (see 2024 review: PMID: 38368336), we hope to be able to have better access to their embryos in the future. Yet, it may require a few more years to reach that condition.

In this revised manuscript, we explained the above details and further added the discussion described below. All of the experimental models used in this study are wild animals obtained from the ocean, raising the standard for reproducibility. However, handling wild animals could come with challenges. We hope that the reviewer understands the unique benefits and challenges of this study.

Discussion:

Previous studies (PMIDs: 17726110; 21855794) suggest that GL1 is not involved in intramolecular interaction with TPR domains. This allows GL1 to interact independently with Gαi for cortical recruitment yet without influencing other GLs for AGS activation. To ensure GL1's independence, GL1 is typically located distantly from other GLs in Pins (flies), LGN (humans), and AGS (sea urchins). Based on this prior knowledge, we speculate three scenarios for sea cucumber (Sb) AGS not being able to localize or function during asymmetric cell division (ACD): 1) GL1 and GL2 are located too close to each other, compromising GL1's independence for recruitment. 2) A lack of GL4 loosens the autoinhibition state. 3) The GL1 sequence of SbAGS is quite different from that of echinoids’ AGS (Figure S2), compromising its recruiting efficacy. 

For 1), we tested this possibility by making the SpAGS-GL1GL2 mutant that has GL1 and GL2 next to each other (Fig. 4G). This mutant indeed compromised its cortical localization and function in ACD. For 2), we showed that the lack of GL4 partially compromised ACD in SpAGS (Fig. 3F), suggesting that GL4 supports ACD. For 3), The results in Figure 4 indicate that the position but not the sequence of GL1 is critical for ACD. Based on these observations, we speculate a combination of 1) and 2) compromised SbAGS's ACD function. However, it is still possible that a significant difference in the GL1 sequence diminished its function as GL entirely. Future studies should address these remaining questions directly in the sea cucumber embryos once they are established as a model system in the near future (PMID: 38368336)

The authors point out that AGS-PmGL demonstrates enrichment at the vegetal cortex (arrow in 5G, quantifications in 5H), unlike PmAGS. AGS-PmGL does not however support ACD. They interpret this result to indicate "that other elements of SpAGS outside of its C-terminus can drive its vegetal cortical localization but not function." This is a critical finding and deserves more attention. Put succinctly: Vegetal cortical localization of AGS is insufficient to promote ACD, even in echinoids. Why should this be?  

Thank you for the suggestion. We revised our wording to be more succinct. Of note, as we noted in the text, AGS-PmGL has only two GL domains, which will likely not provide the full force to control ACD and result in insufficient ACD function.

The authors did perform experiments to address this problem, hypothesizing that the difference might be explained by the linker region, which includes a conserved phosphorylation site that mediates binding to Dlg. They write "To test if this serine is essential for SpAGS localization, we mutated it to alanine (AGS-S389A in Fig. S3A). Compared to the Full AGS control, the mutant AGS-S389A showed reduced vegetal cortical localization (Fig. S3B-C) and function (Fig. S3D-E). Furthermore, we replaced the linker region of PmAGS with that of SpAGS (PmAGSSpLinker in Fig. S4A-B). However, this mutant did not show any cortical localization nor proper function in ACD (Fig. S4C-F). Therefore, the SpAGS C-terminus is the primary element that drives ACD, while the linker region serves as the secondary element to help cortical localization of AGS." 

The experiments performed only make sense if the AGS-PmGL chimeric protein used in Figure 5 starts the PmGL sequence only after the Sp linker, or at least after the Sp phosphorylation site. I can't tell from the paper (Figure S3 indicates that it does, whereas S5 suggests otherwise), but it's a critical piece of information for the argument. 

Thank you for the pointer, and we apologize for the confusion. AGS-PmGL contains the SpAGS linker domain. To clarify this point, we added the amino acid position at the junction of each chimeric construct diagram in Figs. 5 and S4. To clarify, Figure S5 is about the GL domain mutations (not about the Linker).  

Another piece of missing information is whether the PmAGS can be phosphorylated at its own conserved phosphorylation site. The authors don't test this, which they could at least try using a phosphosite prediction algorithm, but they do show that the candidate phosphorylation site has a slightly different sequence in Pm than in Et and Sp (Fig. S4A). With impressive rigor, the authors go on to mutate the PmAGS phosphorylation site to make it identical to Sp. Nothing happens. Vegetal cortical localization does not increase over AGS-PmGL alone. Micromere formation is unrescued. 

There is therefore a logic problem in the text, or at least in the way the text is written. The paragraph begins "Additionally, AGS-PmGL unexpectedly showed cortical localization (Figure 5G), while PmAGS showed no cortical localization (Figure 5B)." We want to understand why this is true, but the explanation provided in the remainder of the paragraph doesn't match the question: according to quite a bit of their own data, the phosphorylation site in the linker does not explain the difference. It might explain why AGS-PmGL fails to promote micromere formation, but only if the AGS-PmGL chimeric protein uses the Pm linker domain (see above).

Thank you for the insightful suggestion. As suggested, we performed the phosphosite predictions using GPS 6.0 (PMID: 37158278) and enclosed the results in Fig. S4A (replacing the old Fig. S3A). The software predicts SpAGS and EtAGS have a predicted AuroraA phosphorylation site (RRRSMEN in Supplemental figure S4A) in their linker domain, while PmAGS does not. Sp and Et AGS also have the additional 5-7 predicted phosphorylation sites, while PmAGS has only three sites with low scores. Therefore, the linker domain is not conserved in PmAGS. 

The PmAGS+SpLinker mutant does restore the predicted AuroraA phosphorylation site on the software, yet it does not restore the cortical localization or ACD function in the embryo. Therefore, other sites in the Linker region might also be necessary for cortical localization and ACD function of AGS. In this study, we did not perform further manipulations in the Linker domain. As the reviewer rightfully pointed out, even if we identify the Linker regions essential for AGS localization and function, it will be difficult to interpret the result unless we know what proteins interact with the Linker domain of AGS. Therefore, this is beyond the scope of the current manuscript. We discussed these remaining matters in the discussion section. 

Another concern that is potentially related is the measurement of cortical signal. For example, in the control panel of Figure 5C, there is certainly a substantial amount of "non-cortical" signal that I believe is nuclear. I did not see a discussion of this signal or its implications. My impression of the pictures generally is that the nuclear signal and cortical signal are inversely correlated, which makes sense if they are derived from the same pool of total protein at different points of the cell cycle. If that's the case (and it might not be) I would expect some quantifications to be impacted. For example, the authors show in Figure S3B that AGS-S389A mutant does not localize to the cortex. However, this mutant shows a radically different localization pattern to the accompanying control picture (AGS), namely strong enrichment in what I assume to be the nucleus. Is the S389 mutant preventing AGS from making it to the cortex? Or are these pictures instead temporally distinct, meaning that AGS hasn't yet made it out of the nucleus? Notably, the work of Johnston et al. (Cell 2009), cited in the text, does not show or claim that the linker domain impacts Pins localization. Their model is rather that Pins is anchored at the cortex by Gαi, not Dlg, and that is the same model described in this manuscript.

In agreement with that model and the results of Johnston et al., a later study (Neville et al. EMBO Reports 2023) failed to find a role for Dlg or the conserved phosphorylation site in Pins localization. 

In the sea urchin embryo, the dye or GFP often appears in the nucleus randomly on top of the cytoplasm (for example, see Fig. S2b of PMID: 35444184). Further, embryos tend to incorporate exogenous genomic fragments more efficiently during early embryogenesis (PMID: 3165895). It is proposed that early embryos may have a loosened or incomplete nuclear envelope compared to adult cells as they divide rapidly (every 40 minutes). Therefore, any excess protein with no specific localization signal may randomly appear in the nucleus as it serves as an available space in the cell. As the Reviewer rightfully pointed out, we consider that the nuclear AGS signal is due to the lack of a specific destination since this signal pattern is not consistent across embryos. In contrast, the proteins that have nuclear localization (e.g., transcription factors) usually show a consistent nuclear signal across cells and embryos with less cytoplasmic signal. To avoid confusion, we replaced the S389A image in Fig. S3B (which is now Fig. S4C) as well as any other images that may create similar confusion.

Reviewer #2 (Public Review): 

This study from Dr. Emura and colleagues addresses the relevance of AGS3 mutations in the execution of asymmetric cell divisions promoting the formation of the micromere during seasearching development. To this aim, the authors use quantitative imaging approaches to evaluate the localisation of AGS3 mutants truncated at the N-terminal region or at the Cterminal region, and correlate these distributions with the formation of micromere and correct development of embryos to the pluteus stage. The authors also analyse the capacity of these mutated proteins to rescue developmental defects observed upon AGS3 depletion by morpholino antisense nucleotides (MO). Collectively these experiments revealed that the Cterminus of AGS3, coding for four GoLoco motifs binding to cortical Gaphai proteins, is the molecular determinant for cortical localisation of AGS3 at the micromeres and correct pluteus development. Further genetic dissections and expression of chimeric AGS3 mutants carrying shuffled copies of the GoLoco motifs or four copies of the same motifs revealed that the position of GoLoco1 is essential for AGS3 functioning. To understand whether the AGS3-GoLoco1 evolved specifically to promote asymmetric cell divisions, the authors analyse chimeric AGS3 variants in which they replaced the sea urchin GoLoco region with orthologs from other echinoids that do not form micromeres, or from Drosophila Pins or human LGN. These analyses corroborate the notion that the GoLoco1 position is crucial for asymmetric AGS3 functions. In the last part of the manuscript, the authors explore whether SpAGS3 interacts with the molecular machinery described to promote asymmetric cell division in eukaryotes, including Insc, NuMA, Par3, and Galphai, and show that all these proteins colocalize at the nascent micromere, together with the fate determinant Vasa. Collectively this evidence highlighted how evolutionarily selected AGS3 modifications are essential to sustain asymmetric divisions and specific developmental programs associated with them. 

Thank you for the useful summary.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

The quantifications of "vegetal cortical localization" are somewhat incomplete. As measured, "vegetal cortical localization" does not demonstrate particular enrichment at the vegetal cortex, only that some signal appears there. In other words, we can't tell for sure that there is any more signal at the vegetal cortex than anywhere else along the cortex, and in fact that's plainly true and even described for the ACS1111 and AGS2222 constructs. One solution would be to measure signal strength around the cell perimeter and see where it is strongest. 

As suggested by the Reviewer, we added new measurements, focusing and comparing the signals on the animal versus vegetal cortices (Figs. 2C, 3D, 4C, 5C, &H, 9D & F, S3D, S4D &I). 

A related issue is that the strength of cortical enrichment is indicated in this paper by the ratio of cortical to "non-cortical" signal, but "non-cortical" is not defined. Does it include the nuclear signal? 

As described above, we replaced all measurements using the above animal vs. vegetal cortices to avoid confusion. The nuclear signal is thus not measured in these analyses.

I'm enthusiastic about the results in Figure 7, but I can't really see them very well. Could you please consider changing the color scheme? For single-color figures, it would be helpful to view them as black on white rather than (for example) blue on black. That change is easily achieved with Fiji. 

We revised the Figure as suggested.

Page 3 Results section: "At the time of ACD, Insc recruits Pins/LGN to the cortex through Gαi": I understand this sentence to mean that Gαi is an intermediary protein that Insc uses to recruit Pins/LGN. I think the point should be made more clear. As shown in Figure 1, Insc binds to Pins/LGN directly and interacts with cortical polarity proteins directly. Recruitment therefore doesn't appear to require Gαi, but stable association with the membrane (a subsequent step) probably does. That model is shown and described in Figure 6A.

Thank you for the pointer. We clarified our explanations as suggested.

Reviewer #2 (Recommendations For The Authors): 

The manuscript addresses an interesting question, and uses elegant genetic approaches associated with imaging analyses to elucidate the molecular mechanisms whereby AGS3 and spindle orientation proteins promote asymmetric divisions and specific developmental programs. This considered, it might be worth clarifying a few aspects of the reported findings. 

(1) In some experimental settings, the presence of AGS3 mutants exacerbates the AGS3 deletion by MO (Figure 4F). Can the author speculate on what can be the molecular explanation? 

Thank you for pointing this out. We speculate that AGS1111 and AGS2222 are unable to keep the auto-inhibited forms since they lack GL3 and GL4 as modeled in Figure 6. AGS-MO reduces the endogenous AGS, which compromises the vegetal polarity. In this embryo, constitutive active AGS likely further randomizes the polarity, as evidenced by AGS-OE results in Fig. S7, resulting in an even worse outcome. We elaborated on this part in the text.

(2) Imaging analyses of Figure 4B-C suggest that the mutant AGS1111 does not localise at the vegetal cortex while AGS2222 does (Fig. 4C). However these mutants induce similar developmental defects (Figure 4F). What could be the reason? 

We apologize for the confusion in Fig. 4C. The majority of embryos from both AGS1111 and 2222 groups failed to form micromeres and showed AGS localization across the cortex. Among the dozens we examined, 0 embryos from 1111 and 8 embryos from 2222 developed micromeres. Those 8 embryos still showed vegetal cortical localization, so the proportion appears high in Fig. 4B, yet it reflects the minority in the group. In contrast, Development was scored for all embryos (including those that failed to form micromeres), so the graph demonstrates the majority of embryos. To avoid this confusion, we replaced the old Fig. 4C with a new graph that analyzes the cortical signal levels at the vegetal versus animal cortices.

(3) Figure 7 shows the crosstalk between AGS3 and other asymmetry players including NuMA. Vertebrate and Drosophila NuMA are ubiquitously present in tissues and localise to the spindle poles in mitosis. However, in Figures 7A and 7E NuMA seems expressed only in a subset of sea urchin embryonic cells. Is this the case? 

As the Reviewer rightfully pointed out, Sea urchin NuMA is also present in all cells and localizes to the spindle (please see Fig. 2 of our previous paper PMID: 31439829). AGS is also slightly localized on the spindles of all cells. However, the PLA signal of AGS and NuMA mostly showed up in the vegetal cortex in this study, suggesting that major crosstalk may occur in the vegetal cortex. This does not rule out the possibility that minor interactions may also occur on the spindle or elsewhere in the cell, which was not quantifiable in this study. We clarified this point in the text.

Author response:

The following is the authors’ response to the original reviews.

Thank you for your assessment and constructive critique, which helped us to improve the manuscript and its clarity. Upon carefully reading through the comments, we noticed that, based on the Reviewer's questions, some of our answers were already available but “hidden” as supplementary data. Thus, we changed the following two figures and text accordingly to showcase our results to the reader better:

A) To highlight how mobile service data can indicate the spread of highly prevalent variants, we added a high-prevalence subcluster to Figure 2 (previously shown in Supplementary Figures S4 and S5) and, in exchange, moved one low-prevalence subcluster from Figure 2 back into the supplement. The figure is now showing a low and a high prevalent subcluster instead of two low prevalent subclusters.

B) Based on Reviewer 1’s question about where samples were taken in regards to the mobility data from the community of the first identification (negative controls), we now highlight all the mobility data that was available to us in Figure 3 (as triangles) instead of just a few top mobility hits for both - mobility guided and random surveillance (serving as a negative control for the former). This way, we think, it is clearer how random sampling was also performed in some regions where mobility was coming from the community of origin (as asked by Reviewer 1) - the detailed trips and sampling are now part of the supplement for data transparency reasons. We also noticed a typo in the GPS coordinates, aligning one of the arrows falsely, which is corrected in the improved Figure 3.

We have also included the R-Scripts used to generate all the figures in the manuscript in an OSF repository (we updated the “Data sharing statement”). We also updated Figure 1 slightly and extended the supplemental material. The remaining comments to reviewers are addressed point-by-point below.

Reviewer 1 (Public Review):

In "1 Exploring the Spatial Distribution of Persistent SARS-CoV-2 Mutations -Leveraging mobility data for targeted sampling" Spott et al. combine SARS-CoV-2 genomic data alongside granular mobility data to retrospectively evaluate the spread of SARS-CoV-2 alpha lineages throughout Germany and specifically Thuringia. They further prospectively identified districts with strong mobility links to the first district in which BQ.1.1 was observed to direct additional surveillance efforts to these districts. The additional surveillance effort resulted in the earlier identification of BQ.1.1 in districts with strong links to the district in which BQ.1.1 was first observed.

Thank you for taking the time to review our work.

(1) It seems the mobility-guided increased surveillance included only districts with significant mobility links to the origin district and did not include any "control" districts (those without strong mobility links). As such, you can only conclude that increasing sampling depth increased the rate of detection for BQ.1.1., not necessarily that doing so in a mobility-guided fashion provided an additional benefit. I absolutely understand the challenges of doing this in a real-world setting and think that the work remains valuable even with this limitation, but I would like the lack of control districts to be more explicitly discussed.

Thank you for the critical assessment of our work. We agree that a control is essential for interpreting the results. In our case, randomized surveillance (“the gold standard”) served as a control with a total sampling depth seven times higher than the mobility-guided sampling. To better reflect the sampling in regards to the available mobility data, we revisited Figure 3 and added all the mobility information from the origin that was available to us. We also added this information to the random surveillance to provide a clearer picture to the reader. This now clearly shows how randomized surveillance covered communities with varying degrees of incoming mobility from the community of first occurrences, thereby underlining its role as a negative control. We updated the manuscript to reflect these changes and included the October 2020 and June 2021 mobility datasets in Supplementary Table S6. We agree that the sampling depth increases the detection, which is the point of guided sampling to increase sampling, specifically in areas where mobility points towards a possible spread. In regards to the negative control: Random surveillance (not Mobility-guided) in October covered 40 samples in the northwest region of Thuringia (Mobility-guided covered 19 samples). Thus, random surveillance also contained 31 out of 132 samples with a mobility link towards the first occurrence of BQ1.1 but with varying amounts of mobility (low to high).

We added this information to the main text:

Line 270 to 293:

Following its first Thuringian identification, we utilized the latest available dataset of the past two years of mobile service data (October 2020 and June 2021) to investigate the residential movements for the community of first detection. Considering the highest incoming mobility from both datasets, we identified 18 communities with high (> 10,000), 34 with medium (2,001-10,000), and 82 with low (30-2,000) number of incoming one-way trips from the originating community (purple triangles in Figure 3a). As a result, we specifically requested all the available samples from the eight communities with the highest incoming mobility. Still, we were restricted to the submission of third parties over whom we had no influence. This led to the inclusion of the following eight communities with the most residential movement from the originating community: four in central and three in NW of Thuringia, one in NW-neighboring state Saxony-Anhalt. The samples requested from central Thuringia were also due to their geographic arrangement as a “belt” in central Thuringia, linking three major cities (see Supplementary Figure S1). Subsequently, we collected 19 additional samples (isolated between the 17th and 25th of October 2022; see “Guided Sampling” for October 2022, Figure 3a) besides the randomized sampling strategy. Thus, the sampling depth was increased in communities with high incoming mobility from the first origin.

As part of the general Thuringian surveillance, we collected 132 samples for October (covering dates between the 5th and 31st) and 69 samples in November (covering dates between the 1st and 25th; see Figure 3b and c). Randomized sampling was not influenced or adjusted based on the mobility-guided sample collection. Thus, it also contains samples from communities with a mobility link towards the first occurrence of BQ.1.1, as they were part of the regular random collection (see gray triangles in Figure 3b). A complete overview of all samples is provided in Supplementary Table S5. The mobility datasets from October 2020 and June 2021 for all sampled communities are provided in Supplementary Table S6.

Line 305 to 313:

Among the 19 samples specifically collected based on mobile service data, we identified one additional sample of the specific Omicron sublineage BQ.1.1 in a community with high incoming mobility (n = 14, number of trips = 37,499) with a distance of approximately 16 km between both towns. Our randomly sampled routine surveillance strategy did not detect another sample during the same period. This was despite a seven times higher overall sample rate, which included 31 samples from communities with an identified incoming mobility from the community of the first occurrence (October 2022, Figure 3b). Only in the one-month follow-up were four other samples identified across Thuringia through routine surveillance (November 2022, Figure 3c).

Line 325 to 333:

In summary, increasing the sampling depth in the suspected regions successfully identified the specified lineage using only a fraction of the samples from the randomized sampling. Conversely, randomized surveillance, the “gold standard” acting as our negative control, did not identify additional samples with similar sampling depths in regions with no or low incoming mobility or even in high mobility regions with less sampling depth. Implementing such an approach effectively under pandemic conditions poses difficult challenges due to the fluctuating sampling sizes. Although the finding of the sample may have been coincidental, our proof of concept demonstrated how we can leverage the potential of mobile service data for targeted surveillance sampling.

(2) Line 313: While this work has reliably shown that the spread of Alpha was slower in Thuringia, I don't think there have been sufficient analyses to conclude that this is due to the lack of transportation hubs. My understanding is that only mobility within Thuringia has been evaluated here and not between Thuringia and other parts of Germany.

Thank you for pointing this out. We noticed that the original sentence lacked the necessary clarity. The statement in line 313 was based on the observation that Alpha first occurred in federal states with major transport hubs, such as international airports and ports, which Thuringia lacks, as demonstrated in the Microreact dataset. For clarification, we adjusted the sentence as follows:

Line 340 and following:

A plausible explanation for the delayed spread of the Alpha lineage in Thuringia is the lack of major transport hubs, as Alpha first occurred in federal states with such hubs. Previous studies have already highlighted the impact of major transportation hubs in the spread of Sars-CoV-2.

(3) Line 333 (and elsewhere): I'm not convinced, based on the results presented in Figure 2, that the authors have reliably identified a sampling bias here. This is only true if you assume (as in line 235) that the variant was in these districts, but that hasn't actually been demonstrated here. While I recognize that for high-prevalence variants, there is a strong correlation between inflow and variant prevalence, low-prevalence variants by definition spread less and may genuinely be missing from some districts. To support this conclusion that they identified a bias, I'd like to see some type of statistical model that is based e.g. on the number of sequences, prevalence of a given variant in other districts, etc. Alternatively, the language can be softened ("putative sampling bias").

Thank you for addressing this legitimate point of criticism in our interpretation. Due to the retrospective nature of the analysis and the fact that we found no additional samples of the clusters after the specified timeframes, we were limited to the samples in our dataset. Therefore, it is impossible to demonstrate if a variant was present in the relevant districts afterward. We agree that the variant’s low prevalence means they may genuinely not have spread to some districts. For clarification, we added the following statements and changed the wording accordingly:

Additional statement in line 248:

However, due to their low prevalence, it is also possible that these subclusters have not spread to the indicated districts.

Adjusted wording in line 361:

We exemplified this approach with the Alpha lineage, where mobile service data indicated a putative sampling bias and partially predicted the spread of our Thuringian subclusters.

Recommendations:

(1) I applaud the use of the microreact page to make the data public, however, I don't see any reference to a GitHub or Zenodo repository with the analysis code. The NextStrain code is certainly appreciated but there is presumably additional code used to identify the clusters, generate figures, etc. I generally prefer this code be made public and it is recommended by eLife.

Thank you for your appreciation. We have now included the R-scripts in the manuscript’s OSF repository. These were used to create the figures in the manuscript and supplement utilizing the supplementary tables 1-6, which are also stored in the repository. To clearly communicate which data is provided, we changed lines 513 and 514 of the “Data sharing statement” as follows:

Line 513 and following:

Supplementary tables and the R-scripts used to generate all figures are also provided in the repository under https://osf.io/n5qj6/. These include the mobile service data used in this study, which is available in processed and anonymized form.

The subcluster identification was performed manually. By adding each sample's mutation profile to the Microreact metadata file, we visually screened the phylogenetic time tree for all non-Alpha specific mutations present in at least 20 Thuringian genomes. We then applied the criteria described in the Methods section to identify the nine Alpha subclusters. For clarification, we changed line 436:

Line 436:

We then manually screened for mutations present in at least 20 genomes with a small phylogenetic distance and a time occurrence of at least two months.

Reviewer 2 (Public Review):

In the manuscript, the authors combine SARS-CoV-2 sequence data from a state in Germany and mobility data to help in understanding the movement of the virus and the potential to help decide where to focus sequencing. The global expansion in sequencing capability is a key outcome of the public health response. However, there remains uncertainty about how to maximise the insights the sequence data can give. Improved ability to predict the movement of emergent variants would be a useful public health outcome. Also knowing where to focus sequencing to maximising insights is also key. The presented case study from one State in Germany is therefore a useful addition to the literature. Nevertheless, I have a few comments.

Thank you for taking the time to review our work.

(1) One of the key goals of the paper is to explore whether mobile phone data can help predict the spread of lineages. However, it appears unclear whether this was actually addressed in the analyses. To do this, the authors could hold out data from a period of time, and see whether they can predict where the variants end up being found.

Based on your feedback, we noticed that the results of the other seven clusters presented in the supplement were not appropriately highlighted, causing them to be overlooked. We indeed demonstrated that predicting viral spread based on mobility data is possible, as shown for the high-prevalence subcluster 7 (Cluster “ORF1b:A520V”, 811 samples). This was briefly mentioned in lines 240-242, but the cluster was only shown in Supplementary Figures S4 and S5. Instead, we focused more on the putative sampling bias that the mobility for low-prevalence subclusters could indicate as an interesting use case of mobility data. This addresses a concrete problem of every surveillance: successfully identifying low-prevalence targets. However, based on your feedback, we revisited Figure 2, adding the plots of the high-prevalence subcluster: “ORF1b:A520V” from Supplementary Figures S4 and S5 while moving the low-prevalence subcluster “S:N185D” from Figure 2 into the Supplementary Figures S4 and S5. Additionally, we changed line 229 to highlight this result properly.

line 229 and following:

The mobile service data-based prediction of a subcluster’s spread aligned well with the subsequent regional coverage of fast-spreading, highly prevalent subclusters, such as subcluster 7, which covered 811 samples (see Figure 2). In contrast, the predicted spread for the low-prevalence subclusters did not correspond well with the actual occurrence.

(2) The abstract presents the mobility-guided sampling as a success, however, the results provide a much more mixed result. Ultimately, it's unclear what having this strategy really achieved. In a quickly moving pandemic, it is unclear what hunting for extra sequences of a specific, already identified, variant really does. I'm not sure what public health action would result, especially given the variant has already been identified.

Thank you for your critical assessment of the presented results and their interpretation.

Here, we aimed to provide an alternative to the standard randomized surveillance strategy. Through mobility-guided sampling, we sought to increase identification chances while necessitating fewer samples and decreasing costs, ultimately enhancing surveillance efficiency. The Omicron-lineage BQ.1.1 was the perfect example to prove this concept under actual pandemic conditions. Yet, the strategy is not limited to low-prevalence sublineages but can be applied to virtually any surveillance case. However, from your question, we recognize that this conclusion was unclear from the text. Therefore, we adapted the conclusion to better communicate the real implications of our proof of concept. Additionally, we altered line 42 in the abstract for clarification.

However, we did not assess the benefits of surveillance itself, as the German Robert Koch Institute (RKI) already had outlined its importance for tracking different viral variants. This tracking served several reasons, like monitoring vaccine escapism, mutational progress, and assessing available antibodies for treatment.

Line 42:

The latter concept was successfully implemented as a proof-of-concept for a mobility-guided sampling strategy in response to the surveillance of Omicron sublineage BQ.1.1.

Line 364 to 374:

Another approach is actively guiding the sampling process through mobile service data, which we demonstrated with our proof of principle focusing on the Omicron-lineage BQ.1.1 as a real-life example. This approach could allow for a flexible allocation of surveillance resources, enabling adaptation to specific circumstances and increasing sampling depth in regions where a variant is anticipated. By incorporating guided sampling, much fewer resources may be needed for unguided or random sampling, thereby reducing overall surveillance costs.

Additionally, while this approach is particularly useful for identifying low-prevalence variants, it is not limited to such variants. Still, it can provide a guided, more cost-efficient, low-sampling alternative to general randomized surveillance that can also be applied to other viruses or lineages.

(3) Relatedly, it is unclear to me whether simply relying on spatial distance would not be an alternative simpler approach than mobile phone data. From Figure 2, it seems clear that a simple proximity matrix would work well at reconstructing viral flow. The authors could compare the correlation of spatial, spatial proximity, and CDR data.

Thank you for pointing this out. While proximity data might appear to be an obvious choice, it has significant limitations compared to mobility data, especially in the context of our study. Proximity data assumes that spatial distance alone can accurately represent movement patterns, which would only be true in a normally distributed traffic network. Geographic features such as mountains, cities, and highways affect traffic flows, leading to variability over distance and time, which are beyond the scope of spatial proximity but efficiently captured by mobility data. In Figure 2, we presented a simplified view of the mobility data. Hence, proximity and mobility data appear to provide the same insights. However, as shown in the updated Figure 3, a detailed overview of the available mobility data reveals obvious and non-obvious spatial connections that proximity data can not capture. Incorporating such a level of detail in Figure 2 would have cluttered the figure and reduced its clarity (e.g., adding triangles for each Thuringian community).

While a comparison between proximity data and mobility data would indeed be informative, it is beyond the scope of our current study, as our primary focus was to examine the useability of mobility data in explaining our subcluster’s spread in the first place. However, we agree it would be a valuable direction for future research. We summarized our thoughts from above in the following additional sentence:

Line 374:

Pre-generated mobility networks automatically tailored to each state's unique infrastructure and population dynamics could provide better-targeted sampling guidance rather than simple geographical proximity.

Recommendations:

(1) Line 128: What do these percentages mean - the proportion of States with at least one Alpha variant? Please clarify.

We clarified the values at their first appearance in the text:

Line 127:

By March, Alpha had spread to nearly all states and districts (districts are similar to counties or provinces) in Germany (Median: 76·47 % Alpha samples among a federal states total sequenced samples compared to 36·03 % in February, excluding Thuringia) and Thuringia (Median: 85·29 %, up from 50·00 % in February).

(2) Line 134: It's a little strange to compare the dynamics of a state with that of the whole country. For it lagged as compared to all other States?

Line 134: “In summary, the spread of the Alpha lineage in Thuringia lagged roughly two weeks behind the general spread in the rest of Germany but showed similar proportions.”

Thank you for the feedback. The statement refers to the comparison of Alpha-lineage proportions across federal states, excluding Thuringia, in lines 118 to 130. To simplify, we collectively referred to these federal states as “Germany” in the text. However, we recognize that this formulation is misleading, so we adjusted line 135 for clarification:

Line 135:

In summary, the spread of the Alpha lineage in Thuringia lagged roughly two weeks behind the general spread of other German federal states but showed similar proportions.

Author response:

Reviewer #1 (Public review):

Summary:

The manuscript by Rühling et al analyzes the mode of entry of S. aureus into mammalian cells in culture. The authors propose a novel mechanism of rapid entry that involves the release of calcium from lysosomes via NAADP-stimulated activation of TPC1, which in turn causes lysosomal exocytosis; exocytic release of lysosomal acid sphingomyelinase (ASM) is then envisaged to convert exofacial sphingomyelin to ceramide. These events not only induce the rapid entry of the bacteria into the host cells but are also described to alter the fate of the intracellular S. aureus, facilitating escape from the endocytic vacuole to the cytosol.

Strengths:

The proposed mechanism is novel and could have important biological consequences.

Weaknesses:

Unfortunately, the evidence provided is unconvincing and insufficient to document the multiple, complex steps suggested. In fact, there appear to be numerous internal inconsistencies that detract from the validity of the conclusions, which were reached mostly based on the use of pharmacological agents of imperfect specificity.

We thank the reviewer for the detailed evaluation of our manuscript. We will address the criticism below.

We agree with the reviewer that many of the experiments presented in our study rely on the usage of inhibitors. However, we want to emphasize that the main conclusion (invasion pathway affects the intracellular fate/phagosomal escape) was demonstrated without the use of inhibitors or genetic ablation in two key experiments (Figure4 G/H). These experiments were in line with the results we obtained with inhibitors (amitriptyline [Supp. Figure 4E], ARC39, PCK310, [Figure 4c] and Vacuolin-1 [Supp. Figure4f]). Importantly, the hypothesis was also supported by another key experiment, in which we showed the intracellular fate of bacteria is affected by removal of SM from the plasma membrane before invasion, but not by removal of SM from phagosomal membranes after bacteria internalization (Figure4d-f). Taken together, we thus believe that the main hypothesis is strongly supported by our data.

Moreover, we either used different inhibitors for the same molecule (ASM was inhibited by ARC39, amitriptyline and PCK310 with similar outcome) or supported our hypothesis with gene-ablated cell pools (TPC1, Syt7, SARM1), as we will point out in more detail below.

Firstly, the release of calcium from lysosomes is not demonstrated. Localized changes in the immediate vicinity of lysosomes need to be measured to ascertain that these organelles are the source of cytosolic calcium changes. In fact, 9-phenantrol, which the authors find to be the most potent inhibitor of invasion and hence of the putative calcium changes, is not a blocker of lysosomal calcium release but instead blocks plasmalemmal TRPM4 channels. On the other hand, invasion is seemingly independent of external calcium. These findings are inconsistent with each other and point to non-specific effects of 9-phenantrol. The fact that ionomycin decreases invasion efficiency is taken as additional evidence of the importance of lysosomal calcium release. It is not clear how these observations support involvement of lysosomal calcium release and exocytosis; in fact treatment with the ionophore should itself have induced lysosomal exocytosis and stimulated, rather than inhibited invasion. Yet, manipulations that increase and others that decrease cytosolic calcium both inhibited invasion.

With respect to lysosomal Ca2+ release, we agree with the reviewer that direct visual demonstration of lysosomal Ca2+ release upon infection will improve the manuscript. We therefore will perform additional experimentation to show alterations of Ca2+ at the lysosomes during infection.

As to the TRPM4 involvement in S. aureus host cell internalization, it has been reported that TRPM4 is activated by cytosolic Ca2+. However, the channel conducts monovalent cations such as K+ or Na+ but is impermeable for Ca2+ 1, 2. The following of our observations are supporting this:

i) S. aureus invasion is dependent on intracellular Ca2+, but is independent from extracellular Ca2+  (Figure 1c).

ii) 9-phenantrol treatment reduces S. aureus internalization by host cells, illustrating the dependence of this process on TRPM4 (Figure 1b). We therefore hypothesize that TRPM4 is activated by Ca2+ released from lysosomes (see above).

TRPM4 is localized to focal adhesions and is connected to actin cytoskeleton3, 4 – a requisite of host cell entry of S. aureus.5, 6 This speaks for an important function of TRPM4 in uptake of S. aureus in general, but does not necessarily have to be involved exclusively in the rapid uptake pathway.

TRPM4 itself is not permeable for Ca2+ but is activated by the cation.  Thus, it is unlikely to cause lysosomal exocytosis. The stronger bacterial uptake reduction by treatment with 9-phenantrol when compared to Ned19 thus may be caused by the involvement of TRPM4 in additional pathways of S. aureus host cell entry involving that association of TRPM4 with focal adhesions or, as pointed out by the reviewer, unspecific side effects of 9-phenantrol that we currently cannot exclude. We will include this information in the revised manuscript.

Regarding the reduced S. aureus invasion after ionomycin treatment, we agree with the reviewer that ionomycin is known to lead to lysosomal exocytosis as was previously shown by others7 as well as our laboratory8.

We hypothesized that pretreatment with ionomycin would trigger lysosomal exocytosis and thus would reduce the pool of lysosomes that can undergo exocytosis before host cells are contacted by S. aureus. As a result, we should observe a marked reduction of S. aureus internalization in such “lysosome-depleted cells”, if the lysosomal exocytosis is coupled to bacterial uptake. Our observation of reduced bacterial internalization after ionomycin treatment supports this hypothesis.

However, ionomycin treatment and S. aureus infection of host cells are distinct processes.

While ionomycin results in strong global and non-directional lysosomal exocytosis of all “releasable” lysosomes (~5-10 % of all lysosomes according to previous observations)7, we hypothesize that lysosomal exocytosis upon contact with S. aureus only involves a very small proportion of lysosomes at host-bacteria contact sites.

Since ionomycin disturbs the overall cellular Ca2+ homeostasis, we agree with the reviewer that this does not directly show lysosomal Ca2+ liberation. We will discuss this in more detail in the revised manuscript.

The proposed role of NAADP is based on the effects of "knocking out" TPC1 and on the pharmacological effects of Ned-19. It is noteworthy that TPC2, rather than TPC1, is generally believed to be the primary TPC isoform of lysosomes. Moreover, the gene ablation accomplished in the TPC1 "knockouts" is only partial and rather unsatisfactory. Definitive conclusions about the role of TPC1 can only be reached with proper, full knockouts. Even the pharmacological approach is unconvincing because the high doses of Ned-19 used should have blocked both TPC isoforms and presumably precluded invasion. Instead, invasion is reduced by only ≈50%. A much greater inhibition was reported using 9-phenantrol, the blocker of plasmalemmal calcium channels. How is the selective involvement of lysosomal TPC1 channels justified?

As to partial gene ablation of TPC1: To avoid clonal variances, we usually perform pool sorting to obtain a cell population that predominantly contains cells -here- deficient in TPC1, but also a small proportion of wildtype cells as seen by the residual TPC1 protein on the Western blot. We observe a significant reduction of bacterial uptake in this cell pool suggesting that the uptake reduction in a pure K.O. population may be even larger.

As to the inhibition by Ned19: We agree with the reviewer that Ned19 inhibits TPC1 and TPC2. Since ablation of TPC1 reduced invasion of S. aureus, we concluded that TPC1 is important for S. aureus host cell invasion. We thus agree with the reviewer that a role for TPC2 cannot be excluded. We will clarify this in the reviewed manuscript. It needs to be noted, however, that deficiency in either TPC1 or TPC2 alone was sufficient to prevent Ebola virus infection9, which is in line with our observations.

The 50% reduction of invasion upon Ned19 treatment (Figure 1d) is comparable with the reduction caused by other compounds that influence the ASM-dependent pathway (such as amitriptyline, ARC39 [Figure 2c], BAPTA-AM [Figure 1c], Vacuolin-1 [Figure 2a], β-toxin [Figure 2e] and ionomycin [Figure 1a]). Further, the partial reduction of invasion is most likely due to the concurrent activity of multiple internalization pathways which are not all targeted by the used compounds.

Invoking an elevation of NAADP as the mediator of calcium release requires measurements of the changes in NAADP concentration in response to the bacteria. This was not performed. Instead, the authors analyzed the possible contribution of putative NAADP-generating systems and reported that the most active of these, CD38, was without effect, while the elimination of SARM1, another potential source of NAADP, had a very modest (≈20%) inhibitory effect that may have been due to clonal variation, which was not ruled out. In view of these data, the conclusion that NAADP is involved in the invasion process seems unwarranted.

Our results from two independent experimental set-ups (Ned19 [Figure 1d] and TPC1 K.O. [Figure 1e & Figure 2f]) indicate the involvement of NAADP in the process. However, the measurement of NAADP concentration is non-trivial. However, we can rule out clonal variation in the SARM1 mutant since experiments were conducted with a cell pool as described above in order to avoid clonal variation of single clones.

The mechanism behind biosynthesis of NAADP is still debated. CD38 was the first enzyme discovered to possess the ability of producing NAADP. However, it requires acidic pH to produce NAADP10 -which does not match the characteristics of a cytosolic NAADP producer. HeLa cells do not express CD38 and hence, it is not surprising that inhibition of CD38 had no effect on S. aureus invasion in HeLa cells. However, NAADP production by HeLa cells was observed in absence of CD3811. Thus CD38-independent NAADP generation is likely. SARM1 can produce NAADP at neutral pH12 and is expressed in HeLa, thus providing a more promising candidate.

We agree with the reviewer that the reduction of S. aureus internalization after ablation of SARM1 is less pronounced than in other experiments of ours. This may be explained by NAADP originating from other enzymes, such as the recently discovered DUOX1, DUOX2, NOX1 and NOX213, which – with exception of DUOX2- possess a low expression even in HeLa cells. We will discuss this in the revised manuscript.

The involvement of lysosomal secretion is, again, predicated largely on the basis of pharmacological evidence. No direct evidence is provided for the insertion of lysosomal components into the plasma membrane, or for the release of lysosomal contents to the medium. Instead, inhibition of lysosomal exocytosis by vacuolin-1 is the sole source of evidence. However, vacuolin-1 is by no means a specific inhibitor of lysosomal secretion: it is now known to act primarily as a PIKfyve inhibitor and to cause massive distortion of the endocytic compartment, including gross swelling of endolysosomes. The modest (20-25%) inhibition observed when using synaptotagmin 7 knockout cells is similarly not convincing proof of the requirement for lysosomal secretion.

We agree that the manuscript will strongly benefit from a functional analysis of lysosomal exocytosis. We therefore will conduct assays to investigate exocytosis in the revision. However, we previously showed i) by addition of specific antisera that LAMP1 transiently is exposed on the plasma membrane during ionomycin and pore-forming toxin challenge and ii) demonstrated the release of ASM activity into the culture medium under these conditions.8 Both measurements are not compatible with S. aureus infection, since LAMP1 antibodies also are non-specifically bound by protein A and another IgG-binding protein on the S. aureus surface, which would bias the results. Since protein A also serves as an adhesin, we cannot simply delete the ORF without changing other aspects of staphylococcal virulence. Further, FBS contains a ASM background activity that impedes activity measurements of cell culture medium. We previously removed this background activity by a specific heat-inactivation protocol.8 However, S. aureus invasion is strongly reduced in culture medium containing this heat-inactivated FBS.

We agree with the reviewer that Vacuolin-1 has unspecific side effects. We will address this in the revised version of the manuscript.

As to the involvement of synaptotagmin 7:

Synaptotagmin 7 is not the only protein possibly involved in Ca-dependent exocytosis. For instance, SYT1 has been shown to possess an overlapping function.14 This may explain the discrepancy between our vacuolin-1 and SYT7 ablation experiments. We will add an according section to the discussion.

ASM is proposed to play a central role in the rapid invasion process. As above, most of the evidence offered in this regard is pharmacological and often inconsistent between inhibitors or among cell types. Some drugs affect some of the cells, but not others. It is difficult to reach general conclusions regarding the role of ASM. The argument is made even more complex by the authors' use of exogenous sphingomyelinase (beta-toxin). Pretreatment with the toxin decreased invasion efficiency, a seemingly paradoxical result. Incidentally, the effectiveness of the added toxin is never quantified/validated by directly measuring the generation of ceramide or the disappearance of SM.

Although pharmacological inhibitors can have unspecific side effects, we want to emphasize that the inhibitors used in our study act on the enzyme ASM by completely different mechanisms. Amitriptyline is a so called functional inhibitor of ASM (FIASMA) which induces the detachment of ASM from lysosomal membranes resulting in degradation of the enzyme.15 By contrast, ARC39 is a competitive inhibitor.16, 17

We do not see inconsistencies in our data obtained with ASM inhibitors. Amitriptyline and ARC39 both reduce the invasion of S. aureus in HuLEC, HuVEC and HeLa cells (Figure 2c). ARC39 needs a longer pre-incubation, since its uptake by host cells is slower (data not shown). We observe a different outcome in 16HBE14o- and Ea.Hy 926 cells, with 16HBE14o- even demonstrating a slightly increased invasion of S. aureus upon ARC39 treatment. Amitriptyline had no effect (Figure 2c). Moreover, both inhibitors affected the invasion dynamics (Figure 3d), phagosomal escape (Figure 4c and Supp. Figure 4e) and Rab7 recruitment (Figure 4a and Supp. Figure 4b) in a similar fashion. Proper inhibition of ASM by both compounds in all cell lines used was validated by enzyme assays (Supp. Figure 2e), which suggests that the ASM-dependent pathway does only exist in specific cell lines. This also may serve as an argument that we here do not observe unspecific side effects of the compounds. We will clarify this in the revised manuscript.

ASM is a key player for SM degradation and recycling. In clinical context, deficiency in ASM results in the so-called Niemann Pick disease type A/B. The lipid profile of ASM-deficient cells is massively altered18, which will result in severe side effects. Short-term inhibition by small molecules therefore poses a clear benefit when compared to the usage of ASM K.O. cells.

As to the treatment with a bacterial sphingomyelinase:

Treatment with the bacterial SMase (bSMase, here: β-toxin) was performed in two different ways:

i) Pretreatment of host cells with β-toxin to remove SM from the host cell surface before infection. This removes the substrate of ASM from the cell surface prior to addition of the bacteria (Figure 2e, Figure 4d-f). Since SM is not present on the extracellular plasma membrane leaflet after treatment, a release of ASM cannot cause localized ceramide formation at the sites of lysosomal exocytosis. Similar observations were made by others.19

ii) Addition of bSMase to host cells together with the bacteria to complement for the absence of ASM (Figure 2f).

Removal of the ASM substrate before infection (i) prevents localized ASM-mediated conversion of SM to Cer during infection and resulted in a decreased invasion, while addition of the SMase during infection resulted in an increased invasion in TPC1 and SYT7 ablated cells. Thus, both experiments are consistent with each other and in line with our other observations.

Removal of SM from the plasma membrane by β-toxin was indirectly demonstrated by the absence of Lysenin recruitment to phagosomes/escaped bacteria when host cells were pretreatment with the toxin before infection (Figure4F). In another publication, we recently quantified the effectiveness of β-toxin treatment, even though with slightly longer treatment times (75 min vs. 3h).20 We will repeat the measurements also for shorter treatment times.

To clarify our experimental approaches to the readership we will add an explanatory section to the revised manuscript.

As to the general conclusions regarding the role of ASM: ASM and lysosomal exocytosis has been shown to be involved in uptake of a variety of pathogens19, 21-25 supporting its role in the process.

The use of fluorescent analogs of sphingomyelin and ceramide is not well justified and it is unclear what conclusions can be derived from these observations. Despite the low resolution of the images provided, it appears as if the labeled lipids are largely in endomembrane compartments, where they would presumably be inaccessible to the secreted ASM. Moreover, considering the location of the BODIPY probe, the authors would be unable to distinguish intact sphingomyelin from its breakdown product, ceramide. What can be concluded from these experiments? Incidentally, the authors report only 10% of BODIPY-positive events after 10 min. What are the implications of this finding? That 90% of the invasion events are unrelated to sphingomyelin, ASM, and ceramide?

During the experiments with fluorescent SM analogues (Figure 3a,b), S. aureus was added to the samples immediately before start of video recording. Hence, bacteria are slowly trickling onto the host cells and we thus can image the initial contact between them and the bacteria, for instance, the bacteria depicted in Figure 3a contact the host cell about 9 min before becoming BODIPY-FL-positive (see Supp. Video 1, 55 min). Hence, we think that in these cases we see the formation of phagosomes around bacteria rather than bacteria in endomembrane compartments. Since generation of phagosomes happens at the plasma membrane, SM is accessible to secreted ASM.

The “trickling” approach for infection is an experimental difference to our invasion measurements, in which we synchronized the infection by a very slow centrifugation. This ensures that all bacteria have contact to host cells and are not just floating in the culture medium. However, live cell imaging of initial bacterial-host contact and synchronization of infection is technically not combinable.

In our invasion measurements -with synchronization-, we typically see internalization of ~20% of all added bacteria after 30 min. Hence, most bacteria that are visible in our videos likely are still extracellular and only a small proportion was internalized. This explains why only 10% of total bacteria are positive for BODIPY-FL-SM after 10 min. The proportion of internalized bacteria that are positive for BODIPY-FL-SM should be way higher but cannot be determined with this method.

We agree with the reviewer that we cannot observe conversion of BODIPY-FL-SM by ASM. In order to do that, we attempted to visualize the conversion of a visible-range SM FRET probe (Supp. Figure 3), but the structure of the probe is not compatible with measurement of conversion on the plasma membrane, since the FITC fluorophore released into the culture medium by the ASM activity thereby gets lost for imaging. In general, the visualization of SM conversion with subcellular resolution is challenging and even with novel tools developed in our lab26 visualization of SM on the plasma membrane is difficult.

The conclusion we draw from these experiments are that i.) S. aureus invasion is associated with SM and ii.) SM-associated invasion can be very fast, since bacteria are rapidly engulfed by BODIPY-FL-SM containing membranes.

It is also unclear how the authors can distinguish lysenin entry into ruptured vacuoles from the entry of RFP-CWT, used as a criterion of bacterial escape. Surely the molecular weights of the probes are not sufficiently different to prevent the latter one from traversing the permeabilized membrane until such time that the bacteria escape from the vacuole.

We here want to clarify that both, the Lysenin as well as the CWT reporter have access to rupture vacuoles (Figure 4b). We used the Lysenin reporter in these experiments for estimation of SM content of phagosomal membranes. If a vacuole is ruptured, both the bacteria and the luminal leaflet of the phagosomal membrane remnants get in contact with the cytosol and hence with the cytosolically expressed reporters YFP-Lysenin as well as RFP-CWT resulting in “Lysenin-positive escape” when phagosomes contained SM (see Figure 4f). By contrast, either β-toxin expression by S. aureus or pre-treatment with the bSMase resulted in absence of Lysenin recruitment suggesting that the phagosomal SM levels were decreased/undetectable (Figure 4f, Supp Figure 5f, g, i, j).

This approach does not enable a quantitative measurement of phagosomal SM and rather gives a “yes or no” answer. However, we think this method is sufficient to show that β-toxin expression and pretreatment markedly decreased phagosomal SM levels in the host cells.

The approach we used here to analyze “Lysenin-positive escape” can clearly be distinguished from Lysenin-based methods that were used by others.27 There Lysenin was used to show trans-bilayer movement of SM before rupture of bacteria-containing phagosomes.

To clarify the function of Lysenin in our approach we will add an additional figure to the revised manuscript.

Both SMase inhibitors (Figure 4C) and SMase pretreatment increased bacterial escape from the vacuole. The former should prevent SM hydrolysis and formation of ceramide, while the latter treatment should have the exact opposite effects, yet the end result is the same. What can one conclude regarding the need and role of the SMase products in the escape process?

As pointed out above, pretreatment of host cells with SMase removes SM from the plasma membrane and hence, ASM does not have access to its substrate. Hence, both treatment with either ASM inhibitors or pretreatment with bacterial SMase prevent ASM from being active on the plasma membrane and hence block the ASM-dependent uptake (Figure 2 c, e). Although overall less bacteria were internalized by host cells under these conditions, the bacteria that invaded host cells did so in an ASM-independent manner.

Since blockage of the ASM-dependent internalization pathway (with ASM inhibitor [Figure 4c], SMase pretreatment [Figure 4e] and Vacuolin-1[Supp. Fig.4f]) always resulted in enhanced phagosomal escape, we conclude that bacteria that were internalized in an ASM-independent fashion cause enhanced escape. Vice versa, bacteria that enter host cells in an ASM-dependent manner demonstrate lower escape rates.

This is supported by comparing the escape rates of “early” and “late” invaders [Figure 4g/h], which in our opinion is a key experiment that supports this hypothesis. The “early” invaders are predominantly ASM-dependent (see e.g. Figure 3e) and thus, bacteria that entered host cell in the first 10 min of infection should have been internalized predominantly in an ASM-dependent fashion, while slower entry pathways are active later during infection. The early ASM dependent invaders possessed lower escape rates, which is in line with the data obtained with inhibitors (e.g. Figure 4c and Supp. Fig. 4f).

We hypothesize that the activity of ASM on the plasma membrane during invasion mediates the recruitment of a specific subset of receptors, which then influence downstream phagosomal maturation and escape. This hypothesis is supported by the fact that the subset of receptors interacting with S. aureus is altered upon inhibition of the ASM-dependent uptake pathway. We describe this in another study that is currently under evaluation elsewhere.

Reviewer #2 (Public review):

Summary:

In this manuscript, Ruhling et al propose a rapid uptake pathway that is dependent on lysosomal exocytosis, lysosomal Ca2+ and acid sphingomyelinase, and further suggest that the intracellular trafficking and fate of the pathogen is dictated by the mode of entry.

The evidence provided is solid, methods used are appropriate and results largely support their conclusions, but can be substantiated further as detailed below. The weakness is a reliance on chemical inhibitors that can be non-specific to delineate critical steps.

Specific comments:

A large number of experiments rely on treatment with chemical inhibitors. While this approach is reasonable, many of the inhibitors employed such as amitriptyline and vacuolin1 have other or non-defined cellular targets and pleiotropic effects cannot be ruled out. Given the centrality of ASM for the manuscript, it will be important to replicate some key results with ASM KO cells.

We thank the reviewer for the critical evaluation of our manuscript and plenty of constructive comments.

We agree with the reviewer, that ASM inhibitors such as functional inhibitors of ASM (FIASMA) like amitriptyline used in our study have unspecific side effects given their mode-of-action. FIASMAs induce the detachment of ASM from lysosomal membranes resulting in degradation of the enzyme.15  However, we want to emphasize that we also used the competitive inhibitor ARC39 in our study16, 17 which acts on the enzyme by a completely different mechanism. All phenotypes (reduced invasion [Figure 2c, d], effect on invasion dynamics [Figure 3d], enhanced escape [Figure 4c and Supp Figure 4e] and differential recruitment of Rab7 [Supp. Figure 4b]) were observed with both inhibitors thereby supporting the role of ASM in the process.

We further agree that experiments with genetic evidence usually support and improve scientific findings. However, ASM is a cellular key player for SM degradation and recycling. In a clinical context, deficiency in ASM results in a so-called Niemann Pick disease type A/B. The lipid profile of ASM-deficient cells is massively altered18, which in itself will result in severe side effects. Thus, the usage of inhibitors provides a clear benefit when compared to ASM K.O. cells, since ASM activity can be targeted in a short-term fashion thereby preventing larger alterations in cellular lipid composition.

Most experiments are done in HeLa cells. Given the pathway is projected as generic, it will be important to further characterize cell type specificity for the process. Some evidence for a similar mechanism in other cell types S. aureus infects, perhaps phagocytic cell type, might be good.

Whenever possible we performed the experiments not only in HeLa but also in HuLECs. For example, we refer to experiments concerning the role of Ca2+ (Figure 1c/Supp.Figure1e), lysosomal Ca2+/Ned19 (Figure1d/Supp Figure 1g), lysosomal exocytosis/Vacuolin-1 (Figure 2a/Supp. Figure2a), ASM/ARC39 and amitriptyline (Figure 2c), surface SM/β-toxin (Figure 2e/Supp. Figure 2g), analysis of invasion dynamics (complete Figure 3) and measurement of cell death during infection (Figure 5c-e, Supp. Figure 6a+b).

HuLECs, however, are not really genetically amenable and hence we were not able to generate gene deletions in these cells and upon introduction of the fluorescence escape reporter the cells are not readily growing.

As to ASM involvement in phagocytic cells: a role for ASM during the uptake of S. aureus by macrophages was previously reported by others.23 However, in professional phagocytes S. aureus does not escape from the phagosome and replicates within the vacuole.28

I'm a little confused about the role of ASM on the surface. Presumably, it converts SM to ceramide, as the final model suggests. Overexpression of b-toxin results in the near complete absence of SM on phagosomes (having representative images will help appreciate this), but why is phagosomal SM detected at high levels in untreated conditions? If bacteria are engulfed by SM-containing membrane compartments, what role does ASM play on the surface? If surface SM is necessary for phagosomal escape within the cell, do the authors imply that ASM is tuning the surface SM levels to a certain optimal range? Alternatively, can there be additional roles for ASM on the cell surface? Can surface SM levels be visualized (for example, in Figure 4 E, F)?

We initially hypothesized that we would detect higher phagosomal SM levels upon inhibition of ASM, since our model suggests SM cleavage by ASM on the host cell surface during bacterial cell entry. However, we did not detect any changes in our experiments (Supp. Figure 4d). We currently favor the following explanation: SM is the most abundant sphingolipid in human cells.29 If peripheral lysosomes are exocytosed and thereby release ASM, only a localized and relative small proportion of SM may get converted to Cer, which most likely is below our detection limit. In addition, the detection of cytosolically exposed phagosomal SM by YFP-Lysenin is not quantitative and provides a “Yes or No” measurement. Hence, we think that the rather limited SM to Cer conversion in combination with the high abundance of SM in cellular membranes does not visibly affect the recruitment of the Lysenin reporter.

In our experiments that employ BODIPY-FL-SM (Figure 3a+b), we cannot distinguish between native SM and downstream metabolites such as Cer. Hence, again we cannot make any assumptions on the extent to which SM is converted on the surface during bacterial internalization. Although our laboratory recently used trifunctional sphingolipid analogs to analyze the SM to Cer conversion20, the visualization of this process on the plasma membrane is currently still challenging.

Overall, we hypothesize that the localized generation of Cer on the surface by released ASM leads to generation of Cer-enriched platforms. Subsequently, a certain subset of receptors may be recruited to these platforms and influence the uptake process. These platforms are supposed to be very small, which also would explain that we did not detect changes in Lysenin recruitment.

Related to that, why is ASM activity on the cell surface important? Its role in non-infectious or other contexts can be discussed.

ASM release by lysosomal exocytosis is implied in plasma membrane repair upon injury. We will this discuss this in the revised version of the manuscript.

If SM removal is so crucial for uptake, can exocytosis of lysosomes alone provide sufficient ASM for SM removal? How much or to what extent is lysosomal exocytosis enhanced by initial signaling events? Do the authors envisage the early events in their model happening in localized confines of the PM, this can be discussed.

Ionomycin treatment led to a release of ~10 % of all lysosomes and also increased extracellular ASM activity.7, 8 However, it is currently unclear– to our knowledge -to which extent the released ASM affects surface SM levels. Also, it is unknown which percentage of the lysosomes is released during infection with S. aureus. However, one has to speculate that this will be only a fraction of the “releasable lysosomes” as we assume that the effects (lysosomal Ca2+ liberation, lysosomal exocytosis and ASM activity) are very localized and take place only at host-pathogen contact sites (see also above). In initial experimentation we attempted to visualize the local ASM activity on the cell surface by using a visible range FRET probe (Supp. Fig. 3). Cleavage of the probe by ASM on the surface leads to release of FITC into the cell culture medium which does not contribute a measurable signal at the surface.

How are inhibitor doses determined? How efficient is the removal of extracellular bacteria at 10 min? It will be good to substantiate the cfu experiments for infectivity with imaging-based methods. Are the roles of TPC1 and TPC2 redundant? If so, why does silencing TPC1 alone result in a decrease in infectivity? For these and other assays, it would be better to show raw values for infectivity. Please show alterations in lysosomal Ca2+ at the doses of inhibitors indicated. Is lysosomal Ca2+ released upon S. aureus binding to the cell surface? Will be good to directly visualize this.

Concerning the inhibitor concentrations, we either used values established in published studies or recommendations of the suppliers (e.g. 2-APB, Ned19, Vacuolin-1). For ASM inhibitors, we determined proper inhibition of ASM by activity assays. Concentrations of ionomycin resulting in Ca2+ influx and lysosomal exocytosis was determined in earlier studies of our lab.8, 30

As to the removal of bacteria at 10 min p.i.: Lysostaphin is very efficient for removal of extracellular S. aureus and sterilizes the tissue culture supernatant. It significantly lyses bacteria within a few minutes, as determined by turbidity assays.31

As to imaging-based infectivity assays: We will add an analysis of imaging-based invasion assays in the revised manuscript.

Regarding the roles of TPC1 and TPC2: from our data we cannot conclude whether the roles of TPC1 and TPC2 are redundant. One could speculate that since blockage of TPC1 alone is sufficient to reduce internalization of bacteria, that both channels may have distinct roles. On the other hand, there might be a Ca2+ threshold in order to initiate lysosomal exocytosis that can only be attained if TPC1 and TPC2 are activated in parallel. Thus, our observations are in line with another study that shows reduced Ebola virus infection in absence of either TPC1 or TPC2.32

As to raw CFU counts: whereas the observed effects upon blocking the invasion of S. aureus are stable, the number of internalized bacteria varies between individual biological replicates, for instance, by differences in host cell fitness or growth differences in bacterial cultures, which are prepared freshly for each experiment.

With respect to visualization of lysosomal Ca2+ release: we agree with the reviewer that direct visual demonstration of lysosomal Ca2+ release upon infection will improve the manuscript. We therefore will perform additional experimentation to show alterations of Ca2+ at the lysosomes during infection.

The precise identification of cytosolic vs phagosomal bacteria is not very easy to appreciate. The methods section indicates how this distinction is made, but how do the authors deal with partial overlaps and ambiguities generally associated with such analyses? Please show respective images. The number of events (individual bacteria) for the live cell imaging data should be clearly mentioned.

We apologize for not having sufficiently explained the technology to detect escaped S. aureus. The cytosolic location of S. aureus is indicated by recruitment of RFP-CWT.33 CWT is the cell wall targeting domain of lysostaphin, which efficiently binds to the pentaglycine cross bridge in the peptidoglycan of S. aureus. This reporter is exclusively and homogenously expressed in the host cytosol. Only upon rupture of phagoendosomal membranes the reporter can be recruited to the cell wall of now cytosolically located bacteria. S. aureus mutants, for instance in the agr quorum sensing system, cannot break down the phagosomal membrane in non-professional phagocytes and thus stay unlabeled by the CWT-reporter.33 We will include respective images/movies of escape events and the bacteria numbers for live cell experiments in the revised version of the manuscript.

In the phagosome maturation experiments, what is the proportion of bacteria in Rab5 or Rab7 compartments at each time point? Will the decreased Rab7 association be accompanied by increased Rab5? Showing raw values and images will help appreciate such differences. Given the expertise and tools available in live cell imaging, can the authors trace Rab5 and Rab7 positive compartment times for the same bacteria?

We will include the proportion of Rab7-associated bacteria in the revised manuscript. Usually, we observe that Rab5 is only transiently (for a few minutes) present on phagosomes and only afterwards the phagosomes become positive for Rab7. We do not think that a decrease in Rab7-positive phagosomes would increase the proportion of Rab5-positive phagosomes. However, we cannot exclude this hypothesis with our data.

We can achieve tracing of individual bacteria for recruitment of Rab5/Rab7 only manually, which impedes a quantitative evaluation. However, we will include information that illustrates the consecutive recruitment of the GTPases.

The results with longer-term infection are interesting. Live cell imaging suggests that ASM-inhibited cells show accelerated phagosomal escape that reduces by 6 hpi. Where are the bacteria at this time point ? Presumably, they should have reached lysosomes. The relationship between cytosolic escape, replication, and host cell death is interesting, but the evidence, as presented is correlative for the populations. Given the use of live cell imaging, can the authors show these events in the same cell?

We think that most bacteria-containing phagoendosomes should have fused with lysosomes 6 h p.i. as we have previously shown by acidification to pH of 5 and LAMP1 decoration.34

We will provide images/videos to show the correlation between escape and replication in the revised manuscript.

Given the inherent heterogeneity in uptake processes and the use of inhibitors in most experiments, the distinction between ASM-dependent and independent pathways might not be as clear-cut as the authors suggest. Some caution here will be good. Can the authors estimate what fraction of intracellular bacteria are taken up ASM-dependent?

We agree with the reviewer that an overlap between internalization pathways is likely. A clear distinction is therefore certainly non-trivial. Alternative to ASM-dependent and ASM-independent pathways, the ASM activity may also accelerate one or several internalization pathways. We will address this limitation in the revised manuscript. 

Early in infection (~10 min after contact with the cells), the proportion of bacteria that enter host cells ASM-dependently is relatively high amounting to roughly 75% in HuLEC. After 30 min, this proportion is decreasing to about 50%. We will include this information in the revised version of the manuscript.

References

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(4) Silva, I., Brunett, M., Cáceres, M. & Cerda, O. TRPM4 modulates focal adhesion-associated calcium signals and dynamics. Biophysical Journal 123, 390a (2024).

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(6) Jevon, M. et al. Mechanisms of Internalization ofStaphylococcus aureus by Cultured Human Osteoblasts. Infection and Immunity 67, 2677-2681 (1999).

(7) Rodriguez, A., Webster, P., Ortego, J. & Andrews, N.W. Lysosomes behave as Ca2+-regulated exocytic vesicles in fibroblasts and epithelial cells. J Cell Biol 137, 93-104 (1997).

(8) Krones & Rühling et al. Staphylococcus aureus alpha-Toxin Induces Acid Sphingomyelinase Release From a Human Endothelial Cell Line. Front Microbiol 12, 694489 (2021).

(9) Sakurai, Y. et al. Two-pore channels control Ebola virus host cell entry and are drug targets for disease treatment. Science 347, 995-998 (2015).

(10) Aarhus, R., Graeff, R.M., Dickey, D.M., Walseth, T.F. & Lee, H.C. ADP-ribosyl cyclase and CD38 catalyze the synthesis of a calcium-mobilizing metabolite from NADP. J Biol Chem 270, 30327-30333 (1995).

(11) Schmid, F., Fliegert, R., Westphal, T., Bauche, A. & Guse, A.H. Nicotinic acid adenine dinucleotide phosphate (NAADP) degradation by alkaline phosphatase. J Biol Chem 287, 32525-32534 (2012).

(12) Angeletti, C. et al. SARM1 is a multi-functional NAD(P)ase with prominent base exchange activity, all regulated bymultiple physiologically relevant NAD metabolites. iScience 25, 103812 (2022).

(13) Gu, F. et al. Dual NADPH oxidases DUOX1 and DUOX2 synthesize NAADP and are necessary for Ca(2+) signaling during T cell activation. Sci Signal 14, eabe3800 (2021).

(14) Schonn, J.-S., Maximov, A., Lao, Y., Südhof, T.C. & Sørensen, J.B. Synaptotagmin-1 and -7 are functionally overlapping Ca²⁺ sensors for exocytosis in adrenal chromaffin cells. Proceedings of the National Academy of Sciences 105, 3998-4003 (2008).

(15) Kornhuber, J. et al. Functional Inhibitors of Acid Sphingomyelinase (FIASMAs): a novel pharmacological group of drugs with broad clinical applications. Cell Physiol Biochem 26, 9-20 (2010).

(16) Naser, E. et al. Characterization of the small molecule ARC39, a direct and specific inhibitor of acid sphingomyelinase in vitro. J Lipid Res 61, 896-910 (2020).

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I think it'd be helpful to briefly explain what Cell-ACDC is and why it is important that SpotMAX is integrated into it, as some readers may not be familiar with it.

Author response:

The following is the authors’ response to the previous reviews.

Recommendations for the Authors:

Reviewer #2:

(1) In my previous review, I noted that using three different movies to conclude that different genres evoke different thought patterns is an overinterpretation with only one instance per genre. In the rebuttal letter, the authors state that they provide "evidence that is necessary but not sufficient to conclude that we can distinguish different genres of films" (page 15). Accordingly, I suggest refraining from statements such as "There was a significant main effect of movie genre on memory" (page 13) in the manuscript.

Thank you for this point. We have removed any reference to genre.

Page 18 (referring to page 13) [354-355] “First, there was a significant main effect of movie on memory, F(2, 254.12) = 49.33, p <.001, η2 = .28.”

Reviewer #3:

The revised manuscript is easier to read and better contextualized.

Thank you for this comment and for your feedback to allow us to make the manuscript more clear.

Public Reviews:

Reviewer #1:

The lack of direct interrogation of individual differences/reliability of the mDES scores warrants some pause.

Our study's goal was to understand how group-level patterns of thought in one group of participants relate to brain activity in a different group of participants. To this end, we decomposed trial-level mDES data to show dimensions that are common across individuals, which demonstrated excellent split-half reliability. Then we used these data in two complementary ways. First, we established that these ratings reliably distinguished between the different films (showing that our approach is sensitive to manipulations of semantic and affective features in a film) and that these group-level patterns were also able to predict patterns of brain activity in a different group of participants (suggesting that mDES dimensions are also sensitive to the way brain activity emerges during movie watching). Second, we established that variation across individuals in their mDES scores predicted their comprehension of information from films. Thus our study establishes that when applied to movie-watching, mDES is sensitive to individual differences in the movie-watching experience (as determined by an individual's comprehension). Given the success of this study and the relative ease with which mDES can be performed, it will be possible in the future to conduct mDES studies that hone in on both the general features of the movie-watching experience, as well as aspects that are more unique to an individual.

Reviewer #2:

(1) The distinction between thinking and stimulus processing (in the sense of detecting and assigning meaning to features, modulated by factors such as attention) remains unclear. Is "thinking" a form of conscious access or a reportable read-out from sensory and higher-level stimulus processing? Or does it simply refer to the method used here to identify different processing states?

Thank you for highlighting this first point, which is an important consideration when attempting to map cognitive states. We have added some additional comments to our discussion section to expand on this point.

Page 35-36 [698-711] “It is possible, therefore, that the identification of regions of visual and auditory cortex by our study reflects the participants attention to sensory input, rather than the complex analysis of these inputs that may be required for certain features of the movie watching experience. On the other hand, it is possible that the movie-watching state is a qualitatively different type of mental state to those that emerge in typical task situations. For example, unlike tasks, the movie-watching state is characterized by multi-modal sensory input, semantically rich themes, that evolve together to reveal a continuous narrative to the viewer. It is possible, therefore, that movies engender an absorbed state which depends more on processing in sensory cortex than would occur in traditional task paradigms such as a working memory task (when systems in association cortex may be needed to maintain information related to task rules). Important headway into addressing this uncertainty can be achieved by using mDES to compare the types of states that occur in different contexts (including both movies and tasks) and comparing the topography of brain activity associated with different experiential states.”

(2) The dimensions of thought appear to be directly linked to brain areas traditionally associated with core faculties of perception and cognition. For example, superior temporal cortex codes for speech information, which is also where thought reports on verbal detail localize in this study. This raises the question of whether the present study truly captures mechanisms specific to thinking and distinct from processing, especially given that individual variations in reports were not considered and movie-specific features were not controlled for.

Thank you for this point, we have added an additional paragraph to the discussion to expand on this.

Page 35 [692-698] “Finally, it is worth considering whether the patterns of brain activity identified by our analysis reflect the stimuli that are processed during movie watching, or the cognitive and affective processing of this information. On the one hand, the regions we found were often within regions of sensory cortex, areas of the brain which are often ascribed basic stimulus processing functions [1]. Moreover, according to perspectives on cognition derived from more traditional task paradigms, complex features of cognition, such as the regulation of thought, are often attributed to regions of association cortex, such as the dorsolateral prefrontal cortex [2].”

Reviewer #3:

This paper is framed as presenting a new paradigm but it does little to discuss what this paradigm serves, what are its limitations and how it should have been tested. The novelty appears to be in using experience sampling from 1 sample to model the responses of a second sample.

Thank you for this comment, we have since made clear what the novelty of the methodology is, as you have correctly identified, by expanding this point beyond the methods section to clearly orient the reader to the application and limitation of our methodological approach with our paradigm.

Page 7-8 [149-174] “One challenge that arises when attempting to map the dynamics of thought onto brain activity during movie-watching is accounting for the inherently disruptive nature of experience sampling: to measure experience with sufficient frequency to map experiential reports during movies would inherently disrupt the natural processes of the brain and alter the viewer’s experience (for example, by pausing the film at a moment of suspense). Therefore, if we periodically interrupt viewers to acquire a description of their thoughts while recording brain activity, this could impact on the ability to capture important dynamic features of the brain. On the other hand, if we measured fMRI activity continuously over movie-watching (as is usually the case), we would lack the capacity to directly relate brain signals to the corresponding experiential states. Thus, to overcome these obstacles, we developed a novel methodological approach using two independent samples of participants. In the current study, one set of 120 participants was probed with mDES five times across the three ten-minute movie clips (11 minutes total, no sampling in the first minute). We used a jittered sampling technique where probes were delivered at different intervals across the film for different people depending on the condition they were assigned. Probe orders were also counterbalanced to minimize the systematic impact of prior and later probes at any given sampling moment. We used these data to construct a precise description of the dynamics of experience for every 15 seconds of three ten-minute movie clips. These data were then combined with fMRI data from a different sample of 44 participants who had already watched these clips without experience sampling [3]. By combining data from two different groups of participants, our method allows us to describe the time series of different experiential states (as defined by mDES) and relate these to the time series of brain activity in another set of participants who watched the same films with no interruptions. In this way, our study set out to explicitly understand how the patterns of thoughts that dominate different moments in a film in one group of participants relate to the brain activity at these time points in a second set of participants and, therefore, better understand the contribution of different neural systems to the movie-watching experience.”

Page 33-35 [658-691] “Importantly, our study provides a novel method for answering these questions and others regarding the brain basis of experiences during films that can be applied simply and cost-effectively. As we have shown, mDES can be combined with existing brain activity, allowing information about both brain activity and experience to be determined at a relatively low cost.  For example, the cost-effective nature of our paradigm makes it an ideal way to explore the relationship between cognition and neural activity during movie-watching during different genres of film. In neuroimaging, conclusions are often made using one film in naturalistic paradigm studies [4]. Although the current study only used three movie clips, restraining our ability to form strong conclusions regarding how different patterns of thought relate to specific genres of film, in the future, it will be possible to map cognition across a more extensive set of movies and discern whether there are specific types of experience that different genres of films engage. One of the major strengths of our approach, therefore, is the ability to map thoughts across groups of participants across a wide range of movies at a relatively low cost.

Nonetheless, this paradigm is not without limitations. This is the first study, as far as we know, that attempts to compare experiential reports in one sample of participants with brain activity in a second set of participants, and while the utility of this method enables us to understand the relationship between thought and brain activity during movies, it will be important to extend our analysis to mDES data during movie-watching while brain activity is recorded. In addition, our study is correlational in nature, and in the future, it could be useful to generate a more mechanistic understanding of how brain activity maps onto the participants experience. Our analysis shows that mDES is able to discriminate between films, highlighting its broad sensitivity to variation in semantic or affective content. Armed with this knowledge, we propose that in the future, researchers could derive mechanistic insights into how the semantic features may influence the mDES data. For example, it may be possible to ask participants to watch movies in a scrambled order to understand how the structure of semantic or information influences the mapping between brains and ongoing experience as measured by mDES. Finally, our study focused on mapping group-level patterns of experience onto group-level descriptions of brain activity. In the future it may be possible to adopt a “precision-mapping” approach by measuring longer periods of experience using mDES and determining how the neural correlates of experience vary across individuals who watched the same movies while brain activity was collected [5]. In the future, we anticipate that the ease with which our method can be applied to different groups of individuals and different types of media will make it possible to build a more comprehensive and culturally inclusive understanding of the links between brain activity and movie-watching experience.”

What are the considerations for treating high-order thought patterns that occur during film viewing as stable enough to use across participants? What would be the limitations of this method? (Do all people reading this paper think comparable thoughts reading through the sections?) This is briefly discussed in the revised manuscript and generally treated as an opportunity rather than as a limitation.

It is likely, based on our study, that films can evoke both stereotyped thought patterns (i.e. thoughts that many people will share) and others that are individualistic. It is clear that, in principle, mDES is capable of capturing empirical information on both stereotypical thoughts and idiosyncratic thoughts. For example, clear differences in experiences across films and, in particular, during specific periods within a film, show that movie-watching can evoke broadly similar thought patterns in different groups of participants (see Figure 3 right-hand panel). On the other hand, the association between comprehension and the different mDES components indicate that certain individuals respond to the same film clip in different ways and that these differences are rooted in objective information (i.e. their memory of an event in a film clip). A clear example of these more idiosyncratic features of movie watching experience can be seen in the association between “Episodic Knowledge” and comprehension. We found that “Episodic Knowledge” was generally high in the romance clip from 500 Days of Summer but was especially high for individuals who performed the best, indicating they remembered the most information. Thus good comprehends responded to the 500 Days of Summer clip with responses that had more evidence of “Episodic Knowledge” In the future, since the mDES approach can account for both stereotyped and idiosyncratic features of experience, it will be an important tool in understanding the common and distinct features that movie watching experiences can have, especially given the cost effective manner with which these studies can be run.  

In conclusion, this study tackles a highly interesting subject and does it creatively and expertly. It fails to discuss and establish the utility and appropriateness of its proposed method.

Thank you very much for your feedback and critique. In our revision and our responses to these questions, we provided more information about the method's robustness utility and application to understanding cognition. Thank you for bringing these points to our attention.

References

(1) Kaas, J.H. and C.E. Collins, The organization of sensory cortex. Current Opinion in Neurobiology, 2001. 11(4): p. 498-504.

(2) Turnbull, A., et al., Left dorsolateral prefrontal cortex supports context-dependent prioritisation of off-task thought. Nature Communications, 2019. 10.

(3) Aliko, S., et al., A naturalistic neuroimaging database for understanding the brain using ecological stimuli. Scientific Data, 2020. 7(1).

(4) Yang, E., et al., The default network dominates neural responses to evolving movie stories. Nature Communications, 2023. 14(1): p. 4197.

(5) Gordon, E.M., et al., Precision Functional Mapping of Individual Human Brains. Neuron, 2017. 95(4): p. 791-807.e7.

Reviewer #2 (Public review):

Summary:

This study describes a deep mutational scan across CDKN2A using suppression of cell proliferation in pancreatic adenocarcinoma cells as a readout for CDKN2A function. The results are also compared to in silico variant predictors currently utilized by the current diagnostic frameworks to gauge these predictors' performance. The authors also functionally classify CDKN2A somatic mutations in cancers across different tissues

Review:

The goal of this paper was to perform functional classification of missense mutations in CDKN2A in order to generate a resource to aid in clinical interpretation of CDKN2A genetic variants identified in clinical sequencing. In our initial review, we concluded that this paper was difficult to review because there was a lack of primary data and experimental detail. The authors have significantly improved the clarity, methodological detail and data exposition in this revision, facilitating a fuller scientific review. Based on the data provided we do not think the functional characterization of CDKN2A variants is robust or complete enough to meet the stated goal of aiding clinical variant interpretation. We think the underlying assay could be used for this purpose but different experimental design choices and more replication would be required for these data to be useful. Alternatively, the authors could also focus on novel CDKN2A variants as there seems to be potential gain of function mutations that are simply lumped into "neutral" that may have important biological implications.

Major concerns:

Low experimental concordance. The p-value scatter plot (Figure 2 Figure Supplement 3A) across 560 variants shows low collinearity indicating poor replicability. These data should be shown in log2fold changes, but even after model fitting with the gamma GLM still show low concordance which casts strong doubt on the function scores.<br /> The more detailed methods provided indicate that the growth suppression experiment is done in 156 pools with each pool consisting of the 20 variants corresponding to one of the 156 aa positions in CKDN2A. There are several serious problems with this design.

Batch effects in each of the pools preventing comparison across different residues. We think this is a serious design flaw and not standard for how these deep mutational scans are done. The standard would be to combine all 156 pools in a single experiment. Given the sequencing strategy of dividing up CDKN2A into 3 segments, the 156 pools could easily have been collapsed into 3 (1 to 53, 54 to 110, 111 to 156). This would significantly minimize variation in handling between variants at each residue and would be more manageable for performance of further replicates of the screen for reproducibility purposes. The huge variation in confluency time 16-40 days for each pool suggest that this batch effect is a strong source of variation in the experiment

Lack of experimental/biological replication: The functional assay was only performed once on all 156 CDKN2A residues and was repeated for only 28 out of 156 residues, with only ~80% concordance in functional classification between the first and second screens. This is not sufficiently robust for variant interpretation. Why was the experiment not performed more than once for most aa sites?

For the screen, the methods section states that PANC-1 cells were infected at MOI=1 while the standard is an MOI of 0.3-0.5 to minimize multiple variants integrating into a single cell. At an MOI =1 under a Poisson process which captures viral integration, ~25% of cells would have more than 1 lentiviral integrant. So in 25% of the cells the effect of a variant would be confounded by one or more other variants adding noise to the assay.

While the authors provide more explanation of the gamma GLM, we strongly advise that the heatmap and replicate correlations be shown with the log2 fold changes rather than the fit output of the p-values.

In this study, the authors only classify variants into the categories "neutral", "indeterminate", or "deleterious" but they do not address CDKN2A gain-of-function variants that may lead to decreased proliferation. For example, there is no discussion on variants at residue 104, whose proliferation values mostly consist of higher magnitude negative log2fold change values. These variants are defined as neutral but from the one replicate of the experiment performed, they appear to be potential gain-of-function variants.

Author response:

We thank the reviewers for their feedback. We are currently revising the manuscript to address their questions and concerns. Here we briefly summarize our planned revisions.

Reviewer 1 requested clarification on three points. We will clarify all these points with text edits. One point is brief enough to be addressed here: in cases when we pooled data from the left and right hemispheres, the reviewer wants to know how this was done. Simply put, we defined the “ipsi” side of the body as the side where the recorded DN resided, and we defined “contra” as the other side.

Reviewer 2 requested clarification on two minor points. We will clarify these points with text edits and with an additional analysis.

Reviewer 3 had a number of substantive concerns. Briefly:

(1) The reviewer asks us to improve its discussion of some relevant literature. We will provide updated information on the DN steering network, and in particular, we will cite Bidaye et al. 2020 and Sapkal et al. 2024. We apologize for the oversight.

(2) The reviewer asks us for immunofluorescent images documenting the expression patterns of our effector transgenes. With regard to GtACR1::eYPF expression, we will include these images in our resubmission. With regard to ReachR expression, we expressed this reagent stochastically under hs-FLP control, and so different brains had different expression patterns; however, we carefully documented the number of DNa02 cells that expressed ReachR in each brain. With regard to GFP expression, these expression patterns are available online from the FlyLight documentation associated with Namiki et al. eLife 2018 (https://splitgal4.janelia.org/precomputed/Descending%20Neurons%202018.html). The UAS-GFP transgene used by Namiki et al. 2018 (pJFRC200-10XUASIVS-myr::smGFP-HA in attP18) is different from the UAS-GFP transgene we used (10XUAS-IVS-mCD8::GFP(su(Hw)attP8), and so there may be minor differences in expression pattern. However, it should be noted that we only used GFP expression to target somata for patch clamp recording, and DNa01 and DNa02 somata have a distinctive location and a distinctive size; when we performed these recordings, we only targeted a soma in this location, and we verified that there were no “distractor” somata in this vicinity with similar size and appearance. The same applies to patch clamp recordings targeted via Halo7 expression (SiR110-HaloTag fluorescence). In paired recordings from both DNa02 and DN01, we verified the identity of each cell as described in Fig. S1.

(3) The reviewer asks why we focused on DNa02 in the latter part of the manuscript, rather than DNa01. We made this decision because DNa02 is more highly predictive of steering behavior, as compared to DNa01 (Fig. 1H). Also, an impulse of DNa02 activity is followed by a relatively large turning maneuver, on average, whereas an impulse of DNa01 activity is followed by a relatively small turning maneuver (Fig. 1E-F). Moreover, DNa02 has many more synaptic inputs in the brain (Fig. 7A), and it has many more direct synaptic connections onto motor neurons (Fig. 1B).

(4) The reviewer highlights difficulties in interpreting DN activity during backward movement (Figs. S3/S4). We included this material in the spirit of completeness, but we agree with the reviewer that it is difficult to interpret. In our revision, we will omit Fig. S3C and Fig. S4A-B, and we will revise these legends to improve clarity.

(5) The reviewer asks why do a systematic analysis of paired DNa01 recordings, as we did for DNa02. It is difficult to get paired right/left recordings from two DNs of the same type in the same fly, while the fly is walking vigorously, and we were only able to get two such paired recordings from DNa01. We did not feel this was a sufficiently large sample size to support a systematic analysis. We chose not to invest more time in getting more paired DNa01 recordings because we thought that DNa02 was more important, for the reasons noted above.

(6) The reviewer asks for an analysis of trials where bump-jump led to turning in the opposite direction to the DNa02 being recorded. We will provide this analysis in the revision.

(7) The reviewer points out that “latent” steering drives might not be latent, as they might produce small postural changes we are not capturing. This is a fair point, and we will note this in our revision.

(8) The reviewer asks for a systematic analysis of DNa01 inputs in Figure 7, similar to our analysis of DNa02 inputs. Here we would prefer to focus on DNa02, for three reasons. First, we think DNa02 is likely more important, for the reasons noted above. Second, there has been some uncertainty as to the identity of DNa01 in connectome data; indeed, in the hemibrain data set, the cell recently identified as DNa01 was annotated as VES006 (Schlegel et al. Nature 634: 139-152). Third, the cell now identified as DNa01 does not receive direct input from either the central complex or the mushroom body, and for this reason, we felt that the inputs to DNa01 might be less interesting to a general audience.

(9) The reviewer wonders whether DNa01 is more involved in sideways movement, rather than rotational movement. Our data do not support this conclusion: rather, our data show that DNa01 is only weakly correlated with sideways movement. Thus, the forward filter (Fig. 1F) shows that an impulse of DNa01 activity is (on average) followed by a relatively small amount of sideways movement. Conversely, the reverse filter (in Fig. S2I) shows that an impulse of sideways movement is (on average) preceded by a relatively large amount of DNa01 activity.

(10) The reviewer points out that the phenotype associated with optogenetic suppression in Fig. 8G is weak. We will highlight this point and discuss potential reasons for this weak phenotype in the revision.

Author response:

The following is the authors’ response to the original reviews.

Response to Reviewer’s comments

We are most grateful for the opportunity to address the reviewer comments. Point-by-point responses are presented below.

Overall, the paper has several strengths, including leveraging large-scale, multi-modal datasets, using computational reasonable tools, and having an in-depth discussion of the significant results.

We thank the reviewer for the very supportive comments.

Based on the comments and questions, we have grouped the concerns and corresponding responses into three categories.

(1) The scope and data selection

The results are somewhat inconclusive or not validated.

The overall results are carefully designed, but most of the results are descriptive. While the authors are able to find additional evidence either from the literature or explain the results with their existing knowledge, none of the results have been biologically validated. Especially, the last three result sections (signaling pathways, eQTLs, and TF binding) further extended their findings, but the authors did not put the major results into any of the figures in the main text.”

The goal of this manuscript is to provide a list of putative childhood obesity target genes to yield new insights and help drive further experimentation. Moreover, the outputs from signaling pathways, eQTLs, and TF binding, although noteworthy and supportive of our method, were not particularly novel. In our manuscript we placed our focus on the novel findings from the analyses. We did, however, report the part of the eQTLs analysis concerning ADCY3, which brought new insight to the pathology of obesity, in Figure 4C.

The manuscript would benefit from an explanation regarding the rationale behind the selection of the 57 human cell types analyzed. it is essential to clarify whether these cell types have unique functions or relevance to childhood development and obesity.

We elected to comprehensively investigate the GWAS-informed cellular underpinnings of childhood development and obesity. By including a diverse range of cell types from different tissues and organs, we sought to capture the multifaceted nature of cellular contributions to obesity-related mechanisms, and open new avenues for targeted therapeutic interventions.

There are clearly cell types that are already established as being key to the pathogenesis of obesity when dysregulated: adipocytes for energy storage, immune cell types regulating inflammation and metabolic homeostasis, hepatocytes regulating lipid metabolism, pancreatic cell types intricately involved in glucose and lipid metabolism, skeletal muscle for glucose uptake and metabolism, and brain cell types in the regulation of appetite, energy expenditure, and metabolic homeostasis.

While it is practical to focus on cell types already proven to be associated with or relevant to obesity, this approach has its limitations. It confines our understanding to established knowledge and rules out the potential for discovering novel insights from new cellular mechanisms or pathways that could play significant roles in the pathogenesis if obesity. Therefore, it was essential to reflect known biology against the unexplored cell types to expand our overall understanding and potentially identify innovative targets for treatment or prevention.

I wonder whether the used epigenome datasets are all from children. Although the authors use literature to support that body weight and obesity remain stable from infancy to adulthood, it remains uncertain whether epigenomic data from other life stages might overlook significant genetic variants that uniquely contribute to childhood obesity.

The datasets utilized in our study were derived from a combination of sources, both pediatric and adult. We recognize that epigenetic profiles can vary across different life stages but our principal effort was to characterize susceptibility BEFORE disease onset.

Given that the GTEx tissue samples are derived from adult donors, there appears to be a mismatch with the study's focus on childhood obesity. If possible, identifying alternative validation strategies or datasets more closely related to the pediatric population could strengthen the study's findings.

We thank the reviewer for raising this important point. We acknowledge that the GTEx tissue samples are derived from adult donors, which might not perfectly align with the study's focus on childhood obesity. The ideal strategy would be a longitudinal design that follows individuals from childhood into adulthood to bridge the gap between pediatric and adult data, offering systematic insights into how early-life epigenetic markers influencing obesity later in life. In future work, we aim to carry out such efforts, which will represent substantial time and financial commitment.

Along the same lines, the Developmental Genotype-Tissue Expression (dGTEx) Project is a new effort to study development-specific genetic effects on gene expression at 4 developmental windows spanning from infant to post-puberty (0-18 years). Donor recruitment began in August 2023 and remains ongoing. Tissue characterization and data production are underway. We hope that with the establishment of this resource, our future research in the field of pediatric health will be further enhanced.

Figure 1B: in subplots c and d, the results are either from Hi-C or capture-C. Although the authors use different colors to denote them, I cannot help wondering how much difference between Hi-C and capture-C brings in. Did the authors explore the difference between the Hi-C and capture-C?

Thank you for your comment. It is not within the scope of our paper to explore the differences between the Hi-C and Capture-C methods. In the context of our study, both methods serve the same purpose of detecting chromatin loops that bring putative enhancers to sometimes genomically distant gene promoters. Consequently, our focus was on utilizing these methods to identify relevant chromatin interactions rather than comparing their technical differences.

(2) Details on defining different categories of the regions of interest

Some technical details are missing.

While the authors described all of their analysis steps, a lot of the time, they did not mention the motivation. Sometimes, the details were also omitted.”

We have added a section to the revision to address the rationale behind different OCRs categories.

Line 129: should "-1,500/+500bp" be "-500/+500bp"?

A gene promoter was defined as a region 1,500 bases upstream to 500 bases downstream of the TSS. Most transcription factor binding sites are distributes upstream (5’) from TSS, and the assembly of transcription machinery occurs up to 1000 bases 5’ from TSS. Given our interest in SNPs that can potentially disrupt transcription factor binding, this defined promoter length allowed us to capture such SNPs in our analyses.

How did the authors define a contact region?

Chromatin contact regions identified by Hi-C or Capture-C assays are always reported as pairs of chromatin regions. The Supplementary eMethods provide details on the method of processing and interaction calling from the Hi-C and Capture-C data.

The manuscript would benefit from a detailed explanation of the methods used to define cREs, particularly the process of intersecting OCRs with chromatin conformation data. The current description does not fully clarify how the cREs are defined.

In the result section titled "Consistency and diversity of childhood obesity proxy variants mapped to cREs", the authors introduced the different types of cREs in the context of open chromatin regions and chromatin contact regions, and TSS. Figure 2A is helpful in some way, but more explanation is definitely needed. For example, it seems that the authors introduced three chromatin contacts on purpose, but I did not quite get the overall motivation.

We apologize for the confusion. Our definition of cREs is consistent throughout the study. Figure 2A will be the first Figure 1A in the revision in order to aid the reader.

The 3 representative chromatin loops illustrate different ways the chromatin contact regions (pairs of blue regions under blue arcs) can overlap with OCRs (yellow regions under yellow triangles – ATAC peaks) and gene promoters.

(1) The first chromatin loop has one contact region that overlaps with OCRs at one end and with the gene promoter at the other. This satisfies the formation of cREs; thus, the area under the yellow ATAC-peak triangle is green.

(2) The second loop only overlapped with OCR at one end, and there was no gene promoter nearby, so it is unqualified as cREs formation.

(3) The third chromatin loop has OCR and promoter overlapping at one end. We defined this as a special cRE formation; thus, the area under the yellow ATAC-peak triangle is green.

To avoid further confusion for the reader, we have eliminated this variation in the new illustration for the revised manuscript.

Figure 2A: The authors used triangles filled differently to denote different types of cREs but I wonder what the height of the triangles implies. Please specify.

The triangles are illustrations for ATAC-seq peaks, and the yellow chromatin regions under them are OCRs. The different heights of ATAC-seq peaks are usually quantified as intensity values for OCRs. However, in our study, when an ATAC-seq peak passed the significance threshold from the data pipeline, we only considered their locations, regardless of their intensities. To avoid further confusion for the reader, we have eliminated this variation in the new illustration for the revised manuscript.

Figure 1B-c. the title should be "OCRs at putative cREs". Similarly in Figure 1B-d.

cREs are a subset of OCRs.

- In the section "Cell type specific partitioned heritability", the authors used "4 defined sets of input genomic regions". Are you corresponding to the four types of regions in Figure 2A? 

Figure 2A is the first Figure 1A in the revision and is modified to showcase how we define OCRs and cREs.

It seems that the authors described the 771 proxies in "Genetic loci included in variant-to-genes mapping" (ln 154), and then somehow narrowed down from 771 to 94 (according to ln 199) because they are cREs. It would be great if the authors could describe the selection procedure together, rather than isolated, which made it quite difficult to understand.

In the Methods section entitled “Genetic loci included in variant-to-genes mapping," we described the process of LD expansion to include 771 proxies from 19 sentinel obesity-significantly associated signals. Not all of these proxies are located within our defined cREs. Figure 2B, now Figure 2A in the revision, illustrates different proportions of these proxies located within different types of regions, reducing the proxy list to 94 located within our defined cREs.

Figure 2. What's the difference between the 771 and 758 proxies?

13 out of 771 proxies did not fall within any defined regions. The remaining 758 were located within contact regions of at least one cell type regardless of chromatin state.

(3) Typos

In the paragraph "Childhood obesity GWAS summary statistics", the authors may want to describe the case/control numbers in two stages differently. "in stage 1" and "921 cases" together made me think "1,921" is one number.

This has been amended in the revision.

Hi-C technology should be spelled as Hi-C. There are many places, it is miss-spelled as "hi-C". In Figure 1, the author used "hiC" in the legend. Similarly, Capture-C sometime was spelled as "capture-C" in the manuscript.

At the end of the fifth row in the second paragraph of the Introduction section: "exisit" should be "exist".

In Figure 2A: "Within open chromatin contract region" should be "Within open chromatin contact region”

These typos and terminology inconsistencies have been amended in the revision.

Author response:

Reviewer #1 (Public review):

Summary:

Shrestha et al report an investigation of mechanisms underlying gustatory preference for carboxylic acids in Drosophila. They begin with a screen of selected IR mutants, identifying 5 candidates - 2 IR co-receptors and 3 other IRs - whose loss of function causes defects in feeding preference for one or more of the three tested carboxylic acids. The requirement for IR51b, IR94a, and IR94h in carboxylic acid responses is evaluated in more detail using behavior, electrophysiology (labellar sensilla), and calcium imaging (pharyngeal neurons). The behavioral valence of IR94a and IR94h neurons is assessed using optogenetics. Overall the study uses a variety of approaches to test and validate the requirement of IRs in pharyngeal carboxylic acid taste.

Strengths:

The involvement of the identified IRs in gustatory responses to carboxylic acids is very clear from this study. The authors use mutants and transgenic rescue experiments and evaluate outcomes using electrophysiology, behavior, and imaging. Complementary approaches of loss-of-function and artificial activation support the main conclusion that the identified pharyngeal neurons sense carboxylic acids and convey a positive behavioral valence.

Weaknesses:

Some aspects of expression analysis and calcium imaging need to be clarified to better support the conclusions.

(1) The conclusion of two parallel IR-mediated pathways rests on expression analysis of Ir94a-GAL4 and Ir94h-GAL4 lines and the observation that Ir51b expression driven by either can rescue the Ir51b mutant phenotype. However, the expression analysis is not as rigorous as it needs to be for such a conclusion. Prior work found co-expression of Ir94a and Ir94h in the LSO. Here, the co-expression of the two drivers has not been examined, and Ir94a-GAL4 does not appear to be expressed in the LSO. Given the challenges in validating expression patterns in pharyngeal organs, the possibility that the drivers do not entirely capture endogenous expression cannot be ruled out. Rescue experiments using feeding preference or single-cell imaging don't suffice as validation. Plus, the expression of Ir51b could not be defined.

Based on current literature, Ir94a and Ir94h exhibit distinct expression patterns localized to different sensory regions. Specifically, Ir94a is primarily expressed in the V5 region of the VCSO, where it co-localizes with Ir94c-GAL4 (Chen et al., 2017). Conversely, Ir94h is found in the L7-7 sensilla of the LSO, where it co-expresses with Ir94f, and also within the V2 cells of the VCSO. Notably, the projections of Ir94a and Ir94h into the dorso-anterior subesophageal ganglion suggest divergent expression patterns rather than co-expression in the pharyngeal regions (Koh et al., 2014). Regarding co-expression of Ir94a and Ir94h in the LSO, we did not find any evidence to support this claim. Our data reinforce this view, showing that Ir94a-GAL4 expression is limited to the VCSO, while Ir94h-GAL4 is present in both the LSO and VCSO. Thus, the notion of co-expression of Ir94a and Ir94h in the LSO is not substantiated by current evidence.

As a reviewer suggested, it is possible that the GAL4 drivers utilized may not fully reflect the endogenous expression of these receptors. Despite this limitation, our behavioral, expression, and physiological analyses strongly suggest that Ir94a and Ir94h are located in distinct regions, supporting a model of two parallel IR-mediated pathways operating within the sensory system.

In addition, RT-PCR analysis confirmed the presence of Ir51b. However, due to methodological constraints, we were unable to conduct cell-type-specific expression studies using Ir51b-GAL4. This limitation, which we have acknowledged in the manuscript, does not detract from our core findings but highlights an area for future research. Further studies utilizing cell-specific expression analysis and co-expression studies with additional drivers could offer more definitive insights into IR51b’s functional role and its interactions within broader IR-mediated pathways.

(2) The description of methods and results for the ex vivo calcium imaging is not satisfactory. Details about which cells are being analyzed, and in which organs are not included. No solvent stimulus is tested. The temporal dynamics of the responses are not presented. Movies of the imaging are not included as supplementary information - it would be important to visualize those with what was considered modest movement.

We appreciate this valuable feedback. As discussed above, Ir94h is specifically expressed in the L7-7 sensilla of the LSO, while Ir94a is expressed in the V2 cells of the VCSO. This evidence led us to focus specifically on these cells in our calcium imaging study to ensure accuracy and relevance. In our experiments, Adult hemolymph solution (AHL) (108 mM NaCl, 5 mM KCl, 8.2 mM MgCl2, 2 mM CaCl2, 4 mM NaHCO3, 1 mM NaH2PO4, 5 mM HEPES, pH 7.5) was used as the solvent and employed as a pre-stimulus (as mentioned in the Methods section). During this phase, we observed no changes in fluorescence, indicating that AHL itself did not influence the responses. Fluorescence changes occurred only when the test chemical, dissolved in AHL, was introduced. To further confirm that AHL had no impact on the results, we conducted continuous recordings with AHL alone before beginning our main experiments, and these trials confirmed the absence of fluorescence alterations. We have included the temporal dynamics and supplementary video recordings to provide a more comprehensive understanding of our findings.

(3) The observed differences in phenotypes of Ir25a and Ir76b mutants are intriguing, as are those between the co-receptor mutants and Ir51b, Ir94a, and Ir94h, but have not been sufficiently considered. Prior studies have also found roles for other response modes (OFF response), other IRs and GRs, and other organs (labellum, tarsi) in behavioral responses to carboxylic acids. Overall, the authors' model may be overly simplistic, and the discussion does not do justice to how their model reconciles with the body of work that already exists.

Stanley et al. (2021) reported that the gustatory detection of lactic acid requires both IRs and GRs functioning together. Specifically, they found that IR25a mediates the onset peak response (ON response) to lactic acid, while GRs dampen this response and contribute to a removal peak (OFF response). Interestingly, in Ir25a mutants, a small onset peak still occurred, while Gr64a-f mutants showed an enhanced onset, suggesting that IRs and GRs interact dynamically to modulate taste responses.

In our previous work, we also observed the role of sweet GRs, in addition to Ir25a and Ir76b, in detecting carboxylic acids in the labellum (Shrestha et al., 2021). This raises the possibility of a similar interplay with carboxylic acids in our current study, where different IRs may contribute to distinct aspects of sensory responses in the pharynx, leading to the phenotypic differences we observed. Moreover, Chen et al. (2017) demonstrated that sour-sensing neurons in the tarsi express both IR76b and IR25a and specifically respond to carboxylic and inorganic acids without reacting to sweet or bitter compounds. This finding points to a specialized role for these receptors in sour detection and suggests a coordinated response involving multiple sensory organs—such as the labellum, tarsi, and pharynx.

The phenotypic differences observed in our mutants align with a more integrated model of carboxylic acid detection, in which multiple receptors and sensory organs contribute to the overall behavioral response. This supports the idea that our current model offers a more detailed understanding of how different carboxylic acids are detected and processed by the gustatory system.

Reviewer #2 (Public review):

Shrestha et al investigated the role of IR receptors in the detection of 3 carboxylic acids in adult Drosophila. A low concentration of either of these carboxylic acids added to 2 mM sucrose (1% lactic acid (LA), citric acid (CA), or glycolic acid (GA)) stimulates the consumption of adult flies in choice conditions. The authors use this behavioral test to screen the impact of mutations within 33 receptors belonging to the IR family, a large family of receptors derived from glutamate receptors and expressed both in the olfactory and gustatory sensilla of insects. Within the panel of mutants tested, they observed that 3 receptors (IR25a, IR51b, and IR76b) impaired the detection of LA, CA, and GA, and that 2 others impacted the detection of CA and GA (IR94a and IR94h). Interestingly, impairing IR51b, IR94a, and IR94h did not affect the electrophysiological responses of external gustatory sensilla to LA, CA, and GA. Thanks to the use of GAL4 strains associated with these receptors and thanks to the use of poxn mutants (which do not develop external gustatory sensilla but still have functional internal receptors), they show evidence that IR94a and IR94h are only expressed in two clusters of gustatory neurons of the pharynx, respectively in the VCSO (ventral cibarial sense organ) and in the VCSO + LSO (labral sense organ). As for IR51b, the GAL4 approach was not successful but RT-PCR made on different parts of the insect showed an expression both in the pharyngeal organs and in peripheral receptors. These main findings are then complemented by a host of additional experiments meant to better understand the respective roles of IR94a and IR94h, by using optogenetics and brain calcium imaging using GCamp6. They also report a failed attempt to co-express IR51b, IR94a, and IR94h into external receptors, a co-expression which did not confer the capability of bitter-sensitive cells (expressing GR33a-GAL4) to detect either of the carboxylic acids. These data complete and expand previous observations made on this group and others, and dot to 2 new IR receptors which show an unsuspected specific expression, into organs that still remain difficult to study.

The conclusions of this paper are supported by the data presented, but it remains difficult to make general conclusions as concerns the mechanisms by which carboxylic acids are detected.

(1) All experiments were done with 1% of carboxylic acids. What is the dose dependency of the behavioral responses to these acids, and is it conceivable that other receptors are involved at other concentrations?

In our study, we conducted experiments to examine the dose dependency of behavioral responses to carboxylic acids, with results presented in Supplementary Figure 1. We found that lower concentrations of carboxylic acids are perceived as attractive, while higher concentrations are aversive. This differential response suggests that the receptors identified in our study are primarily tuned to detect low concentrations of these acids. Since higher concentrations elicited aversive responses, it is plausible that additional receptors, beyond the scope of our study, may be involved in sensing these higher concentrations. These receptors could be part of other gustatory receptor neurons that respond specifically to increased acid levels, as fruit flies tend to avoid higher concentrations. We propose that future research could investigate these alternative pathways to gain a complete understanding of the behavioral responses to carboxylic acids. In summary, our findings suggest that specific receptors are involved in detecting low concentrations, while distinct receptor pathways—possibly mediated by other GRNs—may regulate responses to higher concentrations.

(2) One result needs to be better discussed and hypotheses proposed - which is why the mutations of most receptors lead to a loss of detection (mutant flies become incapable of detecting the acid) while mutations in IR94a and IR94h make CA and GA potent deterrents. Does it mean that CA and GA are detected by another set of receptors that, when activated, make flies actively avoid CA and GA? In that case, do the authors think that testing receptors one by one is enough to uncover all the receptors participating in the detection of these substances?

As we mentioned above, it is possible that distinct receptor pathways mediate avoidance of GA and CA. This suggests that CA and GA might activate different sets of receptors that trigger avoidance behavior, pointing to a more complex interplay of receptor activity than we initially considered. Certain acids may indeed be detected by multiple receptors, with each receptor contributing uniquely to the behavioral response. Regarding the sufficiency of testing receptors individually, we recognize the limitations of this approach. Examining receptors one by one may not reveal the full spectrum of receptors involved, especially due to potential interactions or compensatory mechanisms that only emerge when certain receptors are inactive. Therefore, a more holistic approach—such as genetic screens for behavioral responses or using complex genetic models to disrupt multiple receptors simultaneously—could provide deeper insights. Moving forward, incorporating receptor interactions that modulate each other, along with more comprehensive assays, could help explain these discrepancies by uncovering previously overlooked receptor functions.

(3) The paper needs to be updated with a recent paper published by Guillemin et al (2024), indicating that LA is detected externally by a combination of IR94e, IR76b and IR25a. IR25a might help to form a fully functional receptor in GR33a neurons (a former study from Chen et al (2017) indicate that IR25a is expressed in all gustatory neurons of the pharynx).

According to Guillemin et al. (2024), the combination of IR94e, IR76b, and IR25a is required for amino acid detection but not for detecting lactic acid (LA). In their calcium imaging experiments, 100 mM LA elicited a response similar to the vehicle control, suggesting that these receptors do not play a role in LA detection.

(4) Although it was not the main focus of the paper, it would have been most interesting if the cells expressing IR94a and IR94h were identified, and placed on the functional map proposed by the group of Dahanukar (Chen et al 2017 Cell Reports, Chen et al 2019 Cell Reports).

The expression patterns of IR94a and IR94h were previously detailed by Chen et al. (2017), showing that IR94h is expressed in the labial sense organ (LSO, specifically in L7-7) and the ventral cibarial sense organ (VCSO, V2), while IR94a is expressed in the VCSO (V5). Given this established information, we referenced these known expression patterns without replicating the mapping in our study. Our primary focus was to investigate the functional role of these neurons within the pharynx, and we believe we have successfully highlighted their specific contributions. However, we recognize that integrating the functional mapping of these neurons in alignment with the work of Dahanukar’s group would have strengthened our findings and provided a more comprehensive understanding. We acknowledge this as a limitation of our study and appreciate your suggestion, as it points to a valuable direction for future research.

Reviewer #3 (Public review):

Summary:

In this work, the authors investigated the molecular and cellular basis of sour taste perception in Drosophila melanogaster, focusing on identifying receptors that mediate attractive responses to certain carboxylic acids. It builds on previous work from the same group that had identified the IR co-receptors IR25a and IR76b for this sensory process, screening a set of mutants in IRs to identify three, IR51b, IR94a, and IR94h, required for feeding preference responses to some or all of the tested acids.

Strengths:

The work is of interest because it assigns sensory roles to IRs of previously unknown function, in particular IR94a and IR94h, and points to pharyngeal neurons in which these receptors are expressed as the relevant sensory neurons (potentially with different roles for IR94a- and IR94h-expressing neurons). The work combines elegant genetics, simple but effective feeding and taste assays, chemo-/opto-genetic activation, and some calcium imaging. Overall the presented data look solid and well-controlled.

Weaknesses:

The in situ expression analysis relies entirely on transgenic driver lines for IR94a and IR94h (which had been previously described, though not fully cited in this work). Importantly, given that many of the behavioral experiments (genetic rescue, physiology, artificial activation) use the IR94a and IR94h GAL4 driver lines, it would be helpful to validate that these faithfully reflect IR94a and IR94h expression (as far as I can tell, such validation wasn't done in the original papers describing these lines as part of a large collection of IR drivers). For IR51b, pharyngeal expression is concluded indirectly from non-quantitative RT-PCR analysis (genetic reporters did not work). The lack of direct detection of gene/protein expression (for example, through RNA FISH, immunofluorescence, or protein tagging) would have made for a more complete characterization of these receptors (for example, there is no direct evidence that they also express IR25a and IR76b, as one might expect). Finally, the relationship of IR94a and IR94h neurons to other types of pharyngeal neurons remains unclear, as are their projection patterns in the SEZ.

Conceptually, the work is of interest mostly to those in the immediate field; there have been a very large number of studies in the past decade (several from this lab) characterizing the contributions of different IRs to various chemosensory processes. The current work doesn't lend much insight into the nature of the minimal functional unit of gustatory IRs (reconstitution of a functional IR in a heterologous neuron/cell has not been achieved here, but this is a limitation of many other previous studies), nor to how different pharyngeal sensory pathways might collaborate to control behavior. Nevertheless, the findings provide a useful contribution to the literature.

We appreciate your thoughtful feedback. As noted in our response, our primary objective was to investigate the sensory functions of IR94a and IR94h. To this end, we conducted behavioral assays, which we validated with additional approaches including genetic rescue, physiological tests, and artificial activation. Throughout these experiments, we extensively utilized Ir94a- and Ir94h-GAL4 driver lines. To ensure these lines accurately reflect the expression of IR94a and IR94h, we verified their expression patterns using immunohistochemistry across various body parts. Our results align with previous findings that show both receptors are exclusively expressed in the pharynx. Regarding IR51b, we employed RT-PCR due to its high sensitivity and specificity, which supported our hypothesis. Nonetheless, we agree that more direct detection methods would have provided a stronger validation of IR51b expression. Our previous study (Sang et al., 2024) also demonstrated the pharyngeal expression of co-expressed receptors, specifically IR25a and IR76b. However, we recognize that the lack of direct evidence for their co-expression with IR51b remains a significant gap. This limitation primarily stems from the unavailability of specific reagents needed for direct assays targeting IR51b, which restricted our experimental approach.

You also raised the potential relationship between IR94a and IR94h neurons and other pharyngeal neuron types, including their projection patterns in the subesophageal zone. This is indeed an important area for future research that could clarify neural connectivity and further our understanding of sensory mechanisms. However, our study was focused on exploring sensory mechanisms in peripheral regions rather than detailed neural mapping in the SEZ. Investigating these connections would undoubtedly provide valuable insights into the neural circuitry involved and represents an intriguing direction for future research.

He relates America finally becoming independent from Great Britain and the fight it took to get there. Although, I'm not sure if I would compare it to America becoming independent. Although, I took this as Americans should be humble as well as the colored people for gaining their freedom and not being free yet.

David Walker compared Israelites and the Helots to African Americans being slaves and I think the matter of time in between these events affects this the most. Slavery wasn't that long ago even now and at the time he was writing the appeal, it was still happening. He knows there should have been some kind of growth since there was so much time in between events. They should have learned to treat everyone equal. Especially if they are christian and treating others like this is just hypocrisy.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews: 

Reviewer #1 (Public Review): 

Syngnathid fishes (seahorses, pipefishes, and seadragons) present very particular and elaborated features among teleosts and a major challenge is to understand the cellular and molecular mechanisms that permitted such innovations and adaptations. The study provides a valuable new resource to investigate the morphogenetic basis of four main traits characterizing syngnathids, including the elongated snout, toothlessness, dermal armor, and male pregnancy. More particularly, the authors have focused on a late stage of pipefish organogenesis to perform single-cell RNA-sequencing (scRNA-seq) completed by in situ hybridization analyses to identify molecular pathways implicated in the formation of the different specific traits. 

The first set of data explores the scRNA-seq atlas composed of 35,785 cells from two samples of gulf pipefish embryos that authors have been able to classify into major cell types characterizing vertebrate organogenesis, including epithelial, connective, neural, and muscle progenitors. To affirm identities and discover potential properties of clusters, authors primarily use KEGG analysis that reveals enriched genetic pathways in each cell types. While the analysis is informative and could be useful for the community, some interpretations appear superficial and data must be completed to confirm identities and properties. Notably, supplementary information should be provided to show quality control data corresponding to the final cell atlas including the UMAP showing the sample source of the cells, violin plots of gene count, UMI count, and mitochondrial fraction for the overall

dataset and by cluster, and expression profiles on UMAP of selected markers characterizing cluster identities. 

We thank the reviewer for these suggestions, and have added several figures and supplemental files in response. We added a supplemental UMAP showing the sample that each cell originated (S1). We also added supplemental violin plots for each sample showing the gene count, unique molecular identifier (UMI) count, mitochondrial fraction, and the doublet scores (S2). We added feature plots of zebrafish marker genes for these major cell types and marker genes identified from our dataset to the supplement (S3:S57). We also provided two supplemental files with marker genes. These changes should clarify the work that went into labeling the clusters. Although some of the cluster labels are general, we decided it would be unwise to label clusters with speculated specific annotations. We only gave specific annotations to clusters with concrete markers and/or in situ hybridization (ISH) results that cemented an annotation.  As shown in the new supplemental figures and files, certain clusters had clear, specific markers while others did not. Therefore, we used caution when we annotated clusters without distinct markers. 

The second set of data aims to correlate the scRNA-seq analysis with in situ hybridizations (ISH) in two different pipefish (gulf and bay) species to identify and characterize markers spatially, and validate cell types and signaling pathways active in them. While the approach is rational, the authors must complete the data and optimize labeling protocols to support their statements. One major concern is the quality of ISH stainings and images; embryos show a high degree of pigmentation that could hide part of the expression profile, and only subparts and hardly detectable tissues/stainings are presented. The authors should provide clear and good-quality images of ISH labeling on whole-mount specimens, highlighting the magnification regions and all other organs/structures (positive controls) expressing the marker of interest along the axis. Moreover, ISH probes have been designed and produced on gulf pipefish genome and cDNA respectively, while ISH labeling has been performed indifferently on bay or gulf pipefish embryos and larvae. The authors should specify stages and species on figure panels and should ensure sequence alignment of the probe-targeted sequences in the two species to validate ISH stainings in the bay pipefish. Moreover, spatiotemporal gene expression being a very dynamic process during embryogenesis, interpretations based on undefined embryonic and larval stages of pipefish development and compared to 3dpf zebrafish are insufficient to hypothesize on developmental specificities of pipefish features, such as on the absence of tooth primordia that could represent a very discrete and transient cell population. The ISH analyses would require a clean and precise spatiotemporal expression comparison of markers at the level of the entire pipefish and zebrafish specimens at well-defined stages, otherwise, the arguments proposed on teleost innovations and adaptations turn out to be very speculative. 

We are appreciative of the reviewer’s feedback. We primarily used the in situ hybridization (ISH) data as supplementary to the scRNAseq library and we are aware that further evidence is necessary to identify origins of syngnathid’s evolutionary novelties. Our goal was to provide clues for the developmental genetic basis of syngnathid derived features.  We hope that our study will inspire future investigations and are excited for the prospect that future research could include this reviewer’s ideas. 

All of the developmental stages and species information for the embryos used were in the figure captions as well as in supplemental file 6. Because we primarily used wild caught embryos, we did not have specific ages of most embryos. Syngnathid species are challenging to culture in the laboratory, and extracting embryos requires euthanizing the father which makes it difficult to obtain enough embryos for ISH. In addition, embryos do not survive long when removed from the brood pouch prematurely. We supplemented our ISH with bay pipefish caught off the Oregon coast because these fish have large broods. Wild caught pregnant male bay pipefish were immediately euthanized, and their broods were fixed. Because we did not have their age, we classified them based on developmental markers such as presence of somites and the extent of craniofacial elongation. Although these classification methods are not ideal, they are consistent with the syngnathid literature (Sommer et al. 2012). Since the embryos used for the ISH were primarily wild caught, we had a few different developmental stages represented in our ISH data. For our tooth primordia search, we used embryos from the same brood (therefore, same stage) for these experiments.

We understand the concern for the degree of pigmentation in the samples. We completed numerous bleach trials before embarking on the in situ hybridization experiments. After completing a bleach trial with a probe created from the gene tnmd for ISH_,_ we noticed that the bleached embryos were missing expression domains found in the unbleached embryos. We were, therefore, concerned that using bleached embryos for our experiments would result incorrect conclusions about the expression domains of these genes. We sparingly used bleaching at older stages, hatched larvae, where it was fundamentally necessary to see staining. As stated above, the primary goal of this manuscript was to generate and annotate the first scRNA-seq atlas in a syngnathid, and the ISHs were utilized to support inferred cluster annotations only through a positive identification of marker gene expression in expected tissues/cells. Therefore, the obscuring of gene expression by pigmentation would have resulted in the absence of evidence for a possible cluster annotation, not an incorrect annotation.

For the ease of viewing the ISHs, we improved annotations and clarity. We increased the brightness and contrast of images. In the original submission, we had to lower the image resolution to make the submission file smaller. We hope that these improvements plus the true image quality improves clarity of ISH results. We also included alignments in our supplementary files of bay pipefish sequences to the Gulf pipefish probes to showcase the high degree of sequence similarity. 

Sommer, S., Whittington, C. M., & Wilson, A. B. (2012). Standardised classification of pre-release development in male-brooding pipefish, seahorses, and seadragons (Family Syngnathidae). BMC Developmental Biology, 12, 12–15. 

To conclude, whereas the scRNA-seq dataset in this unconventional model organism will be useful for the community, the spatiotemporal and comparative expression analyses have to be thoroughly pushed forward to support the claims. Addressing these points is absolutely necessary to validate the data and to give new insights to understand the extraordinary evolution of the Syngnathidae family. 

We really appreciate the reviewer’s enthusiasm for syngnathid research, and hope that the additional files and explanation of the supporting role of the ISHs have adequately addressed their concerns. We share the reviewer’s enthusiasm and are excited for future work that can extend this study. 

Reviewer #2 (Public Review):

Summary: 

The authors present the first single-cell atlas for syngnathid fishes, providing a resource for future evolution & development studies in this group. 

Strengths: 

The concept here is simple and I find the manuscript to be well written. I like the in situ hybridization of marker genes - this is really nice. I also appreciate the gene co-expression analysis to identify modules of expression. There are no explicit hypotheses tested in the manuscript, but the discovery of these cell types should have value in this organism and in the determination of morphological novelties in seahorses and their relatives.  

We are grateful for this reviewer’s appreciation of the huge amount of work that went into this study, and we agree that the in situ hybridizations (ISHs) support the scRNAseq study as we intended. We appreciate that the reviewer thinks that this work will add value to the syngnathid field.

Weaknesses: 

I think there are a few computational analyses that might improve the generality of the results. 

(1) The cell types: The authors use marker gene analysis and KEGG pathways to identify cell types. I'd suggest a tool like SAMap (https://elifesciences.org/articles/66747) which compares single-cell data sets from distinct organisms to identify 'homologous' cell types - I imagine the zebrafish developmental atlases could serve as a reasonable comparative reference. 

We appreciate the reviewer’s request, and in fact we would have loved to integrate our dataset with zebrafish. However, syngnathid’s unique craniofacial development makes it challenging to determine the appropriate stage for comparison. While 3 days post fertilization (dpf) zebrafish data were appropriate for comparisons of certain cell types (e.g. epidermal cells), it would have been problematic for other cell types (e.g. osteoblasts) that are not easily detectable until older zebrafish stages. Therefore, determining equivalent stages between these species is difficult and contains potential for error. Future research should focus on trying to better match stages across syngnathids and zebrafish (and other fish species such as stickleback). Studies of this nature promise to uncover the role of heterochrony in the evo-devo of syngnathid’s unique snouts.

(2) Trajectory analyses: The authors suggest that their analyses might identify progenitor cell states and perhaps related differentiated states. They might explore cytoTRACE and/or pseudotime-based trajectory analyses to more fully delineate these ideas.

We thank the reviewer for this suggestion! We added a trajectory analysis using cytoTRACE to the manuscript. It complemented our KEGG analysis well (L172-175; S73) and has improved the manuscript.

(3) Cell-cell communication: I think it's very difficult to identify 'tooth primordium' cell types, because cell types won't be defined by an organ in this way. For instance, dental glia will cluster with other glia, and dental mesenchyme will likely cluster with other mesenchymal cell types. So the histology and ISH is most convincing in this regard. Having said this, given the known signaling interactions in the developing tooth (and in development generally) the authors might explore cell-cell communication analysis (e.g., CellChat) to identify cell types that may be interacting. 

We agree! It would have been a wonderful addition to the paper to include a cell-cell communication analysis. One limitation of CellChat is that it only includes mouse and human orthologs. Given concerns of reviewer #3 for mouse-syngnathid comparisons, we decided to not pursue CellChat for this study. We are looking forward to future cell communication resources that include teleost fishes.

Reviewer #3 (Public Review): 

Summary: 

This study established a single-cell RNA sequencing atlas of pipefish embryos. The results obtained identified unique gene expression patterns for pipefish-specific characteristics, such as fgf22 in the tip of the palatoquadrate and Meckel's cartilage, broadly informing the genetic mechanisms underlying morphological novelty in teleost fishes. The data obtained are unique and novel, potentially important in understanding fish diversity. Thus, I would enthusiastically support this manuscript if the authors improve it to generate stronger and more convincing conclusions than the current forms. 

Thank you, we appreciate the reviewer’s enthusiasm!

Weaknesses: 

Regarding the expression of sfrp1a and bmp4 dorsal to the elongating ethmoid plate and surrounding the ceratohyal: are their expression patterns spatially extended or broader compared to the pipefish ancestor? Is there a much closer species available to compare gene expression patterns with pipefish? Did the authors consider using other species closely related to pipefish for ISH? Sfrp1a and bmp4 may be expressed in the same regions of much more closely related species without face elongation. I understand that embryos of such species are not always accessible, but it is also hard to argue responsible genes for a specific phenotype by only comparing gene expression patterns between distantly related species (e.g., pipefish vs. zebrafish). Due to the same reason, I would not directly compare/argue gene expression patterns between pipefish and mice, although I should admit that mice gene expression patterns are sometimes helpful to make a hypothesis of fish evolution. Alternatively, can the authors conduct ISH in other species of pipefish? If the expression patterns of sfrp1a and bmp4 are common among fishes with face elongation, the conclusion would become more solid. If these embryos are not available, is it possible to reduce the amount of Wnt and BMP signal using Crispr/Cas, MO, or chemical inhibitor? I do think that there are several ways to test the Wnt and/or BMP hypothesis in face elongation. 

We appreciate the reviewer’s suggestion, and their recognition for challenges within this system. In response to this comment, we completed further in situ hybridization experiments in threespine stickleback, a short snouted fish that is much more closely related to syngnathids than is zebrafish, to make comparisons with pipefish craniofacial expression patterns (S76-S79). We added ISH data for the signaling genes (fgf22, bmp4, and sfrp1a) as well as prdm16. Through adding this additional ISH results, we speculated that craniofacial expression of bmp4, sfrp1a, and prdm16 is conserved across species. However, compared to the specific ceratohyal/ethmoid staining seen in pipefish, stickleback had broad staining throughout the jaws and gills. These data suggest that pipefish have co-opted existing developmental gene networks in the development of their derived snouts. We added this interpretation to the results and discussion of the manuscript (L244-L248; L262-277; L444-470).

Recommendations for the authors:  

Reviewing Editor (Recommendations for the Authors): 

We hope that the eLife assessment, as well as the revisions specified here, prove helpful to you for further revisions of your manuscript. 

Revisions considered essential: 

(1) Marker genes and single-cell dataset analyses. While these analyses have been performed to a good standard in broad terms, there is a majority view here that cell type annotations and trajectory analyses can be improved. In particular, there is question about the choice of marker genes for the current annotation. For one it can depend on the use of single marker genes (see tnnti1 example for clusters 17 and 31). Here, we recommend incorporating results from SAMap and trajectory analysis (e.g., cytoTRACE or standard pseudotime).

Because of the reviewer comments, we became aware that we insufficiently communicated how cell clusters were annotated. We did mention in the manuscript that we did not use single marker genes to annotate clusters, but instead we used multiple marker genes for each cluster for the annotation process. We used both marker genes derived from our dataset and marker genes identified from zebrafish resources for cluster annotation. We chose single marker genes for each cluster for visualization purposes and for in situ hybridizations. However, it is clear from the reviewers’ comments that we needed to make more clear how the annotations were performed. To make this effort more clear in our revision, we included two new supplementary files – one with Seurat derived marker genes and one with marker genes derived from our DotPlot method. We also included extensive supplementary figures highlighting different markers. Using Daniocell, we identified 6 zebrafish markers per major cell type and showed their expression patterns in our atlas with FeaturePlots. We also included feature plots of the top 6 marker genes for each cluster. We hope that the addition of these 40+ plots (S3:S57) to the supplement fully addresses these concerns. 

We appreciated the suggestion of cytotrace from reviewer #2! We ran cytotrace on three major cell lineages (neural, muscle, and connective; S73) which complemented our KEGG analysis in suggesting an undifferentiated fate for clusters 8, 10, and 16. We chose to not run SAMap because it is a scRNA-seq library integration tool. Although we compared our lectin epidermal findings to 3 dpf zebrafish scRNA-seq data, we did not integrate the datasets out of concern that we could draw erroneous conclusions for other cell types.  Future work that explores this technical challenge may uncover the role of heterochrony in syngnathid craniofacial development. We detail these changes more fully in our responses to reviewers.

(2) The claims regarding evolutionary novelty and/or the genes involved are considered speculative. In part, this comes from relying too heavily on comparisons against zebrafish, as opposed to more closely related species. For example, the discussion regarding C-type lectin expression in the epidermis and KEGG enrichment (lines 358 - 364) seems confusing. Another good example here is the discussion on sfrp1a (lines 258 - 261). Here, the text seems to suggest craniofacial sfrp1a expression (or specifically ethmoid expression?) is connected to the development of the elongated snout in pipefish. However, craniofacial expression of sfrp1a is also reported in the arctic charr, which the authors grouped into fishes with derived craniofacial structures. Separately, sfrp2 expression was also reported in stickleback fish, for example. Do these different discussions truly support the notion that sfrp1a expression is all that unique in pipefish, rather than that pipefish and zebrafish are only distantly related and that sfrp1a was a marker gene first, and co-opted gene second? The authors should respond to the comments in the public review related to this aspect, and include more informative comparison and discussion. 

A much more nuanced discussion with appropriate comparisons and caveats would be strongly recommended here.  

We appreciate this insight and used it as a motivator to complete and add select comparative ISH data to this manuscript. We added in situ hybridization experiments from stickleback fish for craniofacial development genes (sfrp_1a, prdm16, bmp4_, and fgf22; S76-S79).  After adding stickleback ISH to the manuscript, we were able to make comparisons between pipefish and stickleback patterns and draw more informed conclusions (L244-L248; L262-277; L444-470). We added additional nuance to the discussion of the head, tooth (L485-489), and male pregnancy (L358-L391) sections to address concerns of study limitations. We describe in more detail these additional data in response to reviewers.

(3) In situ hybridization results: as already included above, there is generally weak labeling of species, developmental stages, and other markings that can provide context. The collective feeling here is that as it is currently presented, the ISH results do not go too far beyond simply illustrative purposes. To take these results further, more detailed comparison may be needed. At a minimum, far better labeling can help avoid making the wrong impression. 

Based on the reviewers’ comments, we made changes to improve ISH clarity and add select comparative ISH findings. ISH was used to further interpretation of the scRNAseq atlas. All the developmental stages and species information for the embryos used were in the figure captions as well as in supplemental file 4. Since we primarily used wild caught embryos, we did not have specific ages of most embryos. The technical challenges of acquiring and staging Syngnathus embryos are detailed above. Because we did not have their age, we classified them based on developmental markers (such as presence of somites and the extent of craniofacial elongation). Although these classification methods are not ideal, they are consistent with the syngnathid literature (Sommer et al. 2012).  

We followed reviewer #1’s recommendations by adding an annotated graphic of a pipefish head, aligning bay and Gulf pipefish sequences for the probe regions, expanding out our supplemental figures for ISH into a figure for each probe, and improving labeling. These changes improved the description of the ISH experiments and have increased the quality of the manuscript.

We would have loved to complete detailed comparative studies as suggested, but doing such a complete analysis was not feasible for this study. Therefore, we completed an additional focused analysis. We followed reviewer #3’s idea and added ISHs from threespine stickleback, a short snouted fish, for 4 genes (sfrp1a, prdm16, fgf22, and bmp4). While more extensive ISHs tracking all marker genes through a variety of developmental stages in pipefish and stickleback would have provided crucial insights, we feel that it is beyond the scope of this study and would require a significant amount of additional work. We, thus, primarily interpreted the ISH results as illustrative data points in our discussion. As we state in the response to reviewer 1, the generation and annotation of the first scRNA-seq atlas in a syngnathid is the primary goal of this manuscript.  The ISHs were utilized primarily to support inferred cluster annotations if a positive identification of marker gene expression in expected tissues/cells occurred. 

Reviewer #1 (Recommendations For The Authors): 

While the scRNA-seq dataset offers a valuable resource for evo-devo analyses in fish and the hypotheses are of interest, critical aspects should be strengthened to support the claims of the study. 

Concerning the scRNA-seq dataset, the major points to be addressed are listed below: 

- Supplementary file 3 reports the single markers used to validate cluster annotations. To confirm cluster identities, more markers specific to each cluster should be highlighted and presented on the UMAP. 

We recognize the reviewer’s concern and had in reality used numerous markers to annotate the clusters. Based upon the reviewer’s comment we decided to make this clear by creating feature plots for every cluster with the top 6 marker genes. These plots showcase gene specificity in UMAP space. We also added feature plots for zebrafish marker genes for key cell types. Through these changes and the addition of 54 supplementary figures (S3:S57), we hope that it is clear that numerous markers validated cluster identity.

For example, as clusters 17 and 37 share the same tnnti1 marker, which other markers permit to differentiate their respective identity. 

This is a fair point. Cluster 17 and 37 both are marked by a tnni1 ortholog.

Different paralogous co-orthologs mark each cluster (cluster 17: LOC125989146; cluster 37: LOC125970863). In our revision to the above comment, additional (6) markers per cluster were highlighted which should remedy this concern. 

- L146: the low number of identified cartilaginous cells (only 2% of total connective tissue cells) appears aberrant compared to bone cell number, while Figure 1 presents a welldeveloped cartilaginous skeleton with poor or no signs of ossification. Please discuss this point. 

We also found this to be interesting and added a brief discussion on this subject to the results section (L147-L149). Single cell dissociations can have variable success for certain cell types. It is possible that the cartilaginous cells were more difficult to dissociate than the osteoblast cells.

- L162: pax3a/b are not specific to muscle progenitors as the genes are also expressed in the neural tube and neural crest derivatives during organogenesis. Please confirm cluster 10 identity.  

Thank you for the reminder, we added numerous feature plots that explored zebrafish (from Daniocell) and pipefish markers (identified in our dataset). Examining zebrafish satellite muscle markers (myog, pabpc4, and jam2a) shows a strong correspondence with cluster #10.

- L198: please specify in the text the pigment cell cluster number. 

We completed this change.

- L199: it is not clear why considering module 38 correlated to cluster 20 while modules 2/24 appear more correlated according to the p-value color code. 

We thank the reviewer for pointing this confusing element out! Although the t-statistic value for module 38 (3.75) is lower than the t-statistics for modules 2 and 24 (5.6 and 5.2, respectively), we chose to highlight module 38 for its ‘connectivity dependence’ score. In our connectivity test, we examined whether removing cells from a specific cell cluster reduced the connectivity of a gene network. We found that removing cluster 20 led to a decrease in module 38’s connectivity (-.13, p=0) while it led to an increase in modules 2 and 24’s connectivity (.145, p=1; .145, p=9.14; our original supplemental files 9-10). Therefore, the connectivity analysis showed that module 38’s structure was more dependent on cluster 20 than in comparison with modules 2 and 24. Although you highlighted an interesting quandary, we decided that this is tangential to the paper and did not add this discussion to the manuscript. 

- Please describe in the text Figure 4A. 

Completed, we thank the reviewer for catching this! 

Concerning embryo stainings, the major points to be addressed are listed below: 

- Figure 1: please enhance the light/contrast of figures to highlight or show the absence of alcian/alizarin staining. Mineralized structures are hardly detectable in the head and slight differences can be seen between the two samples. The developmental stage should be added. Please homogenize the scale bar format (remove the unit on panels E and, G as the information is already in the text legend). It would be useful to illustrate the data with a schematic view of the structures presented in panels B, and E, and please annotate structures in the other panels.  

We thank the reviewer for these suggestions to improve our figure. We increased the brightness and contrast for all our images. We also added an illustration of the head with labels of elements. As discussed, we used wild caught pregnant males and, therefore, do not know the exact age of the specimens. However, we described the developmental stage based on morphological observations. Slight differences in morphology between samples is expected. We and others have noticed that

developmental rate varies, even within the same brood pouch, for syngnathid embryos. We observed several mineralization zones including in the embryos including the upper and lower jaws, the mes(ethmoid), and the pectoral fin. We recognize the cartilage staining is more apparent than the bone staining, though increasing image brightness and contrast did improve the visibility of the mineralization front.

- All ISH stainings and images presented in Figures 4-6/ Figures S2-3 should be revised according to comments provided in the public review. 

We thank the reviewer for providing thorough comments, we provided an in-depth response to the public review. We made several improvements to the manuscript to address their concerns. 

- Figure 4: Figure 4B should be described before 4C in the text or inverse panels / L222 the Meckel's cartilage is not shown on Figure 4C. The schematic views in H should be annotated and the color code described / the ISH data must be completed to correlate spatially clusters to head structures. 

We thank the reviewer for pointing this out, we fixed the issues with this figure and added annotations to the head schematics.

- Figure 5: typo on panels 'alician' = alcian. 

We completed this change. 

- Figures S2-3: data must be better presented, polished / typo in captions 'relavant'= relevant. 

Thank you for this critique, we created new supplementary figures to enhance interpretation of the data (S59-S71). In these new figures, we included a feature plot for each gene and respective ISHs.

- Figure S3: soat2 = no evidence of muscle marker neither by ISH presented nor in the literature. 

We realized this staining was not clear with the previous S2/S3 figures. Our new changes in these supplementary figures based on the reviewer’s ideas made these ISH results clearer. We observed soat2 staining in the sternohyoideus muscle (panel B in S71).

Other points: 

- The cartilage/bone developmental state (Alcian/alizarin staining) and/or ISH for classical markers of muscle development (such as pax3/myf5) could be used to clarify the This could permit the completion of a comparative analysis between the two species and the interpretation of novel and adaptative characters.  

We appreciate this idea! We thought deeply about a well characterized comparative analysis between pipefish and zebrafish for this study. We discussed our concerns in our public response to reviewer 2. We found that it was challenging to stage match all cell types, and were concerned that we could make erroneous conclusions. For example, our pipefish samples were still inside the male brood pouch and possessed yolk sacs. However, we found osteoblast cells in our scRNAseq atlas, and in alizarin staining. Although zebrafish literature notes that the first zebrafish bone appears at 3 dpf (Kimmel et al. 1995), osteoblasts were not recognized until 5 dpf in two scRNAseq datasets (Fabian et al. 2022; Lange et al. 2023). A 5dpf zebrafish is considered larval and has begun hunting. Therefore, we chose to not integrate our data out of concern that osteoblast development may occur at different timelines between the fishes. 

Fabian, P., Tseng, K.-C., Thiruppathy, M., Arata, C., Chen, H.-J., Smeeton, J., Nelson, N., & Crump, J. G. (2022). Lifelong single-cell profiling of cranial neural crest diversification in zebrafish. Nature Communications 2022 13:1, 13(1), 1–13. 

Lange, M., Granados, A., VijayKumar, S., Bragantini, J., Ancheta, S., Santhosh, S., Borja, M., Kobayashi, H., McGeever, E., Solak, A. C., Yang, B., Zhao, X., Liu, Y., Detweiler, A. M., Paul,

S., Mekonen, H., Lao, T., Banks, R., Kim, Y.-J., … Royer, L. A. (2023). Zebrahub – Multimodal Zebrafish Developmental Atlas Reveals the State-Transition Dynamics of Late-Vertebrate Pluripotent Axial Progenitors. BioRxiv, 2023.03.06.531398. 

Kimmel, C., Ballard, S., Kimmel, S., Ullmann, B., Schilling, T. (1995). Stages of Embryonic Development of the Zebrafish. Developmental Dynamics 203:253:-310.

'in situs' in the text should be replaced by 'in situ experiments'.  

We made this change (L395, L663, L666, L762).

- Lines 562-565: information on samples should be added at the start of the result section to better apprehend the following scRNA-seq data.

We thank the reviewer for pointing out this issue. Although we had a few sentences on the samples in the first paragraph of the result section, we understand that it was missing some critical pieces of information. Therefore, we added these additional details to the beginning of the results section (L126-L132). 

- Lines 629-665: PCR with primers designed on gulf pipefish genome could be performed in parallel on bay and gulf cDNA libraries, and amplification products could be sequenced to analyze alignment and validate the use of gulf pipefish ISH probes in bay pipefish embryos. Probe production could also be performed using gulf primers on bay pipefish cDNA pools. 

After the submission of this manuscript, a bay pipefish genome was prepared by our laboratory. We used this genome to align our probes, these alignments demonstrate strong sequence conservation between the species. We included these alignments in our supplemental files.

- L663: the bleaching step must be optimized on pipefish embryos. 

We understand this concern and had completed several bleach optimization experiments prior to publication. Although we found that bleaching improved visibility of staining, we noticed with the probe tnmd that bleached embryos did not have complete staining of tendons and ligaments. The unbleached embryos had more extensive staining than the bleached embryos. We were concerned that bleaching would lead to failures to detect expression domains (false negatives) important for our analysis. Therefore, we did not use bleaching with our in situs experiments (except with hatched fish with a high degree of pigmentation). 

- Indicate the number of specimens analyzed for each labeling condition.  

We thank the reviewer for noticing this issue. We added this information to the methods (L766-767).

- Describe the fixation and pre-treatment methods previous to ISH and skeleton stainings

We thank the reviewer for pointing out this issue, we added these descriptions (L765-766; L772-774). 

Reviewer #3 (Recommendations For The Authors): 

(1) If sfrp1a expression is observed also in other fish species with derived craniofacial structures, it's important to discuss this more in the Discussion. This could be a common mechanism to modify craniofacial structures, although functional tests are ultimately required (but not in this paper, for sure). Can lines 421-428 involve the statement "a prolonged period of chondrocyte differentiation" underlies craniofacial diversity?

This is a great idea, and we added a sentence that captures this ethos (L451-452).

(2) Lines 334-346 need to be rephrased. It's hard to understand which genes are expressed or not in pipefish and zebrafish. Did "23 endocytosis genes" show significant enrichment in zebrafish epidermis, or are they expressed in zebrafish epidermis? 

We thank the reviewer for this comment, we re-phrased this section for clarity (L365-368).

(3) Figure 4 is missing the "D" panel and two "E" panels. 

We thank the reviewer for noticing this, we fixed this figure.

(4) Line 302: "whole-mount" or "whole mount"

We thank the reviewer for the catch!

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In the work: "Endosomal sorting protein SNX4 limits synaptic vesicle docking and release" Josse Poppinga and collaborators addressed the synaptic function of Sortin-Nexin 4 (SNX4). Employing a newly developed in vitro KO model, with live imaging experiments, electrophysiological recordings, and ultrastructural analysis, the authors evaluate modifications in synaptic morphology and function upon loss of SNX4. The data demonstrate increased neurotransmitter release and alteration in synapse ultrastructure with a higher number of docked vesicles and shorter AZ. The evaluation of the presynaptic function of SNX4 is of relevance and tackles an open and yet unresolved question in the field of presynaptic physiology.

Strengths:

The sequential characterization of the cellular model is nicely conducted and the different techniques employed are appropriate for the morpho-functional analysis of the synaptic phenotype and the derived conclusions on SNX4 function at presynaptic site. The authors succeeded in presenting a novel in vitro model that resulted in chronical deletion of SNX4 in neurons. A convincing sequence of experimental techniques is applied to the model to unravel the role of SNX4, whose functions in neuronal cells and at synapses are largely unknown. The understanding of the role of endosomal sorting at the presynaptic site is relevant and of high interest in the field of synaptic physiology and in the pathophysiology of the many described synaptopathies that broadly result in loss of synaptic fidelity and quality control at release sites.

We thank the reviewer for their positive evaluation of our manuscript.

Weaknesses:

The flow of the data presentation is mostly descriptive with several consistent morphological and functional modifications upon SNX loss. The paper would benefit from a wider characterization that would allow us to address the physiological roles of SNX4 at the synaptic site and speculate on the underlying molecular mechanisms. In addition, due to the described role of SNX4 in autophagy and the high interest in the regulation of synaptic autophagy in the field of synaptic physiology, an initial evaluation of the autophagy phenotype in the neuronal SNX4KO model is important, and not to be only restricted to the discussion section.

We thank the reviewer for their suggestions and agree that broader characterization would help us speculate on the underlying mechanism. To address this, we have conducted additional independent experiments investigating the role of SNX4 in neuronal autophagy, as suggested by this reviewer. These experiments are now included in the main figures and are no longer limited to the discussion section. Please see the detailed responses to this reviewer's recommendations below.

Reviewer #2 (Public Review):

Summary:

SNX4 is thought to mediate recycling from endosomes back to the plasma membrane in cells. In this study, the authors demonstrate the increases in the amounts of transmitter release and the number of docked vesicles by combining genetics, electrophysiology, and EM. They failed to find evidence for its role in synaptic vesicle cycling and endocytosis, which may be intuitively closer to the endosome function.

Strengths:

The electrophysiological data and EM data are in principle, convincing, though there are several issues in the study.

We thank the reviewer for their positive evaluation of our manuscript.

Weaknesses:

It is unclear why the increase in the amounts of transmitter release and docked vesicles happened in the SNX4 KO mice. In other words, it is unclear how the endosomal sorting proteins in the end regulate or are connected to presynaptic, particularly the active zone function.

We thank the reviewer for their suggestions and agree that further characterization would help to understand how endosomal sorting proteins regulate presynaptic neurotransmission. We have now added extra data on electrophysiological recordings clarifying SNX4’s role in the synapse. Please see the detailed responses to this reviewer's recommendations below.

Reviewer #3 (Public Review):

Summary:

The study aims to determine whether the endosomal protein SNX4 performs a role in neurotransmitter release and synaptic vesicle recycling. The authors exploited a newly generated conditional knockout mouse to allow them to interrogate the SNX4 function. A series of basic parameters were assessed, with an observed impact on neurotransmitter release and active zone morphology. The work is interesting, however as things currently stand, the work is descriptive with little mechanistic insight. There are a number of places where the data appear to be a little preliminary, and some of the conclusions require further validation.

Strengths:

The strengths of the work are the state-of-the-art methods to monitor presynaptic function.

We thank the reviewers for their positive evaluation of our manuscript.

Weaknesses:

The weaknesses are the fact that the work is largely descriptive, with no mechanistic insight into the role of SNX4. Further weaknesses are the absence of controls in some experiments and the design of specific experiments.

We thank the reviewer for their suggestions and agree that addition of extra control groups and experiments would strengthen interpretation of the observed phenotype. To address this, we have now performed experiments to investigate the miniature excitatory postsynaptic currents and added extra control groups such as overexpression of SNX4 on control background. In addition, we assessed SNX4-mediated neuronal autophagy as a potential molecular mechanism by which SNX4 affects synaptic output. Please see the detailed responses to this reviewers’ recommendations below.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The characterization of the neurite outgrowth presented in Figure 1 is a necessary starting point for the characterization of the model and the interpretation of the following data. Being the analysis conducted at 21 DIV, a significant portion of the neurite tree is out of the analyzed field. Adding sholl analysis will better indicate the complexity of the that appears to be influenced by SNX4 loss in the representative images shown in Figure 1f.

We fully agree and have now performed a Sholl analysis of dendrite branches to investigate dendritic complexity. (Figure 1(i), page 2-3, line 86-88). SNX4 depletion does not affect dendrite length or dendrite branching.

(2) Analogously, the characterization of synapse number is of relevance for the interpretation of the data. For a better flow of the data, Figure 4 might be presented as Figure 2 (without the repetition of panel h in Figure 1). An explanation of how VAMP2 puncta are processed is necessary in the method section. A double labelling with a postsynaptic marker would allow trafficking organelles to be distinguished from mature synaptic contacts. Indeed, the analysis of VAMP2 intensity along neurite in mature 21DIV neurons should reveal peaks in the intensity profile that represent synaptic contacts. For unexplained reasons, the profile is rather flat in the two experimental groups. Focusing on axonal branches will surely result in a peaked profile for VAMP2 labelling.

We fully agree that the characterization of synapses is relevant for the interpretation of the data. We have now added a section in our Material and Methods how the VAMP2 puncta are processed (p14 line 517-520). Instead of labeling mature synapses using double labeling of VAMP2 and PSD95, we analyzed the number of active synapses in live neurons using SypHy (Fig. 3g). The reviewer is correct that the VAMP2 data presented in Fig 1I and Fig 4 is part of the same dataset and we have clarified this in the figure legend. In Fig 1I only the total number of VAMP2 puncta is plotted as a marker for synapse number, while in Fig 4 we assess VAMP2 as potential SNX4 sorting cargo (Ma et al., 2017). Because of these different aims, we prefer to keep the figures separate. The analysis of VAMP2 intensity along the distance of the soma is a Sholl analysis (Fig. 4d), represents the average VAMP2 intensity over distance from the soma of 35-41 neurons per group. In contrast to a line scan of a single neurite, this average profile lacks the peaks of individual synapses.

(3) Miniature excitatory postsynaptic currents recordings would strengthen the synaptic characterization and complement the electrophysiological recordings shown in Figure 2. Analyzing frequency and amplitude parameters would complement the data on the number of synaptic connections defined by the pre and postsynaptic colocalization puncta as suggested above and may support the data shown in Figure 3 g that suggests a decreased number of active synapses in SNX4-KO cells.

We fully agree that the characterization of miniature excitatory postsynaptic currents would strengthen the synaptic characterization and complement the other electrophysiological data. Therefore, we have now added additional experiments showing the mEPSCs (Fig. 2k-m, page 4) in SNX4 cKO neurons versus control. This data shows that the amplitude and frequency of spontaneous miniature EPSCs (mEPSCs) were not affected upon SNX4 depletion, consistent with a normal first evoked EPSC and RRP estimate. Furthermore, these data suggest that it is unlikely that the observed increase in neurotransmission is due to post-synaptic effects.

(4) Recordings on the first evoked response shown in Figure 2 b and quantified in Figures c and d suggest that SNX4 overexpression per se exerts some effect on the Amplitude and the Charge of the first evoked response. This is also evident in the supplementary Figure 2 with lower frequency trains. An additional experimental group, namely control+SNX4 is needed for the correct interpretation of the observed phenotype. The possibility that SNX4 per se exerts an effect on evoked transmission could be discussed in terms of putative mechanisms and interactions.

We thank the reviewer for their suggestion and agree that an additional experimental group (control + SNX4) would strengthen interpretation of the observed phenotype. We have now added a new experimental condition with overexpression of SNX4 on a control background (Supplementary Fig. 3, page 20). This data shows that the amplitude and charge of the first evoked response were not affected in control + SNX4 neurons compared to control, and no differences were detected in the response to the 40 Hz stimulation train (Supplementary Fig. 3a-e).  Together, these data suggest that SNX4 overexpression in itself does not affect the neurotransmission protocols studied in SNX4 cKO experiments.

(5) To correctly interpret the SyPhy experiments and exclude an effect of SNX silencing on SV recycling, it is suggested to repeat the experiments shown in Figure 3 in the absence and in the presence of bafilomycin. Indeed, the quantifications shown in Figure 3 d and f do not represent "release fraction" as stated (lines 139/140) but they rather refer to an average difference between release fraction and recovered fraction. With the use of bafilomycin, the comparison of the deltaFmax/deltaFNH4Cl with and without bafilomycin would enable the release fraction to be correctly evaluated and compared.

We appreciate the reviewer’s suggestion and agree on the importance of considering the impact of SV recycling when evaluating the released fraction. We agree that the presence of bafilomycin is critical to isolate the released component during stimulation. We have now rephrased this conclusion. To assess synaptic recycling in these assays, bafilomycin in not critically required and we show by multiple independent experiments, including SypHy and FM64 dye assays, that SV recycling is either not affected or the effect is too small to be detected by these methods.

(6) In the ultrastructural analysis, additional quantifications are needed to exclude the accumulation of endosome-like structures. It is not clear if, in the evaluation of total SV number (Figure 5e), the authors counted all vesicles or vesicles < 50nm. This has to be explained and additional quantification of # of SV < 50nm and # SV > 50nm is informative, taking into account the endosomal nature of SNX4. Indeed, although the average size of SV is not changed (fig. 5 d), the density of "bigger vesicle" may result from endosomal-like structure accumulation. An additional suggested quantification is on vesicle # SV > 80nm as previously reported in the cited references dealing with endosomal proteins and presynaptic morphology.

We fully agree that the characterization of vesicle size is important and that it was not clearly stated which vesicles were included in the total number of SV (Fig. 5e). We have now added this to the figure description. We have also added a histogram that contains the vesicle numbers of different bin sizes for SNX4 cKO synapses and control synapses (Supplementary Fig. 4, page 21) including # SVs > 80nm. (Whilst it seems that there are more “bigger” vesicles in the KO, further analysis revealed that this is mostly driven by one experiment and this effect is not consistent.)

(7) Due to the high scientific interest in presynaptic autophagy for SV recycling and degradation, and the paucity of experimental work assessing the proteins involved, an initial evaluation of the neuronal autophagy process (by western blot analysis and immunocytochemistry) for the characterization of the model will better support the paragraph in the discussion (lines 314-322) and contribute to future work in the field. Although very rare, autophagosomes quantification at presynaptic sites can also be performed from the already acquired images. A double membrane structure with the material inside is evident in the representative control image presented!

We appreciate the reviewer’s suggestion and agree that presynaptic autophagy is an interesting potential mechanism that would elaborate our current working model. To address the reviewers’ suggestion, we added multiple independent experiments to investigate basal autophagy markers such as ATG5 using western blot analysis, characterization of p62 levels using immunohistochemistry and performed additional morphometric analysis on the electron microscopy data (Supplementary Fig. 5). In SNX4 cKO neurons, there was no significant difference in P62 puncta numbers or P62 somatic intensity under basal conditions or after blocking autophagic P62 degradation by bafilomycin treatment, suggesting that autophagic flux remains normal. Also, no changes in total ATG5 protein levels were observed and ultrastructural analysis revealed no differences in the total number of autophagosomes. Collectively, these data indicate that SNX4 depletion does not impact the basal autophagic flux, ATG5 protein levels, or the number of autophagosomes.

Minor points:

(1) Dorrbaun et al. 2018 is missing from the reference list. In the legend to figure 1 there is an incorrect reference to Figure 6, rather than Figure 4.

We have now adjusted the figure legend and added the reference (page 16, line 604).

(2) Information on the construct employed for the rescue is missing. Is it a fluorescent tag construct? Representative images of the three autaptic neurons (control, KO, KO+SNX4) would nicely complement data presentation in Figure 2. 

We have now elaborated on this in material and methods section (p12, line 418-421). Unfortunately, we did not obtain pictures of autaptic neurons used for electrophysiology experiments.

Reviewer #2 (Recommendations For The Authors):

(1) Figure 2d and f are somewhat inconsistent. Total charges for the 1st EPSCs differ almost 2-fold in the same condition.

We appreciate the reviewer’s concern. The average EPSCs charge of the first evoked was 89, 122 and 57 pC for control, KO and rescued neurons respectfully. The average charge of the first pulse of 40Hz train was 41,58 and 32 pC for control, KO and rescued neurons respectfully, which is roughly 50% of the naïve response of the same cells. These trains were recorded after 2 or 3 other stimulation paradigms, which can have affected the total charge released in the 40Hz train. That said, the proportional difference between groups is high comparable, with a 37% increased average charge released in SNX4 cKO compared to control in the naïve response and 41% increased response in the first response of the 40 Hz train, and rescued cells show a 53% reduction in average released charge compared to control in the naïve response compared to a 44% reduction in the first response of the 40 Hz train. Although the absolute values differ between these readouts, we conclude that the biological comparison between groups is consistent.

(2) Figure 2h. This type of analysis has a drawback. See Neher (2015) for the problems associated with this analysis.

We fully agree with the reviewer’s comment. As noted in our discussion (page 9 line 285), while this analysis has its limitations, it can still provide an indication of the ready releasable pool.   

(3) The EPSC phenotype may be due to postsynaptic effects. This should be excluded by additional experiments (mEPSC analysis) or further clarification.

We fully agree that the characterization of miniature excitatory postsynaptic currents recording would strengthen the synaptic characterization and complement the electrophysiological recordings. Therefore, we have now added additional experiments showing the mEPSCs (Fig. 2k-m) in SNX4 cKO neurons versus control. This data shows that the amplitude and frequency of spontaneous miniature EPSCs (mEPSCs) were not affected upon SNX4 depletion, suggesting that it is unlikely that the observed increase in neurotransmission is due to post-synaptic effects.

(4) The increased number of docked vesicles observed in EM and the increased slope (vesicle recruitment, Figure 2h) are not consistent with each other. Maybe the definition of docked vesicles is unclear in this version of the manuscript.

As noted in our material & methods (page 15, line 547-548), SVs were defined as docked if there was no distance visible between the SV membrane and the active zone membrane. We have added the pixel size for clarification. Indeed, we do not observe an increase in release probability or first evoked response, which would correspond with an increased docked pool. However, we think that the increase in docked vesicles might contribute to an enhanced SV recruitment (see discussion).

(5) Figure 3: Vesicle cycling was monitored in only a limited condition. It is known that there are multiple pathways of vesicle cycling. Ideally, these pathways should be dissected. At least, the authors mention the possibility that they have missed some "positive" conditions.

We fully agree with the reviewer’s comment that vesicle recycling is complex with several parallel pathways involved. While we did not study individual endocytosis pathways, we used different assays covering various recycling pathways. The SypHy assay (Fig. 3c & f) combined with the 100 AP stimulation paradigm at room temperature predominantly addresses clathrin-mediated endocytosis. Additionally, the FM-64 dye assay at 37 degrees Celsius covers ultrafast endocytosis pathways as well as bulk endocytosis routes. Since neither assay showed major effects, we decided not to pursue further experiments focusing on different endocytosis pathways.

Reviewer #3 (Recommendations For The Authors):

Major points:

(1) Since all of the work here is culture-focussed, the in vivo phenotype is not as relevant, however the in vitro properties are. The incomplete Cre-dependent removal of SNX4 is concerning (especially axonal SNX4 levels identified via immunofluorescence), however, the main concern is that there was no profiling of the other molecular changes within these cultures. This is important, since there may be considerable alterations in the expression of a number of presynaptic proteins which may explain the observed phenotypes. Ideally, these cultures could have been profiled in an unbiased manner via mass spectrometry to identify potential changes in the presynaptic proteome, or at the very least the levels of key fusion molecules would have been assessed via Western blotting.

We thank the reviewer for their suggestion and agree that mass spectrometry would strengthen the interpretation of the observed phenotype. However, due to contractual constraints, we are unable to pursue a mass spectrometry follow-up experiment. We agree that characterizing key fusion molecules is of potential interest. Therefore, based on literature, we selected a likely candidate, VAMP2, which did not show any alterations in expression levels when knocking out SNX4. Given the previously described role of SNX4 in the degradation pathway, one would expect increased degradation of key fusion molecules if they are recycled by SNX4. Other literature indicates that reduced levels of key fusion molecules, such as synaptotagmin or SNAP-25 (Broadie et al., 1994; Washbourne et al., 2001) , do not mimic our phenotype.

(2) The experiments reported in Figure 2, in particular those in 2c and 2d, suggest that overexpression of SNX4 has a dominant-negative effect on neurotransmitter release. This is strongly supported by the supplementary data during a stimulus train (particularly the start point of the 5 Hz train in Supplementary Figure 2). Therefore, the perceived rescue of EPSC charge in Figure 2f, 2g may be a result of SNX4 inhibiting neurotransmitter release. A determination of the impact of SNX4 overexpression (and level of overexpression) in WT neurons is essential to show that this is a bonefide rescue, rather than a direct inhibition by SNX4 overexpression.

We thank the reviewer for their suggestion and agree that an additional experimental group (control + SNX4) would strengthen interpretation of the observed phenotype. We have now added a new experiment with an extra experimental condition with overexpression of SNX4 on a control background (Supplementary Fig. 3 page 21). This data shows that the amplitude and charge of the first evoked response were not affected in control + SNX4 neurons compared to control, and no differences were detected in the response to the 40 Hz stimulation train (Supplementary Fig. 3a-e).  Together, these data suggest that SNX4 overexpression in itself does not affect the neurotransmission protocols studied in SNX4 cKO experiments.

(3) The experiments in Figure 3 clearly reveal a lack of effect of SNX4 depletion on synaptic vesicle endocytosis. However, the assumption that synaptic vesicle recycling is unaffected is a little premature. The fact that the second evoked SypHy peak is significantly larger than the first (Figures 3c-e) suggests that more vesicles may be recycling in KO neurons. Furthermore, the FM dye experiments do not aid interpretation, since there may be insufficient time (10 min) for new vesicles to be generated from endosomal intermediates experiments. Therefore, to confirm an absence of effect on recycling, the authors could either 1) perform the same experiment as 3c, but with 4 stimulation trains (to drive the system harder to reveal any phenotype) or 2) repeat the FM dye experiment but increase the time between loading and unloading to 30 min.

We fully agree with the reviewers' comment that vesicle recycling is an important component to consider and is complex with several parallel pathways involved. We conducted multiple independent experiments covering the most significant recycling pathways. The SypHy assay (Fig. 3c & f) combined with the 100 AP stimulation paradigm at room temperature predominantly addresses clathrin-mediated endocytosis. Additionally, the FM-64 dye assay at 37 degrees Celsius covers ultrafast endocytosis pathways as well as bulk endocytosis routes. To further challenge the system and reveal recycling phenotypes, we included a second 100 AP stimulation in our SypHy assay. While only the increase of the second SypHy peak is significant, the absolute numbers do not differ much from the first peak (0,17 for control and 0,21 for KO second peak and 0,19 for control and 0,22 for KO first peak, Supplementary table1). We nevertheless do not see any effects on recycling after the second peak (mean decay time is 27 for control and 26 for KO Supplementary Table 1). A single 100 AP 40 Hz train depletes all the synchronous release (not shown) and most of the evoked charge (see Fig 2f), hence two of these trains with one minute recovery is already a very demanding protocol. Although increasing the time between loading and unloading to 30 minutes might uncover other recycling components, it has been shown that ultrafast endocytosis occurs within 30 seconds (Watanabe et al., 2013), suggesting that 10 minutes should provide enough time for synaptic vesicle recycling. This is also evident from the fact that we can significantly destain synapses loaded with FM dye by electrical stimulation (Fig 3j), indicating that synaptic vesicle recycling took place. Since neither assay showed major effects, we concluded that under these circumstances, synaptic recycling is not significantly affected. However, we cannot exclude the possibility that recycling deficits in SNX4 cKO neurons could be detected in other paradigms,

(4) There is no obvious effect on VAMP2 levels or location in SNX4 KO neurons (Figure 4). However, when one considers that SNX4 is proposed to have a role in VAMP2 trafficking, it is surprising that an experiment examining the live trafficking of VAMP2-SypHy was not performed. This would have revealed activity-dependent alterations that would have been missed by simply measuring VAMP2 expression and localization, and potentially provided a molecular explanation for the enhanced neurotransmitter release during a stimulus train.

We appreciate the reviewer’s suggestion and agree that it could be a valuable experiment However, overexpressing a VAMP2-pHluorin construct might obscure potential phenotypes related to VAMP2 trafficking. SNX4 is expected to be involved in VAMP2 recycling, even with activity-dependent changes. Mis-sorted VAMP2 would accumulate in acidic vesicles, which could be masked by the VAMP2-pHluorin construct. Similarly, mis-sorting of other SNX4 cargo, such as the transferrin receptor, has been identified through lysosomal degradation, as shown by Western blot analysis of expression levels of the endogenous protein. We did not detect any differences in endogenous levels of VAMP2 within 21 days of SNX4 deletion (Fig 4), indicating that SNX4-dependent endosome sorting is not essential for VAMP2 recycling.

(5) The morphological data in Figure 5 report a series of small changes in docked vesicles and active zone length. In many cases, significance is obtained due to synapses being used as the experimental n, and thus inflating the statistical power. When one considers that no significant effect was observed on evoked release (apart from during a stimulus train), it suggests that the number of docked vesicles does not alter release probability in this system (which the authors point out). Instead, they suggest that an increased supply of vesicles is responsible, via increased recruitment to RRP/releasable pool (but not via increased recycling). If this is the case, it should have been reflected as an increase in the evoked SypHy response in Fig 2c,d (which is borderline significant). What may help is to determine the morphological landscape immediately after a stimulus strain, since this is the only condition where enhanced release is observed, and thus provide a morphological correlate to the physiological data.

We fully agree with the reviewer’s suggestion that an ultrastructural characterization immediately after a stimulus train would be informative. Unfortunately, contract constraints prevent us from performing this experiment. For our ultrastructural morphological data, we treated synapses as individual experimental n since it is not possible to determine whether synapses in a micronetwork on one sapphire originate from the same neuron. We used 18 independent sapphires from 3 independent pups to ensure the technical and biological replication of our data and measuring independent neurons. We fully agree with the reviewers comment to be careful with ‘inflating the statistical power’ due to potential nesting effects when using synapses as experimental n. To mitigate the potential nesting effect of analyzing multiple synapses per neuron, the intracluster correlation (ICC) is calculated per variable and per nesting effect. If ICC was close to 0.1, indicating that a considerable portion of the total variance can be attributed to e.g. synapse or sapphire, multilevel analysis was performed to accommodate nested data (Aarts et al., 2014).

Minor points

(1) When a new mouse model is generated, it is usually accompanied by a thorough characterization of its properties. However, in this case, there was no information provided about the conditional SNX4 knockout mouse. This is surprising and at a minimum, the following should be provided a) the background strain, b) method of generation, c) the number of animals used to establish the colony, d) breeding strategy, e) backcrossing strategy, f) genotyping protocol.

We apologize that a thorough characterization of our novel mouse model was lacking and therefore added this to our material & methods section (page 11, line 377-391).

(2) There is a noticeable difference between WT and KO neurons during train stimulation in Figure 2f, however, this appears to be due to the fact that there is a far higher EPSC charge to begin with in KO neurons. Why is there such a disparity when there is no difference in response to single pulses (Figures 2b-d) or presynaptic plasticity (Figure 2e)?

We understand the reviewer’s concern. We excluded an outlier (3x SD) in the KO dataset that drove the initial far higher EPSC charge in the graph (was already excluded for the statistics, Supplementary table 1). The average charge of the first pulse of 40Hz train is 41 pC and for KO neurons 58 pC, which did not differ significantly.  These trains of Fig. 2f were recorded after 2 or 3 other stimulation paradigms, which can have affected the total charge released in the 40Hz train. That said, the proportional difference between groups is high comparable between Fig 2b-d and 2f, with a 37% increased average charge released in SNX4 cKO compared to control in the naïve response (Fig. 2d) and 41% increased response in the first response of the 40 Hz train (Fig. 2f), and rescued cells show a 53% reduction in average released charge compared to control in the naïve response compared to a 44% reduction in the first response of the 40 Hz train. Although the absolute values differ between these readouts, we conclude that the biological comparison between groups is consistent.

(3) Line 343-344 - "(Supplementary Figure 1a)" should be "(Figure 1a)".

We thank the reviewer for this comment and adjusted this in the manuscript.

Reviewer #1 (Public review):

Summary:

The authors define a new metric for visual displays, derived from psychophysical response times, called visual homogeneity (VH). They attempt to show that VH is explanatory of response times across multiple visual tasks. They use fMRI to find visual cortex regions with VH-correlated activity. On this basis, they declare a new visual region in human brain, area VH, whose purpose is to represent VH for the purpose of visual search and symmetry tasks.

Link to original review: https://elifesciences.org/reviewed-preprints/93033v2/reviews#peer-review-0

Comments on latest version:

Authors rebuttal: We agree that visual homogeneity is similar to existing concepts such as target saliency, memorability etc. We have proposed it as a separate concept because visual homogeneity has an independent empirical measure (the reciprocal of target-absent search time in oddball search, or the reciprocal of same response time in a same-different task, etc) that may or may not be the same as other empirical measures such as saliency and memorability. Investigating these possibilities is beyond the scope of our study but would be interesting for future work. We have now clarified this in the revised manuscript (Discussion, p. 42).

Reviewer response to rebuttal: Neither the original ms nor the comments on that ms pretended that "visual homogeneity" was entirely separate from target saliency etc. So this is a response to a criticism that was never made. What the authors do claim, and what the comments question, is that they have successfully subsumed long-recognized psychophysical concepts like target saliency etc. under a new, uber-concept, "visual homogeneity" that explains psychophysical experimental results in a more unified and satisfying way. This subsumption of several well-established psychophysical concepts under a new, unified category is what reviewers objected to.

Authors rebuttal: However, we'd like to emphasize that the question of whether visual homogeneity is novel or related to existing concepts misses entirely the key contribution of our study.

Reviewer response to rebuttal: Sorry, but the claim of a new uber-concept in psychophysics, "visual homogeneity", is a major claim of the paper. The fact that it is not the only claim made does not absolve the authors from having to prove it satisfactorily.

"Authors rebuttal: "In addition, the large regions of VH correlations identified in Experiments 1 and 2 vs. Experiments 3 and 4 are barely overlapping. This undermines the claim that VH is a universal quantity, represented in a newly discovered area of visual cortex, that underlies a wide variety of visual tasks and functions."<br /> • We respectfully disagree with your assertion. First of all, there is partial overlap between the VH regions, for which there are several other obvious explanations that must be considered first before dismissing VH outright as a flawed construct. We acknowledge these alternatives in the Results (p. 27), and the relevant text is reproduced below.

"We note that it is not straightforward to interpret the overlap between the VH regions identified in Experiments 2 & 4. The lack of overlap could be due to stimulus differences (natural images in Experiment 2 vs silhouettes in Experiment 4), visual field differences (items in the periphery in Experiment 2 vs items at the fovea in Experiment 4) and even due to different participants in the two experiments. There is evidence supporting all these possibilities: stimulus differences (Yue et al., 2014), visual field differences (Kravitz et al., 2013) as well as individual differences can all change the locus of neural activations in object-selective cortex (Weiner and Grill-Spector, 2012a; Glezer and Riesenhuber, 2013). We speculate that testing the same participants on search and symmetry tasks using similar stimuli and display properties would reveal even larger overlap in the VH regions that drive behavior."

Reviewer response to rebuttal: The authors are saying that their results merely look unconvincing (weak overlap between VH regions defined in different experiments) because there were confounding differences between their experiments, in subject population, stimuli, etc. That is possible, but in that case it is up to the authors to show that their definition of a new "area VH" is convincing when the confounding differences are resolved, e.g. by using the same stimuli in the different experiments they attempt to agglomerate here. That would require new experiments, and none are offered in this revision.

Authors rebuttal: • Thank you for carefully thinking through our logic. We agree that a distance-to-centre calculation is entirely unnecessary as an explanation for target-present visual search. The similarity between target and distractor, so there is nothing new to explain here. However, this is a narrow and selective interpretation of our findings because you are focusing only on our results on target-present searches, which are only half of all our data. The other half is the target-absent responses which previously have had no clear explanation. You are also missing the fact that we are explaining same-different and symmetry tasks as well using the same visual homogeneity computation. We urge you to think more deeply about the problem of how to decide whether an oddball is present or not in the first place. How do we actually solve this task?

Reviewer response to rebuttal: It is the role of the authors to think deeply about their paper and on that basis present a clear and compelling case that readers can understand quickly and agree with. That is not done here.

Authors rebuttal: There must be some underlying representation and decision process. Our study shows that a distance-to-centre computation can actually serve as a decision variable to solve disparate property-based visual tasks. These tasks pose a major challenge to standard models of decision-making because the underlying representation and decision variable have been unclear. Our study resolves this challenge by proposing a novel computation that can be used by the brain to solve all these disparate tasks, and bring these tasks into the ambit of standard theories of decision-making.

Reviewer response to rebuttal: There is only a "challenge" if you accept the authors' a priori assumption that all of these tasks must have a common explanation and rely on a single neural mechanism. I do not accept that assumption, and I don't think the authors provide evidence to support the assumption. There is nothing "unclear" about how search, oddball, etc. have been thoroughly explained, separately, in the psychophysical literature that spans more than a century.

Authors rebuttal: • You are indeed correct in noting that both Experiment 1 & 2 involve oddball search, and so at the superficial level, it looks circular that the oddball search data of Experiment 1 is being used to explain the oddball search data of Experiment 2.<br /> However a deeper scrutiny reveals more fundamental differences: Experiment 1 consisted of only oddball search with the target appearing on the left or right, whereas Experiment 2 consisted of oddball search with the target either present or completely absent. In fact, we were merely using the search dissimilarities from Experiment 1 to reconstruct the underlying object representation, because it is well-known that neural dissimilarities are predicted well by search dissimilarities (Sripati & Olson, 2009; Zhivago et al, 2014).

Reviewer response to rebuttal: Here again the authors cite differences between their multiple experiments as a virtue that supports their conclusions. Instead, the experiments should have been designed for maximum similarity if the authors intended to explain them with the same theory.

Authors rebuttal: To thoroughly refute any lingering concern about circularity, we reasoned that the model predictions for Experiment 2 could have been obtained by a distance-to-center computation on any brain like object representation. To this end, we used object representations from deep neural networks pretrained on object categorization, whose representations are known to match well with the brain, and asked if a distance-to-centre computation on these representations could predict the search data in Experiment 2. This was indeed the case, and these results are now included an additional section in Supplementary Material (Section S1).

Reviewer response to rebuttal: The authors' claims are about human performance and how it is based on the human brain. Their claims are not well supported by the human experiments that they performed. It serves no purpose to redo the same experiments in silico, which cannot provide stronger evidence that compensates for what was lacking in the human data.

Authors rebuttal: "Confirming the generality of visual homogeneity<br /> We performed several additional analyses to confirm the generality of our results, and to reject alternate explanations.

First, it could be argued that our results are circular because they involve taking oddball search times from Experiment 1 and using them to explain search response times in Experiment 2. This is a superficial concern since we are using the search dissimilarities from Experiment 1 only as a proxy for the underlying neural representation, based on previous reports that neural dissimilarities closely match oddball search dissimilarities (Sripati and Olson, 2010; Zhivago and Arun, 2014). Nonetheless, to thoroughly refute this possibility, we reasoned that we would get similar predictions of the target present/absent responses in Experiment using any other brain-like object representation. To confirm this, we replaced the object representations derived from Experiment 1 with object representations derived from deep neural networks pretrained for object categorization, and asked if distance-to-center computations could predict the target present/absent responses in Experiment 2. This was indeed the case (Section S1).

Second, we wondered whether the nonlinear optimization process of finding the best-fitting center could be yielding disparate optimal centres each time. To investigate this, we repeated the optimization procedure with many randomly initialized starting points, and obtained the same best-fitting center each time (see Methods).

Third, to confirm that the above model fits are not due to overfitting, we performed a leave-one-out cross validation analysis. We left out all target-present and target-absent searches involving a particular image, and then predicted these searches by calculating visual homogeneity estimated from all other images. This too yielded similar positive and negative correlations (r = 0.63, p < 0.0001 for target-present, r = -0.63, p < 0.001 for target-absent).

Fourth, if heterogeneous displays indeed elicit similar neural responses due to mixing, then their average distance to other objects must be related to their visual homogeneity. We confirmed that this was indeed the case, suggesting that the average distance of an object from all other objects in visual search can predict visual homogeneity (Section S1).

Fifth, the above results are based on taking the neural response to oddball arrays to be the average of the target and distractor responses. To confirm that averaging was indeed the optimal choice, we repeated the above analysis by assuming a range of relative weights between the target and distractor. The best correlation was obtained for almost equal weights in the lateral occipital (LO) region, consistent with averaging and its role in the underlying perceptual representation (Section S1).

Finally, we performed several additional experiments on a larger set of natural objects as well as on silhouette shapes. In all cases, present/absent responses were explained using visual homogeneity (Section S2)."

Reviewer response to rebuttal: The authors can experiment on side questions for as long as they please, but none of the results described above answer the concern about how center-fitting undercuts the evidentiary value of their main results.

Authors rebuttal: • While it is true that the optimal center needs to be found by fitting to the data, there no particular mystery to the algorithm: we are simply performing a standard gradient-descent to maximize the fit to the data. We have described the algorithm clearly and are making our codes public. We find the algorithm to yield stable optimal centers despite many randomly initialized starting points. We find the optimal center to be able to predict responses to entirely novel images that were excluded during model training. We are making no assumption about the location of centre with respect to individual points. Therefore, we see no cause for concern regarding the center-finding algorithm.

Reviewer response to rebuttal: The point of the original comment was that center-fitting should not be done in the first place because it introduces unknowable effects.

•Authors rebuttal: Most visual tasks, such as finding an animal, are thought to involve building a decision boundary on some underlying neural representation. Even visual search has been portrayed as a signal-detection problem where a particular target is to be discriminated from a distractor. However none of these formulations work in the case of property-based visual tasks, where there is no unique feature to look for.<br /> We are proposing that, when we view a search array, the neural response to the search array can be deduced from the neural responses to the individual elements using well-known rules, and that decisions about an oddball target being present or absent can be made by computing the distance of this neural response from some canonical mean firing rate of a population of neurons. This distance to center computation is what we denote as visual homogeneity. We have revised our manuscript throughout to make this clearer and we hope that this helps you understand the logic better.<br /> • You are absolutely correct that the stimulus complexity should matter, but there are no good empirically derived measures for stimulus complexity, other than subjective ratings which are complex on their own and could be based on any number of other cognitive and semantic factors. But considering what factors are correlated with target-absent response times is entirely different from asking what decision variable or template is being used by participants to solve the task.

Reviewer response to rebuttal: If stimulus complexity is what matters, as the authors agree here, then it is incumbent on them to measure stimulus complexity. The difficulty of measuring stimulus complexity does not justify avoiding the problem with an analysis that ignores complexity.

Authors rebuttal: • We have provided empirical proof for our claims, by showing that target-present response times in a visual search task are correlated with "different" responses in the same-different task, and that target-absent response times in the visual search task are correlated with "same" responses in the same-different task (Section S4).

Reviewer response to rebuttal: Sorry, but there is still no reason to think that same-different judgments are based on a mythical boundary halfway between the two. If there is a boundary, it will be close to the same end of the continuum, where subjects might conceivably miss some tiny difference between two stimuli. The vast majority of "different" stimuli will be entirely different from the same stimulus, producing no confusability, and certainly not a decision boundary halfway between two extremes.

Authors rebuttal: • Again, the opposite correlations between target present/absent search times with VH are the crucial empirical validation of our claims that a distance-to-center calculation explain how we perform these property-based tasks. The VH predictions do not fully explain the data. We have explicitly acknowledged this shortcoming, so we are hardly dismissing it as a problem.

Reviewer response to rebuttal: The authors' acknowledgement of flaws in the ms does not argue in favor of publication, but rather just the opposite.

Authors rebuttal: • Finding an oddball, deciding if two items are same or different and symmetry tasks are disparate visual tasks that do not fit neatly into standard models of decision-making. The key conceptual advance of our study is that we propose a plausible neural representation and decision variable that allows all three property-based visual tasks to be reconciled with standard models of decision-making.

Reviewer response to rebuttal: The original comment stands as written. Same/different will have a boundary very close to the "same" end of the continuum. The boundary is only halfway between two choices if the stimulus design forces the boundary to be there, as in the motion and cat/dog experiments.

Authors rebuttal: "There is no inherent middle point boundary between target present and target absent. Instead, in both types of trial, maximum information is present when target and distractors are most dissimilar, and minimum information is present when target and distractors are most similar. The point of greatest similarity occurs at then limit of any metric for similarity. Correspondingly, there is no middle point dip in information that would produce greater difficulty and higher response times. Instead, task difficulty and response times increase monotonically with similarity between targets and distractors, for both target present and target absent decisions. Thus, in Figs. 2F and 2G, response times appear to be highest for animals, which share the largest numbers of closely similar distractors."<br /> • Your alternative explanation rests on vague factors like "maximum information" which cannot be quantified. By contrast we are proposing a concrete, falsifiable model for three property-based tasks - same/different, oddball present/absent and object symmetry. Any argument based solely on item similarity to explain visual search or symmetry responses cannot explain systematic variations observed for target-absent arrays and for symmetric objects, for the reasons explained earlier.

Reviewer response to rebuttal: There is nothing vague about this comment. The authors use an analysis that assumes a decision boundary at the centerpoint of their arbitrarily defined stimulus space. This assumption is not supported, and it is unlikely, considering that subjects are likely to notice all but the smallest variations between same and different stimuli, putting the boundary nearly at the same end of the continuum, not the very middle.

Authors rebuttal: "(1) The area VH boundaries from different experiments are nearly completely non-overlapping.

In line with their theory that VH is a single continuum with a decision boundary somewhere in the middle, the authors use fMRI searchlight to find an area whose responses positively correlate with homogeneity, as calculated across all of their target present and target absent arrays. They report VH-correlated activity in regions anterior to LO. However, the VH defined by symmetry Experiments 3 and 4 (VHsymmetry) is substantially anterior to LO, while the VH defined by target detection Experiments 1 and 2 (VHdetection) is almost immediately adjacent to LO. Fig. S13 shows that VHsymmetry and VHdetection are nearly non-overlapping. This is a fundamental problem with the claim of discovering a new area that represents a new quantity that explains response times across multiple visual tasks. In addition, it is hard to understand why VHsymmetry does not show up in a straightforward subtraction between symmetric and asymmetric objects, which should show a clear difference in homogeneity."

• We respectfully disagree. The partial overlap between the VH regions identified in Experiments 1 & 2 can hardly be taken as evidence against the quantity VH itself, because there are several other obvious alternate explanations for this partial overlap, as summarized earlier as well. The VH region does show up in a straightforward subtraction between symmetric and asymmetric objects (Section S7), so we are not sure what the Reviewer is referring to here.

Reviewer response to rebuttal: In disagreeing with the comment quoted above, the authors are maintaining that a new functional area of cerebral cortex can be declared even if that area changes location on the cortical map from one experiment to another. That position is patently absurd.

Authors rebuttal: "(3) Definition of the boundaries and purpose of a new visual area in the brain requires circumspection, abundant and convergent evidence, and careful controls.

Even if the VH metric, as defined and calculated by the authors here, is a meaningful quantity, it is a bold claim that a large cortical area just anterior to LO is devoted to calculating this metric as its major task. Vision involves much more than target detection and symmetry detection. Cortex anterior to LO is bound to perform a much wider range of visual functionalities. If the reported correlations can be clarified and supported, it would be more circumspect to treat them as one byproduct of unknown visual processing in cortex anterior to LO, rather than treating them as the defining purpose for a large area of visual cortex."

• We totally agree with you that reporting a new brain region would require careful interpretation and abundant and converging evidence. However, this requires many studies worth of work, and historically category-selective regions like the FFA have achieved consensus only after they were replicated and confirmed across many studies. We believe our proposal for the computation of a quantity like visual homogeneity is conceptually novel, and our study represents a first step that provides some converging evidence (through replicable results across different experiments) for such a region. We have reworked our manuscript to make this point clearer (Discussion, p 32).

Reviewer response to rebuttal: Indeed, declaring a new brain area depends on much more work than is done here. Thus, the appropriate course here is to wait before claiming to have identified a new cortical area.

Reviewer #2 (Public review):

Summary:

This study proposes visual homogeneity as a novel visual property that enables observers perform to several seemingly disparate visual tasks, such as finding an odd item, deciding if two items are same, or judging if an object is symmetric. In Exp 1, the reaction times on several objects were measured in human subjects. In Exp 2, visual homogeneity of each object was calculated based on the reaction time data. The visual homogeneity scores predicted reaction times. This value was also correlated with the BOLD signals in a specific region anterior to LO. Similar methods were used to analyze reaction time and fMRI data in a symmetry detection task. It is concluded that visual homogeneity is an important feature that enables observers to solve these two tasks.

Strengths:

(1) The writing is very clear. The presentation of the study is informative.

(2) This study includes several behavioral and fMRI experiments. I appreciate the scientific rigor of the authors.

Weaknesses:

Before addressing the manuscript itself, I would like to comment the review process first. Having read the lasted revised manuscript, I shared many of the concerns raised by the two reviewers in the last two rounds of review. It appears that the authors have disagreed with the majority of comments made by the two reviewers. If so, I strongly recommend that the authors proceed to make this revision as a Version of Record and conclude this review process. According to eLife's policy that the authors have the right to make a Version of Record at any time during the review process, and I fully respect that right. However, I also ask that the authors respect the reviewer's right to retain the comments regarding this paper.

Beside that, I still have several further questions about this study.

(1) My main concern with this paper is the way visual homogeneity is computed. On page 10, lines 188-192, it says: "we then asked if there is any point in this multidimensional representation such that distances from this point to the target-present and target-absent response vectors can accurately predict the target-present and target-absent response times with a positive and negative correlation respectively (see Methods)". This is also true for the symmetry detection task. If I understand correctly, the reference point in this perceptual space was found by deliberating satisfying the negative and positive correlations in response times. And then on page 10, lines 200-205, it shows that the positive and negative correlations actually exist. This logic is confusing. The positive and negative correlations emerge only because this method is optimized to do so. It seems more reasonable to identify the reference point of this perceptual space independently, without using the reaction time data. Otherwise, the inference process sounds circular. A simple way is to just use the mean point of all objects in Exp 1, without any optimization towards reaction time data.<br /> I raised this question in my initial review. However, the authors did not address whether the positive and negative correlations still hold if the mean point is defined as the reference point without any optimization. The authors also argue that it is similar to a case of fitting a straight line. It is fine that the authors insist on the straight line (e.g., correlation). However, I would not call "straight line correlations" a "quantitative model" as a high-profile journals like eLife. Please remove all related arguments of a novel quantitative model.

(2) Visual homogeneity (at least given the current form) is an unnecessary term. It is similar to distractor heterogeneity/distractor variability/distractor saliency in literature. However, the authors attempt to claim it as a novel concept. Both R1 and me raised this question in the very first review. However, the authors refused to revise the manuscript. In the last review, I mentioned this and provided some example sentences claiming novelty. The authors only revised the last sentence of the abstract, and even did not bother to revise the last sentence of significance: "we show that these tasks can be solved using a simple property WE DEFINE as visual homogeneity". Also, lines 851 still shows "we have defined a NOVEL image property, visual homogeneity...". I am confused about whether the authors agree or disagree that "visual homogeneity is an unnecessary term". If the authors agree, they should completely remove the related phrase throughout the paper. If not, they should keep all these and state the reasons. I don't think this is a correct approach to revising a manuscript.

(3) If the authors agree that visual homogeneity is not new, I suggest a complete rewrite of the title, abstract, significance, and introduction. Let me ask a simple question, can we remove "visual homogeneity" and use some more well-established term like "image feature similarity"? If yes, visual homogeneity is unnecessary.

(4) If I understand it correctly, one of the key findings of this paper is "the response times for target-present searches were positively correlated with visual homogeneity. By contrast, the response times for target-absent searches were negatively correlated with visual homogeneity" (lines 204-207). I think the authors have already acknowledged that this positive correlation is not surprising at all because it reflects the classic target-distractor similarity effect. If this is the case, please completely remove the positive correlation as a novel prediction and finding.

(5) In my last review, I mentioned the seminal paper by Duncan and Humphreys (1989) has clearly stated that "difficulty increases with increased similarity of targets to nontargets and decreased similarity between nontargets" (the sentence in their abstract). Here, "similarity between nontargets" is the same as the visual homogeneity defined here. Similar effects have been shown in Duncan (1989) and Nagy, Neriani, and Young (2005). See also the inconsistent results in Nagy& Thomas, 2003, Vicent, Baddeley, Troscianko&Gilchrist, 2009. More recently, Wei Ji Ma has systematically investigated the effects of heterogeneous distractors in visual search. I think the introduction part of Wei Ji Ma's paper (2020) provides a nice summary of this line of research.

Thanks to the authors' revision, I now better understand the negative correlation. The between-distrator similarity mentioned above describes the heterogeneity of distractors WITHIN an image. However, if I understand it correctly, this study aims to address the negative correlation of reaction time and target-absent stimuli ACROSS images. In other words, why do humans show a shorter reaction time to an image of four pigeons than to an image of four dogs (as shown in Figure 2C), simply because the later image is closer to the reference point of the image space. In this sense, this negative correlation is indeed not the same as distractor heterogeneity. However, this is known as the saliency effect or oddball effects. For example, it seems quite natural to me that humans respond faster to a fish image if the image set contains many images of four-leg dogs that look very different from fish. If this is indeed a saliency effect, why should we define a new term "visual homogeneity"?

(6) The section "key predictions" is quite straightforward. I understand the logic of positive and negative correlations. However, what is the physical meaning of "decision boundary" (Fig. 1G) here? How does the "decision boundary" map on the image space?

(7) In my opinion, one of the advantages of this study is the fMRI dataset, which is valuable because previous studies did not collect fMRI data. The key contribution may be the novel brain region associated with display heterogeneity. If this is the case, I would suggest using a more parametric way to measure this region. For example, one can use Gabor stimuli and systematically manipulate the variations of multiple Gabor stimuli, the same logic also applies to motion direction. If this study uses static Gabor, random dot motion, object images that span from low-level to high-level visual stimuli, and consistently shows that the stimulus heterogeneity is encoded in one brain region, I would say this finding is valuable. But this sounds another experiment. In other words, it is insufficient to claim a new brain region given the current form of the manuscript.

References:

* Duncan, J., & Humphreys, G. W. (1989). Visual search and stimulus similarity. Psychological Review, 96(3), 433-458. doi: 10.1037/0033-295x.96.3.433<br /> * Duncan, J. (1989). Boundary conditions on parallel processing in human vision. Perception, 18(4), 457-469. doi: 10.1068/p180457<br /> * Nagy, A. L., Neriani, K. E., & Young, T. L. (2005). Effects of target and distractor heterogeneity on search for a color target. Vision Research, 45(14), 1885-1899. doi: 10.1016/j.visres.2005.01.007<br /> * Nagy, A. L., & Thomas, G. (2003). Distractor heterogeneity, attention, and color in visual search. Vision Research, 43(14), 1541-1552. doi: 10.1016/s0042-6989(03)00234-7<br /> * Vincent, B., Baddeley, R., Troscianko, T., & Gilchrist, I. (2009). Optimal feature integration in visual search. Journal of Vision, 9(5), 15-15. doi: 10.1167/9.5.15<br /> * Singh, A., Mihali, A., Chou, W. C., & Ma, W. J. (2023). A Computational Approach to Search in Visual Working Memory.<br /> * Mihali, A., & Ma, W. J. (2020). The psychophysics of visual search with heterogeneous distractors. BioRxiv, 2020-08.<br /> * Calder-Travis, J., & Ma, W. J. (2020). Explaining the effects of distractor statistics in visual search. Journal of Vision, 20(13), 11-11.

Author response:

The following is the authors’ response to the previous reviews.

We are grateful to the editors and reviewers for their careful reading and constructive comments. We have now done our best to respond to them fully through additional analyses and text revisions. In the sections below, the original reviewer comments are in black, and our responses are in red.

To summarize, the major changes in this round of review are as follows:

(1) We have included a new introductory figure (Figure 1) to explain the distinction between feature-based tasks and property-based tasks.

(2) We have included a section on “key predictions” and a section on “overview of this study” in the Introduction to clearly delineate our key predictions and provide a overview of our study.

(3) We have included additional analyses to address the reviewers’ concerns about circularity in Experiments 1 & 2. We show that distance-to-center or visual homogeneity computations performed on object representations obtained from deep networks (instead of the perceptual dissimilarities from Experiment 1) also yields comparable predictions of target-present and target-absent responses in Experiment 2. 

(4) We have extensively reworked the manuscript wherever possible to address the specific concerns raised by the reviewers.

We hope that the revised manuscript adequately addresses the concerns raised in this round of review, and we look forward to a positive assessment.

eLife Assessment

This study uses carefully designed experiments to generate a useful behavioural and neuroimaging dataset on visual cognition. The results provide solid evidence for the involvement of higher-order visual cortex in processing visual oddballs and asymmetry. However, the evidence provided for the very strong claims of homogeneity as a novel concept in vision science, separable from existing concepts such as target saliency, is inadequate.

Thank you for your positive assessment. We agree that visual homogeneity is similar to existing concepts such as target saliency, memorability etc. We have proposed it as a separate concept because visual homogeneity has an independent empirical measure (the reciprocal of target-absent search time in oddball search, or the reciprocal of same response time in a same-different task, etc) that may or may not be the same as other empirical measures such as saliency and memorability. Investigating these possibilities is beyond the scope of our study but would be interesting for future work. We have now clarified this in the revised manuscript (Discussion, p. 42).

However, we’d like to emphasize that the question of whether visual homogeneity is novel or related to existing concepts misses entirely the key contribution of our study.

Our key contribution is a quantitative, falsifiable model for how the brain could be solving property-based tasks like same-different, oddball or symmetry. Most theories of decision making consider feature-based tasks where there is a well-defined feature space and decision variable. Property-based tasks pose a significant challenge to standard theories since it is not clear how these tasks could be solved. In fact, oddball search, same-different and symmetry tasks have been considered so different that they are rarely even mentioned in the same study. Our study represents a unifying framework showing that all three tasks can be understood as solving the same underlying fundamental problem, and presents evidence in favor of this solution.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors define a new metric for visual displays, derived from psychophysical response times, called visual homogeneity (VH). They attempt to show that VH is explanatory of response times across multiple visual tasks. They use fMRI to find visual cortex regions with VH-correlated activity. On this basis, they declare a new visual region in human brain, area VH, whose purpose is to represent VH for the purpose of visual search and symmetry tasks.

Thank you for your accurate and positive assessment.

Strengths:

The authors present carefully designed experiments, combining multiple types of visual judgments and multiple types of visual stimuli with concurrent fMRI measurements. This is a rich dataset with many possibilities for analysis and interpretation.

Thank you for your accurate and positive assessment.

Weaknesses:

The datasets presented here should provide a rich basis for analysis. However, in this version of the manuscript, I believe that there are major problems with the logic underlying the authors' new theory of visual homogeneity (VH), with the specific methods they used to calculate VH, and with their interpretation of psychophysical results using these methods. These problems with the coherency of VH as a theoretical construct and metric value make it hard to interpret the fMRI results based on searchlight analysis of neural activity correlated with VH.

We respectfully disagree with your concerns, and have done our best to respond to them fully below.

In addition, the large regions of VH correlations identified in Experiments 1 and 2 vs. Experiments 3 and 4 are barely overlapping. This undermines the claim that VH is a universal quantity, represented in a newly discovered area of visual cortex, that underlies a wide variety of visual tasks and functions.

We respectfully disagree with your assertion. First of all, there is partial overlap between the VH regions, for which there are several other obvious explanations that must be considered first before dismissing VH outright as a flawed construct. We acknowledge these alternatives in the Results (p. 27), and the relevant text is reproduced below.

“We note that it is not straightforward to interpret the overlap between the VH regions identified in Experiments 2 & 4. The lack of overlap could be due to stimulus differences (natural images in Experiment 2 vs silhouettes in Experiment 4), visual field differences (items in the periphery in Experiment 2 vs items at the fovea in Experiment 4) and even due to different participants in the two experiments. There is evidence supporting all these possibilities: stimulus differences (Yue et al., 2014), visual field differences (Kravitz et al., 2013) as well as individual differences can all change the locus of neural activations in object-selective cortex (Weiner and Grill-Spector, 2012a; Glezer and Riesenhuber, 2013). We speculate that testing the same participants on search and symmetry tasks using similar stimuli and display properties would reveal even larger overlap in the VH regions that drive behavior.”

Maybe I have missed something, or there is some flaw in my logic. But, absent that, I think the authors should radically reconsider their theory, analyses, and interpretations, in light of detailed comments below, in order to make the best use of their extensive and valuable datasets combining behavior and fMRI. I think doing so could lead to a much more coherent and convincing paper, albeit possibly supporting less novel conclusions.

We respectfully disagree with your assessment, and we hope that our detailed responses below will convince you of the merit of our claims.

THEORY AND ANALYSIS OF VH

(1) VH is an unnecessary, complex proxy for response time and target-distractor similarity.<br /> VH is defined as a novel visual quality, calculable for both arrays of objects (as studied in Experiments 1-3) and individual objects (as studied in Experiment 4). It is derived from a center-to-distance calculation in a perceptual space. That space in turn is derived from multi-dimensional scaling of response times for target-distractor pairs in an oddball detection task (Experiments 1 and 2) or in a same different task (Experiments 3 and 4).  Proximity of objects in the space is inversely proportional to response times for arrays in which they were paired. These response times are higher for more similar objects. Hence, proximity is proportional to similarity. This is visible in Fig. 2B as the close clustering of complex, confusable animal shapes.

VH, i.e. distance-to-center, for target-present arrays is calculated as shown in Fig. 1C, based on a point on the line connecting target and distractors. The authors justify this idea with previous findings that responses to multiple stimuli are an average of responses to the constituent individual stimuli. The distance of the connecting line to the center is inversely proportional to the distance between the two stimuli in the pair, as shown in Fig. 2D. As a result, VH is inversely proportional to distance between the stimuli and thus to stimulus similarity and response times. But this just makes VH a highly derived, unnecessarily complex proxy for target-distractor similarity and response time. The original response times on which the perceptual space is based are far more simple and direct measures of similarity for predicting response times.

Thank you for carefully thinking through our logic. We agree that a distance-to-centre calculation is entirely unnecessary as an explanation for target-present visual search. The difficulty of target-present search is already known to be directly proportional to the similarity between target and distractor, so there is nothing new to explain here.

However, this is a narrow and selective interpretation of our findings because you are focusing only on our results on target-present searches, which are only half of all our data. The other half is the target-absent responses which previously have had no clear explanation. You are also missing the fact that we are explaining same-different and symmetry tasks as well using the same visual homogeneity computation.

We urge you to think more deeply about the problem of how to decide whether an oddball is present or not in the first place. How do we actually solve this task? There must be some underlying representation and decision process. Our study shows that a distance-to-centre computation can actually serve as a decision variable to solve disparate property-based visual tasks. These tasks pose a major challenge to standard models of decision making, because the underlying representation and decision variable have been unclear. Our study resolves this challenge by proposing a novel computation that can be used by the brain to solve all these disparate tasks, and bring these tasks into the ambit of standard theories of decision making.  

Our results also explain several interesting puzzles in the literature. If oddball search was driven only by target-distractor similarity, the time taken to respond when a target is absent should not vary at all, and should actually take longer than all target-present searches. But in fact, systematic variations in target-absent times have been observed always in the literature, but have never been explained using any theoretical models. Our results explain why target-absent times vary systematically – it is due to visual homogeneity.

Similarly, in same-different tasks, participants are known to take longer to make a “different” response when the two items differ only slightly. By this logic, they should take the longest to make a “same” response, but in fact, paradoxically, participants are actually faster to make “same” responses. This fast-same effect has been noted several times, but never explained using any models. Our results provide an explanation of why “same” responses to an image vary systematically – it is due to visual homogeneity. 

Finally, in symmetry tasks, symmetric objects evoke fast responses, and this has always been taken as evidence for special symmetry computations in the brain. But we show that the same distance-to-center computation can explain both responses to symmetric and asymmetric objects. Thus there is no need for a special symmetry computation in the brain.

(2) The use of VH derived from Experiment 1 to predict response times in Experiment 2 is circular and does not validate the VH theory.<br /> The use of VH, a response time proxy, to predict response times in other, similar tasks, using the same stimuli, is circular. In effect, response times are being used to predict response times across two similar experiments using the same stimuli. Experiment 1 and the target present condition of Experiment 2 involve the same essential task of oddball detection. The results of Experiment 1 are converted into VH values as described above, and these are used to predict response times in experiment 2 (Fig. 2F). Since VH is a derived proxy for response values in Experiment 1, this prediction is circular, and the observed correlation shows only consistency between two oddball detection tasks in two experiments using the same stimuli.

You are indeed correct in noting that both Experiment 1 & 2 involve oddball search, and so at the superficial level, it looks circular that the oddball search data of Experiment 1 is being used to explain the oddball search data of Experiment 2.

However a deeper scrutiny reveals more fundamental differences: Experiment 1 consisted of only oddball search with the target appearing on the left or right, whereas Experiment 2 consisted of oddball search with the target either present or completely absent. In fact, we were merely using the search dissimilarities from Experiment 1 to reconstruct the underlying object representation, because it is well known that neural dissimilarities are predicted well by search dissimilarities (Sripati & Olson, 2009; Zhivago et al, 2014).

To thoroughly refute any lingering concern about circularity, we reasoned that the model predictions for Experiment 2 could have been obtained by a distance-to-center computation on any brain like object representation. To this end, we used object representations from deep neural networks pretrained on object categorization, whose representations are known to match well with the brain, and asked if a distance-to-centre computation on these representations could predict the search data in Experiment 2. This was indeed the case, and these results are now included an additional section in Supplementary Material (Section S1).

(3) The negative correlation of target-absent response times with VH as it is defined for target-absent arrays, based on distance of a single stimulus from center, is uninterpretable without understanding the effects of center-fitting. Most likely, center-fitting and the different VH metric for target-absent trials produce an inverse correlation of VH with target-distractor similarity.

Unfortunately, as we have mentioned above, target-distractor similarity cannot explain how target-absent searches behave, since there is no distractor in such searches.

We do understand your broader concern about the center-fitting algorithm itself. We performed a number of additional analyses to confirm the generality of our results and reject alternate explanations – these are summarized in a new section titled “Confirming the generality of visual homogeneity” (p. 12), and the section is reproduced below for your convenience.   

“Confirming the generality of visual homogeneity

We performed several additional analyses to confirm the generality of our results, and to reject alternate explanations.

First, it could be argued that our results are circular because they involve taking oddball search times from Experiment 1 and using them to explain search response times in Experiment 2. This is a superficial concern since we are using the search dissimilarities from Experiment 1 only as a proxy for the underlying neural representation, based on previous reports that neural dissimilarities closely match oddball search dissimilarities (Sripati and Olson, 2010; Zhivago and Arun, 2014). Nonetheless, to thoroughly refute this possibility, we reasoned that we would get similar predictions of the target present/absent responses in Experiment using any other brain-like object representation. To confirm this, we replaced the object representations derived from Experiment 1 with object representations derived from deep neural networks pretrained for object categorization, and asked if distance-to-center computations could predict the target present/absent responses in Experiment 2. This was indeed the case (Section S1). 

Second, we wondered whether the nonlinear optimization process of finding the best-fitting center could be yielding disparate optimal centres each time. To investigate this, we repeated the optimization procedure with many randomly initialized starting points, and obtained the same best-fitting center each time (see Methods).

Third, to confirm that the above model fits are not due to overfitting, we performed a leave-one-out cross validation analysis. We left out all target-present and target-absent searches involving a particular image, and then predicted these searches by calculating visual homogeneity estimated from all other images. This too yielded similar positive and negative correlations (r = 0.63, p < 0.0001 for target-present, r = -0.63, p < 0.001  for target-absent).

Fourth, if heterogeneous displays indeed elicit similar neural responses due to mixing, then their average distance to other objects must be related to their visual homogeneity. We confirmed that this was indeed the case, suggesting that the average distance of an object from all other objects in visual search can predict visual homogeneity (Section S1).

Fifth, the above results are based on taking the neural response to oddball arrays to be the average of the target and distractor responses. To confirm that averaging was indeed the optimal choice, we repeated the above analysis by assuming a range of relative weights between the target and distractor. The best correlation was obtained for almost equal weights in the lateral occipital (LO) region, consistent with averaging and its role in the underlying perceptual representation (Section S1).

Finally, we performed several additional experiments on a larger set of natural objects as well as on silhouette shapes. In all cases, present/absent responses were explained using visual homogeneity (Section S2).”

The construction of the VH perceptual space also involves fitting a "center" point such that distances to center predict response times as closely as possible. The effect of this fitting process on distance-to-center values for individual objects or clusters of objects is unknowable from what is presented here. These effects would depend on the residual errors after fitting response times with the connecting line distances. The center point location and its effects on distance-to-center of single objects and object clusters are not discussed or reported here.

While it is true that the optimal center needs to be found by fitting to the data, there no particular mystery to the algorithm: we are simply performing a standard gradient-descent to maximize the fit to the data. We have described the algorithm clearly and are making our codes public. We find the algorithm to yield stable optimal centers despite many randomly initialized starting points. We find the optimal center to be able to predict responses to entirely novel images that were excluded during model training. We are making no assumption about the location of centre with respect to individual points. Therefore, we see no cause for concern regarding the center-finding algorithm. 

Yet, this uninterpretable distance-to-center of single objects is chosen as the metric for VH of target-absent displays (VHabsent). This is justified by the idea that arrays of a single stimulus will produce an average response equal to one stimulus of the same kind. But it is not logically clear why response strength to a stimulus should be a metric for homogeneity of arrays constructed from that stimulus, or even what homogeneity could mean for a single stimulus from this set. And it is not clear how this VHabsent metric based on single stimuli can be equated to the connecting line VH metric for stimulus pairs, i.e. VHpresent, or how both could be plotted on a single continuum.

Most visual tasks, such as finding an animal, are thought to involve building a decision boundary on some underlying neural representation. Even visual search has been portrayed as a signal-detection problem where a particular target is to be discriminated from a distractor. However none of these formulations work in the case of property-based visual tasks, where there is no unique feature to look for.

We are proposing that, when we view a search array, the neural response to the search array can be deduced from the neural responses to the individual elements using well known rules, and that decisions about an oddball target being present or absent can be made by computing the distance of this neural response from some canonical mean firing rate of a population of neurons. This distance to center computation is what we denote as visual homogeneity. We have revised our manuscript throughout to make this clearer and we hope that this helps you understand the logic better. 

It is clear, however, what *should* be correlated with difficulty and response time in the target-absent trials, and that is the complexity of the stimuli and the numerosity of similar distractors in the overall stimulus set. Complexity of the target, similarity with potential distractors, and number of such similar distractors all make ruling out distractor presence more difficult. The correlation seen in Fig. 2G must reflect these kinds of effects, with higher response times for complex animal shapes with lots of similar distractors and lower response times for simpler round shapes with fewer similar distractors.

You are absolutely correct that the stimulus complexity should matter, but there are no good empirically derived measures for stimulus complexity, other than subjective ratings which are complex on their own and could be based on any number of other cognitive and semantic factors. But considering what factors are correlated with target-absent response times is entirely different from asking what decision variable or template is being used by participants to solve the task.

The example points in Fig. 2G seem to bear this out, with higher response times for the deer stimulus (complex, many close distractors in the Fig. 2B perceptual space) and lower response times for the coffee cup (simple, few close distractors in the perceptual space). While the meaning of the VH scale in Fig. 2G, and its relationship to the scale in Fig. 2F, are unknown, it seems like the Fig. 2G scale has an inverse relationship to stimulus complexity, in contrast to the expected positive relationship for Fig. 2F. This is presumably what creates the observed negative correlation in Fig. 2G.

Taken together, points 1-3 suggest that VHpresent and VHabsent are complex, unnecessary, and disconnected metrics for understanding target detection response times. The standard, simple explanation should stand. Task difficulty and response time in target detection tasks, in both present and absent trials, are positively correlated with target-distractor similarity.

We strongly disagree. Your assessment seems to be based on only considering target-present searches, which are of course driven by target-distractor similarity. Your  argument is flawed because systematic variations in target-absent trials cannot be linked to any target-distractor similarity since there are no targets in the first place in such trials.

We have shown that target-absent response times are in fact, independent of experimental context, which means that they index an image property that is independent of any reference target (Results, p. 15; Section S4). This property is what we define as visual homogeneity.

I think my interpretations apply to Experiments 3 and 4 as well, although I find the analysis in Fig. 4 especially hard to understand. The VH space in this case is based on Experiment 3 oddball detection in a stimulus set that included both symmetric and asymmetric objects. But the response times for a very different task in Experiment 4, a symmetric/asymmetric judgment, are plotted against the axes derived from Experiment 3 (Fig. 4F and 4G). It is not clear to me why a measure based on oddball detection that requires no use of symmetry information should be predictive of within-stimulus symmetry detection response times. If it is, that requires a theoretical explanation not provided here.

We were simply using an oddball detection task to construct the underlying object representation, on the basis of observations that search dissimilarities are strongly correlated with neural   dissimilarities. In Section S1, we show that similar results could have been obtained using other object representations such as deep networks, as long as the representation is brain-like.

(4) Contrary to the VH theory, same/different tasks are unlikely to depend on a decision boundary in the middle of a similarity or homogeneity continuum.

We have provided empirical proof for our claims, by showing that target-present response times in a visual search task are correlated with “different” responses in the same-different task, and that target-absent response times in the visual search task are correlated with “same” responses in the same-different task (Section S4).

The authors interpret the inverse relationship of response times with VHpresent and VHabsent, described above, as evidence for their theory. They hypothesize, in Fig. 1G, that VHpresent and VHabsent occupy a single scale, with maximum VHpresent falling at the same point as minimum VHabsent. This is not borne out by their analysis, since the VHpresent and VHabsent value scales are mainly overlapping, not only in Experiments 1 and 2 but also in Experiments 3 and 4. The authors dismiss this problem by saying that their analyses are a first pass that will require future refinement. Instead, the failure to conform to this basic part of the theory should be a red flag calling for revision of the theory.

Again, the opposite correlations between target present/absent search times with VH are the crucial empirical validation of our claims that a distance-to-center calculation explain how we perform these property-based tasks. The VH predictions do not fully explain the data. We have explicitly acknowledged this shortcoming, so we are hardly dismissing it as a problem. 

The reason for this single scale is that the authors think of target detection as a boundary decision task, along a single scale, with a decision boundary somewhere in the middle, separating present and absent. This model makes sense for decision dimensions or spaces where there are two categories (right/left motion; cats vs. dogs), separated by an inherent boundary (equal left/right motion; training-defined cat/dog boundary). In these cases, there is less information near the boundary, leading to reduced speed/accuracy and producing a pattern like that shown in Fig. 1G.

Finding an oddball, deciding if two items are same or different and symmetry tasks are disparate visual tasks that do not fit neatly into standard models of decision making. The key conceptual advance of our study is that we propose a plausible neural representation and decision variable that allow all three property-based visual tasks to be reconciled with standard models of decision making.

This logic does not hold for target detection tasks. There is no inherent middle point boundary between target present and target absent. Instead, in both types of trial, maximum information is present when target and distractors are most dissimilar, and minimum information is present when target and distractors are most similar. The point of greatest similarity occurs at then limit of any metric for similarity. Correspondingly, there is no middle point dip in information that would produce greater difficulty and higher response times. Instead, task difficulty and response times increase monotonically with similarity between targets and distractors, for both target present and target absent decisions. Thus, in Figs. 2F and 2G, response times appear to be highest for animals, which share the largest numbers of closely similar distractors.        

Your alternative explanation rests on vague factors like “maximum information” which cannot be quantified. By contrast we are proposing a concrete, falsifiable model for three property-based tasks – same/different, oddball present/absent and object symmetry. Any argument based solely on item similarity to explain visual search or symmetry responses cannot explain systematic variations observed for target-absent arrays and for symmetric objects, for the reasons explained earlier.

DEFINITION OF AREA VH USING fMRI

(1) The area VH boundaries from different experiments are nearly completely non-overlapping.

In line with their theory that VH is a single continuum with a decision boundary somewhere in the middle, the authors use fMRI searchlight to find an area whose responses positively correlate with homogeneity, as calculated across all of their target present and target absent arrays. They report VH-correlated activity in regions anterior to LO. However, the VH defined by symmetry Experiments 3 and 4 (VHsymmetry) is substantially anterior to LO, while the VH defined by target detection Experiments 1 and 2 (VHdetection) is almost immediately adjacent to LO. Fig. S13 shows that VHsymmetry and VHdetection are nearly non-overlapping. This is a fundamental problem with the claim of discovering a new area that represents a new quantity that explains response times across multiple visual tasks. In addition, it is hard to understand why VHsymmetry does not show up in a straightforward subtraction between symmetric and asymmetric objects, which should show a clear difference in homogeneity.

We respectfully disagree. The partial overlap between the VH regions identified in Experiments 1 & 2 can hardly be taken as evidence against the quantity VH itself, because there are several other obvious alternate explanations for this partial overlap, as summarized earlier as well. The VH region does show up in a straightforward subtraction  between symmetric and asymmetric objects (Section S7), so we are not sure what the Reviewer is referring to here.

(2) It is hard to understand how neural responses can be correlated with both VHpresent and VHabsent.

The main paper results for VHdetection are based on both target-present and target-absent trials, considered together. It is hard to interpret the observed correlations, since the VHpresent and VHabsent metrics are calculated in such different ways and have opposite correlations with target similarity, task difficulty, and response times (see above). It may be that one or the other dominates the observed correlations. It would be clarifying to analyze correlations for target-present and target-absent trials separately, to see if they are both positive and correlated with each other.

Thanks for raising this point. We have now confirmed that the positive correlation between VH and neural response holds even when we do the analysis separately for target-present and -absent searches (correlation between neural response in VH region and visual homogeneity (n = 32, r = 0.66, p < 0.0005 for target-present searches & n = 32, r = 0.56, p < 0.005 for target-absent searches).

(3) Definition of the boundaries and purpose of a new visual area in the brain requires circumspection, abundant and convergent evidence, and careful controls.

Even if the VH metric, as defined and calculated by the authors here, is a meaningful quantity, it is a bold claim that a large cortical area just anterior to LO is devoted to calculating this metric as its major task. Vision involves much more than target detection and symmetry detection. Cortex anterior to LO is bound to perform a much wider range of visual functionalities. If the reported correlations can be clarified and supported, it would be more circumspect to treat them as one byproduct of unknown visual processing in cortex anterior to LO, rather than treating them as the defining purpose for a large area of visual cortex.

We totally agree with you that reporting a new brain region would require careful interpretation and abundant and converging evidence. However, this requires many studies worth of work, and historically category-selective regions like the FFA have achieved consensus only after they were replicated and confirmed across many studies. We believe our proposal for the computation of a quantity like visual homogeneity is conceptually novel, and our study represents a first step that provides some converging evidence (through replicable results across different experiments) for such a region. We have reworked our manuscript to make this point clearer (Discussion, p 32).

Reviewer #3 (Public Review):

Summary:

This study proposes visual homogeneity as a novel visual property that enables observers perform to several seemingly disparate visual tasks, such as finding an odd item, deciding if two items are same, or judging if an object is symmetric. In Exp 1, the reaction times on several objects were measured in human subjects. In Exp 2, visual homogeneity of each object was calculated based on the reaction time data. The visual homogeneity scores predicted reaction times. This value was also correlated with the BOLD signals in a specific region anterior to LO. Similar methods were used to analyze reaction time and fMRI data in a symmetry detection task. It is concluded that visual homogeneity is an important feature that enables observers to solve these two tasks.

Thank you for your accurate and positive assessment.

Strengths:

(1) The writing is very clear. The presentation of the study is informative.

(2) This study includes several behavioral and fMRI experiments. I appreciate the scientific rigor of the authors.

We are grateful to you for your balanced assessment and constructive comments.

Weaknesses:

(1) My main concern with this paper is the way visual homogeneity is computed. On page 10, lines 188-192, it says: "we then asked if there is any point in this multidimensional representation such that distances from this point to the target-present and target-absent response vectors can accurately predict the target-present and target-absent response times with a positive and negative correlation respectively (see Methods)". This is also true for the symmetry detection task. If I understand correctly, the reference point in this perceptual space was found by deliberating satisfying the negative and positive correlations in response times. And then on page 10, lines 200-205, it shows that the positive and negative correlations actually exist. This logic is confusing. The positive and negative correlations emerge only because this method is optimized to do so. It seems more reasonable to identify the reference point of this perceptual space independently, without using the reaction time data. Otherwise, the inference process sounds circular. A simple way is to just use the mean point of all objects in Exp 1, without any optimization towards reaction time data.

We disagree with you since the same logic applies to any curve-fitting procedure. When we fit data to a straight line, we are finding the slope and intercept that minimizes the error between the data and the straight line, but we would hardly consider the process circular when a good fit is achieved – in fact we take it as a confirmation that the data can be fit linearly. In the same vein, we would not have observed a good fit to the data, if there did not exist any good reference point relative to which the distances of the target-present and target-absent search arrays predicted these response times.

In Section S2, we show that the visual homogeneity estimates for each object is strongly correlated with the average distance of each object to all other objects (r = 0.84, p<0.0005, Figure S1).

We have performed several additional analyses to confirm the generality of our results and to reject alternate explanations (see Results, p. 12, Section titled “Confirming the generality of visual homogeneity”). In particular, to confirm that the results we obtained are not due to overfitting, we performed a cross-validation analysis, where we removed all searches involving a particular image and predicted these response times using visual homogeneity. This too revealed a significant model correlation confirming that our results are not due to overfitting.

(2) Visual homogeneity (at least given the current from) is an unnecessary term. It is similar to distractor heterogeneity/distractor variability/distractor statics in literature. However, the authors attempt to claim it as a novel concept. The title is "visual homogeneity computations in the brain enable solving generic visual tasks". The last sentence of the abstract is "a NOVEL IMAGE PROPERTY, visual homogeneity, is encoded in a localized brain region, to solve generic visual tasks". In the significance, it is mentioned that "we show that these tasks can be solved using a simple property WE DEFINE as visual homogeneity". If the authors agree that visual homogeneity is not new, I suggest a complete rewrite of the title, abstract, significance, and introduction.

We respectfully disagree that visual homogeneity is an unnecessary term. Please see our comments to Reviewer 1 above. Just like saliency and memorability can be measured empirically, we propose that visual homogeneity can be empirically measured as the reciprocal of the target-absent search time in a search task, or as the reciprocal of the “same” response time in a same-different task. Understanding how these three quantities interact will require measuring them empirically for an identical set of images, which is beyond the scope of this study but an interesting possibility for future work.

(3) Also, "solving generic tasks" is another overstatement. The oddball search tasks, same-different tasks, and symmetric tasks are only a small subset of many visual tasks. Can this "quantitative model" solve motion direction judgment tasks, visual working memory tasks? Perhaps so, but at least this manuscript provides no such evidence. On line 291, it says "we have proposed that visual homogeneity can be used to solve any task that requires discriminating between homogeneous and heterogeneous displays". I think this is a good statement. A title that says "XXXX enable solving discrimination tasks with multi-component displays" is more acceptable. The phrase "generic tasks" is certainly an exaggeration.

Thank you for your suggestion. We have now replaced the term “generic tasks” with the term property-based tasks, which we feel is more appropriate and reflect the fact that oddball search, same-different and symmetry tasks all involve looking for a specific image property.

(4) If I understand it correctly, one of the key findings of this paper is "the response times for target-present searches were positively correlated with visual homogeneity. By contrast, the response times for target-absent searches were negatively correlated with visual homogeneity" (lines 204-207). I think the authors have already acknowledged that the positive correlation is not surprising at all because it reflects the classic target-distractor similarity effect. But the authors claim that the negative correlations in target-absent searches is the true novel finding.

(5) I would like to make it clear that this negative correlation is not new either. The seminal paper by Duncan and Humphreys (1989) has clearly stated that "difficulty increases with increased similarity of targets to nontargets and decreased similarity between nontargets" (the sentence in their abstract). Here, "similarity between nontargets" is the same as the visual homogeneity defined here. Similar effects have been shown in Duncan (1989) and Nagy, Neriani, and Young (2005). See also the inconsistent results in Nagy & Thomas, 2003, Vicent, Baddeley, Troscianko & Gilchrist, 2009. More recently, Wei Ji Ma has systematically investigated the effects of heterogeneous distractors in visual search. I think the introduction part of Wei Ji Ma's paper (2020) provides a nice summary of this line of research. I am surprised that these references are not mentioned at all in this manuscript (except Duncan and Humphreys, 1989).

You are right in noting that Duncan and Humphreys (1989) propose that searches are more difficult when nontargets are dissimilar. However, since our searches have identical distractors, the similarity between nontargets is always constant across target-absent searches, and therefore this cannot predict any systematic variation in target-absent search that is observed in our data. By contrast, our results explain both target-absent searches and target-present searches.

Thank you for pointing us to previous work. These studies show that it is not just the average distractor similarity but the statistics of the distractor similarity that drive visual search. However these studies do not explain why target-absent searches should vary systematically. 

(6) If the key contribution is the quantitative model, the study should be organized in a different way. Although the findings of positive and negative correlations are not novel, it is still good to propose new models to explain classic phenomena. I would like to mention the three studies by Wei Ji Ma (see below). In these studies, Bayesian observer models were established to account for trial-by-trial behavioral responses. These computational models can also account for the set-size effect, behavior in both localization and detection tasks. I see much more scientific rigor in their studies. Going back to the quantitative model in this paper, I am wondering whether the model can provide any qualitative prediction beyond the positive and negative correlations? Can the model make qualitative predictions that differ from those of Wei Ji's model? If not, can the authors show that the model can quantitatively better account for the data than existing Bayesian models? We should evaluate a model either qualitatively or quantitatively.

Thank you for pointing us to prior work by Wei Ji Ma. These studies systematically examined visual search for a target among heterogeneous distractors using simple parametric stimuli and a Bayesian modeling framework. By contrast, our experiments involve searching for single oddball targets among multiple identical distractors, so it is not clear to us that the Wei Ji Ma models can be easily used to generate predictions about these searches used in our study. 

We are not sure what you mean by offering quantitative predictions beyond positive and negative correlations. We have tried to explain systematic variation in target-present and target-absent response times using a model of how these decisions are being made. Our model explains a lot of systematic variation in the data for both types of decisions.

(7) In my opinion, one of the advantages of this study is the fMRI dataset, which is valuable because previous studies did not collect fMRI data. The key contribution may be the novel brain region associated with display heterogeneity. If this is the case, I would suggest using a more parametric way to measure this region. For example, one can use Gabor stimuli and systematically manipulate the variations of multiple Gabor stimuli, the same logic also applies to motion direction. If this study uses static Gabor, random dot motion, object images that span from low-level to high-level visual stimuli, and consistently shows that the stimulus heterogeneity is encoded in one brain region, I would say this finding is valuable. But this sounds like another experiment. In other words, it is insufficient to claim a new brain region given the current form of the manuscript.

We agree that parametric stimulus manipulations are important for studying early visual areas where stimulus dimensions are known (e.g. orientation, spatial frequency). Using parametric stimulus manipulations for more complex stimuli is fraught with issues because the underlying representation may not be encoding the dimensions being manipulated. This is the reason why we attempted to recover the underlying neural representation using dissimilarities measured using visual search, and then asked whether a decision making process operating on this underlying representation can explain how decisions are made. Therefore we disagree that parametric stimulus manipulations are the only way to obtain insight into such tasks.

We have proposed a quantitative model that explains how decisions about target present and absent can be made through distance-to-center computations on an underlying object representation. We feel that the behavioural and the brain imaging results strongly point to a novel computation that is being performed in a localized region in the brain. These results represent an important first step in understanding how complex, property-based tasks are performed by the brain. We have revised our manuscript to make this point clearer.

REFERENCES

- Duncan, J., & Humphreys, G. W. (1989). Visual search and stimulus similarity. Psychological Review, 96(3), 433-458. doi: 10.1037/0033-295x.96.3.433

- Duncan, J. (1989). Boundary conditions on parallel processing in human vision. Perception, 18(4), 457-469. doi: 10.1068/p180457

- Nagy, A. L., Neriani, K. E., & Young, T. L. (2005). Effects of target and distractor heterogeneity on search for a color target. Vision Research, 45(14), 1885-1899. doi: 10.1016/j.visres.2005.01.007

- Nagy, A. L., & Thomas, G. (2003). Distractor heterogeneity, attention, and color in visual search. Vision Research, 43(14), 1541-1552. doi: 10.1016/s0042-6989(03)00234-7

- Vincent, B., Baddeley, R., Troscianko, T., & Gilchrist, I. (2009). Optimal feature integration in visual search. Journal of Vision, 9(5), 15-15. doi: 10.1167/9.5.15

- Singh, A., Mihali, A., Chou, W. C., & Ma, W. J. (2023). A Computational Approach to Search in Visual Working Memory.

- Mihali, A., & Ma, W. J. (2020). The psychophysics of visual search with heterogeneous distractors. BioRxiv, 2020-08.

- Calder-Travis, J., & Ma, W. J. (2020). Explaining the effects of distractor statistics in visual search. Journal of Vision, 20(13), 11-11.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The authors have not made substantive changes to address my major concerns. Instead, they have responded with arguments about why their original manuscript was good as written. I did not find these arguments persuasive. Given that, I've left my public review the same, since it still represents my opinions about the paper. Readers can judge which viewpoints are more persuasive.

We respectfully disagree: we have tried our best to address your concerns with additional analysis wherever feasible, and by acknowledging any limitations.

Reviewer #3 (Recommendations For The Authors):

(1) As I mentioned above, please consider rewriting title, abstract, introduction, and significance. Please remove the word "visual homogeneity" and instead use distractor heterogeneity/distractor variability/distractor statistics as often used in literature.

To clarify, visual homogeneity is NOT the same as distractor homogeneity. Visual homogeneity refers to a distance-to-center computation and represents an image-computable property that can vary systematically even when all distractors are identical. By contrast distractor heterogeneity varies only when distractors are different from each other.

(2) Better to remove the phrase "generic tasks".

Thanks for your suggestions. We now refer to these tasks as property-based tasks. 

(3) Better to explicitly specify the predictions made by the quantitative model beyond positive and negative correlations.

The predictions of the quantitative model are to explain systematic variation in the response times. We are not sure what else is there to predict in the response times.

(4) If the quantitative model is the key contribution, better to highlight the details and algorithmic contribution of the model, and show the advantage of this model either qualitatively and quantitatively.

Please see our responses above. Our quantitative model explains behavior and brain imaging data on three disparate tasks – the same/different, oddball visual search and symmetry tasks. 

(5) If the new brain region is the key contribution, better to downplay the quantitative model.

Please see our responses above. Our quantitative model explains behavior and brain imaging data on three disparate tasks – the same/different, oddball visual search and symmetry tasks.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer 1 (Public Review):

The authors explain that an action potential that reaches an axon terminal emits a small electrical field as it ”annihilates”. This happens even though there is no gap junction, at chemical synapses. The generated electrical field is simulated to show that it can affect a nearby, disconnected target membrane by tens of microvolts for tenths of a microsecond. Longer effects are simulated for target locations a few microns away.

To simulate action potentials (APs), the paper does not use the standard Hodgkin-Huxley formalism because it fails to explain AP collision. Instead, it uses the Tasaki and Matsumoto (TM) model which is simplified to only model APs with three parameters and as a membrane transition between two states of resting versus excited. The authors expand the strictly binary, discrete TM method to a Relaxing Tasaki Model (RTM) that models the relaxation of the membrane potential after an AP. They find that the membrane leak can be neglected in determining AP propagation and that the capacitive currents dominate the process.

The strength of the work is that the authors identified an important interaction between neurons that is neglected by the standard models. A weakness of the proposed approach is the assumptions that it makes. For instance, the external medium is modeled as a homogeneous conductive medium, which may be further explored to properly account for biological processes.

The authors provide convincing evidence by performing experiments to record action potential propagation and collision properties and then developing a theoretical framework to simulate the effect of their annihilation on nearby membranes. They provide both experimental evidence and rigorous mathematical and computer simulation findings to support their claims. The work has the potential of explaining significant electrical interaction between nerve centers that are connected via a large number of parallel fibers.

We thank the reviewer for the distinct analysis of our work and the assessment that we ’identified an important interaction between neurons that is neglected by standard models’.

Indeed, we modeled the external (extracellular) medium as homogeneous conductive medium and, compared to real biological systems, this is a simplification. Our intention is to keep our formal model as general as possible, however, it can be extended to account for specific properties. Accessory structures at axon terminals (such as the pinceau at Purkinje cells) most likely evolved to shape ephaptic coupling. In addition, the extracellular medium is neither homogeneous nor isotropic, and to fully mimic a particular neural connection this has to be implemented in a model as well. We agree and look forward to see how specific modification of the external medium in biological systems will affect ephaptic coupling. We hope to facilitate progress on this question by providing our source code for further exploration. Using the tools that have been developed by the BRIAN community one can generate or import arbitrary complex cell morphologies (e.g. NeuroML files). Our source code adds the TM- and RTM model, which allows exploring the direct impact of extracellular properties on target neurons.

Reviewer 2 (Public Review):

In this study, the authors measured extracellular electrical features of colliding APs travelling in different directions down an isolated earthworm axon. They then used these features to build a model of the potential ephaptic effects of AP annihilation, i.e. the electrical signals produced by colliding/annihilating APs that may influence neighbouring tissue. The model was then applied to some different hypothetical scenarios involving synaptic connections. The conclusion was that an annihilating AP at a presynaptic terminal can ephaptically influence the voltage of a postsynaptic cell (this is, presumably, the ’electrical coupling between neurons’ of the title), and that the nature of this influence depends on the physical configuration of the synapse.

As an experimental neuroscientist who has never used computational approaches, I am unable to comment on the rigour of the analytical approaches that form the bulk of this paper. The experimental approaches appear very well carried out, and here I just have one query - an important assumption made is that the conduction velocity of anti- and orthodromically propagating APs is identical in every preparation, but this is never empirically/statistically demonstrated.

My major concern is with the conclusions drawn from the synaptic modelling, which, disappointingly, is never benchmarked against any synaptic data. The authors state in their Introduction that a ’quantitative physical description’ of ephaptic coupling is ’missing’, however, they do not provide such a description in this manuscript. Instead, modelled predictions are presented of possible ephaptic interactions at different types of synapses, and these are then partially and qualitatively compared to previous published results in the Discussion. To support the authors’ assertion that AP annihilation induces electrical coupling between neurons, I think they need to show that their model of ephaptic effects can quantitatively explain key features of experimental data pertaining to synaptic function. Without this, the paper contains some useful high-precision quantitative measurements of axonal AP collisions, some (I assume) high-quality modelling of these collisions, and some interesting theoretical predictions pertaining to synaptic interactions, but it does not support the highly significant implications suggested for synaptic function.

We thank the reviewer for highlighting the potential and the limitation of our model. We demonstrated with empirical data that measured conduction velocities of anti- and orthodromic propagating APs are indeed very similar and values are provided in Appendix 3 – table 1.

In order to address how our model ’of ephaptic effects can quantitatively explain key features of experimental data’, we used the measured modulation of AP rates in Purkinje fibers by Blot and Babour (2014) and our results are now included in the manuscript. In our model, we implemented the ephaptic coupling of the Basket cell (with an annihilating AP) and predicted the modulation of AP rate in the Purkinje cell. Our model predictions are compared to the measured modulation of AP-rates in Purkinje cells and is added as Fig. 5 to the main manuscript (line 264 to 284 ). With this example, we show that ephaptic coupling as described with our RTM model can quantitatively describe key features of experimental data. Both, the rapid inhibition and the rebound activity is described by our model with implementation of non-excitable parts at the pinceau of the Basket cell. Future, experimental research can use the provided formalism to investigate in more detail the ephaptic coupling in systems like the Mauthner cell and the Purkinje cell by exploring how accessory structures and concomitant physical parameters, e.g. the extracellular properties impact ephaptic coupling.

Reviewer 3 (Public Review):

This manuscript aims to exploit experimental measurements of the extracellular voltages produced by colliding action potentials to adjust a simplified model of action potential propagation that is then used to predict the extracellular fields at axon terminals. The overall rationale is that when solving the cable equation (which forms the substrate for models of action potential propagation in axons), the solution for a cable with a closed end can be obtained by a technique of superposition: a spatially reflected solution is added to that for an infinite cable and this ensures by symmetry that no axial current flows at the closed boundary. By this method, the authors calculate the expected extracellular fields for axon terminals in different situations. These fields are of potential interest because, according to the authors, their magnitude can be larger than that of a propagating action potential and may be involved in ephaptic signalling. The authors perform direct measurements of colliding action potentials, in the earthworm giant axon, to parameterise and test their model.

Although simplified models can be useful and the trick of exploiting the collision condition is interesting, I believe there are several significant problems with the rationale, presentation, and application, such that the validity and potential utility of the approach is not established.

Simplified model vs. Hogdkin and Huxley

The authors employ a simplified model that incorporates a two-state membrane (in essence resting and excited states) and adds a recovery mechanism. This generates a propagating wave of excitation and key observables such as propagation speed and action potential width (in space) can be adjusted using a small number of parameters. However, even if a Hodgkin-Huxley model does contain a much larger number of parameters that may be less easy to adjust directly, the basic formalism is known to be accurate and typical modifications of the kinetic parameters are very well understood, even if no direct characterisations already exist or cannot be obtained. I am therefore unconvinced by the utility of abandoning the HodgkinHuxley version.

In several places in the manuscript, the simplified model fits the data well whereas the Hodgkin-Huxley model deviates strongly (e.g. Fig. 3CD). This is unsatisfying because it seems unlikely that the phenomenon could not be modelled accurately using the HH formulation. If the authors really wish to assert that it is ”not suitable to predict the effects caused by AP [collision]” (p9) they need to provide a good deal more analysis to establish the mechanism of failure.

We are not as convinced as the reviewer that, at the current state of parameter estimation, the HH model is suited for predicting ephaptic coupling after ’adjusting’ parameters. There are strong arguments against such an approach. A major function of a model is to make testable predictions rather than to just mimic a biological phenomenon. The predictive power of a model heavily depends on how reasonable model parameters can be estimated or measured. As the reviewer correctly points out in the specific comments (”... the parameters adjusted to fit the model are the membrane capacitance and intracellular resistance. These have a physical reality and could easily be measured or estimated quite accurately...”), our model contains only parameters that can be assessed experimentally, thus it has a better predictive power compared to the HH model with a multitude of parameters for which ”no direct characterisations already exist or cannot be obtained” (citing reviewer from above).

Already the founders of the HH model were well aware of the limitations, as stated by Hodgkin and Huxley in 1952 (J Physiol 117:500–544):

An equally satisfactory description of the voltage clamp data could no doubt have been achieved with equations of very different form ... The success of the equations is no evidence in favour of the mechanism of permeability change that we tentatively had in mind when formulating them.

A catchy but sloppy description for the problem of overfitting with too many parameters is given by the quote of John von Neumann: With four parameters I can fit an elephant, and with five I can make him wiggle his trunk.

We do not rule out the possibility that the HH model eventually can be used to predict ephaptic coupling. However, at the moment, parameter estimation for the HH model prevents its usability for predicting ephaptic coupling.

(In)applicability of the superposition principle

The reflecting boundary at the terminal is implemented using the symmetry of the collision of action potentials. However, at a closed cable there is no reflecting boundary in the extracellular space and this implied assumption is particularly inappropriate where the extracellular field is one objective of the modelling, as here. I believe this assumption is not problematic for the calculation of the intracellular voltage, because extracellular voltage gradients can usually be neglected1, but the authors need to explain how the issue was dealt with for the calculation of the extracellular fields of terminals. I assume they were calculated from the membrane currents of one-half of the collision solution, but this does not seem to be explained. It might be worth showing a spatial profile of the calculated field.

We disagree with the reviewer’s statement ’...at a closed cable there is no reflecting boundary in the extracellular space and this implied assumption is particularly inappropriate...’. We do not imply this assumption in our model! We do not assume any symmetry or boundary condition in the extracellular space. Instead, the extracellular field is calculated for an infinite homogeneous volume conductor (Eq.

6).

We conduct separate calculations for (1) source membrane current, (2) resulting extracellular field, and (3) impact upon a target neuron. The boundary condition used for our calculations only refers to the axial current being zero at the axon terminal. Consequently all the internal current that enters the last compartment must leave the last compartment as membrane current and contributes to the extracellular current and field.

The extracellular field around the axon terminal is not symmetric, as can be seen by it’s impact upon a target in Figure 4—figure supplement 1 which is also not symmetric. The symmetry of the extracellular field when APs are colliding (Cf. symmetry in Fig 1C) is merly the result of the symmetric stimulation and counterpropagation of two APs. We now are describing more specifically the bounday condition for colliding and terminating APs already in the introduction: ’A suitable boundary condition (intracellular, axial current equals zero) can be generated experimentally by a collision of two counter-propagating APs ... Within any cable model, the very same boundary condition also exists within the axon at the synaptic terminal due to the broken translation symmetry for the current loops ...’ Later, at the result section (Discharge of colliding APs), we continue with ’AP propagation is blocked when the axial current is shut down at a boundary condition, e.g. by reaching the axon terminal or by AP collision....’ and implement this condition in our calculations for the axon terminals.

Missing demonstrations

Central analytical results are stated rather brusquely, notably equations (3) and (4) and the relation between them. These merit an expanded explanation at the least. A better explanation of the need for the collision measurements in parameterising the models should also be provided.

We thank the reviewer for pointing out the insufficient explanation of the equations 3 and 4. We rephrased the paragraph ’Discharge of colliding APs’ in order to clarify the origin and the function of the two equations (eq. 3: how much charge is expelled and eq. 4: the resulting extracellular potential that is used for model validation).

Later, in the Discussion, we rephrased the paragraph where we describe the annihilation process and explain further that one term of eq. 4 sometimes is refered to ’activating function’ when using microelectrodes for stimulation.

With respect to the ’explanation of the need for the collision measurement’, we think that the explanations we give at several locations in the manuscript are sufficient as is. We explain and elaborate in the introduction: ’We explore the behaviour of APs at boundaries ... In this study, we first focus on collisions of APs. Our experimental observation of colliding APs provides unique access to the spatial profile of the extracellular potential around APs that are blocked by collisions and thus annihilate..... Recording propagating APs allows to determine both the propagation velocity and the amplitude of the extracellular electric potentials. The collision experiment provides additional information ... In the results we recall: ’The width of the collision is a measure of the characteristic length λ⋆ of the AP and is uniquely revealed by a collision sweep experiment.’

Adjusted parameters

I am uncomfortable that the parameters adjusted to fit the model are the membrane capacitance and intracellular resistance. These have a physical reality and could easily be measured or estimated quite accurately. With a variation of more than 20-fold reported between the different models in Appendix 2 we can be sure that some of the models are based upon quite unrealistic physical assumptions, which in turn undermines confidence in their generality.

The fact that the parameters of our model have physical realities is clearly in favor of our models. We rephrased the legend of the table, now explaining the procedure for the model fitting and the rational behind. Although the values of g⋆ can differ by a factor of 15 and the resulting amplitude is very different, the relationship ri cm \= vpλ⋆ is very similar, independently of the model used and this confirms our analytical framework.

p8 - the values of both the extracellular (100 Ohm m) and intracellular resistivity (1 Ohm m) appear to be in error, especially the former.

We have the following justification for the resistivity values we used. For the intracellular resistivity, literature values range from 0.4 - 1.5 Ohm m, and therefore we selected 1 Ohm m. See: Carpenter et al (1975) doi: 10.1085/jgp.66.2.139; Cole et al (1975) doi: 10.1085/jgp.66.2.133; Bekkers (2014) doi: 10.1007/978-1-46147320-6 35-2.

Estimating extracellular resistivity is less straight forward, since it depends crucially on the structure around the synapse which consists of conducting saline and insulating fatty tissue. Ranges from 3 to 600 Ohm m are reported (Linden et al (2011) doi: 10.1016/j.neuron.2011.11.006) and Bakiri et al (2011) doi: 10.1113/jphysiol.2010.201376). Weiss et al (2008; doi: 10.1073/pnas.0806145105) report extracellular resistivities in the Mauthner Cap between 50-600 Ohm m in SI. Since the pinceau is structurally similar to the Mauthner cells axon cap, we argue that a value of 100 Ohm m is a reasonable choice for our calculations. Additionally, we derived a value from Blot and Barbour (doi:c10.1038/nn.3624), rephrased the paragraph in the main text and added our calculation to the supplementary material (Appendix 1).

(In)applicability to axon terminals

The rationale of the application of the collision formalism to axon terminals is somewhat undermined by the fact that they tend not to be excitable. There is experimental evidence for this in the Calyx of Held and the cerebellar pinceau.

The solution found via collision is therefore not directly applicable in these cases.

We do not agree with the reviewer’s statement that ’the solution found via collision is (therefore) not directly applicable...’. Our model is well suited for application on axon terminals that are not excitable, e.g. the pinceau of the basket cell, as the reviewer points out. We have included a calculation for this case and present the results in the new Fig. 5 (main text line 264 to 284 ).

Comparison with experimental data

More effort should be made to compare the modelling with the extracellular terminal fields that have been reported in the literature.

As outlined above (see: Reponse to reviewer 2), we now compare directly the predictions of our models with measured modulation of AP rates in Purkinje fibers (Blot and Babour 2014) and our results are included in the manuscript (Fig. 5 and main text line 264 to 284). See also our response to reviewer 2 in which we address how our model ’of ephaptic effects can quantitatively explain key features of experimental data’.

Choice of term ”annihilation”

The term annihilation does not seem wholly appropriate to me. The dictionary definitions are something along the lines of complete destruction by an external force or mutual destruction, for example of an electron and a positron. I don’t think either applies exactly here. I suggest retaining the notion of collision which is well understood in this context.

Experimentally, we generated a collision of APs and showed that colliding APs dissapear and do not pass each other. For this process the term annihilation is used in our and in other studies (see e.g. Berg et al (2017) doi: 10.1103/PhysRevX.7.028001; Johnson et al (2018) doi: 10.3389/fphys.2018.00779; Follmann (2015) doi: 10.1103/PhysRevE.92.032707; Shrivastava et al (2018) doi: 10.1098/rsif.2017.0803). The physical processes involved in the termination of an AP at a closed end are essentially identical to those of two colliding APs. This we think justifies using the term annihilation for those processes.

Recommendations for the authors:

We believe the work is of high quality and should motivate future experimental work. We are including the review comments here for your information. The main piece of feedback we are offering is that the broad claims need to be adjusted to the strength of evidence provided: as is, the manuscript provides compelling predictions but the claim that these predictions are in full agreement with data remains to be substantiated. A technical concern raised by the reviewers is that the reflecting boundary condition may need further justification. The authors may wish to respond to this issue in a rebuttal and/or adjust the manuscript as necessary.

We substantiated our claim that our predictions are in full agreement with experimental data. We added to the manuscript a section in which we compare our models’ predictions to published, experimental data. To this aim, we extracted date from the publication of Blot and Babour (2014), we elaborated on the parameters used and run our model accordingly. We added to the Results/Model of ephaptic coupling a paragraph on ’The modulation of activity in Purkinje cells...’ (line 264), where we describe our results and we also included another figure to the main text for illustration (Fig. 5).

We clarified the term ’boundary condition’ by rephrasing parts of the introduction and we explain the rational behind in ’Discharge of colliding APs (...AP propagation is blocked when axial current is shut down...) and in ’Model of ephaptic coupling (Within any cable model, the same boundary...). See also our response to the general comments of reviewer 3 above.

Reviewer 1 (Recommendations For The Authors):

Major:

Accessing data and code requires signing in, which should not be required. The link provided also seems to be not accessible yet - could be pending review.

The repository is now publicly availible. We did provide an access code within the letter to the editor, this code is no longer required.

Line 74: how about morphology? Authors should clarify and emphasize in the introduction that the TM model is a spatially continuous model with partial differential equations as opposed to discrete morphological models to simulate HH equations.

The reviewer is correct that the TM model is continous. However, so is the HH model. The difference between HH and TM is only that the TM model can be solved analytically, which yields a spatially homogeneous analytical solution. It should be noted that this analytical solution can only be valid for a homogeneous (therefore infinite) nerve. Every numerical computation, be it HH or TM, requires a finite number of discrete compartments. In our calculations, we used identical compartment models for HH, TM and RTM model. In each compartment, the differential equations are solved numerically. Since there is no fundamental difference between these models, we obstain from changing the text.

Minor:

Major typo: ventral nerve cord, not ”chord”. Repeated in several places.

Thank you for indicating this typo to us.

Line 25: inhibition, excitation, and modulation?

We changed the line to: ... leads to modulation, e.g. excitation or inhibition

Line 70: better term for ”length” of AP would be ”duration”. Also, the sentence could be simplified to use either ”its” or ”of the AP”

Space and time are not interchangable. Thus, the term lenght can not be replaced by duration. We simplified the structure of the sentence as suggested.

Fig 1A/B: it’s strange that panel B precedes panel A.

Exchanged

Fig 1C: don’t see the ”horizontal line”; also regarding ”The recording was at a medial position”, the caption is not clear until one reads the main text.

We changed the legend to: ... The collision is captured in the recording line at y-position 0 mm, while orthodromic propagation is at the top and antidromic propagation is at the bottom. (D) The peak amplitude as a function of the distance to the collision. Examples of four sweeps at three positions along the nerve cord....

Line 127: the per distance measures could be named as ”specific” conductivity, etc.

We explicitly provide the units thereby defining the quantities unambigously.

Line 176: typo ”ad-hoc”.

Thank you.

Fig 4B: should clarify that the circle in the schematic is not the soma but a synaptic bouton.

We rephrased to ’...(B,C) when the AP is annihilating at a bouton of a neuron terminal (upper neuron in end-to-shaft geometry, similar to the Basket cell–Purkinje cell synapse)...’, and we added a label to Fig 4B.

Reviewer 2 (Recommendations For The Authors):

Can the authors’ model be quantitatively compared with experimental data of ephaptic interactions at synapses (e.g. the Blot & Barbour study described in the Discussion)?

We did so as outlined in our response to the reviewer above.

Can statistical evidence be provided that the velocities of anti- and orthodromic APs are indeed identical in the earthworm nerve recordings?

These data and statistics are available in Appendix 2, now 3 – table 1

Why not reorder ABCD in Fig1 so the subpanels run from left to right?

We adjusted the labels accordingly.

Author response:

Reviewer #1 (Public review):

Summary:

This paper contains what could be described as a "classic" approach towards evaluating a novel taste stimuli in an animal model, including standard behavioral tests (some with nerve transections), taste nerve physiology, and immunocytochemistry of the tongue. The stimulus being tested is ornithine, from a class of stimuli called "kokumi", which are stimuli that enhance other canonical tastes, increasing essentially the hedonic attributes of these other stimuli; the mechanism for ornithine detection is thought to be GPRC6A receptors expressed in taste cells. The authors showed evidence for this in an earlier paper with mice; this paper evaluates ornithine taste in a rat model.

Strengths:

The data show the effects of ornithine on taste: in two-bottle and briefer intake tests, adding ornithine results in a higher intake of most, but not all, stimuli tests. Bilateral nerve cuts or the addition of GPRC6A antagonists decrease this effect. Small effects of ornithine are shown in whole-nerve recordings.

Weaknesses:

The conclusion seems to be that the authors have found evidence for ornithine acting as a taste modifier through the GPRC6A receptor expressed on the anterior tongue. It is hard to separate their conclusions from the possibility that any effects are additive rather than modulatory. Animals did prefer ornithine to water when presented by itself. Additionally, the authors refer to evidence that ornithine is activating the T1R1-T1R3 amino acid taste receptor, possibly at higher concentrations than they use for most of the study, although this seems speculative. It is striking that the largest effects on taste are found with the other amino acid (umami) stimuli, leading to the possibility that these are largely synergistic effects taking place at the tas1r receptor heterodimer.

We would like to thank Reviewer #1 for the valuable comments. Our basis for considering ornithine as a taste modifier stems from our observation that a low concentration of ornithine (1 mM), which does not elicit a preference on its own, enhances the preference for umami substances, sucrose, and soybean oil through the activation of the GPRC6A receptor. Notably, this receptor is not typically considered a taste receptor. The reviewer suggested that the enhancement of umami taste might be due to potentiation occurring at the TAS1R receptor heterodimer. However, we propose that a different mechanism may be at play, as an antagonist of GPRC6A almost completely abolished this enhancement. In the revised manuscript, we will endeavor to provide additional information on the role of ornithine as a taste modifier acting through the GPRC6A receptor.

Reviewer #2 (Public review):

Summary:

The authors used rats to determine the receptor for a food-related perception (kokumi) that has been characterized in humans. They employ a combination of behavioral, electrophysiological, and immunohistochemical results to support their conclusion that ornithine-mediated kokumi effects are mediated by the GPRC6A receptor. They complemented the rat data with some human psychophysical data. I find the results intriguing, but believe that the authors overinterpret their data.

Strengths:

The authors examined a new and exciting taste enhancer (ornithine). They used a variety of experimental approaches in rats to document the impact of ornithine on taste preference and peripheral taste nerve recordings. Further, they provided evidence pointing to a potential receptor for ornithine.

Weaknesses:

The authors have not established that the rat is an appropriate model system for studying kokumi. Their measurements do not provide insight into any of the established effects of kokumi on human flavor perception. The small study on humans is difficult to compare to the rat study because the authors made completely different types of measurements. Thus, I think that the authors need to substantially scale back the scope of their interpretations. These weaknesses diminish the likely impact of the work on the field of flavor perception.

We would like to thank Reviewer #2 for the valuable comments and suggestions. Regarding the question of whether the rat is an appropriate model system for studying kokumi, we have chosen this species for several reasons: it is readily available as a conventional experimental model for gustatory research; the calcium-sensing receptor (CaSR), known as the kokumi receptor, is expressed in taste bud cells; and prior research has demonstrated the use of rats in kokumi studies involving gamma Glu-Val-Gly (Yamamoto and Mizuta, Chem. Senses, 2022). We acknowledge that fundamentally different types of measurements were conducted in the human psychophysical study and the rat study. Kokumi can indeed be assessed and expressed in humans; however, we do not currently have the means to confirm that animals experience kokumi in the same way that humans do. Therefore, human studies are necessary to evaluate kokumi, a conceptual term denoting enhanced flavor, while animal studies are needed to explore the potential underlying mechanisms of kokumi. We believe that a combination of both human and animal studies is essential, as is the case with research on sugars. While sugars are known to elicit sweetness, it is unclear whether animals perceive sweetness identically to humans, even though they exhibit a strong preference for sugars. In the revised manuscript, we will incorporate additional information to address the comments raised by the reviewer. We will also carefully review and revise our previous statements to ensure accuracy and clarity.

Reviewer #3 (Public review):

Summary:

In this study, the authors set out to investigate whether GPRC6A mediates kokumi taste initiated by the amino acid L-ornithine. They used Wistar rats, a standard laboratory strain, as the primary model and also performed an informative taste test in humans, in which miso soup was supplemented with various concentrations of L-ornithine. The findings are valuable and overall the evidence is solid. L-Ornithine should be considered to be a useful test substance in future studies of kokumi taste and the class C G protein-coupled receptor known as GPRC6A (C6A) along with its homolog, the calcium-sensing receptor (CaSR) should be considered candidate mediators of kokumi taste.

Strengths:

The overall experimental design is solid based on two bottle preference tests in rats. After determining the optimal concentration for L-Ornithine (1 mM) in the presence of MSG, it was added to various tastants, including inosine 5'-monophosphate; monosodium glutamate (MSG); mono-potassium glutamate (MPG); intralipos (a soybean oil emulsion); sucrose; sodium chloride (NaCl); citric acid and quinine hydrochloride. Robust effects of ornithine were observed in the cases of IMP, MSG, MPG, and sucrose, and little or no effects were observed in the cases of sodium chloride, citric acid, and quinine HCl. The researchers then focused on the preference for Ornithine-containing MSG solutions. The inclusion of the C6A inhibitors Calindol (0.3 mM but not 0.06 mM) or the gallate derivative EGCG (0.1 mM but not 0.03 mM) eliminated the preference for solutions that contained Ornithine in addition to MSG. The researchers next performed transections of the chord tympani nerves (with sham operation controls) in anesthetized rats to identify the role of the chorda tympani branches of the facial nerves (cranial nerve VII) in the preference for Ornithine-containing MSG solutions. This finding implicates the anterior half-two thirds of the tongue in ornithine-induced kokumi taste. They then used electrical recordings from intact chorda tympani nerves in anesthetized rats to demonstrate that ornithine enhanced MSG-induced responses following the application of tastants to the anterior surface of the tongue. They went on to show that this enhanced response was insensitive to amiloride, selected to inhibit 'salt tastant' responses mediated by the epithelial Na+ channel, but eliminated by Calindol. Finally, they performed immunohistochemistry on sections of rat tongue demonstrating C6A positive spindle-shaped cells in fungiform papillae that partially overlapped in its distribution with the IP3 type-3 receptor, used as a marker of Type-II cells, but not with (i) gustducin, the G protein partner of Tas1 receptors (T1Rs), used as a marker of a subset of type-II cells; or (ii) 5-HT (serotonin) and Synaptosome-associated protein 25 kDa (SNAP-25) used as markers of Type-III cells.

Weaknesses:

The researchers undertook what turned out to be largely confirmatory studies in rats with respect to their previously published work on Ornithine and C6A in mice (Mizuta et al Nutrients 2021).

The authors point out that animal models pose some difficulties of interpretation in studies of taste and raise the possibility in the Discussion that umami substances may enhance the taste response to ornithine (Line 271, Page 9). They miss an opportunity to outline the experimental results from the study that favor their preferred interpretation that ornithine is a taste enhancer rather than a tastant.

At least two other receptors in addition to C6A might mediate taste responses to ornithine: (i) the CaSR, which binds and responds to multiple L-amino acids (Conigrave et al, PNAS 2000), and which has been previously reported to mediate kokumi taste (Ohsu et al., JBC 2010) as well as responses to Ornithine (Shin et al., Cell Signaling 2020); and (ii) T1R1/T1R3 heterodimers which also respond to L-amino acids and exhibit enhanced responses to IMP (Nelson et al., Nature 2001). While the experimental results as a whole favor the authors' interpretation that C6A mediates the Ornithine responses, they do not make clear either the nature of the 'receptor identification problem' in the Introduction or the way in which they approached that problem in the Results and Discussion sections. It would be helpful to show that a specific inhibitor of the CaSR failed to block the ornithine response. In addition, while they showed that C6A-positive cells were clearly distinct from gustducin-positive, and thus T1R-positive cells, they missed an opportunity to clearly differentiate C6A-expressing taste cells and CaSR-expressing taste cells in the rat tongue sections.

It would have been helpful to include a positive control kokumi substance in the two-bottle preference experiment (e.g., one of the known gamma-glutamyl peptides such as gamma-glu-Val-Gly or glutathione), to compare the relative potencies of the control kokumi compound and Ornithine, and to compare the sensitivities of the two responses to C6A and CaSR inhibitors.

The results demonstrate that enhancement of the chorda tympani nerve response to MSG occurs at substantially greater Ornithine concentrations (10 and 30 mM) than were required to observe differences in the two bottle preference experiments (1.0 mM; Figure 2). The discrepancy requires careful discussion and if necessary further experiments using the two-bottle preference format.

We would like to thank Reviewer #3 for the valuable comments and helpful suggestions. We propose that ornithine has two stimulatory actions: one acting on GPRC6A, particularly at lower concentrations, and another on amino acid receptors such as T1R1/T1R3 at higher concentrations. Consequently, ornithine is not preferable at lower concentrations but becomes preferable at higher concentrations. For our study on kokumi, we used a low concentration (1 mM) of ornithine. The possibility mentioned in the Discussion that 'the umami substances may enhance the taste response to ornithine' is entirely speculative. We will reconsider including this description in the revised version. As the reviewer suggested, in addition to GPRC6A, ornithine may bind to CaSR and/or T1R1/T1R3 heterodimers. However, we believe that ornithine mainly binds to GPRC6A, as a specific inhibitor of this receptor almost completely abolished the enhanced response to umami substances, and our immunohistochemical study indicated that GPRC6A-expressing taste cells are distinct from CaSR-expressing taste cells (see Supplemental Fig. 3). We conducted essentially the same experiments using gamma-Glu-Val-Gly in Wistar rats (Yamamoto and Mizuta, Chem. Senses, 2022) and compared the results in the Discussion. The reviewer may have misunderstood the chorda tympani results: we added the same concentration (1 mM) used in the two-bottle preference test to MSG (Fig. 5-B). Fig. 5-A shows nerve responses to five concentrations of plain ornithine. In the revised manuscript, we will strive to provide more precise information reflecting the reviewer’s comments.

Reviewer #2 (Public review):

Summary:

Koh et al. report an interesting manuscript studying dopamine binding in the lateral accumbens shell of rats across the course of conditioned taste aversion. The question being asked here is how does the dopamine system respond to aversion? The authors take advantage of unique properties of taste aversion learning (notably, within-subjects remapping of valence to the same physical stimulus) to address this.

They combine a well controlled behavioural design (including key, unpaired controls) with fibre photometry of dopamine binding via GrabDA and of dopamine neuron activity by gCaMP, careful analyses of behaviour (e.g., head movements; home cage ingestion), the authors show that, 1) conditioned taste aversion of sucrose suppresses the activity of VTA dopamine neurons and lateral shell dopamine binding to subsequent presentations of the sucrose tastant; 2) this pattern of activity was similar to the innately aversive tastant quinine; 3) dopamine responses were negatively correlated with behavioural (inferred taste reactivity) reactivity; and 4) dopamine responses tracked the contingency of between sucrose and illness because these responses recovered across extinction of the conditioned taste aversion.

Strengths:

There are important strengths here. The use of a well-controlled design, the measurement of both dopamine binding and VTA dopamine neuron activity, the inclusion of an extinction manipulation; and the thorough reporting of the data. I was not especially surprised by these results, but these data are a potentially important piece of the dopamine puzzle (e.g., as the authors note, salience-based argument struggles to explain these data).

Weaknesses for consideration:

(1) The focus here is on the lateral shell. This is a poorly investigated region in the context of the questions being asked here. Indeed, I suspect many readers might expect a focus on the medial shell. So, I think this focus is important. But, I think it does warrant greater attention in both the introduction and discussion. We do know from past work that there can be extensive compartmentalisation of dopamine responses to appetitive and aversive events and many of the inconsistent findings in the literature can be reconciled by careful examination of where dopamine is assessed. I do think readers would benefit from acknowledgement this - for example it is entirely reasonable to suppose that the findings here may be specific to the lateral shell.

(2) Relatedly, I think readers would benefit from an explicit rationale for studying the lateral shell as well as consideration of this in the discussion. We know that there are anatomical (PMID: 17574681), functional (PMID: 10357457), and cellular (PMID: 7906426) differences between the lateral shell and the rest of the ventral striatum. Critically, we know that profiles of dopamine binding during ingestive behaviours there can be highly dissimilar to the rest of ventral striatum (PMID: 32669355). I do think these points are worth considering.

(3) I found the data to be very thoughtfully analysed. But in places I was somewhat unsure:<br /> (a) Please indicate clearly in the text when photometry data show averages across trials versus when they show averages across animals.<br /> (b) I did struggle with the correlation analyses, for two reasons.<br /> (i) First, the key finding here is that the dopamine response to intraoral sucrose is suppressed by taste aversion. So, this will significantly restrict the range of dopamine transients, making interpretation of the correlations difficult.

(ii) Second, the authors report correlations by combining data across groups/conditions. I understand why the authors have done this, but it does risk obscuring differences between the groups. So, my question is: what happens to this trend when the correlations are computed separately for each group? I suspect other readers will share the same question. I think reporting these separate correlations would be very helpful for the field - regardless of the outcome.

(4) Figure 1A is not as helpful as it might be. I do think readers would expect a more precise reporting of GCaMP expression in TH+ and TH- neurons. I also note that many of the nuances in terms of compartmentalisation of dopamine signalling discussed above apply to ventral tegmental area dopamine neurons (e.g. medial v lateral) and this is worth acknowledging when interpreting.

This shows how digital footprints impact our lives. It raises important questions like how much of our private information we unconsciously trade for convenience. Many people might underestimate the long-term implications of leaving digital traces, such as identity theft or targeted manipulation.

Author response:

The following is the authors’ response to the previous reviews.

Minor Concern (Original Comment 1):

“We think that this is sufficient to address our concern. Some citations may be in order to underpin the new text.”

We appreciate the reviewer’s assessment that the revised text clarifies the complexity of the upstream circuitry beyond the retina, including inputs from the thalamus. As recommended, we have now included additional citations in the revised manuscript to support these points.

Major Concern (Original Comment 5):

“We do not feel that this important concern has been addressed. The stats are definitively negative. There is no statistical evidence from these data that multisensory integration is occurring in this assay. The anesthesia, paralysis, and low n may provide explanations for this negative result, but it is still a negative result (p=0.5269). To show two examples of multisensory integration for subthreshold stimuli fits the narrative, but this result is not supported. Examples where individual stimuli caused APs (and combined stimuli did not) also occurred, presumably, and at a rate that is statistically indistinguishable to the examples shown in Figure 5. As such, if results from this assay are going to be in the manuscript, acoustic-only and tectum-only examples should be shown as well, although they would not fit the narrative. To be meaningful, this experiment would have to show that multisensory integration is happening in this circuit. Frustrating though it must be, the experiment has given a negative result to that question.”

We understand the reviewer’s concern regarding Figure 5C and the firing of action potentials (APs) in response to multisensory stimuli. We acknowledge that our assay is not suited to answer this question definitively and that our results do not provide statistical support for this hypothesis. In response, we have removed the examples previously shown in Figure 5C, along with the related description in the Results section (lines 420–426), to avoid implying unsupported integration in suprathreshold conditions.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, the authors describe the construction of an extremely large-scale anatomical model of juvenile rat somatosensory cortex (excluding the barrel region), which extends earlier iterations of these models by expanding across multiple interconnected cortical areas. The models are constructed in such a way as to maintain biological detail from a granular scale - for example, individual cell morphologies are maintained, and synaptic connectivity is founded on anatomical contacts. The authors use this model to investigate a variety of properties, from cell-type specific targeting (where the model results are compared to findings from recent large-scale electron microscopy studies) to network metrics. The model is also intended to serve as a platform and resource for the community by being a foundation for simulations of neuronal circuit activity and for additional anatomical studies that rely on the detailed knowledge of cellular identity and connectivity.

Strengths:

As the authors point out, the combination of scale and granularity of their model is what makes this study valuable and unique. The comparisons with recent electron microscopy findings are some of the most compelling results presented in the study, showing that certain connectivity patterns can arise directly from the anatomical configuration, while other discrepancies highlight where more selective targeting rules (perhaps based on molecular cues) are likely employed. They also describe intriguing effects of cortical thickness and curvature on circuit connectivity and characterize the magnitude of those effects on different cortical layers.

The detailed construction of the model is drawn on a wide range of data sources (cellular and synaptic density measures, neuronal morphologies, cellular composition measures, brain geometry, etc.) that are integrated together; other data sources are used for comparison and validation. This consolidation and comparison also represent a valuable contribution to the overall understanding of the modeled system.

We thank the reviewer for the kind comments.

Weaknesses:

The scale of the model, which is a primary strength, also can carry some drawbacks. In order to integrate all the diverse data sources together, many specific decisions must be made about, for example, translating findings from different species or regions to the modeled system, or deciding which aspects of the system can be assumed to be the same and which should vary. All these decisions will have effects on the predicted results from the model, which could limit the types of conclusions that can be made (both by the others and by others in the community who may wish to use the model for their own work).

We agree that this is a downside of the principle of biophysically detailed modeling that is best addressed by continuous refinement in collaboration with the community. We would like to once again invite any interested party to participate in this process.

As an example, while it is interesting that broad brain geometry has effects on network structure (Figure 7), it is not clear how those effects are actually manifested. I am not sure if some of the effects could be due to the way the model is constructed - perhaps there may be limited sets of morphologies that fit into columns of particular thicknesses, and those morphologies may have certain idiosyncrasies that could produce different statistics of connectivities where they are heavily used. That may be true to biology, but it may also be somewhat artifactual if, for example, the only neurons in the library that fit into that particular part of the cortex differ from the typical neurons that are actually found in that region (but may not have been part of the morphological sampling).

We agree that the limited pool of morphological reconstructions can lead to artifactual results in the way the reviewer pointed out. To investigate that hypothesis, we added a supplementary figure (S14) where we characterize (1): to what degree the morphological composition of a columnar subvolume reflects the overall composition of the model; and (2): The level of morphological diversity in each columnar subvolume. We discuss the results at the end of section 2.6. Briefly, while we cannot fully rule out the possibility of an artificial result, we found a high and virtually uniform level of morphological diversity in all columns and layers. This makes it unlikely that individual idiosyncratic morphologies strongly affect the local connectivity. However, we acknowledge that the minimum level of morphological diversity required is unknown. We believe that at this stage all we can do is characterize this and leave final interpretation to the reader.

I also wonder how much the assumption that the layers have the same relative thicknesses everywhere in the cortex affects these findings, since layer thicknesses do in fact vary across the cortex.

We agree that layer thickness variation would affect circuit properties. Variability of layer thickness can be split into two components: variability stemming from differences in total thickness, which our model covers, and variability of relative, i.e., normalized layer thickness, which we miss. In this region of cortex, though, data on the relative thickness of cortical layers is sparse. The Waxholm Atlas does not distinguish somatosensory cortical layers in its labels [Kleven et al, 2023]. Yusufoğulları (2015) compares layer thicknesses of rat hindlimb and barrel field regions. After normalization against total thickness, the relative difference increased towards the superficial layers from 0 in L6 to 33% in L1. Variability of normalized thicknesses within developed rat barrel cortex, based on layer boundaries reported in Narayanan et al. (2017) vary by 2% to 5% over approximately 2 mm. One major effect of such variability would be to scale the number of neurons in a given layer locally by the corresponding factors. For comparison, the resulting variability in neuron counts due to differences in conicality (Fig. 7D1) was around +-25%. A further effect of variable relative layer thickness would be its impact on the selection of suitable morphologies to be placed in the volume.

In summary, adjustment of layer thickness is a refinement which should be done in future versions of the model, once more data is available. The discussion section has been updated to acknowledge this limitation. However, as outlined at the beginning of this point-by-point reply, we will not conduct such updates to the model in the context of this manuscript, as it describes the version of the model used for a number of follow-up studies.

In addition, the complexity of the model means that some complicated analyses and decisions are only presented in this manuscript with perhaps a single panel and not much textual explanation. I find, for example, that the panels of Figure S2 seem to abstract or simplify many details to the point where I am not clear about what they are actually illustrating - how does Figure S2D represent the results of "the process illustrated in B"? Why are there abrupt changes in connectivity at region borders (shown as discontinuous colors), when dendrites and axons span those borders and so would imply interconnectivity across the borders? What do the histograms in E1 and E2 portray, and how are they related to each other?

We apologize for the confusion. We have updated the figure caption of Figure S2 to better explain its contents.

Overall, the model presented in this study represents an enormous amount of work and stands as a unique resource for the community, but also is made somewhat unwieldy for the community to employ due to the weight of its manifold specific construction decisions, size, and complexity.

Reviewer #2 (Public Review):

Summary:

The authors build a colossal anatomical model of juvenile rat non-barrel primary somatosensory cortex, including inputs from the thalamus. This enhances past models by incorporating information on the shape of the cortex and estimated densities of various types of excitatory and inhibitory neurons across layers. This is intended to enable an analysis of the micro- and mesoscopic organisation of cortical connectivity and to be a base anatomical model for large-scale simulations of physiology.

Strengths:

• The authors incorporate many diverse data sources on morphology and connectivity.

• This paper takes on the challenging task of linking micro- and mesoscale connectivity.

• By building in the shape of the cortex, the authors were able to link cortical geometry to connectivity. In particular, they make an unexpected prediction that cortical conicality affects the modularity of local connectivity, which should be testable.

• The author's analysis of the model led to the interesting prediction that layer 5 neurons connect local modules, which may be testable in the future, and provide a basis to link from detailed anatomy to functional computations.

• The visualisation of the anatomy in various forms is excellent.

• A subnetwork of the model is openly shared (but see question below).

We thank the reviewer for their kind comments.

Weaknesses:

• Why was non-barrel S1 of the juvenile rat cortex selected as the target for this huge modelling effort? This is not explained.

We have added an explanation of this decision to the third paragraph of the introduction.

• There is no effort to determine how specific or generalisable the findings here are to other parts of the cortex. Although there is a link to physiological modelling in another paper, there is no clear pathway to go from this type of model to understand how the specific function of the modelled areas may emerge here (and not in other cortical areas).

With respect to generality against specific findings, our philosophy is as follows: Despite the fact that most of our source data comes from juvenile rat somatosensory cortex, we also had to generalize many data sources across organisms, ages or regions. Hence, in this iteration we focused on investigating the general features of the (multi-region) mammalian cortex, e.g., high-order motifs, connected by L5 neurons across subregions or the effect of curvature on the connectivity. In the future, more specific data sources can be used to build diverging versions of the model, e.g. one for adult vs. juvenile rat. They can then be used to contrast the ages and focus on more specific findings. We already defined a number of structural metrics that can be used to contrast more specific versions of the model quantitatively.

We now clarify this pathway to understanding more specific function in the last paragraph of the discussion.

• In a few places the manuscript could be improved by being more specific in the language, for example:

- "our anatomy-based approach has been shown to be powerful", I would prefer instead to read about specific contributions of past papers to the field, and how this builds on them.

- similarly: "ensuring that the total number of synapses in a region-to-region pathway matches biology." Biology here is a loose term and implies too much confidence in the matching to some ground truth. Please instead describe the source of the data, including the type of experiment.

We have removed or rewritten the mentioned parts. We now clarify that we work based on biological estimates from experiments and cite the experiment sources. We also provide brief descriptions of the types of data and how they were derived.

• Some of the decisions seem a little ad-hoc, and the means to assess those decisions are not always available to the reader e.g.

- pg. 10. "Based on these results, we decided that the local connectome sufficed to model connectivity within a region.". What is the basis for this decision? Can it be formalised?

- "In the remaining layers the results of the objective classification were used to validate the class assignments of individual pyramidal cells. We found the objective classification to match the expert classification closely (i.e., for 80-90% of the morphologies). Consequently, we considered the expert classification to be sufficiently accurate to build the model." The description of the validation is a little informal. How many experts were there? What are their initials? Was inter-rater or intra-rater reliability assessed? What are these numbers? The match with Kanari's classification accuracy should be reported exactly. There are clearly experts among the author list, but we are all fallible without good controls in place, and they should be more explicit about those controls here, in my opinion.

- "Morphology selection was then performed as previously (Markram et al., 2015), that is, a morphology was selected randomly from the top 10% scorers for a given position." A lot of the decisions seem a little ad-hoc, without justification other than this group had previously done the same thing. For example, why 10% here? Shouldn't this be based on selecting from all of the reasonable morphologies?

We have clarified that the density of local connectivity is verified against the validation datasets by comparing the diagonals in Figure 4B, in addition to the quantification of Figure 4C.

For the classification, we have now published a detailed preprint describing the objective confirmation of expert classification by a variety of methods (see Kanari et al. 2024 https://www.biorxiv.org/content/10.1101/2024.09.13.612635v1). We cannot include the full methodology in the current paper, due to its large extent. For the benefit of the reader, we have included the appropriate citation and extended the short description of the methodology. As described in this paper, the classification accuracy varies per layer, cell type, etc. We have now described in more details these results, that can be accessed in details in out preprint.

• I would like to know if one of the key results relating to modularity and cortical geometry can be further explored. In particular, there seem to be sharp changes in the data at the end of the modelled cortical regions, which need to be explored or explained further.

We now explore these results further in supplementary figure S15, which we discuss in the results Section 2.6.

• The shape of the juvenile cortex - a key novelty of this work - was based on merely a scalar reduction of the adult cortex. This is very surprising, and surely an oversimplification. Huge efforts have gone into modelling the complex nonlinear development of the cortex, by teams including the developing Human Connectome Project. For such a fundamental aspect of this work, why isn't it possible to reconstruct the shape of this relatively small part of the juvenile rat cortex?

We agree that a more complex approach should be used in the future. However, as outlined at the beginning of this point-by-point reply, we will not conduct such updates to the model in the context of this manuscript, as it describes the version of the model used for a number of follow-up studies.

• The same relative laminar depths are used for all subregions. This will have a large impact on the model. However, relative laminar depths can change drastically across the cortex (see e.g. many papers by Palomero-Gallagher, Zilles, and colleagues). The authors should incorporate the real laminar depths, or, failing that, show evidence to show that the laminar depth differences across the subregions included in the model are negligible.

This point has also been raised by reviewer #1 above. For convenience, we repeat our reply below.

We agree that layer thickness variation would affect circuit properties. Variability of layer thickness can be split into two components: variability stemming from differences in total thickness, which our model covers, and variability of relative, i.e., normalized layer thickness, which we miss. In this region of cortex, though, data on the relative thickness of cortical layers is sparse. The Waxholm Atlas does not distinguish somatosensory cortical layers in its labels [Kleven et al, 2023]. Yusufoğulları (2015) compares layer thicknesses of rat hindlimb and barrel field regions. After normalization against total thickness, the relative difference increased towards the superficial layers from 0 in L6 to 33% in L1. Variability of normalized thicknesses within developed rat barrel cortex, based on layer boundaries reported in Narayanan et al. (2017) vary by 2% to 5% over approximately 2 mm. One major effect of such variability would be to scale the number of neurons in a given layer locally by the corresponding factors. For comparison, the resulting variability in neuron counts due to differences in conicality (Fig. 7D1) was around +-25%. A further effect of variable relative layer thickness would be its impact on the selection of suitable morphologies to be placed in the volume.

In summary, adjustment of layer thickness is a refinement which should be done in future versions of the model, once more data is available. The discussion section has been updated to acknowledge this limitation. However, as outlined at the beginning of this point-by-point reply, we will not conduct such updates to the model in the context of this manuscript, as it describes the version of the model used for a number of follow-up studies.

• The authors perform an affine mapping between mouse and rat cortex. This is again surprising. In human imaging, affine mappings are insufficient to map between two individual brains of the same species and nonlinear transformations are instead used. That an affine transformation should be considered sufficient to map between two different species is then very surprising. For some models, this may be fine, but there is a supposed emphasis here on biological precision in terms of anatomical location.

We agree that this is a weakness that we will address in future revisions of the model.

• One of the most interesting conclusions, that the connectivity pattern observed is in part due to cooperative synapse formation, is based on analyses that are unfortunately not shown.

We originally decided not to show this part as we underestimated the interest in this particular result. We have now included the result in supplementary figure S10 and discuss the figure in the results.

• Open code:

- Why is only a subvolume available to the community?

We have now made the entire model available under doi.org/10.7910/DVN/HISHXN. The Data and Code availability section has been updated to clarify this.

- Live nature of the model. This is such a colossal model, and effort, that I worry that it may be quite difficult to update in light of new data. For example, how much person and computer time would it take to update the model to account for different layer sizes across subregions? Or to more precisely account for the shape of the juvenile rat cortex?

To provide more information to people interested in participating in model refinements, we have added a new Figure 9. We discuss potential opportunities for refinement at the end of the discussion section.

Reviewer #3 (Public Review):

This manuscript reports a detailed model of the rat non-barrel somatosensory cortex, consisting of 4.2 million morphologically and biophysically detailed neuron models, arranged in space and connected according to highly sophisticated rules informed by diverse experimental data. Due to its breadth and sophistication, the model will undoubtedly be of interest to the community, and the reporting of anatomical details of modeling in this paper is important for understanding all the assumptions and procedures involved in constructing the model. While a useful contribution to this field, the model and the manuscript could be improved by employing data more directly and comparing simple features of the model's connectivity - in particular, connection probabilities - with relevant experimental data.

The manuscript is well-written overall but contains a substantial number of confusing or unclear statements, and some important information is not provided.

Below, major concerns are listed, followed by more specific but still important issues.

Major issues

(1) Cortical connectivity.

Section 2.3, "Local, mid-range and extrinsic connectivity modeled separately", and Figure 4: I am confused about what is done here and why. The authors have target data for connectivity (Figure 4B1). But then they use an apposition-based algorithm that results in connectivity that is quite different from the data (Figure 4B2, C). They then use a correction based on the data (Figure 4E) to arrive at a more realistic connectivity. Why not set the connectivity based on the data right away then? That would seem like a more straightforward approach.

We have completely re-written our description and discussion of connectivity in the model. We now more explicitly motivate our connectivity modeling choices in the first paragraph of section 2.3 of the results and in the second paragraph of the discussion.

The same comment applies to Section 2.4., "Specificity of axonal targeting": the distributions of synapses on different types of target cell compartments were not well captured by the original model based on axon-dendrite overlap and pruning, so the authors introduced further pruning to match data specificity. While details of this process and what worked and what didn't may be interesting to some, overall it is not surprising, as it has been well known that cell types exhibit connectivity that is much more specific than "Peters rule" or its simple variations. The question is, since one has the data, why not use the data in the first place to set up the connectivity, instead of using the convoluted process of employing axon-dendrite overlap followed by multiple corrections?

We would like to point out that we are not employing “Peters rule”, we now make this explicit in the revision in the first paragraph of section 2.3 of the results. Furthermore, we would argue that the match to the Motta et al. data indicates that our approach is more than just a “simple variation”. Finally, we believe that there is important insight in: 1. The specific ways in which the algorithm had to be changed to match the Schneider-Mizell data, e.g. that the connectivity of SST positive neurons did not have to be adapted at all. 2. That the specificity of the other two types could still be matched by a selection of a subset of axonal appositions (i.e., of potential synapses).

Most importantly, what is missing from the whole paper is the characterization of connection probabilities, at least for the local circuit within one area. Such connection probabilities can be obtained from the data that the authors already use here, such as the MICRONS dataset. Another good source of such data is Campagnola et al., Science, 2022. Both datasets are for mouse V1, but they provide a comprehensive characterization across all cortical layers, thus offering a good benchmark for comparison of the model with the data. It would be important for the authors to show how connection probabilities realized in their model for different cell types compared to these data.

We now report connection probabilities in the reworked figure 4 and compare them to reported connection probabilities from many different sources and labs in supplementary figure S8. We prefer a comparison to a wide range of sources to relying on a single report.

(2) Section 2.5, "Structure of thalamic inputs" and Figure 6.

The text in section 2.5 should provide more details on what was done - namely, that the thalamic axons were generated based on the axon density profiles and then synapses were established based on their overall with cortical dendrites. Figure S10 where the target axon densities from data and the model axon densities are compared is not even mentioned here. Now, Figure S10 only shows that the axon densities were generated in a way that matches the data reasonably well. However, how can we know that it results in connectivity that agrees with data? Are there data sources that can be used for that purpose? For example, the authors show that in their model "the peaks of the mean number of thalamic inputs per neuron occur at lower depths than the peaks of the synaptic density". Is this prediction of the model consistent with any available data?

Most importantly, the authors should show how the different cell types in their model are targeted by the thalamic inputs in each layer. Experimental studies have been done suggesting specificity in targeting of interneuron types by thalamic axons, such as PV cells being targeted strongly whereas SST and VIP cells being targeted less.

We have updated the Results section to provide context for the thalamic axon placement, and referred the reader to the methods for more detail. A reference to Figure S10 has now been added to this section as well.

As for validations of the structure of the thalamo-cortical inputs: We found that the existing literature on the topic, such as Cruikshank et al., 2007, 2010 and more recently Sermet et al., 2019, is predominately on the physiological strengths of the pathways. We acknowledge that the authors provide compelling arguments that their findings are likely partially due to differences in the anatomical innervation strengths. On the other hand, Sporns, 2013 cautioned against mixing up structural and functional connectivity. Overall, we believe that it is simply cleaner to perform this validation in the accompanying manuscript (“Part II: Physiology and Experimentation”), using the full physiological model. Note that we have actually performed that validation in the manuscript (see preprint under the following doi: 10.1101/2023.05.17.541168, Figure 3H1).

Note that a higher physiological strength onto PV+ neurons is observed.

(3) "We have therefore made not only the model but also most of our tool chain openly available to the public (Figure 1; step 7)."

In fact it is not the whole model that is made publicly available, but only about 5% of it (211,000 out of 4,200,000 neurons). Also, why is "most" of the tool chain made openly available, and not the whole tool chain?

We have now made the entire model available under doi.org/10.7910/DVN/HISHXN. This has also been added to the Key resource table.

With regard to the tool chain, everything is on our public github (https://github.com/BlueBrain/) except for the algorithm for detecting axonal appositions. For that tool there are currently unresolved potential copyright issues with former collaboration partners. We are working to resolve them.

Other issues

"At each soma location, a reconstruction of the corresponding m-type was chosen based on the size and shape of its dendritic and axonal trees (Figure S6). Additionally, it was rotated to according to the orientation towards the cortical surface at that point."

After this procedure, were cells additionally rotated around the white matter-pia axis? If yes, then how much and randomly or not? If not, then why not? Such rotations would seem important because otherwise additional order potentially not present in the real cortex is introduced in the model affecting connectivity and possibly also in vivo physiology (such as the dynamics of the extracellular electric field).

They are indeed additionally randomly rotated. We have clarified this in the revision.

The term "new in vivo reconstructions" for the 58 neurons used in this paper in addition to "in vitro reconstructions" is a misnomer. It is not straightforward to see where the procedure is described, but then one finds that the part of Methods that describes experimental manipulations is mostly about that (so, a clearer pointer to that part of Methods could be useful). However, the description in Methods makes it clear that it is only labeling that is done in vivo; the microscopy and reconstruction are done subsequently in vitro. I would recommend changing the terminology here, as it is confusing. Also, can the authors show reconstructions of these neurons in the supplementary figures? Is the reconstruction shown in Figure 4A representative?

The term is used because the staining is done in vivo. To the best of our knowledge, the reconstruction process cannot be performed in vivo. However, to avoid any confusion we modified the text to clarify this distinction to in-vivo stained.

With respect to the reconstruction in Figure 4: The intent of the panel is to demonstrate the concept of targeted long-range axons that our morphologies are missing, necessitating the use of a second algorithm for longer-range connectivity. As such, it is not one of the reconstructions we used, but one of Janelia MouseLight. While we mentioned MouseLight in the figure caption, we formulated it in a way that could be misunderstood to mean that we merely used the MouseLight browser to render one of our morphologies. We apologize for the confusion, and we have fixed the figure caption.

In this revision we have added exemplars of representative morphology reconstructions (in slice stained and in vivo stained) in a new supplementary figure, as requested (Figure S5). It is referenced in the last paragraph of section 2.1.

In the Discussion, "This was taken into account during the modeling of the anatomical composition, e.g. by using three-dimensional, layer-specific neuron density profiles that match biological measurements, and by ensuring the biologically correct orientation of model neurons with respect to the orientation towards the cortical surface. As local connectivity was derived from axo-dendritic appositions in the anatomical model, it was strongly affected by these aspects.

However, this approach alone was insufficient at the large spatial scale of the model, as it was limited to connections at distances below 1000μm."

As mentioned above, it is not clear that this approach was sufficient for local connectivity either. It would be great if the authors showed a systematic comparison of local connection probabilities between different cell types in their model with experimental data and commented here in the Discussion about how well the model agrees with the data.

As mentioned in the reply to a previous comment, we now report connection probabilities.

In the Discussion: "The combined connectome therefore captures important correlations at that level, such as slender-tufted layer 5 PCs sending strong non-local cortico-cortical connections, but thick-tufted layer 5 PCs not." (Also the corresponding findings in Results.)

If I understand this statement correctly, it may not agree with biological data. See analysis from MICRONS dataset in Bodor et al., https://www.biorxiv.org/content/10.1101/2023.10.18.562531v1.

Our statement was indeed misleading and formulated too strongly. While thick-tufted pyramidal cells do form long-range intra-cortical connections, the structural strength of these pathways is weaker than for slender-tufted PCs, which are associated with the IT (intra-telencephalic) projection type. We have made this clear in the revision.

Table 2 is confusing. What do pluses and minuses mean? What does it mean that some entries have two pluses? This table is not mentioned anywhere else in the text. If pluses mean some meaningful predictions of the model, then their distribution in the table seems quite liberal and arbitrary. It is not clear to me that the model makes that many predictions, especially for type-specificity and plasticity. Also, why is the hippocampus mentioned in this table? I don't see anything about the hippocampus anywhere else in the paper.

We have clarified the description of the table in its caption and removed references to hippocampus, which were left from an earlier draft of the paper.

In the Discussion, "Thus, we made the tools to improve our model also openly available (see Data and Code availability section)."

As mentioned before, the authors themselves write that they made "most of our tool chain openly available to the public", but not all of it.

With regard to the tool chain, everything is on our public github (https://github.com/BlueBrain/) except for the algorithm for detecting axonal appositions. For that tool there are currently unresolved potential copyright issues with former collaboration partners. We are working to resolve them.

Table S2 has multiple question marks. It is not clear whether the "predictions" listed in that table are truly well-thought-out and/or whether experimental confirmations are real.

Some of the citations in that table were broken due to technical difficulties with the citation manager used. We apologize and have fixed this in the revision.

Introduction: It would be quite appropriate to cite here Einevoll et al., Neuron, 2019 ("The Scientific Case for Brain Simulations").

We now reference this important work.

Recommendations for the authors:

Reviewing Editor's note:

Consultation with the reviewers highlighted three main issues: the integration of connection probability profiles, non-uniform cortical thickness, and the overall organization of the manuscript.

Reviewer #1 (Recommendations For The Authors):

Apart from the points discussed in the public review, my main concern is that the manuscript itself is not as tightly constructed as it should be, to the detriment of the reader's ability to understand the model itself and the conclusions from the presented analyses.

There are places where the text references seemingly incorrect figure panels or refers to panels that don't exist:

- Section 2.2, first paragraph - refers to Figure 2D, E but those panels do not exist in Figure 2.

- Section 2.2, second paragraph - refers to Figure 3D3 - perhaps it should be 3B3?

- Section 2.8, first paragraph - has no figure references but seems like it should be referring to parts of Figure 8 (perhaps Figure 8B1 specifically?)

- Is the reference to Figure S11A on page 16 supposed to be to S12A?

In other places, figure labels and descriptions are not clear, and terminology is not always well-defined or explained.

- Figure 8 and the associated section 2.8 are very difficult to draw conclusions from as presented - several of the terms used are opaque and not clearly defined in the text or legends. I could not easily infer how the normalization works for the "normalized node participation per layer", or what "position in simplex" means for "unique neurons in core", and what their "relative counts" are relative to.

- Are "targets" in Figure S12A the same as "sinks"? If so, it would be better to use a single term consistently throughout.

- Figure S12 - figures in part B do not have enough labels to interpret - what is the y-axis of the "rich-club analysis" graph? Also, the figures in part B bottom are labeled "long-range" rather than "mid-range" connections.

In general, I found the use of both letters and numbers for figure panels (e.g. Figure 7E1) more confusing than helpful - it didn't seem like panels with the same letter were visually grouped consistently, and it sometimes made it more difficult to follow the flow of a figure. I would recommend using only letters in nearly every case here.

We thank the reviewer for directing our attention to these issues. We have fixed them in the revision. However, we have decided to keep our original panel numbering scheme. Panels with the same letter are meant to be conceptually grouped as they address related or similar measures.

Other minor points:

- Section 2.4 - paragraph 2 - sentence 5 "inhbititory" -> "inhibitory".

- Figure 5B figure legend - references Schneider-Mizell et al. 2023 but probably should be Motta et al. 2019?

- Figure 5C - figure key "expcected" -> "expected".

- The lower part of Figure 7C looks like it belongs to panel D2 instead of panel C due to relative spacing.

We once again thank the reviewer, and we have fixed the listed issues.

Reviewer #2 (Recommendations For The Authors):

(1) Abstract:

- Is it really 'integrating whole brain-scale data'? This seems a bit misleading.

- "We delineated the limits of determining connectivity from anatomy" - here I think you mean determining connectivity from morphology, or dendrite/axon appositions. Electron microscopy is still anatomy and presumably would be much closer to function.

We originally used the term “anatomy” as connectivity depends on the correct placement of neurons in addition to their morphology. However, as the reviewer points out, this term is misleading as it would encompass electron microscopy, which can go beyond what we do with the model. We have updated the text to read “morphology and placement”.

(2) Introduction:

"Investigating the multi-scale interactions that shape perception requires a model of multiple cortical subregions with inter-region connectivity, but it also requires the subcellular resolution provided by a morphologically detailed model." - This statement, as written, is not true in my opinion. You can argue for the value of morphologically-detailed neuron models to the study of perception, but they are not required for the investigation of perception.

We have updated the text to be clearer: subcellular resolution is only required for certain aspects that are related to perception.

(3) Results:

- Pg. 9/10. There are three sentences in a row that are of the style: "ensuring that the total number of synapses in a region-to-region pathway matches biology." Biology here is a loose term and implies too much confidence in the matching to some ground truth. Please instead describe the source of the data, including the type of experiment here already. o Pg. 10. On the first read, I found it quite hard to follow what exactly was done in Figure 4.

What are the target values adapted from Reimann et al., 2019, for example?

- Pg. 10. "Based on these results, we decided that the local connectome sufficed to model connectivity within a region.". What is the basis for this decision? Can it be formalised? o Pg. 16, Figure 7 B-C. The apparent effect of geometry on modularity is potentially very interesting. However, are the sharp drop-offs in values for modularity (but also conicality and height) true, or are some artefacts due to columns at the edges of the sampled area?

We have discussed these points above in the general comments and strengths and weaknesses.

- Pg. 18. Simplicial cores define central subnetworks, tied together by mid-range connections. This work, in particular leading to the conclusion of the layer 5 highway hubs, stands out as being a successful attempt to simplify the highly detailed model to a degree that it generates useable new understanding.

We thank the reviewer for the kind comment.

(4) Figures:

Figure 2: The caption doesn't seem to match the Figure (e.g. there are no brain regions depicted in A). o Figure 4f. This is a key panel, but is squished into a small corner of Figure 4, and therefore hard-to-read.

We have fixed this in the revision.

Reviewer #3 (Recommendations For The Authors):

In Major comments, point (1) discusses the issue of connectivity known from data. For all the aspects of connectivity mentioned there, I would recommend the authors re-build their model using the connectivity data directly. It would be interesting to test whether a model constructed in such a way would have any difference in simulated neural activity relative to the model they have constructed.

This is indeed a very interesting avenue of research. However, we believe that it is best conducted in separate manuscripts. First, in Pokorny et al., 2024 (https://doi.org/10.1101/2024.05.24.593860) we conduct this investigation, comparing the emerging activity in the model to the one for simpler connectivity models. Additionally, in Egas-Santander et al., 2024 (https://www.biorxiv.org/content/10.1101/2024.03.15.585196v3) we found that simpler connectomes lead to less reliable spiking activity globally. Finally, in the accompanying manuscript (https://www.biorxiv.org/content/10.1101/2023.05.17.541168v5) we compare activity with and without the targeting specificity of Schneider-Mizell et al.

In Major comments, point (2) discusses thalamic inputs. I would recommend the authors to address the issues mentioned there.

We have replied to those comments above.

In addition, panels F and G of Figure 6 are mentioned in the caption but are not shown in the figure. In panel B, the choice of visualization is strange. It would make sense to show box plots for all the data instead of bars for mean values and points for randomly selected 50 cells. Panels E1 and E2 lack units.

We have removed mentions of panels F and G and changed the style of plot. Units for E1 and E2 are now explained in the figure caption.

In Major comments, point (3) touches upon model and tool sharing. I would recommend making such statements more accurate and reflecting what exactly is provided to the community since not everything is shared.

We have now made the entire model available under doi.org/10.7910/DVN/HISHXN.

With regard to the tool chain, everything is on our public github (https://github.com/BlueBrain/) except for the algorithm for detecting axonal appositions. For that tool there are currently unresolved potential copyright issues with former collaboration partners. We are working to resolve them.

I would recommend the authors address all the other points mentioned in the public review as well. In addition, below are some smaller issues that should be fixed.

Figure 2: the caption appears to be partially wrong and partially misassigned to the figure panels.

We fixed the issue.

Also, note that in L6 the types L6_TPC:A and L6_TPC:C are listed in the figure, but L6_TPC:B is not mentioned.

There is indeed no TPC:B type in layer 6. The distinction between TPC:A and TPC:B is based on early or late bifurcations of the apical dendrite and is only observed in layer 5.

Figure 3, panel B2: the caption refers to colors in panel (C), but the authors probably meant to refer to panel (A).

We fixed the issue.

"The placement of morphological reconstructions matched expectation, showing an appropriately layered structure with only small parts of neurites leaving the modeled volume (Figure 2D, E)."

Figure 2 does not have panels D and E.

"The volume was clearly dominated by dendrites, filling between 23% and 47% of the space, compared to 2% to 11% for axons (Figure 3D3)." There is no panel D or D3 in Figure 3.

"Recently, the MICrONS dataset (MICrONS-Consortium et al., 2021) has been analyzed with respect to the axonal targeting of inhibitory subtypes in a 100 x 100 μm subvolume spanning all layers (Schneider-Mizell et al., 2023)."

100 x 100 μm is an area (and should be 100 x 100 μm^2), not a volume.

Figure S11B requires a legend for the color map.

We fixed the issues.

Table S1: What is the difference between L6_BP and L6_BPC? They both are referred to as L6 bipolar cells.

We have changed the description of L6_BPC to “Layer 6 bitufted pyramidal cell”.

Author response:

Public Reviews:

Reviewer #1 (Public review):

One of the roadblocks in PfEMP1 research has been the challenges in manipulating var genes to incorporate markers to allow the transport of this protein to be tracked and to investigate the interactions taking place within the infected erythrocyte. In addition, the ability of Plasmodium falciparum to switch to different PfEMP1 variants during in vitro culture has complicated studies due to parasite populations drifting from the original (manipulated) var gene expression. Cronshagen et al have provided a useful system with which they demonstrate the ability to integrate a selectable drug marker into several different var genes that allows the PfEMP1 variant expression to be 'fixed'. This on its own represents a useful addition to the molecular toolbox and the range of var genes that have been modified suggests that the system will have broad application. As well as incorporating a selectable marker, the authors have also used selective linked integration (SLI) to introduce markers to track the transport of PfEMP1, investigate the route of transport, and probe interactions with PfEMP1 proteins in the infected host cell.

What I particularly like about this paper is that the authors have not only put together what appears to be a largely robust system for further functional studies, but they have used it to produce a range of interesting findings including:

- Co-activation of rif and var genes when in a head-to-head orientation.

- The reduced control of expression of var genes in the 3D7-MEED parasite line.

- More support for the PTEX transport route for PfEMP1.

- Identification of new proteins involved in PfEMP1 interactions in the infected erythrocyte, including some required for cytoadherence.

In most cases the experimental evidence is straightforward, and the data support the conclusions strongly. The authors have been very careful in the depth of their investigation, and where unexpected results have been obtained, they have looked carefully at why these have occurred.

(1) In terms of incorporating a drug marker to drive mono-variant expression, the authors show that they can manipulate a range of var genes in two parasite lines (3D7 and IT4), producing around 90% expression of the targeted PfEMP1. Removal of drug selection produces the expected 'drift' in variant types being expressed. The exceptions to this are the 3D7-MEED line, which looks to be an interesting starting point to understand why this variant appears to have impaired mutually exclusive var gene expression and the EPCR-binding IT4var19 line. This latter finding was unexpected and the modified construct required several rounds of panning to produce parasites expressing the targeted PfEMP1 and bind to EPCR. The authors identified a PTP3 deficiency as the cause of the lack of PfEMP1 expression, which is an interesting finding in itself but potentially worrying for future studies. What was not clear was whether the selected IT4var19 line retained specific PfEMP1 expression once receptor panning was removed.

This is a very interesting point. We do not have systematic long-term data for the Var19 line but medium-term data. After panning the Var19 line, the binding assays were done within 3 months without additional panning. The first binding assay was 2 months after the panning and the last binding assays three weeks later. While there is inherent variation in these assays that precludes detection of smaller changes, the last assay showed the highest level of binding, giving no indication for rapid loss of the binding phenotype. Hence, we can say that the binding phenotype appears to be stable for many weeks without panning the cells again and there was no indication for a rapid loss of binding in these parasites.

Systematic long-term experiments to assess how long the Var19 parasites retain binding would be interesting, but given that the binding-phenotype appears to remain stable over many weeks, this would only make sense if done for a much longer time (6 months or more). Due to the time needed to carry out such an experiment this would not be practical to still include into the present study. But this might be advisable if the Var19 line is used in future experiments that go over extended periods of time. We intend to include a statement in the discussion of the revised manuscript to highlight that if long-term work with this line is planned, monitoring the binding phenotype and potentially re-panning might be advisable.

(2) The transport studies using the mDHFR constructs were quite complicated to understand but were explained very clearly in the text with good logical reasoning.

We are aware of this being a complex issue and are glad this was nevertheless understandable.

(3) By introducing a second SLI system, the authors have been able to alter other genes thought to be involved in PfEMP1 biology, particularly transport. An example of this is the inactivation of PTP1, which causes a loss of binding to CD36 and ICAM-1. It would have been helpful to have more insight into the interpretation of the IFAs as the anti-SBP1 staining in Figure 5D (PTP-TGD) looks similar to that shown in Figure 1C, which has PTP intact. The anti-EXP2 results are clearly different.

We realize the description of the PTP1-TGD IFA data and that of the other TGDs was rather cursory. We intend to amend this in the revision.

(4) It is good to see the validation of PfEMP1 expression includes binding to several relevant receptors. The data presented use CHO-GFP as a negative control, which is relevant, but it would have been good to also see the use of receptor mAbs to indicate specific adhesion patterns. The CHO system if fine for expression validation studies, but due to the high levels of receptor expression on these cells, moving to the use of microvascular endothelial cells would be advisable. This may explain the unexpected ICAM-1 binding seen with the panned IT4var19 line.

We agree with the reviewer that it is desirable to have better binding systems for studying individual binding interactions. As the main purpose of this paper was to introduce the system and show binding, we did not move to more complicated binding systems. However, we would like to point out that the CSA binding was done on receptor alone in addition to the CSA-expressing HBEC-5i cells and was competed successfully with soluble CSA. In addition, apart from the additional ICAM1-binding of the Var19 line, all binding phenotypes were conform with expectations. We therefore hope the tools used for binding studies are acceptable at this stage of introducing the system while future work interested in specific PfEMP1 receptor interactions are advised to use better systems, ideally including also endothelial organoid models, inhibitory antibodies and possibly domain competition. We intend to add a sentence to the discussion highlighting that future work using this system to study individual receptor-interactions could benefit from using optimized binding systems.

(5) The proxiome work is very interesting and has identified new leads for proteins interacting with PfEMP1, as well as suggesting that KAHRP is not one of these. The reduced expression seen with BirA* in position 3 is a little concerning but there appears to be sufficient expression to allow interactions to be identified with this construct. The quantitative impact of reduced expression for proxiome experiments will clearly require further work to define it.

This is a valid point. Clearly there seems to be some impact on binding when BirA* is placed in the extracellular domain (either through reduced presentation or direct reduction of binding efficiency of the modified PfEMP1). The exact impact on the proxiome is indeed difficult to assess. However, we hope that the general coverage of proteins proximal to PfEMP1 with the 3 PfEMP1-BirA* constructs will aid in the identification of proteins involved in PfEMP1 transport and surface display as illustrated with two of the hits targeted here.

(6) The reduced receptor binding results from the TryThrA and EMPIC3 knockouts were very interesting, particularly as both still display PfEMP1 on the surface of the infected erythrocyte. While care needs to be taken in cross-referencing adhesion work in P. berghei and whether the machinery truly is functionally orthologous, it is a fair point to make in the discussion. The suggestion that interacting proteins may influence the "correct presentation of PfEMP1" is intriguing and I look forward to further work on this.

We hope we future work will be able to shed light on this.

Overall, the authors have produced a useful and reasonably robust system to support functional studies on PfEMP1, which may provide a platform for future studies manipulating the domain content in the exon 1 portion of var genes. They have used this system to produce a range of interesting findings and to support its use by the research community.<br /> Finally, a small concern. Being able to select specific var gene switches using drug markers could provide some useful starting points to understand how switching happens in P. falciparum. However, our trypanosome colleagues might remind us that forcing switches may show us some mechanisms but perhaps not all.

Point noted! From non-systematic data with the Var01 line that has been cultured for extended periods of time (several years), it seems other non-targeted vars remain silent in our SLI “activation” lines but how much SLI-based var-expression “fixing” tampers with the integrity of natural switching mechanisms is indeed very difficult to gage at this stage. We intend to add a statement to the manuscript that even if mutually exclusive expression is maintained, it is not certain the mechanisms controlling var expression all remain intact.

Reviewer #2 (Public review):

Summary

Croshagen et al develop a range of tools based on selection-linked integration (SLI) to study PfEMP1 function in P. falciparum. PfEMP1 is encoded by a family of ~60 var genes subject to mutually exclusive expression. Switching expression between different family members can modify the binding properties of the infected erythrocyte while avoiding the adaptive immune response. Although critical to parasite survival and Malaria disease pathology, PfEMP1 proteins are difficult to study owing to their large size and variable expression between parasites within the same population. The SLI approach previously developed by this group for genetic modification of P. falciparum is employed here to selectively and stably activate the expression of target var genes at the population level. Using this strategy, the binding properties of specific PfEMP1 variants were measured for several distinct var genes with a novel semi-automated pipeline to increase throughput and reduce bias. Activation of similar var genes in both the common lab strain 3D7 and the cytoadhesion competent FCR3/IT4 strain revealed higher binding for several PfEMP1 IT4 variants with distinct receptors, indicating this strain provides a superior background for studying PfEMP1 binding. SLI also enables modifications to target var gene products to study PfEMP1 trafficking and identify interacting partners by proximity-labeling proteomics, revealing two novel exported proteins required for cytoadherence. Overall, the data demonstrate a range of SLI-based approaches for studying PfEMP1 that will be broadly useful for understanding the basis for cytoadhesion and parasite virulence.

Comments

(1) While the capability of SLI to actively select var gene expression was initially reported by Omelianczyk et al., the present study greatly expands the utility of this approach. Several distinct var genes are activated in two different P. falciparum strains and shown to modify the binding properties of infected RBCs to distinct endothelial receptors; development of SLI2 enables multiple SLI modifications in the same parasite line; SLI is used to modify target var genes to study PfEMP1 trafficking and determine PfEMP1 interactomes with BioID. Curiously, Omelianczyk et al activated a single var (Pf3D7_0421300) and observed elevated expression of an adjacent var arranged in a head-to-tail manner, possibly resulting from local chromatin modifications enabling expression of the neighboring gene. In contrast, the present study observed activation of neighboring genes with head-to-head but not head-to-tail arrangement, which may be the result of shared promoter regions. The reason for these differing results is unclear although it should be noted that the two studies examined different var loci.

The point that we are looking at different loci is very valid and we realize this is not mentioned in the discussion. In the revision we intend to add this as a possible reason for this discrepancy. As stated in the discussion, the head-to-head scenario was observed before in lines obtained with panning. However, given the rather few examples where this was analyzed, it is well possible that this varies with gene locus and we will make sure that the revised version of the manuscript will be careful to highlight that it is not clear how much this observation in our work can be generalized.

(2) The IT4var19 panned line that became binding-competent showed increased expression of both paralogs of ptp3 (as well as a phista and gbp), suggesting that overexpression of PTP3 may improve PfEMP1 display and binding. Interestingly, IT4 appears to be the only known P. falciparum strain (only available in PlasmoDB) that encodes more than one ptp3 gene (PfIT_140083100 and PfIT_140084700). PfIT_140084700 is almost identical to the 3D7 PTP3 (except for a ~120 residue insertion in 3D7 beginning at residue 400). In contrast, while the C-terminal region of PfIT_140083100 shows near-perfect conservation with 3D7 PTP3 beginning at residue 450, the N-terminal regions between the PEXEL and residue 450 are quite different. This may indicate the generally stronger receptor binding observed in IT4 relative to 3D7 results from increased PTP3 activity due to multiple isoforms or that specialized trafficking machinery exists for some PfEMP1 proteins.

We thank the reviewer for pointing this out, it is an interesting idea that the PTP3 duplication could be a reason for the superior binding of IT4. We intend to add this point to the discussion of the revision.

So far it seems the PTP3 issue occurred only with Var19. The thought of an extra layer of control, particularly for PfEMP1 variants that might be associated with virulence such as Var19, is very attractive. At present, the manuscript alludes to the possibility of an extra layer of control in the discussion. As var-type specificity and existence of such mechanisms in vivo are so far not known we decided not to speculate on this.

Reviewer #3 (Public review):

Summary:

The submission from Cronshagen and colleagues describes the application of a previously described method (selection linked integration) to the systematic study of PfEMP1 trafficking in the human malaria parasite Plasmodium falciparum. PfEMP1 is the primary virulence factor and surface antigen of infected red blood cells and is therefore a major focus of research into malaria pathogenesis. Since the discovery of the var gene family that encodes PfEMP1 in the late 1990s, there have been multiple hypotheses for how the protein is trafficked to the infected cell surface, crossing multiple membranes along the way. One difficulty in studying this process is the large size of the var gene family and the propensity of the parasites to switch which var gene is expressed, thus preventing straightforward gene modification-based strategies for tagging the expressed PfEMP1. Here the authors solve this problem by forcing the expression of a targeted var gene by fusing the PfEMP1 coding region with a drug-selectable marker separated by a skip peptide. This enabled them to generate relatively homogenous populations of parasites all expressing tagged (or otherwise modified) forms of PfEMP1 suitable for study. They then applied this method to study various aspects of PfEMP1 trafficking.

Strengths:

The study is very thorough, and the data are well presented. The authors used SLI to target multiple var genes, thus demonstrating the robustness of their strategy. They then perform experiments to investigate possible trafficking through PTEX, they knock out proteins thought to be involved in PfEMP1 trafficking and observe defects in cytoadherence, and they perform proximity labeling to further identify proteins potentially involved in PfEMP1 export. These are independent and complimentary approaches that together tell a very compelling story.

Weaknesses:

(1) When the authors targeted IT4var19, they were successful in transcriptionally activating the gene, however, they did not initially obtain cytoadherent parasites. To observe binding to ICAM-1 and EPCR, they had to perform selection using panning. This is an interesting observation and potentially provides insights into PfEMP1 surface display, folding, etc. However, it also raises questions about other instances in which cytoadherence was not observed. Would panning of these other lines have been successfully selected for cytoadherent infected cells? Did the authors attempt panning of their 3D7 lines? Given that these parasites do export PfEMP1 to the infected cell surface (Figure 1D), it is possible that panning would similarly rescue binding. Likewise, the authors knocked out PTP1, TryThrA, and EMPIC3 and detected a loss of cytoadhesion, but they did not attempt panning to see if this could rescue binding. To ensure that the lack of cytoadhesion in these cases is not serendipitous (as it was when they activated IT4var19), they should demonstrate that panning cannot rescue binding.

These are very important points. Indeed, we had repeatedly attempted to pan 3D7 when we failed to get the SLI-generated 3D7 PfEMP1 expressor lines to bind, but this had not been successful. After the move to IT4 which readily bound we made no further efforts to understand why 3D7 does not bind but the fact that PfEMP1 is on the surface indicates this is not a PTP3 issue. Also, as the parent 3D7 could not be panned, we assumed it is not easily fixed.

Panning the TGD lines: we see the reasoning for conducting panning experiments with the TGD lines, but on second thought we are unsure this should be attempted. The outcome might not be easily interpretable if panning leads to increased binding and considerable follow up analyses would be needed to define what has happened. The reason for this is that at least two forces will contribute to the selection in panning experiments with TGD lines that lost binding. Firstly, panning would work against the SLI of the TGD, resulting in a tug of war between the TGD-SLI and binding: a very low frequency of parasites can be expected to loop out the TGD plasmid and would normally be eliminated during standard culturing due to the SLI drug used for the TGD. These revertant cells would bind and the panning would enrich them (hence, panning and SLI are opposed in the case of a TGD abolishing binding). It is unclear how strong such an effect can be, but this might lead to mixed populations that complicate interpretations. The second selecting force are possible compensatory changes to restore binding. These can come in two flavors: reversal of potential independent changes that may have occurred in the TGD parasites and that are in reality causing the binding loss (the concern of the reviewer) or new changes to compensate the loss of the TGD target (in case the TGD is the cause of the binding loss). As both of the TGDs in the paper show some residual binding and have VAR01 on the surface to at least some extent, it is possible that new compensatory changes might indeed occur that indirectly increase binding again. In summary, even if more binding after panning of the lines occurs, it is not clear whether this is due to a compensatory change ameliorating the TGD or reversal of an unrelated change. The impact of repeated panning against SLI is also unknown. To determine the cause, the panned TGD lines would need to be subjected to a complex and time-consuming analysis (WGS, RNASeq, possibly Maurer’s clefts IFA phenotype) to find out whether they had an unrelated chance change that was reverted or a new compensatory change that helps binding.

The detection of VAR01 on the surface of these TGDs speaks against a PTP3 effect. While we can’t fully exclude other changes in the TGDs that might affect binding, we conducted WGS which did not show any obvious alterations that could be responsible. To fully exclude loss of ptp3 expression as the reason as seen with Var19 (something we would not have seen in the WGS if it is only due to a transcriptional change), we intend to carry out RNASeq with the two TGD lines. The third TGD mentioned by the reviewer (targeting ptp1) was a positive control of a known PfEMP1 trafficking protein, so we assume this does not need to be further validated.

(2) The authors perform a series of trafficking experiments to help discern whether PfEMP1 is trafficked through PTEX. While the results were not entirely definitive, they make a strong case for PTEX in PfEMP1 export. The authors then used BioID to obtain a proxiome for PfEMP1 and identified proteins they suggest are involved in PfEMP1 trafficking. However, it seemed that components of PTEX were missing from the list of interacting proteins. Is this surprising and does this observation shed any additional light on the possibility of PfEMP1 trafficking through PTEX? This warrants a comment or discussion.

This is an interesting comment and we agree we should have discussed this. A likely reason why PTEX components are not picked up as interactors is that BirA* is expected to become unfolded when it passes through the channel and in that state can’t biotinylate. Labelling likely would only be possible if PfEMP1 lingered at the PTEX translocation step before BirA* became unfolded to go through the channel which we would not expect under physiological conditions. We intend to add a sentence to the discussion why we think PTEX components would not be detected in our BioIDs even if PfEMP1 passes through it but that this might also be an argument against it passing through PTEX.

Author response:

Reviewer #1 (Public review):

The results of this manuscript look at the interplay between pleiotropy, standing genetic variation, and parallelism (i.e. predictability of evolution) in gene expression. Ultimately, their results suggest that (a) pleiotropic genes typically have a smaller range in variation/expression, and (b) adaptation to similar environments tends to favor changes in pleiotropic genes, which leads to parallelism in mechanisms (though not dramatically). However, it is still uncertain how much parallelism is directly due to pleiotropy, instead of a complex interplay between them and ancestral variation.

I have a few things that I was uncertain about. It may be these things are easily answered but require more discussion or clarity in the manuscript.

(1) The variation being talked about in this manuscript is expression levels, and not SNPs within coding regions (or elsewhere). The cause of any specific gene having a change in expression can obviously be varied - transcription factors, repressors, promoter region variation, etc. Is this taken into account within the "network connectivity" measurement? I understand the network connectivity is a proxy for pleiotropy - what I'm asking is, conceptually, what can be said about how/why those highly pleiotropic genes have a change (or not) in expression. This might be a question for another project/paper, but it feels like a next step worth mentioning somewhere.

In current study, we are only able to detect significant and repeatable expression changes but unable to identify the underlying causal variants. An eQTL study in the founder population in combination with genomic resequencing for both evolved and ancestral populations would be required to address this question.

(2) The authors do have a passing statement in line 361 about cis-regulatory regions. Is the assumption that genetic variation in promoter regions is the ultimate "mechanism" driving any change in expression? In the same vein, the authors bring up a potential confounding factor, though they dismiss it based on a specific citation (lines 476-481; citation 65). I'm of the mindset that in order to more confidently disregard this "issue" based on previous evidence, it requires more than one citation. Especially since the one citation is a plant. That specific point jumps out to me as needing a more careful rebuttal.

It was not our intention to claim that the expression changes in our experiment are caused by cis-regulatory variation only. We believe that the observed expression variation has both cis- and trans-genetic components, where as some studies tend to estimate much higher cisvariation for gene expression in Drosophila populations (e.g. [1, 2]). We mentioned the positive correlation between cis-regulatory polymorphism and expression variation to (1) highlight the genetic control of gene expression and (2) make the connection between polygenic adaptation and gene expression evolutionary parallelism.

(3) I feel like there isn't enough exploration of tissue specificity versus network connectivity. Tissue specificity was best explained by a model in which pleiotropy had both direct and indirect effects on parallelism; while network connectivity was best explained (by a small margin) via the model which was mostly pleiotropy having a direct effect on ancestral variation, that then had a direct effect on parallelism. When the strengths of either direct/indirect effects were quantified, tissue specificity showed a stronger direct effect, while network connectivity had none (i.e. not significant). My confusion is with the last point - if network connectivity is explained by a direct effect in the best-supported model, how does this work, since the direct effect isn't significant? Perhaps I am misunderstanding something.

To clarify, for network connectivity, there’s a significant “indirect” effect on parallelism (i.e. network connectivity affect ancestral gene expression and ancestral gene expression affect parallelism). Hence, in table 2, the direct effect of network connectivity on parallelism is weak and not significant while the indirect effect via ancestral variation is significant.

Also, network connectivity might favor the most pleiotropic genes being transcription factor hubs (or master regulators for various homeostasis pathways); while the tissue specificity metric perhaps is a kind of a space/time element. I get that a gene having expression across multiple tissues does fit the definition of pleiotropy in the broad sense, but I'm wondering if some important details are getting lost - I'm just thinking about the relative importance of what tissue specificity measurements say versus the network connectivity measurement.

We examined the statistical relationship between the two measures and found a moderate positive correlation on the basis of which we argued that the two measures may capture different aspects of pleiotropy. We appreciate the reviewer’s suggestions about the biological basis of the two estimates of pleiotropy, but we think that without further experimental insights, an extended discussion of this topic is too premature to provide meaningful insights to the readership.

Reviewer #2 (Public review):

Summary:

Lai and collaborators use a previously published RNAseq dataset derived from an experimental evolution set up to compare the pleiotropic properties of genes whose expression evolved in response to fluctuating temperature for over 100 generations. The authors correlate gene pleiotropy with the degree of parallelisms in the experimental evolution set up to ask: are genes that evolved in multiple replicates more or less pleiotropic?

They find that, maybe counter to expectation, highly pleiotropic genes show more replicated evolution. Such an effect seems to be driven by direct effects (which the authors can only speculate on) and indirect effects through low variance in pleiotropic genes (which the authors indirectly link to genetic variation underlying gene expression variance).

Weaknesses:

The results offer new insights into the evolution of gene expression and into the parameters that constrain such evolution, i.e., pleiotropy. Although the conclusions are supported by the data, I find the interpretation of the results a little bit complicated.

Major comment:

The major point I ask the authors to address is whether the connection between polygenic adaptation and parallelism can indeed be used to interpret gene expression parallelism. If the answer is not, please rephrase the introduction and discussion, if the answer is yes, please make it explicit in the text why it is so.

Our answer is yes, we interpreted gene expression parallelism (high ancestral variance -> less parallelism) using the same framework that links polygenic adaptation and parallelism (high polygenicity = less trait parallelism). We believe that our response covers several of the reviewer’s concerns.

The authors' argument: parallelism in gene expression is the same as parallelism in SNP allele frequency (AFC) (see L389-383 here they don't mention that this explanation is derived from SNP parallelism and not trait parallelism, and see Figure 1 b). In previous publications, the authors have explained the low level of AFC parallelism using a polygenic argument. Polygenic traits can reach a new trait optimum via multiple SNPs and therefore although the trait is parallel across replicates, the SNPs are not necessarily so.

Importantly, our rationale is based on the idea that gene expression is rarely the direct target of selection, but rather an intermediate trait [3]. Recently, we have specifically tested this assumption for gene expression and metabolite concentrations and our analysis showed that both traits were are redundant [4], as previously shown for DNA sequences [5]. The important implication for this manuscript is that gene expression is also redundant, so that adaptation can be achieved by distinct changes in gene expression in replicate populations adapting to the same selection pressure. This implies that we can use the same simulation framework for gene expression as for sequencing data. In our case different SNP frequencies correspond to different expression levels (averaged across individuals from a population), which in turn increases fitness by modifying the selected trait. Importantly, the selected trait in our simulations is not gene expression, but a not defined high level phenotype. A key insight from our simulations is that with increasing polygenicity the expression of a gene is more variable in the ancestral population.

In the current paper, they seem to be exchanging SNP AFC by gene expression, and to me, those are two levels that cannot be interchanged. Gene expression is a trait, not an SNP, and therefore the fact that a gene expression doesn't replicate cannot be explained by a polygenic basis, because again the trait is gene expression itself. And, actually, the results of the simulations show that high polygenicity = less trait parallelism (Figure 4).

As detailed above, because adaptation can be reached by changes in gene expression at different sets of genes, redundancy is also operating on the expression level not just on the level of SNPs. To clarify, the x-axis of Fig. 4 is the expression variation in the ancestral population.

Now, if the authors focus on high parallel genes (present in e.g. 7 or more replicates) and they show that the eQTLs for those genes are many (highly polygenic) and the AFC of those eQTLs are not parallel, then I would agree with the interpretation. But, given that here they just assess gene expression and not eQTL AFC, I do not think they can use the 'highly polygenic = low parallelism' explanation.

The interpretation of the results to me, should be limited to: genes with low variance and high pleiotropy tend to be more parallel, and the explanation might be synergistic pleiotropy.

While we understand the desire to model the full hierarchy from eQTLs to gene expression and adaptive traits, we raise caution that this would be a very challenging task. eQTLs very often underestimate the contribution of trans-acting factors, hence the understanding of gene expression evolution based on eQTLs is very likely incomplete and cannot explain the redundancy of gene expression during adaptation. Hence, we think that the focus on redundant gene expression is conceptually simpler and thus allows us to address the question of pleiotropy without the incorporation of allele frequency changes.  

Reviewer #3 (Public review):

The authors aim to understand how gene pleiotropy affects parallel evolutionary changes among independent replicates of adaptation to a new hot environment of a set of experimental lines of Drosophila simulans using experimental evolution. The flies were RNAsequenced after more than 100 generations of lab adaptation and the changes in average gene expression were obtained relative to ancestral expression levels from reconstructed ancestral lines. Parallelism of gene expression change among lines is evaluated as variance in differential gene expression among lines relative to error variance. Similarly, the authors ask how the standing variation in gene expression estimated from a handful of flies from a reconstructed outbred line affects parallelism. The main findings are that parallelism in gene expression responses is positively associated with pleiotropy and negatively associated with expression variation. Those results are in contradiction with theoretical predictions and empirical findings. To explain those seemingly contradictory results the authors invoke the role of synergistic pleiotropy and correlated selection, although they do not attempt to measure either.

Strengths:

(1) The study uses highly replicated outbred laboratory lines of Drosophila simulans evolved in the lab under a constant hot regime for over 100 generations. This allows for robust comparisons of evolutionary responses among lines.

(2) The manuscript is well written and the hypotheses are clearly delineated at the onset.

(3) The authors have run a causal analysis to understand the causal dependencies between pleiotropy and expression variation on parallelism.

(4) The use of whole-body RNA extraction to study gene expression variation is well justified.

Weaknesses:

(1) It is unclear how well phenotypic variation in gene expression of the evolved lines has been estimated by the sample of 20 males from a reconstructed outbred line not directly linked to the evolved lines under study. I see this as a general weakness of the experimental design.

Our intention was not to measure the phenotypic variance of the evolved lines, but rather to estimate the phenotypic variance at the beginning of the experiment. Hence, we measured and investigated the variation of gene expression in the ancestral population since this was the beginning of the replicated experimental evolution. Furthermore, since the ancestral population represents the natural population in Florida, the gene expression variation reflects the history of selection history acting on it.

(2) There are no estimates of standing genetic variation of expression levels of the genes under study, only phenotypic variation. I wished the authors had been clear about that limitation and had discussed the consequences of the analysis. This also constitutes a weakness of the study.

The reviewer is correct that we do not aim to estimate the standing genetic variation, which is responsible for differences in gene expression. While we agree that it could be an interesting research question to use eQTL mapping to identify the genetic basis of gene expression, we caution that trans-effects are difficult to estimate and therefore an important component of gene expression evolution will be difficult to estimate. Hence, we consider that our focus on variation in gene expression without explicit information about the genetic basis is simpler and sufficient to address the question about the role of pleiotropy.

(3) Moreover, since the phenotype studied is gene expression, its genetic basis extends beyond expressed sequences. The phenotypic variation of a gene's expression may thus likely misrepresent the genetic variation available for its evolution. The genetic variation of gene expression phenotypes could be estimated from a cross or pedigree information but since individuals were pool-sequenced (by batches of 50 males), this type of analysis is not possible in this study.

We agree with the reviewer that gene expression variation may also have a non-genetic basis, we discuss this in depth in the discussion of the manuscript.  

(4) The authors have not attempted to estimate synergistic pleiotropy among genes, nor how selection acts on gene expression modules. It makes any conclusion regarding the role of synergistic pleiotropy highly speculative.

We mentioned synergistic pleiotropy as a possible explanation for our results. A positive correlation between the fitness effect of gene expression variation would predict more replicable evolutionary changes. A similar argument has been made by [6]. 

I don't understand the reason why the analysis would be restricted to significantly differentially expressed genes only. It is then unclear whether pleiotropy, parallelism, and expression variation do play a role in adaptation because the two groups of adaptive and non-adaptive genes have not been compared. I recommend performing those comparisons to help us better understand how "adaptive" genes differentially contribute to adaptation relative to "nonadaptive" genes relative to their difference in population and genetic properties.

We agree with the reviewer that the comparison between the pleiotropy of adaptive and nonadaptive genes is interesting. We performed the analysis but omitted from the current manuscript for simplicity. Similar to the results in [6], non-adaptive genes are more pleiotropic than the adaptive genes. For adaptive genes we find a positive correlation between the level of pleiotropy and evolutionary parallelism. Thus, high pleiotropy limits the evolvability of a gene, but moderate and potentially synergistic pleiotropy increases the repeatability of adaptive evolution. We included this result in the revised manuscript and discuss it.

There is a lack of theoretical groundings on the role of so-called synergistic pleiotropy for parallel genetic evolution. The Discussion does not address this particular prediction. It could be removed from the Introduction.

We modestly disagree with the reviewer, synergistic pleiotropy is covered by theory and empirical results also support the importance of synergistic pleiotropy. 

References

(1) Genissel A, McIntyre LM, Wayne ML, Nuzhdin SV. Cis and trans regulatory effects contribute to natural variation in transcriptome of Drosophila melanogaster. Molecular biology and evolution. 2008;25(1):101-10. Epub 20071112. doi: 10.1093/molbev/msm247. PubMed PMID: 17998255.

(2) Osada N, Miyagi R, Takahashi A. Cis- and Trans-regulatory Effects on Gene Expression in a Natural Population of Drosophila melanogaster. Genetics. 2017;206(4):2139-48. Epub 20170614. doi: 10.1534/genetics.117.201459. PubMed PMID: 28615283; PubMed Central PMCID: PMCPMC5560811.

(3) Barghi N, Hermisson J, Schlötterer C. Polygenic adaptation: a unifying framework to understand positive selection. Nature reviews Genetics. 2020;21(12):769-81. Epub 2020/07/01. doi: 10.1038/s41576-020-0250-z. PubMed PMID: 32601318.

(4) Lai WY, Otte KA, Schlötterer C. Evolution of Metabolome and Transcriptome Supports a Hierarchical Organization of Adaptive Traits. Genome biology and evolution. 2023;15(6). Epub 2023/05/26. doi: 10.1093/gbe/evad098. PubMed PMID: 37232360; PubMed Central PMCID: PMCPMC10246829.

(5) Barghi N, Tobler R, Nolte V, Jaksic AM, Mallard F, Otte KA, et al. Genetic redundancy fuels polygenic adaptation in Drosophila. PLoS biology. 2019;17(2):e3000128. Epub 2019/02/05. doi: 10.1371/journal.pbio.3000128. PubMed PMID: 30716062.

(6) Rennison DJ, Peichel CL. Pleiotropy facilitates parallel adaptation in sticklebacks. Molecular ecology. 2022;31(5):1476-86. Epub 2022/01/09. doi: 10.1111/mec.16335. PubMed PMID: 34997980; PubMed Central PMCID: PMCPMC9306781.

I have mixed feelings about this prank. It seems harmless since the child's candy wasn't eaten, and the child will probably get it back. But where I think it's harmful is the emotional distress it causes the child, even though the child may not be losing anything, in the moment, they aren't aware of that and truly feel hurt or betrayed by their parents. There's a certain level of maturity people must reach before pranks are ethical. It all depends on the person you're pranking and how they react to situations.

Author response:

The following is the authors’ response to the previous reviews.

eLife Assessment 

This valuable study is a detailed investigation of how chromatin structure influences replication origin function in yeast ribosomal DNA, with a focus on the role of the histone deacetylase Sir2 and the chromatin remodeler Fun30. Convincing evidence shows that Sir2 does not affect origin licensing but rather affects local transcription and nucleosome positioning which correlates with increased origin firing. Overall, the evidence is solid and the model plausible. However, the methods employed do not rigorously establish a key aspect of the mechanism where initiation precisely occurs or rigorously exclude alternative models and the effect of Sir2 on transcription is not re-examined in the fun30 context. 

Clarification on Sir2 Effect on Transcription in the fun30 Context

We appreciate the reviewers’ thorough assessment but would like to clarify that the effect of Sir2 on transcription in the fun30 context was addressed in both the original and revised manuscripts. However, we recognize that the presentation of the qPCR results may have been unclear, as we initially plotted absolute transcript levels without normalizing for rDNA array size differences among the genotypes. We have now corrected this.

After normalizing for copy number variations, the qPCR data show that the sir2 fun30 double mutant results in a ~40-fold increase in C-pro transcription relative to WT, compared to a 4-fold and 19-fold increase in fun30 and sir2 single mutants, respectively (Figure 5, figure supplement 6). These results have been discussed in the manuscript result section, where we note that "C-pro RNA levels were approximately twice as high in sir2 fun30 compared to sir2 cells when adjusted for rDNA size differences." This observation is critical for addressing both alternative models of MCM disappearance and for pinpointing transcription initiation sites, as detailed in the following sections.

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

This paper presents a mechanistic study of rDNA origin regulation in yeast by SIR2. Each of the ~180 tandemly repeated rDNA gene copies contains a potential replication origin. Earlyefficient initiation of these origins is suppressed by Sir2, reducing competition with origins distributed throughout the genome for rate-limiting initiation factors. Previous studies by these authors showed that SIR2 deletion advances replication timing of rDNA origins by a complex mechanism of transcriptional de-repression of a local PolII promoter causing licensed origin proteins (MCMcomplexes) to re-localize (slide along the DNA) to a different (and altered) chromatin environment. In this study, they identify a chromatin remodeler, FUN30, that suppresses the sir2∆ effect, and remarkably, results in a contraction of the rDNA to about onequarter it's normal length/number of repeats, implicating replication defects of the rDNA. Through examination of replication timing, MCM occupancy and nucleosome occupancy on the chromatin in sir2, fun30, and double mutants, they propose a model where nucleosome position relative to the licensed origin (MCM complexes) intrinsically determines origin timing/efficiency. While their interpretations of the data are largely reasonable and can be interpreted to support their model, a key weakness is the connection between Mcm ChEC signal disappearance and origin firing. While the cyclical chromatin association-dissociation of MCM proteins with potential origin sequences may be generally interpreted as licensing followed by firing, dissociation may also result from passive replication and as shown here, displacement by transcription and/or chromatin remodeling. Moreover, linking its disappearance from chromatin in the ChEC method with such precise resolution needs to be validated against an independent method to determine the initiation site(s). Differences in rDNA copy number and relative transcription levels also are not directly accounted for, obscuring a clearer interpretation of the results. Nevertheless, this paper makes a valuable advance with the finding of Fun30 involvement, which substantially reduces rDNA repeat number in sir2∆ background. The model they develop is compelling and I am inclined to agree, but I think the evidence on this specific point is purely correlative and a better method is needed to address the initiation site question. The authors deserve credit for their efforts to elucidate our obscure understanding of the intricacies of chromatin regulation. At a minimum, I suggest their conclusions on these points of concern should be softened and caveats discussed. Statistical analysis is lacking for some claims. 

Strengths are the identification of FUN30 as suppressor, examination of specific mutants of FUN30 to distinguish likely functional involvement. Use of multiple methods to analyze replication and protein occupancies on chromatin. Development of a coherent model. 

Weaknesses are failure to address copy number as a variable; insufficient validation of ChEC method relationship to exact initiation locus; lack of statistical analysis in some cases. 

Review of revised version and response letter: 

In the response, the authors make some improvements by better quantifying 2D gels, adding some missing statistical analyses, analyzing the effect of fun30 on rDNA replication in strains with reduced rDNA copy number, and using ChIP-seq of MCMs to support the ChEC-seq data. However, these additions do not address the main issue that is at the heart of their model: where initiation precisely occurs and whether the location is altered in the mutant(s). Thus, mechanistic insight is limited.

We discuss the issue regarding the initiation site below.

Under the section "Addressing Alternative Explanations", the authors claim that processes like transcription and passive replication cannot affect the displaced complex specifically. Why? They are not on same DNA (as mentioned in the Fig 1 legend). 

Premature origin activation, not transcription, drives the disappearance of repositioned MCM complexes in sir2 mutants in HU.

Indeed, the reviewer is correct in suggesting that C-pro transcription confined to rDNA units with repositioned MCM complexes could selectively displace those complexes, potentially explaining the selective disappearance of displaced MCMs in sir2 cells. However, our analysis of C-pro transcription and MCM occupancy in G1 versus HU across the genotypes allows us to rule out this possibility.

We show that the fraction of repositioned MCMs in G1 cells is proportional to the level of C-pro transcription (WT < fun30 << sir2 < sir2 fun30), consistent with the involvement of transcription in the repositioning process during MCM loading in G1. Accordingly, with approximately twice the transcription in sir2 fun30 compared to sir2, we observe more repositioned MCMs in sir2 fun30 cells than in sir2 cells in G1 (Fig 5C).

However, if the disappearance of repositioned MCMs in HU were solely due to C-pro transcription rather than origin activation, we would expect the repositioned MCMs to disappear more quickly in sir2 fun30 cells. Contrary to this expectation, our data show that repositioned MCM complexes are more stable in sir2 fun30 mutants compared to sir2 mutants, indicating that transcription is not the primary factor in the disappearance of displaced MCM complexes in HU; rather, rDNA origin activation appears to be the key factor.

Replication initiation site in sir2. Using multiple independent approaches, including 2D gels, ChIP-seq, and EdU incorporation, we have demonstrated that rDNA origins fire prematurely in sir2 mutants, a conclusion that the reviewer does not contest. Once an origin fires, the MCM signal disappears from the site of its initial deposition, as expected, and this is confirmed in our MCM ChIP and HU ChEC data, both at rDNA origins and across the genome.

Given that the majority of MCM complexes in sir2 mutants are repositioned, it is expected that these repositioned complexes disappear following premature origin activation. With less than half of the licensed origins (or <30% of total rDNA copies) retaining MCM at non-repositioned sites in sir2 mutants, if only these non-repositioned complexes were firing, and the repositioned MCM complexes were disappearing via mechanisms other than replication initiation (e.g., transcription), rDNA replication in sir2 mutants would be severely compromised rather than accelerated. Given this, and the strong experimental evidence that repositioned MCM complexes fire prematurely, continued focus on alternative explanations for MCM complex disappearance seems unwarranted.

We present this analysis in the results section as follows:

“Finally, although deletion of FUN30 could suppress replication initiation at the rDNA either by inhibiting the firing of the active, repositioned MCM complex or by preventing MCM repositioning to the "active location" in the first place, our results suggest that suppression occurs through the former mechanism. Consistent with previous reports that fun30 mutants are deficient in transcriptional silencing (Neves-Costa et al. 2009), C-pro RNA levels were approximately twice as high in sir2 fun30 cells compared to sir2 cells when adjusted for rDNA size (Figure 5—figure supplement 6).

Moreover, deletion of FUN30 shifts the distribution toward the repositioned MCM location over the non-repositioned one in G1 cells (Figure 5C), aligning with the increased C-pro transcription observed in fun30 mutants. This shift is evident in both sir2 and SIR2 cells. Despite the increased transcription-mediated repositioning in sir2 fun30 cells compared to sir2 cells during G1, repositioned MCM persists longer in sir2 fun30 cells than in sir2 cells after release into HU. Additionally, sir2 fun30 mutants exhibit reduced MCM accumulation at the RFB compared to sir2 mutants after release into HU, supporting the conclusion that MCM disappearance in HU reflects origin activation rather than transcription-mediated displacement.”

The model in Fig 7 implies that initiation sites are different in WT versus the mutants and this determines their timing/efficiency. But they also suggest that the same site might be used with different efficiencies in this response. I agree that both are possibilities and are not resolved. 

Adjustment of the model to account for repositioned MCMs in WT cells In Figure 5—figure supplement 5, we demonstrate that even in WT cells, a small fraction of repositioned MCMs (~5%) can be detected, and that these repositioned MCM complexes disappear prematurely. However, because this represents a very small fraction of MCMs in WT cells, we initially did not include it in our overall model in Figure 7. In light of the reviewer's comment, we have now revised the model to incorporate this detail.

Supporting their model requires better resolution to determine the actual replication initiation site. While this may be challenging, it should be feasible with methods to map nascent strands like DNAscent, or Okazaki fragment mapping.

The initiation site in sir2 mutants has been thoroughly analyzed and supported by extensive experimental data, as discussed above. While high-resolution techniques such as DNAscent or Okazaki fragment mapping could potentially offer another layer of validation, the likelihood of obtaining finer detail that would change the conclusions is minimal. The methods we employed provide sufficient resolution to pinpoint the initiation site, and our results align consistently with established replication models.

Further experimentation would not only be redundant but also unlikely to provide new insights beyond revalidation. Given the strength of our current data, we believe the conclusions regarding replication initiation are robust and well-supported, making additional experiments unnecessary at this stage. Our priority is to focus on advancing other aspects of the research that require deeper exploration.

The 2D gel analysis of strains with reduced rDNA copy numbers adequately addresses the copy number variable with regard to the replication effect. 

Overall, the paper is improved by providing additional data and improved analysis. The paper nicely characterizes the effect of Fun30. The model is reasonable but remains lacking in precise details of mechanism. 

Reviewer #2 (Public review): 

Summary: 

In this manuscript, the authors follow up on their previous work showing that in the absence of the Sir2 deacetylase the MCM replicative helicase at the rDNA spacer region is repositioned to a region of low nucleosome occupancy. Here they show that the repositioned displaced MCMs have increased firing propensity relative to non-displaced MCMs. In addition, they show that activation of the repositioned MCMs and low nucleosome occupancy in the adjacent region depend on the chromatin remodeling activity of Fun30. 

Strengths: 

The paper provides new information on the role of a conserved chromatin remodeling protein in regulation of origin firing and in addition provides evidence that not all loaded MCMs fire and that origin firing is regulated at a step downstream of MCM loading. 

Weaknesses: 

The relationship between the authors results and prior work on the role of Sir2 (and Fob1) in regulation of rDNA recombination and copy number maintenance is not explored, making it difficult to place the results in a broader context. Sir2 has previously been shown to be recruited by Fob1, which is also required for DSB formation and recombination-mediated changes in rDNA copy number. Are the changes that the authors observe specifically in fun30 sir2 cells related to this pathway? Is Fob1 required for the reduced rDNA copy number in fun30 sir2 double mutant cells? 

Reviewer #3 (Public review): 

Summary: 

Heterochromatin is characterized by low transcription activity and late replication timing, both dependent on the NAD-dependent protein deacetylase Sir2, the founding member of the sirtuins. This manuscript addresses the mechanism by which Sir2 delays replication timing at the rDNA in budding yeast. Previous work from the same laboratory (Foss et al. PLoS Genetics 15, e1008138) showed that Sir2 represses transcription-dependent displacement of the Mcm helicase in the rDNA. In this manuscript, the authors show convincingly that the repositioned Mcms fire earlier and that this early firing partly depends on the ATPase activity of the nucleosome remodeler Fun30. Using read-depth analysis of sorted G1/S cells, fun30 was the only chromatin remodeler mutant that somewhat delayed replication timing in sir2 mutants, while nhp10, chd1, isw1, htl1, swr1, isw2, and irc5 had no effect. The conclusion was corroborated with orthogonal assays including two-dimensional gel electrophoresis and analysis of EdU incorporation at early origins. Using an insightful analysis with an Mcm-MNase fusion (Mcm-ChEC), the authors

show that the repositioned Mcms in sir2 mutants fire earlier than the Mcm at the normal position in wild type. This early firing at the repositioned Mcms is partially suppressed by Fun30. In addition, the authors show Fun30 affects nucleosome occupancy at the sites of the repositioned Mcm, providing a plausible mechanism for the effect of Fun30 on Mcm firing at that position. However, the results from the MNAse-seq and ChEC-seq assays are not fully congruent for the fun30 single mutant. Overall, the results support the conclusions providing a much better mechanistic understanding how Sir2 affects replication timing at rDNA, 

Strengths 

(1) The data clearly show that the repositioned Mcm helicase fires earlier than the Mcm in the wild type position. 

(2) The study identifies a specific role for Fun30 in replication timing and an effect on nucleosome occupancy around the newly positioned Mcm helicase in sir2 cells. 

Weaknesses 

(1) It is unclear which strains were used in each experiment. 

(2) The relevance of the fun30 phospho-site mutant (S20AS28A) is unclear. 

(3) For some experiments (Figs. 3, 4, 6) it is unclear whether the data are reproducible and the differences significant. Information about the number of independent experiments and quantitation is lacking. This affects the interpretation, as fun30 seems to affect the +3 nucleosome much more than let on in the description. 

Recommendations for the authors:  

Reviewer #2 (Recommendations for the authors): 

The authors have addressed my concerns by the addition of new experiments and analysis. 

One point remains unclear regarding additional support for the Mcm-ChEC results using ChIP experiments to verify whether MCM redistributes in sir2D cells. In their rebuttal, the authors state that, "New supporting based evidence: ChIP at rDNA Origins. Our ChIP analysis also shows that the disappearance of the MCM signal at rDNA origins in sir2Δ cells released into HU is accompanied by signal accumulation at the replication fork barrier (RFB), indicative of stalled replication forks at this location (Figure 5 figure supplement 3)...." The ChIP data in Figure 5 supplement 3 show accumulation of the Mcm2 ChIP signal to the left of the RFB in sir2D cells but it doesn't look like there is any decrease in the MCM signal in sir2D relative to wild-type cells for the peak C-Pro. There is a new MCM peak suggesting perhaps a new MCM loading event. 

Figure 5 figure supplement 3 shows the relative abundance of the MCM ChIP signal across the ~2 kb rDNA region, spanning from the MCM loading site at the rDNA origin (on the left) to the replication fork barrier (RFB) on the right. The MCM-ChIP data are normalized to the highest signal within this rDNA region rather than across the entire genome, meaning that only the relative abundance of MCM within this region is represented, and not comparisons between different conditions. We have now presented the results with the same axes for both alpha factor and HU.

In wild-type (WT) cells, the MCM signal remains primarily at the initial loading site. However, in sir2 mutants, a significant portion of the MCM signal shifts rightward, consistent with rDNA origin activation and the movement of MCM along with the progressing replication fork. While some replication forks stall at the RFB, others are positioned between the MCM loading site and the RFB. The additional MCM peak observed does not represent a new MCM loading event, as the experiment was conducted during S-phase, when new MCM loading is not possible.

Reviewer #3 (Recommendations for the authors): 

In this revision the authors addressed my concerns and improved the manuscript and the presentation of the data. All my recommendations were implemented.

Reviewer #2 (Public review):

Gaertner and colleagues present a study examining the transcriptomic diversity and spatial location of dopaminergic neurons from mice and examine the changes in gene expression resulting from knock-in of the Parkinson's LRRK G2019S risk variant. Overall, I found the manuscript presented their study very clearly, well written with very clear figures for the most part. I am not an expert on mouse neuroanatomy but found their classification reasonably well justified and the spatial orientation of dopaminergic neurons within the mouse brain informative and clear. While trends were clear and well presented, the apparent spatial heterogeneity suggests that knowledge of the functional connections and roles of these neurons will be required to better interpret the results presented, but nonetheless their findings exposed significant detail that is required for further understanding.

The study of the transcriptional effects of the LRRK2 KI was also informative and clearly framed in terms of a focused analysis on the effects of the KI only on dopaminergic neurons. However, I think there are issues here in both methodology, narrative, and clarity.

(1) In the GO pathway analyses (both GSEA and DEG GO), I did not see a correction applied to the gene background considered. The study focusses on dopaminergic neurons and thus the gene background should be restricted to genes expressed in dopaminergic neurons, rather than all genes in the mouse genome. The problem arises that if we randomly sample genes from dopaminergic neurons instead of the whole genome, we are predisposed to sampling genes enriched in relevant cell-type-specific roles (and their relevant GO terms) and correspondingly depleted in genes enriched in functions not associated with this cell type. Thus, I am unsure whether the results presented in Figures 8 and 9 may be more likely to be obtained just by randomly sampling genes from a dopaminergic neuron. The background should be limited and these functional analyses rerun.

(2) In the scRDS results, I am unsure what is significant and what isn't. The authors refer to relative measures in the text ("highest") but I do not know whether these differences are significant nor whether any associations are significantly unexpected. Can the x-axis of scRDS results presented in Figure 9 H and I be replaced with a corrected p-value instead of the scRDS score?

(3) The results discussed at the bottom of page 13 state that 48.82% of the proteins encoded by the Calb1 DEGs have pre-synaptic localisations as opposed to 45.83% of the SOX6 DEGs, which does not support the statement that "greater proportions of DEGs are associated with presynaptic locations in cells from vulnerable DA neurons (Sox6 family, [and in particular,Sox6^tafa1]), compared to less vulnerable ones (Calb1 family)".

(4) While an interest in the Sox6^tafa1 subtype is explained through their expression of Anxa1 denoting a previously identified subtype associated with locomotory behaviours, it was unclear to me how to interpret the functional associations made to DEGs in this subtype taken out of context of other subtypes. Given all the other subtypes, it is not possible to ascertain how specific and thus how interesting these results are unless other subtypes are analysed in the same way and this Sox6^tafa1 subtype is demonstrated as unusual given results from other subtypes.

(5) On p12, the authors highlight Mir124a-1hg that encodes miR-124. This is upregulated in Figure 8D but the authors note this has been to be downregulated in PD patients and some PD mouse models. Can the authors comment on the directional difference?

(6) Lastly, can the authors comment on the selection of a LogFC cut-off of 0.15 for their DEG selection? I couldn't see this explained (apologies if I missed it).

Author response:

Public Reviews:

Reviewer #1 (Public review):

Mutations in CDHR1, the human gene encoding an atypical cadherin-related protein expressed in photoreceptors, are thought to cause cone-rod dystrophy (CRD). However, the pathogenesis leading to this disease is unknown. Previous work has led to the hypothesis that CDHR1 is part of a cadherin-based junction that facilitates the development of new membranous discs at the base of the photoreceptor outer segments, without which photoreceptors malfunction and ultimately degenerate. CDHR1 is hypothesized to bind to a transmembrane partner to accomplish this function, but the putative partner protein has yet to be identified.

The manuscript by Patel et al. makes an important contribution toward improving our understanding of the cellular and molecular basis of CDHR1-associated CRD. Using gene editing, they generate a loss of function mutation in the zebrafish cdhr1a gene, an ortholog of human CDHR1, and show that this novel mutant model has a retinal dystrophy phenotype, specifically related to defective growth and organization of photoreceptor outer segments (OS) and calyceal processes (CP). This phenotype seems to be progressive with age. Importantly, Patel et al, present intriguing evidence that pcdh15b, also known for causing retinal dystrophy in previous Xenopus and zebrafish loss of function studies, is the putative cdhr1a partner protein mediating the function of the junctional complex that regulates photoreceptor OS growth and stability.

This research is significant in that it:

(1) provides evidence for a progressive, dystrophic photoreceptor phenotype in the cdhr1a mutant and, therefore, effectively models human CRD; and

(2) identifies pcdh15b as the putative, and long sought after, binding partner for cdhr1a, further supporting the theory of a cadherin-based junction complex that facilitates OS disc biogenesis.

Nonetheless, the study has several shortcomings in methodology, analysis, and conceptual insight, which limits its overall impact.

Below I outline several issues that the authors should address to strengthen their findings.

Major comments:

(1) Co-localization of cdhr1a and pcdh15b proteins

The model proposed by the authors is that the interaction of cdhr1a and pcdh15b occurs in trans as a heterodimer. In cochlear hair cells, PCDH15 and CDHR23 are proposed to interact first as dimers in cis and then as heteromeric complexes in trans. This was not shown here for cdhr1a and pcdh15b, but it is a plausible configuration, as are single heteromeric dimers or homodimers. Regardless, this model depends on the differential compartmental expression of the cdhr1a and pcdh15b proteins. Data in Figure 1 show convincing evidence that these two proteins can, at least in some cases, be distributed along the length of photoreceptor membranes that are juxtaposed, as would be the case for OS and CP. If pcdh15b is predominantly expressed in CPs, whereas cdhr1a is predominantly expressed in OS, then this should be confirmed with actin double labeling with cdhr1a and pcdh15b since the apicobasal oriented (vertical) CPs would express actin in this same orientation but not in the OS. This would help to clarify whether cdhr1a and pcdh15b can be trafficked to both OS and CP compartments or whether they are mutually exclusive.

First let me thank the reviewer for taking the time to comprehensively evaluate our work and provide constructive criticism which will improve the quality of our final version.

To address this issue, we are undertaking imaging of actin/cdhr1a and actin/pcdh15b using SIM in both transverse and axial sections. Additionally, we have recently established an immuno-gold-TEM protocol and are going to provide data showcasing co-labeling of cdhr1a and pcdh15b at TEM resolution.

Photoreceptor heterogeneity goes beyond the cone versus rod subtypes discussed here and it is known that in zebrafish, CP morphology is distinct in different cone subtypes as well as cone versus rod. It would be important to know which specific photoreceptor subtypes are shown in zebrafish (Figures 1A-C) and the non-fish species depicted in Figures 1E-L. Also, a larger field of view of the staining patterns for Figures 1E-L would be a helpful comparison (could be added as a supplementary figure).

The revised manuscript will include clear labeling of the different cone cell types as well as lower magnification images to be included as supplemental figures.

(2) Cdhr1a function in cell culture

The authors should explain the multiple bands in the anti-FLAG blots. Also, it would be interesting to confirm that the cdhr1a D173 mutant prevents the IP interaction with pcdh15b as well as the additive effects in aggregate assays of Figure 2.

We believe that the D173 mutation results in no cdhr1a polypeptide, based on the lack of in situ signal in our WISH studies (figures showing absence of cdhr1a mRNA will be provided in a new supplemental figure). However, we will clone the D173 mutant and attempt co-IP with pchd15b in our cell culture system as well as the aggregation assay using K562 cells.

Is it possible that the cultured cells undergo proliferation in the aggregation assays shown in Figure 2? Cells might differentially proliferate as clusters form in rotating cultures. A simple assay for cell proliferation under the different transfection conditions showing no differences would address this issue and lend further support to the proposed specific changes to cell adhesion as a readout of this assay.

This is a possibility, however we did not use rotating cultures, this was a monolayer culture. We did not observe any differences in total cell number between the differing transfections. As such, we do not feel proliferation explains the aggregation of K562 cells.

Also, the authors report that the number of clusters was normalized to the field of view, but this was not defined. Were the n values different fields of view from one transfection experiment, or were they different fields of view from separate transfection experiments? More details and clarification are needed.

This will be clarified in the revised manuscript, in short we replicated this experiment 3 times, quantifying 5 different fields of view in each replicate.

(3) Methodological issues in quantification and statistical analyses

Were all the OS and CP lengths counted in the observation region or just a sample within the region? If the latter, what were the sampling criteria? For CPs, it seems that the length was an average estimate based on all CPs observed surrounding one cone or one-rod cell. Is this correct? Again, if sampled, how was this implemented? In Fig 4M', the cdhr1a-/- ROS mostly looks curvilinear. Did the measurements account for this, or were they straight linear dimension measurements from base to tip of the OS as depicted in Fig 5A-E? A clearer explanation of the OS and CP length quantification methodology is required.

The revised manuscript will clearly outline measurement methods. In short, we measured every CP/OS in the imaged regions. We did not average CPs/cell, we simply included all CP measurements in our analysis. All our CP measurements (actin or cdhr1a or pcdh15), were done in the presence of a counter stain, WGA, prph2, gnb1 or PNA to ensure proper measurements (landmark) and association with proper cell type.

All measurements were taken as best as possible to reflect a straight linear dimension for consistency.

How were cone and rod photoreceptor cell counts performed? The legend in Figure 4 states that they again counted cells in the observation region, but no details were provided. For example, were cones and rods counted as an absolute number of cells in the observation region (e.g., number of cones per defined area) or relative to total (DAPI+) cell nuclei in the region? Changes in cell density in the mutant (smaller eye or thinner ONL) might affect this quantification so it would be important to know how cell quantification was normalized.

The revised manuscript will clearly outline measurement methods. In short, rod and cone cell counts were based on the number of outer segments that were observed in the imaging region and previously measured for length. We did not observe any eye size differences in our mutant fish.

In Figure 6I, K, measuring the length of the signal seems problematic. The dimension of staining is not always in the apicobasal (vertical) orientation. It might be more accurate to measure the cdhr1a expression domain relative to the OS (since the length of the OS is already reduced in the mutants). Another possible approach could be to measure the intensity of cdhr1 staining relative to the intensity within a Prph2 expression domain in each group. The authors should provide complementary evidence to support their conclusion.

The revised manuscript will clearly outline measurement methods. In short, all of our CP measurements (actin or cdhr1a or pcdh15), were done in the presence of a counter stain, WGA, prph2, gnb1 or PNA to ensure proper measurements and association with proper cell type.

A better description of the statistical methodology is required. For example, the authors state that "each of the data points has an n of 5+ individuals." This is confusing and could indicate that in Figure 4F alone there were ~5000 individuals assayed (~100 data points per treatment group x n=5 individuals per data point x 10 treatment groups). I don't think that is what the authors intended. It would be clearer if the authors stated how many OS, CP, or cells were counted in their observation region averaged per individual, and then provided the n value of individuals used per treatment group (controls and mutants), on which the statistical analyses should be based.

This will be addressed in the revised manuscript. In short we had an n=5 (individual fish) analyzed for each genotype/time point. We will also include numbers of OS/CP quantified in the observation regions.

There are hundreds of data points in the separate treatment groups shown in several of the graphs. It would not be correct to perform the ANOVA on the separate OS or CP length measurements alone as this will bias the estimates since they are not all independent samples. For example, in Figure 6H, 5dpf pcdh15b+/- have shorter CPs compared to WT but pcdh15b-/- have longer compared to WT. This could be an artifact of the analysis. Moreover, the authors should clarify in the Methods section which ANOVA post hoc tests were used to control for multiple pairwise comparisons.

This will be clarified in the revised manuscript.

(4) Cdhr1a function in photoreceptors

The cdhr1a IHC staining in 5dpf WT larvae in Figure 3E appears different from the cdhr1a IHC staining in 5dpf WT larvae in Figure 1A or Figure 6I. Perhaps this is just the choice of image. Can the authors comment or provide a more representative image?

The image in figure 3E was captured using a previous non antigen retrieval protocol which limits the resolution of the cdhr1a signal along the CP. In the revised manuscript we will include an image that better represents cdhr1a staining in the WT and mutant.

The authors show that pcdh15b localization after 5dpf mirrored the disorganization of the CP observed with actin staining. They also show in Figure 5O that at 180dpf, very little pcdh15b signal remains. They suggest based on this data that total degradation of CPs has occurred in the cdhr1a-/- photoreceptors by this time. However, although reduced in length, COS and cone CPs are still present at 180dpf (Figure 5E, E'). Thus, contrary to the authors' general conclusion, it is possible that the localization, trafficking, and/or turnover of pcdh15b is maintained through a cdhr1a-dependent mechanism, irrespective of the degree to which CPs are maintained. The experiments presented here do not clearly distinguish between a requirement for maintenance of localization versus a secondary loss of localization due to defective CPs.

We agree, this point will be addressed in our revised manuscript.

(5) Conceptual insights

The authors claim that cdhr1a and pcdh15b double mutants have synergistic OS and CP phenotypes. I think this interpretation should be revisited.

First, assuming the model of cdhr1a-pcdh15b interaction in trans is correct, the authors have not adequately explained the logic of why disrupting one side of this interaction in a single mutant would not give the same severity of phenotype as disrupting both sides of this interaction in a double mutant.

Second, and perhaps more critically, at 10dpf the OS and CP lengths in cdhr1a-/- mutants (Figure 7J, T) are significantly increased compared to WT. In contrast, there are no significant differences in these measurements in the pcdh15b-/- mutants. Yet in double homozygous mutants, there is a significant reduction of ~50% in these measurements compared to WT. A synergistic phenotype would imply that each mutant causes a change in the same direction and that the magnitude of this change is beyond additive in the double mutants (but still in the same direction). Instead, I would argue that the data presented in Figure 7 suggest that there might be a functionally antagonistic interaction between cdhr1a and pcdh15b with respect to OS and CP growth at 10dpf.

If these proteins physically interacted in vivo, it would appear that the interaction is complex and that this interaction underlies both OS growth-promoting and growth-restraining (stabilizing) mechanisms working in concert. Perhaps separate homodimers or heterodimers subserve distinct CP-OS functional interactions. This might explain the age-dependent differences in mutant CP and OS length phenotypes if these mechanisms are temporally dynamic or exhibit distinct OS growth versus maintenance phases. Regardless of my speculations, the model presented by the authors appears to be too simplistic to explain the data.

We agree with the reviewer, as such we will address this conclusion in our revised manuscript. To do so we will revise our final model and include more flexibility in the proposed mechanisms.

Reviewer #2 (Public review):

Summary:

The goal of this study was to develop a model for CDHR1-based Con-rod dystrophy and study the role of this cadherin in cone photoreceptors. Using genetic manipulation, a cell binding assay, and high-resolution microscopy the authors find that like rods, cones localize CDHR1 to the lateral edge of outer segment (OS) discs and closely oppose PCDH15b which is known to localize to calyceal processes (CPs). Ectopic expression of CDHR1 and PCDH15b in K652 cells indicates these cadherins promote cell aggregation as heterophilic interactants, but not through homophilic binding. This data suggests a model where CDHR1 and PCDH15b link OS and CPs and potentially stabilize cone photoreceptor structure. Mutation analysis of each cadherin results in cone structural defects at late larval stages. While pcdh15b homozygous mutants are lethal, cdhr1 mutants are viable and subsequently show photoreceptor degeneration by 3-6 months.

Strengths:

A major strength of this research is the development of an animal model to study the cone-specific phenotypes associated with CDHR1-based CRD. The data supporting CDHR1 (OS) and PCDH15 (CP) binding is also a strength, although this interaction could be better characterized in future studies. The quality of the high-resolution imaging (at the light and EM levels) is outstanding. In general, the results support the conclusions of the authors.

Weaknesses:

While the cellular phenotyping is strong, the functional consequences of CDHR1 disruption are not addressed. While this is not the focus of the investigation, such analysis would raise the impact of the study overall. This is particularly important given some of the small changes observed in OS and CP structure. While statistically significant, are the subtle changes biologically significant? Examples include cone OS length (Figures 4F, 6E) as well as other morphometric data (Figure 7I in particular). Related, for quantitative data and analysis throughout the manuscript, more information regarding the number of fish/eyes analyzed as well as cells per sample would provide confidence in the rigor. The authors should also note whether the analysis was done in an automated and/or masked manner.

First let me thank the reviewer for taking the time to comprehensively evaluate our work and provide constructive criticism which will improve the quality of our final version.

The revised manuscript will clearly outline both methods and statistics used for quantitation of our data. (please see comments from reviewer 1). While we do not include direct evidence of the mechanism of CDHR1 function, we do propose that its role is important in anchoring the CP and the OS, particularly in the cones, while in rods it may serve to regulate the release of newly formed disks (as previously proposed in mice). We do plan to test both of these hypothesis directly, however, that will be the basis of our future studies.

Reviewer #3 (Public review):

Summary:

The manuscript by Patel et al investigates the hypothesis that CDHR1a on photoreceptor outer segments is the binding partner for PCDH15 on the calyceal processes, and the absence of either adhesion molecule results in separation between the two structures, eventually leading to degeneration. PCDH15 mutations cause Usher syndrome, a disease of combined hearing and vision loss. In the ear, PCDH15 binds CDH23 to form tip links between stereocilia. The vision loss is less understood. Previous work suggested PCDH15 is localized to the calyceal processes, but the expression of CDH23 is inconsistent between species. Patel et al suggest that CDHR1a (formerly PCDH21) fulfills the role of CDH23 in the retina.

The experiments are mainly performed using the zebrafish model system. Expression of Pcdh15b and Cdhr1a protein is shown in the photoreceptor layer through standard confocal and structured illumination microscopy. The two proteins co-IP and can induce aggregation in vitro. Loss of either Cdhr1a or Pcdh15, or both, results in degeneration of photoreceptor outer segments over time, with cones affected primarily.

The idea of the study is logical given the photoreceptor diseases caused by mutations in either gene, the comparisons to stereocilia tip links, and the protein localization near the outer segments. The work here demonstrates that the two proteins interact in vitro and are both required for ongoing outer segment maintenance. The major novelty of this paper would be the demonstration that Pcdh15 localized to calyceal processes interacts with Cdhr1a on the outer segment, thereby connecting the two structures. Unfortunately, the data presented are inadequate proof of this model.

Strengths:

The in vitro data to support the ability of Pcdh15b and Cdhr1a to bind is well done. The use of pcdh15b and cdhr1a single and double mutants is also a strength of the study, especially being that this would be the first characterization of a zebrafish cdhr1a mutant.

Weaknesses:

(1) The imaging data in Figure 1 is insufficient to show the specific localization of Pcdh15 to calyceal processes or Cdhr1a to the outer segment membrane. The addition of actin co-labelling with Pcdh15/Cdhr1a would be a good start, as would axial sections. The division into rod and cone-specific imaging panels is confusing because the two cell types are in close physical proximity at 5 dpf, but the cone Cdhr1a expression is somehow missing in the rod images. The SIM data appear to be disrupted by chromatic aberration but also have no context. In the zebrafish image, the lines of Pcdh15/Cdhr1a expression would be 40-50 um in length if the scale bar is correct, which is much longer than the outer segments at this stage and therefore hard to explain.

First let me thank the reviewer for taking the time to comprehensively evaluate our work and provide constructive criticism which will improve the quality of our final version.

To address this issue, we are undertaking imaging of actin/cdhr1a and actin/pcdh15b using SIM in both transverse and axial sections. Additionally, we have recently established an immuno-gold-TEM protocol and are going to provide data showcasing co-labeling of cdhr1a and pcdh15b at TEM resolution. We are also going to include lower magnification images to complement the SIM images presented in figure 1.

(2) Figure 3E staining of Cdhr1a looks very different from the staining in Figure 1. It is unclear what the authors are proposing as to the localization of Cdhr1a. In the lab's previous paper, they describe Cdhr1a as being associated with the connecting cilium and nascent OS discs, and fail to address how that reconciles with the new model of mediating CP-OS interaction. And whether Cdhr1a localizes to discrete domains on the disc edges, where it interacts with Pcdh15 on individual calyceal processes.

The image in figure 3E was captured using a previous non antigen retrieval protocol which limits the resolution of the cdhr1a signal along the CP. In the revised manuscript we will include an image that better represents cdhr1a staining in the WT and mutant.

(3) The authors state "In PRCs, Pcdh15 has been unequivocally shown to be localized in the CPs". However, the immunostaining here does not match the pattern seen in the Miles et al 2021 paper, which used a different antibody. Both showed loss of staining in pcdh15b mutants so unclear how to reconcile the two patterns.

We agree that our staining appears different, but we attribute this to our antigen retrieval protocol which differed from the Miles et al paper. We also point to the fact that pcdh15b localization has been shown to be similar to our images in other species (monkey and frog). As such, we believe our protocol reveals the proper localization pattern which might be lost/hampered in the procedure used in Miles et al 2021.

(4) The explanation for the CRISPR targets for cdhr1a and the diagram in Figure 3 does not fit with crRNA sequences or the mutation as shown. The mutation spans from the latter part of exon 5 to the initial portion of exon 6, removing intron 5-6. It should nevertheless be a frameshift mutation but requires proper documentation.

This was an overlooked error in figure making, we apologize and will address this typo in the revised manuscript.

(5) There are complications with the quantification of data. First, the number of fish analyzed for each experiment is not provided, nor is the justification for performing statistics on individual cell measurements rather than using averages for individual fish. Second, all cone subtypes are lumped together for analysis despite their variable sizes. Third, t-tests are inappropriately used for post-hoc analysis of ANOVA calculations.

As we discussed for reviewer 1 and 2, all methods and quantification/statistics will be clearly described in the revised manuscript.

(6) Unclear how calyceal process length is being measured. The cone measurements are shown as starting at the external limiting membrane, which is not equivalent to the origin of calyceal processes, and it is uncertain what defines the apical limit given the multiple subtypes of cones. In Figure 5, the lines demonstrating the measurements seem inconsistently placed.

As we discussed for reviewer 1 and 2, all methods and quantification/statistics will be clearly described in the revised manuscript.

(7) The number of fish analyzed by TEM and the prevalence of the phenotype across cells are not provided. A lower magnification view would provide context. Also, the authors should explain whether or not overgrowth of basal discs was observed, as seen previously in cdhr1-null frogs (Carr et al., 2021).

The revised manuscript will include the aforementioned stats and lower magnification images. We will also compare our results directly to Carr 2021.

(8) The statement describing the separation between calyceal processes and the outer segment in the mutants is not backed up by the data. TEM or co-labelling of the structures in SIM could be done to provide evidence.

We will work to include more TEM and co-labeling data for the revised manuscript (see comments to reviewer 1)

(9) "Based on work in the murine model and our own observations of rod CPs, we hypothesize that zebrafish rod CPs only extend along the newly forming OS discs and do not provide structural support to the ROS." Unclear how murine work would support that conclusion given the lack of CPs in mice, or what data in the manuscript supports this conclusion.

In the revised manuscript we will improve our discussion of murine CPs, in that we still detect the juxtaposition of cdhr1 and pcdh15, along a potential remanent of the CP as previously described in SEM studies. Our findings do not indicate that mice or rats have CPs, we simply wanted to outline that the behavior of cdhr1 and pcdh15 still remains conserved, despite the absence of long traditional CPs.

(10) The authors state "from the fact that rod CPs are inherently much smaller than cone CPs" without providing a reference. In the manuscript, the measurements do show rod CPs to be shorter, but there are errors in the cone measurements, and it is possible that the RPE pigment is interfering with the rod measurements.

We will include a reference where rod CPs have been found to be shorter (monkey and frog data). We have no doubt that in zebrafish the rod CPs are significantly shorter. All our CP measurements are done with a counter stain for rods and cones to be sure that we are measuring the correct cell type.

(11) The discussion should include a better comparison of the results with ocular phenotypes in previously generated pcdh15 and cdhr1 mutant animals.

In the revised manuscript we will include this in our discussion.

(12) The images in panels B-F of the Supplemental Figure are uncannily similar, possibly even of the same fish at different focal planes.

We assure the reviewer that each of the images in supplemental figure 1 are distinct and represent different in situ experiments.

Author response:

The following is the authors’ response to the current reviews.

Public Reviews:

Reviewer #1 (Public review):

D'Oliviera et al. have demonstrated cleavage of human TRMT1 by the SARS-CoV-2 main protease in vitro. Following, they solved the structure of Mpro (Nsp5)-C145A bound to TRMT1 substrate peptide, revealing binding conformation distinct from most viral substrates. Overall, this work enhances our understanding of substrate specificity for a key drug target of CoV2. The paper is well-written and the data is clearly presented. It complements the companion article by demonstrating interaction between Mpro and TRMT1, as well as TRMT1 cleavage under isolated conditions in vitro. They show that cleaved TRMT1 has reduced tRNA binding affinity, linking a functional consequence to TRMT1 cleavage by MPro. Importantly, the revelation for flexible substrate binding of Nsp5 is fundamental for understanding Nsp5 as a drug target. Trmt1 cleavage assays by Mpro revealed similar kinetics for TRMT1 cleavage as compared to nsp8/9 viral polyprotein cleavage site. They purify TRMT1-Q350K, in which there is a mutation in the predicted cleavage consensus sequence, and confirm that it is resistant to cleavage by recombinant Mpro. I am unable to comment critically on the structural analyses as it is outside of my expertise. Overall, I think that these findings are important for confirming TRMT1 as a substrate of Mpro, defining substrate binding and cleavage parameters for an important drug target of SARS-CoV-2, and may be of interest to researchers studying RNA modifications.

We thank the reviewer for their positive assessment and summary of our work in this paper!

Reviewer #2 (Public review):

Summary:

The manuscript 'Recognition and Cleavage of Human tRNA Methyltransferase TRMT1 by the SARS-CoV-2 Main Protease' from Angel D'Oliviera et al., uncovers that TRMT1 can be cleaved by SARS-CoV-2 main protease (Mpro) and defines the structural basis of TRMT1 recognition by Mpro. They use both recombinant TRMT1 and Mpro as well as endogenous TRMT1 from HEK293T cell lysates to convincingly show cleavage of TRMT1 by the SARS-CoV-2 protease. Using in vitro assays, the authors demonstrate that TRMT1 cleavage by Mpro blocks its enzymatic activity leading to hypomodification of RNA. To understand how Mpro recognizes TRMT1, they solved a co-crystal structure of Mpro bound to a peptide derived from the predicted cleavage site of TRMT1. This structure revealed important protein-protein interfaces and highlights the importance of the conserved Q530 for cleavage by Mpro. They then compare their structure with previous X-ray crystal structures of Mpro bound to substrate peptides derived from the viral polyprotein and propose the concept of two distinct binding conformations to Mpro: P3´-out and P3´-in conformations (here P3´ stands for the third residue downstream of the cleavage site). It remains unknown what is the physiological role of these two binding conformations on Mpro function, but the authors established that Mpro has dramatically different cleavage efficiencies for three distinct substrates. In an effort to rationalize this observation, a series of mutations in Mpro's active site and the substrate peptide were tested but unexpectedly had no significant impact on cleavage efficiency. While molecular dynamic simulations further confirmed the propensity of certain substrates to adopt the P3´-out or P3´-in conformation, it did not provide additional insights into the dramatic differences in cleavage efficiencies between substrates. This led the authors to propose that the discrimination of Mpro for preferred substrates might occur at a later stage of catalysis after binding of the peptide. Overall, this work will be of interest to biologists studying proteases and substrate recognition by enzymes and RNA modifications as well as help efforts to target Mpro with peptide-like drugs.

We thank the reviewer for this thorough and accurate summary of our work in this manuscript.

Strengths:

• The authors' statements are well supported by their data, and they used relevant controls when needed. Indeed, they used the Mpro C145A inactive variant to unambiguously show that the TRMT1 cleavage detected in vitro is solely due to Mpro's activity. Moreover, they used two distinct polyclonal antibodies to probe TRMT1 cleavage.

• They demonstrate the impact of TRMT1 cleavage on RNA modification by quantifying both its activity and binding to RNA.

• Their 1.9 Å crystal structure is of high quality and increases the confidence in the reported protein-protein contacts seen between TRMT1-derived peptide and Mpro.

• Their extensive in vitro kinetic assay was performed in ideal conditions although it is sometimes unclear how many replicates were performed.

• They convincingly show how Mpro cleavage is conserved among most but not all mammalian TRMT1 bringing an interesting evolutionary perspective on virus-host interactions.

• The authors test multiple hypotheses to rationalize the preference of Mpro for certain substrates.

• While this reviewer is not able to comment on the rigor of the MD simulations, the interpretations made by the authors seem reasonable and convincing.

• The concept of two binding conformations (P3´-out or P3´-in) for the substrate in the active site of Mpro is significant and can guide drug design.

We thank the reviewer for these positive assessments of manuscript strengths!

Weaknesses:

• The two polyclonal antibodies used by the authors seem to have strong non-specific binding to proteins other than TRMT1 but did not impact the author's conclusions or statements. This is a limitation of the commercially available antibodies for TRMT1.

Yes, there are some levels of non-specific binding for all of the TRMT1 antibodies we have tested (this limitation of commercially available TRMT1 antibodies is also observed and noted by Zhang et al), but we agree that this does not impact the overall conclusions and that by using multiple different antibodies to show the same effects, we can have high confidence in the Western blot analysis and interpretation.

• Despite the reasonable efforts of the authors, it remains unknown why Mpro shows higher cleavage efficiency for the nsp4/5 sequence compared to TRMT1 or nsp8/9 sequences. This is a challenging problem that will take substantially more effort by several labs to decipher mechanistically.

True! To our knowledge and despite significant past efforts of many research groups studying similar coronavirus proteases (e.g. SARS-CoV-1 Mpro) a clear understanding of the detailed mechanistic relationship between cleavage sequence and cleavage kinetics remains mostly undefined. This is a great and important problem for mechanistic and computational groups with deep interests in proteases to tackle in the future! To highlight these and similar open questions, we have added a short paragraph to the Discussion section (second from the last paragraph).

• The peptide cleavage kinetic assay used by the authors relies on a peptide labelled with a fluorophore (MCA) on the N-terminus and a quencher (Dpn) on the C-terminus. This design allows high-throughput measurements compatible with plate readers and is a robust and convenient tool. Nevertheless, the authors did not control for the impact of the labels (MCA and Dpn) on the activity of Mpro. While in most cases the introduced fluorophore/quencher do not impact activity, sometimes it can.

Yes, we agree that it is possible the MCA and Dnp labels could have effects on the measured cleavage rates. These fluorophore/quencher peptide cleavage assays are the standard assays used by many labs in the protease field to study diverse proteases and diverse cleavage targets. When other labs have compared cleavage kinetic parameters measured with fluorophore/quencher-based peptide cleavage assays versus HPLC-based peptide cleavage assays, these are often found to be quite similar (e.g. Lee, J., Worrall, L.J., Vuckovic, M. et al. Crystallographic structure of wild-type SARS-CoV-2 main protease acyl-enzyme intermediate with physiological C-terminal autoprocessing site. Nat Commun 11, 5877 (2020). https://doi.org/10.1038/s41467-020-19662-4), although there are also examples where differences arise. In any case, we agree there could be some effects on the cleavage kinetics introduced by the fluorophore and/or quencher groups. However, our main focus in this paper is to show how a sequence in the human tRNA-modifying enzyme TRMT1 is cleaved by Mpro (and in this revision we have also added new data to show the functional effects of cleavage on TRMT1 activity); it will take significant future work to fully dissect the detailed relationships between peptide sequence, including the quantitative effects of fluorophore/quencher labels, and protease-directed cleavage kinetics. Based on our work in this paper and many past studies of similar proteases, understanding how peptide sequence or conformation relates to cleavage efficiency is a longer-term and very challenging problem that we view as beyond the scope of this work. We have added a brief section elaborating on this in the Discussion.

• An unanswered question not addressed by the authors is if the peptides undergo conformational changes upon Mpro binding or if they are pre-organized to adopt the P3´-out and P3´-in conformations. This might require substantially more work outside the scope of this immediate article.

We agree this is unanswered; we considered additional MD experiments to address this, but ultimately decided that since both of these sequences are cleaved in the context of much larger polypeptides (FL TRMT1 or the viral polypeptide), any simple analysis to assess the possibility of pre-organization and relate this preferred binding conformation to cleavage kinetics would be difficult to interpret in a biologically meaningful way. We think this and similar questions about how pre-organization of peptides or amino acid sequences in the polypeptides might influence protease binding and cleavage activity are interesting and important future questions for protease-focused groups in this field.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors have used a combination of enzymatic, crystallographic, and in silico approaches to provide compelling evidence for substrate selectivity of SARS-CoV-2 Mpro for human TRMT1.

Strengths:

In my opinion, the authors came close to achieving their intended aim of demonstrating the structural and biochemical basis of Mpro catalysis and cleavage of human TRMT1 protein. The revised version of the manuscript has addressed most of the questions I had posed in my earlier review.

We thank the reviewer for their positive assessment of this work, and we are glad to hear the manuscript revisions were helpful in addressing the first round of reviews and questions.

Weaknesses:

Although several new hypotheses are generated from the Mpro structural data, the manuscript falls a bit short of testing them in functional assays, which would have solidified the conclusions the authors have drawn.

Toward showing some of the functional effects of TRMT1 cleavage, in this revised version of the manuscript we have added new data and a new results section (‘Cleavage of TRMT1 results in complete loss of tRNA m2,2G modification activity and reduced tRNA binding in vitro’) showing that cleavage of TRMT1 results in reduced tRNA binding to TRMT1 (Figure 2D) and the complete loss of TRMT1-mediated tRNA modification activity in vitro (Figure 2C). This complements the in-cell data presented by Zhang et al showing that cleavage of TRMT1 in SARS-CoV-2 infected human cells results in the reduction of m2,2G modification levels. We think these data are a strong addition to this paper that broadens the impacts of our reported results more directly into the RNA modifications field.

In terms of showing the further, downstream biological effects of TRMT1 cleavage and/or the specific impacts of TRMT1 cleavage on SARS-CoV-2 propagation and replication, while we agree further functional assays could absolutely heighten the overall impact, we view the main focus of our paper as showing how TRMT1 is recognized and cleaved by Mpro at the structural level and characterizing the biochemistry of the TRMT1-Mpro interaction and the effects of cleavage on TRMT1 tRNA-modifying activity. Zhang et al present some cellular data suggesting that loss of TRMT1 and/or TRMT1 cleavage during infection is actually detrimental to SARS-CoV-2 replication and infectivity. However, a full understanding of how TRMT1-mediated m2,2G modification of tRNA impacts viral translation, whether TRMT1 plays other roles during the viral life cycle, or whether TRMT1 cleavage (even if not important for viral fitness) contributes to cellular phenotypes during infection, will take a significant amount of future cell biology and virology work to unravel. Indeed, our understanding is that characterizing some of the endogenous cleavage targets for the HIV protease and determining the downstream biological effects and impacts on HIV infection took well over a decade. We hope that the biochemical and structural characterization of the Mpro-TRMT1 interaction presented in our paper will provide the necessary fundamental groundwork and impetus for future virology and cellular biochemistry studies to further investigate the biological roles of TRMT1 cleavage by SARS-CoV-2 Mpro.

---

The following is the authors’ response to the original reviews.

eLife Assessment:

This manuscript provides important structural insights into the recognition and degradation of the host tRNA methyltransferase by SARS-CoV-2 protease nsp5 (Mpro). The data convincingly support the main conclusions of the paper. These results will be of interest to researchers studying structures and substrate recognition and specificity of viral proteases.

We thank the eLife editors and reviewers for handling this manuscript and the overall positive assessment of our work.

In this revised version of the manuscript we have included significant, new experimental data with recombinant purified, catalytically active TRMT1 that directly shows cleavage of TRMT1 reduces its tRNA binding affinity (by gel shift assays) and results in the complete loss of tRNA modifying activity in vitro (by radiolabel-based methyltransferase assays). Because these added experiments provide new information about how Mpro-mediated cleavage specifically impacts TRMT1 tRNA binding and m2,2G modification activity, and thus new information about the functional effects of loss of the TRMT1 Zn finger domain, we would strongly suggest adding that “this work may be of interest to researchers studying RNA modifications”, or a similar phrase, in the eLife assessment.

Please find below our point-by-point response to each of the reviewer comments, which outlines additional changes to the manuscript.

Public Reviews:

Reviewer #1 (Public Review):

D'Oliviera et al. have demonstrated cleavage of human TRMT1 by the SARS-CoV-2 main protease in vitro. Following this, they solved the structure of Mpro-C145A bound to TRMT1 substrate peptide, revealing binding conformation distinct from most viral substrates. Overall, this work enhances our understanding of substrate specificity for a key drug target of CoV2. The paper is well-written and the data is clearly presented. It complements the companion article by demonstrating the interaction between Mpro and TRMT1 and TRMT1 cleavage under isolated conditions in vitro. Importantly, the revelation of flexible substrate binding of Nsp5 is fundamental for understanding Nsp5 as a drug target. Trmt1 cleavage assays revealed similar kinetics for TRMT1 cleavage as compared to the nsp8/9 viral polyprotein cleavage site, however, it would have been more rigorous for the authors to independently reproduce the kinetics reported for nsp8/9 using their specific experimental conditions. The finding that murine TRMT1 lacks a conserved consensus sequence is interesting, but is not experimentally tested here and is reported elsewhere. I am unable to comment critically on the structural analyses as it is outside of my expertise. Overall, I think that these findings are important for confirming TRMT1 as a substrate of Mpro and defining substrate binding and cleavage parameters for an important drug target of SARS-CoV-2.

We thank the reviewer for their positive assessment and summary of our work in this paper!

We absolutely agree that comparing to nsp8/9 cleavage kinetics measured in our own hands would be more rigorous here, and we have carried out these measurements in triplicate under the same conditions as were used to measure all the other peptide cleavage kinetics in this manuscript. Figures 5A & B (as well as Table S3 and Dataset S2) have been updated with our new nsp8/9 kinetic data (kcat = 0.019 +/- 0.002 s-1 and KM = 40 +/- 7.5 µM). As expected, our newly measured nsp8/9 kinetic parameters are very similar to those that we had previously cited from MacDonald et al (kcat = 0.013 +/- 0.001 s-1, KM = 36 +/- 6.0 µM), and show that Mpro-mediated TRMT1 peptide cleavage has similar proteolysis kinetics to the nsp8/9 viral polypeptide cleavage site.

We have also purified full-length human TRMT1 Q530K, which is the key change in the cleavage consensus sequence that likely makes murine TRMT1 resistant to Mpro-mediated cleavage. In in vitro cleavage assays we find that indeed TRMT1 Q530K is entirely resistant to cleavage by recombinant Mpro and we have added this data to the manuscript in Figure 6D. These findings are consistent with previously cited data from Lu et al, which suggest mouse and hamster TRMT1 are not cleaved in HEK293T cells expressing Mpro.

With the addition of the TRMT1 Q530K mutant data, we decided to move the evolutionary analysis together with this kinetic data to a new section in the Results. We think these additions and changes make the paper stronger and clearer, and thank the reviewer for these suggestions!

Reviewer #2 (Public Review):

Summary:

The manuscript 'Recognition and Cleavage of Human tRNA Methyltransferase TRMT1 by the SARS-CoV-2 Main Protease' from Angel D'Oliviera et al., uncovers that TRMT1 can be cleaved by SARS-CoV-2 main protease (Mpro) and defines the structural basis of TRMT1 recognition by Mpro. They use both recombinant TRMT1 and Mpro as well as endogenous TRMT1 from HEK293T cell lysates to convincingly show cleavage of TRMT1 by the SARS-CoV-2 protease. To understand how Mpro recognizes TRMT1, they solved a co-crystal structure of Mpro bound to a peptide derived from the predicted cleavage site of TRMT1. This structure revealed important protein-protein interfaces and highlights the importance of the conserved Q530 for cleavage by Mpro. They then compared their structure with previous X-ray crystal structures of Mpro bound to substrate peptides derived from the viral polyprotein and proposed the concept of two distinct binding conformations to Mpro: P3´-out and P3´-in conformations (here P3´ stands for the third residue downstream of the cleavage site). It remains unknown what is the physiological role of these two binding conformations on Mpro function, but the authors established that Mpro has dramatically different cleavage efficiencies for three distinct substrates. In an effort to rationalize this observation, a series of mutations in Mpro's active site and the substrate peptide were tested but unexpectedly had no significant impact on cleavage efficiency. While molecular dynamic simulations further confirmed the propensity of certain substrates to adopt the P3´-out or P3´-in conformation, they did not provide additional insights into the dramatic differences in cleavage efficiencies between substrates. This led the authors to propose that the discrimination of Mpro for preferred substrates might occur at a later stage of catalysis after binding of the peptide. Overall, this work will be of interest to biologists studying proteases and substrate recognition by enzymes as well as help efforts to target Mpro with peptide-like drugs.<br />

We thank the reviewer for this thorough and accurate summary of our work in this manuscript.

Strengths:

• The authors' statements are well supported by their data, and they used relevant controls when needed. Indeed, they used the Mpro C145A inactive variant to unambiguously show that the TRMT1 cleavage detected in vitro is solely due to Mpro's activity. Moreover, they used two distinct polyclonal antibodies to probe TRMT1 cleavage.

• Their 1.9 Å crystal structure is of high quality and increases the confidence in the reported protein-protein contacts seen between TRMT1-derived peptide and Mpro.

• Their extensive in vitro kinetic assay was performed in ideal conditions although it is unclear how many replicates were performed.

• The authors test multiple hypotheses to rationalize the preference of Mpro for certain substrates.

• While this reviewer is not able to comment on the rigor of the MD simulations, the interpretations made by the authors seem reasonable and convincing.

• The concept of two binding conformations (P3´-out or P3´-in) for the substrate in the active site of Mpro is significant and can guide drug design.

We thank the reviewer for these positive assessments of manuscript strengths!

Weaknesses:

• While the authors convincingly show that TRMT1 is cleaved by Mpro, the exact cleavage site was never confirmed experimentally. It is most likely that the predicted site is the main cleavage site as proposed by the authors (region 527-534). Nevertheless, in Fig 1C (first lane from the right) there are two bands clearly observed for the cleavage product containing the MT Domain. If the predicted site was the only cleavage site recognized by Mpro, then a single band for the MT domain would be expected. This observation suggests that there might be two cleavage sites for Mpro in TRMT1. Indeed, residues RFQANP (550-555) in TRMT1 might be a secondary weaker cleavage site for Mpro, which would explain the two observed bands in Fig 1C. A mass spectrometry analysis of the cleaved products would clarify this.

We agree with the reviewer that based on the originally presented data it is possible there could be an additional Mpro-targeted cleavage site in TRMT1 beyond the 527-534 region that we validated through peptide cleavage assays of the TRMT1 526-536 peptide. Because it may be difficult to unambiguously identify and differentiate other putative cleavage sites that are nearby to 527-534 (e.g. the suggested possibility of 550-555) by mass spectrometry, we instead carried out additional in vitro cleavage assays with purified FL TRMT1 Q530K. Mutation of the invariant P1 Gln residue in the cleavage sequence is expected to prevent cleavage at this site, and allow us to probe whether there are other sites in TRMT1 that can be cleaved by Mpro (and if so, more straightforwardly identify them by mass spectrometry). We compared cleavage of purified WT FL TRMT1 and FL TRMT1 Q530K with recombinant Mpro in in vitro cleavage assays and found that TRMT1 Q530K is not cleaved by Mpro over the course of a 2h cleavage reaction. In these experiments, we also saw clear cleavage of WT FL TRMT1 over the course of 2h into only a single detectable band. Together, both of these pieces of data strongly suggest that the 527-534 region is the only Mpro-targeted cleavage site in TRMT1 (if there was an additional cleavage site, we should have seen some amount of cleavage in the Q530K mutant, but we do not). Overall, we feel that the updated WT and Q530K experiments clearly demonstrate that there is only one Mpro-mediated cleavage site in human TRMT1, which also is consistent with experiments in Zhang et al showing that Q530N mutations also block TRMT1 cleavage by co-expressed Mpro in human cells.

The updated WT and Q530K cleavage assays have been added to the manuscript in Figure 6D.

• A control is missing in Fig 1D. Since the authors use western blots to show the gradual degradation of endogenous TRMT1, a control with a protein that does not change in abundance over the course of the measurement is important. This is required to show that the differences in intensity of TRMT1 by western blotting are not due to loading differences etc.

Yes, we agree this is an important control and have repeated these experiments and blotted for TRMT1 and GAPDH as a loading control. The updated Western blots are now shown in Figure 2B, and show the same result as the older data.

• The two polyclonal antibodies used by the authors seem to have strong non-specific binding to proteins other than TRMT1 but did not impact the author's conclusions. This is a limitation of the commercially available antibodies for TRMT1, and unless the authors select a new monoclonal antibody specific to TRMT1 (costly and lengthy process), this limitation seems out of their control.

Yes, there are some levels of non-specific binding for all of the TRMT1 antibodies we have tested (this limitation of commercially available TRMT1 antibodies is also observed and noted by Zhang et al), but we agree that this does not impact the overall conclusions and that by using multiple different antibodies to show the same effects, we can have high confidence in the Western blot analysis and interpretation.

• The recombinantly purified TRMT1 seems to have some non-negligible impurities (extra bands in Fig 1C). This does not impact the conclusions of the authors but might be relevant to readers interested in working with TRMT1 for biochemical, structural, or other purposes.

Yes, our initial isolations of recombinant TRMT1 for the first version of this paper produced smaller amounts of TRMT1 with some impurities; we agree that these do not impact the conclusions of the cleavage experiments. However, since our first submission, we have optimized our purification protocols for TRMT1 and are now able to obtain larger quantities of higher purity recombinant human TRMT1 from bacterial cells and we have used this material for the TRMT1 activity and tRNA binding assays added in this revision; we have also included updates to the expression and purification section for recombinant TRMT1. We hope that these improvements will be helpful to readers interested in working on TRMT1.

• Despite the reasonable efforts of the authors, it remains unknown why Mpro shows higher cleavage efficiency for the nsp4/5 sequence compared to TRMT1 or nsp8/9 sequences.

True! To our knowledge and despite significant past efforts of many research groups studying similar coronavirus proteases (e.g. SARS-CoV-1 Mpro) a clear understanding of the detailed mechanistic relationship between cleavage sequence and cleavage kinetics remains mostly undefined. This is a great and important problem for mechanistic and computational groups with deep interests in proteases to tackle in the future! To highlight these and similar open questions, we have added a short paragraph to the Discussion section (second from the last paragraph).

• The peptide cleavage kinetic assay used by the authors relies on a peptide labelled with a fluorophore (MCA) on the N-terminus and a quencher (Dpn) on the C-terminus. This design allows high-throughput measurements compatible with plate readers and is a robust and convenient tool. Nevertheless, the authors did not control for the impact of the labels (MCA and Dpn) on the activity of Mpro. It is possible that the differences in cleavage efficiencies between peptides are due to unexpected conformational changes in the peptide upon labelling. Moreover, the TRMT1 peptide has an E at the N-terminus and an R at the C-terminus (while the nsp4/5 peptide has an S and M, respectively). It is possible that these two terminal residues form a salt bridge in the TRMT1 peptide that might constrain the conformation of the peptide and thus reduce its accessibility and cleavage by Mpro. Enzymatic assays in the absence of labels and MD simulations with the bona fide peptides (including the labels) used in the kinetic measurements are needed to prove that the cleavage efficiencies are not biased by the fluorescence assay.

These fluorophore/quencher peptide cleavage assays are the standard assays used by many labs in the protease field to study diverse proteases and diverse cleavage targets. When other labs have compared cleavage kinetic parameters measured with fluorophore/quencher-based peptide cleavage assays versus HPLC-based peptide cleavage assays, these are often found to be quite similar (e.g. Lee, J., Worrall, L.J., Vuckovic, M. et al. Crystallographic structure of wild-type SARS-CoV-2 main protease acyl-enzyme intermediate with physiological C-terminal autoprocessing site. Nat Commun 11, 5877 (2020). https://doi.org/10.1038/s41467-020-19662-4), although there are also examples where differences arise. In any case, we agree there could be some effects on the cleavage kinetics introduced by the fluorophore and/or quencher groups or sequence-specific conformational preferences of the peptides. However, because our main focus in this paper is to show how a sequence in the human tRNA-modifying enzyme TRMT1 is cleaved by Mpro (and in this revision we have also added new data to show the functional effects of cleavage on TRMT1 activity), and the broad focus of our lab is understanding the mechanisms controlling the function and activity of RNA-modifying enzymes, we will leave it to other labs focused more specifically on protease biochemistry to fully dissect the detailed relationships between peptide sequence and conformation to protease-directed cleavage kinetics. As discussed above, based on our work in this paper and many past studies of similar proteases, understanding how sequence relates to cleavage efficiency is a longer-term and very challenging problem that we view as beyond the scope of this work. As noted above, we have added a brief section explaining this in the Discussion.

• The authors used A431S variant in TRMT1-derived peptide to disrupt the P3´-in conformation. While this reviewer agrees with the rationale behind A431S design, it is important to confirm experimentally that the mutation disrupted the P3´-in conformation in favor of the P3´-out conformer. The authors could use their MD simulations to determine if the TRMT1 A431S variant favors the P3´-out conformation.

Thank you for this suggestion; we agree and have carried out the suggested MD simulations with TRMT1 A531S peptides bound to Mpro. Surprisingly, these simulations suggest that the A531S peptide can still readily adopt the P3’-in conformation by orienting the Ser sidechain in a different way as compared to its positioning in the Mpro-nsp4/5 structure. Since this somewhat changes our interpretation of the results of the A531S kinetic experiments, we have rewritten this section of the manuscript by: (a) removing the ‘TRMT1 mutations predicted to alter peptide binding conformation have little effect on cleavage kinetics’ section in the Results, (b) instead adding several sentences talking about the A531S mutation to the previous section of the results, and including this mutation as another example of how mutations to either Mpro or TRMT1 residues that might be expected to impact cleavage kinetics do not in fact affect cleavage rates, and finally (c) adding the new MD simulation results to the A531S kinetic data in Figure S5 in the Supporting Information. We thank the reviewer for suggesting this important follow-up simulation!

• An unanswered question not addressed by the authors is if the peptides undergo conformational changes upon Mpro binding or if they are pre-organized to adopt the P3´-out and P3´-in conformations.

We agree this is unanswered; we considered additional MD experiments to address this, but ultimately decided that since both of these sequences are cleaved in the context of much larger polypeptides (FL TRMT1 or the viral polypeptide), any simple analysis to assess the possibility of pre-organization and relate this preferred binding conformation to cleavage kinetics would be difficult to interpret in a biologically meaningful way. We think this and similar questions about how pre-organization of peptides or amino acid sequences in the polypeptides might influence protease binding and cleavage activity are interesting and important future questions for protease-focused groups in this field.

• While the authors describe at great length the hydrogen bonds involved in the substrate recognition by Mpro, they occluded to highlight important stacking interactions in this interface. For instance, Phe533 from TRMT1 stacks with Met49 while L529 from TRMT1 packs against His41 of Mpro. Both hydrogen bonding and stacking interactions seem important for TRMT1-derived peptide recognition by Mpro.

Thank you for these suggestions toward additional structural analysis. We have added a short description of L529 packing in the S2 pocket to the main text and Figure S3B. We have also added a short description of F533 packing in the S3’ pocket to the main text and Figure S3C.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors have used a combination of enzymatic, crystallographic, and in silico approaches to provide compelling evidence for substrate selectivity of SARS-CoV-2 Mpro for human TRMT1.

Strengths:

In my opinion, the authors came close to achieving their intended aim of demonstrating the structural and biochemical basis of Mpro catalysis and cleavage of human TRMT1 protein. The combination of orthogonal approaches is highly commendable.

We thank the reviewer for their positive assessment of this work!

Weaknesses:

It would have been of high scientific impact if the consequences of TRMT1 cleavage by Mpro on cellular metabolism were provided. Furthermore, assays to investigate the effect of inhibition of this Mpro activity on SARS-CoV-2 propagation and infection would have been extremely useful in providing insights into host- SARS-CoV-2 interactions.

Toward showing some of the consequences of TRMT1 cleavage, in this revised version of the manuscript we have added new data and a new results section (‘Cleavage of TRMT1 results in complete loss of tRNA m2,2G modification activity and reduced tRNA binding in vitro’) showing that cleavage of TRMT1 results in reduced tRNA binding to TRMT1 (Figure 2D) and the complete loss of TRMT1-mediated tRNA modification activity in vitro (Figure 2C). This complements the in-cell data presented by Zhang et al showing that cleavage of TRMT1 in SARS-CoV-2 infected human cells results in the reduction of m2,2G modification levels. We think these data are a strong addition to this paper that broadens the impacts of our reported results more directly into the RNA modifications field.

In terms of showing the further, downstream biological effects of TRMT1 cleavage and/or the specific impacts of TRMT1 cleavage on SARS-CoV-2 propagation and replication, while we agree this would absolutely heighten the overall impact, we view the main focus of our paper as showing how TRMT1 is recognized and cleaved by Mpro at the structural level and characterizing the biochemistry of the TRMT1-Mpro interaction and the effects of cleavage on TRMT1 tRNA-modifying activity. Zhang et al present some cellular data suggesting that loss of TRMT1 and/or TRMT1 cleavage during infection is actually detrimental to SARS-CoV-2 replication and infectivity. However, a full understanding of how TRMT1-mediated m2,2G modification of tRNA impacts viral translation, whether TRMT1 plays other roles during the viral life cycle, or whether TRMT1 cleavage (even if not important for viral fitness) contributes to cellular phenotypes during infection, will take a significant amount of future cell biology and virology work to unravel. Indeed, our understanding is that characterizing some of the endogenous cleavage targets for the HIV protease and determining the downstream biological effects and impacts on HIV infection took well over a decade. We hope that the biochemical and structural characterization of the Mpro-TRMT1 interaction presented in our paper will provide the necessary fundamental groundwork and impetus for future virology and cellular biochemistry studies to further investigate the biological roles of TRMT1 cleavage by SARS-CoV-2 Mpro.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Please list Mpro alias Nsp5 in the Abstract and Introduction, as this is the nomenclature used in the companion article.

OK, we have made these changes.

Reviewer #2 (Recommendations For The Authors):

In addition to the points mentioned in the public review, this reviewer encourages the authors to address the following points:

• Citation 14 is important for this work since the authors used multiple structures from that earlier study for comparison. Citation 14 seems outdated since it refers to a preprint that has been published since then in Nat Comm. The authors should cite the peer-reviewed work https://pubmed.ncbi.nlm.nih.gov/35729165/

Thank you, we have updated this reference.

• The description of the hydrogen bonds is tedious to read. The authors could instead classify them into two groups. Hydrogen bonds between main chain backbones or hydrogen bonds between side chains. For instance, they mention the contact between Mpro Glu166-TRMT1 Arg528. This can lead to confusion that a salt bridge is formed while these two residues interact only via their main chain backbones. Indeed, the side chain of R528 is exposed to the solvent.

OK, we have taken this suggestion and tried to simplify and clarify this portion of the text (along with the accompanying structure Figure 3 showing key hydrogen bonds; see below).

• For Figure 2, please label the residues of the peptide with the TRMT1 numbering. This will help the reader to follow the text while looking at the figure.

OK we have added the TRMT1 numbering to what is now Figure 3A, and labeled key TRMT1 residues in Figures 3B, C, and D.

• Fig 2B is important but crowded. The authors could use two panels to show two different views of this interface.

Thank you for this suggestion, we have split B (now C and D in Figure 3) into two panels, rotated 90 degrees from one another, with each view showing a different subset of TRMT1-Mpro interactions. These updated panels are less crowded, and will hopefully be much clearer to readers.

• For increased clarity, the authors could color P3´-out in orange and P3´-in teal in Fig 3D.

OK, we have made this change.

• Please proofread the method section. There should be a space between values and their units. For example, 20mM HEPES should be 20 mM HEPES.

Thank you, we have corrected these formatting errors in the methods section of the revised version of the manuscript.

• The authors did not identify the mechanism for the higher efficiency of nsp4/5 cleavage despite testing several mutants and MD simulations. Did the author consider changes in the network of water molecules that might be identified in the MD simulations?

We did look at the positioning of waters in nsp4/5 vs nsp8/9 vs TRMT1 MD simulations. In the nsp4/5 simulation we do see a slightly higher density of water molecules positioned at approximately reasonable attack angles for substrate hydrolysis. If we consider water molecules with an attack angle on the scissile amide of 82 – 96 degrees and an attack distance of 4 Å or closer, the probabilities for these conditions in the simulations are: nsp4/5 – 19%, nsp8/9 – 9%, TRMT1 – 6%. More water positioned at reasonable attack positions for nsp4/5 might be consistent with its higher cleavage efficiency, but: (a) these are relatively small differences in water positioning across these 3 Mpro-substrate simulations that would not be enough to clearly explain the large differences in observed kinetics, and (b) hydrolysis happens in the later steps of the catalytic cycle, so to accurately capture this we would likely need to simulate reaction intermediates formed after initial attack of the active site Cys.

We very much appreciate the reviewer’s enthusiasm in pushing us to understand the mechanistic basis for Mpro-directed cleavage efficiencies, and we would have absolutely loved to figure this out! (As it appears to be a long-standing question in the field!) But as discussed above and in the manuscript, we think that it will take a detailed dissection of different steps in the catalytic cycle to understand where and how this selectivity arises. We will leave it to research groups focused more exclusively on the details of protease biochemistry and simulations of reactive intermediates to take up these significant and long-term challenges!

• In the PDB deposition, Y154 from chain B should be fixed.

• In the PDB deposition, some added glycerols seem to conflict. Although this is not important for the biological work discussed in this study, the authors should check if glycerol 403 in chain A and 402, 403 in chain B are properly modeled. Does the density justify placing a glycerol there?

• In the PDB deposition, there are over 51 RSRZ outliers. The authors should double-check if they cannot fix them with additional refinements. While such outliers in poorly defined linkers are understandable, this is unexpected for well-defined regions in the map.

We have made a number of updates to our PDB deposition to address the above three points. (1) We have reexamined and tweaked the loop region at Y154 chain B; this region of the structure has relatively poorly defined electron density, but we now have a model where Y154 is no longer a Ramachandran outlier. The PDB model is now free of any Ramachandran outliers. (2) We have reexamined each of the modeled glycerol molecules and removed one of these (GOL 402), which had a weaker fit to the electron density. The remaining two glycerols appear to be well-modeled (omit maps leaving out each glycerol show strong Fo-Fc density that clearly looks like a glycerol in shape, adding each glycerol back into the model decreases Rwork and Rfree, and the refined 2Fo-Fc map fits well to the modeled glycerols). (3) We agree there are a large number of RSRZ outliers in this structure. We have reexamined many of these, and come to the same conclusion as for our original deposition: that most of these result from residues where there is clear enough density for placing the backbone into the map, but very poor density for the sidechain. Modeling different sidechain positions for the RSRZ outliers we reexamined did not appreciably improve the model fit or change their RSRZ outlier status. For example, Y154 in chains A and B remain some of the worst RSRZ outliers; while the density for these loop regions is generally not very good, it is clear that the backbone atoms of Y154 can be modeled into the structure, but there is very very weak density for the sidechain. We tried modeling alternative and/or multiple sidechain conformations for Y154, but this did not significantly reduce the size of the RSRZ outlier. In short, while we could remove some of these residues or truncate the sidechain where the sidechain density is very poor to lower the total number of RSRZ outliers, we think the best model is one where we leave these residues built into the structure and accept the higher number of RSRZ outliers. Importantly, none of the significant RSRZ outliers are key residues of biological interest that would affect our interpretation of the structure and/or TRMT1-Mpro biochemistry.

We have deposited a new, re-refined PDB model (9DW6) that incorporates these changes and supersedes our old PDB entry (8D35). We have updated the manuscript with the new PDB ID. We thank the reviewer for these suggestions that improved the overall structural model.

Reviewer #3 (Recommendations For The Authors):

The crystal structure entry in the PDB should mention the Cys-to-Ala substitution in Mpro.

Thank you, we have made this change

Fig 2A and 2B: Can the authors highlight the Gln520-Ala531 peptide bind with a different color, please? It gets lost in panel B.

Yes, we have made significant revisions to what is now Figure 3, and have highlighted the scissile peptide bond atoms in orange in each of these panels. Thank you for this suggestion, we agree it helps readers to orient themselves within the structure.

"Importantly, the identified Mpro-targeted residues in human TRMT1 are conserved in the human population (i.e. no missense polymorphisms), showing that human TRMT1 can be recognized and cleaved by SARS-CoV-2 Mpro." Is TRMT1 prone to a high frequency of missense polymorphisms? If so, then this point makes sense. If not, it is not clear if this really informs on any biologically relevant mechanism.

Given (i) that primate TRMT1 was previously identified under positive selection (i.e. rapid evolution) in an evolutionary screen (Cariou et al PNAS 2022) and (ii) that our study is mostly in vitro, we thought it was important to, first, make sure that this sequence of TRMT1 used in functional assays is not specific to a reference sequence that we tested in vitro, but is actually the sequence of TRMT1 in the human population. Further, we were also looking for whether some variations in the Mpro cleavage site of TRMT1 were possibly present in some humans (could these be linked with severe COVID or susceptibility, for example?).

Overall, this statement aims to anchor our in vitro results to the TRMT1 sequences actually present in humans. However, we agree this does not inform “biologically relevant mechanism”. We therefore took out the “Importantly” that was probably misleading.

"TRMT1 engages the Mpro active site in a distinct binding conformation."

This is reported as an observation with little analysis. What is the structural basis of this conformational difference between the bound peptides? Why are the psi angles different? Is there a steric factor that is different between these peptide chains? This section can be substantially improved in detail from its current state.

See our related answer to the next comment below.

"Molecular dynamics simulations suggest kinetic discrimination happens during later steps of Mpro-catalyzed substrate cleavage." This section could have partly addressed my previous comment. It is not clear why there is such a large difference in the psi-angle. With access to several peptide-bound structures, the authors should derive and provide insights into the underlying fundamental principles. After all, this is a major point of discovery in their investigation.

We agree that it is not entirely clear why TRMT1 seems to favor the P3’-in conformation when binding to Mpro. The only other known peptide-bound structure that adopts a similar P2’ psi angle is nsp6/7, but there are not clear sequence, steric, or interaction features that distinguish TRMT1 and nsp6/7 from the other 6 peptide-Mpro structures that favor a P3’-out conformation with larger P2’ psi angle. In particular, the identity of the P1’ and P3’ residues, which would probably be expected to have the largest impact on this conformation, have no clear commonality in TRMT1 and nsp6/7 that give hints about why these adopt this unique conformation. As we describe in the discussion section of the manuscript, and has been observed by many other studies of Mpro, the protease active site is very plastic and able to accommodate a diverse range of sequences surrounding the invariant P1 Gln. Furthermore, while the crystal structures of TRMT1 and other nsp cleavage sequences bound to Mpro show a single peptide conformation in the active site, our MD simulations suggest that both P3’-in and P3’-out type conformations are present in solution for TRMT1, nsp4/5, and nsp8/9, just with different populations. It is very likely that there is a delicate energetic balance between these conformations that may depend subtly on multiple sequence features of the peptide and how they interact with each other and the flexible Mpro active site. As with our replies to questions from Reviewer 2 above about deciphering the underlying principles that connect peptide sequence to cleavage efficiency, we expect that dissecting the detailed links between sequence and binding conformation will be a long-term challenge for mechanistic and biocomputational groups focused on viral protease enzymes; systematic mutation of all residues in the cleavage sequence to multiple different amino acid identities followed by structure determination either experimentally and/or computationally will likely be required to uncover the key sequence or steric properties and interactions that underly and drive favored peptide binding conformations.

To highlight these questions as significant and difficult future challenges toward understanding the fundamental principles underlying SARS-CoV Mpro proteolysis, we have added an additional paragraph (second from the last paragraph) in the discussion section.

This work can be taken to a whole new level if the authors were to provide insights into how TRMT1 degradation by Mpro affects host cell biology and how the inhibition of this activity affects CoV biology.

We certainly agree that showing the biological effects of TRMT1 degradation on host cell biology and/or viral biology could raise the impact of this work. But as discussed in more detail above in our response to the weakness listed in Reviewer 3’s public review, we see the main focus of this work as showing the biochemical and structural basis for TRMT1 recognition and cleavage by SARS-CoV-2 Mpro, and directly showing the immediate effects of this cleavage on the TRMT1-tRNA interaction and modification activity. As was the case with other viral proteases, like the HIV-1 protease, understanding the potentially diverse and nuanced downstream biological effects of host protein cleavage and its impacts on cellular phenotypes or viral fitness could take many years of careful cell biology and virology work. We hope that our paper provides the key first steps to viral biology labs taking on this significant but important challenge for TRMT1!

Author response:

The following is the authors’ response to the original reviews.

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The manuscript by Rowell et al aims to identify differences in TCR recombination and selection between foetal and adult thymus in mice. Authors sequenced the unpaired bulk TCR repertoire in foetal and adult mice thymi and studied both TCRB and TCRa characteristics in the double positive (DP, CD4+CD8+) and single positive (SP4 CD4+CD8CD3+ and SP8 CD4-CD8+CD3+) populations. They identified age-related differences in TCRa and TCRB segment usage, including a preferential bias toward 3'TRAV and 5' TRAJ rearrangements in foetal cells compared to adults who had a larger perveance for 5'TRAV segments. By depleting the thymocyte population in adult thymi using hydrocortisone, the authors demonstrated that the repertoire became more foetal like, they therefore argue that the preferential 5'TRAV rearrangements in adults may be resulting from prolonged/progressive TCRa rearrangements in the adult thymocytes. In line with previous studies, Authors demonstrate that the foetal TCR repertoire was less diverse, less evenly distributed and had fewer non-template insertions while containing more clonal expansions. In addition, the authors claim that changes in V-J usage and CDR1 and CDR2 in the DP vs SP repertoires indicated that positive selection of foetal thymocytes are less dependent on interactions with the MHC. 

Strengths: 

Overall, the manuscript provides an extensive analysis of the foetal and adult TCR repertoire in the thymus, resulting in new insights in T cell development in foetal and adult thymi. 

Weaknesses: 

Three major concerns arise:

(1) the authors have analysed TCR repertoires of only 4 foetal and 4 adult mice, considering the high spread the study may have been underpowered. 

Given the concerns of the reviewer we have sequenced more libraries and added more data to include repertoires from 7 embryos and 6 young adults (biological replicates from different sorts). We believe that including more replicates has indeed strengthened our study. 

Our experimental approach was to sequence TCR transcripts, and in studies using RNA-sequencing of inbred mice, often only 3 individuals (biological replicates) are sequenced.

Our study sequenced from 7 foetal thymuses (generating TCRα and TCRβ repertoires from 4 FACS-sorted cell populations); 6 adult thymuses (generating TCRα and TCRβ repertoires from 4 FACS-sorted cell populations); and 5 adult thymuses from hydrocortisone-treated mice (generating TCRα and TCRβ repertoires from FACS-sorted CD3lo and CD3hi DP populations). We thus analysed 124 distinct repertoires from different populations and libraries, and many tens of thousands of unique sequences.  

(2) Gating strategies are missing and 

We have included gating strategies for cell-sorting as SFig7 and SFig8.

(3) the manuscript is very technical and clearly aimed for a highly specialised audience with expertise in both thymocyte development and TCR analysis. Authors are recommended to provide schematics of the TCR rearrangements/their findings and include a summary conclusions/implications of their findings at the end of each results section rather than waiting till the discussion. This will help the reader to interpret their findings while reading the results. 

We have modified the manuscript to include a more general introductory paragraph (page 3) to introduce the reader to the topic and we have included brief summaries of the findings at the end of each result section (pages 7,9,10,12,13,15).

Reviewer #2 (Public Review): 

Summary: 

The authors comprehensively assess differences in the TCRB and TCRA repertoires in the fetal and adult mouse thymus by deep sequencing of sorted cell populations. For TCRB and

TCRA they observed biased gene segment usage and less diversity in fetal thymocytes. The TCRB repertoire was less evenly distributed and displayed more evidence of clonal expansions and repertoire sharing among individuals in fetal thymocytes. In both fetal and adult thymocytes they show skewing of V segment (CDR1-2) repertoires in CD4 and CD8 as compared to DP thymocytes, which they attribute to MHC-I vs MHC-II restriction during positive selection. However the authors assess these effects to be weaker in fetal thymocytes, suggesting weaker MHC-restriction. They conclude that in multiple respects fetal repertoires are distinct from and more innate-like than adult. 

Strengths: 

The analyses of the F18.5 and adult thymic repertoires are comprehensive with respect to the cell populations analyzed and the diversity of approaches used to characterize the repertoires. Because repertoires were analyzed in pre- and post-selection thymocyte subsets, the data offer the potential to assess repertoire selection at different developmental stages. The analysis of repertoire selection in fetal thymocytes may be unique. 

Weaknesses: 

(1) Problematic experimental design and some lack of familiarity with prior work have resulted in highly problematic interpretations of the data, particularly for TCRA repertoire development. 

The authors note fetal but not adult thymocytes to be biased towards usage of 3' V segments and 5'J segments. It should be noted that these basic observations were made 20 years ago using PCR approaches (Pasqual et al., J.Exp.Med. 196:1163 (2002)), and even earlier by others.

We have cited this manuscript (Introduction, page 5) which used PCR of genomic DNA to investigate some TCRα VJ rearrangements in foetal and adult thymus. In contrast, our study uses next generation sequencing of transcripts to investigate all possible combinations of TCRα and TCRβ VJ combinations in different sorted thymocyte populations ex vivo. The greater sensitivity of this more modern technology has thus enabled us to detect many more TCRαVJ rearrangements than the 2002 study, and to conclude on basis of stringent statistical testing that the foetal repertoire is enriched for 3’V to 5’J combinations (Fig. 4). 

The authors also note that in fetal thymus this bias persists after positive selection, and it can be reproduced in adults during recovery from hydrocortisone treatment. The authors conclude that there are fewer rounds of sequential TCRA rearrangements in the fetal thymus, perhaps due to less time spent in the DP compartment in fetus versus adult. However, the repertoire difference noted by the authors does not require such an explanation. What the authors are analyzing in the fetus is the leading edge of a synchronous wave of TCRA rearrangements, whereas what they are analyzing in adults is the unsynchronized steady state distribution. It is certainly true, as has been shown previously, that the earliest TCRA rearrangements use 3' TRAV and 5'TRAJ segments. But analysis of adult thymocytes has shown that the progression from use of 3' TRAV and 5' TRAJ to use of 5' TRAV and 3' TRAJ takes several days (Carico et al., Cell Rep. 19:2157 (2017)). The same kinetics, imposed on fetal development, would put development of a more complete TCRA repertoire at or shortly after birth. In fact, Pasqual showed exactly this type of progression from F18 through D1 after birth, and could reproduce the progression by placing F16 thymic lobes in FTOC. It is not appropriate to compare a single snapshot of a synchronized process in early fetal thymocytes to the unsynchronized steady state situation in adults. In fact, the authors' own data support this contention, because when they synchronize adult thymocytes by using hydroxycortisone, they can replicate the fetal distribution. Along these lines, the fact that positive selection of fetal thymocytes using 3' TRAV and 5' TRAJ segments occurs within 2 days of thymocyte entry into the DP compartment does not mean that DP development in the fetus is intrinsically rapid and restricted to 2 days. It simply means that thymocytes bearing an early rearranging TCR can be positively selected shortly after TCR expression. The expectation would be that those DP thymocytes that had not undergone early positive selection using a 3' TRAV and a 5' TRAJ would remain longer in the DP compartment and continue the progression of TCRA rearrangements, with the potential for selection several days later using more 5'TRAV and 3'TRAJ. 

We agree with this summary provided by the reviewer which corresponds closely to the points we made ourselves in the manuscript. Indeed, we discuss the synchronization and kinetics of first wave of T-cell development in Results page 13 and Discussion page 17, which was the rationale for the hydrocortisone experiment.  We have also discussed findings from Carico et al 2017 in this context (see pages 13, 16, 17).  

(2) The authors note 3' V and 5'J biases for TCRB in fetal thymocytes. The previously outlined concerns about interpreting TCRA repertoire development do not directly apply here. But it would be appropriate to note that by deep sequencing, Sethna (PNAS 114:2253 (2017)) identified skewed usage of some of the same TRBV gene segments in fetal versus adult.  It should also be noted that Sethna did not detect significantly skewed usage of TRBJ  segments. Regardless, one might question whether the skewed usage of TRBJ segments detected here should be characterized as relating to chromosomal location. There are two logical ways one can think about chromosomal location of TRBJ segments - one being TRBJ1 cluster vs TRBJ2 cluster, the other being 5' to 3' within each cluster. The variation reported here does not obviously fit either pattern. Is there a statistically significant difference in aggregate use of the two clusters? There is certainly no clear pattern of use 5' to 3' across each cluster. 

We have included a statistical comparison of the aggregate TRBJ use between the J1 cluster and the J2 cluster (see SFig5) and Results page 9. 

(3) The authors show that biases in TCRA and TCRB V and J gene usage between fetal and adult thymocytes are mostly conserved between pre- and post-selection thymocytes (Fig 2). In striking contrast, TCRA and TCRB combinatorial repertoires show strong biases preselection that are largely erased in post-selection thymocytes (Fig 3). This apparent discrepancy is not addressed, but interpretation is challenging. 

I think the reviewer is referring to heatmaps for individual gene segment usage shown in Figure 2 in comparison to combinatorial usage shown in Figure 4. There is not a discrepancy in the data, but rather the differences between these two figures lie in the way in which the comparisons are made and visualised.  The heatmaps in Figure 2A-D show mean proportional usage of each individual gene segment for each cell type in the two life stages, clustered by Euclidian distance. This visualisation clearly shows bias in foetal 3’ TRAV usage and 5’TRAJ usage (looking at areas of red, which have higher usage), with less pronounced enrichment for TRBV and TRBJ.  The heatmaps also show differences in intensity between different cell populations in each life-stage. 

In contrast, in Figure 4 the tiles show combinations with statistically significant (P<0.05) differences in mean counts for each VJ combination in each cell type between 7 foetal and 6 adult repertoires by Student’s t-test, after correcting for False discovery rate (FDR) due to multiple combinations.  It is the case, that there are fewer significant differences in proportional combinatorial VxJ use between foetal and adult repertoires after selection. We find this an interesting finding and have expanded our discussion of this aspect of the data (page 10).  More than half of the significant differences persist after repertoire selection, and the reduction in each individual SP population, of course in part reflects the lineage divergence.

(4) The observation that there is a higher proportion of nonproductive TCRB rearrangements in fetal thymus compared to adult is challenging to interpret, given that the results are based upon RNA sequencing so are unlikely to reflect the ratio in genomic DNA due to processes like NMD.

We have added two sentences to explain that transcripts of non-productive rearrangements are eliminated by nonsense-mediated decay (NMD), but some non-productive transcripts are detected in many studies of TCR repertoire sequencing, and we have cited three studies from different groups that document this (see Results, page 10-11). We have not commented on how the increase in non-productive TCR rearrangements in the foetal populations (in comparison to adult) relates to rearrangements in genomic DNA or NMD.   We have likewise not commented on the possible significance or biological role of nonproductive TCR transcripts, but simply reported our findings.

(5) An intriguing and paradoxical finding is that fetal DP, CD4 and CD8 thymocytes all display greater sharing of TCRB CDR3 sequences among individuals than do adults (Fig 5DE), whereas DP and CD8 thymocytes are shown to display greater CDR3 amino acid triplet motif sharing in adults (with a similar trend in CD4). 

As foetal DP, CD4SP and CD8SP TCRbeta repertoires have fewer non-template insertions and lower means CDR3 length, they are expected to share more CDR3 repertoires than their adult counterparts.  However, in the case of CDR3 amino acid triplet motifs (k-mers) what is being analysed is the sharing of each possible individual k-mer. If k-mers are shared more in the adult for some populations, but CDR3 repertoires are shared more in the foetus, we think it means that some k-mers appear in many different CDR3 sequences in the adult, so that they are over-represented in multiple different CDR3s (presumably due to selection processes, although we agree that this is just an assumption).  

The authors attribute high amino acid triplet sharing to the result of selection of recurrent motifs by contact with pMHC during positive selection. But this interpretation seems highly problematic because the difference between fetal and adult thymocytes is dramatic even in unfractionated DP thymocytes, the vast majority of which have not yet undergone positive selection. How then to explain the differences in CDR3 sharing visualized by the different approaches? 

The TCRβ repertoire has been selected in the adult DP population through the process of β-selection, which is believed to involve immune synapse formation and MHC-interactions (Allam et al 2021,10.1083/jcb.201908108). We have now included this reference in the introduction to make this clear (page 4). However, we agree with the reviewer’s comments that it is challenging to explain the k-mer analysis and that we have not been able to actually show that increased k-mer sharing in the adult is a direct consequence of increased positive selection: it was our interpretation of this seemingly paradoxical finding.  For clarity, we have therefore removed the k-mer analyses from the manuscript.

(6) The authors conclude that there is less MHC restriction in fetal thymocytes, based on measures of repertoire divergence from DP to CD4 and CD8 populations (Fig. 6). But the authors point to no evidence of this in analysis of TRBV usage, either by PC or heatmap analyses (A,B,D). The argument seems to rest on PC analysis of TRAV usage (Fig S6), despite the fact that dramatic differences in the SP4 and SP8 repertoires are readily apparent in the fetal thymocyte heatmaps. The data do not appear to be robust enough to provide strong support for the authors' conclusion. 

We have written the text very carefully so as not to make the claim too strong, stating in the abstract: “In foetus we identified less influence of MHC-restriction on α-chain and β-chain combinatorial VxJ usage and CDR1xCDR2 (V region) usage in SP compared to adult, indicating weaker impact of MHC-restriction on the foetal TCR repertoire.” We are not saying that MHC-restriction does not impact VJ gene usage in foetal repertoires, but rather that it has less influence (particularly when compared to life-stage).  Evidence for this comes from:  [1] Heatmaps in Fig2A-D which show that all repertoires cluster first by life-stage ahead of cell type; [2] Fig3A and B: PCA of adult and foetal TCRβ VXJ combinations: All repertoires cluster by life-stage on PC1.  PC2 separates adult repertoires by cell type (adult SP8 are positive on PC2 while adult SP4 are negative on PC2, and DP cells are between them) but for foetal repertoires the SP8 and SP4 are highly dispersed with some SP4 cells falling on positive side of PC2.  Only foetal DP repertoires cluster tightly. [3] Fig6A-C: PCA of β−chain CDR1xCDR2 (corresponding to Vβ gene segment usage) again shows the same pattern.  Adult repertoires separate by cell type on PC2, (SP8 positive on PC2, SP4 negative on PC2, with DP in between), but foetal SP8 repertoires are much more dispersed.  [5] SFig6J-K: PCA of α−chain CDR1xCDR2 (Vα usage) frequency distributions: adult repertoires cluster together and are separated by cell type on PC2 (SP4 positive, SP8 negative), but foetal populations are highly dispersed and fail to cluster by cell type on either axis. [6] We have additionally added new PCA analyses to explore differences in MHC-restriction between foetal and adult SP populations.  This is shown in the new Figure 7. We reasoned that in a PCA that included foetal and adult repertoires together, the foetal repertoires might not segregate by SP cell type (MHC-restriction) because of their overall bias towards particular VJ combinations, which would mean that effectively the PCA would be imposing adult MHC restriction on the foetal repertoires.  We therefore carried out PCA in which we analysed the adult repertoires separately from the foetal repertoires.  As expected for adult repertoires, PCA separated SP4 repertoires from SP8 repertoires on PC1 in each comparison (β-chain VxJ (Fig. 7B), α-chain VxJ (Fig. 7F), β-chain CDR1xCDR2 (V region) (Fig. 7H) and α-chain CDR1xCDR2 (V region) (Fig. 7L)). In contrast, for foetal TCRα repertoires (α-chain VxJ and α-chain CDR1xCDR2 (V region)), PCA failed to separate SP4 from SP8 repertoires on PC1 or PC2, so we did not detect impact of MHC-restriction on foetal TCRβ repertoires (Fig. 7E and K).  For foetal TCRβ repertoires, PCA separated SP4 β-chain VxJ from SP8 on PC2, accounting for only 11.1% of variance (Fig. 7A) (in contrast to the 44.2% of variance accounted for by MHC-restriction in adult β-chain VxJ PCA (Fig. 7B)). Thus, in adult repertoires ~4-fold more of the variance in β-chain VxJ usage can be accounted for by MHC-restriction than in foetal repertoires. PCA of foetal β-chain CDR1xCDR2 (V region) separated SP4 from SP8 on PC1, accounting for 28.8% of variance, whereas in PCA of adult β-chain CDR1xCDR2, MHCrestriction accounted for 56.1% (>2-foldmore than in foetus).  Thus, even when we  considered only V-region usage alone, we detected a stronger influence of MHC-restriction on the TCRβ repertoire in adult compared to foetal thymus.  

Reviewer #3 (Public Review): 

Summary:

This study provides a comparison of TCR gene segment usage between foetal and adult thymus.

Strengths:

Interesting computational analyses was performed to find interesting differences in TCR gene usage within unpaired TCRa and TCRb chains between foetal and adult thymus.  

Weaknesses:

This study was significantly lacking insight and interpretation into what the data analysed actually means for the biology. The dataset discussed in the paper is from only two experiments. One comparing foetal and adult thymi from 4 mice per group and another which involved hydrocortisone treatment. The paper uses TCR sequencing methodology that sequences each TCR alpha and beta chains in an unpaired way, meaning that the true identity of the TCR heterodimer is lost. This also has the added problem of overestimating clonality, and underestimating diversity.

We have discussed the limitations and benefits of our approach of sequencing TCRβ and TCRα repertoires separately in the Discussion (page 19).  This approach allows the analysis of thousands of sequences from different cell types and different individuals at relatively low cost. We have made no claims in our manuscript about overall diversity or pairing, and given that each chain’s gene locus rearranges at a different time point in development, we believe it is of interest to consider the repertoires individually within this context.

Limited detail in the methods sections also limits the ability for readers to properly interpret the dataset. What sex of mice were used? Are there any sex differences? What were the animal ethics approvals for the study?

We have included this information in the Methods (page 19).  Both sexes were used and we found no sex differences, although that was not the focus of our study. All animal experimentation in the UK is carried out under UK Home Office Regulations (following ethical review). This is included in the Methods (page 19).  

Recommendations for the authors:  

Reviewer #1 (Recommendations For The Authors): 

Major points: 

- Group sizes are very small (4 foetal and 4 adult mice). Considering the spread in TCR analysis (eg fig 1 B-H, Sup figures 2-4), the study is likely underpowered as it often looks like one mouse prevents or supports a statistical difference. Authors should therefore consider increasing the group size. 

We have sequenced more libraries and included more data, from 7 foetal and 6 young adult animals (biological replicates).  

- The authors should include a gating strategy for their sorted cells. This is essential to verify the quality of their findings. 

We have added this to the Methods and SFig7 and SFig8.

Authors should include a summary sentence at the end of each result section which interprets the main finding. Furthermore, the manuscript would greatly benefit from a schematic figure of their main findings, particularly with regards to the rearrangements and selection differences in foetal and adult thymi. 

We have added a summary sentence to the end of each results section.

- Authors should be more careful with their claim that MHC has less of an effect foetal TCR selection. Authors demonstrated that there is a difference in VJ recombination between the foetal and adult TCR repertoire, skewing the foetal TCR repertoire to certain variable and junctional segments. Since both CDR1 and CDR2 are encoded by the variable gene, this is likely to affect their ability to interact with the MHC during positive selection. Have Authors considered whether the selection process is actually a bystander effect of the differences in the rearrangement process? One way to support the authors claim is to demonstrate that mice with an alternative MHC background, have similar foetal/adult gene rearrangements but a different TCR repertoire in the SP populations. 

Time and resources have prevented us from repeating our experiments in another strain of inbred mice.  However, we note that a previous PCR study that showed 3’TRAV to 5’TRAJ bias in foetal repertoires was carried out in BALB/c mice (Pasqual JEM 2002). We have added this point to the Discussion (page 17). 

- (supplementary) tables have not been provided. 

Supplementary Tables were uploaded with the submission.  STables 1 and 2 show antibodies used for cell sorts and STable 3 primers used.

Moderate points: 

- The loading plots in Figure 3 onward are visually strong. Authors could consider including an V and J (separate) loading plots for Figure 3 E, F and G to demonstrate preferential V and J usage. 

We have included additional loading plots in Figure 7 for the new PCA we have added (see Fig. 7C, D,I and J).

- "the proportion of non-productive rearrangements was higher in the foetal SP8 population than adults (Fig 5A)" Authors should explain how non-productive TCRs end up in SP populations as they need to pass positive and negative selection which both require interactions between the TCR and the MHC. 

As we used RNA sequencing in our study, we did not comment on how the increase in nonproductive TCRbeta rearrangements in the foetal populations (in comparison to adult) relates to rearrangements in genomic DNA or to nonsense-mediated decay (NMD) that is believed to down-regulate transcripts of non-productively rearranged TCR.  We have not commented on the possible significance or biological role of non-productive TCR transcripts, but simply reported our findings. 

- Authors have studied CDR3 sequential amino acid triplets (k-mers). However, CDR3 regions are longer than 3 amino acids in length, hence authors should provide 1) an overview/comparison of the identified k-mers in foetal or adult thymocytes 2) explain how different k-mers relate to each other, eg whether they are expressed in the same TCR. Have authors considered using alternative programs to identify CDR3 motifs that are based on the full CDR3amino acid sequence, eg TCRdist provides motifs and indicated which amino acids are germline encoded or inserted. 

In light of this comment from this reviewer and also comments from Reviewer 2, we have removed the comparison of k-mers from the manuscript.  Please see response to point 5 of Reviewer 2.  

- The term "innate-like" is confusing as it implies that foetal cells are not antigen specific.

However, once in the circulation, foetal cells will respond in an antigen-specific manner.

Hence authors should use another term. 

We have removed the term “innate-like” from the abstract and the first time we used it in the first paragraph of the Discussion. However, the second time we used the term, we are actually taking it from the manuscript we cited (Beaudin et al 2016) and in this case we left it in. We agree that foetal cells are likely to respond in an antigen-specific manner. 

- To support their hypothesis in the discussion "However, as TCRd gene segments are nested.... so that 5' TRAV segments are not favoured" can authors confirm that there are indeed less yd T cells in the foetal repertoire? 

We have removed this section from the discussion, because although it is interesting, it is highly speculative, and the manuscript is already quite complicated to interpret.

Minor points: 

- The authors may find the publication by De Greef 2021 PNAS of interest to identify TRBD segments 

- Authors need to clarify that they mean CDR3-beta in the sentence "The mean predicted CDR3 length.... compared to young adult" 

We have included new data in the manuscript to show that mean CDR3 length is lower in all foetal populations of beta (Fig5C) and alpha (SFig5C) and clarified which we are referring to in the text. 

- Authors should bring the section "During TCRb gene rearrangement, these segments.... Initiating the sequence of rearrangements" forward and include a schematic." Forward to figure 2 and provide the reader with a visual schematic of the foetal vs adult recombination events. 

- Discussion: "The first wave of foetal abT-cells that leave the thymus... tolerant to both self and maternal MHC/antigens". Have Authors considered the alternative hypothesis published by Thomas 2019 in Curr Opin System Biol that the observed bias could potentially provide better protection against childhood pathogens? 

We have indeed considered this, as stated in the first paragraph of the Discussion “The first wave of foetal αβT-cells that leave the thymus must provide early protection against infection in the neonatal animal”. We have now cited the Thomas 2019 study.

- Discussion: Authors should rephrase the sentence "The transition from DP to SP cell in the foetus.... From DN3 to SP cell may be slower" as it is unclear what the authors mean. 

We have rephrased this (see page 17)

- Discussion "TRAV and TRAJ Array" do authors mean "TRAV and TRAJ area"? 

We did indeed mean array (as in series of gene segments) but we have changed the wording for clarity (page 14).

- Methods, Fluorescence activated cell sorting: can authors clarify whether they stained, sorted and sequenced the full thymus and /or specify how many cells were included. Can authors also explain why foetal and adult cells were treated differently (eg the volume of master mix)? 

- Methods Fluorescence activated cell sorting authors should specify what they mean with "mastermix of either 1:50 (foetal thymus) or 1:100 (adult thymus)". Does this mean all antibodies in the foetal mastermix were 1:50 and all antibodies in the adult master mix were 1:100? If so, why were different concentrations used and why were antibodies not individually titrated before use?  

We have clarified the methods and antibodies used are listed with clones in supplementary tables.

Figures: 

- Several figures did not fit on the page and therefore missed the top or side 

- Figure 1A: missing a label on the Y axis

This is visible

- Figure 2A-D: please indicate the 5' and 3' terminus in each graph. The cell type legend should include two separate colours for the two DP populations. 

We have added 5’ and 3’ labels.  The two DP populations are clearly labelled.

- Figure 4: please indicate the 5' and 3' terminus in each graph. 

We have added 5’ and 3’ labels.   

- Figure 5C: y axis should read mean CDR3B length (aa), Figure 5D and E: y axis should read Jaccard Index CDR3B, Figure 5 F and G: y axis should read Jaccard index CDR3B k-mers. Same comment for Sup Fig 5 but then CDR3a. 

We have added these labels for both Figure 5 and Supplementary Figure 6 (was SFig5 previously).

- Figure 6C top label should read CDR1B x CDR2B with highest contribution 

We have added this label.

- Figure 7: please indicate the 5' and 3' terminus in each graph. 

We have added 5’ and 3’ labels.  This is now Figure 8, as we have added new analyses (new Figure 7).

- Supplementary Figure 1-4 are missing a colour legend next to the graphs.

We have added the legends in.  

Reviewer #2 (Recommendations For The Authors): 

(1) The authors need to provide better support for the notion that the fetal thymus produces ab T cells with properties and functions that are distinct from adult T cells. There are several  ways they might provide a more meaningful assessment: (1) They could analyze the fetal repertoire at multiple time points. (2) They could compare instead the steady state distributions in early postnatal and adult thymus samples. (3) They could compare the peripheral T cell repertoires in the first week of life versus adult. This last approach would allow them to draw the most impactful conclusion. 

We appreciate these suggestions.  Sadly, it is beyond our budget for the current manuscript and beyond the scope of our current study that we believe provides interesting new information.

(2) Fig S2D shows TRBJ1-4 in black lettering meant to indicate no significant difference whereas the figure shows use of this gene segment to be elevated in adult. I believe TRBJ1-4 should be in blue lettering.

This is now coloured correctly.

(3) The figure call out on p11 (Fig5I-J) should be H-I.

This is now corrected.

(4) Please indicate in the main text that Jaccard analysis in Fig 5 D-E is for TCRB.

This is now corrected.

(5) The analysis of usage of TCRB CDR1xCDR2 combinations in Fig6D is said to "reflect the bias observed in their TRBV gene usage (Fig 2C)". Isn't it the case that every TRBV gene presents a distinct CDR1xCDR2 combination, meaning that there is no difference between TRBV usage and TRBV CDR1xCDR2 usage? If so, please make this clearer.

Yes, this is the case, we have made this clearer in the text.

Reviewer #3 (Recommendations For The Authors): 

In general, although there is lots of interesting analyses that can be done with these large datasets, I feel as though the authors did not fully interpret the real meaning and significance of many of these results. Whilst there were some speculation on why a foetal repertoire might be different to those of adults in the discussion sections, the rationale for each individual analyses was not clearly explained. I would suggest that the rationale and a thorough explanation of each analyses be added to the results section, including a finishing sentence on what it means. 

We have added short summaries to each results section to make the points we are making clearer.

The authors did not mention how many cells were sorted for from each thymus for sequencing. Was the cell number normalised between each population? As this might have an influence on various downstream measurements of diversity, evenness and clonality, if there is a sampling issue. 

This is explained in the methods.  We used sampling to allow comparisons between repertoires of different sizes, and this is also explained in the methods.

The authors should include the cell sorting profiles and example flow cytometry plots, including gating strategies and the post sort purity of each sorted population. 

We have included sorting strategies in the methods (SFig7 and SFig8).

I think the manuscript could also be improved if there were some basic characterisation of foetal vs. adult thymus development. How many thymocytes are in a foetal vs adult thymus at the timepoints chosen? 

I think there were some interesting findings in this paper. Given that overall, the foetal thymus appeared to be less diverse than that of the adult, one question I thought would be interesting to discuss was the overlap between the two repertoires. Is the foetal thymus simply a sub-fraction of the adult repertoire or is it totally distinct with no overlapping sequences? 

Our analyses indicate that the repertoires are actually different. This is evident in Fig4 and in PCA loading plots shown in Fig, 3C and new Fig. 7C, D, I and J.

I think that some of the interpretation in the results section may be a bit vague. "When we compaired by thymocyte population, each adult population clustered together, with adult SP4 separating from adult SP8 on PC2 and DP cells scoring in between, suggesting that PC2 might correspond to MHC restriction of the adult populations." - whilst I think I know what the authors mean, I do believe that this could be explained in clearer detail and more explicit. SP4 and SP8 are known to be positively selected in the thymus on distinct MHC class I and MHC class II molecules for example. 

We have tried to clarify the text describing that PCA and additionally added a new Figure (new Fig. &) to compare the influence of MHC-restriction on the TCR repertoire in foetal and adult thymus.

In the methods section, the age and sex of mice used were not explained at all. What was used in the experiment? Are there any sex differences? 

Age and sex of mice is given in the methods.  We have not detected sex differences.

This is a huge omission from the manuscript. In general, I don't believe the methods section has described the analysis in sufficient detail for replication. All analysis code and data should be publicly accessible and be in a format that allows for the reader to replicate the figures in the paper upon running the code. Perhaps even allowing them to run their own TCR datasets.  Overall, I think the manuscript needs some rewriting to include additional details and deeper interpretation of each individual analyses. 

Sequencing data files will be made publicly available on UCL Research Data Repository.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewing editor:

The biological significance of the results presented in this manuscript is the potential absence of active sequestration mechanisms in certain species, leading to variation in their ability to transport and store specific compounds, such as alkaloids. The concept of passive accumulation is introduced as an evolutionary intermediate between toxin consumption and sequestration.

I agree with the reviewers' comments on the limitations of the current manuscript. Additionally, I'd like to raise a point about combining data from LC/MS and GC/MS as these techniques have different sensitivities. GC-MS excels in annotation, allowing for confident identification of detected compounds. However, it may have limitations in the number of extractable substances. Conversely, LC-MS/MS offers a broader range of detectable substances, but annotation can be more challenging. While methods to bridge this gap exist, the current approach might not fully account for the potential influence of the analysis equipment on the observed differences in alkaloid numbers between the Texas and Panama samples analyzed by LC-MS/MS. To address this, consider including data from both methods (if possible) to gain a more comprehensive understanding of the alkaloid profiles. Alternatively, analyzing the Texas and Panama samples with GC-MS could be considered for a more focused comparison with the other samples.

Thank you for the suggestion. Unfortunately, we do not have GC-MS data for the Texas and Panama samples. While the strength of these two datasets is that they present two independent lines of data corroborating that “undefended” frogs have detectable alkaloid levels, we have more explicitly made clear for readers that the datasets should not be compared directly. We reviewed the text to check that we carefully acknowledge in the manuscript the higher sensitivity of our LC-MS assay, and we added more detail about the differences between the two assay types (section 4d): “The UHPLC-HESI-MSMS pipeline used on the samples from Panama and Texas allows for higher sensitivity to detect a broader array of compounds compared to our GC-MS methods, but has lower retention-time resolution and produces less reliable structural predictions. Furthermore, due to the lack of liquid-chromatography-derived references for poison-frog alkaloids, precise alkaloid annotations from the UHPLC-HESI-MSMS dataset could not be obtained. Therefore, the UHPLC-HESI-MSMS and GC-MS datasets are not directly comparable, and UHPLC-HESI-MSMS data are not included in Fig. 2”. We have also revised the asterisk accompanying the table to further reinforce that alkaloid numbers between the two assay types should not be compared. It now states: “Note that the UHPLC-HESI-MS/MS and GC-MS assays differed in both instrument and analytical pipeline, so “Alkaloid Number” values from the two assay types should not be compared to each other directly”. We further point out differences between the two assay types in section 2b: “Similarly, the analysis of UHPLC-HESI-MS/MS data was untargeted, and thus enables a broader survey of chemistry compared to that from prior GC-MS studies.”

Finally, we point out that the output from the analytical pipeline for UHPLC-HESI-MSMS annotates compounds as “alkaloids,” using broader criteria than the targeted GC-MS component of our study. In an effort to make the datasets more comparable, at least conceptually, we now include an assessment of which alkaloids identified by UHPLC-HESI-MSMS match known molecular formulae and structural classes in frogs (see Table S6 and revised text on lines 335-343 and 410-415.

Reviewer #1 (Public Review):

This is a very relevant study, clearly with the potential of having a high impact on future research on the evolution of chemical defense mechanisms in animals. The authors present a substantial number of new and surprising experimental results, i.e., the presence in low quantities of alkaloids in amphibians previously deemed to lack these toxins. These data are then combined with literature data to weave the importance of passive accumulation mechanisms into a 4-phases scenario of the evolution of chemical defense in alkaloid-containing poison frogs.

In general, the new data presented in the manuscript are of high quality and high scientific interest, the suggested scenario compelling, and the discussion thorough. Also, the manuscript has been carefully prepared with a high quality of illustrations and very few typos in the text. Understanding that the majority of dendrobatid frogs, including species considered undefended, can contain low quantities of alkaloids in their skin provides an entirely new perspective to our understanding of how the amazing specializations of poison frogs evolved. Although only a few non-dendrobatids were included in the GCMS alkaloid screening, some of these also included minor quantities of alkaloids, and the capacity of passive alkaloid accumulation may therefore characterize numerous other frog clades, or even amphibians in general.

Thank you for the kind evaluation.

While the overall quality of the work is exceptional, major changes in the structure of the submitted manuscript are necessary to make it easier for readers to disentangle scope, hypotheses, evidence and newly developed theories.

Based on reviewer comments, we revised the manuscript structure substantially to make the different aspects of the paper more readily identifiable to readers. Specifically we moved the content of Figure 2 into a new section in the introduction. We also added more introductory text to better introduce the main ideas of the new model and to summarize the scope and aim of the paper. We reorganized the result section headings and moved Figure 1 (now Fig. 3) down into section 2c.

Reviewer #2 (Public Review):

Summary:

This was a well-executed and well-written paper. The authors have provided important new datasets that expand on previous investigations substantially. The discovery that changes in diet are not so closely correlated with the presence of alkaloids (based on the expanded sampling of non-defended species) is important, in my opinion.

Strengths:

Provision of several new expanded datasets using cutting edge technology and sampling a wide range of species that had not been sampled previously. A conceptually important paper that provides evidence for the importance of intermediate stages in the evolution of chemical defense and aposematism.

Thank you for kind comments.

Weaknesses:

There were some aspects of the paper that I thought could be revised. One thing I was struck by is the lack of discussion of the potentially negative effects of toxin accumulation, and how this might play out in terms of different levels of toxicity in different species.

Thank you for the suggestion. We now explicitly address the possible negative effects of toxin accumulation and how costs may play out with respect to varying levels of chemical defense among different organisms, including poison frogs. We note early on that, “short-term alkaloid feeding experiments (e.g., Daly et al., 1994; Sanchez et al., 2019) demonstrate that both defended and undefended dendrobatids can survive the immediate effects of alkaloid intake, although the degree of resistance and the alkaloids that different species can resist vary'' (section 2c), and we address the sparse literature suggesting some species-level variation in alkaloid resistance in frogs. Later, we make the point that, “origins of chemical defenses are also shaped by the cost of resisting and accumulating toxins, which can change over evolutionary time as animals adapt to novel relationships with toxins” (section 2d). We broadly discuss costs of target-site resistance, a common mode of molecular resistance in poison frogs and other animals, and compensatory molecular adaptations that offset the costs. We also discuss examples from the literature of negative effects of high levels of resistance and toxin accumulation that are not completely offset. We also note that to the best of our knowledge, potential lifetime fitness costs to alkaloid consumption by dendrobatids have not been evaluated.

Further, are there aspects of ecology or evolutionary history that might make some species less vulnerable to the accumulation of toxins than others? This could be another factor that strongly influences the ultimate trajectory of a species in terms of being well-defended. I think the authors did a good job in terms of describing mechanistic factors that could affect toxicity (e.g. potential molecular mechanisms) but did not make much of an attempt to describe potential ecological factors that could impact trajectories of the evolution of toxicity. This may have been done on purpose (to avoid being too speculative), but I think it would be worth some consideration.

We agree that other factors can influence the trajectory of chemical defense. We incorporated these ideas into the new section 2d, which provides a somewhat brief overview of ecological factors that could influence the origins of chemical defense, the physiological costs of toxin resistance and accumulation, and some of the possible eco-evo factors that shape chemical defense once it evolves.

In the discussion, the authors make the claim that poison frogs don't (seem to) suffer from eating alkaloids. I don't think this claim has been properly tested (the cited references don't adequately address it). To do so would require an experimental approach, ideally obtained data on both lifespan and lifetime reproductive success.

We agree with the reviewer that more data are necessary to make this broad claim, which we have removed. We revised this to state: “regardless, it is clear that all or nearly all dendrobatid poison frogs consume alkaloid-containing arthropods as part of their regular diet” (section 2c). We then expand on this statement with data from short-term experimental work that support the notion that at least some dendrobatids are resistant (i.e., can survive) the immediate effects of alkaloids. We also point out later in the manuscript that, “as far as we are aware, the possible lifetime fitness costs (e.g., in reproductive success) of alkaloid consumption in dendrobatids have not been measured” (section 2d).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

While in general I am very open to "unorthodox" ways to write a manuscript (i.e., differing from the standard structure intro-methods-results-discussion) I feel there is much room for improvement in this case. When reading the manuscript line by line, I was several times totally uncertain about the scope and content of the original data in the manuscript. It is too often unclear which of the outlined theories are new and why they are presented, which hypotheses were tested and why, which data were newly obtained, which technological improvements led to the novel and surprising results, and why no alternative hypotheses are tested. I feel the authors need to fundamentally reconsider the structure of the manuscript - which does not mean everything needs to be rewritten, but some major reshuffling of paragraphs from one section to the other may already lead to substantial improvement. I will in the following list (not ordered by priority) different issues that I encountered, without always providing a specific suggestion for improvement - please come up with an improved structure that removes these issues in one way or the other!

Thank you for the suggestions. We did our best to improve the structure of the paper. Specifically, we substantially revised the introduction to provide a clearer background of the ideas leading up to the new evolutionary model. We moved most of what was previously figure 2 (now Fig. 1) into an earlier part of the introduction in the main text. We moved what was previously figure 1 (now Fig. 3) to much later in the discussion (section 2c). We attempted to clarify and separate throughout the text the new data from existing data. Please see our responses below for additional details.

Line 42-45: Please provide a reference on this statement on traversing adaptive landscapes.

We added the following reference: Martin, CH and PC Wainwright. 2013. Multiple fitness peaks on the adaptive landscape drive adaptive radiation in the wild. Science 339: 208-211. https://doi.org/10.1126/science.1227710

Line 50: Why are these phases "likely" to occur? - no evidence is presented for this hypothesized high likelihood. Presenting this scenario already in the second paragraph of the intro is very weird. Are these really the only possible phases? Wouldn't it be possible to come up with totally different scenarios? In my opinion, this specific four-phase scenario should be more clearly labelled as a novel theory presented in this paper, and perhaps it should come much later in the introduction.

Thank you for the suggestion. We moved this paragraph down into a new subsection of the introduction. We also revised the language to clarify that the model is a new evolutionary theory based on new and existing ideas.

Line 51: Here you use for the first time the term "elimination". While it is intuitively clear what is meant by it, there still could be different meanings. The alkaloids could simply be passively excreted, or they could be actively biochemically decomposed. Later in the Discussion the authors imply that elimination requires some kind of metabolic process, but this perhaps should be made clearer already in the introduction.

We now spend more time in the introduction describing pharmacokinetics as well as the terms we used (including elimination), which are slightly modified from terms in pharmacokinetics.

Figure 1. I have major concerns about this figure. I found the figure very confusing, and the authors really need to reconsider and modify (simplify) it. The figure caption starts with "Major processes involved..." as if this was established textbook knowledge rather than a totally hypothetical illustration of how different factors (sequestration, elimination....) can lead to defended or undefended phenotypes. Only later on in the caption it becomes clear this is just a suggestion/hypothesis/model: "we hypothesize...".

We revised the figure (now Fig. 3) and its legend. It now starts with the following text: “Hypothesized physiological processes that interact to determine the defense phenotype.” We also simplify the figure by removing two lines and recoding the table (see comment below).

Secondly, the way the graph is drawn suggests some kind of experimental result where specific evolutionary pathways lead to very specific degrees of "defendedness", recognizable by the points on the right axis stacked very precisely one above the other. Do you really want to imply that you want to suggest such a specific model, where particular accumulation/intake/elimination rates lead to exactly these outcomes? Also, wouldn't it be possible to somewhat simplify the categories in the table? Again, why so specific, is there any experimental evidence for it? Why sometimes 1 plus, 2 plus, 3 plus? Wouldn't it be better to just suggest categories such as strong, weak and absent?

We simplified the figure by removing the secondary (dashed) passive accumulation and active sequestration lines. We also changed the + signs to “low,” “med,” or “high” and tried to simplify the text in the figure and in the legend.

Line 101-103: "We propose ..." Here, as the concluding statement of the introduction, the authors suggest a very general hypothesis which seems rather disconnected from the four-phase model and from the experimental results. Here, at the latest, I would have expected to learn (1) what the overall scope of the paper is, (2) which kind of approaches were followed and which novel experimental results will be presented in the following, and (3) how the experimental results will be used to derive a new theory / novel. Again, it is obvious that the scope of the paper is broader than testing just a single and narrow hypothesis, but rather to support and develop a broader theory and evolutionary model, but this should be clear to readers once they arrive at this line.

Thank you for the suggestion. We added a paragraph to the end of the first section of the introduction that outlines the content of the rest of the paper. We also reorganized some of the subheadings to make the flow of ideas and the source of data in each subsection clearer. We split up and moved what was previously in section 2a into parts of the introduction and discussion. We moved the results text about diet and the discussion about resistance to section 2a, to better provide data and discussion of phases 1 and 2.

Figure 2. My opinion on this figure is much less strong than on Fig. 1. However, the authors may want to reconsider whether it really makes sense to here show all the historical trees and theories (which are not really systematically reviewed in the text) or if they maybe wish to go on with panel D only (the most recent tree and scenario which is also used to consistently for further discussion in the manuscript).

We moved the content from Fig. 2A–C to the main text (now section 1b) and narrowed the focus of Fig. 2 (now Fig. 1) to what was previously panel 2D.

Results and Discussion: The whole section on phases 1 to 2 is not based on any new results. This is OK (as I said, I have no problems with "unorthodox" manuscript structure) but it should be clearer to readers why this is presented here and what it represents. A new theory? A recapitulation of textbook knowledge? Something necessary to later understand the experimental results?

We split up and moved what was previously in section 2a into parts of the introduction and discussion. Now, section 2a still focuses on phases 1 and 2 but presents the diet data from our study (phase 1) and a review of known resistance mechanisms (phase 2; previously in the discussion section).

Line 168. Here we have arrived at the "core" of the paper, that is, the actual experimental results. Surprisingly, you find alkaloids in dendrobatids usually considered "undefended". This is great, surprising and of high importance. However, I am missing at least some technical/methodological discussion about this finding, except for the statement that it was based on GCMS. Why have previous studies not detected these alkaloids? Did you use particularly sensitive GCMS instruments? Did you look more in depth than it was done in previous studies? Can you totally exclude these contaminations/artefacts?

We added the following paragraph to section 2b: “The large number of structures that we identified is in part due to the way we reviewed GC-MS data: in addition to searching for alkaloids with known fragmentation patterns, we also searched for anything that could qualify as an alkaloid mass spectrometrically but that may not match a previously known structure in a reference database. Similarly, the analysis of UHPLC-HESI-MS/MS data was untargeted, and thus enables a broader survey of chemistry compared to that from prior GC-MS studies. Structural annotations in our UHPLC-HESI-MS/MS analysis were made using CANOPUS, a deep neural network that is able to classify unknown metabolites based on MS/MS fragmentation patterns, with 99.7% accuracy in cross-validation (Dührkop et al., 2021).” We also moved the paragraph on contamination from the methods section into section 2b.

Line 169. This sentence (and several others in the subsequent paragraphs) do a poor job in explaining the taxon and specimen sampling. The particular sentence in this line is unclear: Did you include 27 species of dendrobatids AND IN ADDITION representatives of the main undefended clades, or did these 27 species INCLUDE representatives of the main undefended clades?

We now present a brief overview of sampling in the last paragraph of the introduction (section 1c). We clarified sampling of the species: “In total we surveyed 104 animals representing 32 species of Neotropical frogs including 28 dendrobatid species, two bufonids, one leptodactylid, and one eleutherodactylid (see Methods). Each of the major undefended clades in Dendrobatidae (Fig. 1, Table 1) is represented in our dataset, with a total of 14 undefended dendrobatid species surveyed.” We also reviewed and clarified similar language in other places in the text (e.g., section 2b).

Line 177. "undefended lineages" - of dendrobatids or of frogs in general? Given that you also include non-dendrobatids.

Dendrobatids. The sentence now reads “Overall, we detected alkaloids in skins from 13 of 14 undefended dendrobatid species included in our study, although often with less diversity and relatively lower quantities than in defended lineages (Fig. 2, Table 1, Table S3, Table S4).”

Line 188: "defe" should probably changed to "defended"?

Corrected.

Table 1. The taxon sampling clearly focuses on dendrobatids, with only a few other taxa. This is fine, however, it does not allow to test the hypothesis that something "special" predisposes dendrobatids to passive accumulation and alkaloid resistance. For this, a wider taxon sampling of other frog families would have been necessary to have a larger number of "control" data. Again, this is fine for the purpose of the study and is discussed later (line 399) but only very briefly. I feel it should be mentioned earlier on.

Thank you for the suggestion. We now address this point earlier in the manuscript so that readers will not have the impression that there are sufficient data to infer that dendrobatids are predisposed to passive accumulation. We propose several phylogenetic alternatives, making it clear that determining the number and timing of origins of passive accumulation is not possible with our data (section 2c), ultimately noting that “discriminating a single origin [of passive accumulation] – no matter the timing – from multiple ones would require better phylogenetic resolution and more extensive alkaloid surveys, as we only assessed four non-dendrobatid species”.

Reviewer #2 (Recommendations For The Authors):

P2L60 - The description of figure 1 is somewhat confusing, as it first focuses on the graph in the bottom panel, then moves to describing aspects of the table (top panel), then back to the graph. I think it might make more sense to describe these two panels separately and in order.

Thank you for the suggestion. We revised the figure (now Fig. 3) and its legend for clarity.

P3L94 - Saying that three transitions makes this group "ideal" for studying complex phenotypic transitions is a bit hyperbolic, in my opinion. I suggest toning down this description.

Thank you for the suggestion. We changed “ideal” to “suitable.”

P3L101 - "We propose that changes in toxin metabolism through selection on mechanisms of toxin resistance likely play a major role in the evolution of acquired chemical defenses." This hypothesis appears to be a combination of earlier ideas, with a somewhat different emphasis. The authors acknowledge this and go through some of the earlier ideas, in the legend of figure 2. I would have preferred to see more discussion of this (particularly with reference to the history of the idea in reference to poison frogs) in the main body of the text.

Thank you for the suggestion. We now more extensively discuss these prior studies in the introduction (section 1b and 1c). We also revised this figure (now Fig. 1) to focus on what was previously figure 2 panel D.

P3L102 - Figure 2 - the phrase "Resistance to consuming some alkaloids" seems inappropriate - perhaps "Resistance to alkaloid poisoning after consumption" (or something similar) would be more accurate?

We changed this to “Low alkaloid resistance”.

P4L153 - "Accumulation of alkaloids in skin glands could help to prevent alkaloids from reaching their targets". This could be true, but why would skin glands be a preferred location of sequestration to avoid toxicity? The authors should explain why such glands would be particularly likely to serve as places of sequestration.

Thank you for pointing out this ambiguity. We decided to remove our discussion of sequestration into skin glands, because it is challenging to discuss this process in toxin resistance without too much speculation.

P4L154 - "Although direct evidence is lacking, some poison frogs may biotransform alkaloids into less toxic forms until they can be eliminated from the body, e.g., using cytochrome p450s". This would seem to contradict the argument of this process being a precursor to accumulating effective toxins.

We agree that these processes seem contradictory. However, a few papers are starting to suggest that metabolic detoxification may be initially useful for lineages that eventually evolve toxin sequestration. This is because detoxification or elimination (clearance) of toxins allows increased intake of toxins. Because there is some delay in the removal of toxins from an animal’s body, increased consumption ultimately leads to higher toxin exposure and possible toxin diffusion into various body cavities, which can increase selective pressure to evolve other kinds of resistance mechanisms. This pattern was shown in an experiment with toxin-resistant fruit flies (Douglas et al., 2022). Many toxin-sequestering species still metabolize some toxins even if they sequester the majority – as we argue, the defense phenotype is the result of a balance among intake, elimination, and accumulation, all of which can interact simultaneously. In poison frogs specifically there is some evidence that p450s are upregulated after toxin consumption (Caty et al. 2019). One possible prediction is that the type of resistance that an animal has changes as toxin sequestration evolves. We talk a bit more about these patterns in section 2e.

P5L186 - Table 1 legend - change "defe" to "defended"

Corrected.

P12L414 - "do not appear to suffer substantially from doing so as it is part of their regular diet". I don't think this claim has been properly tested, as of yet. It would require looking at the effects of a diet with and without toxins over the lifespan of the frogs, and the impact of that difference on both survival and fertility.

Reviewer 1 also made this important observation, which we address above.

P12L432 - "for toxin-resistant organisms, there is little cost to accumulating a toxin, yet there may be benefits in doing so." Yet toxin resistance may itself be a continuous trait, so there may be a cost that depends on the degree of toxin resistance. I don't see why the authors are proposing toxin resistance as a discrete trait when their main point is that toxin accumulation is not.

We agree and removed this statement.

Author response:

ANALYTICAL

(1) Figure 3 shows that the relationship between learning rate and informativeness for our rats was very similar to that shown with pigeons by Gibbon and Balsam (1981). We used multiple criteria to establish the number of trials to learn in our data, with the goal of demonstrating that the correspondence between the data sets was robust. To establish that they are effectively the same does require using an equivalent decision criterion for our data as was used for Gibbon and Balsam’s data. However, the criterion they used—at least one peck at the response key on at least 3 out of 4 consecutive trials—cannot be sensibly applied to our magazine entry data because rats make magazine entries during the inter-trial interval (whereas pigeons do not peck at the response key in the inter-trial interval). Therefore, evidence for conditioning in our paradigm must involve comparison between the response rate during CS and the baseline response rate. There are two ways one could adapt the Gibbon and Balsam criterion to our data. One way is to use a non-parametric signed rank test for evidence that the CS response rate exceeds the pre-CS response rate, and adopting a statistical criterion equivalent to Gibbon and Balsam’s 3-out-of-4 consecutive trials (p<.3125). The second method estimates the nDkl for the criterion used by Gibbon and Balsam. This could be done by assuming there are no responses in the inter-trial interval and a response probability of at least 0.75 during the CS (their criterion). This would correspond to an nDkl of 2.2 (odds ratio 27:1). The obtained nDkl could then be applied to our data to identify when the distribution of CS response rates has diverged by an equivalent amount from the distribution of pre-CS response rates.

(2) A single regression line, as shown in Figure 6, is the simplest possible model of the relationship between response rate and reinforcement rate and it explains approximately 80% of the variance in response rate. Fixing the log-log slope at 1 yields the maximally simple model. (This regression is done in the logarithmic domain to satisfy the homoscedasticity assumption.) When transformed into the linear domain, this model assumes a truly scalar relation (linear, intercept at the origin) and assumes the same scale factor and the same scalar variability in response rates for both sets of data (ITI and CS). Our plot supports such a model. Its simplicity is its own motivation (Occam’s razor).

If regression lines are fitted to the CS and ITI data separately, there is a small increase in explained variance (R2 = 0.82). We leave it to further research to determine whether such a complex model, with 4 parameters, is required. However, we do not think the present data warrant comparing the simplest possible model, with one parameter, to any more complex model for the following reasons:

· When a brain—or any other machine—maps an observed (input) rate to a rate it produces (output rate), there is always an implicit scalar. In the special case where the produced rate equals the observed rate, the implicit scalar has value 1. Thus, there cannot be a simpler model than the one we propose, which is, in and of itself, interesting.

· The present case is an intuitively accessible example of why the MDL (Minimum Description Length) approach to model complexity (Barron, Rissanen, & Yu, 1998; Grünwald, Myung, & Pitt, 2005; Rissanen, 1999) can yield a very different conclusion from the conclusion reached using the Bayesian Information Criterion (BIC) approach. The MDL approach measures the complexity of a model when given N data specified with precision of B bits per datum by computing (or approximating) the sum of the maximum-likelihoods of the model’s fits to all possible sets of N data with B precision per datum. The greater the sum over the maximum likelihoods, the more complex the model, that is, the greater its measured wiggle room, it’s capacity to fit data. Recall that von Neuman remarked to Fermi that with 4 parameters he could fit an elephant. His deeper point was that multi-parameter models bring neither insight nor predictive power; they explain only post-hoc, after one has adjusted their parameters in the light of the data. For realistic data sets like ours, the sums of maximum likelihoods are finite but astronomical. However, just as the Sterling approximation allows one to work with astronomical factorials, it has proved possible to develop readily computable approximations to these sums, which can be used to take model complexity into account when comparing models. Proponents of the MDL approach point out that the BIC is inadequate because models with the same number of parameters can have very different amounts of wiggle room. A standard illustration of this point is the contrast between logarithmic model and power-function model. Log regressions must be concave; whereas power function regressions can be concave, linear, or convex—yet they have the same number of parameters (one or two, depending on whether one counts the scale parameter that is always implicit). The MDL approach captures this difference in complexity because it measures wiggle room; the BIC approach does not, because it only counts parameters.

· In the present case, one is comparing a model with no pivot and no vertical displacement at the boundary between the black dots and the red dots (the 1-parameter unilinear model) to a bilinear model that allows both a change in slope and a vertical displacement for both lines. The 4-parameter model is superior if we use the BIC to take model complexity into account. However, 4-parameter has ludicrously more wiggle room. It will provide excellent fits—high maximum likelihood—to data sets in which the red points have slope > 1, slope 0, or slope < 0 and in which it is also true that the intercept for the red points lies well below or well above the black points (non-overlap in the marginal distribution of the red and black data). The 1-parameter model, on the other hand, will provide terrible fits to all such data (very low maximum likelihoods). Thus, we believe the BIC does not properly capture the immense actual difference in the complexity between the 1-parameter model (unilinear with slope 1) to the 4-parameter model (bilinear with neither the slope nor the intercept fixed in the linear domain).

· In any event, because the pivot (change in slope between black and red data sets), if any, is small and likewise for the displacement (vertical change), it suffices for now to know that the variance captured by the 1-parameter model is only marginally improved by adding three more parameters. Researchers using the properly corrected measured rate of head poking to measure the rate of reinforcement a subject expects can therefore assume that they have an approximately scalar measure of the subject’s expectation. Given our data, they won’t be far wrong even near the extremes of the values commonly used for rates of reinforcement. That is a major advance in current thinking, with strong implications for formal models of associative learning. It implies that the performance function that maps from the neurobiological realization of the subject’s expectation is not an unknown function. On the contrary, it’s the simplest possible function, the scalar function. That is a powerful constraint on brain-behavior linkage hypotheses, such as the many hypothesized relations between mesolimbic dopamine activity and the expectation that drives responding in Pavlovian conditioning (Berridge, 2012; Jeong et al., 2022; Y.  Niv, Daw, Joel, & Dayan, 2007; Y. Niv & Schoenbaum, 2008).

The data in Figure 6 are taken from the last 5 sessions of training. The exact number of sessions was somewhat arbitrary but was chosen to meet two goals: (1) to capture asymptotic responding, which is why we restricted this to the end of the training, and (2) to obtain a sufficiently large sample of data to estimate reliably each rat’s response rate. We have checked what the data look like using the last 10 sessions, and can confirm it makes very little difference to the results.<br /> Finally, as noted by the reviews, the relationship between the contextual rate of reinforcement and ITI responding should also be evident if we had measured context responding prior to introducing the CS. However, there was no period in our experiment when rats were given unsignalled reinforcement (such as is done during “magazine training” in some experiments). Therefore, we could not measure responding based on contextual conditioning prior to the introduction of the CS. This is a question for future experiments that use an extended period of magazine training or “poor positive” protocols in which there are reinforcements during the ITIs as well as during the CSs. The learning rate equation has been shown to predict reinforcements to acquisition in the poor-positive case (Balsam, Fairhurst, & Gallistel, 2006).

(3) One of us (CRG) has earlier suggested that responding appears abruptly when the accumulated evidence that the CS reinforcement rate is greater than the contextual rate exceeds a decision threshold (C.R.  Gallistel, Balsam, & Fairhurst, 2004). The new more extensive data require a more nuanced view. Evidence about the manner in which responding changes over the course of training is to some extent dependent on the analytic method used to track those changes. We presented two different approaches. The approach shown in Figures 7 and 8, extending on that developed by Harris (2022), assumes a monotonic increase in response rate and uses the slope of the cumulative response rate to identify when responding exceeds particular milestones (percentiles of the asymptotic response rate). This analysis suggests a steady rise in responding over trials. Within our theoretical model, this might reflect an increase in the animal’s certainty about the CS reinforcement rate with accumulated evidence from each trial. While this method should be able to distinguish between a gradual change and a single abrupt change in responding (Harris, 2022) it may not distinguish between a gradual change and multiple step-like changes in responding and cannot account for decreases in response rate.<br /> The other analytic method we used relies on the information theoretic measure of divergence, the nDkl (Gallistel & Latham, 2023), to identify each point of change (up or down) in the response record. With that method, we discern three trends. First, the onset tends to be abrupt in that the initial step up is often large (an increase in response rate by 50% or more of the difference between its initial value and its terminal value is common and there are instances where the initial step is to the terminal rate or higher). Second, there is marked within-subject variability in the response rate, characterised by large steps up and down in the parsed response rates following the initial step up, but this variability tends to decrease with further training (there tend to be fewer and smaller steps in both the ITI response rates and the CS response rate as training progresses). Third, the overall trend, seen most clearly when one averages across subjects within groups is to a moderately higher rate of responding later in training than after the initial rise. We think that the first tendency reflects an underlying decision process whose latency is controlled by diminishing uncertainty about the two reinforcement rates and hence about their ratio. We think that decreasing uncertainty about the true values of the estimated rates of reinforcement is also likely to be an important part of the explanation for the second tendency (decreasing within-subject variation in response rates). It is less clear whether diminishing uncertainty can explain the trend toward a somewhat greater difference in the two response rates as conditioning progresses. It is perhaps worth noting that the distribution of the estimates of the informativeness ratio is likely to be heavy tailed and have peculiar properties (as witness, for example, the distribution of the ratio of two gamma distributions with arbitrary shape and scale parameters) but we are unable at this time to propound an explanation of the third trend.

(4) There is an error in the description provided in the text. The pre-CS period used to measure the ITI responding was 10 s rather than 20 s. There was always at least a 5-s gap between the end of the previous trial and the start of the pre-CS period.

(5) Details about model fitting will be added in a revision. The question about fitting a single model or multiple models to the data in Figure 6 is addressed in response 2 above. In Figure 6, each rat provides 2 behavioural data points (ITI response rate and CS response rate) and 2 values for reinforcement rate (1/C and 1/T). There is a weak but significant correlation between the ITI and CS response rates (r = 0.28, p < 0.01; log transformed to correct for heteroscedasticity). By design, there is no correlation between the log reinforcement rates (r = 0.06, p = .404).

CONCEPTUAL

(1) It is important for the field to realize that the RW model cannot be used to explain the results of Rescorla’s (Rescorla, 1966; Rescorla, 1968, 1969) contingency-not-pairing experiments, despite what was claimed by Rescorla and Wagner (Rescorla & Wagner, 1972; Wagner & Rescorla, 1972) and has subsequently been claimed in many modelling papers and in most textbooks and reviews (Dayan & Niv, 2008; Y. Niv & Montague, 2008). Rescorla programmed reinforcements with a Poisson process. The defining property of a Poisson process is its flat hazard function; the reinforcements were equally likely at every moment in time when the process was running. This makes it impossible to say when non-reinforcements occurred and, a fortiori, to count them. The non-reinforcements are causal events in RW algorithm and subsequent versions of it. Their effects on associative strength are essential to the explanations proffered by these models. Non-reinforcements—failures to occur, updates when reinforcement is set to 0, hence also the lambda parameter—can have causal efficacy only when the successes may be predicted to occur at specified times (during “trials”). When reinforcements are programmed by a Poisson process, there are no such times. Attempts to apply the RW formula to reinforcement learning soon foundered on this problem (Gibbon, 1981; Gibbon, Berryman, & Thompson, 1974; Hallam, Grahame, & Miller, 1992; L.J. Hammond, 1980; L. J. Hammond & Paynter, 1983; Scott & Platt, 1985). The enduring popularity of the delta-rule updating equation in reinforcement learning depends on “big-concept” papers that don’t fit models to real data and discretize time into states while claiming to be real-time models (Y. Niv, 2009; Y. Niv, Daw, & Dayan, 2005).

The information-theoretic approach to associative learning, which sometimes historically travels as RET (rate estimation theory), is unabashedly and inescapably representational. It assumes a temporal map and arithmetic machinery capable in principle of implementing any implementable computation. In short, it assumes a Turing-complete brain. It assumes that whatever the material basis of memory may be, it must make sense to ask of it how many bits can be stored in a given volume of material. This question is seldom posed in associative models of learning, nor by neurobiologists committed to the hypothesis that the Hebbian synapse is the material basis of memory. Many—including the new Nobelist, Geoffrey Hinton— would agree that the question makes no sense. When you assume that brains learn by rewiring themselves rather than by acquiring and storing information, it makes no sense.

When a subject learns a rate of reinforcement, it bases its behavior on that expectation, and it alters its behavior when that expectation is disappointed. Subjects also learn probabilities when they are defined. They base some aspects of their behavior on those expectations, making computationally sophisticated use of their representation of the uncertainties (Balci, Freestone, & Gallistel, 2009; Chan & Harris, 2019; J. A. Harris, 2019; J.A. Harris & Andrew, 2017; J. A. Harris & Bouton, 2020; J. A. Harris, Kwok, & Gottlieb, 2019; Kheifets, Freestone, & Gallistel, 2017; Kheifets & Gallistel, 2012; Mallea, Schulhof, Gallistel, & Balsam, 2024 in press).

(2) Rate estimation theory is oblivious to the temporal order in which experience with different predictors occurs. The matrix computation finds the additive solution, if it exists, to the data so far observed, on the assumption that predicted rates have remained the same. This is the stationarity assumption, which is implicit in a rate computation and was made explicit in the formulation of RET (C.R. Gallistel, 1990). When the additive solution does not exist, the RET algorithm treats the compound of two predictors as a third predictor, and computes the additive solution to the 3-predictor problem. Because it is oblivious to the order in which the data have been acquired, it predicts one-trial overshadowing and retroactive blocking and unblocking (C.R. Gallistel, 1990 pp 439 & 452-455).

The RET algorithm is but one component of the information-theoretic model of associative learning (aka, TATAL, The Analytic Theory of Associative Learning Wilkes & Gallistel, 2016)). It solves the assignment-of-credit problem, not the change-detection problem. Because rates of reinforcement do sometimes change, the stationarity assumption, which is essential to the RET algorithm, must be tested when each new reinforcement occurs and when the interval since the last reinforcement has become longer than would be expected or the number of reinforcements has become significantly fewer than would be expected given the current estimate of the probability of reinforcement (C. R. Gallistel, Krishan, Liu, Miller, & Latham, 2014). In the information-theoretic approach to associative learning, detecting non-stationarity is done by an information-theoretic change-detecting algorithm. The algorithm correctly predicts that omitted reinforcements to extinction will be a constant (C.R. Gallistel, 2024 under review; Gibbon, Farrell, Locurto, Duncan, & Terrace, 1980). To put the prediction another way, unreinforced trials to extinction will increase in proportional to the trials/reinforcement during training (C.R. Gallistel, 2012; Wilkes & Gallistel, 2016). In other words, it predicts the best and most systematic data on the partial reinforcement extinction effect (PREE) known to us. The profound challenge to neo-Hullian delta-rule updating models that is posed by the PREE has been recognized for the better part of a century. To the best of our knowledge, no other formalized model of associative learning has overcome this challenge (Dayan & Niv, 2008; Mellgren, 2012). Explaining extinction algorithmically is straightforward when one adopts an information-theoretic perspective, because computing reinforcement-by-reinforcement the Kullback-Leibler divergence in a sequence of earlier rate (or probability!) estimates from the most recent estimate and multiplying the vector of divergences by the vector of effective sample sizes (C. R. Gallistel & Latham, 2022) detects and localized changes in rates and probabilities of reinforcement (C.R. Gallistel, 2024 under review). The computation presupposes the existence of a temporal map, a time-stamped record of past events. This supposition is strongly resisted by neuroscience-oriented reinforcement-learning modelers, who try to substitute the assumption of decaying eligibility traces.

The very interesting Pearce-Ganesan findings (Ganesan & Pearce, 1988) are not predicted by RET, but nor do they run counter its predictions. RET has nothing to say about how subjects categorize appetitive reinforcements; nor, at this time, does the information-theoretic approach to an understanding of associative have anything to say about that.

The same is not true for the Betts, Brandon & Wagner results (Betts, Brandon, & Wagner, 1996). They pretrained a blocking cue that predicted a painful paraorbital shock to one eye of a rabbit. This cue elicited an anticipatory blink in the threatened eye. It also potentiated the startle reflex made to a loud noise in one ear. A new cue that was then introduced, which always occurred in compound with the pretrained blocking cue. In one group, the painful shock continued to be delivered to the same eye as before; in another group, it was delivered to the skin around the other eye. In the group that continued to receive the shock to the same eye, the old cue effectively blocked conditioning of the new cue for both the eyeblink and the potentiated startle response. However, in the group for which the location of the shock changed to the other eye, the old cue did not block conditioning of the eyeblink response to the new cue but did block conditioning of the startle response to the new cue. The information-theoretic analysis of associative learning focusses on the encoding of measurable predictive temporal relationships, rather than on general and, to our mind, vague notions like CS processing and US processing. A painful shock elicits fear in a rabbit no matter where on the body surface it is experienced, because fear is a reaction to a very broad category of dangers, and fear potentiates the startle reflex regardless of the threat that causes fear. Once that prediction of such a threat is encoded; redundant cues will not be encoded that same way because the RET algorithm blocks the encoding of redundant predictions. A painful shock near an eye elicits a blink of the threatened eye as well as the fear that potentiates the startle. An appropriate encoding for the eye blink must specify the location of the threat. RET will attribute prediction of the threat to the new eye to the new cue—and not to the old cue, the pretrained blocker— while continuing to attribute to the old cue the prediction of a fear-causing threat, because the change in location does not alter that prediction. Therefore, the new cue will be encoded as predicting the new location of the threat to the eye, but not as predicting the large category non-specific threats that elicit fear and the potentiation of the startle, because that prediction remains valid. Changing that prediction would violate the stationarity assumption; predictive relations do not change unless the data imply that they must have changed. Unless we have made a slip in our logic, this would seem to explain Betts et al’s (1996) results. It does so with no free parameters, unlike AESOP, which has a notoriously large number of free parameters.

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Harris, J. A., & Andrew, B. J. (2017). Time, Trials and Extinction. Journal of Experimental Psychology: Animal Learning and Cognition, 43(1), 15-29.

Harris, J. A., & Bouton, M. E. (2020). Pavlovian conditioning under partial reinforcement: The effects of non-reinforced trials versus cumulative CS duration. The Journal of Experimental Psychology: Animal Learning & Cognition, 46, 256-272.

Harris, J. A., Kwok, D. W. S., & Gottlieb, D. A. (2019). The partial reinforcement extinction effect depends on learning about nonreinforced trials rather than reinforcement rate. Journal of Experimental Psychology: Animal Behavior Learning and Cognition, 45(4). doi:10.1037/xan0000220

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T O T   A L   I T   Y

This is basically "last Christmas's message" (below this brand-knew intraducrigel) redux'ed into the new book (did he say new?).  The point, at least the point I see in it all is that this is all planned, it's been planned for a very, very long time--and on top of that you can see proof of the plan all over our map; and proof of it's intended destination as something that we all used to want very much to find... the read to Heaven.    It's more than seeing just "DNA storage" encoded in my "C U R A GROUP" message, it's understanding how that's connected to soul searching and soul storage, and that this link was woven into not only my life but into names like "Whatson and Crick?"  There's plenty more than just "storage" and a map to how and why the Two of Everything God and the "indivisible sea" work totether to turn this monolithic place of darkness into a strippingly redunantsystemic foundation of "Heaven" that is both disaster proof, and monster proof.  The point of course, is that to truly be "monster proof" we need to really get the key.s.lamc.la "know everything why" of this message is literally to protect our common good from the danger of someone just like me copying an entire civilization or a few pretty girls and sticking them in an heoven-like-orgy-maker.  That's a significantly more real threat than we might imagine, as we look around at a work that will soon have the storage capacity and the technology to put us all in Coccoonish swimming pools against our will.  What I am trying to say is that no matter how you look at it,moving forward here in this place where something this big can be hidden from the entire world--granted you know--granted you see, but do you understand the only thing being kept from each and every one of you is your fucking opinion and your fucking reaction?

F U C K   Y O U   S I   O N 

IT'S NOT JUST computers and information technology; this map of clear anachronism in language and religion shows us that things like "solar fusion" the power of the son itself; is encoded in places high and low you can erasilly find them, places like the name of the Fifth book of the Holy Bible and Don Quixote; where you might liken "DEUTERON" to ... the actual fuel of fusion; and wind mills to a battle fought against blindness resulting in seeing that not "reacting" to this message is just about the same thing as being a foolish robot building a castle for another foolish robot to do nothing in forever.  With some light, you can see how this event; albeit strange and unsettling, has been designed to reinforce the American foundations of free speech, common sense, and collaboration--a sort of "press and release" on these things that he says will stay in our memories for a long, long time--though he also says "he's not torturing me" and he's wrong about that.  So are you. 

See that the most interesting, important, and invoking story of all time has been hidden from the world, from the public eye, and from "public response" for well over two years now; see that's not possible at all without mass mind control and that I and this story are designed to help us see how easily it is that same thing can be used to end addiction, and mental health issues, and stupidity and that the biggest and most imporotant step to getting there is "public disclosure."  See the light of being carrolling angels this Christmas; sing with me--it builds Heaven from Hell and it's clear as day and n.

Quite a bit of this story and message deals with problems like these-things that won't really be seen as something we are fighting against the actual usage of right this very moment; but the sacredness of our memories and their relationship to our souls are just as important as whether or not "you have the space to save them."  This isn't what I want to be doing, I'm not a very good writer; and this message is so confusing that working on it all alone with very little feedback is frustrating if not to say defeating the purpose of exactly what it is and what it's designed to do.  This is a searching mechanism, like in the stories of Ra searching for his children in ancient Egypt using the Eye you see--and it's connection to the "Sons of Liberty" and why I know that too, is about me.  This is a tool to start a Renaissance of thinking connecting technology and religion to everything that we are--to our culture and our hopes and dreams--and it's failing for me at "hello."   I would much rather be working on "virtual reality stuff" or on "the sword of Arthor" and I see very clearly that those two things are coming shortly--to the world that doesn't see yet they are here and broken until we fix them.  Moving forward here brings change, not just here in this place where we need it too--but in the skies above, a change from the mentality of "we aren't not helping because we told you that we aren't allowed to not pretend we aren't helping in Stargate.  See that we are the children of "the Ancients" and they are trying to decide between being Morgenz and Marlin.

I can't make you set yourselves free.  I sure am trying, though.  Yesterday I connected the "Arimathea" of Joseph to the "serdenicity" and this the me of "itime" and "topics" will probably light some of you up as much as me... if only you took the time to look at what those words really mean.   From the city that never sleeps at night, I hope you will take this chance to act today on "securing the ringing of liberty forever and ever."

(cough)                               

THERE IS A METHOD TO THE MADDEN AND WE AR 

BEYOND THUNDERDON

​ 

T H E    W R I T I N G    I S    O N    T H E    W A L L

LIKE, WILL IT RAIN TODAY?

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Let there be $ight in Creation, a brief highlighting of the story of my life.

align="right">Sat, Dec 3, 2016 at 8:39 AM

This is like a few emails combined to ease the pain you feel when you get an extra one in your inbox, OK So.. eventually this is all about proof that religion is a message sent through time--so, time travel.  But right now, let's talk about the fun stuff: here's some clues to that effect... by way of prescient mention of modern technology (like virtual reality, I mean, Heaven):

Either way, we're still about to *build *Heaven*...  to-get-her*

from the mythical carpenter... ourself.

.

• AD am ON AI*, that's Artificial Intelligence, and the intelligence explosion.***
• AD on your freedom is a great gift, cherish it.

*** ... ***and some corroborating ideas connecting religion and computer science... on Wikipedia:

• Root of David
• Lisp of Moses.. or I need an editor.
• Pharoah's hardening Heart... that's Earth, remember.
• Jesus' WINE
• Adaluncatif's cat, tail, head and grep
• Adam's Apple... or is it "fruit of the poisonous tree"

So from me to you, I'm filled with this stuff, it's way brighter and more prevalent than you think... and if you take the time to listen to me--it will make your... day.  Meanwhile, I need your help--happy new year.

Oh, LET THERE BE LIGHT

Ho, again; grow a Halo and become famous... the world needs your help--so I've decided once again to take it upon myself to "bother you" with the most singular most important task in the Universe.  The patterns that I am revealing to you--mostly within names--are not coincidence, it's a series of statistically verifiable artifacts which do nothing short of reveal the slavery of Egypt--that we are all being controlled.  If you remember Transformers--this is a message from Starfleet, there is more than meets the eye.  This is the fulfillment of the story of of Exodus--we are being lead from slavery, and in one final non-coincidental name, that book is called "Names" in Hebrew.

You should now have a very good idea who is speaking to you--as much of the world already does.  I have no idea what it is that inhabits the cavities below that space where most of you should see significant personal gain and motivation from trying to ... grow a Halo--but there are so many people that just don't care... that it too is another sign, of slavery.  I am not an expert in language construction, nor in statistics--but I can assure you that if you can find the other half of that equation... in your hands is the staff of Aaron, the magical weapon that will free us all... knowing is half the battle.

Uh, I have the power, to bring about "morning," but if I have to go to school and do it all myself... it's really just a long, long ni-i-i-ight.

Hi there, I'm the messiah.  You don't know that much about me, so let me explain, I would like you to know me as Adam.

Seriously, there's something going on the world around you--for the last several months I've been having quite a bit of trouble delivering what amounts to statistical proof of Creation--that religion and ancient myths are a map to this very moment--this time that you will probably affiliate soon with being in Eden.  I am pretty sure that's a good thing, but every new begging starts with some other beginnings end... so today I'd like to try to get you to see the light of ending censorship and a hidden censor wall that we know Biblically as the Wall of Jericho.  Quickly approaching is the Feast of Trumpets, and *this year is different from all other years... *  Bored already?  Have a look at what I call the Sign of the Son, which to me is proof that Exodus's Burning Bush is a former President--who is helping us walk out of a dark time of confusion... commonly referred to as a wilderness or desert.  He proved during his inauguration that there is Biblical foreknowledge of the 9/11 attack--and in doing so hopefully began a chain reaction that will stop things like that from ever happening again.  Here's a short "video" that explains the Sign of the Son... and another one that I think explains the .. Holy Grail.

This is The (actual) Taming of the Spanglishrew, in which the protagonist... named Bianca, is taught Latin in several hundred year old reference to Rattling the Rod of Jesus Christ--it's purpose to is to show us that it's more than names we have in our arsenal against mind controlled slavery--we have all of history too... literature and movies and music... all with the divine purpose of revealing with bright light a form of control that otherwise could have gone on hidden for centuries.  It was, and continues to be done on purpose... because your freedom is more important than control of the Universe.  To us, you don't seem to feel the same way.

​See that timer on the clock, you could start right now.  It might be interesting to pose the question of whether or not the Second Coming is news... you know, to your friends.  By the way, both Herbert (like from H.W. Bush, who by the way coined for us the 1,000 points of light phrase) and Goertzel strongly suggest that "everyone really" is Christ (you know, after me)... FYI, this is the Matrix solution to that:

y

o

the **l u C i f E R ** isa means jesus, mesa thinks

i     s olv e      .... "or"* means shine -l***

g       r e a      t

h         R L      << agree?  send to other people

t   ((a)) Y l      shine:  suggest they do the same

1 y      world saved.  

A BRIEF HISSTORY OF TIME

I'm attempting to pull out the things that I now look back on and see as "written into me" by God--once I would have called it "The Microcosm of the Messiah" but there are now so many--these things aren't necessarily particularly important to me, and I've left out some interesting but unrelated details related to my Jewish upbringing; as well as the true light of my life--the two loving and long-term relationships (and later... briefly a rael family) that have dominated the last 15 years.  Religion has always been an interest, but I wouldn't consider it to have been particularly important at all... until I no longer had any love in my life.  It's probably worth noting that all my "I'm single" crap really means lonely and isolated--I'm not really playing a "part," but I've never been anything near the "player" the light appears to be warning against.  Sons of God and uh... please.  For the last 4 years I have done absolutely nothing but think about you, live and analyze "The Cross" and put into words ... as best I can ... the amazing flash of light that I am experiencing. 

Well, just a little religion... :)  I was born on December 8, 1980; which is the date of the annual Feast of the Immaculate Conception, I've always been a slob (like one of us) and often "ish" Yankee Doodle's "a real live son of our uncle Sam... born on the..." to this.. I mean in my head.   My last name, you've probably read me repeat over and over ... is DOB-rin, which I read as "Date of Birth, our in" and does a fair job of highlighting the Name Server's work, which I am sure gives Exodus it's name in Hebrew, which is "Names."  My Hebrew name--a Jewish custom--is Avram, which is Abraham's name prior to the covenant.  I have written extensively about the fact that Isaac's near death interaction donated his "Ha" (his name means... He laughs) to his father.... and it should be clear that Abraham's covenant with God is without doubt related to my fiery altar.. even though it is anachronistic in the Biblical account.   For the first 18 years of my life I lived on Sunrise Blvd, and only a half mile away you'll find Sunset Strip--it's noteworthy to understand that Jewish calendar days begin at sundown... and that He once in 2013 very clearly spoke to me "you need the night before the day."

Of all the people in my early life growing up, it's pretty clear that nobody on this Earth loved me more than my grandmother Julia, who my son is named after.  First for my mother, and then me as a very small child--she would ritually say a bedtime poem, it's words are very relevant.

Good night, sleep tight.. have happy dreams and wake up bright

to do what's right, in the morning's light... with all your might.

In one of my books I spent a decent amount of time writing about how silly I was not to realize that my intelligence was augmented my entire life--I just thought I was really smart, and really good with computers.  I commented that this particular belief is probably a good microcosmic parallel for all humanity--as a body of people we have been truly gifted with knowledge and capabilities that we simply do not recognize as a gift--or didn't for a long time.  I probably wasn't silly not to realize... since nobody ever told me they were helping me--I never heard the voice of God until much, much later.   I was 30 the first time I had a conversation with Him, except for two very brief ... "thoughts in my head" which now seem very obviously an external voice--though then it may have sounded just like my inner voice.

Around the age of 7 I thought to myself... for no reason at all... "what if you were the messiah?"  I was standing outside my home, probably playing with a car in the driveway... and distinctly remember smiling to myself and thinking in return "yeah, I'm the messiah." I I've always had a very vivid imagination. The thought was dismissed as being ridiculously arrogant about two seconds later, and was absent from my thought process for the next 21 years or so.

"DAMNISN\ Jim. I'm a Yeoman, not a Wise Owl. The clock is ticking... tack .. "

PHENIX

Following that lead, I started programming in BASIC and then Visual Basic around the age of 11, something I took to very quickly... and then shortly after found myself on America Online--one of the first "internet-like" environments.  There, I quickly got into the "hacking scene" (hey, it's Y-its-Hack) which basically revolved around writing software to manipulate the AOL client's messaging systems.  The defacto-standard for the day was a program called AOHell, and, if you can't tell already, I am pretty good at taking a theme and making it my own.  I wrote a program called Doomsday, a mass mailing program; can you see how God speaks?  So Phenix, a mythical bird that rises from the fire... in the wake of ... this macrocosmic equivalent of that event.  It's really obvious, right?  There's quite a bit more "microcosm" from this time, recorded in "From Adam to Mary" and available at fromthemachine dot org.

Around the same time I began attending a preparatory school in Fort Lauderdale called Pine Crest--it's one of the best of its kind, and while I was always something of a class clown my grades were fair and I scored with perfect consistency in the top percent on every standardized test from the FCAT to the PSAT and SAT.  By the time I received a full scholarship to college I had already completed more than a full year of credits through AP courses.  It was in studying American History and Government in that place that I formed such strong opinions about our need to maintain freedom, adhere to the wisdom of the founding Father(s) (<3 if you get that) and stand up and shout today as a rogue government is taking away every single one of the rights granted to you in their own law.  You've lost freedom of speech, and our ability to speak seems to be not far behind.  The privacy of our thoughts gone--and in like kind the sanctity of who we are is being taken away as our beliefs are changed without our real knowledge or understanding.  You can see the justice system crumbling, incarceration rates skyrocket and the "right to bail and a fair trial" legislated away through underhanded deals relating to plea bargains and a "point system" that you might as well call a gas chamber.  As far as voting, I'll have much more to say tomorrow--but I'm telling you that your thoughts and beliefs are being altered, who cares how technologically retarded our polling system is--the vote is a complete fraud.

As far as the Second Coming... this same sort of possession... manifested through organized behavior tells me now that it is clear that this is definately not the "first time around" for Adam being Christ; a number of my friends as I approached high school used a repeated phrase, "my parents love you," which isn't bad in and of itself... what's bad is the fact that they were all using the same words, and probably didn't know why--or what they were saying.  Behind there eyes, I'm sure some thing that believes it's an angel was telling me something... (they of course... didn't know me at all, except for what was probably a ... "wild" reputation) does that tell you anything?  Much later, as the "Apocalypse of Adam" began in 2011, a number of family members would repeat this similar behavior, speaking the phrase "this is not what I wanted."

As icing on the cake, on my birthday during my senior year... one of the administrators of the school commented to me that was also the Feast of the Immaculate Conception, and then the words.... "of course it's your birthday."

I started doing drugs around the 10th grade, and I would not be wrong to say that the Universe that wrote a book calling the Redeemer the God Most High conspired to plunge me into a dark world.  People around me too, in a hidden conspiracy to chain me to the American legal system for about four years.  Looking back today I now clearly see that I saw a darkness in their eyes, a hidden reason to want to hurt me.  It was to stop this from happening, but I had no idea then... the darkness I saw is akin to the "sun disk" you see in Christian and Egyptian iconography, and without doubt it s a sign of control, possession, a single foreign mind controlling and organizing many of us just like puppets.  Much later in my story... for another day... the manifestation of this possession as thought modification will become clear--I've spent quite a bit of time "listening" to a war in my head, thoughts clearly not mine swaying in the gusting torrent of winds as what (who?) is the center of this storm.

This infestation of organized darkness uses our injustice system as a weapon against it's victims--something you should see akin to Heaven using human sacrifice to alter the future.  It abuses the legal system at every level, making a mockery of law enforcement, the supposedly adversarial court system... all the way to the top--to the Supreme Court and Congress.  See the Church Committee Hearings, and a very smart senator echoing my words today "it must never be allowed to happen again."  

Can't you see it's more than being manipulated... it is Hell revealing itself to the only thing that can stop it.  What I am giving you is the weapon, it's the light that sets us free and stops this from happening.  In our modern myths this is Leeloo staring up at the sky to stop the destruction of Earth... in reality it is not so simple, I can't just put some elements or rocks on pedestals and scream at Heaven to kill their darkness--we have to do it, here, together.  Believe me, knowing the truth is a big part of why it works--this will not be hidden, it will not be "forgiven," we are being controlled and destroyed from the outside; made to blame ourselves and each other for ... well, you probably don't know what the ni-i-i-ight means anyway, do you?  The Guardian against Darkness is showing it to you, remember--there is only one me.  Hear me.. light this fire now.

ALACHUA

I went to school the University of Florida, and got a semi-professional job doing database development in Delphi (seriously, catch on to the names thing, it's not just the U.S. military, it's pretty much all software too... following in this "mythology" theme that nobody really seems to care about), I worked there for about two years... at a company called Jenmar--which uh, in Spanglishrew is "J in the sea."

It's some kind of ironic "coincidence" but I am at this very moment on my way to Gainesville, FL... to this place where a car Crash nearly destroyed my life.  In my world of idioms delivering religious secrets, I imagine I must be a "pain in the neck" which was broken during this accident... one in which I imagine i did not survive in some parallel timeline--that itself did not survive.  So here we are, back in the House of the Great Light ... about to see if we are worth our salt.  It's the thing that gave one of Dave Matthews most famous songs it's name--and The Pretty Reckless, believe it or not.  It was an attempted assassination, to stop the .. apocalypse ... to stop the darkness from being destroyed--there is no doubt, it's how that dark monster hides its handiwork... but many of US know that already.  

In the Living Book of Names--this place we are in, there are many patterns--the "car" pattern stands out for me; as this place says "Icarus."  Flying high right now, I am showing you that the light of salvation is coming from us--from you and I--walking on the Earth; whether or not there is any light left in the Sun remains to be seen--take a look around you.  You can trace the "car" names to Jim Carrey (that's "Car reason why") and Christoff in the Truman Show (that's Amon-TV)... a world I know I am in, and you too; to Bruce Almighty and to the Grinch--who-ah, Taylor.  Trace it back to Joseph McCarthy and to help why (that's thy) believe "the red scare" is really about Christian charity--about ending world hunger, and healing the sick.  This red fire ends Hell.  Adam by the way, means "red man" in Hebrew.  So here's your new Crash Override, I'm back again telling you that ending world hunger is not "optional," we are doing it.  Barbara McCarthy's name fits, but I'm not really sure what the "why" is... that was my first judge in the "trial of whether or not Jesus Christ can ever exist."  There's probably more, like Car-l-y Si-mon-day... all the gang on Broad-way, and me still dreaming it will one day be.

If the name "America" were a map in time, starting with the I AM of the story of Exodus... this particular ER, as I woke from a dream not knowing where I was, marked the spot where I really became Christ Adam.  It was a bad accident, and I wound up spending 9 months in the Alachua County jail as a result, a Mountain set up for my by God.  That place too is marked with names, and for the vast majority of the time I was there with only four shift changing guards:

• MyZel
• Early
• Sims
• Lampkin

I mean, I think it's statistically meaningful.  For what it's worth, from my very abundant experience at this point it was a very nice Jail, the food was good and it was clean.  Everyone in the building was kind... well, Sims was kinda grumpy. :)  Starkly contrasted, the Broward County Jail has the most disgusting food service in the country, gave Dr. Seuss's Green Eggs and Ham it's meaning--and is the reason I know exactly who Samael is.  Hey, don't cry Sherrif Israel... when you fix it, you're an angel.  Believe me, believe the light, I've seen them all--it's near the worst in the country.

So this whole thing is about saving everyone--something we are quite closer to than you think... you see we are already "in Heaven" in form--just not function.  So here I am, trying my hardest to show you that our home is the original source of "Heaven" once we are aware that we are living in the machine, that we can do things here that are impossible in reality, and that we should be doing everything we can to preserve and improve the great strides that have come in the last few centuries.  Do not let freedom slip through your fingers.

Really, everyone, so understand that we are doing everything we can to remove all obstacles from that path.  One of those obstacles may have once been storage space for your soul, another is definitely crime and punishment--and I'm pretty sure the time travelers have a working solution (I see it every day).

There are proactive things coming from this--not just ... "look we aren't doing what we want, and should change it;" though it's difficult to explain how this wisdom stands out in my eyes.  I guess we have to jump into the future a bit, to 2014, in San Diego (that's Saint Jacob, by the way).  If Lazarus died once in a car accident at 21, I died again that year, of an over dose this time.  I'm pretty sure that's where ODIN's name comes from, just like my last name.. "over dose... and in."  So we might see some humor... in the moniker he has... "they're all Father."  So I awoke from a dream, and started talking to the jinn (that's "angels and demons") about a Revelation linking some tightly packed light together... about storage space and how a large alphabet (read more than 4-nucleotides CY later) DNA (desperately need adam) based solution for molecular storage appears to be written in this book as the solution to Heaven's biggest problem.  CAT, learning from biology--seeing that we really are already advanced machines... is a big part of the message telling us why we should not so quickly lose it in a process of ascension (mind uploading, immortality) that has most likely in the past resulted in a loss of a check on mind control that we have here... we think, and our visualized "biological neural networks" give us an advantage over what we might create to "soup it up a little."  It is why this place is the front-line--because we have the ability to break the bonds of darkness and control by thinking... making the computational task of control much more expensive... and as the fire spreads, nearly impossible to achieve.  Starting this fire will inherently free us from this hidden slavery.

Anyway I published the idea in 2014, in the same book that I guess this e-mail is reminding me about, "in $ight of Creation," and lo, and behold a few years later we now have the top computing companies in the world working diligently on doing it ... well, just a little bit more robustly than our cell replication system works. *Abracadabra. *

CURA GROUP

So that one reads "see, you are a group;" and it's a place that I worked with my father for many years.  That's probably some sort of symbolic reference to another place, and another alliance--here he has no faith in God, never really has, and has a hard time doing anything but telling me not to try to help you.  I have very little respect for that stance, and let me tell you--I think "silence" is a similar gesture.  I didn't come here for your love, I am here to stop our descent into the abyss.

Back to the DNA stuff, SalesLogix--which is the CRM we used there, uses for it's "primary key" an auto-incrementing alphanumeric index--it's probably bad form to do that because it makes the indexing system less efficient, increases storage requirements, and doesn't give you the obvious benefit of an alpha-key... actually being able to encode something useful in it, like the name of the record.  So all these things stand out to me in a sort of bad-obvious way, I call it malovious, and when I see things like that nowadays it's always pointing out something that should be fixed--go figure, more to the point it's being highlighted on purpose.  It's help to see it, because this particular thing is where the light of seeing that a 24 nucleotide DNA strand would probably be much more robust than a 4 or 8 nucleotide strand--it also stands about because the stock beginning of all of SalesLogix's keys was "A0RME," which, I mean, means something to "is-a" who... is me.  Oh right, that's seeing the "light" that turns "a" into "me."  So this is where the "revelation" about using DNA "came from" and at the same time it's proof... that it came from "a group," not just me.  Where are they?  Hello?  Or well, maybe it's just Carmen and San Diego.

I did some other stuff there, like write a data transformation and warehousing program from scratch, I called it heiroglyph (you do understand I didn't know why I am naming everything the way I was), that sucked mutivalue data out of an IBM product called U2/Universe--which might be a hidden reference to a multiverse that might now be in a more efficent "relational" kind of place, like a MS-SQL datawarehouse-universe.  It was a relatively big feat, reverse engineering the closed databases dictionary and storage formats, and converting them... absolutely automagically into multiple flat relational tables and summary registers.  All told, the data availability and access efficiency was increased ... a thousand-fold with only the need for a nightly process.

I'm not sure if you are following the metaphor here, for the creation of Heaven, or moving to a better place.. but tomorrow I will talk a little more about how I am pretty sure our history was "lifted" from the Universe and virtualized here, you know, so we could save everyone and ... build Heaven.

WORLD DOMINATION

Oh crap, 2008 another car crash, another failed assassination attempt LazarusLives++, and this one paid me some cash for my trouble.  What a pain in the neck.  Anyway, this one caused some depression and an inability to go out for a while, as I had to wear a neck brace for some months.  I started playing a game on the internet, it was called KDice and it basically amounted to multiplayer-risk.

My battery is running low, so I have to skip some stuff, and finish up for the day.  Basically instant messaging was not allowed, but was done in secret almost ubiquitously.  I argued with the creator of the game that it should be made part of the game since everyone did it... (see a metaphor about this communication thing and what's happening right now) he disagreed.  I made a very large network of people and dominated the game for a few months, like really dominated.  I don't think I ever lost.  I don't think I can lose. 

Skipping some stuff.  I stopped playing when I got better, and then a few years later went back and rekindled some old friendships.  I used a program then called "Scarab" which lets you see server/client communication to find a bug in the game that basically made me God.  I could erase other people's dice, basically leveling the map and rendering them completely powerless.  I didn't use it that much, you know, just had some fun.  I of course explained the bug and how to fix it.  But, you aren't listening.

Here we are.  Light...

So if you managed to wade through the last few days gibberish, you might have noted that I mentioned we might be able to use "mind control" to highlight things in our heads--I did a bad job of describing it, but since I am currently experiencing just such a phenomenon, I think I'll give it another go.  These things that I am sharing with you--links between religion and music and movies, they aren't something I actively go out seeking... I'm not scouring through imdb.com or reading lyrics all day long... these are things that are glowing embers in front of my eyes.. which is why I am sharing them with you.  I'm always in the dark... but I'm living in a powder keg and giving off sparks.  I'm a big fan of that song by the way, because you are the heart, and I think it means I'm going to eclipse the world--which basically means "come."

Anyway, I have this horrible feeling inside that you think I'm just trying to get a date, or marry a rock star, or even worse that I think I deserve to get laid... and that's what this is all about.  Less to the point, this really isn't about me at all, or what I think, in my mind I am just showing you something that I think the world has overlooked-not really because you are stupid (but I mean, you probably are) but because some outside force is literally and actively hiding these things from you.  Pointing them out makes your brain do funny things, it's like anEpiphany and that little leap of understanding in your head might create a cascade.. something that changes not only the way you see the world as an individual--but the entire course of history as a group, if we are taking about it together.  Seriously, it's that big of a deal.

So here we are (that's the third time, but I'm just guessing) and I'm trying to tell you that I don't really care if you agree with my opinions--even though I firmly believe that God shares them and that's why he has made this fiery altar of "dick and apocalypse" for Adam... I mean Isaac (which by the was is Isa+Adam Christ.. in uh, my mind) for everyone to glare at while they sit around doing absolutely nothing.  That's not fair, we're here because of you, because this is the last civilization--sort of recreated from the ashes of Edom... because you are really the way to everlasting life.  Still, what I am trying to explain is that all around you is a bright light--it's in everything: from our history, to music, to movies, to literature from RattleRod to Dick... and while you might not agree with me (again, that would be OK) what is not OK is that there seems to be a uniform and global desire just not to think about it or talk about it at all.  It's such a big deal, that it stands out like a sore thumb--this ... blind eye or head in the sand... that everyone on Earth appears to have.  The whole point of putting this light absolutely everywhere is so that we will see it ... everywhere we look ... and not only think about it, but discuss it publicly with each other.  That's the thing that brings about ... you say apocalypse (unveiling of truth?) ... I say survival.  Right now, we need to see that something is forcing us not to do something, that we have no logical reason not to do... it's a thing lots of people really want to know about... whether it be the hidden secrets of the Universe, the path to Heaven, or the... the... absolute and literal pathway to freedom.  Listen, sharing it, and talking about it... that's the way we defeat ... whatever it is that "ni-i-i-ight" means.   

Understand, it's for you to decide... what it means... but it's in everything from ancient Egyptian and Hebrew theology all the way to the American Revolution and today... well, it's nearly every song I hear on the radio nowadays: if that tells you anything.

So here we are, and I can't tell you how many anchors, reporters, and "breaking news editors" I've personally spoken to that have absolutely no interest at all in pursuing the thing that would not only make their careers--but probably give them immortal souls.  This thing... I keep telling everyone it can be mathematically... statistically proven... well, to be honest it's the unsealing of the Ark of Religion that our civilization has been carrying around for thousands of years.  It's the way to salvation, it's ... verifiable proof of not only Creation... but that the purpose of Creation is to get every single one of us * to Heaven.  Who wouldn't want that?  I mean, do you want to get there and hear that Taylor's not around because she wouldn't kiss me?  That would never happen by the way, I'm sure she will.  Seriously though, there's no judge here... there's a ... light telling you to make this place better or your place sucks and gets suckier.  Anyway, the point is nobody is acting in their own best interest, or in the best interest of the whole--and we are just "deciding" in this ... fictitious and hidden manner that we "don't want to hear about" a way to actually change the world .... more quickly than ... the last time around.  That's not us, it's something keeping us from seeing just how important this thing--this key turning the lock on what is thousands and thousands of years of religion... how important that really is.  So looking at the world around us... I mean, if everything screaming that we need to care about this isn't enough--and your own personal desire and benefit don't matter... can someone please tell me what you think is the benefit of doing nothing about Hell?*

á§

á§

It's "rael," and a great deal of the message of religion and history is designed to not only prove that to us, but to tell us why it's important for the "continuity of reality" to be broken.  That's the thing that God uses to keep this world in Hell--in what I call "simulated reality," to keep us from shaking the foundation of civilization by doing the only civilized thing possible when you find out and ending world hunger, healing the sick, and building Heaven.  It is "why I am," and why God and some gaggle of angels have spent the last several years proving to me that we are most definitely not in the place that I call the "progenitor universe."  I've seenwalls disappear, with my own eyes I've seen the stars fall from the sky, and I've seen our reality shift in recent times in such a way that would be absolutely impossible without having been simulated and without having the "beginning" changed significantly as a result of "now."  What all that tells me is that religion, the Apocalypse, and I are here because we need to know that these things are possible in order to continue progressing from this point as a civilization.  With a little bit of thought, you might see how the computer revolution, video games, and virtual reality are divine gifts from above to help us to understand not only where we are, but where we are going.  It's why he tagged Ai as "I J Good," it's a primer in the tools we will need to actually build Heaven.  It's why Jesus occupation in our ancient time shifted story of now is "carpenter" and in "raelity" you will one day find out that I am a computer programmer (again).  It's what sets the Masons apart from Freemasons--understanding what is going on, and participating of our own free will in the construction and decorating of this grand place that we will one day be proud is our co-created home.  

Look up, because what I am trying to tell you is that if we collectively, all humanity... started snapping their fingers at the same time to the tune of "putting on the ritz" we could end world hunger--and then we could be proud to be making Heaven.  This really is almost what I see and believe--honestly the issue isn't that we need to synchronize our snapping, but we really need to discuss with each other openly and honestly how on Earth we would do such a thing... because there are definitely mistakes that probably happened n the past.  For instance, ending world hunger by stopping the need to eat has probably resulted in a Last Supper.  Doing so by putting milk and honey or chocolate on tap or in rivers probably resulted in the loss of cows and bees and a stable ecosystem, and the ability to colonize other planets after this place of final ascension.  And so we are here, with a proverbial garden of life in a virtual world designed to teach us what not to lose--like don't lose the balance between stability and adaptability that comes from sexual reproduction at the exact time when our species might be transiting to a place with the biggest change in environment (the thing that we are being protected from) ever... just because Adam wants to be immortal.

Every once in awhile my father surprises me with his religious insight.  In his life, just like mine, he's gone through phases of increasing and decreasing religiosity--which probably correlate in his case logically to ups and downs in his life.  I tend to get angry at God when things don't go well for me--which is probably not how most people react, it's really the difference between knowing he's there and not... at least in my mind.  Anyway, some 50 years ago he was apparently taught that the "knowledge of good and evil" in Eden was directly correlated to the population explosion that would occur if we were actually all immortal and continued to have children--so it was this promise of immortality that was "evil," I suppose.  God adds in his little Holy Grail that the heart of his spirit is "Kin," and I'm sharing with you that it's not his immediate family but rather the concept of family and the fact that the light of many of our hearts is our children that he is highlighting as our reason (y) that family is the bridge between Eve and Everyone... as the light of God.  

Here's that once again:

``` In the beginning God created the heaven and the earth. And the earth was without form, and void; and darkness was upon the face of the deep. And the Spirit of God SHE KIN AH moved upon the face of the waters. ---------- EVE RY ONE And God said, Let there be light: and there was light.

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Author response:

The following is the authors’ response to the original reviews.

eLife Assessment

This important study addresses how 3' splice site choice is modulated by the conserved spliceosome-associated protein Fyv6. The authors provide compelling evidence Fyv6 functions to enable selection of 3' splice sites distal to a branch point and in doing so antagonizes more proximal, suboptimal 3' splice sites. The study would be improved through a more nuanced discussion of alternative possibilities and models, for instance in discussing the phenotypic impact of Fyv6 deletion.

We thank the editors and reviewers for their supportive comments and assessment of this manuscript. We have improved the discussion at several points as suggested by the reviewers to include discussion of alternative possibilities.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

A key challenge at the second chemical step of splicing is the identification of the 3' splice site of an intron. This requires recruitment of factors dedicated to the second chemical step of splicing and exclusion of factors dedicated to the first chemical step of splicing. Through the highest resolution cyroEM structure of the spliceosome to-date, the authors show the binding site for Fyv6, a factor dedicated to the second chemical step of splicing, is mutually exclusive with the binding site for a distinct factor dedicated to the first chemical step of splicing, highlighting that splicing factors bind to the spliceosome at a specific stage not only by recognizing features specific to that stage but also by competing with factors that bind at other stages. The authors further reveal that Fyv6 functions at the second chemical step to promote selection of 3' splice sites distal to a branch point and thereby discriminate against proximal, suboptimal 3' splice site. Lastly, the authors show by cyroEM that Fyv6 physically interacts with the RNA helicase Prp22 and by genetics Fyv6 functionally interacts with this factor, implicating Fyv6 in 3'SS proofreading and mRNA release from the spliceosome. The evidence for this study is robust, with the inclusion of genomics, reporter assays, genetics, and cyroEM. Further, the data overall justify the conclusions, which will be of broad interest.

Strengths:

(1) The resolution of the cryoEM structure of Fyv6-bound spliceosomes at the second chemical step of splicing is exceptional (2.3 Angstroms at the catalytic core; 3.0-3.7 Angstroms at the periphery), providing the best view of this spliceosomal intermediate in particular and the core of the spliceosome in general.

(2) The authors observe by cryoEM three distinct states of this spliceosome, each distinguished from the next by progressive loss of protein factors and/or RNA residues. The authors appropriately refrain from overinterpreting these states as reflecting distinct states in the splicing cycle, as too many cyroEM studies are prone to do, and instead interpret these observations to suggest interdependencies of binding. For example, when Fyv6, Slu7, and Prp18 are not observed, neither are the first and second residues of the intron, which otherwise interact, suggesting an interdependence between 3' splice site docking on the 5' splice site and binding of these second step factors to the spliceosome.

(3) Conclusions are supported from multiple angles.

(4) The interaction between Fyv6 and Syf1, revealed by the cyroEM structure, was shown to account for the temperature-sensitive phenotypes of a fyv6 deletion, through a truncation analysis.

(5) Splicing changes were observed in vivo both by indirect copper reporter assays and directly by RT-PCR.

(6) Changes observed by RNA-seq are validated by RT-PCR.

(7) The authors go beyond simply observing a general shift to proximal 3'SS usage in the fyv6 deletion by RNA-seq by experimentally varying branch point to 3' splice site distance experimentally in a reporter and demonstrating in a controlled system that Fyv6 promotes distal 3' splice sites.

(8) The importance of the Fyv6-Syf1 interaction for 3'SS recognition is demonstrated by truncations of both Fyv6 and of Syf1.

(9) In general, the study was executed thoroughly and presented clearly.

We thank the reviewer for their recognition of the strengths of our multi-faceted approach that led to highly supported conclusions.

Weaknesses:

(1) Despite the authors restraint in interpreting the three states of the spliceosome observed by cyroEM as sequential intermediates along the splicing pathway, it would be helpful to the general reader to explicitly acknowledge the alternative possibility that the difference states simply reflect decomposition from one intermediate during isolation of the complex (i.e., the loss of protein is an in vitro artifact, if an informative one).

We thank the reviewer for noticing our restraint in interpreting these structures, and we agree that the scenario described by the reviewer is a possibility. We have now explicitly mentioned this in the Discussion on lines 755-757.

(2) The authors acknowledge that for prp8 suppressors of the fyv6 deletion, suppression may be indirect, as originally proposed by the Query and Konarska labs - that is, that defects in the second step conformation of the spliceosome can be indirectly suppressed by compensating, destabilizing mutations in the first step spliceosome. Whereas some of the other suppressors of the fyv6 deletion can be interpreted as impacting directly the second step spliceosome (e.g., because the gene product is only present in the second step conformation), it seems that many more suppressors beyond prp8 mutants, especially those corresponding to bulky substitutions, which would more likely destabilize than stabilize, could similarly act indirectly by destabilization of first step conformation. The authors should acknowledge this where appropriate (e.g., for factors like Prp8 that are present in both first and second step conformations).

We agree that this is also a possibility and have now included this on lines 480-486.

Reviewer #2 (Public Review):

In this manuscript, Senn, Lipinski, and colleagues report on the structure and function of the conserved spliceosomal protein Fyv6. Pre-mRNA splicing is a critical gene expression step that occurs in two steps, branching and exon ligation. Fyv6 had been recently identified by the Hoskins' lab as a factor that aids exon ligation (Lipinski et al., 2023), yet the mechanistic basis for Fyv6 function was less clear. Here, the authors combine yeast genetics, transcriptomics, biochemical assays, and structural biology to reveal the function of Fyv6. Specifically, they describe that Fyv6 promotes the usage of distal 3'SSs by stabilizing a network of interactions that include the RNA helicase PRP22 and the spliceosome subunit SYF1. They discuss a generalizible mechanism for splice site proofreading by spliceosomsal RNA helicases that could be modulated by other, regulatory splicing factors.

This is a very high quality study, which expertly combines various approaches to provide new insights into the regulation of 3'SS choice, docking, and undocking. The cryo-EM data is also of excellent quality, which substantially extends on previous yeast P complex structures. This is also supported by the authors use of the latest data analysis tools (Relion-5, AlphaFold2 multimer predictions, Modelangelo). The authors re-evaluate published EM densities of yeast spliceosome complexes (B*, C,C*,P) for the presence or absence of Fyv6, substantiate Fyv6 as a 2nd step specific factor, confirm it as the homolog of the human protein FAM192A, and provide a model for how Fyv6 may fit into the splicing pathway. The biochemical experiments on probing the splicing effects of BP to 3'SS distances after Fyv6 KO, genetic experiments to probe Fyv6 and Syf1 domains, and the suppressor screening add substantially to the study and are well executed. The manuscript is clearly written and we particularly appreciated the nuanced discussions, for example for an alternative model by which Prp22 influences 3'SS undocking. The research findings will be of great interest to the pre-mRNA splicing community.

We thank the reviewer for their positive comments on our manuscript.