Author response:

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

Reviewer #1

The authors should include experiments such as Cryo-EM and genetically modified animals to demonstrate the physiological importance of the TMEM81 complex.

While we intend to pursue cryo-EM studies of the putative complex (or subcomplexes thereof), this is clearly not a straightforward endeavor and goes beyond the scope of the present manuscript. Concerning the generation of genetically modified animals, we would like to underline that the majority of the proteins that we used for AlphaFold-Multimer complex predictions were precisely chosen based on the fact that - as detailed in the publications referenced in the Introduction - ablation of the respective genes caused sex-specific infertility due to defects in gamete fusion (the other criterion used for inclusion being structural similarity to IZUMO1 coupled with expression in the testis (IZUMO2-4 and TMEM81), or evidence from other kinds of experiments in the case of human-specific MAIA). Concerning TMEM81, experimental evidence for a direct involvement in gamete fusion is described in the referenced preprint by Daneke et al., which was submitted to bioRxiv concomitantly with the present work.

Reviewer #2

I believe that the manuscript would benefit from the authors providing more information about the systematic search (Figure 4). For example, by indicating for each pair tested the average pDock score in a 2D plot (or table) and as raw data in the supplementary information.

Figure 4 has been modified to report both the top and the mean ranking scores for every interaction. Furthermore, additional metrics for the systematic search summarized in Figure 4, including pDockQ scores, are provided in this manuscript revision as supplementary Table S1.

A global search, such as including all membrane proteins expressed in eggs or sperm, could not only be more informative but could also allow the reader to understand the pDock score discrimination power for this particular subset.

The possibility of carrying out a global search was evaluated by performing preliminary computational experiments on an extended ensemble of sperm and egg proteins. In order to do so, we compiled a list of sperm membrane proteins by referring to 4 proteomic datasets (PMIDs 36384108, 36896575, 31824947, 24082039) and identifying ~600 proteins that were found in at least two of them; among these, 250 were single-pass type I or type II membrane proteins, or GPI-anchored proteins. Similarly, a list of 160 egg surface membrane proteins, excluding multipass and secreted ones, was obtained by comparing oocyte cDNA library NIH_MGC_257_N (Express Genomics, USA) with 4 proteomic datasets (PMIDs 35809850, 36042231, 29025019, 27215607). As we briefly commented at the beginning of the section “Prediction of interactions between human proteins associated with gamete fusion” of the revised manuscript, the tests carried out using the resulting list of sperm and egg proteins suggested that interpreting the results of a global search would be severely complicated by a relatively large number of putative false positives. Moreover, the tests showed that performing a complete systematic search would be beyond our current access to computing power. Based on these observations, we preferred to maintain the present study limited to proteins that had been previously clearly implicated in gamete fusion and/or matched specific structural features of IZUMO1.

Figure 5 could be improved in clarity by schematically indicating to which cell each protein is anchored.

This has been done in the revised version of the manuscript.

Reviewer #3

Major comments

(1) In Figure 1, how the protein of mouse/human IZUMO1 and JUNO is purified is not mentioned in the main text nor in the Methods. Are the mouse IZUMO1-His and mouse JUNO-His transfected together or separately? Are human JUNO-His and human IZUMO1-Myc transfected together into HEK293 cells? And purified by IMAC?

Transfection information has been included in the Methods section “Protein expression, purification and analysis” (previously “Protein expression and purification”). Concerning the purification procedure, we had already stated in the legend of Figure 1 that human JUNOE-His/IZUMO1E-Myc had been purified by IMAC before SEC, and have now done the same for mouse JUNOE-His and IZUMO1E-His.

(2) It would be easier to understand the figure if the author could run a WB to indicate which band above JUNO is specifically IZUMO1-Myc in Figure 1.

This has been done and reported in a new Figure S1 (with the original Figure S1 having now become Figure S2). Details about the antibodies used for immunoblot have been included in both Methods section “Protein expression, purification and analysis” and the Key Resources Table.

(3) Figure 4: Analysis of more proteins that have been suggested as possible candidates for sperm-egg interaction will help to highlight the following results. Also, providing a score for the possibility of interaction might help in selecting those proteins in Figures 5 and 6.

Please refer to the answer to the first question of Reviewer #2.

(4) Figure 7: The authors take advantage of the latest developments in protein structure and interaction to model protein complex formation. However, some experimental experiments such as Co-IP, pull down to support the prediction to verify some of this predicated interaction is necessary.

We agree with the reviewer; however, for the reasons we discussed during our comparison of the biochemical properties of the JUNO/IZUMO1 interaction between mouse and human, pursuing this line of inquiry will likely necessitate an extensive set of parallel experiments using proteins from different species. This work is being planned and will be the focus of future studies. However, as we mentioned at the end of the Abstract, one should also consider that some of these complexes are likely to be highly transient. Because of this, while they may have important regulated roles in vivo (function at a specific time and place), they could be very challenging to detect using standard approaches in vitro. We thus see this as a significant advance that structural modeling could contribute to the identification of such functionally important but transient interactions.

Minor points

(1) In the abstract, "three sperm (IZUMO1, SPACA6 and TMEM81) "should be "three sperm proteins."

The Abstract has been condensed to fit within the suggested 200-word limit and, as part of this, the sentence has been changed to “complex involving sperm IZUMO1, SPACA6, TMEM81 and egg JUNO, CD9”.

(2) How do the predictions of the binary complex IZUMO1/CD9 (Figure S1B) or IZUMO1/CD81 (Figure S1C) suggest "the two egg tetraspanins are interchangeable"? Was it because they are quite similar? Please provide more explanation for this speculation. Interchangeable by function or for complex formation? To support the conclusion, biochemical data is required. Otherwise, it needs to be toned down.

This is because, in the AlphaFold-Multimer predictions of the pentameric complex, CD9 and CD81 are placed in essentially the same way relative to the other subunits.

We have now clarified this at the end of page 6:

“(...) suggest that the two egg tetraspanins are interchangeable because they are predicted to bind to the same region of IZUMO1; (...)”

(3) It would be more reader-friendly if the author could label the name of each protein in the figure in Figure S1, especially when the name is not written in the figure legend.

This has been done in Figure S2 of the revised manuscript (corresponding to original Figure S1).

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

This study examined a universal fractal primate brain shape. However, the paper does not seem well structured and is not well written. It is not clear what the purpose of the paper is. And there is a lack of explanation for why the proposed analysis is necessary. As a result, it is challenging to clearly understand what novelty in the paper is and what the main findings are.

We have now restructured the paper, including a summary of the main purpose and findings as follows:

“Compared to previous literature, we can summarise our main contribution and advance as follows:

(i) We are showing for the first time that representative primate species follow the exact same fractal scaling – as opposed to previous work showing that they have a similar fractal dimension [Hofman1985, Hofman1991], i.e. slope, but not necessarily the same offset, as previous methods had no consistent way of comparing offsets.

(ii) Previous work could also not show direct agreement in morphometrics between the coarse-grained brains of primate species and other non-primate mammalian species.

(iii) Demonstrating in proof-of-principle that multiscale morphometrics, in practice, can have much larger effect sizes for classification applications. This moves beyond our previous work where we only showed the scaling law across [Mota2015] and within species [Wang2016], but all on one (native) scale with comparable effect sizes for classification applications [Wang2021].

In simple terms: we know that objects can have the same fractal dimension, but differ greatly in a range of other shape properties. However, we demonstrate here, that representative primate brains and mammalian brain indeed share a range of other key shape properties, on top of agreeing in fractal dimension. This suggests a universal blueprint for mammalian brain shape and a common set of mechanisms governing cortical folding. As a practical additional outcome of our study, we could show that our novel method of deriving multiscale metrics of brain shape can differentiate subtle shape changes much better than the metrics we have been using so far at a single native scale.”

We plan to use the second paragraph as a plain-language summary of our work.

Additionally, several terms are introduced without adequate explanation and contextualization, further complicating comprehension.

We have now made sure that potential jargon is introduced with context and explanation. For example in Introduction: “This scaling law, relating powers of cortical thickness and surface area metrics, […]”

Does the second section, "2. Coarse-graining procedure", serve as an introduction or a method?

We have now renamed this section to “Coarse-graining Method” to indicate that this is a section about methods. However, to describe the methods adequately, we also expanded this section with introductory texts around the history and motivation of the method to provide context and explanations, as the reviewer rightly requested.

Moreover, the rationale behind the use of the coarse-graining procedure is not adequately elucidated. Overall, it is strongly recommended that the paper undergoes significant improvements in terms of its structure, explanatory depth, and overall clarity to enhance its comprehensibility.

To specifically explain the rationale behind the coarse-graining method, we added several clarifications, including the following paragraph:

“As a starting point for such a coarse-graining procedure, we suggest to turn to a well-established method that measures fractal dimension of objects: the so-called box-counting algorithm [Kochunov2007, Madan2019]. Briefly, this algorithm fills the object of interest (say the cortex in our case) with boxes, or voxels of increasingly larger sizes and counts the number of boxes in the object as a function of box size. As the box size increases, the number of boxes decreases; and in a log-log plot, the slope of this relationship indicates the fractal dimension of the object. In our case, this method would not only provide us with the fractal dimension of the cortex, but, with increasing box size, the filled cortex would also contain less and less detail of the folded shape of the cortex. Intuitively, with increasing box size, the smaller details, below the resolution of a single box, would disappear first, and increasingly larger details will follow -- precisely what we require from a coarse-graining method. We therefore propose to expand the traditional box-counting method beyond its use to measure fractal dimension, but to also analyse the reconstructed cortices as different realisations of the original cortex at the specified spatial scale.”

Reviewer #2 (Public Review):

In this manuscript, Wang and colleagues analyze the shapes of cerebral cortices from several primate species, including subgroups of young and old humans, to characterize commonalities in patterns of gyrification, cortical thickness, and cortical surface area. The work builds on the scaling law introduced previously by co-author Mota, and Herculano-Houzel. The authors state that the observed scaling law shares properties with fractals, where shape properties are similar across several spatial scales. One way the authors assess this is to perform a "cortical melting" operation that they have devised on surface models obtained from several primate species. The authors also explore differences in shape properties between the brains of young (~20 year old) and old (~80) humans. My main criticism of this manuscript is that the findings are presented in too abstract a manner for the scientific contribution to be recognized.

We recognise that our work is at the intersection of complex mathematical concepts and a perplexing biological phenomenon. Therefore, our paper has to strike a balance between scientifically accurate and succinct descriptions whilst giving sufficient space to provide context and explanations.

Throughout, we have now added text to provide more context, but also repeat key statements in plain-english terms.

For example, we added the following text to highlight our key contributions.

“In simple terms: we know that objects can have the same fractal dimension, but differ greatly in a range of other shape properties. However, we demonstrate here, that representative primate brains and mammalian brain indeed share a range of other key shape properties, on top of agreeing in fractal dimension. This suggests a universal blueprint for mammalian brain shape and a common set of mechanisms governing cortical folding. As a practical additional outcome of our study, we could show that our novel method of deriving multiscale metrics of brain shape can differentiate subtle shape changes much better than the metrics we have been using so far at a single native scale.”

(1) The series of operations to coarse-grain the cortex illustrated in Figure 1, constitute a novel procedure, but it is not strongly motivated, and it produces image segmentations that do not resemble real brains.

To specifically explain the rationale behind the coarse-graining method, we added several clarifications, including the following paragraph:

“As a starting point for such a coarse-graining procedure, we suggest to turn to a well-established method that measures fractal dimension of objects: the so-called box-counting algorithm [Kochunov2007, Madan2019]. Briefly, this algorithm fills the object of interest (say the cortex in our case) with boxes, or voxels of increasingly larger sizes and counts the number of boxes in the object as a function of box size. As the box size increases, the number of boxes decreases; and in a log-log plot, the slope of this relationship indicates the fractal dimension of the object. In our case, this method would not only provide us with the fractal dimension of the cortex, but, with increasing box size, the filled cortex would also contain less and less detail of the folded shape of the cortex. Intuitively, with increasing box size, the smaller details, below the resolution of a single box, would disappear first, and increasingly larger details will follow -- precisely what we require from a coarse-graining method. We therefore propose to expand the traditional box-counting method beyond its use to measure fractal dimension, but to also analyse the reconstructed cortices as different realisations of the original cortex at the specified spatial scale.”

We also note in several places in the text that the coarse-grained brains are not to be understood as exact reconstructions of actual brains, but serve the purpose of a model:

“[…] nor are the coarse-grained versions of human brains supposed to exactly resemble the location/pattern/features of gyri and sulci of other primates. The similarity we highlighted here are on the level of summary metrics, and our goal was to highlight the universality in such metrics to point towards highly conserved quantities and mechanisms.”

“Note, of course, that the coarse-grained brain surfaces are an output of our algorithm alone and not to be directly/naively likened to actual brain surfaces, e.g. in terms of the location or shape of the folds. Our comparisons here between coarse-grained brains and actual brains is purely on the level of morphometrics across the whole cortex.”

The process to assign voxels in downsampled images to cortex and white matter is biased towards the former, as only 4 corners of a given voxel are needed to intersect the original pial surface, but all 8 corners are needed to be assigned a white matter voxel (section S2). This causes the cortical segmentation, such as the bottom row of Figure 1B, to increase in thickness with successive melting steps, to unrealistic values. For the rightmost figure panel, the cortex consists of several 4.9-sided voxels and thus a >2 cm thick cortex. A structure with these morphological properties is not consistent with the anatomical organization of a typical mammalian neocortex.

Specifically on the point on increasing cortical thickness with increased level of coarse-graining, we have now added the following paragraph:

“The observation that with increasing voxel sizes, the coarse-grained cortices tend to be smoother and thicker is particularly interesting: the scaling law in Eq. 1 can be understood as thicker cortices (T) form larger folds (or are smoother i.e. less surface area At) when brain size is kept constant (Ae). This way of understanding has also been vividly illustrated by using the analogy of forming paper balls with papers of varying thickness in [Mota2015]: to achieve the same size of a paper ball (Ae), the one that uses thicker paper (T) will show larger folds (or is smoother i.e. less surface area At) than the one using thinner paper. The scaling law can therefore be understood as a physically and biologically plausible statement, and here, we are encouraged that our algorithm yields results in line with the scaling law.”

(2) For the comparison between 20-year-old and 80-year-old brains, a well-documented difference is that the older age group possesses more cerebral spinal fluid due to tissue atrophy, and the distances between the walls of gyri becomes greater. This difference is born out in the left column of Figure 4c. It seems this additional spacing between gyri in 80-year-olds requires more extensive down-sampling (larger scale values in Figure 4a) to achieve a similar shape parameter K as for the 20-year-olds. A case could be made that the familiar way of describing brain tissue - cortical volume, white matter volume, thickness, etc. - is a more direct and intuitive way to describe differences between young and old adult brains than the obscure shape metric described in this manuscript. At a minimum, a demonstration of an advantage of the Figure 4a and 4b analyses over current methods for interpreting age-related differences would be valuable.

We have demonstrated the utility of our new shape metrics in a separate paper [Wang2021]. However, we agree with the reviewer that, in this specific instance, it is much easier to understand the key message without considering the less traditional metrics. We have therefore completely revised this part of the Results section to highlight the advantage of multiscale morphometrics, and used the traditional metric of surface area to illustrate the point. The reasoning in surface area is much easier to follow, both visually and conceptually, exactly as the reviewer described.

(3) In Discussion lines 199-203, it is stated that self-similarity, operating on all length scales, should be used as a test for existing and future models of gyrification mechanisms. First, the authors do not show, (and it would be surprising if it were true) that self-similarity is observed for length scales smaller than the acquired MRI data for any of the datasets analyzed. The analysis is restricted to coarse (but not fine)-graining.

To clarify this point, we have added a supplementary section and the following sentence: “Note this method has also no direct dependency on the original MR image resolution, as the inputs are smooth grey and white matter surface meshes reconstructed from the images using strong (bio-)physical assumptions and therefore containing more fine-grained spatial information than the raw images (also see Suppl. Text 3).”

We are indeed sampling at resolutions down to 0.2mm, which is below MR image resolution. The reviewer is, however, correct that we are only coarse-graining, not “fine-graining”. Coarse-graining, here, relates to more coarse than the smooth surface meshes though, not the MR image.

Therefore, self-similarity on all length scales would seem to be too strong a constraint. Second, it is hard to imagine how this test could be used in practice. Specific examples of how gyrification mechanisms support or fail to support the generation of self-similarity across any length scale, would strengthen the authors' argument.

We agree that spatial scales much below 0.2mm resolution may not be of interest, as these scales are only measuring the fractal properties, or “bumpiness”, of the surface meshes at the vertex level. We have therefore revised our statement in Discussion and clarified it with an example: “Finally, this dual universality is also a more stringent test for existing and future models of cortical gyrification mechanisms at relevant scales, and one that moreover is applicable to individual cortices. For example, any models that explicitly simulate a cortical surface could be directly coarse-grained with our method and compared to actual human and primate data provided here.”

Some additional, specific comments are as follows:

(4) The definition of the term A_e as the "exposed surface" was difficult to follow at first. It might be helpful to state that this parameter is operationally defined as the convex hull surface area.

We agree and introduced this term now at first use: “The exposed surface area can be thought of as the surface area of a piece of cling film wrapped around the brain. Mathematically, for the remaining paper it is the convex hull of the brain surface.”

Also, for the pial surface, A_t, there are several who advocate instead for the analysis of a cortical mid-thickness surface area, as the pial surface area is subject to bias depending on the gyrification index and the shape of the gyri. It would be helpful to understand if the same results are obtained from mid-thickness surfaces.

This point is indeed being investigated independently of this study. Our provisional understanding is that in healthy human brains, at native scale, using the mid (or the white matter) surface introduced a systematic offset shift in the scaling law, but does not affect the scaling slope of 1.25. However, this requires a more in-depth investigation in a range of other conditions, and in the context of the coarse-grained shapes, which is on-going. Nevertheless, the scaling law, at first introduction already, has been using the pial surface area [Mota2015] and all subsequent follow-up studies followed this convention. To make our paper here accessible and directly comparable, we therefore used the same metric. Future work will investigate the utility of other metrics.

(5) In Figure 2c, the surfaces get smaller as the coarse-graining increases, making it impossible to visually assess the effects of coarse-graining on the shapes. Why aren't all cortical models shown at the same scale?

The purpose of rescaling the surfaces is to match the scaling plot (Fig 2A) directly, which are showing shrinking surface areas Ae and At with increasing coarse-graining. Here, we are effectively keeping the size of the box constant and resizing the cortical surface instead, which is mathematically equivalent to changing the box size and keeping the cortical surface constant.

An alternative interpretation of the “shrinking” is, therefore, that with increasingly smaller cortical surfaces, the folding details disappear, as we require from our coarse-graining method. This is also visually apparent, as the reviewer points out. We have added this to the explanation in the text.

If we, however, changed the box size instead, the scaling law plot would be meaningless: for example, Ae would barely change with coarse-graining. We would therefore have needed to introduce more complexity in our analysis in terms of how we can measure the scaling law. Thus, we opted to present the simpler method and interpretation here.

Nevertheless, we agree that a direct comparison would be beneficial and have thus added the videos for each species in supplementary under this link: https://bit.ly/3CDoqZQ Upon completed peer-review, we hope to integrate these directly into eLife’s interactive displays for this figure.

(6) Text in Section 3.2 emphasizes that K is invariant with scale (horizontal lines in Figure 3), and asserts this is important for the formation of all cortices. However, I might be mistaken, but it appears that K varies with scale in Figure 4a, and the text indicates that differences in the S dependence are of importance for distinguishing young vs. old brains. Is this an inconsistency?

We agree that it may be confusing to emphasise a “constant K” in the first set of results across species, and then later highlight a changing K in the human ageing results. To clarify, in the first set of results, we find a constant K relative to a changing S: the range in K across melted primate brains is less than 0.1, whereas in S it is over 1.2. In other words, S changes are an order of magnitude higher than K changes. Hence, we described K as “constant” relative to S.

Nevertheless, K shows subtle changes within individuals, which is what we were describing in the human ageing results. These changes are within the range of K values described in the across species results.

However, in the interest of clarity, we followed the reviewer’s suggestion of simplifying the last set of results on human ageing and therefore the variable K in human ageing now only appears in Supplementary. We have now added clarifications to the supplementary on this point.

Reviewer #3 (Public Review):

Summary:

Through a detailed methodology, the authors demonstrated that within 11 different primates, the shape of the brain matched a fractal of dimension 2.5. They enhanced the universality of this result by showing the concordance of their results with a previous study investigating 70 mammalian brains, and the discordance of their results with other folded objects that are not brains. They incidentally illustrated potential applications of this fractal property of the brain by observing a scale-dependent effect of aging on the human brain.

Strengths:

• New hierarchical way of expressing cortical shapes at different scales derived from the previous report through the implementation of a coarse-graining procedure.

Positioning of results in comparison to previous works reinforcing the validity of the observation.

• Illustration of scale-dependence of effects of brain aging in the human.

Weaknesses:

• The impact of the contribution should be clarified compared to previous studies (implementation of new coarse graining procedure, dimensionality of primate brain vs previous studies, and brain aging observations).

We have now made these changes, particularly by adding two paragraphs to the start of Discussion. One summarising the main contributions above previous work, and one paraphrasing the former in plain English for accessibility.

• The rather small sample sizes, counterbalanced by the strength of the effect demonstrated.

We have now increased the sample size of the human ageing analysis substantially to over 100 subjects and observe the same trends, but with an even stronger effect. We therefore believe that this revision serves as an additional internal validation of our data and methods.

• The use of either averaged or individual brains for the different sub-studies could be made clearer.

We have now added this to our Suppl methods: with the exception of the Marmoset, all brain surface data were derived from healthy individual brains.

• The model discussed hypothetically in the discussion is not very clear, and may not be state-of-the-art (axonal tension driving cortical folding? cf. https://doi.org/10.1115/1.4001683).

We have now added this citation to our Discussion and given it context:

“Indeed, our previously proposed model [Mota2015] for cortical gyrification is very simple, assuming only a self-avoiding cortex of finite thickness experiencing pressures (e.g. exerted by white matter pulling, or by CSF pressure). The offset K, or 'tension term', precisely relates to these pressures, leading us to speculate that subtle changes in K correlate with changes in white matter property [Wang2016, Wang2021]. In the same vein of speculation, the scale-dependence of K shown in this work might therefore be related to different types of white matter that span different length scales, such as superficial vs. deep white matter, or U-fibres vs. major tracts. However, there are also challenges to the axonal tension hypothesis [Xu2010]. Indeed, white matter tension differentials in the developed brain may not explain location of folds, but instead white matter tension may contribute to a whole-brain scale 'pressure' during development that drives the folding process overall.”

Reviewer #3 (Recommendations For The Authors):

Many thanks to the authors for this elegant article. I will only report here on the cosmetics of the article.

We thank the reviewer for their kind words and attention to detail and have made all the suggested changes and revised the paper generally for readability, grammar and spelling.

p2: last line of abstract: 'for a range of conditions in the future'.

p3 l.37: I would not self-describe this method as elegant as this is a subjective property .

p3 l.38: 'that will render' -> I wouldn't use the future here.

p.4 l.59: double spacing before ref [9]?

p.6 l.99: 'approximate a fractal' -> why is 'a' italicized?

p.7 fig.2: I would expect the colours to be detailed in the legend. Are there two data points per species because both hemispheres are treated separately?

p.9 l.134-135: 'similar to and in terms of the universal law 'as valid as' -> please add commas for reading comfort: 'similar to, and, in terms of the universal law, 'as valid as'.

p.9 l. 141: For all the cortices we analysed.

p.9 Fig 3: I find the colours a bit confusing in Figs B and C. I find Fig C a bit confusing: what are all the lines representative of, and more specifically, the two lower lines with a different trajectory?

p.10 l.155: '1̃500' -> '~1500'.

p.13 l. 209: either 'speculate that' of 'wonder if'.

p.14 l.232: 'neuron numbers' -> 'number of neurons'.

p.26 S2 second paragraph: 'gryi' -> 'gyri'.

p.30 l.3: please refrain from starting a sentence with I.e..

p.30 last line before S3.2: 'The algorithmic implementation in MATLAB can be found on Zenodo: TBA' - I guess this is linked to you disclosing the code upon acceptance, but please complete before final submission.

p.34 middle/bottom of page: 'The scheme described in Sec. S3.1' -> double spacing before S3.1?

p.35 l.1: 'We simply replace' -> 'we simply replace' (no capital).

p.36 Fig S5.1: explicit the same colouring of the points and boxes in legend

p.38 Fig. S6.1: briefly describe the use of colours in the legend.

p.39 Fig. S7.1: detail colours in the legend.

p.41 Fig. S7.3: detail colours in the legend.

Author response:

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

Reviewer #1:

Figure 1

• The "matched primary tumors" from TCGA include n=424 from cutaneous melanoma; but it is unclear where this is coming from; the PanCan Atlas for melanoma shows n=81 primary and 367 metastatic tumors. There are also additional large cohorts of ICI-treated metastatic tumors with RNAseq data (e.g. a metastatic melanoma cohort with 100+ patients https://doi.org/10.1038/s41591-019-0654-5) that would increase the numbers here.

We thank the reviewer for their observation. We have replaced references to “primary” cancers as “TCGA” cancers as appropriate. While the TCGA analyses included metastatic samples, the majority of the TCGA tumors in most cohorts correspond to primary cancers or local metastases, a point which we added to the text. We retained Fig. 1D as the representative examples are actual primary samples. We have decided to defer analysis of additional melanoma cohorts for future inquiry.

Figure 2

• What is the basis for the split between high and low Dux4 expressing tumors at 1 TPM? Is it arbitrary, or based on some structure in the distribution? (e.g. bimodal distribution)

Our previous analyses of RNA-seq datasets derived from early embryogenesis samples (PMID: 3132774, 28459457) showed that physiologic levels of DUX4 range from approximately 2 to 10 TPM. We added a description in the methods section, under “Genome annotations, gene expression, and Gene Ontology (GO) enrichment analyses,” of our conservative choice for the threshold: DUX4-positivity defined as expression levels > 1 TPM.

Figure 3

• Overall claim is that Dux4 expression is associated with worse survival in metastatic urothelial carcinomas treated with PD-L1 inhibitor. However, the rationale for the choice of split (Dux4 expression < 0.5 and > 1 TPM) to show is unclear (is this the 25th percentile? 75th percentiles?), and the rationale/interpretation of the "partial adjustment" for TMB by removing the bottom quartile of TMB feels non-rigorous and prone to bias. It doesn't feel like Fig 3bc contributes very much; Figure 4 really is the more rigorous analysis.

We thank the reviewer for these comments and suggestions. We adjusted the analyses in Fig. 3C and Fig. S3 to be consistent with Fig. 1 and Fig. 2, in terms of the choice of split. We also clarified in the text how our initial, crude TMB adjustment served as an important indication for us to pursue more rigorous statistical approaches.

Figure 4

• Dux4 expression is independently associated with worse survival considering other clinical and molecular characteristics

• I would include TGFB in the features considered in the table (in the supplementary but not the main table or forest plots, not sure why not?)

• The choice of Dux4 expression split ( < 0.25 and > 1 TPM) feels arbitrary and is different than the split in Figure 3; what is the rationale for this? Also, how many patients does this exclude? (TPM between 0.25 and 1). What does the continuous value or median split for Dux4 expression give you for the CoxPH model?

• Re: building a predictive model, excluding patients (e.g. between <0.25 and > 1 Dux4 TPM) makes the model difficult to apply (e.g. cannot apply to patients with Dux4 levels in the missing interval); a better predictive model would include all patients in the cohort.

We thank the reviewer for their other suggestions. We have clarified in the text that our choice to define DUX4negative samples as those with DUX4 expression levels < 0.25 TPM was made to preemptively address potential misclassifications due to decreased sensitivity of bulk RNA-seq at very low expression levels (PMID: 18516045). We believe our classifications with the new scheme are more reliable. We have also now specified in the text that our categorization excludes 126 patients. We have decided to not pursue the addition of TGFB or exploration of the use of an alternative split or continuous version of DUX4 expression in the Cox Proportional Hazards analyses but appreciate the suggestions, which we will keep in mind for future studies.

Figure 5

• An RSF (randomized survival forest) model predicts survival in Dux4+ vs Dux4- patient, and the Shapley values for landmark time analyses show time-varying effects of different features.

• In some sense, the authors have already demonstrated that Dux4+ is associated with survival differences in ICI treated patients; so a model that predicts survival applied to Dux4+ and Dux4- patients that shows a difference in survival is unsurprising (even in a training/test set setting given that there is a difference in survival across the entire cohort). The quantified marginal effect (from a predictive perspective) of different features is what is interesting here. In that light, I'd like to see more validation of the model up front, specifically how close the predicted survival is to the actual survival of patients (e.g. the survival curves in Fig 5a but with actual survival of the Dux4- and Dux4+ cohorts superimposed on the predicted probabilities).

We thank the reviewer for this suggestion. We have added a plot showing the superimposed survival probability estimates over time for the RSF and KM models for patients assigned to either the test or training sets in Fig. 5.

SFig 5

• Unclear how the authors got estimates of the # of expected deaths associated with covariates (e.g. "...we measured an increase in the number of predicted deaths associated with DUX4-positivity by approximately 16, over DUX4negative status (Fig S5F-G).") from Shapley values as shown in the indicated figure - is this 16 out of the entire cohort? At a given time point? Would recommend perhaps showing the inferred absolute change in mortality (e.g. 8% absolute increase in mortality)

Mortality is the expected number of deaths for the cohort over the observation window, measured as the sum of the CHF over time. We have clarified this in the Methods section, under “Random Survival Forest, feature importance, and partial dependence.” We have also changed the quantification to show the absolute mortality differences comparing patients with DUX4-negative and -positive tumors; we thank the reviewer for this suggestion. We have also clarified in the text that adjusted mortality was estimated via partial dependence, which operates using the correct units, as opposed to Shapley values, where attribution is scaled. Finally, we changed the referenced figure when discussing changes in mortality associated with TMB and DUX4 status (Fig. S5H-I); we appreciate the reviewer pointing out this error.

Figure S1B-C

• The authors argue that Dux4 expression is not an artifact of FFPE tissue by analyzing a mixed tumor cohort sequenced with both poly-A and hybrid probe capture in matched flash-frozen and FFPE tumor samples, showing that it is 1) detectible both FFPE and flash-frozen tissue and 2) higher levels are detected in polyA sequencing/frozen tissue. However, the reference for this section (D. Robinson et al 2015) is a study of a cohort of prostate cancers with polyA bulk RNAseq sequencing; is this correct/is the data coming from a different study?

• Analysis of scRNAseq (if available) would strengthen their analyses by better delineating the expression and response of interferon-gamma and downstream (e.g. antigen presentation) pathways in specific cell compartments, and potential differences in cell-cell interactions (e.g. using CellPhoneDB) associated with Dux4+ vs Dux4- tumors.

• Do the investigators find similar findings in primary and metastatic tumors sequenced the same way (e.g. tcga primary vs met melanoma, albeit most of the met melanoma are Stage III lymph nodes)?

We thank the reviewer for finding the citation error. We have corrected the manuscript to reflect the correct study we analyzed (PMID: 28783718). We also thank the reviewer for their additional suggestions, which undoubtedly would strengthen the current study. However, we have respectfully decided to defer these additional analyses for future study.

Reviewer #2:

It is strange as a statistician to see BIC and AIC represented as barplots, e.g. Figure 4B. There is no knowledge to be gained through this visual representation that would not otherwise be conveyed by just giving the numbers.

We thank the reviewer for this suggestion. We understand that simply stating the numbers would be equally informative. However, we respectfully decided to retain our current versions of Figures 4 and S4 so that the numbers can be illustrated in a visual manner in the figures, rather than just stated in the text.

Author response:

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

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

• Line 144, after eq. (1). Vectors d_i need to be defined. Are these the mapping of vectors e_i due to the active deformation? It would be useful to state then that d_3 is aligned with r'.

Thank you for your suggestion, and the definition has been added to lines 146-149 for a better understanding of the model.

• Line 144.Authors state a_i(0,0,Z)=0. Shouldn't this be true also for any angle, i.e., a_i(0,Theta, Z)=0?

Thank you, we have revised it in line 144.

• Line 156. G_0 is defined as Diag(1,g_0(t), 1), which seems to be using cylindrical coordinates. Previously, in line 147, vector argument X of \chi is defined with Cartesian coordinates (X,Y,Z). Shouldn't these be also cylindrical?

We are very sorry for this error, our initial configuration is defined with cylindrical coordinates, we have revised it in the manuscript line 151.

• Line 162. "where alpha and beta lie in the range [-pi/2, pi/2]" has already been indicated.

Thank you for your mention, we have deleted duplicate information in line 166.

• Line 171. W is defined as the strain energy density, while in equation (2), symbol W is the total energy (which depends on the previous W). Letters for total elastic and strain energy must be distinguished.

Thank you, we have changed the letter for total energy in Eq.(2).

• Line 176. "we take advantage of the weakness of" -> "we take advantage of the small value of".

We have revised it in line 179.

• Line 177. Why is there a subscript i in p_i? If these do not correspond to penalty p, but to parameters in eqn (3), the latter should have been introduced before this line.

We have revised this error in line 180.

• Line 186. "as the overall elongation \zeta". This parameter, axial extension, has not been defined yet.

Thank you for your mention, the definition of \zeta is now given in line 146.

• Figure 4. Why are the values of g_0 from the elastic model and equations (30)-(32) so non-smooth? Clarify what is being fit and what is the input in the latter equations. Final external radius R_3? Final internal radius R_1'?

(1) To mimic the embryo, we consider a multi-layered cylindrical body so that the shear modulus of each layer is different. The continuity of both deformations and stresses is imposed (see Eq.(26)-Eq.(30). This is the usual treatment for complex morpho-elastic systems. Obviously, $g_0$ originates from the actomyosin cortex so it appears only in the corresponding layer. Finally, all physical quantities such as deformations and stresses must be continuous.

(2) The final outer radius is R_3, which represents the outer radius of C. elegans embryos. In addition to R_3, what we need to consider in this model are R_1’=0.7, R_1’=0.768, R_2=0.8 and R_2’=0.96, these definitions have been added in the caption of Appendix 2—figure 1.

• Line 663, equation (19). Parameter mu is multiplying penalisation term with p, while in equation (2) mu is only affecting the elastic part.

These two different ways of expressing the energy function will ultimately affect the value of p, but the two p are not the same quantities, so they will not affect our results. To avoid misunderstandings, we will replace p in equation (19) with q.

Reviewer #2 (Recommendations For The Authors):

As mentioned in my public summary, I find the writing really not adequate. I provide here a list of specific points that the authors should in my opinion address. As a general comment, I would delete many instances of 'the'.

First, here are figures and whole paragraphs that do not seem to bring anything to the understanding of the phenomenon of C. elegans elongation, notably, Figs. 2, 3C-H, 5m, and 6. Figures 6G and 7 are the only figures containing results it seems. Some elements of the figures are repeated, for example, the illustration of the system's cross-section in Figs 3 and 5.

Thank you for your suggestion, we have made some adjustments to our images to remove some of the duplicate information.

Second, and this is my most important criticism: the mechanism of elongation by releasing elastic stress introduced by muscle contraction is not explained in clear terms anywhere in the text. At least, I was unable to understand it. On p 10 you write "This energy exchange causes the torsion-bending energy to convert into elongation energy, (...)" How this is done is not explained. I assume that the reference state is somehow changed through muscle contraction. The new reference state probably has a longer axis than the one before, but this would then be a plastic deformation and not purely elastic as claimed by the authors (ll 76: "This work aims to answer this paradox within the framework of finite elasticity without invoking cell plasticity (...)"). Is torsion important for this process or is it 'just' another way to store elastic energy in the system?

We perfectly explain most of the exchange of energy between bending, torsion and elongation: indeed, we quantify all aspects of this transformation as the elastic elongation energy, and the dissipation processes which will cost energy. The dissipation evaluated here concerns the rotation of the worm due to the muscle geometry and the viscous friction at the inner surface of the egg. Torsion seems to appear in the late stages and only in some cases. As we show, it comes from a torque induced by the muscles which are not vertical. vertical. Finally, our quantitative predictions of the modelling which recovers most of the experimental published results.

Third, there are a number of strange phrasings and the notation is not helpful in places.

We feel sorry for that, the manuscript is now more precise.

Fourth, the title promises to explain how cyclic muscle contractions reinforce acto-myosin motors. I can't see this done in this work.

The fact that the acto-myosin is reorganized between two sequences of contraction justifies the title. The complete reorganization of the actomyosin network would require a chemico-mechanical model that is not achieved here, perhaps in future work as data become available.

In addition:

We have chosen to respond globally rather than point by point to the referee’s recommendations.

Typographic errors and vocabulary

All English corrections and typos are now included in the main text.

Figures and captions:

Figures and captions have been improved.

• Figure 1: Make the caption and the illustration more coherent. For example, only two cell types are distinguished; in the caption, you mention lateral cells, in the sketch seam cells. What is the difference between acto-myosin and muscle contraction? Muscle contraction is also auto-myosin-based.

(1) The caption for Fig.1 is revised.

(2) From a mechanical point of view, actomyosin bundles in C elegans are orthoradial, whereas muscles are essentially parallel to the main axis of the body are essentially parallel to the main axis of the body, so the geometry is completely different and of extreme importance for deformation. Muscle contractions are quasi-periodic, we do not know the dynamics of the attached molecular motor of myosin. So of course, both contain actin and myosin (not exactly the same proteins), but our model is sensitive to more macroscopic properties.

• Figure 2: I do not find this figure helpful. I might expect such a figure in a grant proposal, but much less in an article.

Figure 2 shows the strategy of our work, we hope that readers can see at a glance what kind of analysis has been done through this figure: since our work is divided into several parts, readers can also unravel the logic through this scheme after reading the whole manuscript. So, this diagram is a guide, and it may be helpful and necessary.

• Figure 3: Figure 3 A, right: What is the dashed line? B You indicate fibers, but your model does not contain fibers, does it? How do I get from the cube to the deformed object? What is the relation of C-H with the rest of the work? Furthermore, you mention seam cells in Fig. 1, but they are absent here. Why can you neglect them? Why introduce them in the first place? E What is a plant vine? F-H What rods are you referring to? Plants do not have muscles, right?

We have modified this figure, and the original Figure 3 now corresponds to Figures 3 and 4.

(1) The dashed line is the centerline after deformation.

(2) The referee is wrong: our model represents the fibers by a higher shear modulus for the actomyosin cortex and for the muscles (see Table Appendix 1) and G_1 reflects the activities of the muscle and actin fibers.

(3) The cube in Figure 3 is a mathematical 3D volume element that is subjected to stresses. Hyperelasticity modelling is based on such a representation.

(4) C-H(new version: Fig.4 A-F): These images show similar deformations: bending and torsion as our C. elegans study. These figures indicate that such deformations are quite common in nature, even if the underlying mechanism is different.

(5) This is a point we have already mentioned: we ignore the difference between the different types of epidermal cells and average their role in the early and second stages of elongation.

(6) The plant vine is the 'botanical vine', see Goriely's article and book.

(7) F-H(new version: Fig.4 D-F) do not have fixed rods, we set a curvature and torsion to fit the actual biological behavior.

(8) Plants do not have muscles, but they grow, and our formalism for growth, pre-strain and material plasticity is very similar to the hyper-elasticity formalism.

• Figure 4: Fig .4 A: "The central or inner part (0 < 𝑅 < 𝑅2, shear modulus 𝜇𝑖) except the muscles which are stiffer." I do not understand.

In the new version, this figure corresponds to Fig.5. The shear modulus of the intrinsic part is very small, but the muscles are harder so we have to consider them separately, we have revised this sentence to avoid misunderstanding.

• Figure 5: Fig 5 A and D: The schematic of the cross-section has appeared already in the previous figure. No need to repeat it here. The same holds for the schematic of the cylindrical embryo. Caption: "But, the yellow region is not an actual tissue layer and it is simply to define the position of muscles." Why do you introduce the yellow region at all? I do not think that it clarifies anything. "Deformation diagram, when left side muscles M_1 and M_2." Something seems to be missing here. Similarly in the next sentence. "the actin fiber orientation changes from the 'loop' to the 'slope'" Do the rings break up and form a helix?

In the new version, this figure corresponds to Fig.6.

(1) We have made revisions to these figures.

(2) The yellow part can show the accurate location of four muscles, which is important for our model and further calculations.

(3) We have revised this sentence in the caption of Fig. 6.

(4) Actin rings do not change to a helix pattern, they will be only sloping.

• Figure 6: Fig 6 A-C These panels do not go beyond Fig 5B. Fig 6D: what are these images supposed to show? They are not really graphs, but microscopy images. The caption is not helpful to understand, what the reader is supposed to see here. Fig 6F: do you really want to plot a linear curve?

In the new version, Fig.5 and Fig.6 respectively correspond to Fig.6 and Fig.7.

(1) Fig.6 shows the simulated images, and Fig.7 A-C is the real calculation results, they are different.

(2) Fig.7 D can show the real condition during C. elegans late elongation, here, we would like to show the torsion of the C. elegans.

(3) Yes, it is our result.

Discussions concerning the biological referee questions:

Ll 75: “how the muscle contractions couple to the acto-myosin activity" Again I find this misleading because muscle contraction relies on auto-myosin activity. Probably, you can find a better expression to refer to the activity of the actomyosin network in the epidermis. Do you propose any mechanism for how muscle contraction increases epidermal contractility? This does not seem to be the mechanism that you propose for elongation, is it?

The actomyosin activity will not stop because of the muscle contraction. Obviously, these two processes cannot be independent. The energy released by a muscle contraction event can and must contribute to the reorganization of the actomyosin network that occurs during the elongation process. Indeed, despite the fact that the embryo elongates, the density of actin cables appears to be maintained, which automatically requires a redistribution of actin monomers. We propose a scenario in which muscle contraction increases actomyosin contractility via energy conversion. We show that after unilateral contraction there is an energy release for this once all dissipation factors are eliminated. We invite the reviewer to re-examine Figure 2 and invite biologists to seriously evaluate the density of molecular motors attached to the circumferential actin cable throughout the stretch process.

Ll 133: "we decide to simplify the geometrical aspect because of the mechanical complexity" This is hardly a justification. Why is it appropriate?

Yes, we would like to offer the reader the simplest modelling with a limiting technicity and a limited number of unknown parameters.

L 135: "active strains" Why not active stress?

The two are equivalent, the choice is dictated by the simplicity of deriving quantitative results for comparison with experiments.

L 170: "hyperelastic" Please, explain this term.

It is the elasticity of very soft samples subjected to large deformations. For classic references, see the books of Ogden, Holzapfel and Goriely, all of which are mentioned in our paper.

Major criticism

Eq. 3 and Ll 227: "𝑝1 is the ratio between the free available myosin population and the attached ones divided by the time of recruitment" Why is the time of recruitment the same for all motors? "inverse of the debonding time" Is it the same as the unbinding rate? Why use the symbol p_2 for it? What is p_3?

The model proposed to justify the increase in the activity of the actomyosin motors during the first phase is a mean-field model: thus all quantities are averaged: we are not considering the theory of a single molecular motor, but a collection in a dynamic environment, so we do not need stochasticity here. Equation (3) concerns the compressive pre-strain, which by definition is a quantity varying between $0$ and $1$ and $X_g=1-G$. ... The debonding time is not the same as the debonding rate. The term $p_3$ indicates saturation and is derived from the law of mass action. The good agreement with the experimental data is shown in Fig.5 (A) and (B). An equivalent model has been developed by (M. Serra et al.).

Serra M, Serrano Nájera G, Chuai M, et al. A mechanochemical model recapitulates distinct vertebrate gastrulation modes[J]. Science Advances, 2023, 9(49)

Ll 275: "This energy exchange causes the torsion-bending energy to convert into elongation energy, leading to a length increase during the relaxation phase, as shown in Fig.1 of Appendix 5." You have posed the puzzle of how contraction leads to elongation, and now that you resolve the puzzle, you simply say that torsion and bending energy are converted into elongation. How? Usually, if I deform an elastic object, it will return to its original configuration after releasing the external forces. Why is this not the case here?

Furthermore, the central result of your work is presented in an Appendix!?

We agree with the referee that an elastic object will return to its initial configuration by releasing stress, i.e. by giving up its accumulated elastic energy to the environment. But the elastic energy has to go somewhere, such as heat. We do not dare to say that the temperature of the worm increases during the muscle contractions.

In fact, the referee's comment also assumes that full relaxation of the stresses is possible, so the object is not a multi-layered specimen and/or it is not enclosed in a box. Most living species are under stress, usually called residual stress. Our skin is under stress. Our fingerprints result from an elastic instability of the epidermis, occurring on foetal life as our brain circumvolutions or our vili. . So, it is obvious that stresses are maintained in multilayered living systems. Closer to the case of C. elegans, the existence of stresses has been demonstrated by experiments with laser ablation fractures in the first stage. The fact that the fractures open proves the existence of stress: if not, there is no opening and only a straight line.

Ll 379: "Although a special focus is made on late elongation, its quantitative treatment cannot avoid the influence of the first stage of elongation due to the acto-myosin network, which is responsible for a prestrain of the embryo." This statement is made repeatedly through the manuscript, but I do not understand, why you could not use an initial state without pre-strain.

This is the basic concept of hyperelasticity. The reference state must be free of stress, so we cannot evaluate the first muscle contraction without treating the first elongation stage.

Grammar, vocabulary and writing errors

ll 31: "the influence of mechanical stresses (...) becomes more complex to be identified and quantified" Is the influence of mechanical stress too complex or too difficult to be identified/quantified?

We have revised it in line 31, “The superposition of mechanical stresses, cellular processes (e.g., division, migration), and tissue organization is often too complex to identify and quantify.”

Ll 41: "The embryonic elongation of C. elegans represents an attractive model of matter reorganization without a mass increase before hatching." Maybe "Embryonic elongation of C. elegans before hatching represents an attractive model of matter reorganization in the absence of growth.".

We have revised it in line 41.

L 42: "It happens after the ventral enclosure (...)" Maybe "It happens after ventral enclosure (...)".

We have revised it in line 42.

Ll 52: "The transition is well defined since the muscle participation makes the embryo rather motile impeding any physical experiments such as laser ablation (...)" Ablation of what?

We have revised it in line 53:The transition is well defined, because the muscle involvement makes the embryo rather motile, and any physical experiments such as laser fracture ablation of the epidermis, which could be performed and achieved in the first period (\cite{vuong2017interplay}), become difficult,.

Ll 59: "a hollow cylinder composed of four parts (seam and dorso-ventral cells)" It is not clear, what the four parts are - in the parenthesis, two are mentioned.

We have revised it in line 59. Fig.1 shows the whole structure, dorsal, ventral and seam cells form four parts of the epidermis.

L 78: "several important issues at this stage remain unsettled" At which stage?

It means the late elongation stage, we have added this information in line 78.

Ll 85: "but how it works at small scales remains a challenge." Maybe "but how it works at small scales remains to be understood.".

We have revised it in line 86.

Ll 99: "the osmolarity of the interstitial fluid" The comes out of the blue. Before you only talked about mechanics, why now osmolarity? Also, the interstitial fluid is only mentioned now. It is important for the dissipative effects that you discuss later, right? If yes, then you should probably introduce it earlier.

For a better understanding, we have change osmolarity into viscosity in line 99.

l 120: "The cortex is composed of three distinct cells" Maybe "distinct cell types".

Thank you, and we have revised it in line 120.

L 121: "cytoskeleton organization and actin network configurations" What is the difference between cytoskeleton organization and actin network configuration? Also, either both should be plural or both singular, I guess.

(1) Cytoskeleton (which involves microtubules) forms the epidermis of C. elegans embryos, and the actin network surrounds the epidermis.

(2) Thank you for your suggestion, we have revised it in line 121.

L 130: "which will be introduced hereafter" Maybe "which will be used hereafter".

We have revised it in line 130.

Ll 148: "The geometric deformation gradient" You usually denote vectors in bold face, so \chi should be bold, right? Define d_i in Eq.(1).

Yes, we have added this information in line 147.

L 172: "auxiliary energy density" Please, explain this term.

We have changed "auxiliary energy density" into "associated energy density" in line 175. Energy density is the amount of energy stored in a given system or region of space per unit volume, the associated energy density in our manuscript can help us to do some calculations.

Ll 188: "Similar active matter can be found in biological systems, from animals to plants as illustrated in Fig.3(C)-(E), they have a structure that generates internal stress/strain when growing or activity. (...)" Why such a general statement during the presentation of the results? The second part of the sentence seems to be incomplete.

Answers: We would like to show our method is general, and can be used in many situations. We have revised the wrong sentence in line 192.

Ll 243: "a bending deformation occurs on the left for active muscles localized on left" Maybe "bending to the left occurs if muscles on the left are activated".

Thank you, we have revised it in line 247.

L 250: "we assume them are perfectly synchronous" Maybe "we assume them to contract simultaneously". We have revised it in line 252.

L 258: "the muscle and acto-myosin activities are assumed to work almost simultaneously." Before it was simultaneously, now only almost!? What does almost mean?

Sorry, we would like to express the same meaning in theses two sentences, we have deleted the word ‘almost’ in line 261.

Ll 294: "one can hypothesize several scenarios" After that, only one scenario is described it seems.

Thank you, we have revised this sentence in line 299.

L 341: "and then is more viscous than water" Maybe "and that is more viscous than water".

We have revised it in line 345.

L 373: "before the egg hatch" Maybe "before the embryo (or larva) hatches"?

We have revised the sentence in line 367.

L 409: "elephant trunk elongated" maybe "elephant trunk elongation".

We have revised it in line 412.

Ll 417: "As one imagines, it is far from triviality (...)" Does this remake help in any way to understand better C. elegans elongation? Also maybe "it is far from trivial".

We have revised it in line 423.

Ll 428: "can map the initial stress-free state B_0 to a state B_1, which reflects early elongation process" Maybe: "maps the initial stress-free state B_0 to a state B_1, which describes early elongation".

We have revised it in line 428.

L 429: "After in the residually stressed (...)" Maybe "Subsequently, we impose an incremental strain filed G_1 that maps the state B_1 to the state B_2, which represents late elongation".

We have revised it in line 429.

l 763: "Modelling details of without pre-strain case" Maybe "Case without pre-strain" or "Modelling in the absence of pre-strain" Similarly for l 784.

We have revised them in line 763 and line 784.

Some questions of definition and understanding

Ll 71: "We can imagine that once the muscle is activated on one side, it can only contract, and then the contraction forces will be transmitted to the epidermis on this side." I do not understand the sentence. Muscle activation leads to contraction, there is nothing to imagine here. Maybe you hypothesize that the muscles are attached to the epidermis such that muscle contraction leads to epidermis deformation?

Yes, four muscle bands are attached to the epidermis, as shown in Fig.1. The deformation does not concern only the epidermis but the whole embryo during the bending events. We have modified the sentence to avoid misunderstanding, the sentence change to “Once the muscle is activated on one side, it can only contract, and then the contraction forces will be transmitted to the epidermis on this side.” in line 71.

Ll 110: "However, it is less widely known that its internal striated muscles share similarities with skeletal muscles found in vertebrates in terms of both function and structure" Is it important for what you report, whether this fact is widely known?

Yes, it is our opinion.

Ll 112: "the role of the four axial muscles (...) is nearly contra-intuitive" Is it or is it not? If yes, why?

Yes it is. Muscles exert contractions, so compressive deformations. Their localization are along the axis of symmetry (up to a small deviation) so they cannot mechanically realize the expected elongation, contrary to the orthoradial actomyosin network.

However, elongation of the C. elegans is observed experimentally, so yes, we think the result contraintuitive.

L 116: "fully heterogeneous cylinder" What is this?

It means that the C. elegans embryo does not have the same elastic properties in different parts (or layers).

L 129: "will collaborate to facilitate further elongation" To facilitate or to drive? If the former, what drives elongation?

Contraction of muscles and actin bundles together drive elongation

Ll 141: "the deformation in each section can be quantified since the circular geometry is lost with the contractions" The deformation could also be quantified if the sections remained circular, right?

Yes. However, circularity is lost during each bending event.

Ll 151: "we need to evaluate the influence of the C. elegans actin network during the early elongation before studying the deformation at the late stage. So, the deformation gradient can be decomposed into: (...) where (...) is the muscle-actomyosin supplementary active strain in the late period" I thought you were now studying the early stage?

In this part, we are outlining how we can study the whole elongation (early and late), not just the early elongation stage. To evaluate the deformation induced by the first contraction of the muscles, we need to know the state of stress of the worm prior to this event, so we also need to recover the early period using the same formalism for the same structure.

L 160: "When considering a filamentary structure with different fiber directions" Which filamentary structure are you talking about?

Fig.3 B shows this model and the filamentary structure, which contains the actin and muscle fibers.

Ll 174: "When the cylinder involves several layers with different shear modulus 𝜇 and different active strains, the integral over 𝑆 covers each layer" I do not understand this sentence. Also, you should probably write 'moduli' instead of modulus.

This implies that when integrating over the whole cross-section S, we need to take into account each layer independently with its own shear modulus and sum the results.

L 176: "weakness of 𝜀" Do you mean \epsilon << 1?

Yes

Ll 178: "Given that the Euler-Lagrange equations and the boundary conditions are satisfied at each order, we can obtain solutions for the elastic strains at zero order 𝐚(𝟎) and at first order 𝐚(𝟏)." Are you thinking about different orders in an \epsilon expansion or the early and the late stages of elongation?

Answers: Different orders are considered only for the late elongation study, the early elongation is treated exactly so do not need a correction in \epsilon.

L 197: "fracture ablation" Please, define.

This is an experiment in which a laser is used to make a cut in a small-scale object of study and then the internal stresses are obtained based on the morphology of the cut, please see the Ref ‘Assessing the contribution of active and passive stresses in C. elegans elongation’. We have added this definition in line 200.

Ll 203: What motivated your choice of notations for the radii R_2'? The inner part of the cylinder is fluid? But above you wrote about a solid cylinder. Why should the inner part be compressible?

(1) We need to define the location of actin cables, which concentrate at the outer periphery.

(2) Our model is a hollow cylinder, and the inner part of the cylinder contains internal organs, tissues, fluids, and so on, so we consider it to be a compressible extremely soft material (Line 213).

Ll 212: "𝑟(𝑅) is the radius after early elongation." And during?

R is variable, r(R) depends on R but also on time t, it represents the radius of C. elegans embryos after the onset of elongation, i.e., after acto-myosin and muscle activities begin.

L 232: \tau_p is probably t_p?

Yes.

L 240: "quite simultaneously" Please, be precise.

In practice, it is difficult to define the concept of simultaneous occurrence unless there is rigorous experimental data to show it, but all we can get in the Ref ‘Remodelage des jonctions sous stress mécanique’, is that it occurs almost simultaneously, which we define as quite simultaneously.

Ll 246: "a short period" What does short mean? Why is it relevant?

From the experimental observations and data, we know that each contraction occurs very rapidly: a few seconds so we define a short period for one contraction.

L 263: "the bending of the model will be increased" Is it really the model that is bent?

Yes, the bending deformation predicted by the model, we have revised in line 266.

Ll 265: "we observed a consistent torsional deformation (Fig.6(E)) that agrees with the patterns seen in the video" In which sense do these configurations agree? I do not see any similarity between panels D and E.

Both show a torsion deformation.

L 267: "torsion as the default of symmetry of the muscle axis" I do not understand.

We discuss two cases in this research, one where the muscle follows the axis of the C. elegans in the initial configuration, and the other where the muscle has a slight angle of deflection, and we have added more information in the manuscript (line 270).

Ll 274: "Each contraction of a pair increases the energy of the system under investigation, which is then rapidly released to the body." Do you mean the elastic energy stored in the epidermis and central part of the embryo?

Yes, the whole body.

Ll 284: "The activation of actin fibers 𝑔𝑎1 after muscle relaxation can be calculated and determined by our model." Have you done it?

Yes, we can obtain the value of g_a1, and then calculate the elongation.

Ll 286 I do not understand, why you write about mutants at this place. Am I supposed to have already understood the basic mechanism of elongation? Why do you now write about the first stage?

I would like to show our formalism can model wild-type and mutant C.elegans, and the comparison results are good.

L 302: "The result is significantly higher than our actual size 210𝜇𝑚." How was significance assessed? Your actual size is probably more than 210µm.

Here, we have considered two situations, one is that the accumulated energy is totally applied to the elongation so that the length will be much larger than the experimental result of 210 µm, the length value that we have obtained by calculation. In the other case, we have considered the energy dissipation, which leads to 210 µm.

L 433: "where 𝜆 is the axial extension due to the pre-strained" Maybe ""where 𝜆 is the axial extension due to the pre-stress".

In our manuscript, we define the pre-strain, not the pre-stress.

L 438: "active filamentary tensor" Please, define.

Active filamentary tensor defines the tensor representing the activities of a cylindrical model composed of different orientations fibers.

Author response:

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

Reviewer #1 (Public Review):

Summary:

This study presents careful biochemical experiments to understand the relationship between LRRK2 GTP hydrolysis parameters and LRRK2 kinase activity. The authors report that incubation of LRRK2 with ATP increases the KM for GTP and decreases the kcat. From this, they suppose an autophosphorylation process is responsible for enzyme inhibition. LRRK2 T1343A showed no change, consistent with it needing to be phosphorylated to explain the changes in G-domain properties. The authors propose that phosphorylation of T1343 inhibits kinase activity and influences monomer-dimer transitions.

Strengths:

The strengths of the work are the very careful biochemical analyses and the interesting result for wild-type LRRK2.

Weaknesses:

A major unexplained weakness is why the mutant T1343A starts out with so much lower activity--it should be the same as wild-type, non-phosphorylated protein. Also, if a monomer-dimer transition is involved, it should be either all or nothing. Other approaches would add confidence to the findings.

We thank the reviewer for these suggestions. We are aware that the T1343A has generally a lower activity compared to the wild type. Therefore, we would like to emphasize that this mutant is the only one not showing an increase in Km values after ATP treatment. Other mutants, also having lower kcat values like T1503A, still show this characteristic change in Km. Our favored explanation for the lower kcat of T1343A is that this mutation lays within a critical region, the so-called ploop, of the Roc domain and is very likely structurally not neutral. Concerning the dimer-monomer transition, we are convinced that there is more than one factor involved in this equilibrium. Most likely, including, but not limited to other LRRK2 domains (e.g. the WD40 domain), binding of co-factors (e.g. Rab29/Rab32 or 14-3-3) and membrane binding. Consistently, also with stapled peptides targeting the Roc or Cor domains we were not able to shift the equilibrium completely to the monomer (Helton et al., ACS Chem Biol. 2021, 16:2326-2338; Pathak et al. ACS Chem Neurosci. 2023, 14(11):1971-1980) We have addressed these points in a revised version of the manuscript.

Reviewer #2 (Public Review):

This study addresses the catalytic activity of a Ras-like ROC GTPase domain of LRRK2 kinase, a Ser/Thr kinase linked to Parkinson's disease (PD). The enzyme is associated with gain-of-function variants that hyper-phosphorylate substrate Rab GTPases. However, the link between the regulatory ROC domain and activation of the kinase domain is not well understood. It is within this context that the authors detail the kinetics of the ROC GTPase domain of pathogenic variants of LRRK2, in comparison to the WT enzyme. Their data suggest that LRRK2 kinase activity negatively regulates the ROC GTPase activity and that PD variants of LRRK2 have differential effects on the Km and catalytic efficiency of GTP hydrolysis. Based on mutagenesis, kinetics, and biophysical experiments, the authors suggest a model in which autophosphorylation shifts the equilibrium toward monomeric LRRK2 (locked GTP state of ROC). The authors further conclude that T1343 is a crucial regulatory site, located in the P-loop of the ROC domain, which is necessary for the negative feedback mechanism. Unfortunately, the data do not support this hypothesis, and further experiments are required to confirm this model for the regulation of LRRK2 activity.

Specific comments are below:

• Although a couple of papers are cited, the rationale for focusing on the T1343 site is not evident to readers. It should be clarified that this locus, and perhaps other similar loci in the wider ROCO family, are likely important for direct interactions with the GTP molecule.

To clarify this point: We, have not only have focused on this specific locus, but instead systematically mutated all known auto-phosphorylation sites with the RocCOR domain (see. supplemental information). Furthermore, it has been shown that this site, at least in the RCKW (Roc to WD40) construct, is quantitatively phosphorylated (Deniston et al., Nature 2020, 588:344-349). We are aware that the T1343 residue is located within the p-loop and that this can impact nucleotide binding capacities (see response to reviewer 1).

We have clarified and addressed these points in a revised version of the manuscript.

• Similar to the above, readers are kept in the dark about auto-phosphorylation and its effects on the monomer/dimer equilibrium. This is a critical aspect of this manuscript and a major conceptual finding that the authors are making from their data. However, the idea that auto-phosphorylation is (likely) to shift the monomer/dimer equilibrium toward monomer, thereby inactivating the enzyme, is not presented until page 6, AFTER describing much of their kinetics data. This is very confusing to readers, as it is difficult to understand the meaning of the data without a conceptual framework. If the model for the LRRK2 function is that dimerization is necessary for the phosphorylation of substrates, then this idea should be presented early in the introduction, and perhaps also in the abstract. If there are caveats, then they should be discussed before data are presented. A clear literature trail and the current accepted (or consensus) mechanism for LRRK2 activity is necessary to better understand the context for these data.

We agree on the reviewer’s opinion. We have revised the introduction accordingly and added a paragraph on page 3 starting from line 27.

• Following on the above concepts, I find it interesting that the authors mention monomeric cytosolic states, and kinase-active oligomers (dimers??), with citations. Again here, it would be useful to be more precise. Are dimers (oligomers?) only formed at the membrane? That would suggest mechanisms involving lipid or membrane-attached protein interactions. Also, what do the authors mean by oligomers? Are there more than dimers found localized to the membrane?

There are multiple studies that have shown that LRRK2 is mainly monomeric in the cytosol while it forms mainly dimeric or higher oligomeric states at membrane (James et al., Biophys. J. 2012, 102, L41–L43; Berger et al., Biochemistry, 2010, 49, 5511–5523). However, we agree with the reviewer that it remains to be determined if the dimeric form is the most active state at the membrane, or a higher oligomeric state. Espescially since a recent study shows that LRRK2 can form active tetramers only when bound to Rab29 (Zhu et al., bioRxiv, 2022, DOI: 10.1101/2022.04.26.489605). We have clarified these points in the introduction of the revised version of the manuscript (page 3, line 27ff).

• Fig 5 is a key part of their findings, regarding the auto-phosphorylation induced monomer formation of LRRK2. From these two bar graphs, the authors state unequivocally that the 'monomer/dimer equilibrium is abolished', and therefore, that the underlying mechanism might be increased monomerization (through maintenance of a GTP-locked state). My view is that the authors should temper these conclusions with caveats. One is that there are still plenty of dimers in the auto-phosphorylated WT, and also in the T1343A mutant. Why is that the case? Can the authors explain why only perhaps a 10% shift is sufficient? Secondly, the T1343A mutant appears to have fewer overall dimers to begin with, so it appears to readers that 'abolition' is mainly due to different levels prior to ATP treatment at 30 deg. I feel these various issues need to be clarified in a revised manuscript, with additional supporting data. Finally, on a minor note, I presume that there are no statistically significant differences between the two sets of bar graphs on the right panel. It would be wise to place 'n.s.' above the graphs for readers, and in the figure legend, so readers are not confused.

Starting with the monomer-dimer equilibrium we are convinced that there is more than the phosphorylation of T1343 (see response to reviewer 1). Therefore a 10% shift in our assay most likely underestimate the effect seen in cells. Consistently, the T1343A mutants show a similar increase in Rab10 phosphorylation assay as the G2019S mutant. This thus shows that the identified feedback mechanism plays an important role in a cellular context. We have addressed this point in the revised manuscript on page 6, line 8ff. As long as the significance indicators in the bar charts are concerned, we agree with reviewer. In order not to overload the figure, we finally decided to include all pairwise comparisons (post-hoc tests) in the supplement.

• Figure 6B, Westerns of phosphorylation, the lanes are not identified and it is unclear what these data mean.

We apologize for this mistake and have added the correct labeling in the revised version of the manuscript.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

The author studies a family of models for heritable epigenetic information, with a focus on enumerating and classifying different possible architectures. The key aspects of the paper are:

• Enumerate all 'heritable' architectures for up-to 4 constituents.

• A study of whether permanent ("genetic") or transient ("epigenetic") perturbations lead to heritable changes

• Enumerated the connectivity of the "sequence space" formed by these heritable architectures

• Incorporating stochasticity, the authors explore stability to noise (transient perturbations)

• A connection is made with experimental results on C elegans.

The study is timely, as there is a renewed interest in the last decade in non-genetic, heritable heterogeneity (e.g., from single-cell transcriptomics). Consequently, there is a need for a theoretical understanding of the constraints on such systems. There are some excellent aspects of this study: for instance, the attention paid to how one architecture "mutates" into another. Unfortunately, the manuscript as a whole does not succeed in formalising nor addressing any particular open questions in the field. Aside from issues in presentation and modelling choices (detailed below), it would benefit greatly from a more systematic approach rather than the vignettes presented.

Despite being foundational, this work was systematic in that (1) for the simple architectures modeled using ordinary differential equations (ODEs) with continuity assumptions, parameters that support steady states were systematically determined for each architecture and then every architecture was explored using genetic changes exhaustively, although epigenetic perturbations were not examined exhaustively because of their innumerable variety; and (2) for the more realistic modeling of architectures as Entity-Sensor-Property systems, the behavior of systems with respect to architecture as well as parameter space that lead to particular behaviors (persistence, heritable epigenetic change, etc.) was systematically explored. A more extensive exploration of parameter space that also includes the many ways that the interaction between any two entities/nodes could be specified using an equation is a potentially ever-expanding challenge that is beyond the scope of any single paper.

Specific aspects that remain to be addressed include the application of multiple notions of heritability to real networks of arbitrary size, considering different types of equations for change of each entity/node, and classifying different behavioral regimes for different sets of parameters.

The key contribution of the paper is an articulation of the crucial questions to ask of any regulatory architecture in living systems rather than the addressing of any question that a field has recognized as ‘open’. Specifically, through the exhaustive listing of small regulatory architectures that can be heritable and the systematic analysis of arbitrary Entity-Sensor-Property systems that more realistically capture regulatory architectures in living systems, this work points the way to constrain inferences after experiments on real living systems. Currently, most experimental biologists engaged in reductionist approaches and some systems biologists examining the function or prevalence of network motifs do not explicitly constrain their models for heritability or persistence. It is hoped that this paper will raise awareness in both communities and lead to more constrained models that minimize biases introduced by incomplete knowledge of the network, which is always the case when analyzing living systems.

Terminology

The author introduces a terminology for networks of interacting species in terms of "entities" and "sensors" -- the former being nodes of a graph, and the latter being those nodes that receive inputs from other nodes. In the language of directed graphs, "entities" would seem to correspond to vertices, and "sensors" those vertices with positive indegree and outdegree. Unfortunately, the added benefit of redefining accepted terminology from the study of graphs and networks is not clear.

The Entities-Sensors-Property (ESP) framework is based on underlying biology and not graph theory, making an ESP system not entirely equivalent to a network or graph, which is much less constrained. The terms ‘entity’, ‘sensor’, and ‘property’ were defined and justified in a previous paper (Jose, J R. Soc. Interface, 2020). While nodes of a network can be parsed arbitrarily and the relationship between them can also be arbitrary, entities and sensors are molecules or collections of molecules that are constrained such that the sensors respond to changes in particular properties of other entities and/or sensors. When considered as digraphs, sensors can be seen as vertices with positive indegree and outdegree. The ESP framework can be applied across any scale of organization in living systems and this specific way of parsing interactions also discretizes all changes in the values of any property of any entity. In short, ESP systems are networks, but not all networks are ESP systems. Therefore, the results of network theory that remain applicable for ESP systems need further investigation.

The key utility of the ESP framework is that it is aligned with the development of mechanistic models for the functions of living systems while being consistent with heredity. In contrast, widely analyzed networks like protein-interaction networks, signaling networks, gene regulatory networks, etc., are not always constrained using these principles.

Model

The model seems to suddenly change from Figure 4 onwards. While the results presented here have at least some attempt at classification or statistical rigour (i.e. Fig 4 D), there are suddenly three values associated with each entity ("property step, active fraction, and number"). Furthermore, the system suddenly appears to be stochastic. The reader is left unsure what has happened, especially after having made the effort to deduce the model as it was in Figs 1 through 3. No respite is to be found in the SI, either, where this new stochastic model should have been described in sufficient detail to allow one to reproduce the simulation.

The Supplementary Information section titled ‘Simulation of simple ESP systems’ provides the requested detailed information and revisions to the writing provide the biologically grounded justification for parsing interacting regulators as ESP systems.

Perturbations

Inspired especially by experimental manipulations such as RNAi or mutagenesis, the author studies whether such perturbations can lead to a heritable change in network output. While this is naturally the case for permanent changes (such as mutagenesis), the author gives convincing examples of cases in which transient perturbations lead to heritable changes. Presumably, this is due the the underlying multistability of many networks, in which a perturbation can pop the system from one attractor to another.

Unfortunately, there appears to be no attempt at a systematic study of outcomes, nor a classification of when a particular behaviour is to be expected. Instead, there is a long and difficult-to-read description of numerical results that appear to have been sampled at random (in terms of both the architecture and parameter regime chosen). The main result here appears to be that "genetic" (permanent) and "epigenetic" (transient) perturbations can differ from each other -- and that architectures that share a response to genetic perturbation need not behave the same under an epigenetic one. This is neither surprising (in which case even illustrative evidence would have sufficed) nor is it explored with statistical or combinatorial rigour (e.g. how easy is it to mistake one architecture for another? What fraction share a response to a particular perturbation?)

As an additional comment, many of the results here are presented as depending on the topology of the network. However, each network is specified by many kinetic constants, and there is no attempt to consider the robustness of results to changes in parameters.

The systematic study of all arbitrary regulatory architectures is beyond the scope of this paper and, indeed, beyond the scope of any one paper. Nevertheless 225,000 arbitrary Entity-Sensor-Property systems were systematically explored and collections of parameters that lead to different behaviors provided (e.g., 78,285 are heritable). These ESP systems more closely mimic regulation in living systems than the coupled ODE-based specification of change in a regulatory architecture.

The example questions raised here are not only difficult to answer, but subjective and present a moving target for future studies. One, ‘how easy is it to mistake one architecture for another?’. Mistaking one architecture for another clearly depends on the number of different types of experiments one can perform on an architecture and the resolution with which changes in entities can be measured to find distinguishing features. Two, ‘What fraction share a response to a particular perturbation?’. ‘Sharing a response’ also depends on the resolution of the measurement after perturbation.

DNA analogy

At two points, the author makes a comparison between genetic information (i.e. DNA) and epigenetic information as determined by these heritable regulatory architectures. The two claims the author makes are that (i) heritable architectures are capable of transmitting "more heritable information" than genetic sequences, and (ii) that, unlike DNA, the connectivity (in the sense of mutations) between heritable architectures is sparse and uneven (i.e. some architectures are better connected than others).

In both cases, the claim is somewhat tenuous -- in essence, it seems an unfair comparison to consider the basic epigenetic unit to be an "entity" (e.g., an entire transcription factor gene product, or an organelle), while the basic genetic unit is taken to be a single base-pair. The situation is somewhat different if the relevant comparison was the typical size of a gene (e.g., 1 kb).

Considering every base being the unit of stored information in the DNA sequence results in the maximal possible storage capacity of a genome of given length. Any other equivalence between entity and units within the genome (e.g., 1 kb gene) will only reduce the information stored in the genome.

Nevertheless, the claim was modified to say that the information content of an ESP system can [italics added] be more extensive than the information content of the genome. This accounts for the possibility of an organism that has an inordinately large genome such that maximal information that can be stored in a particular genome sequence exceeds that stored in a particular configuration of all the contents in a cell.

I thank the reviewer for providing further explanation of this misunderstanding in the second round of review, which helps draw future readers to the sections in the paper that discusses this important point (also see response to Recommendations for the authors).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I thank the author for their efforts in replying to the comments. I have updated my review accordingly; in particular, I have:

(1) Removed my complaint that Heritability is nowhere defined

(2) Removed issues with the presentation of the ODE model in the supplementary information.

I thank the reviewer for raising these issues and acknowledging the improvements made.

However, given that the manuscript is broadly unchanged from the initial one, many of my prior comments remain justified. Some key points:

(1) The manuscript continues to be difficult to read, for the same reasons as I mentioned when reviewing the paper previously.

(2) The utility of the "ESP" formalism is still unclear.

• As the author notes, continuous ODEs are of course an idealisation of a system with discrete copy number.

• However, discussing this is standard fare in any textbook dealing with chemical dynamics and stochastic processes -- see, for instance, the standard textbook by van Kampen.

• This seems little reason to reject ODEs and implement a poorly defined formalism/simulation scheme.

(3) The author claims that many questions raised are "beyond the scope of this study". Indeed, answering all of these questions are beyond the scope of any one study. However, as I initially wrote, the paper would be much stronger if it focused on a particular problem rather than the many vignettes depicted.

The broad scope of this foundational paper necessitates addressing many issues, which may make it a difficult read for some readers. I hope that future work where each paper focuses on one of the aspects raised here will enable the extensive treatment of limited scope as suggested by the reviewer.

The utility of ODEs is much appreciated and was indeed a computationally efficient way of exploring the vast space of regulatory architectures. As stated in the response to the public reviews, the Entity-Sensors-Property framework provides a biologically grounded way of parsing interacting regulators. This approach is aligned with the development of mechanistic models for the functions of living systems while being consistent with heredity. In contrast, widely analyzed networks like protein-interaction networks, signaling networks, gene regulatory networks, etc., are not always constrained using these principles.

On a final note, on the subject of the comparison with DNA:

Perhaps I have misunderstood something. I simply meant that comparing the "maximal information" with 4 HRAs (12.45 bits) is certainly more than the "maximal information" with 4 basepairs (8 bits), but definitely less than the "maximal information" for four 1-kb genes (4^(4000) combinations, so 8000 bits...)

Perhaps the author means that the growth in information of HRAs is faster than exponential. If so, that should be shown and then remarked on.

For this reason, I maintain my comment that the comparison is tenuous.

This issue was addressed once in the results section and again in the discussion section.

The results section states that “The combinatorial growth in the numbers of HRAs with the number of interactors can thus provide vastly more capacity for storing information in larger HRAs compared to that afforded by the proportional growth in longer genomes.”

The discussion section states that “Despite imposing heritability, regulated non-isomorphic directed graphs soon become much more numerous than unregulated non-isomorphic directed graphs as the number of interactors increase (125 vs. 5604 for 4 interactors, Table 1). With just 10 interactors, there are >3x1020 unregulated non-isomorphic directed graphs [60] and HRAs are expected to be more numerous. This tremendous variety highlights the vast amount of information that a complex regulatory architecture can represent and the large number of changes that are possible despite sparsity of the change matrix (Fig. 3).”

Thus, indeed as the reviewer surmises, the combinatorial explosion in information of HRAs with increases in interacting entities is faster than the proportional growth in information of genome sequence with increases in length.

In summary, I thank the reviewers and editors for their help in improving the paper and would like to make the current manuscript the Version of Record.

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

Public Reviews:

Reviewer #1 (Public Review):

The author studies a family of models for heritable epigenetic information, with a focus on enumerating and classifying different possible architectures. The key aspects of the paper are:

• Enumerate all 'heritable' architectures for up to 4 constituents.

• A study of whether permanent ("genetic") or transient ("epigenetic") perturbations lead to heritable changes.

• Enumerated the connectivity of the "sequence space" formed by these heritable architectures.

-Incorporating stochasticity, the authors explore stability to noise (transient perturbations). - A connection is made with experimental results on C elegans.

The study is timely, as there has been a renewed interest in the last decade in nongenetic, heritable heterogeneity (e.g., from single-cell transcriptomics). Consequently, there is a need for a theoretical understanding of the constraints on such systems. There are some excellent aspects of this study: for instance:

• The attention paid to how one architecture "mutates" into another, establishing the analogue of a "sequence space" for network motifs (Fig 3).

• The distinction is drawn between permanent ("genetic") and transient ("epigenetic") perturbations that can lead to heritable changes.

• The interplay between development, generational timescales, and physiological time (as in Fig. 5).

I thank the reviewer for highlighting these aspects of the work.

The manuscript would be very interesting if it focused on explaining and expanding these results. Unfortunately, as a whole, it does not succeed in formalising nor addressing any particular open questions in the field. Aside from issues in presentation and modelling choices (detailed below), it would benefit greatly from a more systematic approach rather than the vignettes presented.

This first paper is foundational and therefore cannot be expected to solve all aspects of the problem of heredity. The work was nevertheless systematic in that (1) for the simple architectures modeled using ordinary differential equations (ODEs) with continuity assumptions, parameters that support steady states were systematically determined for each architecture and then every architecture was explored using genetic changes exhaustively, although epigenetic perturbations were not examined exhaustively because of their wide variety; and (2) for the more realistic modeling of architectures as Entity-Sensor-Property systems, the behavior of systems with respect to architecture as well as parameter space that lead to particular behaviors (persistence, heritable epigenetic change, etc.) was systematically explored. A more extensive exploration of parameter space that also includes the many ways that the interaction between any two entities/nodes could be specified using an equation is a potentially ever-expanding challenge that is beyond the scope of any single paper (see response to additional comments below).

Specific aspects that remain to be addressed include the application of multiple notions of heritability to real networks of arbitrary size, considering different types of equations for change of each entity/node, and classifying different behavioral regimes for different sets of parameters. As is evident from this list of combinatorial possibilities, the space to be explored is vast and beyond the scope of this foundational paper.

The key contribution of the paper is an articulation of the crucial questions to ask of any regulatory architecture in living systems rather than the addressing of any question that a field has recognized as ‘open’. Specifically, through the exhaustive listing for small regulatory architectures that can be heritable and the systematic analysis of arbitrary Entity-Sensor-Property systems that more realistically capture regulatory architectures in living systems, this work points the way to constrain inferences after experiments on real living systems. Currently, most experimental biologists engaged in reductionist approaches and some systems biologists examining the function or prevalence of network motifs do not explicitly constrain their models for heritability or persistence. It is hoped that this paper will raise awareness in both communities and lead to more constrained models that minimize biases introduced by incomplete knowledge of the network, which is always the case when analyzing living systems.

Terminology

The author introduces a terminology for networks of interacting species in terms of "entities" and "sensors" -- the former being nodes of a graph, and the latter being those nodes that receive inputs from other nodes. In the language of directed graphs, "entities" would seem to correspond to vertices, and "sensors" those vertices with positive indegree and outdegree. Unfortunately, the added benefit of redefining accepted terminology from the study of graphs and networks is not clear.

The Entities-Sensors-Property (ESP) framework is based on underlying biology and not graph theory, making an ESP system not entirely equivalent to a network or graph, which is much less constrained. The terms ‘entity’, ‘sensor’, and ‘property’ were defined and justified in a previous paper (Jose, J R. Soc. Interface, 2020). While nodes of a network can be parsed arbitrarily and the relationship between them can also be arbitrary, entities and sensors are molecules or collections of molecules that are constrained such that the sensors respond to changes in particular properties of other entities and/or sensors. When considered as digraphs, sensors can be seen as vertices with positive indegree and outdegree. The ESP framework can be applied across any scale of organization in living systems and this specific way of parsing interactions also discretizes all changes in the values of any property of any entity. In short, ESP systems are networks, but not all networks are ESP systems. Therefore, the results of network theory that remain applicable for ESP systems need further investigation. This justification is now repeated in the paper.

The key utility of the ESP framework is that it is aligned with the development of mechanistic models for the functions of living systems while being consistent with heredity. In contrast, widely analyzed networks like protein-interaction networks, signaling networks, gene regulatory networks, etc., are not always constrained using these principles. In addition, the language of digraphs where sensors can be seen as vertices with positive indegree and outdegree has been also added to aid readers who are familiar with graph theory.

Heritability

The primary goal of the paper is to analyse the properties of those networks that constitute "heritable regulatory architectures". The definition of heritability is not clearly stated anywhere in the paper, but it appears to be that the steady-state of the network must have a non-zero expression of every entity. As this is the heart of the paper, it would be good to have the definition of heritable laid out clearly in either the main text or the SI.

I have now defined the term as used in this paper early, which is indeed as surmised by the reviewer simply the preservation of the architecture and non-zero levels of all entities. I have also highlighted additional notions of heredity that are possible, which will be the focus of future work. These can range from precise reproduction of the concentration and the localization of every entity to a subset of the entities being reproduced with some error while the rest keep varying from generation to generation (as illustrated in Fig. 2 of Jose, BioEssays, 2018). Importantly, it is currently unclear which of these possibilities reflects heredity in real living systems.

Model

As described in the supplementary, but not in the main text, the author first chooses to endow these networks with simple linear dynamics; something like $\partial_t \vec{x} = A x - T x$, where the vector $x$ is the expression level of each entity, $A$ has the structure of the adjacency matrix of the directed graph, and $T$ is a diagonal matrix with positive entries that determines the degradation or dilution rate of each entity. From a readability standpoint, it would greatly aid the reader if the long list of equations in the SI were replaced with the simple rule that takes one from a network diagram to a set of ODEs.

I have abridged the description by eliminating the steady state expression for every HRA as suggested and simply pointed to the earlier version of the paper for those readers who might prefer the explicit derivations of these simple expressions. An overview is now provided for going from any network diagram to a set of ODEs.

The implementation of negative regulation is manifestly unphysical if the "entities" represent the expression level of, say, gene products. For instance, in regulatory network E, the value of the variable z can go negative (for instance, if the system starts with z= and y=0, and x > 0).

Negative values for any entity were avoided in simulations by explicitly setting all such values to zero. This constraint has been added as a note in the section describing the equations for the change of each node/entity in each regulatory network. Specifically, the levels of each entity/sensor was set to zero during any time step when the computed value for that entity/sensor was less than zero. This bounding of the function allows for any approach to zero while avoiding negative values. I apologize for the omission of this constraint from the supplemental material in the last submission. This constraint was used in all the simulations and therefore this change does not affect any of the results presented. In this way, it is ensured that the presence of negative regulation does not lead to negative values.

Formally, the promotion or inhibition of an entity or sensor can be modeled using any function that is either increasing (for promotion) or decreasing (for inhibition). This diversity of possibilities is one of the challenges that prevents exhaustive exploration of all functions. In fact, the use of ODEs after assuming a continuous function is an idealization that facilitates understanding of general principles but is not in keeping with the discreteness of entities or step changes in their values (amount, localization, etc.) observed in living systems. Other commonly used continuous functions include Hill functions for the rate of production of y given as xn/(k + xn) for x activating y, which increases to ~1 as x increases, or given as k/(k + xn) for x inhibiting y, which decreases to ~0 as x increases. Increasing values of ‘n’ result in steeper sigmoidal curves. In reality, levels of all entities/sensors are expected to be discretized by measurement in living systems and the form of the function for any regulation needs empirical measurement in vivo (see response to comment below).

The model seems to suddenly change from Figure 4 onwards. While the results presented here have at least some attempt at classification or statistical rigour (i.e. Fig 4 D), there are suddenly three values associated with each entity ("property step, active fraction, and number"). Furthermore, the system suddenly appears to be stochastic. The reader is left unsure of what has happened, especially after having made the effort to deduce the model as it was in Figs 1 through 3. No respite is to be found in the SI, either, where this new stochastic model should have been described in sufficient detail to allow one to reproduce the simulation.

While ODEs are easier to simulate and understand, they are less realistic as explained above. I have now added more explanation justifying the need for the subsequent simulation of Entity-Sensor-Property systems. I have also expanded the information provided for each aspect of the model (previously outlined in Fig. 4A and detailed within the code) in a Supplementary Information section titled ‘Simulation of simple ESP systems’.

Perturbations

Inspired especially by experimental manipulations such as RNAi or mutagenesis, the author studies whether such perturbations can lead to a heritable change in network output. While this is naturally the case for permanent changes (such as mutagenesis), the author gives convincing examples of cases in which transient perturbations lead to heritable changes. Presumably, this is due the the underlying mutlistability of many networks, in which a perturbation can pop the system from one attractor to another.

Unfortunately, there appears to be no attempt at a systematic study of outcomes, nor a classification of when a particular behaviour is to be expected. Instead, there is a long and difficult-to-read description of numerical results that appear to have been sampled at random (in terms of both the architecture and parameter regime chosen). The main result here appears to be that "genetic" (permanent) and "epigenetic" (transient) perturbations can differ from each other -- and that architectures that share a response to genetic perturbation need not behave the same under an epigenetic one. This is neither surprising (in which case even illustrative evidence would have sufficed) nor is it explored with statistical or combinatorial rigour (e.g. how easy is it to mistake one architecture for another? What fraction share a response to a particular perturbation?)

The systematic study of all arbitrary regulatory architectures is beyond the scope of this paper and, as stated earlier, beyond the scope of any one paper. Nevertheless 225,000 arbitrary Entity-Sensor-Property systems were systematically explored and collections of parameters that lead to particular behaviors provided (e.g., 78,285 are heritable). These ESP systems more closely mimic regulation in living systems than the coupled ODE-based specification of change in a regulatory architecture.

The example questions raised here are not only difficult to answer, but subjective and present a moving target for future studies. One, ‘how easy is it to mistake one architecture for another?’. Mistaking one architecture for another clearly depends on the number of different types of experiments one can perform on an architecture and the resolution with which changes in entities can be measured to find distinguishing features. Two, ‘What fraction share a response to a particular perturbation?’. ‘Sharing a response’ also depends on the resolution of the measurement of entities after perturbation.

As an additional comment, many of the results here are presented as depending on the topology of the network. However, each network is specified by many kinetic constants, and there is no attempt to consider the robustness of results to changes in parameters.

The interpretations presented are conservative determinations of heritability based on the topology of the architecture. In other words, architectures that can be heritable for some set of parameters. Of course, parameter sets can be found that make any regulatory architecture not heritable. As stated earlier, exploring all parameters for even one architecture is beyond the scope of a single study because of the infinitely many ways that the interaction between any two entities can be specified.

DNA analogy

At two points, the author makes a comparison between genetic information (i.e. DNA) and epigenetic information as determined by these heritable regulatory architectures. The two claims the author makes are that (i) heritable architectures are capable of transmitting "more heritable information" than genetic sequences, and (ii) that, unlike DNA, the connectivity (in the sense of mutations) between heritable architectures is sparse and uneven (i.e. some architectures are better connected than others).

In both cases, the claim is somewhat tenuous -- in essence, it seems an unfair comparison to consider the basic epigenetic unit to be an "entity" (e.g., an entire transcription factor gene product, or an organelle), while the basic genetic unit is taken to be a single base-pair. The situation is somewhat different if the relevant comparison was the typical size of a gene (e.g., 1 kb).

Considering every base being the unit of stored information in the DNA sequence results in the maximal possible storage capacity of a genome of given length. Any other equivalence between entity and units within the genome (e.g., 1 kb gene) will only reduce the information stored in the genome.

Nevertheless, the claim has been modified to say that the information content of an ESP system can [italics added] be more extensive than the information content of the genome. This accounts for the possibility of an organism that has an inordinately large genome such that maximal information that can be stored in a particular genome sequence exceeds that stored in a particular configuration of all the contents in a cell.

Reviewer #2 (Public Review):

Summary:

This manuscript uses an interesting abstraction of epigenetic inheritance systems as partially stable states in biological networks. This follows on previous review/commentary articles by the author. Most of the molecular epigenetic inheritance literature in multicellular organisms implies some kind of templating or copying mechanisms (DNA or histone methylation, small RNA amplification) and does not focus on stability from a systems biology perspective. By contrast, theoretical and experimental work on the stability of biological networks has focused on unicellular systems (bacteria), and neglects development. The larger part of the present manuscript (Figures 1-4) deals with such networks that could exist in bacteria. The author classifies and simulates networks of interacting entities, and (unsurprisingly) concludes that positive feedback is important for stability. This part is an interesting exercise but would need to be assessed by another reviewer for comprehensiveness and for originality in the systems biology literature. There is much literature on "epigenetic" memory in networks, with several stable states and I do not see here anything strikingly new.

The key utility of the initial part of the paper is the exhaustive enumeration of all small heritable regulatory architectures. The implications for the abundance of ‘network motifs’ and more generally any part of a network proposed to perform a particular function is that all such parts need to be compatible with heredity. This principle is generally not followed in the literature, resulting in incomplete networks being interpreted as having motifs or modules with autonomous function. Therefore, while the need for positive feedback for stability is indeed obvious, it is not consistently applied by all. For example, the famous synthetic circuit ‘the repressilator’ (Elowitz and Leibler, “A synthetic oscillatory network of transcriptional regulators”, Nature, 2000), which is presented as an example of ‘rational network design’, has three transcription factors that all sequentially inhibit the production of another transcription factor in turn forming a feedback loop of inhibitory interactions. Therefore, the contributions of the factors that promote the expression of each entity is unknown and yet essential for heritability. The comprehensive listing of the heritable regulatory architectures that are simple provide the basis for true synthetic biology where the contributing factors for observed behavior of the network are explicitly considered only after constraining for heredity. Using this principle, the minimal autonomous architecture that can implement the repressilator is the HRA ‘Z’ (Fig. 1).

An interesting part is then to discuss such networks in the framework of a multicellular organism rather than dividing unicellular organisms, and Figure 5 includes development in the picture. Finally, Figure 6 makes a model of the feedback loops in small RNA inheritance in C. elegans to explain differences in the length of inheritance of silencing in different contexts and for different genes and their sensitivity to perturbations. The proposed model for the memory length is distinct from a previously published model by Karin et al. (ref 49).

I thank the reviewer for appreciating this aspect of the paper.

Strengths:

A key strength of the manuscript is to reflect on conditions for epigenetic inheritance and its variable duration from the perspective of network stability.

I thank the reviewer for appreciating the importance of the overall topic.

Weaknesses:

• I found confusing the distinction between the architecture of the network and the state in which it is. Many network components (proteins and RNAs) are coded in the genome, so a node may not disappear forever.

I have added language to clarify the many states of a network versus its architecture (also illustrated in Fig. 4 for ESP systems). Even loss of expression below a threshold can lead to permanent loss if there is not sufficient noise to induce re-expression. For example, consider the simple case of a transcription factor that binds to its own promoter, requiring 10 molecules for the activation of the promoter and thus production of more of the same transcription factor. If an epigenetic change (e.g., RNA interference) reduces the levels to fewer than 10 molecules and if the noise in the system never results in the numbers of the transcription factor increasing beyond 10, the transcription factor has been effectively lost permanently. In this way, reduction of a regulator can lead to permanent change despite the presence of the DNA. Many papers in the field of RNA silencing in C. elegans have provided strong experimental evidence to support this assertion.

• From the Supplementary methods, the relationship between two nodes seems to be all in the form of dx/dt = Kxy . Y, which is just one way to model biological reactions. The generality of the results on network architectures that are heritable and robust/sensitive to change is unclear. Other interactions can have sigmoidal effects, for example. Is there no systems biology study that has addressed (meta)stability of networks before in a more general manner?

Indeed, the relationship between any two entities can in principle be modeled using any function. Extensive exploration of the behavior of any regulatory architecture – even the simplest ones – require simplifications. For example, early work by Stuart Kauffman explored Boolean networks (see ref. 10 in the paper for history and extensive explanations). However, allowing all possible ways of specifying the interactions between components of a network makes analysis both a computational and conceptual challenge.

• Why is auto-regulation neglected? As this is a clear cause of metastable states that can be inherited, I was surprised not to find this among the networks.

Auto-regulation in the sense of some molecule/entity ultimately leading to the production of more of itself is present in every heritable regulatory architecture. Specifically, all auto-regulatory loops rely on a sequence of interactions between two or more kinds of molecules. For example, a transcription factor (TF) binding to the promoter of its own gene sequence, resulting in the production of more TF protein is a positive feedback loop that relies on many interacting factors (transcription, translation, nuclear import, etc.) and can be considered as ‘auto-regulation’ as it is sometimes referred to in the literature. In this sense, every HRA (A through Z) includes ‘auto-regulation’ or more appropriately positive feedback loops. For example, in the HRA ‘A’, x ‘auto-regulates’ itself via y.

• I did not understand the point of using the term "entity-sensor-property". Are they the same networks as above, now simulated in a computer environment step by step (thus allowing delays)?

Please see response to the other reviewer regarding the need for the Entity-SensorProperty framework and how it is distinct from generic networks. Briefly, the ODE-based simple networks, while easy to analyze, are not realistic because of the assumptions of continuity. In contrast ESP systems are more realistic with measurement discretizing changes in property values as is expected in real living systems.

• The final part applies the network modeling framework from above to small RNA inheritance in C. elegans. Given the positive feedback, what requires explanation is how fast the system STOPs small RNA inheritance. A previous model (Karin et al., ref. 49) builds on the fact that factors involved in inheritance are in finite quantity hence the different small RNAs "compete" for amplification and those targeting a given gene may eventually become extinct.

The present model relies on a simple positive feedback that in principle can be modulated, and this modulation remains outside the model. A possibility is to add negative regulation by factors such as HERI-1, that are known to limit the duration of the silencing.

The duration of silencing differs between genes. To explain this, the author introduces again outside the model the possibility of piRNAs acting on the mRNA, which may provide a difference in the stability of the system for different transcripts. At the end, I do not understand the point of modeling the positive feedback.

The previous model (Karin et al., Cell Systems, 2023) can describe populations of genes that are undergoing RNA silencing but cannot explain the dynamics of silencing particular genes. Furthermore, this model also cannot explain cases of effectively permanent silencing of genes that have been reported (e.g., Devanapally et al., Nature Communications, 2021 and Shukla et al., Current Biology, 2021). Finally, the observations of susceptibility to, recovery from, and even resistance to trans silencing (e.g., Fig. 5a in Devanapally et al., Nature Communications, 2021) require an explanation that includes modulation of the HRDE-1-dependent positive feedback loop that maintains silencing across generations.

The specific qualitative predictions regarding the relationship between piRNA-mediated regulation genome-wide and HRDE-1-dependent silencing of a particular gene across generations could guide the discovery of potential regulators of heritable RNA silencing. The equations (4) and (5) in the paper for the extent of modulation needed for heritable epigenetic change provide specific quantitative predictions that can be tested experimentally in the future. I have also revised the title of the section to read ‘Tuning of positive feedback loops acting across generations can explain the dynamics of heritable RNA silencing in C. elegans’ to emphasize the above points.

• From the initial analysis of abstract networks that do not rely on templating, I expected a discussion of possible examples from non-templated systems and was a little surprised by the end of the manuscript on small RNAs.

The heritability of any entity relies on regulatory interactions regardless of whether a templated mechanism is also used or not. For example, DNA replication relies on the interactions between numerous regulators, with only the sequence being determined by the template DNA. The field of small RNA-mediated silencing facilitates analysis of epigenetic changes at single-gene resolution (Chey and Jose, Trends in Genetics, 2022). It is therefore likely to continue to provide insights into heritable epigenetic changes and how they can be modulated. Unfortunately, there are currently no known cases of epigenetic inheritance where the role of any templated mechanism has been conclusively excluded. Future research will improve our understanding of epigenetic states and their modulation in terms of changes in positive feedback loops as proposed in this study and potentially lead to the discovery of such mechanisms that act entirely independent of any template-dependent entity.

Recommendations for the authors:

I thank the reviewers for their specific suggestions to improve the paper.

Reviewer #1 (Recommendations For The Authors):

The paper has many long paragraphs that attempt to explain results, make illustrations, and give intuition. Unfortunately, these are difficult to read. It would aid the reader greatly if these were, say, converted into cartoons (even if only in the SI), or made more accessible in some other way.

I agree with the importance of making the material accessible to readers in multiple ways. I have now added a figure with schematics in the SI titled ‘Illustrations of key concepts’ (new Fig. S2), which collects concepts that are relevant throughout the paper and might aid some readers.

The bulk of the supplementary is currently a collection of elementary mathematics results: to whit, pages 26 to 33 of the combined manuscript carry no more information than a quick description of the general model and the diagrams in Fig 1. Similarly, pages 34 to 39 (non-zero dilution rate), and pages 39 through 58 (response to permanent changes) each express a trivial mathematical point that is more than sufficiently made with one illustrative example.

I agree with the reviewer and have condensed these pages as suggested. I have added a pointer to the earlier version as containing further details for the readers who might prefer the explicit listing of these equations.

Overall, the paper appears to be a collection of numerical results obtained from different models, united by uncertain terminology that is not fully defined in this paper. The most promising aspects of the paper lie either in (a) combinatorially complete enumeration of all regulatory architectures, or (b) relating experimental manipulations in C. elegans to possible underlying regulatory architectures. Focusing on one or the other might improve the readability of the paper.

The two sections of the paper are complementary and when presented together help with the integration of concepts rather than the siloed pursuit of theory versus experimental analysis. When this work was presented at meetings before submission, it was clear that different researchers appreciated different aspects. This divergence is also apparent in the two reviews, with each reviewer appreciating different aspects. I have repeated the definitions and justifications from the earlier paper (Jose, J R Soc Interface, 2020) to provide a more fluid transition between the two complementary sections of the paper. Knowing both sides could aid in the development of models that are not only consistent with measurable quantities (e.g., anything that can be considered an entity) but are also logically constrained (e.g., entities matched with sensors while avoiding any entities that do not have a source of production – i.e., avoiding nodes with indegree = 0).

However, having said that many results of these types are well-known in models of regulatory networks, and it is unclear what precisely warrants the new framework that the author is proposing. Indeed, it would be good to understand in what way the framework here is novel, and how it is distinguished from prior studies of regulatory networks.

The key novelty of the work is the consideration of heritability for any regulation. With the explicit definition of the heritability for a regulatory architecture and the acknowledgement that there can be more than one notion of heredity, this paper now sets the foundation for examining many real networks in this light. I hope that the added justifications for the current framework in the revised paper strengthen these arguments. Future literature reviews on networks in general and how they address heritability or persistence will better define the prevalence of these considerations. Currently, most experimental biologists engaged in reductionist approaches and some systems biologists examining the function or prevalence of network motifs do not explicitly constrain their models for heritability or persistence. It is hoped that this work will raise awareness in both communities and lead to more constrained models that acknowledge incomplete knowledge of the network, which is always the case when analyzing living systems.

Reviewer #2 (Recommendations For The Authors):

Minor points/clarity

• page 1 line 57: "transgenerational waveforms that preserve form and function" is unclear.

This phrase was expanded upon in a previous paper (Jose, BioEssays, 2020). I have now added more explanation in this paper for completeness. The section now reads ‘For example, the localization and activity of many kinds of molecules are recreated in successive generations during comparable stages [1-3]. These recurring patterns can change throughout development such that following the levels and/or localizations of each kind of molecule over time traces waveforms that return in phase with the similarity of form and function across generations [2].’

• page 7 line 3-6: the sentence has an ambiguous structure.

I have now edited this long sentence to read as follows: ‘For systematic analysis, architectures that could persist for ~50 generations without even a transient loss of any entity/sensor were considered HRAs. Each HRA was perturbed (loss-of-function or gain-of-function) after five different time intervals since the start of the simulation (i.e., phases). The response of each HRA to such perturbations were compared with that of the unperturbed HRA.’

• page 9 lines 25-27: the sentence is convoluted: are you defining epigenetic inheritance?

I have simplified this sentence describing prior work by others (Karin et al., Cell Systems, 2023) and moved a clause to the subsequent sentence. This section now reads: ‘Recent considerations of competition for regulatory resources in populations of genes that are being silenced suggest explanations for some observations on RNA silencing in C. elegans [49]. Specifically, based on Little’s law of queueing, with a pool of M genes silenced for an average duration of T, new silenced genes arise at a rate  that is given by M = T’. I have also provided more context by preceding this section with: ‘Although the release of shared regulators upon loss of piRNA-mediated regulation in animals lacking PRG-1 could be adequate to explain enhanced HRDE-1-dependent transgenerational silencing initiated by dsRNA in prg-1(-) animals, such a competition model alone cannot explain the observed alternatives of susceptibility, recovery and resistance (Fig. 6A).’

• page 13 lines 51-53. This last sentence of the discussion is ambiguous/unclear.

I have now rephrased this sentence to read: ‘This pathway for increasing complexity through interactions since before the origin of life suggests that when making synthetic life, any form of high-density information storage that interacts with heritable regulatory architectures can act as the ‘genome’ analogous to DNA.’

• Figure 2: the letters in the nodes are hard to read; the difference between full and dotted lines in the graphs also.

I have enlarged the nodes and widened the gap in the dotted lines to make them clearer. I have also similarly edited Fig. 1 and Fig. S3 to Fig. S9.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

(1) More explanation/description of Fig 3C and 3D would be helpful for readers, including the color code of 3D and black lines shown in both panels.

We have added more description to the legend of Figure 3, and we have used the same color code as in Figure 2, which we now specifically note in the figure legend as well.

(2) Differences between cranial and trunk NCC could be experimentally shown or discussed. Fig 4C shows some differences between these two populations, but in situ, results using Dlc1/Sp5/Pak3 probes in the trunk region may be informative, like Fig 5 supplement 2 for cranial NCCs.

This is an important point. The focus of our study was on cranial neural crest cells, and the single cell sequencing data is therefore truly reflective of only cranial neural crest cells. We have not functionally tested for the roles of Dlc1/Sp5/Pak3 in trunk neural crest cells, however, based on the expression and loss-of-function phenotypes of Sp5 or Pak3 knockout mice, we predict they individually may not play a significant role. It remains plausible that Dlc1 could play an important role in the delamination of trunk neural crest cells, but we have not tested that definitively. Nonetheless, Sabbir et al 2010 showed in a gene trap mouse mutant that Dlc1 is expressed in trunk neural crest cells. Regarding the similarities and differences between cranial and trunk neural crest cells as noted by the reviewer with respect to Figure 4, it’s important to recognize the temporal differences illustrated in Figure 4. Neural crest cell delamination proceeds in a progressive wave from anterior to posterior, but also that the analysis was designed to quantify cell cycle status before and during neural crest cell delamination. We have compared cranial and trunk neural crest cells in more detail in the discussion and also speculate what might happen in the trunk based on what we know from other species.

(3) Discussion can be added about the potential functions of Dlc1 for NCC migration and/or differentiation based on available info from KO mice.

We have added specific details regarding the published Dlc1 knockout mouse phenotype to the discussion, particularly with respect to the craniofacial anomalies which included frontonasal prominence and pharyngeal arch hyperplasia, and defects in neural tube closure and heart development. Although the study didn’t investigate the mechanisms underpinning the Dlc1 knockout phenotype, the craniofacial morphological anomalies would be consistent with a deficit in neural crest cell delamination reducing the number of migrating neural crest cells, as we observed in our Dlc1 knockdown experiments.

Reviewer #2 (Recommendations For The Authors):

The authors used the (Tg(Wnt1-cre)11Rth Tg(Wnt1-GAL4)11Rth/J) line but work from the Bush lab (see Lewis et al., 2013) has demonstrated fully penetrant abnormal phenotypes that affect the midbrain neuroepithelium, increased CyclinD1 expression and overt cell proliferation as measured by BrdU incorporation. The authors should explain why they used this mouse line instead of the Wnt1-Cre2 mice (129S4-Tg(Wnt1-cre)1Sor/J) in the Jackson Laboratory (which lacks the phenotypic effects of the original Wnt1-Cre line), or a "Cre-only" control, or at a minimum explain the steps they took to ensure there were no confounding effects on their study, especially since cell proliferation was a major outcome measure.

This is an important point, and we thank the reviewer for raising it. Yes, it has been reported that the original Wnt1Cre mice exhibit a midbrain phenotype (Ace et al. 2013). However, it has also been noted that Wnt1Cre2 can exhibit recombination in the male germline leading to ubiquitous recombination (Dinsmore et al., 2022). Therefore, to avoid any potential for bias, we used an equal number of cells derived from the Wnt1 and F10N transgenic line embryos in our scRNA-seq, and this included multiple non-Cre embryos. Our scRNA-seq analysis was therefore not dependent upon Wnt1-Cre, but also because we used whole heads not fluorescence sorted cells. However, Wnt1-Cre lineage tracing was advantageous from a computational perspective to help define cells that were premigratory and migratory in concert with Mef2c-lacZ ¬based on their expression of YFP, LacZ or both. We note these specifics more clearly in the methods.

The Results section (line 122) states that scRNA-seq was performed on dissociated cranial tissues but the Methods section (lines 583-584) implies that whole E8.5 mouse embryos were dissociated. Which was dissociated, whole embryos or just cranial tissues? Obviously, the latter would be a better strategy to enrich for cranial neural crest, but the authors also examine the trunk neural crest. This should be clarified in the text.

We apologize that some of the details regarding the tissue isolation were confusing and we have clarified this in the methods and the text. For the record, after isolating E8.5 embryos, we then dissected the head from those embryos, and performed scRNA-seq on dissociated cranial tissues. As the reviewer correctly noted, this approach strategically enriches for cranial neural crest cells.

The authors do not justify why they chose a knockdown strategy, which has its limitations including its systemic injection into the amniotic cavity, its likely global and more variable effects, and its need to be conducted in culture. Why the authors did not instead use a Wnt1-Cre-mediated deletion of Dlc1, which would have been "cleaner" and more specific to the neural crest, is not clear (maybe so they could specifically target different Dcl1 isoforms?). Also, the authors use Sox10 as a marker to count neural crest cells, but Sox10 may only label a subset of neural crest cells and thus some unaffected lineages may not have been counted. The authors should mention what is known about the regulation of Dcl1 by Sox10 in the neural crest. Although the data are persuasive, a second marker for counting neural crest cells following knockdown would make the analysis more robust. Can the authors explain why they did not simply use the Mef2c-F10N-LacZ line and count LacZ-positive cells (if fluorescence signal was required for the quantification workflow, then could they have used an anti-beta Galactosidase antibody to label cells)?

We thank the reviewer for raising these important considerations. It has previously been noted that although Wnt1-Cre is the gold standard for conditional deletion analyses in neural crest cell development, especially migration and differentiation, it is not a good tool for functional studies of the specification and delamination of neural crest cells due to the timing of Wnt1 expression and Cre activation and excision (see Barriga et al., 2015). Therefore, we chose a knockdown strategy instead, and also because it allows us to more rapidly evaluate gene function. We agree that there are limitations to the approach with respect to variability, however, this is outweighed by the ability to repeatedly perform the knockdown at multiple and more relevant temporal stages such as E7.5 (which is prior to the onset of Wnt1-Cre activity), as well as target different isoforms, and also treat large numbers of embryos for quantitative analyses. The advantage of using Sox10 as a marker for counting neural crest cells is that at the time of analysis, cranial neural crest cells are still migrating towards the frontonasal prominences and pharyngeal arches, and the overwhelming majority of these cells are Sox10 positive. Moreover, we can therefore assay every Dlc1 knockdown embryo for Sox10 expression and count the number of migrating neural crest cells. The limitation of using the Mef2c-F10N-LacZ line is that this transgenic line is maintained as a heterozygote, and thus only half the embryos in a litter could reasonably be expected to be lacZ+. But combining Sox10 and Mef2c-F10N-LacZ fluorescent immunostaining for similar analyses in the future is a great idea.

Reviewer #3 (Recommendations For The Authors):

The putative intermediate cells differentially express mRNAs for genes involved in cell adhesion, polarity, and protrusion relative to bona fide premigratory cells (Fig. 2E). This is persuasive evidence, but only differentially expressed genes are shown. Discussing those markers that have not yet changed, e.g. Cdh1 or Zo1 (?), would be instructive and help to clarify the order of events.

We thank the author for this suggestion and we have provided more detail about adherens junction and tight junctions. Cdh1 is not expressed, and although Myh9 and Myh10 are expressed, we did not detect any significant changes. ZO1 is a tight junction protein encoded by the gene Tjp1, which along with other tight junctions protein encoding genes, is downregulated in intermediate NCCs as shown in the Figure 2E.

It is unclear whether the two putative intermediate state clusters differ other than their stage of the cell cycle. Based on the trajectory analysis in Fig. 3C-D, the authors state that these two populations form simultaneously and independently but then merge into a single population. However, without further differential expression, it seems more plausible that they represent a single population that is temporarily bifurcated due to cell cycle asynchrony.

We have addressed the cell cycle question in the discussion by noting that while it is possible the transition states represent a single population that is temporarily bifurcated due to cell cycle asynchrony, if this were true, then we should expect S phase inhibition to eliminate both transition state groups. Instead, our trajectory analyses suggest that the transition states are initially independent, and furthermore, S phase inhibition did not affect delamination of the other population of neural crest cells.

The authors do not present an in-depth comparison of these neural crest intermediate states to previously reported cancer intermediate states. This analysis would reveal how similar the signatures are and thus how extrapolatable these and future findings in delaminating neural crest are to different types of cancer.

We have also added more detail to the discussion to address the potential for similarities and differences in neural crest intermediate states compared to previously reported cancer intermediate states. The challenge, however, is that none of the cancer intermediate states have been characterized at a molecular level. Nonetheless, with the limited molecular markers available, we have not identified any similarities so far, but our datasets are now available for comparison with future cancer EMP datasets.

The reduction in SOX10+ cells may be in part or wholly attributable to inhibition of proliferation AFTER delamination. Showing that there are premigratory NCCs in G2/M at ~E8.0 would bolster the argument that this population is present from the earliest stages.

The presence of premigratory neural crest cells in G2/M is shown by the scRNA-seq data and cell cycle staining data in the neural plate border.

Lines 248-249: The pseudo-time analysis in Fig 3C/D does indicate that the two most mature cell clusters (pharyngeal arch and frontonasal mesenchyme) may arise from common or similar migratory progenitors. However, given the decades of controversy about fate restriction of neural crest cells, the statement that "EMT intermediate NCC and their immediate lineages are not fate restricted to any specific cranial NCC derivative at this timepoint" should be toned down so as to not give the impression that they have identified common progenitors of ectomesenchyme and neuro/glial/pigment derivatives.

We appreciate this comment, because as the reviewer noted, there has been considerable literature and debate about the fate restriction and plasticity of neural crest cells, and indeed we did not intend to imply we have identified common progenitors of ectomesenchyme and neuro/glial/pigment derivatives. That can only be truly functionally demonstrated by clonal lineage tracing analyses. Rather, we interpret our pseudo-time analyses to indicate that irrespective of cell cycle status at the time of delamination, these two populations come together with equivalent mesenchymal and migratory properties, but in the absence of fate determination in the collective of cells. This does not mean that individual cells are common progenitors of both ectomesenchyme and neuro/glial/pigment derivatives. The nuance is important, and we address this more carefully in the text.

Lines 320-321: "...this overlap in expression was notably not observed in older embryos in areas where EMT had concluded". It is unclear whether the markers no longer overlap in older embryos (i.e. segregate to distinct populations) or are simply no longer expressed.

The data in Figure 5 demonstrates the dynamic and overlapping expression of Dlc1, Sp5 and Pak3 in the different clusters of cells as they transition from being neuroepithelial to mesenchymal. In contrast to Sp5 and Pak3, Dlc1 is not expressed by premigratory neural crest cells but is expressed at high levels in all EMT intermediate stage neural crest cells. Later as Dlc1 continues to be expressed in migrating neural crest cells, Pak3 and Sp5 are downregulated. But the absence of overlapping expression in the dorsolateral neural plate at the conclusion of EMT coincides with their downregulation in that territory.

In the final results section on Dlc1, the previously published mutant mouse lines are referenced as having "craniofacial malformation phenotypes". The lack of detail given on what those malformations are (assuming descriptions are available) makes the argument that they may be related to insufficient delamination less persuasive. The degree of knockdown correlates so well with the percentage reduction in migratory neural crest (Fig. 6) that one would imagine a null mutant to have a very severe phenotype.

The inference from the reviewer is correct and indeed Dlc1 null mutant mice do have a severe phenotype. We have added more specific details regarding the craniofacial and other phenotypes of the Dlc1 mutant mice to the discussion. Of note the frontonasal prominences and the pharyngeal arches are hypoplastic in E10.5 Dlc1 mutant embryos, which would be consistent with a neural crest cell deficit. Although a deficit in neural crest cells can be caused my multiple distinct mechanisms, our Dlc1 knockdown analyses suggest that the phenotype is due to an effect on neural crest cell delamination which diminishes the number of migrating neural crest cells.

Use the same y-axis for Fig. 4C/D

This has been corrected.

Fig. 6C: Please note in the panel which gene is being measured by qPCR

This has been corrected to denoted Dlc1.

Lines 108-117: More concise language would be appropriate here.

As requested, we were more succinct in our language and have shortened this section.

The SABER-FISH images are very dim. I realize the importance of not saturating the pixels, but the colors are difficult to make out.

We thank the reviewer for pointing this out and have endeavored to make the SABER-FISH images brighter and easier to see.

Author Response

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

eLife assessment

This study presents valuable findings on the roles of the axon growth regulator Sema7a in the formation of peripheral sensory circuits in the lateral line system of zebrafish. The evidence supporting the claims of the authors is solid, although further work directly testing the roles of different sema7a isoforms would strengthen the analysis. The work will be of interest to developmental neuroscientists studying circuit formation.

Public Reviews:

Reviewer #1 (Public Review):

In this work, Dasguta et al. have dissected the role of Sema7a in fine tuning of a sensory microcircuit in the posterior lateral line organ of zebrafish. They attempt to also outline the different roles of a secreted verses membrane-bound form of Sema7a in this process. Using genetic perturbations and axonal network analysis, the authors show that loss of both Sema7a isoforms causes abnormal axon terminal structure with more bare terminals and fewer loops in contact with presynaptic sensory hair cells. Further, they show that loss of Sema7a causes decreased number and size of both the pre- and post-synapse. Finally, they show that overexpression of the secreted form of Sema7a specifically can elicit axon terminal outgrowth to an ectopic Sema7a expressing cell. Together, the analysis of Sema7a loss of function and overexpression on axon arbor structure is fairly thorough and revealed a novel role for Sema7a in axon terminal structure. However, the connection between different isoforms of Sema7a and the axon arborization needs to be substantiated. Furthermore, an autocrine role for Sema7a on the presynaptic cell is not ruled out as a contributing factor to the synaptic and axon structure phenotypes.

Finally, critical controls are absent from the overexpression paradigm.

Comments: Thank you for your valuable comments. We have analyzed the hair cell scRNA transcriptome data of zebrafish neuromasts from published works and have not identified known expression of receptors of the Sema7A protein, particularly PlexinC1 and Integrin β1 molecules (reference 4 and 15) in hair cells. This result suggests that the Sema7A protein molecule, either secreted or membrane-bound, does not possess its cognate receptor to elicit an autocrine function on the hair cells. Moreover, the GPI-anchored Sema7A lacks a cytosolic domain. So it is unlikely that Sema7A signaling directly induces the formation of presynaptic ribbons. We propose that the decrease in average number and area of synaptic aggregates likely reflects decreased stability of the synaptic structures owing to lack of contact between the sensory axons and the hair cells, which has been identified in zebrafish neuromasts (reference 38).

Thank you for pointing missing critical control experiments. Additional control experiments (lines 333-346) with a new figure (Figure 5) have been added.

These issues weaken the claims made by the authors including the statement that they have identified differential roles for the GPI-anchored verses secreted forms of Sema7a on synapse formation and as a chemoattractant for axon arborization respectively.

Comments: We have rephrased our statement and argue in lines 428-430 that our experiments “suggest a potential mechanism for hair cell innervation in which a local Sema7Asec diffusive cue likely consolidates the sensory arbors at the hair cell cluster and the membrane-anchored Sema7A-GPI molecule guides microcircuit topology and synapse assembly.”

The manuscript itself would benefit from the inclusion of details in the text to help the reader interpret the figures, tools, data, and analysis.

Comments: We have made significant revisions to the text and figures to improve clarity and consistency of the manuscript.

Reviewer #2 (Public Review):

In this work, Dasgupta et al. investigates the role of Sema7a in the formation of peripheral sensory circuit in the lateral line system of zebrafish. They show that Sema7a protein is present during neuromast maturation and localized, in part, to the base of hair cells (HCs). This would be consistent with pre-synaptic Sema7a mediating formation and/or stabilization of the synapse. They use sema7a loss-of-function strain to show that lateral line sensory terminals display abnormal arborization. They provide highly quantitative analysis of the lateral line terminal arborization to show that a number of specific topological parameters are affected in mutants. Next, they ectopically express a secreted form of Sema7a to show that lateral line terminals can be ectopically attracted to the source. Finally, they also demonstrate that the synaptic assembly is impaired in the sema7a mutant. Overall, the data are of high quality and properly controlled. The availability of Sema7a antibody is a big plus, as it allows to address the endogenous protein localization as well to show the signal absence in the sema7a mutant. The quantification of the arbor topology should be useful to people in the field who are looking at the lateral line as well as other axonal terminals. I think some results are overinterpreted though. The authors state: "Our findings demonstrate that Sema7A functions both as a juxtracrine and as a secreted cue to pattern neural circuitry during sensory organ development." However, they have not actually demonstrated which isoform functions in HCs (also see comments below).

Comments: Thank you for making this point. To investigate the presence of both sema7a transcripts in the hair cells of the lateral-line neuromasts, we used the Tg(myo6b:actb1EGFP) transgenic fish to capture the labeled hair cells by fluorescence-activated cell sorting (FACS) and isolated total RNA. Using transcript specific DNA oligonucleotide primers, we have identified the presence of both sema7a transcript variants in the hair cell of the neuromast. Even though we have not developed transcript specific knockout animals, we speculate that the presence of both transcript variants in the hair cell implies that they function in distinct fashion. We have changed our interpretation in lines 32-34 to “Our findings propose that Sema7A likely functions both as a juxtracrine and as a secreted cue to pattern neural circuitry during sensory organ development.”

In future we will utilize the CRISPR/Cas9 technique to target the unique C-terminal domain of the GPI-anchored sema7a transcript variant. We believe that this will only perturb the formation of the full-length Sema7A protein and help us determine the role of the membrane-bound Sema7AGPI molecule as well as the Sema7Asec in sensory arborization and synaptic assembly.

In addition, they have to be careful in interpreting their topology analysis, as they cannot separate individual axons. Thus, such analysis can generate artifacts. They can perform additional experiments to address these issues or adjust their interpretations.

Comments: Thank you for this insightful comment. In a previous eLife publication from our laboratory, we utilized the serial blockface scanning electron micrograph (SBFSEM) technique to characterize the connectome of the neuromast microcircuit where patterns of innervation of all the individual axons can be delineated in five-days-old larvae (reference 8). However, the collective behavior of all the sensory axons that build the innervation network remained enigmatic, especially in a living animal during development. In this paper we addressed how the sensory-axon collective behaves around the clustered hair cells and build the innervation network in living animals during diverse developmental stages. Our analyses have not only identified how the axons associates with the hair cell cluster as the organ matures, but also discovered distinct topological features in the arbor network that emerges during organ maturation, which may influence assembly of postsynaptic aggregates (lines 384-403, Figure 6G-I). We believe that our quantitative approach to capture collective axonal behaviors and their topological attributes during circuit formation have highlighted the importance of understanding network assembly during sensory organ development.

Reviewer #3 (Public Review):

Summary:

This study demonstrates that the axon guidance molecule Sema7a patterns the innervation of hair cells in the neuromasts of the zebrafish lateral line, as revealed by quantifying gain- and loss-of function effects on the three-dimensional topology of sensory axon arbors over developmental time. Alternative splicing can produce either a diffusible or membrane-bound form of Sema7a, which is increasingly localized to the basolateral pole of hair cells as they develop (Figure 1). In sema7a mutant zebrafish, sensory axon arbors still grow to the neuromast, but they do not form the same arborization patterns as in controls, with many arbors overextending, curving less, and forming fewer loops even as they lengthen (Figure 2,3). These phenotypes only become significant later in development, indicating that Sema7a functions to pattern local microcircuitry, not the gross wiring pattern. Further, upon ectopic expression of the diffusible form of Sema7a, sensory axons grow towards the Sema7a source (Figure 4). The data also show changes in the synapses that form when mutant terminals contact hair cells, evidenced by significantly smaller pre- and post-synaptic punctae (Figure 5). Finally, by replotting single cell RNA-sequencing data (Figure 6), the authors show that several other potential cues are also produced by hair cells and might explain why the sema7a phenotype does not reflect a change in growth towards the neuromast. In summary, the data strongly indicate that Sema7a plays a role in shaping connectivity within the neuromast.

Strengths:

The main strength of this study is the sophisticated analysis that was used to demonstrate fine-level effects on connectivity. Rather than asking "did the axon reach its target?", the authors asked "how does the axon behave within the target?". This type of deep analysis is much more powerful than what is typical for the field and should be done more often. The breadth of analysis is also impressive, in that axon arborization patterns and synaptic connectivity were examined at 3 stages of development and in three-dimensions.

Weaknesses:

The main weakness is that the data do not cleanly distinguish between activities for the secreted and membrane-bound forms of Sema7a, which the authors speculate may influence axon growth and synapse formation respectively. The authors do not overstate the claims, but it would have been nice to see some additional experimentation along these lines, such as the effects of overexpressing the membrane-bound form,

Comments: We have accepted this useful suggestion. In lines 333-346 and in Figure 5 we have demonstrated the impact of overexpressing the membrane-bound transcript variant on arborization pattern of the sensory axons.

Some analysis of the distance over which the "diffusible" form of Sema7a might act (many secreted ligands are not in fact all that diffusible), or

Comments: We have reported this in lines 311-317 and in Figure 4F,G.

Some live-imaging of axons before they reach the target (predicted to be the same in control and mutants) and then within the target (predicted to be different).

Comments: We have accepted this useful suggestion. We demonstrate the dynamics of the sensory arbors that are attracted to an ectopic Sema7Asec source in lines 325-332, Figure 4I,J; Figure 4—figure supplement 2A, and Videos 13-16.

Clearly, although the gain-of-function studies show that Sema7a can act at a distance, other cues are sufficient. Although the lack of a phenotype could be due to compensation, it is also possible that Sema7a does not actually act in a diffusible manner within its natural context. Overall, the data support the authors' carefully worded conclusions. While certain ideas are put forward as possibilities, the authors recognize that more work is needed. The main shortcoming is that the study does not actually distinguish between the effects of the two forms of Sema7a, which are predicted but not actually shown to be either diffusible or membrane linked (the membrane linkage can be cleaved). Although the study starts by presenting the splice forms, there is no description of when and where each splice form is transcribed.

Comments: We have utilized the HCR™ RNA-FISH Technology to generate transcript specific probes. To generate transcript-specific HCR probes to distinctly detect the sema7aGPI (NM_001328508) and the sema7asec (NM_001114885) transcripts, Molecular Instruments could design only 11 probes against the sema7aGPI transcript and only one probe against the sema7asec transcript (personal correspondence with Mike Liu, PhD, Head of Operations and Product Development Lead Molecular Instruments, Inc.). The HCR probe against the sema7aGPI transcript showed a very faint signal. Unfortunately, the HCR probe against the sema7asec transcript failed to detect the presence of any transcript. For robust detection of transcripts, the protocol demands a minimum of 20 probes. We believe that the very low number of probes against our transcripts is the primary reason for the absence of a signal.

We therefore utilized fluorescence-activated cell sorting (FACS) to capture the labeled hair cells and isolated total RNA to perform RT-PCR using transcript specific DNA oligonucleotide primers. We identified the presence of both the secreted and the membrane-bound transcripts at four-days-old neuromasts (lines 80-84, Figure 1B-D).

Additionally, since the mutants are predicted to disrupt both forms, it is a bit difficult to disentangle the synaptic phenotype from the earlier changes in circuit topology - perhaps the change at the level of the synapse is secondary to the change in topology.

Comments: Thank you for the insightful suggestion. We have analyzed the relationship between the sensory arbor network topology and the distribution of postsynaptic structures (lines 384-403, Figure 6G-I). We identified that the distribution of the postsynaptic aggregates is closely associated with the topological attributes of the sensory circuit. We further clarify the potential origin of disrupted synaptic assemblies in sema7a-/- mutants in lines 380-382 and lines 417-420.

Further, the authors do not provide any data supporting the idea that the membrane bound form of Sema7a acts only locally. Without these kinds of data, the authors are unable to attribute activities to either form.

Comments: We have accepted this useful suggestion and have prepared the Figure 5 with the necessary details.

The main impact on the field will be the nature of the analysis. The field of axon guidance benefits from this kind of robust quantification of growing axon trajectories, versus their ability to actually reach a target. This study highlights the value of more careful analysis and as a result, makes the point that circuit assembly is not just a matter of painting out paths using chemoattractants and repellants, but is also about how axons respond to local cues. The study also points to the likely importance of alternative splice forms and to the complex functions that can be achieved using different forms of the same ligand.

Reviewer #4 (Public Review):

Summary:

The work by Dasgupta et al identifies Sema7a as a novel guidance molecule in hair cell sensory systems. The authors use the both genetic and imaging power of the zebrafish lateralline system for their research. Based on expression data and immunohistochemistry experiments, the authors demonstrate that Sema7a is present in lateral line hair cells. The authors then examine a sema7a mutant. In this mutant, Sema7a proteins levels are nearly eliminated. Importantly, the authors show that when Sema7a is absent, afferent terminals show aberrant projections and fewer contacts with hair cells. Lastly the authors show that ectopic expression of the secreted form of Sema7a is sufficient to recruit aberrant terminals to non-hair cell targets. The sema7a innervation defects are well quantified. Overall, the paper is extremely well written and easy to follow.

Strengths:

(1) The axon guidance phenotypes in sema7a mutants are novel, striking and thoroughly quantified.

(2) By combining both loss of function sema7a mutants and ectopic expression of the secreted form of Sema7a the authors demonstrate the Sema7a is both necessary and sufficient to guide sensory axons

Weaknesses:

(1) Control. There should be an uninjected heatshock control to ensure that heatshock itself does not cause sensory afferents to form aberrant arbors. This control would help support the hypothesis that exogenously expressed Sema7a (via a heatshock driven promoter) is sufficient to attract afferent arbors.

Comments: Thank you for the suggestion. We have added the uninjected heatshock control experiment in Figure 5 and described experimental details in the text, lines 343-345.

(2) Synapse labeling. The numbers obtained for postsynaptic labeling in controls do not match up with the published literature - they are quite low. Although there are clear differences in postsynaptic counts between sema7a mutants and controls, it is worrying that the numbers are so low in controls. In addition, the authors do not stain for complete synapses (pre- and post-synapses together). This staining is critical to understand how Sema7a impacts synapse formation.

Comments: Thank you for raising this issue. We believe the low average numbers of the postsynaptic punctae in control neuromasts arise from lack of formation of postsynaptic aggregates beneath the immature hair cells, which are abundant in early stages of neuromast maturation. We have performed exhaustive analysis on the formation of pre- and postsynaptic structures and have identified how their distribution changes along neuromast development in control larvae. We have further analyzed how such distribution is perturbed in the sema7a-/- mutants. We do not think analyzing the complete synapse structure will add much to our understanding of how Sema7A influence synapse formation and maintenance.

(3) Hair cell counts. The authors need to provide quantification of hair cell counts per neuromast in mutant and control animals. If the counts are different, certain quantification may need to be normalized.

Comments: We have added the raw data with the hair cell counts in both control and sema7a-/- mutants across developmental stages. The homozygous sema7a-/- mutants have slightly less hair cells and we have normalized all our topological analyses by the corresponding hair cell numbers for each neuromast in each experiment (lines 669-675).

(4) Developmental delay. It is possible that loss of Sema7a simply delays development. The latest stage examined was 4 dpf, an age that is not quite mature in control animals. The authors could look at a later age, such as 6 dpf to see if the phenotypes persist or recover.

Comments: The homozygous sema7a-/- mutants are unviable and die at 6 dpf. We therefore restricted our analysis till 4 dpf. The association of the sensory arbors with the clustered hair cells gradually decreases as the neuromasts mature from 2 dpf to 4dpf in the sema7a-/- mutants (lines 174-176, Figure 2I). Moreover, in the sema7a-/- mutants the sensory axons throw long projections that keep getting farther away from the clustered hair cells as the neuromast matures from 2 dpf to 4 dpf (lines 166-168, Figure 2H; Figure 2—figure supplement 1K,L). These observations suggest that if the phenotypes in the sema7a-/- mutants were due to developmental delays, then we should have seen a recovery of disrupted arborization patterns over time. But instead, we observe a further deterioration of the arborization patterns and other architectural assemblies. These findings confirm that the observed phenotypes in the sema7a-/- mutants are not due to delayed development of the larvae, but a specific outcome for the loss of Sema7A protein.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major concerns:

Issue 1: One of the most interesting conclusions in this manuscript is the function of the GPIanchored vs. secreted form of Sema7a in axon structure and synapse formation. In lines 357360 of the discussion (for example) the authors state that they have shown that the GPIanchored form of Sema7a is responsible for contact-mediated synapse formation while the secreted form functions as a chemoattractant for axon arbor structure. "We have discovered dual modes of Sema7A function in vivo: the chemoattractive diffusible form is sufficient to guide the sensory arbors toward their target, whereas the membrane-attached form likely participates in sculpting accurate neural circuitry to facilitate contact-mediated formation and maintenance of synapses." However, the data do not support this conclusion. Specifically, no analysis is done showing unique expression of either isoform in hair cells and no functional analysis is done to conclusively determine which isoform is important for either phenotype.

Comments: We have shown that both sema7a transcripts are expressed in the hair cells of four-day-old neuromasts (lines 78-84, Figure 1C,D). Ectopic expression of the sema7asec transcript variant robustly attracts the lateral-line sensory arbors toward itself, whereas ectopic expression of the sema7aGPI variant fails to impart sensory guidance from a distance, suggesting that the membrane-bound form likely participates in contact-mediated neural guidance. These experiments decisively show, for the first time in zebrafish, the dual modes of Sema7A function in vivo. However, we agree that the sema7aGPI transcript-specific knockout animal would be essential to conclusively prove that the membrane-attached form is primarily involved in forming accurate neural circuitry and contact-mediated formation and maintenance of synapses. Hence, we have very carefully stated in lines 427-428 that “the membrane-attached form likely participates in sculpting accurate neural circuitry to facilitate contact-mediated formation and maintenance of synapses”. We will follow up on this suggestion in our upcoming manuscript that will incorporate transcript-specific genetic ablations.

Though the authors present RT-PCR analysis of sema7a isoforms, it is not interpretable. The second reverse primer will also recognize the full-length transcript (from what I can gather) so it does not simply show the presence of the secreted form. Is there a unique 3'UTR for the short transcript that can be used? Additionally, for the GPI-anchored version can you use a forward primer that is not present in the short isoform? This would shed some light on the respective levels of both transcripts.

Comments: The C-termini of the two transcript variants are distinct and we have designed distinct primers that will selectively bind to each transcript (lines 503-511). Since, we have not performed quantitative polymerase chain reaction (qPCR), relative levels of each transcript are hard to determine.

Alternatively, and perhaps of more use, in situ hybridization using unique probes for each isoform would allow you to determine which are actually present in hair cells.

Comments: We have tried this approach and explained the point earlier (refer to lines 203212 of this response letter).

To decisively state that these isoforms have unique functions in axon terminal structure and synapse formation, other experiments are also essential. For example, RNA-mediated rescue analyses using both isoforms would tell you which can rescue the axonal structure and synapse size/number phenotypes. Overexpression of the GPI-anchored form, like the secreted form in Figure 4, would allow you to determine if only the secreted form can cause abnormal axon extension phenotypes. Expression of both forms in hair cells (using a myo6b promotor for example) would allow assessment of their role in presynapse formation.

Comments: We have ectopically expressed the sema7aGPI transcript variant near the sensory arbor network and observed that Sema7A-GPI fails to impart sensory axon guidance from a distance.

Thank you for suggesting the rescue experiments. We are in the process of generating CRISPR/Cas9-mediated transcript-specific knockout animals. We are currently preparing another manuscript that incorporates the above-mentioned rescue experiments to dissect the role of each transcript in regulating arbor topology and synapse formation.

For the overexpression experiments, expression of mKate alone (with and without heat shock) is also a critical control to include.

Comments: We have incorporated two control experiments: (1) larvae injected with hsp70:sema7asec-mKate2 plasmid that were not heat shocked and (2) Uninjected larvae that were heatshocked. We think these two controls are sufficient to demonstrate that the abnormal arborization patterns are not artifacts generated due to plasmid injection and heatshocking.

Issue 2: A second concern is the lack of data showing support cell and hair cell formation and function is unaffected. Analysis of support and hair cell number with loss of Sema7a as well as simple analyses of mechanotransduction (FM4-64) would help alleviate concerns that phenotypes are due to disrupted neuromast formation and basic hair cell function rather than a specific role for Sema7a in this process.

Comments: We have measured the hair cell numbers in both control and sema7a-/- mutants across developmental stages. We have added this to our submitted raw data.

We have utilized the styryl fluorophore FM4-64 to test the mechanotransduction function of the hair cells in sema7a-/- mutants. We have detailed our finding in lines 137141 and in Figure 2—figure supplement 1C,D.

Expression analysis of Sema7a receptors would also help strengthen the argument for a specific effect on lateral line afferent axons.

Comments: Thank you for this suggestion. Currently, we do not possess an RNA transcriptome dataset for the lateral line ganglion. This deficit limits a systematic screen for lateral-line sensory neuronal gene expressions either through antibody stains or via HCRmediated in situ techniques. In future we plan to develop an RNA transcriptome for the lateral-line ganglion and identify potential binding partners for Sema7A.

Issue 3: The manuscript could also be improved to include more detail in some areas and less in others. In general, each section has a fairly long lead up but lacks important experimental details that would help the reader interpret the data. For example:

Figure 1: What is the label for the lateral line axons? Is it a specific transgenic? The legend states that 3 asterisks indicate p<0.0001. What about the other asterisk combinations?

Comments: We have clarified these issues in lines 118-121 and in lines 906-907.

Figure 2: For the network analysis, are the traces for all axons that branch to innervate the neuromast?

Comments: Yes, we have traced the entire arbor containing all the axons that branched from the lateral line nerve and extended toward the clustered hair cells. The three-dimensional traces depict a skeletonized representation of the arbor network.

Can the tracing method distinguish individual axons?

Comments: No, our goal is to understand how the axon-collective behave around the clustered hair cells during development.

How do you know where an end is versus continued looping?

Comments: We have categorically defined the topological attributes in lines 187-191 and in Figure 3A.

Also, are all neuromasts similarly affected or is there a divergence based on which organ you are imaging? What neuromast was imaged in this and other figures?

Comments: Yes, all the neuromasts in the trunk and tail regions were affected similarly by the sema7a mutation. We did not observe any region-specific phenotypic outcome. We consistently imaged the trunk neuromasts, particularly the second, third, and fourth neuromasts.

Discussion: The short discussion failed to put these findings into context or to discuss how this unique topological arrangement of axon terminals impacts function.

Comments: We have added a new segment, lines 432-448, in the discussion section which mentions the potential role of the topological features in arranging the distribution pattern of the postsynaptic densities and thereby potentially influencing the network’s ability to gather sensory inputs through properly placed postsynaptic aggregates.

Can you speculate on how the looping structure may alter number of synaptic contacts per axon for instance? For this, it would be useful to know if normally the synapses form on loops versus bare terminals.

Comments: Thank you for this insightful suggestion. We have performed detailed analysis, as mentioned in lines 384-397, to characterize the distribution of the postsynaptic densities between the two topological attributes.

Does this looping facilitate single axons contacting more hair cells of the same polarity? Would that be beneficial?

Comments: Looping behaviors indeed facilitate the contact between the axons and the hair cells. As we have observed, the primary topological attribute that the sensory arbor network underneath the clustered hair cells adopts is a loop. The bare terminals are predominantly projected transverse to the clustered hair cells and lack contact with them. Whether a single axon, being part of a loop, preferentially contacts hair cells of same polarity is yet to be determined. We can address this question by mosaic labeling a single axon in the arbor network and determine its association with the hair cells. We intend to do these experiments in our upcoming manuscript.

Minor concerns:

(1) For the stacked charts quantifying topological features, I found interpreting them challenging. Is it possible to put these into overlapping histograms or line graphs to better compare wild type to mutant directly?

Comments: Thank you for your suggestion. We tried several ways to represent our data and found that the stacked charts optimally signify our analysis and depict the characteristic phenological differences between the control and the sema7a-/- mutants.

(2) There are numerous strong statements throughout not directly supported by the data, e.g. lines 110-113; 206-208; 357-360 and others. These should be tempered.

Comments: For lines 110-113, we have updated this section with new experiments and the new segment is represented in lines 115-126.

For lines 206-208, we have updated the statement to “This result suggests that the stereotypical circuit topology observed in the mature organ may emerge through transition of individual arbors from forming bare terminals to forming closed loops encircling topological holes” in lines 225-227.

Reviewer #2 (Recommendations For The Authors):

The authors should be careful about making any assumptions which form of sema7a is active in NMs. Their RT-PCR demonstrates presence of both isoforms in a whole animal; however, whether they are similarly present in HCs is not investigated here.

Comments: We have addressed this concern and have updated the manuscript with new experiments, detailed in lines 78-84.

Also, there is an issue of translation and trafficking to the membrane with subsequent secretion. An important experiment that would address this question is expressing two sema7a isoforms in mutant HCs and asking whether this can suppress the mutant phenotype.

Comments: Thank you for suggesting the rescue experiments. We are in the process of generating CRISPR/Cas9-mediated transcript-specific knockout animals. We are currently preparing another manuscript that incorporates the above-mentioned rescue experiments to dissect the role of each transcript in regulating arbor topology and synapse formation.

Presumably, sema7a is trafficked to the membrane during HC maturation. This is consistent with the authors' observation that sema7a localization is changing as NM mature. However, actin-sema7a co-labeling does not actually show whether sema7a is on the membrane. Labeling HCs with a membrane marker (transgene) would be much more convincing. Alternatively, can the authors show sema7a localization actually correlates with the presence of sensory axon terminals? They already have immunos that label both. Thus, this should be pretty straightforward.

Comments: Thank you for these suggestions. We have addressed these issues in lines 112114, and in lines 119-126.

Figure 2 should have a control panel, so the reduced sema7a staining can be compared to the control side-by-side.

Comments: We have depicted Sema7A staining in control neuromasts in multiple images, including Figure 1E, Figure 1H, and in Figure 2—figure supplement 1B. We have kept the control panel in the supplementary figure due to space restrictions in Figure 2.

Arborization topology: While I appreciate the very careful characterization of the topology for wild-type and mutant NMs, I think it would be much more informative to mark individual axons and then analyze their topology. The main reason is that the authors cannot really distinguish whether some aspects of topology they describe are really due to the densely packed overlapping terminals of multiple axons or these are really characteristic, higher order organization of individual axons. Because of this, they cannot be certain what is really happening with sema7a mutant terminals. Related to the point above. While it is clear that the overall topology is abnormal in the mutant, the authors should be careful in concluding that sema7a regulates specific aspects of it. The overall structure is probably highly interconnected perturbing one parameter would likely affect all the others.

Comments: Thank you for this comment. In a previous eLife publication from our laboratory, we utilized the serial blockface scanning electron micrograph (SBFSEM) technique to characterize the connectome of the neuromast microcircuit where patterns of innervation of all the individual axons can be delineated in five-days-old larvae (reference number 8). However, the collective behavior of all the sensory axons that build the innervation network remained enigmatic, especially in a living animal during development. In this paper we addressed how the sensory axon-collective behave around the clustered hair cells and build the innervation network in living animals during diverse developmental stages. Our analyses have not only identified how the axon-collective associates itself with the hair cell cluster as the organ matures, but also discovered distinct topological features in the arbor network that emerges during organ maturation, which may influence assembly of postsynaptic aggregates (lines 384-403, Figure 6G-I). We believe that our quantitative approach to capture collective axonal behaviors and their topological attributes during circuit formation have highlighted the importance of understanding network assembly during sensory organ development.

Experiments with the secreted sema7a isoform would be much more informative if they were compared/contrasted to the GPI anchored isoform.

Comments: We added a new section, lines 338-351, and a new Figure 5 to address this issue.

The phenotype of ectopic projections in sema7a overexpression experiments is pretty dramatic, especially given the fact that these were performed in wild-type animals. Does this mean that the phenotype would be even more dramatic in sema7a mutants, as they have more bare axon terminals according to the authors' analysis. Have the authors attempted this type of experiments?

Comments: That is an interesting suggestion. We have not tested that yet. Our guess is that in the sema7a-/- mutants, the abundant bare terminals will be far more sensitive to an ectopic source of Sema7A. But even in the sema7a-/- mutants, other chemotropic cues are still functional, which may impart certain restrictions on how many bare terminals are allowed to leave the neuromast region.

Reviewer #3 (Recommendations For The Authors):

(1) No raw data are shown, such that it is difficult to assess variability across animals or within animals, just the overall trends within the whole dataset. Raw data need to be shown for every measurement, at least in supplemental figures. It would also be useful to reliably show control next to mutant in the same plot, as it is a bit hard to compare across panels, which occurs in several figures.

Comments: We have uploaded all the raw data related to each experiment.

(2) Given the focus on the two possible forms of Sema7a, the authors should use HCR or another form of reliable in situ hybridization to show the spatiotemporal pattern of expression of each isoform.

Comments: We have utilized the HCR™ RNA-FISH Technology to generate transcript specific probes. To generate transcript-specific HCR probes to distinctly detect the sema7aGPI (NM_001328508) and the sema7asec (NM_001114885) transcripts, Molecular Instruments could design only 11 probes against the sema7aGPI transcript and only one probe against the sema7asec transcript (personal correspondence with Mike Liu, PhD, Head of Operations and Product Development Lead Molecular Instruments, Inc.). The HCR probe against the sema7aGPI transcript showed a very faint signal. Unfortunately, the HCR probe against the sema7asec transcript failed to detect the presence of any transcript. For robust detection of transcripts, the protocol demands a minimum of 20 probes. We believe that the very low number of probes against our transcripts is the primary reason for the lack of a signal.

(3) The authors should explain the criteria used to select the 22 embryos used to analyze the effects of expressing diffusible Sema7a.

Comments: We have explained this in lines 291-292. We identified 22 mosaic sema7asecmKate2 integration events, in which a single mosaic ectopic integration had occurred near the network of sensory arbors, from a total of almost 100 integrations. We rejected events where the sema7asec-mKate2 integration occurred either farther away from the sensory arbor network or had happened in multiple neighboring cells.

(4) Although arbors were imaged in live embryos, time is never presented as a variable, so I cannot tell whether axon topology was changing as the images were collected. This needs to be clarified.

Comments: We imaged the trunk neuromasts of both control and sema7a-/- mutant live zebrsfish larvae at 2, 3, and 4 dpf. We imaged the control and the sema7a-/- mutants of each developmental stage in parallel, within a span of two hours, and repeated these experiments multiple times to gather almost a hundred larvae from each genotype. Even though the sensory arbor network is dynamic, we believe imaging both the genotypes in parallel and within a span of two hours, and averaging almost a hundred larvae from each genotype minimize the temporal variability observed in the arbor architecture.

(5) Ideally, the authors should use CRISPR/cas-9 to create a mutation in the C-terminus that would prevent production of the GPI-anchored form and not of the diffusible form. I understand if this is too much work to do in a short time, and would be satisfied with another experiment that could distinguish roles for at least one isoform more clearly. For instance, it would be interesting to see an analysis of how far an axon can be from a source to detect diffusible Sema7a (live imaging would be ideal for this) and then to show that the effect is different when the membrane bound form is expressed.

Comments: Thank you for this comment. We are currently working in generating transcript specific knockout animals.

We have added live timelapse video microscopy data in lines 330-337, Figure 4H-J, Figure 4—figure supplement 2, Video15,16.

We have added a new segment analyzing the membrane-bound transcript variant in lines 338-351.

Reviewer #4 (Recommendations For The Authors):

Feedback to authors

Overall, this is a very important and novel study. Currently the manuscript does need revision.

Major concerns:

(1) Controls. For the ectoptic expression of Sema7a, injection of a construct expressing Sema7a under a heatshock promoter is used to drive ectopic expression. No heatshock (injected) animal are used as a control. In many systems heatshock can impact neuron morphology. And heatshock proteins are required for normal neurite and synapse formation. Please examine sensory axons in uninjected wildtype animals with heatshock.

Comments: We have added this control experiment in a new segment, explained in detail in lines 348-350 and Figure 5.

(2) Synapse staining - regarding Figure 5 and related supplement

Understanding whether guidance defects ultimately impact synapse formation is an important aspect of this paper. Therefore, is necessary to have accurate measurements of the number of complete synapses, and the overall numbers of pre- and postsynaptic components. Currently the data plotted in Figure 5 is extensive, but the way the data is laid out, the relevant comparisons are challenging to make. Perhaps include this quantification in the supplement, and move the data from the supplement to the main figure? The quantifications in the supplement are easier to follow and easier to compare between genotypes.

Comments: We have performed exhaustive analysis on the formation of pre- and postsynaptic structures and have identified how their distribution changes along neuromast development in control larvae. We have further analyzed how such distribution is perturbed in the sema7a-/- mutants. We believe that showing only the average numbers will not reveal the changes in the distribution of the synaptic structures during development and across genotypes.

Looking at the data itself, there seems to be some discrepancies with the synaptic counts compared to published work. While the CTBP numbers seem in order, the Maguk numbers do not. In both mutant and control there are many hair cells without any Maguk puncta/aggregates-leading to 0.75-1 postsynapses per hair cell (Figure 5 supplement H-I). Typically, the numbers should be more comparable to what was obtained for CTBP, 3-4 puncta per cells (Figure 5 supplement B-C), especially by 3-4 dpf. 3-4 CTPB or Maguk puncta per cell is based on previously published immunostaining and EM work.

The Maguk immunostaining, especially at early stages (2-3 dpf) is challenging. To compound a challenging immunostain, around 2019 Neuromab began to outsource the purification of their Maguk antibody. After this outsourcing our lab was no longer able to get reliable label with the Maguk antibody from Neuromab.

Millipore sells the same monoclonal antibody and it works well: https://www.emdmillipore.com/US/en/product/Anti-pan-MAGUK-Antibody-clone-K2886,MM_NF-MABN72

I would recommend this source.

Comments: Thank you for suggesting the new MAGUK antibody. We have utilized this new MAGUK antibody from Millipore and added a new segment in lines 389-408. In future publication we will utilize this antibody to capture the postsynaptic densities in the sensory arbors.

The discrepancies in the postsynaptic punctae number in our control larvae may arise due to the reliability of the Neuromab MAGUK antibody. We have utilized this same antibody to stain the sema7a-/- mutants and have observed a significant decrease in MAGUK punctae number and area. On grounds of keeping parity between the control and the sema7a-/- mutants, we have decided to keep our experimental results in the manuscript.

In addition to a more accurate Maguk label, a combined pre- and post-synaptic label is essential to understand whether synapses pair properly in the sema7a mutants. This can be accomplished using subtype specific antibodies using goat anti-mouse IgG1/Maguk and goat anti-mouse IgG2a/CTBP secondaries.

Comments: Thank you for suggesting this. We are preparing another manuscript in which we will utilize this technique along with other suggestions to tease apart the role of distinct transcript variants in regulating neural guidance and synapse formation.

(3) Does sema7a lesion impact the number of hair cells per neuromast? If hair cell numbers are reduced several of the quantifications could be impacted.

Comments: We have added the raw data with the hair cell counts in both control and sema7a-/- mutants across developmental stages. The homozygous sema7a-/- mutants have slightly less hair cells and we have normalized all our topological analyses by the corresponding hair cell numbers for each neuromast in each experiment (lines 669-675).

(4) Could innervation just be developmentally delayed in sema7a mutants? At 4 dpf the sensory system is just starting to come online and could still be in the process of refinement. Did you look at slightly older ages, after the sensory system is functional behaviorally, for example, 6 dpf? Do the cores phenotypes (synapse defects and excess arbors) persist at 6 dpf in the sema7a mutants?

Comments: The homozygous sema7a-/- mutants are unviable and start to die at 6 dpf. We therefore restricted our analysis until 4 dpf. The association of the sensory arbors with the clustered hair cells gradually decreases as the neuromasts mature from 2 dpf to 4dpf in the sema7a-/- mutants (lines 174-176, Figure 2I). Moreover, in the sema7a-/- mutants the sensory axons throw long projections that keep getting farther away from the clustered hair cells as the neuromast matures from 2 dpf to 4 dpf (lines 166-168, Figure 2H; Figure 2—figure supplement 1K,L). These observations suggests that if the phenotypes in the sema7a-/- mutants were due to developmental delays, then we should have seen a recovery of disrupted arborization patterns over time. But instead, we observe a further deterioration of the arborization patterns and other architectural assemblies. These findings confirm that the observed phenotypes in the sema7a-/- mutants are not due to delayed development of the larvae, but a specific outcome for the loss of Sema7A protein.

Minor comments to address:

Results

Page 4 lines 89-91. For the readers, explain why you examined levels in Sema7a in rostral and caudal hair cells. Also, this sentence is, in general, a little bit misleading-initially reading that there is no difference in Sema7a at 1.5-4 dpf.

Comments: In lines 44-48, we explain that the hair cells in the neuromast contain mechanoreceptive hair cells of opposing polarities that help them detect water currents from opposing directions. In lines 93-106, we tested whether the Sema7A level varies between the two polarities. We observed that the Sema7A level is similar between the two polarities of hair cells, but the average Sema7A intensity increases significantly over the developmental period of 2 dpf to 4 dpf in both rostrally and caudally polarized hair cells.

Page 10-11 Lines 263-270. What was the frequency of these 2 outcomes- out of the 22 cases with ectopic expression?

Comments: We have explained this in lines 291-292. We identified 22 mosaic sema7asecmKate2 integration events, in which a single mosaic ectopic integration had occurred near the network of sensory arbors, from a total of almost 100 integrations. We rejected events where the sema7asec-mKate2 integration occurred either farther away from the sensory arbor network or had happened in multiple neighboring cells.

Discussion

Page 14 Lines 359-360. There is not enough evidence provided in this work to suggest that the membrane attached form of Sema7a is playing a role. Both the secreted and membrane form are gone in the sema7a mutants. If the membrane attached form was specifically lesioned, and resulted in a phenotype, then there would be sufficient evidence. Currently there is strong evidence for a distinct role for the secreted form. Although the authors qualify the outlined statement with the word 'likely', stating this possibility in the discussion take-home is misleading.

Comments: In future we will utilize the CRISPR/Cas9 technique to target the unique Cterminal domain of the GPI-anchored sema7a transcript variant. We believe that this will only perturb the formation of the full-length Sema7A protein and help us differentiate between the roles of the membrane-bound Sema7AGPI molecule and the secreted Sema7Asec in sensory arborization and synaptic assembly.

It might be interesting in either the intro or discussion to reference the role Sema3F in axon guidance in the mouse auditory epithelium. https://elifesciences.org/articles/07830

Comments: We have added this reference in lines 61-64.

Figures

Please indicate on one of your Figures where the mutation is (roughly) in the sema7a mutant (in addition to stating it in the results).

Comments: We have added this information in Figure 2—figure supplement 1A.

Either state or indicate in a Figure where the epitope used to make the Sema7a antibody-to show that the antibody is predicted to recognize both isoforms.

Comments: We have stated the details of the epitope in lines 528-529.

Figure 2-S1 what is the scale in panel A, is it different between mutant and wildtype?

Comments: We have updated the images. New images are depicted in Figure 2—figure supplement 1A.

Methods

What were the methods used to quantify synapse number and area?

Comments: We have added a new section in lines 702-708 to explain the measurement techniques.

Author Response

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

Reply to comments:

(1) It was not clear why the phylogenetic analysis included non-validated GPCRs that clustered with the validated peptidergic receptors. Would restricting the phylogenetic analyses only to confirmed peptidergic GPCRs alter the topology of the tree and subsequent conclusions of independent expansion?

Thank you for this comment. In general, phylogenetic analyses become more robust if a larger diversity and fuller complement of sequences are included. With very sparse sampling, sequences that are homologous but not orthologous may be misleadingly grouped together, because intermediate sequences have been left out. For tree building, we thus did not want to focus only on experimentally validated receptors but also on all receptors that are phylogenetically related to the validated receptors. Only this approach can ensure a comprehensive exploration of the relationship of peptidergic receptors. The broader phylogenetic approach was also essential to identify orthologs to the experimentally validated Nematostella receptors across other cnidarian species.

(2) Clearly, other neuropeptide signaling systems in cnidarians remain to be discovered but this paper represents a huge step forward.

We appreciate this assessment of the paper. We agree that many systems remain to be discovered. Our paper will also help with the identification of further receptors both in Nematostella as well as other cnidarian species. Please note that we have made specific receptor-ligand predictions for several cnidarian species based on our phylogenetic analysis. Our phylogenies could also help prioritize the study of the remaining orphan Nematostella GPCRs.

(3) There are limitations in what can be interpreted from single cell transcriptomic data but the data nevertheless provide the foundations for future studies involving i). detailed anatomical analysis of neuropeptide and neuropeptide receptor expression in N. vectensis using mRNA in situ hybridization and/or immunohistochemical methods and ii). functional analysis of the physiological/behavioral roles of neuropeptide signaling systems in N. vectensis

We fully agree with this comment. The analysis of the available single-cell sequence resources clearly represents only the first step of anatomical and functional analyses. Our aim was to place the identified peptide-receptor interactions into a whole-organism context with cell type resolution, to highlight the potential complexity of peptidergic signaling in this organism and to facilitate the exploration and conceptualisation of our biochemical screen.

Comments to authors

(1) In future, when preparing manuscripts, please use page and line numbers; it makes the task below for reviewers much easier!

We appreciate the suggestion and will do this for future manuscripts.

(2) In the abstract the term "extensively wired" is used. In the context of neuropeptide mediated volume transmission this may not be an appropriate term to use because use of the word "wired" is likely to be associated with point-to-point type classical synaptic transmission; "extensively connected" would be better.

Thank you for this comment. We have changed the text in the abstract to “extensively connected”.

(3) Introduction: Please change "seven-transmembrane proteins and show a slower evolutionary rate than proneuropeptide..." to "seven-transmembrane proteins that show a slower evolutionary rate than proneuropeptide..."

Changed.

(4) Under the section "Creation of a Nematostella neuropeptide library, what is meant by "our regular expressions"? This needs to be rephrased to make it clearer what is meant.

We have now rephrased the relevant sentence to make our approach clearer.

“This predicted secretome was filtered with regular expressions to detect sequences with the repetitive dibasic cleavage sites (K and R in any combination) and amidation sites, using a custom script from a previous publication (Thiel et al., 2021).”

and later:

“Based on the MS data, we included the additional, non-dibasic N-terminal cleavage sites into our script that uses regular expressions to search for repetitive cleavage sites (Thiel et al., 2024) and re-screened the predicted secretome.”

(5) Under the section "Creation of a Nematostella neuropeptide library" the phrase "differ in the length of their N-terminus" needs to be changed to "differ in the length of their N-terminal region". The N-terminus is, as its name implies, one end of the peptide/protein so it can't have a length as such.

Changed.

(6) Under the section "Analysis of metazoan class A GPCRs and selection of N. vectensis neuropeptide-receptor candidates",

Change:

"For a more detailed analysis, we then reduced our sampled species to the cnidarian, the bilaterian with experimentally confirmed GPCRs and Petromyzon marinus, and the two placozoan species (Figure 2B)."

To

"For a more detailed analysis, we then reduced our sampled species to cnidarians, bilaterians with experimentally confirmed GPCRs and Petromyzon marinus, and two placozoan species (Figure 2B)."

Changed.

(7) Under the section "Analysis of metazoan class A GPCRs and selection of N. vectensis neuropeptide-receptor candidates" - change "We re-run" to "We re-ran"

Changed.

(8) Throughout the paper reference is made to a variety of neuropeptides that have or are predicted to have an N-terminal pyroglutmate. However, these are referred to without indicating this post-translational modification e.g. QGRFamide.

This should be corrected throughout the paper, in the text, and figures. Two abbreviations for pyroglutamate are used in the literature:

pQ, which shows that the encoded amino acid is Q (Glutamine)

pE, which shows that the post-translationally modified amino-acid is glutamate (E)

In the neuropeptide field, pQ seems to be more widely used than pE, so our recommendation would be to use pQ.

In the revised version we now write pyroQ whenever we refer to the actual peptide. We now only use the peptide name without indicating this modification when we refer to the precursor of these peptides.

(9) The title for Figure 5 is rather short and vague. A title like "Tissue-specific expression of neuropeptide precursors and receptors in Nematostella" seems more appropriate

We appreciate the reviewer's input, and we have made the change accordingly. The revised figure legend now reads: “Tissue-specific expression of neuropeptide precursors and receptors (GPCRs) in N. vectensis.”

(10) All of the figures in the paper have been saved in bitmap format (e.g. tiff), which means that the resolution of the figures may end up being poor in the published article. All of the figures in this paper should be saved in vector format (e.g. eps) so that there is no loss of resolution when the size of the file/figure is reduced.

We have now uploaded all figures in vector format (.eps or .pdf) to prevent any loss of resolution.

(11) In Figure 3 - supplement 2 - the neuropeptides are referred to here as PRGamides and GPRGamides. Some consistency is needed here. And in Figure B, the G of one of the GPRGamides is not shown in black.

Thank you for spotting this mistake. We now give the correct peptide sequence in parenthesis as "GPRGamide". We also highlighted the missing GPRGamide in the figure.

Author Response

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

Reviewer #1 (Public Review):

Summary:

In the manuscript by Chen et al. entitled, "The retina uncouples glycolysis and oxidative phosphorylation via Cori-, Cahill-, and mini-Krebs-cycle", the authors look to provide insight on retinal metabolism and substrate utilization by using a murine explant model with various pharmacological treatments in conjunction with metabolomics. The authors conclude that photoreceptors, a specific cell within the explant, which also includes retinal pigment epithelium (RPE) and many other types of cells, are able to uncouple glycolytic and Krebs-cycle metabolism via three different pathways: 1) the mini-Krebs-cycle, fueled by glutamine and branched-chain amino acids; 2) the alanine-generating Cahill-cycle; and 3) the lactate-releasing Cori-cycle. While intriguing if determined to be true, these cell-specific conclusions are called into question due to the ex vivo experimental setup with the inclusion of RPE, the fact that the treatments were not cell-specific nor targeted at an enzyme specific to a certain cell within the retina, and no stable isotope tracing nor mitochondrial function assays were performed. Hence, without significant cell-specific methods and future experimentation, the primary claims are not supported.

Strengths:

This study attempts to improve on the issues that have limited the results obtained from previous ex vivo retinal explant studies by culturing in the presence of the RPE, which is a major player in the outer retinal metabolic microenvironment. Additionally, the study utilizes multiple pharmacologic methods to define retinal metabolism and substrate utilization.

Weaknesses:

A major weakness of this study is the lack of in vivo supporting data. Explant cultures remove the retina from its dual blood supply. Typically, retinal explant cultures are done without RPE. However, the authors included RPE in the majority of experimental conditions herein. However, it is unclear if the metabolomics samples included the RPE or not. The inclusion of the RPE, which is metabolically active and can be altered by the treatments investigated herein, further confounds the claims made regarding the neuroretina. Considering the pharmacologic treatments utilized with the explant cultures are not cell-specific and/or have significant off-target effects, it is difficult to ascertain that the metabolic changes are secondary to the effects on photoreceptors alone, which the authors claim. Additionally, the explants are taken at a very early age when photoreceptors are known to still be maturing. No mention or data is presented on how these metabolic changes are altered in retinal explants after photoreceptors have fully matured. Likewise, significant assumptions are made based on a single metabolomics experiment with no stable isotope tracing to support the pathways suggested. While the authors use immunofluorescence to support their claims at multiple points, demonstrating the presence of certain enzymes in the photoreceptors, many of these enzymes are present throughout the retina and likely the RPE. Finally, the claims presented here are in direction contradiction to recent in vivo studies that used cell-specific methods when examining retinal metabolism. No discussion of this difference in results is attempted. Response: We agree with the reviewer that in vivo studies could be very interesting indeed. However, technologically it will be extremely difficult to (repeatedly/continuously) sample the retina of an experimental animal and to combine this with an interventional study, with a subsequent metabolomic analysis. We do not currently have access to such technology nor are we aware of any other lab in the world capable of doing such studies. Moreover, virtually all prior studies on retinal metabolism have been done on explanted retina without RPE. This includes the seminal studies by Otto Warburg in the 1920s. As opposed to this, our retinal samples for also all the metabolomic analyses included the RPE, except for the no RPE condition that was used as a comparator for the earlier investigations.

We note that our metabolomic analysis was done for all five experimental conditions where each condition included at least five independent samples (each derived from different animals).

The reviewer is correct to say that our organotypic explant cultures are early post-natal, with explantation performed at post-natal day 9 and culturing until day 15. Since our retinal explant system has been validated extremely well over more than three decades of pertinent research (see for instance: Caffe et al., Curr Eye Res. 8:1083-92, 1989), we are confident that photoreceptors mature in vitro in ways that are very similar to the in vivo situation. As far as studies in adult retina (i.e. three months or older) are concerned, this is indeed an important question that will be addressed in future studies. Studies employing stable isotope labelling may also be very informative and are planned for the future, also in order to properly determine fluxes. This will likely require an extension to our NMR hardware with an 15N channel probe, something that we plan on implementing in the future.

We are aware that a number of questions relating to retinal metabolism are controversial and that the use of other methodology or experimental systems may lead to alternative interpretations. We have now included citations of other studies that use, for example, conditional and/or inducible knock-outs or in vivo blood sampling (e.g. Wang et al., IOVS 38:48-55, 1997; Yu et al., Invest Ophthalmol Vis Sci. 46:4728-33, 2005; Swarup et al., Am J Physiol Cell Physiol. 316:C121-C133, 2019; Daniele et al., FASEB Journal 36:e22428, 2022) and discuss the pros and cons of such approaches (e.g. in Lines 376-384; 454-472).

Reviewer #2 (Public Review):

Summary:

The authors aim to learn about retinal cell-specific metabolic pathways, which could substantially improve the way retinal diseases are understood and treated. They culture ex vivo mouse retinas for 6 days with 2 - 4 days of various drug treatments targeting different metabolic pathways or by removing the RPE/choroid tissue from the neural retina. They then look at photoreceptor survival, stain for various metabolic enzymes, and quantify a broad panel of metabolites. While this is an important question to address, the results are not sufficient to support the conclusions.

Strengths:

The questions the authors are exploring at extremely valuable and I commend the authors and working to learn more about retina metabolism. The different sensitivity of the cones to various drugs is interesting and may suggest key differences between rods and cones. The authors also provide a thoughtful discussion of various metabolic pathways in the context of previous publications.

Weaknesses:

As the authors point out, ex vivo culture models allow for control over multiple aspects of the environment (such as drug delivery) not available in vivo. Ex vivo cultures can provide good hints as to what pathways are available between interacting tissues. However, there are many limitations to ex vivo cultures, including shifting to a very artificial culture media condition that is extremely different than the native environment of the retina. It is well appreciated that cells have flexible metabolism and will adapt to the conditions provided. Therefore, observations of metabolic responses obtained under culture conditions need to be interpreted with caution, they indicate what the tissue is doing under those specific conditions (which include cells adapting and dying).

Chen et al use pharmacological interventions to the impact of various metabolic pathways on photoreceptor survival and "long term" metabolic changes. The dose and timing of these drug treatments are not examined though. It is also hard to know how these drugs penetrate the tissue and it needs to be validated that the intended targets are being accurately hit. These relatively long-term treatments should be causing numerous downstream changes to metabolism, cell function, and survival, which makes looking at a snapshot of metabolite levels hard to interpret. It would be more valuable to look at multiple time points after drug treatment, especially easy time points (closer to 1 hr). The authors use metabolite ratios to make conclusions about pathway activity. It would be more valuable to directly measure pathway activity by looking a metabolite production rates in the media and/or with metabolic tracers again in time scales closer to minutes and hours instead of days.

It is not clear from the text if the ex vivo samples with RPE/choroid intact are analyzed for metabolomics with the RPE/choroid still intact or if this is removed. If it is not removed, the comparison to the retina without RPE/choroid needs to be re-interpreted for the contribution of metabolites from the added tissue. The composition of the tissue is different and cannot be disentangled from the changes to the neural retina specifically.

While the data is interesting and may give insights into some rod and cone-specific metabolic susceptibility, more work is needed to validate these conclusions. Given the limitations of the model the authors have over-interpreted their findings and the conclusions are not supported by the results. They need to either dramatically limit the scope of their conclusions or validate these hypotheses with additional models and tools.

Response: We thank the reviewer for the insightful comments and agree that some of our interpretations may have been phrased too determinedly. We have therefore rephrased and toned down our conclusions in many instances in the text, and changed the manuscript title to now read “Retinal metabolism: Evidence for uncoupling of glycolysis and oxidative phosphorylation via Cori-, Cahill-, and mini-Krebs-cycle”.

Nevertheless, when considering the major known metabolic pathways and their possible impact on metabolite patterns after the experimental manipulations used here, we believe our interpretations to be consistent with the data obtained. Conversely, the previously suggested retinal aerobic glycolysis cannot explain most of the data we have obtained. Even further, also a predominant use of the classical “full” Krebs-cycle/OXPHOS would not explain the metabolite patterns found (e.g. alanine, N-acetylaspartate (NAA)). While this does not in itself mean that our interpretations are all correct, they seem plausible in view of the data at hand and will hopefully stimulate further research on retinal energy metabolism using complementary technologies that were not available to us for the purpose of this study.

We comment that our organotypic retinal explant cultures, while they do contain their very own, native RPE, do not comprise the choroidal vasculature (in our explantation procedure the RPE readily detaches from the choroid).

As far as the drugs used on retinal explants are concerned, we note that:

(1) all three compounds used are extremely well validated, with literally thousands of studies and decades of research to their credit (i.e., 1,9-dideoxyforskolin: >270 publications since 1984; Shikonin: >1000 publications since 1977; FCCP: >2800 publications since 1967),

(2) all experimental conditions show clear and differential drug effects, as shown, for instance, by the principal component analysis in Figure1I and the cluster analysis in Figure2A,

(3) the response patterns observed for key metabolites match the anticipated drug effects (e.g. decreased glucose consumption with 1,9-dideoxyforskolin; decreased lactate levels with Shikonin; lactate accumulation with FCCP).

One can therefore be reasonably certain that these drugs did penetrate the explanted retina and that their respective drug targets were hit. Assessing dose-responses would certainly be interesting, however, the aim of this initial study was not pharmacodynamics but a general manipulation of energy metabolism. Moreover, given the extensive validation of these drugs, off-target effects seem not very likely at the concentrations used.

We agree with the reviewer that using a longitudinal, time-series type of analysis could give additional insights. We note that each additional time-point will require retinae from 25 animals and a very resource-intensive and time-consuming metabolomic analysis, together with a significantly more complex multivariate analysis (metabolite, experimental condition, time). This is a completely new undertaking that is simply not feasible as an extension of the present study.

To look at pathway activity in more direct ways is very good idea, to this end we aim to implement in the future an idea put forward by the reviewers, namely 13C-labeling and additionally 15N-labeling and tracing for specific metabolic fuels (e.g. glucose, lactate and anaplerotic amino acids such as glutamate and branched chain amino acids).

The reviewer is of course correct to say that the culture condition is somewhat artificial and that this may have introduced changes in the metabolism. However, as noted above in the first response to reviewer #1, the organotypic retinal culture system, using a defined medium, free of serum and antibiotics, has been extremely well studied and validated for decades (cf. Caffé et al., Curr Eye Res. 8:1083-92, 1989). Importantly, this system allows to maintain retinal viability, histotypic organization, and function over many weeks in culture. Moreover, most previous studies on retinal metabolism have also used explanted retina – acute or cultured – i.e. experimental approaches that are similar to what we have used and that may be liable to their own artefactual changes in metabolism. This includes the seminal, 1920s studies by Otto Warburg, or the 1980s studies by Barry Winkler, the results of which the reviewers do not seem to doubt.

We further agree that studying retinal metabolism in a situation closer to in vivo conditions would be thrilling, however to our knowledge to date there is no retina model that fully mimics the complex interplay of the blood metabolome with metabolic tissue activity. This likely means that for each metabolic condition to study (e.g. hyperglycemia, cachexia, etc.), a fairly large number of animals will need to be sacrificed for the molecular investigation of ex vivo retinal biopsies, which would mean a tremendous animal burden.

We hope the reviewer will appreciate that the revised manuscript now includes numerous improvements, along with new, additional datasets and figures, references to further relevant literature, and – as mentioned above – a more cautious phrasing of our interpretations and conclusions, including a more careful wording for the manuscript title.

Reviewer #3 (Public Review):

Summary:

The neural retina is one of the most energetically active tissues in the body and research into retinal metabolism has a rich history. Prevailing dogma in the field is that the photoreceptors of the neural retina (rods and cones) are heavily reliant on glycolysis, and as oxygen tension at the level of photoreceptors is very low, these specialized sensory neurons carry out aerobic glycolysis, akin to the Warburg effect in cancer cells. It has been found that this unique metabolism changes in many retinal diseases, and targeting retinal metabolism may be a viable treatment strategy. The neural retina is composed of 11 different cell types, and many research groups over the past century have contributed to our current understanding of cell-specific metabolism of retinal cells. More recently, it has been shown in mouse models and co-culture of the mouse neural retina with human RPE cultures that photoreceptors are reliant on the underlying retinal pigment epithelium for supplying nutrients. Chen and colleagues add to this body of work by studying an ex vivo culture of the developing mouse retina that maintained contact with the retinal pigment epithelium. They exposed such ex vivo cultures to small molecule inhibitors of specific metabolic pathways, performing targeted metabolomics on the tissue and staining the tissue with key metabolic enzymes to lay the groundwork for what metabolic pathways may be active in particular cell types of the retina. The authors conclude that rod and cone photoreceptors are reliant on different metabolic pathways to maintain their cell viability - in particular, that rods rely on oxidative phosphorylation and cones rely on glycolysis. Further, their data support multiple mechanisms whereby glycolysis may occur simultaneously with anapleurosis to provide abundant energy to photoreceptors. The data from metabolomics revealed several novel findings in retinal metabolism, including the use of glutamine to fuel the mini-Krebs cycle, the utilization of the Cahill cycle in photoreceptors, and a taurine/hypotaurine shuttle between the underlying retinal pigment epithelium and photoreceptors to transfer reducing equivalents from the RPE to photoreceptors. In addition, this study provides robust quantitative metabolomics datasets that can be compared across experiments and groups. The use of this platform will allow for rapid testing of novel hypotheses regarding the metabolic ecosystem in the neural retina.

Strengths:

The data on differences in the susceptibility of rods and cones to mitochondrial dysfunction versus glycolysis provides novel hypothesis-generating conjectures that can be tested in animal models. The multiple mechanisms that allow anapleurosis and glycolysis to run side-by-side add significant novelty to the field of retinal metabolism, setting the stage for further testing of these hypotheses as well.

Weaknesses:

Almost all of the conclusions from the paper are preliminary, based on data showing enzymes necessary for a metabolic process are present and the metabolites for that process are also present. However, to truly prove whether these processes are happening, C13 labeling or knock-out or over-expression experiments are necessary. Further, while there is good data that RPE cultures in vitro strongly recapitulate RPE phenotypes in vivo, ex vivo neural retina cultures undergo rapid death. Thus, conclusions about metabolism from explants should either be well correlated with existing literature or lead to targeted in vivo studies. This paper currently lacks both.

Response: As mentioned above in the first answers to reviewers #1 and #2, we think of our study as a starting point that may provide novel directions for a whole series of investigations into retinal energy metabolism. Especially the use of novel technologies may in the future allow to decipher the different metabolic phenotypes of the 100+ distinct retinal cell types by in situ spatial metabolomics and lipidomics. Currently, we still have to limit the scope of our studies to only certain aspects of this topic. We thus agree that some of our interpretations need to be formulated more carefully and we have done so in the revised version of our manuscript. We also agree with the reviewer that carbon (13C) labelling and tracing studies will be very informative and will engage in such studies in the future. Besides 13C, we aim to further employ 15N labelled substrates, which is especially suitable to study the destiny of amino acids.

As far as our organotypic retinal explant system is concerned, it is arguably one of the best validated such systems available (see responses to reviewers #1 and #2). While the reviewer is correct to say that the neuroretina without RPE degenerates relatively quickly in vitro, in our system, with the neuroretina and its native RPE cultured together, we can routinely culture the retina for four weeks or more, without major cell loss (Söderpalm et al., IOVS 35:3910-21, 1994; Belhadj et al., JoVE 165, 2020). Thus, our retinal cultures with RPE do not undergo rapid death. Within the time-frame of the present study (6 days in vitro) culturing-induced cell death is minimal and unlikely to influence our analyses. For further, more detailed answers to the reviewers’ questions please see our detailed point-to-point response below.

We agree with the reviewer that eventually in vivo studies will be important to confirm our interpretations. As mentioned in our initial response to reviewer #1, such studies will be very challenging and new technologies may need to be developed before in vivo investigations can deliver the answers to the questions at hand (see answer to question Rev#3.17 below), especially if the cross-play between substrate availability from the blood metabolome and the retinal metabolic pathway activity shall be studied.

Recommendations For The Authors

Reviewer #1 (Recommendations For The Authors):

Rev#1.1. The animals should be screened for and lack rd8.

Response: This is a pertinent question from the reviewer. Ever since we first became aware of the presence of rd8 mutations in certain mouse lines from major vendors (e.g. Charles River, Jackson Labs) in around 2010, we have setup regular screening of all our mouse lines for this Crb1 mutation. Accordingly, the mouse lines used in this study were confirmed to be free of the rd8 / Crb1 mutation. A corresponding remark has now been inserted into the SI materials and methods section (Lines 37-38).

Rev#1.2. GLUT1 looks significantly different from in vivo to in vitro. Recommend co-staining with RHO and cone markers (PNA or CAR) to further delineate where it is being expressed. The in vitro cultures appear to have much shorter outer segments (OS). Considering OS biosynthesis is thought to drive a good deal of metabolic adaptations, how relevant is the in vitro model system to what is truly occurring in vivo?

Response: The GLUT1 staining shown in Figure 1 displays the in vivo situation. Since may not have been entirely clear from the previous figure legend, we have now labelled this as “in vivo retina” and distinguish it from “in vitro” samples in the legend to Figure 1 (Lines 774-778). As far as the comparison of GLUT1 staining in vivo (Figure 1A3) vs. in vitro (Figure S1C3) is concerned, in both situations a strong RPE labelling is clearly visible, with essentially no GLUT1 label within the neuroretina.

Nevertheless, to better delineate the expression of GLUT1 in the outer retina, we have now performed an additional co-staining with rhodopsin (RHO) as rod marker and peanut agglutinin (PNA) as cone marker, as suggested by the reviewer (new supplemental Figure S1). In brief, this co-staining confirms the strong expression of GLUT1 in the RPE, while there is essentially no GLUT1 detectable in rod or cone photoreceptors.

Retinal explants in long-term cultures do indeed have somewhat shorter outer segments compared to same age in vivo counterparts (Caffe et al., Curr Eye Res. 8:1083-1092, 1989). However, in the short-term cultures (6 DIV) and at the age studied here (P15) outer segments have only just started to grow out and are around 10 - 12 µm long, both in vitro and in vivo (cf. LaVail, JCB 58:650-661, 1978). Thus, the metabolism required for outer segment synthesis should be equivalent when in vitro and in vivo situations are compared. For considerations on outer segments in retinal explant cultures see also Rev#3.2 and Rev#3.29.

Rev#1.3. Also, recent publications have shown that GLUT1 is expressed in the neuroretina including rods, cones, and muller glia. Was GLUT1 not appreciated in these cells in your ex vivo samples and if so, why? Likewise, these same studies previously demonstrated GLUT1 resulted in rod degeneration but not cone. The results presented here differ significantly. Why the difference in results and is it secondary to the in vitro vs. in vivo setting? Furthermore, the authors state that they thought the no RPE situation would be similar to the GLUT1 inhibitor experimental condition but instead, they were vastly different. Is this secondary to the fact that GLUT1 is expressed outside the RPE.

Response: We are aware that there is a controversy regarding GLUT1 expression in the neuroretina, please see also our response to question Rev#3.1 below. As far as our immunostaining for GLUT1 on in vivo retina is concerned, we find an unambiguous and very marked expression of GLUT1 in RPE cells, at both basal and apical sides. Compared to the RPE, the neuroretina appears devoid of GLUT1 staining. However, at very high gamma values a faint staining in the neuroretina becomes visible, a staining which from its appearance – processes spanning the entire width of the retina – is most compatible with Müller glia cells. Under normal circumstances we would have dismissed such a faint staining as background and false positive. Given the sometimes very contradicting reports in the literature, we cannot fully exclude a weak expression of GLUT1 also in cells other than the RPE, with Müller glial cells perhaps being the most likely candidate. At any rate, GLUT1 expression in the neuroretina can only be much weaker than in the RPE, making its relevance for overall retinal metabolism unclear.

As far as recent publications studying GLUT1 in the retina are concerned, we know of the study by Daniele et al. (FASEB Journal 36:e22428, 2022), which used a rod-specific, conditional knock-out of GLUT1 and found a relatively slow rod degeneration. We are not aware of a selective GLUT1 knock-out in cones, nor are we aware of conditional GLUT3 knock-outs in the retina. For further discussion of the Daniele et al. study please see Rev#3.13.

The reviewer is right, initially we were thinking that, since GLUT1 was expressed only (predominantly) in RPE, the metabolic response to GLUT1 inhibition should look similar to the no RPE situation. However, this initial hypothesis did not consider a key fact: The RPE builds the blood retinal barrier and the tight-junction coupled RPE cells are a barrier to any larger molecule, including glucose. Removing the barrier by removing the RPE dramatically increases the availability of glucose to the retina, a phenomenon that is likely exacerbated by the expression of the high affinity/high capacity GLUT3 on photoreceptors (cf. Figure S1A). In other words, when the RPE is removed the outer retina is “flooded” with glucose and we believe that this is probably the main factor that explains why the metabolic response to GLUT1 inhibition (1,9-DDF group) is so different from the no RPE condition.

We have now included an additional corresponding explanation in the discussion (Lines 422-429). Furthermore, we have added an entire new subchapter to the discussion to debate the expression of glucose transporters in the outer retina (Lines 454-472).

Rev#1.4. Shikonin's mechanism of action via protein aggregation and lack of specificity for PKM2 vs PKM1 at 4uM is an experimental limitation that needs to be taken into account. All treatments utilized are not cell-specific.

Response: While the reviewer is correct to say that Shikonin may have multiple cellular targets and a diverse range of possible applications as an anti-inflammatory, antimicrobial, or anticancer agent (cf. Guo et al., Pharmacol. Res. 149:104463, 2019), numerous studies support its specificity for PKM2 over PKM1, at concentrations ranging from 1 – 10 µM (Chen et al., Oncogene 30:4297-306, 2011; Zhao et al., Sci. Rep. 8:14517, 2018; Traxler et al., Cell Metab. 34:1248-1263, 2022). We settled for 4 μM as an intermediate concentration, considering its effectiveness and specificity in previous studies. We have now inserted references detailing the specificity and concentration range of Shikonin into the SI Materials and Methods section (Line 62).

The concern that “all treatments” are not cell-specific is debatable. Certainly, any given compound may have off-target effects, yet, since the compounds we used in our study have all been studied for decades (see above, initial response to Reviewer #2), their off-target profile is well established and unlikely to play an important role here. Moreover, in our study the cell specificity does not come from the compounds used but from where their targets are expressed. As shown in Figure 1A and in Figure S1C, Shikonin´s target PKM2 is almost exclusively expressed in photoreceptor inner segments. Hence, it seems very reasonable to expect that the vast majority of the metabolomic changes observed by Shikonin treatment are related to photoreceptors. We note that this assertion would still be true even if there was a low-level expression of PKM2 in other retinal cell types and/or if Shikonin had moderate off-target effects on other enzymes since the bulk of the effect on the quantitative metabolomic dataset would still originate from PKM2 inhibition in photoreceptors.

Rev#1.5. What was the method of cone counting in Figure 1?

Response: Cones were counted per 100 µm of retinal circumference based on an arrestin-3 staining (cone arrestin, CAR).

This information is now included in the SI Materials and Methods section under “Microscopy, cell counting, and statistical analysis” (Lines 99-100).

Rev#1.6. How do you know that FCCP is not altering RPE ox phos, disrupting the outer retinal microenvironment and leading to cell death, and therefore, the effects seen are not photoreceptor-specific but rather downstream from the initial insult in RPE?

Response: We propose that FCCP will be acting on both photoreceptors and RPE cells (and all other retinal cell types) at essentially the same time, over the experimental time-frame. Thus, OXPHOS should be inhibited in all cells simultaneously. However, FCCP will primarily affect cells that actually use OXPHOS to a large extent, while cells relying on other metabolic pathways (e.g. glycolysis) will hardly be affected.

We believe the very strong effect of FCCP, seen exclusively in rod photoreceptors, to be a direct drug effect. While we cannot not fully exclude an indirect effect via the RPE – as proposed by the reviewer – we think this to be unlikely because:

(1) RPE viability was not compromised by FCCP treatment.

(2) If the reviewer´s hypothesis was correct, then also cone photoreceptors should have been affected (e.g. because now the RPE consumes all glucose, leaving nothing for cones). However, cones were essentially unaffected by the FCCP treatment, making a dependence on RPE OXPHOS unlikely. Especially so, because blocking GLUT1 and glucose import on the RPE with 1,9-DDF had only relatively minor effects on rod photoreceptor viability but strongly affected cones. This indicates that the RPE is mainly shuttling glucose through to photoreceptors, especially to cones, and this function does not seem to be impaired by FCCP treatment.

(3) We found that enzymes required for Krebs-cycle and OXPHOS activity (i.e. citrate synthase, fumarase, ATP synthase γ) are predominantly expressed in photoreceptors but virtually absent from RPE (Figure 3D, see also answer to following question).

(4) The density of mitochondria (i.e. the target for FCCP) is far lower in RPE than in photoreceptors, as evidenced also by the COX staining shown in Figure 1A. Hence, photoreceptors are far more likely to be hit by FCCP treatment than RPE cells.

To accommodate the reviewer´s concern, we have now added a further comment into the discussion (Lines 440-442).

Rev#1.7. While Figure 3D is interesting, it offers no significant insight into mechanisms as the enzyme levels are not being compared to control nor is mitochondrial fitness in these conditions being assessed, which would provide greater insight than just showing that these enzymes are present in the inner segments, which are known to be rich in mitochondria. Additionally, stating that the low ATP is secondary to decreased Krebs cycle activity and ox phos based on merely ATP levels is not supported by metabolite levels minus citrate nor ox phos enzyme levels or oxygen consumption. Also, citrate is purported to be decreased in the table in Figure 2 in the no RPE condition; however, Supplemental Figure 2 demonstrates this change is not significant then the same data is presented in Supplemental Figure 3 and it is statistically significant again. Why the difference in data and why is the same data being shown multiple times?

Response: The immunostaining shown in Figure 3D shows the in vivo retina, or in other words the localization of enzymes in the native situation. Since this may not have been obvious in the previous manuscript version, we have added a corresponding comment to the legend of Figure 3 (Line 806). The localization of the Krebs-cycle/OXPHOS enzymes citrate synthase, fumarase, and ATP synthase mainly to photoreceptors, but not (or much less) to RPE, is another piece of evidence supporting the idea that OXPHOS is predominantly performed by photoreceptors (see also answer to previous question Rev#1.6).

The decreased ATP levels (together with citrate, aspartate, NAA) shown in Figure 3 in the no RPE group, are an indication that photoreceptor Krebs-cycle activity may be decreased but not abolished in the absence of RPE. Importantly, GTP levels are not reduced in the no RPE group (Figure 2). Since large amounts of GTP can only by synthesized by either SUCLG-1 in the Krebs-cycle or by NDK-mediated exchange with ATP, the most plausible interpretation is that Krebs-cycle dependent ATP-synthesis was decreased in the no RPE situation, but that the (mini) Krebs-cycle or Cahill-cycle, notably the step from succinyl-CoA to succinate, was running. Since there is no RPE in this group, this strongly suggests important Krebs-cycle/OXPHOS activity in photoreceptors where the majority of the corresponding enzymes are located (see above).

We thank the reviewer for pointing out that the information on group comparisons may not have been presented with sufficient clarity. In the figures mentioned by the reviewer the data is shown and compared in different contexts: the table in Figure 2B and the data in Figure S3 (now renumbered to Figure S5) refer to two-way comparisons of treatment condition to control, to elucidate individual treatment effects. Meanwhile Figure S2 (now supplementary Figure S3) refers to a 5-way comparison for a general overview that puts all five groups in context with each other. These differences in comparisons and normalization to the respective common standards entail the use of different statistical tools, resulting in different p-values. The statistical testing approaches and thresholds are now disclosed in the figure legends, and additionally in the SI Materials and Methods section (Lines 145-155).

Rev#1.8. When were the ex vivo samples taken for metabolomics, and if taken when significant TUNEL staining and cell death have occurred, are the changes in metabolism due to cell death or a true indication of differential metabolism? Furthermore, it is unclear if the metabolomics samples included the RPE or not. Considering these treatments will affect most cells in the retina and the RPE, which is included in the ex vivo samples, it is difficult to ascertain that these changes are secondary to the effects on photoreceptors alone.

Response: The samples for metabolomics included the RPE (except for the no RPE condition) and were taken at the same time as the tissues for histological preparations and TUNEL assays, i.e. they were all taken at post-natal day 15. This has now been clarified in the SI Materials and Methods section (Lines 108-110).

We cannot entirely exclude an effect of ongoing cell death caused by the different drug treatments on the retinal metabolome. However, since in the experimental treatments cell death was still comparatively low (even in the FCCP condition, overall cell death was only around 10% of the total retina), and the metabolomic analysis considered the entire tissue, the impact of cell death per se on the total metabolome will be comparatively minor (≤ 10%, i.e. within the typical error margin of the metabolomic analysis).

As mentioned above, the drug treatments should in principle affect all retinal cells at the same time. However, only cells that express the drug targets (i.e. 1,9-DDF targets GLUT1 in RPE cells, Shikonin targets PKM2 in photoreceptors; cf. Figure 1A) should react to the treatment. Even FCCP, in the paradigm employed, will only affect those cells that rely heavily on OXPHOS. Our data indicates that while this is almost certainly the case for rods; cones, RPE cells, and essentially all of the inner retina, are not affected by FCCP treatment, strongly suggesting that OXPHOS is of minor importance for these cell populations.

Rev#1.9. Why were the FCCP and no RPE groups compared? If they have similar metabolite patterns as noted in Figure 2, would that suggest that FCCP's greatest effect is on the ox phos of RPE and the metabolite patterns are secondary to alterations in RPE metabolism? Also, the increase in citrate and decrease in NAD may be related to effects on RPE mitochondrial metabolism when comparing these groups, and the disruption of RPE metabolism may then result in PARP staining of photoreceptors.

Response: The reason for the pair-wise comparison of the no RPE and FCCP groups initially was indeed the similarity in metabolite patterns. This was now rephrased accordingly in the results section “Photoreceptors use the Krebs-cycle to produce GTP” (Lines 218-219). The interpretation that the reviewer proposed here is interesting, but does not conform with the data analysis of this and other group comparisons.

Instead, the similarity between the metabolic patterns found in the no RPE and FCCP groups further supports the idea that a lack of RPE decreases retinal OXPHOS and increases glycolysis. This interpretation is based on the following observations:

(1) Mitochondrial density in the RPE is far lower than in photoreceptors (see COX staining in Figure 1A), thus quantitatively the metabolite pattern caused by a disruption of OXPHOS (via FCCP treatment) will be dominated by metabolites generated by photoreceptors. For the same reason the depletion of retinal NAD+, and the concomitant increase in photoreceptor PAR accumulation after FCCP treatment, is unlikely to be due to changes in RPE.

(2) Similarly, citrate synthase (CS) was found to be almost exclusively expressed in photoreceptor inner segments, with little expression in RPE (Figure 3D). Hence, the quantitative increase of citrate levels after FCCP treatment can only originate in photoreceptors.

(3) The comparison of the control (with RPE) against the no RPE group suggested an increase in (aerobic) glycolysis in the absence of RPE, evidenced notably by a retinal accumulation of lactate, BCAAs, and glutamate (Figure 3A). The very same metabolite pattern is seen for the FCCP treatment (Figure 1B) indicating a marked upregulation of glycolysis (Figure 6C). The latter observation suggests that photoreceptors, after disruption of OXPHOS switch to an exclusively glycolytic metabolism, which, however, rods cannot sustain (Figure 1C, D).

(4) Glucose consumption and lactate release is increased in the no RPE group vs. control (new Supplementary Figure 4). A similar increase in glucose consumption and lactate production is seen in the FCCP group suggesting that also the no RPE situation disrupts OXPHOS in photoreceptors.

Rev#1.10. The conclusions being reached are difficult to interpret secondary to the experimental procedures and the fact that the treatments are not cell-specific and RPE is included with the neuroretina as well. Likewise, stating FCCP is altering the Krebs cycle in the neuroretina is difficult to believe as there are no changes in the Krebs cycle when compared to the control, which also has RPE.

Response: We agree with the reviewer, that some of the conclusions may have been somewhat speculative. Accordingly, we have toned down our conclusions in several instances in the text, notably in abstract, introduction, and discussion.

When it comes to Krebs cycle intermediates a key limitation of our study is indeed the lack of carbon-tracing and metabolic flux analysis as noted by the reviewers, a limitation that we now highlight more strongly in the discussion of the revised manuscript (Lines 545-549). While it is highly probable that the flux of Krebs cycle intermediates is altered by FCCP, our steady-state data does not show significant changes in the metabolites citrate, fumarate, and succinate. However, our study does show a highly significant decrease in GTP levels, which as explained above, is a key indicator of Krebs cycle activity/inactivity. Moreover, while GTP levels were reduced also in the no RPE group, GTP was still significantly higher in the no RPE group compared to the FCCP treatment. Our interpretation of this finding is that there is Krebs-cycle/OXPHOS activity in the neuroretina, which is abolished by FCCP.

Rev#1.11. Supplemental Figure 4C and D states that GAC inhibition affected only photoreceptors, but GAC is expressed throughout the retina and so the inhibition is altering glutamine-glutamate homeostasis throughout the retina. Clearly, based on histology, one can see that the architecture of the retina, especially at the highest dose, is lost likely because all cells are being affected. So it is not photoreceptor-specific and even at low doses one can see that the inner retina is edematous. Moreover, with such a high amount of TUNEL staining in the ONL, are rods more affected than cones?

Response: In our hands the immunostaining for Glutaminase C (GAC) labelled predominantly cone inner segments, the OPL, and perhaps bipolar cells (Figure S1A). The deleterious effects mentioned by the reviewer are only seen at the highest concentration of the GAC inhibitor compound 968. This concentration (10 µM) is 100-fold higher than the dose that produces a significant loss of cones in the outer retina (0.1 µM). We therefore think that this data points to the extraordinary reliance of cones on glutamine and glutamate. As can be seen from the images (Figure S4C) illustrating the effects of 0.1 and 1 µM Compound 968 treatment, the ONL thickness is not significantly reduced by the GAC inhibitor. This strongly indicates that at these doses the rods are not affected by GAC inhibition.

Rev#1.12. The no RPE vs 1,9 DDF data may be interpreted as preventing glucose transport in the RPE increases BCAA catabolism by the RPE, which has been shown to utilize BCAA in culture systems. To this end, when the RPE is not present, the BCAA is increased as compared to the control with RPE.

Response: Our original interpretation of this data was that after GLUT1 inhibition and a correspondingly reduced retinal glucose uptake, the retina switched to an increasing use of anaplerotic substrates, including BCAAs. This is supported by the concomitant upregulation of the Cahill-cycle product alanine and the mini-Krebs-cycle product N-acetylaspartate (NAA). Yet, we agree with the reviewer that BCAAs could also be consumed by the RPE. We have now changed our conclusion at the end of the results chapter “Reduced retinal glucose uptake promotes anaplerotic metabolism“ to also highlight this possibility (Lines 261-262).

Rev#1.13. It is unclear why so much effort is comparing the no RPE group to the treatment groups and not comparing the control group to the different treatment groups.

Response: Previous studies – including the seminal studies of Otto Warburg from the early 1920s – had always used retina without RPE. This “no RPE” situation is therefore something of a reference for our entire study, which is why we dedicated more effort to its analysis. We have now inserted a corresponding remark into the manuscript (Lines 182-184).

Rev#1.14. The conclusions are significantly overstated especially with regards to rods versus cones as these are not cell-specific treatments. For example, the control vs 1,9 DDF vs FCCP clearly shows that there is mitochondrial dysfunction due to decreased NAD, increased AMP/ATP ratio, decreased Asp but increased Gln, and a compensatory increase in lactate production.

Response: We agree with the reviewer and have tried to phrase our statements in more measured fashion. Notably, we have toned down our statements in the title, abstract, results, discussion, and several of the subchapter headings.

Rev#1.15. While metabolic conclusions are drawn on serine/lactate ratio, this ratio is driven by the drastic changes in lactate and not so much serine in the treatment conditions as it was rather stable. Likewise, substrates beyond glucose have the potential to fuel the TCA cycle and make GTP via SUCLG1, such as fatty acids, other AAs, etc. Therefore, this ratio may not tell the entire story about anaplerotic metabolism. Furthermore, knowing that RPE utilize BCAAs to fuel their TCA cycle, the no RPE condition may simply have increased BCAAs due to lack of metabolism by the RPE, which drives the GTP/BCAA ratio. To state that the neuroretina was utilizing BCAAs for anaplerosis is not well supported based on the current data. Similarly, what is to say that the GTP/lactate ratio in the no RPE situation is not driven by the fact that the RPE is no longer present to act as acceptor of retinal lactate production or that more glucose is reaching the retina since the RPE is not present to accept and utilize that produced. Glucose uptake was not assessed to further address these issues.

Response: We agree with reviewer that metabolite ratios may not tell the full story underlying retinal metabolism however based on the robustness of using quantitative and highly reproducible NMR data, they are an important part of the metabolomics toolbox. The reviewer correctly observed that the changes in lactate levels are more dramatic than in serine. Still, also serine was significantly increased in the no RPE, 1,9-DDF, and Shikonin groups. Together with the lactate changes (same or opposite direction) the resulting serine/lactate ratios display marked alterations.

When it comes to the supply of other potential energy substrates mentioned by the reviewer, i.e. fatty acids or amino acids other than BCAAs, these are only supplied in minimal amounts in the defined, serum free R16 medium (Romijn, Biology of the Cell, 63, 263-268, 1988) and – if used to any important extent – would be rapidly depleted by the retina. Thus, for a culture period of 2 days in vitro between medium changes these energy sources are not available and thus cannot be used by the retina.

Our conclusion that the retina is using anaplerosis is based not only on the observations made in the no RPE group but also on, for instance, the metabolite ratios seen in the 1,9-DDF treatment group. In this group decreased glycolytic activity may correspond to increased serine synthesis and anaplerosis.

As far as glucose uptake is concerned, we have analysed the medium samples at P15 (equivalent to the retina tissue collection time point) and now present data that addresses this question more directly via the consumption of glucose from and release of lactate to the culture medium (New Supplementary Figure 4C, D). This new dataset provides another independent observation showing that:

(1) Glucose consumption/lactate release (i.e. aerobic glycolysis) is high in the no RPE situation but low in the control situation. In other words, retinal aerobic glycolysis is most likely stimulated by the absence of RPE.

(2) 1,9-DDF treatment decreases glucose consumption/lactate release as would be expected from a GLUT1 blocker. Since ATP and GTP production are high nonetheless, this indicates that other substrates (i.e. anaplerosis) were used for retinal energy production, in agreement with the analysis shown in Figure 6C.

(3) The FCCP treatment, which disrupts oxidative ATP-production, increases glucose consumption/lactate release in way similar to the no RPE situation. Yet, the no RPE retina can still generate sizeable amounts of GTP but not ATP. Together, this provides further evidence that neuroretinal OXPHOS is decreased in the absence of RPE.

Rev#1.16. The evidence for the mini-Krebs cycle is intriguing but weak considering it is based on certain enzymes being expressed in the photoreceptors, which had already been shown to be present in other publications, and a single ratio of metabolites that is increased in FCCP. One would expect this ratio to be increased under FCCP regardless. There is no stable isotope tracing with certain fuels to confirm the existence of the mini-Krebs cycle.

Response: We thank the reviewer for this suggestion. We agree that our evidence for the mini-Krebs-cycle (and the Cahill-cycle) may be to some extent circumstantial and additional technologies would help to obtain further supportive data. Still, here we would like to invite the reviewer to a thought experiment where he/she could try and interpret our data without considering the Cahill- or the mini-Krebs-cycle. At least we ourselves, when we engaged into such thought experiments, were unable to explain the data observed without these alternative energy-producing cycles. Most notably, we were unable to explain the strong accumulation of either alanine or N-acetyl-aspartate (NAA) when only considering glycolysis and (full) Krebs-cycle metabolism. Of course, this may still be considered “weak” evidence, and we expect that future studies including complementary technologies will either confirm or expand our interpretation of the existing data set.

The suggestion to perform stable isotope-labelled tracing with potential alternative fuels (e.g. glutamate, glutamine, pyruvate, etc.) is very attractive indeed. While such studies are likely to shed further light on the metabolic pathways proposed, this will entail very extensive experimental work, with multiple different conditions and concentrations and variety of analysis methods that is currently not feasible (e.g. a 1.7 mm NMR probe equipped with a 15N channel) as an extension of the present manuscript. Nevertheless, we will certainly consider this approach for future follow-up studies once such techniques are available and will screen for suited collaboration partners. A corresponding comment on such future possibilities has now been inserted into the discussion (Lines 545-549).

Rev#1.17. The discussion does not mention how this data contradicts a recent in vivo study looking at Glut1 knockout in the retina (Daniele et al. FASEB. 2022) or previous in vivo studies that suggest cones may be less sensitive to changes in glucose levels (Swarup et al. 2019). This is a key oversight.

Response: We thank the reviewer for pointing this out. We now included these studies in the revised discussion in a new subchapter on the expression of glucose transporters in the outer retina (Lines 454-472). For a critical review of the Daniele et al., 2022 study please also see our more detailed response to question Rev#3.13 below.

Rev#1.18. GAC is expressed in more than just cones so making cell-specific statements regarding fuel utilization is not well supported.

Response: Our immunostaining for GAC revealed a strong expression in cone inner segments (Figure S1A3). While this does not exclude (relatively minor) expression in other retinal cell types, cones are likely to be more reliant on GAC activity than other cell types. See also answer above.

Rev#1.19. Suggesting that rods utilize the mini-Krebs cycle based on AAT2 being seen in the inner segments without at least co-staining for RHO or PNA is weak evidence for such a cycle. AAT looks to be expressed in the inner segments of all photoreceptors.

Response: We have taken up this suggestion from the reviewer and now provide an additional co-staining for AAT1 and AAT2 with rhodopsin. Note that in response to a pertinent comment from Reviewer #3 we have changed the abbreviation for aspartate aminotransferase from “AAT” to the more commonly used “AST” throughout the manuscript.

New images showing a co-staining for AST1 and AST2 with rhodopsin now replace the former image set in Figure 7D. In brief, the new images show the expression of both AST1 and AST2 across the retina, with, notably an expression in the inner segments of photoreceptors but not in the outer segments, where rhodopsin is expressed.

Reviewer #3 (Recommendations For The Authors):

Rev#3.1. The staining for the glucose transporters GLUT1 and GLUT3 does not reflect what has previously been published by two different groups that were validated by cell-specific knockout mice. As mentioned by the author GLUT1 and GLUT3 have differences in transport kinetics, which would affect their metabolism. Therefore, the lack of GLUT1 in photoreceptors would suggest that photoreceptor metabolism is not faithfully replicated in this system. This difference from the previous literature should be discussed in the discussion.

Response: As the reviewer pointed out, the expression of GLUT1 in the retina is somewhat controversial, with much older literature showing expression on the RPE, while some more recent studies claim GLUT1 expression in photoreceptors. For a brief discussion of our GLUT1 immunostaining please see also our answer to question Rev#1.3 above.

Although the retinal expression of GLUT1 was besides the focus of our study, we feel we must address this point in more detail: In the brain the generally accepted setup for GLUT1 and GLUT3 expression is that low-affinity GLUT1 (Km = 6.9 mM) is expressed on glial cells, which contact blood vessels, while high-affinity GLUT3 (Km = 1.8 mM) is expressed on neurons (Burant & Bell, Biochemistry 31:10414-20, 1992; Koepsell, Pflügers Archiv 472, 1299–1343, 2020). This setup matches decreasing glucose concentration with increasing transporter affinity, for an efficient transport of glucose from blood vessels, to glial cells, to neurons. In the retina, the cells that contact the choroidal blood vessels are the tight-junction-coupled RPE cells. As shown by us and many others, RPE cells strongly express GLUT1 (cf. Figure 1A-3.). To warrant an efficient glucose transport from the RPE to photoreceptors, photoreceptors must express a glucose transporter with higher glucose affinity than GLUT1. We show that this is indeed the case with photoreceptors expressing GLUT3 (cf. Supplemental Figure 1-5.). While a part teleological explanation does not per se prove that our data is correct, at least our data is plausible. In contrast, the glucose transporter setup sometimes claimed in the literature is biochemically implausible, i.e. for the flow of metabolites (glucose) to go against a gradient of transporter affinities, and we are not aware of an example of such a setup occurring anywhere in nature.

However, at this point we cannot exclude low levels of GLUT1 expression on Müller glia cells or even photoreceptors. This expression could, for instance, be relevant in cases where cells were shuttling excess glucose – perhaps produced through gluconeogenesis – onwards to other retinal cells. Still, GLUT1 expression can only be minor when compared to RPE since a major expression would destroy the glucose affinity gradient (see above) required for efficient glucose shuttling into the energy hungry photoreceptors.

To address this request by the reviewer (and also reviewer #1) we now discuss the question of glucose transporter expression in the outer retina in a new subchapter of the discussion (Lines 454-472).

Rev#3.2. Photoreceptor metabolism and aerobic glycolysis are tied to photoreceptor function, as demonstrated by Dr. Barry Winkler. The authors should provide data or mention (if previously published) about photoreceptor OS growth and function in this system.

Response: The studies of Barry Winkler (e.g. Winkler, J Gen Physiol. 77, 667-692, 1981) confirmed the original work of Otto Warburg and expanded on the idea that the neuroretina was using aerobic glycolysis. Importantly, Winkler used a very similar experimental setup as Warburg has used, namely explanted rat retina without RPE. In light of our data where we compare metabolism of mouse retina with and without RPE – where retina cultured without RPE confirms the data of Warburg and Winkler – it appears most likely that the purported aerobic glycolysis occurs mostly in the absence of RPE but only to a lower extent in the native retina.

Photoreceptor outer segment outgrowth is somewhat slower in the organotypic retinal explant cultures compared to the in vivo situation (cf. Caffe et al., Curr Eye Res. 8:1083-1092, 1989 with LaVail, JCB 58:650-661, 1978; see also answer to reviewer #1). Importantly, organotypic retinal explant cultures and their photoreceptors are fully functional and remain so for extended periods in culture (Haq et al., Bioengineering 10:725, 2023; Tolone et al., IJMS 24:15277, 2023). This information has now been added to the manuscript discussion section, into the new subchapter “The retina as an experimental system for studies into neuronal energy metabolism” (Lines 367-395).

Rev#3.3. It is unclear from the description of the experiment in both the results and methods if 1,9DDF, Shikonin, and FCCP were added to both apical and basal media compartments or one or the other and should be specified. The details of what was on the apical compartment would be helpful, as the model is supposed to allow for only nutrients from the basal compartment (as indicated by the authors themselves). Is the apical compartment just exposed to air? How does this affect survival?

Response: In organotypic retinal explant cultures the RPE rests on the permeable culturing membrane such that the basal side is contact with the membrane and the medium below (far schematic drawing see Figure S1B), while the apical side is covered by a thin film of medium created by the surface tension of water (Caffe et al., Curr Eye Res. 1989; Belhadj et al., JoVE, 2020). This thin liquid film ensures sufficient oxygenation and is an important factor that allows the retinal explant to remain viable for several weeks in culture. If the retinal cultures were submerged by the medium, their viability – especially that of the photoreceptors – would drop dramatically and would typically be below 3-5 days. Therefore, in the retinal organotypic explant cultures used here, the nutrients and the drugs applied do indeed reach the outer retina from the basal side, i.e. similar as they would in vivo.

To address this question from the reviewer, corresponding clarifications have been inserted into the SI Materials and Methods section (Lines 64-66).

Rev#3.4. As the metabolomic data obtained was quantitative, several metabolites discussed should be analyzed in terms of ratios, for example, Glutathione and glutathione disulfide should be reported as a ratio. In addition as ATP, ADP, and AMP were measured, they can used to calculate the energy charge of the tissue.

Response: We thank the reviewer for these suggestions and have created corresponding graphs for GSH / GSSG ratio and energy charge. These new graphs have now been added to the SI datasets, to the new Supplementary Figure 4. To accommodate other requests from the Reviewers, this new Figure also contains additional new datasets on glucose and lactate concentrations (see further comments above and below). Please note that all later SI Figures have been renumbered accordingly.

In brief, the ratios for GSH/GSSG show no significant changes between control and the different experimental groups. Meanwhile, the adenylate energy charge of the retinal tissues show a significant decrease in the energy charge for the Shikonin group and the FCCP group. Note that in the new Supplementary Figure 4A, the dotted lines indicate the energy charge window typical for most healthy cells (0.7 – 0.95).

Rev#3.5. I think a missed opportunity when discussing the possible taurine/hypotaurine shuttle would be the impact on the osmosis of the subretinal space as taurine has been hypothesized as a major osmolyte.

Response: This is another interesting recommendation from the reviewer. To address this point, we have now introduced a corresponding paragraph and references in the discussion of the manuscript (Lines 503-504; 512-514).

Rev#3.6. In Figure 3, the distribution of these enzymes should also be studied under the no RPE condition as the culture treatment took several days for these metabolic changes to occur.

Response: The images shown in Figure 3D are from the in vivo retina. Since this may not have been very clear in the previous manuscript version, we have now added a corresponding explanation to the legend of Figure 3. As far as we can tell, the expression and localization of neuroretinal enzymes does not change in cultured retina, during the culture period (compare Figure 1A with Supplementary Figure S1C). However, when it comes to the metabolite taurine its production (localization) changes dramatically in the no RPE situation where taurine is essentially undetectable by immunostaining (not shown but see metabolite data in Figure 2A, Figure 3A).

Rev#3.7. In Figures 4 and 5, it is unclear why the experimental groups were not compared to the control and requires further explanation. Furthermore, the authors should justify the concentrations of drugs used as the cell death could have risen from toxicity to the drugs and not due to disruption of metabolism.

Response: The reviewer is right, the rationale for these comparisons may not have been laid out with sufficient clarity. In Figure 4 the no RPE and FCCP groups are compared because both groups showed similar metabolite changes towards the control situation. The no RPE to FCCP comparison thus focussed on the details of the – at first seemingly minor – differences between these two groups. This has now been clarified in the corresponding part of the results (Lines 218-219).

In Figure 5A, B we compare the no RPE and 1,9-DDF groups with each other, notably because the data obtained seemingly contradicted our initial expectation that these two groups should show similar metabolite patterns. Also here, we have now inserted an additional explanation for this choice of comparisons (Lines 252-253).

In Figure 5C, D we compare the Shikonin and FCCP groups with each other. The idea behind this comparison was that in the 1st group glycolysis was blocked while in the 2nd group OXPHOS was inhibited, or in other words here were compared what happened when the two opposing ends of energy metabolism were manipulated in opposite directions. This reasoning is now given in the results section (Lines 265-268).

As far as the choice of drugs and concentrations is concerned, we used only compounds that have been extremely well validated through up to five decades of scientific research (see initial response to Reviewer #2 above). We therefore are confident that at the concentrations employed the results obtained stem from drug effects on metabolism and not from generic, off-target toxicity. Then again, as we show, prolonged (i.e. 4 days) block of energy metabolism pathways does cause cell death.

Rev#3.8. In line 203, the authors discuss GTP as being primarily a mitochondrial metabolite, however, photoreceptors would require a localized source of GTP synthesis in the outer segments as part of phototransduction, and therefore GTP in photoreceptors cannot be a mitochondrial-specific reaction in photoreceptors. Furthermore, the authors mentioned NDK as being a possible source of GTP, but they do not show NDK localization despite it being reported in the literature to be localized in the OS.

Response: The question as to the source of GTP in photoreceptor outer segments is indeed highly relevant. For GTP production in mitochondria see the answer to the next question below (Rev#3.9). An early study showed nucleoside-diphosphate kinases (NDK) to be expressed on the rod outer segments of bovine retina (Abdulaev et al., Biochemistry 37:13958-13967, 1998). More recently NDK-A was shown to be strongly expressed in photoreceptor inner segments (Rueda et al., Molecular Vision 22:847-885, 2016). We now refer to both studies in the results section of the manuscript (Line 227-228).

Rev#3.9. In the "Impact on glycolytic activity, serine synthesis pathway, and anaplerotic metabolism" section, the authors claim in the no RPE group glycolytic activity was higher due to a depressed GTP-to-lactate ratio. However, this reviewer is under the impression that GTP production in photoreceptors is not mitochondrial specific, so this ratio doesn't make sense (I could be mistaken, however). A better ratio would have been pyruvate/lactate or glucose/lactate when discussing increased glucose consumption.

Response: We appreciate the reviewers’ comment, yet we do indeed believe we can show that GTP-production in our experimental context is mainly mitochondrial. As explained in the manuscript results section (“Photoreceptors use the Krebs-cycle to produce GTP”), there are essentially only two possibilities for a photoreceptor to produce sizeable amounts of GTP. In the mitochondria via SUCLG1 – i.e. an enzyme highly expressed in photoreceptor inner segments (Figure 5D) – and the cytoplasm via NDK from excess ATP. The claim about the depressed GTP-to-lactate ratio in the no RPE situation takes this into account. Importantly, since in the no RPE situation ATP-levels are significantly lower than GTP, here GTP can only be produced via SUCLG1 and OXPHOS. Moreover, this contrasts with the FCCP group where mitochondrial OXPHOS is disrupted and both ATP and GTP are depleted.

As far as ratios with pyruvate and glucose are concerned, we agree that these could potentially be very interesting to analyse. Unfortunately, in our retinal tissue 1H-NMR spectroscopy- based metabolomics analysis the levels of both pyruvate and glucose were below the detection limits which likely reflects their rapid metabolic turnover (cf. table S1). While this might be attributable to the marked consumption of these metabolites within the tissue, it does not allow for us to calculate the suggested ratios to lactate. Then again, in the supernatant medium which was collected at the same time point as the retina tissue, we can readily detect glucose and lactate levels, for this data please see the new Supplementary Figure 4.

Rev#3.10. Aspartate aminotransferase should be abbreviated as AST, as it is more commonly noted.

Response: In response to this comment from the reviewer, we have changed the abbreviation for aspartate aminotransferase from AAT to AST throughout the manuscript.

Rev#3.11. In the discussion the assumptions of the ex vivo culture systems should be clearly stated. One that was not mentioned, but affects the implications of the data, is that the retinas used in this study are from the developing mouse eye. Another important assumption that was made in this paper was that the changes in retinal metabolism were due to photoreceptors even though the whole neural retina was included.

Response: The reviewer is correct; we have added these two points to the discussion section of the manuscript. Notably, we now included a new subchapter “The retina as an experimental system for studies into neuronal energy metabolism” (Lines 367-395) to present different in vitro and in vivo test systems.

Rev#3.12. Starting at line 347: As the authors know, the RPE has been shown to be highly reliant on mitochondrial function, and disruption of RPE mitochondrial metabolism leads to photoreceptor degeneration (numerous papers have shown this). Furthermore, the lower levels of lactate detected in their explants when RPE was present suggests that lactate is actively transported out of the neural retina by the RPE.

Response: The reviewer is right about lactate being exported from the retina to the blood stream in vivo, or, in our in vitro study, to the culture medium. In the new dataset showing glucose and lactate concentrations in the culture medium (new Supplementary Figure 4C, D), we show that without RPE (no RPE group) and the retina releases more significantly lactate into the medium than control retina with RPE. At the same time the no RPE retina consumes more glucose than control retina.

Rev#3.13. Line 360: Again, in mouse photoreceptors (by bulk RNAseq and scRNAseq), there is no GLUT3 expression (encoded by slc2a3). It was also recently shown by Dr. Nancy Philp's lab that rod photoreceptors express GLUT1, encoded by slc2a1 (PMCID: PMC9438481). The differences reported in this study and previous studies should be discussed.

Response: Although this comment may not make us very popular, we are somewhat sceptical of RNAseq data (especially single cell RNAseq) since the underlying methodology – at the current level of technological development – is notoriously unreliable when it comes to the assessment of low abundance transcripts and suffers from apoor batch reproducibility, compared to NMR based metabolomics. Due to methodological constraints RNAseq have a propensity to display erroneously high or low expression. Moreover, and perhaps even more important, dissociated cells in scRNAseq studies undergo rapid gene expression changes that can significantly falsify the image obtained (Rajala et al., PNAS Nexus 2:1-12, 2023). Finally, it cannot be emphasized enough that mRNA expression profiles DO NOT equate protein expression and there are numerous examples for divergent expression profiles when mRNA and protein is compared.

The Daniele et al. study (FASEB Journal 36:e22428, 2022; PMCID: PMC9438481) used in situ hybridization to study the mRNA expression of GLUT1 (slc2a1) and GLUT3 (slc2a3). In line with our comment just above, the Daniele et al. study may provide for an example of divergence between mRNA and protein expression, since it seemingly showed only minor expression of GLUT1/slc2a1 in the RPE, i.e. precisely in the one cell type that is well-known for its very strong GLUT1 protein expression.

Furthermore, Daniele et al. used a conditional GLUT1 knock-out in photoreceptors induced by repeated Tamoxifen injections. The photoreceptor GLUT1 knock-out led to a relatively mild phenotype with only about 45% of the outer nuclear layer lost over a 4-months time-course. This is in stark contrast with the FCCP or the 1,9-DDF treatment, which would ablate nearly all rod photoreceptors in under one or two weeks, respectively.

As a side note, Tamoxifen is an oestrogen receptor antagonist (with partial agonistic behaviour) with a long history of causing retinal and photoreceptor damage. Notably, oestrogen receptor signalling is important for maintaining photoreceptor viability (Nixon & Simpkins, IOVS 53:4739-47, 2012; Xiong et al., Neuroscience 452:280-294, 2021). Therefore, the relatively minor effects of the conditional GLUT1 KO in photoreceptors found in Daniele et al. may have been confounded by direct tamoxifen photoreceptor toxicity. On a wider level, this possible confounding factor related to the use of Tamoxifen points to general problems associated with certain forms of genetic manipulations.

We now mention the controversy around the expression of glucose transporters in the retina, including the Daniele et al. study in a new subchapter of the discussion on "Expression of glucose transporters in the outer retina” (Lines 454-472).

Rev#3.14. Lines 370-372: FCCP caused a strong cell death phenotype in rods, however under stress rods upregulate the secretion of RdCVF, which leads to cone photoreceptor survival by the upregulation of aerobic glycolysis in cones. The data should be re-interpreted in the context of this previous literature.

Response: We thank the reviewer for this comment; however, we could not find a reference that would state that “…under stress rods upregulate the secretion of RdCVF”. What we did find was a reference stating that similar factors such as thioredoxins (TRX80) are secreted from blood monocytes under stress (Sahaf & Rosén, Antioxid Redox Signal 2:717-26, 2000). However, we consider these cells to be too dissimilar to rod photoreceptors to warrant a corresponding comment. Moreover, the research group who discovered RdCVF originally showed that rod-secreted RdCVF cannot prevent cone degeneration if the corresponding Nxnl1 gene is knocked-out in cones, arguing for a cell-autonomous mechanism of RdCVF -dependent cone protection (Mei et al., Antioxid Redox Signal. 24:909-23, 2016).

Since it is very possible that we may have missed the correct reference(s), we would welcome further guidance by the reviewer.

Rev#3.15. Line 374: 1,9-DDF caused a 90% loss of cones, however, previous studies by Dr. Nancy Philp have shown glucose deprivation in the outer retina affects primarily rod photoreceptors. The differences should be discussed.

Response: We thank the reviewer for directing us to these studies. As mentioned above (Rev#3.13.) the Daniele et al. 2022 study yielded only relatively mild effects for a rod-specific conditional GLUT1 KO on photoreceptor viability. Similarly, in an earlier study (Swarup et al., Am J Physiol Cell Physiol. 316: C121–C133, 2019) the Philp group found that also a GLUT1 KO in the RPE caused only a minor phenotype in the photoreceptor layer. We would argue that if glucose, and by extension aerobic glycolysis, were indeed of major importance for (rod) photoreceptor survival, the degenerative effect of these genetic GLUT1 ablations should have been devastating and should have destroyed most of the outer retina in a matter of days. The fact that this was not seen in both studies is another piece of independent evidence that rod photoreceptors do not rely to any major extent on glycolytic metabolism.

The two studies from the Philp lab (Swarup et al., 2019; Daniele et al., 2022) are now cited in the discussion (Lines 417-419 and 458-460).

Rev#3.16. Line 375: Yes Dr. Claudio Punzo and Dr. Leveillard Thierry along with other groups have shown glycolysis is required to maintain cone survival when under stress, however, the authors should emphasize that it is under stress that this is observed.

Response: In response to this comment we have now specifically extended our corresponding remark in the discussion of the manuscript (Lines 446-447).

Rev#3.17. The section "Cone photoreceptors use the Cahill-cycle". The presence of ALT in photoreceptors was surprising and suggests alternatives to the Cori reaction. However, previous measurements of glucose and lactate from localized in vivo cannulation of animal eyes suggest the majority of glucose taken up by the retina is released back to the blood as lactate. Again, this section should discuss this idea in terms of the previous literature.

Response: Here, we believe the reviewer is referring to studies performed in the late 1990s where, in anaesthetized cats, the lactate concentration in blood samples obtained from choroidal vein cannulation was compared against that in blood samples obtained from femoral arteries (Wang et al., IOVS 38:48-55, 1997). We note that a more relevant in vivo measurement of retinal glucose consumption and lactate production would likely require the simultaneous cannulation of the central retinal artery (CRA) and the central retinal vein (CRV). This would need to be combined with repeated (online) blood sampling, drug applications, and subsequent metabolomic analysis. We are not aware of any in vivo studies where such procedures have been successfully performed and further miniaturization and increased sensitivity of metabolomic analytic equipment will likely be required before such an undertaking may become feasible. Even so, such studies may not be feasible in small rodents (mice, rats) and may instead require larger animal species (e.g. dog, monkey) to overcome limitations in eye and blood sample size.

We have now extended the discussion of our manuscript with a new subchapter on “The retina as an experimental system for studies into neuronal energy metabolism”. Within this new subchapter we now present two different in vivo experimental approaches that addressed retinal energy metabolism (Lines 376-384). Moreover, we now present new data on retinal lactate release to the culture medium, showing, for instance, a strong increase in lactate release in the no RPE condition compared to control (new Supplementary Figure 4).

Rev#3.18. Lines 431-433: The study cited suggested that the mitochondrial AST was detected in other cells, in agreement with the data shown. However, the authors' statements in this section are misleading as they do not take into consideration the contribution of AST from other cell types.

Response: The reviewer is right, we found both AST1 and AST2 to be expressed not only in photoreceptor inner segments but also in the inner retina, especially in the inner plexiform layer (new Figure 6D). Since this might indicate mini-Krebs-cycle activity also in retinal synapses, we have added a corresponding comment to the discussion (Lines 540-543).

Grammatical and wording fixes:

Rev#3.19. Line 98 - "the recycling of the photopigment, retinal."

Response: We have inserted a comma after “photopigment”.

Rev#3.20. Results section and Figure 1 start without providing context for the model system where staining is being done.

Response: We have added this information to the beginning of the results section (Lines 105-106).

Rev#3.21. Supplementary Figure 2 is not mentioned in the main text - there is no context for this figure.

Response: Supplementary Figure 2 was originally referenced in the legend to Figure 2. We now mention supplementary figure 2 (now renumbered to supplementary figure S3) also in the main text, in the results section under “Experimental retinal interventions produce characteristic metabolomic patterns” (Line 148).

Rev#3.22. Volcano plot in Supplementary Figures 3, 5, 6, 7, and 8 don't indicate what Log2(FC) is in reference to.

Response: The log2 fold change (FC) is calculated as follows: log2 (fold change) = log2 (mean metabolite concentration in condition A) - log2 (mean metabolite concentration in condition B) where condition A and condition B are two different experimental groups being compared. This is now explained in the SI Materials and Methods (Lines 145-147) and indicated in abbreviated form in the figure legends. Please note that supplemental figures have now been renumbered due to the insertion of an additional, new Figure.

Rev#3.23. Line 331 - –a“d allowed to analyze the..." ”s incorrect phrasing.

Response: This phrasing was changed.

Rev#3.24. Line 343 "c“cled" ”

Response: This phrasing was changed.

Rev#3.25. Line 446 is misworded.

Response: This phrasing was changed.

Technical questions:

Rev#3.26. At what point after explant was the IHC done in Supplemental Figure 1? If early, but experiments are done later, there's’a chance things are more disorganized at the end of the experiment.

Response: Staining and metabolomics analysis were both done at the end of each experiment, at the same time, at P15. This is now mentioned in the SI materials and methods section (Lines 67, 108-110).

Rev#3.27. FCCP affects plasma membrane permeability, which is particularly critical in neurons that undergo repolarization and depolarization - –ow do we know FCCP on cell death via metabolism? See: https://www.sciencedirect.com/science/article/pii/S2212877813001233

Response: The reviewer is correct, a significant permeabilization of cell membranes in general would likely cause extensive neuronal cell death, unrelated to a disruption of OXPHOS. However, the FCCP concentration used here (5 µM) is at the lower end of what was used in the mentioned Kenwood et al. study (Mol Metab. 3:114-123, 2014) and the effect on cell membrane permeability in tissue culture is likely to be rather small, as opposed to what was seen by Kenwood et al. in cultures of individual cells. This view is supported by the fact that in our FCCP treatments, we did not observe any significant increases of cell death in any retinal cell type (including RPE) other than in rod photoreceptors. Together with the fact that only photoreceptors strongly express Krebs-cycle/OXPHOS related enzymes, this strongly suggests that the FCCP effects seen by us were due to disruption of OXPHOS.

Rev#3.28. Numerous metabolite comparisons are being made throughout the manuscript – what type of multiple hypothesis testing corrections are utilized? Only certain figures mention multiple hypothesis testing (e.g. Figure 6).

Response: In general, in this manuscript we used two different statistical methods: 1) For two-group comparisons, we used an unpaired, two-tailed t-test, which reports a p-value with 95% confidence interval without additional multiple hypothesis testing (e.g. in Figure 2, Suppl. Figures 4, 6, 7, 8). 2) For multiple group comparisons we used a one-way ANOVA analysis with Tukey’s multiple comparisons post-hoc test (except suppl. Figure 9 where Fisher´s LSD post-hoc test was used). The information on which statistical test was used for what dataset is now given in the figure legends and in the SI Material and Methods section.

Rev#3.29. For Figure 3, how do we know that the removal of RPE is causing the metabolite changes due to RPE-PR coupling? How do you rule out the fact that it isn’t just: I – a thicker physical barrier between media and the neural retina that is causing the changes, or II – removal of RPE from PR causes OS shearing and a stress response that alters metabolism?

Response: We believe these concerns can be ruled out: The RPE cells are linked by tight junctions and are not “just a thicker barrier” but a barrier that is almost impermeable for most metabolites unless they are carried by specific transporters. Outer segment shearing via RPE removal would indeed be a concern if we had used adult retina. However, we explanted that retina at P9 when it does not possess any sizeable outer segments yet. As a matter of fact, photoreceptors grow out outer segments only after P9.

Rev#3.30. While 1,9-dideoxyforskolin blocks GLUT1, it is known to have other effects, including on potassium channels. How do we know the effects of 1,9-dideoxyforskolin are specific to GLUT1? Utilizing a GLUT1 KO and showing no additional effects when adding 1,9-dideoxyforskolin would be helpful as a control.

Response: This is a good suggestion from the reviewer. We note that this is technically not easy to achieve as it would require an RPE-specific knock-out that should be inducible at a given experimental time-point, in a quantitative manner. The study by Swarup et al. (see above Rev#3.13.) used an RPE specific knock-out that was, however, not inducible. Moreover, if the corresponding inducible knock-out animals could be generated, then the stochastic nature of the inducing treatment would probably affect only a limited number of cells within a given cell population. In our experimental context, a less than quantitative knock-out would significantly complicate interpretation of results, even to the point that no additional insight might be gained.

Rev#3.31. The analysis in Figure 6, even with attempts to control drug treatments, is highly speculative. One really needs animals with predominately cones vs. predominately rods to do this analysis (e.g. with NRL mice).

Response: The reviewer is right, the analysis shown in Figure 6 was an explorative approach to try and deduce features of rod and cone metabolism. This is now mentioned in the results section (Lines 282-284). Since the experiments were not initially intended to address such questions, by necessity the interpretations remain speculative. The comparison of mouse mutants in which there are either no cones (e.g. cpfl1 mouse) or no rods (e.g. NRL knock-out mouse) may allow to disentangle the metabolic contributions of rods and cones. We appreciate the suggestion from the reviewer and have now inserted a relating suggestion for future studies into the discussion section (Lines 450-452).

Rev#3.32. Overall, much of the paper suggests intriguing pathways, but without C13 tracing or relevant genetic knock-outs, the pathways would have to be speculative rather than definitive.

Response: We agree with the reviewer that further research, including 13C and 15N-tracing studies, will be necessary to evaluate which pathway(s) are used by what retinal cell type under what condition. Still, the high robustness and quantitative nature of the NMR metabolomics data allows us to draw pathway conclusions based on metabolites that are unique to specific pathways/cell types or using ratios. We now relate to the advantages of such carbon-tracing studies in the discussion of the manuscript (Lines 545-549).

Stylistic suggestions:

Rev#3.33. This is a very dense paper to read. It would be helpful for each figure to have a summary diagram of the relevant metabolite changes and how they fit together. Further, for those not metabolism-inclined, defining the mini-Kreb’s, Cahill, and Cori cycles and their brief implications at some point early in the manuscript would be helpful.

Response: We have been thinking a lot about how we could add in the suggested summary diagrams into each figure. Unfortunately, whatever idea we contemplated would have significantly increased the complexity of the figures, while the actual benefit in terms of improved understandability was unclear.

However, we did include the suggestion from the reviewer to present the terms Cori, Cahill-, and mini-Krebs-cycle already in the introduction and we hope that this has improved the understandability of the manuscript overall (Lines 79-92).

Rev#3.34. More discussion about the step-by-step ways that the mini-Kreb’s reaction “uncouples” glycolysis from the Kreb’s cycle would be helpful. What do you mean by “uncouple” in this context?

Response: We thank the reviewer for this suggestion. Uncoupling in this context means that glycolysis and Krebs cycle are not metabolically coupled to each other via pyruvate. Instead both pathways can run independently from each other and in parallel, as long as the Krebs-cycle uses glutamate, BCAAs or other amino acids as fuels. We now also address this point already in the introduction of the manuscript (Lines 87-90).

Conceptual questions:

Rev#3.35. As the proposal that PR undergo heavy amounts of OXPHOS is controversial, it would be helpful for the authors to review the literature on lactate production by the retina and what studies have shown previously about retina use of lactate, specifically lactate making its way into TCA cycle intermediates, suggesting OXPHOS, in PRs.

Response: In response to this question we have added several new references to the introduction and discussion of the manuscript. The question of lactate production (aerobic glycolysis) vs. the use of OXPHOS is now discussed in Lines 77-81, Lines 367-384.

Rev#3.36. Why would cones die more in the no RPE condition? The authors suggest this has something to do with GLUT1 expression on RPE and the transport of glucose to cones. Even if we accept that cones are highly glycolytic, loss of RPE should expose the neural retina to even more glucose in your experimental set-up.

Response: This is a very interesting question from the reviewer. Indeed, loss of the RPE and blood-retinal barrier function should increase photoreceptor access to glucose, even more so if they are expressing high affinity GLUT3. In the discussion (Lines 420-424), we speculate that this may trigger the Crabtree effect, shutting down OXPHOS and causing the cells to exclusively rely on glycolysis. This, however, will likely not yield sufficient ATP to maintain their viability, so that they “starve” to death even in the presence of ample glucose. Since cones require at least twice as much ATP as rods, they may be more sensitive to a Crabtree-dependent shut-down of OXPHOS. However, if this speculation was correct then the question remains why the FCCP treatment, which abolishes OXPHOS more directly, does not cause cone death. Here, we again can only speculate that high glucose may have additional toxic effects on cones that are independent of OXPHOS. We now try to present this reasoning in the discussion (Lines 426-429).

Author Response

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

We thank the reviewers and editors for their comments, as well as for the time dedicated to make useful suggestions that have contributed to improve the manuscript. We have responded to the concerns raised by the reviewers, and after that, we have also responded to the different points highlighted in the Recommendations for the authors:

Reviewer #1

While in vivo injury was used to assess regeneration from subsets of PNS neurons, different in vitro neurite growth or explant assays were used for further assessments. However, the authors did not assess whether the differential "regenerative" responses in vivo could be recapitulated in vitro. Such results will be important in interpreting the results.

We included a supplementary figure evaluating the neurite extension in vitro and updated the text accordingly.

Intriguingly, even in individual groups of PNS neurons, not all neurons regenerate to the same extent. It is known that the distance between the cell body and the lesion site affects neuronal injury responses. It would be interesting to test this in the observed regeneration.

Although it is true that the distance can affect the outcome, here we used a physiological model where all neurons are lesioned at the same point in the nerve. Not only distance is different for motoneurons, but also the microenvironment surrounding their somas and therefore the direct comparison of these neurons with sensory neurons is limited. We extended the discussion on this matter in the new manuscript.

Fig 1: The authors quantified the number of regenerating axons at two different time points. However, the total numbers of neurons/axons in each subset are different. The authors should use these numbers to normalize their regenerative axons.

Figure 1D shows the normalization of data from figure 1C (normalized against the number of control axons in each neuron type). This has been clarified in the text.

Fig 2-5: In explaining differential regeneration of individual groups of neurons, there are at least two possibilities: (1). Each group of neurons has different injury/regenerative responses; (2). The same set of injury/regenerative responses are differentially activated. Some data in this manuscript suggested the latter possibility. But some other data point in the opposite direction. It would be informative for the authors to analyze/discuss this further.

From our point of view, these two options can be considered differential response to injury and could be potentially used for the modulation of regeneration. However, if the second possibility is correct, the regenerative program could be more influenced by the time chosen to study the response. Given the importance of this, we added some discussion about this topic.

Fig 6: Is it possible to assess the regenerative effects of knockdown Med12 after in vivo injury?

It is possible, but it is out of the scope of this work. Here, we aimed to describe the regenerative response and validate our data by testing a potential target for specific regeneration. Future studies will focus on the modulation of this specific regeneration both in vitro and in vivo.

Reviewer #2

It seems that the most intriguing outcome of this paper revolves around the role of Med12 in nerve regeneration. The authors should prioritize this finding. Drawing a conclusion regarding Med12's role in proprioceptor regeneration based solely on this in vitro model may be insufficient. This noteworthy result requires further investigation using more animal models of nerve regeneration.

The main goal of this work was to compare the regenerative responses of different neuron subpopulations. We modulated Med12 to validate our data and the potential of our findings. Unfortunately, investigating in depth the role of Med12 in regeneration is out of the scope of this paper. For this reason, we did not prioritise this finding here. As this finding was striking, we strongly agree that the next step should be studying how it modulates regeneration.

One critique revolves around the authors' examination of only a single time point within the dynamic and continuously evolving process of regeneration/reinnervation. Given that this process is characterized by dynamic changes, some of which may not be directly associated with active axon growth during regeneration, and encompasses a wide range of molecular alterations throughout reinnervation, concentrating solely on a single time point could result in the omission of critical molecular events.

We agree that this is probably the main limitation of this study, as we discussed in the text. We chose 7 days postinjury as a standard time point widely described in literature and to have a correlate with our histological data. Although the main aim was to compare populations, analyzing an additional time point after injury could add valuable information.

Reviewer #3

No concerns were expressed by that reviewer.

Recommendations for the authors:

The authors should assess whether the differential "regenerative" responses in vivo could be recapitulated in vitro.

We included a supplementary figure evaluating the neurite extension in vitro and updated the text accordingly.

Optional:

It will be interesting to test if the distances between the cell body and the lesion site contribute to the observed differences in individual subsets of PNS neurons.

Figure 1D shows the normalization of data from figure 1C (normalized against the number of control axons in each neuron type). This has been clarified in the text.

Fig 2-5: In explaining differential regeneration of individual groups of neurons, there are at least two possibilities: (1). Each group of neurons has different injury/regenerative responses; (2). The same set of injury/regenerative responses are differentially activated. Some data in this manuscript suggested the latter possibility. But some other data point in the opposite direction. At least the authors should discuss these.

From our point of view, these two options can be considered differential response to injury and could be potentially used for the modulation of regeneration. However, if the second possibility is correct, the regenerative program could be more influenced by the time chosen to study the response. Given the importance of this, we added some discussion about this topic.

While the paper is technically well-executed, the conclusions and some of the findings appear to be incomplete and challenging to draw meaningful conclusions from. This manuscript presents some interesting findings, but the title is quite broad and may suggest that the authors have unveiled fundamental mechanisms explaining the varying regenerative abilities of peripheral axons. However, the results do not substantiate such a conclusion. Further comments and suggestions follow.

We eliminated the word “regenerative (response)” from the title, as it could lead to think that all changes seen in these neurons are related only to regeneration. We think that “Neuron-specific RNA-sequencing reveals different responses in peripheral neurons after nerve injury” highlights the differences between neurons that we found without misleading towards thinking that we described regenerative mechanisms in all neurons.

What's notably absent here is the validation of certain genes found with the ribosomes, especially those highlighted in the subsequent figures. The question arises as to whether the changes depicted in the figures align with changes in the DRGs in vivo. Is there concordance between the presence of these genes and their transcriptional changes? It would greatly enhance the study's value if the authors could show evidence of upregulation or downregulation of certain genes over time in tissue sections, utilizing techniques such as in situ hybridization or immunocytochemistry.

We selected some factors that were specifically upregulated in subsets of neurons to corroborated by immunohistochemistry these findings. Changes in the immunofluorescence of P75 in motoneurons and ATF2 in cutaneous mechanoreceptors, were evaluated in controls and animals that received a nerve crush one week before. Supplementary figures with the images have been added.

The authors discovered intriguing distinctions, such as the presence of specific signaling pathways unique to neurons projecting to muscle as opposed to those projecting to the skin. Among these pathways were those associated with receptor tyrosine kinases like VEGF, erbB, and neurotrophin signaling among others. The question now arises: do these pathways play a role in natural peripheral regeneration processes? To answer this, it is imperative to conduct in vivo studies. However, the authors employed an in vitro DRG neurite outgrowth assay to demonstrate that various types of neurons exhibit different responses to the presence of different neurotrophins. This does not reflect what actually happens in vivo. While neurotrophins indeed play a role in neuron survival and axon extension during development, their role in postnatal periods changes over time, and it remains unclear whether they play any role in the natural regenerative processes of the peripheral nerve. Therefore, this experiment may not be directly relevant in this case, especially during the early axon extension period of the regenerating axons. if the authors aim to establish a causal link with neurotrophin signaling, it becomes crucial to conduct in vivo experiments by manipulating the expression of key molecules like the receptors.

It has been widely described that different types of peripheral neurons have a differential expression of Trk receptors, even in the adult, and that these respond differentially to neurotrophins. In our study, we do not stablish a causal relationship between the expression of Trk and neurite extension, but instead we show (as many others) that distinct neurons respond differentially to these neurotrophins. The fact that in vivo studies fail to show a clear effect does not necessarily mean that neurotrophins are not specific. It might mean that their effect is not strong enough to be a useful guide in the complex microenvironment found after an injury. For instance, NGF acts on TrkA (present in some neurons), but in vivo it has been shown to accelerate the clearance of myelin debris in Schwann cells (Li et al., 2020), which could facilitate regeneration of all type of axons, masking any potential specific effect on the subtypes of neurons expressing TrkA. In contrast, in an in vitro setting on neuronal cultures, the specific neuronal effect can be more evident.

Additionally, it's worth noting that another paper utilizing the same methodology and experimental setup (PMID: 29756027, "Translatome Regulation in Neuronal Injury and Axon Regrowth" by Rozenbaum et al.) exists. Are there any significant differences or shared findings with that study?

This study shows the transcriptomic response after an injury 4, 12 and 24 hours after an injury in a very similar experimental setup. They focus on comparing the neuronal vs the glial response to the injury, using a Ribotag line that tags ribosomes from all neurons in the DRG rather than specific neuron subtypes. As the time postinjury (24h vs 7 days) and the cell types studied are different, we could not directly compare our results. We did see an upregulation in both datasets of previously described growth-associated genes (Jun, Atf3, Sox11, Sprr1a, Gal…). We included the article in the references for its relevance in the topic.

It would be helpful for readers to illustrate the finding of the fastest axon regeneration of nociceptors by showing fluorescence micrographs of the nerve samples in addition to the graphs shown in Fig. 1 C/D.

In figure 1B, we show fluorescence micrographs of the nerves 7 days postinjury. As explained in the results, we counted the number of axons at 2 distances from the injury, we did not analyse the fastest axon. This is due to technical reasons: 7 days after the injury the fastest axon has surpassed our evaluation point, which was the further distance that we could assess in our experimental setting in a consistent manner. If the reviewer thinks that we need to include more images from our evaluations (from 9 dpi for example), we could prepare a new figure.

The labeling in Fig. 2B is confusing. Is the CHAT immunoreactivity shown in the last panel illustrated by green or red signals? Is the red signal counterstaining with beta-tubulin?

The labelling was changed in the figure to increase clarity.

The references to the supplementary data throughout the manuscript are confusing. For example, where can the "Supp data 2" be found? (mention on p. 14 in the merged pdf file). Are they referring to the Excel spreadsheets?

We divided the supplementary material in supplementary figures/table (found in the pdf) and supplementary data. Supplementary data refers to excel spreadsheets found outside the pdf file. We hope this will be clearer after the final formatting of the article.

What does the following statement on p. 14 mean?: "The caveat in these analyses was that molecular classification by these approaches may be arbitrary, and not reflective of protein repurposing." This reviewer notes that these databases consider the fact that components participate in different pathways.

Indeed, we aimed to explain that many proteins participate in different pathways, and this is a limitation of the enrichment analysis. We modified the sentence in the text.

First paragraph on p. 15: The PPAR and AMPK pathways have much broader roles, and are not only "related to fatty acid metabolism". This factual inaccuracy should be corrected in the manuscript.

The sentence has been corrected.

The authors should consider showing increased TGF-beta signaling in their neurons after downregulation of Med12 given the previous implication of TGF-beta signaling in axon regeneration.

We tried to demonstrate the effect of our knockdown in TGF-beta pathway by analyzing the expression of typical targets from this pathway by qPCR in our cultures. However, we could not detect any difference. We think that this can have two explanations: (1) as only a few cells upregulate Med12 whereas many cells downregulate it, the effect is masked (presumably only proprioceptors will have a significant difference in this pathway and, thus, it would be very difficult to see the effect), or (2) Med12 is not exerting its effect through this pathway. We added a supplementary figure with these data and discussed it in the manuscript.

It would be helpful to eliminate typos and improve syntax/grammar/style.

We revised the text to improve style.

Author Response

Public Reviews:

Reviewer #1

Strengths:

Overall, the work is novel and moves the field of Alzheimer's disease forward in a significant way. The manuscript reports a novel concept of aberrant activity in VIP interneurons during the early stages of AD thus contributing to dysfunctions of the CA1 microcircuit. This results in the enhancement of the inhibitory tone on the primary cells of CA1. Thus, the disinhibition by VIP interneurons of Principal Cells is dampened. The manuscript was skillfully composed, and the study was of strong scientific rigor featuring well-designed experiments. Necessary controls were present. Both sexes were included.

We express our gratitude to the reviewer for their keen appreciation of our efforts and their enthusiasm for the outcomes of this research.

Limitations:

(1) The authors attributed aberrant circuit activity to the accumulation of "Abeta intracellularly" inside IS-3 cells. That is problematic. 6E10 antibody recognizes amyloid plaques in addition to Amyloid Precursor Protein (APP) as well as the C99 fragment. There are no plaques at the ages 3xTg mice were examined. Thus, the staining shown in Figure 1a is of APP/C99 inside neurons, not abeta accumulations in neurons. At the ages of 3-6 months, 3xTg starts producing abeta oligomers and potentially tau oligomers as well (Takeda et al., 2013 PMID: 23640054; Takeda et al., 2015 PMID: 26458742 and others). Emerging literature suggests that abeta and tau oligomers disrupt circuit function. Thus, a more likely explanation of abeta and tau oligomers disrupting the activity of VIP neurons is plausible.

The Reviewer correctly points out that 3xTg-AD mice typically do not exhibit plaques before 6 months of age, with limited amounts even up to 12 months, particularly in the hippocampus. To the best of our knowledge, the 6E10 antibody binds to an epitope in APP (682-687) that is also present in the Abeta (3-8) peptide. Consequently, 6E10 detects full-length APP, α-APP (soluble alpha-secretase-cleaved APP), and Abeta (LaFerla et al., 2007). Nonetheless, we concur with the Reviewer's observation that the detected signal includes Abeta oligomers and the C99 fragment, which is currently considered an early marker of AD pathology (Takasugi et al., 2023; Tanuma et al., 2023). Studies have demonstrated intracellular accumulation of C99 in 3-month-old 3xTg mice (Lauritzen et al., 2012), and its binding to the Kv7 potassium channel family, which results in inhibiting their activity (Manville and Abbott, 2021). If a similar mechanism operates in IS-3 cells, it could explain the changes in their firing properties observed in our study. Consequently, we will revise the manuscript to include this crucial information in both the Results and Discussion sections.

(2) Authors suggest that their animals do not exhibit loss of synaptic connections and show Figure 3d in support of that suggestion. However, imaging with confocal microscopy of 70micron thick sections would not allow the resolution of pre- and post-synaptic terminals. More sensitive measures such as electron microscopy or array tomography are the appropriate techniques to pursue. It is important for the authors to either remove that data from the manuscript or address the limitations of their technique in the discussion section. There is a possibility of loss of synaptic connections in their mouse model at the ages examined.

We appreciate the Reviewer’s perspective on the techniques used for imaging synaptic connections. While we acknowledge the limitations of confocal microscopy for resolving pre- and post-synaptic structures in thick sections, we respectfully disagree regarding the exclusive suitability of electron microscopy (EM). Our approach involved confocal 3D image acquisition using a 63x objective at 0.2 um lateral resolution and 0.25 Z-step, providing valuable quantitative insights into synaptic bouton density. Despite the challenges posed by thick sections, this method together with automatic analysis allows for careful quantification. Although EM offers unparalleled resolution, it presents challenges in quantification. We will ensure to include the important details regarding image acquisition and analysis in the revised manuscript.

Reviewer #2 (Public Review):

Summary:

The submitted manuscript by Michaud and Francavilla et al., is a very interesting study describing early disruptions in the disinhibitory modulation exerted by VIP+ interneurons in CA1, in a triple transgenic model of Alzheimer's disease. They provide a comprehensive analysis at the cellular, synaptic, network, and behavioral level on how these changes correlate and might be related to behavioral impairments during these early stages of the disease.

Main findings:

3xTg mice show early Aß accumulation in VIP-positive interneurons.

3xTg mice show deficits in a spatially modified version of the novel object recognition test. - 3xTg mice VIP cells present slower action potentials and diminished firing frequency upon current injection.

3xTg mice show diminished spontaneous IPSC frequency with slower kinetics in Oriens / Alveus interneurons.

3xTg mice show increased O/A interneuron activity during specific behavioral conditions.

3xTg mice show decreased pyramidal cell activity during specific behavioral conditions.

Strengths:

This study is very important for understanding the pathophysiology of Alzheimer´s disease and the crucial role of interneurons in the hippocampus in healthy and pathological conditions.

We are thankful to the reviewer for their insightful recognition of our efforts and their enthusiasm for the results of this research.

Weaknesses:

Although results nicely suggest that deficits in VIP physiological properties are related to the differences in network activity, there is no demonstration of causality.

RE: We completely agree with the reviewer's observation regarding the lack of demonstration of causality in our results. Investigating causality in the relationship between deficits in VIP physiological properties and differences in network activity is indeed a crucial aspect of this project. However, achieving this goal will require a significant amount of time and dedicated manipulations in a new mouse model (VIP-Cre-3xTg). We appreciate the importance of this line of investigation and consider it as a priority for our future research endeavors.

Author Response

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

eLife assessment

This fundamental study provides an unprecedented understanding of the roles of different combinations of NaV channel isoforms in nociceptors' excitability, with relevance for the design of better strategies targeting NaV channels to treat pain. Although the experimental combination of electrophysiological, modeling, imaging, molecular biology, and behavioral data is convincing and supports the major claims of the work, some conclusions need to be strengthened by further evidence or discussion. The work may be of broad interest to scientists working on pain, drug development, neuronal excitability, and ion channels.

Reviewer #1 (Public Review):

Summary:

In this work, Xie, Prescott, and colleagues have reevaluated the role of Nav1.7 in nociceptive sensory neuron excitability. They find that nociceptors can make use of different sodium channel subtypes to reach equivalent excitability. The existence of this degeneracy is critical to understanding neuronal physiology under normal and pathological conditions and could explain why Nav subtype-selective drugs have failed in clinical trials. More concretely, nociceptor repetitive spiking relies on Nav1.8 at DIV0 (and probably under normal conditions in vivo), but on Nav1.7 and Nav1.3 at DIV4-7 (and after inflammation in vivo).

The conclusions of this paper are mostly well supported by data, and these findings should be of broad interest to scientists working on pain, drug development, neuronal excitability, and ion channels.

Strengths:

(1.1) The authors have employed elegant electrophysiology experiments (including specific pharmacology and dynamic clamp) and computational simulations to study the excitability of a subpopulation of DRGs that would very likely match with nociceptors (they take advantage of using transgenic mice to detect Nav1.8-expressing neurons). They make a strong point showing the degeneracy that occurs at the ion channel expression level in nociceptors, adding this new data to previous observations in other neuronal types. They also demonstrate that the different Nav subtypes functionally overlap and are able to interchange their "typical" roles in action potential generation. As Xie, Prescott, and colleagues argue, the functional implications of the degenerate character of nociceptive sensory neuron excitability need to be seriously taken into account regarding drug development and clinical trials with Nav subtype-selective inhibitors.

Weaknesses:

(1.2) The next comments are minor criticisms, as the major conclusions of the paper are well substantiated. Most of the results presented in the article have been obtained from experiments with DRG neuron cultures, and surely there is a greater degree of complexity and heterogeneity about the degeneracy of nociceptors excitability in the "in vivo" condition. Indeed, the authors show in Figures 7 and 8 data that support their hypothesis and an increased Nav1.7's influence on nociceptor excitability after inflammation, but also a higher variability in the nociceptors spiking responses. On the other hand, DRG neurons targeted in this study (YFP (+) after crossing with Nav1.8-Cre mice) are >90% nociceptors, but not all nociceptors express Nav1.8 in vivo. As shown by Li et al., 2016 ("Somatosensory neuron types identified by high-coverage single-cell RNA-sequencing and functional heterogeneity"), there is a high heterogeneity of neuron subtypes within sensory neurons. Therefore, some caution should be taken when translating the results obtained with the DRG neuron cultures to the more complex "in vivo" panorama.

We agree that most but not all Nav1.8+ DRG cells are nociceptors and that not all nociceptors express Nav1.8. We targeted small neurons that also express (or at some point expressed) Nav1.8, thus excluding larger neurons that express Nav1.8. This allowed us to hone in on a relatively homogeneous set of neurons, which is crucial when testing different neurons to compare between conditions (as opposed to testing longitudinally in the same neuron, which is not feasible). We expect all neurons are degenerate but likely on the basis of different ion channel combinations. Indeed, even within small Nav1.8+ neurons, other channels that we did not consider likely contribute to the degenerate regulation (as now better reflected in the revised Discussion).

That said, there are multiple sources of heterogeneity. We suspect that heterogeneity is more increased after inflammation than after axotomy because all DRG neurons experience axotomy when cultured whereas neurons experience inflammation differently in vivo depending on whether their axon innervates the inflamed area (now explained on lines 214-215). This is not so much about whether the insult occurs in vivo or in vitro, but about how homogeneously neurons are affected by the insult. Granted, neurons are indeed more likely to be heterogeneously affected in vivo since conditions are more complex. But our goal in testing PF-71 in behavioral tests (Fig. 8) was to show that changes observed in nociceptor excitability in Figure 7, despite heterogeneity, were predictive of changes in drug efficacy. In short, we establish Nav interchangeability by comparing neurons in culture (Figs 1-6), but we then show that similar Nav shifts can develop in vivo (Fig 7) with implications for drug efficacy (Fig 8). Such results should alert readers to the importance of degeneracy for drug efficacy (which is our main goal) even without a complete picture of nociceptor degeneracy or DRG neuron heterogeneity. Additions to the Discussion (lines 248-259, 304-308) are intended to highlight these considerations.

(1.3) Although the authors have focused their attention on Nav channels, it should be noted that degeneracy concerning other ion channels (such as potassium ion channels) could also impact the nociceptor excitability. The action potential AHP in Figure 1, panel A is very different comparing the DIV0 (blue) and DIV4-7 examples. Indeed, the conductance density values for the AHP current are higher at DIV0 than at DIV7 in the computational model (supplementary table 5). The role of other ion channels in order to obtain equivalent excitability should not be underestimated.

We completely agree. We focused on Nav channels because of our initial observation with TTX and because of industry’s efforts to develop Nav subtype-selective inhibitors, whose likelihood of success is affected by the changes we report. But other channels are presumably changing, especially given observed changes in the AHP shape (now mentioned on lines 304-308). Investigation should be expanded to include these other channels in future studies.

Reviewer #2 (Public Review):

Summary:

The authors have noted in preliminary work that tetrodotoxin (TTX), which inhibits NaV1.7 and several other TTX-sensitive sodium channels, has differential effects on nociceptors, dramatically reducing their excitability under certain conditions but not under others. Partly because of this coincidental observation, the aim of the present work was to re-examine or characterize the role of NaV1.7 in nociceptor excitability and its effects on drug efficacy. The manuscript demonstrates that a NaV1.7-selective inhibitor produces analgesia only when nociceptor excitability is based on NaV1.7. More generally and comprehensively, the results show that nociceptors can achieve equivalent excitability through changes in differential NaV inactivation and NaV expression of different NaV subtypes (NaV 1.3/1.7 and 1.8). This can cause widespread changes in the role of a particular subtype over time. The degenerate nature of nociceptor excitability shows functional implications that make the assignment of pathological changes to a particular NaV subtype difficult or even impossible.

Thus, the analgesic efficacy of NaV1.7- or NaV1.8-selective agents depends essentially on which NaV subtype controls excitability at a given time point. These results explain, at least in part, the poor clinical outcomes with the use of subtype-selective NaV inhibitors and therefore have major implications for the future development of Nav-selective analgesics.

Strengths:

(2.1) The above results are clearly and impressively supported by the experiments and data shown. All methods are described in detail, presumably allow good reproducibility, and were suitable to address the corresponding question. The only exception is the description of the computer model, which should be described in more detail.

We failed to report basic information such as the software, integration method and time step in the original text. This information is now provided on lines 476-477. Notably, the full code is available on ModelDB plus all equations including the values for all gating parameters are provided in Supplementary Table 5 and values for maximal conductance densities for DIV0 and DIV7 models are provided in Supplementary Table 6. Changes in conductance densities to simulate different pharmacological conditions are reported in the relevant figure legends (now shown in red). We did not include model details in the main text to avoid disrupting the flow of the presentation, but all the model details are reported in the Methods, tables and/or figure legends.

(2.2) The results showing that nociceptors can achieve equivalent excitability through changes in differential NaV inactivation and expression of different NaV subtypes are of great importance in the fields of basic and clinical pain research and sodium channel physiology and pharmacology, but also for a broad readership and community. The degenerate nature of nociceptor excitability, which is clearly shown and well supported by data has large functional implications. The results are of great importance because they may explain, at least in part, the poor clinical outcomes with the use of subtype-selective NaV inhibitors and therefore have major implications for the future development of Nav-selective analgesics.

In summary, the authors achieved their overall aim to enlighten the role of NaV1.7 in nociceptor excitability and the effects on drug efficacy. The data support the conclusions, although the clinical implications could be highlighted in a more detailed manner.

Weaknesses:

As mentioned before, the results that nociceptors can achieve equivalent excitability through changes in differential NaV inactivation and NaV expression of different NaV subtypes are impressive. However, there is some "gap" between the DRG culture experiments and acutely dissociated DRGs from mice after CFA injection. In the extensive experiments with cultured DRG neurons, different time points after dissociation were compared. Although it would have been difficult for functional testing to examine additional time points (besides DIV0 and DIV47), at least mRNA and protein levels should have been determined at additional time points (DIV) to examine the time course or whether gene expression (mRNA) or membrane expression (protein) changes slowly and gradually or rapidly and more abruptly.

Characterizing the time course of NaV expression changes is worthwhile but, insofar as such details are not necessary to establish that excitability is degenerate, it was not include in the current study. Furthermore, since mRNA levels do not parallel the functional changes in Nav1.7 (Figure 6A), we do not think it would be helpful to measure mRNA levels at intermediate time points. Measuring protein levels would be more informative, however, as now explained on lines 362-369, neurons were recorded at intermediate time points in initial experiments and showed a lot of variability. Methods that could track fluorescently-tagged NaV channels longitudinally (i.e. at different time points in the same cell) would be well suited for this sort of characterization, but will invariably lead to more questions about membrane trafficking, phosphorylation, etc. We agree that a thorough characterization would be interesting but we think it is best left for a future study.

It would also be interesting to clarify whether the changes that occur in culture (DIV0 vs. DIV47) are accompanied by (pro-)inflammatory changes in gene and protein expression, such as those known for nociceptors after CFA injection. This would better link the following data demonstrating that in acutely dissociated nociceptors after CFA injection, the inflammationinduced increase in NaV1.7 membrane expression enhances the effect of (or more neurons respond to) the NaV1.7 inhibitor PF-71, whereas fewer CFA neurons respond to the NaV1.8 inhibitor PF-24.

These are some of the many good questions that emerge from our results. We are not particularly keen to investigate what happens over several days in culture, since this is not so clinically relevant, but it would be interesting to compare changes induced by nerve injury in vivo (which usually involves neuroinflammatory changes) and changes induced by inflammation. Many previous studies have touched on such issues but we are cautious about interpreting transcriptional changes, and of course all of these changes need to be considered in the context of cellular heterogeneity. It would be interesting to decipher if changes in NaV1.7 and NaV1.8 are directly linked so that an increase in one triggers a decrease in the other, and vice versa. But of course many other channels are also likely to change (as discussed above), and they too warrant attention, which makes the problem quite difficult. We look forward to tackling this in future work.

The results shown explain, at least in part, the poor clinical outcomes with the use of subtypeselective NaV inhibitors and therefore have important implications for the future development of Nav-selective analgesics. However, this point, which is also evident from the title of the manuscript, is discussed only superficially with respect to clinical outcomes. In particular, the promising role of NaV1.7, which plays a role in nociceptor hyperexcitability but not in "normal" neurons, should be discussed in light of clinical results and not just covered with a citation of a review. Which clinical results of NaV1.7-selective drugs can now be better explained and how?

We wish to avoid speculating on which particular clinical results are better explained because our study was not designed for that. Instead, our take-home message (which is well supported; see Discussion on lines 309-321) is that NaV1.7-selective drugs may have a variable clinical effect because nociceptors’ reliance on NaV1.7 is itself variable – much more than past studies would have readers believe. At the end of the results (line 235), which is, we think, what prompted the reviewer’s comment, we point to the Discussion. The corollary is that accounting for degeneracy could help account for variability in drug efficacy, which would of course be beneficial. The challenge (as highlighted in the Abstract, lines 21-22) is that identifying the dominant Nav subtype to predict drug efficacy is difficult. We certainly don’t have all the answers, but we hope our results will point readers in a new direction to help answer such questions.

Another point directly related to the previous one, which should at least be discussed, is that all the data are from rodents, or in this case from mice, and this should explain the clinical data in humans. Even if "impediment to translation" is briefly mentioned in a slightly different context, one could (as mentioned above) discuss in more detail which human clinical data support the existence of "equivalent excitability through different sodium channels" also in humans.

We are not aware of human data that speak directly to nociceptor degeneracy but degeneracy has been observed in diverse species; if anything, human neurons are probably even more degenerate based on progressive expansion of ion channel types, splice variants, etc. over evolution. Of course species differences extend beyond degeneracy and are always a concern for translation, because of a species difference in the drug target itself or because preclinical pain testing fails to capture the most clinically important aspects of pain (which we mention on line 35). Line 39 now reiterates that these explanations for translational difficulties are not mutually exclusive, but that degeneracy deserves greater consideration that is has hitherto received. Indeed, throughout our paper we imply that degeneracy may contribute to the clinical failure of Nav subtype-specific drugs, but those failures are certainly not evidence of degeneracy. In the Discussion (line 320-321), we now cite a recent review article on degeneracy in the context of epilepsy, and point out how parallels might help inform pain research. We wish we had a more direct answer to the reviewer’s request; in the absence of this, we hope our results motivate readers to seek out these answers in future research.

Although speculative, it would be interesting for readers to know whether a treatment regimen based on "time since injury" with NaV1.7 and NaV1.8 inhibitors might offer benefits. Based on the data, could one hypothesize that NaV1.7 inhibitors are more likely to benefit (albeit in the short term) in patients with neuropathic pain with better patient selection (e.g., defined interval between injury and treatment)?

We like that our data prompt this sort of prediction. However, this is potentially complicated since the injury may be subtle, which is to say that the exact timing may not be known. There are scenarios (e.g. postoperative pain) where the timing of the insult is known, but in other cases (e.g. diabetic neuropathy) the disease process is quite insidious, and different neurons might have progressed through different stages depending on how they were exposed to the insult. Our own experiments with CFA are a case in point. Notwithstanding the potential difficulties about gauging the time course, any way of predicting which Nav subtype is dominant could help more strategically choose which drug to use.

Reviewer #3 (Public Review):

Summary:

In this study, the authors used patch-clamp to characterize the implication of various voltagegated Na+ channels in the firing properties of mouse nociceptive sensory neurons. They report that depending on the culture conditions NaV1.3, NaV1.7, and NaV1.8 have distinct contributions to action potential firing and that similar firing patterns can result from distinct relative roles of these channels. The findings may be relevant for the design of better strategies targeting NaV channels to treat pain.

Strengths:

The paper addresses the important issue of understanding, from an interesting perspective, the lack of success of therapeutic strategies targeting NaV channels in the context of pain. Specifically, the authors test the hypothesis that different NaV channels contribute in a plastic manner to action potential firing, which may be the reason why it is difficult to target pain by inhibiting these channels. The experiments seem to have been properly performed and most conclusions are justified. The paper is concisely written and easy to follow.

Weaknesses:

(1) The most critical issue I find in the manuscript is the claim that different combinations of NaV channels result in equivalent excitability. For example, in the Abstract it is stated that: "...we show that nociceptors can achieve equivalent excitability using different combinations of NaV1.3, NaV1.7, and NaV1.8". The gating properties of these channels are not identical, and therefore their contributions to excitability should not be the same. I think that the culprit of this issue is that the authors reach their conclusion from the comparison of the (average) firing rate determined over 1 s current stimulation in distinct conditions. However, this is not the only parameter that determines how sensory neurons convey information. For instance, the time dependence of the instantaneous frequency, the actual firing pattern, may be important too. Moreover, the use of 1 s of current stimulation might not be sufficient to characterize the firing pattern if one wants to obtain conclusions that could translate to clinical settings (i.e., sustained pain). A neuron in which NaV1.7 is the main contributor is expected to have a damping firing pattern due to cumulative channel inactivation, whereas another depending mainly on NaV1.8 is expected to display more sustained firing. This is actually seen in the results of the modelling.

This concern seems to boil down to how equivalent is equivalent? The spike shape or the full inputoutput curve for a DIV0 neuron (Nav1.8-dominant) is never equivalent to what’s seen in a DIV47 neuron (Nav1.7-dominant), but nor are any two DIV0 neurons strictly equivalent, and likewise for any two DIV4-7 neurons. Our point is that DIV0 and DIV4-7 neurons are a far more similar (less discriminable) in their excitability than expected from the qualitative difference in their TTX sensitivity (and from repeated claims in the literature that Nav1.7 is necessary for spike generation in nociceptors). Nav isoforms need not be identical to operate similarly; for instance, Nav1.8 tends to activate at “suprathreshold” voltages, but this depends on the value of threshold; if threshold increases, Nav1.8 can activate at subthreshold voltages (see Fig 5). We have modified lines 155- 175 to help clarify this.

We completely agree that firing rate is not the only way to convey sensory information, and of course injecting current directly into the cell body via a patch pipette is not a natural stimulus. These are all factors to keep in mind when interpreting our data. Nonetheless, our data show that excitability is similar between DIV0 and DIV 4-7, so much so that data from any one neuron (without pharmacological tests or capacitance measurements) would likely not reveal if that cell is DIV0 or DIV4-7; this “indiscriminability” qualifies as “equivalent” for our purposes, and is consistent with phrasing used by other authors studying degeneracy. Notably, not every DIV4-7 neuron exhibits spike height attenuation (see Fig. 1A), likely because of concomitant changes in the AHP that were not captured in our computer model or directly tested in our experiments. This highlights that other channel changes may also contribute to degeneracy and the maintenance of repetitive spiking.

(2) In Fig. 1, is 100 nM TTX sufficient to inhibit all TTX-sensitive NaV currents? More common in literature values to fully inhibit these currents are between 300 to 500 nM. The currents shown as TTX-sensitive in Fig. 1D look very strange (not like the ones at Baseline DIV4-7). It seems that 100 nM TTX was not enough, leading to an underestimation of the amplitude of the TTXsensitive currents.

As now summarized in Supplementary Table 3 (which is newly added), 100 nM TTX is >20x the EC50 for Nav1.3 and Nav1.7 (but is still far below the EC50 for Nav1.8). Based on this, TTXsensitive channels are definitely blocked in our TTX experiments.

(3) Page 8, the authors conclude that "Inflammation caused nociceptors to become much more variable in their reliance of specific NaV subtypes". However, how did the authors ensure that all neurons tested were affected by the CFA model? It could be that the heterogeneity in neuron properties results from distinct levels of effects of CFA.

We agree with the reviewer. We also believe that variable exposure to CFA is the most likely explanation for the heightened variability in TTX-sensitivity reported in Figure 7 (now more clearly explained on lines 214-215). One could try co-injecting a retrograde dye with the CFA to label cells innervating the injection site, but differential spread of the CFA and dye are liable to preclude any good concordance. Alternatively, a pain model involving more widespread (systemic) inflammation might cause a more homogeneous effect. But, our main goal with CFA injections was to show that a Nav1.8®Nav1.7 switch can occur in vivo (and is therefore not unique to culturing), and that demonstration is true even if some neurons do not switch. Subsequent testing in Figure 8 shows that enough neurons switch to have a meaningful effect in terms of the behavioral pharmacology. So, notwithstanding tangential concerns, we think our CFA experiments succeeded in showing that Nav channels can switch in vivo and that this impacts drug efficacy.

Recommendations for the authors:

All reviewers agreed that these results are solid and interesting. However, the reviewers also raised several concerns that should be addressed by the authors to improve the strength of the evidence presented. Revisions considered to be essential include:

(1) Discuss how degeneracy concerning other ion channels (such as potassium ion channels) could also impact nociceptor excitability (reviewer #1). Additionally, the translation of results from DRG neuron cultures to "in vivo" nociceptors should be better discussed.

We have added a new paragraph to the Discussion (line 248-259) to remind readers that despite our focus on Nav channels, other ion channels likely also change (and that these changes involve diverse regulatory mechanisms that require further investigation). Likewise, despite our focus on the changes caused by culturing neurons, we remind readers that subtler, more clinically relevant in vivo perturbations can likewise cause a multitude of changes. We end that paragraph by emphasizing that although accounting for all the contributing components is required to fully understand a degenerate system, meaningful progress can be made by studying a subset of the components. We want to emphasize this because there is some middle ground between focusing on one component at a time (which is the norm) vs. trying to account for everything (which is an infeasible ideal). Additional text on lines 304-308 also addresses related points.

(2) Discuss how different combinations of NaV channels result in equivalent excitability, in the context of the experimental conditions used (see main comment by reviewer #3). It should also be discussed in more detail which human clinical data support the existence of "equivalent excitability through different sodium channels" also in humans (reviewer #2).

Regarding the first part of this comment, reviewer 3 wrote in the public review that “The gating properties of these channels are not identical, and therefore their contributions to excitability should not be the same.” Differences in gating properties are commonly used to argue that different Nav subtypes mediate different phases of the spike, for example, that Nav1.7 initiates the spike whereas Nav1.8 mediates subsequent depolarization because Nav1.7 and Nav1.8 activate at perithreshold and suprathrehold voltages, respectively (see lines 134-135, now shown in red). But such comparison is overly simplistic insofar as it neglects the context in which ion channels operate. For instance, if Nav1.7 is not expressed or fully inactivates, voltage threshold will be less negative, enabling Nav1.8 to contribute to spike initiation; in other words, previously “suprathreshold” voltages become “perithreshold”. Figure 5 is dedicated to explaining this context-sensitivity; specifically, we demonstrate with simulations how Nav1.8 takes over responsibility for initiating a spike when Na1.7 is absent or inactivated. Text on lines 155- 184 has been edited to help clarify this. Regarding the second part of this comment, we are not aware of any direct evidence from human sensory neurons that different sodium channels produce equivalent excitability, but that is certainly what we expect. We suggest that failure of Nav subtype-specific drugs is, at least in part, because of degeneracy, but such failures do not demonstrate degeneracy unless other contributing factors can be excluded (which they can’t). Recognizing degeneracy is difficult, and so variability that might be explained by degeneracy will go unexplained or attributed to other factors unless, by design or serendipity, experiments quantify the effects of degeneracy (as we have attempted to do here). We now cite a recent review article on degeneracy and epilepsy (line 320), which addresses relevant themes that might help inform pain research; for instance, most existing antiseizure medications act on multiple targets whereas more recently developed single-target drugs have proven largely ineffective. This is similar to but better documented than for analgesics. With this in mind, we revised the text to emphasize the circumstantial nature of existing evidence and the need to test more directly for degeneracy (lines 320-323).

(3) Extend the discussion about the poor clinical outcomes with the use of subtype-selective NaV inhibitors. In particular, the promising role of NaV1.7, which plays a role in nociceptor hyperexcitability but not in "normal" neurons, should be discussed in light of clinical results and not just covered with a citation of a review. Which clinical results of NaV1.7-selective drugs can now be better explained and how? (reviewer #2)

As discussed above, we are cautious avoid speculating on which clinical results are attributable to degeneracy. Instead, our take-home message (see Discussion, lines 309-323) is that NaV1.7selective drugs may have a variable clinical effect because nociceptors’ reliance on NaV1.7 is itself variable – much more than past studies would have readers believe. The corollary is that accounting for degeneracy could help account for variability in drug efficacy, which would of course be beneficial. The challenge (as highlighted in the Abstract, lines 21-22) is that identifying the dominant Nav subtype to predict drug efficacy is not trivial. Interpreting clinical data is also complicated by the fact that we are either dealing with genetic mutations (with unclear compensatory changes) or pharmacological results (where NaV1.7-selective drugs have a multitude of problems that might contribute to their lack of efficacy, separate from effects of degeneracy). We have striven to contextualize our results (e.g. last paragraph of results, lines 222-235). We think this is the most we can reasonably say based on the limitations of existing clinical data.

(4) Provide a clearer and more detailed description of the computational model (reviewers #2 and #3).

We added important details on line 476-477 but, in our honest opinion, we think our computational model is thoroughly explained. The issue seems to boil down to whether details are included in the Results vs. being left for the Methods, tables and figure legends. We prefer the latter.

(5) Better clarify the effects of the CFA model, to provide further evidence relating inflammation with nociceptors variability (reviewers #2 and #3)

As explained in response to a specific point by reviewer #3, we believe that variable exposure to CFA explains the heightened variability in TTX-sensitivity reported in Figure 7 (now explained on lines 214-215). One could try co-injecting a retrograde dye with the CFA to label cells innervating the injection site, but differential spread of the inflammation and dye are liable to preclude any good concordance. Alternatively, a pain model involving more widespread (systemic) inflammation might cause a more homogeneous effect. But, our main goal with CFA injections was to show that a Nav1.8®Nav1.7 switch can occur in vivo (and is therefore not unique to culturing); that demonstration holds true even if some neurons do not switch. Subsequent testing (Fig 8) shows that enough neurons switch to drug efficacy assessed behaviorally. This is emphasized with new text on lines 225-227. Overall, we think our CFA experiments succeed in showing that Nav channels can switch in vivo and, despite variability, that this occurs in enough neurons to impact drug efficacy.

(6) Revise the text according to all recommendations raised by the reviewers and listed in the individual reviews.

Detailed responses are provided below for all feedback and changes to the text were made whenever necessary, as identified in our responses.

Reviewer #1 (Recommendations For The Authors):

Minor points/recommendations:

Protein synthesis inhibition by cercosporamide could be the direct cause of a smaller-thanexpected increase in Nav1.7 levels at DIV5. But for Nav1.8, there is a mitigation in the increased levels at DIV5, that only could be explained by several indirect mechanisms, including membrane trafficking and posttranslational modifications (phosphorylation, SUMOylation, etc.) on Nav1.8 or protein regulators of Nav1.8 channels. The authors suggest that "translational regulation is crucial", but also insinuate that other processes (membrane trafficking, etc.) could contribute to the observed outcome. It is difficult to assess the relative importance of these different explanations without knowing the exact mechanisms that are acting here.

We agree. We relied on electrophysiology (and pharmacology) to measure functional changes, but we wanted to verify those data with another method. We expected mRNA levels to parallel the functional changes but, when that did not pan out, we proceeded to look at protein levels. Perhaps we should have stopped there, but by blocking protein translation, we show that there is not enough Nav1.7 protein already available that can be trafficked to the membrane. That does not explain why Nav1.8 levels drop. Our immunohistochemistry could not tease apart membrane expression from overall expression, which limits interpretation. We have enhanced the text to discuss this (lines 200-204), but further experiments are needed. Though admittedly incomplete, our initial finding help set the stage for future experiments on this matter.

Page 15, typo: "contamination from genomic RNA" -> "contamination from genomic DNA" (appears twice).

This has been corrected on lines 420 and 421.

Page 17: I could not find the computer code at ModelDB (http://modeldb.yale.edu/267560). It seems to be an old web link. It should be available at some web repository.

We confirmed that the link works. Entry is password-protected (password = excitability; see line 476). Password protection will be removed once the paper is officially published.

Page 19, reference 36, typo: "Inhibitio of" -> "Inhibition of".

This has been corrected (line 557).

Page 33, typo: "are significantly larger than differences at DIV1" -> "are significantly larger than differences at DIV0".

This has been corrected (line 796).

Page 35, figure 6 legend. The number of experiments (n) is not indicated for panel C data.

N = 3 is now reported (line 828).

Reviewer #2 (Recommendations For The Authors):

p. 3/4 and Data of Fig. 6: It should be commented on why days 1-3 were not investigated. An investigation of the time course (by higher frequency testing) would certainly have an added value because it would be possible to deduce whether the changes develop slowly and gradually, or whether the excitability induced by different NaVs changes suddenly. At least mRNA and protein levels should be determined at additional time points to examine the time course or whether gene expression (mRNA) or membrane expression (protein) changes slowly and gradually or rapidly and more abruptly. It would also be interesting to clarify whether the changes that occur in culture (DIV0 vs. DIV4-7) are accompanied by (pro-)inflammatory changes in gene and protein expression, such as those known for nociceptors after CFA injection. Or is the latter question clear in the literature?

We now explain (lines 362-369) that intermediate time points (DIV1-3) were tested in initial current clamp recordings. Those data showed that TTX-sensitivity stabilized by DIV4 and differed from the TTX-insensitivity observed at DIV0. TTX-sensitivity was mixed at DIV1-3 and crosscell variability complicated interpretation. Subsequent experiments were prioritized to clarify why NaV1.7 is not always critical for nociceptor excitability, contrary to past studies. Our efforts to measure mRNA and protein levels were primarily to validate our electrophysiological findings; we are also interested in deciphering the underlying regulatory processes but this is an entire study on its own. Unfortunately, the existing literature does not help or point to an explanation for the Nav1.7/1.8 shift we observed.

Our evidence that mRNA levels do not parallel functional changes argues against pursuing transcriptional changes in Nav1.7, though transcriptional changes in other factors might be important. Interpretation of immuno quantification would be complicated by the high variability we observed with the physiology at intermediate time points and, furthermore, we cannot resolve surface expression from overall expression based on available antibodies. Methods conducive to longitudinal measurements would be more appropriate (as now mentioned on line 367-369). In short, a lot more work is required to understand the mechanisms involved in the switch, but we think the existing demonstration suffices to show that NaV1.7 and NaV1.8 protein levels vary, with crucial implications for which Nav subtype controls nociceptor excitability, and important implications for drug efficacy. Explaining why and how quickly those protein levels change will be no small feat is best left for a future study.

p. 4 and following: In order to enable the interpretation of the used concentration of PF-24, PF71, and ICA, the respective IC50 should be indicated.

A table (now Supplementary Table 3; line 861) has been added to report EC50 values for all drugs for blocking NaV1.7, NaV1.8 and NaV1.3. The concentrations we used are included on that table for easy comparison.

p. 5, end of the middle paragraph: Here it should be briefly explained -for less familiar readers- why NaV1.1 cannot be causative (ICA inhibits NaV1.1 and 1.3).

We now explain (lines 117-120) that NaV1.1 is expressed almost exclusively in medium-diameter (A-delta) neurons whereas NaV1.3 is known to be upregulated in small-diameter neurons, and so the effect we observe in small neurons is most likely via blockade NaV1.3.

p. 6, lines 4/5: At least once it should read computer model instead of model.

“Computer” has been added the first time we refer to DIV0 or DIV4-7 computer models (lines 138-139)

p. 6: the difference between Fig. 4B and Fig. 4 - Figure suppl. 1 should be mentioned briefly.

We now explain (lines 150-154) that Fig. 4B involves replacing a native channel with a different virtual channel (to demonstrate their interchangeability) whereas and Fig. 4 - Figure supplement 1 involves replacing a native channel with the equivalent virtual channel (as a positive control).

p. 6/7: the text and the conclusions regarding Figure 5 are difficult to follow. Somewhat more detailed explanations of why which data demonstrate or prove something would be helpful.

The text describing Figure 5 (lines 155-175) has been revised to provide more detail.

p. 7, last sentence of the first paragraph: How is this supported by the data? Or should this sentence be better moved to the discussion?

This sentence (now lines 182-184) is designed as a transition. The first half – “a subtype’s contribution shifts rapidly (because of channel inactivation)” – summarizes the immediately preceding data (Figure 5). The second half – “or slowly (because of [changes in conductance density])” – introduces the next section. The text show in square brackets has been revised. We hope this will be clearer based on revisions to the associated text.

p. 7, second paragraph, line 3: Please delete one "at both".

Corrected

p. 7, second paragraph: Please explain why different time points (DIV4-7, DIV5, or DIV7) were used or studied.

Initial electrophysiological experiments determined that TTX sensitivity stabilized by DIV 4 (see response to opening point) and we did not maintain neurons longer than 7 days, and so neurons recorded between DIV4 and 7 were pooled. If non-electrophysiological tests were conducted on a specific day within that range, we report the specific day, but any day within the DIV4-7 range is expected to give comparable results. This is now explained on lines 365-367.

p. 8: the text regarding Fig. 7 should also include the important data (e.g. percentage of neurons showing repetitive spinking) mentioned in the legend.

This text (lines 216-220) has been revised to include the proportion of neurons converted by PF71 and PF-24 and the associated statistical results.

Fig. 1: third panel (TTX-sensitive current...) of D & Fig. 2 subpanel of A (Nav1.8 current...). These panels should be explained or mentioned in the text and/or legends.

We now explain in the figure legends (lines 708-710; 714-715; 736-738) how those currents are found through subtraction.

Fig. 2 - figure supplement 2. One might consider taking Panel A to Fig. 2 so that the comparison to DIV0 is apparent without switching to Suppl. Figs.

We left this unchanged so that Figures 2 and 3 are equivalently organized, with negative control data left to the supplemental figures. Elife formatting makes it easy to reach the supplementary figure from the main figure, so we hope this won’t be an impediment to readers.

Fig. 6 C, middle graph (graph of Nav1.7): Please re-check, whether DIV5 none vs. 24 h and none vs. 120 h are really significantly different with such a low p-value.

We re-checked the statistics and the difference pointed out by the reviewer is significant at p=0.007. We mistakenly reported p<0.001 for all comparisons, and so this p value has been corrected; all the other p values are indeed <0.001. Notably, the data are summarized as median ± quartile because of their non-Gaussian distribution; this is now explained on line 827 (as a reminder to the statement on lines 461-462). Quartiles are more comparable to SD than to SEM (in that quartiles and SD represent the distribution rather than confidence in estimating the mean, like SEM), and so medians can differ very significantly even if quartiles overlap, as in this case.

Reviewer #3 (Recommendations For The Authors):

(1) A critical issue in the manuscript is the use of teleological language. It is likely that this is not the intention, but careful revision of the language should be done to avoid the use of expressions that confer purpose to a biological process. Please, find below a list of statements that I consider require correction.

• In the Abstract, the first sentence: "Nociceptive sensory neurons convey pain signals to the CNS using action potentials". Neurons do not really "use" action potentials, they have no will or purpose to do so. Action potentials are not tools or means to be "used" by neurons. Other examples of misuse of the verb "use" are found in several other sentences:

"...nociceptors can achieve equivalent excitability using different combinations of NaV1.3, NaV1.7, and NaV1.8"

"Flexible use of different NaV subtypes - an example of degeneracy - compromises..."

"Nociceptors can achieve equivalent excitability using different sodium channel subtypes" "...degeneracy - the ability of a biological system to achieve equivalent function using different components..."

"...nociceptors can achieve equivalent excitability using different sodium channel subtypes..."

"Our results show that nociceptors can achieve similar excitability using different NaV channels" "...the spinal dorsal horn circuit can achieve similar output using different synaptic weight combinations..."

"Contrary to the view that certain ion channels are uniquely responsible for certain aspects of neuronal function, neurons use diverse ion channel combinations to achieve similar function" "In summary, our results show that nociceptors can achieve equivalent excitability using different NaV subtypes"

“Use” can mean to put into action (without necessarily implying intention). Based on definitions of the word in various dictionaries, we feel we are well within the realm of normal usage of this term. In trying to achieve a clear and succinct writing style, we have stuck with our original word choice.

• At the end of page 5 and in the legend of Fig. 7, the word "encourage" is not properly used in the sentence "The ability of NaV1.3, NaV1.7 and NaV1.8 to each encourage repetitive spiking is seemingly inconsistent with the common view...". Encouraging is really an action of humans or animals on other humans or animals.

Like for “use”, we verified our usage in various dictionaries and we do not think that most readers will be confused or disturbed by our word choice. We use “encourage” to explain that increasing NaV1.3, NaV1.7 or NaV1.8 can increase the likelihood of repetitive spiking; we avoided “cause” because the probability of repetitive spiking is not raised to 100%, since other factors must always be considered.

• In the Abstract and other places in the manuscript, the word "responsibility" seems to be wrongly employed. It is true that one can say, for instance, on page 4 last paragraph "we sought to identify the NaV subtype responsible for repetitive spiking at each time point". However, to confer channels with the human quality of having "responsibility" for something does not seem appropriate. See also page 8 last paragraph, the first paragraph of the Discussion, and the three paragraphs of page 11.

Again, we must respectfully disagree with the reviewer. We appreciate that this reviewer does not like our writing style but we do not believe that our style violates English norms.

(2) In the first sentence of the Abstract, nociceptive sensory neurons do not convey "pain signals". Pain is a sensation that is generated in the brain.

“Pain” is used as an adjective for “signal” and is used to help identify the type of signal. Nonetheless, since the word count allowed for it, we now refer to “pain-related signals” (line 10).

(3) I do not see the point of plotting the firing rate as a function of relative stimulus amplitude (normalized to the rheobase, e.g., Fig. 1A bottom panels, Fig. 2B, bottom-right, Fig. 2 Supp2A right, Fig. 3 B bottom panels, etc) instead of as a function of the actual stimulus amplitude. I have the impression that this maneuver hides information. This is equivalent to plotting the current amplitudes as a function of the voltage normalized by the voltage threshold for current activation, which is obviously not done.

This is how the experiments were performed, so it would be impossible to perform the statistical analysis using the absolute amplitudes post-hoc; specifically, stimulus intensities were tested at increments defined relative to rheobase rather than in absolute terms. There are pros and cons to each approach, and both approaches are commonly used. Notably, we report the value of rheobase on the figures so that readers can, with minimal arithmetic, convert to absolute stimulus intensities. No information is hidden by our approach.

(4) On page 4 it is stated that "We show later that similar changes develop in vivo following inflammation with consequences for drug efficacy assessed behaviourally (see Fig. 8), meaning the NaV channel reconfiguration described above is not a trivial epiphenomenon of culturing". However, what happens in culture may have nothing in common with what happens in vivo during inflammation. Thus, the latter data may not serve to answer whether the culture conditions induce artifacts or not. I suggest tuning down this statement by changing "meaning" to "suggesting".

On line 97, we now write “suggesting”.

(5) Page 5, first paragraph, I miss a clear description of the mathematical models. Having to skip to the Methods section to look for the details of the models as the artifices introduced to simulate different conditions is rather inconvenient.

So as not to disrupt the flow of the presentation with methodological details, we only provide a short description of the model in the Results. We have slightly expanded this to point out that the conductance-based model is also single-compartment (line 111). We provide a very thorough description of our model in the Methods, especially considering all the details provided in Supplementary Tables 1, 5 and 6. We also report conductance densities and % changes in figure legends (lines 722, 747-748; now shown in red). This is also true for Figure 3-figure supplement 2 (lines 756-759). We tried very hard to find a good balance that we hope most readers will appreciate.

(6) Page 6, second paragraph, simulations do not serve to "measure" currents.

The sentence been revised to indicate that simulations were used to “infer” currents during different phases of the spike (line 155).

(7) Page 7, regarding the tile of the subsection "Control of changes in NaV subtype expression between DIV0 and DIV4-7", the authors measured the levels of expression, but not really the mechanisms "controlling" them. I suggest writing "changes in NaV subtype expression between DIV0 and DIV4-7"

We have removed “control of” from the section title (line 185)

(8) What was the reason for adding a noise contribution in the model?

We now explain that noise was added to reintroduce the voltage noise that is otherwise missing from simulations (line 474). For instance, in the absence of noise, membrane potential can approach voltage threshold very slowly without triggering a spike, which does not happen under realistically noisy conditions. Of course membrane potential fluctuates noisily because of stochastic channel opening and a multitude of other reasons. This is not a major issue for this study, and so we think our short explanation should suffice.

(9) Please, define the concept of degeneracy upon first mention.

Degeneracy is now succinctly defined in the abstract (line 20).

Author Response

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

eLife assessment

This paper represents important findings when identifying untargeted metabolomics and its differences between metabolomes of different biological samples. GromovMatcher is the fantasy name for the soft development. The main idea behind it is built on the assumption of featuring and matching complex datasets. Although the manuscript reflects a solid analysis, it remains incomplete for validation with putative non-curated datasets.

We are grateful to the eLife editor for taking the time and effort to assess our manuscript.

We are however unsure of what the editor means by “it remains incomplete for validation with putative non-curated datasets”. As noted by Reviewer 2, manually curated datasets that could be used for validation are scarce. Most publicly available datasets do not contain sufficient information to establish a ground truth matching on which GromovMatcher, M2S, or metabCombiner can be tested. Even in the case where such a ground truth matching can be established, it must be performed by-hand through a manual matching process which is extremely time-consuming and requires very specific expertise. This, in our opinion, only highlights the need for automatic alignment methods such as metabCombiner, M2S or GromovMatcher.

We do agree that the performance of GromovMatcher (and its competitors) needs to be validated further, and we plan to continue validating GromovMatcher as additional data becomes available in EPIC and other cohorts. With that in mind, the lack of publicly available validation data is the reason why we conducted such an extensive simulation study, arguably more comprehensive than previous validations, exploring challenging settings that we believe reflect real-life scenarios (main text “Validation on ground-truth data” and Appendix 3). We would like to stress that this allows us to highlight previously ignored limitations of the previously published methods, metabCombiner and M2S.

We wish to thank the editor and reviewers for their time and efforts in reviewing our manuscript which led to many significant additions to our paper. Namely we:

• Performed an additional sensitivity analysis (Appendix 3) exploring how an imbalance in the number of features or samples between two studies being matched (e.g. the dataset split), affects the quality of matchings found by GromovMatcher, metabCombiner, and M2S.

• Investigated how changing or removing the reference dataset (Appendix 5) in the EPIC study (main text “Application to EPIC data”), affects the results of GromovMatcher.

• Improved alignment matrix visualizations in Fig. 3a for all four methods tested on the validation data, to highlight more clearly which feature matches were correctly identified or missed.

The revised paper is uploaded as the file “main_elife_revision.pdf” where all revisions are highlighted in blue as well as a copy “main_elife_revision_nohighlights.pdf” where revisions are not highlighted.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors have implemented the Optimal Transport algorithm in GromovMatcher for comparing LC/MS features from different datasets. This paper gains significance in the proteomics field for performing meta-analysis of LC/MS data.

Strengths:

The main strength is that GromovMatcher achieves significant performance metrics compared to other existing methods. The authors have done extensive comparisons to claim that GromovMatcher performs well.

Weaknesses:

There are two weaknesses.

(1) When the number of features is reduced the precision drops to ~0.8.

We would like to clarify that this drop in precision occurs in the challenging setting where only a small proportion of metabolites are shared between both datasets (e.g., the overlap – or proportion of shared features - was 25% in our simulation study). When two untargeted metabolic datasets share only 25% of their features, this is a challenging setting for any automated matching method as the vast majority 75% of the features in both datasets must remain unmatched.

In such settings, the reviewer correctly observes that the precision of GromovMatcher algorithms (GM and GMT) drops within the range of 0.80 - 0.85 (Figure 3b, top left panel). Such a precision of 0.8 or larger is still competitive compared with the alternative methods MetabCombiner (mC) and M2S whose precisions drop below 0.8 (see main text Fig. 3b, top left panel).

Precision is measured as the number of metabolite pairs correctly matched divided by all matches identified by a method. In other words, even in the challenging setting when the number of shared features (true matches) between both datasets is small (e.g. low 25% overlap), upwards of 80% of the feature matches found by GromovMatcher are correct which is a very encouraging result.

(2) How applicable is the method for other non-human datasets?

We thank the reviewer for raising this question. The crux of the matter concerning the application to animal data revolves around the hypothesis that correlations between metabolites in two different studies are preserved. Theoretically, the metabolome operates under similar principles in humans, governed by an underlying network of biochemical reactions. Consequently, in comparable human populations, the GM hypothesis is likely to hold to some extent.

However, in practice, application to animal data is more complicated. Animal studies tend to have smaller sample sizes and often stem from intervention-driven scenarios, such as mice subjected to specific diets or chemicals. This results in deliberate alterations in metabolic structures which makes finding two comparable animal studies less likely. To investigate the reviewer’s question, we have searched through the two predominant LC-MS dataset repositories (MetaboLights and NIH Metabolomics Workbench) but did not find any pairs of comparable animal studies due to the reasons mentioned above. One potential strategy to navigate this issue could entail regressing the metabolic intensities against the variables that notably differ between the two animal populations and running GM using the residual intensities. This would be an interesting direction for future research and additional validation would be needed to test the robustness of GM in this setting.

Reviewer #2 (Public Review):

Summary:

The goal of untargeted metabolomics is to identify differences between metabolomes of different biological samples. Untargeted metabolomics identifies features with specific mass-to-charge ratio (m/z) and retention time (RT). Matching those to specific metabolites based on the model compounds from databases is laborious and not always possible, which is why methods for comparing samples on the level of unmatched features are crucial.

The main purpose of the GromovMatcher method presented here is to merge and compare untargeted metabolomes from different experiments. These larger datasets could then be used to advance biological analyses, for example, for the identification of metabolic disease markers. The main problem that complicates merging different experiments is m/z and RT vary slightly for the same feature (metabolite).

The main idea behind the GromovMatcher is built on the assumption that if two features match between two datasets (that feature I from dataset 1 matches feature j from dataset 2, and feature k from dataset 1 matches feature l from dataset 2), then the correlations or distances between the two features within each of the datasets (i and k, and j and l) will be similar. The authors then use the Gromov-Wasserstein method to find the best matches matrix from these data.

The variation in m/z between the same features in different experiments is a user-defined value and it is initially set to 0.01 ppm. There is no clear limit for RT deviations, so the method estimates a non-linear deviation (drift) of RT between two studies. GromovMatcher estimates the drift between the two studies and then discards the matching pairs where the drift would deviate significantly from the estimate. It learns the drift from a weighted spline regression.

The authors validate the’performance of their GromovMatcher method by a validation experiment using a dataset of cord blood. They use 20 different splits and compare the GromovMatcher (both its GM and GMT iterations, whereby the GMT version uses the deviation from estimated RT drift to filter the matching matrix) with two other matching methods: M2S and metabCombiner.

The second validation was done using a (scaled and centered) dataset of metabolics from cancer datasets from the EPIC cohort that was manually matched by an expert. This dataset was also used to show that using automatic methods can identify more features that are associated with a particular group of samples than what was found by manual matching. Specifically, the authors identify additional features connected to alcohol consumption.

Strengths:

I see the main strength of this work in its combination of all levels of information (m/z, RT, and higher-order information on correlations between features) and using each of the types of information in a way that is appropriate for the measure. The most innovative aspect is using the Gromov-Wasserstein method to match the features based on distance matrices.

We thank the reviewer for acknowledging this strength of our proposed GromovMatcher method.

The authors of the paper identify two main shortcomings with previously established methods that attempt to match features from different experiments: a) all other methods require fine-tuning of user-defined parameters, and, more importantly, b) do not consider correlations between features. The main strength of the GromovMatcher is that it incorporates the information on distances between the features (in addition to also using m/z and RT).

Weaknesses:

The first, minor, weakness I could identify is that there seem not to be plenty of manually curated datasets that could be used for validation.

We thank the reviewer for raising this issue concerning manually curated validation data.

Manually curated datasets available for validation purposes are indeed scarce. This stems from the laborious nature of matching features across diverse studies, hence the need for automatic matching methods. Our future strategy involves further validation of the GromovMatcher approach as more data becomes accessible in EPIC and other cohorts.

The scarcity of real-life publicly available datasets that can be used for validation purpose is the reason why we conducted an extensive simulation study (main text “Validation on ground-truth data” and Appendix 3). It is notably thorough, arguably more comprehensive than previous validations, utilizes real-life untargeted data, and imitates situations where data originates from distinct untargeted metabolomics studies, complete with realistic noise parameters encompassing RT, mz, and feature intensities. Our validation study comprehensively explores the performance of GromovMatcher, M2S, and metabCombiner, including in challenging realistic settings where there is a nonlinear drift in retention times, varying levels of feature overlaps between studies, normalizations of feature intensities, as well as imbalances in the number of features and samples present in the studies being matched.

The second is also emphasized by the authors in the discussion. Namely, the method as it is set up now can be directly used only to compare two datasets.

This is indeed a limitation that is common to all three methods considered in this paper. However, all these methods, GromovMatcher, M2S, and metabCombiner, can still be used to compare and pool multiple datasets using a multi-step procedure. Namely, this can be done by designating a 'reference' dataset and aligning all studies to it one by one. We take this exact approach in our paper when aligning the CS, HCC, and PC studies of the EPIC data in positive mode (main text “Application to EPIC data”). Namely, the HCC and PC studies are both aligned to the CS study by running GromovMatcher twice, and after obtaining these matchings, our analysis is restricted to those features in HCC and PC that are present in the CS study.

After the reviewer’s comment, we have added an additional sensitivity analysis in Appendix 5, to compare the results produced by GromovMatcher depending on the choice of the reference study. Namely, setting the reference study to either the CS study or the HCC study, GromovMatcher identified 706 and 708 common features respectively, with an overlap of 640 features. This highlights that the choice of reference does matter to some extent. In our original analysis of the EPIC data, choosing CS as the reference was motivated by the fact that CS had the largest sample size (compared to HCC and PC) and a subset of features in HCC and PC were already matched by experts to the CS study which we could use for validation (see Loftfield et al. (2021). J Natl Cancer Inst.).

As mentioned in the discussion section of our manuscript, the recently proposed multimarginal Gromov-Wasserstein algorithm (Beier, F., Beinert, R., & Steidl, G. (2023). Information and Inference) could potentially allow multiple metabolomic studies to be matched using one optimization routine (e.g. without the designation of a ‘reference study’ for matching). We have not explored this possibility in depth yet as fast numerical methods for multimarginal GW are still in their infancy. Also, such multimarginal methods rely on the computation and storage of coupling or matching matrices that are tensors where the number of dimensions is equal to the number of datasets being matched. Therefore, multimarginal methods have large memory costs, which currently precludes their application for the matching of multiple metabolomics datasets.

Reviewer #2 (Recommendations For The Authors):

(1) I was struggling with the representation used in Figure 3a. The gray points overlayed over the green points on a straight line are difficult to visually quantify. I found that my eyes mainly focused on the pattern of the red dots.

Figure 3a has been modified to improve visual clarity. Namely we have consistently reordered the rows and columns of the coupling matrices such that the true positive matches (green points) are spatially separated from the false negative matches (red points). Now the fraction of true positive and false negative matches can be appreciated much more clearly by eye in Figure 3a.

(2) I would also like to add the caveat that I cannot judge whether the authors used the other two methods that they compare with GromovMatcher (the M2S and metabCombiner) optimally. But I also do not see any evidence that they did not. Hopefully one of the other reviewers can address that.

We appreciate the reviewer for highlighting the comparison of our approach GromovMatcher to the other existing methods M2S and MetabCombiner (mC). Both M2S and mC depend on tens of hyperparameters each with a discrete or continuous set of values that must be properly optimized to infer accurate matchings between dataset features. We detail in Appendix 2 how the hyperparameters of the M2S and mC methods are optimally tuned to achieve the best possible performance on the validation ground-truth data. Namely, both in the simulation study and on EPIC data, we grid-search over all important hyperparameters in the M2S and mC methods and choose those parameter combinations that result in the highest F1 score, averaged over 20 random trials. We remark that no such hyperparameter optimization was performed for our GromovMatcher method. As shown in Figures 3 and 4 of the main text, we find that GromovMatcher outperforms M2S and mC even in these cases when the hyperparameters of M2S and mC are tuned to predict optimal feature matchings.

Given the large combinatorial space of hyperparameter choices, we believe we have thoroughly tested the important hyperparameter combinations that users of M2S and mC would be likely to explore in their own research.

(3) Validation

(3a) The first validation is done on a split cord blood dataset. I could not clearly see from the paper how sensitive the result is to the dataset split.

We are grateful for the reviewer’s question and have included new experiments in Appendix 3 which show how the results of GromovMatcher, M2S, and MetabCombiner are affected by the dataset split. In our original manuscript, our validation ground-truth experiment began with an untargeted metabolomic dataset consisting of n = 499 samples and p = 4,712 metabolic features which is split equally into two datasets consisting of an equal number of samples n1 = n2 and an equal number of metabolic features p1 = p2. The features of these equal-sized datasets would then be matched by our method.

Now in Appendix 3 (Figs. 1-3) we show the sensitivity of all three alignment methods (GromovMatcher, M2S, and MetabCombiner) when we vary the fraction of samples in dataset 1 over dataset 2 given by n1/ n2, the overlap in shared features between both datasets, and the fraction of metabolic features in dataset 1 that are not present in dataset 2 which affects the feature sizes of both datasets p1/ p2. We find that all alignment methods are able to maintain a consistent precision and recall score when these three dataset split parameters are varied. GromovMatcher achieves a higher precision and recall than M2S and MetabCombiner for all choices of dataset split, agreeing with the validation experiment results from the main text (see main text Fig. 3). All three methods tested decrease in precision (without dropping in recall) when dataset 1 and dataset 2 contain an equal number of unshared features (e.g. when p1 = p2). Therefore, these sensitivity experiments in Appendix 3 show that our results in the main text are performed in the most challenging setting for the dataset split.

(3b) The second validation was done using a (scaled and centered) dataset of metabolics from cancer datasets from the EPIC cohort that was manually matched by an expert. Here the authors observe that metabCombiner has good precision, but lags in recall. And M2S has a very similar performance to GromovMatcher. The authors explain this by the fact that the drift in RT between the two experiments is mostly linear and thus does not affect the M2S performance. Can the authors find a different validation dataset where the drift in RT is not linear? If yes, it would be interesting to add it to the paper.

We thank the reviewer for raising this question. As mentioned above, curated validation datasets such as the EPIC study analyzed in our paper are very rare and we do not currently have a validation study with a nonlinear retention time drift.

Nevertheless, we performed an additional analysis of simulated data (reported in Appendix 2 – “M2S hyperparameter experiments” and Appendix 2 – Table 1) that demonstrates the decrease in M2S performance when the simulated drift is nonlinear. As presented in Appendix 2 – Table 1, in a low overlap setting with a linear drift which corresponds to the EPIC data, precision and recall were 0.831 and 0.934 respectively, instead of 0.769 and 0.905 in the main analysis where the drift was nonlinear.

Author Response

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

We want to thank the reviewers for their thoughtful analysis and questions.

A brief overview of the changes to the manuscript is provided here, with individual responses to the reviewer comments following.

The methods section has been expanded to better explain the techniques used in our analyses. CTCF binding data section has likewise been expanded, to include more detail on the dataset and our analysis of its contents. All other requested clarifications have been added to areas of the results.

Beyond specific requests from the reviewers, we made the following changes.

We felt that a particular terminology choice on our part resulted in some confusion: the use of “SNPs” to refer to genetic variants within our Diversity Outbred samples. While we used SNPs that lay closest to the center of our haplotype predictions as our representative loci for each linkage disequilibrium block, this was done for computational purposes only. We did not focus most of our analyses on the haplotypes themselves, because of the uncertainty of which variants within an LD block actually participated in the genetic-epigenetic interactions we imputed.

Thus, we edited the text to remove mention of “SNPs” unless our analysis did directly and deliberately profile SNPs themselves. In all other cases, we now refer to “haplotypes”, “genetic variants”, or “variants”. This should help increase clarity in the manuscript as a whole.

A small error was discovered within the labelling and processing of regression model outputs in chromosome 14. A consistency check was run on all chromosomes, finding that only Chr 14 was affected. Chr 14 was rerun in its entirety to verify its results, with the previous results now archived within our databases uploaded on Synapse (see Methods for a link). All relevant calculations and figures were regenerated, resulting in an average shift of 1% or less across the manuscript. All analyses remain highly statistically significant.

Responses to comments from Reviewer #1

Methods

• Sequencing depth was retrieved from the original publication on the primary multiomics dataset. (Line 105-106)

• A line was added regarding initial mouse genome alignment for the original publication: we explain the GigaMUGA genotyping array, used for the DO mESC samples. For our ChIP-seq data, we reword to specify: we used liftovers from imputed strain-specific genomes to B6 mm10. (Lines 108-110; 116-120; 168-170)

• Aneuploidy removal is expanded upon in a similar fashion: the original QC identified chromosome-level gene expression differences to remove aneuploid samples. (Line 111)

• Mention of the pre-publication use of an alternative null model has been removed, given its lack of relevance to the rest of the text. While it was interesting to compare to the standard null model, it amounts to a side note that distracts from the focus of the paper. (Line 137-139).

• Descriptive subheadings have been added.

Results - Line 179 (now Line 191) now points to Methods.

• Line 189-200 (now Line 188-204): language altered to better explain our intent: We wished to perform an intrachromosomal scan across the whole genome for non-additive genetic-epigenetic interactions. However, there were computational limits to how many possible combinations of gene, haplotype, and ATAC-seq peak we could feasibly test. We thus generated a random subset of possible combinations. This was also performed to identify target regions for focused analyses.

• Line 195 (now line 206, expanded on in Line 210): Clarification added on the significance of our result: if non-additive genetic-epigenetic interactions were not a significant explanatory factor for gene expression, we would expect to see no enrichment of low p-value results. Instead, we see 0.07% of our models coming in at adj. p < 1x10-7.

• Line 199 (now Line 216): The requested calculations were run, and are now included in table S3. We found that within 4 Mb of a given gene, less than 10% of variants and ATAC peaks within clustered closer to each other than they did to the gene they affected.

Please note that this figure has a level of uncertainty due to linkage disequilibrium. Thus, rather than precisely answering the question “[are there haplotype-ATAC pairs] that are in the same locality but further away from the gene?”, we asked "is the ATAC peak closer than the gene to the point where we have the highest confidence of correctly calling the interacting genotype?". The relevant code has been deposited in our Synapse repository (see Methods for link).

• Line 205 (now restructured in Line 221-228): The text has been edited to specify our intent. We are referring to a set of TAD-focused regression models we generated (see Methods) that comprehensively included all possible interactions between genes, and all haplotypes and ATAC peaks within +/- 1 TAD of the gene.

• (Line 227): We specified that the previously-published TAD boundary dataset we used was retrieved from the Bing Ren lab’s Hi-C projects, which imputed locations of TAD boundaries in B6 mESCs.

• We have relabeled Figure 1 and tweaked the surrounding text to clear up some confusing aspects. The Euler plots in Figure 1D-E reflect the fact that each ATAC-seq peak and haplotype can be in multiple relationships with local genes and regulatory factors. Some of these relationships will be simple correlation between their presence and gene expression, while others may co-regulate alongside independent regulatory factors, or engage in non-additive regulatory interactions.

Because these non-additive regulatory interactions have not been comprehensively studied, we wished to determine whether there were any regulatory factors within our data that would not be detected as significant via more conventional methods, such as correlation analysis, mediation analysis, or regression analysis without an interaction term. Our Euler plots show that there are large subsets of both ATAC-seq peaks and haplotypes that are exclusively found in non-additive interactions. Thus, our justification for focusing on non-additive interactions for the rest of the paper.

• Line 256 (now Line 252-255): We further clarified the above in this section: correlation and mediation analyses were previously completed by the team which initially analyzed the DO mESC dataset (Skelly et al. 2020, Cell Stem Cell). They performed a correlation analysis between open chromatin and gene expression (Skelly et al. Fig. 2A), and identified expression quantitative trait loci (eQTL) (Skelly et al. Fig. 2E). We felt that more direct comparisons to the Skelly et al. data would distract readers from our focus on genetic-epigenetic interactions. Thus, we limited our discussion of non-interacting regulatory relationships to Figures 1-2, and a brief mention in Figure 5.

• Line 290 (now Line 337): We pulled promoter locations from the FANTOM5 database of mouse promoters, and included analysis in both the text and Figure S4A-B.

• (Line 475-476): we clarified “DO founder SNPs” to “SNPs from the non-reference DO founder strains”.

• Line 472 (restructured in Lines 531-564): We have expanded on this section, including answers to the reviewer’s questions regarding ChIP-seq peak counts, overlap with the TAD map we used for our other analyses, and expanded upon strain-specific CTCF binding we identified in our ChIP-seq analysis.

Responses to comments from Reviewer #2:

(1) Typo corrected.

(2) Lines 194-195 (now line 206, expanded on in Line 210): We have expanded upon the intent and expectations of our analysis. In summary: if non-additive genetic-epigenetic interactions were not a significant explanatory factor for gene expression, we would expect to see no enrichment of low p-value results. Thus, we would expect 0.0000001% of results to reach adj. p < 1x10-7. Instead, we see 0.07% of our models coming in at adj. p < 1x10-7, four orders of magnitude greater than expected.

(3) Lines 226-230 (Expanded on in Lines 252-276): We have relabeled Figure 1 and tweaked the surrounding text to clear up some confusing aspects. The percentages in the text are derived from the data summarized in the Euler plots in Figure 1D-E. These plots reflect the fact that each ATAC-seq peak and haplotype can be in multiple relationships with local genes and regulatory factors. Some of these relationships will be simple correlation between their presence and gene expression, while others may co-regulate alongside independent regulatory factors, or engage in non-additive regulatory interactions.

(4) Line 261-263 (now lines 299-300): A companion to Figure 2B has been added (Fig. S3), which provides interaction counts for each ATAC-seq peak that contributed to Figure 2B. A horizontal line is included to highlight the locations of the highly-interacting ATAC peaks.

(5) Analysis regarding Figure 3B had been removed from its original context. It has now been restored to the manuscript (Line 368-371).

Author Response

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

Reviewer #1 (Recommendation for the authors)

I only have one comment for improvement of this study and it has to do with the comparison of simulators that they conducted. There are many other simulators around now, including scDesign3, spaSim, SPIDER, SRTSIM, etc. Are any of those methods worth including in the comparison?

Indeed, many of the mentioned simulators did not exist when we initially developed synthspot, and upon closer examination, they are not directly comparable to our tool.

• scDesign3: The runtime of scDesign3 is quite long as a result of its generative model. The example provided in its tutorial only simulates 183 genes and takes over seven minutes when using four cores on a system with Intel Xeon E5-2640 CPUs running at 2.5GHz. In a small downsampling analysis, we simulated 10, 50, 100, and 150 genes with scDesign3 and observed runtimes of 30, 130, 245, and 360 seconds, respectively. This seems to indicate a linear relationship between the number of genes and the runtime, therefore rendering it unsuitable for simulating whole-transcriptome datasets for deconvolution.

• spaSim: spaSim focuses on modelling cell locations in different tissue structures but does not provide gene expression data. It is designed for testing cell colocalization capabilities rather than simulating gene expression.

• SPIDER: Although SPIDER appears to have some overlap with our work, it seems to be in the early stages of development. The GitHub repository contains only two scripts without any documentation, and the preprint does not provide instructions on how to use the tool.

• SRTSim: SRTSim explicitly states in its publication that it is not suitable for evaluating cell type deconvolution, as its focus is on simulating gene expression data without modelling cell type composition.

• scMultiSim: scMultiSim, like scDesign3, is limited in its capability to model the entire transcriptome.

Nonetheless, the inherent modularity of our Nextflow framework makes it possible for users to simply run the deconvolution methods on data that has been simulated by other simulators if need be.

Additionally, we have added the following rationale for why we developed synthspot in “Synthspot allows simulation of artificial tissue patterns”:

“On the other hand, general-purpose simulators are typically more focused on other inference tasks, such as spatial clustering and cell-cell communication, and are unsuitable for deconvolution. For instance, generative models and kinetic models like those of scDesign3 and scMultiSim are computationally intensive and unable to model entire transcriptomes. SRTSim focuses on modeling gene expression trends and does not explicitly model tissue composition, while spaSim only models tissue composition without gene expression.”

The other aspect of the simulation comparison that I'm missing is some kind of spatial metric. There are metrics about feature correlation, sample-sample correlation, library size, etc. But, what about spatial correlation (e.g., Moran's I or similar). Perhaps comparing the distribution of Moran's I across genes in a simulated and real dataset would be a good first start.

We would like to clarify that synthspot does not actually simulate the spatial location of spots, but synthetic regions where spots from the same region share similar compositions. Hence, incorporating a spatial metric in the comparison is not feasible. However, as RCTD is the only method that explicitly uses spot locations in its model (Supplementary Table 2, "Location information"), we believe that generating synthetic datasets with actual coordinates would not significantly impact the conclusions of the study.

Reviewer #2 (Public Review)

On the other hand, the authors state that in silver standard datasets one half of the scRNA-seq data was used for simulation and the other half was used as a reference for the algorithms, but the method of splitting the data, i.e., at random or proportionally by cell type, was not specified.

The data was split proportionally by cell type. To clarify this, we have included an additional sentence in the main text under the first paragraph of “Cell2location and RCTD perform well in synthetic data”, as well as in Figure S2.

Reviewer #2 (Recommendation for the authors)

Figure legends in Figures 3, 4 and across most Supplementary material are almost illegible. Please consider increasing font size for better readability.

Thank you for bringing this to our attention. The font size has been increased for all main and supplementary figures. Additionally, the supplementary figures have also been exported in higher resolution.

Supplementary Notes Figure 2c reads "... total count per sampled multiplied by..."

This has been adapted, as well as the captions of Supplementary Notes Figure 3c and 4c which had the same typo.

Review #3 (Public review)

The simulation setup has a significant weakness in the selection of reference single-cell RNAseq datasets used for generating synthetic spots. It is unclear why a mix of mouse and human scRNA-seq datasets were chosen, as this does not reflect a realistic biological scenario. This could call into question the findings of the "detecting rare cell types remains challenging even for top-performing methods" section of the paper, as the true "rare cell types" would not be as distinct as human skin cells in a mouse brain setting as simulated here.

We appreciate the reviewer’s concern and would like to clarify that within one simulated dataset, we never mix mouse and human scRNA-seq data together. The synthetic spots generated for the silver standards are always sampled from a single scRNA-seq or snRNA-seq dataset. Specifically, for each of the seven public scRNA-seq datasets, we generate synthetic datasets with one of nine abundance patterns, resulting in a total of 63 synthetic datasets. These abundance patterns only affect the sampling priors that are used—the spots are still created with combinations of cells sampled from the same dataset.

Furthermore, it is unclear why the authors developed Synthspot when other similar frameworks, such as SRTsim, exist. Have the authors explored other simulation frameworks?

While there are other simulation frameworks available now, synthspot was designed to specifically address the requirements of our study, offering unique capabilities that make it suitable for deconvolution evaluation. Moreover, many of the simulators did not exist when we initially developed our tool. We have added the following rationale for why we developed synthspot in “Synthspot allows simulation of artificial tissue patterns”:

“On the other hand, general-purpose simulators are typically more focused on other inference tasks, such as spatial clustering and cell-cell communication, and are unsuitable for deconvolution. For instance, generative models and kinetic models like those of scDesign3 and scMultiSim are computationally intensive and unable to model entire transcriptomes. SRTSim focuses on modeling gene expression trends and does not explicitly model tissue composition, while spaSim only models tissue composition without gene expression.”

In our response to Reviewer 1 copied below, we also outline specific reasons why other simulators were not suitable for our benchmark:

• scDesign3: The runtime of scDesign3 is quite long as a result of its generative model. The example provided in its tutorial only simulates 183 genes and takes over seven minutes when using four cores on a system with Intel Xeon E5-2640 CPUs running at 2.5GHz. In a small downsampling analysis, we simulated 10, 50, 100, and 150 genes with scDesign3 and observed runtimes of 30, 130, 245, and 360 seconds, respectively. This seems to indicate a linear relationship between the number of genes and the runtime, therefore rendering it unsuitable for simulating whole-transcriptome datasets for deconvolution.

• spaSim: spaSim focuses on modelling cell locations in different tissue structures but does not provide gene expression data. It is designed for testing cell colocalization capabilities rather than simulating gene expression.

• SPIDER: Although SPIDER appears to have some overlap with our work, it seems to be in the early stages of development. The GitHub repository contains only two scripts without any documentation, and the preprint does not provide instructions on how to use the tool.

• SRTSim: SRTSim explicitly states in its publication that it is not suitable for evaluating cell type deconvolution, as its focus is on simulating gene expression data without modelling cell type composition.

• scMultiSim: scMultiSim, like scDesign3, is limited in its capability to model the entire transcriptome.

Finally, we would have appreciated the inclusion of tissue samples with more complex structures, such as those from tumors, where there may be more intricate mixing between cell types and spot types.

We acknowledge the reviewer's suggestion and have incorporated a melanoma dataset from Karras et al. (2022) in response to this suggestion. This study profiled melanoma tumors by using both scRNA-seq and spatial technologies. The scRNA-seq consists of eight immune cell types and seven melanoma cell states. We have included this study as an additional silver standard and case study, the latter of which is presented in a separate section following the liver analysis (and a corresponding section in Methods).

We found that method performances on synthetic datasets generated from this melanoma dataset follow previous trends (Figure S3-S5). However, the inclusion of the case study led to the following changes in the overall rankings: cell2location and RCTD are now tied for first place (previously RCTD ranked first), and Seurat and SPOTlight have swapped places. Despite these changes, the core messages and conclusions of our paper remain unchanged. All relevant figures (Figures 1a, 2, 3a, 4a, 6b, 7a, S3-S6, S9) have been updated to incorporate these new analyses and results.

Review #3 (Recommendation for the authors)

To maintain consistency in the results, it is recommended to exclude the human scRNAseq set when generating synthetic spots. Furthermore, addressing the other significant weaknesses mentioned earlier would be beneficial.

Please refer to our response to the public review where we address the same remark.

It is essential to differentiate this work from previous benchmarking and simulation frameworks.

In addition to the rationale on why we developed our own framework (see response to the public review), we have included the following text in the discussion that highlights our versatile approach when using a real spatial dataset for evaluation:

“In the case studies, we demonstrated two approaches for evaluating deconvolution methods in datasets without an absolute ground truth. These approaches include using proportions derived from another sequencing or spatial technology as a proxy, and leveraging spot annotations, e.g., zonation or blood vessel annotations, that typically have already been generated for a separate analysis.”

Furthermore, we conducted an extra analysis in the liver case study, generating synthetic datasets with one experimental protocol and using the remaining two as separate references (Figure S13). This further illustrates the usefulness of our simulation framework, which we mentioned by appending this sentence in the discussion:

“As in our silver standards, users can select the abundance pattern most resembling the real tissue to generate the synthetic spatial dataset, as we have also demonstrated in the liver case study.”

Author Response

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

Reviewer #1

(1) Since you only included patients with early-onset preeclampsia in the study, I suggest revising the title to "Identification of novel syncytiotrophoblast membrane extracellular vesicle derived protein biomarkers in early-onset preeclampsia...."

We have changed our title to early-onset preeclampsia.

(2) Under methods, you state that placenta was obtained from women undergoing elective cesarean section. Was this because all the study patients were delivered before the onset of labor? Or were laboring patients specifically excluded from the study?

Indeed, labor influences the extracellular vesicles (EVs) generated. To ensure consistency in our samples and avoid this variable, we chose placentas obtained from elective cesarean sections (CS) for our study.

(3) In Table 1 on page 10, the 8th row (Birth weight grams) needs to be reformatted. The mean birthweights for normal pregnancy and preeclampsia should be the same.

We have reformatted the table and using ranges instead of brackets.

(4) In the legend for Table 1, the sentence beginning on page 10, line 227, and continuing onto page 11, line 228, does not make sense. Part of the sentence was omitted inadvertently.

We have modified this sentence to :

Detergent treatment, which could break down EVs, with NP-40 confirmed that the majority (99%) of our samples were largely vesicular since only 0.1 ± 0.12% of BODIPY FL N-(2-aminoethyl)-maleimide and PLAP double-positive events were detected (a reduction of 99%) (Figure 1E and 1H).'

(5) As you acknowledge, the sample size (12 patients) was small. This is understandable because early-onset preeclampsia occurs in <1% of parturients. You could collaborate with other centers in future studies to increase the sample size.

Thank you very much for your comment. We are willing to cooperate on future research and will try to expand our sample size in subsequent studies.

Reviewer #2 (Recommendations For The Authors):

(1) This is one of the many "catalogue" papers where placental exosome proteins in preeclampsia are profiled. Thus, the manuscript lacks novelty. The only novelty factor is the authors have isolated exosomes by a different method and even separated the small and large exosomes. However, there is no mention of how these exosomes differ from each other in terms of their functionality. Thus it is hard to judge the biological significance of this work.

We appreciate your insights regarding the novelty of our study. While numerous papers have profiled placental exosome proteins in preeclampsia, our methodology for enriching sSTB-EVs (exosomes) offers a distinct perspective. We believe that the separation of sSTB-EVs (exosomes) and medium/large STB-EVs (microvesicles) introduces a differentiation that extends beyond mere profiling, with implications for their functionality. There are previous studies showed that the different sizes of placenta EVs have distinct characteristics (Zabel RR, et al. Enrichment and characterization of extracellular vesicles from ex vivo one-sided human placenta perfusion. Am J Reprod Immunol. 2021 Aug;86(2)). Furthermore, the way cells internalize and respond to EVs may depend on the size of the EV (Zhuang X et al. Treatment of brain inflammatory diseases by delivering exosome encapsulated anti-inflammatory drugs from the nasal region to the brain. Mol Ther. 2011 Oct;19(10).) Therefore, it would be important for future studies to distinguish different sizes of EVs for the research.

(2) The authors must demonstrate that these two types of EVs are also produced in vivo by detecting them in the serum of women.

Thank you for the comment. Many previous studies have shown the two types of placental EVs in women's blood. Nakahara et al.'s (PMCID: PMC7755551) extensive review compiles studies that have specifically isolated various subtypes of placenta-derived EVs from maternal circulation. We have also readdressed it in the introduction.

(3) The authors must compare the proteomes of serum-derived placental exosomes and the proteome of the STBs isolated from the perfusion experiments to judge how overlapping the outcomes are from those produced naturally and those produced under ex vivo conditions.

We appreciate the reviewer's suggestion to compare the proteomes of serum-derived placental sSTB-EVs (exosomes) with those from STBs isolated through perfusion experiments. Indeed, such a comparison would provide valuable insights into the similarities and differences between naturally produced and ex vivo-generated sSTB-EVS (exosomes). However, isolating placental EVs from maternal circulation for comprehensive proteomic profiling presents challenges. It requires a significant amount of serum or plasma sample that will be sufficient to enable the isolation of placenta-specific EVs amongst numerous EVs in the circulation. In addition, it will require multiple intricate steps such as ultracentrifugation followed by immunoprecipitation. Each of these steps can potentially lead to the loss of EVs. Additionally, given the high concentration of lipoproteins in plasma relative to EVs, there's a significant risk of obtaining low-purity isolates from the outset. These challenges might compromise the comparability of results between placenta-specific EVs from maternal circulation and those from ex vivo perfusion. Nevertheless, we acknowledge the value of such an endeavor and will consider incorporating this aspect in future studies as the EV and proteomic methodology and technology improve and become more sensitive.

(4) I have a major issue with the chosen study subjects. While the study title and the manuscript mention preeclampsia, as per the inclusion criteria mentioned in lines 88-90, the patients will be HELLP syndrome. Please clarify what was used and modify the manuscript accordingly.

Thank you very much for finding this error. Our patients had none of the features that would qualify them for HELLP syndrome. We have edited to:

PE was defined as new (after 20 weeks) systolic blood pressure of 140 mmHg or diastolic pressure of 90 mmHg, proteinuria (protein/creatinine ratio of 30 mg/mmol or more). None of our patients had maternal acute kidney injury, liver dysfunction, neurological features, hemolysis, or thrombocytopenia.

(5) It is hard to reconcile how only 15 proteins were identified in the placental extract while 300+ in EVs. There is a methodological issue in the mass spec or extraction. With such widely different denominators in the total proteins identified, it is hard to compare the outcomes in terms of the three sample types.

We acknowledge the reviewer's concerns regarding the disparity in protein counts between the placental extract and the EVs. Ultimately, more is not necessarily better. Several factors might contribute to this discrepancy. Firstly, it is plausible that certain proteins exhibit selective affinity to varying sizes of EVs, leading to a more diverse range of proteins than the placental extract. We were also stringent in our analysis to enable us to select proteins whose biological differences are more likely to be reproducible with a different validatory method like a western blot. Additionally, although the placental extract might contain a higher total protein concentration, it doesn't necessarily translate to a richer diversity of disease-specific proteins. Considering these nuances when comparing protein outcomes across sample types is helpful.

(6) I am unable to understand the terms least differentially expressed and most differentially expressed. Do the authors mean upregulated and downregulated? Please clarify and use the terms appropriately by providing fold change values.

We appreciate the reviewer's request for clarification. We intended to provide a relative measure of expression for the terms 'least differentially expressed' and 'most differentially expressed'. The terms are roughly equitable to down- and upregulated. Regarding EVs, we avoid using the terms 'upregulated' and 'downregulated' as EVs act as transporters and do not possess regulatory functions per se. However, for the placenta, we recognize the relevance of these terms.

(7) The data presented is very superficial and lacks methodological details. The authors should provide the total number of targets achieved after mass spec. The cutoffs used the FDRs and other details.

We apologize for the omission. We have added these details to the method section.

(8) It is not clear how were these differentially abundant proteins identified. What was the cutoff used? Was it identified in all the replicates?

We apologize for the omission. We have added these details to the method section.

(9) How many samples were subjected to the discovery cohort, and how many were in the validation cohort? Were they the same or different? If the samples were different, how many PE samples had differentially abundant proteins by both methods?

The study utilized 12 samples for initial discovery and another 12 for western blot validation. The validation samples specifically targeted proteins of interest, rather than undergoing another comprehensive mass spectrometry analysis.

(10) It is striking that the authors report the expression of prostatic acid phosphatase in the placenta. In my understanding of placental biology, this gene or protein is not known to be expressed by the placenta. Please perform immunofluorescence to demonstrate that this protein is indeed produced in the STBs

Research has revealed that even though it's called prostate-specific antigen, it's created in tissues other than the prostate, such as the placenta. Here are a couple of references to support this claim: PMID: 10634405, PMID: 7533063, PMID: 8939403, and PMID: 8945610. Hence it is likely not beneficial to demonstrate what many researchers have already demonstrated.

(11) Please validate the differential abundance of these proteins in the exosomes isolated from the plasma of women with and without preeclampsia. A serial measurement will be of high value to determine how early as compared to hypertension, these biomarkers can predict preeclampsia.

We are validating each EV-carried marker individually in the circulation (plasma or serum), localizing them in the placenta, and performing downstream functional analysis. This article is already lengthy and would likely be too cumbersome to include the details of all individual proteins in this manuscript. However, we have already published papers on Siglec 6 (PMID: 32998819) and Neprilysin (PMID: 30929513), and others will be published soon. We agree that there will be a lot of value to serial measurement, not just in terms of how early as compared to hypertension, these biomarkers can predict preeclampsia but also as potentially a more sensitive or specific test. This would be the subject of subsequent papers.

(12) The authors are recommended to carry out immunofluorescence to localize the differentially abundant proteins in the placental sections and show that they are specific to STBs.

We have already provided a similar response earlier (see response to point 11). In addition, while it is preferable, the biomarkers don't necessarily need to be specific to STB. Not all biomarkers are mechanistic agents/targets, and not all mechanistic agents are biomarkers. However, mechanistic agents should preferably be placental-specific. For example, the total sFLT1, the most studied biomarker, is not exclusively synthesized in the placenta, even though the placental-specific isoform represents a small fraction of the total sFLT-1. For example, in the non-placental world, alkaline phosphatase (ALP) is not exclusively produced by the liver but is a ‘biomarker’ of cholestatic disease.

(13) Table 1 should give the range and SD could be given as + instead of the bracket.

Thank you for your suggestion. We have edited it accordingly.

(14) It is necessary to provide the gestational age of the onset of hypertension to get a judgment of how long these women were preeclamptic, culminating in HELLP.

We want to emphasize that none of our patients experienced HELLP syndrome. In the results section, we have included the gestational age at the time of diagnosis in the table for preeclampsia. It's crucial to understand that the gestational age at diagnosis is distinct from the gestational age when hypertension initially appeared. Detecting the exact gestational age of hypertension onset would be challenging, and it would likely require a prospective or randomized clinical trial with continuous monitoring, possibly on a daily basis. However, our study is retrospective. Thus we can only comment on the gestational age at diagnosis

(15) For newborns the term Sex is used and not gender

Thank you for your suggestion. We have edited it accordingly.

(16) Figure 2 is stretched and hard to read

Thank you for your suggestion. We have edited it accordingly by creating two separate images to promote readability.

(17) Line 278 change the sentence "there fifteen (15) proteins in the placenta" to "there were fifteen (15) proteins in the placenta"

Thank you for your suggestion. We have edited it accordingly.

(18) Line 288 you mean least and not lease

Thank you for your suggestion. We have edited it accordingly.

Author Response

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

eLife assessment

This important study advances our knowledge of how parasites evade the host complement immune system. The new cryo-EM structure of the trypanosome receptor ISG65 bound to complement component C3b is highly compelling and well-supported by biochemical experiments. This work will be of broad interest to parasitologists, immunologist, and structural biologists.

We thank the reviewers and editorial team for this assessment of our work.

Public Reviews:

Reviewer #1 (Public Review):

The authors set out to use structural biology (cryo-EM), surface plasmon resonance, and complement convertase assays to understand the mechanism(s) by which ISG65 dampens the cytoxicity/cellular clearance to/of trypanosomes opsonised with C3b by the innate immune system.

The cryo-EM structure adds significantly to the author's previous crystallographic data because the latter was limited to the C3d sub-domain of C3b. Further, the in vitro convertase assay adds an additional functional dimension to this study.

The authors have achieved their aims and the results support their conclusions.

The role of complement in immunity to T. brucei (or lack thereof) has been a significant question in molecular parasitology for over 30 years. The identification of ISG65 as the C3 receptor and now this study providing mechanistic insights represents a major advance in the field.

Reviewer #2 (Public Review):

This is an excellent paper that uses structural work to determine the precise role of one of the few invariant proteins on the surface of the African trypanosome. This protein, ISG65, was recently determined to be a complement receptor and specifically a receptor of C3, whose binding to ISG65 led to resistance to complement-mediated lysis. But the molecular mechanism that underlies resistance was unknown.

Here, through cryoEM studies, the authors reveal the interaction interface (two actually) between ISG65 and C3, and based on this, make inferences regarding downstream events in the complement cascade. Specifically, they suggest that ISG65 preferably binds the converted C3b (rather than the soluble C3). Moreover, while conversion to a C3bB complex is not blocked, the ability to bind complement receptors 1 and 3 is likely blocked.

Of course, all this is work on proteins in isolation and the remaining question is - can this in fact happen on the membrane? The VSG-coated membrane is supposed to be incredibly dense (packed at the limits of physical density) and so it is unclear whether the interactions that are implied by the structural work can actually happen on the membrane of a live trypanosome. This is not necessarily a dig but it should be addressed in the manuscript perhaps as a caveat.

We thank the reviewer for their positive response our work. We fully agree with the reviewer about the caveats which come from this work being done in a biochemical context. We have addressed this in lines 223-24 and 327-333.

Reviewer #3 (Public Review):

The authors investigate the mechanisms by which ISG65 and C3 recognize and interact with each other. The major strength is the identification of eco-site by determining the cryoEM structure of the complex, which suggests new intervention strategies. This is a solid body of work that has an important impact on parasitology, immunology, and structural biology.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

A paper by Sulzen et al was published online on 27th April in Nature Communications that has a similarity (the cryo-EM structure) to this paper. This does not detract from the value of this paper. The authors should, however, include a "compare and contrast" section in this paper to explain similarities and differences in the conclusions. For example, while this paper demonstrates that ISG65 does not prevent C3 convertase activity, the Sulzen paper suggests it does prevent C5 convertase activity. The compatibility of these conclusions should be discussed.

Two studies of ISG65 were published shortly after submission of this manuscript (Sulzen et al and Lorenzen et al) and we have added a brief comparison of the conclusions of these papers here. These mentions include lines 151, 155-6, 201-2, 274-278, 292-93 and 321-323. For a more in-depth comparison we have published an opinion piece in Trends in Parasitology, which discusses all three of these papers and which we also now reference here.

Could the authors comment as to whether they think the association of C3b with the unstructured region of ISG65 comes about via S-S shuffling? I.e., is C3B first thioester linked to VSG and then this rearranges to ISG65 through C3b-ISG65 proximity?

We thank the reviewer for the interesting suggestion. However, we are not aware of evidence showing that C3b, which has been conjugated to a target protein through its covalent ester bond, then becomes transferred to a second target protein. As ISG65 can bind to C3 as well as C3b, we think that the conjugate could form when ISG65-bound C3 converts to C3b, becomes reactive and, through proximity, is most likely to conjugate to ISG65. Whether this occurs to a substantial degree in trypanosomes, or whether it is more likely that ISG65 interacts with C3b which is already VSG-conjugated, requires further experiments. We have edited lines 217-222 to make this point more clearly.

Reviewer #3 (Recommendations For The Authors):

The authors previously reported that ISG65 C-terminus is so flexible and is not resolved in their 2022 ISG65-C3d (TED of C3b) crystal structure, which is the same case here in the cryo-EM structure of ISG65-C3b. Thus, I am wondering how C3b might find the flexible C-terminus and form a covalent bond.

We think that the answer to the reviewer’s question relates to local concentration. When two reactive compounds are not attached together, then they diffuse freely in three-dimensions and their likelihood of colliding and reacting is subject to the randomness of Brownian motion. However, if they bind together through an interaction distinct from the reactive residues, then this increases their relative local concentration and the likelihood of collision and reaction taking place. In the case of ISG65, this is coupled with the ability of ISG65 to bind to C3 before it converts to C3b and becomes reactive. The interaction of ISG65 with C3/C3b will therefore bring together the reactive residues and increases the probability that they will collide and form a conjugate. Our control with BSA, which does not bind to C3/C3b, and does not form these conjugates supports this conclusion. We have edited lines 217-222 to clarify.

I also find it puzzling that deleting L2 or L3 in ISG65, which they found forming additional contracts with CUB domain of C3b (12 times binding tighter), does not affect the ISG65-C3b conjugate formation in the in vitro C3 convertase formation assay.

When we consider the affinities that the L2 and L3 loop deletions variants have for ISG65, and the concentration of ISG65 in the C3 convertase assay, we would predict that the conjugates still form with the L2 and L3 variants. This binding would therefore increase the relative local concentration of the reactive residues and ensure preferential conjugate formation, as we observe.

(1) Page 2 bottom line, "In particular, loop 2 forms a direct contact with the CUB domain of ISG65, centered around an electrostatic", ISG65 should be C3b.

We thank the reviewer for spotting this. It has been corrected.

(2) Page 4, "We found that ISG65 does not complete with either factor B or Factor D and does not block the binding of factor Bb (Figure 3b). This suggests that the C3 convertase can form in the presence of ISG65", "complete" should be "compete".

It has been corrected.

(3) Page 4, "revealed that in the presence of ISG65 a high molecular weight band appeared, which we identified through mass spectrometry to be a conjugate of ISG65 with C3b". There is no mass spectrometry data in the manuscript to support this.

We agree with the reviewer that this data should be included in the paper and have now added it as Supplementary Table 3.

(4) Page 5, "By inhibiting binding of CR2 to C3d, ISG65 will reduce the likelihood that B-cell receptor binding to trypanosome antigens will result in B-cell activation and antibody production." - this sentence is a bit confusing.

We have clarified this point in lines 243-245.

(5) Related to Figure 2a. "This structure reveals the two distinct interfaces formed between ISG65 and C3b (Figure 2a)." It would be clearer to label where interface 1 and interface 2 are in Figure 2a.

We have now labelled interfaces 1 and 2 above the insets in Figure 2a.

(6) Related to Figure 2C. I suggest mutagenesis to validate ISG65 L2/L3 - C3b CUB domain interaction, i.e. mutate ISG65 (N188, R187, Y190) and perform SPR with C3b.

We agree with the reviewer that this experiment was a valuable validation of our structural data. To achieve this aim, we changed our SPR assay, coupling C3 variants to the chip surface in an orientation which would match their conjugation to a pathogen and allowing us to reliably compare the affinities of ISG65 variants. We then assessed the binding of ISG65, ISG65∆L2, and the ISG65L2N188A,H189A,Y190A proposed by the reviewer. As predicted from the structure, both loop 2 deletion and mutation reduced the affinity for C3b but did not affect the affinity for C3d, suggesting that the difference in affinity of ISG65 for C3b and C3d is due to the observed interface 2. This new data is described in lines 150-168 and is presented in Figure 2c.

(7) Related to Figure 3a. Is the C3b only structure in the presence of ISG65 the real C3b only? Discussion can be added.

Our cryoEM analysis of the ISG65-C3b mixture yielded three dimensional classes which contained clear density for ISG65 and those in which there was no density for ISG65. While the reviewer is technically correct, and we cannot be 100% sure that there is not an entirely disordered ISG65 attached to these ‘unbound’ C3b, we think that this is extremely unlikely. In either case, these ‘unbound’ C3b are indistinguishable from other structures of C3b and the argument in the paper stands. We have added a clause in lines 178-179 to make this point.

(8) Related to Figure 3e. There is no label for WT and deletion mutants. Also, L1 and L3 deletion does not seem to show on the gel.

We have added these labels.

Author Response

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

We thank the Reviewing Editor and two additional reviewers for the insightful input they gave us on the first version of our manuscript on allosteric activity regulation of the anaerobic ribonucleotide reductase from Prevotella copri. We have revised the manuscript in the light of the reviewers' comments. In particular, we have added additional experiments using hydrogen-deuterium exchange mass spectrometry (HDX-MS) to probe the accessibility and mobility of different parts of the protein structure in the apo-state and in the presence of dATP/CTP and ATP/CTP. The results strongly confirm the binding of nucleotides to the activity and specificity sites, as seen biochemically and structurally. In the question of mobility of the glycyl radical domain the HDX-MS experiments suggest an increased mobility in the presence of dATP, though the results are not as clear-cut as for the nucleotide binding. The HDX-MS analyses are complicated by the fact that they reflect all species in solution, which are evidently multiple for all states of PcNrdD. Finally, we have rephrased key parts of the results and discussion, and modified the title, to avoid any implication that we believe the glycyl radical domain becomes extensively disordered, rather that it becomes more mobile to the extent that it cannot be seen in the cryo-EM structures.

eLife assessment

This study advances our understanding of the allosteric regulation of anaerobic ribonucleotide reductases (RNRs) by nucleotides, providing valuable new structural insight into class III RNRs containing ATP cones. The cryo-EM structural characterization of the system is solid, but other aspects of the manuscript, which are incomplete, could be improved by including additional functional characterization and more evidence for the proposed mechanism of inhibition by dATP. The work will be of interest to biochemists and structural biologists working on ribonucleotide reductases and other allosterically regulated enzymes.

Public Reviews:

Reviewer #1 (Public Review):

The goal of this study is to understand the allosteric mechanism of overall activity regulation in an anaerobic ribonucleotide reductase (RNR) that contains an ATP-cone domain. Through cryo-EM structural analysis of various nucleotide-bound states of the RNR, the mechanism of dATP inhibition is found to involve order-disorder transitions in the active site. These effects appear to prevent substrate binding and a radical transfer needed to initiate the reaction.

Strengths of the manuscript include the comprehensive nature of the work - including numerous structures of different forms of the RNR and detailed characterization of enzyme activity to establish the parameters of dATP inhibition. The manuscript could be improved, however, by performing additional experiments to establish that the mechanism of inhibition can be observed in other contexts and it is not an artifact of the structural approach. Additionally, some of the presentations of biochemical data could be improved to comply with standard best practices.

The work is impactful because it reports initial observations about a potentially new mode of allosteric inhibition in this enzyme class. It also sets the stage for future work to understand the molecular basis for this phenomenon in more detail.

We thank the editor and reviewers for their positive evaluation of the potential impact of our work. We completely agree that hypotheses based on structural data require orthogonal experimental verification. However, the number and consistency of the cryo-EM structures speak in favour of the data being representative of conditions in solution. We feel that in particular cryo-EM data should be relatively free of artefacts, e.g. biased or incorrect relative domain orientations, compared to crystallography, where crystal packing effects can affect these parameters. As we write in response to Reviewer #2, it has been difficult to propose a direct structural mechanism for transmission of the allosteric signal from the a-site in the ATP-cone to the active site and GRD given that the ATP-cones and linker are disordered in the dATP-bound dimers and only partly ordered in the dATP-bound tetramers. Further verification experiments will be performed in future but are outside the scope of the present article.

We will improve the presentation of the biochemical data in a revised version.

General comments:

(1) It would be ideal to perform an additional experiment of some type to confirm the orderdisorder phenomena observed in the cryo-EM structures to rule out the possibility that it is an artifact of the structure determination approach. Circular dichroism might be a possibility?

Circular dichroism reports only on the approximate relative proportions of helix, sheet and loop structure in a protein, thus we believe that it would not be a sensitive enough tool to distinguish between ordered and disordered states. We are considering what alternative methods might be appropriate.

(2) Does the disordering phenomenon of one subunit in the ATP-bound structures have any significance - could it be related to half-of-sites activity? Does this RNR exhibit half-of-sites activity?

Half-of-sites activity has not been biochemically proven in any ribonucleotide reductase in spite of the fact that it was first suggested in 1987 (PMID: 3298261). However, strong structural indication was recently published in the form of the holo-complex of the class Ia ribonucleotide reductase from Escherichia coli, which is highly asymmetrical and in which productive contacts forming an intact proton-coupled electron transfer pathway are only formed between one of two pairs of monomers (PMID: 32217749). We have not been able to prove half-of-sites activity for PcNrdD due to low overall radical content, but the structural results are indeed consistent with such an activity.

(3) Does the disordering of the GRD with dATP bound have any long-term impact on the stability of the Gly radical? I realize that the authors tested the ability to form the Gly radical in the presence of dATP in Fig. 4 of the manuscript. But it looks like they only analyzed the samples after 20 min of incubation. Were longer time points analyzed?

Radical content was measured after 5 min and 20 min incubation; 5 min incubations (not included in the manuscript) consistently gave higher radical content compared to 20 min incubation. Longer time points were not analysed, as we assumed that the radical content would be even lower after 20 min.

(4) Did the authors establish whether the effect of dATP inhibition on substrate binding is reversible? If dATP is removed, can substrates rebind?

This is an interesting question. We measured KDs for dATP in the micromolar range and are hence confident that dATP binding is reversible. Our measurements do not, however, directly prove that inhibition of the enzyme is reversible. Nevertheless, it is worth noting that the protein as purified was precipitated and analysed by the UV-visible spectrum. The aspurified PcNrdD contained 30% nucleotide contamination. The as-purified sample was then analysed by HPLC and we identified a major peak, corresponding to dATP/dADP. Therefore, purification conditions had to be optimised to remove the nucleotides. This is evidence that PcNrdD that has “seen” dATP can subsequently bind substrates in the presence of ATP. We will describe the purification more clearly in a revision.

(5) In some figures (Fig. 6e, for example), the cryo-EM density map for the nucleotide component of the model is not continuous over the entire molecule. Can the authors comment on the significance of this phenomenon? Were the ligands validated in any way to ensure that the assignments were made correctly?

Indeed we sometimes saw discontinuous density for the nucleotides, both in the active site and in the specificity site. However, the break was almost always near the C5’ carbon atom, which is common to all nucleotides. While we cannot readily explain this phenomenon, the nucleotides refined well with full occupancy, giving B-factors similar to those of the surrounding protein atoms. The identity of the nucleotide could always be inferred from a) the size of the base (purine or pyrimidine); b) the known nucleotide combinations added to the protein before grid preparation; c) prior knowledge on the combinations of effector and substrate that have been found valid for all RNRs since the first studies of allosteric specificity regulation.

Reviewer #2 (Public Review):

This manuscript describes the functional and structural characterization of an anaerobic (Class III) ribonucleotide reductase (RNR) with an ATP cone domain from Prevotella copri (PcNrdD). Most significantly, the cryo-EM structural characterization revealed the presence of a flap domain that connects the ATP cone domain and the active site and provides structural insights about how nucleotides and deoxynucleotides bind to this enzyme. The authors also demonstrated the catalytic functions and the oligomeric states. However, many of the biochemical characterizations are incomplete, and it is difficult to make mechanistic conclusions from the reported structures. The reported nucleotide-binding constants may not be accurate because of the design of the assays, which complicates the interpretation of the effects of ATP and dATP on PcNrdD oligomeric states. Importantly, statistical information was missing in most of the biochemical data. Also, while the authors concluded that the dATP binding makes the GRD flexible based on the absence of cryo-EM density for GRD in the dATP-bound PcNrdD, no other supports were provided. There was also a concern about the relevance of the proposed GRD flexibility and the stability of Gly radical. Overall, the manuscript provides structural insights about Class III RNR with ATP cone domain and how it binds ATP and dATP allosteric effectors. However, ambiguity remains about the molecular mechanism by which the dATP binding to the ATP cone domain inhibits the Class III RNR activity.

Strengths:

(1) The manuscript reports the first near-atomic resolution of the structures of Class III RNR with ATP domain in complex with ATP and dATP. These structures revealed the NxN flap domain proposed to form an interaction network between the substrate, the linker to the ATP cone domain, the GRD, and loop 2 important for substrate specificity. The structures also provided insights into how ATP and dATP bind to the ATP cone domain of Class III RNR. Also, the structures suggested that the ATP cone domain is directly involved in the tetramer formation by forming an interaction with the core domain in the presence of dATP. These observations serve as an important basis for future study on the mechanism of Allosteric regulation of Class III RNR.

(2) The authors used a wide range of methodologies including activity assays, nucleotide binding assays, oligomeric state determination, and cryo-EM structural characterization, which were impressive and necessary to understand the complex allosteric regulation of RNR.

(3) The activity assays demonstrated the catalytic function of PcNrdD and its ability to be activated by ATP and low-concentration dATP and inhibited by high-concentration dATP.

(4) ITC and MST were used to show the ability of PcNrdD to bind NTP and dATP.

(5) GEMMA was used successfully to determine the oligomeric state of PcNrdD, which suggested that PcNrdD exists in dimeric and tetrameric forms, whose ratio is affected by ATP and/or dATP.

Weaknesses:

(1) Activity assays.

The activity assays were performed under conditions that may not represent the nucleotide reduction activity. The authors initiated the Gly radical formation and nucleotide reduction simultaneously. The authors also showed that the amount of Gly radical formation was different in the presence of ATP vs dATP. Therefore, it is possible that the observed Vmax is affected by the amount of Gly radical. In fact, some of the data fit poorly into the kinetic model. Also, the number of biological and technical replicates was not described, and no statistical information was provided for the curve fitting.

The highest turnover activity of PcNrdD measured in presence of ATP was 1.3 s-1 (470 nmol/min/mg), a kcat comparable to recently reported values for anaerobic and aerobic RNRs from Neisseria bacilliformis, Leeuwenhoekiella blandensis, Facklamia ignava, Thermus virus P74-23, and Aquifex aeolicus (PMID: 25157154, PMID: 29388911, PMID: 30166338, PMID: 34314684, PMID: 34941255). The general trend illustrated in Figure 1 is that ATP has an activating effect on enzyme activity, whereas high concentrations of dATP have an inactivating effect on activity, which cannot be explained by suboptimal assay conditions since our EPR results consistently show that more radical is formed in incubations with dATP compared to incubations with ATP. Curve fitting methods used are listed in Materials and Methods (as specified in the Figure 1 legend), and standard errors for all specified curve fitting results (from triplicate experiments) are shown in Figure 1.

(2) Binding assays.

The interpretation of the binding assays is complicated by the fact that dATP binds both a- and s-sites and ATP binds a- and active sites. dATP may also bind the active site as the product. It is unknown if ATP binds s-site in PcNrdD. Despite this complexity, the binding assays were performed under the condition that all the binding sites were available.

Therefore, it is not clear which event these assays are reporting.

Both ITC and MST experiments involving ATP and dATP binding to the a-site were performed in the presence of at least 1 mM GTP substrate (5 mM in MST) to fill the active site, and 1 mM dTTP effector to fill the s-site (specified in the legend to Figure 2). These conditions enable binding of ATP or dATP only to the a-site in the ATP-cone.

(3) Oligomeric states.

Due to the ambiguity in the kinetic parameters and the binding constants determined above, the effects of ATP and dATP on the oligomeric states are difficult to interpret. The concentrations of ATP used in these experiments (50 and 100 uM) were significantly lower than KL determined by the activity assays (780 uM), while it is close to the Kd values determined by ITC or MST (~25 uM). Since it is unclear what binding events ITC and MST are reporting, the data in Figure 3 does not provide support for the claimed effects of ATP binding. For the effects of dATP, the authors did not observe a significant difference in oligomeric states between 50 or 100 uM dATP alone vs 50 uM dATP and 100 uM CTP. The former condition has dATP ~ 2x higher than the Kd and KL (Figure 1b) and therefore could be considered as "inhibited". On the other hand, NrdD should be fully active under the latter condition. Therefore, these observations show no correlation between the oligomeric state and the catalytic activity.

The results in Figure 3 show that at in presence of 100 µM ATP plus 100 µM CTP the oligomeric equilibrium is 64% dimers plus 36% tetramers, and in presence of 50-100 µM dATP the oligomeric equilibrium is 32% dimers and 68% tetramers. We agree that there is no clear and strong correlation between oligomeric state and inhibition. We will also try to make it clearer in a revised version. Meanwhile, in order to add some clarity to our observations, SEC experiments at higher nucleotide concentrations will be done to strengthen our observations.

(4) Effects of dATP binding on GRD structure

One of the key conclusions of this manuscript is that dATP binding induces the dissociation of GRD from the active site. However, the structures did not provide an explanation for how the dATP binding affects the conformation of GRD or whether the dissociation of GRD is a direct consequence of dATP binding or it is due to the absence of nucleotide substrate. Also, Gly radical is unlikely to be stable when it is not protected from the bulk solvent. Therefore, it is unlikely that the GRD dissociates from the active site unless the inhibition by dATP is irreversible. Further evidence is needed to support the proposed mechanism of inhibition by dATP.

We admit that it has been difficult to propose a direct structural mechanism for transmission of the allosteric signal from the a-site in the ATP-cone to the active site and GRD given that the ATP-cones and linker are disordered in the dATP-bound dimers and that the linker can only be partly modelled in the dATP-bound tetramers. Most likely dATP binding causes a change in the dynamics of the linker region and NxN flap that directly affects substrate binding and simultaneously causes disorder of the GRD, given that all are part of a connected system (described as “nexus” in the manuscript). The structures determined in the presence of dATP and CTP show that CTP cannot bind in the absence of an ordered NxN flap.

In any case a major conclusion of the work is that dATP does not inhibit the anaerobic RNR by prevention of glycyl radical formation but by prevention of its subsequent transfer. We agree that further evidence is required to support the proposed mechanism, but given the extent of the data already presented in the manuscript, we feel that such studies should be the subject of a future publication.

(5) Functional support for the observed structures.

Evidence for connecting structural observations and mechanistic conclusions is largely missing. For example, the authors proposed that the interactions between the ATP cone domain and the core domain are responsible for tetramer formation. However, no biochemical evidence was provided to support this proposal. Similarly, the functional significance of the interaction through the NxN flap domain was not proved by mutagenesis experiments.

We did actually make mutants to verify the observed interactions, but several of them did not behave well in our hands, e.g. with regard to protein stability. Since we have no evidence that oligomerisation is coupled to inhibition, and since we did not observe any conservation between protein sequences in the interaction area, we chose not to pursue this point further. The main merit of the tetramer structures is that they allowed a high-resolution view of dATP binding to the ATP-cone and a comparison to previously-observed ATP-cones. Nevertheless, mutation experiments, also including the NxN flap, could be the subject of future work.

Reviewer #3 (Public Review):

The manuscript by Bimai et al describes a structural and functional characterization of an anaerobic ribonucleotide reductase (RNR) enzyme from the human microbe, P. copri. More specifically, the authors aimed to characterize the mechanism by how (d)ATP modulates nucleotide reduction in this anaerobic RNR, using a combination of enzyme kinetics, binding thermodynamics, and cryo-EM structural determination. One of the principal findings of this paper is the ordering of a NxN 'flap' in the presence of ATP that promotes RNR catalysis and the disordering of both this flap and the glycyl radical domain (GRD) when the inhibitory effector, dATP, binds. The latter is correlated with a loss of substrate binding, which is the likely mechanism for dATP inhibition. It is important to note that the GRD is remote (>30 Ang) from the binding site of the dATP molecule, suggesting long-range communication of the structural (dis)ordering. The authors also present evidence for a shift in oligomerization in the presence of dATP. The work does provide evidence for new insights/views into the subtle differences of nucleotide modulation (allostery) of RNR through long-range interactions.

The strengths of the work are the impressive, in-depth structural analysis of the various regulated forms of PcRNR by (d)ATP using cryo-EM. The authors present seven different models in total, with striking differences in oligomerization and (dis)ordering of select structural features, including the GRD that is integral to catalysis. The authors present several, complementary biochemical experiments (ITC, MST, EPR, kinetics) aimed at resolving the binding and regulatory mechanism of the enzyme by various nucleotides. The authors present a good breadth of the literature in which the focus of allosteric regulation of RNRs has been on the aerobic orthologues.

Given the resolution of some of the structures in the remote regions that appear to be of importance, the rigor of the work could have been improved by complementing this experimental studies with molecular dynamics (MD) simulations to reveal the dynamics of the GRD and loops/flaps at the active site.

We have discussed with expert colleagues the possibility of carrying out MD simulations on the different states in order to study the differential effects of ATP and dATP binding on the dynamics of the GRD. However, they felt that the chance of obtaining meaningful results was low, particularly since some structural elements are missing from the models for both forms, in particular the linker between the ATP-cone and the core.

The biochemical data supporting the loss of substrate binding with dATP association is compelling, but the binding studies of the (d)ATP regulatory molecules are not; the authors noted less-than-unity binding stoichiometries for the effectors.

Most of the methods used measure only binding strength, not the number of binding sites (N), whereas ITC also measures number of sites. N is dependent on the integrity of the protein, i.e. the number of protein molecules in a preparation that are involved in binding, and quite often gives lower values than the theoretical number of binding sites.

Also, the work would benefit from additional support for oligomerization changes using an additional biochemical/biophysical approach.

SEC (chromatography), GEMMA (mass spectrometry) and cryo-EM were used to study oligomerization. Since each method has restrictions on nucleotide concentrations as well as protein concentrations that can be used, the results are not directly comparable, but all three methods indicate nucleotide dependent oligomerization changes. The SEC results will be included in a revised version.

Overall, the authors have mostly achieved their overall aims of the manuscript. With focused modifications, including additional control experiments, the manuscript should be a welcomed addition to the RNR field

Recommendations for the authors: Reviewer #1 (Recommendations For The Authors):

(1) The last sentence of the abstract is not complete. The structures implicate a complex network of interactions in ... ? What do they implicate?

A couple of words seem to have been missed from the abstract. We have rewritten the end of the abstract to emphasise better that the dynamical transitions involve a linked network of interactions and not just the GRD.

(2) A reference is needed in the second sentence of the introduction.

We have added a reference as requested.

(3) Page 2, paragraph 2. The authors state "two beta subunits (NrdB) harboring a stable radical." This is not accurate. First of all, each beta subunit harbors its own cysteine oxidant.

And in several subclasses, that oxidant is not a stable radical but an oxidized metal cluster. Please revise to improve accuracy and also provide appropriate references.

We have revised the description and added a recent reference.

(4) Page 4, Fig. 1, panels C and D. The fit of the curve to the data is pretty poor. Is there an explanation? Could the data be improved in some way? In general, it is also best practice nowadays to show the individual data points in addition to the error bars in plots like the ones shown in Figure 1. Please modify the plots to include the individual data points in this figure - and probably also the subsequent figures showing binding data.

We have modified relevant panels in Figures 1, 2 and 5 as requested.

(5) Page 12, first paragraph. The authors state that one of the monomers in the ATP-CTP structure is well ordered and the other is less ordered. It would be ideal to show in a figure the basis for this conclusion using the cryo-EM maps. The "less ordered" monomer appears to be fully modeled.

Since the 2-fold axis of the dimer is vertical, the GRD of the left-hand monomer is hidden from view at the back of the molecule in Figure 6. For this monomer there was a small amount of density that allowed modelling of part of the glycyl radical loop (though not the tip containing the radical Gly itself) and the NxN flap, albeit with significantly higher mobility. We have illustrated this through an additional supplement for Figure 6 (figure supplement 2) in which the B-factors of the residues are shown both as a ribbon with radius proportional to the B-factor and through colouring. We hope that the four views in Figure 6 (figure supplement 2) together illustrate the relative mobility of different parts of the dimer.

It would also be ideal to show the basis for the conclusion that the entire GRD is disordered in the dATP-bound dimer structure.

Thank you for this suggestion. We have added a fifth supplement to Figure 8 in which we show the cryo-EM reconstruction for the dATP-bound dimer in two orientations, with the ATP-CTP-bound structure superimposed, which clearly shows that the entire GRD, the ATPcones, linker and NxN flap are all disordered in both monomers.

Reviewer #2 (Recommendations For The Authors):

(1) Units to describe enzyme activity.

• The unit for the specific activity in the main text (nmol/min•mg) is unusual. It is most likely a typo of nmol/min/mg or nmol/(min•mg).

We have changes to nmol/min/mg in the text.

• The unit for the Vmax is unusual and should not be confused with the specific activity. By definition, Vmax is the velocity of a reaction at a defined enzyme concentration/amount. For example, if an assay of 10 mg enzyme yielded 470 nmol of product in 1 min, Vmax is 470 nmol/min, whereas the specific activity is 47 nmol/min/mg.

The velocity as calculated above is ca 1.3 s-1. We have added kcat values to accompany the specific activities given.

(2) Steady-state kinetic analysis.

• The steady-state kinetic analysis in Figure 1 needs to be repeated. While the nonlinear curve fitting for Figure 1a is reasonable, those in Figures 1b, 1c, and 1d were outside the error range. Consequently, the reported kinetic parameters are unlikely accurate. The authors should repeat the assays with different enzyme preparation to account for all the errors. If the fit curve is still outside the error range, the kinetic model is likely incorrect, and the authors need to investigate different kinetic models.

The replotted Figure 1 now includes two different experiments for 1b (four replicates in total).

• The authors should report the number of replicates and the statistical data for the curve fitting.

The figure legend has been updated with statistical data for all curve fits, and the number of replicates has been added.

• The authors should report Vmax, Ki, and KL for Figure 1d.

Results in Figures 1c and 1d are less straightforward than those in Figures 1a and 1b where the s-site is filled with dTTP, favouring binding of GTP to the active site. The curve fit in Figure 1c is disturbed at high concentrations of ATP, which plausibly competes with the CTP substrate and results in inhibition by formed dATP. The curve fit in Figure 1d is less certain since reduction of substrate is low due to intrinsic CTP reduction in absence of effector and partially overlapping activation and inhibition effects of dATP.

• The authors should consider presenting the data in a log scale because of the complex nature of the activation/inhibition at the lower concentrations of dATP.

Log scale plots are included as insets in Figures 1b and 1d.

• The basal level of CPT reduction in the absence of an effector nucleotide should be reported with an error.

The error value has been added in the figure legend for the basal level of CTP reduction in the absence of effector.

(3) Equations for the kinetic analysis.

-The equations should be numbered and referred to in the Figure 1 legend.

All equations are specified and numbered in Materials and Methods. The equation used for each curve fit in the panels in Figure 1 is specified in the figure legend.

-KL must be defined in the main text. I suppose this is Kd for ATP or dATP. The equation for KL determination is missing brackets for dNTP.

KL (the concentration of an allosteric effector that gives half maximal enzyme activity) is defined in Materials and Methods where the equation is described. KL is not the same as KD (the dissociation constant for a ligand and its receptor). Brackets have been added to equation 1.

• I believe dNTP in the first equation is incorrect because ATP was the ligand for Figures 1A and 1C.

[dNTP] in the first equation has been changed to [NTP/dNTP] to indicate that both ribonucleotides and deoxyribonucleotides can bind.

• The second equation can be expressed as dATP as I believe this is the only ligand that inhibits the enzyme.

We prefer to keep the more general [dNTP] in the equation.

• The equation used for the fitting in Figure 1d must be defined more clearly than "a combination of the two equations".

The equation used for the curve fit in Figure 1d has been specified as equation 3 in Materials and Methods.

(4) Design of the activity assays

It is not clear if the activity assays report the rate of glycyl radical formation or nucleotide reduction. The authors mixed NrdD and NrdG and initiated the reaction by adding formate (essential for nucleotide reduction) and dithionite (Gly radical formation). The Gly radical formation is slow (in min time scale). The authors reported that ATP/dATP affected the rate of Gly radical formation and in the presence of ATP, Gly radical formation was incomplete even after 20 min. Therefore, it is possible that within the timescale of the activity assays (5 min), the reactions could be partially limited by the Gly radical formation, which may be the reason for the poor curve fitting.

Activity assays were performed with 5 min pre-incubation without dithionite and formate (no glycyl radical formation) and 10 min incubation after addition of dithionite and formate (glycyl radical formation plus substrate reduction). During earlier tests, NrdD and NrdG were first preincubated in the presence of dithionite (glycyl radical formation) and after addition of formate the substrate reduction was monitored during 20 min. These experiments resulted in lower enzyme activity, whereas higher activity was achieved only upon formate addition to the preincubation reaction. We suppose that the presence of dithionite, which is a strong reducing agent, affected NrdD stability and the reaction was stabilised by the presence of formate at an earlier stage of the reaction. For the EPR conditions used in the paper, 5 min incubation gave higher radical content compared to 20 min, and the reported activity assay gave highest activity after 10 min incubation; kcat of 1.3 s-1.

(5) Methods section for the activity assays.

• The concentration of dTTP, ATP, and dATP used in the assays must be described.

We thank the reviewer for pointing out this omission and we have now specified the concentrations used.

• Although the authors mentioned that they changed the concentration of dTTP, such data were not presented. Is this correct? Did the authors fix the dTTP concentration for the GTP reduction?

We apologise for the ambiguity and have specified that the dTTP concentration was fixed at 1 mM in the GTP experiments and that only the ATP or dATP concentrations were varied.

(6) Discrepancy between Ki/KL and Kd.

• There is a significant ambiguity remaining about the binding event that the ITC and MST results are reporting. Although dATP binds to both a- and s-sites and ATP binds to both active site and a-site, only a single binding event was observed in both cases. To distinguish the dATP binding to a- and s-sites and the active site, the authors should perform binding assays using mutant enzymes with only one of the binding sites available for dATP/ATP binding.

MST and ITC were performed in presence of substrate (1 mM GTP) and s-site effector (1 mM dTTP in ITC experiments, and 5 mM dTTP in MST experiments), thus dATP is blocked from binding to the s-site and ATP from binding to the active site.

• There are significant differences between Kd determined by MST or ITC and Ki/KL determined by the activity assays. Kd measurements were performed in the absence of the substrate nucleotides, while the assays required substrates. There may be complications from the presence of NrdG and the Gly radical formation. The authors must clearly describe all these complications and the discrepancy between Kd and Ki/KL.

MST, ITC and enzyme assays were all performed in the presence of substrate, and enzyme assays also contained NrdG, which was not present in the MST and ITC analyses. While KD is a thermodynamic constant representing the affinity of ligand to its binding site - in our case an effector nucleotide to the ATP-cone, KL is a kinetic constant (the allosteric effector concentration that gives half maximal activity) representing the relationship between the effector concentration and the reaction speed and is affected by the enzyme turnover number (kcat). The relationship between KD, KL and Ki is further complicated by conformational and possibly oligomeric state changes of NrdD upon binding of allosteric effectors, which occurs on a slower time scale than the rapid exchange of nucleotides in allosteric sites.

• The results of ATP/dATP copurification experiments shown in Figure 2 - figure supplement 1 show the preference of dATP binding over ATP. However, the results do not necessarily support the competition between ATP and dATP for binding to the ATP cone domain. It is still possible that dATP binding to the s-site diminishes the binding of ATP to the a-site.

Our aim was to exclude the possibility that ATP and dATP can bind to the ATP-cone at the same time and not to study competition between the two. Nevertheless, to eliminate the possibility that dATP binding to the s-site could affect nucleotide binding to the a-site, in two out of three conditions described in the supplementary figure, the experiments were performed in the presence of dTTP to prevent binding of dATP to the s-site.

(7) Oligomeric states.

• The authors must present the GEMMA results without ATP or dATP. Otherwise, the effects of ATP and dATP on the oligomeric state are not clear.

We cannot report GEMMA results without ATP or dATP because apo-PcNrdD was unstable in the GEMMA buffer and clogged the capillaries. Instead, SEC analysis was performed on apo-PcNrdD in a more suitable buffer and showed a homogeneous peak corresponding to a dimer (included as Figure 3 - figure supplement 1).

• Figure 3 does not support the induction of a2 upon ATP binding. The concentrations of ATP used in these experiments (50 and 100 uM) were significantly lower than KL determined by the activity assays (780 uM), while it is close to the Kd values determined by ITC or MST (~25 uM). Since it is unclear what binding events ITC and MST are reporting, the data in Figure 3 does not provide support for the claimed effects of ATP binding.

MST and ITC were performed in the presence of substrate (1 mM GTP) and s-site effector (1 mM dTTP in ITC experiments, and 5 mM dTTP in MST experiments), and they thus measure binding of ATP or dATP to the ATP cone. SEC analysis with 2 µM apo-PcNrdD and higher nucleotide concentrations (1 mM) was performed, confirming the presence of both dimers and tetramers in solution at different ratios depending on the addition of ATP or dATP. The SEC analysis, included as Figure 3 - figure supplement 1, confirms the existence of an equilibrium in solution.

• The effects of dATP must be presented more clearly. The authors did not observe a significant difference in oligomeric states between 50 or 100 uM dATP vs 50 uM dATP and 100 uM CTP. The former condition has dATP ~ 2x higher than the Kd and KL (Figure 1b) and therefore could be considered as "inhibited". On the other hand, NrdD should be fully active under the latter condition. The absence of difference in the oligomeric states between these two different conditions suggested to me that the oligomeric state does not regulate the NrdD activity. The authors seemed to indicate the same conclusion, but did not describe it clearly.

We agree that the oligomeric state most likely does not regulate the NrdD activity and hope to have explained this better in the revised version.

• Figure 3 legend mentioned a and b, but the figure was not labeled.

We have corrected this.

• The authors should triplicate the analysis and report the errors.

Five scans were added for each trace to increase the signal-to-noise level (included in figure legend).

(8) EPR characterization of Gly radical

• The amount of Gly radical must be quantified by EPR. The authors must report how much NrdD has Gly radical.

The concentration of NrdD (1 µM) in the activity assays is too low to be quantified by EPR. In the EPR experiment the glycyl radical content is given in the figure legend.

• The authors claim that the Gly radical environment was similar based on the doublet feature. However, the double feature comes from the hyperfine splitting with α proton whose orientation relative to the radical p-orbital would not be affected by the conformation or the environment. Thus, this conclusion is incorrect and must be removed.

We thank the reviewer for the clarifying comment and have removed our suggestion in the text.

(9) Gly711 should be shown in Fig. 6e to help readers understand the last paragraph on page 12.

The figure reference has been changed to Fig. 7, where this is shown more clearly. In Fig. 6e, inclusion of Gly711 would obscure other important information.

(10) GRD structure with dATP

The disorder of GRD in the presence of dATP does not agree with the formation of Gly radical under the same conditions. Gly radical is unlikely stable if it is extensively exposed to solvent. Most likely, the observed cryo-EM structures represent the conformation irrelevant to Gly radical formation.

We agree that the glycyl radical is unlikely to be stable if exposed to solvent. We believe that the GRD is not completely disordered but most likely made more mobile through rigid body movements of the domain to an extent that makes it invisible in the cryo-EM maps. It is most likely still in the vicinity of the active site, shielding the glycyl radical. Our new HDX-MS results show a small but tangible increase in mobility of the GRD in the presence of dATP compared to ATP. Of course the differences in dynamics remain to be confirmed. It is worth noting that the group of Catherine Drennan at MIT published a conference abstract more than a year ago that suggested a similar pattern of ordered/dynamic GRDs, based on crystal structures, though the details have not yet been published (https://doi.org/10.1096/fasebj.2022.36.S1.R3407).

We also agree that the cryo-EM structures do not show the GRD conformation relevant to Gly radical formation, as this has been shown spectroscopically for the GRE pyruvate formate lyase to require large conformational changes in the GRD and also the presence of the activase. However, revealing this conformation would be a completely different project. We postulate that inactivation proceeds by prevention of radical transfer to the substrate, not by prevention of its formation.

We have altered the wording in several places in the revised manuscript, including the title, to avoid using the term “disorder”, as this may imply (partial) unfolding, and we certainly do not wish to imply that.

(11) The difference between dATP and ATP binding

From the presented structures, it was not clear how the absence of 2'-OH affects the oligomeric state and the structure of the GRD. The low resolution of the ATP-bound structure precluded the comparison between the ATP and dATP-bound structures.

We agree that a detailed analysis of the differences between ATP- and dATP-bound structures requires higher resolution structures, particularly of the ATP-bound form. This will be the subject of future studies.

(12) Conclusion about the disordered GRD.

-The authors should describe the reason why the dATP binding affected the structure of GRD. The authors did not discuss why dATP binding affected the folding or mobility of GRD. Since this is the key conclusion of this manuscript and the authors are making this conclusion based on the absence of the ordered GRD structure (hence the negative results), the authors should carefully describe why the dATP binding does not allow the binding/folding of GRD in the position observed in the ATP-bound structure.

As mentioned in our response to point 4 in this reviewer’s Public Review, it is difficult to propose a direct structural mechanism for transmission of the allosteric signal from the a-site in the ATP-cone to the active site and GRD given that the ATP-cones and linker are disordered in the dATP-bound dimers and that the linker cannot be completely modelled even in the dATP-bound tetramers. Our first hypotheses were that the ATP-cone might work by a steric occlusion mechanism, but the reality appears more complex. Most likely dATP binding causes a change in the dynamics of the linker region and NxN flap that directly affects substrate binding and simultaneously causes higher mobility of the GRD, given that all are part of a connected system. The structures determined in the presence of dATP and CTP show that CTP cannot bind in the absence of an ordered NxN flap. We hope that future structural studies of NrdDs from other organisms may shed further light on this mechanism.

• The authors should test if the dATP inhibition is reversible for PcNrdD. If dATP binding induces dissociation of GRD from the active site and makes GRD flexible, Gly radical would most likely be quenched by formate or other components in the assay solution. If dATP inhibition is reversible, it is hard to believe that Gly radical dissociates completely from the active site.

As-purified PcNrdD contains dATP and can after removal of bound nucleotides bind substrate in presence of ATP. The as-purified PcNrdD protein contained 30% nucleotide contamination. After precipitation, HPLC analysis identified a major peak corresponding to dATP/dADP. Purification conditions were optimised to remove the nucleotides and we have added this information to the purification description.

(13) Functional support for the observed structures.

Similar to X-ray crystallography, cryo-EM is a highly selective method that requires the selection of particles that can be analyzed with sufficient resolution. This means that the analysis could be biased towards the protein conformations stable on the cryo-EM grid. Consequently, testing the structural observations by functional characterization of mutant enzymes is critical. However, the authors did not perform such functional characterizations and made conclusions purely based on the structural observations.

We acknowledge this limitation. We constructed several mutations located at the tetrameric interface between the ATP-cone and the core protein based on the cryo-EM structure of dATP loaded NrdD. Unfortunately, these mutant proteins were unstable and led to protein cleavage.

(14) Other minor points:

• In the introduction, the authors stated "The presence and function of the ATP-cone domain distinguish anaerobic RNRs from the other members of the large glycyl radical enzyme (GRE) family that are otherwise structurally and mechanistically related (Backman et al., 2017)." This statement is misleading because GREs are functionally diverse.

We have removed the words “and mechanistically” to reduce ambiguity.

• p. 12, e.g. should be removed.

We are not sure what is meant here. Does the reviewer mean p. 21 “The interactions are mostly hydrophobic but are reinforced by several H-bonds, e.g. between Gln3D-Gln458A, Ser53D–Gln458A, Arg11D-Asp468A, the main chain amide of Ile12D and Tyr557A.”?

Reviewer #3 (Recommendations For The Authors):

Overall, the work presents an impressive and in-depth structural view of the conformational changes stemming from the interactions of (d)ATP allosteric effector molecules that are interrelated to RNR function. The manuscript is written clearly and provides a solid overview of RNR chemistry. The cryo-EM data show striking differences between ATP and dATP bound forms, though in select regions, the resolution is not good enough for strong interpretations of the finer details.

(1) In cryo-EM structures, dATP appears to shift the oligomerization equilibrium from nearly all dimeric forms (absence of dATP) to a mixture of both dimeric and tetrameric species (presence of dATP). The examination of the oligomeric composition in solution using the GEMMA - a mass spectral technique - showed somewhat similar trends, though given the magnitude of the differences, it was less compelling. Have the authors considered a complementary solution technique, such as analytical SEC or dynamic light scattering that could provide further support for the change in oligomerization as observed in the cryo-EM?

SEC analysis with 2 µM apoPcNrdD and higher nucleotide concentrations (1 mM) was performed, confirming the presence of both dimer and tetramer in solution at different ratios depending on the addition of ATP or dATP. The SEC analysis, included as Figure 3 - figure supplement 1, confirms the existence of an equilibrium in solution.

(2) The protein as isolated from the final SEC shows a predominant peak corresponding to aggregate protein. It would be helpful if the authors ran an analytical SEC on the protein sample that is more refined to see how much soluble dimer/tetramer vs. aggregate protein there is. This could impact the kinetic and thermodynamic analysis of effector interactions. Further, the second major peak is labeled as 'monomer'. Is the protein isolated as a monomer and then forms dimer upon effector binding? It is unclear. The authors should consider presenting the SEC standards for the given column and buffer condition so that a reasonable estimate of the oligomerization status of the isolated protein can be assigned.

Can the reviewer possibly have believed that Figure 1 - supplementary figure 2a shows PcNrdD rather than PcNrdG? The figure supplement corresponds to the as-isolated SEC analysis of the activase (PcNrdG), which shows the presence of two main peaks of aggregates and monomer. The monomeric peak was reinjected and showed no presence of further aggregation states. Currently it is not known which oligomeric state the activase harbours upon binding to PcNrdD and glycyl radical formation. None of the other SEC figures in the MS has any predominant peak corresponding to aggregated protein.

(3) More details are needed for the ITC section. The ITC methods are not clear. What is the exact composition of the ligand solution being titrated into the protein solution? It is unclear how the less-than-unity binding stoichiometry was determined and what it means. Is the n value for the monomer, dimer, or tetramer forms? It is concerning that n < 1 is observed for dATP binding in the ITC whereas there are 3 dATP bound/subunit in the cryo-EM. For completeness, titration of a buffer into protein solution (no ligand) should be conducted and presented to demonstrate that the heats produced in Figure 2 correspond to the ligand only (and not a buffer mismatch).

ITC experiments were performed in the presence of 1 mM GTP (c-site) and 1 mM dTTP (ssite). Unlike other parameters in ITC analyses, the N value is usually the least accurate of all fitted parameters and strongly depends on the concentration of the active protein in the sample. N values described in the current study are in the same range as values reported for ATP-cones in other RNRs and NrdR (Rozman Grinberg & al 2018a, 2018b, 2022 McKethan and Spiro 2013). The results most likely reflect two high-affinity binding sites for dATP and one high affinity binding site for ATP. Different nucleotide concentrations were used in the cryoEM and ITC experiments.

(4) It is intriguing that the binding of dATP doesn't quell the glycyl radical. In fact, it appears that, as the authors suggest, the amount of glycyl radical might be increased in these samples. However, the cryo-EM data indicates that the GRD is disordered. It is unclear how these would be correlated, as one would not expect a disordered structural element to maintain such a potent oxidant.

As already written above, we do not wish to imply that the GRD is completely or even highly disordered, just that its dynamics increase in the presence of dATP. Otherwise we completely agree that a very exposed Gly radical is incompatible with its stability. It could be that the amount of disorder is exaggerated somewhat by the vitrification process in cryo-EM. We have tried to reword some of the text to emphasise higher mobility rather than disorder.

It has been difficult to propose a direct structural mechanism for transmission of the allosteric signal from the a-site in the ATP-cone to the active site and GRD given that the ATP-cones and linker are disordered in the dATP-bound dimers and that the linker can not be completely modelled even in the dATP-bound tetramers. We initially thought that a steric occlusion mechanism might be at play, but the reality appears more complex. Most likely dATP binding causes a change in the dynamics of the linker region and NxN flap that directly affects substrate binding and simultaneously causes higher mobility of the GRD, given that all are part of a connected system. The structures determined in the presence of dATP and CTP show that CTP cannot bind in the absence of an ordered NxN flap. We hope that future structural studies of NrdDs from other organisms may shed further light on this mechanism.

(5) It is a bit difficult to keep track of the myriad of structural information and differences amongst the various nucleotide-dependent conditions. It would be useful for the authors to add a summary figure that depicts the various oligomers, orientations, and (dis)ordered structural elements with cartoon representations.

Thank you for this suggestion. It has been added as Figure 11.

(6) The mechanism by which (d)ATP binding changes the (dis)ordering of select loops based on the current cryo-EM data is unclear (even the authors agree). The addition of molecular dynamics (MD) simulations on two different structures to reveal the network or structural communication would be a great addition to the work and validate the structural data.

We have discussed this with a colleague who is an expert in MD. Their advice was that such simulations would be very difficult given that some amino-acids are missing in both of the relevant starting structures (ATP-CTP and dATP-CTP dimer) and could give very variable results. Thus we chose to do complementary experiments with hydrogen-deuterium exchange mass spectrometry (HDX-MS) instead. The results are included in the revised manuscript.

Minor points

(1) There are some conflicting reports as to whether P. copri is considered a human 'pathogen'. According to Yeoh, et al Scientific Reports 2022, P. copri is one of the predominant microbes in the human gut and is linked to a positive impact on metabolism. Perhaps the addition of a citation that provides support for it as a pathogen would clarify the statement on p. 3.

We have added a recent reference (Nii T, Maeda Y, Motooka D, et al. (2023) Genomic repertoires linked with pathogenic potency of arthritogenic Prevotella copri isolated from the gut of patients with rheumatoid arthritis. Ann Rheum Dis 82: 621-629. doi: 10.1136/annrheumdis-2022-222881).

(2) In Figure 3, the number of dimers/tetramers for dATP (100 uM) does not add up to 100.

What is the other 2%?

Thank you for pointing this out - it has been corrected.

(3) The data in Figures 5C and D do show slight changes that could be fit and interpreted as a 'weak' interaction. Thus, the statement on p 9 "where dATP-loaded PcNrdD could bind neither GTP nor CTP" should be changed to indicate that the interactions are weak (or that the nucleotides weakly associate).

The text and the figure have been changed according to the reviewer’s suggestion.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

Firstly, the authors place a great deal of emphasis on the impact of the Hif1-a inhibitor PX-478. The literature surrounding this inhibitor and its mode of action indicates that it is not a direct inhibitor of activity but that its greatest impact is on the production of Hif1-a. The authors do include another inhibitor as a control, Echinomycin, but it does not appear to be as biologically active and the panel of experiments conducted with this is extremely limited. I would be more comfortable with a full Seahorse experimental panel for Echinomycin, similar to SFig 2.G as performed with PX-478.

We thank the reviewer for their comment highlighting the different mechanisms of action of the HIF-1α inhibitors used in this article. While echinomycin inhibits the binding of HIF-1α to the hypoxia response element (HRE) thereby blocking HIF-1a DNA binding capability, PX-478 inhibits HIF-1α deubiquitination, decreases HIF-1α mRNA expression, and reduces HIF-1α translation. We have included a paragraph explaining this phenomenon in the new version of the manuscript (page 9). In addition, we extended the panel of experiments performed with echinomycin, which confirmed a marked inhibition of the glycolytic pathway when DCs were stimulated with irradiated Mtb in the presence of echinomycin as assessed by SCENITH (new Figure S3H).

Similarly, it would be of value to have Seahorse profiling that directly excludes FAO from the metabolic profile through the use of Etomoxir as an inhibitor of fatty acid oxidation, which one would assume would have no impact on the metabolic response.

In order to estimate the contribution of FAO towards fueling protein synthesis in DCs stimulated with iMtb, the FAO inhibitor etomoxir was incorporated to the SCENITH method as previously described (Adamik et al., 2022). Overall, FAO dependence was found to be less than 10% in DCs, regardless of their activation state. While mitochondrial dependence is reduced after iMtb stimulation, there is no difference in FAO dependence, suggesting that OXPHOS is primarily driven by glucose in iMtb-stimulated cells. This is consistent with HIF1α-induced increase of glucose metabolism-related genes. We have adjusted the results section to include this new result (new Figure S1).

Aside from these minor points, I believe this to be a rigorous study.

Reviewer #2 (Recommendations For The Authors):

In Fig. 1 and Fig. 2, the authors conclude that Mtb rewires the metabolism of Mo-DCs and induces both glycolysis and OXPHOS. The data shows that infection with iMtb or Mtb increases glucose uptake and lactate release, suggesting an increase in glycolysis. However, an increase in lactate is not a measure of glycolysis. Lactate is a byproduct of glycolysis; the end product of glycolysis is pyruvate.

We are grateful for the reviewer's comment, as it gives us the opportunity to explain the conceptual framework on which we based our study. Traditionally, pyruvate has been considered to be the end product of glycolysis when oxygen is present and lactate the end product under hypoxic conditions. Numerous studies have shown that lactate is produced even under aerobic conditions (Brooks, 2018). Therefore, we frame this work in accordance with this view that states that glycolysis begins with glucose as its substrate and terminates with the production of lactate as its main end product (Rogatzki, Ferguson, Goodwin, & Gladden, 2015; Schurr, 2023; Schurr & Schurr, 2017).

Secondly, since the authors have access to the Agilent Extracellular Flux Analyzer, they should have performed detailed ECAR/OCR measurements to conclusively demonstrate that both glycolysis and OXPHOS are increased in Mo. This is especially important for OXPHOS because the only readout shown for OXPHOS is an increase in mitochondrial mass (figure 1 G, H), which is not acceptable. Overall, the data does not indicate that Mtb triggers OXPHOS in the dendritic cells. It only indicates dead iMtb increases the mass of mitochondria in DCs.

The reviewer’s advice is well appreciated. However, we would like to clarify what may be a misunderstanding; that is, the assays alluded to by the reviewer were not performed on monocytes but on DCs. As advised by the reviewer, we now include the OCR measurements by Seahorse and describe the figures according to their order of appearance in the new version of the manuscript.

What happens to the mitochondrial mass when infected with live Mtb?

In response to the reviewer’s question, we determined the mitochondrial mass in infected DCs with live Mtb. In contrast to DCs treated with irradiated Mtb, those infected with live bacteria showed a clear reduction of their mitochondrial mass (modified Figure 1G). This result indicates that, although both Mtb-infected and irradiated Mtb-exposed DCs show a clear increase in their glycolytic activity, divergent responses are observed in terms of mitochondrial mass.

It will be best if the authors indicate in the figure headings that dead Mtb was used.

We agree with the reviewer. For figures 1-3, we applied the term “Mtb” in the figure headings since both irradiated and viable bacteria were used for the corresponding experiments. In figures 4-5, the term “iMtb” (alluding to irradiated Mtb) was used in the figure headings as suggested by the reviewer. For the remaining figures, the term “iMtb” was indicated in their legends when dead bacteria weres used to stimulate DCs.

E.g., Figure 1F; what does live Mtb do to GLUT1 levels etc etc?

In response to the reviewer’s question, we included new data about Glut1 expression in DCs infected with live Mtb in the latest version of the manuscript. In line with the increase in glucose uptake shown in figure 1B, we observed an increase in the percentage of Glut1 positive DCs upon Mtb infection (new Figure 1F, lower panels). The increase in Glut1 expression strengthens the notion that DCs activates their glycolytic activity in response to the infection, as demonstrated by the elevated release of lactate, glucose consumption, HIF-1α expression, LDHA expression (Figure 1) and glycolytic activity (Figure 2, SCENITH results with viable Mtb). Therefore, these data strongly support the induction of glycolysis by Mtb (either viable or irradiated) in DCs.

Also, we found that they were still able to activate CD4+ T cells from PPD+ donors in response to iMtb. This activation of CD4 T cells with iMtb in the presence of a HIF-1alpha inhibitor is expected, as iMtb is dead and not virulent. What happens when the cells are infected with live virulent Mtb?

We would like to clarify the main purpose of the DC-T cells co-culture assays in the presence of the HIF-1α inhibitors. To characterize the impact of HIF-1α on DC functionality, we assessed the capacity of DCs to activate autologous CD4+ T cells when stimulated with iMtb in the presence of HIF-1α inhibitors. To this end, we used iMtb merely as a source of antigens to load DCs and evaluate the effect of HIF-1α inhibition on the activation of antigen-specific T cell. The use of viable Mtb may introduce confounding factors, such as pathogen-triggered inhibitory mechanisms (e.g., EsxH secretion by Mtb, (Portal-Celhay et al., 2016)), which would prevent us from reaching conclusions about the role of HIF-1α. Thus, we consider that the use of live bacteria for this experiment is out of the scope of this manuscript.

The authors demonstrated that CD16+ monocytes from TB patients have higher glycolytic capacity than healthy controls Fig 7. The authors should differentiate TB patient monocytes into DCs and measure their bioenergetics to test if infection alters their glycolysis and OXPHOS.

In agreement with the reviewer, the determination of metabolic pathways in DCs differentiated from monocytes of TB patients is a key aspect of this work. Accordingly, the bioenergetic determinations of DCs generated from monocytes from TB patients versus healthy subjects are now illustrated in Figures 6F (lactate release) and 6G (SCENITH profile).

In the discussion, the authors state that "pathologically active glycolysis in monocytes from TB patients leads to poor glycolytic induction and migratory capacities of monocyte-derived DCs." However, the data from Fig. 1 and 2 show that treatment with iMtb or Mtb induces glycolysis in MoDCs. How do the authors explain these contrasting results?

We thank the reviewer for pointing out this issue. Figures 1 and 2 show DCs differentiated from monocytes of healthy donors (HS). In this case, DCs from HS respond to Mtb by inducing a glycolytic and migratory profile. Yet, in the case of monocytes isolated from TB patients, these cells exhibit an early glycolytic profile from the beginning of differentiation, ultimately yielding DCs with low glycolytic capacity and low migratory activity in response to Mtb. We included this explanation in the discussion (page 18) to better clarify this issue.

Also, the term "pathological" active glycolysis (Introduction and Discussion) is an inappropriate term.

As requested by the reviewer, we excluded the term “pathological” to describe the phenomenon reported in this study.

Lastly, it should be shown whether the DCs generated from CD16+ monocyte from TB patients generate tolerogenic and/or aberrant DCs, which have lower glycolytic and migration capacity compared to the CD16- monocyte population. In Figure 7B, the authors should discuss why the CD16+ monocyte population has lower glycolytic capacity compared to CD16- monocytes in healthy donors. Furthermore, in contrast to the TB patients, do DCs generated from CD16+ monocyte in healthy donors have increased glycolytic and migration capacity compared to CD16- monocyte (because these monocytes showed lower glycolytic capacity)? Furthermore, if there is no difference in glycolytic capacity among the three monocyte populations in TB patients, on what basis was it concluded that DCs generated only from the CD16+ monocyte population may be the cause of lower migration capacity? The authors state in Figure 7F that the DMOG pretreatment matches the situation where the Mo-DCs from TB patients showed reduced migration. Did the authors check the Hif-1alpha levels in monocytes obtained from TB patients?

We appreciate this in-depth analysis by the reviewer because it allows us to clarify some interpretations of the SCENITH results in Figure 7B. It is important to keep in mind that with the SCENITH technique we can only infer about the relative contributions between the metabolic pathways, without alluding to the absolute magnitudes of such contributions. In this regard, it is key to note that the amount of lactate released during the first hours of the TB monocyte culture is much higher than that released by monocytes from healthy subjects (HS, Figure 7A), even when most of monocytes, which are CD14+ CD16-, have comparable glycolytic capacities between HS and TB. Another example to illustrate how to interpret SCENITH results can be found in Figure 2, where a lower mitochondrial dependence is observed in iMtb-stimulated DCs (Figure 2A), while the absolute ATP production associated to OXPHOS is indeed higher as measured by Seahorse (Figure 2D). Therefore, the glycolytic capacity is not a direct readout of the magnitude of glycolysis, but of its contribution to total metabolism. The low levels of lactate released from HS monocytes likely reflects their low activation state and low metabolic activity compared to TB monocytes. In this regard, we have previously demonstrated that monocytes from pulmonary TB patients display an activated phenotype (Balboa et al., 2011). The fact that there is no difference between the glycolytic capacities of TB and HS CD16- monocytes indicates that their proportional contributions to protein synthesis are comparable (again, without inferring about their absolute values, which may be very different).

Beyond the previous clarification, the reviewer's proposal to isolate subsets of monocytes is a very interesting idea. However, the experimental approach is very difficult based on the amount of blood we can obtain from patients. The cohort of patients included in this work comprises very severe patients and we are given up to 15-20 ml of peripheral blood from each. This volume of blood yields up to 10 million PBMC with approximately 1 million monocytes. If we separate the monocyte subsets, the recovered cells per condition will be insufficient to perform the intended assays.

Nevertheless, we incorporate new evidence that TB disease is associated with an increased activation and glycolytic profile of circulating CD16+ monocytes.

i) First, we show that the baseline glycolytic capacity of CD16+ monocytes correlates with time since the onset of TB-related symptoms (new Figure 7C).

ii) Second, we performed high-throughput GeneSet Enrichment Analysis (GSEA) on transcriptomic data (GEO accession number: GSE185372) of CD14+CD16-, CD14+CD16+ and CD14dimCD16+ monocytes isolated from individuals with active TB, latent TB (IGRA+), as well as from TB negative healthy controls (IGRA-). We found enrichments that, unlike oxidative phosphorylation, glycolysis tends to increase in active TB in both CD14+CD16+ and CD14dimCD16+ monocytes (new Figure 7D).

iii) We measured the expression of HIF-1α in monocyte subsets by FACS and found that this transcription factor is expressed at higher levels in CD16+ monocyte subsets from TB patients compared to their counterparts from healthy donors (new Figure 8 A). We consider this result justifies the assays shown in Figure 8B-C, in which we prematurely activated HIF-1α in healthy donor monocytes during early differentiation to DCs and measured its impact on the migration of the generated DCs.

In the Discussion, the authors mention that circulating monocytes from TB patients differentiate from DCs with low immunogenic potential. However, the authors have not shown any immunological defect in any of their data with monocytes from TB patients. In the proxy model mentioned in Figure 7, they have in fact shown that these preconditioned DCs have higher CD86 expression. Can the authors explain/show data to justify the statement in the first paragraph of the Discussion?

We agree with the reviewer on this observation. Our findings are limited to the generation of DCs with low migratory potential (low chemotactic activity towards CCL21 of DC differentiated from TB patient monocytes shown in figure 6H and of DC generated from pre-conditioned monocytes shown in figure 8C). We have modified that part of the discussion to better clarify this point, replacing migratory with immunogenic.

The authors should note that oxamate is a competitive inhibitor of the enzyme lactate dehydrogenase and not glycolysis. Also, LDHA catalyzes the conversion from pyruvate to lactate and not the other way around (Results, page 6).

This comment relates to the first one by the reviewer, in which the dogma of glycolysis was discussed. According to the new conception of glycolysis, it begins with glucose as its substrate and terminates with the production of lactate as its main end product.

The following statements by the authors on page 6 are incorrect: "Because irradiated and viable Mtb induced comparable activation of glycolysis, we subsequently performed all our assays with irradiated Mtb only in the rest of the study due to biosafety reasons." and: "To our knowledge, this is the first study addressing the metabolic status and migratory activity of Mo-DCs from TB patients."

We deleted the first sentence and reworded the second sentence as "To our knowledge, this is the first study to address how the metabolic status of monocytes from TB patients influences the migratory activity of further differentiated DCs".

The Discussion reads as if live Mtb was used in the experiments, which is not the case. This should be corrected.

We changed Mtb for iMtb when it was the case in the discussion. In some cases, Mtb stimulation was used instead of Mtb infection.

Minor Comments:

(1) In Figure 1F legend "Quantification of Glut1+ cells plotted to the right". The underlined part should be "plotted below".

It was corrected.

(2) In Figure 1H. Please describe the quantitation method and describe how many cells or the number/size of fields were used to quantitate mitochondria.

For mitochondrial morphometric analysis, TEM images were quantified with the ImageJ “analyze particles” plugin in thresholded images, with size (μm2) settings from 0.001 to infinite. For quantification, 8–10 cells of random fields (1000x magnification) per condition were analyzed. We included this information in the methods section of the new version of the manuscript.

(3) Please mention the number of independent experimental repeats for each experimental data set and figure.

In each figure, the number of independent experiments is indicated by individual dots.

(4) In Figure 2A legend, "PER; left panel" should be PER; lower panel and "OCR; right panel" should be OCR; upper panel.

It was corrected.

References for reviewers

Adamik, J., Munson, P. V., Hartmann, F. J., Combes, A. J., Pierre, P., Krummel, M. F., … Butterfield, L. H. (2022). Distinct metabolic states guide maturation of inflammatory and tolerogenic dendritic cells. Nature Communications 2022 13:1, 13(1), 1–19. https://doi.org/10.1038/s41467-022-32849-1

Balboa, L., Romero, M. M., Basile, J. I., Sabio y Garcia, C. A., Schierloh, P., Yokobori, N., … Aleman, M. (2011). Paradoxical role of CD16+CCR2+CCR5+ monocytes in tuberculosis: efficient APC in pleural effusion but also mark disease severity in blood. Journal of Leukocyte Biology. https://doi.org/10.1189/jlb.1010577

Brooks, G. A. (2018). Cell Metabolism The Science and Translation of Lactate Shuttle Theory. Cell Metab. https://doi.org/10.1016/j.cmet.2018.03.008

Portal-Celhay, C., Tufariello, J. M., Srivastava, S., Zahra, A., Klevorn, T., Grace, P. S., … Philips, J. A. (2016). Mycobacterium tuberculosis EsxH inhibits ESCRT-dependent CD4+ T-cell activation. Nature Microbiology, 2, 16232. https://doi.org/10.1038/NMICROBIOL.2016.232

Rogatzki, M. J., Ferguson, B. S., Goodwin, M. L., & Gladden, L. B. (2015). Lactate is always the end product of glycolysis. Frontiers in Neuroscience, 9(FEB), 125097. https://doi.org/10.3389/FNINS.2015.00022/BIBTEX

Schurr, A. (2023). From rags to riches: Lactate ascension as a pivotal metabolite in neuroenergetics. Frontiers in Neuroscience, 17, 1145358. https://doi.org/10.3389/FNINS.2023.1145358/BIBTEX

Schurr, A., & Schurr, A. (2017). Lactate, Not Pyruvate, Is the End Product of Glucose Metabolism via Glycolysis. Carbohydrate. https://doi.org/10.5772/66699

Author Response

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

Thank you for your continued review and for providing insightful suggestions. Below, I share some unpublished new findings related to the MYRF ChIP, comment on the potential interplay between myrf-1 and myrf-2, and describe the modifications we've implemented to address the reviewers' comments.

(1) MYRF-1 ChIP

Our collaboration with the modERN (Model Organism Encyclopedia of Regulatory Networks) project has recently yielded MYRF ChIP data. The results demonstrate clear and consistent MYRF binding across samples, notably on the lin-4 promoter. Given the significant detail and extensive description required to adequately present these findings, we have decided it is impractical to include them in the current paper. These results will be more suitably published in a separate ongoing study focused on MYRF's regulatory targets during larval development.

(2) Inter-regulation between myrf-1 and myrf-2

We acknowledge the interpretation that myrf-2 may act as a genetic antagonist to myrf-1, as suggested by the delayed arrest in myrf-1; myrf-2 double mutants and a trend towards increased lin-4 expression in myrf-2 mutants. Additionally, our unpublished data suggest an elevated myrf-2 expression peak in myrf-1 null mutants during the L1-L2 transition, indicating a potential mutual repressive interaction between myrf1 and myrf-2.

On the other hand, myrf-1 and myrf-2 exhibit functional redundancy in DD synaptic rewiring and lin-4 expression. A gain of function in myrf-2 promotes early DD synaptic rewiring. Furthermore, three independent co-immunoprecipitation analyses targeting myrf-1::gfp, myrf-2::gfp, and pan-1::gfp confirm a tight association between myrf-1 and myrf-2 in vivo. These findings challenge the notion of myrf-2 primarily antagonizing myrf-1, or vice versa.

We propose a model where myrf-1 and myrf-2 collaborate and are functionally redundant, with compensatory elevated expression when one paralog is absent. For instance, the loss of myrf-1 triggers upregulation of myrf-2, which, though insufficient on its own, accelerates the transcriptional program and exacerbates system deterioration, leading to accelerated death. How exactly this takes place is currently unclear. We notice the MYRF binding on both myrf-1 and myrf-2 genes in MYRF-ChIP.

Given the complexity of these interactions, we have chosen not to delve deeply into this discussion in the paper without more direct evidence, which would require detailed analysis.

(3) Revisions Addressing Reviewer Suggestions

(a) We have revised our interpretation of the mScarlet signal changes in myrf-1(ybq6) and myrf-2(ybq42) mutants to reflect a more nuanced understanding of their potential genetic relationship, as highlighted in the main text.

“The mScarlet signals exhibit a marked reduction in the putative null mutant myrf-1(ybq6) (Figure 1D, E). Intriguingly, in the putative null myrf-2(ybq42) mutants, there is a noticeable trend towards increased mScarlet signals, although this increase does not reach statistical significance (Figure 2C, D).”

(b) In response to feedback on Figure 2 and the characterization of lin-4(umn84) mutants, we've included a new series of images showing lin-4(umn84)/+ and lin-4(umn84) signals through larval stages, presented as Figure 2 Figure Supplement 2. This addition clarifies the functional status of lin-4 nulls in our study.

“Our observations revealed that mScarlet signals were not detected early L1 larvae (Figure 2C-F; Figure 2 Figure Supplement 2).”

(c) To improve the clarity of Fig 6, we've added indicator arrows in the red, green, and merge channels, enhancing the visualization of the signals.

We appreciate the opportunity to clarify these points and hope that our revisions and additional data address the concerns raised.

Author Response

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

Reviewer #1:

The very detailed insights gained by the authors into allosteric regulation require very specialized techniques in this study. This poses a challenge to communicate the methods, the results, and the meaning of the results to a broader audience. In some places, the authors overcome this challenge better than in others.

Following this reviewer’s suggestions, we have extensively revised the text, making the text more understandable to a broader audience.

The manuscript does not show up on BioRxiv.

The manuscript is now deposited in Biorxv (doi: 10.1101/2023.09.12.557419)

Fig3: GS-ES2 transition: the changes appear minimal in the illustration.

As suggested by this reviewer, we have re-examined the GS-ES2 transition and clearly defined the structural characteristics of the conformationally excited state 2 (ES2) state. As shown in the revised Fig.3 of the main text, the ground state (GS) features a π-π packing between the aromatic rings of F100 and Y156, as well as a cation-π stacking between R308 and F102. In the ES2 state, these above interactions are disrupted, while a new π-π packing interaction is formed between F100 and F102. We added new comments in the main text clarifying these structural interactions that characterize each state.

GS-ES1 transition: how is the K72-E91 salt bridge disrupted? How do you define the formation/disruption of a salt bridge? The current figure does not make this very clear and the K72-E91 salt bridge appears to be intact in ES1. Maybe the authors could replace the dotted K72-E91 line with a dotted line and distance?

As stated above, we revised Fig. 3 highlighting the differences between the two states. The K72 and E91 salt bridge is formed when the distance between Nε of K72 and Oε of E91 is shorter than 4.0 Å (the typical cutoff for a salt bridge). In the ES1 state, the outward movement of the αC helix increases the distance over 4.5 Å, disrupting the salt bridge.

L251: Could the authors remind the reader why they are only comparing V104 and I150? Could they give a little context as to why they consider the agreement to be good? It appears that they would be statistically different, so a little context for what comprises a good agreement in the literature may be helpful.

Our mutagenesis studies show that V104 and I150 are key residues for allosteric communication, and if mutated, result in well-folded but inactive kinases (Sci Adv. doi: 10.1126/sciadv.1600663). Importantly, V104 and I150 show two distinct populations in the CEST experiments that can be directly related to the GS and ES states. Regarding the fitting of these residues, we obtained a good agreement with the direction of the chemical shifts, which supports the hypothesized GS -> ES structural transition. The lack of a quantitative agreement between the chemical shifts of the experimental and simulated excited state is not surprising for two reasons a) all state-of-the art simulations fall short in sampling slow conformational interconversions, and b) the uncertainty of the SHIFTX algorithm for the prediction of 13C chemical shifts of methyl groups is quite large. Finally, we would like to point out that most NMR relaxation-dispersion experiments (CEST and CPMG) are performed for the backbone 15N, 13Calpha and 1H resonances, which have been used to calculate the structures of the intermediate states (Neudecker, P. et. al Science, 2012, 336,doi: 10.1126/science.1214203) and yield reasonable agreement with the prediction for metastable states derived from Markov Models (Olsson, S. J. Am. Chem. Soc., 2017,139,doi:10.1021/jacs.6b09460). To the best of our knowledge, there is no literature reporting on calculations of the 13C CEST profiles for methyl groups from MD simulations, and remarkably, we found a reasonably good agreement between experimental and predicted chemical shifts (see Fig.5C).

Just to clarify: the calculated CS values are informed by experimental CS values that were used in the calculation?

We used the backbone chemical shifts as the restraints only in the metadynamics simulations. We used the chemical shifts of the methyl groups and their corresponding excited states to verify the ES2 state.

Figure 8: in its current form this potentially exciting result is lost on the average reader.

we modified Fig. 8 of the main text, making the intra- and inter-residue correlations visible to the reader.

Reviewer #2:

While the alphaC-beta4 loop is a conserved feature of protein kinases, the residues within this loop vary across various kinase families and groups, enabling group and family-specific control of activity through cis and trans acting elements. F102 in PKA interacts with co-conserved residues in the C-tail, which has been proposed to function as a cis regulatory element. The authors should elaborate on the conformational changes in the C-tail, particularly in the arginine that packs against F102, in the results and discussion. This would further extend the impact and scope of the manuscript, which is currently confined to PKA.

As suggested by this reviewer, we re-analyzed the time-dependent interactions between F102 and R308 at the C-tail. As this reviewer suspected, these interactions differentiate the ES2 from the GS state. In the GS state, there is a stable cation-π interaction between F102 and R308, which becomes transient in the ES2 state (Fig. 3). For the F100A mutant, the interactions between F102 and R308 have lower occurrence relative to the WT enzyme, i.e., a weaker interaction between the αC-β4 loop and the C-tail (see new Figure 6 - figure supplement 1). The latter supports our conclusion that the structural coupling between the C-tail and the two lobes of the enzyme decreases for the F100A mutant. We added more comments in the main text.

FAIR standards of making the data accessible and reproducible are not directly addressed.

We have deposited all our NMR data on the Data Repository Site at the University of Minnesota, DRUM (https://hdl.handle.net/11299/261043).

The MD data and conformational states would be a valuable resource for the community and should be shared via some open-source repositories.

Due to the large size of the simulations (>500 GB), we could not deposit them in the Data Repository Site at the University of Minnesota (DRUM). We are actively working with the personnel at DRUM to upload all the trajectories in an alternate site. However, these data will be available to the public immediately upon request.

The authors state that ES1 and ES2 states are novel and not observed in previous crystal structures. The authors should quantify this through comparisons with PKA inactive states and with other AGC kinases.

We apologize for the confusion. We now clarify that the ES1 is a well-known inactivation pathway. As suggested by this reviewer, we now report a few examples of active and inactive conformations of PKA-C and other kinases (see new Figure 3 – figure supplement 2.). Briefly, ES1 corresponds to the typical αC-out conformation found for PKA-C bound to inhibitors or in R194A mutant. A similar conformation is present for Src, Abl, and CDK2. The C-out conformation features a disrupted β3K-αCE salt bridge, which is key for active kinases. In contrast, the transition GS-ES2 is not present in the inactive conformations deposited in the PDB.

Based on the results, can the authors speculate on the impact of oncogenic mutations in the alphaCbeta4 loop mutations in PKA?

We now include additional comments and another citation that further supports our findings. In short, the activation of a kinase is generated by mutation insertions that stabilize the αC-β4 loop as pointed out by Kannan and Zhang (see references 28, 30, and 68). In contrast, mutations that destabilize this allosteric site (e.g., F100A) are inactivating, disrupting the structural couplings of the two lobes (our work).

Reviewer #3:

The manuscript is somewhat difficult to read even for kinase experts, and even harder for the layman. The difficulty partially arises from mixing technical description of the simulations with structural interpretation of the results, which is more intuitive, and partially arises from the assumption that readers are familiar with kinase architecture and its key elements (the aC helix, the APE motif, etc).

We revised the text and modified Fig. 1 in the main text to make the paper more accessible to the general audience.

The authors haven't done a good job describing the ES2 state intuitively. From my examination of the figures, it appears that in the ES2 state, the kinase domain is more elongated and the N and the C lobes are relatively less engaged than in the ground state. This may or may not be exactly, but a more intuitive description of the ES2 state is needed.

As suggested by this reviewer, we include a better description of the ES2 state of the kinase and the structural details of the inactivation pathway. Also, we checked the radius of gyration of the two lobes for GS and ES2. ES2 is slightly more elongated with an Rg of 20.3 ± 0.1 Å as compared to the GS state (20.0 ± 0.2 Å). This marginal difference is consistent with our characterization of the local packing around the C-4 loop, in which the lack of stable interaction with E and C-tail in the ES2 state makes the overall structure less compact.

The authors need to introduce and give a brief description of technical terms such as CV (collective variable), PC (principal component) etc.

We now specify both collective variables and principal components and include those definitions in the Method section. Briefly, to characterize the complex conformational transitions of PKA-C, we utilize collective variables (Figure 2 – figure supplement 1). We chose these variables based on structural motifs described in the literature to define local and global structural transitions (Camilloni C., Vendruscolo, M, Biochemistry, 2015,54,7470; Kukic, P. et al. Structure, 2015,23, 745). On the other hand, we utilized the principal component analysis to compare the conformational changes of the kinase in the same two-dimensional space, revealing the two lowest frequencies that define the global motions of the enzyme (Figures 7C, D, and E).

The following paper should be discussed as it discussed similar ATP/substrate binding of Src kinase based on an extensive network that largely overlaps with the discussed PKA network. Foda, et al. "A dynamically coupled allosteric network underlies binding cooperativity in Src kinase." Nature communications 6.1 (2015): 5939.

We apologize for missing this citation. Indeed, it makes our finding more general as allosteric cooperativity is key in other kinases such as Src and ERK2. We included this in the Discussion section.

The CHESCA analysis appears to be an add-on that doesn't add much value. It is difficult to direct. I'd suggest considering removing it to the SI.

We understand this concern. We rewrote part of the paper to make the NMR analysis of the correlated chemical shifts described by the CHESCA matrices linked to the MD calculations.

Author Response

Reviewer #1 (Public Review):

Theoretical principles of viscous fluid mechanics are used here to assess likely mechanisms of transport in the ER. A set of candidate mechanisms is evaluated, making good use of imaging to represent ER network geometries. Evidence is provided that the contraction of peripheral sheets provides a much more credible mechanism than the contraction of individual tubules, junctions, or perinuclear sheets.

The work has been conducted carefully and comprehensively, making good use of underlying physical principles. There is a good discussion of the role of slip; sensible approximations (low volume fraction, small particle size, slender geometries, pragmatic treatment of boundary conditions) allow tractable and transparent calculations; clear physical arguments provide useful bounds; stochastic and deterministic features of the problem are well integrated.

We thank the reviewer for their positive assessment of our work.

There are just a couple of areas where more discussion might be warranted, in my view.

(1) The energetic cost of tubule contraction is estimated, but I did not see an equivalent estimate for the contraction of peripheral sheets. It might be helpful to estimate the energetic cost of viscous dissipation in generated flows at higher frequencies.

This is a good point. We will also include an energetic cost estimate for the contractions of peripheral sheets in the revised manuscript.

The mechanism of peripheral sheet contraction is unclear: do ATP-driven mechanisms somehow interact with thermal fluctuations of membranes?

The new energetic estimates in the revision might help constrain possible hypotheses for the mechanism(s) driving peripheral sheet contraction, and suggest if a dedicated ATP-driven mechanism is required.

(2) Mutations are mentioned in the abstract but not (as far as I could see) later in the manuscript. It would be helpful if any consequences for pathologies could be developed in the text.

We are grateful for this suggestion. The need to rationalise pathology associated with the subtle effects of ER-morphogens’ mutations is indeed pointed out as one factor motivating the study of the interplay between ER structure and performance. In the revised manuscript, we plan to include a brief discussion potentially linking ER morphogenes’ malfunction to luminal transport, integrating additional freshly published data.

Reviewer #2 (Public Review):

Summary:

This study explores theoretically the consequences of structural fluctuations of the endoplasmic reticulum (ER) morphology called contractions on molecular transport. Most of the manuscript consists of the construction of an interesting theoretical flow field (physical model) under various hypothetical assumptions. The computational modeling is followed by some simulations

Strengths:

The authors are focusing their attention on testing the hypothesis that a local flow in the tubule could be driven by tubular pinching. We recall that trafficking in the ER is considered to be mostly driven by diffusion at least at a spatial scale that is large enough to account for averaging of any random flow occurring from multiple directions [note that this is not the case for plants].

We thank the reviewer. We have indeed explored here the possibilities of active transport, focusing especially on transport over the length scale of single tubules, as a result of structural fluctuations, and found tubular pinching to be ineffective compared to e.g. peripheral sheets fluctuations. In the revised version we plan to add text mentioning what is known about the ER in plants.

Weaknesses:

The manuscript extensively details the construction of the theoretical model, occupying a significant portion of the manuscript. While this section contains interesting computations, its relevance and utility could be better emphasized, perhaps warranting a reorganization of the manuscript to foreground this critical aspect.

Overall, the manuscript appears highly technical with limited conclusive insights, particularly lacking predictions confirmed by experimental validation. There is an absence of substantial conclusions regarding molecular trafficking within the ER.

We sought to balance the theoretical/computational details of our model with the biophysical conclusions drawn from its predictions. Given the model's complexity and novelty, it was essential to elucidate the theoretical underpinnings comprehensively, in order to allow others to implement it in the future with additional, or different, parameters. To maintain clarity and focus in the main text, we have judiciously relegated extensive technical details to the methods section or supplementary materials, and divided the text into stand-alone section headings allowing the reader to skip through to conclusions.

The primary focus of our manuscript is to introduce and explore, via our theoretical model, the interplay between ER structure dynamics and molecular transport. Our approach, while in silico, generates concrete predictions about the physical processes underpinning luminal motion within the ER. For instance, our findings challenge the previously postulated role of small tubular contractions in driving luminal flow, instead highlighting the potential significance of local flat ER areas—empirically documented entities—for facilitating such motion.

Furthermore, by deducing what type of transport may or may not occur within the range of possible ER structural fluctuations, our model offers detailed predictions designed to bridge the gap between theoretical insight and experimental verification. These predictions detail the spatial and temporal parameters essential for effective transport, delineating plausible values for these parameters. We hope that the model’s predictions will invite experimentalists to devise innovative methodologies to test them. We plan to introduce text edits to the revised version to clarify these.

Author Response

eLife assessment

The authors report that optogenetic inhibition of hippocampal axon terminals in retrosplenial cortex impairs the performance of a delayed non-match to place task. The significance of findings elucidating the role of hippocampal projections to the retrosplenial cortex in memory and decision-making behaviors is important. However, the strength of evidence for the paper's claims is currently incomplete.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This is a study on the role of the retrosplenial cortex (RSC) and the hippocampus in working memory. Working memory is a critical cognitive function that allows temporary retention of information for task execution. The RSC, which is functionally and anatomically connected to both primary sensory (especially visual) and higher cognitive areas, plays a key role in integrating spatial-temporal context and in goal-directed behaviors. However, the specific contributions of the RSC and the hippocampus in working memory-guided behaviors are not fully understood due to a lack of studies that experimentally disrupt the connection between these two regions during such behaviors.

In this study, researchers employed eArch3.0 to silence hippocampal axon terminals in the RSC, aiming to explore the roles of these brain regions in working memory. Experiments were conducted where animals with silenced hippocampal axon terminals in the RSC performed a delayed non-match to place (DNMP) task. The results indicated that this manipulation impaired memory retrieval, leading to decreased performance and quicker decision-making in the animals. Notably, the authors observed that the effects of this impairment persisted beyond the light-activation period of the opsin, affecting up to three subsequent trials. They suggest that disrupting the hippocampal-RSC connection has a significant and lasting impact on working memory performance.

Strengths:

They conducted a study exploring the impact of direct hippocampal inputs into the RSC, a region involved in encoding spatial-temporal context and transferring contextual information, on spatial working memory tasks. Utilizing eArch3.0 expressed in hippocampal neurons via the viral vector AAV5-hSyn1-eArch3.0, they aimed to bilaterally silence hippocampal terminals located at the RSC in rats pre-trained in a DNMP task. They discovered that silencing hippocampal terminals in the RSC significantly decreased working memory performance in eArch+ animals, especially during task interleaving sessions (TI) that alternated between trials with and without light delivery. This effect persisted even in non-illuminated trials, indicating a lasting impact beyond the periods of direct manipulation. Additionally, they observed a decreased likelihood of correct responses following TI trials and an increased error rate in eArch+ animals, even after incorrect responses, suggesting an impairment in error-corrective behavior. This contrasted with baseline sessions where no light was delivered, and both eArch+ and control animals showed low error rates.

Weaknesses:

While I agree with the authors that the role of hippocampal inputs to the RSC in spatial working memory is understudied and merits further investigation, I find that the optogenetic experiment, a core part of this manuscript that includes viral injections, could be improved. The effects were rather subtle, rendering some of the results barely significant and possibly too weak to support major conclusions.

We thank Reviewer#1 for carefully and critically reading our manuscript, and for the valuable comments provided. The judged “subtlety” of the effects stems from a perspective according to which a quantitatively lower effect bears less biological significance for cognition. We disagree with this perspective and find it rather reductive for several reasons.

Once seen in the context of the animal’s ecology, subtle impairments can be life-threatening precisely because of their subtlety, leading the animal to confidently rely on a defective capacity, for such events as remembering the habitual location of a predator, or food source.

Also, studies in animal cognition often undertake complete, rather than graded, suppression of a given mechanism (in the same sense as that of “knocking out” a gene that is relevant for behaviour), leading to a gravelly, rather that gradually, impaired model system, to the point of not allowing a hypothetical causal link to be mechanistically revealed beyond its mere presence. This often hinders a thorough interpretation of the perturbed factor’s role. If a caricatural analogy is allowed, it would be as if we were to study the role of an animal’s legs by chopping them both off and observing the resulting behaviour.

In our study we conclude that silencing HIPP inputs in RSC perturbs cognition enough to impair behaviour while not disabling the animal entirely, as such allowing for behaviour to proceed, and for our observation of graded, decreased (not absent), proficiency under optogenetic silencing. So rather than weak, we would say the results are statistically significant, and biologically realistic.

Additionally, no mechanistic investigation was conducted beyond referencing previous reports to interpret the core behavioral phenotypes.

We fully agree with this being a weakness, as we wish we could have done more mechanistic studies to find out exactly what is Arch activation doing to HIPP-RSC transmission, which neurons are being affected, and perhaps in the future dissect its circuit determinants. We have all these goals very present and hope we can address them soon.

Reviewer #2 (Public Review):

The authors examine the impact of optogenetic inhibition of hippocampal axon terminals in the retrosplenial cortex (RSP) during the performance of a working memory T-maze task. Performance on a delayed non-match-to-place task was impaired by such inhibition. The authors also report that inhibition is associated with faster decision-making and that the effects of inhibition can be observed over several subsequent trials. The work seems reasonably well done and the role of hippocampal projections to retrosplenial cortex in memory and decision-making is very relevant to multiple fields. However, the work should be expanded in several ways before one can make firm conclusions on the role of this projection in memory and behavior.

We thank Reviewer#2 for carefully and critically reading our manuscript, and for the valuable comments provided.

(1) The work is very singular in its message and the experimentation. Further, the impact of the inhibition on behaviour is very moderate. In this sense, the results do not support the conclusion that the hippocampal projection to retrosplenial cortex is key to working memory in a navigational setting.

As we have mentioned in response to Reviewer#1, the judged “very moderate” effect stems from a perspective according to which a quantitatively lower effect bears less biological significance for cognition, precluding its consideration as “key” for behaviour. We disagree with this perspective and find it rather reductive for several reasons. Once seen in the context of the animal’s ecology, quantitatively lower impairments in working memory are no less key for this cognitive capacity, and can be life-threatening precisely because of their subtlety, leading the animal to confidently rely on a defective capacity, for such events as remembering the habitual location of a predator, or food source. Furthermore, studies in animal cognition often undertake complete, rather than graded, suppression of a given mechanism (in the same sense as “knocking out” a gene that is relevant for behaviour), leading to a gravelly, rather that gradually, impaired model system, to the point of not allowing a hypothetical causal link to be mechanistically revealed beyond its mere presence. This often hinders a thorough interpretation of its role.

In our study we conclude that silencing HIPP inputs in RSC perturbs behaviour enough to impair behaviour while not disabling the animal entirely, as such allowing for behaviour to proceed, and our observation of graded, decreased (not absent), proficiency under optogenetic silencing. So rather than weak, we would say the results are statistically significant, and biologically realistic.

(2) There are no experiments examining other types of behavior or working memory. Given that the animals used in the studies could be put through a large number of different tasks, this is surprising. There is no control navigational task. There is no working memory test that is non-spatial. Such results should be presented in order to put the main finding in context.

It is hard to gainsay this point. The more thorough and complete a behavioural characterization is, the more informative is the study, from every angle you look at it. While we agree that other forms of WM would be quite interesting in this context, we also cannot ignore the fact that DNMP is widely tested as a WM task, one that is biologically plausible, sensitive to perturbations of neural circuitry know to be at play therein, and fully accepted in the field. Faced with the impossibility of running further studies, for lack of additional funding and human resources, we chose to run this task.

A control navigational task would, in our understanding, be used to assess whether silencing HIPP projections to RSC would affect (spatial?) navigation, rather than WM, thus explaining the observed impairment. To this we have the following to say: Spatial Navigation is a very basic cognitive function, one that relies on body orientation relative to spatial context, on keeping an updated representation of such spatial context, (“alas”, as memory), and on guiding behaviour according to acquired knowledge about spatial context. Some of these functions are integral to spatial working memory, as such, they might indeed be affected.

Dissecting the determinants of spatial WM is indeed an ongoing effort, one that was not the intention of the current study, but also one that we have very present, in hope we can address in the future.

A non-spatial WM task would indeed vastly solidify our claims beyond spatial WM, onto WM. We have, for this reason, changed the title of the manuscript which now reads “spatial working memory”.

(3) The actual impact of the inhibition on activity in RSP is not provided. While this may not be strictly necessary, it is relevant that the hippocampal projection to RSP includes, and is perhaps dominated by inhibitory inputs. I wonder why the authors chose to manipulate hippocampal inputs to RSP when the subiculum stands as a much stronger source of afferents to RSP and has been shown to exhibit spatial and directional tuning of activity. The points here are that we cannot be sure what the manipulation is really accomplishing in terms of inhibiting RSP activity (perhaps this explains the moderate impact on behavior) and that the effect of inhibiting hippocampal inputs is not an effective means by which to study how RSP is responsive to inputs that reflect environmental locations.

We fully agree that neural recordings addressing the effect of silencing on RSC neural activity is relevant. We do wish we could have provided more mechanistic studies, to find out exactly what is Arch activation doing to HIPP-RSC transmission, which neurons are being affected, and thus dissecting its circuit determinants. We have all these goals very present and hope we can address them soon. Subiculum, which we mention in the Introduction, is indeed a key player in this complex circuitry, one whose hypothetical influence is the subject of experimental studies which will certainly reveal many other key elements.

(4) The impact of inhibition on trials subsequent to the trial during which optical stimulation was actually supplied seems trivial. The authors themselves point to evidence that activation of the hyperpolarizing proton pump is rather long-lasting in its action. Further, each sample-test trial pairing is independent of the prior or subsequent trials. This finding is presented as a major finding of the work, but would normally be relegated to supplemental data as an expected outcome given the dynamics of the pump when activated.

We disagree that this finding is “trivial”, and object to the considerations of “normalcy”, which we are left wondering about.

In lack of neurophysiological experiments (for the reasons stated above) to address this interesting finding, we chose to interpret it in light of (the few) published observations, such being the logical course of action in scientific reporting, given the present circumstances.

Evidence for such a prolonged effect in the context of behaviour is scarce (to our knowledge only the one we cite in the manuscript). As such, it is highly relevant to report it, and give it the relevance we do in our manuscript, rather than “relegating it to supplementary data”, as the reviewer considers being “normal”.

In the DNMP task the consecutive sample-test pairs are explicitly not independent, as they are part of the same behavioural session. This is illustrated by the simple phenomenon of learning, namely the intra-session learning curves, and the well-known behavioral trial-history effects. The brain does not simply erase such information during the ITI.

(5) In the middle of the first paragraph of the discussion, the authors make reference to work showing RSP responses to "contextual information in egocentric and allocentric reference frames". The citations here are clearly deficient. How is the Nitzan 2020 paper at all relevant here?

Nitzan 2020 reports the propagation of information from HIPP to CTX via SUB and RSC, thus providing a conduit for mnemonic information between the two structures, alternative to the one we target, thus providing thorough information concerning the HIPP-RSC circuitry at play during behaviour.

Alexander and Nitz 2015 precisely cite the encoding, and conjunction, of two types of contextual information, internal (ego-) and external (allocentric).

The subsequent reference is indeed superfluous here.

We thank the Reviewer#2 for calling our attention to the fact that references for this information are inadequate and lacking. We have now cited (Gill et al., 2011; Miller et al., 2019; Vedder et al., 2017) and refer readers to the review (Alexander et al., 2023) for the purpose of illustrating the encoding of information in the two reference frames. In addition, we have substantially edited the Introduction and Discussion sections, and suppressed unnecessary passages.

(6) The manuscript is deficient in referencing and discussing data from the Smith laboratory that is similar. The discussion reads mainly like a repeat of the results section.

Please see above. We thank Reviewer#2 for this comment, we have now re-written the Discussion such that it is less of a summary of the Results and more focused on their implications and future directions.

Author Response

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

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Hats off to the authors for taking time to decipher the seemingly subtle but important differences between the Gnai2/3 double mutant and Ptx mutant phenotypes. These results further illustrate the dynamic requirement of Gnai/0 in hair bundle establishment. I have some minor suggestions for the authors to consider and it is up to the authors to decide whether to incorporate them:

We decided to make the current (revised) version the version of record, and we explain why below. Please include these comments in the review+rebuttal material.

(1) The abstract could be modified to reflect the revised interpretations of the results.

Response: the abstract is high-level and the changes in interpretation in the revised manuscript do not modify the message there. Briefly, the abstract only states that Gnai2; Gnai3 double mutants recapitulate two defects previously only observed with pertussis toxin. There is no claim about the timing or dose of GNAI proteins involved.

(2) The three rows of OHCs are like a different beast from each other. Mireille Montcouquiol's lab has demonstrated that there is a differential requirement for Gnai3 in hair bundle orientation among the three rows of OHCs. The results described in this manuscript support this notion as well.

To clarify, Gnai3 inactivation does not affect OHC orientation. Only pertussis toxin, and in this work Gnai2; Gnai3 double mutants, do. The Montcouquiol lab showed different degree of OHC1, OHC2 and OHC3 misorientation upon use of pertussis toxin in vitro using cochlear explants (Ezan et al 2013). We showed the same thing in vivo using transgenic models (Tarchini et al 2013; Tarchini et al 2016). The different OHC responses by row and corresponding citations are mentioned in several locations in the manuscript, including first on line 112 in the Introduction and in Fig. 1C in a graphical summary.

(3) I wonder if "compensate" or "redundancy" may be a better term to use than "rescue" in the Discussion and figure.

Use of “rescue” in the Discussion is line 603 and 604. We think that “rescue” is appropriate to refer to the ability of GNAI2 to compensate for the loss of GNAI1 and GNAI3 in mutant context. We would argue that these different wordings are largely interchangeable and do not change the message.

Author Response

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

We really appreciate the time the reviewers spent reading and commenting on the original manuscript. Although they were positive already, we decided to spend some time to address the main comments with new experiments as thoroughly as possible in a new manuscript version. We also heavily edited some sections accordingly.: 1) we delayed pertussis toxin activation in hair cells with Atoh1-Cre to show that the resulting misorientation phenotype is delayed compared to FoxG1-Cre results, as also seen in Gnai2; Gnai3 double mutants. It follows that Gnai2; Gnai3 and pertussis mutants do share a similar misorientation profile, and that GNAI proteins are required to normally reverse OHC1-2 (from medial to lateral), but also to maintain the lateral orientation, at least transiently. 2) We experimentally verified that one of our GNAI antibodies can indeed detect GNAI1, and consequently that absence of signal in Gnai2; Gnai3 double mutants is evidence that GNAI1 is not involved in apical hair cell polarization. We believe these changes strengthen the manuscript and its conclusions.

Reviewer #1 (Public Review):

A subclass of inhibitory heterotrimeric guanine nucleotide-binding protein subunits, GNAI, has been implicated in sensory hair cell formation, namely the establishment of hair bundle (stereocilia) orientation and staircase formation. However, the former role of hair bundle orientation has only been demonstrated in mutants expressing pertussis toxin, which blocks all GNAI subunits, but not in mutants with a single knockout of any of the Gnai genes, suggesting that there is a redundancy among various GNAI proteins in this role. Using various conditional mutants, the authors concluded that GNAI3 is the primary GNAI proteins required for hair bundle morphogenesis, whereas hair bundle orientation requires both GNAI2 and GNAI3.

Strength

Various compound mutants were generated to decipher the contribution of individual GNAI1, GNAI2, GNAI3 and GNAIO in the establishment of hair bundle orientation and morphogenesis. The study is thorough with detailed quantification of hair bundle orientation and morphogenesis, as well as auditory functions.

Weakness

While the hair bundle orientation phenotype in the Foxg1-cre; Gnai2-/-; Gnai3 lox/lox (double mutants) appear more severe than those observed in Ptx cKO mutants, it may be an oversimplification to attribute the differences to more GNAI function in the Ptx cko mutants. The phenotypes between the double mutants and Ptx cko mutants appear qualitatively different. For example, assuming the milder phenotypes in the Ptx cKO is due to incomplete loss of GNAI function, one would expect the Ptx phenotype would be reproducible by some combination of compound mutants among various Gnai genes. Such information was not provided. Furthermore, of all the double mutant specimens analyzed for hair bundle orientation (Fig. 8), the hair bundle/kinocilium position started out normally in the lateral quadrant at E17.5 but failed to be maintained by P0. This does not appear to be the case for Ptx cKO, in which all affected hair cells showed inverted orientation by E17.5. It is not clear whether this is the end-stage of bundle orientation in Ptx cKO, and the kinocilium position started out normal, similar to the double mutants before the age of analysis at E17.5. Understanding these differences may reveal specific requirements of individual GNAI subunits or other factors are being affected in the Ptx mutants.

This criticism was very useful and prompted new experiments as well as a change in data presentation and a fundamental rewrite regarding hair cell orientation. These changes are detailed below. Of note, however, please let us clarify that the original manuscript did show that the ptxA orientation phenotype is reproduced to some extent in Gnai2; Gnai3 double mutants (previously Fig. 8 and corresponding text line 505). We showed that OHC1-2 are also inverted in the double mutant, although at a later differentiation stage. We recognize that similarities in hair cell misorientation between ptxA and Gnai2; Gnai3 DKO were not explained and discussed well enough. This part of the manuscript has been re-worked extensively, and we hope that along with new results, comparisons between mutant models are easier to follow and understand. We notably fully adopted the idea that there are qualitative differences between ptxA and Gnai2; Gnai3 mutants, and not only a difference in the remaining “dose” of GNAI activity.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Comments related to clarification of the weakness:

(1) In general, hair bundle orientation in the double mutants is established in the lateral quadrant of the cochlea before being inverted (Fig. 8). These results are intriguing because the lateral orientation is the correct position for these hair bundles normally and Gnai proteins are thought to be required to get the kinocilium to the lateral position. This process appears to proceed normally in the double mutants but the kinocilium reverted to the medial default position over time, which suggests that Gnai2 and Gnai3 are only required for the maintenance and not the establishment of the kinocilium in the lateral position. Is this phenotype qualitatively similar in the Ptx cKO?

We addressed these issues with two types of modifications to the data:

(1) We modified the eccentricity threshold used at E17.5 in Fig. 8 (orientation) to be more stringent, using 0.4 (instead of 0.25 previously) in both controls and mutants. This means that we now only graph the orientation of cells where eccentricity is more marked. The rationale is that at early stages, it is challenging to distinguish immature vs defective near-symmetrical cells. We kept a threshold of 0.25 at P0 when the hair cell apical surface is larger and better differentiated (Fig. 8C-D). Importantly, the dataset remains rigorously identical. This change usefully highlights that a large proportion of OHC1 is in fact inverted (oriented medially) at E17.5 in Gnai2; Gnai3 double mutants at the cochlear mid, as also seen in the ptxA model at the same stage and position (see new Fig. 8A). At the E17.5 base (Fig. 8B), a slightly more mature position, the outcome is unchanged (the majority of OHC1 are inverted using either a 0.25 or 0.4 threshold in double mutants and in ptxA).

Interestingly however, the orientation trend is unchanged for OHC2: OHC2 remain oriented largely laterally (i.e. normally) at the E17.5 mid and base in Gnai2; Gnai3 double mutants even with a raised eccentricity thresholds, whereas by contrast OHC2 in ptxA are inverted at these stage and positions. In the double mutant, OHC2 only become inverted at the P0 base (Fig. 8D). This suggests that there are similarities (OHC1) but also differences (OHC2s) between the two mouse models, and that double mutants show a delay in adopting an inverted orientation compared to ptxA. Of note, OHC2 have been shown to differentiate later than OHC1 (for example, Anniko 1983 PMID:6869851).

(2) To directly test the idea that the misorientation phenotype (inverted OHC1-2) is comparable between the two models but delayed in Gnai2; Gnai3 mutants, we performed a new experiment and added new results in the manuscript. We delayed ptxA action by using Atoh1-Cre (postmitotic hair cells) instead of FoxG1-Cre (otic progenitors). Remarkably, this produced a pattern of OHC1-2 misorientation more similar to Gnai2; Gnai3 mutants: at the E17.5 base and P0 apex, OHC2 were still largely oriented laterally (normally) in Atoh1-Cre; ptxA as in Gnai2; Gnai3 mutants whereas at the P0 base a large proportion of OHC2 were inverted (Fig. 8 Supp 1B). OHC1 were inverted at all stages and positions in the Atoh1-Cre as in the FoxG1-Cre; ptxA model. For Atoh1-Cre; ptxA, we only illustrated OHC1 and OHC2 and did not add E17.5 mid or P0 mid results because other cell types and stage/positions did not provide additional insight. In addition, we are well aware that the full FoxG1-Cre; ptxA and Gnai2; Gnai3 results for 4 cells types (IHC, OHC1-3) and 5 stages/positions is already a lot of data for cell orientation.

These results suggest that:

(a) The normal reversal of OHC1-2 to adopt a lateral orientation needs to be maintained, at least transiently, and that maintenance also relies on GNAI/O (Results starting line 529. Disussion line 621).

(b) ptxA is more severe than Gnai2; Gnai3 when it comes to OHC1-2 orientation (Figure 9, role b). Oppositely, Gnai2; Gnai3 is obviously more severe when it comes to symmetry-breaking (Fig. 9, role a) and hair bundle morphogenesis (Fig. 9, c). It follows that the two early GNAI/O activities are qualitatively different and not just based on dose. This is essentially what this Reviewer correctly pointed out, and we have fully edited both Results and Discussion accordingly. We now speculate that the difference may lie in the identity of the necessary GNAI/O protein for each role. Any GNAI/O proteins acting as a switch downstream of the GPR156 receptor may relay orientation information (Fig. 9, role b), making ptxA a particularly effective disruption strategy since it downregulates all GNAI/O proteins. In contrast, symmetry-breaking may rely more specifically on GNAI2 and GNAI3, and ptxA is not expected to achieve a loss-of-function of GNAI2 and GNAI3 as extensive as a double targeted genetic inactivation of the corresponding genes. Please see new Results starting line 526 and Discussion starting line 603. We consequently abandoned the notion that increased doses of GNAI/O is required for each role, and we also clarify that symmetry-breaking (a) and orientation (b) occur at the same time (Fig. 9).

(2) P0 may not be late enough a stage to access phenotype maturity in the double mutants. For example, it is not clear from the basal PO results whether the IHC will acquire an inverted phenotype or just misorientation in the lateral side.

For context, the OHC1-2 misorientation pattern in the ptxA model at P0 does represent the end stage, as the same pattern is observed in adults (illustrated in Fig. 2A). In addition, OHC1-2 that express ptxA are inverted as soon as they break planar symmetry, and this was established at E16.5 in a previous publication where ptxA and Gpr156 misorientation patterns were compared and shown to be identical (Kindt et al., 2021 Supp. fig. 5C-D). However, we clearly failed to mention these important results in the original manuscript. We now cite Figure 2 for adult defects (line 522), and provide a citation for OHC1-2 inversion being observed from earliest stage of hair cell differentiation (Kindt et al., 2021) (line 519).

The vast majority of Gnai2; Gnai3 double mutants die before weaning but the single specimen we managed to collect at P21 also showed inverted OHC1-2 (representative example in Fig. 2A). Again, we previously failed to point out this important result. We now do so line 214 and 555. This is another evidence that OHC1-2 misorientation is in fact similar in the ptxA and Gnai2; Gnai3 models (but milder and delayed in the latter).

When it comes to IHCs and OHC3s however, the situation is less clear. These cell types are mildly misoriented in ptxA and Gpr156 mutants, but IHCs in particular appear severely misoriented in Gnai2; Gnai3 mutants based on the position of the basal body (Fig. 8). However, very dysmorphic hair bundles can pull on the basal body via the kinocilium and affect its position, which obscures hair cell orientation inferred from the basal body and subsequent interpretations. We do not delve on IHC and OHC3 and their orientation in Gnai2; Gnai3 mutants in the revision since we do not observe similar orientation defects in a different mouse model and lack sufficient adult data.

Suggestions to improve upon the manuscript for readers:

(1) Line 294, indicate on the figure the staining in bare zone and tips of stereocilia on row 1.

Pertains to Figure 4. In A, we now point out the bare zone and stereocilia tips with arrow and arrowheads, respectively (as in other figures).

(2) Fig.8 schematic diagram, the labels of the line and 90o side by side is misleading.

We added black ticks for 0, 90, 180, 270 degree references. In contrast, the hair cell angle represented was switched to magenta.

(3) Fig. 7 legend, redundancy towards the end of the paragraph.

Thank you for catching this issue. A large portion of the legend was indeed accidentally repeated and is now deleted.

(4) Line 490-493, Another plausible explanation is that other factors besides Gnai2 and Gnai3 are involved in breaking symmetry during bundle establishment.

We now acknowledge that other proteins besides GNAI/O may be involved (Discussion line 614). That said, the notion that we do not achieve sufficient and/or early enough GNAI loss is supported for example by the Beer-Hammer 2018 study where no defects in symmetry-breaking or orientation were reported in their Gnai2 flox/flox; Gnai3 flox/flox model (Discussion new Line 637).

(5) Line 518, the base were largely inverted (Figure 8B). Should Fig 8A be cited instead of 8B?

Fig. 8B has graphs for the E17.5 cochlear base where OHC1-2 are inverted in both ptxA and Gnai2;3 DKO models. Fig. 8A has graphs of the E17.5 cochlear mid (less differentiated hair cells) where an inversion was not obvious previously, but is now clear although only partial in Gnai2; Gnai3 DKO (see above; raised eccentricity threshold). In the context of the previous text, this citation was thus correct. However, this section has been heavily modified to better compare Gnai2; Gnai3 DKO and ptxA and is hopefully less confusing in the revised version.

Reviewer #2 (Public Review):

Jarysta and colleagues set out to define how similar GNAI/O family members contribute to the shape and orientation of stereocilia bundles on auditory hair cells. Previous work demonstrated that loss of particular GNAI proteins, or inhibition of GNAIs by pertussis toxin, caused several defects in hair bundle morphogenesis, but open questions remained which the authors sought to address. Some of these questions include whether all phenotypes resulting from expression of pertussis toxin stemmed from GNAI inhibition; which GNAI family members are most critical for directing bundle development; whether GNAI proteins are needed for basal body movements that contribute to bundle patterning. These questions are important for understanding how tissue is patterned in response to planar cell polarity cues.

To address questions related to the GNAI family in auditory hair cell development, the authors assembled an impressive and nearly comprehensive collection of mouse models. This approach allowed for each Gnai and Gnao gene to be knocked out individually or in combination with each other. Notably, a new floxed allele was generated for Gnai3 because loss of this gene in combination with Gnai2 deletion was known to be embryonic lethal. Besides these lines, a new knockin mouse was made to conditionally express untagged pertussis toxin following cre induction from a strong promoter. The breadth and complexity involved in generating and collecting these strains makes this study unique, and likely the authoritative last word on which GNAI proteins are needed for which aspect of auditory hair bundle development.

Appropriate methods were employed by the authors to characterize auditory hair bundle morphology in each mouse line. Conclusions were carefully drawn from the data and largely based on excellent quantitative analysis. The main conclusions are that GNAI3 has the largest effect on hair bundle development. GNAI2 can compensate for GNAI3 loss in early development but incompletely in late development. The Gnai2 Gnai3 double mutant recapitulates nearly all the phenotypic effects associated with pertussis toxin expression and also reveals a role for GNAIs in early movement of the basal body. Although these results are not entirely unexpected based on earlier reports, the current results both uncover new functions and put putative functions on more solid ground.

Based on this study, loss of GNAI1 and GNAO show a slight shortening of the tallest row of stereocilia but no other significant changes to bundle shape. Antibody staining shows no change in GNAI localization in the Gnai1 knockout, suggesting that little to no protein is found in hair cells. One caveat to this interpretation is that the antibody, while proposed to cross-react with GNAI1, is not clearly shown to immunolabel GNAI1. More than anything, this reservation mostly serves to illustrate how challenging it is to nail down every last detail. In turn, the comprehensive nature of the current study seems all the more impressive.

(1) The original manuscript quantified stereocilia properties in Gnai1 and Gnai2 single mutants, and in Gnai1; Gnai2 double mutants using non-parametric t-tests (Mann-Whitney) for comparisons. This approach indeed suggested subtle reduction in row 1 height in IHCs in all 3 mutants. We did not quantify stereocilia features in Gnao1 mutants but could not observe defects (new Fig. 2 Supp. 1E-F). In fact, we could not observe defects in Gnai1 and Gnai2 single mutants, and in Gnai1; Gnai2 double mutants either. For this reason we have been ambivalent about reporting defects for Gnai1 and Gnai2 single and Gnai1; Gnai2 double mutants.

In the revision, we applied a nested (hierarchical) t-test to avoid pseudo-replication (Eisner 2021; PMID: 33464305; https://pubmed.ncbi.nlm.nih.gov/33464305/). In our data, the nested t-tests structure measurements by animal instead of having all stereocilia or other cell measurements treated as independent values. This more stringent approach no longer finds row 1 height reduction significant in single Gnai1 or Gnai2 mutants, or in Gnai1; Gnai2 double mutants. We modified the text accordingly in Results and Discussion. Nested t-tests were applied uniformly across the manuscript and, besides IHC measurements in Fig. 2, now also apply to bare zone surface area in Fig. 6 and eccentricity in Fig. 7. For these experiments in contrast, previous conclusions are not changed. We think that this more careful statistical treatment is a closer representation of the data in term of the conclusions we can safely make.

(2) The reviewer's criticism about antibody specificity is accurate and fair, and is fully addressed in the revised manuscript. First, we provide a phylogeny cartoon as Figure 1A to compare the GNAI/O proteins and highlight how closely related they are in sequence. To validate the assumption that our approach would detect GNAI1 if it were present in hair cells, we took a new dual experimental approach in the revision. First, we electroporated Gnai1, Gnai2 and Gnai3 expression constructs in the E13.5 inner ear and tested whether the two GNAI antibodies used in the study can detect ectopic GNAI1 in Kolliker organ. This revealed that “ptGNAI2” detects GNAI1 very well (in addition to GNAI2), but that “scbtGNAI3” does not detect GNAI1 efficiently (although it does detect GNAI3 very well). To verify in vivo that “ptGNAI2” can detect endogenous GNAI1, we immunolabeled the gallbladder epithelium in Gnai1 mutants and littermate controls using the “ptGNAI2” antibody. Based on IMPC consortium data* about the Gnai1 LacZ mouse strain, Gnai1 is specifically expressed in the adult gallbladder. We could verify that signals detected in the Gnai1 mutants were visually reduced in comparison to littermate controls. We now added this validation step in Results line 309 and the data in Fig. 4 Supp. 1A-B).

*https://www.mousephenotype.org/data/genes/MGI:95771

Reviewer #2 (Recommendations For The Authors):

Minor comments that may marginally improve clarity.

Abstract line 24: delete "nor polarized" because polarization cannot be assessed since the protein is undetectable.

This is a fair point, now deleted.

Consider revising: Lines 80-82; 188-202 (the order in which the mutants were presented was hard to follow for me); 239-240.

Lines 80-82: Used to read as "Ptx recapitulates severe stereocilia stunting and immature-looking hair bundles observed when GPSM2 or both GNAI2 and GNAI3 are inactivated."

Line 88: Was now changed to "Ptx provokes immature-looking hair bundles with severely stunted stereocilia, mimicking defects in Gpsm2 mutants and Gnai2; Gnai3 double mutants".

Lines 188-202: This was the first paragraph describing adult stereocilia defects in the different Gnai/o mouse strains. We completely rewrote the entire section to reflect the order in which the strains appear in Figure 2, hopefully making the text easier to follow because it better matches panels in Fig. 2 . We also made several other modifications to streamline comparisons and better introduce the orientation defects that are later detailed at neonate stages.

Lines 239-240: Used to read "GNAI2 makes a clear contribution since stereocilia defects increase in severity when GNAI loss extends from GNAI3 to both GNAI2 and GNAI3".

Line 247: Was now changed for "GNAI2 makes a clear contribution since Gnai3neo stereocilia defects dramatically increase in severity when GNAI2 is absent as well in Gnai2; Gnai3 double mutants."

Line 164: hardwired is unclear. Conserved?

We modified this sentence as follows: Line 171: "We reasoned that apical HC development is probably highly constrained and less likely to be influenced by genetic heterogeneity compared to susceptibility to disease, for example."

Line 299: It is not clear why GNAI1 is a better target than GNAI3. This phrase is repeated in line 303, I suspect inadvertently. Is there evidence that this antibody detects GNAI1, perhaps in another tissue? Line 308: GNAI1 may also not be detected by this antibody.

Please see point 2 above. We removed these hypothetical statements entirely and we instead now experimentally show that one of the two commercial antibodies used can readily detect GNAI1 (yet does not detect signal in hair cells when GNAI2 and GNAI3 are absent in Fig. 4F).

Author Response

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

Reviewer #2 (Public Review):

Major Weaknesses:

The assertion that MOCAT can be rapidly applied in hospital pathology departments seems overstated due to the limited availability of light-sheet microscopes outside research labs. In the first rebuttal letter, authors explain the limitations of other microscopes more readily available in hospitals. This explanation relies on your own investigations and practical experience on the matter, so including them in some part of the manuscript would be beneficial.

We appreciate the reviewer's comments and have added a discussion on the limitations of microscopes that are more readily available in hospitals in our text:

Revised manuscript, line 305-316:

“3.3 Microscopy options for imaging centimeter-sized specimens

Optical sectioning techniques are crucial for obtaining high-quality volumetric images. Techniques such as confocal microscopes, multi-photon microscopy, and light-sheet microscopy filter out-of-focus signals, resulting in sharp images of individual planes. In our study, we used light-sheet microscopy and multi-point confocal (i.e., spinning disc) for imaging centimeter-sized specimens because of their scanning speeds. While two-photon and confocal microscopy offer high-resolution imaging of smaller volumes, they are not ideal for scanning entire tissues because of their prolonged scanning times.”

Non-optical sectioning wide-field fluorescence microscopes, like the Olympus BX series or ZEISS Axio imager series, can also be used to scan samples up to about 3.5mm thick with long working distance objective lenses. In these cases, deconvolution algorithms are required to eliminate out-of-focus signals. However, it should be noted that the epifluorescence system might reduce fluorescent intensity in deeper regions within the samples.”

Refractive index matching is a critical point in the protocol, the one providing final transparency. Authors utilized the commercial solutions NFC1 and NFC2 (Nebulem, Taiwan) with a known refractive index, but for which its composition is non-disclosable. My knowledge on the organic chemistry around refractive index matching is limited, but if users don't really know what is going on in this final step, the whole protocol would rely on a single world-wide provider and troubleshooting would be fishing. I suggest that you try to validate the approach with solutions of known composition, or at least provide the solutions sold by other providers.

We appreciate the reviewer's suggestions. Based on our experience, the CUBIC-R solution developed by Ueda's team also serves as an effective RI-matching solution in the MOCAT pipeline. Its only drawback is the potential reddening of the specimen, likely due to the light-responsive component, antipyrine. We have now added this information to the Methods section:

Revised manuscript, line 492-496:

“Refractive index (RI) matching. Before imaging, the specimens were RI-matched by being immersed in NFC1 (RI = 1.47) and NFC2 (RI = 1.52) solutions (Nebulum, Taipei, Taiwan). Each immersion lasted for one day at room temperature. Alternatively, RI-matching can also be accomplished by immersing specimens in a 1:1 dilution of CUBIC-R[28] for one day, followed by pure CUBIC-R for an additional day.“

Reviewer #2 (Recommendations For The Authors):

A comment on the name of the protocol, MOCAT. I am sorry to bring this now, and not before. But, I strongly recommend another name for the procedure. My concern is that the present name "MOCAT" refers to the problem, and NOT to the actual solution provided by you. See, the problem to solve is: to perform Multiplex labeling Of Centimeter-sized Archived Tissue (MOCAT), but it says nothing about HOW you did it: heat-induced antigen retrieval and Tween20-delipidation for centimeter-scale FFPE specimens. In summary, I strongly recommend that the acronym of the procedure refers more to the "solution" than to the "problem", and for me this is important because otherwise the acronym is not fair with present and future techniques pretending to provide a novel solution to the same problem. Another way to put it is that researchers can own their proposed solutions, but they do not own the problem to be solved.

We appreciate the reviewer's suggestions. In response to their concerns, we have renamed the procedure presented in this study as Heat-Induced FFPE-based Tissue Clearing, with the acronym HIF-Clear. This change reflects the critical step in our procedure. Corresponding updates have also been made in the manuscript.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

This manuscript aims to understand the biological mechanisms underlying neuropsychiatric symptoms in Parkinson's disease by characterizing subtypes of neurons in the dorsal raphe nucleus and defining their susceptibility to the degeneration of dopaminergic and adrenergic systems in the brain. This study was well-designed, the results were presented beautifully, and the manuscript was well-written. Here are some comments that may help to improve the overall quality of this work.

We thank the reviewer for the kind comments.

Major concerns:

The current study utilized an intrastriatal 6-OHDA injection, which raises the possibility that the observed electrophysiological and morphological changes of DRN5-HT and DRNDA neurons (Figs 3-6) may be due to the direct effects of 6-OHDA to DRN5-HT and DRNDA neurons projecting to the dorsal striatum (at least for DRN5-HT neurons). This possibility requires further clarification and discussion.

6-OHDA is a catecholamine neurotoxin with low selectivity for serotonin neurons. However, changes in the levels of serotonin have been observed with high doses of 6OHDA. In our study, we used lower concentrations of 6-OHDA, which did not affect the levels of serotonin (Suppl. Fig 4D), or the number of DRN5-HT neurons (Suppl. Fig. 5B). Concerning the possible effect of 6-OHDA on DRNDA neurons, we did not observe any modification in the number of these cells in response to the administration of 6-OHDA (Suppl. Fig. 5C), (lines 170-175).

How does the loss of nigrostriatal dopamine neurons affect the electrophysiology and morphology of DRNDA neurons (Figs. 5-6)? What are the potential circuit mechanisms?

The dopaminergic system in the midbrain and the DRN constitute two highly interconnected nuclei and hence there are multiple possible circuit mechanisms that could explain how loss of nigrostriatal dopaminergic neurons affects DRNDA neurons: First, DRNDA neurons are directly innervated by dopaminergic neurons in the SNc and VTA and hence loss of SNc inputs might evoke acute as well as homeostatic changes in DRNDA (Lin et al., 2020; Pinto et al., 2019). Second, midbrain dopaminergic neurons are in turn innervated by the DRN (Watabe-Uchida et al., 2012) and loss of postsynaptic dopaminergic neurons might affect all neuron types in the DRN that target the midbrain. Finally, GABAergic populations in the midbrain have been shown to target DRN5-HT neurons and might potentially also target other local cell types such as DRNDA (Li et al., 2019). Another possible pathway is the bidirectional connection between the striatum and the DRN (Pollak-Dorocic et al, 2014). DA depletion in the striatum may affect the GABAergic projection to the DRN and in turn modify the properties of postsynaptic DRN neurons.

The potential circuit mechanisms are now included in the introduction (lines 58-59).

Whether these intrastriatal 6-OHDA mice exhibited nonmotor deficits (e.g., anxiety) that may be related to the observed changes in the DRN? Such behavioral data would enhance the overall conclusions of this work.

The PD model utilized in this study displays non-motor deficits, including depression- and anxiety-like behavior (Masini et al. 2021, Ztaou et al., 2018). This is now highlighted in the manuscript (lines 167-169).

Minor issues:

The panels of Fig. 2 should be re-labelled to match the descriptions in the main text (L. 142-158).

Fig.2 now matches the descriptions in the main text.

Fig 4D was missing from the figure, which does not match the descriptions in the main text (L. 193-204:)

Fig. 4D includes the parameters describing the dendritic branching and starts with the last graph on the right in the second row of the panel.

Line 409: Extra "as" after "average"

Corrected in revised manuscript.

Fig 3G: Missed asterisks.

Corrected in revised manuscript (Fig. 3G)

Details of how action parameters were quantified should be stated and specified in the methods.

We have now added a section called ‘Quantification of electrophysiological parameters’ in the methods where we explain how the electrophysiological properties are defined and quantified (lines 407-439).

"Parkinson's disease" in the title should be revised to "parkinsonism"

Corrected in revised manuscript.

Reviewer #2 (Recommendations For The Authors):

(1) Throughout the paper, there are numerous inaccuracies and inconsistencies in the figures, which impede the clear understanding of this paper. For example, there are discrepancies between the labeling of the main figures (sub-panels) and the corresponding manuscript (Figure 2, Figure 4).

Corrected in the revised manuscript.

The statistical presentations are inaccurate in several figures (Figure 3E, 3G), making it difficult to distinguish which data is statistically meaningful. Furthermore, the number of cells presented in each figure is ambiguous in the figure legend. It would be better to avoid expressions such as 'n = 28 - 43 cells per group', as in line 456 (Figure 1I). Please provide the exact number of cells for each graph.

We agree with the reviewer, and we have now added the precise n numbers for each panel in the corresponding legends in Fig 1, Fig 3, and Fig 5. Please note that some analysis was restricted to recordings where neurons fired close to their average spontaneous firing frequency (e.g. 1Hz for DRN5-HT) to allow for a fair comparison of the data across groups and that therefore the n numbers vary in different panels.

In some figures, the value of n in the graph seems different from the value of n in the figure legends (Figure 2G-I, Figure 4, Figure 6). Collectively, these inaccurate figures and the manuscript weaken the general credibility of the data presented.

We apologize for the misunderstanding, but in the type of chosen graph, equal values are overlapped. The numbers described in the figure legend are correct.

(2) Some of the authors' claims in this paper are not supported by quantitative analysis, but only by sample recording traces or simple descriptions. For example, in line 97, the authors mentioned, "no differences when comparing TH-positive to TH-negative neurons".

But there are no data actually analyzing these two groups in Supplementary Figure 2A.

In addition, in line 103, there is a claim that "DRN DA neurons showed that they share several properties characteristics of other DA populations located in the SNc and the ventral tegmental area". However, this claim is backed up only by a few sample traces in Figure 1E.

The statement (lines 110-111), "a relative constant action potential (AP) amplitude", is also not supported by appropriate quantitative analysis but only by sample recording traces.

In our study we found a small subset of DAT-tdTomato positive neurons which did not stain positive for TH after the slice recordings. In 5 of 6 of these neurons (recorded in sham), the electrophysiological properties did not differ from other TH-positive neurons. This is visualized in Suppl. Fig 2A. The absence of any statistical difference was also confirmed by a Mann Whiteny U test comparing the TH negative to the TH positive DRNDA neurons (no significant differences in all 6 of 6 properties shown in Suppl. Fig 2A). Additionally, all these cells were DAT-positive, further supporting their classification as dopaminergic neurons. Therefore, we suspect that the lack of TH staining is likely caused by the tissue processing itself. Please note that all our immunohistochemistry was run on slices after several hours of patch-clamping procedures. Finally, including or excluding this small subset of neurons in the present study does not change any of the results presented and data was therefore pooled. We have now clarified this in more detail in the results section and in Suppl. Fig 2A (lines 100-103).

We have moved the comparison of hallmark properties found in DRNDA neurons as well as in dopaminergic neurons in the midbrain from the results section to the discussion (lines 281-283).

The claim that DRN5HT neurons have a comparatively constant action potential amplitude compared to DRNDA neurons is supported by quantitative analysis shown in Fig 1I (left panel, “AP drop rate”), while the representative example traces are shown in Fig 1G.

(3) In the legend of Figure 2, the mouse used in this experiment is mentioned with two different names (wild-type mice in line 463 and sham-lesion mice in line 465). Is this a mistake? Or did the authors intentionally use the brain samples from sham-lesion mice for Figure 2?

Figure 2 shows data in control conditions (Sham-lesion in our case), both from wild-type and Dat-Tomato. The text has been changed to avoid misunderstandings.

(4) While the primary claim of this paper is the differential alterations of DRN 5-HT and DA neurons in a mouse PD model, the observed changes in the DRN neurons of the 'DA only lesion model' are comparatively minor to the 'DA and NA lesions model'. Therefore, it looks like NA depletion has a more critical role in the DRN neurons of 6OHDA-lesion mice than DA depletion. To understand the results of this paper better, it would be great if the authors can provide additional data from the 'NA only lesion model'.

We agree with the reviewer, and we have now added a new set of experiments in which we selectively lesioned noradrenergic cells by injecting 6-OHDA unilaterally into the LC. The new data are presented in supplementary figure 6 in the revised manuscript. We find that selective lesioning of the NA system affects DRNDA and DRN5-HT neurons mildly, suggesting that the concomitant lesion of the DA and NA systems is particularly impactful (possibly because of interactions between these two systems).

(5) In Figure 3B and Figure 5B, only the 6-OHDA+DMI group shows significant differences from the sham group. This finding might be attributed to the effect of DMI itself, not to the nigrostriatal DA degeneration without NA degeneration. Thus, adding the 'DMI-only group' in all experiments will strengthen the conclusion of this paper.

The effect of one acute administration of desipramine was temporally limited to the stereotactic intervention (line 373-375), which was performed several weeks before the electrophysiological and morphological analyses. Given that the half-life of desipramine is approximately 24 hrs (Nagy and Johansson, 1975), we believe that its impact was limited to the neuroprotection of NA-neurons from 6-OHDA toxicity.

(6) DRN 5-HT neurons are known to exhibit cellular heterogeneity, and in particular their electrophysiological properties are quite heterogeneous (Bernat Kocsis. 2006; J.V. Schweimer. et al. 2011). Furthermore, 5-HT neurons in the distinct subregions of the DRN display different membrane properties (LaTasha K. Crawford, 2010). Therefore, not all DRN 5-HT neurons can be regarded as electrophysiologically identical. Given that the molecular identity of all recorded cells was confirmed with neurobiotin in this paper, it would be better to show that recorded cells are not biased toward certain subregions of DRN.

In addition, providing more comprehensive descriptions of the electrophysiological features used in PCA analysis would be beneficial in understanding the electrophysiological profiling of DRN neurons explained in this paper.

Although several studies have revealed electrophysiological and molecular heterogeneity within the DRN5-HT population, we did not observe any significant differences within the DRN5-HT neurons recorded in this study. We compared the properties of DRN5HT neurons recorded more anterior to those recorded in the posterior

DRN as well as neurons found in more ventral locations to those in more dorsal locations (data not shown). We would like to point out that the largest differences within serotonergic neuron populations described by previous studies were often found when comparing those located in the medial raphe nucleus (MRN) to those found in the DRN. Calizo et al., (2011) showed for example significant differences in the input resistance and AHP amplitude between MRN5HT and DRN5HT neurons. These two properties as well as the AP amplitude, AP threshold, AP duration, and tau did however not differ between DRN subregions in their study - and neither in ours. We extended our Suppl. Fig 1 and mapped the location of DRN5HT and DRNDA neurons recorded in sham (Suppl. Fig 1D).

Overall, we’ve sampled neurons along the anterior-posterior and dorsal-ventral axes of the DRN, while on the medial-lateral axis, recorded DRN neurons were located medially.

We agree with the reviewer that a comprehensive description of the electrophysiological features was missing in the manuscript, and we have therefore added a new section in the materials and methods where we explain in detail how each parameter was measured and analyzed (‘Quantification of electrophysiological parameters’, lines 407-439). This section also provides detailed information about the five properties underlying the PCA shown in figure 1 (i.e. delay to the first action potential, action potential drop rate, action potential rise time, duration of the afterhyperpolarization, and capacitance).

(7) Some sample images presented in this paper contain information that can conflict with the previous research. In Figures 4B and 6B, TH expression was significantly increased in the DMI pretreatment group compared to the control group. However, several studies have shown that the administration of DMI decreases TH expression levels (Komori et al.1992; Nestler et al.1990). Therefore, it would be great if the authors further explained how the pretreatment of DMI with 6-OHDA affects TH level within the DRN.

Figure 4B and 6B do not show any quantification of TH expression. The difference observed in the representative pictures is casual and due to the variable expression of TH across the slice. Moreover, as mentioned in the response to point 5, mice were subjected to a single injection of DMI immediately preceding the stereotactic intervention (line 373375). In contrast, the increase in TH expression reported by Komori et al. 1992 and Nestler et al. 1990 was observed in response to chronic (two weeks) administration of DMI.

(8) This paper lacks direct evidence to demonstrate whether DMI pretreatment could effectively protect against NA depletion. Therefore, in addition to TH expression levels, it is important to provide data to confirm the intact NA levels (or NA axons) after DMI treatment.

NA levels in the striatum were measured by Enzyme-linked immunosorbent assay and reported in Suppl.Fig.4 in the revised manuscript.

(9) It would be great if the authors specifically explained why 6-OHDA was injected into the striatum (neither MFB nor SNc) to make a mouse model of PD.

Mice were injected in the dorsal striatum to produce a partial bilateral lesion of the dopamine and noradrenaline systems. This model reproduces the initial stages of PD and also recapitulates several non-motor symptoms of PD, including affective disorders, which may be related to changes in serotonergic and dopaminergic transmission in the dorsal raphe. In contrast, injections in the MFB and SNc quickly produce a severe motor phenotype closer to a late stage of the disease and cannot be done bilaterally. <br /> The striatal model has been successfully used in other publications (Kravitz et al., 2010, Masini et al., 2021, Ztaou et al., 2018, Chen et al., 2014, Branchi et al., 2008, Marques et al. 2019, Tadaiesky et al., 2008, Matheus et al., 2016, Silva et al., 2016).

(10) Supplementary Figures 2 and 3 were erroneously cut on the right side. These figure images should be replaced with the correct ones.

We thank the reviewer for noticing and we have now replaced the figures with the correct ones.

(11) There should be more explanations about tdTomato-positive but non-TH neurons in Supplementary Figure 2. It is strange to regard TH-negative neurons as DA neurons although these neurons have DA neuron-like electrophysiological properties. If these tdTomato-positive but non-TH neurons cannot release DA, can we say these are DA neurons?

In our study we found a small subset of DAT-tdTomato positive neurons which did not stain positive for TH afterwards. In 5 of 6 of these neurons (recorded in sham), the electrophysiological properties did not differ from other TH-positive neurons. This is visualized in Suppl. Fig 2A. The absence of any statistical difference was also confirmed by a Mann Whiteny U test comparing the TH-negative to the TH-positive DRNDA neurons (no significant differences in all 6 of 6 properties shown in SF2A). Additionally, all these cells were DAT-positive, further supporting their classification as dopaminergic neurons. Therefore, we suspect that the lack of TH staining is likely caused by the tissue processing itself. Please note that all our immunohistochemistry was run on slices after several hours of patch-clamping procedures. Finally, including or excluding this small subset of neurons in the present study does not change any of the results presented and data was therefore pooled. We have now clarified this in more detail in the results section and in Suppl. Fig 2A (lines 100-103).

Reviewer #3 (Recommendations For The Authors):

The authors report using a parametric statistical test, the t-test. The t-test makes the assumption that the data are normally distributed. Most biological data is not distributed normally, and with smaller datasets, it is difficult to say whether the underlying distribution would be normally distributed. I would recommend using the non-parametric versions of the same test (eg Mann-Whitney U test), which is likely to give a similar result while being more conservative given the potential for non-normal distribution.

All electrophysiological data were first tested for normality before running the corresponding statistical test (either t-test for normal distributed data or Mann-Whitney U test for non-normally distributed data). The morphological data are now analyzed by the Mann-Whitney U test (lines 484-494).

The authors state that mice were treated with 6-OHDA at 3 months, then brain slices were prepared 3 weeks later, making them about 4 months old. I could not find the age of sham/control mice and 6-OHDA/desipramine mice in the methods section. Were sham/controls and 6-OHDA slices prepared in an interleaved fashion?

Sham and 6-OHDA+DMI mice underwent surgery at 3 months and the brain slices were prepared 3 weeks later, as the 6-OHDA mice. We have now clarified this in the methods (line 381).

While desipramine is relatively selective as a norepinephrine reuptake inhibitor, it also can prevent serotonin reuptake. Could this mechanism also protect DRN neurons from the effects of 6-OHDA?

Even if desipramine has some affinity for the serotonin reuptake, this affinity is 100-fold less than the one described for the noradrenaline reuptake (Richelson and Pfenning, 1984, Gillman, 2007). Moreover, in our study the 6-OHDA injection in the dorsal striatum did not cause any direct damage to the DRN5-HT, as shown by the 5-HT measurement and DRN5-HT counting (Suppl. Fig. 4D, Suppl. Fig. 5A,B), so we can exclude that the effects observed in the DMI+6-OHDA group are related to a protection of the serotonergic system exerted by a single injection of desipramine.

On line 168, the authors use the abbreviation NA for noradrenergic. Was this abbreviation previously defined in the manuscript?

Yes, the abbreviation is defined in the introduction (line 73).

On line 45, the authors cite that the DRN-5HT subpopulation accounts for 30-50% of the DRN neurons. It would be helpful to know approximately what percentage of the DRN neurons belong to the DRNDA subpopulation as well.

To the best of our knowledge, there is unfortunately no detailed analysis of the prevalence of DRNDA neurons in mice available. Previous studies in rats have estimated that this population comprises around 1000 neurons (Descarries et al., 1986). According to Calizo et al. (2011), the number of any non-serotonergic neuron population (releasing dopamine or other neurotransmitters) in the DRN is one third to one tenth less than the number of DRN5-HT neurons. But please note that this study was also performed in rats (line 55).

While I appreciate that the authors did not over-interpret their findings, it would be useful to comment (in the Discussion) on how their findings could/should be used in interpreting other studies using 6-OHDA, as well as the relationship of their findings to loss of 5-HT and/or DRN neurons in Parkinson's Disease itself.

In the manuscript, we refer to the utility of the 6-OHDA model for the study of a wide range of non-motor symptoms. We have now described, in this model, how the loss of midbrain dopaminergic and noradrenergic neurons affects the electrophysiological and morphological properties of DRN5-HT and DRNDA neurons. This information will allow for a more precise assessment of the mechanisms involved in the affective and cognitive aspects of PD symptomatology (lines 354-356).

Author Response

We are writing this response letter with regards to the insightful feedback you provided on our manuscript titled: "A metabolic modeling-based framework for predicting trophic dependencies in native rhizobiomes of crop plants" submitted for consideration in eLife.

We sincerely appreciate the thorough and constructive reviews, seeing and fitting the intentions behind our work. We intend to fully address all points raised by the reviewers in our revised manuscript. Specifically, we plan to incorporate targeted revisions to address concerns raised during the review process, with focus on process benchmarking and validation of our framework to enhance its reliability and accuracy.

We believe that the current revision would improve the consistency and quality of the framework, making it a suitable tool for the characterization of microbial trophic interactions in diverse biological landscapes.

Thank you once again for both your time and dedication in reviewing our manuscript, as well as the constructive review.

Author Response

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

eLife assessment

This valuable study provides insights into the IDA peptide with dual functions in development and immunity. The approach used is solid and helps to define the role of IDA in a two-step process, cell separation followed by activation of innate defenses. The main limitation of the study is the lack of direct evidence linking signaling by IDA and its HAE receptors to immunity. As such the work remains descriptive but it will nevertheless be of interest to a wide range of plant cell biologists.

We thank the reviewers for thoroughly reading our manuscript. We have used their comments and suggestions- to improve the manuscript. Below is a response to the reviewer's comments.

Public Reviews:

Reviewer #1 (Public Review):

The paper titled 'A dual function of the IDA peptide in regulating cell separation and modulating plant immunity at the molecular level' by Olsson Lalun et al., 2023 aims to understand how IDAHAE/HSL2 signalling modulates immunity, a pathway that has previously been implicated in development. This is a timely question to address as conflicting reports exist within the field. IDL6/7 have previously been shown to negatively regulate immune signalling, disease resistance and stress responses in leaf tissue, however IDA has been shown to positively regulate immunity through the shedding of infected tissues. Moreover, recently the related receptor NUT/HSL3 has been shown to positively regulate immune signalling and disease resistance. This work has the potential to bring clarity to this field, however the manuscript requires some additional work to address these questions. This is especially the case as it contracts some previous work with IDL peptides which are perceived by the same receptor complexes.

Can IDA induce pathogen resistance? Does the infiltration of IDA into leaf tissue enhance or reduce pathogen growth? Previously it has been shown that IDL6 makes plants more susceptible. Is this also true for IDA? Currently cytoplasmic calcium influx and apoplastic ROS as overinterpreted as immune responses - these can also be induced by many developmental cue e.g. CLE40 induced calcium transients. Whilst gene expression is more specific is also true that treatment with synthetic peptides, which are recognised by LRR-RKs, can induce immune gene expression, especially in the short term, even when that is not there in vivo function e.g. doi.org/10.15252/embj.2019103894.

We thank the reviewer for the concerns raised and agree that further experiments including pathogen assays would strengthen the link between IDA signaling and immunity and we plan for such experiments in future work. We have however, modified the discussion to include the possible role of IDA induced Ca2+ and ROS during development. We have recently published a preprint (accepted for publication in JXB) ( (Galindo-Trigo et al., 2023, https://doi.org/10.1101/2023.09.12.557497)) strengthening the link between IDA and defense by identifying WRKY transcription factors that regulate IDA expression through a Y1H assay.

This paper shows that receptors other than hae/hsl2 are genetically required to induce defense gene expression, it would have been interesting to see what phenotype would be associated with higher order mutants of closely related haesa/haesa-like receptors. Indeed recently HSL1 has been shown to function as a receptor for IDA/IDL peptides. Could the triple mutant suppress all response? Could the different receptors have distinct outputs? For example for FRK1 gene expression the hae hsl2 mutant has an enhanced response. Could defence gene expression be primarily mediated by HSL1 with subfunctionalisation within this clade?

We agree that it would be interesting to also include HSL1 in our studies. However, the focus of this study has been on HAE and HSL2 and we wanted to explore their role in IDA induced defense responses. Including HSL1 in these studies will require generation of multiple transgenic lines and repeating most of the experiments and are experiments we will consider in a follow up study together with pathogen assays (that would also address the main concern raised in the comment above). We have however, modified the text to include the known function of HSL1 and discuss the possibility of subfunctionalisation of this receptor clade.

One striking finding of the study is the strong additive interaction between IDA and flg22 treatment on gene expression. Do the authors also see this for co-treatment of different peptides with flg22, or is this unique function of IDA? Is this receptor dependent (HAE/HSL1/HSL2)?

This is a good question. Since our study focuses on the IDA signaling pathway we preferentially tested if the additive effect observed between flg22 and mIDA was also observed when mIDA was combined with another peptide involved in defense. The endogenous peptide PIP1, has previously been shown to amplify flg22 signaling (Hou et al 2014, doi:10.1371/journal.ppat.1004331 ). In this study it is shown that co-treatment with flg22 and PIP1 gives increased resistance to Pseudomonas PstDC3000 compared to when plants are treated with each peptide separately. In the same study, the authors also show reduced flg22 induce transcriptional activity of two defense related genes WRKY33 and PR in the receptor like kinase7 (rlk7) mutant (the receptor perceiving PIP1) (). To investigate whether PIP1 would give the same additive effect with mIDA as that observed between flg22 and mIDA, we co-treated seedlings with PIP1 and mIDA. We observed no enhanced transcriptional activity of FRK1, MYB51 and PEP3 in tissue from plants treated with both PIP1 and mIDA peptides compared to single exposure. These results are presented in supplementary figure 11. In conclusion we do not think mIDA acts as a general amplifier of all immune elicitors in plants.

It is interesting how tissue specific calcium responses are in response to IDA and flg22, suggesting the cellular distribution of their cognate receptors. However, one striking observation made by the authors as well, is that the expression of promoter seems to be broader than the calcium response. Indicating that additional factors are required for the observed calcium response. Could diffusion of the peptide be a contributing factor, or are only some cells competent to induce a calcium response?

It is interesting that the authors look for floral abscission phenotypes in cngc and rbohd/f mutants to conclude for genetic requirement of these in floral abscission. Do the authors have a hypothesis for why they failed to see a phenotype for the rbohd/f mutant as was published previously? Do you think there might be additional players redundantly mediating these processes?

It is a possibility that diffusion of the peptide plays a role in the observed response. In a biological context we would assume that the local production of the peptides plays an important role in the cellular responses. In our experimental setup, we add the peptide externally and we can therefore assume that the overlaying cells get in contact with the peptide before cells in the inner tissues and this could be affecting the response recorded However, our results show that there is a differences between flg22 and mIDA induced responses even when the application of the peptides is performed in the same manner, indicating that the difference in the response is not primarily due to the diffusion rate of the peptides but is likely due to different factors being present in different cells. To acquire a better picture of the distribution of receptor expression in the root tissue and to investigate in which cells the receptors have an overlapping expression pattern, we have included results in figure 6 showing plant lines co-expressing transcriptional reporters of FLS2 and HAE or HSL2.

Can you observe callose deposition in the cotyledons of the 35S::HAE line? Are the receptors expressed in native cotyledons? This is the only phenotype tested in the cotyledons.

We thank the reviewer for this valuable comment. We have now conducted callose deposition assay on the 35S:HAE line. And Indeed, we observe callose depositions when cotyledons from a 35S:HAE line is treated with mIDA. We have included these results in figure 4 and have adjusted the text regarding the callose assay accordingly. In addition, we have analyzed the promoter activity of pHAE in cotelydons and we observe weak promoter activity. These results are included as supplementary figure 1d.

Are flg22-induced calcium responses affected in hae hsl2?

The experiment suggested by the reviewer is an important control to ensure that the hae hsl2-Aeq line can respond to a Ca2+ inducing peptide signaling through a different receptor than HAE or HSL2. One would expect to see a Ca2+ response in this line to the flg22 peptide. We performed this experiment and surprisingly we could not detect a flgg22 induced Ca2+ signal in the hae hsl2 mutnt. As it is unlikely that the Ca2+ response triggered by flg22 is dependent on HAE and HSL2 we have to assume that the lack of response is due to a malfunction of the Aeq sensor in this line. As a control to measure the amount of Aeq present in the cells we treat the Aeq seedlings with 2 M CaCl2 and measure the luminescence constantly for 180 seconds (Ranf et al., 2012, DOI10.1093/mp/ssr064). The CaCl2 treatment disrupts the cells and releases the Aeq sensor into the solution where it will react with Ca2+ and release the total possible response in the sample (Lmax) in form of a luminescent peak. When treating the hae hsl2-Aeq line with CaCl2we observe a luminescent peak, indicating the presence of the sensor, however, the response is reduced compared to WT seedlings expressing Aeq. Given the sensitivity of FLS2 to flg22 one would still expect to see a Ca2+ peak in the hae hsl2-Aeq line even if the amount of sensor is reduced. Given that this is not the case, we have to assume that localization or conformation of the sensor is somehow affected in this line or that there is another biological explanation that we cannot explain at the moment.

We have therefore opted on omitting the results using the hae hsl2 Aeq lines from the manuscript and are in the process of mutating HAE and HSL2 by CRISPR-Cas9 in the Aeq background to verify that the mIDA triggered Ca2+ response is dependent on HAE and HSL2.

Reviewer #2 (Public Review):

Lalun and co-authors investigate the signalling outputs triggered by the perception of IDA, a plant peptide regulating organs abscission. The authors observed that IDA perception leads to a transient influx of Ca2+, to the production of reactive oxygen species in the apoplast, and to an increase accumulation of transcripts which are also responsive to an immunogenic epitope of bacterial flagellin, flg22. The authors show that IDA is transcriptionally upregulated in response to several biotic and abiotic stimuli. Finally, based on the similarities in the molecular responses triggered by IDA and elicitors (such as flg22) the authors proposed that IDA has a dual function in modulating abscission and immunity. The manuscript is rather descriptive and provide little information regarding IDA signalling per se. A potential functional link between IDA signalling and immune signalling remains speculative.

We thank the reviewer for the concerns raised and agree that further experiments including pathogen assays would strengthen the link between IDA signaling and immunity and plan for such experiments in future work.

Reviewer #3 (Public Review):

Previously, it has been shown the essential role of IDA peptide and HAESA receptor families in driving various cell separation processes such as abscission of flowers as a natural developmental process, of leaves as a defense mechanism when plants are under pathogenic attack or at the lateral root emergence and root tip cell sloughing. In this work, Olsson et al. show for the first time the possible role of IDA peptide in triggering plant innate immunity after the cell separation process occurred. Such an event has been previously proposed to take place in order to seal open remaining tissue after cell separation to avoid creating an entry point for opportunistic pathogens.

The elegant experiments in this work demonstrate that IDA peptide is triggering the defenseassociated marker genes together with immune specific responses including release of ROS and intracellular CA2+. Thus, the work highlights an intriguing direct link between endogenous cell wall remodeling and plant immunity. Moreover, the upregulation of IDA in response to abiotic and especially biotic stimuli are providing a valuable indication for potential involvement of HAE/IDA signalling in other processes than plant development.

We are pleased that the reviewer finds our findings linking IDA to defense interesting and would like to thank the reviewer for this positive feedback.

Strengths:

The various methods and different approaches chosen by the authors consolidates the additional new role for a hormone-peptide such as IDA. The involvement of IDA in triggering of the immunity complex process represents a further step in understanding what happens after cell separation occurs. The Ca2+ and ROS imaging and measurements together with using the haehsl2 and haehsl2 p35S::HAE-YFP genotypes provide a robust quantification of defense responses activation. While Ca2+ and ROS can be detected after applying the IDA treatment after the occurrence of cell separation it is adequately shown that the enzymes responsible for ROS production, RBOHD and RBOHF, are not implicated in the floral abscission.

Furthermore, IDA production is triggered by biotic and abiotic factors such as flg22, a bacterial elicitor, fungi, mannitol or salt, while the mature IDA is activating the production of FRK1, MYB51 and PEP3, genes known for being part of plant defense process.

Thank you.

Weaknesses:

Even though there is shown a clear involvement of IDA in activating the after-cell separation immune system, the use of p35S:HAE-YFP line represent a weak point in the scientific demonstration. The mentioned line is driving the HAE receptor by a constitutive promoter, capable of loading the plant with HAE protein without discriminating on a specific tissue. Since it is known that IDA family consist of more members distributed in various tissues, it is very difficult to fully differentiate the effects of HAE present ubiquitously.

We agree on this statement. Nevertheless, it is important to note that the responses we have observed are not detectable in WT plants that do not (over)express the HAE receptors. Suggesting that the ROS and callose deposition are induced by the addition of mIDA peptide and not the potential presence of the endogenous IDL peptides.

The co-localization of HAE/HSL2 and FLS2 receptors is a valuable point to address since in the present work, the marker lines presented do not get activated in the same cell types of the root tissues which renders the idea of nanodomains co-localization (as hypothetically written in the discussion) rather unlikely.

Thank you for raising an important aspect of our study. It is true that not all cells in the root which have promoter activity for FLS2 also exhibit promoter activity for either HAE or HSL2. However, we have observed that certain cells in the roots show promoter activity for both receptors. In the revised version of the manuscript, we have included plants expression a transcriptional promoter for both FLS2 and HAE or HSL2 using different fluorescent proteins. We have investigated overlapping promoter activity both at sites of lateral roots, in the tip of the primary root and in the abscission zone. Our results show overlapping expression of the transcriptional reporters in certain cells, indicating that FLS2 and HAE or HSL2 are likely to be found in some of the same cells during plant development. We also observe cells where only one or none of the promoters are active.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Supplementary Figure 3: re-labelling of y axis; 200 than 200,00 for clarity.

This has been addressed.

Supplementary Figure 2: It would be good to include the age of the seedlings used to study calcium influx in the legend.

This has been addressed.

Supplementary Figure 1: rephrase 'IDA induces ROS production in Arabidopsis'.

This has been addressed.

The use of chelating agents to establish the need of calcium from extracellular space is a clear experiment supporting the calcium response phenotype specific to IDA treatment in seedlings. Removing the last asparagine (N) and using it as a peptide that fails to elicit calcium response could simply be because of the peptide is smaller in length or different chemical properties. Therefore, a scrambled sequence would have been a better control.

We thank the reviewer for the suggestion of using a scrambled peptide as a negative control, however we find it unlikely that mIDA∆N69 could induce any activity based on previous work. Results from crystal structure of mIDA bound to the HAE receptor and ligand-receptor interaction studies (10.7554/eLife.15075 ) show that the last asparagine in the mIDA peptide is essential for detectable binding to the HAE receptor and that a peptide lacking this amino acid does not have any activity. We will however, in future experiments also include a scrambled version of the peptide as an additional control.

Reviewer #2 (Recommendations For The Authors):

Please find below specific comments:

(1) Most of the molecular outputs triggered by IDA can be considered as common molecular marks of plant peptides signalling, they do not represent strong evidences of a potential function of IDA in modulating immunity. For instance, perception of CIF peptides, which control the establishment of the Casparian strips, regulate the production of reactive oxygen species, and the transcription of genes associated with immune responses (Fujita et al., The EMBO Journal 2020). It should also be considered that FRK1, whose function remains unknown, may be involved in both immunity and abscission and that the upregulation of FRK1 upon IDA treatment is not indicative of active modulation of immune signalling by IDA.

This is a fair point raised by the reviewer and we now address in the manuscript that ROS and Ca2+ are hallmarks of both plant development and defense. The function of FRK1 is not known however, it is unlikely that the upregulation of FRK1 in response to mIDA plays a role in the developmental progression of abscission as it is not temporally regulated during the abscission process, thus making it an unlikely candidate in the regulation of cell separation (Cai & Lashbrook, 2008, https://doi.org/10.1104/pp.107.110908). We do however agree that further experiments including pathogen assays would strengthen the link between IDA signaling and immunity and plan for such experiments in future work.

(2) It remains unknown whether IDA modulate immunity. For instance, does IDA perception promote resistance to bacteria (bacterial proliferation, disease symptoms)? Is IDA genetically required for plant disease resistance immunity? Is the IDA signalling pathway genetically required for transcriptional changes induced by flg22, such as increase in FRK1 transcripts? In addition, the authors propose that the proposed function of IDA in modulating immune signalling prevents bacterial infection in tissue exposed to stress(es). Does loss of function of IDA or of its corresponding receptors leads to changes in the ability of bacteria to colonise plant root upon stress(es)?

Please see the comment above regarding pathogen assays.

(3) Several aspects of the work appear to correspond to preliminary investigation. For instance, the authors analyse loss of function mutant for genes encoding for Ca2+ permeable channels (CNGCs) which are transcriptionally active during the onset of abscission (Sup. Figure 5). None of the single mutants present an abscission defect. These observations provide no information regarding the identity of the channel(s) involved in IDA-induced calcium influx.

We agree with the reviewer that we have not been able to identify the channels responsible for the IDA-induced calcium influx. Given the redundancy for many of the members of this multigenic family a future approach to identify proteins responsible for the IDA triggered calcium response could be to create multiple KO mutants by CRISPR Cas9.

(4) Using H2DCF-DA, the authors observed a decrease in ROS accumulation in the abscission zone of rbohd/rbohf double KO line (Sup Figure 5c) but describe in the text that ROS production in this zone does not depend on RBOHD and RBOHF (L220). Please clarify.

This has now been clarified in the text.

(5) The authors describe that rbohd/rbohf double KO present a lower petal break-strength, which they describe as an indication of premature cell wall loosening, and that petals of rbohd/rbohf abscised one position earlier than in WT. Yet, the authors postulate that IDA-induced ROS production does not regulate abscission but may regulate additional responses. Instead the data seems to indicate that ROS production by RBOHD and RBOHF regulate the timing of abscission. In addition, it would have been interesting to test whether IDA signalling pathway regulate ROS production in the abscission zone.

The rbohd and rbohf double mutants show several phenotypes associated to developmental stress, the mild phenotype observed with regards to premature abscission (by one position) could be caused by the phenotype of the double mutant rather than related to ROS production. Indeed, it has been suggested that the lignified brace in the AZ dependent on ROS production by the aforementioned RBOHs in necessary for the correct concentration of cell modifying enzymes (Lee et al., 2018, https://doi.org/10.1016/j.cell.2018.03.060). The precocious abscission in this double mutant clearly shows this not to be the case. We have tried to do a ROS burst assay on AZ tissue/flowers with the mIDA peptide but have not been successful with this approach. A ROS sensor expressed in AZ tissue would be a valuable tool to address whether IDA signalling regulates ROS production in AZs.

(6) In Sup. Figure5a, it would be of interest to have a direct comparison of the transcript accumulation of the presented CNGCs and RBOHDs with other of these multigenic families.

The CNGCs and RBOH gene expression profile shown in the figure are the family members expressed during the developmental progress of floral abscission in stamen AZs. Since there is no difference in the temporal expression of the other family members (and most are either not expressed or very weakly expressed in this tissue) it is not possible to do this comparison (Cai & Lashbrook, 2008, https://doi.org/10.1104/pp.107.110908).

(7) L251-253, since IDAdeltaN69 cannot be perceived by its receptors, the absence of induction of pIDA::GUS by IDAdeltaN69 compared to flg22 cannot be seen as a sign of specificity in peptideinduced increase in IDA promotor activity.

We have rephased this in the text

(8) Please provide quantitative and statistical analysis of the calcium measurement presented in sup figure 3.

This has been addressed.

(9) L339-341; This sentence is unclear to me, please rephrase.

We have rephased this in the text

Reviewer #3 (Recommendations For The Authors):

(1) In order to assess the role of CNGCs in abscission process, it would be more interesting to see the effect on the Ca2+ pattern and ROS signaling after application of mIDA on cngc and rbohf rbohd mutants.

We agree in this statement and the studies on mIDA induced ROS and Ca2+ on these mutants will provide valuable information to the regulation of the response. We are in the process of making the lines needed to be able to perform these experiments. However, since it requires crossing of genetically encoded sensors into each mutant, and generation of higher order mutants this is a long process.

(2) With regard to the ROS production (Sup Fig. 1), the application of mIDA can trigger ROS in p35S::HAE:YFP lines, but not in the wild-type plant, which is according to the text "most likely due to the absence of HAE expression" in leaves. The experiment on callose deposition is performed in wild-type cotyledons where no callose deposition could be observed after mIDA treatment (Fig. 4a,b). The conclusion from text is that IDA "is not involved in promoting deposition of callose as a long-term defence response". It appears more likely that neither ROS nor callose can be observed in wild-type plants due to the lack of HAE expression. Therefore, the callose experiment should include the p35S::HAE:YFP lines. The experiment as it is does not allow to draw any conclusion on HAE/IDA involvement in callose formation.

We fully agree with this comment, thank you for pinpointing this out. We have now performed the callose experiment with the 35S:HAE lines. Please see our answer to reviewer #1.

(3) Between Sup Fig. 3 and Sup Fig. 5 two different systems were used to asses the floral stage. An adjustment of the floral stages would be easier to convey the levels of HAE/HSL2 expression and hence potentially with the onset of cell-wall degradation.

We now used the same system to assess floral stages throughout the whole manuscript.

(4) For the Fig. 1 and 2, it will be helpful to mention the genotype used for imaging/quantification of Ca2+.

This has been addressed.

(5) Some of the abbreviations are not introduced as full-text at their first time use in the text, such as: mIDA (Line 68), Ef-Tu (line 85), NADPH (line 77).

The abbreviations have now been introduced.

(6) In the legend of Fig. 5 (lines 897 and 898)- in the figure description, the box plots are identified as light gray and dark gray, while in the panel a of the figure the box plots are colored in red and blue.

Thank you for pointing this out, this has now been corrected.

(7) In figure 1 and 2. the authors write that the number of replicates is 10 (n=10) but data represents a single analysis. Please provide the quantitative ROI analysis, demonstrating that the observed example is representative. This is particularly important since the authors claim very specific changes in pattern of Ca signaling between mIDA and FLG22 treatments (Line 148).

(8) Figure 4: please use alternative scaling on the Y axis instead of breaks.

This has now been fixed.

(9) Figure 5: it is not clear what n=4 refers to when the authors state three independent replicates. In figure 6 they state 4 technical reps and 3 biological reps. Please ensure this is similar across all descriptions.

We have now ensured the correct information in all descriptions.

Author Response

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

eLife assessment

This study presents valuable findings on Legionella pneumophila effector proteins that target host vesicle trafficking GTPases during infection and more specifically modulate ubiquitination of the host GTPase Rab10. The evidence supporting the claims of the authors is solid, although it remains unclear how modification of the GTPase Rab10 with ubiquitin supports Legionella virulence and the impact of ubiquitination during LCV formation. The work will be of interest to colleagues studying animal pathogens as well as cell biologists in general.

We greatly appreciate the positive and valuable feedback from the editors and the reviewers. According to their suggestions, we added many new experimental data and implications of our findings in Legionella virulence in terms of the biological process of its replication niche. Please find our point-to-point responses below.

Public Reviews:

Reviewer #1 (Public Review):

In this manuscript, Kubori and colleagues characterized the manipulation of the host cell GTPase Rab10 by several Legionella effector proteins, specifically members of the SidE and SidC family. They show that Rab10 undergoes both conventional ubiquitination and noncanonical phosphoribose-ubiquitination, and that this posttranslational modification contributes to the retention of Rab10 around Legionella vacuoles.

Strengths

Legionella is an emerging pathogen of increasing importance, and dissecting its virulence mechanisms allows us to better prevent and treat infections with this organism. How Legionella and related pathogens exploit the function of host cell vesicle transport GTPases of the Rab family is a topic of great interest to the microbial pathogenesis field. This manuscript investigates the molecular processes underlying Rab10 GTPase manipulation by several Legionella effector proteins, most notably members of the SidE and SidC families. The finding that MavC conjugates ubiquitin to SdcB to regulate its function is novel, and sheds further light into the complex network of ubiquitin-related effectors from Lp. The manuscript is well written, and the experiments were performed carefully and examined meticulously.

Weaknesses

Unfortunately, in its current form this manuscript offers only little additional insight into the role of effector-mediated ubiquitination during Lp infection beyond what has already been published. The enzymatic activities of the SidC and SidE family members were already known prior to this study, as was the importance of Rab10 for optimal Lp virulence. Likewise, it had previously been shown that SidE and SidC family members ubiquitinate various host Rab GTPases, like Rab33 and Rab1. The main contribution of this study is to show that Rab10 is also a substrate of the SidE and SidC family of effectors. What remains unclear is if Rab10 is indeed the main biological target of SdcB (not just 'a' target), and how exactly Rab10 modification with ubiquitin benefits Lp infection.

Reviewer #1 (Recommendations for The Authors):

Major points of concern

(1) The authors show that SdcB increases Rab10 levels on LCVs at later times of infection and conclude that this is its main biological role. An alternative explanation may be that Rab10 is not 'the main' target of SdcB but merely 'a' target, which may explain why the effect of SdcB on Rab10 accumulation on LCV is only detectable after several hours of infection. An unbiased omics-based approach to identify the actual host target(s) of SdcB may be needed to confirm that Rab10 modification by SdcB is biologically relevant.

We totally agree with your comment that SdcB should have multiple targets considering the abundance of ubiquitin observed on the LCVs when SdcB was expressed (Figure 3). However, the effect of SdcB on Rab10 accumulation at the later time point (7 h) (current Figure 4e) was well supported by the new data showing that the SdcB-mediated ubiquitin conjugation to Rab10 was highly detected at this time point (new Figure 4c). We have tried the comprehensive search of interaction partners of the ANK domain of SdcB. This analysis is planned to be included in our on-going study. We therefore decided not to add the data in this manuscript.

(2) The authors show that Rab10 within cell lysate is ubiquitinated and conclude that ubiquitination of Rab10 is directly responsible for its retention on the LCV. What is the underlying molecular mechanism for this retention? Are GAP proteins prevented from binding and deactivating Rab10. This may be worth testing.

It would be a fantastic hypothesis that a Rab10GAP is involved in the regulation of Rab10 localization on the LCV. However, as far as we know, GAP proteins against Rab10 have not been identified yet. It should be an important issue to be addressed when a Rab10GAP will be found.

(3) Related to this, an alternative explanation would be that Rab10 retention is an indirect effect where inactivators of Rab10, such as host cell GAP proteins, are the main target of SidE/C family members and sent for degradation (see point #1). Can the authors show that Rab10 on the LCV is indeed ubiquitinated?

The possible involvement of a putative Rab10GAP is currently untestable as it is not known. To address whether Rab10 located on the LCV is ubiquitinated nor not, we conducted the critical experiments using active Rab10 (QL) and inactive Rab10 (TN) (new Figure 4a, new Figure 4-figure supplement 1). As revealed for Rab1 (Murata et al., Nature Cell Biol. 2006; Ingmundson et al., Nature 2007), Rab10 is expected to be recruited to the LCV as a GDPbound inactive form and converted to a GTP-bound active form on the LCV. The new results clearly demonstrated that GTP-locked Rab10QL is preferentially ubiquitinated upon infection, strongly supporting the model; Rab10 is ubiquitinated “on the LCV” by the SidE and SidC family ligases.

(4) Also, on what residue(s) is Rab10 ubiquitinated? Jeng et. al. (Cell Host Microbe, 2019, 26(4): 551-563)) suggested that K102, K136, and K154 of Rab10 are modified during Lp infection. How does substituting those residues affect the residency of Rab10 on LCVs? Addressing these questions may ultimately help to uncover if the growth defect of a sidE gene cluster deletion strain is due to its inability to ubiquitinate and retain Rab10 on the LCV.

Thank you for the suggestion. We conducted mutagenesis of the three Lys residues of Rab10 and applied the derivative on the ubiquitination analysis (new Figure 1-figure supplement 1). The Lys substitution to Ala residues did not abrogate the ubiquitination upon Lp infection. This result indicates that ubiquitination sites are present in the other residue(s) including the PR-ubiquitination site(s), raising possibility that disruption of sidE genes would be detrimental for intracellular growth of L. pneumophila because of failure of Rab10 retention.

(5) The authors proposed that "the SidE family primarily contributes towards ubiquitination of Rab10". In this case, what is the significance of SdcB-mediated ubiquitination of Rab10 during Lp infection?

We found that the major contribution of SdcB is retention of Rab10 until the late stage of infection. This claim was supported by our new data (new Figure 4c) as mentioned above (response to comment #1).

(6) The contribution of SdcB to ubiquitination of Rab10 relative to SidC and SdcA is unclear. SidC is shown to be unaffected by MavC. In this case, SidC can ubiquitinate Rab10 regardless of the regulatory mechanism of SdcB by MavC. This is not further being examined or discussed in the manuscript.

The effect of intrinsic MavC is apparent at the later stage (9 h) of infection (Figure 7c) when SdcB gains its activity (see above). We therefore do not think that the contribution of MavC on the SidC/SdcA activities, which are effective in the early stage, would impact on Rab10 localization. However, without specific experiments addressing this issue, possible MavC effects on SidC/SdcA would be beyond the scope in this manuscript.

(7) When is Rab10 required during Lp infection? The authors showed that Rab10 levels at LCV are rather stable from 1hr to 7hr post infection. If MavC regulates the activity of SdcB, when does this occur?

While the Rab10 levels on the LCV (~40 %) are stable during 1-7 h post infection (Figure 2b), it reduced to ~20% at 9 h after infection (Figure 7c) (the description was added in lines 304-306). Rab10 seems to be required for optimal LCV biogenesis over the early to late stages, but may not be required at the maturation stage (9 h). We validated the effect of MavC on the Rab10 localization at this time point (Figure 7c). These observations allowed us to build the scheme described in Figure 7d. We revised the illustration in new Figure 7d according to the helpful suggestions from both the reviewers.

(8) Previous analyses by MS showed that ubiquitination of Rab10 in Lp-infected cells decreases over time (from 1 hpi to 8 hpi - Cell Host Microbe, 2019, 26(4): 551-563). How does this align with the findings made here that Rab10 levels on the LCV and likely its ubiquitination levels increase over time?

We carefully compared the Rab10 ubiquitination at 1 h and 7 h after infection (new Figure 1figure supplement 1b). This analysis showed that the level of its ubiquitination decreased over time in agreement with the previous report. Nevertheless, Rab10 was still significantly ubiquitinated at 7 h, which we believe to cause the sustained retention of Rab10 on the LCV at this time point. We added the observation in lines 146-148.

(9) Polyubiquitination of Rab10 was not detected in cells ectopically producing SdcB and SdeA lacking its DUB domain (Figure 7 - figure supplement 2). Does SdcB actually ubiquitinate Rab10 (see also point #5)? Along the same line, it is curious to find that the ubiquitination pattern of Rab10 is not different for LpΔsidC/ΔsdcA compared to LpΔsidC/dsdcA/dsdcB (Figure 1C). The actual contribution of SdcB to ubiquitinating Rab10 compared to SidC/SdcA thus needs to be clarified.

Thank you for the important point. We currently hypothesize that SidC/SdcA/SdcB-mediated ubiquitin conjugation can occur only in the presence of PR-ubiquitin on Rab10 (either directly on the PR-ubiquitin or on other residue(s) of Rab10). Failure to detect the polyubiquitination in the transfection condition (Figure 7-figure supplement 2) suggests that this specific ubiquitin conjugation can occur in the restricted condition, i.e. only “on the LCV”. We added this description in the discussion section (lines 334-335). No difference between the ΔsidCΔsdcA and ΔsidCΔsdcAΔsdcB strains (Figure 1C, 1h infection) can be explained by the result that SdcB gains activity at the later stages (see above).

Minor comments In Figure 4b and 7b, the authors show a quantification of "Rab10-positive LCVs/SdcBpositive LCVs". Whys this distinction? It begs the question what the percentile of Rab10positive/SdcB-negative LCVs might be?

We took this way of quantification as we just wanted to see the effect of SdcB on the Rab10 localization. To distinguish between SdcB-positive and negative LCVs, we would need to rely on the blue color signals of DAPI to visualize internal bacteria, which we thought to be technically difficult in this specific analysis.

The band of FLAG-tagged SdcB was not detected by immunoblot using anti-FLAG antibody (Figure 5). The authors hypothesized that "disappearance of the SdcB band can be caused by auto-ubiquitination, as SdcB has an ability to catalyze auto-ubiquitination with a diverse repertoire of E2 enzymes. This can be easily confirmed by using MG-132 to inhibit proteasomal degradation of polyubiquitinated substrates.

We conducted the experiment using MG-132 as suggested and found that proteasomal degradation is not the cause of the disappearance of the band (new Figure 5-figure supplement 2, added description in lines 228-233). SdcB is actually not degraded. Instead, its polyubiquitination causes its apparent loss by distributing the SdcB bands in the gel.

In Figure 5F, the authors mentioned that "HA-UbAA did not conjugate to SdcB", whereas "shifted band detected by FLAG probing plausibly represents conjugation of cellular intrinsic Ub". The same argument was made in Figure 6B. These claims should be confirmed by immunoblot using anti-Ub antibody.

Thank you. We added the data using anti-Ub antibody (P4D1) (Figure 6f, new third panel).

Figure 7A: In cell producing MavC, SdcB is clearly present on LCV. However, in Figure 5A, SdcB was not detected by immunoblot in cells ectopically expressing MavC-C74A. What is the interpretation for these results?

SdcB was not degraded in the cells, but just its apparent molecular weight shift occurred by polyubiquitination (see above). The detection of SdcB in the IF images (Figure 7a) supported this claim.

Reviewer #2 (Public Review):

This manuscript explores the interplay between Legionella Dot/Icm effectors that modulate ubiquitination of the host GTPase Rab10. Rab10 undergoes phosphoribosyl-ubiquitination (PR-Ub) by the SidE family of effectors which is required for its recruitment to the Legionella containing vacuole (LCV). Through a series of elegant experiments using effector gene knockouts, co-transfection studies and careful biochemistry, Kubori et al further demonstrate that:

(1) The SidC family member SdcB contributes to the polyubiquitination (poly-Ub) of Rab10 and its retention at the LCV membrane.

(2) The transglutaminase effector, MavC acts as an inhibitor of SdcB by crosslinking ubiquitin at Gln41 to lysine residues in SdcB.

Some further comments and questions are provided below.

(1) From the data in Figure 1, it appears that the PR-Ub of Rab10 precedes and in fact is a prerequisite for poly-Ub of Rab10. The authors imply this but there's no explicit statement but isn't this the case?

Yes, we think that it is the case. We revised the description in the text accordingly (lines 326327).

(2) The complex interplay of Legionella effectors and their meta-effectors targeting a single host protein (as shown previously for Rab1) suggests the timing and duration of Rab10 activity on the LCV is tightly regulated. How does the association of Rab10 with the LCV early during infection and then its loss from the LCV at later time points impact LCV biogenesis or stability? This could be clearer in the manuscript and the summary figure does not illustrate this aspect.

Thank you for pointing the important issue. Association of Rab10 with the LCV is thought to be beneficial for L. pneumophila as it is the identified factor which supports bacterial growth in cells (Jeng et al., 2019). We speculate that its loss from the LCV at the later stage of infection would also be beneficial, since the LCV may need to move on to the maturation stage in which a different membrane-fusion process may proceed. As this is too speculative, we gave a simple modification on the part of discussion section (lines 356-358). We also modified the summary figure (revised Figure 7d) as illustrated with the time course.

(3) How do the activities of the SidE and SidC effectors influence the amount of active Rab10 on the LCV (not just its localisation and ubiquitination)

We agree that it is an important point. We tested the active Rab10 (QL) and inactive Rab10 (TN) for their ubiquitination and LCV-localization profiles (new Figure 4ab, new Figure 4figure supplement 1 and 2). These analyses led us to the unexpected finding that the active form of Rab10 is the preferential target of the effector-mediated manipulation. See also our response to Reviewer 1’s comment #3. Thank you very much for your insightful suggestion.

(4) What is the fate of PR-Ub and then poly-Ub Rab10? How does poly-Ub of Rab10 result in its persistence at the LCV membrane rather than its degradation by the proteosome?

We have not revealed the molecular mechanism in this study. We believe that it is an important question to be solved in future. We added the sentence in the discussion section (lines 376378).

(5) Mutation of Lys518, the amino acid in SdcB identified by mass spec as modified by MavC, did not abrogate SdcB Ub-crosslinking, which leaves open the question of how MavC does inhibit SdcB. Is there any evidence of MavC mediated modification to the active site of SdcB?

The active site of SdcB (C57) is required for the modification (Figure 5b), but it is not likely to be the target residue, as the MavC transglutaminase activity restricts the target residues to Lys. It would be expected that multiple Lys residues on SdcB can be modified by MavC to disturb the catalytic activity.

(6) I found it difficult to understand the role of the ubiquitin glycine residues and the transglutaminase activity of MavC on the inhibition of SdcB function. Is structural modelling using Alphafold for example helpful to explain this?

We conducted the Alphafold analysis of SdcB-Ub. Unfortunately, when the Glycine residues of Ub was placed to the catalytic pocket of SdcB, Q41 of Ub did not fit to the expected position of SdcB (K518). Probably, the ternary complex (MavC-Ub-SdcB) would cause the change of their entire conformation. A crystal structure analysis or more detailed molecular modeling would be required to resolve the issue.

(7) Are the lys mutants of SdbB still active in poly-Ub of Rab10?

We performed the experiment and found that K518R K891R mutant of SdcB still has the E3 ligase activity of similar level with the wild-type upon infection (new Figure 6-figure supplement 2) (lines 283-284). The level was actually slightly higher than that of the wildtype. This result may suggest that the blocking of the modification sites can rescue SdcB from MavC-mediated down regulation.

Reviewer #2 (Recommendations For The Authors):

see above

Author Response

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

eLife assessment

This valuable study applies voltage clamp fluorometry to provide new information about the function of serotonin-gated ion channels 5-HT3AR. The authors convincingly investigate structural changes inside and outside the orthosteric site elicited by agonists, partial agonists, and antagonists, helping to annotate existing cryo-EM structures. This work confirms that the activation of 5-HT3 receptors is similar to other members of this well-studied receptor superfamily. The work will be of interest to scientists working on channel biophysics but also drug development targeting ligand-gated ion channels.

Public Reviews:

All reviewers agreed that these results are solid and interesting. However, reviewers also raised several concerns about the interpretation of the data and some other aspects related to data analysis and discussion that should be addressed by the authors. Essential revisions should include:

(1) Please try to explicitly distinguish between a closed pore and a resting or desensitized state of the pore, to help in clarity.

(2) Add quantification of VCF data (e.g. sensor current kinetics, as suggested by reviewer #2) or better clarify/discuss the VCF quantitative aspects that are taken into account to reach some conclusions (reviewer #3).

(3) Review and add relevant foundational work relevant to this study that is not adequately cited.

(4) Revise the text according to all recommendations raised by the reviewers and listed in the individual reviews below.

We have revised the text to address all four points. See the answers to referees’ recommendations.

Reviewer #1 (Public Review):

Summary:

This study brings new information about the function of serotonin-gated ion channels 5-HT3AR, by describing the conformational changes undergoing during ligands binding. These results can be potentially extrapolated to other members of the Cys-loop ligand-gated ion channels. By combining fluorescence microscopy with electrophysiological recordings, the authors investigate structural changes inside and outside the orthosteric site elicited by agonists, partial agonists, and antagonists. The results are convincing and correlate well with the observations from cryo-EM structures. The work will be of important significance and broad interest to scientists working on channel biophysics but also drug development targeting ligand-gated ion channels.

Strengths:

The authors present an elegant and well-designed study to investigate the conformational changes on 5-HT3AR where they combine electrophysiological and fluorometry recordings. They determined four positions suitable to act as sensors for the conformational changes of the receptor: two inside and two outside the agonist binding site. They make a strong point showing how antagonists produce conformational changes inside the orthosteric site similarly as agonists do but they failed to spread to the lower part of the ECD, in agreement with previous studies and Cryo-EM structures. They also show how some loss-of-function mutant receptors elicit conformational changes (changes in fluorescence) after partial agonist binding but failed to produce measurable ionic currents, pointing to intermediate states that are stabilized in these conditions. The four fluorescence sensors developed in this study may be good tools for further studies on characterizing drugs targeting the 5-HT3R.

Weaknesses:

Although the major conclusions of the manuscript seem well justified, some of the comparison with the structural data may be vague. The claim that monitoring these silent conformational changes can offer insights into the allosteric mechanisms contributing to signal transduction is not unique to this study and has been previously demonstrated by using similar techniques with other ion channels.

The referee emphasizes that “some of the comparison with the structural data may be vague”. To better illustrate the structural reorganizations seen in the cryo-EM structures and that are used for VCF data interpretation, we added a new supplementary figure 3. It shows a superimposition of Apo, setron and 5-HT bond structures, with reorganization of loop C and Cys-loop consistent with VCF data.

Reviewer #2 (Public Review):

Summary:

This study focuses on the 5-HT3 serotonin receptor, a pentameric ligand-gated ion channel important in chemical neurotransmission. There are many cryo-EM structures of this receptor with diverse ligands bound, however assignment of functional states to the structures remains incomplete. The team applies voltage-clamp fluorometry to measure, at once, both changes in ion channel activity, and changes in fluorescence. Four cysteine mutants were selected for fluorophore labeling, two near the neurotransmitter site, one in the ECD vestibule, and one at the ECD-TMD junction. Agonists, partial agonists, and antagonists were all found to yield similar changes in fluorescence, a proxy for conformational change, near the neurotransmitter site. The strength of the agonist correlated to a degree with propagation of this fluorescence change beyond the local site of neurotransmitter binding. Antagonists failed to elicit a change in fluorescence in the vestibular the ECD-TMD junction sites. The VCF results further turned up evidence supporting intermediate (likely pre-active) states.

Strengths:

The experiments appear rigorous, the problem the team tackles is timely and important, the writing and the figures are for the most part very clear. We sorely need approaches orthogonal to structural biology to annotate conformational states and observe conformational transitions in real membranes- this approach, and this study, get right to the heart of what is missing.

Weaknesses:

The weaknesses in the study itself are overall minor, I only suggest improvements geared toward clarity. What we are still missing is application of an approach like this to annotate the conformation of the part of the receptor buried in the membrane; there is important debate about which structure represents which state, and that is not addressed in the current study.

Reviewer #3 (Public Review):

Summary:

The authors have examined the 5-HT3 receptor using voltage clamp fluorometry, which enables them to detect structural changes at the same time as the state of receptor activation. These are ensemble measurements, but they enable a picture of the action of different agonists and antagonists to be built up.

Strengths:

The combination of rigorously tested fluorescence reporters with oocyte electrophysiology is a solid development for this receptor class.

Weaknesses:

The interpretation of the data is solid but relevant foundational work is ignored. Although the data represent a new way of examining the 5-HT3 receptor, nothing that is found is original in the context of the superfamily. Quantitative information is discussed but not presented.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Here are some suggestions that may help to improve the manuscript: - Page 6, point 2), typo: "L131W is positioned more profound in each ECD, its side chain (...)"

“profound” have been corrected into “profoundly”

• Fig 1C: Why not compare 5-HT responses for the four sensors studied? If the reason is the low currents elicited by 5-HT on I160C/Y207W sensor, could you comment on this effect that is not observed for the other full agonist tested (mCPBG)?

The point of this figure (Fig 1G) is to show currents that desensitize to follow the evolution of the fluorescence signal during desensitization, that’s why for the I160C/Y207W sensor where 5-HT become a partial agonist we have judge more appropriate to use mCPBG acting as a more potent agonist to elicit currents with clear desensitization component. We have added a sentence in the legend of the figure to explain this choice more clearly.

• Page 9, paragraph 2: "However, concentration-response curves on V106C/L131W show a small yet visible decorrelation of fluorescence and current (...)" Statistical analysis on EC50c and EC50f will help to see this decorrelation.

Statistical analysis (unpaired t test) has been added to figure 3 panel A.

• Page 10, paragraph 1: the authors describe how "different antagonists promote different degrees of local conformational changes". Does it have any relation to the efficacy or potency of these antagonists? Is there any interpretation for this result?

Since setrons are competitive antagonists, the concept of efficacy of these molecules is unclear. Concerning potency, no correlation between affinity and fluorescence variation is observed. For instance, ondansetron and alosetron bind with similar nanomolar affinity to the 5-HT3R (Thompson & Lummis Curr Pharm Des. 2006;12(28):3615-30) but elicit different fluorescence variations on both S204C and I160C/Y207W sensors.

• Fig. 1 panel A, graph to far right: axis label is cut ("current (uA)/..."). Colors of graph A - right are not clearly distinguishable e.g. cyan from green.

The fluorescent green color that describes the mutant has been changed into limon color which is more clearly distinguishable from cyan.

• Why is R219C/F142W not selected in the study? Are the signals comparable to the chosen R219C/F142W?

We have chosen not to select R219C/F142W because the current elicited by this construct was lower than the current elicited by the construct R219C/Y140W. Moreover, the residue F142 belongs to the FPF motif from Cys-loop that is essential for gating (Polovinkin et al, 2018, Nature).

• Fig. 1 legend typo: "mutated in tryptophan”

“in” has been changed by “into”

• Fig. 2: yellow color (graphs in panel B) is very hard to read.

Yellow color has been darkened to yellow/brown to allow easy reading.

• Fig. 4 is too descriptive and undermines the information of the study. It could be improved e.g. by representing specific structures or partial structures involved. As an additional minor comment, some colors in the figure are hard to differentiate, e.g. magenta and purple.

We have added relevant specific structures involved, namely loop C, the Cys-loop and pre-M1 loop to clarify. The intensity of magenta and purple has been increased to help differentiate the two sensor positions.

• Fig S1C: it is confusing to see the same color pattern for the single mutants without the W. I would recommend to label each trace to make it clearer.

Labelling of the traces corresponding to the single mutants has been added.

• Fig S2: Indicating the statistical significance in the graph for the mutants with different desensitization properties compared to the WT receptor will help its interpretation.

The statistical significance of the difference in the desensitization properties has been added to Figure S2.

Reviewer #2 (Recommendations For The Authors):

Overall comments for the authors:

Selection of cysteine mutants and engineered Trp sites is clear and logical. VCF approach with controls for comparing the functionality of WT vs. mutants, and labeled with unlabeled receptor, is well explained and satisfying. The finding that desensitization involves little change in ECD conformation makes sense. It is somewhat surprising, at least superficially, to find that competitive antagonists promote changes in fluorescence in the same 'direction' and amplitude as strong agonists, however, this is indeed consistent with the structural biology, and with findings from other groups testing different labeling sites. Importantly, the team finds that antagonist-binding changes in deltaF do not spread beyond the region near the neurotransmitter site. The finding that most labeling sites in the ECD, in particular those not in/near the neurotransmitter site, fail to report measurable fluorescence changes, is noteworthy. It contrasts with findings in GlyR, as noted by the authors, and supports a mechanism where most of each subunit's ECD behaves as a rigid body.

Specific questions/comments:

I am confused about the sensor current kinetics. Results section 2) states that all sensors share the same current desensitization kinetics, while Results section 5) states that the ECD-TMD site and the vestibule site sensors exhibit faster desensitization. SF1C, right-most panel of R219C suggests the mutation and/or labeling here dramatically changes apparent activation and deactivation rates measured by TEVC. Both activation and deactivation upon washout appear faster in this one example. Data for desensitization are not shown here but are shown in aggregate in earlier panels. It is a bit surprising that activation and deactivation would both change but no effect on desensitization. Indeed, it looks like, in Fig. 1G, that desensitization rate is not consistent across all constructs. Can you please confirm/clarify?

TEVC and VCF recordings in this study show a significant variability concerning both the apparent desensitization and desactivation kinetics. This is illustrated concerning desensitization in TEVC experiments in figure S2, where the remaining currents after 45 secondes of 5-HT perfusion and the rate constants of desensitization are measured on different oocytes from different batches. Therefore, the differences in desensitization kinetics shown in fig 1.G are not significant, the aim of the figure being solely to illustrate that no variation of fluorescence is observed during the desensitization phase. A sentence in the legend of fig 1.G has been added to precise this point. We also revised the first paragraph of result section 5, clearly stating that the slight tendency of faster desensitization of V106C/L131W and R219C/Y140W sensors is not significant.

An alternative to the conclusion-like title of Results section 2) is that the ECD (and its labels) does not undergo notable conformational changes between activated and desensitized states.

This is a good point and we have added a sentence at the end of results section 2 to present this idea.

I find the discussion paragraph on partial agonist mechanisms, starting with "However," to be particularly important but at times hard to follow. Please try to revise for clarity. I am particularly excited to understand how we can understand/improve assignments of cryo-EM structures using the VCF (or other) approaches. As examples of where I struggled, near the top of p. 11, related to the partial agonist discussion, there is an assumption about the pore being either activated, or resting. Is it not also possible that partial agonists could stabilize a desensitized state of the pore? Strictly speaking, the labeling sites and current measurements do not distinguish between pre-active resting and desensitized channel conformations/states. However, the cryo-EM structures can likely help fill in the missing information there- with all the normal caveats. Please try to explicitly distinguish between a closed pore and a resting or desensitized state of the pore, to help in clarity.

We have revised the section, and hope it is clearer now. We notably state more explicitly the argument for annotation of partial agonist bound closed structures as pre-active, mainly from kinetic consideration of VCF experiments. We also mention and cite a paper by the Chakrapani group published the 4th of January 2024 (Felt et al, Nature Communication), where they present the structures of the m5HT3AR bound to partial agonists, with a set of conformations fully consistent with our VCF data.

This statement likely needs references: "...indirect experiments of substituted cysteine accessibility method (SCAM) and VCF experiments suggested that desensitization involves weak reorganizations of the upper part of the channel that holds the activation gate, arguing for the former hypothesis."

Reference Polovinkin et al, Nature, 2018, has been added.

I respectfully suggest toning down this language a little bit: "VCF allowed to characterize at an unprecedented resolution the mechanisms of action of allosteric effectors and allosteric mutations, to identify new intermediate conformations and to propose a structure-based functional annotation of known high-resolution structures." This VCF stands strongly without unclear claims about unprecedented resolution. What impresses me most are the findings distinguishing how agonists/partial agonists/antagonists share a conserved action in one area and not in another, the observations consistent with intermediate states, and the efforts to integrate these simultaneous current and conformation measurements with the intimidating array of EM structures.

We thank the referee for his positive comments. We have removed “unprecedented resolution” and revised the sentences.

It is beyond the scope of the current study, but I am curious what the authors think the hurdles will be to tracking conformation of the pore domain- an area where non-cryo-EM based conformational measurements are sorely needed to help annotate the EM structures.

We fully agree with the referee that structures of the TMD are very divergent between the various conditions depending on the membrane surrogate. We are at the moment working on this region by VCF, incorporating the fluorescent unnatural amino acid ANAP.

Minor:

(1) P. 5, m5-HT3R: Please clarify that this refers to the mouse receptor, if that is correct.

OK, “mouse” has been added.

(2) Fig. 1D, I suggest moving the 180-degree arrow to the right so it is below but between the two exterior and vestibular views.

Ok, it has been done.

(3) Please add a standard 2D chemical structure of MTS-TAMRA, and TAMRA attached to a cysteine, to Fig 1.

A standard chemical structure has been added for the two isomers of MTS-TAMRA.

(4) Please label subpanels in Fig. 1G with the identity of the label site.

The subpanels have been labelled.

Reviewer #3 (Recommendations For The Authors):

This is solid work but I mainly have suggestions about placing it in context.

(1) Abstract "Data show that strong agonists promote a concerted motion of all sensors during activation, "

The concept of sensors here is the fluorescent labels? I did not find this meaningful until I read the significance statement.

We have specified “fluorescently-labelled” before sensors in the abstract.

(2) p4 "each subunit in the 5-HT3A pentamer...." this description would be identical for any pentameric LGIC so the authors should beware of a misleading specificity. This goes for other phrases in this paragraph. However, the summary of the 5HT specific results is very good.

About the description of the structure, we added “The 5-HT3AR displays a typical pLGIC structure, where….”.

(3) This paper is very nicely put together and generally explains itself well. The work is rigorous and comprehensive. But the meaning of quenching (by local Trp) seems straightforward, but it is not made explicit in the paper. Why doesn't simple labelling (single Cys) at this site work? And can we have a more direct demonstration of the advantage of including the Trp (not in the supplementary figure?) All this information is condensed into the first part of figure 1 (the graph in Figure 1A). Figure 1 could be split and the principle of the introduced quenching could be more clearly shown

detailed in a few more sentences the principle of the TrIQ approach. In addition, to be more explicit, the significative differences of fluorescence comparing sensors with and without tryptophan have been added in Figure 1, panel screening and a sentence have been added in the legend of this figure.

(4) p10 "VCF measurements are also remarkably coherent with the atomic structures showing an open pore (so called F, State 2 and 5-HT asymmetric states), "

This statement is intriguing. What do these names or concepts represent? Are they all the same thing? Where do the names come from? What is meant here? Three different concepts, all consistent? Or three names for the same concept?

We have tried to clarify the statement by making reference to the PDB of the structures.

(5) "Fluorescence and VCF studies identified similar intermediate conformations for nAChRs, ⍺1-GlyRs and the bacterial homolog GLIC(21,32-35). "

Whilst this is true, the motivation for such ideas came from earlier work identifying intermediates from electrophysiology alone (such as the flip state (Burzomato et al 2004), the priming state (Mukhatsimova 2009) and the conformational wave in ACh channels grosman et al 2000). It would be appropriate to mention some of this earlier work.

We have incorporated and described these references in the discussion. Of note, we fully quoted these references in our previous papers on the subject (Menny 2017, Lefebvre 2021, Shi 2023), but the referee is right in asking to quote them again.

(6) "A key finding of the study is the identification of pre-active intermediates that are favored upon binding of partial agonists and/or in the presence of loss-of-function mutations. "

Even more fundamental, the idea of a two-state equilibrium for neurotransmitter receptors was discarded in 1957 according to the action of partial agonists.

DEL CASTILLO J, KATZ B (1957) Interaction at end-plate receptors between different choline derivatives. Proc R Soc Lond B Biol Sci

So to discover this "intermediate" - that is, bound but minimal activity - in the present context seems a bit much. It is a big positive of this paper that the results are congruent with our expectations, but I cannot see value in posing the results as an extension of the 2-state equilibrium (for which there are anyway other objections).

As for intermediates being favoured by loss of function mutations, this concept is already well established in glycine receptors (Plested et al 2007, Lape et al 2012) and doubtless in other cases too.

I do get the point that the authors want to establish a basis in 5-HT3 receptors, but these previous works suggest the results are somewhat expected. This should be commented on.

We also agree. We replace “key finding” by “key observation”, quote most of the references proposed, and explicitly conclude that “The present work thus extends this idea to the 5HT3AR, together with providing structural blueprints for cryo-EM structure annotation”.

(7) "In addition, VCF data allow a quantitative estimate of the complex allosteric action of partial agonists, that do not exclusively stabilize the active state and document the detailed phenotypes of various allosteric mutations."

Where is this provided? If the authors are not motivated to do this, I have some doubts that others will step in. If it is not worth doing, it's probably not worth mentioning either.

Language has been toned down by “In addition, VCF data give insights in the action of partial agonists, that do not exclusively stabilize the active state and document the phenotypes of various allosteric mutations."

(8) Figure 1G please mark which construct is which.

This has been added into Figure 1G

Author Response

Provisional response

We would like to thank the reviewers for taking the time to review our manuscript, for providing useful suggestions for improvement, and for highlighting the significance of our approach.

Reviewer #1 (Public Review):

Summary:

The authors demonstrate that it is possible to carry out eQTL experiments for the model eukaryote S. cerevisiae, in "one pot" preparations, by using single-cell sequencing technologies to simultaneously genotype and measure expression. This is a very appealing approach for investigators studying genetic variation in single-celled and other microbial systems, and will likely inspire similar approaches in non-microbial systems where comparable cell mixtures of genetically heterogeneous individuals could be achieved.

Strengths:

While eQTL experiments have been done for nearly two decades (the corresponding author's lab are pioneers in this field), this single-cell approach creates the possibility for new insights about cell biology that would be extremely challenging to infer using bulk sequencing approaches. The major motivating application shown here is to discover cell occupancy QTL, i.e. loci where genetic variation contributes to differences in the relative occupancy of different cell cycle stages. The authors dissect and validate one such cell cycle occupancy QTL, involving the gene GPA1, a G-protein subunit that plays a role in regulating the mating response MAPK pathway. They show that variation at GPA1 is associated with proportional differences in the fraction of cells in the G1 stage of the cell cycle. Furthermore, they show that this bias is associated with differences in mating efficiency.

We thank the reviewer for recognizing the strengths of our overall approach and our dissection of the functional consequences of the W82R variant of GPA1.

Weaknesses:

While the experimental validation of the role of GPA1 variation is well done, the novel cell cycle occupancy QTL aspect of the study is somewhat underexploited. The cell occupancy QTLs that are mentioned all involve loci that the authors have identified in prior studies that involved the same yeast crosses used here. It would be interesting to know what new insights, besides the "usual suspects", the analysis reveals. For example, in Cross B there is another large effect cell occupancy QTL on Chr XI that affects the G1/S stage. What candidate genes and alleles are at this locus?

We thank the reviewer for this suggestion. We plan to expand the section on cell cycle occupancy QTL in our revision.

And since cell cycle stages are not biologically independent (a delay in G1, could have a knock-on effect on the frequency of cells with that genotype in G1/S), it would seem important to consider the set of QTLs in concert.

We thank the reviewer for this suggested clarification. In our revision, we will clarify that the cell cycle occupancy phenotype represents the proportion of cells assigned to a given stage. As the reviewer correctly notes, a change in the proportion of cells in one stage may alter the proportion of cells in other stages, and this could result in cell cycle occupancy QTL for multiple stages. We will make efforts to consider the cell cycle occupancy QTLs in concert in the revised manuscript.

Reviewer #2 (Public Review):

Boocock and colleagues present an approach whereby eQTL analysis can be carried out by scRNA-Seq alone, in a one-pot-shot experiment, due to genotypes being able to be inferred from SNPs identified in RNA-Seq reads. This approach obviates the need to isolate individual spores, genotype them separately by low-coverage sequencing, and then perform RNA-Seq on each spore separately. This is a substantial advance and opens up the possibility to straightforwardly identify eQTLs over many conditions in a cost-efficient manner. Overall, I found the paper to be well-written and well-motivated, and have no issues with either the methodological/analytical approach (though eQTL analysis is not my expertise), or with the manuscript's conclusions.

We thank the reviewer for recognizing the significant contributions our work makes to the field.

393 segregant experiment:

For the experiment with the 393 previously genotyped segregants, did the authors examine whether averaging the expression by genotype for single cells gave expression profiles similar to the bulk RNA-Seq data generated from those genotypes? Also, is it possible (and maybe not, due to the asynchronous nature of the cell culture) to use the expression data to aid in genotyping for those cells whose genotypes are ambiguous? I presume it might be if one has a sufficient number of cells for each genotype, though, for the subsequent one-pot experiments, this is a moot point.

We thank the reviewer for this comment. While we could expand the analysis along these lines, this is not relevant for the subsequent one-pot eQTL experiments, as the reviewer notes, and is therefore beyond the scope of the manuscript. We will make the data available so that anyone interested can try these analyses.

Figure 1B:

Is UMAP necessary to observe an ellipse/circle - I wouldn't be surprised if a simple PCA would have sufficed, and given the current discussion about whether UMAP is ever appropriate for interpreting scRNA-Seq (or ancestry) data, it seems the PCA would be a preferable approach. I would expect that the periodic elements are contained in 2 of the first 3 principal components. Also, it would be nice if there were a supplementary figure similar to Figure 4 of Macosko et al (PMID 26000488) to indeed show the cell cycle dependent expression.

We thank the reviewer for this comment. We too have been following the debate on the utility of UMAP for scRNA-seq, and in our revision we will provide an alternative visualization of the cell cycle. We will also generate a supplementary figure similar to Figure 4 of Macosko et al. to visualize cell-cycle-dependent gene expression.

Aging, growth rate, and bet-hedging:

The mention of bet-hedging reminded me of Levy et al (PMID 22589700), where they saw that Tsl1 expression changed as cells aged and that this impacted a cell's ability to survive heat stress. This bet-hedging strategy meant that the older, slower-growing cells were more likely to survive, so I wondered a couple of things. It is possible from single-cell data to identify either an aging, or a growth rate signature? A number of papers from David Botstein's group culminated in a paper that showed that they could use a gene expression signature to predict instantaneous growth rate (PMID 19119411) and I wondered if a) this is possible from single-cell data, and b) whether in the slower growing cells, they see markers of aging, whether these two signatures might impact the ability to detect eQTLs, and if they are detected, whether they could in some way be accounted for to improve detection.

We thank the reviewer for this comment and suggested analyses. We are not sure whether one can see gene expression signatures of aging in yeast scRNA-seq data. We believe that such analyses are beyond the scope of this work, but we will make the data available so that anyone interested can try them.

AIL vs. F2 segregants:

I'm curious if the authors have given thought to the trade-offs of developing advanced intercross lines for scRNA-Seq eQTL analysis. My impression is that AIL provides better mapping resolution, but at the expense of having to generate the lines. It might be useful to see some discussion on that.

We thank the reviewer for their comment. We will include some discussion of the trade-offs of different experimental designs in our revision.

10x vs SPLit-Seq

10x is a well established, but fairly expensive approach for scRNA-Seq - I wondered how the cost of the 10x approach compares to the previously used approach of genotyping segregants and performing bulk RNA-Seq, and how those costs would change if one used SPLiT-Seq (see PMID 38282330).

We will provide some ballpark estimates of the costs, and we will discuss the trade-offs of different scRNA-seq technologies in our revision

Author Response

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

We are very grateful to both Reviewers, the Reviewing Editor and the Senior Editor for carefully reviewing our manuscript and for providing useful comments and suggestions that further improved the quality of our work. We appreciate that our work is perceived to substantially advance the understanding of osteoblast migration and that the experiments are found to be rigorous and to provide conclusive evidence. We also look forward to reaching a broad audience in the field. Below we provide a point-by-point response to each suggestion made by the reviewers and explain how we included their recommendations in the revised manuscript.

Public Reviews

Reviewer #1 (Public Review):

Summary:

The authors were trying to achieve that Tgif1 expression is regulated by EAK1/2 and PTH in a timedependent manner, and its roles in suppressing Pak3 for facilitating osteoblast adhesion. The authors further tried to show that the Tgif1- Pak3 signaling plays a significant role in osteoblast migration to the site of bone repair and bone remodeling.

Strengths:

• In a previous study, it was demonstrated that Tgif1 is a target gene of PTH, and the absence of Tgif1 failed to increase bone mass by PTH treatment (Saito et al., Nat Commun., 2019). In this study, the authors found that Tgif1-Pak3 signaling prompts osteoblast migration through osteoblast adhesion to prompt bone regeneration. This novel finding provides a better understanding of how Tgif1 expression in osteoblasts regulates adherence, spreading, and migration during bone healing and bone remodeling.

• The authors demonstrated that ERK1/2 and PTH regulate Tgif1 expression in a time-dependent manner and its role in suppressing Pak3 through various experimental approaches such as luciferase assay, ChIP assay, and gene silencing. These results contribute to the overall strength of the article.

We thank the reviewer for acknowledging the novelty of our findings as well as the strength of the manuscript.

Weaknesses:

• The authors need to further justify why they focused on Pak3 in the introduction by mentioning its known function for cell adhesion.

We thank the reviewer for this suggestion. We mention in the introduction that we further investigated Pak3 due to its implication in cell adhesion (page 6, lines 7-8).

• Some results indicated statistically significant but small changes. The authors need to explain in the discussion part why they believe this is the major mechanism or why there may be some other possible mechanisms.

We agree with this comment. We are confident that our work identified an important mechanism by which Tgif1 regulates cellular features of osteoblasts. However, it is certainly possible that other mechanisms may exist as well. We discuss this point in the revised manuscript (page 18, lines 16-17).

• The study does not include enough in vivo data to claim that this mechanism is crucial for bone healing and bone remodeling in vivo.

Re: We agree with this point and have modified the abstract accordingly by replacing “crucial” with “implicated in” as well as the text by changing “crucial” to “important” (page 2, line 9). Furthermore, we discuss this limitation in the revised manuscript (page 18, lines 9-14).

Reviewer #2 (Public Review):

Summary:

Bolamperti S. et al. 2023 investigate whether the expression of TG-interacting factor (Tgif1) is essential for osteoblastic cellular activity regarding morphology, adherence, migration/recruitment, and repair. Towards this end, germ-line Tgif1 deletion (Tgif1-/-) mice or male mice lacking expression of Tgif1 in mature osteoblastic and osteocytic cells (Dmp1-Cre+; Tgif1fl/fl) and corresponding controls were studied in physiological, bone anabolic, and bone fracture-repair conditions. Both Tgif1-/- and Dmp1-Cre+; Tgif1fl/fl exhibited decreased osteoblasts on cancellous bone surfaces and adherent to collagen I-coated plates. Tgif1-/- mice exhibit impaired healing in the tibial midshaft fracture model, as indicated by decreased bone volume (BV/Cal.V), osteoid (OS/BS), and low osteoblasts (number and surface). Likewise, both Tgif1-/- and Dmp1-Cre+; Tgif1fl/fl show impaired PTH 1-34, (100µg/kg, 5x/wk for 3 wks) osteoblast activation in vivo, as detected by increases in quiescent bone surfaces. Mechanistic in vitro studies then utilized primary osteoblasts isolated from Tgif1-/- mice and siRNA Tgif1 knockdown OCY454 cells to further investigate and identify the downstream Tgif1 target driving these osteoblastic impairments. In vitro, Tgif1-/- osteoblastic and Tgif1 knockdown OCY454 cells exhibit decreased migration, abnormal morphology, and decreased focal adhesions/cells. Unexpectantly though, localization assays revealed Tgif1 to primarily concentrate in the nucleus and not to co-localize with focal adhesions (paxillin, talin). Also, the expression of major focal adhesion components (paxillin, talin, FAK, Src, etc.) or the Cdc42 family was not altered by loss of Tgif1 expression. In contrast, PAK3 expression is markedly upregulated by loss of Tgif1. In silico analysis followed by mechanistic molecular assays involving ChIP, siRNA (Tgif1, PAK3), and transfection (rat PAK3 promoter) techniques show that Tgif1 physically binds to a specific site in the PAK3 promoter region. Further, the knockdown of PAK3 rescues the Tgif1-deficient abnormal morphology in OCY454 cells. This is the first study to identify the novel transcriptional repression of PAK3 by Tgif1 as well as the specific Tgif1 binding site within the PAK3 promoter.

Strengths:

This work has a plethora of strengths. The co-authors achieved their aim of eliciting the role of Tgif1 expression in osteoblastic cellular functions (morphology, spreading/attachment, migration).

Further, this work is the first to depict the novel mechanism of Tgif1 transcriptional repression of PAK3 by a thorough usage of mechanistic molecular assays (in silico analysis, ChIP, siRNA, transfection etc.). The conclusions are well supported and justified by these findings, as the appropriate controls, sample sizes (statistical power), statistics, and assays were fully utilized. The claims and conclusions are justified by the data.

Re: We are grateful to this reviewer for recognizing the novelty, strengths, and rigor of our study and for acknowledging that the data convincingly support the conclusions drawn.

Weaknesses:

The discussion section could be expanded with a few sentences regarding limitations to the current study and potential future directions.

Re: In the revised manuscript, we are discussing limitations of the work and describe possible future directions (page 18, line 9-14).

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors):

(1) The cell spreading and migration assay is quite artificial. Trypsinized osteoblasts and quiescent osteoblasts are totally different. The authors need to cite papers from other groups to justify whether the cell spreading and migration assay is appropriate to achieve the goals of this study.

Re: The reviewer is right that in vitro assays are often artificial and do not necessarily fully reflect in vivo situations. We have taken this aspect into account and discuss it in the revised manuscript (page 18, lines 9-10). In addition, we have included references from other groups who have used similar assays to study cell spreading and migration (Dejaeger M et al., 2017 and Dang et al., 2018).

(2) Page 13 Line 15: The statement "Osteoblasts are greatly impaired in the ability to migrate into the repair zone" is an overstatement. The experiments in Figure 5 do not necessarily reflect osteoblast migration activities. The authors need to rephrase the sentence or need to show observation of earlier time points (e.g., 1 week after fracture) in their bone healing experiments. The number of osteoblasts/surface in Tgif1+/+ and Tgif1-/- mice at different time points during bone healing should be a good indicator for the migration of osteoblasts to the repair site.

Re: We understand the critique that a time course or lineage tracing experiments would provide better evidence for the statement of osteoblast migration into the repair zone. To avoid overinterpretations we have removed the sentence from the revised manuscript.

(3) Page 14, Line 24: Regarding the sentence "The observation that Tgif1 is crucial for osteoblast adherence, spreading, and migration", the authors need to clearly mention this statement is based on the in vitro experiments. The animal studies are not enough to claim that the mechanism is crucial for adherence, spreading, and migration.

Re: We thank the reviewer for pointing out this limitation. We have clarified that the finding that Tgif1 is crucial for osteoblast adherence, spreading and migration was made in vitro (page 14, line 22).

(4) The authors need to demonstrate the suppression of Pak3 expression in PTH-treated mice in vivo, in addition to the in vitro culture system (Fig. 7C and 7D).

Re: We agree with the reviewer that this experiment would be very insightful. However, this is beyond the scope of the current work. Nevertheless, to take this valid point into consideration, we mention it in the discussion as potential future direction (page 18, lines 11-14).

(5) The authors need to demonstrate that the pharmacologic suppression of Pak3 in Tgif1-/- mice reduces the % of quiescent surface/BS in vivo.

Re: This point is also well taken, and we agree that a suppression of Pak3 in Tgif1-deficient mice would be very informative to support our in vitro findings. However, this may also be part of future investigations. This is emphasized in the discussion of the revised manuscript (page 18, lines 11-14).

Figures (Minor)

Fig. 1:

Fig. 1A

Arrows need to indicate a more precise position.

Re: The position of the arrows has been optimized.

Fig. 1DE

What are blue/red bars (genotypes)?

Re: The colors indicate the genotypes. A legend has been added to the revised figure.

Fig. 1K

Quantification data is needed.

Re: Thank you for this suggestion. We added a quantification of the data (Fig. 1L, M; page 8, lines 3-4; page 21, lines 5-6)

Fig. 2A

Show the representative high-magnification image of round (non-spread) cells.

Re: Representative high-magnification images (insets) are provided in the revised figure 2A.

Fig. 5

Red arrows need to indicate a more precise position.

Re: The arrows have been repositioned.

Fig. 6A, C

Red arrows need to indicate a more precise position.

Re: The arrows have been repositioned.

Reviewer #2 (Recommendations For The Authors):

(1) The microscopy images and analyses are excellent.

Re: We thank the reviewer for acknowledging the quality of our microscopy studies.

(2) Since the Tgif1-/- mouse has low osteoclast numbers, is it possible that this is a contributing factor to the delays/impairment in bone healing, given that resorption also has a role in fracture repair? Since the focus of these studies is on osteoblastic cells, this point is a little out of scope. However, would the authors consider exploring this further in the discussion section?

Re: This point is well taken by the reviewer, and we agree that osteoclasts could certainly play a role in the impaired fracture healing. To acknowledge this aspect, we followed the recommendation and discuss this aspect in the revised manuscript (page 16, lines 22-24).

Revisions

Would the authors consider slightly re-wording the title? Tgif1 suppresses PAK3 expression; however, Tgif1-deficiency leads to the unregulated elevation of PAK3 expression.

Re: Thank you for pointing this out. We agree with the reviewer and adapted the title accordingly.

Suggestions

(1) Is it possible that apoptosis and/or anoikis is being induced by Tgif1 deficiency in osteoblastic cells?

Re: We do not have data towards this direction and although Tgif1-deficient osteoblasts are overall viable and well expanding, we cannot fully exclude this possibility.

(2) For the fracture study, any differences in overall callus size? Would it be possible to perform micro-CT imaging with some of these samples?

Re: There is no difference in non-mineralized callus size between Tgif1+/+ and Tgif1-/- mice. However, there is less mineralized bone per callus area in Tgif1-/- mice, confirming an impaired osteoblast phenotype. As suggested by the reviewer, we added representative micro-CT images and the respective information to the revised manuscript (Fig 5F; pages 19-20).

(3) Fracture repair experiment-is PAK3 expression downregulated with fracture injury; and/or, is PAK3 upregulated by loss of Tgif1 expression?

Re: Unfortunately, we do not have data to answer this very interesting question and it would need to be addressed in future studies. This is mentioned in the revised discussion (page 18, lines 12-14).

(4) Fig 7F. within PTH treated cells, is the light blue SCR sphericity statistically different than the light green siTgif1 + siPAK3 ? While the statement of the "lack of both, Tgif1 and PAK3 prevented PTH-induced decrease in cell sphericity" is supported by the lack of differences between dark green vs. light green; is it also possible that this is due to the siPAK3 returning sphericity to control (scr) levels? (i.e. hitting a floor limit of detection).

Re: We thank the reviewer for this thoughtful question. There is no statistically significant difference between light blue and light green. Silencing PAK3 restores the impaired capacity to spread that occurs in the absence of Tgif1 to the level of scr controls (significant difference between dark and light red vs. dark and light green and no difference between either dark or light blue vs. dark or light green). However, unlike in the (scr) controls, in the absence of both Tgif1 and PAK3, the cells do not respond to PTH (statistically significant difference between dark and light blue, no difference between dark and light green). Based on the data, cells can reach sphericity of less than 0.2 and thus it is unlikely that sphericity is “hitting the floor level of detection” in these groups.

Author Response

We would like to thank the reviewers for their positive comments and valuable suggestions for improvements to the manuscript. We intend to revisit the discussion to clarify our interpretation of how azithromycin resistance mutations impact the transmission potential of P. falciparum and expand on the differences between mouse and human malaria. Additionally, we intend to adjust the title to better align with the revised interpretation of the main findings. These changes will be reflected in the revised manuscript to be submitted as the eLife Version of Record.

Author Response

Reviewer #1 (Public Review):

Summary:

The current study aims to quantify associations between the regular use of proton-pump inhibitors (PPI) - defined as using PPI most days of the week during the last 4 weeks at one cross-section in time - with several respiratory outcomes up to several years later in time. There are 6 respiratory outcomes included: risk of influenza, pneumonia, COVID-19, other respiratory tract infections, as well as COVID-19 severity and mortality).

Strengths:

Several sensitivity analyses were performed, including i) estimation of the e-value to assess how strong unmeasured confounders should be to explain observed effects, ii) comparison with another drug with a similar indication to potentially reduce (but not eliminate) confounding by indication.

Thank you for pointing out the strengths of our article. We also sincerely thank the reviewer for raising several concerns and providing significant suggestions to improve our manuscript. We will revise our manuscript according to our provisional responses.

Weaknesses:

(1) The main exposure of interest seems to be only measured at one time-point in time (at study enrollment) while patients are considered many years at risk afterwards without knowing their exposure status at the time of experiencing the outcome. As indicated by the authors, PPI are sometimes used for only short amounts of time. It seems biologically implausible that an infection was caused by using PPI for a few weeks many years ago.

We agree with the reviewer, and this is one of the limitations of the UK Biobank data. We might identify potential long-term PPI users by defining the users that have certain indications, since they tend to regularly take PPI for a long period rather than only short amounts of time. We will evaluate the effect modification for the subgroup of potential long-term PPI users.

(2) Previous studies have shown that by focusing on prevalent users of drugs, one often induces several biases such as collider stratification bias, selection bias through depletion of susceptible, etc.

Due to the limitations of the data from the UK Biobank, including the lack of information on the initiation of medications and close follow-up, we can only use prevalent user design to evaluate the associations between PPI use and respiratory outcomes. We will further discuss it in the limitation section.

(3) It seems Kaplan Meier curves are not adjusted for confounding through e.g. inverse probability weighting. As such the KM curves are currently not informative (or the authors need to make clearer that curves are actually adjusted for measured confounding).

We will provide Kaplan Meier curves adjusted for confounding by inverse probability weighting according to the reviewer’s suggestion.

(4) Throughout the manuscript the authors seem to misuse the term multivariate (using one model with e.g. correlated error terms to assess multiple outcomes at once) when they seem to mean multivariable.

We will correct the misused terms throughout the manuscript according to the reviewer’s suggestions.

(5) Given multiple outcomes are assessed there is a clear argument for accounting for multiple testing, which following the logic of the authors used in terms of claiming there is no association when results are not significant may change their conclusions. More high-level, the authors should avoid the pitfall of stating there is evidence of absence if there is only an absence of evidence in a better way (no statistically significant association doesn't mean no relationship exists).

We will revise our interpretation of the results, especially for those without statistically significant associations based on the reviewer’s advice.

(6) While the authors claim that the quantitative bias analysis does show results are robust to unmeasured confounding, I would disagree with this. The e-values are around 2 and it is clearly not implausible that there are one or more unmeasured risk factors that together or alone would have such an effect size. Furthermore, if one would use the same (significance) criteria as used by the authors for determining whether an association exists, the required effect size for an unmeasured confounder to render effects 'statistically non-significant' would be even smaller.

We agree with the reviewer that there might still exist one or more unmeasured risk factors that have effect sizes larger than 2. Therefore, we could not state that the results are robust to unmeasured confounding based on the current analysis, and this would be a limitation of our study. We will add the above information to the discussion section.

(7) Some patients are excluded due to the absence of follow-up, but it is unclear how that is determined. Is there potentially some selection bias underlying this where those who are less healthy stop participating in the UK biobank?

We will provide the details for the determination of absence of follow-up in the UK Biobank and illustrate whether it potentially induced selection bias.

(8) Given that the exposure is based on self-report how certain can we be that patients e.g. do know that their branded over-the-counter drugs are PPI (e.g. guardium tablets)? Some discussion around this potential issue is lacking.

In the data collection of the UK Biobank, the participants can enter the generic or trade name of the treatment on the touchscreen to match the medications they used. We will discuss this important issue in the discussion section.

(9) Details about the deprivation index are needed in the main text as this is a UK-specific variable that will be unfamiliar to most readers.

We will provide details about the deprivation index in the manuscript.

(10) It is unclear how variables were coded/incorporated from the main text. More details are required, e.g. was age included as a continuous variable and if so was non-linearity considered and how?

Age was included as a continuous variable. We will provide information on whether non-linearity was considered in our manuscript.

(11) The authors state that Schoenfeld residuals were tested, but don't report the test statistics. Could they please provide these, e.g. it would already be informative if they report that all p-values are above a certain value.

We will provide the test statistics for the Schoenfeld residuals.

(12) The authors would ideally extend their discussion around unmeasured confounding, e.g. using the DAGs provided in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832226/, in particular (but not limited to) around severity and not just presence/absence of comorbidities.

We will use the DAGs provided by the article (PMC7832226) to extend our discussion around unmeasured confounding, especially the severity of comorbidities.

(13) The UK biobank is known to be highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. The potential problems this might create in terms of collider stratification bias - as highlighted here for example: https://www.nature.com/articles/s41467-020-19478-2 - should be discussed in greater detail and also appreciated more when providing conclusions.

We agree with the reviewer that the highly selective nature of the UK Biobank might create collider stratification bias for the evaluation of COVID-19-related outcomes. We will further discuss this in detail and be cautious when generating conclusions.  

Reviewer #2 (Public Review):

Summary:

Zeng et al investigate in an observational population-based cohort study whether the use of proton pump inhibitors (PPIs) is associated with an increased risk of several respiratory infections among which are influenza, pneumonia, and COVID-19. They conclude that compared to non-users, people regularly taking PPIs have increased susceptibility to influenza, pneumonia, as well as COVID-19 severity and mortality. By performing several different statistical analyses, they try to reduce bias as much as possible, to end up with robust estimates of the association.

Strengths:

The study comprehensively adjusts for a variety of critical covariates and by using different statistical analyses, including propensity-score-matched analyses and quantitative bias analysis, the estimates of the associations can be considered robust.

We thank the reviewer for demonstrating the strengths of our articles. We will further revise our manuscript according to the reviewer’s suggestions.

Weaknesses:

As it is an observational cohort study there still might be bias. Information on the dose or duration of acid suppressant use was not available, but might be of influence on the results. The outcome of interest was obtained from primary care data, suggesting that only infections as diagnosed by a physician are taken into account. Due to the self-limiting nature of the outcome, differences in health-seeking behavior might affect the results.

We will try to adjust or provide discussions about the above factors, including the dose/duration of PPI use, outcome assessment, and health-seeking behavior.

Author Response

eLife assessment

In this study, the authors offer a theoretical explanation for the emergence of nematic bundles in the actin cortex, carrying implications for the assembly of actomyosin stress fibers. As such, the study is a valuable contribution to the field actomyosin organization in the actin cortex. While the theoretical work is solid, experimental evidence in support of the model assumptions remains incomplete. The presentation could be improved to enhance accessibility for readers without a strong background in hydrodynamic and nematic theories.

To address the weaknesses identified in this assessment, we plan to expand the description of the theoretical model to make it more accessible to a broader spectrum of readers. We will discuss in more detail the relation between the different mathematical terms and physical processes at the molecular scale, as well as the experimental evidence supporting the model assumptions. We will also discuss more explicitly how our results are relevant to different systems exhibiting actomyosin nematic bundles beyond stress fibers.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this article, Mirza et al developed a continuum active gel model of actomyosin cytoskeleton that account for nematic order and density variations in actomyosin. Using this model, they identify the requirements for the formation of dense nematic structures. In particular, they show that self-organization into nematic bundles requires both flow-induced alignment and active tension anisotropy in the system. By varying model parameters that control active tension and nematic alignment, the authors show that their model reproduces a rich variety of actomyosin structures, including tactoids, fibres, asters as well as crystalline networks. Additionally, discrete simulations are employed to calculate the activity parameters in the continuum model, providing a microscopic perspective on the conditions driving the formation of fibrillar patterns.

Strengths:

The strength of the work lies in its delineation of the parameter ranges that generate distinct types of nematic organization within actomyosin networks. The authors pinpoint the physical mechanisms behind the formation of fibrillar patterns, which may offer valuable insights into stress fiber assembly. Another strength of the work is connecting activity parameters in the continuum theory with microscopic simulations.

We thank the referee for these comments.

Weaknesses:

This paper is a very difficult read for nonspecialists, especially if you are not well-versed in continuum hydrodynamic theories. Efforts should be made to connect various elements of theory with biological mechanisms, which is mostly lacking in this paper. The comparison with experiments is predominantly qualitative.

We agree with the referee that the manuscript will benefit from a better description of the theoretical model and the results in relation with specific molecular and cellular mechanisms. We will further emphasize how a number of experimental observations in the literature support our model assumptions and can be explained by our results. A quantitative comparison is difficult for several reasons. First, many of the parameters in our theory have not been measured, and in fact estimates in the literature often rely on comparison with hydrodynamic models such as ours. Second, the effective physical properties of actomyosin gels can vary wildly between cells, which may explain the diversity of forms, dynamics and functions. For these reasons, we chose to delineate regimes leading to qualitatively different emerging architectures and dynamics. In the revised manuscript, we will make this point clearer and will further study the literature to seek quantitative comparison.

It is unclear if the theory is suited for in vitro or in vivo actomyosin systems. The justification for various model assumptions, especially concerning their applicability to actomyosin networks, requires a more thorough examination.

We thank the referee for this comment. Our theory is applicable to actomyosin in living cells. To our knowledge, reconstituted actomyosin gels currently lack the ability to sustain the dynamical steady-states involved in the proposed self-organization mechanism, which balance actin flows with turnover. In addition to actomyosin gels in living cells, in vitro systems based on encapsulated cell extracts can also sustain such dynamical steady states [e.g. https://doi.org/10.1038/s41567-018-0413-4], and therefore our theory may be applicable to these systems as well. Of course, with advancements in the field of reconstituted systems, this may change in the near future. We will explicitly discuss this point in the revised manuscript.

The classification of different structures demands further justification. For example, the rationale behind categorizing structures as sarcomeric remains unclear when nematic order is perpendicular to the axis of the bands. Sarcomeres traditionally exhibit a specific ordering of actin filaments with alternating polarity patterns.

We agree and will avoid the term “sarcomeric”.

Similarly, the criteria for distinguishing between contractile and extensile structures need clarification, as one would expect extensile structures to be under tension contrary to the authors' claim.

We plan to clarify this point by representing in a main figure the stress profiles across dense nematic structures (currently in Supp Fig 2), along with a more detailed description. In short, depending on the parameter regime, the competition between active and viscous stresses in the actin gel determine whether the emergent structures are extensile or contractile. In our system tension is positive in all directions at all times. However, in “contractile” structures, tension is larger along the bundle, whereas in “extensile” structures, tension is larger perpendicular to the bundle. This is consistent with the common expression for active stress of incompressible nematic systems [see e.g. https://doi.org/10.1038/s41467-018-05666-8], that takes the form –zQ, where z is positive for an extensile system, showing that in this case active tension is negative along the nematic direction. This point, also been raised by another referee, will be clarified and connected to existing literature.

Additionally, its unclear if the model's predictions for fiber dynamics align with observations in cells, as stress fibers exhibit a high degree of dynamism and tend to coalesce with neighboring fibers during their assembly phase.

In the present work, we focus on the self-organization of a periodic patch of actomyosin gel. However, in adherent cells boundary conditions play an essential role, e.g. with inflow at the cell edge as a result of polymerization and exclusion at the nucleus. In ongoing work, we are studying with the present model the dynamics of assembly and reconfiguration of dense nematic structures in domains with boundary conditions mimicking in adherent cells, as suggested by the referee. We would like to note, however, that the prominent stress fibers in cells adhered to stiff substrates, so abundantly reported in the literature, are not the only instance of dense nematic actin bundles, and may not be representative of physiologically relevant situations. In the present manuscript, we emphasize the relation of the predicted organizations with those found in different in vivo contexts not related to stress fibers, such as the aligned patterns of bundles in insects (trachea, scales in butterfly wings), in hydra, or in reproductive organs of C elegans; the highly dynamical network of bundles observed in C elegans early embryos; or the labyrinth patters of micro-ridges in the apical surface of epidermal cells in fish. We will further emphasize these points in the revised manuscript.

Finally, it seems that the microscopic model is unable to recapitulate the density patterns predicted by the continuum theory, raising questions about the suitability of the simulation model.

We thank the referee for raising this question, which needs further clarification. The goal of the microscopic model is not to reproduce the self-organized patterns predicted by the active gel theory. The microscopic model lacks essential ingredients, notably a realistic description of hydrodynamics and turnover. Our goal with the agent-based simulations is to extract the relation between nematic order and active stresses for a small homogeneous sample of the network. This small domain is meant to represent the homogeneous active gel prior to pattern formation, and it allows us to substantiate key assumptions of the continuum model leading to pattern formation, notably the dependence of isotropic and deviatoric components of the active stress on density and nematic order (Eq. 7) and the active generalized stress promoting ordering.

We should mention that reproducing the range of out-of-equilibrium mesoscale architectures predicted by our active gel model with agent-based simulations seems at present not possible, or at least significantly beyond the state-of-the-art. We note for instance that parameter regimes in which agent-based simulations of actin gels display extended contractile steady-states are non-generic, as these simulations often lead to irreversible clumping (as do many reconstituted contractile systems), see e.g. https://doi.org/10.1038/ncomms10323 or https://doi.org/10.1371/journal.pcbi.1005277. Very few references report sustained actin flows or the organization of a few bundles (https://doi.org/10.1371/journal.pcbi.1009506). While agent-based cytoskeletal simulations are very attractive because they directly connect with molecular mechanisms, active gel continuum models are better suited to describe out-ofequilibrium emergent hydrodynamics at a mesoscale. We believe that these two complementary modeling frameworks are rather disconnected in the literature, and for this reason, we have attempted substantiate our continuum modeling with discrete simulations. In the revised manuscript, we will better frame the relationship between them.

Reviewer #2 (Public Review):

Summary:

The article by Waleed et al discusses the self organization of actin cytoskeleton using the theory of active nematics. Linear stability analysis of the governing equations and computer simulations show that the system is unstable to density fluctuations and self organized structures can emerge. While the context is interesting, I am not sure whether the physics is new. Hence I have reservations about recommending this article.

We thank the referee for these comments. In the revised manuscript, we will highlight the novelty of the paper in terms of the theoretical model, the mechanism of patterning of dense nematic structures, the nature and dynamics of the resulting architectures, their relation with the experimental record, and the connection with microscopic models.

We will emphasize the fact that nematic architectures in the actin cytoskeleton are characterized by a co-localization of order and density (and strong variations in each of these fields), that recent work shows that isotropic and nematic organizations coexist and are part of a single heterogeneous network, that the emergence and maintenance of nematic order requires active contraction, and that the assembly and maintenance of dense nematic bundles involves convergent flows. None of these key features can be described by the common incompressible models of active nematics. To address this, we develop here a compressible and density dependent model for an active nematic gel. We will carefully justify that the proposed model is meaningful for actomyosin gels, and we will highlight the commonalities and differences with previous models of active nematics.

Strengths:

(i) Analytical calculations complemented with simulations (ii) Theory for cytoskeletal network

Weaknesses:

Not placed in the context or literature on active nematics.

We agree with the referee that the manuscript requires a better contextualization of the work in relation with the very active field of active nematics. In the revised manuscript, we will clearly describe the relation of our model with existing ones.

Reviewer #3 (Public Review):

The manuscript "Theory of active self-organization of dense nematic structures in the actin cytoskeleton" analysis self-organized pattern formation within a two-dimensional nematic liquid crystal theory and uses microscopic simulations to test the plausibility of some of the conclusions drawn from that analysis. After performing an analytic linear stability analysis that indicates the possibility of patterning instabilities, the authors perform fully non-linear numerical simulations and identify the emergence of stripelike patterning when anisotropic active stresses are present. Following a range of qualitative numerical observations on how parameter changes affect these patterns, the authors identify, besides isotropic and nematic stress, also active self-alignment as an important ingredient to form the observed patterns. Finally, microscopic simulations are used to test the plausibility of some of the conclusions drawn from continuum simulations.

The paper is well written, figures are mostly clear and the theoretical analysis presented in both, main text and supplement, is rigorous. Mechano-chemical coupling has emerged in recent years as a crucial element of cell cortex and tissue organization and it is plausible to think that both, isotropic and anisotropic active stresses, are present within such effectively compressible structures. Even though not yet stated this way by the authors, I would argue that combining these two is of the key ingredients that distinguishes this theoretical paper from similar ones. The diversity of patterning processes experimentally observed is nicely elaborated on in the introduction of the paper, though other closely related previous work could also have been included in these references (see below for examples).

We thank the referee for these comments and for the suggestion to emphasize the interplay of isotropic and anisotropic active tension, which is possible only in a compressible gel. We thank the suggestions of the referee to better connect with existing literature.

To introduce the continuum model, the authors exclusively cite their own, unpublished pre-print, even though the final equations take the same form as previously derived and used by other groups working in the field of active hydrodynamics (a certainly incomplete list: Marenduzzo et al (PRL, 2007), Salbreux et al (PRL, 2009, cited elsewhere in the paper), Jülicher et al (Rep Prog Phys, 2018), Giomi (PRX, 2015),...). To make better contact with the broad active liquid crystal community and to delineate the present work more compellingly from existing results, it would be helpful to include a more comprehensive discussion of the background of the existing theoretical understanding on active nematics. In fact, I found it often agrees nicely with the observations made in the present work, an opportunity to consolidate the results that is sometimes currently missed out on. For example, it is known that self-organised active isotropic fluids form in 2D hexagonal and pulsatory patterns (Kumar et al, PRL, 2014), as well as contractile patches (Mietke et al, PRL 2019), just as shown and discussed in Fig. 2. It is also known that extensile nematics, \kappa<0 here, draw in material laterally of the nematic axis and expel it along the nematic axis (the other way around for \kappa>0, see e.g. Doostmohammadi et al, Nat Comm, 2018 "Active Nematics" for a review that makes this point), consistent with all relative nematic director/flow orientations shown in Figs. 2 and 3 of the present work.

We thank the referee for these suggestions. Indeed, in the original submission we had outsourced much of the justification of the model and the relevant literature to a related pre-print, but this is not reasonable. In the revised manuscript, we will discuss our model in the context of the state-of-the-art, emphasizing connections with existing results.

The results of numerical simulations are well-presented. Large parts of the discussion of numerical observations - specifically around Fig. 3 - are qualitative and it is not clear why the analysis is restricted to \kappa<0. Some of the observations resonate with recent discussions in the field, for example the observation of effectively extensile dynamics in a contractile system is interesting and reminiscent of ambiguities about extensile/contractile properties discussed in recent preprints (https://arxiv.org/abs/2309.04224). It is convincingly concluded that, besides nematic stress on top of isotropic one, active self-alignment is a key ingredient to produce the observed patterns.

We thank the referee for these comments. We will expand the description of the results around Figure 3. We are reluctant to extend the detailed analysis of emergent architectures and dynamics to the case \kappa > 0 as it leads to architectures not observed, to our knowledge, in actin networks. We will expand the characterization of emergent contractile/extensile networks by describing the distribution of the different components of the stress tensor across the bundles and will place our results in the context of related recent work.

I compliment the authors for trying to gain further mechanistic insights into this conclusion with microscopic filament simulations that are diligently performed. It is rightfully stated that these simulations only provide plausibility tests and, within this scope, I would say the authors are successful. At the same time, it leaves open questions that could have been discussed more carefully. For example, I wonder what can be said about the regime \kappa>0 (which is dropped ad-hoc from Fig. 3 onward) microscopically, in which the continuum theory does also predict the formation of stripe patterns - besides the short comment at the very end? How does the spatial inhomogeneous organization the continuum theory predicts fit in the presented, microscopic picture and vice versa?

We thank the referee for this compliment. We think that the point raised by the referee is very interesting. It is reasonable to expect that the sign of \kappa will not be a constant but rather depend on S and \rho. Indeed, for a sparse network with low order, the progressive bundling by crosslinkers acting on nearby filaments is likely to produce a large active stress perpendicular to the nematic direction, whereas in a dense and highly ordered region, myosin motors are more likely to effectively contract along the nematic direction whereas there is little room for additional lateral contraction by additional bundling. In the revised manuscript, we envision to further deepen in this issue in two ways. First, we plan to perform additional agent-based simulations in a regime leading to kappa > 0. Second, we will modify the active gel model such that kappa < 0 for low density/order, so that a fibrillar pattern is assembled, and kappa > 0 for high density/order, so that the emergent fibers are highly contractile.

Overall, the paper represents a valuable contribution to the field of active matter and, if strengthened further, might provide a fruitful basis to develop new hypothesis about the dynamic self-organisation of dense filamentous bundles in biological systems.

Author Response

We would like to thank the editorial board and the reviewers for their assessment of our manuscript and their constructive feedback that we believe will make our manuscript stronger and clearer. Please find below our provisional response to the public reviews; these responses outline our plan to address the concerns of the reviewers for a planned resubmission. Our responses are written in red.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this paper, Misic et al showed that white matter properties can be used to classify subacute back pain patients that will develop persisting pain.

Strengths:

Compared to most previous papers studying associations between white matter properties and chronic pain, the strength of the method is to perform a prediction in unseen data. Another strength of the paper is the use of three different cohorts. This is an interesting paper that provides a valuable contribution to the field.

We thank the reviewer for emphasizing the strength of our paper and the importance of validation on multiple unseen cohorts.

Weaknesses:

The authors imply that their biomarker could outperform traditional questionnaires to predict pain: "While these models are of great value showing that few of these variables (e.g. work factors) might have significant prognostic power on the long-term outcome of back pain and provide easy-to-use brief questionnaires-based tools, (21, 25) parameters often explain no more than 30% of the variance (28-30) and their prognostic accuracy is limited.(31)". I don't think this is correct; questionnaire-based tools can achieve far greater prediction than their model in about half a million individuals from the UK Biobank (Tanguay-Sabourin et al., A prognostic risk score for the development and spread of chronic pain, Nature Medicine 2023).

We agree with the reviewer that we might have under-estimated the prognostic accuracy of questionnaire-based tools, especially, the strong predictive accuracy shown by Tangay-Sabourin 2023. In the revised version, we will change both the introduction and the discussion to reflect the the questionnaires based prognostic accuracy reported in the seminal work by TangaySabourin. We do note here, however, that the latter paper while very novel is unique in showing the power of questionnaires. In addition, the questionnaires we have tested in our cohort did not show any baseline differences suggestive of prognostic accuracy.

Moreover, the main weakness of this study is the sample size. It remains small despite having 3 cohorts. This is problematic because results are often overfitted in such a small sample size brain imaging study, especially when all the data are available to the authors at the time of training the model (Poldrack et al., Scanning the horizon: towards transparent and reproducible neuroimaging research, Nature Reviews in Neuroscience 2017). Thus, having access to all the data, the authors have a high degree of flexibility in data analysis, as they can retrain their model any number of times until it generalizes across all three cohorts. In this case, the testing set could easily become part of the training making it difficult to assess the real performance, especially for small sample size studies.

The reviewer raises a very important point of limited sample size and of the methodology intrinsic of model development and testing. We acknowledge the small sample size in the “Limitations” section of the discussion. In the resubmission, we will acknowledge the degree of flexibility that is afforded by having access to all the data at once. However, we will also note that our SLF-FA based model is a simple cut-off approach that does not include any learning or hidden layers and that the data obtained from Open Pain were never part of the “training” set at any point at either the New Haven or the Mannheim site. Regarding our SVC approach we follow standard procedures for machine learning where we never mix the training and testing sets. The models are trained on the training data with parameters selected based on crossvalidation within the training data. Therefore, no models have ever seen the test data set. The model performances we reported reflect the prognostic accuracy of our model. Finally, as discussed by Spisak et al., 1 the key determinant of the required sample size in predictive modeling is the ” true effect size of the brain-phenotype relationship” which we think is the determinant of the replication we observe in this study. As such the effect size in the New Haven and Mannheim data is Cohen’s d >1.

Even if the performance was properly assessed, their models show AUCs between 0.65-0.70, which is usually considered as poor, and most likely without potential clinical use. Despite this, their conclusion was: "This biomarker is easy to obtain (~10 min 18 of scanning time) and opens the door for translation into clinical practice." One may ask who is really willing to use an MRI signature with a relatively poor performance that can be outperformed by self-report questionnaires?

The reviewer is correct, the model performance is poor to fair which limits its usefulness for clinical translation. We wanted to emphasize that obtaining diffusion images can be done in a short period of time and, hence, as such models predictive accuracy improves, clinical translation becomes closer to reality. In addition, our findings are based on old diffusion data and limited sample size coming from different sites and different acquisition sequences. This by itself would limit the accuracy especially that evidence shows that sample size affect also model performance (i.e. testing AUC)1. In the revision, we will re-word the sentence mentioned by the reviewer to reflect the points discussed here. This also motivates us to collect a more homogeneous and larger sample.

Overall, these criticisms are more about the wording sometimes used and the inference they made. I think the strength of the evidence is incomplete to support the main claims of the paper.

Despite these limitations, I still think this is a very relevant contribution to the field. Showing predictive performance through cross-validation and testing in multiple cohorts is not an easy task and this is a strong effort by the team. I strongly believe this approach is the right one and I believe the authors did a good job.

We thank the reviewer for acknowledging that our effort and approach were the right ones.

Minor points:

Methods:

I get the voxel-wise analysis, but I don't understand the methods for the structural connectivity analysis between the 88 ROIs. Have the authors run tractography or have they used a predetermined streamlined form of 'population-based connectome'? They report that models of AUC above 0.75 were considered and tested in the Chicago dataset, but we have no information about what the model actually learned (although this can be tricky for decision tree algorithms).

We apologize for the lack of clarity; we did run tractography and we did not use a predetermined streamlined form of the connectome. We will clarify this point in the methods section.

Finding which connections are important for the classification of SBPr and SBPp is difficult because of our choices during data preprocessing and SVC model development: (1) preprocessing steps which included TNPCA for dimensionality reduction, and regressing out the confounders (i.e., age, sex, and head motion); (2) the harmonization for effects of sites; and (3) the Support Vector Classifier which is a hard classification model2. Such models cannot tell us the features that are important in classifying the groups. Our model is considered a black-box predictive model like neural networks.

Minor:

What results are shown in Figure 7? It looks more descriptive than the actual results.

The reviewer is correct; Figure 7 and supplementary Figure 4 are both qualitatively illustrating the shape of the SLF.

Reviewer #2 (Public Review):

The present study aims to investigate brain white matter predictors of back pain chronicity. To this end, a discovery cohort of 28 patients with subacute back pain (SBP) was studied using white matter diffusion imaging. The cohort was investigated at baseline and one-year follow-up when 16 patients had recovered (SBPr) and 12 had persistent back pain (SBPp). A comparison of baseline scans revealed that SBPr patients had higher fractional anisotropy values in the right superior longitudinal fasciculus SLF) than SBPp patients and that FA values predicted changes in pain severity. Moreover, the FA values of SBPr patients were larger than those of healthy participants, suggesting a role of FA of the SLF in resilience to chronic pain. These findings were replicated in two other independent datasets. The authors conclude that the right SLF might be a robust predictive biomarker of CBP development with the potential for clinical translation.

Developing predictive biomarkers for pain chronicity is an interesting, timely, and potentially clinically relevant topic. The paradigm and the analysis are sound, the results are convincing, and the interpretation is adequate. A particular strength of the study is the discovery-replication approach with replications of the findings in two independent datasets.

We thank reviewer 2 for pointing to the strength of our study.

The following revisions might help to improve the manuscript further.

Definition of recovery. In the New Haven and Chicago datasets, SBPr and SBPp patients are distinguished by reductions of >30% in pain intensity. In contrast, in the Mannheim dataset, both groups are distinguished by reductions of >20%. This should be harmonized. Moreover, as there is no established definition of recovery (reference 79 does not provide a clear criterion), it would be interesting to know whether the results hold for different definitions of recovery. Control analyses for different thresholds could strengthen the robustness of the findings.

The reviewer raises an important point regarding the definition of recovery. To address the reviewers concern we will add a supplementary figure showing the results in the Mannheim data set if a 30% reduction is used as a recovery criterion. We would like to emphasize here several points that support the use of different recovery thresholds between New Haven and Mannheim. The New Haven primary pain ratings relied on visual analogue scale (VAS) while the Mannheim data relied on the German version of the West-Haven-Yale Multidimensional Pain Inventory. In addition, the Mannheim data was pre-registered with a definition of recovery at 20% and is part of a larger sub-acute to chronic pain study with prior publications from this cohort using the 20% cut-off3. Finally, a more recent consensus publication4 from IMMPACT indicates that a change of at least 30% is needed for a moderate improvement in pain on the 0-10 Numerical Rating Scale but that this percentage depends on baseline pain levels.

Analysis of the Chicago dataset. The manuscript includes results on FA values and their association with pain severity for the New Haven and Mannheim datasets but not for the Chicago dataset. It would be straightforward to show figures like Figures 1 - 4 for the Chicago dataset, as well.

We welcome the reviewer’s suggestion; we will therefore add these analyses to the results section of our manuscript upon resubmission

Data sharing. The discovery-replication approach of the present study distinguishes the present from previous approaches. This approach enhances the belief in the robustness of the findings. This belief would be further enhanced by making the data openly available. It would be extremely valuable for the community if other researchers could reproduce and replicate the findings without restrictions. It is not clear why the fact that the studies are ongoing prevents the unrestricted sharing of the data used in the present study.

Reviewer #3 (Public Review):

Summary:

Authors suggest a new biomarker of chronic back pain with the option to predict the result of treatment. The authors found a significant difference in a fractional anisotropy measure in superior longitudinal fasciculus for recovered patients with chronic back pain.

Strengths:

The results were reproduced in three different groups at different studies/sites.

Weaknesses:

The number of participants is still low.

We have discussed this point in our replies to reviewer number 1.

An explanation of microstructure changes was not given.

The reviewer points to an important gap in our discussion. While we cannot do a direct study of actual tissue micro-structure, we will explore further the changes observed in the SLF by calculating diffusivity measures and discuss possible explanations of these changes.

Some technical drawbacks are presented.

We are uncertain if the reviewer is suggesting that we have acknowledged certain technical drawbacks and expects further elaboration on our part. We kindly request that the reviewer specify what particular issues they would like us to address so that we can respond appropriately.

(1) Spisak T, Bingel U, Wager TD. Multivariate BWAS can be replicable with moderate sample sizes. Nature 2023;615:E4-E7.

(2) Liu Y, Zhang HH, Wu Y. Hard or Soft Classification? Large-margin Unified Machines. J Am Stat Assoc 2011;106:166-177.

(3) Loffler M, Levine SM, Usai K, et al. Corticostriatal circuits in the transition to chronic back pain: The predictive role of reward learning. Cell Rep Med 2022;3:100677.

(4) Smith SM, Dworkin RH, Turk DC, et al. Interpretation of chronic pain clinical trial outcomes: IMMPACT recommended considerations. Pain 2020;161:2446-2461.

Author Response

eLife assessment

The authors used electrophysiology in brain slices and computer modeling and suggest that layer 2/3 pyramidal neurons of the mouse cortex express functional HCN channels, despite little evidence in the past that they are present. The study is useful at the present time, but results are incomplete because the methods, data, and analyses do not always support the conclusions.

Public Reviews:

Reviewer #1 (Public Review):

The manuscript by Oleh et al. uses in vitro electrophysiology and compartmental modeling (via NEURON) to investigate the expression and function of HCN channels in mouse L2/3 pyramidal neurons. The authors conclude that L2/3 neurons have developmentally regulated HCN channels, the activation of which can be observed when subjected to large hyperpolarizations. They further conclude via blockade experiments that HCN channels in L2/3 neurons influence cellular excitability and pathway-specific EPSP kinetics, which can be neuromodulated. While the authors perform a wide range of slice physiology experiments, concrete evidence that L2/3 cells express functionally relevant HCN channels is limited. There are serious experimental design caveats and confounds that make drawing strong conclusions from the data difficult. Furthermore, the significance of the findings is generally unclear, given modest effect sizes and a lack of any functional relevance, either directly via in vivo experiments or indirectly via strong HCN-mediated changes in known operations/computations/functions of L2/3 neurons.

Specific points:

(1) The interpretability and impact of this manuscript are limited due to numerous methodological issues in experimental design, data collection, and analysis. The authors have not followed best practices in the field, and as such, much of the data is ambiguous and/or weak and does not support their interpretations (detailed below). Additionally, the authors fail to appropriately explain their rationale for many of their choices, making it difficult to understand why they did what they did. Furthermore, many important references appear to be missing, both in terms of contextualizing the work and in terms of approach/method. For example, the authors do not cite Kalmbach et al 2018, which performed a directly comparable set of experiments on HCN channels in L2/3 neurons of both humans and mice. This is an unacceptable omission. Additionally, the authors fail to cite prior literature regarding the specificity or lack thereof of Cs+ in blocking HCN. In describing a result, the authors state "In line with previous reports, we found that L2/3 PCs exhibited an unremarkable amount of sag at 'typical' current commands" but they then fail to cite the previous reports.

We thank the reviewer for the thorough examination of our manuscript; however, we strongly disagree with many of the raised concerns for several reasons, as detailed in an initial response below:

To address the lack of certain citations, we would like to emphasize that in the introduction section, we did focus on a several decades-long line of investigation into the HCN channel content of layer 2/3 pyramidal cells (L2/3 PCs), where there has undoubtedly been some controversy as to their functional contribution. We did not explicitly cite papers that claimed to find no/little HCN channels/sag- although this would be a significant list of pubs from some excellent senior investigators, as we wanted to avoid shining a negative light on otherwise excellent publications. However, we plan to address this more clearly in the upcoming revision.

Just to take an example: in the publication mentioned by the reviewer (Kalmbach et al 2018), the investigators did not carry out voltage clamp recordings. Furthermore, the reported input resistance values in the aforementioned paper were far above other reports in mice (Routh et al. 2022, Brandalise et al 2022, Hedrick et al 2012; which were similar and our findings here), suggesting that recordings in Kalmbach were carried out at membrane potentials where HCN activation is less available (Routh, Brager and Johnston 2022).

Another reason for some mixed findings in the field is undoubtedly due to the small/nonexistent sag in L2/3 current clamp recordings in mice. We also found a small sag, and that we have shown to be explained by the following: The ‘sag’ potential is a biphasic voltage response emerging from a relatively fast passive membrane response and a slower Ih activation. In L2/3 PCs, hyperpolarization-activated currents are apparently faster than previously described and are located proximally (our findings here). Therefore, their recruitment in mouse L2/3 PCs is on a similar timescale as the passive membrane response, resulting in a more monophasic response. Again, we plan to include a full set of citations in the updated introduction section, to highlight the importance of HCN channels in L2/3 PCs in mice and other species. The justification for using cesium (i.e., ‘best practices’) is detailed in the next paragraph.

(2) A critical experimental concern in the manuscript is the reliance on cesium, a nonspecific blocker, to evaluate HCN channel function. Cesium blocks HCN channels but also acts at potassium channels (and possibly other channels as well). The authors do not acknowledge this or attempt to justify their use of Cs+ and do not cite prior work on this subject. They do not show control experiments demonstrating that the application of Cs+ in their preparation only affects Ih. Additionally, the authors write 1 mM cesium in the text but appear to use 2 mM in the figures. In later experiments, the authors switch to ZD7288, a more commonly used and generally accepted more specific blocker of HCN channels. However, they use a very high concentration, which is also known to produce off-target effects (see Chevaleyre and Castillo, 2002). To make robust conclusions, the authors should have used both blockers (at accepted/conservative concentrations) for all (or at least most) experiments. Using one blocker for some experiments and then another for different experiments is fraught with potential confounds.

To address the concerns regarding the usage of cesium to block HCN channels, we would like to state that neither cesium nor ZD-7288 are without off-target effects, however in our case the potential off-target effects of external cesium were deemed less impactful, especially concerning AP firing output experiments. Extracellular cesium has been widely accepted as a blocker of HCN channels (Lau et al. 2010, Wickenden et al. 2009, Rateau and Ropert 2005, Hemond et al. 2009, Yang et al. 2015, Matt et al. 2010). However, it is known to act on potassium channels as well, which has mostly been demonstrated with intracellular application (Puil et al. 1981, Fleidervish et al. 2008, Williams et al. 1991, 2008). However, we acknowledge off-target effects and we will better cite the appropriate literature in our manuscript in the revision.

Although we performed internal control experiments during the recordings, these were not included in the manuscript- which we plan to correct in the revision. These are detailed as follows: during our recordings cesium had no significant effect on action potential halfwidth, ruling out substantial blocking of potassium channels, nor did it affect any other aspects of suprathreshold activity. Furthermore, we observed similar effects on passive properties (resting membrane potential, input resistance) following ZD-7288 as with cesium, which we will also update in our figures. We did acknowledge that ZD-7288 is a widely accepted blocker of HCN, and for this reason we carried out some of our experiments using this pharmacological agent instead of cesium. However, these experiments were always supported by complementary findings using external cesium. For example, the effect of ZD-7288 on EPSPs was confirmed by similar synaptic stimulation experiments using cesium. This is important, as synaptic inputs of L2/3 PCs are modulated by both dendritic sodium (Ferrarese et al. 2018) and calcium channels (Landau 2022), therefore the application of ZD-7288 alone may have been difficult to interpret in isolation.

On the other hand, ZD-7288 suffers from its own side effects, such as a substantial effect on sodium channels (Wu et al. 2012) and calcium channels (Sánchez-Alonso et al. 2008, Felix et al. 2003). As our aim was to provide functional evidence for the importance of HCN channels, we deemed these effects unacceptable in experiments where AP firing output (e.g., in cell-attached experiments) was measured.

(3) A stronger case could be made that HCN is expressed in the somatic compartment of L2/3 cells if the authors had directly measured HCN-isolated currents with outside-out or nucleated patch recording (with appropriate leak subtraction and pharmacology). Whole-cell voltage-clamp in neurons with axons and/or dendrites does not work. It has been shown to produce erroneous results over and over again in the field due to well-known space clamp problems (see Rall, Spruston, Williams, etc.). The authors could have also included negative controls, such as recordings in neurons that do not express HCN or in HCN-knockout animals. Without these experiments, the authors draw a false equivalency between the effects of cesium and HCN channels, when the outcomes they describe could be driven simply by multiple other cesium-sensitive currents. Distortions are common in these preparations when attempting to study channels (see Williams and Womzy, J Neuro, 2011). In Fig 2h, cesium-sensitive currents look too large and fast to be from HCN currents alone given what the authors have shown in their earlier current clamp data. Furthermore, serious errors in leak subtraction appear to be visible in Supplementary Figure 1c. To claim that these conductances are solely from HCN may be misleading.

We disagree with the argument that “Whole-cell voltage-clamp in neurons with axons and/or dendrites does not work”. Although this method is not without its confounds (i.e. space clamp), it is still a useful initial measure as demonstrated countless times in the literature. However, the reviewer is correct that the best approach to establish the somatodendritic distribution of ion channels is by direct somatic and dendritic outside-out patches. Due to the small diameter of L2/3 PC dendrites, these experiments haven’t been carried out yet in the literature for any other ion channel either to our knowledge. Mapping this distribution may be outside the scope of the current manuscript, but it was hard for us to ignore the sheer size of the Cs+ sensitive hyperpolarizing currents in whole cell. Thus, we will opt to report this data.

Also, we should point out that space clamp-related errors manifest in the overestimation of frequency-dependent features, such as activation kinetics, and underestimation of steady-state current amplitudes. The activation time constant of our measured currents are somewhat faster than previously reported- reducing major concerns regarding space clamp errors. Furthermore, we simply do not understand what “too large… to be from HCN currents” means. We would like to ask the reviewer to point out what the “serious errors in leak subtraction” are, as the measured currents are similar in shape and correction artifacts to previously reported HCN currents (Meng et al. 2011, Li 2011, Zhao et al. 2019, Yu et al. 2004, Zhang et al. 2008, Spinelli et al. 2018, Craven et al. 2006, Ying et al. 2012, Biel et al. 2009).

Furthermore, we would be grateful if the reviewer would mention the other possible ion channels that are activated at hyperpolarized voltages, have the same voltage dependence as HCN currents, do not show inactivation, influence both input resistance and resting membrane potential, and are blocked by low concentration extracellular cesium.

(4) The authors present current-clamp traces with some sag, a primary indicator of HCN conductance, in Figure 2. However, they do not show example traces with cesium or ZD7288 blockade. Additionally, the normalization of current injected by cellular capacitance and the lack of reporting of input resistance or estimated cellular size makes it difficult to determine how much current is actually needed to observe the sag, which is important for assessing the functional relevance of these channels. The sag ratio in controls also varies significantly without explanation (Figure 6 vs Figure 7). Could this variability be a result of genetically defined subgroups within L2/3? For example, in humans, HCN expression in L2/3 varies from superficial and deep neurons. The authors do not make an effort to investigate this. Regardless of inconsistencies in either current injection or cell type, the sag ratio appears to be rather modest and similar to what has already been reported previously in other papers.

We thank the reviewer for pointing out that our explanation for the modest sag ratio might have not been sufficient to properly understand why this measurement cannot be applied to layer 2/3 pyramidal cells. We will clarify this section in the results section. Briefly: sag potential emerges from a relatively (compared to Ih) fast passive membrane response and a slower HCN recruitment. The opposing polarity and different timescales of these two mechanisms results in a biphasic response called “sag” potential. However, if the timescale of these two mechanisms is similar, the voltage response is not predicted to be biphasic. We have shown that hyperpolarization activated currents in our preparations are fast and proximal, therefore they are recruited during the passive response (see Figure 2g.). This means that although a substantial amount of HCN currents are activated during hyperpolarization, their activation will not result in substantial sag. Therefore, sag ratio measurement is not necessarily applicable to approximate the HCN content of L2/3 PCs. We would like to emphasize that sag ratio measurements are correct in case of other cell types, and our aim is not to discredit the method, but rather to show that it cannot be applied in case of mouse L2/3 PCs.

Our own measurements, similar to others in the literature show that L2/3 PCs exhibit modest sag ratios, however, this does not mean that HCN is not relevant. Ih activation in L2/3 PCs does not manifest in large sag potential but rather in a continuous distortion of steady-state responses (Figure 2b.). The reviewer is correct that L2/3 PCs are non-homogenous, therefore we sampled along the entire L2/3 axis. This yielded some variability in our results (i.e., passive properties); yet we did not observe any cells where hyperpolarizing-activated/Cs+-sensitive currents could not be resolved. As structural variability of L2/3 cells does result in variability in cellular capacitance, we compensated for this variability by injecting cellular capacitance-normalized currents. Our measured cellular capacitances were in accordance with previously published values, in the range of 50-120 pF. Therefore, the injected currents were not outside frequently used values. Together, we would like to state that whether substantial sag potential is present or not, initial estimates of the HCN content for each L2/3 PC should be treated with caution.

(5) In the later experiments with ZD7288, the authors measured EPSP half-width at greater distances from the soma. However, they use minimal stimulation to evoke EPSPs at increasingly far distances from the soma. Without controlling for amplitude, the authors cannot easily distinguish between attenuation and spread from dendritic filtering and additional activation and spread from HCN blockade. At a minimum, the authors should share the variability of EPSP amplitude versus the change in EPSP half-width and/or stimulation amplitudes by distance. In general, this kind of experiment yields much clearer results if a more precise local activation of synapses is used, such as dendritic current injection, glutamate uncaging, sucrose puff, or glutamate iontophoresis. There are recording quality concerns here as well: the cell pictured in Figure 3a does not have visible dendritic spines, and a substantial amount of membrane is visible in the recording pipette. These concerns also apply to the similar developmental experiment in 6f-h, where EPSP amplitude is not controlled, and therefore, attenuation and spread by distance cannot be effectively measured. The outcome, that L2/3 cells have dendritic properties that violate cable theory, seems implausible and is more likely a result of variable amplitude by proximity.

To resolve this issue, we will make a supplementary figure showing elicited amplitudes, which showed no significant distance dependence and minimal variability. We thank the reviewer for suggesting an amplitude-halfwidth comparison control. To address the issue of the non-visible spines, we would like to note that these images are of lower magnification. The presence of dendritic spines was confirmed in every recorded pyramidal cell observed using 2P microscopy.

We would like to emphasize that although our recordings “seemingly” violated the cable theory, this is only true if we assume a completely passive condition. As shown in our manuscript, cable theory was not violated, as the presence of NMDA receptor boosting explained the observed ‘non-Rallian’ phenomenon. We plan to clarify this in the fully revised manuscript.

(6) Minimal stimulation used for experiments in Figures 3d-i and Figures 4g-h does not resolve the half-width measurement's sensitivity to dendritic filtering, nor does cesium blockade preclude only HCN channel involvement. Example traces should be shown for all conditions in 3h; the example traces shown here do not appear to even be from the same cell. These experiments should be paired (with and without cesium/ZD). The same problem appears in Figure 4, where it is not clear that the authors performed controls and drug conditions on the same cells. 4g also lacks a scale bar, so readers cannot determine how much these measurements are affected by filtering and evoked amplitude variability. Finally, if we are to believe that minimal stimulation is used to evoke responses of single axons with 50% fail rates, NMDA receptor activation should be minimal to begin with. If the authors wish to make this claim, they need to do more precise activation of NMDA-mediated EPSPs and examine the effects of ZD7288 on these responses in the same cell. As the data is presented, it is not possible to draw the conclusion that HCN boosts NMDA-mediated responses in L2/3 neurons.

As stated in the figure legends, the control and drug application traces are from the same cell, both in figure 3 and figure 4, and the scalebar is not included as the amplitudes were normalized for clarity. We have address the effects of dendritic filtering above in answer (5), and cesium blockade above in answer (2). To reiterate, dendritic filtering alone cannot explain our observations, and cesium is often a better choice for blocking HCN channels compared to ZD-7288, which blocks sodium channels as well. When an excitatory synaptic signal arrives onto a pyramidal cell in typical conditions, neurotransmitter sensitive receptors transmit a synaptic current to the dendritic spine. This dendritic spine is electrically isolated by the high resistance of the spine neck and due to the small membrane surface of the spine, the synaptic current elicits remarkably large voltage changes. These voltage changes can be large enough to depolarize the spine close to zero millivolts upon even single small inputs (Jayant et al. 2016). Therefore, to state that single inputs arriving to dendritic spines cannot be large enough to recruit NMDA receptor activation is incorrect. This is further exemplified by the substantial literature showing ‘miniature’ NMDA recruitment via stochastic vesicle release alone.

(7) The quality of recordings included in the dataset has concerning variability: for example, resting membrane potentials vary by >15-20 mV and the AP threshold varies by 20 mV in controls. This is indicative of either a very wide range of genetically distinct cell types that the authors are ignoring or the inclusion of cells that are either unhealthy or have bad seals.

Although we are aware of the diversity of L2/3 PCs, resolving further layer depth differences is outside the scope of our current manuscript. However, as shown in Kalmbech et al, resting membrane potential can greatly vary (>15-20 mV) in L2/3 PCs depending on distance from pia. We acknowledge that the variance in AP threshold is large and could be due to genetically distinct cell types. Therefore, we plan to present AP peak/width information in the revision, which showed a significantly smaller variability, therefore validating our recording conditions.

(8) The authors make no mention of blocking GABAergic signaling, so it must be assumed that it is intact for all experiments. Electrical stimulation can therefore evoke a mixture of excitatory and inhibitory responses, which may well synapse at very different locations, adding to interpretability and variability concerns.

We thank the reviewer for pointing out our lack of detail regarding the GABAergic signaling blocker SR 95531. We did include this drug in our recordings of signal summation, so GABAergic responses did not contaminate our recordings. We plan to clarify in the revision.

(9) The investigation of serotonergic interaction with HCN channels produces modest effect sizes and suffers the same problems as described above.

We do not agree with the reviewer that 50% drop in neuronal AP firing responses (Figure 7b) was a modest effect size. Thus we plan to keep this data in the manuscript.

(10) The computational modeling is not well described and is not biologically plausible. Persistent and transient K channels are missing. Values for other parameters are not listed. The model does not seem to follow cable theory, which, as described above, is not only implausible but is also not supported by the experimental findings.

The model was downloaded from the Cell Type Database from the Allen Institute, with only minor modifications including the addition of dendritic HCN channels and NDMA receptors- which were varied along a wide parameter space to find a ‘best fit’ to our observations. These additions were necessary to recapitulate our experimental findings. We agree the model likely does not fully recapitulate all aspects of the dendrites, which as we hope to convey in this manuscript, are not fully resolved in mouse L2/3 PCs. This is a published neuronal model, and despite its potential shortcomings, is one among a handful of open-source neuronal models of fully reconstructed L2/3 PCs. We are open to improvement suggestions.

Reviewer #2 (Public Review):

Summary:

This paper by Olah et al. uncovers a previously unknown role of HCN channels in shaping synaptic inputs to L2/3 cortical neurons. The authors demonstrate using slice electrophysiology and computational modeling that, unlike layer 5 pyramidal neurons, L2/3 neurons have an enrichment of HCN channels in the proximal dendrites. This location provides a locus of neuromodulation for inputs onto the proximal dendrites from L4 without an influence on distal inputs from L1. The authors use pharmacology to demonstrate the effect of HCN channels on NMDA-mediated synaptic inputs from L4. The authors further demonstrate the developmental time course of HCN function in L2/3 pyramidal neurons. Taken together, this a well-constructed investigation of HCN channel function and the consequences of these channels on synaptic integration in L2/3 pyramidal neurons.

Strengths:

The authors use careful, well-constrained experiments using multiple pharmacological agents to asses HCN channel contributions to synaptic integrations. The authors also use a voltage clamp to directly measure the current through HCN channels across developmental ages. The authors also provide supplemental data showing that their observation is consistent across multiple areas of the cerebral cortex.

Weaknesses:

The gradient of the HCN channel function is based almost exclusively on changes in EPSP width measured at the soma. While providing strong evidence for the presence of HCN current in L2/3 neurons, there are space clamp issues related to the use of somatic whole-cell voltage clamps that should be considered in the discussion.

We thank the reviewer for pointing out our careful and well-constrained experiments and for making suggestions. The potential effects of space clamp errors will be detailed in the discussion section (see extended explanations under Reviewer 1).

Reviewer #3 (Public Review):

Summary:

The authors study the function of HCN channels in L2/3 pyramidal neurons, employing somatic whole-cell recordings in acute slices of visual cortex in adult mice and a bevy of technically challenging techniques. Their primary claim is a non-uniform HCN distribution across the dendritic arbor with a greater density closer to the soma (roughly opposite of the gradient found in L5 PT-type neurons). The second major claim is that multiple sources of long-range excitatory input (cortical and thalamic) are differentially affected by the HCN distribution. They further describe an interesting interplay of NMDAR and HCN, serotonergic modulation of HCN, and compare HCN-related properties at 1, 2 and 6 weeks of age. Several results are supported by biophysical simulations.

Strengths:

The authors collected data from both male and female mice, at an age (6-10 weeks) that permits comparison with in vivo studies, in sufficient numbers for each condition, and they collected a good number of data points for almost all figure panels. This is all the more positive, considering the demanding nature of multi-electrode recording configurations and pipette-perfusion. The main strength of the study is the question and focus.

Weaknesses:

Unfortunately, in its present form, the main claims are not adequately supported by the experimental evidence: primarily because the evidence is indirect and circumstantial, but also because multiple unusual experimental choices (along with poor presentation of results) undermine the reader's confidence. Additionally, the authors overstate the novelty of certain results and fail to cite important related publications. Some of these weaknesses can be addressed by improved analysis and statistics, resolving inconsistent data across figures, reorganizing/improving figure panels, more complete methods, improved citations, and proofreading. In particular, given the emphasis on EPSPs, the primary data (for example EPSPs, overlaid conditions) should be shown much more.

However, on the experimental side, addressing the reviewer's concerns would require a very substantial additional effort: direct measurement of HCN density at different points in the dendritic arbor and soma; the internal solution chosen here (K-gluconate) is reported to inhibit HCN; bath-applied cesium at the concentrations used blocks multiple potassium channels, i.e. is not selective for HCN (the fact that the more selective blocker ZD7288 was used in a subset of experiments makes the choice of Cs+ as the primary blocker all the more curious); pathway-specific synaptic stimulation, for example via optogenetic activation of specific long-range inputs, to complement / support / verify the layer-specific electrical stimulation.

We thank the reviewer for their very careful examination of our manuscript and helpful suggestions. We will address the concerns raised in the review and present substantially more raw traces in our figures. Although direct dendritic HCN mapping measurements are likely outside the scope of the current manuscript due to the morphological constraints presented by L2/3 PCs (which explains why no other full dendritic nonlinearity distribution has been described in L2/3 PCs with this method), we will nonetheless supplement our manuscript with additional suggested experiments. For example we plan to include the excellent suggestion of pathway-specific optogenetic stimulation to further validate the disparate effect of HCN channels for distal and proximal inputs. We will also include control measurements using different internal solutions. We agree that ZD-7288 is a widely accepted blocker of HCN channels. However, the off-target effects on sodium channels may have significantly confounded our measurements of AP output using extracellular stimulation. Therefore we chose cesium as the primary blocker for those experiments, but did validate several other Cs+-based results with ZD-7288. These controls will also be represented in a more clear fashion in a new supplementary figure.

Author Response

We thank all the reviewers for their comments and insight. We plan to address the comments and recommendations in the revised version of the manuscript. Provisional response on key points are given below.

Reviewer #1 (Public Review):

Summary:

In this manuscript, Chowdhury and co-workers provide interesting data to support the role of G4-structures in promoting chromatin looping and long-range DNA interactions. The authors achieve this by artificially inserting a G4-containing sequence in an isolated region of the genome using CRISPR-Cas9 and comparing it to a control sequence that does not contain G4 structures. Based on the data provided, the authors can conclude that G4-insertion promotes long-range interactions (measured by Hi-C) and affects gene expression (measured by qPCR) as well as chromatin remodelling (measured by ChIP of specific histone markers).

Whilst the data presented is promising and partially supports the authors' conclusion, this reviewer feels that some key controls are missing to fully support the narrative. Specifically, validation of actual G4-formation in chromatin by ChIP-qPCR (at least) is essential to support the association between G4-formation and looping. Moreover, this study is limited to a genomic location and an individual G4-sequence used, so the findings reported cannot yet be considered to reflect a general mechanism/effect of G4-formation in chromatin looping.

Strengths:

This is the first attempt to connect genomics datasets of G4s and HiC with gene expression. The use of Cas9 to artificially insert a G4 is also very elegant.

Weaknesses:

Lack of controls, especially to validate G4-formation after insertion with Cas9. The work is limited to a single G4-sequence and a single G4-site, which limits the generalisation of the findings.

In an earlier study, we reported intracellular G4 formation in the hTERT promoter region in human cells (Sharma et al., Cell Reports, 2021). Exactly the same stretch of DNA was taken for insertion here. This is mentioned in the current manuscript as- “The array of G4-forming sequences used for insertion was previously reported to form stable G4s in human cells.” under the paragraph titled “Insertion of an array of G4s in an isolated locus” in the Results section. As the reviewer points out, we understand that intracellular G4 formation needs to be confirmed upon insertion at the non-native location. These experiments/results will be included in the revised version.

To directly address the second point we are attempting insertion of the same G4-sequence at another locus. Experiments/results on this, and if the insertion is successful, how the insertion affects chromatin organization and nearby gene expression will be included in the revised manuscript.

Reviewer #2 (Public Review):

Summary:

Roy et al. investigated the role of non-canonical DNA structures called G-quadruplexes (G4s) in long-range chromatin interactions and gene regulation. Introducing a G4 array into chromatin significantly increased the number of long-range interactions, both within the same chromosome (cis) and between different chromosomes (trans). G4s functioned as enhancer elements, recruiting p300 and boosting gene expression even 5 megabases away. The study proposes a mechanism where G4s directly influence 3D chromatin organization, facilitating communication between regulatory elements and genes.

Strength:

The findings are valuable for understanding the role of G4-DNA in 3D genome organization and gene transcription.

Weaknesses:

The study would benefit from more robust and comprehensive data, which would add depth and clarity.

(1) Lack of G4 Structure Confirmation: The absence of direct evidence for G4 formation within cells undermines the study's foundation. Relying solely on in vitro data and successful gene insertion is insufficient.

As pointed out in response to the above comment, direct evidence of G4 formation by the stretch of DNA was published by us earlier (Sharma et al., Cell Reports, 2021). We understand here it is important to check/confirm this at the insertion site. These experiments are being initiated.

(2) Alternative Explanations: The study does not sufficiently address alternative explanations for the observed results. The inserted sequences may not form G4s or other factors like G4-RNA hybrids may be involved.

G4 formation at the insertion site will be checked to confirm. It has been reported G4 structures associate with R-loops to strengthen CTCF binding and enhance chromatin looping (Wulfridge et al., 2023). This can discussed further for readers.

(3) Limited Data Depth and Clarity: ChIP-qPCR offers limited scope and considerable variation in some data makes conclusions difficult.

Variation with one of the primers in a few ChIP-qPCR experiments (in Figures 2 and 3D) we have noted. The change however was statistically significant, and consistent with the overall trend across experiments (Figures 2, 3 and 4). Enhancer function, in addition to ChIP, was confirmed using other assays like 3C and RNA expression.

(4) Statistical Significance and Interpretation: The study could be more careful in evaluating the statistical significance and magnitude of the effects to avoid overinterpreting the results.

As pointed out, the manuscript will be revised to ensure we are not overinterpreting any results.

Reviewer #3 (Public Review):

Summary:

This paper aims to demonstrate the role of G-quadruplex DNA structures in the establishment of chromosome loops. The authors introduced an array of G4s spanning 275 bp, naturally found within a very well-characterized promoter region of the hTERT promoter, in an ectopic region devoid of G-quadruplex and annotated gene. As a negative control, they used a mutant version of the same sequence in which G4 folding is impaired. Due to the complexity of the region, 3 G4s on the same strand and one on the opposite strand, 12 point mutations were made simultaneously (G to T and C to A). Analysis of the 3D genome organization shows that the WT array establishes more contact within the TAD and throughout the genome than the control array. Additionally, a slight enrichment of H3K4me1 and p300, both enhancer markers, was observed locally near the insertion site. The authors tested whether the expression of genes located either nearby or up to 5 Mb away was up-regulated based on this observation. They found that four genes were up-regulated from 1.5 to 3-fold. An increased interaction between the G4 array compared to the mutant was confirmed by the 3C assay. For in-depth analysis of the long-range changes, they also performed Hi-C experiments and showed a genome-wide increase in interactions of the WT array versus the mutated form.

Strengths:

The experiments were well-executed and the results indicate a statistical difference between the G4 array inserted cell line and the mutated modified cell line.

Weaknesses:

The control non-G4 sequence contains 12 point mutations, making it difficult to draw clear conclusions. These mutations not only alter the formation of G4, but also affect at least three Sp1 binding sites that have been shown to be essential for the function of the hTERT promoter, from which the sequence is derived. The strong intermingling of G4 and Sp1 binding sites makes it impossible to determine whether all the observations made are dependent on G4 or Sp1 binding. As a control, the authors used Locked Nucleic Acid probes to prevent the formation of G4. As for mutations, these probes also interfere with two Sp1 binding sites. Therefore, using this alternative method has the same drawback as point mutations. This major issue should be discussed in the paper. It is also possible that other unidentified transcription factor binding sites are affected in the presented point mutants.

Since the sequence we used to test the effects of G4 structure formation is highly G-rich, we had to introduce at least 12 mutations to be sure that a stable G4 structure would not form in the mutated control sequence. Sp1 has been reported to bind to G4 structures (Raiber et al., 2012). So, Sp1 binding could also be associated with the G4-dependent enhancer functions observed here. We also appreciate that apart from Sp1, other unidentified transcription factor binding sites might be affected by the mutations we introduced. We will discuss these possibilities in the revised version of the manuscript.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors establish a recombinant insect cell expression and purification scheme for the antiviral Dicer complex of C. elegans. In addition to Dicer-1, the complex harbors two additional proteins, the RIG-I-like helicase DRH-1, and the dsRNA-binding protein RDE-4. The authors show that the complex prefers blunt-end dsRNA over dsRNAs that contain overhangs. Furthermore, whereas ATP-dependent dsRNA cleavage only exacerbates regular dsRNA cleavage activity, the presence of RDE-4 is essential to ATP-dependent and ATP-independent dsRNA cleavage. Single-particle cryo-EM studies of the ternary C. elegans Dicer complex reveal that the N-terminal domain of DRH-1 interacts with the helicase domain of DCR-1, thereby relieving its autoinhibitory state. Lastly, the authors show that the ternary complex is able to processively cleave long dsRNA, an activity primarily relying on the helicase activity of DRH-1.

Strengths:

First thorough biochemical characterization of the antiviral activity of C. elegans Dicer in complex with the RIG-I-like helicase DRH-1 and the dsRNA-binding protein RDE-4. • Discovery that RDE-4 is essential to dsRNA processing, whereas ATP hydrolysis is not.

Discovery of an autoinhibitory role of DRH-1's N-terminal domain (in analogy to the CARD domains of RIG-I).

First structural insights into the ternary complex DCR-1:DRH-1:RDE-4 by cryo-EM to medium resolution.

Trap experiments reveal that the ternary DCR-1 complex cleaves blunt-ended dsRNA processively. Likely, the helicase domain of DRH-1 is responsible for this processive cleavage.

We thank the reviewer for this accurate and thoughtful summary of the strengths of our study. We note that although ATP hydrolysis is not essential for dsRNA processing, it is essential for promoting an alternative, and dramatically more efficient, cleavage mechanism that is wellsuited for processing viral dsRNA.

Weaknesses:

Cryo-EM Structure of the ternary Dicer-1:DRH-1:RED-4 complex to only medium resolution.

We agree with the reviewer that our structures are only of modest resolution. We continue to work towards a higher resolution structure of this conformationally heterogeneous complex. We do want to emphasize that despite our modest resolution, our structures provide novel insights into how the factors in the antiviral complex interact with each other, and also allow us to compare our findings to other Dicer systems. For example, the dsRNA binding protein RDE-4 binds the Hel2i subdomain, and this is similar to accessory dsRNA binding proteins of other Dicers, including human and Drosophila. Most importantly, for the first time, we uncover the interaction of DRH-1 with C. elegans Dicer; our structures show DRH-1's N-terminal domain interacting with Dicer's helicase domain. This observation spurred our experiments that showed the N-terminal domain of DRH-1, like the analogous domain of RIG-I, enables an autoinhibited conformation. While RIG-I autoinhibition is relieved by dsRNA binding, we do not observe this with C. elegans DRH-1 and speculate that instead it is the interaction with Dicer's helicase domain that relieves autoinhibition.

High-resolution structure of the C-terminal domain of DRH-1 bound to dsRNA does not reveal the mechanism of how blunt-end dsRNA and overhang-containing one are being discriminated.

The cryo-EM structure of DCR1:DRH-1:RDE-4 in the presence of ATP only reveals the helicase and CTD domains of DRH-1 bound to dsRNA. No information on dsRNA termini recognition is presented. The paragraph seems detached from the general flow of the manuscript.

We agree with the reviewer that our paper would be improved with a high-resolution structure of DRH-1 bound to the dsRNA terminus to better understand terminus discrimination. Since we did not obtain a high-resolution structure of DRH-1 bound to the dsRNA terminus, we could not comment on how DRH-1 discriminates termini. However, our structure of DRH-1’s helicase and CTD bound to the middle of the dsRNA does provide important evidence that DRH-1 translocates along dsRNA, which is crucial for our interpretation of DRH-1’s ATPase function in the antiviral complex. Furthermore, our analysis of the DRH-1:dsRNA contacts reveals just how well conserved DRH-1 is with mammalian RLRs.

The antiviral DCR-1:DRH-1:RDE-4 complex shows largely homologous activities and regulation than Drosophila Dicer-2.

It is unclear to us why this is a weakness. In our Discussion in the section “Relationship to previously characterized Dicer activities,” we compare and contrast the C. elegans antiviral complex and the most well characterized antiviral Dicer: Drosophila Dcr2. While it might not be surprising that two invertebrate activities that both must target viral dsRNA have similar enzymatic properties, we find this remarkable given that Dcr2 orchestrates cleavage with a single protein, while two helicases and a dsRNA binding protein cooperate in the C. elegans reaction. Our careful biochemical analyses reveal how the three proteins cooperate. In vivo, C. elegans Dicer must function to cleave pre-miRNAs, endogenous siRNAs as well as viral dsRNA, and we speculate that the use of diverse accessory factors allows C. elegans Dicer to carry out these distinct tasks.

Reviewer #2 (Public Review):

Summary:

To investigate the evolutionary relationship between the RNAi pathway and innate immunity, this study uses biochemistry and structural biology to investigate the trimeric complex of Dicer1, DRH-1 (a RIGI homologue), and RDE-4, which exists in C. elegans. The three subunits were co-expressed to promote stable purification of the complex. This complex promoted ATPdependent cleavage of blunt-ended dsRNAs. A detailed kinetic analysis was also carried out to determine the role of each subunit of the trimeric complex in both the specificity and efficiency of cleavage. These studies indicate that RDE-4 is critical for cleavage while DRC-1 is primarily involved in the specificity of the reaction, and DRH-1 promotes ATP hydrolysis. Finally, a moderate density (6-7 angstrom) cryo-EM structure is presented with attempts to position each of the components.

Strengths:

(1) Newly described methods for studying the C. elegans DICER complex.

(2) New structure, albeit only moderate resolution.

(3) Kinetic study of the complex in the presence and absence of individual subunits and mutations, provides detailed insight into the contribution of each subunit.

Weaknesses:

(1) Limited insight due to limited structural resolution.

(2) No attempts to extend findings to other Dicer or RLR systems.

Overall, we agree with the assessment of this reviewer, and we thank them for their efforts in evaluating our manuscript. Whenever possible we have discussed the similarities and differences of the C. elegans Dicer to other Dicers and RLR systems. We are unsure how we could have expanded upon this further (as suggested in point 2).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor recommendations to the authors:

Page 10: To assess the role of ATP hydrolysis for dsRNA binding, please refrain from using the term "fuzzy band" as a qualitative measure of RNA binding to the ternary complexes.

We searched our entire manuscript and did not find the term “fuzzy band.” We did describe some of the bands in the gel shift assays as “diffuse.” This is an accurate description of the bands we see in our gels and distinguishes them from other more well-defined bands.

Page 13: "positioned internally" - please explain "internally" better here.

We agree with the reviewer that “positioned internally” is confusing. In our revised manuscript we have changed this sentence to (Page 13, line 1):

“Under these conditions, we obtained a 2.9 Å reconstruction of the helicase and CTD domains of DRH-1 bound to the middle region of the dsRNA, rather than its terminus (Figures 4C and S9), suggesting that DRH-1 hydrolyzes ATP to translocate along dsRNA.”

Page 13: Please re-consider the detailed description of the dsRNA:DRH-1 contacts.

We feel it is very important to illustrate and describe these contacts, which will be of interest to those who study mammalian RLRs.

Figure 1C/D: Please write "minus/+ ATP" on top of the gels to make this distinction more clearly visible.

In our original manuscript the gels are labeled with “minus ATP” (panel C) or “5mM ATP” (panel D) on the left to indicate both gels in panel C and both gels in panel D have the same conditions. This is also stated in the figure legend. We have not made revisions in response to this comment because we think it is already clear.

Figure 2: Please explain R = RDE-4 in a clearly visible legend.

We agree with the author that the illustration above the gels was not explained clearly. In our revised manuscript we have added the sentence below to the beginning of Figure legend 2A. “Cartoons indicate complexes and variants, with mutations in DCR-1 (green) and DRH-1 (blue) indicated with the amino acid change, and the presence of RDE-4 (R) represented with a purple circle.”

Figure 4A: Please label the DRH-1 helicase domain and the C-terminal domain.

We agree with the reviewer that we could more clearly define our labeled domains. In the revised manuscript we have added a sentence to the legend of Figure 4A: “The domains of DCR-1, DRH-1, and RDE-4 are color coded the same as in Fig 1A. For simplicity, only domains discussed in the text are labeled.”

Reviewer #2 (Recommendations For The Authors):

This study is complete in that all necessary controls and data are included and the authors are careful in their interpretation so as to not overstate the data or conclusions. The only suggestion is that further extension of the study to address the weaknesses above would increase the breadth of impact of this work.

We thank the reviewer for their thoughtful comments. Weaknesses are addressed above in public reviews, and we will add again that we agree that a higher resolution structure would provide additional insight. In ongoing research, we are working towards this goal.

Author Response

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

We thank the reviewers for their careful comments. We sincerely agree with the comments from both reviewers, and noticed the word “cell transplantation”, throughout the manuscript including the title, was confusing. We revised the manuscript to clarify the aim of the study, and to express the conclusion more straightforwardly.

Response to the reviewers:

We interpret the data of the present study as the color of each RPE cell is a temporal condition which does not necessarily represent the quality (e.g. for cell transplantation) of the cells. We consider this may be applicable not only in vitro but also in vivo, although we do not know whether RPE shows heterogeneous level of pigmentation in vivo.

As our concern for iPSC-RPE is always about their quality for cell transplantation, maybe we haven’t fairly evaluated the scientific significance obtained from the present study.

Another thing we noticed was, although we used the term “cell transplantation” to explain what we meant by “quality” of the cells, we agree this was confusing. The aim of the study was not to show how the pigmentation level of transplant-RPE affects the result of cell transplantation, but to show the heterogeneous gene expression of iPSC-derived RPE cells, and the less correlation of the heterogeneity with pigmentation level. We went through the manuscript, including the title, to more straightforwardly lead this conclusion: the degree of pigmentation had some but weak correlation with the expression levels of functional genes, and the reason for the weakness of the correlation may be because the color is a temporal condition (as we interpreted from the data) that is different from more stable characteristics of the cells.

We agree that “cell transplantation” in the title (and other parts) was misleading. So, we changed the title, and removed the phrase that led as if the aim of the study was to show something about cell transplantation or in vivo results.

Also, to face scientifically significant results obtained from the present study appropriately, we discussed more about the correlation of the pigmentation level with some functional genes, and brought this as one of the conclusions of the manuscript.

Author Response

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

We thank the two reviewers for their very thoughtful suggestions and the editors for writing the eLife assessment. We will submit a revised manuscript that addresses most comments and include a point-by-point response to the reviewers. We will provide evidence that overexpression of the HtrA1 protease and knockdown of its inhibitor SerpinE2 reduce the development of neural crest-derived cartilage elements in the head of Xenopus embryos. This will be done by whole mount in situ hybridization, using a probe for the chondrogenic marker Sox9. We will also provide two time-lapse movies showing (1) collective migration of cranial neural crest cells in culture and (2) failure of these cells to adhere to fibronectin upon SerpinE2 depletion. We will discuss in more depth how the SerpinE2-HtrA1 proteolytic pathway and its target, the heparan sulfate proteoglycan Syndecan-4, might regulate FGF signaling and suggest a model, in which serpin secreted by the leader cells and the protease released by the follower cells might establish a chemotactic FGF gradient for the directed migration of the neural crest cohort. The criticism that other factors such as proliferation and cell survival might contribute to the observed craniofacial phenotypes upon misexpression of SerpinE2 and HtrA1, and that it remains unclear to what extent the mechanism reported here is conserved in the trunk neural crest is valid. The reason we focused on the more amenable cranial neural crest in the Xenopus embryo and used a multitude of approaches – structure-function studies, biochemical analyses, in vitro explant assays and epistatic experiments in vivo – was to validate a central finding: that an extracellular proteolytic pathway involving a serpin, a protease and a proteoglycan regulates by a double inhibition mechanism collective cell migration.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

(1) Methods, please state the sex of the mice.

This has now been added to the methods section:

“Three to nine month old Thy1-GCaMP6S mice (Strain GP4.3, Jax Labs), N=16 stroke (average age: 5.4 months; 13 male, 3 female), and 5 sham (average age: 6 months; 3 male, 2 female), were used in this study.”

(2) The analysis in Fig 3B-D, 4B-C, and 6A, B highlights the loss of limb function, firing rate, or connections at 1 week but this phenomenon is clearly persisting longer in some datasets (Fig. 3 and 6). Was there not a statistical difference at weeks 2,3,4,8 relative to "Pre-stroke" or were comparisons only made to equivalent time points in the sham group? Personally, I think it is useful to compare to "pre-stroke" which should be more reflective of that sample of animals than comparing to a different set of animals in the Sham group. A 1 sample t-test could be used in Fig 4 and 6 normalized data.

On further analysis of our datasets, normalization throughout the manuscript was unnecessary for proper depiction of results, and all normalized datasets have been replaced with nonnormalized datasets. All within group statistics are now indicated within the manuscript.

(3) Fig 4A shows a very striking change in activity that doesn't seem to be borne out with group comparisons. Since many neurons are quiet or show very little activity, did the authors ever consider subgrouping their analysis based on cells that show high activity levels (top 20 or 30% of cells) vs those that are inactive most of the time? Recent research has shown that the effects of stroke can have a disproportionate impact on these highly active cells versus the minimally active ones.

A qualitative analysis supports a loss of cells with high activity at the 1-week post-stroke timepoint, and examination of average firing rates at 1-week shows reductions in the animals with the highest average rates. However, we have not tracked responses within individual neurons or quantitatively analyzed the data by subdividing cells into groups based on their prestroke activity levels. We have amended the discussion of the manuscript with the following to highlight the previous data as it relates to our study:

“Recent research also indicates that stroke causes distinct patterns of disruption to the network topology of excitatory and inhibitory cells [73], and that stroke can disproportionately disrupt the function of high activity compared to low activity neurons in specific neuron sub-types [61]. Mouse models with genetically labelled neuronal sub-types (including different classes of inhibitory interneurons) could be used to track the function of those populations over time in awake animals.”

(4) Fig 4 shows normalized firing rates when moving and at rest but it would be interesting to know what the true difference in activity was in these 2 states. My assumption is that stroke reduces movement therefore one normalizes the data. The authors could consider putting non-normalized data in a Supp figure, or at least provide a rationale for not showing this, such as stating that movement output was significantly suppressed, hence the need for normalization.

On further analysis of our datasets, normalization throughout the manuscript was unnecessary for proper depiction of results, and all normalized datasets have been replaced with nonnormalized datasets.

(5) One thought for the discussion. The fact that the authors did not find any changes in "distant" cortex may be specific to the region they chose to sample (caudal FL cortex). It is possible that examining different "distant" regions could yield a different outcome. For example, one could argue that there may have been no reason for this area to "change" since it was responsive to FL stimuli before stroke. Further, since it was posterior to the stroke, thalamocortical projects should have been minimally disturbed.

We would like to thank the reviewer for this comment. We have amended the discussion with the following:

“Our results suggest a limited spatial distance over which the peri-infarct somatosensory cortex displays significant network functional deficits during movement and rest. Our results are consistent with a spatial gradient of plasticity mediating factors that are generally enhanced with closer proximity to the infarct core [84,88,90,91]. However, our analysis outside peri-infarct cortex is limited to a single distal area caudal to the pre-stroke cFL representation. Although somatosensory maps in the present study were defined by a statistical criterion for delineating highly responsive cortical regions from those with weak responses, the distal area in this study may have been a site of activity that did not meet the statistical criterion for inclusion in the baseline map. The lack of detectable changes in population correlations, functional connectivity, assembly architecture and assembly activations in the distal region may reflect minimal pressure for plastic change as networks in regions below the threshold for regional map inclusion prior to stroke may still be functional in the distal cortex. Thus, threshold-based assessment of remapping may further overestimate the neuroplasticity underlying functional reorganization suggested by anaesthetized preparations with strong stimulation. Future studies could examine distal areas medial and anterior to the cFL somatosensory area, such as the motor and pre-motor cortex, to further define the effect of FL targeted stroke on neuroplasticity within other functionally relevant regions. Moreover, the restriction of these network changes to peri-infarct cortex could also reflect the small penumbra associated with photothrombotic stroke, and future studies could make use of stroke models with larger penumbral regions, such as the middle cerebral artery occlusion model. Larger injuries induce more sustained sensorimotor impairment, and the relationship between neuronal firing, connectivity, and neuronal assemblies could be further probed relative to recovery or sustained impairment in these models.”

Minor comments:

Line 129, I don't necessarily think the infarct shows "hyper-fluorescence", it just absorbs less white light (or reflects more light) than blood-rich neighbouring regions.

Sentence in the manuscript has been changed to:

“Resulting infarcts lesioned this region, and borders could be defined by a region of decreased light absorption 1 week post-stroke (Fig 1D, Top).”

Line 130-132: the authors refer to Fig 1D to show cellular changes but these cannot be seen from the images presented. Perhaps a supplementary zoomed-in image would be helpful.

As changes to the morphology of neurons are not one of the primary objectives of this study, and sampled resolution was not sufficiently high to clearly delineate the processes of neurons necessary for morphological assessment, we have amended the text as follows:

“Within the peri-infarct imaging region, cellular dysmorphia and swelling was visually apparent in some cells during two photon imaging 1-week after stroke, but recovered over the 2 month poststroke imaging timeframe (data not shown). These gross morphological changes were not visually apparent in the more distal imaging region lateral to the cHL.”

Lines 541-543, was there a rationale for defining movement as >30mm/s? Based on a statistical estimate of noise?

Text has been altered as follows:

“Animal movement within the homecage during each Ca2+ imaging session was tracked to determine animal speed and position. Movement periods were manually annotated on a subset of timeseries by co-recording animal movement using both the Mobile Homecage tracker, as well as a webcam (Logitech C270) with infrared filter removed. Movement tracking data was low pass filtered to remove spurious movement artifacts lasting below 6 recording frames (240ms). Based on annotated times of animal movement from the webcam recordings and Homecage tracking, a threshold of 30mm/s from the tracking data was determined as frames of animal movement, whereas speeds below 30mm/s was taken as periods of rest.”

Lines 191-195: Note that although the finding of reduced neural activity is in disagreement with a multi-unit recording study, it is consistent with other very recent single-cell Ca++ imaging data after stroke (PMID: 34172735 , 34671051).

Text has been altered as follows:

“These results indicate decreased neuronal spiking 1-week after stroke in regions immediately adjacent to the infarct, but not in distal regions, that is strongly related to sensorimotor impairment. This finding runs contrary to a previous report of increased spontaneous multi-unit activity as early as 3-7 days after focal photothrombotic stroke in the peri-infarct cortex [1], but is in agreement with recent single-cell calcium imaging data demonstrating reduced sensoryevoked activity in neurons within the peri-infarct cortex after stroke [60,61].”

Fig 7. I don't understand what the color code represents. Are these neurons belonging to the same assembly (or membership?).

That is correct, neurons with identical color code belong to the same assembly. The legend of Fig 7 has been modified as follows to make this more explicit:

“Fig 7. Color coded neural assembly plots depict altered neural assembly architecture after stroke in the peri-infarct region. (A) Representative cellular Ca2+ fluorescence images with neural assemblies color coded and overlaid for each timepoint. Neurons belonging to the same assembly have been pseudocolored with identical color. A loss in the number of neural assemblies after stroke in the peri-infarct region is visually apparent, along with a concurrent increase in the number of neurons for each remaining assembly. (B) Representative sham animal displays no visible change in the number of assemblies or number of neurons per assembly.”

Reviewer #2 (Recommendations For The Authors):

Materials and methods

Identification of forelimb and hindlimb somatosensory cortex representations [...] Cortical response areas are calculated using a threshold of 95% peak activity within the trial. The threshold is presumably used to discriminate between the sensory-evoked response and collateral activation / less "relevant" response (noise). Since the peak intensity is lower after stroke, the "response" area is larger - lower main signal results in less noise exclusion. Predictably, areas that show a higher response before stroke than after are excluded from the response area before stroke and included after. While it is expected that the remapped areas will exhibit a lower response than the original and considering the absence of neuronal activity, assembly architecture, or functional connectivity in the "remapped" regions, a minimal criterion for remapping should be to exhibit higher activation than before stroke. Please use a different criterion to map the cortical response area after stroke.

We would like to thank the reviewer for this comment. We agree with the reviewer’s assessment of 95% of peak as an arbitrary criterion of mapped areas. To exclude noise from the analysis of mapped regions, a new statistical criterion of 5X the standard deviation of the baseline period was used to determine the threshold to use to define each response map. These maps were used to determine the peak intensity of the forelimb response. We also measured a separate ROI specifically overlapping the distal region, lateral to the hindlimb map, to determine specific changes to widefield Ca2+ responses within this distal region. We have amended the text as follows and have altered Figure 2 with new data generated from our new criterion for cortical mapping.

“The trials for each limb were averaged in ImageJ software (NIH). 10 imaging frames (1s) after stimulus onset were averaged and divided by the 10 baseline frames 1s before stimulus onset to generate a response map for each limb. Response maps were thresholded at 5 times the standard deviation of the baseline period deltaFoF to determine limb associated response maps. These were merged and overlaid on an image of surface vasculature to delineate the cFL and cHL somatosensory representations and were also used to determine peak Ca2+ response amplitude from the timeseries recordings. For cFL stimulation trials, an additional ROI was placed over the region lateral to the cHL representation (denoted as “distal region” in Fig 2E) to measure the distal region cFL evoked Ca2+ response amplitude pre- and post-stroke. The dimensions and position of the distal ROI was held consistent relative to surface vasculature for each animal from pre- to post-stroke.”

Animals

Mice used have an age that goes from 3 to 9 months. This is a big difference given that literature on healthy aging reports changes in neurovascular coupling starting from 8-9 months old mice. Consider adding age as a covariate in the analysis.

We do not have sufficient numbers of animals within this study to examine the effect of age on the results observed herein. We have amended the discussion with the following to address this point:

“A potential limitation of our data is the undefined effect of age and sex on cortical dynamics in this cohort of mice (with ages ranging from 3-9 months) after stroke. Aging can impair neurovascular coupling [102–107] and reduce ischemic tolerance [108–111], and greater investigation of cortical activity changes after stroke in aged animals would more effectively model stroke in humans. Future research could replicate this study with mice in middle-age and aged mice (e.g. 9 months and 18+ months of age), and with sufficient quantities of both sexes, to better examine age and sex effects on measures of cortical function.”

Statistics

Please describe the "normalization" that was applied to the firing rate. Since a mixedeffects model was used, why wasn't baseline simply added as a covariate? With this type of data, normalization is useful for visualization purposes.

On further analysis of our datasets, normalization throughout the manuscript was unnecessary for the visualization of results, and all normalized datasets have been replaced with nonnormalized datasets. All within group comparisons are now indicated throughout the manuscript and in the figures.

Introduction

Line 93 awake, freely behaving but head-fixed. That's not freely. Should just say behaving.

Sentence has been edited as follows:

“We used awake, behaving but head-fixed mice in a mobile homecage to longitudinally measure cortical activity, then used computational methods to assess functional connectivity and neural assembly architecture at baseline and each week for 2 months following stroke.”

110 - 112 The last part of this sentence is unjustified because these areas have been incorrectly identified as locations of representational remapping.

We agree with the reviewer and have amended the manuscript as follows after re-analyzing the dataset on widefield Ca2+ imaging of sensory-evoked responses: “Surprisingly, we also show that significant alterations in neuronal activity (firing rate), functional connectivity, and neural assembly architecture are absent within more distal regions of cortex as little as 750 µm from the stroke border, even in areas identified by regional functional imaging (under anaesthesia) as ‘remapped’ locations of sensory-evoked FL activity 8-weeks post-stroke.”

Results

149-152 There is no observed increase in the evoked response area. There is an observed change in the criteria for what is considered a response.

We agree with the reviewer. Text has been amended as follows:

“Fig 2A shows representative montages from a stroke animal illustrating the cortical cFL and cHL Ca2+ responses to 1s, 100Hz limb stimulation of the contralateral limbs at the pre-stroke and 8week post-stroke timepoints. The location and magnitude of the cortical responses changes drastically between timepoints, with substantial loss of supra-threshold activity within the prestroke cFL representation located anterior to the cHL map, and an apparent shift of the remapped representation into regions lateral to the cHL representation at 8-weeks post-stroke. A significant decrease in the cFL evoked Ca2+ response amplitude was observed in the stroke group at 8-weeks post-stroke relative to pre-stroke (Fig 2B). This is in agreement with past studies [19–25], and suggests that cFL targeted stroke reduces forelimb evoked activity across the cFL somatosensory cortex in anaesthetized animals even after 2 months of recovery. There was no statistical change in the average size of cFL evoked representation 8-weeks after stroke (Fig 2C), but a significant posterior shift of the supra-threshold cFL map was detected (Fig 2D). Unmasking of previously sub-threshold cFL responsive cortex in areas posterior to the original cFL map at 8-weeks post-stroke could contribute to this apparent remapping. However, the amplitude of the cFL evoked widefield Ca2+ response in this distal region at 8-weeks post-stroke remains reduced relative to pre-stroke activation (Fig 2E). Previous studies suggest strong inhibition of cFL evoked activity during the first weeks after photothrombosis [25]. Without longitudinal measurement in this study to quantify this reduced activation prior to 8-weeks poststroke, we cannot differentiate potential remapping due to unmasking of the cFL representation that enhances the cFL-evoked widefield Ca2+ response from apparent remapping that simply reflects changes in the signal-to-noise ratio used to define the functional representations. There were no group differences between stroke and sham groups in cHL evoked intensity, area, or map position (data not shown).”

A lot of the nonsignificant results are reported as "statistical trends towards..." While the term "trend" is problematic, it remains common in its use. However, assigning directionality to the trend, as if it is actively approaching a main effect, should be avoided. The results aren't moving towards or away from significance. Consider rewording the way in which these results are reported.

We have amended the text to remove directionality from our mention of statistical trends.

R squared and p values for significant results are reported in the "impaired performance on tapered beam..." and "firing rate of neurons in the peri-infarct cortex..." subsections of the results, but not the other sections. Please report the results in a consistent manner.

R-squared and p-values have been removed from the results section and are now reported in figure captions consistently.

Discussion

288 Remapping is defined as "new sensory-evoked spiking". This should be the main criterion for remapping, but it is not operationalized correctly by the threshold method.

With our new criterion for determining limb maps using a statistical threshold of 5X the standard deviation of baseline fluorescence, we have edited text throughout the manuscript to better emphasize that we may not be measuring new sensory-evoked spiking with the mesoscale mapping that was done. We have edited the discussion as follows:

“Here, we used longitudinal two photon calcium imaging of awake, head-fixed mice in a mobile homecage to examine how focal photothrombotic stroke to the forelimb sensorimotor cortex alters the activity and connectivity of neurons adjacent and distal to the infarct. Consistent with previous studies using intrinsic optical signal imaging, mesoscale imaging of regional calcium responses (reflecting bulk neuronal spiking in that region) showed that targeted stroke to the cFL somatosensory area disrupts the sensory-evoked forelimb representation in the infarcted region. Consistent with previous studies, this functional representation exhibited a posterior shift 8-weeks after injury, with activation in a region lateral to the cHL representation. Notably, sensory-evoked cFL representations exhibited reduced amplitudes of activity relative to prestroke activation measured in the cFL representation and in the region lateral the cHL representation. Longitudinal two-photon calcium imaging in awake animals was used to probe single neuron and local network changes adjacent the infarct and in a distal region that corresponded to the shifted region of cFL activation. This imaging revealed a decrease in firing rate at 1-week post-stroke in the peri-infarct region that was significantly negatively correlated with the number of errors made with the stroke-affected limbs on the tapered beam task. Periinfarct cortical networks also exhibited a reduction in the number of functional connections per neuron and a sustained disruption in neural assembly structure, including a reduction in the number of assemblies and an increased recruitment of neurons into functional assemblies. Elevated correlation between assemblies within the peri-infarct region peaked 1-week after stroke and was sustained throughout recovery. Surprisingly, distal networks, even in the region associated with the shifted cFL functional map in anaesthetized preparations, were largely undisturbed.”

“Cortical plasticity after stroke Plasticity within and between cortical regions contributes to partial recovery of function and is proportional to both the extent of damage, as well as the form and quantity of rehabilitative therapy post-stroke [80,81]. A critical period of highest plasticity begins shortly after the onset of stroke, is greatest during the first few weeks, and progressively diminishes over the weeks to months after stroke [19,82–86]. Functional recovery after stroke is thought to depend largely on the adaptive plasticity of surviving neurons that reinforce existing connections and/or replace the function of lost networks [25,52,87–89]. This neuronal plasticity is believed to lead to topographical shifts in somatosensory functional maps to adjacent areas of the cortex. The driver for this process has largely been ascribed to a complex cascade of intra- and extracellular signaling that ultimately leads to plastic re-organization of the microarchitecture and function of surviving peri-infarct tissue [52,80,84,88,90–92]. Likewise, structural and functional remodeling has previously been found to be dependent on the distance from the stroke core, with closer tissue undergoing greater re-organization than more distant tissue (for review, see [52]).”

“Previous research examining the region at the border between the cFL and cHL somatosensory maps has shown this region to be a primary site for functional remapping after cFL directed photothrombotic stroke, resulting in a region of cFL and cHL map functional overlap [25]. Within this overlapping area, neurons have been shown to lose limb selectivity 1-month post-stroke [25]. This is followed by the acquisition of more selective responses 2-months post-stroke and is associated with reduced regional overlap between cFL and cHL functional maps [25]. Notably, this functional plasticity at the cellular level was assessed using strong vibrotactile stimulation of the limbs in anaesthetized animals. Our findings using longitudinal imaging in awake animals show an initial reduction in firing rate at 1-week post-stroke within the peri-infarct region that was predictive of functional impairment in the tapered beam task. This transient reduction may be associated with reduced or dysfunctional thalamic connectivity [93–95] and reduced transmission of signals from hypo-excitable thalamo-cortical projections [96]. Importantly, the strong negative correlation we observed between firing rate of the neural population within the peri-infarct cortex and the number of errors on the affected side, as well as the rapid recovery of firing rate and tapered beam performance, suggests that neuronal activity within the peri-infarct region contributes to the impairment and recovery. The common timescale of neuronal and functional recovery also coincides with angiogenesis and re-establishment of vascular support for peri-infarct tissue [83,97–100].”

“Consistent with previous research using mechanical limb stimulation under anaesthesia [25], we show that at the 8-week timepoint after cFL photothrombotic stroke the cFL representation is shifted posterior from its pre-stroke location into the area lateral to the cHL map. Notably, our distal region for awake imaging was directly within this 8-week post-stroke cFL representation. Despite our prediction that this distal area would be a hotspot for plastic changes, there was no detectable alteration to the level of population correlation, functional connectivity, assembly architecture or assembly activations after stroke. Moreover, we found little change in the firing rate in either moving or resting states in this region. Contrary to our results, somatosensoryevoked activity assessed by two photon calcium imaging in anesthetized animals has demonstrated an increase in cFL responsive neurons within a region lateral to the cHL representation 1-2 months after focal cFL stroke [25]. Notably, this previous study measured sensory-evoked single cell activity using strong vibrotactile (1s 100Hz) limb stimulation under aneasthesia [25]. This frequency of limb stimulation has been shown to elicit near maximal neuronal responses within the limb-associated somatosensory cortex under anesthesia [101]. Thus, strong stimulation and anaesthesia may have unmasked non-physiological activity in neurons in the distal region that is not apparent during more naturalistic activation during awake locomotion or rest. Regional mapping defined using strong stimulation in anesthetized animals may therefore overestimate plasticity at the cellular level.”

“Our results suggest a limited spatial distance over which the peri-infarct somatosensory cortex displays significant network functional deficits during movement and rest. Our results are consistent with a spatial gradient of plasticity mediating factors that are generally enhanced with closer proximity to the infarct core [84,88,90,91]. However, our analysis outside peri-infarct cortex is limited to a single distal area caudal to the pre-stroke cFL representation. Although somatosensory maps in the present study were defined by a statistical criterion for delineating highly responsive cortical regions from those with weak responses, the distal area in this study may have been a site of activity that did not meet the statistical criterion for inclusion in the baseline map. The lack of detectable changes in population correlations, functional connectivity, assembly architecture and assembly activations in the distal region may reflect minimal pressure for plastic change as networks in regions below the threshold for regional map inclusion prior to stroke may still be functional in the distal cortex. Thus, threshold-based assessment of remapping may further overestimate the neuroplasticity underlying functional reorganization suggested by anaesthetized preparations with strong stimulation. Future studies could examine distal areas medial and anterior to the cFL somatosensory area, such as the motor and pre-motor cortex, to further define the effect of FL targeted stroke on neuroplasticity within other functionally relevant regions. Moreover, the restriction of these network changes to peri-infarct cortex could also reflect the small penumbra associated with photothrombotic stroke, and future studies could make use of stroke models with larger penumbral regions, such as the middle cerebral artery occlusion model. Larger injuries induce more sustained sensorimotor impairment, and the relationship between neuronal firing, connectivity, and neuronal assemblies could be further probed relative to recovery or sustained impairment in these models. Recent research also indicates that stroke causes distinct patterns of disruption to the network topology of excitatory and inhibitory cells [73], and that stroke can disproportionately disrupt the function of high activity compared to low activity neurons in specific neuron sub-types [61]. Mouse models with genetically labelled neuronal sub-types (including different classes of inhibitory interneurons) could be used to track the function of those populations over time in awake animals. A potential limitation of our data is the undefined effect of age and sex on cortical dynamics in this cohort of mice (with ages ranging from 3-9 months) after stroke. Aging can impair neurovascular coupling [102–107] and reduce ischemic tolerance [108–111], and greater investigation of cortical activity changes after stroke in aged animals would more effectively model stroke in humans. Future research could replicate this study with mice in middle-age and aged mice (e.g. 9 months and 18+ months of age), and with sufficient quantities of both sexes, to better examine age and sex effects on measures of cortical function.”

315 - 317 Remodelling is dependent on the distance from the stroke core, with closer tissue undergoing greater reorganization than more distant tissue. There is no evidence that the more distant tissue undergoes any reorganization at all.

We agree with the reviewer that no remodelling is apparent in our distal area. We have removed reference to our study showing remodeling in the distal area, and have amended the text as follows:

“Likewise, structural and functional remodeling has previously been found to be dependent on the distance from the stroke core, with closer tissue undergoing greater re-organization than more distant tissue (for review, see [52]).”

412-414 The authors speculate that a strong stimulation under anaesthesia may unmask connectivity in distal regions. However, the motivation for this paper is that anaesthesia is a confounding factor. It appears to me that, given the results of this study, the authors should argue that the functional connectivity observed under anaesthesia may be spurious.

The incorrect word was used here. We have corrected the paragraph of the discussion and amended it as follows:

“Consistent with previous research using mechanical limb stimulation under anaesthesia [25], we show that at the 8-week timepoint after cFL photothrombotic stroke the cFL representation is shifted posterior from its pre-stroke location into the area lateral to the cHL map. Notably, our distal region for awake imaging was directly within this 8-week post-stroke cFL representation. Despite our prediction that this distal area would be a hotspot for plastic changes, there was no detectable alteration to the level of population correlation, functional connectivity, assembly architecture or assembly activations after stroke. Moreover, we found little change in the firing rate in either moving or resting states in this region. Contrary to our results, somatosensoryevoked activity assessed by two photon calcium imaging in anesthetized animals has demonstrated an increase in cFL responsive neurons within a region lateral to the cHL representation 1-2 months after focal cFL stroke [25]. Notably, this previous study measured sensory-evoked single cell activity using strong vibrotactile (1s 100Hz) limb stimulation under aneasthesia [25]. This frequency of limb stimulation has been shown to elicit near maximal neuronal responses within the limb-associated somatosensory cortex under anesthesia [101]. Thus, strong stimulation and anaesthesia may have unmasked non-physiological activity in neurons in the distal region that is not apparent during more naturalistic activation during awake locomotion or rest. Regional mapping defined using strong stimulation in anesthetized animals may therefore overestimate plasticity at the cellular level.”

Figures

Figure 1 and 2: Scale bar missing.

Scale bars added to both figures.

Figure 2: The representative image shows a drastic reduction of the forelimb response area, contrary to the general description of the findings. It would also be beneficial to see a graph with lines connecting the pre-stroke and 8-week datapoints.

The data for Figure 2 has been re-analyzed using a new criterion of 5X the standard deviation of the baseline period for determining the threshold for limb mapping. Figure 2 and relevant manuscript and figure legend text has been amended. In agreement with the reviewers observation, there is no increase in forelimb response area, but instead a non-significant decrease in the average forelimb area.

Author Response

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

We greatly thank you and the reviewers for your expert comments and valuable suggestions on our manuscript. After reading these comments, we realized that the previous version of the manuscript contained some weak points. Surely, the issues raised by the six reviewers were of great help in the revision of our manuscript.

According to the comments, we have now fully revised the manuscript to address most of the questions and suggestions. In addition, we reworded some parts of the Introduction, Results and Discussion, Figures, Figure legends and Experimental Methods to increase the rigor of our conclusions.

Overall, you will see that we have paid serious attention to all the concerns and criticisms expressed by reviewers. Addressing these various issues has most certainly allowed us to prepare a much-improved manuscript and for this we offer our hearty thanks.

Reviewer #1 (Public Review):

Summary:

The organization of cell surface receptors in membrane nanodomains is important for signaling, but how this is regulated is poorly understood. In this study, the authors employ TIRFM single-molecule tracking combined with multiple analyses to show that ligand exposure increases the diffusion of the immune receptor FLS2 in the plasma membrane and its co-localization with remorin REM1.3 in a manner dependent on the phosphosite S938. They additionally show that ligand increases the dwell time of FLS2, and this is linked to FLS2 endocytosis, also in a manner dependent on S938 phosphorylation. The study uncovers a regulatory mechanism of FLS2 localization in the nanodomain crucial for signaling.

Strengths:

TIRFM single-molecule tracking, FRAP, FRET, and endocytosis experiments were nicely done. The role of S938 phosphorylation is convincing.

Weaknesses:

Question 1: The model suggests that S938 is phosphorylated upon flg22 treatment. This is actually not known.

Reply: Thank you for your expert comments. Although the phosphorylation of Ser-938 upon flg22 treatment is not known, the model presented in the manuscript is based on previous studies that have shown the importance of Ser-938 phosphorylation for the function of FLS2 (Cao et al, 2013). When it is mutated to the phosphorylation-mimicking residues aspartate or glutamate, immune responses remain normal. These findings suggest that the phosphorylation of Ser-938 plays a critical role in activating defense mechanisms upon flagellin detection (Cao et al, 2013). Now we added the results of Cao et al. (2013) to the introduction to strengthen in the revised manuscript.

Question 2: In addition, the S938D mutant does not show constitutively increased diffusion and co-localization with remorin. It is necessary to soften the tone in the conclusion.

Reply: We appreciate the valuable suggestions from the reviewer. Based on our findings, we observed that the phosphorylation of Ser-938 significantly impacts the dynamics of flg22-induced FLS2. However, it does not alter the diffusion coefficient of FLS2 itself. In the revised manuscript, we have carefully adjusted the conclusion by softening the tone to reflect these findings.

Question 3: The introduction (only two paragraphs) and discussion are not properly written in the context of the current understanding of plant receptors in nanodomains. The authors basically just cited a few publications of their own, and this is not acceptable.

Reply: We accepted the criticisms here. Now, we have reworded the introduction and discussion sections to improve clarity. Furthermore, we have incorporated several new reports on plant receptors in nanodomains into the revised manuscript. Besides, we deleted some publications from our own group, while citing the latest references on plant receptors and nanodomains.

Reviewer #2 (Public Review):

Summary:

The research conducted by Yaning Cui and colleagues delves into understanding FLS2-mediated immunity. This is achieved by comparing the spatiotemporal dynamics of an FLS2-S938A mutant and FLS2-WT, especially in relation to their association with the remorin protein. To delineate the differences between the FLS2-S938A mutant and FLS2-WT, they utilized a plethora of advanced fluorescent imaging techniques. By analyzing surface dynamics and interactions involving the receptor signal co-receptor BAK1 and remorin proteins, the authors propose a model of how FLS2 and BAK1 are assembled and positioned within a remorin-specific nano-environment during FLS2 ligand-induced immune responses.

Strengths:

These techniques offer direct visualizations of molecular dynamics and interactions, helping us understand their spatial relationships and interactions during innate immune responses. Advanced cell biology imaging techniques are crucial for obtaining high-resolution insights into the intracellular dynamics of biomolecules. The demonstrated imaging systems are excellent examples to be used in studying plant immunity by integrating other functional assays. Weaknesses:

It's essential to acknowledge that every fluorescence-based method, just like biochemical assays, comes with its unique limitations. These often pertain to spatial and temporal resolutions, as well as the sensitivity of the cameras employed in each setup. Meticulous interpretation is pivotal to guarantee an accurate depiction and to steer clear of potential misunderstandings when employing specific imaging systems to analyze molecular attributes. Moreover, a discerning interpretation and accurate image analysis can offer invaluable guidance for future studies on plant signaling molecules using these nice cell imaging techniques. For instance, although single-particle analysis couldn't conclusively link FLS2 and remorin, FLIM-FRET effectively highlighted their ligand-triggered association and the disengagement brought on by mutations. While these methodologies seemed to present differing outcomes, they were described in the manuscript as harmonious. In reality, these differences could highlight distinct protein populations active in immune responses, each accentuated differently by the respective imaging techniques due to their individual spatial and temporal limitations. Addressing these variations is imperative, especially when designing future imaging explorations of immune complexes.

Reply: Thank you for your insightful comments and suggestions. We appreciate your expertise in fluorescence-based methods and the importance of careful interpretation and accurate image analysis. We agree with you that different imaging techniques may have their limitations and can highlight distinct aspects of protein dynamics and interactions.

In our study, we used single-particle analysis and FLIM-FRET to investigate the spatiotemporal dynamics of FLS2 and its association with remorin. While single-particle analysis did not conclusively link FLS2 and remorin, FLIM-FRET effectively highlighted their ligand-triggered association and the disengagement caused by mutations. We acknowledge that these techniques may have different spatial and temporal resolutions, leading to the discrepancy in their results. However, after the normalized treatment, we can provide very similar conclusions. Accordingly, we have revised the manuscript.

Reviewer #3 (Public Review):

Summary:

Receptor kinases (RKs) perceive extracellular signals to regulate many processes in plants. FLS2 is an RK that acts as a pattern-recognition receptor (PRR) to recognize bacterial flagellin and activate pattern-triggered immunity (PTI). PRRs such as FLS2 have been previously shown to reside within PM nanodomains, which can regulate downstream PTI signaling. In the current manuscript, Cui et al use single particle tracking to characterize the effect of previously-described phosposite mutants (FLS2-S938A/D) on the PM organization, endocytosis, and signaling functions of FLS2. The authors confirm that FLS2-S938D but not -S938A is functional for flg22-induced responses, while also demonstrating that phopshodead mutation at this site (S938A) prevents flg22-induced sorting into nanodomains and endocytosis. These results are consistent with S938 being an important phosphorylation site for FLS2 function, however, they fall short of demonstrating that membrane disorganization of FLS2-938A is responsible for downstream signaling defects.

Strengths:

The authors' experiments (single particle tracking, co-localization, etc) do a good job of demonstrating how a non-functional version of FLS2 (S938A) does not alter its spatio-temporal dynamics, nanodomain organization, and endocytosis in response to flg22, suggesting that these require a functional receptor and are regulated by intracellular signaling components.

Weaknesses:

Question 1: The authors do not provide direct evidence that S938 phosphorylation specifically affects membrane organization, rather than FLS2 signaling more generally. All evidence is consistent with S938A being a non-functional version of FLS2, wherein an activated/functional receptor is required for all downstream events including membrane re-organization, downstream signalling, internalization, etc. Furthermore, the authors never demonstrate that this site is phosphorylated in planta in the basal or flg22-elicited state.

Reply: Sorry that we did not describe clearly in the original manuscript. In fact, we found in our study that the phosphorylation of the Ser-938 site influences the efficient sorting of FLS2 into AtRem1.3-associated microdomains rather than membrane organization, as depicted in Figure 2. Furthermore, we found that the immune responses are disrupted when Ser-938 is mutated to alanine, which is consistent with previously reported results (Cao et al, 2013). However, they remain normal when mutated to the phosphorylation-mimicking residues aspartate or glutamate. These results suggest that the phosphorylation of Ser-938 is crucial for activating defense mechanisms upon flagellin detection. Although the phosphorylation of Ser-938 in plant at the basal or flg22-elicited state is not known, the model presented in the manuscript is based on the results of our current investigation together with those in the previous study that have shown the importance of Ser-938 phosphorylation for FLS2 function (Cao et al, 2013).

Question 2: As written, the manuscript also has numerous scientific issues, including a misleading/incomplete description of plant immune signaling, lack of context from previous work, and extensive use of inappropriate references.

Reply: We accept the criticism here. After reading the comments, we realized the problem. Now we have revised the misleading or incomplete description of plant immune signaling, added the context of previous works and deleted inappropriate references in the revised manuscript.

Reviewer #1 (Recommendations For The Authors):

Question 1: The description of the data has no details. How many biological repeats were done? How were statistical analyses done? What is the concentration of flg22? How was the calcium flux done (Fig. 4A)? The method also lacks details and relevant references.

Reply: We apologize for the lack of detail in presenting the data. Following your suggestion, we added comprehensive figure legends that provide clear explanations for each figure. Additionally, we included supplementary information on the measurement methods and references pertaining to calcium flux in the revised manuscript.

Question 2: Data in Fig. 4 basically repeated the 2013 PLoS Pathog paper. Why were these experiments even performed? Were GFP-tagged FLS2 lines used in these experiments? If this is the case, the data just verified that the GFP-tagged FLS2 functions as expected and should be moved to supporting data.

Reply: Thanks for the expert suggestions. In our study, we utilized GFP-tagged FLS2 lines to generate FLS2-S938 mutants and conducted experiments to investigate the flg22-induced immune response. Although some experiments in Figure 4 are similar to those reported (Cao et al, 2013), we provided a more detailed analysis of the immune response. The comprehensive analysis included early immune responses and late immune responses, e.g., the activation of a calcium burst, mitogen-activated protein kinases (MAPKs), the induction of immune-responsive genes and callose deposition, ultimately resulting in the inhibition of plant growth. As some results are analogous to the previous paper, we transfer some of the experiments as suggested, including the analysis of MAPKs and callose deposition, to the supporting data section of the revised manuscript.

Question 3: Flg22-induced FLS2-BAK1 association does not require S938, this is consistent with prior study that flg22 acts as a molecular glue for the ectodomains of FLS2 and BAK1 (Sun et al., 2013 Science). This needs to be cited.

Reply: Yes, we agree with the comment. Now we added an additional sentence in the revised manuscript: “ This aligns with the previous finding that flg22 acts as a molecular glue for FLS2 and BAK1 ectodomains (Sun et al., 2013).”

Question 4: Line 50, the references cited do not match what they say here.

Reply: We are sorry for the mistake in citing inappropriate references. In the revised manuscript, we deleted this sentence as well as the incorrect reference.

Question 5: Line 105, "flg22 can act as a ligand-like factor". It is a ligand!

Reply: Sorry for the mistake. Now, the sentence was corrected in the revised manuscript by deleting the word “like”.

Question 6: Line 107, FLS2/BAK1 heterodimerization, not heteroologomerization.

Reply: Now we used “heterodimerization” to replace “heteroologomerization” in the revised manuscript.

Question 7: Line 114, are these really the best references to cite here?

Reply: After reading the comment, we found the references were not suitable here. Now we changed references by citing “(Martinière et al., 2021)” in the revised manuscript.

Question 8: Lines 123-124, the sentence is incomplete.

Reply: In the revised manuscript, we reworded the sentence to make it complete now. We changed “In a previous investigation, we demonstrated that flg22 induces FLS2 translocation from AtFlot1-negative to AtFlot1-positive nanodomains in the plasma membrane, implying a connection between FLS2 phosphorylation and membrane nanodomain distribution (Cui et al., 2018). To validate this, we assessed the association of FLS2/FLS2S938D/FLS2S938A with membrane microdomains, using AtRem1.3-associated microdomains as representatives (Huang et al., 2019).” in the revised manuscript.

Question 9: Lines 169-170, Why is this "most important"?

Reply: Sorry for the unsuitable description. As we have dramatically changed the manuscript, this sentence was deleted from the new version.

Reviewer #2 (Recommendations For The Authors):

Here are some specific areas of ambiguity in the study to be improved.

Question 1: Clarity in statistical analysis is necessary. Many figure legends omit details such as the sample size "n", and the nature of the measurements, like ROIs, images, and dots, the size of the seedlings, etc.

Reply: We appreciated this suggestion, which was raised by the reviewer I as well. Now, we provided the details for each figure, including the sample size, the nature of the measurements in the revised manuscript.

Question 2: Additional background about the choice of FLS2-S938 mutant would be beneficial, given that this mutant doesn't affect the BAK1 interaction but nullifies several PTI responses.

Reply: Yes, we agreed that some additional background is required for the FLS2-S938 mutant. Therefore, we added a sentence here: “FLS2 Ser-938 mutations impact flg22-induced signaling, while BAK1 binding remains unaffected, thereby suggesting Ser-938 regulates other aspects of FLS2 activity (Cao et al., 2013).” in the revised manuscript.

Question 3: A specific segment "... Using CLSM, Fluorescence Correlation Spectroscopy (FCS) and Western blotting, we found that the endocytic vesicles of FLS2S938D increased significantly after flg22 treatment (Figure 3B-3E)..." is not easy to follow. The author may want to differentiate these methods and highlight them by indicting them as endocytic vesicle counting, receptor density on PM measurement by FCS, and WB-based protein degradation characterization to understand such mixed descriptions better. By the way, "Number of Endocytosis" should be "number of endocytic vesicles". Endocytosis is a process and uncountable.

Reply: We thank the reviewer for kindly reminding us to differentiate experimental methods. Therefore, we changed the sentences in the revised manuscript: “Employing confocal laser-scanning microscopy (CLSM) during 10μM flg22 treatment, we tracked FLS2 endocytosis and quantified vesicle numbers over time (Figure 3B). It is evident that both FLS2 and FLS2S938D vesicles appeared 15 min after-flg22 treatment, significantly increasing thereafter (Figure 3C). Notably, only a few vesicles were detected in FLS2S938A-GFP, indicating Ser-938 phosphorylation's impact on flg22-induced FLS2 endocytosis. Additionally, fluorescence correlation spectroscopy (FCS) (Chen et al., 2009) monitored molecular density changes at the PM before and after flg22 treatment (Figure S3F). Figure 3D shows that both FLS2-GFP and FLS2S938D-GFP densities significantly decreased after flg22 treatment, while FLS2S938A-GFP exhibited minimal changes, indicating Ser-938 phosphorylation affects FLS2 internalization. Western blotting confirmed that Ser-938 phosphorylation influences FLS2 degradation after flg22 treatment (Figure 3E), consistent with single-molecule analysis findings.” Besides, we also changed “number of endocytosis” to “the number of endocytic vesicles” in Figure 3C as suggested.

Question 4: In Figure 1 E, a discrepancy exists where the total percentages in the red and black columns don't sum up to 100%, while other groups look right. This needs clarification.

Reply: We are sorry for our carelessness in making the data incomplete. Now we thoroughly supplemented, collated, and rechecked the data in Figure 1E. Due to an oversight during the production of the figure, some data was inadvertently omitted, resulting in the red column not reaching 100%. Besides, we checked the data in the black column again, and the total percentage indeed added up to 100%.

Question 5: Although Figure 1F uses UMAP analysis to differentiate between FLS2WT and A mutants, only data pertaining to the "D" mutant is shown.

Reply: Thank you for the expert comments. Because there are several images in Figure 1, we only selected the data related to the “D” mutant as a representative for display. As suggested, we have added all the UMAP images in the revised supplement figure S1F.

Question 6: There are apparent inconsistencies in the FRAP results, particularly regarding the initial recovery points post-bleaching. A detailed statistical analysis, supplemented with FRAP images over time, should be included for clarity. Were they bleached to a similar ground level before monitoring their recovery? The data points from "before" and "after "bleaching were not shown. I found the red and blue curves showed similar recovery slop, which suggests no long-distance movement changes for all three FLS2 versions, with or without flg22. This is opposite from the conclusions made by the author.

Reply: Thank you for the expert comments. After reading the comments, we recognized this terrible problem. Therefore, we carried out a new FRAP experiment. The new results showed that, following complete bleaching of three samples of FLS2 to ground level, the recovery rates of FLS2 and FLS2S938D under flg22 treatment were significantly higher compared to the control group (Fig. 1G). In contrast, the recovery rates of the FLS2S938A-GFP after flg22 treatment remain similar to that before treatment (Fig. 1G), indicating that the Ser-938 phosphorylation site indeed affects the flg22-induced lateral diffusion of FLS2 at the PM. The new results are basically consistent with the motion range of single-molecule results, which is not contradictory to long-distance movement changes. Accordingly, we incorporated the new time-lapse FRAP images into Figure 1G and S1B.

Question 7: There's a potential typo in Figure 1B regarding the bar size. It could neither possibly be 200 um nor 200 nm. Figure 1A also needs a scale bar.

Reply: Apologies for the mistake. We now corrected “200 μm” to “2 μm”. Besides, we also included a scale bar in Figure 1A in the revised manuscript.

Question 8: Due to the unreliable tracking for a long-time by Imaris, the authors analyzed the tracks within 10s and quantified very short live particles under 4s. Such 4S surface retention for a receptor does not seem to match functional endocytic internalization time for cargo. Even after the endocytic adaptor module recruitment, it would take at least more than 10s to finish the internalization. In the field of endocytosis, these events are often described as abortive endocytic events. However, the disappearance of cargoes, FLS2 in this case, indicates internalization into the cytoplasm, which is interesting. May the author discuss more on how these short events analyzed enhance our understanding of the functional behavior of FLS2?

Reply: We greatly appreciated the valuable comments provided by the reviewer. After thorough consideration, we acknowledged that in our original manuscript, we failed to distinguish the short-lived from the long-lived particles and vaguely put them collectively into the internalized particles. We realized that and it is inappropriate to ambiguously categorize all particles as internalized. Therefore, we added the sentence “Additionally, numerous FLS2 exhibited short-lived dwell times, indicating abortive endocytic events associated with the endocytic pathway and signal transduction (Bertot et al., 2018)” in the revised manuscript.

Question 9: Figure 2D should be comprehensive, presenting data for the WT, A, and D versions.

Reply: Yes, we agreed with the suggestions. Now, we added several representative images for the WT, A, and D versions in the revised manuscript.

Question 10: In Figure 2D, TIRM-SIM should be a typo and rectified to TIRF-SIM. Also, a detailed explanation of the TIRF-SIM setup and its specifics would be important. The imaging approach of SIM, especially the time duration for finishing all frames before reconstruction, is essential to rationalize its use in capturing and measuring an appropriate speed range of particle movement. May the author elaborate on the technique details and the use of TIRF-SIM for colocalization analysis? To clarify these, the author may provide additional TIRF-only movies of FLS2 (WT, A, D) and AtRem1.3 for comparison with TIRF-SIM still images.

Reply: Sorry for the mistake. In the revised manuscript, we have corrected “TIRM-SIM” to “TIRF-SIM”. In order to rationalize its use in capturing and measuring an appropriate speed range of particle movement, we included a more detailed description of the imaging approach and the colocalization analysis of TIRF-SIM in the Materials and Methods section as follows: “The SIM images were taken by a 60 × NA 1.49 objective on a structured illumination microscopy (SIM) platform (DeltaVision OMX SR) with a sCMOS camera (Camera pixel size, 6.5 μm). The light source for TIRF-SIM included diode laser at 488 nm and 568 nm with pixel sizes (μm) of 0.0794 and 0.0794 (Barbieri et al., 2021). For the dual-color imaging, FLS2/FLS2S938A/FLS2S938D-GFP (488 nm/30.0%) and AtRem1.3-mCherry (561 nm/30.0%) were excited sequentially. The exposure time of the camera was set at 50 ms throughout single-particle imaging. The time interval for time-lapse imaging was 100 ms, the total time was 2s, and the total time points were 21s. The Imaris intensity correlation analysis plugin was used to calculate the co-localization ratio.” in the revised manuscript. Furthermore, we provided additional TIRF-SIM movies of FLS2 (WT, A, D) and AtRem1.3.

Question 11: The colocalization displayed in Figure 2D is hard to tell. A colocalization ratio of FLS2-AtRem1.3 is shown as ~0.8%, which has only ~0.2% difference from the flg22-treated condition. "n" of Figure 2F should be specified in the legend, such as a line with a specific length, or an ROI with a specific area size.

Reply: Thank you for the expert comments. Although the increased colocalization after flg22 treatment is not high, the change is statistically significant as compared with the wild type. We agreed that every fluorescence-based method, like biochemical analysis, has its own unique limitations, which were raised by the Reviewer #2 (Public Review) as well. In order to provide strong evidence, we also carried out the FLIM-FRET experiment as a supplement, which can effectively detect their ligand-triggered association or disassociation. From figure 2G and H, we clearly found that the co-localization of FLS2/FLS2S938D-GFP with AtRem1.3-mCherry significantly increase in response to flg22 treatment (FLS2-GFP control: 2.45 ± 0.019 s; FLS2-GFP flg22-treated: 2.39 ± 0.016 s; FLS2S938D-GFP control: 2.42 ± 0.010 ns; FLS2S938D-GFP flg22-treated: 2.35 ± 0.028 ns). In contrast, FLS2S938A-GFP shows no significant changes (control: 2.53 ± 0.011 ns; flg22-treated: 2.56 ± 0.013 ns), indicating that Ser-938 phosphorylation influences efficient sorting of FLS2 into AtRem1.3-associated microdomains. Following the suggestion of the reviewer, we now rearranged the order of 2E and 2F, in which N represents the entire image region used for analysis rather than a specific region of interest.

Question 12: I appreciate the nice results of the FLIM-FRET results for FLS2-Rem1.3. Figure 2H should be supplemented with additional representative images of all FLS2 variants including WT and mutants.

Reply: Thanks for your warm encouragement. As suggested, we added all the representative images in the revised manuscript.

Question 13: The unit of the X-axis of Figure 2E can not be pixel. Should it be, um? In the method, the author could specify the camera model and magnification for TIRF-SIM to understand pixel size of the image better.

Reply: Sorry for the mistake here. Indeed, the unit of the X-axis in Figure 2E should be μm. Now we correct this mistake in Figure 2E in the revised manuscript. Besides, we included a detailed description of the imaging approach of TIRF-SIM in the Materials and Methods section as follows: “The SIM images were taken by a 60 × NA 1.49 objective on a structured illumination microscopy (SIM) platform (DeltaVision OMX SR) with a sCMOS camera (Camera pixel size, 6.5 μm)”.

Question 14: "... as shown in A..." in Figure Legend 2E should be "... as shown in D..."

Reply: Thanks for pointing out this mistake. In the revised manuscript, we used “as shown in D” to replace “as shown in A”.

Question 15: I recommend that the authors exercise caution when drawing conclusions based on the Rem1.3 data and when representing the "microdomain" concept in their final model. While Rem1.3 punctate is a nanometer-sized protein cluster specific to its identity, its shape can be categorized as a nanodomain. Conceptually, however, it neither universally represents all nanodomains nor microdomains, as depicted in Figure 4. We should exercise caution to prevent providing misleading information to the field.

Reply: We thank the reviewer for expert comments. To avoid misleading conclusions, we changed “nanodomains” to “AtRem1.3-associated microdomains” in the revised manuscript. Besides, we have also made modifications to Figure 4.

Reviewer #3 (Recommendations For The Authors):

Question 1: The manuscript needs to be extensively re-written and has severe issues as-is. Many references are either not quite appropriate or are completely unrelated to the use in the text. In general, the current state-of-the-art of PTI and RK signaling is not correctly described or incorporated.

Reply: We accepted the criticisms here. As suggested, we thoroughly rewrote the manuscript to address the concerns raised. Furthermore, we have thoroughly checked and revised the manuscript by removing 21 irrelevant references and adding 30 relevant references. We also incorporated the most up-to-date descriptions of the PTI and RK signaling pathways.

Question 2: Receptor-like kinase (RLK) should generally be receptor kinase (RK) as receptor functions are now well established.

Reply: Yes, we agreed with your expert comment here. Now, we changed “Receptor-like kinase (RLK)” into “receptor kinase (RK)” in the revised manuscript.

Question 3: Line 20 - is this really true?

Reply: Sorry for the mistake. In the revised manuscript, we changed “However, the mechanisms underlying the regulation of FLS2 phosphorylation activity at the plasma membrane in response to flg22 remain largely enigmatic.” to “However, the dynamic FLS2 phosphorylation regulation at the plasma membrane in response to flg22 needs further elucidation.”

Question 4: S938D sorts better in response to Flg22; S938A is unaffected - suggests phosphorylation of S938 is not dynamic in response to Fig 22 but is required for pre-elicitation sorting. Overall, there is a chicken-and-egg problem in this paper: which comes first, immune/signalling functionality or nanodomain sorting? And which is explaining the defects of S938A?

Reply: We thank the reviewer for expert suggestions. In fact, the previous studies showed that membrane microdomains serve as signaling platforms that mediate cargo protein sorting and protein-protein interactions in a variety of contexts (Goldfinger et al. 2017). Since our previous research showed that the disruption of membrane microdomains affected flg22-induced immune signaling (Cui et al. 2018), we speculate that the immune signal occurred after entering the membrane microdomains.

As shown in Figure 1 and 2, ligand exposure leads to an increase in diffusion coefficient and enhanced co-localization with REM1.3, both of which are dependent on the phosphorylation of the Ser-938 site. Deducing from these results, we inferred that the defects in S938A resulted largely from its failure to sort into membrane microdomains. The phosphorylation of the Ser-938 site can regulate FLS2 into functional AtRem1.3-associated microdomains, thereby affecting flg22-induced plant immunity.

Question 5: Line 37 conserved, not conservative (though not technically true - the domain organization is conserved but the ECDs are not conserved).

Reply: Thank you for pointing this mistake out. In the revised manuscript, we used “conserved” to replace “conservative”.

Question 6: Lines 40-42 - not all phosphorylation sites are within the kinase domain, for example, sites are well-described on the JM and/or C-tail regions outside of the kinase domain.

Reply: We accepted the criticisms here. We have corrected the sentence to “with phosphorylation sites mainly located in PKC” in the revised manuscript.

Question 7: Line 42 - what is BIK1? Intro to relevant topics is severely lacking.

Reply: Sorry for the incomplete introduction here. We added the relevant introduction of BIK1 by adding that “Upon recognizing flg22, FLS2 interacts with the co-receptor Brassinosteroid-Insensitive 1-associated Kinase 1 (BAK1), initiating phosphorylation events through the activation of receptor-like cytoplasmic kinases (RLCKs) such as BOTRYTIS-INDUCED KINASE 1 (BIK1) to elicit downstream immune responses (Chinchilla et al., 2006; Li et al., 2016b; Majhi et al., 2021). ” in the revised manuscript.

Question 8: Lines 42-44 - not sure this sequence of events is being properly described (e.g. BIK1 release is unlikely to precede activation by BAK1/SERKs).

Reply: We apologize for not expressing this sentence clearly. Now, we reworded the sentence: “Upon recognizing flg22, FLS2 interacts with the co-receptor Brassinosteroid-Insensitive 1-associated Kinase 1 (BAK1), initiating phosphorylation events through the activation of receptor-like cytoplasmic kinases (RLCKs) such as BOTRYTIS-INDUCED KINASE 1 (BIK1) to elicit downstream immune responses (Chinchilla et al., 2006; Li et al., 2016b; Majhi et al., 2021).” in the revised manuscript.

Question 9: Line 61 - S938 was identified by Cao et al (2013) based on in vitro MS, but was functionally validated using genetic assays, not based on MS.

Reply: Thank you for your comments. Now, we changed the sentence: “In vitro mass spectrometry (MS) identified multiple phosphorylation sites in FLS2. Genetic analysis further identified Ser-938 as a functionally important site for FLS2 in vivo (Cao et al., 2013).” in the revised manuscript.

Question 10: Line 68-69 - phospho-dead and phospho-mimic, not phosphorylated/non-phosphorylated.

Reply: We thank the reviewer for expert suggestions. In the revised manuscript, we changed the sentence by replacing “phosphorylated/non-phosphorylated” with “phospho-mimic” and “phospho-dead”.

Question 11: Lines 104-106 - this is wildly misleading. Flg22 is more than a ligand-like factor, as it is a bona fide ligand, and the heterodimerization with BAK1/SERKs is extremely well-established (and relevant foundational papers should be cited here in place of the authors' previous work).

Reply: We apologize for the incorrect expression here. After reading the comments, we realized the problem which was raised by the reviewer I as well. Now, we changed “ligand-like factor” to “ligand”. Besides, we cited the new references “(Orosa et al., 2018)” to replace the references of our group in the revised manuscript.

Question 12: Lines 107-112 - again, this is confusing. There is a decade of (uncited, undiscussed) work previously establishing that heterodimerization of RK-co-receptor complexes is mediated by extracellular ligand binding and independent of intracellular phosphorylation.

Reply: We thank the reviewer for expert suggestions. Now, we added several sentences in the revised manuscript: “Therefore, we further investigated if Ser-938 phosphorylation affects FLS2/BAK1 heterodimerization. Tesseler segmentation, FRET-FLIM, and smPPI analyses revealed no impact of Ser-938 phosphorylation on FLS2/BAK1 heterodimerization (Figure 2A-C and S2). This aligns with the previous finding that flg22 acts as a molecular glue for FLS2 and BAK1 ectodomains (Sun et al., 2013), confirming the independence of FLS2/BAK1 heterodimerization from phosphorylation, with these events occurring sequentially.”

Question 13: Line 119 - this is the wrong citation - Yu et al 2020 is a review and does not cover RALFs; correct citation is Gronnier et al 2022 eLife.

Reply: In the revised manuscript, we updated the reference from “ (Yu et al., 2020)” to “(Gronnier et al., 2022)”.

Question 14: Lines 123-124 - this sentence is incomplete.

Reply: Sorry for the incomplete sentence. Now we reworded the sentence to “In a previous investigation, we demonstrated that flg22 induces FLS2 translocation from AtFlot1-negative to AtFlot1-positive nanodomains in the plasma membrane, implying a connection between FLS2 phosphorylation and membrane nanodomain distribution (Cui et al., 2018). To validate this, we assessed the association of FLS2/FLS2S938D/FLS2S938A with membrane microdomains, using AtRem1.3-associated microdomains as representatives (Huang et al., 2019).” in the revised manuscript.

Question 15: Line 126 - this requires a reference.

Reply: Yes, we added a new reference: “(Huang et al., 2019)” in the revised manuscript.

Question 16: Lines 125-128 - should clarify that the authors are not looking at direct interaction between FLS2 and REM1.3.

Reply: Sorry for the inappropriate expressions here. In the revised manuscript, we reworded the sentence as follows: “To validate this, we assessed the association of FLS2/FLS2S938D/FLS2S938A with membrane microdomains, using AtRem1.3-associated microdomains as representatives (Huang et al., 2019)” .

Question 17: Line 138 - these are odd references to use for such a broad statement.

Reply: Now the inappropriate references cited here have been deleted.

Question 18: Line 161 - incorrect reference, again.

Reply: Sorry for this mistake. In the revised manuscript, we reworded the sentence and changed the reference.

Question 19: Lines 160-165 - this is very confusing and misleading. I would suggest just having a short section introducing PTI earlier on (with appropriate references).

Reply: As suggestion, we reworded and added a section in the revised manuscript as follows: “PTI plays a pivotal role in host defense against pathogenic infections (Lorrai et al., 2021; Ma et al., 2022). Previous studies demonstrated that FLS2 perception of flg22 initiates a complex signaling network with multiple parallel branches, including calcium burst, mitogen-activated protein kinases (MAPKs) activation, callose deposition, and seedling growth inhibition (Baral et al., 2015; Marcec et al., 2021; Huang et al., 2023). Our focus was to investigate the significance of Ser-938 phosphorylation in flg22-induced plant immunity. Figure 4A-F illustrates diverse immune responses in FLS2 and FLS2S938D plants following flg22 treatment. These responses encompass calcium burst activation, MAPKs cascade reaction, callose deposition, hypocotyl growth inhibition, and activation of immune-responsive genes. In contrast, FLS2S938A (Figure S4A-D) exhibited limited immune responses, underscoring the importance of Ser-938 phosphorylation for FLS2-mediated PTI responses”.

Question 20: Line 166 - these are not appropriate references, again.

Reply: Thank you for the suggestion. In the revised manuscript, we removed the inappropriate references. Besides, we added new references by citing: “(Baral et al., 2015; Marcec et al., 2021)”.

Question 21: Lines 169-173 - this is not relevant, the inhibition of growth by elicitors is extremely well-documented (though not by the refs cited here).

Reply: We reworded the sentence and deleted the inappropriate reference in the revised manuscript.

Question 22: Lines 174-175 - I don't see why this is unexpected, as nanodomain organization of PRRs has been previously described.

Reply: Sorry for the inappropriate expressions here. As we have dramatically changed the manuscript, this sentence was deleted from the new version.

References we added into the revised manuscript

Baral A, Irani NG, Fujimoto M, Nakano A, Mayor S, Mathew MK. 2015. Salt-induced remodeling of spatially restricted clathrin-independent endocytic pathways in Arabidopsis root. Plant Cell 27:1297-315. DOI: 10.1105/tpc.15.00154, PMID: 25901088

Barbieri L, Colin-York H, Korobchevskaya K, Li D, Wolfson DL, Karedla N, Schneider F, Ahluwalia BS, Seternes T, Dalmo RA, Dustin ML, Li D, Fritzsche M. 2021. Two-dimensional TIRF-SIM-traction force microscopy (2D TIRF-SIM-TFM). Nature Communications 12:2169. DOI: 10.1038/s41467-021-22377-9, PMID: 33846317

Bertot L, Grassart A, Lagache T, Nardi G, Basquin C, Olivo-Marin J, Sauvonnet N. 2018. Quantitative and statistical study of the dynamics of clathrin-dependent and -independent endocytosis reveal a differential role of endophilinA2. Cell Reports 22: 1574–1588. DOI:org/10.1016/j.celrep.2018.01.039, PMID: 29425511

Bücherl CA, Jarsch IK, Schudoma C, Segonzac C, Mbengue M, Robatzek S, MacLean D, Ott T, Zipfel C. 2017. Plant immune and growth receptors share common signalling components but localise to distinct plasma membrane nanodomains. eLife 6:e25114. DOI: https://doi.org/10.7554/eLife.25114, PMID: 28262094

Chen Y, Munteanu AC, Huang YF, Phillips J, Zhu Z, Mavros M, Tan W. 2009. Mapping receptor density on live cells by using fluorescence correlation spectroscopy. Chemistry 15:5327-36. DOI: https://doi.org/10.1002/chem.200802305, PMID: 19360825

Chinchilla, D., Bauer, Z., Regenass, M., Boller, T., and Felix, G. 2006. The Arabidopsis receptor kinase FLS2 binds flg22 and determines the specificity of flagellin perception. Plant Cell 18:465-476. doi:10.1105/tpc.105.036574, PMID: 16377758

Gada KD, Kawano T, Plant LD, Logothetis DE. 2022. An optogenetic tool to recruit individual PKC isozymes to the cell surface and promote specific phosphorylation of membrane proteins. The Journal of Biological Chemistry 298:101893. DOI: https://doi.org/10.1016/j.jbc.2022.101893, PMID: 35367414

Gronnier J, Franck CM, Stegmann M, DeFalco TA, Abarca A, von Arx M, Dünser K, Lin W, Yang Z, Kleine-Vehn J, Ringli C, Zipfel C. 2022. Regulation of immune receptor kinase plasma membrane nanoscale organization by a plant peptide hormone and its receptors. eLife 11:e74162. DOI: https://doi.org/10.7554/eLife.74162, PMID: 34989334

Hohmann U, Lau K, Hothorn M. 2017. The structural basis of ligand perception and signal activation by receptor kinases. Annual Review of Plant Biology 68:109–137. DOI: https://doi.org/10.1146/annurev-arplant-042916-040957, PMID: 28125280.

Huang D, Sun Y, Ma Z, Ke M, Cui Y, Chen Z, Chen C, Ji C, Tran TM, Yang L, Lam SM, Han Y, Shu G, Friml J, Miao Y, Jiang L, Chen X. 2019. Salicylic acid-mediated plasmodesmal closure via Remorin-dependent lipid organization. Proceedings of the National Academy of Sciences 116:21274–21284. DOI: https://doi.org/10.1073/pnas.1911892116, PMID: 31575745

Huang Y, Cui J, Li M, Yang R, Hu Y, Yu X, Chen Y, Wu Q, Yao H, Yu G, Guo J, Zhang H, Wu S, Cai Y. 2023. Conservation and divergence of flg22, pep1 and nlp20 in activation of immune response and inhibition of root development. Plant Science 331:111686. DOI: https://doi.org/10.1016/j.plantsci.2023.111686, PMID: 36963637

Jiao C, Gong J, Guo Z, Li S, Zuo Y, Shen Y. 2022. Linalool activates oxidative and calciμm burst and CAM3-ACA8 participates in calciμm recovery in Arabidopsis leaves. International Journal of Molecular Sciences, 23:5357. DOI: https://doi.org/10.3390/ijms23105357, PMID: 35628166

Kim TJ, Lei L, Seong J, Suh JS, Jang YK, Jung SH, Sun J, Kim DH, Wang Y. 2018. Matrix rigidity-dependent regulation of Ca2+ at plasma membrane microdomains by FAK visualized by fluorescence resonance energy transfer. Advanced science, 6:1801290. DOI: https://doi.org/10.1002/advs.201801290, PMID: 30828523

Kontaxi C, Kim N, Cousin MA. 2023. The phospho-regulated amphiphysin/endophilin interaction is required for synaptic vesicle endocytosis. Journal of Neurochemistry 166:248–264. DOI: https://doi.org/10.1111/jnc.15848, PMID: 37243578

Lee Y, Phelps C, Huang T, Mostofian B, Wu L, Zhang Y, Tao K, Chang YH, Stork PJ, Gray JW, Zuckerman DM, Nan X. 2019. High-throughput, single-particle tracking reveals nested membrane domains that dictate KRasG12D diffusion and trafficking. eLife 8:e46393. DOI: https://doi.org/10.7554/eLife.46393, PMID: 31674905

Li B, Meng X, Shan L, He P. 2016a. Transcriptional regulation of pattern-triggered immunity in plants. Cell Host Microbe 19:641-50. DOI: 10.1016/j.chom.2016.04.011, PMID: 27173932

Li L, Kim P, Yu L, Cai G, Chen S, Alfano JR, Zhou JM. 2016b. Activation-dependent destruction of a co-receptor by a pseudomonas syringae effector dampens plant immunity. Cell Host Microbe 20:504-514. DOI: https://doi.org/10.1016/j.chom.2016.09.007, PMID: 27736646.b

Lorrai R, Ferrari S. 2021. Host cell wall damage during pathogen infection: mechanisms of perception and role in plant-pathogen interactions. Plants (Basel) 10:399. DOI: https://doi.org/10.3390/plants10020399, PMID: 33669710

Marcec MJ, Tanaka K. 2021. Crosstalk between Calcium and ROS signaling during flg22-triggered immune response in Arabidopsis leaves. Plants 11:14. DOI: 10.3390/plants11010014. PMID: 35009017

Ma M, Wang W, Fei Y, Cheng HY, Song B, Zhou Z, Zhao Y, Zhang X, Li L, Chen S, Wang J, Liang X, Zhou JM. A surface-receptor-coupled G protein regulates plant immunity through nuclear protein kinases. 2022. Cell Host Microbe 30:1602-1614. DOI: 10.1016/j.chom.2022.09.012. Epub 2022 Oct 13. PMID: 36240763.

Martinière A, Zelazny E. 2021. Membrane nanodomains and transport functions in plant. Plant Physiology 187:1839–1855. DOI: https://doi.org/10.1093/plphys/kiab312, PMID: 35235669

Majhi, B.B., Sobol, G., Gachie, S., Sreeramulu, S., and Sessa, G. 2021. BRASSINOSTEROID-SIGNALLING KINASES 7 and 8 associate with the FLS2 immune receptor and are required for flg22-induced PTI responses. Molecular Plant Pathology 22:786-799. DOI:https://doi.org/10.1111/mpp.13062, PMID: 33955635

Mitra SK, Chen R, Dhandaydham M, Wang X, Blackburn RK, Kota U, Goshe MB, Schwartz D, Huber SC, Clouse SD. 2015. An autophosphorylation site database for leucine-rich repeat receptor-like kinases in Arabidopsis thaliana. The Plant Journal 82:1042–1060. DOI: https://doi.org/10.1111/tpj.12863, PMID: 25912465

Orosa B, Yates G, Verma V, Srivastava AK, Srivastava M, Campanaro A, De Vega D, Fernandes A, Zhang C, Lee J, Bennett MJ, Sadanandom A. 2018. SμmO conjugation to the pattern recognition receptor FLS2 triggers intracellular signalling in plant innate immunity. Nature Communications 9:5185. DOI: https://doi.org/10.1038/s41467-018-07696-8, PMID: 30518761

Sun Y, Li L, Macho AP, Han Z, Hu Z, Zipfel C, Zhou JM, Chai J. 2013. Structural basis for flg22-induced activation of the Arabidopsis FLS2-BAK1 immune complex. Science 342:624-628. DOI: https://doi.org/10.1126/science.1243825, PMID: 24114786

Vitrac H, Mallampalli VKPS, Dowhan W. 2019. Importance of phosphorylation/dephosphorylation cycles on lipid-dependent modulation of membrane protein topology by posttranslational phosphorylation. The Journal of Biological Chemistry 294:18853–18862. DOI: https://doi.org/10.1074/jbc.RA119.010785, PMID: 31645436

Xue Y, Xing J, Wan Y, Lv X, Fan L, Zhang Y, Song K, Wang L, Wang X, Deng X, Baluška F, Christie JM, Lin J. 2018. Arabidopsis blue light receptor phototropin 1 undergoes blue light-induced activation in membrane microdomains. Molecular Plant 11:846-859. DOI: 10.1016/j.molp.2018.04.003, PMID: 29689384

Xing J, Ji D, Duan Z, Chen T, Luo X. 2022. Spatiotemporal dynamics of FERONIA reveal alternative endocytic pathways in response to flg22 elicitor stimuli. New Phytologist 235: 518-532. DOI: 10.1111/nph.18127, PMID: 35358335

Zhai K, Liang D, Li H, Jiao F, Yan B, Liu J, Lei Z, Huang L, Gong X, Wang X, Miao J, Wang Y, Liu JY, Zhang L, Wang E, Deng Y, Wen CK, Guo H, Han B, He Z. 2021. NLRs guard metabolism to coordinate pattern- and effector-triggered immunity. Nature 601:245-251. DOI: https://doi.org/10.1038/s41586-021-04219-2, PMID: 34912119

Zhong YH, Guo ZJ, Wei MY, Wang JC, Song SW, Chi BJ, Zhang YC, Liu JW, Li J, Zhu XY, Tang HC, Song LY, Xu CQ, Zheng HL. 2023. Hydrogen sulfide upregulates the alternative respiratory pathway in mangrove plant Avicennia marina to attenuate waterlogging-induced oxidative stress and mitochondrial damage in a calciμm-dependent manner. Plant Cell and Environment 46:1521-1539. DOI: https://doi.org/10.1111/pce.14546, PMID: 36658747

Inappropriate references we deleted from the revised manuscript

Schulze S, Yu L, Hua C, Zhang L, Kolb D, Weber H, Ehinger A, Saile SC, Stahl M, Franz-Wachtel M, Li L, El Kasmi F, Nürnberger T, Cevik V, Kemmerling B. 2022. The Arabidopsis TIR-NBS-LRR protein CSA1 guards BAK1-BIR3 homeostasis and mediates convergence of pattern- and effector-induced immune responses. Cell Host Microbe 30:1717-1731.e6. DOI: 10.1016/j.chom.2022.11.001, PMID: 36446350

Wang Q, Zhao Y, Luo W, Li R, He Q, Fang X, Michele RD, Ast C, von Wirén N, Lin J. 2013. Single-particle analysis reveals shutoff control of the Arabidopsis ammonium transporter AMT1;3 by clustering and internalization. Proceedings of the National Academy of Sciences of the United States of America 110:13204-9. DOI: 10.1073/pnas.1301160110, PMID: 23882074

Eichel K, Jullié D, von Zastrow M. β-Arrestin drives MAP kinase signalling from clathrin-coated structures after GPCR dissociation. Nature Cell Biology 18:303-10. DOI: 10.1038/ncb3307, PMID: 26829388

Van Itallie CM, Anderson JM. Phosphorylation of tight junction transmembrane proteins: Many sites, much to do. Tissue Barriers 6:e1382671. DOI: 10.1080/21688370.2017.1382671, PMID: 29083946

Monje-Galvan V, Warburton L, Klauda JB. Setting up all-atom molecular dynamics simulations to study the interactions of peripheral membrane proteins with model lipid bilayers. Methods in Molecular Biology 1949:325-339. DOI: 10.1007/978-1-4939-9136-5_22, PMID: 30790265.

Trotta A, Bajwa AA, Mancini I, Paakkarinen V, Pribil M, Aro EM. The role of phosphorylation dynamics of CURVATURE THYLAKOID 1B in plant thylakoid membranes. Plant Physiology 181:1615-1631. DOI: 10.1104/pp.19.00942, PMID: 31615849

Dorrity MW, Saunders LM, Queitsch C, Fields S, Trapnell C. Dimensionality reduction by UMAP to visualize physical and genetic interactions. Nature Communications 11:1537. DOI: 10.1038/s41467-020-15351-4, PMID: 32210240

Sato KI, Tokmakov AA. Membrane microdomains as platform to study membrane-associated events during Oogenesis, Meiotic Maturation, and Fertilization in Xenopus laevis. Methods in Molecular Biology 920:59-73. DOI: 10.1007/978-1-4939-9009-2_5, PMID: 30737686.

Ozolina NV, Kapustina IS, Gurina VV, Bobkova VA, Nurminsky VN. Role of plasmalemma microdomains (Rafts) in protection of the plant cell under Osmotic stress. Journal of Membrane Biology 254:429-439. DOI: 10.1007/s00232-021-00194-x, PMID: 34302495

Boutté Y, Moreau P. Plasma membrane partitioning: from macro-domains to new views on plasmodesmata. Frontiers in Plant Science 5:128. DOI: 10.3389/fpls.2014.00128. PMID: 24772114

Yu M, Cui Y, Zhang X, Li R, Lin J. Organization and dynamics of functional plant membrane microdomains. Cellular and Molecular Life Sciences 77:275-287. DOI: 10.1007/s00018-019-03270-7, PMID: 31422442

Zhao Z, Li M, Zhang H, Yu Y, Ma L, Wang W, Fan Y, Huang N, Wang X, Liu K, Dong S, Tang H, Wang J, Zhang H, Bao Y. Comparative proteomic analysis of plasma membrane proteins in rice leaves reveals a vesicle trafficking network in plant immunity that is provoked by Blast Fungi. Frontiers in Plant Science 13:853195. DOI: 10.3389/fpls.2022.853195, PMID: 35548300

Hilgemann DW, Dai G, Collins A, Lariccia V, Magi S, Deisl C, Fine M. Lipid signaling to membrane proteins: From second messengers to membrane domains and adapter-free endocytosis. Journal of General Physiology 150:211-224. DOI: 10.1085/jgp.201711875, PMID: 29326133

Joshi R, Paul M, Kumar A, Pandey D. Role of calreticulin in biotic and abiotic stress signalling and tolerance mechanisms in plants. Gene 714:144004. DOI: 10.1016/j.gene.2019.144004, PMID: 31351124

Chen Y, Cao C, Guo Z, Zhang Q, Li S, Zhang X, Gong J, Shen Y. Herbivore exposure alters ion fluxes and improves salt tolerance in a desert shrub. Plant Cell and Environment 43:400-419. DOI: 10.1111/pce.13662, PMID: 31674033

Chi Y, Wang C, Wang M, Wan D, Huang F, Jiang Z, Crawford BM, Vo-Dinh T, Yuan F, Wu F, Pei ZM. Flg22-induced Ca2+ increases undergo desensitization and resensitization. Plant Cell and Environment 44:3563-3575. DOI: 10.1111/pce.14186, PMID: 34536020

Zhang M, Su J, Zhang Y, Xu J, Zhang S. Conveying endogenous and exogenous signals: MAPK cascades in plant growth and defense. Current Opinion in Plant Biology 45:1-10. DOI: 10.1016/j.pbi.2018.04.012, PMID: 29753266

Arnaud D, Deeks MJ, Smirnoff N. RBOHF activates stomatal immunity by modulating both reactive oxygen species and apoplastic pH dynamics in Arabidopsis. Plant Journal 116:404-415. DOI: 10.1111/tpj.16380, PMID: 37421599

Zou Y, Wang S, Zhou Y, Bai J, Huang G, Liu X, Zhang Y, Tang D, Lu D. Transcriptional regulation of the immune receptor FLS2 controls the ontogeny of plant innate immunity. Plant Cell.30:2779-2794. DOI: 10.1105/tpc.18.00297, PMID: 30337428

Ngou BPM, Jones JDG, Ding P. Plant immune networks. Trends in Plant Science 27:255-273. DOI: 10.1016/j.tplants.2021.08.012, PMID: 34548213.

Yu M, Liu H, Dong Z, Xiao J, Su B, Fan L, Komis G, Šamaj J, Lin J, Li R. 2017. The dynamics and endocytosis of Flot1 protein in response to flg22 in Arabidopsis. Journal of Plant Physiology 215:73–84. DOI: https://doi.org/10.1016/j.jplph.2017.05.010, PMID: 28582732

Author Response

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

Response to Reviewer 1:

• We agree with the reviewer’s overall assessment of this manuscript.

• Because multiple secreted proteins are changed between the control and experimental groups, some of them could be causal and others corelative in the context of enhancing compensatory glucose production in response to elevated glycosuria. Through future studies we will determine the causal factors that trigger the increase in glucose production.

• Yes, we will correct the typographical errors in a revised version of this manuscript.

Response to Reviewer 2:

• We agree with reviewer on their comment about potential sex differences we may have missed in this study. Therefore, we will include this limitation in discussion section of a revised manuscript.

• The reviewer’s statement ‘The methods of that publication indicate that all experiments were completed within 14 days of inducing the Glut2 knockout’ is incorrect. In the referred publication, we had explicitly mentioned in methods that ‘All of the experiments, except those using a diet-induced obesity mouse model or noted otherwise, were completed within 14 days of inducing the Glut2 deficiency.’ Please see figures 5h-l and 6 in that previous publication, which demonstrate that all the experiments were not completed within 14 days of inducing renal Glut2 deficiency. Per the reviewer’s advice, in the present manuscript we will include the timeline of the experiments (which in some cases is 4 months beyond inducing glycosuria) with all the figure legends. In addition, for a separate project (which is unpublished) we have measured glycosuria up to 1 year after inducing renal Glut2 deficiency. Therefore, the glycosuria observed in the renal Glut2 KO mice is not temporary.

• In our previous response to the reviewer, we had already mentioned which control group was used in this study. Please see our response to the second reviewer’s point 3. As mentioned to the reviewer, we had used Glut2-loxp/loxp mice as the control group, which is also described multiple times in the figure legends of our previous paper that reported the phenotype of renal Glut2 KO mice and is cited in this manuscript so we don’t have to repeat the same information. Per the reviewer’s advice, we will also include the information in a revised version of this manuscript.

• We request the reviewer to look at figure 1, showing an increase in glucose production in renal Glut2 KO mice and figure 3, which demonstrates that an afferent renal denervation reduces blood glucose levels by 50%. The afferent renal denervation (ablation of afferent renal nerves) does reduce blood glucose levels in renal Glut2 KO mice. Therefore, the use of the word ‘promote’ in the title is accurate and appropriate to reflect the role of the afferent renal nerves in contributing to about 50% increase in blood glucose levels in renal Glut2 KO mice. Regarding the reviewer's comment on changes in Crh gene expression, please look at figure 3. Ablation of renal afferent nerves decreases hypothalamic Crh gene expression and other mediators of the HPA axis by 50%. Therefore, the afferent renal nerves do contribute to regulating blood glucose levels, at least in part, by the HPA axis (which is widely known to change blood glucose levels). The use of words such as ‘required’ or ‘necessary’ in the title may have indicated causal role or could have been misleading here; therefore we have purposely used ‘promote’ in the title to accurately reflect the findings of this study.

• Because we observed an increase in hepatic glucose production in renal Glut2 KO mice (Fig. 1) - which was reduced by 50% after selective afferent renal denervation (Fig. 3) - in the graphical abstract we are suggesting a neural connection between the kidney-brain-liver or an endocrine factor(s) to account for these changes in blood glucose levels as also described in the discussion section. We can include a question mark ‘?’ in the graphical abstract to show that further studies are need to validate these proposed mechanisms; however, we cannot just remove the arrow as advised by the reviewer.

• Per the reviewer’s advice, in the methods we will include the dilutions used for each assay.

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

Reviewer #1 (Recommendations For The Authors):

It would be helpful to the reader to specify in Figure 1a-c whether data were directly measured or calculated.

We have now clarified this in method section of the revised manuscript. The glucose production was directly measured and then fractional contribution of the tissues was calculated from the former data. We have also included a reference research paper to further clarify the method.

The methods section would be strengthened by clarifying the order in which experiments were performed, the age of the mice at each time point, and whether different cohorts were used for different techniques.

We have included additional details in the method section with proper citations. For in-depth protocols we have cited our previous publications.

It would be helpful to explain or provide a reference for how the post-mortem background activity measurement was performed.

We have included this explanation in the revised manuscript.

Similarly, details regarding the collection of blood for ACTH and corticosterone measurement are needed for the reader to evaluate whether the results are confounded by stress at the time of collection.

We have added these details in the method section.

I recommend stating, if accurate, that you used mixed-sex groups because your previous study found no sex differences in the phenotype of renal Glut2 KO mice.

Yes, we have included these details in the revised manuscript.

Sentence 239 is difficult to follow. Also, line 287 contains a contraction.

We have revised the sentence per the reviewer’s advice.

A graphical abstract would be helpful, bearing in mind conclusive vs suggestive findings.

Yes, we have included the graphical abstract with the revised manuscript.

Reviewer #2 (Recommendations For The Authors):

Minor Comments to the Authors

(1) The Methods also need to specify more of the critical details of the ELISAs, including the dilution factors used, and whether the values reported are dilution-corrected. Also, there is no description of how insulin was measured.

We have included these details in the method section. The assay dilutions were performed per manufacturers’ instructions.

(2) The Methods do not sufficiently describe how Crh mRNA was quantified in the hypothalamus. Presumably, they examined only the paraventricular nucleus? How many sections were used for in situ hybridization? How were the brains processed? What thickness of section was used? When were the brains collected?

We have included these details in the method section and cited our previous publications for in-depth protocols. Some of the information is also available in the figure legends.

(3) The number of mice that were used for plasma proteomics is not indicated.

The number of mice is indicated using individual symbols or points presented on the bar graphs.

Author Response

Reviewer #2 (Public Review):

In this manuscript, Chen et al. reported that the core binding factor beta (Cbfβ), a heterodimeric subunit of the RUNX family transcription factors (TFs), is crucial in maintaining cartilage homeostasis and counteracting traumatic OA pathology. Using mouse models in which Cbfβ is conditionally inactivated in the Col2a1+ and Acan+ cells, the authors claimed that Cbfβ ablation led to articular cartilage (AC) degeneration, which is associated with aberrant cartilage gene expression and chondrocyte signaling, particularly the elevated Wnt/Catenin and the decreased Hippo/YAP and TGFβ signaling. The authors further showed that Cbfβ transcripts are decreased in human OA cartilage, and sustaining Cbfβ expression in mouse knee joints mitigated the severity of surgery-evoked OA.

On the whole, the work reported is interesting and exciting. Genetic and biochemical data support key statements. Both in vivo and in vitro experiments were well designed with proper controls; semiquantitative data were digitalized and processed for statistical significance. Furthermore, new findings were adequately discussed in contrast to the current available knowledge. However, the conceptual novelty of this study is slightly compromised by recent publications showing that Cbfβ reduction is associated with OA (Che et al. 2023; Li et al. 2021). Also, the authors claimed that multiple signaling pathways were affected by Cbfβ ablation in cartilage cells; many of them, however, are indirect effects given the nature of Cbfβ as a TF. The authors also showed that pSMAD2/3 and active βCatenin decreased and increased upon Cbfβ depletion in the mouse AC cartilage. However, how the deficiency of Cbfβ, a widely expressed TF, affected the posttranslational modification of SMAD2/3 and βCatenin is unclear and needs further discussion. Overall, Cbfβ's role in cartilage and OA pathology is an emerging area of study; the authors provided a set of genetic evidences showing that Cbfβ is indispensable for cartilage homeostasis.

We thank the reviewer for the positive appraisal of our manuscript. We greatly appreciate the insightful comments and critiques. In accordance with the reviewer’s suggestions, we have thoroughly revised all parts of the manuscript. We are glad that the reviewers considered our work to be of interest, and we are grateful for this opportunity to resubmit our manuscript. With regard to concerns of novelty of our study, Li et al’s study only reported the relationship between abnormal Cbfβ expression in human cartilage and osteoarthritis. Che et al’s study employed Cbfβf/fAggrecan-cre mice, while our study used a novel inducible Cbfβf/fCol2α1CreERT mouse model. While the Aggrecan-creERT system provides valuable insights into the role of Cbfβ in differentiated cartilage cells and its implications in the advanced stages of osteoarthritis, our current study also used Cbfβf/fCol2α1-CreERT aimed to explore the gene's function from a broader perspective. Previous study points out that Col2α1 is expressed in both early and late stage of chondrogenesis, including skeletal mesenchymal cells, perichondrium and presumptive joint cells, but aggrecan is expressed specifically in differentiated chondrocytes(1). However, studies show that not only differentiated chondrocytes but also chondrocyte progenitors are involved in OA pathogenesis(2). In our current study, the Col2α1-CreERT system allowed us to investigate Cbfβ's role not only in mature chondrocytes but also in early chondroprogenitor cells, offering a comprehensive view of Cbfβ’s involvement in cartilage in osteoarthritis. Therefore, the use of the Cbfβf/fCol2α1-CreERT mouse mutant strain was instrumental in expanding our understanding of Cbfβ's multifaceted role in osteoarthritis, highlighting its importance not only in mature cartilage but also in the early stages of cartilage formation and differentiation. In addition to the different types of Cre used compared to our previous study, our current study also used gain-of-function approach in ACLT-induced OA disease model to understand the potential therapeutic function of Cbfβ in OA pathological condition. Adding our current findings to our previous research, we can now piece together a more complete picture of Cbfβ's role across the entire spectrum of cartilage development in osteoarthritis.

We agree with the reviewer that how the deficiency of Cbfβ, a widely expressed TF, affected the posttranslational modification of SMAD2/3 and βCatenin is unclear and needs further exploration. So far there is no clear explanation of this, which is why we used RNA-seq and heatmap analysis to examine other genes expression which could help to uncover the mechanism underlying these results. Interestingly, Che et al’s result showed that TGFB signaling (P-Smad3) increased in Cbfβf/fAggrecan-cre mice, while our data showed that TGFB signaling (both PSmad3 and Smad3) decreased in Cbfβf/fCol2α1-CreERT mice as shown in our results in Figure 8. These results were also confirmed by RNA-seq analysis as shown in the heatmaps in figure 5.

These differences could be the result of different mouse ages used in our study and Che et al’s study.

1. Blaney Davidson EN, van de Loo FA, van den Berg WB, van der Kraan PM. How to build an inducible cartilagespecific transgenic mouse. Arthritis Res Ther. 2014;16(3):210.

2. Tong L, Yu H, Huang X, Shen J, Xiao G, Chen L, et al. Current understanding of osteoarthritis pathogenesis and relevant new approaches. Bone Res. 2022;10(1):60.

Reviewer #3 (Public Review):

The authors comprehensively demonstrated the Cbfβ gene, which is involved in articular cartilage homeostasis, can promote articular cartilage regeneration and repair in osteoarthritis (OA) through regulating Hippo/YAP signaling TGF-β signaling, and canonical Wnt signaling. First, the authors demonstrated the deletion of Cbfβ can induce the OA phenotypes including decreased articular cartilage and osteoblasts, and increased osteoclasts and subchondral bone hyperplasia, and induce the early onset of OA. Additionally, the authors showed that the deficiency of Cbfβ in cartilage can increase canonical Wnt signaling and decrease TGF-β and Hippo signaling. Finally, the authors demonstrated that the overexpression of Cbfβ can inhibit Wnt signaling and enhance Hippo/YAP signaling in knee joints articular cartilage of ACLT-induced OA mice and protect against ACLT-induced OA. The manuscript is overall well-constructed, and the authors provided evidence to support their findings.

In Fig. 7I, it could be better to show the statistical analysis between normal and AAV-mediated Cbfβ ACLT mice groups.

We thank the reviewer for bringing this to our attention. In the revised figure 7I, we have included the statistical analysis between normal and AAV-mediated Cbfβ ACLT mice groups.

In Fig. 9H-K, in the quantification analysis, the OARSI score in the DMM+AAV-YFP group is higher than in the sham group significantly. However, the SO staining results appear to show no significant difference between the DMM+AAV-luc-YFP group (Fig. 9I) and the sham group (Fig. 9H).

We thank the reviewer for bringing this to our attention. Although both the sham and DMM+AAV-luc-YFP group stain positive for SO, the SO stain intensity of the DMM+AAV-lucYFP group is noticeably lower. In addition, SO staining is not the only parameter which is included in the OARSI score. We also evaluated the cartilage thickness, proteoglycan structure, and Cartilage surface fibrillation index. Our evaluation to determine the OARSI score relies on the qualities of the whole joint, not only the magnified portion. For convenience we have also outlined the region of positive SO stain in the revised figure 9I

Author Response

eLife assessment

This important study provides a new, apparently high-performance algorithm for B cell clonal family inference. The new algorithm is highly innovative and based on a rigorous probabilistic analysis of the relevant biological processes and their imprint on the resulting sequences, however, the strength of evidence regarding the algorithm's performance is incomplete, due to (1) a lack of clarity regarding how different data sets were used for different steps during algorithm development and validation, resulting in concerns of circularity, (2) a lack of detail regarding the settings for competitor programs during benchmarking, and (3) method development, data simulation for method validation, and empirical analyses all based on the B cell repertoire of a single subject. With clarity around these issues and application to a more diverse set of real samples, this paper could be fundamental to immunologists and important to any researcher or clinician utilizing B cell receptor repertoires in their field (e.g., cancer immunology).

We apologize for the long delay in implementing the suggested changes. Some of the co-authors had some personal issues that made it hard to efficiently work on the revision.

We have addressed all the essential points below, as well as all the detailed comments of each reviewer in the following pages.

Due to the journal’s guidelines we have to upload an “all black” version of the manuscript as the main version. We have uploaded a revised manuscript with the changes marked in red as a “Related Manuscript file”, which appears at the very end of the Merged Manuscript File, after all the Figures, and at the end of the list of files on the webpage. We apologize for this inconvenience.

In addition, we have added an extension of HILARy to deal with paired-chain repertoires, and have benchmarked the new method on a recently published synthetic dataset. This new analysis is now presented in new Fig. 5.

Reviewer #1 (Public Review):

Identifying individual BCR/Ab chain sequences that are members of the same clone is a longstanding problem in the analysis of BCR/Ab repertoire sequencing data. The authors propose a new method designed to be scalable for application to huge repertoire data sets without sacrificing accuracy. Their approach utilizes Hamming Distance between CDR3 sequences followed by clustering for a fast, high-precision approach to classifying pairs of sequences as related or not, and then refines the classification using mutation information from germline-encoded regions. They compare their method with other state-of-the-art methods using synthetic data.

The authors address an important problem in an interesting, innovative, and rigorous way, using probabilistic representations of CDR3 differences, frequencies of shared and not-shared mutations, and the relationships between the two under hypotheses of related pairs and unrelated pairs, and from these develop an approach for determining thresholds for classification and lineage assignment. Benchmarking shows that the proposed method, the complete method including both steps, outperforms other methods.

Strengths of the method include its theoretical underpinnings which are consistent with an immunologist's intuition about how related and unrelated sequences would compare with each other in terms of the metrics to use and how those metrics are related to each other.

I have two high-level concerns:

(1) It isn't clear how the real and synthetic data are being used to estimate parameters for the classifier and evaluate the classifier to avoid circularity. It seems like the approach is used to assign lineages in the data from [1], and then properties of this set of lineages are used to estimate parameters that are then used to refine the approach and generate synthetic data that is used to evaluate the approach. This may not be a problem with the approach but rather with its presentation, but it isn't entirely clear what data is being used and where for what purpose. An understanding of this is necessary in order to truly evaluate the method and results.

The reviewer is correct in their understanding of the pipeline. It should be stressed that the lineages used to guide the generation of the synthetic data was done on VJl classes for which the clustering was easy and reliable, and should therefore be largely model independent.

We have added an explanation in the main text of why the re-use of real data lineages inferred by HILARy doesn’t bias the procedure, since it’s done on a subset of lineages within VJl classes that are easy to infer (section “Test on synthetic dataset”).

(2) Regarding the data used for benchmarking - given the intertwined fashion by which the classification approach and synthetic data generation approach appear to have been developed, it is not surprising that the proposed approach outperforms the other methods when evaluated on the synthetic data presented here. It would be better to include in the benchmark the data used by the other methods to benchmark themselves or also generate synthetic data using their data generation procedures.

We agree with the reviewer that a test of the method on an independent synthetic dataset is important for its applicability and to compare to other methods.

We have added a new synthetic dataset from the group that designed the partis method to our benchmark. Our method still performs competitively, on par with partis—which was developed and tested on that dataset—and better than other methods. The results are presented in revised Fig. 4 (panels E-G), and Figure 4–figure supplement 1 as a function of the mutation rate.

In addition, we have used that dataset to benchmark a new version of HILARy that also uses the light chain. We present the results in new Figures 5 and Figure 4–figure supplement 1.

An improved method for BCR/Ab sequence lineage assignment would be a methodologic advancement that would enable more rigorous analyses of BCR/Ab repertoires across many fields, including infectious disease, cancer, autoimmune disease, etc., and in turn, enable advancement in our understanding of humoral immune responses. The methods would have utility to a broad community of researchers.

Reviewer #2 (Public Review):

This manuscript describes a new algorithm for clonal family inference based on V and J gene identity, sequence divergence in the CDR3 region, and shared mutations outside the CDR3. Specifically, the algorithm starts by grouping sequences that have the same V and J genes and the same CDR3 length. It then performs single-linkage clustering on these groups based on CDR3 Hamming distance, then further refines these groups based on shared mutations.

Although there are a number of algorithms that use a similar overall strategy, a couple of aspects make this work unique. First, a persistent challenge for algorithms such as this one is how to set a cutoff for single-linkage clustering: if it is too low, then one separates clusters that should be together, and if too high one joins together clusters that should be separate. Here the authors leverage a rich collection of probabilistic tools to make an optimal choice. Specifically, they model the probability distributions of within- and between-cluster CDR3 Hamming distances, with parameters depending on CDR3 length and the "prevalence" of clonal sequence pairs (i.e. family size distribution). This allows the algorithm to make optimal choices for separating clusters, given the particular chosen distance metric, and assuming the sample in question has been accurately modeled. Second, the algorithm uses a highly efficient means of doing single-linkage clustering on nucleotide sequences.

This leads to a fast and highly performant algorithm on data meant to replicate the original sample used in algorithm design. The ideas are new and beautifully developed. The application to real data is interesting, especially the point about dN/dS.

However, the paper leaves open the question of how this inference algorithm works on samples other than the one used for simulation and as a template for validation. If I understand the simulation procedure correctly - that one takes a collection of inferred trees from the real data, then re-draws the root sequence and the identity of the mutations on the branches - then the simulated data should be very close to the data used to develop the methods in the paper. This consideration seems especially important given that key methods in this paper use mutation counts and overall mutation counts are preserved.

Repertoires come in all shapes and sizes: infants to adults, healthy to cancerous, and naive to memory to plasma-cell-just-after-vaccination. If this is being proposed as a general-purpose clonal inference algorithm rather than one just for this sample, then a more diverse set of validations are needed.

We agree that testing the method on a differently generated dataset is a useful check. We should point out, however, that our synthetic dataset is not as biased as it may seem. In particular, it is based on trees from VJl classes that we predicted are very easy to cluster, which means that they are truly faithful to the data, and not dependent on the particular algorithm used to infer them. The big advantage over this synthetic dataset over others is that it recapitulates the power law statistics of clone size distribution, as well as the diversity of mutation rates. To us, it still represents a more useful benchmark than synthetic datasets generated by population genetics models, which miss most of this very broad variability.

However, to check how the method generalizes to other datasets, we repeated our validation procedure on the dataset used to evaluate Partis in Ralph et al 2022. The new results are discussed in the main text and in new panels of Fig. 4 in the same form as the previous comparisons. We also added a comparison of performance as a function of mutation rate in the new Figure 4–figure supplement 1.

It is unclear how to run the code. The software repo has a nice readme explaining the file layout, dependencies, and input file format, but the repo seems to be lacking an inference.ipynb mentioned there which runs an analysis. Perhaps this is a typo and refers to inference.py, which in addition to the documented cdr3 clustering, seems to have functions to run both clustering methods. However, it does not seem to have any documentation or help messages about how to run these functions.

We have completely overhauled the github to provide a detailed step by step explanation of how to run the code. The code is now easily installable using pip.

The results are not currently reproducible, because the simulated data is not available. The data availability statement says that no data have been generated for this manuscript, however simulated data has been generated, and that is a key aspect of the analysis in the paper.

We have uploaded the simulated data to zenodo, as well as provided scripts in the github to run the benchmarks.

More detail is needed to understand the timing comparisons. The new software is clearly written to use many threads. Were the other software packages run using multiple threads? What type of machine was used for the benchmarks?

All timing comparisons were made based on a single VJl class on a 14 double-threaded CPU computer. HILARy uses all 28 threads, and other methods were run with default settings, with multi-threading allowed.

We have clarified the specifications of the computer.

Reviewer #3 (Public Review):

B cell receptors are produced through a combination of random V(D)J recombination and somatic hypermutation. Identifying clonal lineages - cells that descend from a common V(D)J rearrangement - is an important part of B cell repertoire analysis. Here, the authors developed a new method to identify clonal lineages from BCR data. This method builds off of prior advances in the field and uses both an adaptive clonal distance threshold and shared somatic hypermutation information to group B cells into clonal lineages.

The major strength of this paper is its thorough quantitative treatment of the subject and integration of multiple improvements into the clonal clustering process. By their simulation results, the method is both highly efficient and accurate.

The only notable weakness we identified is that much of the impact of the method will depend on its superiority to existing approaches, and this is not convincingly demonstrated by Fig. 4. In particular, little detail is given on how the other clonal clustering programs were run, and this can significantly impact their performance. More specifically:

We have added a new benchmark to address these concerns, presented in Fig. 4 and in new figure 4 – figure supplement 1 as a function of a controllable mutation rate.

(1) Scoper supports multiple methods for clonal clustering, including both adaptive CDR3 distance thresholds (Nouri and Kleinstein, 2018) and shared V-gene mutations (Nouri and Kleinstein, 2020). It is not clear which method was used for benchmarking. The specific functions and settings used should have been detailed and justified. Spectral clustering with shared V gene mutations would be the most comparable to the authors' method. Similar detail is needed for partis.

In the updated version I use the 2020 version. The 2018 is very similar to simple single linkage so will be removed from the benchmark.

(2) It is not clear how the adaptive thresholds and shared mutation analysis in the authors' method differ from prior approaches such as scoper and partis.

We have changed the paragraph in the discussion section about the benchmark to highlight the innovative aspects and differences with previous approaches.

(3) The scripts for performing benchmarking analyses, as well as the version numbers of programs tested, are not available.

We have added to the github all the scripts used for benchmarking. We have added details about the version numbers in the data and code availability section of the methods.

(4) Similar to above, P. 10 describes single linkage hierarchical clustering with a fixed threshold as a "crude method" that "suffers from inaccuracy as it loses precision in the case of highlymutated sequences and junctions of short length." As far as we could tell, this statement is not backed up by either citations or analyses in the paper. It should not be difficult for the authors to test this though using their simulations, as this method is also implemented in scoper.

We have added this method to our benchmark to support that point. The results are presented in Figure 4 – figure supplement 2.

References

Nouri N, Kleinstein SH. 2020. Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data. PLOS Comput Biol 16:e1007977. doi:10.1371/journal.pcbi.1007977

Nouri N, Kleinstein SH. 2018. A spectral clustering-based method for identifying clones from high- throughput B cell repertoire sequencing data. Bioinformatics 34:i341-i349. doi:10.1093/bioinformatics/bty235

We have changed citation [22] to refer to the 2018 paper. The 2020 paper is citation [18].

Author Response

We acknowledge the editors and reviewers for their careful and thoughtful review of the preprint. Their comments and suggestions will be very useful in improving the manuscript's revised version, which we plan to submit in the coming weeks.

Author Response

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

Reviewing Editor

We thank you for clarifying several of the questions raised by the reviewers. Since the study has otherwise largely stayed unchanged, we will leave the eLife assessment as “before”:

We respectfully disagree because we addressed all concerns raised by the two reviewers except one (below), which was not satisfactorily answered according to reviewer 1; it has now been addressed (new S3 Fig).

Reviewer #1 (Recommendations For The Authors):

The authors addressed most of my previous comments. However, there is one important point that was not satisfactorily addressed "The band intensities on Western blots in Fig. 4 and Fig. 5 are not quantified, and the numbers of repeats are also not provided" The response that "It is not straightforward to quantify and describe the intensity of the bands of these numerous with different fate outcomes." In the revision, they mentioned at least three repeats were performed. If so, it's not entirely clear why they couldn't quantify the western blots results. Including quantitative data will strengthen the rigor of the findings.

Quantitative data from Fig. 4 and Fig. 5 are now provided as S3 Fig and described in the manuscript (lines 170-175; 184-188).

Author Response

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

Reviewer #1:

(1) It is not entirely clear why a tumor-free model is chosen to study immune responses, as immune responses can differ significantly with or without tumor-bearing. A more detailed explanation is needed.

We appreciate the question. As stated in the original submission, tumor-free mouse models are commonly used to assess off-target outcomes of anti-neoplastic therapies. We have expanded on this point and acknowledged this shortcoming in the revised manuscript (lines 264-265).

(2) Immune responses in isolated macrophages, neutrophils, and bone marrow cells require priming with LPS, while such responses are not observed in vivo. There is no explanation for these differences.

The reviewer raises an excellent point. The assembly of inflammasomes such as those nucleated by NLRP3 requires priming signals, which increase the levels of this sensor, which are kept low in homeostatic conditions to prevent spontaneous unwanted inflammation. While LPS is commonly used in vitro as an inducer of priming signals, these cues are triggered in vivo by various molecules, including pro-inflammatory cytokines. We have provided a rationale for the use of LPS in vitro in the revised manuscript (lines 144-145).

(3) The band intensities on Western blots in Fig. 4 and Fig. 5 are not quantified, and the numbers of repeats are also not provided. This additional information is recommended.

While caspase-1, caspase-3, GSDMD, and GSDME but not AIM 2 and NLRP3 are activated upon proteolytic cleavage. It is not straightforward to quantify and describe the intensity of the bands of these numerous with different fate outcomes. We regret for not mentioning the numbers of repeats in the original submission. This information has now been provided in figure legends where necessary.

(4) Many abbreviations are used throughout the text, and some of the full names are not provided.

Full names are required at the first introduction.

We agree. We have provided full names at the first introduction (lines 21, 23, 86).

(5) Fig. 5B needs a label on the X axis.

We regret the confusion: X axis was for both Fig. 5B and 5C. We have made the change in the new Fig. 5.

Reviewer #2:

The following specific points could be addressed to further improve the quality of the manuscript:

(1) Concerning data presented in Figure 1, 3D micro-CT reconstructions of the entire femurs could be shown instead of just the trabecular bone. Data on cortical bone loss are important. It would be important to show histological (sagittal) sections of the bones at baseline, treated with Doxorubicin or vehicle, and quantify osteoblasts in addition to osteoclasts. Is there increased bone marrow adiposity in Doxorubicin-treated mice? The data with vehicle should be shown in the main figures not just in the supplemental data.

We thank the reviewer for the suggestion. We have now provided 3 D micro-CT reconstructions of a representative femur containing both trabecular and cortical bones (S1B Fig). Only the metaphyseal area is shown because we did not originally scan the entire femur.

Quantification of osteoblast number is not a reliable measurement, the reason why we carried out dynamic histomorphometry to assess the effect of doxorubicin on bone formation (original S1D Fig/new S1E Fig).

Unfortunately, we did not determine the effects of doxorubicin on bone marrow adiposity. However, to address the reviewer’s comment, we have mentioned in the revised manuscript adipogenic effects of doxorubicin based on the literature (lines 264-265).

(2) Concerning data presented in Figure 2, how long after Doxorubicin injection is leukopenia observed (beyond the 72-hour timepoint)? Does cell-count return to baseline 4 weeks after treatment (when the bone phenotype is characterized)? Why use 12-week-old mice here and 10week-old animals for the rest of the study?

We appreciate the question. We did not measure leukopenic effects of doxorubicin beyond the 72-hour timepoint based on the following: i) bones are analyzed in mice injected only once with a single dose of doxorubicin; ii) leukopenia is a side effect of doxorubicin whose blood levels should be undetectable 4 weeks after its administration although we did not measure them experimentally. Our premise is that osteopenia observed in doxorubicin-exposed mice is the result of early events that occur after the administration of the drug.

We apologize for the confusion. We assessed baseline bone mass by VivaCT using 10-week-old mice; doxorubicin was injected 2 weeks when mice were 12-week-old. We have clarified this point in the revised manuscript (line 301).

(3) It would be important to evaluate local inflammation in bones collected from wild-type and mutant mice. Are ASC specks, Cit-H3, and MPO present in the bone marrow? The expression of some components of the inflammasomes or relevant pathways could be assessed in bone samples deprived of bone marrow and in the bone marrow.

This is a good point. Although we were not able to reliably measure Cit-H3 and MPO in bone marrow fluid, our data shown in Figs. 3-6, 7A-D are from bone marrow cells.

(4) Data presented in western blots should be quantified. The ratio of signal intensity obtained for beta-actin over the signal obtained for a given protein should be calculated for each experimental condition (especially in Figure 5, where beta-actin levels fluctuate a lot).

Please see the response to question #1. Fluctuations in β-actin levels are likely related to doxorubicin cytotoxic effects as mentioned in the original submission (lines 150, 194, 253). Despite this caveat, IL-1β levels are stimulated by this drug.

(5) In Figure 7, BV/TV of WT and mutant mice at baseline should be quantified and shown. Sagittal histological sections of the femur should be shown. 3D micro-CT reconstructions of the entire femur could be shown instead of just the trabecular bone. Osteoblasts and bone resorption should be quantified. Data obtained with vehicle should be quantified and shown in the main figure. The control and LPS conditions should be better defined. Does it include vehicle?

Please see the response to reviewer 1’s question #1.

We have now provided 3 D micro-CT reconstructions of a representative femur containing both trabecular and cortical bone (S3A, B Fig).

LPS was dissolved in PBS (vehicle), which was used as control. We have now replaced vehicle with PBS in Fig. 7.

(6) For all figures, the number of biological replicates should be mentioned in the legends, as well as the statistical tests used for the analyses.

We have now included this information in the legends where necessary.

(7) Some of the scientific rationales are not totally clear and could be better explained in the text. For example, it is written on page 6 "studies mainly on male mice and revolved around innate immune responses" and "we focused on neutrophils because of their high turnover rate and short lifespan", but it is not clear why. The rationale (page 10) for assessing bone mass in "mice globally lacking AIM2 and/or NLRP3" is not totally clear either. The argument is that systemic inflammation leads to bone loss but the effects obtained with the total ablation of AIM2 and NLRP3 do not prove strictly speaking that systemic inflammation really matters (in this current study, although we know from many other studies that it clearly does matter). We could imagine, for example, that bone mass would be preserved in AIM2 KO mice only because the inflammasome is impaired in osteoblasts and/or osteoclasts, but not in any other cell types. Conversely one could imagine that bone would be preserved only because inflammation is preserved in the gut, for example. The use of global knockouts unfortunately does not tell us much about the importance of systemic versus local effects of the inflammasomes. It shows that reducing inflammation, either in specific organs or globally, limits bone loss in doxorubicin-treated mice. This result is important but it was fully expected since doxorubicin has been reported to induce systemic inflammation, and since many studies have shown that systemic inflammation leads to bone loss.

We appreciate the comments. We have clarified the rationale for focusing on neutrophils (lines 129-130) and AIM2 and NLRP inflammasomes (lines 209-211). We have also now down played the concept of inflammasome-mediated systemic inflammation in doxorubicin-induced bone loss.

Author Response

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

Public Reviews:

Roget et al. build on their previous work developing a simple theoretical model to examine whether ageing can be under natural selection, challenging the mainstream view that ageing is merely a byproduct of other biological and evolutionary processes. The authors propose an agent-based model to evaluate the adaptive dynamics of a haploid asexual population with two independent traits: fertility timespan and mortality onset. Through computational simulations, their model demonstrates that ageing can give populations an evolutionary advantage. Notably, this observation arises from the model without invoking any explicit energy tradeoffs, commonly used to explain this relationship.

The model’s results are based on both numerical simulations and formal mathematical analysis.

Additionally, the theoretical model developed here indicates that mortality onset is generally selected to start before the loss of fertility, irrespective of the initial values in the population. The selected relationship between the fertility timespan and mortality onset depends on the strength of fertility and mortality effects, with larger effects resulting in the loss of fertility and mortality onset being closer together. By allowing for a trans-generational effect on ageing in the model, the authors show that this can be advantageous as well, lowering the risk of collapse in the population despite an apparent fitness disadvantage in individuals. Upon closer examination, the authors reveal that this unexpected outcome is a consequence of the trans-generational effect on ageing increasing the evolvability of the population (i.e., allowing a more effective exploration of the parameter landscape), reaching the optimum state faster.

The simplicity of the proposed theoretical model represents both the major strength and weakness of this work. On one hand, with an original and rigorous methodology, the logic of their conclusions can be easily grasped and generalised, yielding surprising results. Using just a handful of parameters and relying on direct competition simulations, the model qualitatively recapitulates the negative correlation between lifespan and fertility without requiring energy tradeoffs. This alone makes this work an important milestone for the rapidly growing field of adaptive dynamics, opening many new avenues of research, both theoretically and empirically.

We thank the reviewers and editor for highlighting the importance of the work presented here.

On the other hand, the simplicity of the model also makes its relationship with living organisms difficult to gauge, leaving open questions about how much the model represents the reality of actual evolution in a natural context.

We presented both in results and discussion how the mathematical trade-offs between fertility and survival time give rise to (xb, xd) configuration representative of existing aging modes.

In particular, a more explicit discussion of how the specifics of the model can impact the results and their interpretation is needed. For example, the lack of mechanistic details on the trans-generational effect on ageing makes the results difficult to interpret.

We discussed the role of the transgenerational Lansing effect played to its function, there is no need for a particular mechanism beyond that function of transgenerational negative effect. We reinforce this in the discussion by adding the following sentence “Regarding the nature of the transgenerational effect, our model is agnostic and the mere transmission of any negative effect would be sufficient to exert the function.“

Even if analytical results are obtained, most of the observations appear derived from simulations as they are currently presented. Also, the choice of parameters for the simulations shown in the paper and how they relate to our biological knowledge are not fully addressed by the authors.

The long time limit of the system with and without the Lansing effect is based on analytical results later confirmed using numerical simulations. The choice of parameters is explained in the introduction as being the minimum ones for defining a living organism. As for the parameters’ values, our numerical analysis gives a solution for any ib, id, xb and xd on R+, making the choice of initial value a mere random decision.

Finally, the conclusions of evolvability are insufficiently supported, as the authors do not show if the wider genotypic variability in populations with the ageing trans-generational effect is, in fact, selected.

We do not show nor claim that evolvability per se is selected for but that the apparent advantage given by this transgenerational effect seems to be mediated by an increased genotypic/phenotypic variability conferred to the lineage that we interpreted as evolvability.

Recommendations for the authors

(1) The authors could use the lineage tracing results for the evolvability aspect. Specifically, within subpopulations featuring the Lansing effect, it would be valuable to explore whether individuals with parental age greater than the mortality onset (a > x_d) demonstrate higher fitness compared to individuals with a < x_d. Additionally, an examination of how this variation evolves over time could provide further insights into the dynamics of the proposed model.

We thank the reviewer for this suggestion. This is an ongoing work in the group, especially in the context of varying environmental conditions.

(2) In all simulations, I_b = I_d = 1, resulting in total fertility (x_b * I_b) equating to x_b, while x_d is proportional to life expectancy. Considering an exploration of the implications of this parameter setting, the authors could frame x_d as a 'lifespan cost', potentially allowing for the model to be conceptualised in terms of energetic tradeoffs. This might offer additional perspectives on the dynamics of the model and its alignment with biological principles.

We discuss how the apparent trade-offs given by the model depending on ib and id values can be related to the interpretation of such trade-offs that has been accepted for most of the past century. Our claim here in the discussion is that one does not need such energetic trade-off for the fertility/longevity trade-offs to appear. Such energetic trade-off is not a “biological principle” but merely an accepted interpretation of a fertility/longevity trade-off that is not even a general mechanism.

(3) Considering the necessity of variation in x_d for the observed patterns, an exploration could be undertaken by the authors to examine a model where x_d is simply variable without inheritance. This could involve centring x_d at some value d with some variance σ_d for all individuals. In such a scenario, it may be observed whether the same convergence of x_b - x_d occurs without requiring x_d to be selected. Furthermore, similar consequences of the Lansing effect could potentially be identified.

This was done early on during our work and did not show any major changes in the model’s behaviour beyond the time of convergence. We did not include it to the final manuscript because of the low added value to an already long and complex manuscript.

(4) While it may not be necessary to alter the model itself, it is suggested that the authors consider acknowledging the potential consequences of certain modelling decisions that might be perceived as biologically unrealistic. Notable examples include assumptions such as fertility from birth and zero mortality prior to x_d. These assumptions, such as infertility from birth, could be viewed as distinctive features, and it might be worth mentioning that parental care of offspring could have co-evolved with such features. This is particularly relevant considering the energy tradeoff hypothesis that has been postulated.

Although inspired from results obtained in Drosophila, mice, nematodes and zebrafish, the model is so far haploid and asexual, thus involving individuals likely more similar to unicellulars. In these conditions, infertility from birth did not seem relevant to us. However, the model and codes are accessible online and we hope that others will use it to address such questions. It is interesting though to notice that ageing appears here without such constraint.

Additionally, the consideration that all organisms face a non-trivial mortality rate at every age, not solely from physiological causes, reflects the reality within which selection operates.

We thought this was the best way to reflect, an environment with a limited carrying capacity. A more complex model is under construction to take into account the fact that older individuals might be more sensitive to it than younger ones.

(5) While acknowledging the technical rigour applied by the authors, it is suggested that further attention be given to conducting a comprehensive 'reality check' associated with the chosen parameters, particularly regarding the biological relevance of the results. For instance, the authors argue that offspring of old organisms do not, on average, live similarly to their parents. However, it is noted that studies in the haploid asexual organism yeast, akin to what the authors model (albeit not necessarily yeast), revealed that the average lifespan of yeast progeny born from young or old mothers is very similar.

We do not claim that progeny of old parents live less long than that of younger parents on average, we say that it happens in the progeny of physiologically old parents, representing at most 10% of the population in our numerical simulations.

The authors cite experimental evolution in Drosophila progeny conceived later in the life of the parent, indicating that the onset of mortality in these progeny occurs late, sometimes even after the end of the fertility period (Burke et al., 2016; Rose et al., 2002). While the authors report their own previous studies with divergent results, independent experiments have suggested an increase of x_d following an artificial increase of x_b (Luckinbill and Clare, 1985; Sgro et al., 2000). A more in-depth consideration of these contrasting observations and their potential implications for the current model could enhance the overall robustness of the study.

The increase of x_d following an artificial increase of x_b is predicted by our model as discussed. The divergence of observations between studies is alas hard to assess.

(6) To enhance readability and maintain consistency, it is suggested that the authors homogenise the description of key parameters, specifically x_b and x_d, throughout the text. This could contribute to improved clarity and rigour. One recommendation is to refer to x_b consistently as the 'fertility span' and x_d as the 'mortality onset' for the sake of uniformity in terminology.

We have modified the text accordingly.

(7) At various points in the text, the assertion is made that observations have indicated a tradeoff between fertility and longevity. It is recommended that the authors provide references or data to substantiate this claim. This addition would contribute to the empirical grounding of the mentioned tradeoff and strengthen the overall support for the assertions made in the study.

We added the following references to the discussion Lemaitre et al., 2015, Kirkwood, 2005 and Rodrigues and Flatt 2016.

(8) The statement claiming that the model is 'able to describe all types of ageing observed in the wild' should be moderated. As the authors themselves acknowledge, the model is referred to as a 'toy model,' and it is made clear that it cannot capture, nor is intended to capture, the entire diversity observed in life. Adjusting this statement to reflect the limited scope and purpose of the model would enhance precision and accuracy in the presentation of its capabilities.

Although a toy model, its possible configurations encompass all the possible configurations described so far across the diversity of ageing throughout the tree of life from negligible senescence with no loss of fertility (x_b and x_d >> 0) to menopause-like configurations (x_b >> x_d) through fast mortality increase post reproduction (x_b = x_d). Replacing our current square functions would allow age-dependant decrease or increase of fertility and/or risks of mortality onsets.

(9) To bolster the biological relevance of the study, it is strongly recommended that the authors cross-check the results of their simulations with previously published experimental findings. This approach would serve to strengthen the alignment between the model outcomes and observed biological phenomena. Additionally, placing greater emphasis on the biological relevance aspects throughout the text would contribute to a more robust and comprehensive exploration of the study's implications.

In the present manuscript we have tried to cite a certain number of results from artificial selection experiments on life history traits in order to strengthen the interpretations of our model’s behaviour. There are numerous other studies, going in the same direction or not, but we do not think that it would be relevant to add an exhaustive list of them. Nevertheless, we added Stearns et al., 2000 that adds extrinsic high mortality to the evolution of life history traits.

(1) For enhanced clarity, it is suggested that the x-axis in Figure 1 be labelled as 'age.' Considering this adjustment could contribute to clearer visual communication of the data.

We agree with the reviewer and modified the figure accordingly.

(!!) The addition of graphical legends is recommended for Figures 3-5, as well as the supplementary figures. Including these legends would provide essential context and improve the interpretability of the figures for readers.

We agree with the reviewer and modified the figure accordingly.

(12) For improved distinction of the ranges indicated by quantiles in Figure 3, it is suggested that the authors consider enhancing visual clarity. One approach could involve making the middle quantile thicker or using a different line type. Additionally, it is recommended to explore the calculation of the highest density 90% intervals rather than the 1-9 deciles. This adjustment could contribute to a clearer representation of the data distribution in the figure.

We named the different deciles directly on the figure to improve readability.

(13) It is observed that the mathematical proofs in Annex 1 are not displaying properly in the PDF. Additionally, there seem to be missing and broken references for the Annex. This issue may be related to LaTeX formatting. The authors could consider revisiting the formatting of Annex 1 to ensure the correct display of mathematical proofs and address the referencing concerns, possibly by checking and rectifying any LaTeX-related issues.

The latex file of the supplementary was not correctly compiled. It is now corrected.

(14) There is inconsistency in the text regarding the reference to the Annex, with both 'Annex' and 'Annexe' being used interchangeably. To maintain uniformity, it is suggested that the authors consistently use either 'Annex' or 'Annexe' throughout the text. This adjustment would contribute to a more polished presentation of the supplementary material.

We corrected them accordingly.

(15)There appears to be a typographical error in the name of Supplementary Figure 3.

We corrected it accordingly.

Author Response

eLife assessment

The authors present evidence that small extracellular vesicles can be secreted from cells inside larger vesicles that they call amphiectosomes, which then tear to release their small vesicle contents. There are questions and concerns relating to the quality of the data and the in vivo significance of the observations. The findings are potentially important but the data are incomplete and the claims are only partially supported.

We agree that the in vivo significance and details of the molecular background of amphiectosome release remains to be studied further. However, as Reviewer 2 indicated, our data in this Short Report may have a substantial impact on our understanding of EV biogenesis. Therefore, we considered it was important to publish our data as soon as possible because it may significantly impact other EV biogenesis studies.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors' research group had previously demonstrated the release of large multivesicular body-like structures by human colorectal cancer cells. This manuscript expands on their findings, revealing that this phenomenon is not exclusive to colorectal cancer cells but is also observed in various other cell types, including different cultured cell lines, as well as cells in the mouse kidney and liver. Furthermore, the authors argue that these large multivesicular body-like structures originate from intracellular amphisomes, which they term "amphiectosomes." These amphiectosomes release their intraluminal vesicles (ILVs) through a "torn-bag mechanism." Finally, the authors demonstrate that the ILVs of amphiectosomes are either LC3B positive or CD63 positive. This distinction implies that the ILVs either originate from amphisomes or multivesicular bodies, respectively.

Strengths:

The manuscript reports a potential origin of extracellular vesicle (EV) biogenesis. The reported observations are intriguing.

Weaknesses:

It is essential to note that the manuscript has issues with experimental designs and lacks consistency in the presented data. Here is a list of the major concerns:

(1) The authors culture the cells in the presence of fetal bovine serum (FBS) in the culture medium. Given that FBS contains a substantial amount of EVs, this raises a significant issue, as it becomes challenging to differentiate between EVs derived from FBS and those released by the cells. This concern extends to all transmission electron microscopy (TEM) images (Figure 1, 2P-S, S5, Figure 4 P-U) and the quantification of EV numbers in Figure 3. The authors need to use an FBS-free cell culture medium.

(1) Although FBS indeed contains bovine EVs, however, the presence of very large multivesicular EVs (amphiectosomes) that our manuscript focuses on has never been observed and reported. For reported size distributions of EVs in FBS, please find a few relevant references below:

PMID: 29410778, PMID: 33532042, PMID: 30940830 and PMID: 37298194

All the above publications show that the number of lEVs > 350-500 nm is negligible in FBS. The average diameter of MV-lEVs (amphiectosomes) described in our manuscript is around 1.00-1.50 micrometre.

(1) When we demonstrated the TEM of isolated EVs, we consistently used serum- free conditioned medium (Fig2 P-S, Fig2S5 J, O) as described previously (Németh et al 2021, PMID: 34665280).

(2) Our TEM images show cells captured in the process of budding and scission of large multivesicular EVs excluding the possibility that these structures could have originated from FBS.

(3) In addition, in our confocal analysis, we studied Palm-GFP positive, cell-line derived MV-lEVs. Importantly, in these experiments, FBS-derived EVs are non-fluorescent, therefore, the distinction between GFP positive MV-lEVs and FBS-derived EVs was evident.

(4) In addition, culturing cells in FBS-free medium (serum starvation) significantly affects autophagy. Given that in our study, we focused on autophagy related amphiectosome secretion, we intentionally chose to use FBS supplemented medium.

(5) Even though the authors of this manuscript are not familiar with the technological details how FBS is processed before commercialization, it is reasonable to assume that the samples are subjected to sterile filtration (through a 0.22 micron filter) after which MV-lEVs cannot be present in the commercial FBS samples.

(2) The data presented in Figure 2 is not convincingly supportive of the authors' conclusion. The authors argue that "...CD81 was present in the plasma membrane-derived limiting membrane (Figures 2B, D, F), while CD63 was only found inside the MV-lEVs (Fig. 2A, C, E)." However, in Figure 2G, there is an observable CD63 signal in the limiting membrane (overlapping with the green signals), and in Figure 2J, CD81 also exhibits overlap with MV-IEVs.

Both CD63 and CD81 are tetraspanins known to be present both in the membrane of sEVs and in the plasma membrane of cells (for references, please see Uniprot subcellular location maps: https://www.uniprot.org/uniprotkb/P08962/entry#subcellular_location https://www.uniprot.org/uniprotkb/P60033/entry#subcellular_location). However, according the feedback of the reviewer, for clarity, we will delete the implicated sentence from the text.

(3) Following up on the previous concern, the authors argue that CD81 and CD63 are exclusively located on the limiting membrane and MV-IEVs, respectively (Figure 2-A-M). However, in lines 104-106, the authors conclude that "The simultaneous presence of CD63, CD81, TSG101, ALIX, and the autophagosome marker LC3B within the MV-lEVs..." This statement indicates that CD63 and CD81 co-localize to the MV-IEVs. The authors need to address this apparent discrepancy and provide an explanation.

There must be a misunderstanding because we did not claim or implicate in the text that that “CD81 and CD63 are exclusively located on the limiting membrane and MV-IEVs”. Here we studied co-localization of the above proteins in the case intraluminal vesicles (ILVs). In Fig 2. we did not show any analysis of limiting membrane co-localization.

(4) The specificity of the antibodies used in Figure 2 should be validated through knockout or knockdown experiments. Several of the antibodies used in this figure detect multiple bands on western blots, raising doubts about their specificity. Verification through additional experimental approaches is essential to ensure the reliability and accuracy of all the immunostaining data in this manuscript.

We will consider this suggestion during the revision of the manuscript.

(5) In Figures 2P-R, the morphology of the MV-IEVs does not resemble those shown in Figures 1-A, H, and D, indicating a notable inconsistency in the data.

EM images in Figure2 P-R show sEVs separated from serum-free conditioned media as opposed to MV-lEVs, which were in situ captured in in fixed tissue cultures (Fig1). Therefore, the two EV populations necessarily have different size and structure. Furthermore, Fig. 1 shows images of ultrathin sections while in Figure 2P-R, we used a negative-positive contrasting of intact sEV-s without embedding and sectioning.

(6) There are no loading controls provided for any of the western blot data.

Not even the latest MISEV 2023 guidelines give recommendations for proper loading control for separated EVs in Western blot (MISEV 2023 , DOI: 10.1002/jev2.12404 PMID: 38326288). Here we applied our previously developed method (PMID: 37103858), which in our opinion, is the most reliable approach to be used for sEV Western blotting. For whole cell lysates, we used actin as loading control (Fig3_S2B).

Additionally, for Figures 2-S4B, the authors should run the samples from lanes i-iii in a single gel.

Please note that in Figure 2- S4B, we did run a single gel, and the blot was cut into 4 pieces, which were tested by anti-GFP, anti-RFP, anti-LC3A and anti-LC3B antibodies. Full Western blots are shown in Fig.3_S2 B, and lanes “1”, “2” and “3” correspond to “i”, “ii” and “iii” in Fig.2_S4, respectively.

(7) In Figure 2-S4, is there co-localization observed between LC3RFP (LC3A?) with other MV-IFV markers? How about LC3B? Does LC3B co-localize with other MV-IFV markers?

In the Supplementary figure Figure 2-S4 we showed successful generation of HEK293T-PalmGFP-LC3RFP cell line. In this case we tested the cells, and not the released MV-lEVs. LC3A co-localized with the RFP signal as expected.

(8) The TEM images presented in Figure 2-S5, specifically F, G, H, and I, do not closely resemble the images in Figure 2-S5 K, L, M, N, and O. Despite this dissimilarity, the authors argue that these images depict the same structures. The authors should provide an explanation for this observed discrepancy to ensure clarity and consistency in the interpretation of the presented data.

As indicated in Material and Methods, Fig 2_S5 F, G, H and I are conventional TEM images fixed by 4% glutaraldehyde 1% OsO4 2h and embedded into Epon resin with a post contrasting of 3.75% uranyl acetate 10 min and 12 min lead citrate. Samples processed this way have very high structure preservation and better image quality, however, they are not suitable for immune detection. In contrast, Fig.2._S5 K,L,M,N shows immunogold labelling of in situ fixed samples. In this case we used milder fixation (4% PFA, 0.1% glutaraldehyde, postfixed by 0.5% OsO4 30 min) and LR-White hydrophilic resin embedding. This special resin enables immunogold TEM analysis. The sections were exposed to H2O2 and NaBH4 to render the epitopes accessible in the resin. Because of the different applied techniques, the preservation of the structure is not the same. In the case of Fig.2 J, O, separated sEVs were visualised by negative-positive contrast and immunogold labelling as described previously (PMID: 37103858).

(9) For Figures 3C and 3-S1, the authors should include the images used for EV quantification. Considering the concern regarding potential contamination introduced by FBS (concern 1), it is advisable for the authors to employ an independent method to identify EVs, thereby confirming the reliability of the data presented in these figures.

In our revised manuscript, we will provide all the images used for EV quantification in Figure 3C. Given that Figures 3C and 3-S1 show MV-lEVs released by HEK293T-PlamGFP cells, the possible interference by FBS-derived non-fluorescent EVs can be excluded.

(10) Do the amphiectosomes released from other cell types as well as cells in mouse kidneys or liver contain LC3B positive and CD63 positive ILVs?

Based on our confocal microscopic analysis, in addition the HEK293T-PalmGFP cells, HT29 and HepG2 cells also release similar LC3B and CD63 positive MV-lEVs. Preliminary evidence shows MV-lEV secretion by additional cell types.

Author Response

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

Reviewer #1 (Public Review):

The present study by Berger et al. analyzes to what extent memory formation is dependent on available energy reserves. This has been dealt with extensively in the case of aversive memory formation, but only very sparsely in the case of appetitive memory formation. It has long been known that an appetitive memory in flies can only be formed by starvation. However, the authors here additionally show that not only the duration of starvation plays a role, but also determines which form of memory (short- or long-term memory) is formed. The authors demonstrated that internal glycogen stores play a role in this process and that this is achieved through insulin-like signaling in octopaminergic reward neurons that integrates internal energy stores into memory formation. Here, the authors suggest that octopamine plays a role as a negative regulator of different forms of memory.

The study sheds light on an old question, to what extent the octopaminergic neuronal system plays a role in the formation of appetitive memory, since in recent years only the dopaminergic system has been in focus. Furthermore, the data are an interesting contribution to the ongoing debate whether insulin receptors play a role in neurons themselves or in glial cells. The experiments are very well designed and the authors used a variety of behavioural experiments, genetic tools to manipulate neuronal activity and state-of-the-art imaging techniques. In addition, they not only clearly demonstrated that octopamine is a negative regulator of appetitive memory formation, but also proposed a mechanism by which the insulin receptor in octopaminergic neurons senses the internal energy status and then controls the activity of those neurons. The conclusions are mostly supported by the data, but some aspects related to the experimental design, some explanations and literature references need more clarification and revision.

(1) Usually, long-term memory (LTM) is tested 24 hours after training. Here, the authors usually refer to LTM as a memory that is tested 6 hours after training. The addition of a control experiment to show that LTM that the authors observe here lasts longer would increase the power of this study immensely.

We thank the reviewer for this comment, as it helped greatly to clarify the matter.

We measured memory of control and mutant flies 24 h after the training and included the data into the manuscript (Figure 1B and summarized in a model in Figure 2C). We show that control flies develop an intermediate type of memory, that is depending on the length of starvation either anesthesia-sensitive or resistant. Mutants lacking octopamine develop either anesthesia-sensitive or resistant long-term memory.

(2) The authors define here another consolidated memory component as ARM, when they applied a cold-shock 2 hours after training. However, some publications showed that LTM is formed after only one training cycle (Krashes et al 2008, Tempel et al 1983). This makes it difficult to determine, whether appetitive ARM can be formed. Furthermore, one study showed that appetitive ARM is absent after massed training (Colomb et al 2009). Therefore, the conclusion could be also, that different starvation protocols, would lead to different stabilities of LTM. Therefore, additional experiments could help to clarify this opposing explanation. From these results, it can then be concluded either that different stable forms of LTM are formed depending on the starvation state, or that two differently consolidated memory phases (LTM, ARM) are formed, as has already been shown for aversive memory. This is also important for other statements in the manuscript, and therefore the authors should address this. For example, the findings about the insulin receptor (is it two opposing memories or different stabilities of LTM).

The flies indeed develop different types of memory depending on the length of starvation and the internal energy supply.

Reviewer #2 (Public Review):

How organism physiological state modulates establishment and perdurance of memories is a timely question that the authors aimed at addressing by studying the interplay between energy homeostasis and food-related conditioning in Drosophila. Specifically, they studied how starvation modulates the establishment of short-term vs long-term memories and clarified the role of the monoamine Octopamine in food-related conditioning, showing that it is not per se involved in formation of appetitive short-term memories but rather gates memory formation by suppressing LTM when energy levels are high. This work clarifies previously described phenotypes and provides insight about interconnections between energy levels, feeding and formation of short-term and long-term food-related memories. In the absence of population-specific manipulation of octopamine signaling, it however does not reach a circuit-level understanding of how these different processes are integrated.

Strengths

• Previous studies have documented the impact of Octopamine on different aspects of food-related behaviors (regulation of energy homeostasis, feeding, sugar sensing, appetitive memory...), but we currently lack a clear understanding of how these different functions are interconnected. The authors have used a variety of experimental approaches to systematically test the impact of internal energy levels in establishment of appetitive memory and the role of Octopamine in this process.

• The authors have used a range of approaches, performed carefully controlled experiments and produced high quality data.

Weaknesses

(1) In the tbh mutant flies, Tyramine -to- Octopamine conversion is inhibited, resulting not only in a lack of Octopamine, but also in elevated levels of Tyramine. If and how elevated levels of Tyramine contributes to the described phenotypes is unclear. In the current version of the manuscript, only one set of experiments (Figure 2) has been performed using Octopamine agonist. This is particularly important in light of recent published data showing that starvation modifies Tyramine levels. (2) Octopamine (and its precursor Tyramine) have been implicated in numerous processes, complicating the analysis of the phenotypes resulting from a general inhibition of tbh.

We thank the reviewer for raising these points. The observed memory defects of the Tbh mutants can be solely explained by loss of octopamine. We included models into the manuscript to illustrate this (Figure 2 C and Figure 7E).

To address whether the elevated levels of tyramine observed in Tbh mutants interfere with food consumption, we analyzed the effect of increased levels of tyramine and octopamine on food consumption. We included the data (Figure S2). An increase in tyramine levels did not result in a change in food intake, rather the increase in octopamine levels reduced food intake. Our data show that the reduction of food intake observed in starved Tbh mutants is due to the increased internal energy supply.

(3) The manuscript explores various aspects of the impact of energy levels on food-related behaviors and the underlying sensing and effector mechanism, both in wild-type and tbh mutants, making it difficult to follow the flow of the results.

We included models illustrating the results to clarify the content of the manuscript.

Reviewer #3 (Public Review):

In this manuscript, Berger et al. study how internal energy storage influence learning and memory. Since in Drosophila melanogaster, octopamine (OA) is involved in the regulation of energy homeostasis they focus on the roles of OA. To do so they use the tyramine-β-hydroxylase (Tbh) mutant that is lacking the neurotransmitter OA and study short term memory (STM), long-term memory (LTM) and anesthesia-resistant memory (ARM). They show that the duration of starvation affects the magnitude of both short- and long-term memory. In addition, they show that OA has a suppressive effect on learning and memory. In terms of energy storage, they show that internal glycogen storage influences how long sucrose is remembered and high glycogen suppresses memory. Finally, they show that insulin-like signaling in octopaminergic neurons, which is also related to internal energy storage, suppresses learning and memory.

This is an important study that extends our knowledge on OA activity in learning and memory and the effects the metabolic state has on learning and memory. The authors nicely use the genetic tools available in flies to try and unravel the complex circuitry of metabolic state level, OA activity and learning and memory.

Nevertheless, I do have some comments that I think require attention:

(1) The authors use RNAi to reduce the level of glycogen synthase or glycogen phosphorylase. These manipulations are expected to affect the level of glycogen. Using specific drivers the authors attempt to manipulate glycogen level at the muscles and fat bodies and examine how this affects learning and memory. The conclusions of the authors arise solely from the manipulation intended (i.e. the genetics). However, the authors also directly measured glycogen levels at these organs and those do not follow the manipulation intended, i.e. the RNAi had very limited effect on the glycogen level. Nevertheless, these results are ignored.

We agreed with the reviewer and repeated the experiments. While we could not detect differences in whole animals, we detected differences in tissues enriched for muscles or fat, e.g. thorax or abdomen. We added the data.

(2) The authors claim in the summary that OA is not required for STM. However, according to one experiment OA is required for STM as Tbh mutants cannot form STM. In another experiment OA is suppressive to STM as wt flies fed with OA cannot form STM. Therefore, it is very difficult to appreciate the actual role of OA on STM.

During mild starvation, the internal energy supply is greater in Tbh mutants than in control flies. This information is integrated into the reward system via insulin receptor signaling. Therefore, the association between the odorant and sucrose is not meaningful to the mutants and no STM is formed. At the same time there is no release of octopamine and therefore no repression of LTM. In starved animals, octopamine suppresses food intake (we added the data). This is consistent with a function of Octopamine as a signal for the presence of food. Depending on when the signal comes, this might suppress the formation of STM or LTM.

(3) The authors use t-test and ANOVA for most of the statistics, however, they did not perform normality tests. While I am quite sure that most datasets will pass normality test, nevertheless, this is required.

Thanks for pointing this out. We have included a description in the “Materials and Methods” section that explains how we tested the data for normal distribution. We corrected the figure legends accordingly.

“We used the Shapiro-Wilk test (significance level P < 0.05) followed by a QQ-Plot chart analysis to determine whether the data were normal distributed. “

(4) While it is logical to assume that OA neurons are upstream to R15A04 DA neurons, I am not sure this really arises from the experiment that is presented here. It is well established that without activity in R15A04 DA neurons there is no LTM. Since OA acts to decrease LTM, can one really conclude anything about the location of OA effect when there is no learning?

Normally control flies did not form memory 6 h after training, only Tbh mutants. We wanted to investigate what kind of memory develops in Tbh mutants. During the experiments of the manuscript, we kept the training procedure constant.