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Referee #1

Evidence, reproducibility and clarity

Summary: Sharma, et al. report the characterization of the polar tube (PT) from the microsporidian species, Vairimorpha necatrix, using a combination of optical microscopy, cryo-ET, and proteomics. The polar tube is a fascinating invasion apparatus which mediates the translocation of the parasite into the inside of a host cell to initiate infection. Similar to results obtained previously in other species, the authors show that PT firing in Vairimorpha necatrix is extremely fast, occurring on the order of 1 sec, and that the extruded PT is over 100 microns long in this species. Using cryo-ET to image the PT at a high resolution, they find that it exists in two major states: both an empty state and a state filled with cargo, and that the thickness of the tube wall changes when cargo is present. Strikingly, the authors observed that one of the cargo components, the ribosomes, are organized ordered array that may have helical symmetry. Finally, the authors took advantage of a naturally occurring "His tag" on PTP3 to affinity purify PTP3-containing protein complexes and analyze the composition using proteomics.

Major comments

ln 139-140: The absolute handedness of something can be very tricky to determine by cryo-ET (but certainly is possible). Variable hardware configurations between microscopes and differing conventions between software packages (e.g., for what direction is a positive tilt angle) can lead to inversion of the apparent handedness in the final tomogram. How certain are the authors that the absolute handedness is indeed right handed, as this seems to vary between the various display items in the manuscript? For example, in Fig 1c, my impression is that ribosome helices are left handed, as they are also in the supplemental movie. If this isn't known with certainty, perhaps it would be sufficient to describe the apparent helical symmetry but state that the handedness is ambiguous.

Minor comments

ln 39-40: Perhaps also cite the E. cuniculi genome paper?

ln 97-98: It is interesting that the PT shortens in V. necatrix as well, and while I can pick this out in some of the individual traces in Sup Fig. 1b, it seems to get washed out in the trend line and isn't super obvious. If it isn't to laborious, it could be nice to add a panel showing the quantification of this (e.g., plotting the final length of each PT as a percentage of the maximum length achieved).

ln 98-100: Strictly speaking, I don't think the referenced figure shows the sporoplasm being transformed into an extended conformation, only that it is spherical upon exit. Simply reword this to make clear that the deformations are inferred to occur but not directly observed.

Because PT firing is so fast, the probability of trapping a PT in the process of transporting cargo would be pretty low. So then why does the PT still contain cellular cargo like ribosomes inside in the tomograms? Should these not have emerged in the sporoplasm which would enter the host cell? Are these "defective" spores that have failed to complete sporoplasm transport? Perhaps this is worth discussing.

ln 118: The authors note an apparent correlation between the phase of germination and the thickness of the tube wall but don't specify what this correlation is. Is it thicker in the early phase and thinner in later phase, or vice versa? One could imagine "empty" tubes existing before or after sporoplasm transport, for example, so I'm not sure I follow how the phase is being inferred from the tomograms.

ln 119-120: What is the evidence that the outer layer is made of PTPs, or that it is even protein (for example, as opposed to cell wall-like carbohydrate polymers)? I think this seems like a very reasonable hypothesis, but I would suggest explaining the logic and ensuring the degree of uncertainty is conveyed clearly. In light of this, I would also suggest changing figure labels, etc, that refer to the PTP layer (e.g., Fig. 3, PTPc and PTPe labels).

ln 121, 123: "sheathed by a thin layer" and "enveloped by a thick outer layer": is this an additional layer being described? Or is this referring to the putative PTP layer, and that its thickness is variable?

ln 125-126: While I understand how some features, like ribosomes, proteasomes, and generic membrane compartments could be identified, it is unclear to me how one would recognize the nucleus when inside the PT, nor are any examples shown. If the data is clear, perhaps the authors could show it in a figure? Otherwise, I suggest removing the claim regarding the nucleus.

The arrangement of the ribosomes in a subset of tubes is really fascinating! While the number of observations is relatively small (n=5), it seems like it should be possible to comment preliminarily on whether there is much variability in their helical arrangement. Do the helical parameters vary much between observations? Does the til, pitch, etc vary much, are the 5 occurrences very similar? Is there any sign that they are associated with a membrane? Also, since the ribosomes form a lattice-like arrangement, it seems like it would be possible to trace ribosome helices in both the left and right handed directions. How did the authors decide between the two possibilities? This doesn't seem to be discussed.

Fig. 2e: Are the two different colors/orientations meant to represent the two protamers of the ribosome dimer? When refined subvolumes are mapped back onto the original tomogram do the authors observe a similar crystalline arrangement of particles as in their segmentation? Are the orientations of the ribosomes correlated, and do the provide any evidence for the dimeric arrangement mentioned? The PlaceObjects plugin for Chimera can be very helpful for visualizing this: https://www.biochem.mpg.de/7939908/Place-Object

Supp figure 4(b-d): Perhaps these models could be colored by pLDDT scores (with a key indicating the color scheme), so the reader can assess the quality of the predictions?

How were the measurements of the membrane thickness and putative PTP layer carried out? On the tomogram projections? STAs? How were the boundaries of the layers established (e.g., map threshholding if STA?)? This information appears to be missing from the methods.

Some tubes that are labeled as 'PTempty' actually contain cargo and look dense (example supp. Fig 2c, left and middle panels). Is it fair to classify these as empty tubes?

Fig. 3d: I am not entirely clear on what is being shown here. Are independent reconstructions of PTcargo and PTempty superposed (aligned on membrane)? The description in the figure legend doesn't clearly say what is being displayed. I think it might be more clear to show these side-by-side instead of superposed (i.e., 4 panels instead of 2).

Sup Fig 1: Define S and SP in legend or just spell out on figure? Missing x-axis label on panel b.

Fig. 4b and Sup Fig 2a: The depictions of the PT in the spore here are left-handed. In a few species, the coil of the PT was found to form a right-handed helix (Jaroenlak, et al.), and it seems plausible that this may be a general feature that would be conserved across microsporidia. I appreciate that it might not be actually known to be right-handed in V. necatrix, but if there is no strong data either way, perhaps it would make sense for these depictions of the PT to be right-handed.

I think all three of us are more or less in consensus about this manuscript, and I largely agree with the other reviewers comments. I think after addressing reviewer suggestions, this will be a pretty nice story.

Significance

Overall, this manuscript from Sharma, et al. presents interesting new findings about the structure and cargo transport function of the microsporidian PT. Microsporidia infect a wide range of hosts, including humans, and how the PT mediates parasite entry into cells is poorly understood. The approaches used in this study are appropriate for tackling the questions at hand, and appear to be generally well executed and interpreted. The observation that ribosomes assemble into an array within the PT is very unexpected and quite fascinating, and may be of broader interest to researchers working on ribosome structure and function, in addition to researchers studying microsporidia. The approach to investigating proteins interacting with PTP3 was quite elegant, and yielded a list of potential interactors that appears to be of very high quality and is highly plausible based on the literature field. We think this work is a substantial advance in the field and provides important new insights into the organization of the PT. - Please define your field of expertise with a few keywords to help the authors contextualize your point of view:

Structural biology, microsporidia - Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

We are not experts in proteomics/mass spectrometry

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Reply to the reviewers

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

The authors describe a broad-scale phylogenetic survey of chemokine-related ligand and receptors from representative vertebrates, invertebrates, and viruses. They collect ligand and receptor sequences from available genome sequences, and use phylogenetic and CLANS analysis to group these into similar gene types. They then overlay these onto a validated species phylogeny in order to evaluate relationships of orthology and paralogy to pinpoint gene duplication and loss events. They carry out these analyses for canonical chemokine ligands receptors and for other closely related protein families. They conclude that the canonical chemokine system is restricted to vertebrates but that closely related ligands and receptors can be found in invertebrate chordates. More divergent but related gene systems are found in more distant invertebrates. They define more limited expansions of some ligand-receptor systems in certain jawed vertebrate groups and specifically in mammals.

Overall, the paper addresses a complex and important system of signaling proteins with a rigorous and comprehensive set of analyses. The finding will be of interest to a diverse group of scientists. My comments listed below mainly consist of suggestions to help clarify the presentation.

1. Pg 2, Lns 21-24: The canonical and non-canonical chemokine subclasses are introduced in the abstract without definition. A very brief explanation would be useful.

We've included a brief description of "non-canonical" components in the abstract (lines 21-24). These non-canonical components fall into at least one of three categories: 1) molecules with sequence similarities to canonical components, 2) those that bind to a canonical component (either ligand or receptor), 3) those involved in chemokine-like functions, such as chemoattraction. More comprehensive explanations and examples of these non-canonical components are provided in the Introduction section.

1. Some general contexts of chemokine functions are listed, including inflammation and homeostasis. A little more detail of how these signals are used and the molecular consequences of signaling may be useful in the introduction to set the biological context of the analysis (e.g., how do the signals regulate homeostasis?).

We have added at the beginning of the introduction (lines 39 – 46) some details of how chemokine signalling typically occurs at a mechanistic level. We also provided few examples of homeostatic functions regulated by chemokine signalling and clarified different expression strategies for inflammatory versus homeostatic chemokines.

It may help to summarize the known chemokine and chemokine-related gene systems in some type of table at the beginning of the results. This could serve as a convenient reference to guide the reader through the more detailed results. The manuscript addresses a complex set of ligands and receptors with names that may be confusing to the non-expert.

We agree with the reviewer on this and moved Table S1 to the main text (now Table 1). This table contains all the information on ligands, receptors, and relative citations (lines 741-744).

Pg 5, Ln 98: Fig 1C is introduced before Fig 1B. Can the panels be switched or the descriptions be rearranged?

We have switched the panels in Figure 1. Now, Figure 1A and 1B refer to CLANS analyses and Figure 1C and 1D refer to phylogenetic trees of ligand groups. We have corrected all the references in the main text and in Figure 1 caption. Now the panels are mentioned in the correct alphabetical order within the text.

Cytokine and chemokine ligands are small proteins that diverge quickly in different species and are difficult to identify in divergent genomes even within vertebrates. Conclusions about the absence of these types of factors are notorious for being disproven in subsequent analyses. Some discussion of what may have been missed in the survey for homologs (or reasons to think that ligands were not missed) would be useful in the Discussion.

We concur with the reviewer's observation, and we used three distinct strategies to address the issue:

1. E-value Threshold Adjustment: Initially, we utilized a relatively low e-value threshold of These three strategies collectively contribute to a more robust and comprehensive approach to address the challenges associated with the bioinformatic identification of canonical and non-canonical chemokines. We briefly mentioned the technical difficulty of working with short sequences in our Introduction (lines 75-76).

Reviewer #1 (Significance (Required)):

This paper presents a thorough analysis of chemokines and related gene systems across a wide phylogenetic landscape. The authors have expertise in these gene families and in the techniques that they use to identify and relate family members. The chemokines are an important set of signals that are used across several biological systems. These findings will be of wide interest to immunologists, neurobiologists, developmental and evolutionary biologists.

We thank reviewer 1 for their comments – they have been very valuable to improve our manuscript.

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

This paper applies phylogenetic clustering methods to a large taxonomical sampling to interrogate the relationship between canonical and non-canonical chemokine ligands and receptors. The results suggest that 1) unrelated proteins evolved "chemokine-like" ligand function multiple times independently; and 2) all the canonical and non-canonical chemokine receptors (except ACKR1) originated from a single duplication in the vertebrate stem group, which also gave rise to many GPCRs. In addition, the authors characterized the complement of canonical and non-canonical components in the common ancestor of vertebrates and identified several other ligands and receptors with potential chemokine related properties.

Comments: 1. There are many places in the paper, too many to list, where the authors refer to chemokine receptors but call them 'chemokines'.

We have corrected this oversight throughout the manuscript.

In Figure 1, CX3CL is referred to as 'X3CL'

We have corrected this. Now CX3CL is referred to correctly in Figure 1. We also found that it was incorrectly spelt in Figure 2 as well and corrected it there too.

1. CXCL17 was originally reported to be chemokine-like based on sequence threading methods. The authors refer to a 2015 paper indicating that it has chemokine-like activity at GPR35, which had been renamed provisionally CXCR8. To my knowledge that result was not based on direct binding data but inferred from a functional response. Moreover, to my knowledge it has not been independently confirmed. Instead there is a recent paper in JI from the Pease lab showing extensive experimental results that fail to demonstrate CXCL17 activity at GPR35. This uncertainty regarding a potential mistake in the literature should be addressed and integrated in the points made about CXCL17 being an outlier.

We thank the reviewer for pointing this out. To account for this suggestion, we have modified the text as follows:

Lines 105-108: “The distinction between CXCL17 and all other canonical chemokines is consistent with our receptor results showing that the potential receptor for CXCL17, GPR35 (41), is also not within the canonical chemokine receptor group (see below). Although it is important to note that recent studies fail to demonstrate CXCL17 activity at GPR35 (42, 43).”

Lines 240-241: “Another orphan GPCR, GPR35, had been proposed as a potential chemokine receptor (41); however, this was later questioned (42, 43) and GPR35 is still generally considered orphan (55–57).”

Lines 312-315: “CXCL17 is mammal-specific and likely unrelated to canonical chemokines (similar to its controversial putative receptor, GPR35 (41-43), that is not a canonical chemokine receptor).”

References: [41] J. L. Maravillas-Montero, et al., Cutting Edge: GPR35/CXCR8 Is the Receptor of the Mucosal Chemokine CXCL17. The Journal of Immunology 194, 29–33 (2015).

[42] S.-J. Park, S.-J. Lee, S.-Y. Nam, D.-S. Im, GPR35 mediates lodoxamide-induced migration inhibitory response but not CXCL17-induced migration stimulatory response in THP-1 cells; is GPR35 a receptor for CXCL17? British Journal of Pharmacology 175, 154–161 (2018).

[43] N. A. S. B. M. Amir, et al., Evidence for the Existence of a CXCL17 Receptor Distinct from GPR35. The Journal of Immunology 201, 714–724 (2018).

[55] S. Xiao, W. Xie, L. Zhou, Mucosal chemokine CXCL17: What is known and not known. Scandinavian Journal of Immunology 93, e12965 (2021).

[56] S. P. Giblin, J. E. Pease, What defines a chemokine? – The curious case of CXCL17. Cytokine 168, 156224 (2023).

[57] J. Duan, et al., Insights into divalent cation regulation and G13-coupling of orphan receptor GPR35. Cell Discov 8, 1–12 (2022).

Can the authors use alpha fold to address whether any of these non-canonical molecules actually is predicted to fold like a chemokine? More generally, based on the paper's analysis, how do the authors propose to define a chemokine? It is well-accepted that chemokines are defined by structure, not function (e.g. limited truncation of any chemokine abrogates activity, but it is still a chemokine structurally, not semantically, folds like a chemokine, aligns with other chemokines).

In response to the recommendation from reviewer 2 to incorporate AlphaFold data, we leveraged AFDB Clusters (foldseek.com), a recently developed tool that clustered over 200 million Uniprot proteins based on their predicted AlphaFold structures (as described in this Nature paper: https://www.nature.com/articles/s41586-023-06510-w). We utilised this pre-computed dataset of clustered proteins to query with representative human proteins, both canonical and non-canonical chemokine ligands, and the results are summarised in the table below. Notably, we observed that canonical chemokines were distributed across different AlphaFold clusters, each corresponding to different ligand types (e.g., CC and CXC). Interestingly, despite this, all these clusters exhibited similar descriptions (e.g. CC or CXC), indicating that the method effectively recovers well-characterized chemokines. Conversely, when analysing non-canonical chemokine ligands, none of them were classified within the canonical chemokine clusters. This observation strongly suggests that canonical and non-canonical ligands do not share the same protein fold. Additionally, we identified intriguing correlations between these structure-based clusters and the results from our phylogenetic analyses. For instance, CXCL14 was clustered within a CC-type group, consistent with our reconciled tree positioning it within the broader CC-type clade (as shown in Figure 2A). Similarly, CXCL16 formed its own unique cluster, which aligns with our CLANS analysis, where it is the last group to connect with canonical chemokines (illustrated in Figure 1A and Figure S1). Furthermore, TAFA5 was found in a distinct cluster, mirroring our phylogenetic analyses that place it as the most basal TAFA clade (as depicted in Figure 2A and Figure S19). While these findings are intriguing, we acknowledge that additional in-depth analyses, beyond the scope of this paper, will be necessary to confirm these results.

In response to the reviewer's inquiry regarding how to define a chemokine, it is essential to recognise that many proteins can exhibit similar 3D structures without being considered homologous. A notable example is the opsins, which are present in both bacteria and animals. Despite sharing a common 3D structure that is characterised by seven transmembrane domains (TMDs) and serves similar functions, they are not regarded as homologous, as highlighted in this study (https://doi.org/10.1186/gb-2005-6-3-213). Considering these findings, we propose that, like various other gene families, the primary criterion for assessing protein homology should be rooted in shared evolutionary ancestry and common origin, and this should take precedence over structural similarities.

Human gene

Uniprot Accession

AFDB Cluster

Accession

Description

Canonical CKs

CXCL14

O95715

A0A3Q3M453

C-C motif chemokine

CCL24

O00175

A0A4X1T574

C-C motif chemokine

CX3CL1

P78423

A0A7J8CF84

C-X3-C motif chemokine ligand 1

CXCL1

P09341

A0A1S2ZIJ4

C-X-C motif chemokine

CXCL13

O43927

A0A1S2ZIJ4

C-X-C motif chemokine

CXCL8

P10145

A0A1S2ZIJ4

C-X-C motif chemokine

CCL20

P78556

A0A6P7X7F3

C-X-C motif chemokine

XCL1

P47992

A0A6P7X7F3

C-X-C motif chemokine

CXCL16

Q9H2A7

A0A6P8SIS6

C-X-C motif chemokine 16

CCL27

Q9Y4X3

A0A1L8GBB9

SCY domain-containing protein

CCL1

P22362

A0A3B4A358

SCY domain-containing protein

CCL5

P13501

A0A3B4A358

SCY domain-containing protein

CCL28

Q9NRJ3

A0A3Q0SB19

SCY domain-containing protein

CXCL12

P48061

A0A401SMI2

SCY domain-containing protein

CXCL17

CXCL17

Q6UXB2

No cluster found

No cluster found

TAFA

TAFA1

Q7Z5A9

Q96LR4

Chemokine-like protein TAFA-4

TAFA2

Q8N3H0

Q96LR4

Chemokine-like protein TAFA-4

TAFA3

Q7Z5A8

Q96LR4

Chemokine-like protein TAFA-4

TAFA4

Q96LR4

Q96LR4

Chemokine-like protein TAFA-4

TAFA5

Q7Z5A7

A0A7M4EYY1

TAFA chemokine like family member 5

CYTL

CYTL1

Q9NRR1

A0A673GVE4

Cytokine-like protein 1

CKLFSF

CMTM5

Q96DZ9

A0A4W2H069

CKLF like MARVEL transmembrane domain containing 5

CMTM8

Q8IZV2

U3IR50

CKLF like MARVEL transmembrane domain containing 7

CMTM7

Q96FZ5

A0A6G1PQK5

CKLF-like MARVEL transmembrane domain-containing protein 7

CMTM6

Q9NX76

A0A814ULI9

Hypothetical protein

CKLF

Q9UBR5

A0A3M0K8M7

MARVEL domain-containing protein

CMTM1

Q8IZ96

A0A3M0K8M7

MARVEL domain-containing protein

MAL

P21145

A0A402F5Z5

MARVEL domain-containing protein

CMTM2

Q8TAZ6

A0A6G1S7Y0

MARVEL domain-containing protein

PLP2

Q04941

A0A667IJ27

Proteolipid protein 2

CMTM3

Q96MX0

A0A3B1ILJ1

Zgc:136605

CMTM4

Q8IZR5

A0A3B1ILJ1

Zgc:136605

PLLP

Q9Y342

A0A3B1ILJ1

Zgc:136605

Chemokine genes are found on many human chromosomes with large clusters on chromosome 2 and 17. Can the authors address the syntenic relationships phylogenetically?

There are cases where synteny data have been used to infer the relationship between species (e.g. https://doi.org/10.1038/s41586-023-05936-6); however, to our knowledge, they cannot be used to infer the pattern of gene duplications and losses, as we have done here with gene tree to species tree reconciliations. However, the two approaches are extremely powerful combined and compared as they provide independent evidence. For example, with our phylogenetic analysis of chemokine ligands, we found that CXCL1-10 plus CXCL13 form a monophyletic clade (Figure 2A); this is consistent with their location on the human chromosome 4 (Zlotnik and Yoshie 2012). Similarly, most of the CC-type chemokines, that we find monophyletic in our trees, are located in a locus in human chromosome 17. Likewise, chemokine receptor phylogenetic relationships are largely consistent with macro and micro syntenic patterns. Most of the chemokine receptors are on human chromosome 3 (Zlotnik and Yoshie 2012) and they all belong to a large monophyletic clade in our tree (Figure 4A). Smaller clusters also maintain correspondence, such as the mini cluster of CXCR1 and CXCR2 on human chromosome 2 corresponding to a monophyletic clade in our phylogenetic analysis (Figure 4A).

We have incorporated the above considerations in our manuscript at the lines:

• Lines 140-148 (ligands)

• Lines 256-272 (receptors)

• Lines 375 – 483 (discussion)

The authors indicate that 'CXCL8 is present in all jawed vertebrates except in the cartilaginous fishes lineage'. However, they should point out that CXCL8 is not represented in mice. The notion that the repertoire of chemokine and chemokine receptor genes can be different in even closely related species as well as in individuals of the same species is well-documented but not mentioned here.

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We thank the reviewer for these suggestions, and we have modified the text in lines 137-138.

The analysis suggests that chemokine gene repertoires start small and grow non-linearly to 45 in mammals. However DeVries et al (JI 2005) published that zebrafish have the most chemokines, 63, and chemokine receptors, 24. Do the authors disagree? This should be addressed.

The significant increase in the number of ligands and receptors in zebrafish, compared to their last common mammalian ancestor, can be attributed to an additional round of whole-genome duplication (WGD) (https://doi.org/10.1016/S0955-0674(99)00039-3).

Concerning ligands, the count in zebrafish varies from 63 in DeVries et al. 2005 to 111 in Nomiyama et al. 2008, and to 35 in our study. This variation can be attributed to several factors:

1. Genome Versions: The disparities may arise from the use of different versions of the zebrafish genome. We utilised an improved version known for its higher contiguity and reduced fragmentation (https://www.nature.com/articles/nature12111). It is possible that the additional ligands identified by DeVries, Nomiyama, and others were partial sequences.
2. Methodology: Methodological differences are at play. DeVries et al. employed tblastN, while we opted for BLASTP. Nomiyama et al. do not specify the type of BLAST performed.
3. Stringency: We collected our sequences based on a BLASTP search using as query sequences only manually curated sequences from UniProt. This additional precaution allowed us to identify sequences with high-confidence chemokine ligand characteristics.
4. Sequence Characteristics: Ligands typically have shorter sequences and exhibit less sequence conservation compared to receptors. Zebrafish represents a case in which working with short sequences may lead to missed homologs.
5. Species-Specific Nature: Our approach successfully recovered the complete set of ligands in other species, such as humans and mice. Zebrafish appears to be an exception rather than the norm. When it comes to receptors, which typically have longer sequences, making it easy to identify distant homologs, our results closely mirror those of DeVries in 2005. In our study, we identified 28 canonical receptors, compared to their count of 24. However, it is worth highlighting that within our dataset, four of these receptors appear as species-specific duplications, potentially indicating that they are actually isoforms or related variants.

Nonetheless, it is essential to emphasise that our work does not aim to precisely reconstruct the entire complement of ligands and receptors in zebrafish or other species. Achieving this would require further validation, including the expression analysis of potential transcripts.

Did the authors find any species in which a chemokine/chemokine receptor pair are not found together? That is, if the system is irreducibly complex, requiring both a ligand and receptor, the probability of both genes arising simultaneously is essentially zero. So how do the authors theorize that such a system actually arose, and is there any evidence in their data set for convergence of separately evolved ligand and receptor?

Our data strongly support the hypothesis that the canonical chemokine system originated within the stem group of vertebrates, likely as a consequence of two rounds of genome duplication. This likely accounts for the simultaneous emergence of both ligands and receptors. While the receptors (both canonical and non) can be traced back to a single-gene duplication event (with the exception of ACKR1), the evolution of ligand families capable of interacting with chemokine receptors occurred independently, although further experiments are required to validate this in vivo in a broader set of organisms. In our study, we successfully identified the complete set of receptors and ligands in well-established model systems like humans and mice. However, when it comes to interactions between ligands and receptors outside these model organisms, the picture becomes less clear. Similarly, the exact pairings of non-canonical components are also not fully clarified (see lines 404-406). As a result, speculating about evolutionary conservation in these contexts requires caution and further investigation. It's worth noting that chemokines and their corresponding chemokine receptors do not necessarily evolve in tandem. Since they are encoded by different genes, they evolved from separate duplication events occurring at different points in evolutionary history. In certain instances, due to the system's flexibility, chemokines binding orthologous receptors may not be orthologous themselves but may have independently acquired the ability to activate the same receptor in various species.

Line 180, 181 and elsewhere: GPCR1 and GPCR33 should be GPR1 and GPR33

We have corrected this throughout the manuscript.

Line 185: ACKR1 exceptionalism is noted, but there is no discussion of the remarkable structure-function paradox that the most distantly related chemokine receptor is also the most highly promiscuous receptor, binding many but not all CC and CXC chemokines with high affinity.

We added in the discussion section this consideration regarding the wide binding of ACKR1 (Lines 341-343) and its ability to bind both CC and CXC chemokines (DOI: 10.1126/science.7689250 and 10.3389/fimmu.2015.00279), highlighting the intriguing contrast with the fact that it is the most distantly related receptor.

Line 196: the viral receptors cluster with the vertebrate receptors, suggesting that the viruses captured the receptor gene from the host. Authors might mention this obvious point regarding origins, and discuss how it relates to the monophyly and paraphyly that emerges from the phylogenetic analysis.

We added a comment to the discussion section (Lines 348-352) regarding the potential origins of the viral chemokine receptors.

Any discussion of chemokine-like convergent evolution presupposes that the activity is real and actually occurs in vivo. The authors should make clear to what extent the existing literature supports this. As mentioned above, CXCL17 interaction with GPR35 has been challenged in vitro and has never been demonstrated to occur in vivo. To what extent is the same limitation a problem in considering co-evolution of the other non-canonical chemokines? I agree that classification based solely on function is inappropriate, but so is phylogenetic analysis without direct knowledge of in vivo function. It is no feasible to address this in a phylogenetic analysis, but there ought to be at least one species in which the non-canonicals have been rigorously shown to act at specific receptors in vivo before grouping them with the canonicals in a co-evolutionary sense.

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We agree with the referee that evidence of real chemokine-like activity is important to consider the activity in vivo.

In our work, the molecules examined were chosen based on previous evidence of chemokine-like sequence similarity, ability to bind canonical components and/or chemokine-like function. For example, CKLF (also called CKLF1) has been shown, through calcium mobilisation and chemotaxis assays using the human cell line HEK293, to bind CCR4 and to induce cell migration via CCR4 respectively (https://doi.org/10.1016/j.lfs.2005.05.070). Numerous papers are studying the in vitro and in vivo effects of CKLF in murein and human models (https://doi.org/10.1016/j.cyto.2017.12.002), therefore, we found it compelling to investigate its evolutionary relationship with canonical chemokines. Similarly, CYTL1, that had been predicted to possess an IL8-like fold (https://doi.org/10.1002/prot.22963), has been found to bind CCR2 (https://doi.org/10.4049/jimmunol.1501908) and in vitro and in vivo studies showed chemotactic activity for neutrophils (https://doi.org/10.1007/s10753-019-01116-9). Ongoing research into this molecule are focusing on a wide array of immune functions (https://doi.org/10.1007/s00018-019-03137-x).

We mentioned these considerations in our introduction to explain why we were interested in investigating these molecules (lines 50-57). We have also added a line in the Discussion (lines 323-324) where we reinforce the idea that in vitro and in vivo experiments for all chemokine-like molecules are required to validate computation predictions.

The discussion of homeostatic vs inflammatory chemokine/receptors in the last section of the Discussion would be enhanced by pointing out that the chemokine specificities are numerically totally different for these two groupings, homeostatics tending to have monogamous ligand-receptor relationships and inflammatories being highly promiscuous.

To account for the reviewer’s comment, we have added this consideration in a paragraph of the discussion (see Line 389-394).

Reviewer #2 (Significance (Required)):

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Much of the paper's results are confirmatory of previous work based on less extensive sequence analysis. One could say more generally that unrelated chemical forms, not just unrelated proteins, have chemokine-like ligand function. For example leukotriene B4 is a powerful leukocyte chemoattractant for neutrophils working through a GPCR. That proteins might also independently evolve common functions does not add insight beyond what is already appreciated. The notion that chemokine receptors have a common ancestor is also generally accepted and that ACKR1 is an outlier is already appreciated. The present work adds phylogenetic and statistical precision to these points.

Our discoveries clarify various aspects of the chemokine system's evolution, and we are confident that the "phylogenetic and statistical precision" of our findings will provide a solid cornerstone for future research aimed at unravelling the function and evolution of the system. Specifically, our work clarified:

1. The presence only in Vertebrates: We have confirmed, through a comprehensive taxonomic sampling (we use many more species than previous works), that the chemokine system is exclusive to vertebrates. However, intriguingly, we identified a TAFA chemokine-like family in urochordates.
2. Relationships between Ligands: We conducted a thorough examination of the relationships between canonical and non-canonical ligands and suggested that several unrelated molecules might have evolved independently their ability to interact with the chemokine receptors. We appreciate the comment of the reviewer regarding the fact that unrelated chemical forms such as leukotriene B4 may have chemokine-like functions. However, in our work all the non-canonical components examined are proteins and as such could have an evolutionary relationship with chemokines. Furthermore, we chose to consider only proteins that showed multiple lines of evidence implicating them in the chemokine system and that are currently the topic of interest in the field (see replies to reviewer 1’s comment #5 and to reviewer 2’s comment #12). Seeing the general interest in the topic, and especially seeing as this had never been clarified before, in this work, we set ourselves the goal to investigate the evolutionary relationship amongst these non-canonical ligands and canonical chemokines.
3. Duplication Events: We pinpoint the specific gene duplication events responsible for the emergence of chemokine receptors.
4. Atypical Receptor Paraphyly: Our work highlights the paraphyletic nature of atypical receptors, in contrast to previous research (see https://doi.org/10.1155/2018/9065181).
5. Viral Receptor Phylogenetics: To our knowledge, this is the first work to investigate the phylogenetic affinities of viral receptors.
6. GPCR182 and Atypical Receptor Affinities: We clarify the affinity of GPCR182 with atypical receptor 3, offering different insights compared to prior studies (see figure S3C in https://doi.org/10.1038/s41467-020-16664-0).
7. Additionally, our study represents the first analysis of the chemokine system in the basal vertebrate hagfish and provides insights into the ancestral form of the chemokine system.
8. Ultimately, our research identifies numerous molecules and receptors with potential chemokine functions. In conclusion, we contribute to resolving uncertainties surrounding the system's origin, including the complex duplication events that have shaped receptor evolution. As evident from the extensive comments provided by the reviewer, our work addresses various controversies in the field (e.g. the inclusion of CXCL17 as a chemokine). Nonetheless, like any new set of findings, our work amalgamates confirmatory results (as highlighted in point 1) with innovative discoveries (as outlined in points 2-8). However, the latter category significantly outweighs the former, underscoring the richness of novel insights.

Finally, we would like to thank reviewer 2 for their comments, as these have contributed to greatly improve our manuscript.

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Referee #2

Evidence, reproducibility and clarity

This paper applies phylogenetic clustering methods to a large taxonomical sampling to interrogate the relationship between canonical and non-canonical chemokine ligands and receptors. The results suggest that 1) unrelated proteins evolved "chemokine-like" ligand function multiple times independently; and 2) all the canonical and non-canonical chemokine receptors (except ACKR1) originated from a single duplication in the vertebrate stem group, which also gave rise to many GPCRs. In addition, the authors characterized the complement of canonical and non-canonical components in the common ancestor of vertebrates and identified several other ligands and receptors with potential chemokine related properties.

Comments:

1. There are many places in the paper, too many to list, where the authors refer to chemokine receptors but call them 'chemokines'.
2. In Figure 1, CX3CL is referred to as 'X3CL'
3. CXCL17 was originally reported to be chemokine-like based on sequence threading methods. The authors refer to a 2015 paper indicating that it has chemokine-like activity at GPR35, which had been renamed provisionally CXCR8. To my knowledge that result was not based on direct binding data but inferred from a functional response. Moreover, to my knowledge it has not been independently confirmed. Instead there is a recent paper in JI from the Pease lab showing extensive experimental results that fail to demonstrate CXCL17 activity at GPR35. This uncertainty regarding a potential mistake in the literature should be addressed and integrated in the points made about CXCL17 being an outlier.
4. Can the authors use alpha fold to address whether any of these non-canonical molecules actually is predicted to fold like a chemokine? More generally, based on the paper's analysis, how do the authors propose to define a chemokine? It is well-accepted that chemokines are defined by structure, not function (e.g. limited truncation of any chemokine abrogates activity, but it is still a chemokine structurally, not semantically, folds like a chemokine, aligns with other chemokines).
5. Chemokine genes are found on many human chromosomes with large clusters on chromosome 2 and 17. Can the authors address the syntenic relationships phylogenetically?
6. The authors indicate that 'CXCL8 is present in all jawed vertebrates except in the cartilaginous fishes lineage'. However, they should point out that CXCL8 is not represented in mice. The notion that the repertoire of chemokine and chemokine receptor genes can be different in even closely related species as well as in individuals of the same species is well-documented but not mentioned here.
7. The analysis suggests that chemokine gene repertoires start small and grow non-linearly to 45 in mammals. However DeVries et al (JI 2005) published that zebrafish have the most chemokines, 63, and chemokine receptors, 24. Do the authors disagree? This should be addressed.
8. Did the authors find any species in which a chemokine/chemokine receptor pair are not found together? That is, if the system is irreducibly complex, requiring both a ligand and receptor, the probability of both genes arising simultaneously is essentially zero. So how do the authors theorize that such a system actually arose, and is there any evidence in their data set for convergence of separately evolved ligand and receptor?
9. Line 180, 181 and elsewhere: GPCR1 and GPCR33 should be GPR1 and GPR33
10. Line 185: ACKR1 exceptionalism is noted, but there is no discussion of the remarkable structure-function paradox that the most distantly related chemokine receptor is also the most highly promiscuous receptor, binding many but not all CC and CXC chemokines with high affinity.
11. Line 196: the viral receptors cluster with the vertebrate receptors, suggesting that the viruses captured the receptor gene from the host. Authors might mention this obvious point regarding origins, and discuss how it relates to the monophyly and paraphyly that emerges from the phylogenetic analysis.
12. Any discussion of chemokine-like convergent evolution presupposes that the activity is real and actually occurs in vivo. The authors should make clear to what extent the existing literature supports this. As mentioned above, CXCL17 interaction with GPR35 has been challenged in vitro and has never been demonstrated to occur in vivo. To what extent is the same limitation a problem in considering co-evolution of the other non-canonical chemokines? I agree that classification based solely on function is inappropriate, but so is phylogenetic analysis without direct knowledge of in vivo function. It is no feasible to address this in a phylogenetic analysis, but there ought to be at least one species in which the non-canonicals have been rigorously shown to act at specific receptors in vivo before grouping them with the canonicals in a co-evolutionary sense.
13. The discussion of homeostatic vs inflammatory chemokine/receptors in the last section of the Discussion would be enhanced by pointing out that the chemokine specificities are numerically totally different for these two groupings, homeostatics tending to have monogamous ligand-receptor relationships and inflammatories being highly promiscuous.

Significance

Much of the paper's results are confirmatory of previous work based on less extensive sequence analysis. One could say more generally that unrelated chemical forms, not just unrelated proteins, have chemokine-like ligand function. For example leukotriene B4 is a powerful leukocyte chemoattractant for neutrophils working through a GPCR. That proteins might also independently evolve common functions does not add insight beyond what is already appreciated. The notion that chemokine receptors have a common ancestor is also generally accepted and that ACKR1 is an outlier is already appreciated. The present work adds phylogenetic and statistical precision to these points.

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Referee #1

Evidence, reproducibility and clarity

The authors describe a broad-scale phylogenetic survey of chemokine-related ligand and receptors from representative vertebrates, invertebrates, and viruses. They collect ligand and receptor sequences from available genome sequences, and use phylogenetic and CLANS analysis to group these into similar gene types. They then overlay these onto a validated species phylogeny in order to evaluate relationships of orthology and paralogy to pinpoint gene duplication and loss events. They carry out these analyses for canonical chemokine ligands receptors and for other closely related protein families. They conclude that the canonical chemokine system is restricted to vertebrates but that closely related ligands and receptors can be found in invertebrate chordates. More divergent but related gene systems are found in more distant invertebrates. They define more limited expansions of some ligand-receptor systems in certain jawed vertebrate groups and specifically in mammals.

Overall, the paper addresses a complex and important system of signaling proteins with a rigorous and comprehensive set of analyses. The finding will be of interest to a diverse group of scientists. My comments listed below mainly consist of suggestions to help clarify the presentation.

1. Pg 2, Lns 21-24: The canonical and non-canonical chemokine subclasses are introduced in the abstract without definition. A very brief explanation would be useful.
2. Some general contexts of chemokine functions are listed, including inflammation and homeostasis. A little more detail of how these signals are used and the molecular consequences of signaling may be useful in the introduction to set the biological context of the analysis (e.g., how do the signals regulate homeostasis?).
3. It may help to summarize the known chemokine and chemokine-related gene systems in some type of table at the beginning of the results. This could serve as a convenient reference to guide the reader through the more detailed results. The manuscript addresses a complex set of ligands and receptors with names that may be confusing to the non-expert.
4. Pg 5, Ln 98: Fig 1C is introduced before Fig 1B. Can the panels be switched or the descriptions be rearranged?
5. Cytokine and chemokine ligands are small proteins that diverge quickly in different species and are difficult to identify in divergent genomes even within vertebrates. Conclusions about the absence of these types of factors are notorious for being disproven in subsequent analyses. Some discussion of what may have been missed in the survey for homologs (or reasons to think that ligands were not missed) would be useful in the Discussion.

Significance

This paper presents a thorough analysis of chemokines and related gene systems across a wide phylogenetic landscape. The authors have expertise in these gene families and in the techniques that they use to identify and relate family members. The chemokines are an important set of signals that are used across several biological systems. These findings will be of wide interest to immunologists, neurobiologists, developmental and evolutionary biologists.

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Reply to the reviewers

The authors do not wish to provide a response at this time

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Referee #3

Evidence, reproducibility and clarity

There is mounting evidence pointing towards an association of HSV with ApoE and Alzheimer's disease. Although it has been shown that ApoE impacts HSV-1 spread in animal models in an isoform specific fashion, the molecular relationship between the virus and ApoE is unclear. The present study probes the role of ApoE on the viral life cycle, clearly an important aspect if one is to better understand how the virus may influence the disease. Using assays monitoring various steps of viral replication, the authors report that ApoE perturbs the interaction of the virus with the cell surface, both during the initial binding and following viral release. Furthermore, they show that ApoE 3 and 4 exert a proviral effect, with a smaller impact with ApoE 2.

General Comments

The comprehensive study addresses an important point that may help clarify the interaction between HSV-1 and Alzheimer's disease. It major strength is that it is systematic and elegantly performed. The paper convincingly shows that ApoE impact cell adhesion at the cell surface. In general, the data have properly been analyzed statistically. Some aspects are however enigmatic. First of all, how can ApoE be proviral if it prevents the initial binding of the virus to the cells? Second, less viral binding should mean less entry (as shown in figure 2) and subsequently fewer genome copies, but that is not what is reported in figure 3. This is not clearly stated in the discussion (lines 457-459 and again in lines 490-491). Finally, if ApoE is unstable as indicated on lines 495-497, how can it be active later on at 24 hpi and prevent viral release? How do the authors reconcile these observations? Of interest, ApoE 3 has a slightly greater impact on viral growth than other isoforms (fig 1), which is not quite fitting the model that ApoE4 is the main culprit for Alzheimer's disease. Could the authors comment? Where are the ApoE proteins normally expressed in cells? At the cell surface or intracellularly? This may provide a hint as to where the virus picks it up when incorporating it. Immunofluorescence would be a great addition (e.g., Huh-7 cell line). Similarly, does the virus impact the expression level of ApoE? One could resolve the dilemma that ApoE blocks the initial binding of the virus but stimulates viral release at later time points if the virus induces ApoE at those late times. Furthermore, if the Vero or SH-SY5Y cells don't normally express ApoE, then it should not be important for the virus in that context. How about keratinocytes, the normal host of the virus? These considerations should be addressed in the manuscript by Western blotting at different time points.

Significance

This study adds an interesting twist and advances the infectious etiology model of Alzheimer's disease and should appeal to a broad authorship ranging from the neurobiology to virology.

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Referee #2

Evidence, reproducibility and clarity

In the manuscript "Recruitment of apolipoprotein E facilitates Herpes simplex virus 1 release", by Liu et al, the authors investigate the effect of ApoE protein on HSV-1 replication. Treatment of infected cultures with ApoE proteins appeared to increase the production of infectious titer. The authors perform several experiments to determine which steps of the virus replication cycle are affected. ApoE proteins reduce virus attachment, but subsequent reductions in entry are explained by the reduction in attachment, suggesting that the efficiency of entry is not affected. Viral DNA replication and cell-surface virus amounts appear to be unaffected, but the release of virus titer to the supernatant is increased. These results suggest that ApoE effects virus replication in two ways: reduces attachment if inoculum, but subsequently increases release of progeny. To determine whether this is the case, the authors then measure the release of attached virus particles from native membranes in the presence or absence of ApoE4, and derived from cells inducibly expressing ApoE4.

The manuscript is generally well written and the experiments generally appear to be performed well. However, the importance and impact of this manuscript are limited by two major weaknesses:

1. It seems that effects are only seen with high concentrations of ApoE. How does this concentration compare to what would be found in blood plasma/tissues/secreted by Huh-7 cells? Thus, these results may not be biologically relevant. It is difficult to determine what concentrations of ApoE are used in some cases, e.g. Fig 6. Please provide this information in the figure or figure caption.
2. While there are some interesting results here, this manuscript does not get to the point of establishing mechanism. In the discussion, it is speculated that ApoE functions via GAGs/HSPGs, which are known to affect HSV-1 attachment/release. It would make the manuscript much stronger to include experiments adding soluble heparin or treating cells with heparinase, or producing gC-null virus particles, to see if this abolishes the attachment/release effects of ApoE.

Minor points:

Fig. 3B: It is difficult to compare virus genomes by qPCR to virus titer of supernatants. If ApoE is promoting release of cell surface virus, why does an increase in titer in the supernatants not show a corresponding decrease in cell surface virus?

Fig. 6B: "Spdi I" term is not used in the results section or figure legend. I do not know what "Spdi I" means.

Fig. 6E, Fig 7: Normalized values can be misleading. Please provide raw values. Please show dissociation curves, as in Fig. 6D, for Fig.7.

It would be nice to perform the same analysis of supernatant vs. cell surface vs. intracellular virus as in Fig. 3B, and the release on ice measures as in Fig 4, using the inducible expression HEK cell line.

Discussion: Degradation of ApoE (line 495-500). The degradation of ApoE in these infection experiments could be measured by e.g. western blot of cell supernatants. This suggestion is a bit troubling: If the ApoE is degraded during the first replication cycle, how is it able to have an ongoing effect? How can ApoE simultaneously be present to promote release of progeny, while being degraded so as not to prevent attachment to subsequent cells.

I do not see that "GMK AH-1" cells are available from ATCC, as stated in the methods. Is this a synonym for Vero cells?

Although I understand what is meant, "dissolvent" is not a common term. "diluent" or "vehicle" is more common.

Significance

General assessment: The manuscript is generally well written and the experiments generally appear to be performed well. However, the significance of this manuscript are limited by two major weaknesses:

1. It seems that effects are only seen with high concentrations of ApoE. How does this concentration compare to what would be found in blood plasma/tissues/secreted by Huh-7 cells? Thus, these results may not be biologically relevant. It is difficult to determine what concentrations of ApoE are used in some cases, e.g. Fig 6. Please provide this information in the figure or figure caption.
2. While there are some interesting results here, this manuscript does not get to the point of establishing mechanism. In the discussion, it is speculated that ApoE functions via GAGs/HSPGs, which are known to affect HSV-1 attachment/release. It would make the manuscript much stronger to include experiments adding soluble heparin or treating cells with heparinase, or producing gC-null virus particles, to see if this abolishes the attachment/release effects of ApoE.

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Referee #1

Evidence, reproducibility and clarity

This manuscript attempts to identify the molecular basis for the reported interactions between apolipoprotein E (ApoE) and herpes simplex virus 1 (HSV1), known to be a significant marker for Alzheimer's disease. The authors employ a combination of cellular and in vitro assays designed to assess the effect of ApoE on different stages of the HSV1 life cycle. These experiments reveal an effect of ApoE on virus binding to and detachment from cell membranes, but not in other aspects of the viral life cycle. Further, the isoform ApoE4 was found to be the most effective in exerting these changes, possibly due to competitive binding with cell surface receptors that can associate with both ApoE and HSV1. Prior studies were referenced appropriately. For the most part, sufficient details about the data acquisition and analysis workflow were included in the study, although a couple of exceptions have been noted. More information about number of data sets for each panel and statistical analysis need to be included in the manuscript (figure legends and a separate section in Materials & Methods). Some of the key conclusions from the data require additional context and information to justify their interpretation in the present form. The additional experiments suggested are reasonable in terms of time and resources and are critical for strengthening the key conclusions in the manuscript. These have been noted below. Comments on evidence, reproducibility and clarity

Major Comments:

1. The authors mention in the Discussion section that they have ruled out interaction of ApoE with HSV1 glycoproteins B, C, D, and E based on immunoprecipitation data that is not included in the manuscript. In view of this, how do they justify using HSV1 gC as a marker for checking for association of HSV1 with ApoE (Fig 5)? Further, the authors should consider including said immunoprecipitation data in the manuscript, since those would be of immense value in further studies looking for other interaction partners of ApoE (as the authors have stated in the Discussion section).
2. How strong are the interactions between ApoE and HSV-1? In other words, what fraction of the available ApoE could be expected to associate stably with HSV1? Can ApoE or ApoE-associated complexes act as a trap for the virus and therefore represent latent virus pools in cells? How were possible contributions to HSV1 detachment from other cellular factors associated with ApoE ruled out?
3. The Discussion section of the manuscript clearly places the detected interaction of ApoE with HSV1 in the context of previous literature on related facets of the crosstalk between ApoE and viral infections. The authors may also consider including a paragraph on the more physicochemical attributes of ApoE interaction with HSV1, which would make the Discussion section more well-rounded and provide some background for understanding the biophysical experiments reported here (Fig. 6, 7). For example, how do the dissociation rates they report in Fig. 6 compare to those reported earlier for viruses on SLBs or cellular membranes? Could these dissociation rates be readily converted to (at least semi-quantitative) estimates of the thermodynamics of ApoE binding to HSV1 or would other factors need to be explicitly considered for such analytical exercises? Would it be difficult to measure binding affinities using HSV1 and purified ApoE by complementary approaches such as calorimetry or surface plasmon resonance? On a related note, what kind of ApoE concentration could HSV1 encounter in a cellular milieu and would it be in the range (5 μM) at which they report significant effects of ApoE on HSV1? What could be expected to happen at even higher concentrations of ApoE (reported for other cellular pathologies)?<br /> Why is the isoform dependence of ApoE effects observed predominantly in case of HSV1? Could this be related to the more complex fusion machinery available to this virus?
4. It is strongly recommended that details about ApoE purification and characterization are included in the manuscript, along with appropriate references. It is also not clear how a 4h period was deemed to be sufficient for incubation with ApoE (Fig. 2).
5. The data in Fig 4 is not entirely sufficient to support claims of ApoE enrichment in virus particles released into the supernatant. The authors may consider including additional experiments to check for (and quantify, if possible) ApoE levels in these virus fractions (since these conditions are drastically different than that used for reporting co-sedimentation of ApoE and HSV-gC in Fig. 6B).
6. Fig 5: It is not clear why the authors tested only for gC (especially when they note that co-immunoprecipitation experiments have ruled out gC as a possible interaction partner for ApoE; also see comment 1). Is the shift in the ApoE band to higher kD values from fraction 1 to 6 significant? What do the error bars in Fig 5B represent, if data was generated from two independent replicates? How do you reconcile the very high viral titer of fraction 3 (Fig 5C) with the moderate level of gC_HSV-1 in the same fraction (Fig 5B)? Does this indicate heterogeneity in gC content across seemingly equivalent viral titers (fractions 1-3, based on Fig 5C) ?
7. Several inconsistencies were noted in figures and figure legends that could affect a clear understanding of the data by readers. For all figures, please indicate clearly if no notation for statistical tests denote an absence of significance (ns) or that significance was not tested (such as for Fig. 2B, C). Please include sufficient information regarding number of independent replicates for each panel (i.e., what do error bars represent). For t-tests, please indicate clearly the reference data sets used for testing statistical significance and define symbols used for different p-values only for that specific figure. For example, a p-value corresponding to * is defined in the legend to Fig. 2, although that p-value is not indicated in the figure.

Minor Comments:

1. P. 4: abbreviation HSPG is not defined
2. Inconsistent figure formatting noted in terms of non-uniform axis labels, color coding, inclusion of error bars, clear label of all lanes in blots. These should be reviewed and modified as appropriate.
3. Fig 1A: The authors may consider reverting the order of ApoE concentration (ascending, instead of descending) in X-axis label to make it more intuitive.
4. Line 167: please specify that data in Fig S1b refers only to SH-SY5Y cells.
5. In Fig 2A, value for dissolvent should be set to 100%, since rest of the data are normalized with respect to that.
6. Line 279 refers to Fig. 3C (not present in the manuscript).
7. Ladders not clearly visible in some blots, such as that for 50 kD in Fig S1 and Fig 5A (HSV1 infected panel).
8. Please indicate clearly in the Materials & Methods if ApoE induction (Fig. 7) is performed in HEK cells or HEK-293T cells.

Significance

This study sheds light on the molecular basis of the interaction between HSV1 and ApoE and represents a conceptual advance in the field. Since such interactions have been reported to be a marker for patients at high risk of developing Alzheimer's disease, these findings would be important in designing future clinical studies on prognostic and diagnostic advances in neurodegenerative diseases. As such, this manuscript would be of interest to a broad spectrum of scientists and clinicians including virologists, biochemists, and biophysicists.

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Reply to the reviewers

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

Hermanns et al. report a robust biochemical and structural characterization of the TSSM virulence effector of Burkholderia pseudomallei, the causative agent of melioidosis, with TSSM being the only USP-type deubiquitinase of bacterial origin. The authors demonstrate ubiquitin specificity, and the capacity of the DUB to cleave most Ubiquitin chain types in vitro. By solving crystal structures of TSSM in isolation as well as in covalent complex with Ubiquitin, the authors rationalize how the truncated fold (compared to all other eukaryotic USPs) is capable of binding ubiquitin. The most surprising finding is the conformation of the so-called little finger loop, unique to TSSM, which engages the distal Ubiquitin in a manner not seen in any other DUB. These findings are convincingly validated by point mutations in biochemical assays and bioinformatic analyses. The structures also yielded information on the fold an N-terminal Ig-like domain with unknown function.

The authors are encouraged to address the following main points before publication can be supported:

• On page 6, the authors state "the little finger loop contacts hydrophobic residues around Ile-44 of ubiquitin, an important interaction interface that is not observed in USP7 and is not part of the canonical USP: ubiquitin interface". The latter statement I believe is simply wrong as Ile44 is at the centre of the large canonical USP:ubiquitin interaction interface observed in all structurally studied USP proteins so far (the recognition is through varying residues, that are not always strictly hydrophobic, but it is always contacted). This is occurring at the hinge between thumb and fingers. In USP7, this is for example occurring through the hydrophobic part of Arg301. The authors are referred to O'Dea et al. Nat Chem Biol 2023, where same conformation of the equivalent residue in USP36 is discussed in the context of Ile44-Ubiquitin vs. Fubi recognition.

We thank reviewer #1 for this detailed explanation. Initially we made this statement based on the fact that in other USPs, the interaction is not always hydrophobic. However, we now agree with reviewer #1 and have removed this statement from the manuscript.

• This should also be phrased very precisely, as Ile44 itself is not actually contacted by the little finger loop (see Fig. 3c), but other hydrophobic residues commonly included in the Ile44 patch are. However, also those are always in the Ubiquitin:USP interface.

These section was removed in line with the previous point. Additionally, we added the information that Ile44 itself is not contacted to the main text.

• It appears as if the little finger loop takes up the exact position of what is commonly referred to as blocking loop 1 (terminology introduced by Hu et al EMBO J 2005, which the authors could also use when referring to canonical USPs). The connectivity in the domain is not equivalent to BL1 though (this could be shown with topology diagrams of a canonical USP and of TSM), but the relative position is. Importantly, BL1 in USPs typically engage the Ile36 patch with a large aromatic residue (e.g., a Trp in USP30 or a Tyr in USP7), however, in TSSM Ubiquitin is rotated such that the Ile44 patch is recognised at the exact spot where normally Ile36 is engaged. Can the authors comment on this interpretation? This reviewer would also find a comparison to the canonical USP recognition mode in the main figures beneficial (however one that is less crowded than in the SI) to make this very important finding come across well. Even if the loop is more like a BL1 (which is highly diverse in the USP family, see reference above), it can still be called "little finger", but the analogies should be carefully phrased throughout the text (e.g., page 9: "functionally but not structurally analogous" would not be correct if it does not replace the fingers which contact Phe4 as BL1 is not part of the fingers).

The reviewer is correct. The positioning of BL1 from canonical USPs and the TssM Littlefinger loop is superficially similar. Since the Littlefinger is derived from a different part of the sequence (Fig 1a) and contacts a different patch of ubiquitin, it is still distinct from the BL1. We have incorporated this information into the main text and generated a new superposition to show the structural similarities (Supplementary Fig 3c). Additionally, we highlighted BL1 and Littlefinger in Fig1a to show the different position within the amino acid sequence. Since BL1 and Littlefinger are distinct from each other we still classify the Littlefinger as functionally (but not structurally) analogous to the fingers domains. Both serve as the main contacts to ubiquitin and (partially) interact with the Ile44 patch, while BL1 contacts the Ile36 patch.

• The assessment of the molecular weight of apo TSSM in solution by SEC is flawed in its current form (page 7, Fig. 4b). There are only two, seemingly randomly chosen, comparison proteins, and the GST-Ub is unsuitable for 36 kDa because it is an obligate dimer (due to the GST) and as a fusion protein has a much higher hydrodynamic radius than a globular protein. There are commercial reference proteins that can be used which have defined oligomerisation states and are (reasonably globular) so that the retention times can be quantitatively analysed. Such a reference, which also includes proteins of smaller sizes, must be used. Alternatively, the authors should use light-scattering (SEC-MALS or SEC-RALS) to unequivocally demonstrate the molecular weight / oligomeric state of TSSM.

The experiment has been re-done using commercially proteins routinely used for the calibration of SEC columns. Additionally, a 75pg column was used to achieve a better resolution of the proteins. The new data confirm the original conclusion that TssM behaves like a monomer in solution.

• The abstract ends with speculation on the presence of the strand-swapping in live cells and of the role of the Ig-fold domain, but not a proper conclusion. The same applies to the ending of the results section (page 8), where it is not clear where the structural analysis of the Ig-like domains is leading. The authors then speculate about sugar binding (page 10) in the discussion. The latter could be substantiated by an analysis of the residues in the possible sugar binding site (if present in their TSSM), and the text be more rounded off at these regions.

We admit that our manuscript does not reveal the function of the Ig-like domain and the strand swap observed in the apo structure. To really address these questions, an infection model would be required, which is outside the scope of our manuscript focussing on the TssM mechanism. As have showed that deletion of the Ig-domain does not alter the DUB properties, the remaining questions cannot be addressed in vitro. To address the concerns of the reviewer, we have changed the ending of the result section (page 8) to summarize our in vitro data on the Ig-like domain. We have also clarified the paragraph discussing the possibility of sugar binding on pages 10/11. In brief, this speculation was not based on the identification of a possible sugar binding site, but rather on the presumed positioning of the Ig-like domain relative to the bacterial surface and its evolutionary relationship to other sugar-binding domains.

• The authors show in Fig. 1b TSSM reactivity with both Ubiquitin and Nedd8 probes but then qualify the Nedd8-reactivity with the assays shown in Figs. 1cd, which currently looks like it is an issue with the comparison of probe to substrate. However, their probe assay is very long with 18 h. It should be repeated at shorted times (for Ub and Nedd8-PA only) to test if the strong Ub preference seen in the kinetic assay can also be visualized with the probes.

We measured the requested time curve and added the data to the newly generated Supplementary Figure 1. Using very short time points of 3 – 20min, a preferential reactivity with the ubiquitin probe became visible, which supports the AMC results.

• Moreover, the authors prepare Ubl-PA probes by aminolysis of C-terminal thioesters but at very different stoichiometries as reported for Ub-PA (e.g., in Gersch et al. NSMB 2017). The precise amounts should be cross-checked. This reviewer would not be surprised if the conditions of only 2-fold excess of propargylamine over Ubl thioester and the high amount of NaOH led also to hydrolysis of the C-terminus, which by size exclusion chromatography would not be separated. For all probes, intact mass spectra of the used aliquots must be shown to demonstrate the identity of the used reagents. The presence of other species of course does not per se disqualify the assay in Fig. 1b.

Our protocol for generating the Ub-PA probe follows exactly the one described in Gersch et al 2017. While this publication describes the amounts and concentrations of the individual reagents, our description of the protocol documents their final concentration in the reaction mixture. We double-checked the data and found them to be identical. Thus, there is no reason to assume elevated amounts of hydrolysis products. Moreover, the purity of our probes is regularly controlled by intact mass spectrometry (shown below for Ub-PA). The observed ~5% of hydrolysis product should not pose a problem for the assays, which routinely involve a 10x excess of probe over DUB. As documented in the Materials section, the Nedd-PA probe was obtained from Monique Mulder in Leiden.

In addition, the following minor points should be addressed:

• On page 2, the authors state that the 1-2 DUBs typically present in bacteria either target K63-chains or lack specificity (without reference), but on page 10 they state that bacteria typically only have 1 DUB with little specificity. This is contradicting and should be fixed.

We have rephrased the introductory sentence on page 2 and added a reference. The issue on page 10 has been corrected

• On page 3, the authors make the case for TSSM from B. pseudomallei by calling it representative. This reviewer would appreciate if the authors could expand on this towards the end of the manuscript and comment on whether they expect the identified Ubiquitin recognition mode to be present also in all (!) other DUB-TSSM proteins, at least all analysed bioinformatically. Importantly, they should include the sequence of TSSM of B. mallei (the only in vitro studied TSSM so far) into their analyses in the SI.

We have expanded the paragraph (page 9) where we discuss the conservation of the Littlefinger-based ubiquitin recognition mode. Among the TssM-like DUBs that we identified, only those from other Burkholderia species contain the Littlefinger conservation and are predicted (by alphafold) to use this region for S1-ubiquitin recognition. The two non-Burkholderia TssM-like DUBs (one from Chromobacterium sinusclupearum, the other from an unidentified bacterial metagenome) lack the Littlefinger region and the associated ubiquitin recognition mode. This is documented in Supplementary Fig 4. We also added B. mallei to the alignment in Supplementary Fig 4a, although it is almost identical to the B. pseudomallei sequence.

• On page 4, the authors start with TSSM (193-490) but do not comment on the role of the first 192 residues. What was the rational for these boundaries?

The N-terminal 192 amino acids are predicted to be unstructured (by alphafold, IUPRED, etc). We therefore decided to use the entire folded part of TssM for the first experiments. The information has been added to the manuscript and a domain scheme was added to the supplementary data (Supplementary Figure 1a).

• On page 4, the authors introduce the use of the fluorogenic AMC substrates with "more quantitative analysis", however, the experiments are rather minimalistic. Only one substrate concentration, and two enzyme concentrations (at least for one substrate) are used, and the data are not really analysed in a quantitative manner. Through curve fitting of the existing data (with fixed restraints) the authors may be able to determine an estimate of the Ub/Nedd8-specificity factor. Moreover, negative controls should be shown in Figure 1c to demonstrate that the Nedd8-curve is above a possible baseline drift.

Following this reviewer’s suggestion, we estimated a 116x better cleavage of ubiquitin-AMC by determination of the initial velocities using the linear range of the data presented in Figure 1c.

For ensuring better clarity, we have omitted the negative control data from Fig 1 panel c (comparing Ub-AMC and Nedd8-AMX) but are showing them in panel 1d (comparing Nedd8-AMC to negative control). This figure clearly shows that Nedd8-AMC is really cleaved, whereas no baseline drift is visible.

• On page 4, the authors mention a possible relationship with the Josephin family (which they later disprove through structural comparisons, page 6). It may be helpful to briefly explain the rational (i.e., why one would even consider a relationship with the Josephin family DUB given the higher homology to the USP fold).

The rationale for considering a relationship to the Josephin family is explained at the end of the introduction section (page 3). We never considered TssM to be closer related to Josephins than to USPs. We rather speculated that the entire USP and Josephin families are distantly related to each other, and that TssM-like USPs might form a kind of missing link. This latter aspect has been disproved (page 6), since the TssM structure is no more Josephin-like than that of any other USP.

• On page 5, the "shortened C-terminus" compared to USP7 is mentioned. This is misleading, as USP7 has an elongated C-terminus compared to most other USPs, and so the TSSM C-terminus is the canonical ending of the USP fold.

The section has been rephrased and now points out that TssM shares the canonical USP C-terminus.

• On page 6, "ubiquitin has multiple specific contacts" - why are the contacts named "specific"?

We have removed the word ‘specific’

• On page 10, the TSSM from C. sinusclupearum appears without any context. Chromobacterium should be spelled out, and it should be discussed if this is the odd one out. It would also be appreciated if the authors could state whether their bioinformatic analysis is comprehensive (i.e., do only the known or all Burkholderia strains have TSSM with a DUB profile). And why is there a A0A1J5... sequence included in Supp Fig. 3a without any context?

We agree that this part of the text was confusing. We have now bundled all information on other TssM-like sequences on page 9. There, we explain that TssM-like DUBs are only found in selected Burkholderia lineages, and spell out some examples. We also introduce Chromobacterium sinusclupearum with its full name and explain that the remaining non-Burkholderia TssM homolog is from an unidentified metagenomic sequence. This is also the reason why we refer to this sequence by its Uniprot accession number.

• Some minor polishing in the methods would increase consistency (cloning is only mentioned for pOPIN-K, but TSSM is also expressed from pOPIN-S; mutants are only mentioned for the 292-490 construct, but Fig. 2d shows one in the 193-490 construct; Hampton Additive Screens I-III are likely an internal name and not used by Hampton itself; "different TSSM concentrations (as indicated in the figure legends" are mentioned in the methods, but e.g. in the caption to Fig. 1 no concentration is given).

We have added the pOPIN-S cloning method and corrected the name of the additive screen. We now provide all mutant data in the 292-490 background (Figure 2d was updated accordingly). We made sure that all figure captions mention the enzyme concentrations.

• Likewise, Table 1 needs polishing as commas and points are used interchangeably.

Table 1 has been corrected

• In Fig. 2c, it looks like the catalytic His is built such that there is no hydrogen bonding between Cys and Asp - is there a particular reason for this? If not, the side chain should be flipped so that the nitrogens are positioned for ideal hydrogen bonding.

Fig 2c had been accidentally exported from an early version of the structure. It has now been replaced by a panel that uses the final and deposited version of the structure. Here, the His position is correct.

• In Fig. 3, dotted lines are introduced as "hydrophobic interactions" as per the captions, but some are clearly hydrogen bonds (e.g., from the amide backbone), and for some others one does not see as they emerge from a cartoon.

In Fig 3, dotted lines are not generally introduced as "hydrophobic interactions". Instead, the individual panels have their own definition for the dotted lines. c) hydrophobic d) hydrogen bonds e) hydrophobic.

• In Fig. 4, context should be given to "3VYP" and why it is used here.

We have added an explanation of 3VYP to the figure legend (The reason for showing it is also explained in the main text)

**Referees cross-commenting**

Reviewer comments appear to be fair, balanced and complementary.

Re Reviewer 2's comment on the pre-print: It includes a structure of TSSM bound to ubiquitin (but not of the apo protein). I am not sure if it is appropriate to follow up on the esterase activity. Firstly, there are no tools for it commercially available or readily made in vitro, and secondly it would appear a bit "copycat"-like. Especially since the molecular determinants of what makes a DUB a good esterase are still elusive. A narrower focus of this manuscript, but done very well (also according to what reviewer 3 suggested), might be a more fitting option.

Reviewer #1 (Significance (Required)):

The findings are novel and of high relevance to the broad ubiquitin and bacterial pathogen communities as the study addresses an enigmatic USP-type deubiquitinase which, as the authors reveal, recognizes Ubiquitin in an entirely different way than its eukaryotic counterparts. This is basic research of pronounced conceptual and mechanistic advance, as it demonstrates that the USP domain can be much more diversified than previously assumed. The structural analysis is very thorough, the data are scholarly presented, and interactions/mechanisms carefully validated by mutations.

The strongest point is clearly the structural analysis of Ubiquitin recognition by this extremely truncated USP fold, and the introduction of the little finger motif. The manuscript does not provide cellular validation of the findings, which is fine to this reviewer for the DUB catalysis part. For the part of the N-terminal domain, the manuscript would benefit from a cellular validation or some localisation studies, however, this is beyond what is established in the authors' lab, and therefore has not been asked. This in turn limits the study to a very thorough in-vitro analysis of this DUB for TSSMs with DUB activity, using conventional substrates like polyUb chains and fluorogenic substrates, and providing convincing conclusions.

My expertise lies in DUBs, biochemistry, and structural biology, and I this believe to have sufficient expertise to evaluate the majority of the findings except the bioinformatic algorithms used which are however not an emphasis in this manuscript.

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

In this manuscript, the authors solved crystal structures of apo-TssM and its complex with Ubiquitin. Together with the biochemical assays, the authors highlighted the differences of TssM from other USP family. TssM do not contain the classical finger domain while it has little finger domain that authors defined. The Ile44 patch on the ubiquitin is mainly used for interacting with TssM.

For the clarity of there findings several points should be edited.

1) In Fig 2d, the authors used different constructs for testing catalytic residues. It will be better to be consistent. Though the authors showed that the deletion of Ig-like domain does not affect the catalytic activity of TssM by showing the AMC assay, it does not guarantee that the effect of mutation on catalytic sites is same for both construct.

The experiment has been repeated with TssM292-490 C308A and the panel in Fig 2d has been replaced

.

2) In fig 2d, authors need to put the label to indicate which linkage of di-Ub is used. Authors did it for figure 2f.

Chain type was already stated in the legend, but was now also added to the respective panel.

3) By showing AMC assay (fig 2e) and K63 Ub2 cleavage assay (fig 2f), authors concluded that the deletion of Ig-like domain does not affect the activity of TssM. However, as authors found that the Ig-like domain forms dimer at least in their crystallization condition, one cannot exclude the possibility of the role of Ig-like domain in recognising different ubiquitin chains. I would clarify the words by saying "The Ig-like domain does not affect the cleavage K63-Ub2), or authors can expand the cleavage assay with all the linkages.

We agree with reviewer #2 and rephrased the corresponding sentence.

4) In the apo structure, authors found a domain swapped dimer. One can expect that this dimer is crystallographic artifact and not found in the nature. Indeed, authors could not observe this dimer in the solution when they performed SEC analysis and there was no effect on the catalytic activity when the Ig-like domain is deleted. Because there is no clear evidence of functional importance of this dimer in the manuscript, authors need to clarify about this dimer.

We agree with reviewer #2 that there is no evidence for functional importance of the dimer. We had already addressed this issue in our discussion section, where we hypothesized a potential in vivo function. We have now rephrased this section of the discussion in order to make it more precise.

**Referees cross-commenting**

Agree with Reviewer #1's comment on my points.

Reviewer #2 (Significance (Required)):

The structure of TssM is recently reported in a preprint ( https://doi.org/10.21203/rs.3.rs-2986327/v1) from Pruneda (OHSU). In this preprint, they suggested the role of TssM as Ubiquitin esterase which is not explored in the this manuscript. As it is already published and freely available, authors can explore the role of TssM in that direction as well. Because the preprint do not contain the complex structure of TssM with Ubiquitin, authors can also examine the roles of Ile44 patch-interacting residues on the catalytic activities.

Said preprint contains the TssM~Ub complex structure, but not the structure of the apo form. The role of the Ile44-patch contacting residues is also already analysed in regards of esterase activity. As explained in the ‘general points’ at the top of the rebuttal letter, the two manuscripts were meant to be submitted simultaneously. Due to a delay in our PDB deposition/validation, we submitted our version two weeks later. Thus, we share the opinion of reviewer #1 that it would be inappropriate to use our competitors’ data and analyse esterase activity in our manuscript. Instead, we added a reference to the competing preprint to our discussion section and briefly compare the key findings. The authors of the preprint have agreed to reciprocate and reference our preprint in their revised manuscript.

Also, authors can compare their little finger structure with the preprint.

See point above.

In general, this manuscript is providing several interesting points to the readers working on the ubiquitin, structures of proteins, host-pathogen interactions and especially those who studying deubiquitinases.

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

In this study, Hermanns et al. have examined the Burkholderia TssM deubiquitinase (DUB) for its ubiquitin chain cleavage specificity using in vitro analyses and for a structural rationalization of its specificity by solving crystal structures with and without covalently bound ubiquitin. TssM was previously shown to be a DUB of the USP family capable of cleaving several ubiquitin chain types. From bioinformatic comparisons, the authors inferred that TssM lacks the 'fingers' domain of classic USP enzymes, and this was shown in the co-crystal structure to be replaced by a 'Littlefinger' loop. The work here is well described and appears to be overall technically solid.

My enthusiasm is reduced for several reasons. First, there is no analysis in an (infected) cell model to evaluate the significance of the TssM mutations, for example, the mutations in ubiquitin-interacting surfaces that are not seen in classic USPs. Second, there is very little quantitation of cleavage rates; while I would not demand derivation of kinetic values for all mutants, the qualitative treatment was not always convincing. For example, Y443A was said to cause "strongly reduced cleavage" (Fig. 4h) whereas E378A was said to "only mildly affect cleavage" (Fig. 4i): to my eye, these cleavage rates are only slightly different (2-3 fold?).

In order to support the findings of our gel based assay and to get more quantitative data, we tested all mutants against Ub-AMC. The results are depicted in supplementary figures 3d/f/g and correspond to results of the chain cleavage assays.

Finally, there is a preprint available on Research Square (doi: 10.21203/rs.3.rs-2986327/v1) that shows a potent esterase activity of TssM against ubiquitinated LPS, which is probably its key role in avoiding surveillance and elimination by the host. This paper, although still a preprint, is far more quantitative, includes similar crystallographic data as in the current paper, and describes cellular assays of function. Even without the RS preprint, the Hermanns et al. study provides a fairly modest advance in our knowledge of TssM function; with the preprint, its novelty is, unfortunately, severely reduced.

As explained in the ‘general points’ at the top of the rebuttal letter, the two manuscripts were meant to be submitted simultaneously. Due to a delay in our PDB deposition/validation, we submitted our version two weeks later. The competing work is currently under review at a top-tier journal and far from being accepted. We therefore ask to consider the significance of our own manuscript based on the peer-reviewed state-of-the-art.

In our revised manuscript, we have added a brief discussion of the pre-published results. The authors of the preprint have agreed to reciprocate and address our data in their revised manuscript.

Minor comments:

Full genus names should be spelled out when they first appear in the text (such as Burkholderia and Chromobacterium).

Genus names are now spelled out.

Fig. 3a is described out of sequence.

A reference to figure 3a was missing in the beginning of the chapter. The reference was added and the figure is now described in sequence.

In Fig 4a, there still seem to be extensive contacts between monomers but the viewing angle could be misleading.

There are no extensive contacts between the two DUB monomers in the right panel (complex structure). We slightly changed the viewing angle in Fig 4a to make this clearer.

**Referees cross-commenting**

I also agree with Reviewer #1's comments

Reviewer #3 (Significance (Required)):

Even without the Research Square preprint (doi: 10.21203/rs.3.rs-2986327/v1), the Hermanns et al. study provides a fairly modest advance in our knowledge of TssM function; but with the preprint, its novelty is, unfortunately, severely reduced.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

In this study, Hermanns et al. have examined the Burkholderia TssM deubiquitinase (DUB) for its ubiquitin chain cleavage specificity using in vitro analyses and for a structural rationalization of its specificity by solving crystal structures with and without covalently bound ubiquitin. TssM was previously shown to be a DUB of the USP family capable of cleaving several ubiquitin chain types. From bioinformatic comparisons, the authors inferred that TssM lacks the 'fingers' domain of classic USP enzymes, and this was shown in the co-crystal structure to be replaced by a 'Littlefinger' loop. The work here is well described and appears to be overall technically solid.

My enthusiasm is reduced for several reasons. First, there is no analysis in an (infected) cell model to evaluate the significance of the TssM mutations, for example, the mutations in ubiquitin-interacting surfaces that are not seen in classic USPs. Second, there is very little quantitation of cleavage rates; while I would not demand derivation of kinetic values for all mutants, the qualitative treatment was not always convincing. For example, Y443A was said to cause "strongly reduced cleavage" (Fig. 4h) whereas E378A was said to "only mildly affect cleavage" (Fig. 4i): to my eye, these cleavage rates are only slightly different (2-3 fold?). Finally, there is a preprint available on Research Square (doi: 10.21203/rs.3.rs-2986327/v1) that shows a potent esterase activity of TssM against ubiquitinated LPS, which is probably its key role in avoiding surveillance and elimination by the host. This paper, although still a preprint, is far more quantitative, includes similar crystallographic data as in the current paper, and describes cellular assays of function. Even without the RS preprint, the Hermanns et al. study provides a fairly modest advance in our knowledge of TssM function; with the preprint, its novelty is, unfortunately, severely reduced.

Minor comments:

Full genus names should be spelled out when they first appear in the text (such as Burkholderia and Chromobacterium).

Fig. 3a is described out of sequence.

In Fig 4a, there still seem to be extensive contacts between monomers but the viewing angle could be misleading.

Referees cross-commenting

I also agree with Reviewer #1's comments

Significance

Even without the Research Square preprint (doi: 10.21203/rs.3.rs-2986327/v1), the Hermanns et al. study provides a fairly modest advance in our knowledge of TssM function; but with the preprint, its novelty is, unfortunately, severely reduced.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

In this manuscript, the authors solved crystal structures of apo-TssM and its complex with Ubiquitin. Together with the biochemical assays, the authors highlighted the differences of TssM from other USP family. TssM do not contain the classical finger domain while it has little finger domain that authors defined. The Ile44 patch on the ubiquitin is mainly used for interacting with TssM.

For the clarity of there findings several points should be edited.

1. In Fig 2d, the authors used different constructs for testing catalytic residues. It will be better to be consistent. Though the authors showed that the deletion of Ig-like domain does not affect the catalytic activity of TssM by showing the AMC assay, it does not guarantee that the effect of mutation on catalytic sites is same for both construct.
2. In fig 2d, authors need to put the label to indicate which linkage of di-Ub is used. Authors did it for figure 2f.
3. By showing AMC assay (fig 2e) and K63 Ub2 cleavage assay (fig 2f), authors concluded that the deletion of Ig-like domain does not affect the activity of TssM. However, as authors found that the Ig-like domain forms dimer at least in their crystallization condition, one cannot exclude the possibility of the role of Ig-like domain in recognising different ubiquitin chains. I would clarify the words by saying "The Ig-like domain does not affect the cleavage K63-Ub2), or authors can expand the cleavage assay with all the linkages.
4. In the apo structure, authors found a domain swapped dimer. One can expect that this dimer is crystallographic artifact and not found in the nature. Indeed, authors could not observe this dimer in the solution when they performed SEC analysis and there was no effect on the catalytic activity when the Ig-like domain is deleted. Because there is no clear evidence of functional importance of this dimer in the manuscript, authors need to clarify about this dimer.

Referees cross-commenting

Agree with Reviewer #1's comment on my points.

Significance

The structure of TssM is recently reported in a preprint ( https://doi.org/10.21203/rs.3.rs-2986327/v1) from Pruneda (OHSU). In this preprint, they suggested the role of TssM as Ubiquitin esterase which is not explored in the this manuscript. As it is already published and freely available, authors can explore the role of TssM in that direction as well.

Because the preprint do not contain the complex structure of TssM with Ubiquitin, authors can also examine the roles of Ile44 patch-interacting residues on the catalytic activities.

Also, authors can compare their little finger structure with the preprint.

In general, this manuscript is providing several interesting points to the readers working on the ubiquitin, structures of proteins, host-pathogen interactions and especially those who studying deubiquitinases.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Hermanns et al. report a robust biochemical and structural characterization of the TSSM virulence effector of Burkholderia pseudomallei, the causative agent of melioidosis, with TSSM being the only USP-type deubiquitinase of bacterial origin. The authors demonstrate ubiquitin specificity, and the capacity of the DUB to cleave most Ubiquitin chain types in vitro. By solving crystal structures of TSSM in isolation as well as in covalent complex with Ubiquitin, the authors rationalize how the truncated fold (compared to all other eukaryotic USPs) is capable of binding ubiquitin. The most surprising finding is the conformation of the so-called little finger loop, unique to TSSM, which engages the distal Ubiquitin in a manner not seen in any other DUB. These findings are convincingly validated by point mutations in biochemical assays and bioinformatic analyses. The structures also yielded information on the fold an N-terminal Ig-like domain with unknown function.

The authors are encouraged to address the following main points before publication can be supported:

• On page 6, the authors state "the little finger loop contacts hydrophobic residues around Ile-44 of ubiquitin, an important interaction interface that is not observed in USP7 and is not part of the canonical USP: ubiquitin interface". The latter statement I believe is simply wrong as Ile44 is at the centre of the large canonical USP:ubiquitin interaction interface observed in all structurally studied USP proteins so far (the recognition is through varying residues, that are not always strictly hydrophobic, but it is always contacted). This is occurring at the hinge between thumb and fingers. In USP7, this is for example occurring through the hydrophobic part of Arg301. The authors are referred to O'Dea et al. Nat Chem Biol 2023, where same conformation of the equivalent residue in USP36 is discussed in the context of Ile44-Ubiquitin vs. Fubi recognition.
• This should also be phrased very precisely, as Ile44 itself is not actually contacted by the little finger loop (see Fig. 3c), but other hydrophobic residues commonly included in the Ile44 patch are. However, also those are always in the Ubiquitin:USP interface.
• It appears as if the little finger loop takes up the exact position of what is commonly referred to as blocking loop 1 (terminology introduced by Hu et al EMBO J 2005, which the authors could also use when referring to canonical USPs). The connectivity in the domain is not equivalent to BL1 though (this could be shown with topology diagrams of a canonical USP and of TSM), but the relative position is. Importantly, BL1 in USPs typically engage the Ile36 patch with a large aromatic residue (e.g., a Trp in USP30 or a Tyr in USP7), however, in TSSM Ubiquitin is rotated such that the Ile44 patch is recognised at the exact spot where normally Ile36 is engaged. Can the authors comment on this interpretation? This reviewer would also find a comparison to the canonical USP recognition mode in the main figures beneficial (however one that is less crowded than in the SI) to make this very important finding come across well. Even if the loop is more like a BL1 (which is highly diverse in the USP family, see reference above), it can still be called "little finger", but the analogies should be carefully phrased throughout the text (e.g., page 9: "functionally but not structurally analogous" would not be correct if it does not replace the fingers which contact Phe4 as BL1 is not part of the fingers).
• The assessment of the molecular weight of apo TSSM in solution by SEC is flawed in its current form (page 7, Fig. 4b). There are only two, seemingly randomly chosen, comparison proteins, and the GST-Ub is unsuitable for 36 kDa because it is an obligate dimer (due to the GST) and as a fusion protein has a much higher hydrodynamic radius than a globular protein. There are commercial reference proteins that can be used which have defined oligomerisation states and are (reasonably globular) so that the retention times can be quantitatively analysed. Such a reference, which also includes proteins of smaller sizes, must be used. Alternatively, the authors should use light-scattering (SEC-MALS or SEC-RALS) to unequivocally demonstrate the molecular weight / oligomeric state of TSSM.
• The abstract ends with speculation on the presence of the strand-swapping in live cells and of the role of the Ig-fold domain, but not a proper conclusion. The same applies to the ending of the results section (page 8), where it is not clear where the structural analysis of the Ig-like domains is leading. The authors then speculate about sugar binding (page 10) in the discussion. The latter could be substantiated by an analysis of the residues in the possible sugar binding site (if present in their TSSM), and the text be more rounded off at these regions.
• The authors show in Fig. 1b TSSM reactivity with both Ubiquitin and Nedd8 probes but then qualify the Nedd8-reactivity with the assays shown in Figs. 1cd, which currently looks like it is an issue with the comparison of probe to substrate. However, their probe assay is very long with 18 h. It should be repeated at shorted times (for Ub and Nedd8-PA only) to test if the strong Ub preference seen in the kinetic assay can also be visualized with the probes.
• Moreover, the authors prepare Ubl-PA probes by aminolysis of C-terminal thioesters but at very different stoichiometries as reported for Ub-PA (e.g., in Gersch et al. NSMB 2017). The precise amounts should be cross-checked. This reviewer would not be surprised if the conditions of only 2-fold excess of propargylamine over Ubl thioester and the high amount of NaOH led also to hydrolysis of the C-terminus, which by size exclusion chromatography would not be separated. For all probes, intact mass spectra of the used aliquots must be shown to demonstrate the identity of the used reagents. The presence of other species of course does not per se disqualify the assay in Fig. 1b.

In addition, the following minor points should be addressed:

• On page 2, the authors state that the 1-2 DUBs typically present in bacteria either target K63-chains or lack specificity (without reference), but on page 10 they state that bacteria typically only have 1 DUB with little specificity. This is contradicting and should be fixed.
• On page 3, the authors make the case for TSSM from B. pseudomallei by calling it representative. This reviewer would appreciate if the authors could expand on this towards the end of the manuscript and comment on whether they expect the identified Ubiquitin recognition mode to be present also in all (!) other DUB-TSSM proteins, at least all analysed bioinformatically. Importantly, they should include the sequence of TSSM of B. mallei (the only in vitro studied TSSM so far) into their analyses in the SI.
• On page 4, the authors start with TSSM (193-490) but do not comment on the role of the first 192 residues. What was the rational for these boundaries?
• On page 4, the authors introduce the use of the fluorogenic AMC substrates with "more quantitative analysis", however, the experiments are rather minimalistic. Only one substrate concentration, and two enzyme concentrations (at least for one substrate) are used, and the data are not really analysed in a quantitative manner. Through curve fitting of the existing data (with fixed restraints) the authors may be able to determine an estimate of the Ub/Nedd8-specificity factor. Moreover, negative controls should be shown in Figure 1c to demonstrate that the Nedd8-curve is above a possible baseline drift.
• On page 4, the authors mention a possible relationship with the Josephin family (which they later disprove through structural comparisons, page 6). It may be helpful to briefly explain the rational (i.e., why one would even consider a relationship with the Josephin family DUB given the higher homology to the USP fold).
• On page 5, the "shortened C-terminus" compared to USP7 is mentioned. This is misleading, as USP7 has an elongated C-terminus compared to most other USPs, and so the TSSM C-terminus is the canonical ending of the USP fold.
• On page 6, "ubiquitin has multiple specific contacts" - why are the contacts named "specific"?
• On page 10, the TSSM from C. sinusclupearum appears without any context. Chromobacterium should be spelled out, and it should be discussed if this is the odd one out. It would also be appreciated if the authors could state whether their bioinformatic analysis is comprehensive (i.e., do only the known or all Burkholderia strains have TSSM with a DUB profile). And why is there a A0A1J5... sequence included in Supp Fig. 3a without any context?
• Some minor polishing in the methods would increase consistency (cloning is only mentioned for pOPIN-K, but TSSM is also expressed from pOPIN-S; mutants are only mentioned for the 292-490 construct, but Fig. 2d shows one in the 193-490 construct; Hampton Additive Screens I-III are likely an internal name and not used by Hampton itself; "different TSSM concentrations (as indicated in the figure legends" are mentioned in the methods, but e.g. in the caption to Fig. 1 no concentration is given).
• Likewise, Table 1 needs polishing as commas and points are used interchangeably.
• In Fig. 2c, it looks like the catalytic His is built such that there is no hydrogen bonding between Cys and Asp - is there a particular reason for this? If not, the side chain should be flipped so that the nitrogens are positioned for ideal hydrogen bonding.
• In Fig. 3, dotted lines are introduced as "hydrophobic interactions" as per the captions, but some are clearly hydrogen bonds (e.g., from the amide backbone), and for some others one does not see as they emerge from a cartoon.
• In Fig. 4, context should be given to "3VYP" and why it is used here.

Referees cross-commenting

Reviewer comments appear to be fair, balanced and complementary.

Re Reviewer 2's comment on the pre-print: It includes a structure of TSSM bound to ubiquitin (but not of the apo protein). I am not sure if it is appropriate to follow up on the esterase activity. Firstly, there are no tools for it commercially available or readily made in vitro, and secondly it would appear a bit "copycat"-like. Especially since the molecular determinants of what makes a DUB a good esterase are still elusive. A narrower focus of this manuscript, but done very well (also according to what reviewer 3 suggested), might be a more fitting option.

Significance

The findings are novel and of high relevance to the broad ubiquitin and bacterial pathogen communities as the study addresses an enigmatic USP-type deubiquitinase which, as the authors reveal, recognizes Ubiquitin in an entirely different way than its eukaryotic counterparts. This is basic research of pronounced conceptual and mechanistic advance, as it demonstrates that the USP domain can be much more diversified than previously assumed. The structural analysis is very thorough, the data are scholarly presented, and interactions/mechanisms carefully validated by mutations.

The strongest point is clearly the structural analysis of Ubiquitin recognition by this extremely truncated USP fold, and the introduction of the little finger motif. The manuscript does not provide cellular validation of the findings, which is fine to this reviewer for the DUB catalysis part. For the part of the N-terminal domain, the manuscript would benefit from a cellular validation or some localisation studies, however, this is beyond what is established in the authors' lab, and therefore has not been asked. This in turn limits the study to a very thorough in-vitro analysis of this DUB for TSSMs with DUB activity, using conventional substrates like polyUb chains and fluorogenic substrates, and providing convincing conclusions.

My expertise lies in DUBs, biochemistry, and structural biology, and I this believe to have sufficient expertise to evaluate the majority of the findings except the bioinformatic algorithms used which are however not an emphasis in this manuscript.

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Reply to the reviewers

The authors do not wish to provide a response at this time.

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Referee #3

Evidence, reproducibility and clarity

Summary

In this manuscript Salvador-Garcia et al. examine how several mutations in the Dynein heavy chain (DHC) influence Dynein function in an in vitro reconstituted system and in Drosophila embryos. Most importantly, they identify a novel substitution mutation (S3372C, generated as a by-product of a targeted CRISPR mutagenesis screen) that leads to a novel phenotype specifically in early Drosophila embryos (metaphase arrest) and that only impairs DHC function under load in vitro. Most surprisingly, the metaphase arrest in embryos does not appear to be due to a failure to inactive the spindle-assembly-checkpoint (SAC), a known DHC function. This suggests that DHC has a hitherto unappreciated function in allowing spindles in the Drosophila embryo to progress from metaphase-to-anaphase.

The manuscript is generally well written and conveys the major conclusions clearly and concisely. The data is generally of high quality, and largely supports the main conclusions, although there is one set of relatively straight-forward experiments that I think would be an important addition (see major comment #1, below).

Major Comments

1. The observation that the S3372C mutation causes a mitotic arrest that is not SAC dependent (i.e. it still largely occurs even in a Mad2 mutant background) is very surprising, and is the basis of the authors claim of a new, DHC-dependent, mechanism that allows embryo spindles to progress into anaphase. I think it would be important to assess whether the SAC components are still localising to the kinetochores in these S3372C, Mad2 double mutants (e.g. is Rod still recruited to high-levels in the double mutant?). If the SAC components are still being recruited to the spindle (suggesting that they are still detecting that the spindle is not ready to go into anaphase), is it worth considering that Mad2 may not be essential for SAC function in these embryos? I say this because I find it hard to imagine how any, presumably mechanical, failure at the kinetochore that leads to the improper metaphase/anaphase transition in the S3372C mutants, would signal to the rest of the spindle to not transition to anaphase if the SAC is truly inactivated. Do the authors think these embryos have a completely unrelated surveillance system that detects the S3372C-dependent error (whatever that is) and arrests the spindles specifically in embryos? Or is the error itself sufficient to cause a spindle-wide arrest, which seems improbable?
2. I was surprised the authors made no attempt to quantify the level of over-accumulation of Dlic (Figure 6) or Rod (Figure 7) (and the lack of over-accumulation in other regions of the spindle). The images are convincing, so I don't doubt that this is the case, but I think some sort of quantification would be useful and I don't think it would be hard to come up with a way to do this (even just drawing a ROI around the approximate areas of interest). It would also be interesting to know whether other proteins like Spc25 (Figure 6) and Cdc20 (Figure S6) are recruited to normal levels at kinetochores.

Minor Comments

1. In the Discussion the authors state: "Our discovery of a missense mutation that strongly affects nuclear divisions in the embryo without disrupting other dynein functions offers a unique tool to study the mitotic roles of the motor". This should be reworded, as it suggests that the mutation effects all mitotic functions of DHC, which is clearly not the case (and also applies only to the embryo).
2. I think it worth more explicitly stating that there is no evidence that the defects the S3372C mutation lead to in the in vitro reconstituted system are the cause of the in vivo defects observed in the embryo. The authors are careful not to directly claim this, and I agree with their assertion that this is the most "parsimonious explanation" for their data, but I'm sure they would agree that this is far from proven, and it might be worth emphasising this point a little more.

Significance

This is a well conducted study that significantly extends the author's previous work on how mutations in DHC (initially indentified in human patients) effect DHC function (Hoang et al., PNAS, 2017). The paper reports the striking central finding that the S3372C mutation produces a very unusual mitotic arrest phenotype specifically in Drosophila embryos, and the authors link this to the also striking finding that this mutation only disrupts DHC function in vitro when DHC is working under load. As mentioned above, this link is not proven here, but this is a solid working hypothesis that is potentially of significant interest to those working on molecular motors and their role in fundamental cell biology and human disease.

I am a cell biologist with expertise in the cytoskeleton, particularly during early Drosophila embryogenesis.

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Referee #2

Evidence, reproducibility and clarity

In the manuscript by Salvador-Garcia et al., the authors assess the physiological consequences of dynein mutations in flies and in vitro. In addition to characterizing the manner by which disease causing mutations affect fly development and some aspects of their cell physiology, the authors focus on sporadic mutations that arose during the course of generating their mutant fly lines. Of particular interest was a mutation in the dynein MTBD: S3722C. This mutation caused very interesting phenotypes in flies (e.g., infertility in females likely due to mitotic arrest) as well as in reconstituted motility assays (e.g., reduced stall force). The authors posit that the mitotic arrest phenotype is a consequence of a dynein's role in initiating anaphase onset, and that this role is distinct from its well established role in silencing the spindle assembly checkpoint.

The paper is very well written, and the data are of high quality. Most of the claims - with one major exception (described below) - are well supported by the data. I have a few comments that might help to strengthen the conclusions.

Major comment:

1. In brief, I'm not convinced the mitotic arrest phenotype is not a consequence of impaired SAC silencing by the mutant dynein. The main tool the authors use to support their claim is a Mad2 mutant. They use this to determine if preventing SAC function (with the mutant Mad2) can override the ability of S3722C cells to progress into anaphase. The Mad2 mutant does in fact increase the proportion of cells exiting mitosis (from 0.8 to 14% of cells); however, the low number (14%) suggests that an inability to silence the SAC is not the reason the cells are not entering anaphase (i.e., it is SAC silencing-independent). My question is how penetrant the Mad2 mutant is? For example, how many cells with this Mad2 mutant would exit mitosis if the authors perturbed mitosis some other way (e.g., treatment with high concentrations of nocodazole)? If the number is still low (~14%), then this might be why the mutant can't rescue the mitotic exit phenotype for S3722C cells, and would challenge the following statements: "...it suggests that this can make, at best, a minor contribution to the mitotic arrest phenotype"; and "Remarkably, the MTBD mutation does not appear to block anaphase progression in embryos by preventing the well-characterized role of kinetochore-associated dynein in silencing the SAC, as the defect persists when the checkpoint is inactivated by mutation of Mad2. Collectively, these observations indicate that kinetochore dynein has a novel role in licensing the transition from metaphase to anaphase." Although previous studies might have assessed the penetrance of this mutant in other cell types, given the cell specificity of the mitotic arrest phenotype for S3722C (in embryonic cells, but not L3 neuroblasts), it will be important to provide such additional evidence in embryonic cells (e.g., nocodazole treatment of embryonic cells) to support these statements, especially in light of the bold conclusions and hypotheses they are making (e.g., "For example, the apparent variability in tension between sister kinetochores in S3372C embryos, which could reflect abnormal force generation by the mutant motor complex, might prevent APC/C activation through the complex series of signaling events that respond to chromosome biorientation."). Although it would be fascinating if the authors are correct that dynein provides another role in licensing anaphase onset, the well-established role for dynein in checkpoint silencing currently seems like the most parsimonious explanation.

Minor comments:

1. The S3722C mutant appears to accumulate to higher-than-normal levels at KTs and to some extent along the spindle MTs. In addition to the representative images, it would be helpful to see a quantitation of this phenomenon for WT, S3722C, and S3722C/+ along with statistics.
2. Although the mean inter-KT distance was unchanged between WT and S3722C cells, the authors note that the deviation from the mean was higher. Could this simply be a consequence of more highly dynamic oscillations of KT pairs (similar to that seen with Hec1-S69D in DeLuca et al., JCB 2018)? More dynamic oscillations could potentially lead to more variable distances between KT pairs.
3. It is interesting that the S3722C/Mad2-mutant cells are enriched in telophase (Fig. 7G). Does this not suggest another arrest point for these cells?
4. The authors state: "Immunostaining revealed that whereas α1-tubulin was present throughout the spindle apparatus, α4-tubulin was enriched at the spindle poles (Figure S7A)." Although I agree the a4-tubulin appears somewhat enriched at the poles with respect to a1-tubulin, a quantitation (with statistics) would be needed to support this claim. That being said, I agree the isotype is unlikely to account for the S3722C phenotype.
5. Trapping data show reduced stall force, yet increased stall time at low resistive forces for the mutant. This finding could potentially account for the reduced velocity of GFP-Rod noted in cells; however, I wonder if the authors noted altered velocity for dynein-driven bead movement under load in their trapping assays? This information would be useful to include in their manuscript.
6. Is there a defocused spindle pole phenotype in the mutant cells? The cells in Video 1 and Fig. S6c appear to show as much, although other cells do not.
7. The authors state: "This may reflect the relatively short length of Drosophila neurons making them less sensitive to partially impaired cargo transport." Could the extent of the phenotypes also be related to the lifespan of the flies? Do any of the diseases caused by these mutations have late-onset in patients? I wonder if a subtle defect in dynein behavior might not manifest for numerous years due to only minor changes in motility?

Referees cross-commenting

I wanted to reiterate my skepticism regarding the possibility that their data strongly support a SAC-independent role for dynein in the metaphase-to-anaphase transition (it seems Reviewer #3 might agree with me). I don't think it's impossible, but I'm not convinced they've made a very strong case for this model, which is noted in the title. The fact that proteins are accumulating at aligned kinetochores in the mutant cells (e.g., Rod and DLIC) in fact are consistent with a SAC silencing defect. Along these lines, I think reviewer #1's point regarding RZZ is a good one (that the mutant dynein is incapable of evicting RZZ specifically from aligned KTs), and should be tested prior to publication.

My primary concern is that their conclusion is based entirely on the fact that the Mad2 mutant does not fully restore mitotic exit to the dynein mutant cells. Given the cell-specificity of their dynein phenotype (in embryonic cells, but not L3 neuroblasts), I think testing the penetrance of their Mad2 mutant in the embryonic cells would need to be assessed. In my review I suggested nocadazole, when I realized I meant to say reversine (oops!).

Significance

The manuscript by Salvador-Garcia et al. is a very interesting study dissecting the physiological consequences of dynein mutations in flies and in vitro. This study will be of high interest to those in the dynein/molecular motor field, as well as those that study mitosis and kinetochore function. One of the most interesting findings in the study is the identification of a mutation in dynein that specifically impacts its motility in conditions of high load. This mutant provides a novel tool to dissect load-dependent transport for dynein in other systems. Moreover, the study suggests a novel role for dynein in promoting anaphase onset; if the authors can provide additional support for this claim, the impact of this study would be greater.

Field of expertise: molecular motors, kinetochore function

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Referee #1

Evidence, reproducibility and clarity

The authors start out by examining the cellular and organismal effects of 6 human disease-linked mutations, as well as the mouse legs-at-odd-angles (Loa) mutation, after introducing the mutations into the D. melanogaster dynein heavy chain (Dhc) by genome editing. This reveals an overall correlation between the severity of the effect on dynein motor activity in vitro (determined in a previous study) and the penetrance of the corresponding mutant phenotype in the fly, with a couple of interesting exceptions that illustrate the value of performing structure-function analysis of dynein in animal models. The authors then focus on an additional missense mutation in the Dhc microtubule binding domain, fortuitously generated by imprecise editing, that results in a striking phenotype. The S3372C mutation supports normal development, including normal axonal transport of mitochondria and asymmetric mitosis of larval neuroblasts, but female flies are infertile. Through elegant genetics, ectopic disulfide bond formation with a nearby residue is ruled out as the cause of the maternal effect. S3372C results in metaphase arrest of early embryonic divisions, characterized by over-accumulation of dynein light intermediate chain (Dlic) and the dynein recruitment factor Rod at kinetochores, as well as by reduced poleward streaming of Rod along spindle microtubules. Surprisingly, the S3372C-induced metaphase arrest cannot by bypassed by inhibiting the spindle assembly checkpoint, implying that dynein promotes the metaphase-to-anaphase transition not solely through its known function in spindle assembly checkpoint silencing. In vitro motility and optical trapping experiments show that the mutant motor performs normally in a load-free regime but exhibits reduced peak force production and excessive pausing under load. Furthermore, molecular dynamics simulations reveal how the mutation affects dynein's interaction with microtubules, including a change in the positioning of the stalk. The authors conclude that the S3372C mutation specifically perturbs high-load functions of dynein, explaining the selective phenotype observed in vivo.

The experiments are technically on a very high level, the results are presented in a clear manner, and the conclusions are fully supported by the data.

Minor suggestions (optional):

• In the first part of the paper, where Dhc mutations associated with neurological disease are examined, the H3808P and F579/Loa mutations are shown to cause mis-accumulation of synaptic vesicles in axons. The authors may want to perform this assay for the K129I, R1557Q, and K3226T mutations, as this would strengthen the comparative analysis of in vitro versus in vivo effects, summarized in Figure 1C. For example, K129I has a more severe effect in vitro than the Loa mutation, but the Loa mutation has a more pronounced phenotype on the organismal level. Would this also be the case in a cell-based assay?
• The observation that the metaphase arrest of S3372C mutant embryos cannot be alleviated by the checkpoint-defective Mad2 mutant is very intriguing, as is the observation that Dlic and the RZZ subunit Rod over-accumulate at/near kinetochores. As discussed by the authors, one possibility is that the arrest is a consequence of dynein's failure to disassemble the corona by stripping, but, surprisingly, in a manner unrelated to dynein's role in SAC silencing. In this regard, it is interesting to note that fly RZZ mutants do not undergo metaphase arrest in the early embryo (Williams and Goldberg, 1994; Défachelles et al., 2015), whereas knockdown of Spindly, which functions in dynein recruitment downstream of RZZ, does lead to arrest (see Figure 2 in Clemente et al., 2018; PMID 29615558). Taken together, this raises the possibility that it is the failure to remove RZZ (and other associated corona components) from kinetochores that inhibits anaphase onset in S3372C embryos. It would therefore be interesting to test whether the metaphase arrest in S3372C embryos is alleviated in RZZ mutants.

Referees cross-commenting

The Mad2 mutant the authors use is a P-element insertion that was described by Buffin et al 2007 as a null mutant with regards to SAC signaling (it also does not produce any detectable protein by Western blot; Figure 1b). Nevertheless, since the analysis in Buffin et al was restricted to larval brains, I agree with reviewer #2 that it remains to be formally demonstrated that this Mad2 mutant fully abolishes the SAC in the early embryo. Unfortunately, as far as I am aware, reversine does not work well in Drosophila. An alternative would be to combine Dhc(S3372C) with the other Mad2 mutant used by Buffin et al, which (besides not producing detectable protein) lacks the Mad1 binding domain and can therefore be expected to be a definitive checkpoint null in all tissues.

Significance

The cytoplasmic dynein 1 motor complex participates in a multitude of cellular processes that require microtubule minus-end-directed motility. Whereas in vitro reconstitution efforts have led to a detailed understanding of the motor's motile properties, the essential requirement of dynein for development has made it challenging to dissect how the motor contributes to specific aspects of intracellular organization and cell division in vivo. The need for a mechanistic understanding of how dynein motility is used for diverse functions in vivo is underscored by missense mutations in the motor subunit that cause human neurological disease. In this interesting and insightful study, Salvador-Garcia and colleagues characterize several missense mutations in dynein heavy chain (Dhc) using biochemical assays and genetic approaches is the fly, which reveals the distinct effects of disease-causing mutations in vivo and uncovers an unanticipated novel function of dynein in regulating mitotic progession.

This beautifully executed study has important implications for dynein's role in mitosis, in particular its role at the kinetochore, and is of broad interest to cell biologists studying the cytoskeleton, as it demonstrates that examining motor mutants with altered mechanical properties in vivo can reveal specific motor functions.

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Reply to the reviewers

Response to Reviewers

We are grateful to the Reviewers for their insightful thoughts and suggestions for improving the manuscript for publication. We have addressed all Reviewers’ comments, and detailed responses have been provided below (in blue font). We have uploaded a revised manuscript version, and have made a few small improvements to the text to improve readability. Line and figures numbers refer to the revised version of the manuscript.

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

In this study, Wenner et al. used various in vitro methods, including transposon mutagenesis, screening of known regulatory proteins and isolation of spontaneous mutants to discover 11 mutations in genes that promote bacterial growth under succinate-mediated inhibition. Through additional experiments, the manuscript provides evidence for factors that underlie several layers of succinate regulation. These layers include sRNAs, OxyRS, succinate transport antibiotics and rRNA. The study then characterized the molecular mechanisms regulating succinate utilization by these mutations, revealing a RpoS-independent mechanism for succinate uptake via the dctA transporter and mechanisms for RpoS regulation.

Overall, the manuscript is very unfocused and uneven in the level of details of each of these factors and could be much more compelling if more focus was given to several factors and providing more mechanistic insight of these factors.

We thank Reviewer #1 for the constructive criticism and suggestions. We do recognize the limitations of our study, clearly more work is required to unravel the complex phenomenon of the inhibition of succinate utilisation by Salmonella. We welcome Reviewer #1’s suggestions to shorten the manuscript, which has allowed us to focus the paper on our key findings.

Major comments

1. The authors discuss virulence-mediated succinate but disregard some important features of succinate utilization, only referring to dctA and disregarding the overlap with other C4-dicaroxy transporters (Spiga, wolf, PMID). Furthermore, the study found that a mutation in the IscR binding site on the DctA promoter region reversed the effects of succinate-dependent growth inhibition generated under aerobic conditions but other succinate transporters are expressed under different physiological conditions (Janausch et al. 2002, Spiga et al. 2017). Does the IscR binding site motif can be found in promoters of other succinate transporters? Analysis of IscR in aerobic/ anaerobic conditions can be useful. Do mutations in IcsR lead to increased expression of other succinate transporters in aerobic or anaerobic conditions?

The Reviewer’s question of the regulatory role of IscR on anaerobic C4-dicarboxylate transporters is particularly relevant in the context of the role of succinate catabolism in pathogenesis and could be studied in a follow-up investigation. However, further analysis of the influence of mutations that modulate the expression or activity of IscR are beyond the scope of our study. Here, we have focused on succinate utilisation under in vitro, aerobic conditions: under these conditions, growth upon succinate is robustly repressed, allowing the selection of Succ+ mutants. To emphasise that our study was done under aerobic conditions, we have rephrased the Introduction (line 93).

Transposon screen - There is no comprehensive description of the results and it is not clear why mutations found in the evolution experiments or regulatory proteins that were shown to allow bacteria growth under succinate treatment were not detected in the transposon screening?

Different selection protocols were used to isolate the Succ+ mutants and the experimental approaches are detailed in the Methods and in the strain list for each mutant (Supplementary_Resource_Table S1). Selection was performed in liquid M9+Succ for Tn5 mutagenesis (in the rpoS2X background), or in solid and liquid M9+Succ media for the spontaneous mutants (the mutations are all listed and detailed in Table 1).

Therefore, the different selection conditions and the presence of an extra rpoS copy may have favored certain mutants, especially when the pools of Tn5 mutants were grown with succinate together (mutants in competition).

We recognize that our experimental approach had limitations, and that a Tn-seq methodology would have been more comprehensive. However, the robustness of the phenotypes of the mutants (all re-constructed and complemented, when possible) demonstrated that the genes of interest had direct impact upon the control of succinate metabolism with novel implications for the field.

Figure 4: The authors claim: "that the fast growth of the Δhfq and Δpnp strains reflected both the dysregulation of the sRNA-mediated repression of sdh and the activation of rpoS translation". However, they provide no evidence for SDH regulation. The experiment is correlative, the activity of pnp regulating rpoS was done with overexpression without the proper controls. The authors should look at rpoS expression in Δpnp. It does not seem reasonable that transcription of SDH mRNA can explain lack of succinate utilization. What about the SDH protein? is it at all changed? The authors claim "none of the sRNA mutants tested displayed the same fast-growing pattern of the Δhfq mutant" but they action can involve completely different mechanisms, that the authors do not study. This part does not seem to contribute any novel information on Δhfq and Δpnp on Succ+ with the sRNAs seem not to provide any clear mechanism. The authors should consider removing this part or moving to supplementary.

We appreciate this comment, and agree that this section does not provide critical novel insight. However, our findings provide valuable data concerning the role that Hfq, PNPase and sRNAs play in succinate utilisation. Therefore, we have briefly mentioned the role of Hfq, PNPase and sRNAs in the main text (Lines 333-338) and moved the original Figure 4 to the Supplementary (Figure S5), with a supplementary text section (Supplementary Text T1).

If OxyS, an Hfq-binding sRNA, is related to Succ+ in Δhfq, then why all the other sRNAs are relevant? This is not clear. The authors could have focused here on the oxyS instead of other sRNAs. "The same plasmid did not stimulate the growth of the ΔoxyR strain indicating that a functional OxyR is required for growth in M9+Succ (Fig 5D)" - is it because of other targets of OxyR?

The reviewer’s interpretation is correct. To clarify this point, we have rephased the sentence (Lines 274-276) to “The same plasmid did not stimulate the growth of the ∆oxyR strain indicating that other OxyR-dependent genes are required to grow under this condition”

It seems that an RNA-seq analysis in the conditions of succinate growth with OxyRmut vs. WT could hint towards this.

Indeed, it would be very interesting to compare the transcriptomic landscape of the WT and of the oxyRmut mutant and other Succ+ mutants in succinate minimal medium. However, the lack of growth of S. Typhimurium WT in M9+Succ, would make these experiments unlikely to succeed.

"We previously showed that Hfq inactivation boosted succinate utilization (Fig 4A), but in the oxyRmut genetic background the same Hfq inactivation dramatically reduced growth and extended the duration of lag time in M9+Succ (Fig 5 E)"

The reviewer is correct, we had hypothesised that Hfq is necessary to stimulate succinate utilisation by OxyS. Therefore, we have rephrased to: “We previously showed that Hfq inactivation boosted succinate utilisation, but in the oxyRmut genetic background the same Hfq inactivation dramatically reduced growth and extended the duration of lag time in M9+Succ (Fig 4E). Collectively, our findings show that the OxyS sRNA orchestrates the de-inhibition of succinate utilisation in concert with Hfq” (Lines 278-281)

• this seems like an interesting finding, but the authors don't offer any follow-up? Is it related to oxyS activity?

The role of Hfq on succinate utilisation appeared to be dual, we have added a sentence to this effect (Lines 335-338).

Figure 6: "OxyS acts as an indirect repressor of RpoS expression, probably via the titration of Hfq". the yobF::sfgfp activity was significantly lower in the oxyRmut strain (~2-fold repression), confirming that OxyS represses the expression of the yobF cspC operon in Salmonella - can the authors show this directly with oxyS in succinate?

Because Salmonella WT and ∆oxyS strains do not grow in succinate media (M9+Succ), we had to investigate the regulation of yobF-cspC operon with a translational gene fusion in non-selective LB media.

Why use OxyRmut here? This is indirect.

In Figure 5C we first used the oxyRmut Succ+ strain to demonstrate that this mutation leads to the repression of yobF-cspC. In Figure 6F, we used the oxyRmut allele to allow a constitutive expression of oxyS WT or oxySGG : allele oxySGG was introduced into the chromosome and relies on an active OxyR to be transcribed. The direct role of OxyS is demonstrated in Figure 5 E &F.

The authors already show that OxyRmut does not act solely via Oxys...can the authors directly show RpoS and SDH levels by qRT-PCR in ΔcspC? Again - the appropriate control for RpoS overexpression in the WT was not done (Fig. 6G). Furthermore, expression analysis of the sdhCDAB operon over the background of the oxyR mutant will confirm the author suggestion for the mechanism by which the OxyS-driven inhibition of CspC expression impacts upon the catabolism of succinate.

The reviewer’s comments are valid, more work is required to understand how OxyS stimulates succinate utilisation via the repression of cspC. The fact that Salmonella WT does not grow with succinate as a sole carbon source makes such comparisons technically challenging. Yes, the repressive role of CspC remains enigmatic. However, RNA-seq data following growth in LB media have already been provided by others, suggesting that CspEC may repress TCA cycle genes in Salmonella (PMID: 28611217), consistent with the repression of succinate catabolism by CspC.

The fact that the plasmid-borne overexpression of rpoS completely represses growth upon succinate in the ∆rpoS background (Figure S3 B) validated the usage of the prpoS plasmid in other genetic backgrounds, in order to reveal whether the other Succ+ mutations were stimulating succinate utilisation via rpoS repression or not. Because WT Salmonella does not grow in M9+Succ, presenting the growth curve of the WT strain carrying the prpoS plasmid would not be informative here, and would make the figure overly complex.

Figure 7: the authors check growth in M9+succ in the absence of DctA - but the experiment duration should be carried out for longer, as previous experiments with WT (intact dctA in Fig. 2A) and check if in the absence of dctA there are mutations that allow succinate growth.

We agree with the reviewer’s comment and we have performed a new growth curve (over 65 hours) of the ∆dctA strain to clarify that DctA is the only succinate transporter involved in Salmonella growth under our experimental conditions (Figure S8).

It seems that the results here contradict some of the previous - if succinate uptake through dctA is intact then there is no repression of SDH? rpoS? In figure 7E - is this difference only through dctA activity?

The reviewer is raising an important point and it is possible that the de-repression of succinate uptake via DctA could impact upon the expression of the succinate catabolic genes and more work is required to understand this phenomenon. We have discussed this possibility in the main text (Lines 424-432) and in Figure 8C.

It seems that icsR is not repressing dctA expression to WT levels - are there other factors? Can the authors show that dctA repression by IscR is direct?

We agree with the reviewer, we have not shown that IscR represses dctA directly. Electrophoretic mobility shift assays could be performed to prove that IscR interacts with the dctA promoter region, but this would be beyond the scope of the paper. We have clearly stated in the discussion that indirect effects of iscR on dctA expression cannot be ruled out (Lines 419-422).

Figure 9 is very descriptive and does not provide any evidence to support the authors hypothesis. The authors should either provide more substantial evidence connecting ribosomal RNA levels and succinate utilization and similarly Cm concentrations or either remove this part or move it to the supplementary.

We agree that the data do not conclusively support the hypothesis, but we believe that the impact of anti-SD mutation and chloramphenicol on Salmonella carbon metabolism are valuable observations for the community. Therefore, we have moved the data to supplementary Figures S11 and S12 in the revised version, with a supplementary text section (Supplementary Text T2). We also removed this aspect from the model Figure (Figure 8) and only mentioned the phenomenon briefly in the main text, Lines 482-485.

Can any of the mutations characterized in this work be found in the genome of Newport or LT2 strains that can grow with succinate as a sole carbon source? (Fig 1)

Very good questions. Yes, S. Typhimurium strain LT2 has an altered rpoS allele that attenuates virulence of the strain in the murine infection model (PMID: 8975913) and promotes growth with succinate (PMID: 33593945). We have added a sentence and cited the reference at Lines 129-131.

To address the S. Newport question, we performed an analysis of the genome of the S. Newport strain LSS-48, and did not identify any mutations in regulatory or catabolic genes that could explain the faster growth on M9+succinate. However, in comparison with fast-growing enteric bacteria (i.e. E. coli MG1655) or Succ+ S. Typhimurium mutants, S. Newport LSS-48 grows much slower on succinate and has an intermediate growth phenotype. It remains unclear why S. Newport does grow better than other serovars.

Although the author suggested that regulation of succinate uptake is critical for Salmonella colonization and virulence in various metabolic conditions, the study lacks sufficient evidence to support these claims and further research is necessary to establish these statements.

We agree that our findings are not directly linked to Salmonella host colonisation or virulence. However, we do believe that our study will contribute to a better understanding of Salmonella metabolic control, in the context of pathogenesis. To address Reviewer #3’s comment, we have moderated our claims about the likely impact of our findings on the understanding of Salmonella pathogenesis in the Perspective section.

Minor comments

1. Table summarizing the growth curves lag phase of the different mutants might help in the data interpretation.

We appreciate the Reviewer’s suggestion and have prepared a supplementary figure (Figure S4) indicating the average lag time of the Succ+ mutants and of the complemented mutants.

In lines 245-248 the author describes the eleven novel Succ+ mutations however in this gene list only ten gene names are mentioned. DctA is missing from this list.

We appreciate the Reviewer’s comment and we have modified the sentences in the revised manuscript (Line 244).

** Referees cross-commenting**

I agree with both reviewer that there is a large amount of data in the paper, and willing to accept their point that asking for further experiments would exceed the scope of the paper. In that case, the authors should address the mechanistic options in the discussion

Reviewer #1 (Significance (Required)):

In this work, Wenner et al. characterized the molecular mechanisms regulating Salmonella growth inhibition when succinate is the sole carbon source in the culture. This work revealed new layer of regulations for rpoS activation, the sigma factor previously characterized to control this growth inhibition mechanism. In addition, this work revealed novel RpoS-independent mechanisms for succinate utilization and highlighted the crucial role of succinate processing in Salmonella physiology.

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

In the manuscript titled "Salmonella succinate utilisation is inhibited by multiple regulatory systems", Wenner et al., explored how Salmonella regulates the utilization of succinate, an important carbon source for Salmonella gut colonization as well as a molecule that regulates intracellular adaptation in the SCV. As Salmonella exhibits a slow growth rate when succinate is provided as the sole carbon source, the authors explored the underlying genetic regulation by isolating fast-growing mutants (Succ+) using an experimental evolution approach. By combining the screen for mutants lacking key regulatory proteins, an elegantly designed Tn5 transposon mutagenesis, and selection of spontaneous Succ+ mutants, the authored identified a library of mutations that led to the Succ+ phenotype. Using classical bacterial genetics, Wenner et al characterized how Hfq, PNPase and cognate sRNA inhibit succinate utilization. They went on to show, clearly and convincingly, that IscR inhibits growth upon succinate by repressing DctA expression, and succinate utilization can also be repressed by RbsR and FliST via RpoS. Lastly, they provided evidence supporting that anti-Shine-Dalgarno mutations and low concentrations of chloramphenicol can boost succinate utilization. Overall, this paper is well written, and the experiments were rigorously designed and executed. This is a beautiful example of deciphering complex regulatory nodes in the succinate utilization using elegant genetics approaches. Very nicely done!

We thank the Reviewer #2 for the very positive evaluation of our work and the constructive comments.

Minor issues:

1. While rpoS2X strain is an clever way to avoid the selection of Succ+ rpoS mutants, it is unclear why "identified an iraP::Tn5 mutant was an effective validation of the use of the rpoS2X genetic background". IraP stabilizes Rpos, and this mutant could have been selected in the wild-type background (rpoS1X).

The reviewer’s comment is helpful, we have removed this sentence from the revised manuscript.

The description between line 356-357 is confusing as it reads like the author constructed a "oxyRmut oxySGG pPL-OxySGG" strain, while the experiments that followed actually used a " ∆oxyS, yobF::sfgf, pPL-OxySGG" strain.

We have modified these sentences in the revised manuscript (Lines 303-308).

An alternative explanation for the Succi+ phenotype in aSD mutant and bacteria treated with low Cm is the reduced translation fidelity, which leads to selectively degradation of inhibitors of succinate utilization.

We thank Reviewer #2 for the suggestion. This phenomenon is really enigmatic and as previously discussed in Reviewer #1’s section, we have now moved Figure 9 to supplementary data. Further discussion of how the aSD mutations and chloramphenicol can affect Salmonella succinate metabolism would require a lot more experimental data.

** Referees cross-commenting**

Most of the comments from Reviewer 1 are valid but excessive. Most of the experiments presented in this paper were rigorously controlled and executed. While some parts of the paper could be more mechanistic but they could also leave room for future studies. Also, some of the points raised, the 1st major concern, for example, may have exceeded the scope of the paper.

We agree. We have performed a new experiment (Figure S8) to address Reviewer #1’s comments.

Reviewer #2 (Significance (Required)):

Overall, this paper is well written, and the experiments were rigorously designed and executed. This is a beautiful example of deciphering complex regulatory nodes in the succinate utilization using elegant genetics approaches.

We appreciate Reviewer #2’s feedback that the quality of the text and our experiments was viewed so highly.

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

In this work, Wenner and colleagues use experimental evolution to define a range of spontaneous mutations in Salmonella that allow it to overcome its aversion to using succinate as a carbon source in vitro.

This work cites the literature extensively and the scholarship is very very good. I appreciate the effort they put into the manuscript, which made it easy to read. Quite a relief to get a paper in this good of shape compared to most.

We appreciate Reviewer #3’s positive comments on our work and the constructive suggestions.

Shortcomings - although I don't think they are necessary for *this* paper to be published include:

• not defining what could be 'bad' about eating succinate in the wrong place. The fact that succinate import is a problem (dctA is what is being regulated ant its a transporter) suggests one of the following: (1) excess succinate would block the utilization of fumarate by fumarate reductase, (2) succinate is a powerful buffer and, if protonated, would acidify the cytoplasm of Salmonella if it were brought in - note that there is a lot of work on RpoS controlling cytoplasmic acidification, (3) a drop in succinate (because Salmonella eats it) would allow more flux by macrophages or the microbiota in a bad way...maybe the Salmonella 'wants' macrophages to have lots of succinate *because* its pro-inflammatory (and therefore more tetrathionate for its friends...etc), (4) it could be the transporter that also bring in antimicrobial itaconate?...so the succinate phenotype is a red herring and really this is about preventing taconite from getting into the cell?

We thank the reviewer for all these suggestions and for highlighting the reasons why the avoidance of Salmonella utilising succinate is a key point. We have emphasized this key question to conclude our manuscript (Lines 500-501). Whilst all the hypotheses are valid, we believe that further speculation should not be added to the “Perspective” section.

• no proof that any of this is relevant in infection except citing old papers. Again - this work is already VERY expansive and we could propose experiments until the end of time. Next paper should take the dctA and other mutations and put them into mice to see if they fail in either germ free mice (no microbial produced succinate around) or in systemic infections.

The reviewer’s comment is welcomed. As discussed in our response to Reviewer #1, we have scaled back our discussion of the impact of our findings for the understanding of Salmonella pathogenesis*. *

Most of the mutations they find are 'regulatory' and the only proximal effector of succinate utilization seems to be dctA...suggesting that dctA expression is the 'rate limiting' or 'blocked' step that decides whether succinate is being used or not.

We agree that dctA regulation is a central element of the story. As discussed in Reviewer #1 comments, it is not clear how de-repression of dctA leads to the increased catabolism of succinate in the presence of RpoS (particularly because RpoS represses several succinate catabolic genes, PMID: 24810289 and PMID: 25578965). We also discovered other Succ+ mutants that did not affect DctA expression but stimulated growth on succinate as a sole carbon source. Consequently, it is uncertain whether the uptake of succinate is really the limiting factor. We have added sentences about this paradox, Lines 424-432.

The data is extensive and generally well controlled. Where appropriate they either complement mutations or reconstruct them denovo. The findings of the various genes range in novelty but many are new.

** Referees cross-commenting**

I agree that the work was valid and well controlled. The 'story' was a bit disjointed at times primarily because the range of mutations identified were diverse and pleiotropic. Given the large amount of data already in the paper and the nature of the mutations identified I worry about embarking on an endless cycle of new experiments. I think it's at a publishable stopping point.

In response to Reviewer #3 & #1’s comments, we have now improved the flow of the manuscript.

Reviewer #3 (Significance (Required)):

This seemingly mundane phenotype (Salmonella 'choosing' to not use succinate even though it's perfectly capable of doing so) has been known for years but only recently has its potential relevance become more clear in the context of infection and microbiota metabolism.

The authors propose that succinate utilization is to be used at the right time and right place.

I sympathize with the authors that they keep hitting very pleiotropic regulators (RpoS has ten million upstream inputs and outputs. The ribosome? How is that going to be figured out in one or two simple experiments?). My money is on figuring out exactly how dctA is regulated and whether there's differences in the dctA regulation between E. coli and Klebsiella/Salmonella.

So I think the work is extensive and generally well done. I think the paper will be well cited...and I think it's importance will grow over time and it will continue to be relevant years from now. I can't say that about most work in the field.

We agree with Reviewer #3’s assessment that other scientists in the Salmonella field are likely to cite our paper, and to perform experiments that will build on our findings in the future.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #3

Evidence, reproducibility and clarity

In this work, Wenner and colleagues use experimental evolution to define a range of spontaneous mutations in Salmonella that allow it to overcome its aversion to using succinate as a carbon source in vitro.

This work cites the literature extensively and the scholarship is very very good. I appreciate the effort they put into the manuscript, which made it easy to read. Quite a relief to get a paper in this good of shape compared to most.

Shortcomings - although I don't think they are necessary for this paper to be published include:

• not defining what could be 'bad' about eating succinate in the wrong place. The fact that succinate import is a problem (dctA is what is being regulated ant its a transporter) suggests one of the following: (1) excess succinate would block the utilization of fumarate by fumarate reductase, (2) succinate is a powerful buffer and, if protonated, would acidify the cytoplasm of Salmonella if it were brought in - note that there is a lot of work on RpoS controlling cytoplasmic acidification, (3) a drop in succinate (because Salmonella eats it) would allow more flux by macrophages or the microbiota in a bad way...maybe the Salmonella 'wants' macrophages to have lots of succinate because its pro-inflammatory (and therefore more tetrathionate for its friends...etc), (4) it could be the transporter that also bring in antimicrobial itaconate?...so the succinate phenotype is a red herring and really this is about preventing taconite from getting into the cell?
• no proof that any of this is relevant in infection except citing old papers. Again - this work is already VERY expansive and we could propose experiments until the end of time. Next paper should take the dctA and other mutations and put them into mice to see if they fail in either germ free mice (no microbial produced succinate around) or in systemic infections.

Most of the mutations they find are 'regulatory' and the only proximal effector of succinate utilization seems to be dctA...suggesting that dctA expression is the 'rate limiting' or 'blocked' step that decides whether succinate is being used or not.

The data is extensive and generally well controlled. Where appropriate they either complement mutations or reconstruct them denovo. The findings of the various genes range in novelty but many are new.

** Referees cross-commenting**

I agree that the work was valid and well controlled. The 'story' was a bit disjointed at times primarily because the range of mutations identified were diverse and pleiotropic. Given the large amount of data already in the paper and the nature of the mutations identified I worry about embarking on an endless cycle of new experiments. I think it's at a publishable stopping point.

Significance

This seemingly mundane phenotype (Salmonella 'choosing' to not use succinate even though it's perfectly capable of doing so) has been known for years but only recently has its potential relevance become more clear in the context of infection and microbiota metabolism.

The authors propose that succinate utilization is to be used at the right time and right place.

I sympathize with the authors that they keep hitting very pleiotropic regulators (RpoS has ten million upstream inputs and outputs. The ribosome? How is that going to be figured out in one or two simple experiments?). My money is on figuring out exactly how dctA is regulated and whether there's differences in the dctA regulation between E. coli and Klebsiella/Salmonell.

So I think the work is extensive and generally well done. I think the paper will be well cited...and I think it's importance will grow over time and it will continue to be relevant years from now. I can't say that about most work in the field.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

In the manuscript titled "Salmonella succinate utilisation is inhibited by multiple regulatory systems", Wenner et al., explored how Salmonella regulates the utilization of succinate, an important carbon source for Salmonella gut colonization as well as a molecule that regulates intracellular adaptation in the SCV. As Salmonella exhibits a slow growth rate when succinate is provided as the sole carbon source, the authors explored the underlying genetic regulation by isolating fast-growing mutants (Succ+) using an experimental evolution approach. By combining the screen for mutants lacking key regulatory proteins, an elegantly designed Tn5 transposon mutagenesis, and selection of spontaneous Succ+ mutants, the authored identified a library of mutations that led to the Succ+ phenotype. Using classical bacterial genetics, Wenner et al characterized how Hfq, PNPase and cognate sRNA inhibit succinate utilization. They went on to show, clearly and convincingly, that IscR inhibits growth upon succinate by repressing DctA expression, and succinate utilization can also be repressed by RbsR and FliST via RpoS. Lastly, they provided evidence supporting that anti-Shine-Dalgarno mutations and low concentrations of chloramphenicol can boost succinate utilization. Overall, this paper is well written, and the experiments were rigorously designed and executed. This is a beautiful example of deciphering complex regulatory nodes in the succinate utilization using elegant genetics approaches. Very nicely done!

Minor issues:

1. While rpoS2X strain is an clever way to avoid the selection of Succ+ rpoS mutants, it is unclear why "identified an iraP::Tn5 mutant was an effective validation of the use of the rpoS2X genetic background". IraP stabilizes Rpos, and this mutant could have been selected in the wild-type background (rpoS1X).
2. The description between line 356-357 is confusing as it reads like the author constructed a "oxyRmut oxySGG pPL-OxySGG" strain, while the experiments that followed actually used a " ∆oxyS, yobF::sfgf, pPL-OxySGG" strain.
3. An alternative explanation for the Succi+ phenotype in aSD mutant and bacteria treated with low Cm is the reduced translation fidelity, which leads to selectively degradation of inhibitors of succinate utilization.

** Referees cross-commenting**

Most of the comments from Reviewer 1 are valid but excessive. Most of the experiments presented in this paper were rigorously controlled and executed. While some parts of the paper could be more mechanistic but they could also leave room for future studies. Also, some of the points raised, the 1st major concern, for example, may have exceeded the scope of the paper.

Significance

Overall, this paper is well written, and the experiments were rigorously designed and executed. This is a beautiful example of deciphering complex regulatory nodes in the succinate utilization using elegant genetics approaches.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

In this study, Wenner et al. used various in vitro methods, including transposon mutagenesis, screening of known regulatory proteins and isolation of spontaneous mutants to discover 11 mutations in genes that promote bacterial growth under succinate-mediated inhibition. Through additional experiments, the manuscript provides evidence for factors that underlie several layers of succinate regulation. These layers include sRNAs, OxyRS, succinate transport antibiotics and rRNA. The study then characterized the molecular mechanisms regulating succinate utilization by these mutations, revealing a RpoS-independent mechanism for succinate uptake via the dctA transporter and mechanisms for RpoS regulation.

Overall, the manuscript is very unfocused and uneven in the level of details of each of these factors and could be much more compelling if more focus was given to several factors and providing more mechanistic insight of these factors.

Major comments

1. The authors discuss virulence-mediated succinate but disregard some important features of succinate utilization, only referring to dctA and disregarding the overlap with other C4-dicaroxy transporters (Spiga, wolf, PMID). Furthermore, the study found that a mutation in the IscR binding site on the DctA promoter region reversed the effects of succinate-dependent growth inhibition generated under aerobic conditions but other succinate transporters are expressed under different physiological conditions (Janausch et al. 2002, Spiga et al. 2017). Does the IscR binding site motif can be found in promoters of other succinate transporters? Analysis of IscR in aerobic/ anaerobic conditions can be useful. Do mutations in IcsR lead to increased expression of other succinate transporters in aerobic or anaerobic conditions?
2. Transposon screen - There is no comprehensive description of the results and it is not clear why mutations found in the evolution experiments or regulatory proteins that were shown to allow bacteria growth under succinate treatment were not detected in the transposon screening?
3. Figure 4: The authors claim: "that the fast growth of the Δhfq and Δpnp strains reflected both the dysregulation of the sRNA-mediated repression of sdh and the activation of rpoS translation". However, they provide no evidence for SDH regulation. The experiment is correlative, the activity of pnp regulating rpoS was done with overexpression without the proper controls. The authors should look at rpoS expression in pnp. It does not seem reasonable that transcription of SDH mRNA can explain lack of succinate utilization. What about the SDH protein? is it at all changed? The authors claim "none of the sRNA mutants tested displayed the same fast-growing pattern of the Δhfq mutant" but they action can involve completely different mechanisms, that the authors do not study. This part does not seem to contribute any novel information on Δhfq and Δpnp on Succ+ with the sRNAs seem not to provide any clear mechanism. The authors should consider removing this part or moving to supplementary.
4. If OxyS, an Hfq-binding sRNA, is related to Succ+ in Δhfq, then why all the other sRNAs are relevant? This is not clear. The authors could have focused here on the oxyS instead of other sRNAs. "The same plasmid did not stimulate the growth of the ΔoxyR strain indicating that a functional OxyR is required for growth in M9+Succ (Fig 5D)" - is it because of other targets of OxyR? It seems that an RNA-seq analysis in the conditions of succinate growth with OxyRmut vs. WT could hint towards this. "We previously showed that Hfq inactivation boosted succinate utilization (Fig 4A), but in the oxyRmut genetic background the same Hfq inactivation dramatically reduced growth and extended the duration of lag time in M9+Succ (Fig 5 E)" - this seems like an interesting finding, but the authors don't offer any follow-up? Is it related to oxyS activity?
5. Figure 6: "OxyS acts as an indirect repressor of RpoS expression, probably via the titration of Hfq". the yobF::sfgfp activity was significantly lower in the oxyRmut strain (~2-fold repression), confirming that OxyS represses the expression of the yobF cspC operon in Salmonella - can the authors show this directly with oxyS in succinate? Why use OxyRmut here? This is indirect. The authors already show that OxyRmut does not act solely via Oxys...can the authors directly show RpoS and SDH levels by qRT-PCR in ΔcspC? Again - the appropriate control for RpoS overexpression in the WT was not done (Fig. 6G). Furthermore, expression analysis of the sdhCDAB operon over the background of the oxyR mutant will confirm the author suggestion for the mechanism by which the OxyS-driven inhibition of CspC expression impacts upon the catabolism of succinate
6. Figure 7: the authors check growth in M9+succ in the absence of DctA - but the experiment duration should be carried out for longer, as previous experiments with WT (intact dctA in Fig. 2A) and check if in the absence of dctA there are mutations that allow succinate growth. It seems that the results here contradict some of the previous - if succinate uptake through dctA is intact then there is no repression of SDH? rpoS? In figure 7E - is this difference only through dctA activity? It seems that icsR is not repressing dctA expression to WT levels - are there other factors? Can the authors show that dctA repression by IscR is direct?
7. Figure 9 is very descriptive and does not provide any evidence to support the authors hypothesis. The authors should either provide more substantial evidence connecting ribosomal RNA levels and succinate utilization and similarly Cm concentrations or either remove this part or move it to the supplementary.
8. Can any of the mutations characterized in this work be found in the genome of Newport or LT2 strains that can grow with succinate as a sole carbon source? (Fig 1)
9. Although the author suggested that regulation of succinate uptake is critical for Salmonella colonization and virulence in various metabolic conditions, the study lacks sufficient evidence to support these claims and further research is necessary to establish these statements.

Minor comments

1. Table summarizing the growth curves lag phase of the different mutants might help in the data interpretation.
2. In lines 245-248 the author describes the eleven novel Succ+ mutations however in this gene list only ten gene names are mentioned. DctA is missing from this list.

** Referees cross-commenting**

I agree with both reviewer that there is a large amount of data in the paper, and willing to accept their point that asking for further experiments would exceed the scope of the paper. In that case, the authors should address the mechanistic options in the discussion

Significance

In this work, Wenner et al. characterized the molecular mechanisms regulating Salmonella growth inhibition when succinate is the sole carbon source in the culture. This work revealed new layer of regulations for rpoS activation, the sigma factor previously characterized to control this growth inhibition mechanism. In addition, this work revealed novel RpoS-independent mechanisms for succinate utilization and highlighted the crucial role of succinate processing in Salmonella physiology.

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

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Reply to the reviewers

__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __

The manuscript investigates the role of PAT1 gene family in Arabidopsis thaliana. Though the PAT1 protein has been previously investigated and displayed immune-related and developmental phenotypes, the other two members of the family, PATH1 and PATH2, have not been well studied. The authors set out to understand the role of these proteins in relation to the role of PAT1. They thus generated single, double, and triple mutants of the possible combinations of PAT1 genes and examined their phenotypes. As the study focused on the developmental effects of PAT1, the mutants were generated on the background of the summ2 mutant to avoid phenotypes related to immune response. The authors notice a developmental difference between the pat1 mutant combinations, suggesting that PAT1 acts differently than PATH1 and PATH2 and that the PATH proteins serve a redundant function. They also performed RNA-seq analysis to identify differentially-regulated genes in the mutant combinations. The study is interesting and well-executed, yet I believe some questions should still be addressed:

__Our response: __We thank the reviewer for acknowledging the significance of our findings. Please see our detailed answers to the reviewer’s suggestions in the following.

1. The research mainly focuses on the developmental phenotype of pat mutants but also tests the interaction of PATH proteins with RNA decapping enzymes to check their function and localization during different treatments. I found it a bit confusing since Figure 1 also shows the developmental phenotype of the mutants. I think editing the order of the figures would make the overall story more coherent.

__Our response: __We agree with the reviewer thus we moved old Fig 1C to new Fig 3A, we believe the new figure orders make the overall story more coherent.

My main concern is the correlation between the developmental phenotype of the mutants and the gene expression. Leaf samples for RNA extraction were taken when the plants were 6 weeks old, and the developmental phenotype is very evident. It is thus not possible to tell whether the differences in gene expression are a cause or effect of the developmental phenotype. I think performing qPCR of selected candidates at earlier developmental times might help solve this issue, as well as the characterization of younger plants for the developmental phenotypes (such as leaf number).

__Our response: __We followed the reviewer’s suggestions and performed qRT-PCR on IAA19, IAA29, SAUR23 and PIL2 in pats mutants under different developmental stages (Line 162, 169; Fig S4), we also characterized leaf number of pats mutants from younger stages (Line 109; new Fig 3C).

Overall, the manuscript is missing data regarding replicate numbers in the IP and confocal microscopy experiments.

__Our response: __We thank the reviewer for pointing this it out, the replicate numbers are provided now in our new figure legends.

Minor comments:

1. Figure 1C - the authors should add a picture of Col0 plants as well as the mutants.

Our response: To be reader friendly, the picture of Col-0 plant is added in Fig S1A. For the reviewer’s information, plant pictures in FigS1A and old Fig1C (new Fig 3A) were taken at the same time. 2.

Figure 3 - Calculating the leaf-to-petiole ratio in the different mutants would be good.

Our response: We now calculate PBR (petiole blade ratio) of all pats mutants in Fig3F (Line 121).

Figure 4 - the details in the figure are very unclear, especially in the PCA. It would be good to display the data in 2D for PC1 and PC3 and change the colors a bit.

Our response: We agree with the reviewer; thus, we remade the PCA plot from RNA-seq reads data in a 2D style and also changed the colors for each mutant (Fig 4A). We need to point out that the PCs number also changed because the old PCA plot were made by mistake from expression data.

Reviewer #1 (Significance (Required)): Both PATH proteins have been less investigated than PAT1, and in that sense, the work is novel. However, it seems that most of the phenotype is attributed to PAT1 rather than the other family members, limiting the interest to the broad plant science community.

Our response: We appreciate the reviewer think our work is novel. We agree that PAT1 plays the main role during plant development (old Line 171), however the pat triple mutant exhibit the most severe dwarfism as well as the most mis-regulated genes compared to any single or double mutants, indicating all 3 PATs are essential for development.

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

Zuo et al., characterize the role of three cytoplasmic mRNA-decay activator proteins PAT1, PATH1 and PATH2 in the context of plant development and leaf morphology in Arabidopsis thaliana and Nicotiana benthamiana. The authors show that the triple pat mutant displays the most severe dwarfism of all combinatorial mutants. Through treatment with different stimulants the authors found that only IAA treatment induces the three homologues to form condensates (possibly PBs), while PAT1 forms condensates upon every tested stimulus. An extensive RNA seq experiment revealed miss-regulation of several hundred genes in the higher order mutants, several of which were involved in auxin responsive and leaf morphology determinant genes.

__Our response: __We thank the reviewer for the peer review. Please see our detailed answers to the reviewer’s suggestions in the following.

Major points: 1.Title is not meaningful as is and, in my opinion, does not reflect the main findings in the manuscript.

Our response: We now changed our title into “PAT mRNA decapping factors are required for proper development in Arabidopsis”.

The results section could benefit from improved flow between the paragraphs and more reasoning for the next steps taken to help readers understand the aims of the authors.

Our response: We followed the reviewer’s suggestion and modified the wording in our result part(Line 79,81,94,146-151).

L46: "So far little is known about the functions of these three PATs in plant development.", The authors themselves have studied these proteins in the context of seed germination and ABA control, as well as apical hook formation and auxin responses. Should at least be mentioned and the results discussed in this context.

Our response: We thank the reviewer for noticing our other work and we now included this information in the new introduction and discussion part (Line56&237).

What are the expression levels and patterns of PATH1 and PATH2 compared to PAT1? Is anything known about spatial or temporal regulation of these proteins?

Our response: All three PATs are expressed in roots, stems, leaves, flowers, siliques, and seeds during the whole developmental stages, PAT1 has higher expression level in leaves but lower expression levels in petals. (Klepikova et al., 2016;

https://www.arabidopsis.org/servlets/TairObject?id=138009&type=locus for PAT1; https://www.arabidopsis.org/servlets/TairObject?id=38646&type=locus for PATH1 and https://www.arabidopsis.org/servlets/TairObject?id=128694&type=locus for PATH2).

Figure 1:

o I do not agree that the authors have shown that "PATH1 and PATH2 are also mRNA decapping factors", rather that these proteins can co-localize (and possibly interact) with LSM1. Decapping assays for example with the known PAT1 de-capping targets from their previous work and their extensive mutant collection could be used to test this.

Our response: We thank the reviewer for pointing it out and reminding us about the characterized mRNA decapping target from our previous work, we now include the decapping assays in new Fig5 (Line 197).

From the BiFC experiment (Figure 1B) it looks like PATs are mostly soluble in the cytoplasm (like LSM1) and might be stress-induced components of PBs (like LSM1). Do PATs co-localize with other canonical PB markers that are more prone to condensation, like DCPs or VCS? BiFC could be performed after IAA treatment to confirm that the cytoplasmic foci are indeed LSM1-positive PBs.

Our response: We agree with the reviewer that PATs behave more like LSM1. Given time limit of the project, we unfortunately are not able to check the colocalization of PATs with DCPs or VCS. However, we performed BIFC after IAA treatment, and the cytoplasmic foci are indeed LSM1-positive foci (new Fig1B).

A: please provide uncropped images of all Western blots in supplemental data.

Our response: To be reader friendly, we decide to show the original western blots here (see in the file named "RC-Full-revision"), instead of in supplemental data. However, we will leave the final decision to the editor.

I applaud the authors for establishing this great higher order mutant collection that will be very useful for researchers in the field. However, I am confused about the description of these mutants. If I understood it correctly, these mutants were already used in a previous study by the authors, namely “Zuo, Z., et al., Molecular Plant-Microbe Interactions, 35(2), 125-130.” & Zuo, Z., et al., (2021). FEBS letters, 595(2), 253-263.” In this study the authors refer to a BioRxiv “Zuo, Z., et al., (2019).” As the reference for these Arabidopsis lines. Is this current manuscript a continuation of the BioRxiv? Please elaborate whether these lines have been used and described In previous studies.

Our response: We truly appreciate the reviewer for acknowledging the significance of our work. These pats mutants have been used in the FEBS letters paper (2021), MPMI paper (2022), and the new published paper in Life Science Alliance (2023, but preprinted in BioRxiv 2019 and 2022). However, they have not been fully described or characterized in any of the mentioned published stories. Characterization of these pats mutants were originally only included in preprint 2019 which was cited in FEBS letters paper (2021) and MPMI paper (2022).

L72: Is the strong developmental phenotype of the higher order mutants persistent under long day conditions? Considering the strong developmental phenotypes of the mutants, the flowering transition and morphology could be an interesting trait to study. Why did you choose short day conditions for this study?

Our response: The pat triple mutant also has strong developmental phenotype under long day condition and exhibits early flowering phenotype. We are currently preparing a manuscript regarding mRNA decay and flowering. We did not “choose” short day condition, we just started with short day condition and observed phenotypical differences hence we kept this condition.

L78: This statement is hard to see in Figure 1C and best described for Figure 3A.

Our response: We now change this statement for Fig 3.

L82: Please include a reasoning for testing PATs localization after hormone treatment. Do you have any indication that other PB proteins behave similar to either PAT1 or the PATHs after hormone treatment to substantiate that these foci observed are indeed PBs? What is known about PBs after hormone treatment in planta?

Our response: We were interested in investigating if all three PAT proteins may also form PBs in Arabidopsis thus we tested PATs localization with/without hormone treatment (old Line 84, new line 81). For the reviewer’s interest we also observe LSM1 localization after hormone treatment (Fig 2). PBs have been published to respond to light, cold treatment, PAMPs, ABA, ACC and auxin (Line 39-42).

Figure 2:

o How does the localization of LSM1 change under the same treatments? Does ist behave like PAT1 or the homologues?

Our response: Please see our new Fig 2 for LSM1 localization, and it behaves more like PAT1.

Which part of the root was imaged for this experiment? Is it possible that the observed foci are ARF-condensates as reported by Jing et al., 2022? Do you observe a gradual change in numbers or morphology along the root?

Our response: We use root elongation zone for this experiment. We don’t know if the foci are ARF-condensates, but it’s possible to study in the future. If the reviewer is interested, we are happy to share our materials. We do observe more foci in the cell division zone and less in the mature zone.

How did the authors decide on the concentrations for the stimulant treatments? Have you tried different doses, and could the responses be dose-dependent?

Our response: We did not try different doses; we searched for and applied the commonly used concentrations for different hormones.

A representative image is not sufficient for quantitative responses, like RNA granule condensation. Please provide a quantification of stimulant-induced foci after the different treatments.

Our response: Please see the quantification in our new Fig 2.

L91: Does that mean that most co-precipitated signal comes from the soluble fraction and not PB-localized? Would an RNAse treatment step eliminate the co-precipitation (optional)?

Our response: We believe it means LSM1 and PATs are in the same complex regardless of PB localization.

L92/93: Or alternatively that PAT1 localizes to PBs independent of the stress, while PATHs are signal-specific PB components?

Our response: We think PAT1 aggregates upon broad stimuli/stress, while PATHs respond to specific/limited stimuli, for example, auxin.

Figure 3:

o I wonder if these results fit better in conjunction with Figure 1, either as a combined figure or move before Figure 2.

Our response: We agree with the reviewer thus we moved old Fig 1C into Fig 3.

It is interesting that path2/pat1, while being dwarfed, is less serrated compared to pat1 or path1/pat1. Can you find any indications in your RNAseq set which genes might be involved?

Our response: ANAC016 might be involved, but more research needs to be done to confirm it and this is not the focus of the current project.

Indicate statistical test used to determine p-value

Our response: We now indicate the statistic test in Materials and Methods part (Line 369).

L116/L117: Doesn't the result in Figure 3E indicate that PATH1 and PATH2 are not fully redundant, but that PATs have specific and narrow roles in leaf development? L116 goes against your statement in L150 & L160. What is known about the expression patterns of PAT1, PATH1 and PHATH2?

Our response: We agree and thus modified our statement (Line 137). All three PATs are expressed in roots, stems, leaves, flowers, siliques, and seeds during the whole developmental stages. Please also see our answer to major comment #4.

L123: PC3 only explains 0.55% of the variance, so differences along this axis will be overinflated. In my interpretation the pat1/path2 mutant is clustering apart from the other higher order mutants, which is also reflected in the leaf phenotypes. A 2D PCA would be sufficient to describe most of the variation.

Our response: We agree and thus we changed the PCA plot into a 2D style, please also see our response to reviewer 1 minor comment #3.

Figure 4: o A: The 3D-PCA inflates the differences between higher order mutants along PC3, even though this axis explains only 0.55% of the variance, maybe a 2D-PCA would more intuitively cluster the samples together?

Our response: Please see our new PCA plot in Fig4A.

B: Please explain the scale in the figure legend and which genes were included? Only DEGs between triple mutant and summ2-8 or DEGs that were different in at least one higher order mutant?

Our response: We now explained more details in the figure legends. The genes which were included in Fig4B were DEGs that were differently expressed in at least one of the pat mutants.

C: several comparisons are missing from the upset-plot. Please show the complete plot, also is there a white box laid over the second bar in the upper graph? It would help the reader, if the results section would explain the plots and the comparisons took. Which differences are the authors interested in?

Our response: We covered all the comparisons we wanted to show, but we thank the reviewer for suggesting a more detailed explanation and we therefore explain Fig4C more in detail in Line 146. There is no white box over the second bar, it’s only 1 gene mis-regulated specifically by PATH1 (mis-regulated in plants with path1 mutation).

From Figure 4B, the triple mutant has an almost inverted expression of mis-regulated genes. High expression genes are now lowly expressed and vice-versa. Has this been reported for other RNA decay mutants before?

Our response: Our RNA-seq data indicate the pat tripe mutant has more than 1000 mis-regulated genes and based on microarray data on 2-week-old lsm1alsm1b plants (Perea-Resa et al, 2012), more than 600 genes are misregulated in lsm1alsm1b mutant.

How do you explain that mutants in RNA decay have a large group of repressed transcripts and a large group of enriched transcripts? Wouldn't you suspect a general higher expression in RNA decay mutants or which kind of feedback loop would you propose is happening here? Also, since both kinds of expression changes are recorded in your RNA seq can you speculate on the specificity? Why are some genes up- and others downregulated? Would you suspect that transcription factors are under PATs control?

Our response: We assume that the mRNA decapping machinery target genes should accumulate in mRNA decapping mutants, pat mutants in our case. On the other hand, the down-regulated genes could be target genes of other mRNA degradation pathways such as exosome pathway (Line 257); We agree with the reviewer that the down regulated genes in pat triple could also be negatively regulated by the mRNA decapping targets which could be transcription factor genes. For example, our previous research indicates the transcription factor gene ASL9/LBD3 is mRNA decapping targets under PATs control.

Where is the sequencing data deposited? This dataset can be of great value for researchers in the field, but the raw data needs to be made commonly available.

Our response: We thank the reviewer for acknowledging the significance of our work. The raw data has been submitted to NCBI, accession number is PRJNA1006171(Line 307)

Minor points:

1. Check order and nomenclature for protein / gene names in Abstract and Introduction

Our response: We now carefully double check the order and nomenclature for protein / gene names in abstract and introduction (Line 8,11,14,18,19,24)

L26 / L83 "aggregate" implies non-functionality, I would use "concentrate", "condensate" or "accumulate".

Our response: We thank the reviewer for pointing it out, we now use “concentrate” (Line 29&80)

L35, L45 & L54 all state the same. Maybe remove at least one mention to reduce redundancy?

Our response: We modified these statements hopefully in a satisfactory way. (Line 56)

L211: Did you use the same imaging settings for all lines?

Our response: We used the same settings for all the lines and treatment (Line 284)

L217: RNA quality "control" word missing?

Our response: The word “control” is added in Line 296

L477: Authors should cite the newest version of their BioRxiv: Zuo, Z., Roux, M. E., Chevalier, J. R., Dagdas, Y. F., Yamashino, T., H�jgaard, S. D., ... & Petersen, M. (2022). The mRNA decapping machinery targets LBD3/ASL9 to mediate apical hook and lateral root development in Arabidopsis. bioRxiv, 2022-07.

Our response: The latest version is cited in our new manuscript (Line 42)

Figure 3B-F, Figure 4C: check spelling on the axis titles.

Our response: We carefully checked the spelling on the axis titles in our new manuscript.

Reviewer #2 (Significance (Required)):

This manuscript represents a continuation of the author's characterization of the 3 PAT1s in Arabidopsis development after Zuo et al., 2021; Zuo et al., 2022a; Zuo et al., 2022b. The mutants and the corresponding RNA sequencing experiments are of value to the community working on RNA regulation and degradation or plant development. While the initial findings are interesting, the authors do not explore the stimulus-induced condensation differences between the homologues or try to link the extreme differences in expression profiles mechanistically or functionally. I think the manuscript could greatly benefit from contextualizing their work within the frame of their previous studies and what is known about PBs in terms of plant development. While the RNA seq is a comprehensive data set, a closer examination and a better representation of the results would help readers to access the findings.

__Our response: __We thank the reviewer for the constructive criticism. We hope the reviewer is satisfied by our modified manuscript.

Reviewer expertise: RNA granule biology, Arabidopsis, molecular biology

__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __

Summary:

In the study "PAT mRNA decapping factors function specifically and redundantly during development in Arabidopsis" authors investigate potential specific functions of Arabidopsis PAT1 orthologs in plant development. Authors observe differences in rosette phenotypes (leaf size, serration and number) of single and multiple mutants of PAT1 gene family, show variation in translocation of the corresponding PAT1 proteins to processing bodies under a set of stress conditions and perform transcriptomics on the established mutants to elucidate the impact of individual PATs on posttranscriptional regulation of plant gene expression. Authors conclude that PAT1 orthologs have both overlapping and specific roles in plant development.

__Our response: __We thank the reviewer for the peer review. Please see our detailed answers to the reviewer’s suggestions in the following.

Major comments:

1. The study contains intersting transcriptomics data that will be of use for the scientific community. However, analysis of the transcriptomics results could be discussed a bit more in depth. Authors could express their opinion about what gene expression changes might be caused by direct degradation via PAT1-dependent decapping mechanism and what changes are more likely to have occurred indirectly via other factors.

__Our response: __We followed the reviewer’s suggestion and thus we analysed and discussed more in depth about the transcriptomic data (Line145, 220 &232)

The intersting phenotypic observations are currently poorly linked to the transcriptomics/qPCR data provided, resulting in a somewhat fragmented story flow.

__Our response: __We appreciate the reviewer thought the pat mutants’ phenotype are interesting, however we disagre with the reviewer on the statement of “poorly linked to the transcriptomics/ qPCR data”. For instance, downregulation of developmental and auxin responsive genes could explain the stunt growth phenotype in the pat triple mutant. Furthermore, the published petiole elongation regulator genes XTR7/XTH15 and PIL2/PIF6 exhibit decreased expression level only in mutants with shorter petioles. Nevertheless, we hope our new data and analysis will satisfy the reviewer.

The transcriptomics was performed on the 6-weeks old plants. It would be helpful to learn more about authors reasoning for choosing this developmental stage for sampling. Why did authors decide against sampling at the earlier stages, before the observed leaves phenotypes were established?

__Our response: __The pat mutants growth phenotypes showed bigger difference among each other at the late stage, therefore we performed RNA-seq on these samples. But we agree with the reviewer (also reviewer 1, major comment #2), transcriptomic shift at earlier stage could also be responsible for the observed phenotype, thus we performed qRT-PCR on the pat mutants at earlier stages for certain genes to examine this (Line 162 &169)

Authors obtained intriguing results on specific translocation of PAT1, PATH1 and PATH2 to processing bodies in the root cells upon various stresses. Perhaps root transcriptomics of single PAT1, PATH1 and PATH2 knockouts under control conditions, treatment that translocate all three proteins to PBs(IAA) and selectively translocate only PAT1 (e.g. cytokinin) could shed more light on the redundancy an specificity of these proteins as the mRNA decapping factors.

__Our response: __We appreciate the reviewer found our findings interesting. The specific translocation of PAT1, PATH1 and PATH2 to PBs in the root cells upon various stimuli indicates functional specificity and redundancy in cellular level which correlates with mutants’ growth phenotype. However, we agree with the reviewer that root transcriptomic data on pat mutants are very interesting, we are more than willing to share these mutants with peers who want to persue this in more detail.

Do authors consider PAT1, PATH1 and PATH2 to be localized to different PBs sub-populations? It could be intersting to check co-localization of PAT1, PATH1 and PATH2 under various stress conditions. Could authors elaborate on their view of PBs composition and fate to which different PAT1s are recruited?

__Our response: __We agree with the reviewer that it’s interesting to check co-localization of PAT1, PATH1 and PATH2. We observed partial localization of CFP-PATH2(in blue) and Venus-PAT1(in yellow) when transiently expressed in Benthmiana. But for permanent lines, we failed at observing separate CFP-PATH2(Blue) signal due to too much signal leakage from Venus-PAT1(Green). Given the fact that PATs function redundantly, we would assume they are partially co-localized in cellular level.

Could authors speculate what features in the PAT1 protein might cause it being recruited to PBs more efficiently (or better to say, under a broader range of stresses) in comparison to PATH1 and 2?

__Our response: __The release of ribosome-free mRNPs induces PB formation (Brengues et al., 2005). We suspect PAT1 could bind broader mRNAs compared to PATH1 and PATH2, therefor PAT1-mRNPs could form PBs more efficiently. Moreover, Sachdev et al found yeast PAT1 enhances the condensation of Dhh1 and RNA and PAT1-DHH1 interaction is essential for PB assembly (Sachdev et al., 2019), we assume PAT1 might have better interaction with DHH1 compared to PATH1 and PATH2 thus promote PB formation more efficiently. Please see our discussion part (Line 252)

Are all three Arabidopsis PAT paralogs co-expressed in the same tissues /developmental stages?

__Our response: __Please see our response to reviewer 2 major comment #4.

Could authors elaborate a bit more why the triple pat1 knockout has a much more severe phenotype in comparison to a single pat1 loss-of-function mutant or any of the double pat1 mutants. Do authors observe complementary changes in the PAT1 genes expression in the mutant lines, e.g. is PATH1 expressed at a higher level in the absence of PAT1 and PATH2?

__Our response: __We now elaborate more about the reason why triple pat1 knockout has the most severe phenotype in the multiple pat mutants (Line 210). We do see higher transcriptional level of PAT1 in path1-4path2-1summ2-8 and also higher transcriptional level of PATH1 in pat1-1path2-1summ2-8 but the same PATH2 transcriptional level in pat1-1path1-4summ2-8 compared to summ2-8 (Fig S1C, Line 104)

Please provide the name of the used statistical test in all figure legends.

__Our response: __We now provide the statistical test in “Material and Methods” part (Line 367).

Minor comments:

1. Authors might want to reconsider the title as it is somewhat too vague in its current form.

__Our response: __We now changed our title into “ PAT mRNA decapping factors are required for proper developmental in Arabidopsis”

Line 9: explanation of PAT1 and PATH1 and 2 abbreviations is best placed at the first mentioning of the name.

__Our response: __We carefully followed the reviewer’s suggestion (Line 10)

Line 10: mRNA degradation is rather a posttranscriptional regulation of gene expression.

__Our response: __We agree and changed our statement in the new ms (Line 9).

Lines 11 and 12: path1 and path2 abbreviation are not explained. Please note that on the Figure 1A the same proteins are labelled as PAT1H1 and PAT1H2

__Our response: __We thank the reviewer for pointing it out, we now have PATH1 and PATH2 abbreviations explained in Line 10 and also correct the labels in Fig 1A.

Lines 22-25: Would you be so kind to rephrase or elaborate on what yoPBu mean. LSM1-7/PAT1 complex are known to bind oligoadenylated transcripts indeed and even stabilize their 3' ends, it is not clear what "engage transcripts containing deadenylated tails" means in this context.

__Our response: __We hope we now rephrase the statement in a clear way (Line 25)

Line 29: for the sake of clarity, it might be beneficial to list the known activators of the decapping DCP2 enzyme, including the VCS. Generally the introduction could benefit from a bit more in depth review of the decapping mechanism.

__Our response: __We hope the more detailed introduction will satisfy the reviewer (Line 27).

Line 51:"other 2 PATs" => "other two PATs". Generally the text is quite well written, but might need a bit of polishing.

__Our response: __The text is corrected now (Line 64).

Authors are absolutely correct in their attempt to provide full information about mutant backgrounds. However, for the sake of comprehension, it would be great to grant the double and triple mutants in the summ2 background shorter and more legible names. For example, the pat1-1path1-4path2-1summ2-8 mutant could be named as pat1/h1/h2/s.

__Our response: __We originally used pat1/h1/h2/s for the triple but a colleague pointed out “h1” or “h2” are not proper gene names and suggested us to rename them. But we agree that the double and triple pat names are comprehensive, to compromise we change the triple pat mutants into pat triple.

Figure 1B:

• it would be intersting to have authors opinion on why PBs are formed in this case under non-stress(?) conditions.

__Our response: __Forming PBs is a dynamic process, and we assume that even under normal conditions, there is still ongoing mRNA decay and translational repression which should be seen as some background level of PBs (Line 85).

Please note that expressing only the N-terminal part of CFP is a weak negative control for BiFC. No restoration of CFP can occur in such case and thus it is a given that no fluorescence can be observed in these samples. For example, co-expression of nCFP-PAT1 with cCFP-GUS, would be a more rigorous negative control, better aligned with the coIP experiments.

__Our response: __We had nCFP-Gus with cCFP-LSM1 as real negative control in old Fig 1B (bottom lane). We also agree with the reviewer that only the N-terminal part of CFP is a weak negative control for BiFC, therefore we removed the weak control and only left the rigorous negative control (new Fig 1B).

Please note that some arrows point at a structure that seems to be not discernible a signal.

__Our response: __It’s due to the poor quality of the picture from the PDF file, arrows in the original high-resolution figure do point at discernible foci.

Figure 1C: It might be helpful to also include a Col-0 WT plant

__Our response: __Col-WT plant is now included in Fig S1A.

It is not clear how qPCR data and complementation lines help to characterize the established PATH1 and PATH2 loss-of-function mutants. There is no immunodetection of the corresponding proteins in the knockouts, qPCR shows no dramatic decrease in the transcript level of PATH1 and H2 and the phenotypes of complemented lines presented in the Fig S1E at a glance look quite similar to the phenotypes of the corresponding knockout mutants. Complementation lines are not used for any other experiments in this study and it is not clear why authors decided to include this material into the article.

__Our response: __To characterize the path1 and path2 mutants, we first did qRT-PCR to check the transcriptional level expression, but like the reviewer mentioned, there was no dramatic decrease indicating the mutations of path1-4 and path2-1 did not change PATH1 and PATH2 transcriptional level expression. We also tried to raise antibodies against PATH1 and PATH2, however the antibodies failed to recognize any PAT proteins. Therefore, we used the complementation lines to characterize the mutations in PATH1 and PATH2. Since path1 and path2 single mutants don’t have obvious growth phenotype and the dwarf pat triple is barely possible to transform, we had to complement the pat1path1 and pat1path2 double mutants. If the reviewer takes a closer look, the growth phenotype of the complementation lines Venus-PATH1/ pat1-1path1-4summ2-8 and Venus-PATH2/ pat1-1path2-1summ2-8 are similar to pat1-1summ2-8 but not the background pat double mutants. The complementation lines were also used to study PATH1 and PATH2 cellular localization.

Figure S1C misses labels indicating what detection of what gene is shown on what chart.

__Our response: __We thank the reviewer for pointing it out, the gene names are indicated now in new FigS1C.

Experiments to visualize PBs under various stress stimuli were conducted on roots for the Figure 2 while coIP was performed on the green tissue. Could authors elaborate on whether PB formation could be expected to be the same in different plant organs? Somewhat related to the same topic, Figure 2 contains micrographs obtained on meristematic, transition and elongation root zones, in which epidermal cells are present at various developmental stages. Since PAT proteins are suggested to impact plant development, it might be prudent to obtain observations for all samples at the same developmental stage. Could authors provide their opinion about how representative the provided micrographs are for all root zones? Furthermore, Venus-PATH2 under ACC treatment shows punctate localization only in a single cell out of the three-ish cells visible on the micrograph, potentially indicating differences in PAT2 recruitment to PBs in trichoblasts and atrichoblasts. This in itself could be an intersting observation helpful for elucidating the specific roles of PAT1 orthologs.

__Our response: __CoIP results from Benthamiana leaves indicate Arabidopsis PATs and LSM1 are in the same complex, and PB visualization in root area suggests PATs respond to different hormone treatments. flg22 treatment has been published to induce PB formation in Arabidopsis root but dissemble PBs in Arabidopsis protoplasts, indicating a tissue specific manner of PB formation. We randomly chose 1 picture/treatment from 9 (3 plants * bio-triplicates) which showed the same. However, we thank the reviewer for pointing out the confocal pictures we chose were not all from elongation zone, we now carefully checked all our confocal pictures and made sure they are from the same developmental stages. We also discuss more of PATH2 localization in response to ACC (Line 251).

Figure 4C would greatly benefit from a more detailed description in the main text and figure legend of what authors show/conclude.

__Our response: __We thank the reviewer for the suggestion hence we describe Fig 4C in more detail in our new manuscript (Line 146).

Figure 5, please avoid using the same color for the bars for the triple pat knockout and the control summ2-8 line

__Our response: __We changed the colour scheme for all the mutants (new Fig 4E).

Figure 5B legend should include the name of the statistical test.

__Our response: __We now include the name of the statistical test in “Material and Methods” (Line 367).

Figure S2: The coIP experiment is a bit difficult to interpret due to the extremely low protein quantities in some of the input samples. Perhaps a repetition with more balanced input quantities would be beneficial. The figure legend does not contain information on how normalized intensity values were obtained.

__Our response: __We used the same amount of total protein for each sample (3mg) for each IP, PATH1 and PATH2 don’t express as high as PAT1. The numbers indicate the comparative ratio between PAT-HA protein signal and LSM1-GFP signal, and PAT1-HA/LSM1-GFP under non-treatment condition is normalized as 1.

Line 130: Fig S2 is referenced but Fig S3 is meant

__Our response: __We thank the reviewer for pointing out our mistake, the correct figure is now referenced.

Reviewer #3 (Significance (Required)):

Strength:

Regulation of gene expression by mRNA decay is an extremely intersting topic and is highly relevant in plant stress and developmental biology. This study provides a more in depth view on the potential specific roles of the three PAT1 orthologs in Arabidopsis plants. Authors established loss-of-function mutants of the corresponding genes and performed transcriptomics analysis that will be a valuable source for future studies. Furthermore, microscopy analysis of PATH1 and PATH2 translocation to PBs indicates their potential specific roles in plant stress response.

Weakness: The current version of this study suffers from vague presentation of the results. Starting from the title and ending with discussion authors provide a "general" view on their results and do not go into detailed interpretations. Thus, no mechanistic insight has been derived or at least suggested from the wealth of the transcriptomics, phenotypic and microscopy data.

The introduction should provide more detailed information on what is known on the PAT1 role in the mRNA decapping pathway and its relevance for plant stress response and development.

Please note, that the above mentioned suggestion of different sampling for transcriptomics analysis is not meant as a request for this particular study, but rather as an illustration of an expectation a reader would built while following the current version of the text. A thorough description of the strategy for transcriptomics and a more in depth analysis might significantly strengthen the study's coherence and impact.

Advance:

At this stage, the study looks more like an incremental advance of the work from the same laboratory performed for the single PAT1 protein. However, as mentioned in the comments above, the study might be made significantly stronger by elaborating the results analysis and highlighting potential discoveries.

Audience:

The topic of this study is of a significant interest to a broad audience performing research in plant stress biology and also developmental plant biology.

__Our response: __We thank the reviewer for acknowledging the significance of our work and the structural criticism. We hope our detailed answers to the reviewer’s suggestions and the additional data we included in the manuscript will satisfy the reviewer.

Reviewer's and co-reviewer's fields of expertise:

Molecular Biology, Plant cell biology, Plants Stress response, Autophagy, Stress granules

__Reviewer #4 (Evidence, reproducibility and clarity (Required)): __

PAT1 (Protein Associated with Topoisomerase II) are RNA-binding proteins involved in the control of mRNA decay in the cytoplasm. Plants possess multiple PAT1 family members, three in Arabidopsis, PAT1, PATH1 and PATH2. According to the literature, the pat1 mutant shows dwarfism and de-repressed immunity. In this paper, Zou et al. describe the function of PATH1 and PATH2. Two pieces of evidence are consistent with their role in the control of mRNA decay. First, Co-IP and bimolecular Fluorescence Complementation assays in tobacco indicate physical interaction and co-localization of PAT1, PATH1 or PATH2 with LSM1 (Fig. 1), which is a protein present in decapping complexes that form the cytoplasmic foci involved in mRNA decay. Second, PAT1, PATH1 and PATH2 are present in these cytoplasmic Processing Bodies (Fig. 2). Zou et al. generated path1 and path2 mutants, double mutants with pat1 and the triple mutant using independent alleles and the summ2 background to avoid autoimmunity interference. The mutants show leaf growth (Fig. 3) and gene expression (Fig. 4) phenotypes that are not exactly similar among the different family members, but there is significant redundancy revealed by these phenotypes.

__Our response: __We thank the reviewer for the peer review. Please see our detailed answers to the reviewer’s suggestions in the following.

1. The conclusions are straight forward and, apparently, well supported by the data. However, the authors should confirm that when they provide the number of replicates (n) in the legends to the figures, this actually refers to the number of biological replicates. The statements should be based on true biological replicates (not technical replicates). The statistical tests should also be explicitly indicated (including that used to identify DEG in the RNAseq experiment).

__Our response: __We carefully went through our figures and made sure the number of replicates (n) were correctly stated in figure legends and the statistical tests were indicated (Line 367)

Reviewer #4 (Significance (Required)):

The results are useful but mainly descriptive. Personally, I am interested in the mechanisms involved in the control of growth and the manuscript does not mechanistically link the action of PAT1, PATH1 and PATH2 to the transcriptome and the latter to the growth patterns.

__Our response: __We thank the reviewer for acknowledging the significance of our work of characterizing PATs and we hope our new data could satisfy the reviewer in regarding to “mechanistical link”.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

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Referee #4

Evidence, reproducibility and clarity

PAT1 (Protein Associated with Topoisomerase II) are RNA-binding proteins involved in the control of mRNA decay in the cytoplasm. Plants possess multiple PAT1 family members, three in Arabidopsis, PAT1, PATH1 and PATH2. According to the literature, the pat1 mutant shows dwarfism and de-repressed immunity. In this paper, Zou et al. describe the function of PATH1 and PATH2. Two pieces of evidence are consistent with their role in the control of mRNA decay. First, Co-IP and bimolecular Fluorescence Complementation assays in tobacco indicate physical interaction and co-localization of PAT1, PATH1 or PATH2 with LSM1 (Fig. 1), which is a protein present in decapping complexes that form the cytoplasmic foci involved in mRNA decay. Second, PAT1, PATH1 and PATH2 are present in these cytoplasmic Processing Bodies (Fig. 2). Zou et al. generated path1 and path2 mutants, double mutants with pat1 and the triple mutant using independent alleles and the summ2 background to avoid autoimmunity interference. The mutants show leaf growth (Fig. 3) and gene expression (Fig. 4) phenotypes that are not exactly similar among the different family members, but there is significant redundancy revealed by these phenotypes.

The conclusions are straight forward and, apparently, well supported by the data. However, the authors should confirm that when they provide the number of replicates (n) in the legends to the figures, this actually refers to the number of biological replicates. The statements should be based on true biological replicates (not technical replicates). The statistical tests should also be explicitly indicated (including that used to identify DEG in the RNAseq experiment).

Significance

The results are useful but mainly descriptive. Personally, I am interested in the mechanisms involved in the control of growth and the manuscript does not mechanistically link the action of PAT1, PATH1 and PATH2 to the transcriptome and the latter to the growth patterns.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

Summary:

In the study "PAT mRNA decapping factors function specifically and redundantly during development in Arabidopsis" authors investigate potential specific functions of Arabidopsis PAT1 orthologs in plant development. Authors observe differences in rosette phenotypes (leaf size, serration and number) of single and multiple mutants of PAT1 gene family, show variation in translocation of the corresponding PAT1 proteins to processing bodies under a set of stress conditions and perform transcriptomics on the established mutants to elucidate the impact of individual PATs on posttranscriptional regulation of plant gene expression. Authors conclude that PAT1 orthologs have both overlapping and specific roles in plant development.

Major comments:

• The study contains intersting transcriptomics data that will be of use for the scientific community. However, analysis of the transcriptomics results could be discussed a bit more in depth. Authors could express their opinion about what gene expression changes might be caused by direct degradation via PAT1-dependent decapping mechanism and what changes are more likely to have occurred indirectly via other factors.
• The intersting phenotypic observations are currently poorly linked to the transcriptomics/qPCR data provided, resulting in a somewhat fragmented story flow.
• The transcriptomics was performed on the 6-weeks old plants. It would be helpful to learn more about authors reasoning for choosing this developmental stage for sampling. Why did authors decide against sampling at the earlier stages, before the observed leaves phenotypes were established?
• Authors obtained intriguing results on specific translocation of PAT1, PATH1 and PATH2 to processing bodies in the root cells upon various stresses. Perhaps root transcriptomics of single PAT1, PATH1 and PATH2 knockouts under control conditions, treatment that translocate all three proteins to PBs(IAA) and selectively translocate only PAT1 (e.g. cytokinin) could shed more light on the redundancy an specificity of these proteins as the mRNA decapping factors.
• Do authors consider PAT1, PATH1 and PATH2 to be localized to different PBs sub-populations? It could be intersting to check co-localization of PAT1, PATH1 and PATH2 under various stress conditions. Could authors elaborate on their view of PBs composition and fate to which different PAT1s are recruited?
• Could authors speculate what features in the PAT1 protein might cause it being recruited to PBs more efficiently (or better to say, under a broader range of stresses) in comparison to PATH1 and 2?
• Are all three Arabidopsis PAT paralogs co-expressed in the same tissues /developmental stages?
• Could authors elaborate a bit more why the triple pat1 knockout has a much more severe phenotype in comparison to a single pat1 loss-of-function mutant or any of the double pat1 mutants. Do authors observe complementary changes in the PAT1 genes expression in the mutant lines, e.g. is PATH1 expressed at a higher level in the absence of PAT1 and PATH2?
• Please provide the name of the used statistical test in all figure legends.

Minor comments:

• Authors might want to reconsider the title as it is somewhat too vague in its current form.
• Line 9: explanation of PAT1 and PATH1 and 2 abbreviations is best placed at the first mentioning of the name.
• Line 10: mRNA degradation is rather a posttranscriptional regulation of gene expression.
• Lines 11 and 12: path1 and path2 abbreviation are not explained. Please note that on the Figure 1A the same proteins are labelled as PAT1H1 and PAT1H2
• Lines 22-25: Would you be so kind to rephrase or elaborate on what you mean. LSM1-7/PAT1 complex are known to bind oligoadenylated transcripts indeed and even stabilize their 3' ends, it is not clear what "engage transcripts containing deadenylated tails" means in this context.
• Line 29: for the sake of clarity, it might be beneficial to list the known activators of the decapping DCP2 enzyme, including the VCS. Generally the introduction could benefit from a bit more in depth review of the decapping mechanism.
• Line 51:"other 2 PATs" => "other two PATs". Generally the text is quite well written, but might need a bit of polishing.
• Authors are absolutely correct in their attempt to provide full information about mutant backgrounds. However, for the sake of comprehension, it would be great to grant the double and triple mutants in the summ2 background shorter and more legible names. For example, the pat1-1path1-4path2-1summ2-8 mutant could be named as pat1/h1/h2/s.
• Figure 1B:
• it would be intersting to have authors opinion on why PBs are formed in this case under non-stress(?) conditions.
• Please note that expressing only the N-terminal part of CFP is a weak negative control for BiFC. No restoration of CFP can occur in such case and thus it is a given that no fluorescence can be observed in these samples. For example, co-expression of nCFP-PAT1 with cCFP-GUS, would be a more rigorous negative control, better aligned with the coIP experiments.
• Please note that some arrows point at a structure that seems to be not discernible a signal.
• Figure 1C: It might be helpful to also include a Col-0 WT plant
• It is not clear how qPCR data and complementation lines help to characterize the established PATH1 and PATH2 loss-of-function mutants. There is no immunodetection of the corresponding proteins in the knockouts, qPCR shows no dramatic decrease in the transcript level of PATH1 and H2 and the phenotypes of complemented lines presented in the Fig S1E at a glance look quite similar to the phenotypes of the corresponding knockout mutants. Complementation lines are not used for any other experiments in this study and it is not clear why authors decided to include this material into the article.
• Figure S1C misses labels indicating what detection of what gene is shown on what chart.
• Experiments to visualize PBs under various stress stimuli were conducted on roots for the Figure 2 while coIP was performed on the green tissue. Could authors elaborate on whether PB formation could be expected to be the same in different plant organs? Somewhat related to the same topic, Figure 2 contains micrographs obtained on meristematic, transition and elongation root zones, in which epidermal cells are present at various developmental stages. Since PAT proteins are suggested to impact plant development, it might be prudent to obtain observations for all samples at the same developmental stage. Could authors provide their opinion about how representative the provided micrographs are for all root zones? Furthermore, Venus-PATH2 under ACC treatment shows punctate localization only in a single cell out of the three-ish cells visible on the micrograph, potentially indicating differences in PAT2 recruitment to PBs in trichoblasts and atrichoblasts. This in itself could be an intersting observation helpful for elucidating the specific roles of PAT1 orthologs.
• Figure 4C would greatly benefit from a more detailed description in themain text and figure legend of what authors show/conclude.
• Figure 5, please avoid using the same color for the bars for the triple pat knockout and the control summ2-8 line
• Figure 5B legend should include the name of the statistical test.
• Figure S2: The coIP experiment is a bit difficult to interpret due to the extremely low protein quantities in some of the input samples. Perhaps a repetition with more balanced input quantities would be beneficial. The figure legend does not contain information on how normalized intensity values were obtained.
• Line 130: Fig S2 is referenced but Fig S3 is meant

Significance

Strength: Regulation of gene expression by mRNA decay is an extremely intersting topic and is highly relevant in plant stress and developmental biology. This study provides a more in depth view on the potential specific roles of the three PAT1 orthologs in Arabidopsis plants. Authors established loss-of-function mutants of the corresponding genes and performed transcriptomics analysis that will be a valuable source for future studies. Furthermore, microscopy analysis of PATH1 and PATH2 translocation to PBs indicates their potential specific roles in plant stress response.

Weakness: The current version of this study suffers from vague presentation of the results. Starting from the title and ending with discussion authors provide a "general" view on their results and do not go into detailed interpretations. Thus, no mechanistic insight has been derived or at least suggested from the wealth of the transcriptomics, phenotypic and microscopy data.<br /> The introduction should provide more detailed information on what is known on the PAT1 role in the mRNA decapping pathway and its relevance for plant stress response and development. Please note, that the above mentioned suggestion of different sampling for transcriptomics analysis is not meant as a request for this particular study, but rather as an illustration of an expectation a reader would built while following the current version of the text. A thorough description of the strategy for transcriptomics and a more in depth analysis might significantly strengthen the study's coherence and impact.

Advance: At this stage, the study looks more like an incremental advance of the work from the same laboratory performed for the single PAT1 protein. However, as mentioned in the comments above, the study might be made significantly stronger by elaborating the results analysis and highlighting potential discoveries.

Audience: The topic of this study is of a significant interest to a broad audience performing research in plant stress biology and also developmental plant biology.

Reviewer's and co-reviewer's fields of expertise:

Molecular Biology, Plant cell biology, Plants Stress response, Autophagy, Stress granules

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Referee #2

Evidence, reproducibility and clarity

Zuo et al., characterize the role of three cytoplasmic mRNA-decay activator proteins PAT1, PATH1 and PATH2 in the context of plant development and leaf morphology in Arabidopsis thaliana and Nicotiana benthamiana. The authors show that the triple pat mutant displays the most severe dwarfism of all combinatorial mutants. Through treatment with different stimulants the authors found that only IAA treatment induces the three homologues to form condensates (possibly PBs), while PAT1 forms condensates upon every tested stimulus. An extensive RNA seq experiment revealed miss-regulation of several hundred genes in the higher order mutants, several of which were involved in auxin responsive and leaf morphology determinant genes.

Major points:

• Title is not meaningful as is and, in my opinion, does not reflect the main findings in the manuscript.
• The results section could benefit from improved flow between the paragraphs and more reasoning for the next steps taken to help readers understand the aims of the authors.
• L46: "So far little is known about the functions of these three PATs in plant development.", The authors themselves have studied these proteins in the context of seed germination and ABA control, as well as apical hook formation and auxin responses. Should at least be mentioned and the results discussed in this context.
• What are the expression levels and patterns of PATH1 and PATH2 compared to PAT1? Is anything known about spatial or temporal regulation of these proteins?
• Figure 1:
  • I do not agree that the authors have shown that "PATH1 and PATH2 are also mRNA decapping factors", rather that these proteins can co-localize (and possibly interact) with LSM1. Decapping assays for example with the known PAT1 de-capping targets from their previous work and their extensive mutant collection could be used to test this.
  • From the BiFC experiment (Figure 1B) it looks like PATs are mostly soluble in the cytoplasm (like LSM1) and might be stress-induced components of PBs (like LSM1). Do PATs co-localize with other canonical PB markers that are more prone to condensation, like DCPs or VCS? BiFC could be performed after IAA treatment to confirm that the cytoplasmic foci are indeed LSM1-positive PBs.
  • A: please provide uncropped images of all Western blots in supplemental data.
• I applaud the authors for establishing this great higher order mutant collection that will be very useful for researchers in the field. However, I am confused about the description of these mutants. If I understood it correctly, these mutants were already used in a previous study by the authors, namely "Zuo, Z., et al., Molecular Plant-Microbe Interactions, 35(2), 125-130." & Zuo, Z., et al., (2021). FEBS letters, 595(2), 253-263." In this study the authors refer to a BioRxiv "Zuo, Z., et al., (2019)." as the reference for these Arabidopsis lines. Is this current manuscript a continuation of the BioRxiv? Please elaborate whether these lines have been used and described in previous studies.

• L72: Is the strong developmental phenotype of the higher order mutants persistent under long day conditions? Considering the strong developmental phenotypes of the mutants, the flowering transition and morphology could be an interesting trait to study. Why did you choose short day conditions for this study?

• L78: This statement is hard to see in Figure 1C and best described for Figure 3A.

• L82: Please include a reasoning for testing PATs localization after hormone treatment. Do you have any indication that other PB proteins behave similar to either PAT1 or the PATHs after hormone treatment to substantiate that these foci observed are indeed PBs? What is known about PBs after hormone treatment in planta?
• Figure 2:
  • How does the localization of LSM1 change under the same treatments? Does ist behave like PAT1 or the homologues?
  • Which part of the root was imaged for this experiment? Is it possible that the observed foci are ARF-condensates as reported by Jing et al., 2022? Do you observe a gradual change in numbers or morphology along the root?
  • How did the authors decide on the concentrations for the stimulant treatments? Have you tried different doses, and could the responses be dose-dependent?
  • A representative image is not sufficient for quantitative responses, like RNA granule condensation. Please provide a quantification of stimulant-induced foci after the different treatments.
• L91: Does that mean that most co-precipitated signal comes from the soluble fraction and not PB-localized? Would an RNAse treatment step eliminate the co-precipitation (optional)?
• L92/93: Or alternatively that PAT1 localizes to PBs independent of the stress, while PATHs are signal-specific PB components?
• Figure 3:
  • I wonder if these results fit better in conjunction with Figure 1, either as a combined figure or move before Figure 2.
  • It is interesting that path2/pat1, while being dwarfed, is less serrated compared to pat1 or path1/pat1. Can you find any indications in your RNAseq set which genes might be involved?
  • Indicate statistical test used to determine p-value
• L116/L117: Doesn't the result in Figure 3E indicate that PATH1 and PATH2 are not fully redundant, but that PATs have specific and narrow roles in leaf development? L116 goes against your statement in L150 & L160. What is known about the expression patterns of PAT1, PATH1 and PHATH2?
• L123: PC3 only explains 0.55% of the variance, so differences along this axis will be overinflated. In my interpretation the pat1/path2 mutant is clustering apart from the other higher order mutants, which is also reflected in the leaf phenotypes. A 2D PCA would be sufficient to describe most of the variation.
• Figure 4:
  • A: The 3D-PCA inflates the differences between higher order mutants along PC3, even though this axis explains only 0.55% of the variance, maybe a 2D-PCA would more intuitively cluster the samples together?
  • B: Please explain the scale in the figure legend and which genes were included? Only DEGs between triple mutant and summ2-8 or DEGs that were different in at least one higher order mutant?
  • C: several comparisons are missing from the upset-plot. Please show the complete plot, also is there a white box laid over the second bar in the upper graph? It would help the reader, if the results section would explain the plots and the comparisons took. Which differences are the authors interested in?
  • From Figure 4B, the triple mutant has an almost inverted expression of mis-regulated genes. High expression genes are now lowly expressed and vice-versa. Has this been reported for other RNA decay mutants before?
• How do you explain that mutants in RNA decay have a large group of repressed transcripts and a large group of enriched transcripts? Wouldn't you suspect a general higher expression in RNA decay mutants or which kind of feedback loop would you propose is happening here? Also, since both kinds of expression changes are recorded in your RNA seq can you speculate on the specificity? Why are some genes up- and others downregulated? Would you suspect that transcription factors are under PATs control?
• Where is the sequencing data deposited? This dataset can be of great value for researchers in the field, but the raw data needs to be made commonly available.

Minor points:

• Check order and nomenclature for protein / gene names in Abstract and Introduction
• L26 / L83 "aggregate" implies non-functionality, I would use "concentrate", "condensate" or "accumulate".
• L35, L45 & L54 all state the same. Maybe remove at least one mention to reduce redundancy?
• L211: Did you use the same imaging settings for all lines?
• L17: RNA quality "control" word missing?
• L477: Authors should cite the newest version of their BioRxiv: Zuo, Z., Roux, M. E., Chevalier, J. R., Dagdas, Y. F., Yamashino, T., Højgaard, S. D., ... & Petersen, M. (2022). The mRNA decapping machinery targets LBD3/ASL9 to mediate apical hook and lateral root development in Arabidopsis. bioRxiv, 2022-07.
• Figure 3B-F, Figure 4C: check spelling on the axis titles.

Significance

This manuscript represents a continuation of the author's characterization of the 3 PAT1s in Arabidopsis development after Zuo et al., 2021; Zuo et al., 2022a; Zuo et al., 2022b. The mutants and the corresponding RNA sequencing experiments are of value to the community working on RNA regulation and degradation or plant development. While the initial findings are interesting, the authors do not explore the stimulus-induced condensation differences between the homologues or try to link the extreme differences in expression profiles mechanistically or functionally. I think the manuscript could greatly benefit from contextualizing their work within the frame of their previous studies and what is known about PBs in terms of plant development. While the RNA seq is a comprehensive data set, a closer examination and a better representation of the results would help readers to access the findings.

Reviewer expertise: RNA granule biology, Arabidopsis, molecular biology

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Referee #1

Evidence, reproducibility and clarity

The manuscript investigates the role of PAT1 gene family in Arabidopsis thaliana. Though the PAT1 protein has been previously investigated and displayed immune-related and developmental phenotypes, the other two members of the family, PATH1 and PATH2, have not been well studied. The authors set out to understand the role of these proteins in relation to the role of PAT1. They thus generated single, double, and triple mutants of the possible combinations of PAT1 genes and examined their phenotypes. As the study focused on the developmental effects of PAT1, the mutants were generated on the background of the summ2 mutant to avoid phenotypes related to immune response. The authors notice a developmental difference between the pat1 mutant combinations, suggesting that PAT1 acts differently than PATH1 and PATH2 and that the PATH proteins serve a redundant function. They also performed RNA-seq analysis to identify differentially-regulated genes in the mutant combinations. The study is interesting and well-executed, yet I believe some questions should still be addressed:

• The research mainly focuses on the developmental phenotype of pat mutants but also tests the interaction of PATH proteins with RNA decapping enzymes to check their function and localization during different treatments. I found it a bit confusing since Figure 1 also shows the developmental phenotype of the mutants. I think editing the order of the figures would make the overall story more coherent.
• My main concern is the correlation between the developmental phenotype of the mutants and the gene expression. Leaf samples for RNA extraction were taken when the plants were 6 weeks old, and the developmental phenotype is very evident. It is thus not possible to tell whether the differences in gene expression are a cause or effect of the developmental phenotype. I think performing qPCR of selected candidates at earlier developmental times might help solve this issue, as well as the characterization of younger plants for the developmental phenotypes (such as leaf number).
• Overall, the manuscript is missing data regarding replicate numbers in the IP and confocal microscopy experiments.

Minor comments:

• Figure 1C - the authors should add a picture of Col0 plants as well as the mutants.
• Figure 3 - Calculating the leaf-to-petiole ratio in the different mutants would be good.
• Figure 4 - the details in the figure are very unclear, especially in the PCA. It would be good to display the data in 2D for PC1 and PC3 and change the colors a bit.

Significance

Both PATH proteins have been less investigated than PAT1, and in that sense, the work is novel. However, it seems that most of the phenotype is attributed to PAT1 rather than the other family members, limiting the interest to the broad plant science community.

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Reply to the reviewers

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

The study largely focuses on the use of a 293 cell line that lacks a functional Dicer gene originally identified by the Cullen group. Baldaccini use this cell line, referred to as NoDice cells, to reconstitute various Dicer isoforms that have thus far been described in a variety of settings (e.g., stem cells and oocytes). Collectively, these data demonstrate the capacity of certain N-terminal truncations of Dicer to inhibit Sindbis virus and reduce the presence of viral dsRNA, supporting some of the observations made thus far concerning an antiviral role for mammalian Dicer. For other viruses, this impact was significantly more modest (SFV reduction is less than a log) or was not observed at all (VSV and SARS-CoV-2). The authors then go on to characterize the nature of the observed antiviral activity and ultimately implicate PKR and the induction of NF-kB in priming the cell's antiviral defenses. Importantly, the group also found that this antiviral activity neither required the nuclease activity of Dicer nor the kinase activity of PKR - providing evidence against antiviral RNAi in mammals. In all, the data would seem to suggest that Dicer can act as a dsRNA sensor and can mediate the activation of an NF-kB response - akin to what is observed in response to NOD or some TLR engagement. In all, it is the opinion of this reviewer that this work brings additional clarity to a concept that remains controversial in the field and therefore embodies something meaningful for the community.

With that said, there are a few issues that require additional attention. The first of these is textual. The introduction of the paper accurately describes the evidence in support of mammalian RNAi but does not invest the same time in discussing the data to the contrary. For example, Seo et al demonstrated that virus infection results in poly-adp-ribosylation of RISC preventing RNAi activity (PMID: 24075860), Uhl et al showed that IFN-induced ADAR1 resolves dsRNA in the cell and prevents RNAi (PMID: 37017521), and Tsai et al showed that virus-derived small RNAs are not loaded into the RISC in a manner that would enable antiviral activity (PMID 29903832). None of this work is referenced in this manuscript and it generates an unbalanced introduction as it relates to the controversy surrounding the idea of RNAi in mammals.

Reply: We thank the reviewer for their positive comments and suggestions. In the revised version of this manuscript, we will rewrite the introduction to take into account the published data that are not in favor of an antiviral role of RNAi in mammals and we will add the suggested references

The second issue that would further strengthen this paper relates to the fact that the authors spend a considerable amount of time discussing the data of Figure 6 and 7 as conditions that are defined by a Dicer that can not be processive in its nuclease activity (WT) vs. one that can (N1). However, there seems to be little consideration about the fact that the introduction of WT Dicer into these cells also restores miRNA biology whereas N1 appears to remain only partially functional (based on the data of Fig 3E). Given this, it seems the authors should verify that the high baseline of NFkB signaling that is being observed when comparing WT to N1 is not a product of restored miRNA function in WT cells, in contrast to the hypotheses outlined in the manuscript. This could be addressed by silencing Drosha or DGCR8 in the Dicer knockout cells prior to their reconstitution of Dicer. In the opinion of this reviewer, this experimental control would significantly strengthen the conclusions the authors are making here.

Reply: This would indeed be an ideal experiment to rule out the contribution of miRNAs in the observed phenotype. We believe however that this particular experiment would prove difficult to realize given that we reconstitute Dicer expression by lentiviral transduction and keep the cells under selection for a couple of weeks before using them for further experiments. This time frame is therefore not compatible with the use of siRNA to knock-down Drosha or DGCR8. Alternatively, we could knock them out by CRISPR-Cas9, but this would take too long and is not feasible in the frame of this work.

We can however address the concern regarding the role played by miRNAs in the observed phenotype of the Dicer N1 cells. Indeed, we can determine the miRNA profile from our small RNA sequencing data and compare them between the Dicer WT and Dicer N1 cells. We have done this comparison and could not find striking differences in miRNA expression between the two cell lines. We will add this additional piece of evidence in our revised manuscript.

Reviewer #1 (Significance (Required)):

In the manuscript entitled, "Canonical and non-canonical contributions of human Dicer helicase domain in antiviral defense" Baldaccini et al. describe their findings concerning the ability of certain N-terminal deletion variants of Dicer in contributing to mammalian antiviral activity. The concept of a functional antiviral RNAi system in mammals is a contentious one with many publications including data both in support of its existence and opposing this idea. In this manuscript, Baldaccini et al. perform a wide range of well-controlled experiments to specifically address aspects of those reports to both provide clarity in what has been documented thus far and to expand on those concepts further.

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

Summary

Whether RNAi is used as an antiviral mechanism in mammals has been a hotly debated issue. The research team previously published several papers on the roles of Dicer in siRNA/miRNA biogenesis and in antiviral responses. They have recently reported that the helicase domain of human Dicer specifically interacts with several proteins that are involved in the IFN response, including PKR. In this study, Baldaccini et al. investigated the involvement of Dicer in antiviral response using various mutants of human Dicer. They showed that deletion mutants of helicase domain exhibit antiviral activity that requires the presence of PKR. They further demonstrated that one of the mutants, N1-Dicer showed antiviral activity in an RNAi-independent manner but depending on the presence of either native PKR or kinase deficient mutants. Transcriptomic analysis revealed that numerous genes involved the IFN and inflammatory response were upregulated in the cells that express N1-Dicer, which is likely due to an increased activation of the NFκB pathway. Based on these findings, the authors propose that Dicer may act as antiviral molecule using its helicase domain, which representing a novel non-canonical function of Dicer.

Major comments:

1.The results from experiments with SARS-CoV2 are intriguing (Fig.2). The authors speculated that NFkb activation is in favor of the replication of this virus. It would be interesting to see the infection and replication of SARS-CoV2 in PKR deficient cells and cells expressing PKR mutants (as described in Fig.5). The results may prove/disapprove the authors' speculation and yield additional findings.

Reply: We thank the reviewer for this suggestion. We have cells that are double knock-out for Dicer and PKR (NoDice/∆PKR) that were transduced to stably express Dicer WT or Dicer N1 and further transduced to express ACE2. We will infect those cell lines with SARS-CoV-2, which will allow us to see whether the difference in viral accumulation can still be observed in the absence of PKR. However, it might prove more difficult to reconstitute PKR expression (WT or mutants) in these cells since they are already transduced twice with two different constructs (Dicer and ACE2).

Western blot analysis. In the method section, it is stated that proteins were quantified with Bradford method and equal loading was verified by Ponceau S staining. The members of also probed with gamma-tubulin (It was stated that antibodies against alpha-tubulin was used in the method section) as a loading control, however, the bend intensity of tubulin shows great variations among different lanes in several figures while Ponceau S staining is similar (Fig.s, 4, 5, and 8). The differences compromise the accuracy of the results.

Reply: We apologize for the difference in Tubulin signal in some of our western blots. There are several possibilities to explain those inconsistencies between Ponceau staining and Tubulin blotting, including an effect of viral infection on Tubulin expression. To remove ambiguities around this issue, we could quantify the signal across several blot replicates and provide the quantification after normalization. In addition, we would like to stress that regarding quantification of the infection, we think that the plaque assay experiments are more reliable than quantification of western blot signals.

3.RNA-seq analysis revealed that Dicer N1 cells have significantly increased expression levels of signaling molecules in type I IFN response even in uninfected cells. While this provides a potential explanation for the antiviral phenotype of N1-Dicer cells. I wonder why the expression levels of type I IFNs (probably the most potent antiviral molecules) were not analyzed in WT and Dicer N1 cells. Measurement of the levels of IFNα and IFNβ by ELISA in the cells before and after infection could provide the important and direct data to support their conclusion.

Reply: This an interesting suggestion but unfortunately, we do not believe that it would possible to quantify IFNα and IFNβ by ELISA in the cell line that we used in our experiments. Indeed, the level of expression might just be too low to be able to measure something meaningful. We could measure the induction of IFNβ expression at the mRNA level by RT-qPCR though. However, we do not believe that the observed increased expression of genes that belong to the type I IFN response is solely the effect of an increased production of IFN. These genes are also under the control of other transcription factors, including NF-kB for some of them, and it might prove difficult to make a direct link with IFNα or IFNβ production.

4.While the data presented in Fig. 5 provides convincing evidences that the antiviral activity of mediated by PKR against SINV is independent of its kinase activity in N1-Dicer cells. An interesting question is that whether antiviral activity associated with PKR is N1-Dicer dependent, which could be addressed by comparing the viral infection of NoDice∆PKR and NoDicer expressing PKR mutants.

Reply: Yes indeed, we have generated NoDice/∆PKR cells expressing PKR WT or mutant and we will infect them with SINV to confirm whether the presence of Dicer N1 is needed for the observed phenotype.

5.In the concluding paragraph of the discussion, the authors presented an oversimplified discerption of a complex model that involves a crosstalk between IFN-I and RNAi and Dicer-PKR interaction, which is difficult for the reader to compose a clear picture of mechanisms involved. It could be helpful to use a schematic illustration to summarize the action model of PKR incorporated with the canonical and non-canonical Dicer functions.

Reply: We will add a schematic model in the revised version of our manuscript to summarize our main findings.

Minor comments:

1.It stated that NoDice FHA-Dicer WT #4 and NoDice FHA:Dicer N1 110 #6 are referred to as Dicer WT and Dicer N1 cells (p.6). For simplicity, Dicer WT and Dicer N1 cells should be used throughout manuscript, including in all figures. The labels in the figures are difficult to read and are confusing in some cases.

Reply: This will be changed in the revised version to increase the clarity of the figures.

2.It is to note that p-PKR was only detected at in N1-Dicer cells at 24 hpi (Fig.8A). This is an interesting observation that was not discussed. It appears that this could be due to a delayed viral replication since these cells are already in an elevated antiviral state. This possibility could be tested by examining viral replication and dsRNA accumulation at more time points in the experiments described in Fig.1.

Reply: We have performed a kinetic of infection at more time points and we will incorporate these experiments in the revision.

3.The authors may point out the limitations of the studies. For examples, all cells used in the study are engineered HEK cell lines and were tested with limited number of viruses. As such, the observations may reflect Dicer-PKR interaction under artificially overexpressed conditions, but how the model established from the current study applies to primary cells require further investigation.

Reply: This is indeed important, we will add a sentence about this in the discussion.

Reviewer #2 (Significance (Required)):

The findings reported in this study shed some new light on a long-debated issue regarding the potential roles of RNAi as physiologically relevant antiviral mechanism in mammals. Identification of a new antiviral function of Dicer helicase domain via interaction with PKR is a new advancement of the field, and it also adds a new dimension to a complex subject that overlaps of innate immunity , RNA biology, and developmental biology associated with Dicer.

Field of expertise: Innate immunity, cell signaling, cytokine biology

Areas that that I do not have sufficient expertise to evaluate: Small RNA cloning, sequencing and, analysis.

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

This work by Baldaccini et al. explores the interplay between Dicer and the antiviral protein PKR in the context of viral infection. It builds on a previous publication of the team which demonstrates that the Dicer helicase interacts with multiple RNA binding proteins, including PKR (see Montavon et al.). In this work from 2021, they demonstrate that an artificially-truncated form of Dicer (Dicer-N1) lacking part of the helicase is antiviral against RNA viruses in a PKR-dependent fashion. This was an interesting finding because the field largely assumed that Dicer-N1 performs its antiviral function via canonical dicing of dsRNA, as part of an antiviral RNAi pathway. The present manuscript follows up on this initial discovery and deciphers the specifics of Dicer-N1 antiviral phenotype, as well as delineates the interplay between Dicer's helicase and PKR. The authors main claims are as follow:

1. i) Dicer-N1 antiviral effect does not require its catalytic activity, therefore is completely RNAi-independent.
2. ii) Neither does it require canonical PKR activation, but relies instead on NF-kB-driven inflammation. The origin of this inflammation is not studied.
3. ii) Truncated Dicers other than Dicer-N1 are antiviral through RNAi, but are also PKR-dependent. The authors claims are mostly supported by the data, although I suggest below some improvements regarding experimental approaches and data presentation. This work details in an interesting manner the interplay between the machinery of RNAi and the classical pathway of innate immunity (PKR). As explained by the authors, there is solid data in the literature demonstrating the mutual exclusivity of IFN and antiviral RNAi in differentiated cells. This mostly goes through the receptors LGP2, which inhibits dsRNA dicing by Dicer. The authors data suggest that, conversely, Dicer may play a role in preventing the unwanting activation of PKR (a non-canonical activation leading to inflammation). Given that PKR activation does not depend on virus, the authors discuss potential mechanisms of PKR triggering. This is an interesting topic that deserves further investigation (not necessarily within the frame of this work - it can be a follow-up). Another interesting piece of information is that different truncated Dicers behave differently with respect to implementing antiviral RNAi. Whilst Dicer-N1 isn't proficient in doing so, the other forms are. It shows that lab-generated truncations do not fully recapitulate what is observed with existing truncated Dicers (DicerO and aviD).

Experimental design and data interpretation

1. The authors should compare infection between different cell lines across a range of time points (ie, a virus growth curve). In Fig 4E for example, I worry that cells expressing or not PKR will reach the plateau of viral particle accumulation at different time points. One could imagine that cells lacking PKR do not show any differences in particle production at 24h, but do at earlier time points.

Reply: This is an interesting suggestion, we can perform a kinetic experiment by looking at more time points to address this point. This will allow us to determine the time needed for every cell line to reach the plateau of infection.

Western blots should be accompanied with proper quantifications plotted as bar graph with biological replicates (p-PKR, p-eIF2a and capsid).

Reply: We have biological replicates for our western blot experiments, and we will quantify those to better determine the observed changes. However, in the case of p-eIF2a, we do not think it is pertinent to measure it since there are other kinases than PKR that are known to induce eIF2a phosphorylation upon SINV infection. It might therefore not prove very informative to precisely quantify this particular signal.

Microscopy images should be properly quantified across biological replicates (Fig 1&2 for the J2 staining, for example).

Reply: We could do a proper quantification of the J2 signal across replicates, but we do not think it would bring much to our message. Here, we mostly used J2 staining as a qualitative indication that the infection was impacted or not. We have a proper quantification of the effect with our plaque assay experiments, which are way more robust to determine the levels of infection between conditions.

Confounding factors hinder the interpretation of siRNA accumulation (Suppl Fig 2): i) the efficiency of dsRNA dicing from different Dicers will generate different amounts of siRNAs from a given amount of dsRNA and ii) the higher antiviral response translates into decreased infection, so decreased dsRNA substrate. I suggest that the authors normalise the amount of viral siRNAs over the total amount of viral genomes. This should allow to assess if Dicer-N1 is better at dicing dsRNA than WT in these conditions.

Reply: This is a valid concern and we agree that it is important to be able to normalize small RNA reads between conditions before reaching a conclusion. The problem is that there is no easy way to do this since we do not get a direct measurement of viral genomes accumulation from our small RNA sequencing data. To better compare the two conditions, we could normalize the individual viral siRNA to the total number of viral reads. Another problem that we face is that we are looking here at the AGO-loaded small RNAs, which makes it more difficult to assess dicing efficiency since not every generated siRNA might be loaded into an Argonaute protein. In fact, this has been proposed by the Cullen laboratory in a paper published in 2018 (Tsai et al. doi: 10.1261/rna.066332.118). They showed that although viral siRNAs were generated during IAV infection, those were inefficiently loaded and thus did not significantly impacted the infection.

In Fig 8, the authors should verify that phospho-p65 increase depends on PKR by repeating the experiment in PKR KO cells.

Reply: Yes, good point. We will check what happens to phosphorylation of p65 in PKR KO cells. In addition, we can also measure the effect on a known NF-kB target by RT-qPCR (e.g. PTGS2).

Data representation

1. Levels of phospho-PKR and eIF2a need to be normalised on the total amount of PKR and eIF2a, respectively. The authors should quantify the blots and present bar graphs with biological replicates and statistics.

Reply: As mentioned above in our reply to point 2, we can add the quantification for phospho PKR, but we do not think it is pertinent to do it for eIF2a.

Could the authors add the names of representative genes on the volcano plots of Fig 7?

Reply: Yes, this will be done.

Points of discussion

1. In Fig 4C, catalytically-dead mutants of truncated Dicers (other than N1) do not display an antiviral effect. Presumably, such proteins implement canonical antiviral RNAi. Is there a reason why the authors interpret this data as Dicers being "partially" antiviral through RNAi (l. 92). This data instead suggest that is it totally dependent on RNAi.

Reply: Indeed, and we do not say the contrary. It seems that some of this helicase-truncated Dicer proteins can act through RNAi. However, they also depend on PKR, so in the end it might be a combination of the two that allows their antiviral effect.

Gurung et al. demonstrate that PKR is activated in Dicer KO mouse ES cells, which results in phosphorylation of eIF2a at steady-state. This is different from the authors' data, in which PKR activation does not affect eiF2a phosphorylation. Could the authors discuss this discrepancy?

Reply: The problem that we face here is that SINV is known to also activate GCN2 and therefore eIF2a phosphorylation does not strictly rely on PKR in our experimental conditions. In addition, we did not check eIF2a phosphorylation in Dicer KO cells, but we always compare Dicer WT and Dicer N1 expressing cells.

Do the authors expect that truncated Dicers other than N1 trigger an inflammatory response such as the one described for N1? Would it be possible to have this antiviral inflammatory response in conjunction with antiviral RNAi?

Reply: This goes back to Point 1 mentioned previously. We think indeed that there might be a dual action of Dicer and that it will be important to check whether in other cellular systems or animal model such a phenomenon can be observed as well. This is a point that we did address in the discussion of our manuscript (line 522-525).

Reviewer #3 (Significance (Required)):

This is a study that conceptually advances the field of antiviral RNAi in mammals, including its interplay with the machinery of innate immunity. It is of interest for virologists and immunologists. My expertise is centered on the mechanisms of innate immunity in mammalian cells, including antiviral RNAi.

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Referee #3

Evidence, reproducibility and clarity

This work by Baldaccini et al. explores the interplay between Dicer and the antiviral protein PKR in the context of viral infection. It builds on a previous publication of the team which demonstrates that the Dicer helicase interacts with multiple RNA binding proteins, including PKR (see Montavon et al.). In this work from 2021, they demonstrate that an artificially-truncated form of Dicer (Dicer-N1) lacking part of the helicase is antiviral against RNA viruses in a PKR-dependent fashion. This was an interesting finding because the field largely assumed that Dicer-N1 performs its antiviral function via canonical dicing of dsRNA, as part of an antiviral RNAi pathway. The present manuscript follows up on this initial discovery and deciphers the specifics of Dicer-N1 antiviral phenotype, as well as delineates the interplay between Dicer's helicase and PKR. The authors main claims are as follow:

i) Dicer-N1 antiviral effect does not require its catalytic activity, therefore is completely RNAi-independent.

ii) Neither does it require canonical PKR activation, but relies instead on NF-kB-driven inflammation. The origin of this inflammation is not studied.

ii) Truncated Dicers other than Dicer-N1 are antiviral through RNAi, but are also PKR-dependent.

The authors claims are mostly supported by the data, although I suggest below some improvements regarding experimental approaches and data presentation. This work details in an interesting manner the interplay between the machinery of RNAi and the classical pathway of innate immunity (PKR). As explained by the authors, there is solid data in the literature demonstrating the mutual exclusivity of IFN and antiviral RNAi in differentiated cells. This mostly goes through the receptors LGP2, which inhibits dsRNA dicing by Dicer. The authors data suggest that, conversely, Dicer may play a role in preventing the unwanting activation of PKR (a non-canonical activation leading to inflammation). Given that PKR activation does not depend on virus, the authors discuss potential mechanisms of PKR triggering. This is an interesting topic that deserves further investigation (not necessarily within the frame of this work - it can be a follow-up). Another interesting piece of information is that different truncated Dicers behave differently with respect to implementing antiviral RNAi. Whilst Dicer-N1 isn't proficient in doing so, the other forms are. It shows that lab-generated truncations do not fully recapitulate what is observed with existing truncated Dicers (DicerO and aviD).

Experimental design and data interpretation

1. The authors should compare infection between different cell lines across a range of time points (ie, a virus growth curve). In Fig 4E for example, I worry that cells expressing or not PKR will reach the plateau of viral particle accumulation at different time points. One could imagine that cells lacking PKR do not show any differences in particle production at 24h, but do at earlier time points.
2. Western blots should be accompanied with proper quantifications plotted as bar graph with biological replicates (p-PKR, p-eIF2a and capsid).
3. Microscopy images should be properly quantified across biological replicates (Fig 1&2 for the J2 staining, for example).
4. Confounding factors hinder the interpretation of siRNA accumulation (Suppl Fig 2): i) the efficiency of dsRNA dicing from different Dicers will generate different amounts of siRNAs from a given amount of dsRNA and ii) the higher antiviral response translates into decreased infection, so decreased dsRNA substrate. I suggest that the authors normalise the amount of viral siRNAs over the total amount of viral genomes. This should allow to assess if Dicer-N1 is better at dicing dsRNA than WT in these conditions.
5. In Fig 8, the authors should verify that phospho-p65 increase depends on PKR by repeating the experiment in PKR KO cells.

Data representation

1. Levels of phospho-PKR and eIF2a need to be normalised on the total amount of PKR and eIF2a, respectively. The authors should quantify the blots and present bar graphs with biological replicates and statistics.
2. Could the authors add the names of representative genes on the volcano plots of Fig 7?

Points of discussion

1. In Fig 4C, catalytically-dead mutants of truncated Dicers (other than N1) do not display an antiviral effect. Presumably, such proteins implement canonical antiviral RNAi. Is there a reason why the authors interpret this data as Dicers being "partially" antiviral through RNAi (l. 92). This data instead suggest that is it totally dependent on RNAi.
2. Gurung et al. demonstrate that PKR is activated in Dicer KO mouse ES cells, which results in phosphorylation of eIF2a at steady-state. This is different from the authors' data, in which PKR activation does not affect eiF2a phosphorylation. Could the authors discuss this discrepancy?
3. Do the authors expect that truncated Dicers other than N1 trigger an inflammatory response such as the one described for N1? Would it be possible to have this antiviral inflammatory response in conjunction with antiviral RNAi?

Significance

This is a study that conceptually advances the field of antiviral RNAi in mammals, including its interplay with the machinery of innate immunity. It is of interest for virologists and immunologists. My expertise is centered on the mechanisms of innate immunity in mammalian cells, including antiviral RNAi.

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Referee #2

Evidence, reproducibility and clarity

Summary

Whether RNAi is used as an antiviral mechanism in mammals has been a hotly debated issue. The research team previously published several papers on the roles of Dicer in siRNA/miRNA biogenesis and in antiviral responses. They have recently reported that the helicase domain of human Dicer specifically interacts with several proteins that are involved in the IFN response, including PKR. In this study, Baldaccini et al. investigated the involvement of Dicer in antiviral response using various mutants of human Dicer. They showed that deletion mutants of helicase domain exhibit antiviral activity that requires the presence of PKR. They further demonstrated that one of the mutants, N1-Dicer showed antiviral activity in an RNAi-independent manner but depending on the presence of either native PKR or kinase deficient mutants. Transcriptomic analysis revealed that numerous genes involved the IFN and inflammatory response were upregulated in the cells that express N1-Dicer, which is likely due to an increased activation of the NFκB pathway. Based on these findings, the authors propose that Dicer may act as antiviral molecule using its helicase domain, which representing a novel non-canonical function of Dicer.

Major comments:

1.The results from experiments with SARS-CoV2 are intriguing (Fig.2). The authors speculated that NFkb activation is in favor of the replication of this virus. It would be interesting to see the infection and replication of SARS-CoV2 in PKR deficient cells and cells expressing PKR mutants (as described in Fig.5). The results may prove/disapprove the authors' speculation and yield additional findings. 2. Western blot analysis. In the method section, it is stated that proteins were quantified with Bradford method and equal loading was verified by Ponceau S staining. The members of also probed with gamma-tubulin (It was stated that antibodies against alpha-tubulin was used in the method section) as a loading control, however, the bend intensity of tubulin shows great variations among different lanes in several figures while Ponceau S staining is similar (Fig.s, 4, 5, and 8). The differences compromise the accuracy of the results. 3.RNA-seq analysis revealed that Dicer N1 cells have significantly increased expression levels of signaling molecules in type I IFN response even in uninfected cells. While this provides a potential explanation for the antiviral phenotype of N1-Dicer cells. I wonder why the expression levels of type I IFNs (probably the most potent antiviral molecules) were not analyzed in WT and Dicer N1 cells. Measurement of the levels of IFNα and IFNβ by ELISA in the cells before and after infection could provide the important and direct data to support their conclusion. 4.While the data presented in Fig. 5 provides convincing evidences that the antiviral activity of mediated by PKR against SINV is independent of its kinase activity in N1-Dicer cells. An interesting question is that whether antiviral activity associated with PKR is N1-Dicer dependent,, which could be addressed by comparing the viral infection of NoDice∆PKR and NoDicer expressing PKR mutants. 5.In the concluding paragraph of the discussion, the authors presented an oversimplified discerption of a complex model that involves a crosstalk between IFN-I and RNAi and Dicer-PKR interaction, which is difficult for the reader to compose a clear picture of mechanisms involved. It could be helpful to use a schematic illustration to summarize the action model of PKR incorporated with the canonical and non-canonical Dicer functions.

Minor comments:

1.It stated that NoDice FHA-Dicer WT #4 and NoDice FHA:Dicer N1 110 #6 are referred to as Dicer WT and Dicer N1 cells (p.6). For simplicity, Dicer WT and Dicer N1 cells should be used throughout manuscript, including in all figures. The labels in the figures are difficult to read and are confusing in some cases. 2.It is to note that p-PKR was only detected at in N1-Dicer cells at 24 hpi (Fig.8A). This is an interesting observation that was not discussed. It appears that this could be due to a delayed viral replication since these cells are already in an elevated antiviral state. This possibility could be tested by examining viral replication and dsRNA accumulation at more time points in the experiments described in Fig.1. 3.The authors may point out the limitations of the studies. For examples, all cells used in the study are engineered HEK cell lines and were tested with limited number of viruses. As such, the observations may reflect Dicer-PKR interaction under artificially overexpressed conditions, but how the model established from the current study applies to primary cells require further investigation.

Significance

The findings reported in this study shed some new light on a long-debated issue regarding the potential roles of RNAi as physiologically relevant antiviral mechanism in mammals. Identification of a new antiviral function of Dicer helicase domain via interaction with PKR is a new advancement of the field, and it also adds a new dimension to a complex subject that overlaps of innate immunity , RNA biology, and developmental biology associated with Dicer.

Field of expertise: Innate immunity, cell signaling, cytokine biology

Areas that that I do not have sufficient expertise to evaluate: Small RNA cloning, sequencing and, analysis.

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Referee #1

Evidence, reproducibility and clarity

The study largely focuses on the use of a 293 cell line that lacks a functional Dicer gene originally identified by the Cullen group. Baldaccini use this cell line, referred to as NoDice cells, to reconstitute various Dicer isoforms that have thus far been described in a variety of settings (e.g., stem cells and oocytes). Collectively, these data demonstrate the capacity of certain N-terminal truncations of Dicer to inhibit Sindbis virus and reduce the presence of viral dsRNA, supporting some of the observations made thus far concerning an antiviral role for mammalian Dicer. For other viruses, this impact was significantly more modest (SFV reduction is less than a log) or was not observed at all (VSV and SARS-CoV-2). The authors then go on to characterize the nature of the observed antiviral activity and ultimately implicate PKR and the induction of NF-kB in priming the cell's antiviral defenses. Importantly, the group also found that this antiviral activity neither required the nuclease activity of Dicer nor the kinase activity of PKR - providing evidence against antiviral RNAi in mammals. In all, the data would seem to suggest that Dicer can act as a dsRNA sensor and can mediate the activation of an NF-kB response - akin to what is observed in response to NOD or some TLR engagement. In all, it is the opinion of this reviewer that this work brings additional clarity to a concept that remains controversial in the field and therefore embodies something meaningful for the community.

With that said, there are a few issues that require additional attention. The first of these is textual. The introduction of the paper accurately describes the evidence in support of mammalian RNAi but does not invest the same time in discussing the data to the contrary. For example, Seo et al demonstrated that virus infection results in poly-adp-ribosylation of RISC preventing RNAi activity (PMID: 24075860), Uhl et al showed that IFN-induced ADAR1 resolves dsRNA in the cell and prevents RNAi (PMID: 37017521), and Tsai et al showed that virus-derived small RNAs are not loaded into the RISC in a manner that would enable antiviral activity (PMID 29903832). None of this work is referenced in this manuscript and it generates an unbalanced introduction as it relates to the controversy surrounding the idea of RNAi in mammals.

The second issue that would further strengthen this paper relates to the fact that the authors spend a considerable amount of time discussing the data of Figure 6 and 7 as conditions that are defined by a Dicer that can not be processive in its nuclease activity (WT) vs. one that can (N1). However, there seems to be little consideration about the fact that the introduction of WT Dicer into these cells also restores miRNA biology whereas N1 appears to remain only partially functional (based on the data of Fig 3E). Given this, it seems the authors should verify that the high baseline of NFkB signaling that is being observed when comparing WT to N1 is not a product of restored miRNA function in WT cells, in contrast to the hypotheses outlined in the manuscript. This could be addressed by silencing Drosha or DGCR8 in the Dicer knockout cells prior to their reconstitution of Dicer. In the opinion of this reviewer, this experimental control would significantly strengthen the conclusions the authors are making here.

Significance

In the manuscript entitled, "Canonical and non-canonical contributions of human Dicer helicase domain in antiviral defense" Baldaccini et al. describe their findings concerning the ability of certain N-terminal deletion variants of Dicer in contributing to mammalian antiviral activity. The concept of a functional antiviral RNAi system in mammals is a contentious one with many publications including data both in support of its existence and opposing this idea. In this manuscript, Baldaccini et al. perform a wide range of well-controlled experiments to specifically address aspects of those reports to both provide clarity in what has been documented thus far and to expand on those concepts further.

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Reply to the reviewers

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

Authors used organoid technology to study the effects of the serum from lupus patients on intestinal epithelium. By culturing organoids derived from human colon crypts, they specifically determined the response of epithelial cells to inflammatory mediators present in lupus serum. Using bulk and scRNA-seq, authors found that secretory cells function and differentiation were impaired as well as the mitochondrial metabolism. These effects were shown to be mediated by type 1 interferon in combination with other pro-inflammatory cytokines present in lupus serum.

The reduction of mucus secretion after SLE-serum treatment and the downregulation of tight junctions' genes seem to indicate an increased permeability of the epithelial barrier, thus it would be interesting to determine the expression and distribution of tight junction proteins and to test in the organoids whether the paracellular permeability is increased upon SLE-serum treatment. These analyses will give a functional result of this in vitro model.

If the organoids take a few days to culture and the material is available, the measurement of paracellular permeability may take no more than 2 weeks. It is true that they will need a microneedle to inject the FITC-Dextran 4K into the organoids and record the images for 24h.

• We thank the reviewer for suggesting this experiment. Reviewer #2 had the same suggestion. The results obtained elevate our overall results and the quality of our research.
• Tight junction protein expression is cell-type dependent as shown in literature1,2 and in our scRNA-seq analysis (Suppl. Table 7). Additionally, we observed that the expression changes of ZO-1(TJP1; Fig. 7A) are accumulated in colonocytes. That might be the reason we could not detect significant changes in organoid sections with staining for tight junction proteins ZO-1 and Occludin and analyzing all cell types. The data of the permeability assay however was able to show that the function of the tight junctions is altered. If this change is caused by changes in protein abundance as suggested by reported transcriptomic changes remains to be elucidated in future research using single-cell proteomic approaches.

• For the permeability assay we stimulated organoid monolayers for 72h with SLE or control serum. The measured translocation of FITC showed an increased barrier leakiness in SLE compared to control condition. These functional results not only support our findings presented in our study that the epithelial barrier is altered upon SLE serum stimulation, but they also provide the definitive proof of concept highlighting the crucial connection between SLE and intestinal barrier integrity. Furthermore, it shows that changes on transcriptional level and alterations in cell type composition translate into a barrier dysfunction which could have potentially detrimental effects in vivo. The data has been added to Figure 1H, line 722-731; 746-751.

  I would like to know which of the donor's cells were used from figure 2 on and why.

• Organoid line I was used for the 24h stimulation and for the 72h stimulation whereas organoid line II was used for the 72h stimulation only. Due to limitations in serum availability we had to limit the experiments that exceeded the initial transcriptomic analysis to one organoid line. After confirming that the serum was affecting both organoid lines, we continued using organoid line II.

  The bioinformatics analyses using gene expression data and scRNASeq were well done. No comments.

  Reviewer #1 (Significance (Required)):

  For the field of autoimmunity, to study the crosstalk between the systemic response and the gut epithelium response results quite important as the increased permeability of the gut epithelial barrier has been suggested to fuel the systemic inflammation in lupus. However, as the author mention, there is not enough information about the interaction of epithelial cells and the systemic inflammatory mediators in lupus. This system can be useful to determine a personalized treatment for patients by testing the effect of individual serum on organoids. Moreover, the use of organoids can be extended to study the gut epithelium response in other autoimmune diseases mediated by type 1 interferon.

  Increased permeability of the gut epithelial barrier has been related with lupus development. In humans, it is not known whether it is a cause or consequence, but in lupus mouse models it has been demonstrated that there is a reduction of the systemic autoimmune response concomitant with a reduction of gut permeability. The authors have validated an in vitro model that can be used to study how gut epithelium is affected by systemic inflammatory mediators and that will help to develop novel therapeutic approaches or personalize treatments.

Thank you for your insightful comments. We are encouraged to see that the reviewer has accurately grasped the core purpose and implications of our research. The intricate relationship between gut epithelial barrier permeability and lupus development is indeed vital for expanding our scientific knowledge and for future therapeutic breakthroughs. We are happy that the overall message we aimed to convey with our research was well captured and appreciated by the reviewer. We believe our in vitro model will serve as a foundation for further detailed studies and for the development of therapeutic strategies in this field. Your acknowledgment of our work inspires us to persist in our research efforts.

Interest stakeholders: Clinical and basic researchers in autoimmunity, gastroenterology, and rheumatology.

My field of expertise is systemic and organ-specific autoimmunity at cellular and molecular level. My work covers autoimmunity and gut microbiota. I study how B cells regulate the microbiota composition and how that microbiota impacts gut permeability and inflammation in mouse lupus models. On the other hand, the bioinformatics analyses are well-done for both bulk RNASeq and scRNASeq.

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

The group of Dr. Resnik-Docampo provides a very elegant study on two patient lines for SLE. The study is definitely very interesting and opens many scientific avenues that are worthy of being explored further. Major comments: -Barrier integrity or its alterations can be tested in organoids with specific dyes, I feel this would give definitive proof of concept.

• We thank the reviewer for the suggestion. Reviewer #1 had the same suggestion. The results obtained elevate our overall results and the quality of our research.

• For the permeability assay we stimulated organoid monolayers for 72h with SLE or control serum. The measured translocation of FITC showed an increased barrier leakiness in SLE compared to control condition. These functional results not only support our findings presented in our study that the epithelial barrier is altered upon SLE serum stimulation, but they also provide the definitive proof of concept highlighting the crucial connection between SLE and intestinal barrier integrity. Furthermore, it shows that changes on transcriptional level and alterations in cell type composition translate into a barrier dysfunction which could have potentially detrimental effects in vivo. The data has been added to Figure 1H, line 722-731; 746-751.

  -In supplementary figure 1 a caspase 3 staining is presented, please show a positive control for caspase 3 staining on organoids or alternstively use a different method to prove no differential cell death.

• Due to the processing of the organoids it is difficult to have a positive control. However, we can confirm that the used cleaved Caspase 3 antibody is able to detect cell death by the following staining. There are positive cells in the center of the organoid where dead cells accumulate. Additionally in Figure 1C we show with a cytotoxicity assay measuring LDH release that there is no significant difference between both groups. Furthermore, brightfield imaging showed no obvious differences and the DEGs show also no evidence of increased cell death.

  -Serum from SLE patients reduces drastically Edu positivity, it would be interesting to see a clonogenicity assay to see whether this reflects on reduced stem cell clonogenic potential

• We agree. However, this analysis goes beyond the time and material limitations we have in our project.

  -Goblet cells in the colon are very heterogeneous, which subpopulations of goblet cell are reduced? how does this affect mucus composition?

• We see that subpopulation GC4 is significantly reduced upon SLE serum stimulation (Fig. 7C and Suppl. Fig. 7C). Overall, we see a trend of a general reduction of all GC subpopulation and an indication that there might be also a shift in subtype abundance. We would need to increase the study population to be able to draw any conclusions on how exactly the GC subpopulations change. So far not much is known about how the different GC subpopulations affect mucus composition. This field is completely understudied especially in the human intestine. Future projects should focus on mucus composition and how it is changed with changes of the subpopulations (even under physiological conditions).

• We thank the reviewer for the question about the mucus composition. We included the analysis of FCGBP protein abundance (Fig. 4C and Suppl. Fig. 4D; line 245-247) in our study. The increased FCGBP protein abundance upon SLE serum stimulation which is in line with our transcriptomic changes complements our data and supports our hypothesis that the mucus composition is altered. This new data improves the quality of our research.

  Minor comments: - Please provide an hypothesis on how mitochondrial alterations are linked to altered lineage progeny of stem cells. This should be discussed more in depth.

• It is known that cells in the crypt compartment that undergo rapid division depend on glycolysis for ATP production once the cell differentiates it switches to oxidative phosphorylation.3 In vitro it has been shown that differentiation coincides with the switch to oxidative phosphorylation.4 There is a complex interplay between Notch signaling and FoxO transcription factors which is driving differentiation and cell fate decision.5 Especially the differentiation towards the secretory lineage highly depends on the metabolic switch from glycolysis to oxidative phosphorylation. Furthermore, even mucus secretion itself is dependent on oxidative phosphorylation.6 This is in line with our differentiation experiment where less oxidative phosphorylation coincided with the absence of goblet cells. We hypothesize that the changed cellular composition upon SLE serum stimulation is at least partly reflected in the altered mitochondrial function. If the decrease in the secretory lineage alone can explain the seen mitochondrial changes needs to be further elucidated. While a detailed examination at single-cell level analysis of mitochondrial function might be able to answer if they drive the altered cell differentiation, such an in-depth analysis is beyond the scope of this article and would be best addressed in a dedicated study on the topic. Within the scope of our analysis and considering the still limited knowledge of mitochondrial function in specific cell types of the human colon we included some more discussion (line 696-698 and 702-703).

  -Many antimicrobial peptides are changed, this could reflect on microbiome composition as well as mucus composition and properties, which I am sure will be the topic of future studies. This should be discussed more in depth.

• We hypothesize that the alterations in antimicrobial peptide expression along with the seen changes in major mucus components would be translated into changes in mucus composition. Since the mucus serves as the niche for gut microbiota this could lead to changes in microbiome composition. It is of high interest to analyze the mucus composition of the stimulated organoids. Furthermore, assays analyzing the killing capacity of the potentially secreted antimicrobial peptides could help us to understand the relevance of the observed changes. We addressed this in line 714-715 and line 750-752.

  Reviewer #2 (Significance (Required)):

  The study is useful for both broad and specialized audiences. The findings are interesting and of relevance to the field of SLE, gut epithelial biology. The strength of the manuscript is that it opens many scientific avenues, its weakness is that they are not mechanistically dissected to the fullest rendering the study a bit descriptive. Nonetheless, I consider positively the manuscript after a minor revision given the major message of the paper can be proven.

Thank you for your constructive feedback. We are genuinely encouraged by your recognition of the utility and relevance of our study for both broad and specialized audiences in the fields of SLE and gut epithelial biology. Your acknowledgment resonates with our larger objectives: beyond merely exploring the specific connection between SLE and intestinal leakiness, our aim has been to create a methodological approach that illuminates novel avenues to study complex diseases. It is deeply heartening to realize that this overarching message and intent were clearly understood and agreed upon by the reviewer. We recognize and appreciate your insights into the strengths and areas for improvement of our manuscript, and we are committed to addressing the highlighted points to further enhance our contribution to the field.

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

Inga Viktoria Hensel et al. used colon organoid to study the impact of lupus patients' serum on gut epithelial barrier. The exposure of SLE serum on colon organoids increased gene expression related to cell cycle, chromosome organization, mitochondrial function as well as interferon signaling, but downregulated that related to secretion, cytoskeleton, and anchoring junctions of the cells. Higher type I IFN in the SLE serum and unregulated interferon signature genes post stimulation suggest a potential role of type I interferon in this process. The addition of a type 1 interferon receptor (IFNAR1) antagonist, Anifrolumab, blocked the stimulation function of SLE serum but the combination of IFN-2α and control serum failed to recapitulate the results from SLE serum, suggesting that more than one cytokine was involved. SLE serum exposure altered metabolic profiles of organoids with a significant increase of basal respiration and ATP production. Stimulating organoid with SLE serum confirmed an alteration in cell differentiation with a loss of secretory lineage. scRNA-seq analysis revealed that colon organoid had all major cell types from colon in vivo. SLE serum stimulation shifted cell differentiation with decreased number of goblet cells and downregulated mucin, AMP and other components that were required for gut barrier integrity.

Finally, the authors performed a gene expression analysis of colon biopsies derived from SLE patients and healthy controls. While the authors should be commended to attempt a validation of the results obtained with organoids, the small sample size and patient heterogeneity prevented a statistical analysis. Some genes involved in absorption and ion transport as well as secretory lineage showed a similar trend with organoid assay, suggesting that colon organoids may be a good tool for future studies. However, it is noticeable that the biopsies from SLE patients did not show the IFN signature and the decreased in Muc2 expression, which dominated the gene signature of organoids exposed to SLE serum. There is no information about the disease activity of the SLE patients, as well as their IFN activity, which makes difficult to interpret these results.

• We thank the reviewer for the questions and suggestions. They helped us to improve our manuscript and make it more concise for the reader.

Specific concerns: 1. Line 107, Why did The authors use 72 hours post treatment. Are other timepoints available and have similar results?

• We used two different time points, 24h and 72h. The nature of the organoid culture limits the total length of the experiment. Organoids can be cultured for a maximum of 5-7 days. Differentiation from the stem cell to the fully differentiated cell takes 5-7 days. Preliminary results showed that serum stimulation prior to day 2 led to a decrease in organoid survival, most likely because serum stimulation in general induces differentiation. We therefore chose 72h as the stimulation that mimics a chronic exposure as a differentiating cell would face in vivo. With this time span we were able to see manifestations in cell differentiation changes but avoided beginning cell death to prolonged culture. However, we also wanted to understand a more acute exposure to the serum. Therefore, we chose 24h as a second stimulation duration. With this time point we were able to detect initial changes in cell fate decision which was important in the interpretation of the accumulated effects seen after 72h.

Figure 1D, how do the authors explain the heterogeneity among SLE samples (2, 3, 4, 5) on organoid line II? These samples do not seem to correlate with cytokine levels shown in Fig. 2. This issue may be worth exploring further, such as correlation between cytokine levels and gene expression.

• The heterogeneity seen among the SLE samples most likely reflects the complex composition of the serum itself. A similar heterogeneity (PC1 axis) is also seen for the controls. The epithelial cells eventually will react to all contained factors showing an integrated response that makes it difficult to correlate to a single cytokine.

Line 144, the 2 outlier SLE serum samples are not same between organoid lines with NO. 1&5 in Organoid line II and with NO. 1&4 in Organoid line I. The statement is misleading.

• We corrected the statement according to the suggestions (Line 146-148).

Line 169, IFN-a2 and IL-6 are not significantly different.

• The statement was rephrased (Line 166-174).

Line 179-180, Reduced fitness of organoids exposed to SLE serum is an overstatement. It was not directly tested, and there is no difference in apoptosis.

• The term ‘reduced fitness’ referred to the results seen in the mitochondrial stress test. We rephrased the parts to make the statement more concise (Line 182).

Line 242-243: SLE serum stimulation induced MUC2 high expression in Organoid II but lower level in organoid I (Figure 4B & Figure S4C). This is a major discrepancy that needs to be addressed.

• This discrepancy comes from the different differentiation dynamics observed in both organoid lines. Therefore, for the downstream analysis we considered the results from both lines to have a robust analysis. With the results from the 24h timepoint and the scRNA-seq which were performed with either of the lineages respectively, we can be certain that the overall seen effect on the secretory lineage is a valid finding (we addressed this in line 229-232).

Line 251-252: How do authors make sure that "we were facing an effect on the differentiation process rather than cell type loss"?

• We can exclude an increase of cell death since we did not see changes in LDH release (Fig. 1C) and cleaved Caspase 3 abundance (Suppl. Fig. 1A) when we compared both conditions. Additionally, the data from the 24h stimulation time point showed the reduction of transcription factors (Fig. 4J) important for differentiation which manifested in a reduction of secretory cell markers upon longer stimulation (72h) (edited statement in line 256-258).

Line 259, How about apoptosis gene levels here?

• Apoptosis markers BAX and BCL2 are not amongst the differentially expressed genes. (see Suppl. Table 4).

Line 290 : It has been shown that the response of colon explants to IFN-α was variable among donors (https://www.sciencedirect.com/science/article/pii/S2352345X16301084#undfig1). This study should be cited. Was the response to IFNa tested on both organoids?

• The reference was included (Line 669-671). The response to IFNα was only tested in organoid line II given the limited availability of the serum that was used for co-stimulation. Overall, however, we saw an effect in both donor lines and less response was rather connected to the serum, not the organoid donor. In the publication reported interindividual heterogeneity depends on the therewith connected release of IL-18. Future studies could include analysis of cytokine release from the organoids after serum stimulation and a higher number of organoid lines to validate our findings.

Line 305-307: Is single cell sequencing from single organoid line or from combined? Do two organoid lines show different distributions?

• scRNA-seq was performed using organoid line II. Due to limited resources, we unfortunately could not include another organoid line.

  Reviewer #3 (Significance (Required)):

  While there is mounting evidence of an altered intestinal barrier integrity in SLE patients, there is little insights in the mechanisms. Using colon organoids is a novel approach with great potentials to investigate this issue. The strongest signature found by the authors was type IFN, which is indicates that the colon epithelial cells respond in a similar manner to other cell types. The secretory and absorption genes are of potential greater interest to unraveling mechanisms to gut alteration in SLE. This study is of interest for audiences interested in lupus basic and clinic research, as well as investigators working of gut barrier integrity.

Thank you for your constructive feedback. We are encouraged by your acknowledgment of our novel approach using colon organoids to explore the altered intestinal barrier integrity in SLE patients. Your emphasis on the significance of the type IFN signature and the importance of secretory and absorption genes aligns with our perspective.

Incorporating the intestinal barrier functional experiment emphasizes the translational nature of our study. We agree with your view that our findings are relevant for those involved in both basic and clinical lupus research, as well as specialists in intestinal biology. Your encouraging feedback strengthens our conviction in the wider significance of our work. We are motivated to keep bridging the gap between these two essential areas of study.

References

1. Pearce, S. C. et al. Marked differences in tight junction composition and macromolecular permeability among different intestinal cell types. BMC Biol. 16, 1–16 (2018).
2. Kishida, K., Pearce, S. C., Yu, S., Gao, N. & Ferraris, R. P. Nutrient sensing by absorptive and secretory progenies of small intestinal stem cells. Am. J. Physiol. Liver Physiol. 312, G592–G605 (2017).
3. Rath, E., Moschetta, A. & Haller, D. Mitochondrial function — gatekeeper of intestinal epithelial cell homeostasis. Nat. Rev. Gastroenterol. Hepatol. 15, 497–516 (2018).
4. Rodríguez-Colman, M. J. et al. Interplay between metabolic identities in the intestinal crypt supports stem cell function. Nature 543, 424–427 (2017).
5. Ludikhuize, M. C. et al. Mitochondria Define Intestinal Stem Cell Differentiation Downstream of a FOXO/Notch Axis. Cell Metab. 32, 889-900.e7 (2020).
6. Sünderhauf, A. et al. Loss of Mucosal p32/gC1qR/HABP1 Triggers Energy Deficiency and Impairs Goblet Cell Differentiation in Ulcerative Colitis. Cmgh 12, 229–250 (2021).

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Referee #3

Evidence, reproducibility and clarity

Inga Viktoria Hensel et al. used colon organoid to study the impact of lupus patients' serum on gut epithelial barrier. The exposure of SLE serum on colon organoids increased gene expression related to cell cycle, chromosome organization, mitochondrial function as well as interferon signaling, but downregulated that related to secretion, cytoskeleton, and anchoring junctions of the cells. Higher type I IFN in the SLE serum and unregulated interferon signature genes post stimulation suggest a potential role of type I interferon in this process. The addition of a type 1 interferon receptor (IFNAR1) antagonist, Anifrolumab, blocked the stimulation function of SLE serum but the combination of IFN-2α and control serum failed to recapitulate the results from SLE serum, suggesting that more than one cytokine was involved. SLE serum exposure altered metabolic profiles of organoids with a significant increase of basal respiration and ATP production. Stimulating organoid with SLE serum confirmed an alteration in cell differentiation with a loss of secretory lineage. scRNA-seq analysis revealed that colon organoid had all major cell types from colon in vivo. SLE serum stimulation shifted cell differentiation with decreased number of goblet cells and downregulated mucin, AMP and other components that were required for gut barrier integrity.

Finally, the authors performed a gene expression analysis of colon biopsies derived from SLE patients and healthy controls. While the authors should be commended to attempt a validation of the results obtained with organoids, the small sample size and patient heterogeneity prevented a statistical analysis. Some genes involved in absorption and ion transport as well as secretory lineage showed a similar trend with organoid assay, suggesting that colon organoids may be a good tool for future studies. However, it is noticeable that the biopsies from SLE patients did not show the IFN signature and the decreased in Muc2 expression, which dominated the gene signature of organoids exposed to SLE serum. There is no information about the disease activity of the SLE patients, as well as their IFN activity, which makes difficult to interpret these results.

Specific concerns:

1. Line 107, Why did The authors use 72 hours post treatment. Are other timepoints available and have similar results?
2. Figure 1D, how do the authors explain the heterogeneity among SLE samples (2, 3, 4, 5) on organoid line II? These samples do not seem to correlate with cytokine levels shown in Fig. 2. This issue may be worth exploring further, such as correlation between cytokine levels and gene expression.
3. Line 144, the 2 outlier SLE serum samples are not same between organoid lines with NO. 1&5 in Organoid line II and with NO. 1&4 in Organoid line I. The statement is misleading.
4. Line 169, IFN-a2 and IL-6 are not significantly different.
5. Line 179-180, Reduced fitness of organoids exposed to SLE serum is an overstatement. It was not directly tested, and there is no difference in apoptosis.
6. Line 242-243: SLE serum stimulation induced MUC2 high expression in Organoid II but lower level in organoid I (Figure 4B & Figure S4C). This is a major discrepancy that needs to be addressed.
7. Line 251-252: How do authors make sure that "we were facing an effect on the differentiation process rather than cell type loss"?
8. Line 259, How about apoptosis gene levels here?
9. Line 290 : It has been shown that the response of colon explants to IFN-α was variable among donors (https://www.sciencedirect.com/science/article/pii/S2352345X16301084#undfig1). This study should be cited. Was the response to IFNa tested on both organoids?
10. Line 305-307: Is single cell sequencing from single organoid line or from combined? Do two organoid lines show different distributions?

Significance

While there is mounting evidence of an altered intestinal barrier integrity in SLE patients, there is little insights in the mechanisms. Using colon organoids is a novel approach with great potentials to investigate this issue. The strongest signature found by the authors was type IFN, which is indicates that the colon epithelial cells respond in a similar manner to other cell types. The secretory and absorption genes are of potential greater interest to unraveling mechanisms to gut alteration in SLE. This study is of interest for audiences interested in lupus basic and clinic research, as well as investigators working of gut barrier integrity.

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Referee #2

Evidence, reproducibility and clarity

The group of Dr. Resnik-Docampo provides a very elegant study on two patient lines for SLE. The study is definitely very interesting and opens many scientific avenues that are worthy of being explored further.

Major comments:

• Barrier integrity or its alterations can be tested in organoids with specific dyes, I feel this would give definitive proof of concept.
• In supplementary figure 1 a caspase 3 staining is presented, please show a positive control for caspase 3 staining on organoids or alternstively use a different method to prove no differential cell death.
• Serum from SLE patients reduces drastically Edu positivity, it would be interesting to see a clonogenicity assay to see whether this reflects on reduced stem cell clonogenic potential
• Goblet cells in the colon are very heterogeneous, which subpopulations of goblet cell are reduced? how does this affect mucus composition?

Minor comments:

• Please provide an hypothesis on how mitochondrial alterations are linked to altered lineage progeny of stem cells. This should be discussed more in depth.
• Many antimicrobial peptides are changed, this could reflect on microbiome composition as well as mucus composition and properties, which I am sure will be the topic of future studies. This should be discussed more in depth.

Significance

The study is useful for both broad and specialised audiences. The findings are interesting and of relevance to the field of SLE, gut epithelial biology. The strength of the manuscript is that it opens many scientific avenues, its weakness is that they are not mechanistically dissected to the fullest rendering the study a bit descriptive. Nonetheless, I consider positively the manuscript after a minor revision given the major message of the paper can be proven.

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Referee #1

Evidence, reproducibility and clarity

Authors used organoid technology to study the effects of the serum from lupus patients on intestinal epithelium. By culturing organoids derived from human colon crypts, they specifically determined the response of epithelial cells to inflammatory mediators present in lupus serum. Using bulk and scRNA-seq, authors found that secretory cells function and differentiation were impaired as well as the mitochondrial metabolism. These effects were shown to be mediated by type 1 interferon in combination with other pro-inflammatory cytokines present in lupus serum.

The reduction of mucus secretion after SLE-serum treatment and the downregulation of tight junctions' genes seem to indicate an increased permeability of the epithelial barrier, thus it would be interesting to determine the expression and distribution of tight junction proteins and to test in the organoids whether the paracellular permeability is increased upon SLE-serum treatment. These analyses will give a functional result of this in vitro model.

If the organoids take a few days to culture and the material is available, the measurement of paracellular permeability may take no more than 2 weeks. It is true that they will need a microneedle to inject the FITC-Dextran 4K into the organoids and record the images for 24h.

I would like to know which of the donor's cells were used from figure 2 on and why.

The bioinformatics analyses using gene expression data and scRNASeq were well done. No comments.

Significance

For the field of autoimmunity, to study the crosstalk between the systemic response and the gut epithelium response results quite important as the increased permeability of the gut epithelial barrier has been suggested to fuel the systemic inflammation in lupus. However, as the author mention, there is not enough information about the interaction of epithelial cells and the systemic inflammatory mediators in lupus. This system can be useful to determine a personalized treatment for patients by testing the effect of individual serum on organoids. Moreover, the use of organoids can be extended to study the gut epithelium response in other autoimmune diseases mediated by type 1 interferon.

Increased permeability of the gut epithelial barrier has been related with lupus development. In humans, it is not known whether it is a cause or consequence, but in lupus mouse models it has been demonstrated that there is a reduction of the systemic autoimmune response concomitant with a reduction of gut permeability. The authors have validated an in vitro model that can be used to study how gut epithelium is affected by systemic inflammatory mediators and that will help to develop novel therapeutic approaches or personalize treatments.

Interest stakeholders: Clinical and basic researchers in autoimmunity, gastroenterology, and rheumatology.

My field of expertise is systemic and organ-specific autoimmunity at cellular and molecular level. My work covers autoimmunity and gut microbiota. I study how B cells regulate the microbiota composition and how that microbiota impacts gut permeability and inflammation in mouse lupus models. On the other hand, the bioinformatics analyses are well-done for both bulk RNASeq and scRNASeq.

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Reply to the reviewers

We thank the reviewers to appreciating the depth and quality of the results presented in our manuscript. We are happy that he/she has appreciated our contemporary approach to addressing an important problem but also a thorny and debated topic in the field that has lasted for over 30 years since it was first proposed by Mike Berridge. Our work not only addresses mechanisms in cultured human cells but also for the very first time addresses the key problem of Li action in the human brain using human iPSC derived forebrain cortical neurons.

We are delighted with the reviewer’s comment that “This effort highlighted the author's commendable goal to develop a thoughtful story and not just another publication.” We thank the reviewer for appreciating our detailed multi-disciplinary approach to addressing a long standing (> 4 decades) problem of key significance in human psychiatry and neuroscience. We thank him/her for noting that the work deserves to be presented in a journal with a cross-disciplinary focus. A few technical points that have been noted by the reviewer have been addressed below. Most of these can be effectively addressed by modest rewriting of text to make certain points clearer. The one experiment that has been suggested can be done and will be completed within 1 month.

1. Description of the planned revisions

Reviewer #1 (Evidence, reproducibility and clarity):

The authors functionally define in the inositol monophosphate phosphatase IMPA1, as the true target of lithium regulating phosphatidylinositol turnover and calcium signalling. While the observed IMPA1 inhibition by lithium led to the historical 'inositol depletion hypothesis' over the past 30+ years were published evidence both in support and against this concept. These contradictory sets of results have led to decreased interest in phosphoinositides as the signalling pathway affected by the therapeutic action of lithium in bipolar disorder (BD) patients. The remarkable results shown here will revert this trend since the data clearly demonstrate a key role of IMPA1 in setting the rate of phosphatidylinositol turnover, and consequentially the extent of calcium signalling. While the data are consistent with the 'inositol depletion hypothesis' the authors do not prove or disprove the validity of this hypothesis since the actual levels of inositol were not measured in their experiments. However, this is not a criticism, since quantifying cellular inositol is complex, it is just a suggestion for future work. After clarifying the points listed below this work will be suitable for publication.

• The experiments using inositol rich DMEM (reported on page 17 and in Fig 2I,J) require a better explanation and an adequate material and method section. It is not clear if the 'normal/control' condition uses inositol-free DMEM. The standard concentration of inositol in DMEM is 40uM. Thus, are the ~155uM (28 mg/litre) added by the authors at the high end of the 40uM? Given that FBS contains inositol have the authors used dialyzed serum? While adding 155uM of inositol on either inositol-free medium or to medium containing 40uM inositol does not alter the author's message, this technical information are important for the reproducibility of the data presented and to understand how HEK293T manages inositol homeostasis.

The standard concentration of inositol in DMEM high Glucose media (Dulbecco’s Modified Eagle Medium; Life Technologies) is 40 µM (7 mg/ litre) and our HEK293T cells were maintained under standard conditions at (37oC, with 5% CO2) in this media, supplemented with 10% Foetal bovine serum (FBS). This FBS was not dialyzed, so it might contain trace amounts of inositol.

For our inositol rich DMEM, 117 μl of 100 μM of inositol (18 mg/ml) was added to 100 ml of DMEM high Glucose media, supplemented with 10% FBS- this led to the final effective concentration of inositol in the media ~155 µM (28 mg/ litre). This media was referred to as the inositol rich media and used for the inositol supplementation in Fig. 2I-J.

Therefore, the inositol supplementation we refer to is effectively raising the extracellular inositol concentration from ca. 40mm to 155mM which is 3X elevation. This information has been added to the results section.

• It would be helpful to know if store operated calcium entry is altered in impa1-/--M1 cells. This information would nicely complement Fig.3 C-E data.

We have studied store operated calcium entry in IMPA1-/- cells and it is decreased. The quantification of this reduction can be added to the paper.

• In the Introduction at the end of page 4, the evidence not supporting the inositol depletion hypothesis is correctly discussed. This section lacks the discussion of another work questioning this theory (PMID: 30171184). The conclusion of this work is also in agreement with the authors finding that lithium affects the rare/turnover (lines 490/506) of PIP2 synthesis.

Saiardi A, Mudge AW. Lithium and fluoxetine regulate the rate of phosphoinositide synthesis in neurons: a new view of their mechanisms of action in bipolar disorder. Transl Psychiatry. 2018 Aug 31;8(1):175. doi: 10.1038/s41398-018-0235-2. PMID: 30171184.

This paper suggests that lithium mediated inhibition of IMPase leads to an accumulation of IP1 and this elevated IP1 leads to a competitive inhibition of the rate of synthesis of PI, and hence turnover of PIP2. Combined with lithium’s inhibition of inositol uptake, this inhibition of PI synthesis can lead to the mood stabilizing effect of lithium, rather than the inositol depletion. This point will be added to our manuscript and the reference cited.

• In the material and methods, Liquid Chromatography Mass spectrometry is abbreviated to LCMS while in the main text (line 493) LC-MS is used. The dashed version should be used throughout the manuscript.

Liquid Chromatography Mass spectrometry will be abbreviated to LC-MS throughout the text in the revised version.

• I suggest to define (line 500) phosphatidylinositol 4-phosphate as (PI(4)P simplified as PIP). This will be consistent with the phosphatidylinositol 4,5-bisphosphate abbreviation as (PIP2) as reported in the introduction (line 97)

PIP refers to all the functional isoforms of Phosphatidylinositol phosphate- PI 3P, PI 4P and PI 5P. By the LC-MS/MS analysis, we had measured the total PIP masses but we cannot distinguish between the individual functional isomers of PIP. However, pre-existing literature suggests that PI 4P is the most abundant isoform of PIP present in cell- its level is approximately 50 folds higher than that of PI 5P (Rameh et al., 1997). Hence we can suggest that the change in the total mass of PIP (as seen by the LC-MS/MS) is mainly reflective of the PI 4P.

• Line 646: Instead of using [this study] the authors should refer to the Figure panels supporting the discussed argument.

The identity of the channels mediating Ca2+ transients in this system was shown by us in Sharma et al., 2020. In the revision, we will cite this paper.

Referees cross-commenting

Reviewer #2 main message is identical to my. The work is a "contemporary re-evaluation of the inositol depletion hypothesis" but it does settle the debate. Say that reviewer #2 also recognises the importance of the work in defining IMPA1 as the only lithium target affecting the PI cycle removing GSK3 from the picture. Additionally, we agree that the thorough transcriptional analysis of the effect of lithium on human cortical neurons will be very informative for any researcher interested in psychiatric disorders.

Reviewer #2 requests are rational and not demanding. Most queries require extra information or the reformatting of the data presented.

Reviewer #1 (Significance):

The submitted manuscript addresses an important topic. The authors developed HEK293 stable expressing muscarinic receptor to study the effect of lithium (without or after receptor activation) on PI(4,5)P2 turnover using two approaches, by microscopy and biochemically by LC-MS. These analyses were followed by a thorough characterization of the effect of lithium on calcium signalling. The generation of HEK293 impa1-/- line has allowed the authors to demonstrate that the observed effect of lithium on PI(4,5)P2 turnover and calcium signalling were IMPA1 dependent. The authors pushed the work to a higher level by studying the effect of lithium on iPSC-derived human cortical neurons demonstrating that lithium reduces neurons excitability and calcium signalling. Although previously published attempts failed to generate IMPA1 deficient human cortical neurons the authors managed to produce iPSC impa1-/- but, as reported and consistent with previous literature, this cell line failed to differentiate into neurons. This effort highlighted the author's commendable goal to develop a thoughtful story and not just another publication. The work is complemented by a very informative transcriptional analysis characterising the effect of lithium on human cortical neurons. Noteworthy is also the author's efforts to functionally and transcriptionally define the effect of another lithium target, GSK-3. These experiments emphasize that GSK-3 does not phenocopy the effect of lithium. This is another utterly important message of the paper.

In conclusion, the authors presented an easily readable, comprehensive, and experimentally convincing story. Furthermore, the developed experimental tools (HEK293-m1AchR) and the extensive data set (transcriptomic analysis) will be instrumental to further studies aimed at elucidating mechanistically how phosphoinositide signalling affects BD pathophysiology.

Reviewer #2 (Evidence, reproducibility and clarity):

This manuscript seeks to test if inhibition of the phosphoinositide (PI) cycle is the relevant pathway targeted by lithium in bipolar affective disorder (BPAD). Firstly, a cultured model system (HEK293T) is used to test the effects of lithium on the PI cycle. Using PI(4,5)P2 probes along with mass spectrometry, Li is shown to inhibit PI(4,5)P2 re-synthesis after PLC activation, though not to perturb pre-stimulus levels. Release of calcium from intracellular stores along with refilling from extracellular calcium is also inhibited - though there are no effects on stored calcium capacity. Crucially, with the exception of the calcium refilling step, these effects if Li can be abolished by genetic ablation of IMPA1, the proposed molecular target of Li. Having established the affected pathway, the manuscript then studies the effects of Li treatment on iPSC-differentiated cultured cortical neurons. Spontaneous and muscarinic evoked calcium transients are shown to be abolished by Li. None of these effects in HEK293T or neurons can be recapitulated by an inhibitor of GSK3beta, another proposed target for Li. Finally, a transcriptomic analysis of Li treated neurons is presented, showing down regulation of relevant genes, especially genes involved in neuronal calcium signaling and glutamatergic signaling.

The inositol depletion hypothesis has been debated for nearly four decades. As it stands, this manuscript does not settle this debate once and for all, but it does add some novel and important insights: that 1) IMPA1 is certainly the target of lithium, at least in terms of the PI cycle and 2) Lithium treatment can lead to longer-term transcriptional changes in neuronal calcium and glutamatergic signaling that can dampen excitability. The paper is on the whole clearly written, and the data are easy to follow. That said, there are a number of areas where the manuscript is lacking key details, or where the results do not fully support the conclusions. Specific suggestions for amendment are as follows:

(1) The PH-PLCdetla1 PH domain has been used to follow PI(4,5)P2 turnover in HEK293T cells. Although long established, the manuscript does not discuss the fact that this domain also binds to IP3, which given high enough concentrations, can compete the PH domain off the membrane. As such, what is being measured is the convolution of PI(4,5)P2 decreases and IP3 increases (see for example doi: 10.1083/jcb.200301070 ). Ideally, a non-IP3 binding probe would have been used, such as the Tubby c-terminal domain (doi: 10.1186/1471-2121-10-67; doi: 10.1113/jphysiol.2008.153791). As it stands, the failure of the PH domain to return to the membrane after Li treatment reported in figures 1G, 3F and 3L could either be due to a failure of PI(4,5)P2 re-synthesis, or a failure to breakdown IP3 - either of which are plausible explanations given inhibition of IMPA1. This concern is somewhat mitigated by the inclusion of mass determinations of the lipids in figure 1H-J, which support the PI(4,5)P2 re-synthesis defect. However, the potential problems with interpretation of the data with the PH domain should be discussed.

PH-PLCδ-GFP probe is used in the field as a biosensor for PI 4,5-P2 (Chakrabarti et al., 2015; Várnai and Balla, 1998) and has been used to monitor the PIP2 turn-over rate. However, as the reviewer has pointed out, this probe also has an affinity towards IP3 (Xu et al., 2003) and therefore the failure of the probe to return to the plasma membrane could also, in principle, reflect the accumulation of IP3.

We are well aware of this discussion in the field and to make sure that our measurements using the PH-PLCδ-GFP probe are indeed a true reflection of PIP2 re-synthesis , we have also used a biochemical method to establish the levels of PIP2. Our measurements of the total mass of PIP2 by LC-MS/MS corroborate our findings using the probe, on the delay in PIP2 resynthesis. Nonetheless, we will explicitly mention this point in the discussion.

Drawback of the Tubby c-terminal domain-

Most of the biosensors for PI 4,5-P2 have distinctive advantage and disadvantages- PH-PLCδ-GFP probe is a more sensitive reporter but its IP3 binding may compromise its accuracy to measure PI 4,5-P2 changes. However, the Tubby c-terminal domain has exhibited lower sensitivity to report on changes of PI 4,5-P2 during PLC activation, although being more specific in its affinity towards PI 4,5-P2 (Szentpetery et al., 2009). Furthermore, recent studies have revealed that Tubby c-terminal domain can also bind to PI 3,4-P2 as well as PI 3,4,5-P3 (Hammond and Balla, 2015). Lastly a very recent study has noted that in contrast to PH-PLCδ-GFP probe, the tubby domain binds selectively to certain domains of the plasma membrane at membrane contact sites making it not a detector of PIP2 levels across the plasma membrane (Thallmir et,al., 2023, PMCID: PMC10445746 DOI: 10.1242/jcs.260848 ).

(2) The strongest evidence for the effects of IMPA1 inhibition coming from inositol depletion are given by the experiment reported in figure 2I and J, where inositol supplementation rescues calcium mobilization. This should also be performed for the PIP2 re-synthesis experiments.

We thank the reviewer for this suggestion.

We will perform this experiment and add the results to the revised manuscript. Duration estimated for this experiment- 30 days.

(3) It is implicit in the manuscript that DMEM does not contain inositol. This is not true; Life technologies' formulation for DMEM contains 40 micromol/l myo-inositol, which is sufficient to support activity of both proton/myo-inositol and sodium/myo-inositol symporters (HMIT and SMIT). On the face of it, therefore, inositol depletion seems unlikely. The reviewer wonders what concentration of added inositol mediated the rescue? This key fact is missing from the manuscript. At the very least, the details should be included and the reason for rescue of already inositol replete cells discussed. Ideally, the key experiments would be repeated with inositol-free medium and supplementation.

Repeated cycles of GPCR linked PLC signalling depend on a stable and on a continuous supply of PIP2 at the cell membrane, which in turn depends on the cytoplasmic pool of inositol in the cell. Inositol pool can be maintained by three avenues- via the recycling of the inositol by the stepwise dephosphorylation of IP3, de novo synthesis of inositol from glucose 6-phosphate and the transport of inositol from the extracellular environment across the plasma membrane. Li inhibits IMPase, an enzyme that dephosphorylates inositol monophosphate to generate free inositol.

Due to Li’s inhibition of IMPase, the inositol pool cannot be regenerated by the first two avenues since both of them need IMPase. However, restriction of the inositol pool by Li’s inhibition of IMPase can be bypassed by the transport of inositol from the extracellular media via SMIT (Sodium-dependent myo-inositol co-transporter) and/or HMIT (Proton-dependent myo-inositol co-transporter). At steady state, the low amount of inositol in the DMEM media (the inositol concentration in normal DMEM media being approximately 7 mg/litre or 40 µM) might be sufficient for maintaining the inositol pool and thereby PIP2 levels at the steady state. But this amount of inositol in the DMEM media appeared to be limiting to sustain the inositol pool and thereby PIP2 levels under conditions of hyperactivated PIP2 cycle. This is likely the reason why Li mediated inositol depletion (by inhibition of IMPA1) leads to a decreased rate of PIP2 synthesis at the plasma membrane as well as decreased PLC mediated Ca2+ release, in the background of activated PLC.

However, when the cells were grown in an inositol rich DMEM media (inositol concentration is ca. 28 mg/litre or approximately 155 µM; which is similar to the inositol concentration in the cerebrospinal fluid (Swahn, 1985; Shetty et al., 1996)); transport of extracellular inositol by SMIT/HMIT could sustain a continuous level of PIP2 levels even in a PLC activated background. This explains the rescue of the decreased PLC mediated Ca2+ release phenotype in the control-M1 cells grown in inositol supplemented DMEM despite the Li treatment.

This section can be added to the Results section (page 17) in the revised version.

(4) The introduction refers to lithium as a "non-competitive" inhibitor of IMPA1. This is erroneous, as lithium is in fact an uncompetitive inhibitor. This is a key distinction: since the uncompetitive inhibitor blocks the enzyme:substrate complex, it is most effective where substrate accumulates the most - in this context, sites of intense PLC activity. This was central to Berridge's inositol depletion hypothesis. Also, the Allison et al citation is incorrect here. The correct citation is PMID: 2833231.

Li inhibits IMPA1 in an uncompetitive manner.

This will be corrected in the revised version of the manuscript, with the appropriate references.

Ref cited by reviewer- Gee NS, Ragan CI, Watling KJ, Aspley S, Jackson RG, Reid GG, Gani D, Shute JK. The purification and properties of myo-inositol monophosphatase from bovine brain. Biochem J. 1988 Feb 1;249(3):883-9. doi: 10.1042/bj2490883. PMID: 2833231; PMCID: PMC1148789.

Ref- Hallcher LM, Sherman WR. The effects of lithium ion and other agents on the activity of myo-inositol-1-phosphatase from bovine brain. J Biol Chem. 1980 Nov 25;255(22):10896-901. PMID: 6253491.

(5) other key experimental details are missing from the figures/figure legends/results and or methods. Namely, what concentration of carbachol was used? What was the optimum concentration of thapsigargin? For figure 2 B-C, was carbachol used to evoke calcium mobilization?

For the agonist mediated Ca2+ release, 20 µM of carbachol was used. This is mentioned in the Materials and Methods section (line 238); however, we will mention this in the figure legends and results for clarity, in the revised version.

For the store depletion in the SOCE experiments, 10 µM of thapsigargin was used. This is mentioned in the Materials and Methods section (line 249); however, we will mention this in the figure legends and results for clarity.

In Fig. 2B, C, Carbachol was used to evoke calcium mobilization in the cells. This is mentioned in the Results section (line 510)- we will mention this in the figure legends for clarity.

(6) The effects of IMPA1 knockout and rescue in figure 3F are rather unconvincing. All treatment groups' means fall within 1 SD; are the changes statistically significant? Plotting 95% C.I. or standard error may be more informative for these experiments.

This can be addressed in the revised version.

Minor comments:

() There are some inconsistencies in the figure panels. Arrows labelled "CCh" are used to denote CCh addition in e.g. Fig. 3D, whereas simple arrows are used elsewhere e.g. Fig. 3F or no arrow at all e.g. Fig. 3B.

All the points where CCh has been added to stimulate PLC will be denoted with an arrow labelled Cch for clarity and consistency. This will be addressed in the revised version.

() Details of what the error denotes is missing in Figs. 2B-C, 3B-C, F - as is N for 3A.

Whiskers in box plots show the minimum and maximum values with a line at the median. This will be addressed in the revised version.

() Fig. 2D: It should be made explicit that arrows indicate TTX addition in the figure. More importantly, it should be clarified whether this is also the case for Fig. 5D? Transients do not appear to be depleted by this addition.

This has been mentioned in the figure legends for the figure (line 1077)- we will address this in the revised version.

Few of the neuronal transients are not abolished by TTX- this variability can be addressed by other representative traces.

() In figure 6C, it is stated that "there was no down regulation in transcripts for SCN1A (Nav1.1) or SCN9A (Nav7.1)" but this is not strictly true from the data; the Li-treated cells definitely trend lower. The effect is clearly not statistically significant, so although it is not possible to state that they reduced, this is not the same thing as being able to assert that they are not reduced. Perhaps it could be more helpful to plot the size of the reduction between these transcripts?

This will be addressed as- “there was no significant downregulation in transcripts for SCN1A (Nav1.1) or SCN9A (Nav7.1)".

The difference in the transcript level for SCN1A (Nav1.1) or SCN9A (Nav7.1) can be plotted for further clarification.

Reviewer #2 (Significance):

The manuscript is ultimately a contemporary re-evaluation of the inositol depletion hypothesis for Li treatment of BPAD, first proposed by Berridge in 1989. The manuscript certainly does not end the debate - other pathways and mechanisms for lithium's actions on a complex human behavioral phenotype will surely persist. However, it does add some important new insights: firstly, that IMPA1 is certainly the target mediating the effects of Li on the PI cycle; and secondly, that long-term, Li may exert effects at the transcriptional level to down regulate calcium and glutamatergic signaling in the brain. However, there is no mechanism presented to link these two findings. The work is therefore of interest to researchers with an interest on studying psychiatric disorders, basic mechanisms of neuronal excitability as well as molecular mechanisms of cell signaling. It therefore deserves to be published in a journal with a cross disciplinary focus.

2. Description of the revisions that have already been incorporated in the transferred manuscript

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

3. Description of analyses that authors prefer not to carry out

Nil

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Referee #2

Evidence, reproducibility and clarity

This manuscript seeks to test if inhibition of the phosphoinositide (PI) cycle is the relevant pathway targeted by lithium in bipolar affective disorder (BPAD). Firstly, a cultured model system (HEK293T) is used to test the effects of lithium on the PI cycle. Using PI(4,5)P2 probes along with mass spectrometry, Li is shown to inhibit PI(4,5)P2 re-synthesis after PLC activation, though not to perturb pre-stimulus levels. Release of calcium from intracellular stores along with refilling from extracellular calcium is also inhibited - though there are no effects on stored calcium capacity. Crucially, with the exception of the calcium refilling step, these effects if Li can be abolished by genetic ablation of IMPA1, the proposed molecular target of Li. Having established the affected pathway, the manuscript then studies the effects of Li treatment on iPSC-differentiated cultured cortical neurons. Spontaneous and muscarinic evoked calcium transients are shown to be abolished by Li. None of these effects in HEK293T or neurons can be recapitulated by an inhibitor of GSK3beta, another proposed target for Li. Finally, a transcriptomic analysis of Li treated neurons is presented, showing down regulation of relevant genes, especially genes involved in neuronal calcium signaling and glutamatergic signaling.

The inositol depletion hypothesis has been debated for nearly four decades. As it stands, this manuscript does not settle this debate once and for all, but it does add some novel and important insights: that 1) IMPA1 is certainly the target of lithium, at least in terms of the PI cycle and 2) Lithium treatment can lead to longer-term transcriptional changes in neuronal calcium and glutamatergic signaling that can dampen excitability. The paper is on the whole clearly written, and the data are easy to follow. That said, there are a number of areas where the manuscript is lacking key details, or where the results do not fully support the conclusions. Specific suggestions for amendment are as follows:

1. The PH-PLCdetla1 PH domain has been used to follow PI(4,5)P2 turnover in HEK293T cells. Although long established, the manuscript does not discuss the fact that this domain also binds to IP3, which given high enough concentrations, can compete the PH domain off the membrane. As such, what is being measured is the convolution of PI(4,5)P2 decreases and IP3 increases (see for example doi: 10.1083/jcb.200301070 ). Ideally, a non-IP3 binding probe would have been used, such as the Tubby c-terminal domain (doi: 10.1186/1471-2121-10-67; doi: 10.1113/jphysiol.2008.153791). As it stands, the failure of the PH domain to return to the membrane after Li treatment reported in figures 1G, 3F and 3L could either be due to a failure of PI(4,5)P2 re-synthesis, or a failure to breakdown IP3 - either of which are plausible explanations given inhibition of IMPA1. This concern is somewhat mitigated by the inclusion of mass determinations of the lipids in figure 1H-J, which support the PI(4,5)P2 re-synthesis defect. However, the potential problems with interpretation of the data with the PH domain should be discussed.
2. The strongest evidence for the effects of IMPA1 inhibition coming from inositol depletion are given by the experiment reported in figure 2I and J, where inositol supplementation rescues calcium mobilization. This should also be performed for the PIP2 re-synthesis experiments.
3. It is implicit in the manuscript that DMEM does not contain inositol. This is not true; Life technologies' formulation for DMEM contains 40 micromol/l myo-inositol, which is sufficient to support activity of both proton/myo-inositol and sodium/myo-inositol symporters (HMIT and SMIT). On the face of it, therefore, inositol depletion seems unlikely. The reviewer wonders what concentration of added inositol mediated the rescue? This key fact is missing from the manuscript. At the very least, the details should be included and the reason for rescue of already inositol replete cells discussed. Ideally, the key experiments would be repeated with inositol-free medium and supplementation.
4. The introduction refers to lithium as a "non-competitive" inhibitor of IMPA1. This is erroneous, as lithium is in fact an uncompetitive inhibitor. This is a key distinction: since the uncompetitive inhibitor blocks the enzyme:substrate complex, it is most effective where substrate accumulates the most - in this context, sites of intense PLC activity. This was central to Berridge's inositol depletion hypothesis. Also, the Allison et al citation is incorrect here. The correct citation is PMID: 2833231.
5. other key experimental details are missing from the figures/figure legends/results and or methods. Namely, what concentration of carbachol was used? What was the optimum concentration of thapsigargin? For figure 2 B-C, was carbachol used to evoke calcium mobilization?
6. The effects of IMPA1 knockout and rescue in figure 3F are rather unconvincing. All treatment groups' means fall within 1 SD; are the changes statistically significant? Plotting 95% C.I. or standard error may be more informative for these experiments.

Minor comments:

• There are some inconsistencies in the figure panels. Arrows labelled "CCh" are used to denote CCh addition in e.g. Fig. 3D, whereas simple arrows are used elsewhere e.g. Fig. 3F or no arrow at all e.g. Fig. 3B.
• Details of what the error denotes is missing in Figs. 2B-C, 3B-C, F - as is N for 3A.
• Fig. 2D: It should be made explicit that arrows indicate TTX addition in the figure. More importantly, it should be clarified whether this is also the case for Fig. 5D? Transients do not appear to be depleted by this addition.
• In figure 6C, it is stated that "there was no down regulation in transcripts for SCN1A (Nav1.1) or SCN9A (Nav7.1)" but this is not strictly true from the data; the Li-treated cells definitely trend lower. The effect is clearly not statistically significant, so although it is not possible to state that they reduced, this is not the same thing as being able to assert that they are not reduced. Perhaps it could be more helpful to plot the size of the reduction between these transcripts?

Significance

The manuscript is ultimately a contemporary re-evaluation of the inositol depletion hypothesis for Li treatment of BPAD, first proposed by Berridge in 1989. The manuscript certainly does not end the debate - other pathways and mechanisms for lithium's actions on a complex human behavioral phenotype will surely persist. However, it does add some important new insights: firstly, that IMPA1 is certainly the target mediating the effects of Li on the PI cycle; and secondly, that long-term, Li may exert effects at the transcriptional level to down regulate calcium and glutamatergic signaling in the brain. However, there is no mechanism presented to link these two findings. The work is therefore of interest to researchers with an interest on studying psychiatric disorders, basic mechanisms of neuronal excitability as well as molecular mechanisms of cell signaling. It therefore deserves to be published in a journal with a cross disciplinary focus.

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Referee #1

Evidence, reproducibility and clarity

The authors functionally define in the inositol monophosphate phosphatase IMPA1, as the true target of lithium regulating phosphatidylinositol turnover and calcium signalling. While the observed IMPA1 inhibition by lithium led to the historical 'inositol depletion hypothesis' over the past 30+ years were published evidence both in support and against this concept. These contradictory sets of results have led to decreased interest in phosphoinositides as the signalling pathway affected by the therapeutic action of lithium in bipolar disorder (BD) patients. The remarkable results shown here will revert this trend since the data clearly demonstrate a key role of IMPA1 in setting the rate of phosphatidylinositol turnover, and consequentially the extent of calcium signalling. While the data are consistent with the 'inositol depletion hypothesis' the authors do not prove or disprove the validity of this hypothesis since the actual levels of inositol were not measured in their experiments. However, this is not a criticism, since quantifying cellular inositol is complex, it is just a suggestion for future work. After clarifying the points listed below this work will be suitable for publication.

The experiments using inositol rich DMEM (reported on page 17 and in Fig 2I,J) require a better explanation and an adequate material and method section. It is not clear if the 'normal/control' condition uses inositol-free DMEM. The standard concentration of inositol in DMEM is 40uM. Thus, are the ~155uM (28 mg/litre) added by the authors at the high end of the 40uM? Given that FBS contains inositol have the authors used dialyzed serum? While adding 155uM of inositol on either inositol-free medium or to medium containing 40uM inositol does not alter the author's message, this technical information are important for the reproducibility of the data presented and to understand how HEK293T manages inositol homeostasis.<br /> It would be helpful to know if store operated calcium entry is altered in impa1-/--M1 cells. This information would nicely complement Fig.3 C-E data.<br /> In the Introduction at the end of page 4, the evidence not supporting the inositol depletion hypothesis is correctly discussed. This section lacks the discussion of another work questioning this theory (PMID: 30171184). The conclusion of this work is also in agreement with the authors finding that lithium affects the rare/turnover (lines 490/506) of PIP2 synthesis.<br /> In the material and methods, Liquid Chromatography Mass spectrometry is abbreviated to LCMS while in the main text (line 493) LC-MS is used. The dashed version should be used throughout the manuscript.<br /> I suggest to define (line 500) phosphatidylinositol 4-phosphate as (PI(4)P simplified as PIP). This will be consistent with the phosphatidylinositol 4,5-bisphosphate abbreviation as (PIP2) as reported in the introduction (line 97)<br /> Line 646: Instead of using [this study] the authors should refer to the Figure panels supporting the discussed argument.

Referees cross-commenting

Reviewer #2 main message is identical to my. The work is a "contemporary re-evaluation of the inositol depletion hypothesis" but it does settle the debate. Say that reviewer #2 also recognises the importance of the work in defining IMPA1 as the only lithium target affecting the PI cycle removing GSK3 from the picture. Additionally, we agree that the thorough transcriptional analysis of the effect of lithium on human cortical neurons will be very informative for any researcher interested in psychiatric disorders.

Reviewer #2 requests are rational and not demanding. Most queries require extra information or the reformatting of the data presented.

Significance

The submitted manuscript addresses an important topic. The authors developed HEK293 stable expressing muscarinic receptor to study the effect of lithium (without or after receptor activation) on PI(4,5)P2 turnover using two approaches, by microscopy and biochemically by LC-MS. These analyses were followed by a thorough characterization of the effect of lithium on calcium signalling. The generation of HEK293 impa1-/- line has allowed the authors to demonstrate that the observed effect of lithium on PI(4,5)P2 turnover and calcium signalling were IMPA1 dependent. The authors pushed the work to a higher level by studying the effect of lithium on iPSC-derived human cortical neurons demonstrating that lithium reduces neurons excitability and calcium signalling. Although previously published attempts failed to generate IMPA1 deficient human cortical neurons the authors managed to produce iPSC impa1-/- but, as reported and consistent with previous literature, this cell line failed to differentiate into neurons. This effort highlighted the author's commendable goal to develop a thoughtful story and not just another publication. The work is complemented by a very informative transcriptional analysis characterising the effect of lithium on human cortical neurons. Noteworthy is also the author's efforts to functionally and transcriptionally define the effect of another lithium target, GSK-3. These experiments emphasize that GSK-3 does not phenocopy the effect of lithium. This is another utterly important message of the paper.

In conclusion, the authors presented an easily readable, comprehensive, and experimentally convincing story. Furthermore, the developed experimental tools (HEK293-m1AchR) and the extensive data set (transcriptomic analysis) will be instrumental to further studies aimed at elucidating mechanistically how phosphoinositide signalling affects BD pathophysiology.

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Referee #3

Evidence, reproducibility and clarity

In this work, the authors generate a multi-omic dataset (RNA, proteomic and metabolomic) from fibroblast cell-lines of human and bat origins, in study of the specific differences in bat that allows them to have a good cancer resistance and longevity. They specifically focus on metabolic differences between humans and bats. They perform differential analysis followed by GO enrichment analysis to highlight differences related to the electron transport chain both at the level of RNA and protein abundance. They then use FBA sampling and specific constraints to propose an hypothesis of reverse direction of the second complex of the ETC, as well as better resistance to ROS, which they support with several subsequent experiments.

Overall, the paper is very well written, the findings are presented clearly and efficiently. For the most part, the assumption and limits of the study are clearly stated by the author (notably with respect to the limits of using only cell lines). In my opinion, the goal of the paper, which is presented as a stepping stone into further characterisation of the metabolic differences between human and bat for potential oncological research benefits, is clearly stated and appropriate.

There are however several points that I think are important to address inorder to improve the quality of the scientific work and its interest for the rest of the scientific community.

Major

The authors state:<br /> "We then set the lower bound of the PaLung Complex I reaction flux to a value equal to 70% of its theoretical maximum. Similarly, we set the upper bound of the WI-38 Complex I reaction at a value equal to 30% of its theoretical maximum value. This ensured that the PaLung model would have higher flux through the Complex I reaction, in comparison to the WI-38 model."

How do the results hold with different thresholds ? Are these findings robust with e.g. in ranges between 10 to 50% (90-50%) (instead of only 30% and 70%). Furthermore, the histogram figures doesnt seem to reflect a 70% of maximum lower bound for complex I (threshold at a value of 30 seems like extremity of tail).

Number of differentially expressed genes is extremely high because such cutoffs are not really meaningful given the comparison between two organisms. No need to refer to the 6247 above cutoff as differentially regulated genes (see: https://elevanth.org/blog/2023/07/17/none-of-the-above/ and https://daniel-saunders-phil.github.io/imagination_machine/posts/if-none-of-the-above-then-what/ for pointers toward current best practice in biological statistics). Enough to simply note that 6247 are above the cutoffs, which suggest a drastic (and expected) difference in expression profiles between the two organisms.

Please highlight the RNA and proteomic analysis assumption and present results within those boundaries (e.g. how are the transcript matched between human and bat, the use of human gene ontologies, etc...). Are the human GO set definitions relevant in bat (it is a common practice with mice and rats, are bats close ?)?

Are oxphos and hypoxia responses the most extreme pathway scores in the GSEA ? Instead of barcode plots that are generally not a very useful use of figure space, use fig 1C to show the top e.g.20 (positive and negative) pathway scores so that we can see how much those two actually stand out. Same for the proteomic analysis. Also, need to show an unbiased side by side comparison of the pathway enrichments for RNA and proteomic, the reported results in main text and figures are too cherry picked to be of interest as they stand.

Finally, and very importantly, please upload ALL the code used for the analysis, with instructions to run it and all the required inputs and source files. The computational analysis is only as credible as it is easy to reproduce.

Minor

Introduce GeTMM, what are its key specificities ?

Fig 1C code bar plot useless, simply report ES and NES and pathway absolute rank in text.

Report Foldchange/p-value/rank of complex-I members and other genes of interest for the narrative of the paper.

Referees cross-commenting

I also think the comments from the other reviewers are appropriate.

Significance

In my opinion, the goal of the paper, which is presented as a stepping stone into further characterisation of the metabolic differences between human and bat for potential oncological research benefits, is clearly stated and appropriate.

Broadly interesting for oncological research.

My espertise is multi-omic data analysis and integration with prior knowledge in the context of complexe diseases.

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Referee #2

Evidence, reproducibility and clarity

Jagannathan et al. performed a multi-omics comparative analysis between fibroblast cells from bats of the species P. alecto and humans. Using a combination of transcriptomics, proteomics and metabolomics, the authors showed differences in central metabolism between the cells of the two species. Specifically, the authors noted higher expression of Complex I components of the electron transport chain, as well as low activity of the Complex II. The computational modeling suggested that the latter is indicative of a state resembling the state of ischemia. Furthermore, the expression of antioxidant components was interpreted as higher in the bat compared to human cells, which is in accordance with previous reports.

Overall, it is a comprehensive multi-omics approach performed with a very interesting biological object, such as a bat. However, some aspects, especially the part of bioinformatic analysis, needs to be enhanced. The existence of differences in mitochondrial metabolism is also not surprising, given the evolutionary distance and very different ecology and lifestyle between the two species, but a more mechanistic follow-up would be of greater interest. Nevertheless, it is the first study using integrated omics approach of that sort done on bats.

Major:

1. The authors compared a fibroblast cell line derived from adult bats with a human embryonic cell line. Please discuss whether mitochondrial metabolism in embryonic cells might be different and how it could have affected the obtained results. Please describe in more detail how the cells were established, what population doubling they were used at (both bat and human cells). Were the cells cultured in atmospheric oxygen or low-oxygen conditions. The exposure of cells to atmospheric oxygen might affect the many mitochondrial parameters measured in this study and could influence the main finding about ischemic-like state. Additionally, please mention in the limitations of the study that only biological n=1 was compared (since cells only from 1 individual per species was used in experimental groups), despite n=3 technical replicates.
2. Reference genomes for bats are not as well annotated as for human. Downregulation of a pathway may result from some genes being excluded from the analysis because of poor annotation of the P. Alecto genome compared to human. The authors state: "Genes with counts per million (CPM) < 1 in more than 3 out of 6 samples were discarded from downstream analysis". So, if the gene was not annotated, was it assigned a zero value and discarded? Was it discarded if it was zero in one species (e.g. bat) or set to 0? If such genes were excluded, while in reality not being mapped, they could have skewed the pathway analysis.
3. All conclusions are based on high-throughput data, however it is accepted that some validation should be provided. Please provide qPCR or WB (if good antibodies are available) validation for several most significantly differentially expressed genes supporting the pathways identified in Figure 2 (preferably supporting the conclusions about Complexes I/II).
4. The major findings of this paper were based on the omic data, followed by some experimental validations. However, the quality of these omic data or the results are not solid enough to motivate the authors to validate these findings. For example, both of the GO terms enriched by the DEGs in Fig.1 are not the top terms as claimed by the authors (not even significant after multiple test correction). Also, even though the 2 GO terms in Fig.2 are quite significant, the expression pattern seems not very consistent among the replicates, which make the enrichments not so solid. This highlights an inconsistency among different omic datasets, which may generate some conflicting results. For example, the low level of metabolites from TCA cycle (Fig.4c) seems not consistent with the high level of TCA-related protein, as described in Fig.2c & d. For the purpose of improving the manuscript quality, the authors may have to evaluate the consistency among the multiple omic datasets or to optimize their bioinformatic pipeline to enhance the results.
5. The dominant up-regulation of complex I in ETC is interesting and is the main finding of this paper. However, no experimental evidence was provided to prove the greater activity of Complex I, for example, metabolites changes. In addition, the genes encoding proteins belong to ETC complex I, II, III and IV vary a lot, with much more genes encoding complex I. Therefore, the author should consider the background gene number when they compare the up-regulated gene number differences in each complex. For example, a fisher-exact test could be done to see if complex I has significantly more genes been up-regulated than a random expectation.
6. If the main findings of this paper can be further confirmed by additional experiments or data, it will be a very nice paper. This could be a potential mechanism that bats used to switch metabolism modes between two metabolic extremes: flight and hibernation, which require high and low energy. However, the usage of only the lung fibroblasts of human and bat may limit the ability of generalizing this 'ischemic-like state' of ETC in most of the bats tissue/organs. While I agree what the authors mentioned in the discussion section, that to extend to primary cells of other species can help generalize this finding, studying the metabolism state of different cell type of bats (e.g., muscle cells responsible for flight; myocytes and neurons for hibernation) probably can provide more insights into the evolution of various interesting phenotypes of bats.

Minor:

• the author may have to add the p value or FDR for each GSEA plot, even though some of the FDR are not significant. Also, it will be better to show the normalized enrichment score (NES) instead of the ES.
• the gene set name in several supplementary tables contains many '%' characters and those needs to be removed.
• in Line 302, "...combined with the earlier findings of downregulated OxPhos expression and low OCR, we conclude...". If my understanding is right, the authors only mentioned the up-regulation of Oxphos expression, instead of down-regulation. This sentence may need to be clarified.
• How did mitochondrial DNA content per cell compared between the two species? Could the results be affected by the number and size of the mitochondria per cell in each species? An indirect measurement of mitochondrial DNA yield in the fractionation experiment would be the total DNA amount that was obtained in mitochondrial fractions per cell lysed.

Significance

The work is significant considering the limitations stated above. This may be considered a pilot study of brand significance.

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Referee #1

Evidence, reproducibility and clarity

Summary

In this study, the authors conducted a multi-omics analysis comparing cells from the long-lived bat, Pteropus alecto, and human cells. Their findings revealed that bat cells express higher levels of mitochondrial complex I components and exhibit a lower rate of oxygen consumption. Moreover, computational modeling suggested that the activity of complex II in bat cells might be low or even reversed, similar to the conditions observed during ischemia. The decrease in central metabolites and the increased ratio of succinate to fumarate in bat cells might indicate an ischemia-like metabolic state. Despite having high mitochondrial ROS levels, bat cells exhibit higher levels of total glutathione and a higher ratio of NADPH to NADP. Additionally, bat cells showed resistance to glucose deprivation and induction of ferroptosis.

Major comments

1. Regarding Figure 1A, the authors mention 'n = 3' for a single cell line. Does this refer to three different passages or three independent experiments? Please provide a more detailed description to clarify.
2. In relation to Figures 1C and 1D, the authors state in the figure legend that the 'GSEA analysis identifies Respiratory electron transport and Cellular response to hypoxia as the top metabolic pathways that are differentially regulated between PaLung and WI-38 cells.' (Lines 140-144). However, the criteria for selecting these terms as the top metabolic pathways is not clear. In the lists in Supplementary Tables 2 and 3, the authors' proposed term, 'Respiratory electron transport,' is ranked 126th, and 'Cellular response to hypoxia' is ranked 79th. Conversely, terms related to the TCA cycle are ranked 66th and 82nd, and another term that seems to be related to hypoxia, 'OXYGEN-DEPENDENT PROLINE HYDROXYLATION OF HYPOXIA-INDUCIBLE FACTOR ALPHA,' is ranked 62nd. Could the authors please provide a clarification for their choice of 'Respiratory electron transport' and 'Cellular response to hypoxia' as the top metabolic pathways?
3. In the Materials and Methods section (lines 419-421), the authors mention, 'GSEA was run against the complete Gene ontology biological process (GO BP) gene set list (containing 18356 gene sets).' However, they narrow down the gene dataset for analysis (lines 136-138, 'we filtered our gene dataset to contain only genes listed under the Gene ontology category Cellular Metabolic Process (GO ID:0044237), resulting in a truncated list of 4794 genes.'). I'm concerned that this selective approach might introduce bias into the resultant pathways. Is this selective approach commonly employed in this type of analysis? And isn't there a need for adjustments to avoid potential bias?
4. The authors noted that the number of differentially expressed genes (DEGs) is quite high (6,247 out of 14,986) as per lines 134-135, stating that "The number of differentially expressed genes (6,247) was extremely high, suggesting that multiple pathways are differentially regulated between the two species." However, this large number of DEGs could indicate either an improper correction procedure or a need for a more stringent threshold. The authors should address this issue to avoid potential misinterpretation of the results.
5. In Figure 2B, the samples labeled as W1 and P1 appear to be outliers. This raises questions about the integrity of the sampling or analysis process. Please describe about this.
6. Regarding the GSEA analysis of Fig. 2, they are using the full set of GSEA. However, this reviewer is wondering if this is appropriate when analyzing mitochondrial fractions, as I believe using the entire GSEA set could introduce a bias. Is this a common approach? Shouldn't the authors be focusing on mitochondrial-related sets within the GSEA, and then determining the upregulated and downregulated pathways from there?
7. The authors describe in lines 195-197, "GSEA-flagged upregulation in OxPhos was driven mostly by the upregulation of Complex I subunits, for both the proteomic and transcriptomic data (Figure 2G, Supplementary Figure S1D)." However, within this analysis, the number of genes composing each subgroup of the mitochondrial Complexes are 44 for Complex I, 4 for Complex II, 10 for Complex III, and 19 for Complex IV (https://www.genenames.org/data/genegroup/#!/group/639). The authors mention that the genes of Complex I were dominant in the ETC, but, might this just be reflecting the original difference in the number of genes? As this reviewer believes this could have a significant impact on the authors' current claims, this reviewer suggest the authors to carefully reconsider this point, comparing the actual results with the proportion expected from the difference in gene numbers. (Even in Fig. S1D, it appears to correlate with the number of genes: C1 39.3%, C3 10.7%, C4 10.7%, C2 3.5%)
8. As pointed out in Major Point 7, if the authors' claim of enrichment in Complex I is indeed due to the large number of genes included in the Complex I subgroup (https://www.genenames.org/data/genegroup/#!/group/639), can the assumption of High Complex I flux truly be considered valid? In that case, this constraints model would become inappropriate, and the validity of the inferred low or reverse activity of Complex II would be diminished. Therefore, a careful re-examination is desirable.
9. (option, takes about 1-2 months). This reviewer believes that the authors' most important claim, concerning the high activity of Complex I and the low activity of Complex II, lacks strong evidence as no biochemical data of the activities of each mitochondrial complex are presented to substantiate this. Unless additional biochemical experimental data is provided, the assertions should be toned down. While the abstract mentions "complex II activity may be low or reversed," it is stated with certainty in line 108 of the introduction, "associated with the low or reverse activity of Complex II." Based on the present data, this reviewer believes that the claim remains speculative. Therefore, I suggest moderating the overall argument or adding the biochemical data. While the results from metabolomics are supportive, they do not serve as direct evidence.
10. Regarding Figure 5, the title of the figure states "lower antioxidant response", but it doesn't seem that the data in the figure actually shows a lower antioxidant response.
11. In lines 109-110 of the Introduction, the authors state, "we confirmed our prediction of ischemic-like basal metabolism in PaLung cells by characterizing the response of bat cells to cellular stresses such as oxidative stress, nutrient deprivation, and a type of cell death related to ischemia, viz. ferroptosis." However, can the assertion that the cells are in an ischemic-like state be confirmed simply because they are resistant to several types of cellular stress?

Minor points:

1. The authors mention the use of cufflinks/Tophat for mapping/quantification. However, support for these software programs has ended and the creators of these programs themselves recommend using the successor programs. I recommend re-analysis using a more current pipeline (such as HISAT2/StringTie, STAR/RSEM, etc.). Furthermore, the transcriptomics section of the methods should also include the program used for cleaning and trimming.
2. As for the Oxygen Consumption Rate (OCR) data presented in Figure 2F, it makes sense that it's low at the basal level. However, it's perplexing that it is also low even under uncoupled conditions, especially considering the high energy demand associated with flight in this species. Could the authors provide their interpretation on this apparent contradiction?
3. In line 156, the authors mention that 'Profiling detected a total of 1,469 proteins.' Please provide more details in the explanation. Specifically, does this total of 1,469 proteins represent a combined count from both humans and bats, or is this the number of proteins for which orthologs could be identified in both species, just like the authors did with the transcript results.
4. In Supplementary Table 4, only 127 mitochondrial proteins are listed out of the 405 proteins mentioned in "Of these 405 proteins, we identified 127 to be core mitochondrial proteins (lines 161-163)". As there is no explanation for this within Supplementary Table 4, it would be better to include one.
5. In line 472, the phrase "GO BB gene set list" is used. Could this potentially be a typographical error, and should it instead be "GO BP gene set list"?
6. In the volcano plot of Fig. S3B, it appears that the side with lower P/W values generally corresponds with lower p-values. I wonder if there might have been any oversight or mistake in the data analysis process that could explain this observation?
7. In lines 249-252, it is stated, "The low or negative flux values for Complex II in our PaLung simulations indicate that the electrons obtained from Complex I may accumulate at Complex II or potentially even get consumed by Complex II operating in reverse (bypassing the rest of the ETC) in PaLung cells." However, isn't the basic process of electron transfer done through Complex I-III-IV, independent of Complex II?
8. Regarding Figure 4F, the authors state, 'PaLung cells displayed higher viability than WI-38 cells after glucose deprivation (Figure 4F).' However, in addition to the cell images, it would be beneficial to perform experimental quantification of cell death to provide more rigorous data. Additionally, the cells appear to be over-confluent, which might influence the results. Also, scale bars should be included in all photos, including Fig. 6.
9. Regarding Figure 5B, it is stated that 'the expression levels of differentially expressed antioxidant genes' are shown, but it includes those that are not significant. It would be helpful if the authors could clarify how this gene set was selected.
10. Regarding Figure 6C, the values for total glutathione seem to significantly differ from those in Figure 5C. An explanation for this discrepancy would be appreciated to ensure the consistency and reliability of the data.

Referees cross-commenting

I think the comments from the other reviewers are appropriate.

Significance

Collectively, these intriguing results from the interspecies comparison provide novel insights into the differences in metabolism and cellular characteristics between bat and human cells. However, the study has some limitations, notably certain weaknesses in the data and potential overstating of certain interpretations. Addressing these issues would enhance the overall quality and robustness of the manuscript. Furthermore, if feasible, conducting a biochemical analysis of each mitochondrial complex activity would solidify the authors' main conclusions.

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Referee #3

Evidence, reproducibility and clarity

Summary:

This paper provides an elegant investigation into the dysmorphology of skeletal structures upon loss of the extracellular Fraser Complex through use of a zebrafish fras1 mutant. The adult phenotypes of this mutant had not been previously explored in detail. The authors use transgenics, histological staining and immunostaining to visualize the morphological defects, then examine Fraser Complex localization and effects on Shh signaling upon loss of Fras1. They analyse both steady state and regeneration contexts. Most importantly, they demonstrate the blistering that has been shown to occur in larval fins, also occurs during regeneration of the adult fins, and that there is a disorganization of the pre-osteoblasts due to basement membrane corruption. This work provides novel investigation into how Fraser Complex leads to skeletal defects.

Major comments:

Overall the data presented by the authors is logical and very well presented. They make reasonable claims that are supported by sound experimentation. Statistics are used appropriately and the authors combine different approaches to make their points. For example they draw on previous single cell RNA-seq Data sets to define the cell type expressing Fraser complex components but then also use immunostaining and ins situ hybridisation to localise expression domains. I thought the analysis using the ptch1:kaede line to be particularly elegant and informative.

My first major question relates to Figure 7. The authors cage their analysis under the impetus of understanding how the distal anomalies and skeletal defects in Fraser Syndrome arise in development. They present convincing images of distal blistering during regeneration. Were similar blisters seen at any stage during adult fin development prior to the full fin formation? The authors might have noted this during their imaging of the ptch1 reporter Figure 6 Supplement 1. Or they might need to look earlier. Figure 7 is informative analysis. It's a shame to limit it to regeneration and not look during adult fin formation which would have direct inferences for human development.

Secondly, was osteoblast morphology affected as well as their patterning in the fras1 mutants? Perhaps the authors could look at zns-5 antibodies or sp7:egfp line in transverse cryosections to assess if there was a change in osteoblast morphology. Figure 5B' certainly suggests they might be more cuboidal and have lost their flattened shape. This change in osteoblast morphology has been noted in other ECM mutants affecting the lepidotrichia.

Minor comments:

I have only a few minor comments.<br /> Line 55 Introduction. For clarity change this sentence to "variable expressivity and incomplete penetrance reminiscent of the variability seen in distal limb defects in humans with Fraser Syndrome."

Referring to Figure 2, is each fin ray thicker in fras1 mutants than in WT? It appears so but might be just an optical illusion. Would be easy to measure and state in the text.

Line 242, 243 and Figure 5. The text refers to Fig 5C' and Fig 5D', but I could only find Fig 5C' and Fig 6C'. Do you mean E, F??

Related you claim that in fras1 mutants, frem2 protein remains intracellular. How do you know that protein signal is intracellular yet in the WT it is extracellular? Please reword this or show more conclusively. This is also repeated on line 360 in the discussion.

Fig 6B, D are not referred to in the main text.

I couldn't find details of the Runx2 antibody in the materials and methods

Significance

This paper provides novel insights into how loss of Fraser Complex might alter morphology of post-embryonic structures, and gives novel, visual indication of the effect basement membrane disruption has on osteoblast patterning. This will give novel guidance to explaining the basis of skeletal presentations of Fraser Syndrome for basic researchers interested in the importance of osteoblast environment on patterning and clinicians examining similar rare skeletal congenital syndromes.

It elegantly demonstrates that cellular topology, organisation and morphology is just as important as signalling in defining organ growth and shape. The authors also suggest this model of an indirect disruption of osteoblast patterning might explain why there is such variability in distal skeletal phenotype severity in Fraser patients. This is a valid and reasonable hypothesis.

My background directly relates to mutant analysis of zebrafish fins

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this manuscript, Robbins et al. develop the adult zebrafish caudal fin as a model for limb deformities in Fraser syndrome. They show that the zebrafish fras1 mutant can survive to adulthood and investigate how loss of fras1 influences the caudal fin during its regeneration. The fin ray branching defects and chondral bone defects examined in this study show a degree of variability akin to many defects found in Fraser syndrome, including limb defects. The paper is an easy read, has beautiful imaging, and shows compelling quantification. As such, the authors set up the zebrafish fras1 mutant caudal fin as a novel and interesting platform to investigate the onset and variability of limb defects in Fraser syndrome.

Major comments:

Figure 7 makes some critical claims based solely on H&E staining; better markers are needed. I also strongly recommend that the authors repeat this Figure 7 experiment with immunolabel or other techniques that mark the epithelial and mesenchymal layers distinctly. In particular, since the author's discussion focuses on osteoblast positioning, it seems important to mark the osteoblasts alongside the epithelium.

The authors posit that background mutations explain Fraser syndrome variation, but it's worth also considering non-genomic origins of variation, including potential micro-environmental differences and/or stochasticism. The caudal fin seems like an ideal system to test the idea - one could injure the fin and test how well initial defects predict defects after regeneration. It looks like the authors started to do so in panel 3R, but don't comment on the finding. Also, it looks like the initial severity doesn't completely predict regenerated severity - for instance, the least severe fin pre-injury becomes the most severe fin post-injury. I recommend explaining panel 3R in results text and returning to it in the discussion, to contextualize how this finding might influence understanding of fras1-dependent variability. OPTIONAL: Variability could also be compared between different fins in the same animal; does the severity of caudal fin defect correlate with severity in the pelvic or dorsal fin? If so or if not, what might this suggest about the origins of phenotypic variation in this model?

The study is focused on core Fraser complex, particularly fras1 and the Frem genes which are mutually-stabilizing components of anchoring cords that link nascent epithelia to underlying mesenchyme. The authors could broaden the paper's impact and scope by considering other proteins bound by the core Fraser complex, such as AMACO, Integrins, Npnt, fibrillin, etc. Likewise, examination of Collagen expression, such as collagen VII, may help explain why there is a dissipation over time for the requirement for fras1 to prevent blistering. It may be outside the scope of this study to delve deep into these 'nearby genes' but it seems reasonable to examine them bioinformatically in the scRNA-seq (perhaps adding a supplemental figure if the results are unsatisfying), or at least to discuss them and thereby contextualize the overall findings.

Minor comments:

Figure 8 feels like a visual abstract, summarizing findings and contextualizing existing models to the caudal fin, instead of putting forward a truly new model. It focuses on the concept that Fras1 is needed for epithelial-mesenchymal attachments and that Fraser-components stabilize one another; both of these ideas are over a decade old. Although the concepts are cited appropriately within the discussion narrative, it would be nice if Figure 8 did a bit more. The model could be revised in a way that offers new insights into how the specific defects seen in this study arise, by hinting at answers to some of the many questions raised by the study. What is the source of variation? Why do the fin rays fail to branch in the mutant? (could there be more to that decision than simple osteoblast mispositioning?) Why does defect severity change during regeneration vs. development? Why does an extra hypural cartilage form in the mutant? Is there any similarity between the failure to branch fin rays and the presence of fusions in chondral skeleton? These fusions could also be compared and contrasted with non-limb skeletal fusions? It's certainly optional to tackle all of these issues, but discussing any of them could increase the manuscript's impact and establish interesting fodder for future papers. These changes are important, but I place this remark in 'minor comments', because an improved final figure would not be critical to publication in some journals.

This sentence on line 273-274 is confusing and should be revised: "However, the Fraser Complex at least supports the Shh/Smo downstream cell behaviors that split pre-osteoblasts given frequent ray branching defects in fras1-/- mutants." What do the authors mean by "at least supports"? The phrasing may imply 'Provides physical support essential to,' but that reading does not explain why the fin rays branch in Shh expressing cells regardless of the presence of Fras1. (this comment is actually 'minor')

The Figure 7 title states that the Fraser complex is needed for normal "epithelial-mesenchymal tissue layering." I am not certain what the authors mean by this. The phrase written in the figure title implies that mesenchymal cells start appearing inside of epithelial regions, but that interpretation doesn't match the figure nor results text. An alternative read - one consistent with the final figure - is that the authors are simply trying to show that the blisters form at the interface of the fin and underlying mesenchyme. If this is the primary thrust of the argument, then it could be stated plainly - and shown more clearly in the results section. (this comment is also truly 'minor')

Significance

This manuscript establishes the zebrafish adult caudal fin as a new model to investigate epithelial-mesenchymal interaction defects and skeletal variation caused by loss of the central Fraser complex gene. It is an important contribution to existing models because of the similarity between patterning mechanisms in the caudal fin and tetrapod limbs, which are variably disrupted in Fraser's syndrome. This variability is itself of interest, because the profound variability of Fraser phenotype offers an experimental model for high-variance diseases, which remain poorly understood. The caudal fin is an exciting system to study variation, in particular because it can be cut and regrown rapidly with phenotypic severity compared between regenerates in a clonal system; that regenerative tractability is particularly potent when paired with the zebrafish transgenic toolbox, as the authors show in Figure 6. The present manuscript feels almost complete and yet it opens up many questions, suggesting that it can serve as a good foundation for future studies.

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Referee #1

Evidence, reproducibility and clarity

Robbins and colleagues present a new zebrafish model for studying the rare disorder Fraser Syndrome in a well written manuscript. The authors present an interesting phenotype caused by a mutation in fras1: mutants have small, misshapen fins with rays that frequently fail to form branches. The authors show that the Fras1 protein localizes to the distal portions of regenerating fin rays and that in its absence, another component of the Fraser Complex (Frem2) is decreased and mislocalized. The authors show that Fras1 is not required for Shh/Smo signal transduction but that branching is nonetheless disrupted.

Major comments:

Since the basement membrane ends up being such a big part of the story, it would add support to the model to specifically present a laminin stain in both WT and the mutant. This could potentially support to the idea that the FPC is integrating the BM with the osteoblasts and it would be helpful to see where the BM is relative to the 'blisters.'

The fras1-/- mutants show a near-absence of Frem2 protein; the authors conclude that Fras1 supports Frem2 secretion and assembly into Fraser Complex-containing BM. However have the authors considered the possibility that Fras1 directly or indirectly regulates Frem2 transcription? (The model in Fig 8 seems to show a relative increase in Frem2, which should be altered)<br /> Do the other components of the FPC (Frem1a/b, Frem3) show similarly disrupted levels and localization? As presented, since the Frem1a/b, Frem3 are not actually examined in a fras1-/- context, it is not justified to show their intracellular localization in the model in Fig 8b.

The relative activities of osteoblasts and osteoclasts may regulate the relative location of branches (Cardeira-da-Silva et al 2022). In the fras1 system mutant, it would be beneficial to examine osteoclasts to rule out the idea that Fras1 is simply required for osteoclast activity. This could additional lend support to the idea that disrupted integration between the BE and Obs underlies that failure of fras1-/- mutants to form branches. This could be done within a few months by crossing the mutant into an osteoclast reporter, or more rapidly by using an osteoclast specific antibody or ISH.

The authors look only at the meristic presence/absence of fin ray branches, and do not take fin length into account. Longer rays are more likely to form branches and the fras1-/- have shorter fins. Some of the branches may be absent in the mutant background simply because the rays are not long enough to have formed them. How far are the branches from the body in each background? Where do the branches form along the total length of the rays?<br /> In the regeneration experiments, do the mutants regenerate at the same relative rate as WTs? Could the decrease in the number of branched rays simply be due to the fact that the mutants may not have not regenerated to their original length by 28 dpa? Has the distance from the body to branch changed? These are all addressable with the data the authors have already collected.

Minor comments:

The protruding lower jaw phenotype mentioned in the results should either be shown, or if it is redundant with previous publications please add the citation here.

Please show Amputation planes in Fig 4E-G. It is notable that Fras1 seems to be present in the proximal region (proximal to the amputation plane?). This seems like it deserves mention in the manuscript.

In line 195, please clarify what you mean by ray proportions. The proportion of longest/shortest, presumably?

Please change the title of Fig 3: Fras1 mutants robustly restore skeletal patterning. Also, the ray length ratio in Fig 3Q has been tested with an unpaired t test. It should be a paired test as in R.

This is an unbelievably a minor point, but for consistency with the other panels I think that in Fig 5 E and F should be C' and D'.

The titles of Fig 6 and Fig 7 should indicate indicate Fras1 not the Fraser Complex. Eg. Fras1 is not essential for sonic hedgehog/smoothed signal transduction during fin regeneration

There are also a few grammar errors that should be fixed.

Significance

Robbins and colleagues present a new zebrafish model for studying the rare disorder Fraser Syndrome.

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Reply to the reviewers

First and foremost, we would like to extend our thanks to the two referees and managing editor of Review Commons for their constructive feedback and careful consideration of this manuscript. We have taken into consideration all of the suggestions from the reviewers and address all points below and in the following sections.

Minor comments from Reviewer #1:

“It is not clear why the authors used cell number as a measure of viability compared to the MTS assay used in figure 2.”

Response:

In Fig. 2, both MTS assay and CellTiter-Blue assay are used to assess cell viability at 24h and 72h, respectively, whereas counterstaining with DAPI is used to quantify cell number in viral infection assays. Quantification of viral infection using immunofluorescence requires fixation and permeabilization of the cells which is not compatible with assessment of metabolic activity through either the MTS assay or CellTiter-Blue assay post-imaging. One can perform these assays (e.g., MTS and CellTiter-Blue) prior to imaging, however there were concerns regarding viral assay image quantification after running these assays due to the metabolic demand of dye processing which may influence susceptibility to viral infection/propagation, and fluorescence of the metabolic sensor interfering with subsequent IF staining.

DAPI staining is not meant to show viability per se, however, one can gate for fragmented cell nuclei (indicative of lysed cell) to remove dead cells or nuclear debris from the measurement. However, pre-apoptotic or dying cells cannot be accounted for in this measurement.

In our case, due to the rapid doubling time of the immortalized cell lines used (e.g., Vero E6 ~24h, Calu-3 ~35-48h, L929 ~24h), we are able to see large differences in cell number as infected or lysed cells fail to replicate over the 48h experiment. Thus, DAPI staining can be used as a proxy to determine the overall health of the culture but is not directly a measure of cell viability.

Minor comments from Reviewer #1:

“Why did the authors adopt a different pre-treatment infection protocol for SARS-CoV-2 compared to MHV and OC43?”

Response:

The pre-treatment protocol was developed by collaborators in the BSL3 facility as standard practice for rapid treatment/screening of multiple compounds in a BSL3 lab where hands-on time was limited due to immense research focus on infectious disease (e.g., SARS-CoV-2) at the time. We screened many different nanoparticle formulations with this protocol before assessing nanoparticle effect on MHV and OC43 where we also used standardized protocols.

In reference to comments from Reviewer #2:

“The substance of the work is very interesting, but experience shows that the idea that by testing one isolate you can generalize about all other viruses is not accurate. Viruses mutate. SARS-CoV-2 has shown an exceptional capacity for mutation, and the mechanism by which viruses enter cells by endocytosis or membrane fusion plays a role in their exposure to products concentrated in the lysosome. Viruses that enter by membrane fusion (including certain isolates of SARS-CoV-2) should not a priori be as sensitive because they are not subject to phagolysosome fusion.”

“In this sense, it is important to evaluate efficacy on several viral strains and not on a single strain, as has long been the case for acute viral infections. As with HIV, viruses can present natural or acquired resistances linked to evolution under selection pressure or not.<br /> In practice, we cannot rely on testing two viruses, one that has disappeared (Wuhan) and the first virus of the Omicron generation (B1), to assess the therapeutic capacity of a new strategy.”

Response:

We would like to highlight that MFQ-NP efficacy was evaluated in several different coronavirus models (MHV, HCoV-OC43 and SARS-CoV-2) in addition to two distinct viral strains (SARS-CoV-2 WT-WA1 and Omicron BA.1) in this study. Not only this, but we have selected viruses from distinct Betacoronavirus lineages which infect different species (homo sapiens and mus musculus). At the time, we had chosen Omicron BA.1 as our model strain as it was the dominant strain in circulation and was temporally separated and genetically distinct from the original WT-WA1 strain.

We can appreciate that viruses mutate, and SARS-CoV-2 has exemplified this through its life cycle. Due to this rapid mutation rate, it is challenging to assess the current dominant variant (e.g., XBB.1.16 as of writing this) and complete manuscript preparation in addition to full peer-review prior to the emergence of a new, potentially more relevant, dominant variant. Additionally, it remains challenging to accurately predict the emergence and genotypic/phenotypic changes of new strains before they arise, necessitating investigation on ancestral strains or, at the least, the current dominant strain.

Lastly, we do not wish to speculate/generalize that MFQ-NPs or a similar approach works for “all other viruses”. As exemplified by efficacy studies in three Betacoronavirus lineages, and one of which using two distinct strains, we do argue that this approach is an effective means to inhibit Betacoronavirus infection. We speculate in the discussion that MFQ-NPs may be used as either a prophylactic or treatment for an array of other respiratory coronaviruses, however we neither show data or speculate efficacy against viruses outside of the coronavirus family.

In reference to comments from Reviewer #1:

“The claim that MFQ may impact cell entry is not supported by the data in the paper. At minimum, the impact of treatment on expression of viral entry receptors for all 3 viruses should be performed, viral attachment assays (see PMID 35176124) and viral pseudoparticle assays. Further the conclusion that MFQ inhibits replication as well as entry is not fully supported by the data presented. This could be improved using a single cycle infection experiment using a synchronised infection protocol. The gold standard to determine impacts on replication would be the use of a viral replicon however I appreciate the technical difficulties in performing these experiments.”

Response:

We agree that the mechanism by which MFQ-NPs inhibit coronavirus infection has not been fully interrogated through this work. We do have preliminary evidence addressed in Fig. 4 which suggests that mechanistically MFQ-NPs may work through targeting pH-dependent protease activity and lysosomal function downstream of viral uptake. However, we have not yet investigated the effect that MFQ-NPs may have on viral entry receptors or viral attachment.

To that end, we are proposing to perform RT-qPCR to measure changes in expression level of key membrane bound proteins responsible for viral uptake. For these assays, we plan to treat Calu-3 cells with MFQ-NPs, unloaded PGC-NPs, equivalent concentration of molecular MFQ, or DMSO as control to gauge expression level of ACE2 (Hs01085333_m1), TMPRSS2 (Hs01122322_m1), sialate O-acetyltransferase gene (CasD1, Hs01082700_m1), and sialic acid acetylesterase gene (SIAE, Hs00405149_m1) relative to expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH, Hs02786624_g1) using TaqMan probes (ThermoFisher, USA). Additionally, we will also measure the expression level of Cathepsin L (Hs00964650_m1). Cathepsin L is not a transmembrane protein, however strong evidence suggests that this lysosomal protease is essential for S protein processing and viral membrane – endolysosomal membrane fusion in the SARS-CoV-2 endocytic infection route.

These proteins are chosen as ACE2 and TMPRSS2 are known mediators of SARS-CoV-2 uptake and fusion, and HCoV-OC43 relies on uptake via sialoglycan-based receptors with 9-O-acetylated sialic acid (9-O-Ac-Sia) as a key component. Although CasD1 and SIAE themselves are not transmembrane receptors for HCoV-OC43, they regulate the addition or removal of O-acetyl ester groups from sialic acids, respectively.

Similarly, we plan to treat L929 cells with MFQ-NPs, unloaded PGC-NPs, equivalent concentration of molecular MFQ, or DMSO as control to gauge expression level of CEACAM1 (Mm04204476_m1) relative to expression of GAPDH (Mm99999915_g1). MHV spike protein binds to murine carcinoembryonic antigen-related cell adhesion molecule 1a (mCEACAM1a) facilitating infection. NP treatment duration will be consistent with viral infection assays (e.g., 48 h) prior to RNA isolation and qPCR.

Results from PCR measurements may warrant further investigation into protein level expression measured by Western Blotting. However, we plan to begin with PCR as blotting for multiple membrane bound proteins is considerably challenging and higher cost than qPCR.

In addition to expression of viral entry receptors, we will also perform a viral attachment assay and internalization assay to further interrogate MFQ’s mechanism of action. To determine whether mefloquine inhibits SARS-CoV-2 binding/attachment, we will perform viral binding assays in Calu-3/ Vero E6-TMPRSS2-T2A-ACE2 cells that express ACE2 at high levels, and HEK293T cells that express ACE2 at lower levels. Assays will be performed using SARS-CoV-2 Spike pseudo-typed lentivirus which expresses a fluorescent reporter upon mammalian cell infection. SARS-CoV-2 Spike pseudo-virus is advantageous as it mimics SARS-CoV-2 entry mechanisms; however, it can be handled at BSL2, and it can be used to accurately quantify viral uptake since this virus is not replication competent.

SARS-CoV-2 can be internalized primarily via receptor-mediated endocytosis in cells which do not express TMPRSS2 (e.g., Vero E6) or via direct plasma membrane fusion in cells which do express TMPRSS2 (e.g., Calu-3 and Vero E6-TMPRSS2-T2A-ACE2). To test whether mefloquine inhibits the endocytosis of SARS-CoV-2, Vero E6 cells will be pre-treated with effective concentrations of MFQ-NPs, equivalent concentration of molecular MFQ, or unloaded PGC-NPs/DMSO as controls. Next, cells will be inoculated with SARS-CoV-2 Spike pseudo-virus at MOI 0.5 at 37 oC for 1 h. Cells will then be washed with PBS to remove unbound virus, media containing treatments will be reintroduced, and the pseudoviral reporter fluorescence will be measured 18-24 h after inoculation.

To test whether mefloquine inhibits TMPRSS2-mediated fusion during SARS-CoV-2 infection we will use TMPRS2 expressing Vero E6-TMPRSS2-T2A-ACE2 and Calu-3 cells. Cells will be pre-treated with Leupeptin/Pepstatin (inhibitors of endolysosomal proteases), camostat mesylate (serine protease inhibitor), MFQ-NPs, molecular MFQ, unloaded PCG-NPs, or DMSO as control for 1 h, and then inoculated with SARS-CoV-2 Spike pseudo-virus (MOI = 0.5). Cells will then be washed with PBS to remove unbound virus, media containing treatments will be reintroduced, and the pseudoviral reporter fluorescence will be measured 18-24 h after inoculation.

Minor comments from Reviewer #1

_<br /> “Further discussion in the introduction as to the potential mechanism of action of MFQ should be included. I would also suggest the authors read the work by Elizabeth Campbell and Bruno Canard concerning the potential difficulties in designing direct acting antivirals for coronaviruses.”

“The figure legends lack detail concerning the number of replicates and statistical comparisons. For viral infections MOIs used are also absent.”_

Response:

We have included a brief summary describing what the field knows of the mechanism of action of MFQ. Namely that MFQ does not directly inhibit the virus/cell membrane attachment process, rather MFQ somehow inhibits viral entry after attachment. We speculate a few mechanisms by which this may occur, which include: (1) inhibiting viral membrane fusion with the cell membrane or endolysosomal membrane, (2) inhibiting proteases responsible for processing SARS-CoV-2 S protein and exposing the fusion peptide, (3) modulating expression levels of ACE2, TMPRSS2, and/or cathepsin, or (4) promoting exocytosis of SARS-CoV-2 particles after uptake.

While it is not the primary goal of the manuscript to determine this mechanism of action, we have evidence currently that MFQ inhibits endolysosomal proteolysis (e.g., mechanism 2 above). Through viral attachment assays and evaluation of receptor expression levels we will also probe mechanism 3 in the revision process.

We have updated the figure legends and methods section to include further details concerning replicates and statistical comparisons. For viral infections we had previously listed the MOIs used in the Materials & Methods section but have also included them in the figure legends.

In reference to comments from Reviewer #2:

“On the one hand, both the introduction and the abstract contain too many elements that give a biased view of the extremely controversial literature. For example, the activity of Hydroxychloroquine and its toxicity have been explored most extensively on the "C19Early" website, which reports on trials carried out in a multitude of countries, including more than 300 trials with Hydroxychloroquine, and it is unreasonable in a scientific paper, designed to last, to report on the major beliefs at a given moment in order to develop a work that has nothing to do with this debate. The same applies to the efficacy of the vaccine. It is difficult to say that the vaccine was poorly distributed, with 20 billion doses, making it the most widely distributed vaccine in the history of mankind in such a short space of time, with results that were not as spectacular as the studies predicted, since the epidemic continued at a comparable level. I suggest that the authors concentrate on their work rather than getting involved in the controversies that are developing around treatments and vaccination.”

Response:

When possible, we have tried to highlight the mixed/controversial results, both positive and negative, of pre-existing therapeutics targeting SARS-CoV-2 and COVID-19 and cited them accordingly. Conjectural phrases such as “selective pressure may lead to 3CLpro mutations conferring nirmatrelvir resistance to new viral mutants” or “there is a growing concern that Molnupiravir, especially when administered at sub therapeutic doses, may result in the creation of more virulent SARS-CoV-2 mutants” are supported by observations in the literature and have been proposed by other experts in the field. Otherwise, we have revised the text to remove any unsupported speculative phrases.

We believe it is worth noting the existing therapeutic strategies in the field to provide context and rationale for our differentiated approach. We have extensively explored the C19Early site as well, and although it does a fantastic job of compiling relevant literature, this site also appears to have a biased view. Throughout preparation of this manuscript, we have considered FDA recommendations and clinical practice in prophylactic protection/treatment of COVID-19 paramount in guiding our introduction and discussion.

We have removed phrasing regarding the limited distribution of vaccines.

In reference to comments from Reviewer #1:

_“The authors claim that MFQ loaded nanoparticles have reduced cytotoxicity compared to 'free MFQ' dissolved in DMSO, however with the NP data and free MFQ data not plotted with the same units this conclusion is hard to reach (Fig.2). While I appreciate the molar units are presented in the text - the reduction in cytotoxicity with MFQ-NP appears to be relatively minor in the both the Calu3 and Vero cell models (doubling in IC50 in both instances). This may indicate quite a narrow therapeutic window for antiviral efficacy without unwanted cytotoxicity. Can the authors replot the data on scales using either molar or ug/ml and use the same dose range for all treatments to enable statistical determination as to whether MFQ-NP significantly reduce cytotoxicity.

To test the antiviral efficacy of the MFQ-NP the authors adopt 2 infection systems, either treating cells pre or post infection. The authors either use fluorescently tagged reporter viruses (OC-43/MHV) or immunofluorescence to visualise and quantify viral infection in cell-line models. Given a key aim of this paper is to determine whether MFQ-NP rather than free MFQ is a superior treatment option, it is challenging to assess this with the data presented in figures 5-6. The units of treatment between NP, MFQ-NP and free MFQ again differ, and the molar dose range of MFQ-NP and free NP is not the same. This makes it very hard to conclude whether MFQ-NPs are more effective then free MFQ. Formal dose response curves with the same dose of empty-NPs, MFQ-NPs and free MFQ are needed here, preferably with match cell viability data using the same assay as figure 2.”_

Response:

We will include additional plots with axis scaling for MFQ-NPs as concentration of MFQ in µM rather than concentration of NPs in µg/mL for further comparison to the free MFQ group. We chose concentration of NPs to match with the equivalent unloaded NP controls. Unfortunately, it would not be possible to create a formal dose response curve with the same dose of empty-NPs and free MFQ as those entities only co-exist in the MFQ-NPs treatment group.

We agree with the observation that the therapeutic window is likely narrow in vitro. This is observed in the viral inhibition and cytotoxicity experiments, where the most efficacious dosing of NPs (e.g., 50 – 100 µg/mL) in inhibiting viral infection is similar to the IC50 value (e.g., 54 µg/mL in Calu-3) at 24 h. Similarly, we see a biphasic dose response in our protease activity assay, suggesting that low doses actually increase endolysosomal protease activity which may promote viral infection.

We speculate this dose limiting toxicity and narrow therapeutic window will likely be improved more drastically in vivo (ongoing continuation of this study), however in vitro we still see at least a minor improvement in MFQ tolerability.

In reference to comments from Reviewer #1:

“Finally while animal experiments are likely beyond the scope of this study, use of air-liquid interface cultures of lung epithelial cells would be a significant improvement to the work and provide further support to their conclusions in a physiologically relevant system.”

Response:

While we appreciate the suggestion of ALI models and animal models to further assess MFQ-NPs in a more physiologically relevant system, we agree that these studies would be beyond the scope of this study. This investigation is planned as a continuation of the current study.

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Referee #2

Evidence, reproducibility and clarity

This work is interesting but poses two problems.

On the one hand, both the introduction and the abstract contain too many elements that give a biased view of the extremely controversial literature. For example, the activity of Hydroxychloroquine and its toxicity have been explored most extensively on the "C19Early" website, which reports on trials carried out in a multitude of countries, including more than 300 trials with Hydroxychloroquine, and it is unreasonable in a scientific paper, designed to last, to report on the major beliefs at a given moment in order to develop a work that has nothing to do with this debate. The same applies to the efficacy of the vaccine. It is difficult to say that the vaccine was poorly distributed, with 20 billion doses, making it the most widely distributed vaccine in the history of mankind in such a short space of time, with results that were not as spectacular as the studies predicted, since the epidemic continued at a comparable level. I suggest that the authors concentrate on their work rather than getting involved in the controversies that are developing around treatments and vaccination.<br /> The substance of the work is very interesting, but experience shows that the idea that by testing one isolate you can generalize about all other viruses is not accurate. Viruses mutate. SARS-CoV-2 has shown an exceptional capacity for mutation, and the mechanism by which viruses enter cells by endocytosis or membrane fusion plays a role in their exposure to products concentrated in the lysosome. Viruses that enter by membrane fusion (including certain isolates of SARS-CoV-2) should not a priori be as sensitive because they are not subject to phagolysosome fusion.

In this sense, it is important to evaluate efficacy on several viral strains and not on a single strain, as has long been the case for acute viral infections. As with HIV, viruses can present natural or acquired resistances linked to evolution under selection pressure or not.<br /> In practice, we cannot rely on testing two viruses, one that has disappeared (Wuhan) and the first virus of the Omicron generation (B1), to assess the therapeutic capacity of a new strategy.

Significance

This work is interesting but poses two problems.

On the one hand, both the introduction and the abstract contain too many elements that give a biased view of the extremely controversial literature. For example, the activity of Hydroxychloroquine and its toxicity have been explored most extensively on the "C19Early" website, which reports on trials carried out in a multitude of countries, including more than 300 trials with Hydroxychloroquine, and it is unreasonable in a scientific paper, designed to last, to report on the major beliefs at a given moment in order to develop a work that has nothing to do with this debate. The same applies to the efficacy of the vaccine. It is difficult to say that the vaccine was poorly distributed, with 20 billion doses, making it the most widely distributed vaccine in the history of mankind in such a short space of time, with results that were not as spectacular as the studies predicted, since the epidemic continued at a comparable level. I suggest that the authors concentrate on their work rather than getting involved in the controversies that are developing around treatments and vaccination.<br /> The substance of the work is very interesting, but experience shows that the idea that by testing one isolate you can generalize about all other viruses is not accurate. Viruses mutate. SARS-CoV-2 has shown an exceptional capacity for mutation, and the mechanism by which viruses enter cells by endocytosis or membrane fusion plays a role in their exposure to products concentrated in the lysosome. Viruses that enter by membrane fusion (including certain isolates of SARS-CoV-2) should not a priori be as sensitive because they are not subject to phagolysosome fusion.

In this sense, it is important to evaluate efficacy on several viral strains and not on a single strain, as has long been the case for acute viral infections. As with HIV, viruses can present natural or acquired resistances linked to evolution under selection pressure or not.<br /> In practice, we cannot rely on testing two viruses, one that has disappeared (Wuhan) and the first virus of the Omicron generation (B1), to assess the therapeutic capacity of a new strategy.

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Referee #1

Evidence, reproducibility and clarity

Summary

Petcherski et al describe a nanoparticle delivery system to deliver mefloquine, a inhibitor of viral endocytosis, into cell line models of coronavirus replication. They describe the generation of these nanoparticles and test their antiviral efficacy against 3 different beta coronaviruses OC43, MHV and SARS-CoV-2, including the omicron variant.

Major Comments

The authors claim that MFQ loaded nanoparticles have reduced cytotoxicity compared to 'free MFQ' dissolved in DMSO, however with the NP data and free MFQ data not plotted with the same units this conclusion is hard to reach (Fig.2). While I appreciate the molar units are presented in the text - the reduction in cytotoxicity with MFQ-NP appears to be relatively minor in the both the Calu3 and Vero cell models (doubling in IC50 in both instances). This may indicate quite a narrow therapeutic window for antiviral efficacy without unwanted cytotoxicity. Can the authors replot the data on scales using either molar or ug/ml and use the same dose range for all treatments to enable statistical determination as to whether MFQ-NP significantly reduce cytotoxicity.

To test the antiviral efficacy of the MFQ-NP the authors adopt 2 infection systems, either treating cells pre or post infection. The authors either use fluorescently tagged reporter viruses (OC-43/MHV) or immunofluorescence to visualise and quantify viral infection in cell-line models. Given a key aim of this paper is to determine whether MFQ-NP rather than free MFQ is a superior treatment option, it is challenging to assess this with the data presented in figures 5-6. The units of treatment between NP, MFQ-NP and free MFQ again differ, and the molar dose range of MFQ-NP and free NP is not the same. This makes it very hard to conclude whether MFQ-NPs are more effective then free MFQ. Formal dose response curves with the same dose of empty-NPs, MFQ-NPs and free MFQ are needed here, preferably with match cell viability data using the same assay as figure 2.

The claim that MFQ may impact cell entry is not supported by the data in the paper. At minimum, the impact of treatment on expression of viral entry receptors for all 3 viruses should be performed, viral attachment assays (see PMID 35176124) and viral pseudoparticle assays. Further the conclusion that MFQ inhibits replication as well as entry is not fully supported by the data presented. This could be improved using a single cycle infection experiment using a synchronised infection protocol. The gold standard to determine impacts on replication would be the use of a viral replicon however I appreciate the technical difficulties in performing these experiments.

Finally while animal experiments are likely beyond the scope of this study, use of air-liquid interface cultures of lung epithelial cells would be a significant improvement to the work and provide further support to their conclusions in a physiologically relevant system.

Minor Comments

It is not clear why the authors used cell number as a measure of viability compared to the MTS assay used in figure 2.

Why did the authors adopt a different pre-treatment infection protocol for SARS-CoV-2 compared to MHV and OC43?

Further discussion in the introduction as to the potential mechanism of action of MFQ should be included. I would also suggest the authors read the work by Elizabeth Campbell an Bruno Canard concerning the potential difficulties in designing direct acting antivirals for coronaviruses.

The figure legends lack detail concerning the number of replicates and statistical comparisons. For viral infections MOIs used are also absent.

Significance

This work has good potential but is let down by the execution of the experimental design. The use of NP for antiviral drugs is a very interesting area and could greatly contribute to drug design for this viral family.

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

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

In this study, the authors made a two-component homing modification gene drive in Anopheles coluzii with a different strategy than usual. The final drive itself targets and disrupts the saglin gene that is nonessential for mosquitoes, but important for the malaria parasite. The drive uses several gRNAs, and some of these target the Lp gene where an anti-malaria antibody is added, fused to the native gene (this native gene is also essential, removing nonfunctional resistance alleles at this locus). In general, the system is promising, though imperfect. Some of the gRNAs self-eliminate due to recombination of repetitive elements, and the fusion of the antimalaria gene had a modest fitness cost. Additionally, the zpg promoter was unable to operate at high efficiency, requiring use of the vasa promoter, which suffers from maternal deposition and somatic expression (the latter of which increased fitness costs at the Lp target). The manuscript has already undergone some useful revisions since its earliest iteration, so additional recommended revisions are fairly modest.

Line 43-45: The target doesn't need to be female sterility. It can be almost any haplosufficient but essential target (female sterility works best, so it has gotten the most study, but others have been studied too).

--- We agree. However, this paragraph focused on previous achievements in malaria mosquitoes, for which suppression gene drives spreading lethality rather than female sterility have not been reported to our knowledge. Even the targeting of doublesex, which is a sex determination rather than female fertility gene, results in female sterility (Kyrou et al. 2018). However, we inserted the possibility of female killing by X-shredder GD (Simoni et al., 2020).

Line 69: A quick motivation for studying Anopheles coluzii should be added here (since gambiae is discussed immediately before this).

---Thank you for drawing our attention to this point. We modified the sentence to:

_Here, we present the engineering of the Lipophorin (Lp) essential gene in Anopheles coluzzii, a prominent member of the A. gambiae species complex and a major malaria vector in sub-Saharan Africa.

_

Introduction section: It might be helpful to break up the introduction into additional paragraphs, rather than just two.

--- We followed this suggestion and broke up the introduction into 5 paragraphs to make it more breathable.

Introduction last part: The last part of the introduction reads more like an abstract or conclusions section. Perhaps a little less detail would fit better here, so the focus can be on introducing the new drive components and targets

--- We have followed this suggestion and substantially shortened this last part of the introduction.

Line 207-213: This material could go in the methods section. There are some other examples in the results that could be similarly shortened and rearranged to give a more concise section.

--- We moved the long description from lines 207-213 to the Methods as suggested, and summarized it simply as:

Only mosquitoes displaying GFP parasites visible through the cuticle were used to infect mice.

We emphasize this point because in subsequent experiments using Saglin knockout mosquitoes, this enrichment for infected mosquitoes will probably attenuate the Plasmodium-blocking phenotype caused by Saglin KO, since mosquitoes lacking Saglin tend to be less infected (Klug et al., 2023). Elsewhere in the Results, we still provide detailed descriptions of procedures because we believe they aid understanding and assessing the quality of the experiments.

Line 283-287: I couldn't find the data for this.

--- Indeed we only summarized the data about the progeny of the [zpg-Cas9; GFP-RFP] line crossed to WT, as we didn’t judge these results worth detailing. Here is our record from one such cross:

GFP-RFP females x WT males  486 (50.7%) GFP+ and 472 (49.3%) GFP- larvae

GFP-RFP males x WT females  1836 (48.9%) GFP+ and 1925 (51.1%) GFP- larvae

This shows no significant gene drive. However in these progenies, a few GFP+ and non-RFP larvae, and a few RFP+ non-GFP larvae were noted by visual examination under the fluorescence microscope, without counting them precisely. Their existence testified to some weak homing activity mediated by zpg-Cas9 in the Lp locus.

We modified the sentence as follows to support our conclusion, and we propose to leave these detailed numbers here in our response, which will be published along with the paper.

In spite of the presence of the zpg-Cas9 and gRNA-encoding cassettes in the GFP-RFP allele, it was inherited in about 50% of male or female progenies, demonstrating little homing activity of the GFP-RFP locus after crosses to WT, except for the appearance of rare GFP-only or RFP-only progeny larvae, …

Line 291: Replace "lied" with "was".

----done.

Line 356: Homing in the zygote would be considered very unusual and is thus worthy of more attention. While possible (HDR has been shown for resistance alleles in the zygote/early embryo), this would be quite distinct from the mechanism of every other reliable gene drive that has been reported. Is the flow cytometry result definitely accurate? By this, I mean: could the result be explained by just outliers in the group heterozygous for EGFP, or perhaps some larvae that hatched a little earlier and grew faster? Perhaps larvae get stuck together here on occasion or some other artifact? Was this result confirmed by sequencing individual larvae?

---- We agree with your skepticism, especially given that the same is not seen in Suppl Fig 2A with a similar genotype setup, i.e., the vasa gene drive at the Lp locus, or in the G1 of populations 6 or 8 at the Saglin locus (Suppl. File 2). Unfortunately, it would take too much time at this point to re-create this line (which has been discarded) to re-examine this issue. Therefore, we acknowledge that another explanation than homing in the zygote may account for this result. Based on our empirical experience COPAS outputs are reliable: such outliers from the heterozygous population are usually not seen, and we always sort neonate larvae a few hours from hatching. Those 6% homozygous-looking larvae may come from a contamination with male pupae when female pupae were manually sorted for the cross to WT males, a human error that we cannot exclude. In this case, the true GFP inheritance would be closer to 79% than to 85%. For these reasons, we must back up from our initial statement as follows:

The progeny of these triple-transgenic females crossed to WT males showed markedly better homing rates (>79% GFP inheritance)

And edit the figure legend of Figure 4B to account for the alternative possibility of a contamination with males:

6% of individuals appeared to be homozygous, revealing either unexpected homing in early embryos due to maternal Cas9 deposition, or accidental contamination of the cross with a few transgenic males.

Results in general: Why is there no data for crosses with male drive heterozygotes? Even if some targets are X-linked, performance at others is important (or did I miss something and they are all X-linked). I see some description near line 400, but this sort of data is figure-worthy (or at least a table).

--- For the only example of functioning split gene drive at the Lipophorin locus on chromosome III, we do show homing results from heterozygous GD males in Suppl. Fig. 2A (91.2% homing in males inferred from ((40.7+53.1+1.8)-50)x2). We added this calculation of the homing rates in the figure legend. For full drive constructs in the Saglin locus on chromosome X (our final functional design), in addition to the data described in the text near line 400, male data showing “teleguided” homing at the Lipophorin locus on chromosome II is shown in Suppl. File 2 (see G2 of population 7, showing close to 100% homing at the GFP locus); the same data (less easy to assess) being converted into the G2 point of the graphs in Figure5.

Lines 362-367: What data (figure/table) does this paragraph refer to?

--- We apologize for the fact that this sentence was misleading. In this population, the genotype frequencies were not tracked at each generation but measured once after 7 generations. We rephrased (now lines 401-403) and now provide the measured values directly in the text:

We maintained one mosquito population of Lp::Sc2A10 combined with SagGDzpg (initial allele frequencies: 25% and 33%, respectively) and measured genotype frequencies after 7 generations. This showed an increase in the frequency of both alleles (G7: GFP allelic frequency = 59.2%, phenotypic expression of DsRed in >90% of larvae, n=4282 larvae),

Lines 405-406: There may be a typo or miscalculation for the DsRed inheritance and homing rate here. Should DsRed inheritance be 90.7%?

--- Thank you for spotting this. You are right, DsRed inheritance would be 90.7% if the homing rate were 81.4% as we mistakenly wrote. Actually DsRed inheritance was really 80.7% so our mistake was in calculating the homing rate: 61.4% is the correct value ((80.7-50)x2), now corrected in the manuscript.

Figure 5: The horizontal axis font size for population 8 is a little smaller than the others.

--- True. Corrected.

Line 454: In addition to drive conversion only occurring in females and the somatic fitness costs, embryo resistance from the vasa promoter would prevent the daughters of drive females from doing drive conversion. This means that drive conversion would mostly just happen with alleles that alternate between males and females.

--- We agree with this idea, although the impact of this phenomenon will depend on the extent of resistance allele formation in early embryos. We observed (Fig. 6) that failed homing mutagenesis in Saglin is not that intense, the sequenced non-drive alleles that were exposed 1-4 times to mutagenic activity in females either being mostly wild-type, or carrying mutations that often still left one or two gRNA target sites intact and vulnerable to another round of Cas9 activity. Therefore, alleles passed on from female to female may still undergo drive conversion to a large extent, that future experiments may be able to quantify.

Line 481: Deletions between gRNAs certainly happen, but I wouldn't necessarily expect this to be the "expectation". In our 2018 PNAS paper, it happened in 1/3 of cases. There were less I think in our Sciences Advances 2020 and G3 2022 paper. All of these were from embryo resistance from maternal Cas9 (likely also the case with your drive due to the vasa promoter). When looking at "germline" resistance alleles, we have recently noticed more large deletions.

--- We agree that the early embryo with maternally deposited Cas9 is probably the most prominent source of mutations at gRNA target sites. Perhaps naïvely we imagined that it would be easier for cells to repair two closely spaced DNA breaks by eliminating the intervening sequence, rather than stitching each break individually. Given that we sequenced many alleles carrying a single mutation, the lack of larger deletions may be explained by lower rates of Cas9 activity in Saglin, with mostly a single break at a time, due to limiting Cas9 amounts and their partial saturation with Lp gRNAs, and/or lesser accessibility of the Saglin locus compared to Lipophorin… We deleted the phrase “Contrarily to our expectation”.

Figure 6C: It may be nice to show the wild-type and functional resistance sequence side-by-side.

--- done

Lines 642-644: This isn't necessarily the case. At saglin, the nonfunctional resistance alleles may still be able to outcompete the drive allele in the long run. This wasn't tested, but it's likely that the drive allele has at least some small fitness costs.

--- We agree. We inserted this comment in a parenthesis in the text (now lines 644-645):

Unlike the first approach, this design may allow Cas9 and gRNA-coding genes to persist indefinitely within the invaded mosquito population (unless nonfunctional resistance alleles outcompete the drive allele in the long run).

A few comments on references to some of my studies:

Champer, Liu, et al. 2018a and 2018b citations are the same paper.

--- Duplicate in our reference library. Corrected.

For Champer, Kim, et al. 2021 in Molecular Ecology, there was a recent follow-up study in eLife that shows the problem is even worse in a mosquito-specific model (possibly of interest as an alternate or supporting citation): https://elifesciences.org/articles/79121

--- Citation added (line 68).

One of my other previous studies was not cited, but is quite relevant to the manuscript: https://www.science.org/doi/10.1126/sciadv.aaz0525<br /> This paper demonstrates multiplexed gRNAs and also models them, showing their advantages and disadvantages in terms of drive performance. Additionally, it models and discusses the strategy of targeting vector genes that are essential for disease spread but not the vectors themselves (the "gene disruption drive"), showing that this can be a favorable strategy if gene knockout has the desired effect (nonfunctional resistance alleles contribute to drive success).

--- your 2020 study will indeed now be useful to inform the design of multiplex gRNAs for various gene drives designs, in terms of number of gRNAs, distribution of their target sites, necessity to generate loss-of-function rather than functional resistance allele in the target gene (such as our Lp and Saglin pro-parasitic genes)… The notion of Cas9 saturation with increasing gRNA numbers is also important. When we initiated this project in 2018, we only had intuitive notions that multiplex gRNAs could improve the durability of GD and increase the chances of resistance alleles to be loss-of-function. We thus arbitrarily maximized the number of gRNAs for each of the two targets: 3 for each target in one design, 3 and 4 in another, which, according to your modelling, is luckily close to the optimal numbers for each locus. We now cite your paper as a GD design tool in the discussion about pathways to optimizing our system:

To further optimize GD design, modeling studies can now aid in determining the optimal number of gRNAs in a multiplex, depending on the specific GD design and purpose (Champer et al., 2020)__.

In addition to this and to the stabilization of multiplex gRNA arrays, other paths to improvement (…)

This one is less relevant, but is still a "standard" homing modification rescue type drive that could be mentioned (and owes its success to multiplexing): https://www.pnas.org/doi/abs/10.1073/pnas.2004373117<br /> The recoded rescue method was also used in mosquitoes (albeit without gRNA multiplexing) by others, so this may be a better one to mention: https://www.nature.com/articles/s41467-020-19426-0

--- We added the two references on what is now Line 663:

Lp::Sc2A10 depends on SagGD for its long-term persistence and spread in a population, and SagGD depends on Lp::Sc2A10 as a rescue allele of the essential Lp target for its survival. This design can be seen as a two-locus variation of rescue-type GDs (Adolfi et al., 2020; Champer et al., 2020)

Sincerely,<br /> Jackson Champer

Referees cross-commenting<br /> Other comments look good. One thing that I forgot to mention: for the 7-gRNA construct with tRNAs, the authors mentioned that it was harder to track, but it sounds like they obtained some data for it that showed similar performance. Even if this one is not featured, perhaps they can still report the data in the supplement?

--- This GD required examination of the mosquitoes at late developmental stages, such as the pupa, to score red fluorescence under control of the OpIE2 promoter, that is unfortunately late-active when expressed from the Lp locus. We precisely scored only the first 128 pupae arising from the progeny of the first obtained G1 [SagGD/+ ; Lp-2A10/+] females crossed to WT males. Among these:

• 115 were GFP+, DsRed+ (89.8%)

• 12 were GFP+, DsRed- (9.3%)

• 1 was GFP-, DsRed- (<1%)

This allowed us to roughly estimate the homing rates at 98.2% at the Lipophorin locus and 79.7% at the Saglin locus, which is similar to the other construct without tRNA spacers.

These approximate rates were confirmed by visual examination of progenies in two subsequent generations of [SagGD/+; Lp-2A10/+] males and females backcrossed to WT.

Reviewer #1 (Significance):

Overall, this study represents a useful advance. Aside from being the first report for gene drive in A. coluzii, it also is the first that investigates the gene disruption strategy and is the first report of gRNA multiplexing in Anopheles. The study can thus be considered high impact. There are also other aspects of the study that are of high interest to gene drive researchers in particular (several drives were tested with some variations).

--- We are grateful for your positive, constructive and in-depth analysis of our study!

Reviewer #2 (Evidence, reproducibility and clarity):

The authors initially created a transgenic mosquito colony expressing the Sc2A10 antibody fused to the lipid transporter Lipophorin, and tested the transmission-blocking activity of this transgene. Building off of previous findings that the Sc2A10 antibody inhibits sporozoite infectivity when expressed in mosquito salivary glands, the authors showed that found it was also efficient at inhibiting sporozoite infectivity when secreted into the hemolymph expressed under the lipophorin endogenous promoter in An. coluzzii. They then designed and tested two different gene drives utilizing the Sc2A10-Lipophorin fusion protein. In the first, the authors used a recoded allele of Lp-Sc2A10 while simultaneously utilizing gRNAs that targeted endogenous Lp in an effort to select for mosquitoes that expressed transgenic Lp-Sc2A10 due to the essential nature of Lp. However, this drive was unsuccessful because recoded Lp is necessarily heterozygous while the GD is entering the population, and Lp proved to be largely haploinsufficient. Further, the zpg promoter expressing cas9 was not effective in promoting homing of the gRNAs. In the second gene drive that was tested, authors made use of the endogenous Saglin locus, which expresses a natural agonist for Plasmodium, and is thus desirable to target for disruption in a gene drive that aims to reduce vector competence for Plasmodium. This gene drive also uses recoded Lp-Sc2A10 to replace the wild-type Lp allele, thus selecting for Sc2A10 expression, however this drive is not dependent on fitness of individuals with only one functional copy of Lp.<br /> The authors discovered that the efficacy of the zpg promoter to drive homing of cas9 is locus-dependent, limiting the success of their gene drive designs. They do show, however, that the Saglin gene drive succeeds at reaching high frequencies in mosquito populations using instead the vasa promoter to express cas9, and that these transgenic mosquitoes are able to reduce infectivity of sporozoites in a bite-back mouse model. However, they observe gene drive refractory mutations in the Lp gene, despite its highly conserved nature, showcasing the difficulty of avoiding drive resistance even in small populations of mosquitoes, and also observed deletions of gRNAs targeting both Lp and Saglin, further highlighting possible shortcomings in gene drive approaches. Together, these findings are useful to the field in walking the readers through an interesting and promising approach for a novel gene drive, and illustrating the challenges in engineering an efficacious and long-lasting drive.

Major comments:

As the authors are able to observe Plasmodium within mosquitoes, it would be useful to have these data in the manuscript pertaining to the prevalence and intensity of infection in mosquitoes prior to bite-back assays. If there are data or images that the authors could include, it would be helpful to show if there is a possibility that infection intensity is a variable that contributes to whether or not mice develop an infection. It would also be interesting to note whether there is a different in infection (oocysts or sporozoites) between transgenic mosquitoes and wild type mosquitoes.

--- This is a valuable suggestion. Please note that, in order to evaluate the transmission-blocking properties of the Lp-2A10 allele (acting at the sporozoite level), we discarded non-infected mosquitoes prior to bite-back experiments, so that infection prevalence was 100% in the mosquitoes retained for the bite-back. We have not systematically compared parasite loads between transgenic and control mosquitoes. In some experiments comparing Lp-2A10 mosquitoes and their control, we dissected a subset of the mosquito midguts after bite-back to visually ascertain that they showed roughly equivalent oocyst numbers between transgenic and controls. However, we have not precisely recorded these data. It is possible that slightly decreased lipid availability in Lp::2A10 mosquitoes (their lipophorin allele producing slightly less Lp than the WT) negatively affects the parasite, as suggested by previous studies highlighting the role of host lipophorin-derived lipids for parasite development in the mosquito (Costa et al, Nat Commun 2018; Werling et al. Cell 2019; Kelsey et al. PLoS Path 2023).

In the case of Lp-2A10 mosquitoes additionally containing a GD in Saglin, it is expected that they should carry lower parasite numbers than their controls, an effect of the Saglin knockout mutation alone (Klug et al., PLoS Path 2023). Re-inforcing the transmission blocking effect of the 2A10 antibody by reducing parasite loads via the Saglin KO was indeed our intention. Hence, having selected the most infected mosquitoes for our bite-back experiments likely attenuated this desired effect, but we still observed a 90% transmission decrease when the two modifications were combined, compared to a 70% decrease with Lp-2A10 alone. We do not plan to perform additional infections experiments for the current manuscript on Plasmodium berghei expressing Pf-CSP, but we do intend to record parasite counts in a follow-up study with an optimized SagGD transgene and Plasmodium falciparum infections. This will be of high relevance for potential future applications in malaria control.

The authors also go into significant detail in the discussion exploring ideas of how to optimize or improve this specific gene drive design. The authors should also stress further the applicability of their discoveries in other gene drive designs, and emphasize the lessons they learned in the difficulties encountered in this study and how these findings could guide others in their decision making process when choosing targets or elements to include in a potential gene drive approach.

--- We feel that we already emphasized these lessons in the manuscript, in the discussion and when justifying the chosen strategies in the Results section. Lessons for future designs include:

• inserting an antimalarial factor into an essential endogenous gene, preserving its function, can provide many benefits (high expression level, secretion signal that can be hijacked, endogenous introns can be hijacked to host a marker, inactivation by mutagenesis or epigenetic silencing being more difficult…);

• a distant-locus gene drive (as here in Saglin) could potentially drive several antimalarial cargoes at the same time, inserted in different loci;

• non-essential mosquito genes agonistic to Plasmodium are attractive host loci for a GD, an already old idea illustrated here by the case of Saglin;

• multiplex gRNAs are a viable approach to reduce the formation of GD-resistant alleles in essential genes and/or to increase the frequency of loss-of-function alleles, which will either disappear if the gene is essential or decrease vector competence if the gene is pro-parasitic. Hence gRNAs targeting intron sequences should be avoided in order to preserve this benefit, as illustrated by one of our Lp gRNAs targeting the first intron and that contributed to generate the only Lp viable resistance allele identified in this study;

• To increase long-term stability of the GD construct, repeats should be minimized in gRNA multiplexes through the use of a single promoter and various spacers (tRNAs, ribozymes?) – it remains to be seen if the 76-nucleotide gRNA constant sequence itself, necessarily repeated, will stimulate unit losses in a gRNA multiplex;

• The best promoter to restrict Cas9 expression to the germ line may be zpg in some but not all loci; the vasa promoter causing maternal Cas9 deposition may still be envisaged if resistance allele formation can be prevented by other means (targeting hyper-conserved essential sequence, multiplexing the gRNAs against an essential gene…).

Minor comments:

Line 44 - female sterility but also female killing approaches to crash pop. like X shredder, if authors would like to expand

--- Female killing citation of Simoni et al, 2020 added (line 45).

Lines 48-60 - Authors should add some references from the literature surrounding ethics and ecology studies related to gene drive release

--- we added: (e.g., National Academies of Science, Engineering, and Medicine, 2016; Courtier-Orgogozo et al., 2017; de Graeff et al., 2021) on lines 49-51.

Line 114 - Given the only moderate impacts of Saglin's role in Plasmodium invasion, I am not sure this saglin deletion is a convincing benefit for GD as it is probably not impactful enough alone - can the authors soften this statement?

--- while it’s correct that Saglin KO mosquitoes show a significant decrease only in P. berghei oocyst counts and not in prevalence when mosquitoes are heavily infected, they do show a significant decrease in both counts and prevalence upon infection with P. berghei and, most importantly_, P. falciparum_ when parasite loads are lower —a situation that is more physiological (e.g. prevalence of 65% and 13% in WT and Sag(-)KI mosquitoes, respectively, upon infection with P. falciparum - Klug et al., PLoS Path 2023). Therefore, for human-relevant P. falciparum infections, an impactful decrease in vector competence can be legitimately expected.

Line 126 -Can the authors provide rationale for expressing Sc2A10 with Lp instead of expressing it from salivary glands?

--- There are three reasons for this. First, we knew from the cited Isaacs et al. papers that the 2A10 antibody was efficient against transmission when expressed in the fat body, and from unpublished work (Maria Pissarev, Elena Levashina and Eric Marois) that anti-CSP ScFvs expressed in the fat body of transgenic mosquitoes blocked sporozoite transmission as efficiently as when expressed from salivary glands. This is certainly favored by the easy sporozoite accessibility to the antibody when both are in mosquito hemolymph. Of note, the transmission blocking results suggest that the binding of ScFv to CSP withstands the crossing of the salivary gland epithelium by sporozoites. Second, we were looking for a host gene expressed as high as possible to produce high levels of Sc2A10 antibody. Third, the host gene must be essential so that resistance alleles would not be viable.

We agree that it would also be possible to use a salivary gene instead of Lp as a host for this antimalarial factor. In this case, a same-locus gene drive may have functioned, but the advantages of the host locus being an essential gene would be lost, at least partially, as genetic ablation of the salivary gland, albeit slowing blood uptake, does not prevent mosquito viability and reproduction (Yamamoto et al., PLoS Path 2016).

Line 140 - Can authors give any comment on why these regions of Lp were chosen to be recoded / targeted with gRNAs?

--- inserting Sc2A10 just after the cleaved Lp secretion signal, and N-terminally to the rest of the Lp protein, was the goal, so that 2A10 would be secreted together with Lp and separated from both signal peptide and Lp by naturally occurring proteolysis. This constrained the choice of the target site to be at the junction between signal peptide and the remainder of Lp protein. An alternative design could have been to insert it between the two subunits ApoLpI and ApoLpII, with duplication of the protease cleavage site, or on the C-terminal extremity of the protein, but there would have been no intron in the immediate vicinity to knock-in a selection marker at the same time.

Line 171 - "stoichiometric"

--- Corrected.

Line 186 - Can the authors comment or speculate on why the expression levels of the fusion protein are expected to be lower than endogenous Lp?

--- We did not expect this. It is hard to predict whether and explain how insertion of exogenous sequences in a gene can alter its expression. Possible explanations include: the existence of harder-to-translate mRNA sequences in the Sc2A10 moiety; the addition of seven exogenous amino acids on the N-terminal side of ApoLpII (mentioned in M&M) possibly modifying the stability of the Lp protein; the modification of the intron sequence perturbing efficient intron excision and/or pre-mRNA expression due to the disruption of regulatory elements or to the new presence of the GFP gene in the antisense orientation (albeit expressed in the nervous system and not in the fat body); the presence of the exogenous Tub56D transcription terminator used to arrest GFP transcription possibly possessing bidirectional termination activity and lowering the mRNA level of the Lp allele…

Line 211 - Why were 6 mosquitoes used for these assays, and 10 mosquitoes used in later assays (Line 223)?

--- Mice were always exposed to groups of 10 mosquitoes, but not all 10 mosquitoes were necessarily biting the mice. We retained mice bitten by at least 6 mosquitoes for further analysis (M&M, lines 871-873 of the revised file).

Line 212 - I would also suggest using letters (Suppl. Table 2A,B,C etc) to refer the specific experiments and sections in the Table.

--- Implemented.

Line 225- 228 - The authors should mention in the text that homozygotes and heterozygotes do not differ in infection assays.

--- Added: Therefore, heterozygous mosquitoes showed a transmission blocking activity comparable to that seen in homozygotes.

Line 249 - Can the author comment on the impacts of population influx / exchange on the idea that the GD cassette need only be transiently in the population?

--- If Lp::Sc2A10 is fixed in the population and the GD gone, indeed an influx of WT alleles through mosquito immigration will begin to replace the antimalarial factor and drive it to extinction due to its fitness cost. As mentioned in the final paragraph of the discussion, this could be seen as an advantage to restore the original natural state—hopefully after malaria eradication! However, we regard a situation where Lp::2A10 never reaches fixation as more likely, with its spread being re-ignitable by updated GDs (line 741 of the revised file).

Line 273 - Can the authors comment on why this may have occurred more frequently than the expected integration of the GD cassette?

--- When a chromosome break is repaired, each side of the cut must recombine with the repair template. A possible explanation for our observation is that one side of the break recombined with the injected repair plasmid, while the other recombined with the intact sister chromosome (physiologically probably the preferred option). Since this situation still leaves truncated chromosomes, another repair event can join the plasmid-bearing chromosome end to the sister chromosome. The observation that complex rearrangement occurred frequently suggests that such events can be very common, but will usually go undetected due to the absence of genetic markers. Here, GFP on the intact sister chromosome served as a genetic marker to betray its unexpected involvement in the repair process.

Line 314 - Not all fitness costs are apparent through standard laboratory rearing as was performed in Klug et al. Authors could consider "no known fitness cost" instead.

--- We agree. This is what we meant by “no fitness cost in laboratory mosquitoes”. We changed this to “no fitness cost at least in laboratory conditions (Klug et al., 2023)” to make clear that this was tested.

Line 407 - don't start new paragraph (same with 409)

--- we removed these two lines, as we realized they contained an error, and made a correction on line 420 of the revised manuscript.

Line 408 - I'm not sure it's clear why all these populations were kept for a different number of generations - can the authors clarify?

--- Populations 1 and 2 were the oldest founder populations, therefore maintained for the longest time. As described in the text, all other populations were derived from populations 1 and 2 later in time by outcrossing a subset of individuals to WT mosquitoes. For these derived populations, we reset the clock of generation counting to 0 as we monitored the homing phenomenon “from scratch” in transgenic males crossed to WT, and in transgenic females crossed to WT. Resetting the clock resulted in an apparent lower number of generations for these derived populations. In addition, some of them were discarded early, usually after reaching a stable state, as it was difficult to maintain so many populations in parallel over a long period of time.

Line 558 - "10/12 mice" not immediately clear - the authors could be more specific about how data was combined here

--- Thank you for pointing out this ambiguity. We replaced by: the absence of infection in a total of 10 out of 12 mice showed… (line 561)

Line 586 - Since there do appear to be some fitness costs associated with the Sc2A10 version of Lp, might it be expected that fitness costs imposed by the transgene itself could lead to selection pressures leading to its loss? Or do the authors think that these fitness costs are prevented from causing selection against Sc2A10 due to the design of the transgene such that its translation is a prerequisite for Lp's translation? Is the fact that its removal occurs more rapidly than Lp's any indication that selection against the persistence of Sc2A10 may occur?

--- Yes, we believe that Lp::Sc2A10 will progressively disappear, replaced by the WT allele, as shown in Figure 1C, in the absence of a GD stimulating its maintenance and spread. In the Lp::Sc2A10 transgene, translation of Sc2A10 is indeed a prerequisite for Lp translation, imposing a degree of genetic stability of this transgene in terms of sequence integrity, but this does not mean that the locus cannot be outcompeted by the WT under natural selection, so that long-term persistence of Lp::Sc2A10 depends on the presence of the GD, as outlined in lines 669-672. As the GD itself can disappear due to the accumulation of resistance alleles, we expect a progressive lift of its pressure to maintain Lp::Sc2A10 and both loci to be progressively lost, a form of reversibility that may be regarded as desirable (lines 773-776 in v2, 741-743 in v3). Alternatively, both transmission blocking alleles could be maintained by releasing an updated version of the dual GD.

Line 659 - add some further detail to this - how do you envision this to occur?

--- We have deleted this paragraph, as it hypothesized that SagGD could frequently be transmitted to the next generation in the absence of Lp::2A10, which is not the case (it would be lethal, and Lp::2A10 homing is anyway extremely efficient). After a putative field release of [SagGD / Y; Lp::2A10/ Lp::2A10] males, both transgenes should rapidly be introgressed in the field’s genetic background.

Line 635 - Long paragraph, should be broken up or removal of text. Some of these ideas could possibly be made more concise to improve readability. There are many different hypotheticals that are expanded upon in the discussion.

--- We admit that this paragraph in the discussion was long and dense. We have split it into 4 smaller paragraphs to better separate the concepts that we want to discuss, and have deleted the part mentioned in the above point.

Line 677 - This scenario seems potentially unrealistic considering the only subtle impacts of Saglin deletion on vector competence, and the potential for population exchange in mosquito populations to dilute out these alleles if the drive begins to fail. Can the author comment or potentially decrease emphasis on such scenarios?

--- while Saglin KO mosquitoes show a moderate decrease of infection prevalence in the context of high infections, the Saglin KO decreases parasite loads in all cases, and most importantly, also prevalence upon physiological infections with P. falciparum (Klug et al., PLoS Path 2023 and see our response to your comment to line 114 above). This yields a higher proportion of non-infected mosquitoes. Therefore, the impact of Saglin mutations should be stronger for the epidemiology of human infections with P. falciparum than in laboratory models of infections where parasite loads are very high.

We agree that mosquito migration in natural populations would progressively dilute out the beneficial alleles once the GD effect ceases. The epidemiological impact is difficult to predict and will strongly depend on the durability of the GD and on the intensity of genetic influx from adjacent mosquito populations.

Line 708 - Can the authors speculate on why zpg is sensitive to local chromatin and elaborate on possible solutions or consequences for other drive ideas? This seems broadly important.

--- We do not precisely know why the zpg promoter is more sensitive to local influences than the vasa promoter, but this phenomenon seems common for other promoters as well (e.g., the sds3 promoter as opposed to the shu promoter in Aedes aegypti (Anderson et al., Nat Comm 2023)). It is possible that the vasa promoter is better insulated from local repressive influences, perhaps by insulating elements akin to gypsy insulators in Drosophila. Knowledge of genetic insulators active for mosquito genes is lacking as far as we know. Characterization of efficient mosquito insulators, for example if one could be identified within vasa, and their combination with zpg or sds3 promoter elements, could potentially improve the locus-independent activity of such promoters. Alternatively, a natural and ideal promoter may still be found showing both an optimal window of expression of Cas9 in the germline, and little susceptibility to local repression.

Line 737 - The suggestion of releasing laboratory-selected resistance alleles in the absence of further context may be provocative and unnecessary here.

--- We didn’t intend to sound provocative, but are interested in the idea of simple resistance alleles with limited sequence alteration that could be selected in the lab, and released to block a gene drive that turned undesirable, so we wanted to share it with the reader. Mutations in the Lp and Saglin loci, preserving their functions, can be limited to one or few nucleotide changes in the gRNA target sites, as illustrated by the mutants we sequenced. Lab population of GD mosquitoes can, therefore, be a source of GD refractory mutants that could be leveraged in recall strategies.

Line 850 - unnecessary comma

--- Corrected.

Line 854 - change to "after infection, moquitoes were "

--- Changed.

Figure 1 - Not clear what is intended to be communicated by shapes portraying proteins / subunits - consider more detailed illustration of mosquito fat body cells synthesizing and secreting proteins rather than words in text box with arrow to clearly demonstrate the point of this figure.

--- We propose a new version of figure 1 to better illustrate the fat body origin of Lp and 2A10. We have also re-worked the graphic design to improve several figures.

Figure 3 - I recommend rearranging this figure so that B comes before C, visually. The proportions for the design of in B should also match those used for A.

--- We have followed these recommendations in the new Figure 3, and also used more logical color codes for the gRNAs and their target genes.

Figure 5 - It is unclear to me why some Populations were maintained for such different lengths of time.

--- Same point as above for line #408: Populations 1 and 2 are the oldest founder populations, therefore maintained for the longest time. As described in the text, all other populations were derived from populations 1 and 2 later in time by outcrossing to WT mosquitoes, resulting in a lower number of generations for these derived populations. In addition, some of them were discarded earlier, usually after reaching a stable state, as it was not possible to maintain so many populations in parallel for a long period of time.

Figure 7 - Ladder should be labeled on the gel. It may also be helpful for the author to indicate clearly exactly which mosquitoes were shown by sequencing to have these different deletions, as it is occasionally unclear based on band sizing.

--- we have added the ladder sizes as well as a numbering of individual mosquitoes on Figure 7. We sequenced 4 gel-purified small -type B- amplicons of Population 1 individually (#1, 2, 4, 6), and a pool of 4 type B amplicons from Population 7 (pooled #2, 4, 5, 6) as well as two samples of several pooled gel-purified large -type A- amplicons from Population 2 (pool of samples #2, 3, 4, 5, 6, 8, 9, 11, 12) and from Population 7 ( pool of #1, 3, 7, 11, 12). This information now also appears in the material and methods section (PCR genotyping of the SagGDvasa gRNA array).

Line 996 - given that there is a size band on the right line of this gel also, can authors crop the gel image to eliminate unnecessary lanes a and b from this figure without losing information needed to interpret this blot?

--- we agree that this would make the message easier to understand, but cropping lanes a and b would place WT control and Lp::Sc2A10 homozygotes on two separate images, even if a size marker is present on each. We prefer keeping the raw image to facilitate direct comparison of the band sizes, making clear that this was a single protein gel.

Line 1070 - 12 out of how many sequenced mosquitoes?

--- 12 mosquitoes from each of these four populations served as PCR templates to generate figure 7. A subset of amplicons were sequenced individually or pooled, as described above and now in Methods. All sequencing reactions of type A and type B amplicons showed consistent results.

Line 1078 - Can remove some detail like % of agarose, and replication of results with different polymerase as these are already in methods.

--- Done.

Line 1098 - "Unbless"

--- Corrected

Reviewer #2 (Significance):

This study illustrates a wide range of issues pertinent for gene drive implementation for malaria control, and as such is of value to the field of entomologists, genetic engineers, parasitologists and public health professionals. The gene drive designs explored for this study are interesting largely from a basic biology perspective pertinent mostly to specialists in the field of genetic engineering and vector biology, but highlight challenges associated with this technology that could also be of interest to a broader audience. A transmission blocking gene drive has not yet been achieved in malaria mosquitoes, and is thus a novel space for exploration. As a medical entomologist that works predominantly outside of the genetic engineering space, I have appreciated the detail the authors have provided with regard to their rationale and findings, even when these findings were inconsistent with the authors' primary objectives or expectations.

--- Thank you for your positive assessment and for this in-depth evaluation of our data.

Reviewer #3 (Evidence, reproducibility and clarity):

The study by Green et al. generated a gene drive targeting both Saglin and Lipophorin in the Anopheles mosquito, with a view to blocking Plasmodium parasite transmission. This is a highly complex but elegant study, which could significantly contribute to the design of novel strategies to spread antimalarial transgenes in mosquitoes.<br /> Overall, this is a complex study which, for a non-specialist reader gets quite technical and heavy in most parts. Despite this, there are key points showing that suppression gene drive may not be the way forward in this instance. However, I would advise explaining certain elements in more detail for the benefit of the general readers. I only have minor points for the authors to address:<br /> 1) Please point out for the general reader that Anopheles coluzzii belongs to the gambiae complex, since you explain that gambiae are the major malaria spreaders in sub-Saharan Africa.

--- done in the introduction (lines 71-73) also in response to Rev. 1

2) The authors pretty much give all results in the last part of the introduction, could the intro be shortened by removing these parts, or just highlighting in a single paragraph the main take home message?

--- We have condensed this part to highlight the take home messages in the last paragraph, also in response to Rev. 1.

3) Why is Vg mentioned? It is only mentioned once and doesn't have any other mention through the manuscript.

--- this introduces the two proteins that are by far the most abundant, and present at similar levels, in the hemolymph of blood-fed females, Vg being also prominent on the Coomassie stained gel of fig.1. We mention Vg also because it represents another excellent candidate locus to host anti-plasmodium factors, as discussed later on lines 600-610 of the Discussion section.

4) Please make it clearer for non-specialists why Cecropin wasn't used.

---On lines 630-636 we explain that we decided to leave out Cecropin to avoid potential additional fitness costs due to expression at all life stages in the fat body, as opposed to solely in the midgut after blood meal (Isaacs et al. PNAS 2012); and to avoid complexifying the anti-Plasmodium Lipophorin locus in a way that could further reduce the functionality of the Lp gene. We also had prior knowledge from unplublished work that Sc2A10 alone was sufficient to block sporozoite infectivity.

5) Why were homozygous and not heterozygous transgenics transfected if there is such as fitness cost to homozygous mosquitoes?

--- the fitness cost of homozygous mosquitoes is actually mild, unnoticeable if homozygotes are bred in the absence of competing heterozygotes and wild-types (lines 151-156). Microinjection experiments to obtain the different versions of SagGD were, therefore, performed on either the heterozygous or homozygous line. As for infection assays, the anticipated effect of gene drive is to promote homozygosity at the Lp::Sc2A10 locus. For this reason, it made sense to test the vector competence of homozygotes, in addition to the fact that the Plasmodium-blocking phenotype was expected to be stronger (and thus, easier to document) with two copies of the transgene. Only after obtaining a large dataset from infection assays with homozygotes did we test heterozygotes and found that they actually had a similar phenotype.

6) Line 211 - what was the average number of infected mosquitoes used per infection for each mosquito strain?

--- As described in the text (lines 204-206 of v2; 208-212 of the revision) and in the Methods (lines 868-873), non-infected mosquitoes were discarded prior to performing the experiment using 10 infected mosquitoes per mouse, and we discarded mice bitten by fewer than 6 mosquitoes. So at least 6 infected mosquitoes bit each mouse (often 8-9).

7) Line 219 - please be clearer regarding this being infection detected in the blood.

--- We replaced « infection » with « detectable parasitemia in the blood »

8) Line 320 - please clarify why the zpg promoter was used.

--- The advantages of zpg are mentioned in lines 257-258 and 320-322 (revised file).

9) Line 375 - what was the rationale for using so many gRNAs?

--- 3 or 4 gRNAs against Lipophorin and 3 gRNAs against Saglin, amounting to a total of 6 or 7 gRNAs against the two loci. The rationale is explained on lines 249-253 : the goal was to maximize the chance of causing loss-of-function mutations in the essential Lp gene and to favor elimination of GD resistant alleles by natural selection, in case of failed homing. For Saglin which is a non-essential gene, we wanted to ensure loss-of-function of failed homing alleles to achieve a reduction in vector competence, even if GD-resistant alleles accumulate. We sought to make this rationale clearer by adding a sentence on lines 328-332:

Multiplexing the gRNAs was intended to promote the formation of loss-of-function alleles in case of failed homing at the Lp and Saglin loci: non-functional alleles of the essential Lp gene would be eliminated by natural selection while non-functional Saglin alleles would reduce vector competence.

Line 555 - please state how long post bite back parasite appears in infected mice.

--- We changed this sentence to : …two of these six mice developed parasitemia six days after infection<br /> (line 556).

Reviewer #3 (Significance):

This is potentially a highly significant study that could provide a vital mechanism for generating efficient gene drives. Although highly technical and complex in most parts, with a little clarification in certain areas this manuscript could be of great value to a general readership.

--- Thank you for your appreciation and thoughtful evaluation of our manuscript.

Reviewer #4 (Evidence, reproducibility and clarity):

The authors hijacked the Anopheles coluzzii Lipophorin gene to express the antibody 2A10, which binds sporozoites of the malaria parasite Plasmodium falciparum. The resulting transgenic mosquitoes showed a reduced ability to transmit Plasmodium.

The authors also designed and tested several CRISPR-based gene drives. One targets Saglin gene and simultaneously cleaves the wild-type Lipophorin gene, aiming to replace the wildtype version with the Sc2A10 alele while bringing together the Saglin gene drive.

Drive-resistant alleles were present in population-caged experiments, the Saglin-based gene drive reached high levels in caged mosquito populations though, and simultaneously promoted the spread of the antimalarial Lp::Sc2A10 allele.

This work contributes to the design of novel strategies to spread antimalarial transgenes in mosquitoes. It also displays issues related to using multiplexing gene-drive designs due to DNA rearrangements that could prevent the efficient spread of the gene drive in the long term.

This is tremendous work considering how many transgenic lines and genetic crosses are performed using mosquitoes. The conclusions are supported by the data presented, and some modifications regarding the experimental design description through text/figure improvements would facilitate the reading and flow of the paper.

Here some questions/comments:

• Line 124-125: Reference?

--- added

• Line 133-134: Reference?

--- added

• Table 1: It seems the authors have some issues recovering a good amount Sc2A10 from hemolymph samples. Is this a problem of the antibody per se? Is it the Lp endogenous promoter weak? Could this be improved by placing the antibody in a different genomic region? Alternatives could be discussed.

--- The 2A10 antibody must be initially produced in the same, very high, amounts as the Lp endogenous protein with which it is co-translated. Therefore, its low relative abundance must result from faster turnover or stickiness to tissue, as hypothesised on lines 176-177. We believe that virtually any other endogenous promoter would be weaker than Lp and produce lower Sc2A10 levels.

• Fig.1B: It would be nice to have a representation of the genome after integration. You could add a B' panel or just another schematic under the current one.

--- In agreement with this suggestion and that of rev. 3, we added a new panel in 1B.

• Supplementary Fig.1b: Could the authors explain the origin of the (first) zpg promoter used? Is it from An. Coluzzii? It seems they use a different one in the gene drive designs later (see comments below too).

--- We initially cloned a PCR-amplified zpg promoter region of the same size as the version published by Kyrou et al., from genomic DNA from our colony of A. coluzzii. The resulting promoter fragment harbored several single nucleotide polymorphisms (SNPs) compared to published sequences, as typically observed when cloning genomic fragments due to high genetic diversity in Anopheles species. Such SNPs are not usually expected to affect promoter activity, but are difficult to distinguish from PCR mutations which, in turn, could decrease or abolish promoter activity if mutating an essential transcription factor binding site. For this reason, our next constructs were based on the validated zpg sequences from Kyrou et al. The first cloning strategy was described in the results section but was missing in the material and method section. This is now corrected (lines 773-779).

• Fig.3: Please, correct to A, B, C order. Current one is A, C, B.

--- Done.

Could the authors include a schematic of the final mosquito genome after integration? I can see they are targeting two different locations (Saglin and Lp). It is unclear though from the figure where the Sc2A10-GFP is coming from. I understand this represents the mosquito genome as you injected heterozygous animals already containing the Sc2A10-GFP. Maybe label the Sc2A10-GFP as mosquito genome or similar? A schematic showing mosquito embryos already carrying this and then the plasmid being injected could help.

--- Figure 3 does not represent the injection of new transgenic constructs. Instead, it shows the conversion process of chromosomes X and II in a germ cell carrying both transgenes in the heterozygous state, to illustrate how the dual gene drive can spread in a population after WT mosquitoes mated with transgenics carrying both the SagGD and Lp-2A10 alleles. We have re-worked the graphic design of this figure and modified its title to make this more clear.

• Line 330-331: Do you know the transgenesis efficiency? Did the authors make single or pools for crossing and posterior screening? It would be interesting to know about transgenesis rates to inform the community.

--- we no longer perform single crosses for transgenesis, as batch crosses ensure higher recovery of transgenics due to the collective reproductive behavior (swarming) in Anopheles. Therefore, we cannot precisely calculate the transgenesis efficiency. However, >60 positive G1s from a pool of 36 G0 males crossed to WT females is indicative of a rather high integration efficiency. We consistently observe high efficiency of transgene integration when using the CRISPR/Cas9 system, that we estimate to be about 5-fold more efficient than docking site transgenesis, and much more efficient than piggyBac mediated transgenesis.

• Line 357/Fig.4B: Could the authors explain in the text GFP+ vs. GFP++?

--- GFP++ was meant to indicate higher intensity of GFP fluorescence than GFP+, due to two copies of the transgene versus one, but see our response to reviewer 1’s comment to line 356 about the questionability of homing in the zygote.

• Line 357: Where is the vasa promoter that made the "rescue" coming from? Is it amplified from Coluzzii? Please, include this explanation for clarification. Why the authors think the zpg from Kyrou et al 2018 works for the cassette integration but not for homing? They discuss positional effects, any references showing that?

--- We amplified the vasa promoter from A. coluzzii using primers CggtctcaATCCcgatgtagaacgcgagcaaa and CggtctcaCATAttgtttcctttctttattcaccgg (annealing sequence underlined) to have a fragment equivalent to that (vas2) characterized in Papathanos et al, 2009. We have now added this information in the Methods under Plasmid construction. This is the only source of vasa promoter used in this work.

About zpg promoter activity : we have past experience suggesting that promoters, such as the hsp70 promoter from Drosophila, can be sufficient to express enzymatic activities in embryos injected with helper plasmids, even though the same promoters appear to become inactive once integrated in the genome. This may be due to injected “naked” plasmids being readily accessible to the transcription machinery, unlike organized chromatin. A recent reference showing genomic positional influences on promoter efficiency is Anderson et al., 2023, which we have added on line 710 of the Discussion.

• Line 362: No reference to figure nor table.

--- These data (numbers from a COPAS analysis) are provided directly in the text in this sentence (which has been clarified in response to Reviewer 1). See lines 364-369 of the revision.

• Line 417: The text brings the reader back to Fig.3C. Could the authors move this panel for easier flow of the paper?

--- We agree that positioning of this panel in Figure 3 is a bit awkward, but this western blot pertains to the characterization of the insertion shown in Fig. 3. Placing it after COPAS analyses would be equally awkward.

• Line 472-474: How many WT alleles were recovered? It is not stated unless I missed anything, which is possible.

--- We refrained from providing a quantification of this, and focussed on qualitative results, as we didn't trust the quantitative representativity of our high-throughput amplicon sequencing results in terms of allele frequency in the sampled mosquito population. A large fraction of sequenced reads corresponded to PCR artefacts such as primer dimers and unspecific short amplicons, potentially affecting the relative frequencies of gene-specific amplicons. However, among the sequenced gene-specific amplicons, WT alleles were the majority (lines 474-475).

• Fig.5. Could the authors discuss why the observed DsRed-gene drive drop in population 1 at ~18 generation? The population gets to the point where only 50% of the population carries the Cas9-DsRed cassette. Considering that the Saglin gene drive only converts through females (inserted into the X chr.), and some indels could be generated by generation 20, how do you explain the great recovery until fully spreading into the population?

--- We agree that this is somewhat puzzling. We don’t have a satisfactory explanation beyond stochastic effects, possibly promoted by population bottlenecks: although we strived to maintain these populations at a high number of individuals at each generation, we cannot exclude that at a given generation only a relatively small fraction of individuals contributed to the next generation, leading to fluctuations in allelic frequencies. This would be possible particularly for populations 1 and 2, which were not monitored frequently between generations 10 and 18, at which point additional populations 5-8 were established and it was decided that close monitoring of all populations was important.

It seems to me populations 3-8 are new cage experiments by randomly picking mosquitoes from populations 1 and 2 (at a specific generation) and mixing them with WT individuals. Could the authors explain the reasoning for these experiments? I believe populations 3-8 deserves a different figure (main or supplementary) describing how they were seeded. It is confusing having everything together as these experiments were performed differently way and for a different reason compared to populations 1 and 2. Some cage schematics and drawings would help in understanding the protocol strategy for populations 3-8.

--- This is correct for populations 3 and 4 that indeed originated from randomly picking mosquitoes from populations 1 and 2 at generation 10 and mixing them with WT individuals. Populations 5, 6, 7 and 8 are crosses between generation 16 transgenic partners of one sex to WT of the other sex, as indicated above the COPAS diagrams provided in Suppl. File 2. We apologize for having insufficiently described how each population was assembled and now provide more details (lines 422-429, in the figure 5 legend, and G0 crosses spelled out on top of each population diagram). In setting up these populations, we wanted to test the effects of various routes by which the transgenes may be introduced into a wild mosquito population: release of unsorted transgenic males + females, or release of one sex only (probably males in the field, but the crosses with transgenic females as with transgenic males also served to re-quantify homing in the second generation of each cross).

The modified text reads as follows:

Populations 3 and 4 were established by mixing randomly selected transgenic mosquitoes (both males and females of generation 10) from populations 1 and 2, respectively, with wild-types, to mimic what may occur in a mixed-sex field release. Populations 5-8 were established by crossing single-sex transgenic mosquitoes to WT of the opposite sex, both to mimic a single-sex field release and to re-assess homing efficiency after 16 generations.

Also, could you add homozygous and heterozygous labels in the figure legend to help understanding the different lines.

--- As indicated on the side of the figure and in the figure legend, lines don’t represent homozygous vs. heterozygous frequency, but allele frequency (continuous lines), and frequency of mosquitoes carrying the transgene (dotted lines). In the figure legend we now provided the calculation formulas for gene frequency: [ 2 x (number of homozygotes) + (number of heterozygotes)] / 2 x (total number of larvae) for the autosomal Lp::2A10 transgene, and [ 2 x (number of homozygotes) + (number of heterozygotes) ] / 1.5 x (total number of larvae) for the X-linked SagGD transgene.

• Fig.6: The authors sequenced non-DsRed individuals from generations 3-4. The authors also mentioned they sequenced mosquitoes from generation 32 (Fig.7). Interestingly, they observed that these mosquitoes were missing a piece of the cassette (they contained 2 gRNAs instead of 7). Since the amplicons only cover the gRNA portion, a PCR covering the Zpg-Cas9 portion would be ideal to confirm that only the gRNAs are missing. Sampling DsRed+ mosquitoes from generations 3, 18 and 31 (populations 1 and 2) and carrying out these experiments is recommended. Although unlikely, I would be worried about the Cas9 being deleted due to unexpected DNA rearrangements; in that case, the cassette would contain the DsRed marker alone.

--- Thank you for this suggestion. We no longer have DNA samples from the earlier generations. Thus, we genotyped 7 DsRed positive male mosquitoes from each of current populations 1, 2 and 7 (generation 41 since transgenesis) for the presence of Cas9. We detected a Cas9-specific amplicon of 1.6 kb in 21/21 sampled DsRed positive mosquitoes, in parallel to the same shortened gRNA arrays detected in earlier generations. This suggests that the Cas9 part of the transgene was not affected by the loss of gRNA units. We made a panel C in Figure 7 showing these results and mentioned them on lines 537-538. Of note, the Cas9 moiety of the gene drive construct shows no repetitive sequence and should therefore not be as unstable as the gRNA multiplex array. The observed excisions of gRNA expression units were strictly due to recombinations between repeated U6 promoter sequences (Fig. 7).

The authors employ 3 different gRNAs that are 43 and 310 nts apart. It has been shown that only 20 nt lack of homology produces an important reduction on gene drive performance (Lopez del Amo et al 2020, Nat Comms). Also, it has been shown that gRNA multiplexing approaches should be kept with a low number of gRNAs, 2 being maybe the best one depending on the design (Samuel Champer 2020, Sciences Advances). This could be discussed more.

--- Thank you for this suggestion. These results were not published when this study was initiated, so that our gene drive constructs could only be designed on empirical bases. For gRNA numbers, see the new discussion point and inclusion of a reference to the study by S. Champer et al., on line 700-702. The reduction of drive performance with longer non-homologous stretches is indeed also a very important point, that we now discuss on lines 713-717, citing your study:

Finally, tighter clustering of gRNA target sites at target homing loci, especially Saglin, should improve gene drive performance by reducing the length of DNA sequences flanking the cut site that bear no homology to the repair template on the sister chromosome and need to be resected by the repair machinery to allow homing (López Del Amo et al., 2020)__.

Reviewer #4 (Significance):

There are different novelty aspects from my point of view in this work. While most of the scientists focus on developing CRISPR-based gene drives in An. Stephensi and gambiae, this work employs An. Coluzzii. Some limitations regarding fitness cost associated with the Lp gene were also noted and discussed by the authors.

--- To be fair, earlier gene drive studies were performed on the G3 laboratory strain, traditionally named A. gambiae, although it is probably itself a hybrid strain from gambiae and coluzzii. Still, the Ngousso strain from Cameroon that was used in this study is thought to be a bona fide A. coluzzii. We have also added a reference to a recent paper (Carballar-Lejarazu et al., 2023) that also describes a population modification GD in A. coluzzii.

First, they show that An. Coluzzii mosquitoes infect less when containing the antimalarial effector cassette inserted in their genomes. Second, a gene drive is showing super-Mendelian inheritance in An. Coluzzii, which would be the second example of a gene drive in these mosquitoes so far to my knowledge.

I believe this is the first manuscript experimentally using multiplexing approaches (multiple gRNAs) in mosquitoes (all previous works I saw were performed in flies). While previous gene-drive works employ only one gRNA in mosquitoes, this works explores the use of different gRNAs targeting nearby locations to potentially improve HDR rates and gene drive spread. Although they observe gene drive activity, they also show DNA rearrangements due to the intrinsic nature of multiplexing gene drives that can generate multiple DNA double-strand breaks, impeding proper HDR and clean replacement of the wildtype alleles. This is important from a technical point of view as it shows this approach requires optimization. They included 3 gRNAs targeting the Saglin gene, and trying 2gRNAs instead could be interesting for future investigations.

--- We now discussed optimization with the help of modeling, in response to Reviewer 1, on lines 701-702.

This work will be very useful for the CRISPR-based gene drive field, which seeks to develop genome editing tools to control mosquito populations and reduce the impact of vector-borne diseases such as malaria.

This reviewer intended to understand the work and provide constructive feedback to the best of my abilities. I apologize in advance if I misunderstood anything.

--- Thank you for your appreciation, insight, and constructive evaluation of our manuscript.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #4

Evidence, reproducibility and clarity

The authors hijacked the Anopheles coluzzii Lipophorin gene to express the antibody 2A10, which binds sporozoites of the malaria parasite Plasmodium falciparum. The resulting transgenic mosquitoes showed a reduced ability to transmit Plasmodium.

The authors also designed and tested several CRISPR-based gene drives. One targets Saglin gene and simultaneously cleaves the wild-type Lipophorin gene, aiming to replace the wildtype version with the Sc2A10 alele while bringing together the Saglin gene drive.

Drive-resistant alleles were present in population-caged experiments, the Saglin-based gene drive reached high levels in caged mosquito populations though, and simultaneously promoted the spread of the antimalarial Lp::Sc2A10 allele.

This work contributes to the design of novel strategies to spread antimalarial transgenes in mosquitoes. It also displays issues related to using multiplexing gene-drive designs due to DNA rearrangements that could prevent the efficient spread of the gene drive in the long term.

This is tremendous work considering how many transgenic lines and genetic crosses are performed using mosquitoes. The conclusions are supported by the data presented, and some modifications regarding the experimental design description through text/figure improvements would facilitate the reading and flow of the paper.

Here some questions/comments:

• Line 124-125: Reference?
• Line 133-134: Reference?
• Table 1: It seems the authors have some issues recovering a good amount Sc2A10 from hemolymph samples. Is this a problem of the antibody per se? Is it the Lp endogenous promoter weak? Could this be improved by placing the antibody in a different genomic region? Alternatives could be discussed.
• Fig.1B: It would be nice to have a representation of the genome after integration. You could add a B' panel or just another schematic under the current one.
• Supplementary Fig.1b: Could the authors explain the origin of the (first) zpg promoter used? Is it from An. Coluzzii? It seems they use a different one in the gene drive designs later (see comments below too).
• Fig.3: Please, correct to A, B, C order. Current one is A, C, B.<br /> Could the authors include a schematic of the final mosquito genome after integration? I can see they are targeting two different locations (Saglin and Lp). It is unclear though from the figure where the Sc2A10-GFP is coming from. I understand this represents the mosquito genome as you injected heterozygous animals already containing the Sc2A10-GFP. Maybe label the Sc2A10-GFP as mosquito genome or similar? A schematic showing mosquito embryos already carrying this and then the plasmid being injected could help.
• Line 330-331: Do you know the transgenesis efficiency? Did the authors make single or pools for crossing and posterior screening? It would be interesting to know about transgenesis rates to inform the community.
• Line 357/Fig.4B: Could the authors explain in the text GFP+ vs. GFP++?
• Line 357: Where is the vasa promoter that made the "rescue" coming from? Is it amplified from Coluzzii? Please, include this explanation for clarification. Why the authors think the zpg from Kyrou et al 2018 works for the cassette integration but not for homing? They discuss positional effects, any references showing that?
• Line 362: No reference to figure nor table.
• Line 417: The text brings the reader back to Fig.3C. Could the authors move this panel for easier flow of the paper?
• Line 472-474: How many WT alleles were recovered? It is not stated unless I missed anything, which is possible.
• Fig.5. Could the authors discuss why the observed DsRed-gene drive drop in population 1 at ~18 generation? The population gets to the point where only 50% of the population carries the Cas9-DsRed cassette. Considering that the Saglin gene drive only converts through females (inserted into the X chr.), and some indels could be generated by generation 20, how do you explain the great recovery until fully spreading into the population?

It seems to me populations 3-8 are new cage experiments by randomly picking mosquitoes from populations 1 and 2 (at a specific generation) and mixing them with WT individuals. Could the authors explain the reasoning for these experiments? I believe populations 3-8 deserves a different figure (main or supplementary) describing how they were seeded. It is confusing having everything together as these experiments were performed differently way and for a different reason compared to populations 1 and 2. Some cage schematics and drawings would help in understanding the protocol strategy for populations 3-8.

Also, could you add homozygous and heterozygous labels in the figure legend to help understanding the different lines.

• Fig.6: The authors sequenced non-DsRed individuals from generations 3-4. The authors also mentioned they sequenced mosquitoes from generation 32 (Fig.7). Interestingly, they observed that these mosquitoes were missing a piece of the cassette (they contained 2 gRNAs instead of 7). Since the amplicons only cover the gRNA portion, a PCR covering the Zpg-Cas9 portion would be ideal to confirm that only the gRNAs are missing. Sampling DsRed+ mosquitoes from generations 3, 18 and 31 (populations 1 and 2) and carrying out these experiments is recommended. Although unlikely, I would be worried about the Cas9 being deleted due to unexpected DNA rearrangements; in that case, the cassette would contain the DsRed marker alone.

The authors employ 3 different gRNAs that are 43 and 310 nts apart. It has been shown that only 20 nt lack of homology produces an important reduction on gene drive performance (Lopez del Amo et al 2020, Nat Comms). Also, it has been shown that gRNA multiplexing approaches should be kept with a low number of gRNAs, 2 being maybe the best one depending on the design (Samuel Champer 2020, Sciences Advances). This could be discussed more.

Significance

There are different novelty aspects from my point of view in this work. While most of the scientists focus on developing CRISPR-based gene drives in An. Stephensi and gambiae, this work employs An. Coluzzii. Some limitations regarding fitness cost associated with the Lp gene were also noted and discussed by the authors.

First, they show that An. Coluzzii mosquitoes infect less when containing the antimalarial effector cassette inserted in their genomes. Second, a gene drive is showing super-Mendelian inheritance in An. Coluzzii, which would be the second example of a gene drive in these mosquitoes so far to my knowledge.

I believe this is the first manuscript experimentally using multiplexing approaches (multiple gRNAs) in mosquitoes (all previous works I saw were performed in flies). While previous gene-drive works employ only one gRNA in mosquitoes, this works explores the use of different gRNAs targeting nearby locations to potentially improve HDR rates and gene drive spread. Although they observe gene drive activity, they also show DNA rearrangements due to the intrinsic nature of multiplexing gene drives that can generate multiple DNA double-strand breaks, impeding proper HDR and clean replacement of the wildtype alleles. This is important from a technical point of view as it shows this approach requires optimization. They included 3 gRNAs targeting the Saglin gene, and trying 2gRNAs instead could be interesting for future investigations.

This work will be very useful for the CRISPR-based gene drive field, which seeks to develop genome editing tools to control mosquito populations and reduce the impact of vector-borne diseases such as malaria.

This reviewer intended to understand the work and provide constructive feedback to the best of my abilities. I apologize in advance if I misunderstood anything.

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Referee #3

Evidence, reproducibility and clarity

The study by Green et al. generated a gene drive targeting both Saglin and Lipophorin in the Anopheles mosquito, with a view to blocking Plasmodium parasite transmission. This is a highly complex but elegant study, which could significantly contribute to the design of novel strategies to spread antimalarial transgenes in mosquitoes.<br /> Overall, this is a complex study which, for a non-specialist reader gets quite technical and heavy in most parts. Despite this, there are key points showing that suppression gene drive may not be the way forward in this instance. However, I would advise explaining certain elements in more detail for the benefit of the general readers. I only have minor points for the authors to address:

1. Please point out for the general reader that Anopheles coluzzii belongs to the gambiae complex, since you explain that gambiae are the major malaria spreaders in sub-Saharan Africa.
2. The authors pretty much give all results in the last part of the introduction, could the intro be shortened by removing these parts, or just highlighting in a single paragraph the main take home message?
3. Why is Vg mentioned? It is only mentioned once and doesn't have any other mention through the manuscript.
4. Please make it clearer for non-specialists why Cecropin wasn't used.
5. Why were homozygous and not heterozygous transgenics transfected if there is such as fitness cost to homozygous mosquitoes?
6. Line 211 - what was the average number of infected mosquitoes used per infection for each mosquito strain?
7. Line 219 - please be clearer regarding this being infection detected in the blood.
8. Line 320 - please clarify why the zpg promoter was used.
9. Line 375 - what was the rationale for using so many gRNAs?<br /> Line 555 - please state how long post bite back parasite appears in infected mice.

Significance

This is potentially a highly significant study that could provide a vital mechanism for generating efficient gene drives. Although highly technical and complex in most parts, with a little clarification in certain areas this manuscript could be of great value to a general readership.

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Referee #2

Evidence, reproducibility and clarity

The authors initially created a transgenic mosquito colony expressing the Sc2A10 antibody fused to the lipid transporter Lipophorin, and tested the transmission-blocking activity of this transgene. Building off of previous findings that the Sc2A10 antibody inhibits sporozoite infectivity when expressed in mosquito salivary glands, the authors showed that found it was also efficient at inhibiting sporozoite infectivity when secreted into the hemolymph expressed under the lipophorin endogenous promoter in An. coluzzii. They then designed and tested two different gene drives utilizing the Sc2A10-Lipophorin fusion protein. In the first, the authors used a recoded allele of Lp-Sc2A10 while simultaneously utilizing gRNAs that targeted endogenous Lp in an effort to select for mosquitoes that expressed transgenic Lp-Sc2A10 due to the essential nature of Lp. However, this drive was unsuccessful because recoded Lp is necessarily heterozygous while the GD is entering the population, and Lp proved to be largely haploinsufficient. Further, the zpg promoter expressing cas9 was not effective in promoting homing of the gRNAs. In the second gene drive that was tested, authors made use of the endogenous Saglin locus, which expresses a natural agonist for Plasmodium, and is thus desirable to target for disruption in a gene drive that aims to reduce vector competence for Plasmodium. This gene drive also uses recoded Lp-Sc2A10 to replace the wild-type Lp allele, thus selecting for Sc2A10 expression, however this drive is not dependent on fitness of individuals with only one functional copy of Lp.

The authors discovered that the efficacy of the zpg promoter to drive homing of cas9 is locus-dependent, limiting the success of their gene drive designs. They do show, however, that the Saglin gene drive succeeds at reaching high frequencies in mosquito populations using instead the vasa promoter to express cas9, and that these transgenic mosquitoes are able to reduce infectivity of sporozoites in a bite-back mouse model. However, they observe gene drive refractory mutations in the Lp gene, despite its highly conserved nature, showcasing the difficulty of avoiding drive resistance even in small populations of mosquitoes, and also observed deletions of gRNAs targeting both Lp and Saglin, further highlighting possible shortcomings in gene drive approaches. Together, these findings are useful to the field in walking the readers through an interesting and promising approach for a novel gene drive, and illustrating the challenges in engineering an efficacious and long-lasting drive.

Major comments:

As the authors are able to observe Plasmodium within mosquitoes, it would be useful to have these data in the manuscript pertaining to the prevalence and intensity of infection in mosquitoes prior to bite-back assays. If there are data or images that the authors could include, it would be helpful to show if there is a possibility that infection intensity is a variable that contributes to whether or not mice develop an infection. It would also be interesting to note whether there is a different in infection (oocysts or sporozoites) between transgenic mosquitoes and wild type mosquitoes.

The authors also go into significant detail in the discussion exploring ideas of how to optimize or improve this specific gene drive design. The authors should also stress further the applicability of their discoveries in other gene drive designs, and emphasize the lessons they learned in the difficulties encountered in this study and how these findings could guide others in their decision making process when choosing targets or elements to include in a potential gene drive approach.

Minor comments:

Line 44 - female sterility but also female killing approaches to crash pop. like X shredder, if authors would like to expand

Lines 48-60 - Authors should add some references from the literature surrounding ethics and ecology studies related to gene drive release

Line 114 - Given the only moderate impacts of Saglin's role in Plasmodium invasion, I am not sure this saglin deletion is a convincing benefit for GD as it is probably not impactful enough alone - can the authors soften this statement?

Line 126 -Can the authors provide rationale for expressing Sc2A10 with Lp instead of expressing it from salivary glands?

Line 140 - Can authors give any comment on why these regions of Lp were chosen to be recoded / targeted with gRNAs?

Line 171 - "stoichiometric"

Line 186 - Can the authors comment or speculate on why the expression levels of the fusion protein are expected to be lower than endogenous Lp?

Line 211 - Why were 6 mosquitoes used for these assays, and 10 mosquitoes used in later assays (Line 223)?

Line 212 - I would also suggest using letters (Suppl. Table 2A,B,C etc) to refer the specific experiments and sections in the Table.

Line 225- 228 - The authors should mention in the text that homozygotes and heterozygotes do not differ in infection assays.

Line 249 - Can the author comment on the impacts of population influx / exchange on the idea that the GD cassette need only be transiently in the population?

Line 273 - Can the authors comment on why this may have occurred more frequently than the expected integration of the GD cassette?

Line 314 - Not all fitness costs are apparent through standard laboratory rearing as was performed in Klug et al. Authors could consider "no known fitness cost" instead.

Line 407 - don't start new paragraph (same with 409)

Line 408 - I'm not sure it's clear why all these populations were kept for a different number of generations - can the authors clarify?

Line 558 - "10/12 mice" not immediately clear - the authors could be more specific about how data was combined here

Line 586 - Since there do appear to be some fitness costs associated with the Sc2A10 version of Lp, might it be expected that fitness costs imposed by the transgene itself could lead to selection pressures leading to its loss? Or do the authors think that these fitness costs are prevented from causing selection against Sc2A10 due to the design of the transgene such that its translation is a prerequisite for Lp's translation? Is the fact that its removal occurs more rapidly than Lp's any indication that selection against the persistence of Sc2A10 may occur?

Line 659 - add some further detail to this - how do you envision this to occur?

Line 635 - Long paragraph, should be broken up or removal of text. Some of these ideas could possibly be made more concise to improve readability. There are many different hypotheticals that are expanded upon in the discussion.

Line 677 - This scenario seems potentially unrealistic considering the only subtle impacts of Saglin deletion on vector competence, and the potential for population exchange in mosquito populations to dilute out these alleles if the drive begins to fail. Can the author comment or potentially decrease emphasis on such scenarios?

Line 708 - Can the authors speculate on why zpg is sensitive to local chromatin and elaborate on possible solutions or consequences for other drive ideas? This seems broadly important.

Line 737 - The suggestion of releasing laboratory-selected resistance alleles in the absence of further context may be provocative and unnecessary here.

Line 850 - unnecessary comma

Line 854 - change to "after infection, moquitoes were "

Figure 1 - Not clear what is intended to be communicated by shapes portraying proteins / subunits - consider more detailed illustration of mosquito fat body cells synthesizing and secreting proteins rather than words in text box with arrow to clearly demonstrate the point of this figure.

Figure 3 - I recommend rearranging this figure so that B comes before C, visually. The proportions for the design of in B should also match those used for A.

Figure 5 - It is unclear to me why some Populations were maintained for such different lengths of time.

Figure 7 - Ladder should be labeled on the gel. It may also be helpful for the author to indicate clearly exactly which mosquitoes were shown by sequencing to have these different deletions, as it is occasionally unclear based on band sizing.

Line 996 - given that there is a size band on the right line of this gel also, can authors crop the gel image to eliminate unnecessary lanes a and b from this figure without losing information needed to interpret this blot?

Line 1070 - 12 out of how many sequenced mosquitoes?

Line 1078 - Can remove some detail like % of agarose, and replication of results with different polymerase as these are already in methods.

Line 1098 - "Unbless"

Significance

This study illustrates a wide range of issues pertinent for gene drive implementation for malaria control, and as such is of value to the field of entomologists, genetic engineers, parasitologists and public health professionals. The gene drive designs explored for this study are interesting largely from a basic biology perspective pertinent mostly to specialists in the field of genetic engineering and vector biology, but highlight challenges associated with this technology that could also be of interest to a broader audience. A transmission blocking gene drive has not yet been achieved in malaria mosquitoes, and is thus a novel space for exploration. As a medical entomologist that works predominantly outside of the genetic engineering space, I have appreciated the detail the authors have provided with regard to their rationale and findings, even when these findings were inconsistent with the authors' primary objectives or expectations.

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Referee #1

Evidence, reproducibility and clarity

In this study, the authors made a two-component homing modification gene drive in Anopheles coluzii with a different strategy than usual. The final drive itself targets and disrupts the saglin gene that is nonessential for mosquitoes, but important for the malaria parasite. The drive uses several gRNAs, and some of these target the Lp gene where an anti-malaria antibody is added, fused to the native gene (this native gene is also essential, removing nonfunctional resistance alleles at this locus). In general, the system is promising, though imperfect. Some of the gRNAs self-eliminate due to recombination of repetitive elements, and the fusion of the antimalaria gene had a modest fitness cost. Additionally, the zpg promoter was unable to operate at high efficiency, requiring use of the vasa promoter, which suffers from maternal deposition and somatic expression (the latter of which increased fitness costs at the Lp target). The manuscript has already undergone some useful revisions since its earliest iteration, so additional recommended revisions are fairly modest.

Line 43-45: The target doesn't need to be female sterility. It can be almost any haplosufficient but essential target (female sterility works best, so it has gotten the most study, but others have been studied too).

Line 69: A quick motivation for studying Anopheles coluzii should be added here (since gambiae is discussed immediately before this).

Introduction section: It might be helpful to break up the introduction into additional paragraphs, rather than just two.

Introduction last part: The last part of the introduction reads more like an abstract or conclusions section. Perhaps a little less detail would fit better here, so the focus can be on introducing the new drive components and targets

Line 207-213: This material could go in the methods section. There are some other examples in the results that could be similarly shortened and rearranged to give a more concise section.

Line 283-287: I couldn't find the data for this.

Line 291: Replace "lied" with "was".

Line 356: Homing in the zygote would be considered very unusual and is thus worthy of more attention. While possible (HDR has been shown for resistance alleles in the zygote/early embryo), this would be quite distinct from the mechanism of every other reliable gene drive that has been reported. Is the flow cytometry result definitely accurate? By this, I mean: could the result be explained by just outliers in the group heterozygous for EGFP, or perhaps some larvae that hatched a little earlier and grew faster? Perhaps larvae get stuck together here on occasion or some other artifact? Was this result confirmed by sequencing individual larvae?

Results in general: Why is there no data for crosses with male drive heterozygotes? Even if some targets are X-linked, performance at others is important (or did I miss something and they are all X-linked). I see some description near line 400, but this sort of data is figure-worthy (or at least a table).

Lines 362-367: What data (figure/table) does this paragraph refer to?

Lines 405-406: There may be a typo or miscalculation for the DsRed inheritance and homing rate here. Should DsRed inheritance be 90.7%?

Figure 5: The horizontal axis font size for population 8 is a little smaller than the others.

Line 454: In addition to drive conversion only occurring in females and the somatic fitness costs, embryo resistance from the vasa promoter would prevent the daughters of drive females from doing drive conversion. This means that drive conversion would mostly just happen with alleles that alternate between males and females.

Line 481: Deletions between gRNAs certainly happen, but I wouldn't necessarily expect this to be the "expectation". In our 2018 PNAS paper, it happened in 1/3 of cases. There were less I think in our Sciences Advances 2020 and G3 2022 paper. All of these were from embryo resistance from maternal Cas9 (likely also the case with your drive due to the vasa promoter). When looking at "germline" resistance alleles, we have recently noticed more large deletions.

Figure 6C: It may be nice to show the wild-type and functional resistance sequence side-by-side.

Lines 642-644: This isn't necessarily the case. At saglin, the nonfunctional resistance alleles may still be able to outcompete the drive allele in the long run. This wasn't tested, but it's likely that the drive allele has at least some small fitness costs.

A few comments on references to some of my studies:

Champer, Liu, et al. 2018a and 2018b citations are the same paper.

For Champer, Kim, et al. 2021 in Molecular Ecology, there was a recent follow-up study in eLife that shows the problem is even worse in a mosquito-specific model (possibly of interest as an alternate or supporting citation): https://elifesciences.org/articles/79121

One of my other previous studies was not cited, but is quite relevant to the manuscript: https://www.science.org/doi/10.1126/sciadv.aaz0525<br /> This paper demonstrates multiplexed gRNAs and also models them, showing their advantages and disadvantages in terms of drive performance. Additionally, it models and discusses the strategy of targeting vector genes that are essential for disease spread but not the vectors themselves (the "gene disruption drive"), showing that this can be a favorable strategy if gene knockout has the desired effect (nonfunctional resistance alleles contribute to drive success).

This one is less relevant, but is still a "standard" homing modification rescue type drive that could be mentioned (and owes its success to multiplexing): https://www.pnas.org/doi/abs/10.1073/pnas.2004373117<br /> The recoded recuse method was also used in mosquitoes (albeit without gRNA multiplexing) by others, so this may be a better one to mention: https://www.nature.com/articles/s41467-020-19426-0

Sincerely,<br /> Jackson Champer

Referees cross-commenting<br /> Other comments look good. One thing that I forgot to mention: for the 7-gRNA construct with tRNAs, the authors mentioned that it was harder to track, but it sounds like they obtained some data for it that showed similar performance. Even if this one is not featured, perhaps they can still report the data in the supplement?

Significance

Overall, this study represents a useful advance. Aside from being the first report for gene drive in A. coluzii, it also is the first that investigates the gene disruption strategy and is the first report of gRNA multiplexing in Anopheles. The study can thus be considered high impact. There are also other aspects of the study that are of high interest to gene drive researchers in particular (several drives were tested with some variations).

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Reply to the reviewers

We are grateful to the Referees for their detailed evaluation of our data and insightful remarks. We have now addressed all comments and amended the manuscript accordingly. Below we detail how we have addressed each point.

Reviewer #1 (Evidence, reproducibility and clarity):

Summary:

In this manuscript Lafzi et al. present a novel computational framework (ISCHIA) for the analysis of spatial occurrence patterns, be it of cells or transcript species, found in spatial transcriptomics datasets. The authors also show its applications in finding differentially co-occurring ligand-receptor pairs, as well as inter-species analysis to find conserved cell signalling pathways. ISCHIA consists of a well-documented R package and utilizes empirical probabilistic estimations of non-random co-occurrence, as used in the field of ecology, which to my knowledge is novel in the field. The authors also validate their predictions using an orthogonal technology (in situ hybridization-based spatial transcriptomics), which is a nice addition to the computational work presented in the manuscript.

Major:

When determining the composition classes, the authors discard 4 out of 8 clusters of composition classes, partly due to being highly patient-specific. It's unclear how sensitive ISCHIA is for batch effects which might affect the measured cellular fractions. Given that the presence of batch effects is highly likely with ST methods, due to the sample processing procedures, it would help the reader/potential user to estimate the impact these could have on the resulting output. It would also be useful to plot a version of the UMAP with sample labels as to see if the remaining clusters are properly mixed (at least between replicates of the same condition).

We thank the reviewer for the comment. We have included in Supplemental Figure S1c a UMAP colored by sample (patients), and a barplot in d) that illustrates how CC8 and CC4 are quite specific to patients 2 and 3. These graphs illustrate how the composition classes 1, 3, 5 and 6 are represented in all patients, albeit in different abundances. We would like to point out that the choice to exclude 4 out of 8 composition classes was not dictated by batch effects, but rather by the specific morphology of the samples available to us. Indeed, we excluded composition classes that were mapping to submucosal and muscular areas because only 2 out of 4 colon resections contained these anatomical compartments, and because we wanted to focus on cell type co-occurrences in the epithelial and subepithelial layers.<br /> Additionally, in order to explain the sensitivity of ISCHIA for batch effects or sample-specific variation, we performed principal component (PC) analysis, and measured the standard deviation explained by each PC on the cell type deconvolution matrix (which is used to calculate composition clusters). Next, we tried to find an association of the covariate “batch” with the PCs that explain a high amount of variation in the data. In the deconvoluted matrix, while the first 4 PCs explain more than 80% of the variation in the data, we couldn’t find association of the covariate “batch” to any of the first 4 PCs. We now include this analysis in the Result section:

Principal component analysis of the deconvolution matrix reveals no association of a particular sample with the first 4 principal components, which cumulatively explain 80% of the variance in the data (Supplemental Figure 2c, d). K-means clustering of the deconvolution matrix revealed 8 CCs of co-localizing cell types present in all samples (Fig 1c, d).

While other methods assign a cell-type identity to spots based on the most abundant cell type detected by deconvolution algorithm, ISCHIA summarizes spot gene expression data in a presence-absence matrix. ISCHIA is therefore robust to variation in expression levels due to batch effects. This was also recently reported in the analytical tool Starfysh (He et al. 2022), which performs similar clustering of spots based on cell type composition. This is included in the Discussion:

While preserving the complexity of the cell type composition of the analyzed tissue, composition-based clustering of spots also confers robustness towards variations in expression levels due to batch effects. Indeed, other spatial analysis methods such as Starfysh66 have found that finding inter-sample commonalities using composition-based clusters is easier compared to finding common transcriptome-based clusters between samples. Still, batch analysis and, if needed, correction of the ST data is recommended prior to analysis with ISCHIA.

Additionally, it would help to illustrate that the biological findings reported in the manuscript are supported across more than 1 biological replicate.

We agree with the reviewer that the sample size of Visium and Resolve datasets is limited, however greater than 1 (3 and 4 patients, respectively). ISCHIA is meant as a tool for hypothesis generation. Its findings require independent functional validation in model systems and bigger patient cohorts.

In the LR analysis, the authors state that ISCHIA's predictions are agnostic to gene expression levels, as the authors model expression as a Boolean (gene count threshold > 0). Wouldn't low expression levels result in increased drop-out due to imperfect sensitivity? This would likely inflate false negative predictions at low expression levels.

We agree with the reviewer that dropouts due to low capture rate of Visium will lead to false negative predictions. Indeed, we chose to set the threshold for gene count > 0 to account for the sparsity of captured transcripts. In single cell RNA sequencing analysis, gene expression is analyzed in cell clusters rather than in single cells. Similarly, ISCHIA calculates LR co-occurrence in composition classes, that is clusters of spots of similar cell composition. Aggregating spots in composition classes thus mitigates the effects of low capture rate and consequent false negative predictions. We now include a sentence explaining this concept in the Results section:

The count threshold is a user defined parameter that can be increased to restrict the co-occurrence analysis to highly expressed ligands and receptors. To account for the sparsity of ST data, ISCHIA calculates LR co-occurrence within composition classes, that is clusters of spots with similar cell mixtures. Aggregating spots in composition classes thus mitigates the effects of low transcript capture rate and consequent false negative predictions.

The authors show the enrichment of particular pathways/genesets in differential gene expression comparing interacting vs noninteracting spots (through LR expression) within the same CC. It is however unclear if this enrichment stems from a random sampling of the CC (with possible confounding factors such as batch effect, QC metrics, which might also have a spatial component such as localized tissue degradation) or from the actual interaction. Adding a measure of uncertainty, such as by permuting over interaction-labels to generate a proper null distribution, would help the user to ascertain the robustness of the results. For clarity, it would also be good to add how this is exactly computed to the Methods section.

We thank the reviewer for this remark and now perform a gene expression noise estimation. As suggested by the reviewer, we employed a permutation-based approach to assess the significance of differentially expressed genes (DEGs) identified by comparing spots that are double positive for expression of a ligand-receptor pair vs spots that are not expressing any of the genes in a specific CC. To do so, we performed 1000 random sampling of spots into two groups, and calculated DEGs between these groups, consequently generating a null distribution of DEGs. We next calculated Monte Carlo p-values for the LR-associated DEGs, comparing the initially computed p-values DEG with the null distribution, and adjusting them for multiple testing using FDR. Significant adjusted p-values were indicative of genes whose differential expression was robust and unlikely to result from random spot sampling.

From the Method section:<br /> For the calculation of L-R-associated DEGs, ISCHIA computes differential gene expression between spots that are double positive or double negative for a given L-R pair. The significant DEGs are then used for pathway enrichment with any tool of choice, such as EnrichR (https://bio.tools/enrichr). We employed a permutation-based approach to assess the significance of the obtained DEGs. Specifically, we generated a null distribution of DEGs (noise estimation) by 1000-fold random sampling of spots into two groups, and calculating DEGs between these groups. Next FDR-adjusted Monte Carlo p-values were calculated for each LR-associated DEG, comparing the initially computed p-values DEG from with the null distribution, and subsequently adjusting for multiple testing. DEGs with Monte Carlo FDRs < 0.05 are likely specific to the presence of a given LR and unlikely to result from random spot sampling.

It's unclear if the p-values in the manuscript are adjusted for multiple comparisons or not. Given the number of hypotheses being tested here, this is a crucial issue.

We agree with the reviewer that this is a crucial issue. In the ecology papers we consulted and in the original co-occur R package (Griffith et al. 2016), multiple testing correction was not applied when computing co-occurrence of species. We believe however, that it is necessary to correct p values when computing co-occurrence of ligands and receptor pairs, and now implement FDR correction in the ISCHIA pipeline. We have amended all the relative figures and tables.

The authors don't really mention any of the existing state-of-the-art methods (e.g. Squidpy, Spacemake, Giotto, ...). This doesn't necessitate a full benchmark, but at least the authors should then state qualitatively what the difference is between the chosen approach and already available packages, with their respective added advantages/disadvantages.

We thank the reviewer for the remark and we have compared the analysis performed by ISCHIA with other state-of-the-art tools. The main difference between ISCHIA with respect to other methods such as Spacemake (Sztanka-Toth et al. 2022), Squidpy (Palla et al. 2022) and Giotto (Del Rossi et al. 2022), is that ISCHIA computes cell-type and LR co-occurrence within individual spots, not between neighboring spots. We include here an extensive comparison with other methods for this reviewer, and now discuss the differences between ISCHIA and other tools in the manuscript.

For neighborhood analysis, Spacemake and Squidpy use spatial coordinates of spots to identify neighbors among them (neighborhood sets are defined as a fixed number of adjacent spots in a square or hexagonal grid). Squipdy computes co-occurrence of clusters in spatial dimensions, however it uses the coordinates of spots and clusters to calculate co-occurrence of entire clusters of spots, not of cell-types within spots. This approach ignores the missing data between spots, as well as the multicellular nature of each spot. Similarly to Squidpy, Giotto assigns a score of a cell type to each spot upon deconvolution, to further identify the spatial patterns of the major cell taxonomies across all the spots on the tissue. For image-based spatial technologies with single cell resolution, Giotto creates a neighborhood graph of the single-cells to study gene expression patterns. This is similar to the approach we used to validate our predictions from Visium data in the Resolve dataset.

Tangram (Biancalani et al. 2021) is a deep learning approach to harmonize sc/snRNA-seq data with in situ, histological, and anatomical data, toward a high-resolution, integrated atlas. Tangram focuses on learning spatial gene-expression maps transcriptome-wide at single-cell resolution, and relating those to histological and anatomical information from the same specimens. However, it does not address cell-cell and ligand receptor interactions, nor co-occurrences from spatial data. Therefore, Tangram can be used to improve the deconvolution step of ISCHIA, to improve the definition of cellular composition in multicellular spatial spots.

Starfysh (He et al. 2022) is a computational toolbox for joint modeling of ST and histology data, dissection of refined cell states, and systematic integration of multiple ST datasets from complex tissues. It uses an auxiliary deep generative model that incorporates archetypal analysis and any known cell state markers to avoid the need for a single-cell-resolution reference. Starfysh also clusters spots based on cell type composition, and terms group of spots with similar composition “spatial hubs”. They use spatial hubs to integrate multiple samples, and to uncover regions with varying composition of cell states. As we propose in ISCHIA, the Starfysh authors also suggest that finding inter-sample commonalities using spatial hubs is easier compared to finding common clusters between samples. Starfysh addresses the co-localization of cell states by calculating the spatial correlation index (SCI) within a certain hub and penalizing the calculated correlation with a weight matrix τ in a way that : τ_(between two spots i,j)=1 if the coordinate distance of spot i and spot j was less than √3 else τ_(between two spots i,j)=0 . While this approach provides a measure of cell state co-localization across a spatial hub, it looks at the problem from an inter-spot perspective, similar to Squipdy and Giotto. Again, this is different from ISCHIA that calculates co-occurrence within spots.

In conclusion, none of the current approaches focuses on addressing the co-occurrence of cell types and molecules within individual Visium spots. As the analysis is fundamentally different, we did not perform a full quantitative benchmark. We agree, however, that these differences need to be addressed in the manuscripts. To illustrate the different results obtained, we ran ISCHIA on a Visium slide of a coronal section of the mouse brain, which was also analyzed using Squidpy (https://squidpy.readthedocs.io/en/stable/notebooks/tutorials/tutorial_visium_hne.html) and Giotto (https://rubd.github.io/Giotto_site/articles/mouse_visium_brain_201226.html#part-9-spatial-network). We clustered the spots based on cell composition and then ran celltype co-occurrence analysis within each composition class (Supplementary Figure 1).

From the Results section:

State-of-the-art analysis tools for Visium data often treat every spot as a single datapoint, and compute co-localization, network or cell-cell interactions analysis between neighboring spots (inter-spot analysis). We hypothesized that CNs would be best reconstructed within individual spots (intra-spot analysis), as their mixed transcriptome contains information about locally occurring cell types, expressed ligands and receptors, and activated signaling pathways. As inferring CNs in each individual spot separately would be noisy, sparse, computationally intensive, and would lack statistical power, ISCHIA first divides the tissue into clusters of spots with similar cellular composition - termed composition classes (CCs) (Fig 1a). CCs are thus groups of spots containing similar mixtures of cells, or cellular communities, e.g, all spots capturing colonic crypts. To achieve the division of the tissue into CCs, spot transcriptomes are deconvoluted, yielding a cell type composition matrix (spot × contribution of each cell type), which is then subjected to dimensionality reduction and k-means clustering. ISCHIA allows for both reference-based deconvolution, with tools such as SPOTlight7 or RCTD8, and reference-free deconvolution9. Upon deconvolution, ISCHIA summarizes spot gene expression data in a cell type presence-absence matrix, where each listed cell type is associated with a probability to be present in a given spot (p > 0.1). Each spot is thus represented as a mixture of cell types, and similar mixtures are then clustered together in CCs. We applied ISCHIA on a publicly available Visium slide of a coronal section of the mouse brain (10x Genomics), using as a reference for deconvolution a scRNA-seq dataset of ~14,000 adult mouse cortical cells from the Allen Institute10. Composition-based clustering of the spots yielded 5 CCs, which broadly reflect the annotated anatomical regions (Supplemental Figure 1a). ISCHIA then computes cell type co-occurrence for every CC separately, identifying spatial association of cells in close proximity (Supplemental Figure 1b). Intra-spot analysis reconstructs cellular networks with cell types as nodes, and is distinct from inter-spot networks analysis employed by other tools on this sample, in which spots are used as nodes11,12.

We also discuss differences between ISCHIA and other tools in the Discussion:

ISCHIA differs from other analysis tools for Visium data in that it predicts CNs within spots and not across spots. Indeed, spot data from sequencing-based ST methods such as Visium, simultaneously captures information about 1) cell types, 2) expressed LR genes, and 3) associated transcriptional responses at multiple spatially restricted locations. As proximity is a prerequisite for juxtacrine and paracrine cell-cell communication, which in turn constitutes the basis for the coordinated function of CNs, we hypothesized that CNs would best be reconstructed within individual spots, rather than across neighboring spots. To increase robustness, spots are grouped in clusters of similar cellular composition, termed composition classes. Composition-based clustering of the tissue represents a major advantage of this method, and distinguished it from other methods, such as Squidpy11 or Giotto12, that assign an identity to each spot based on marker gene expression or on the most abundant cell type. While preserving the complexity of the cell type composition of the analyzed tissue, composition-based clustering of spots also confers robustness towards variations in expression levels due to batch effects. Indeed, other spatial analysis methods such as Starfysh66 have found that finding inter-sample commonalities using composition-based clusters is easier compared to finding common transcriptome-based clusters between samples. Still, batch analysis and, if needed, correction of the ST data is recommended prior to analysis with ISCHIA. Composition-based clustering of spots allows to restrict downstream analysis to similar mixtures of cells, filtering out transcriptome heterogeneity arising from distinct cellular compositions, which might act as a confounder variable when performing differential gene expression or cell-cell interaction predictions.<br /> To reconstruct CNs, ISCHIA performs co-occurrence analysis of cell types within CCs. Other tools build a neighborhood graph using spatial coordinates of spots and a fixed number of adjacent spots11,12,66, and therefore ignoring the missing data between spots as well as the multicellular nature of each spot, ISCHIA leverages the inherent proximity of mixed transcriptomes within individual spots to infer cellular neighborhoods. Hence, the cell types within the spots, rather than the spots themselves, are the nodes of the CN. This approach allows for reconstruction of much smaller CNs, operating in close spatial proximity, a prerequisite for juxtacrine and paracrine signaling between cells. ISCHIA further predicts LR interaction as edges connecting cell types within spots, not across multiple spots. Finally, by integrating co-occurrence of cell types, co-occurrence of LR pairs, and associated gene signatures, ISCHIA infers CN function.

Minor:

When a priori testing for LR interactions without restricting these interactions to predicted interactions, it would be informative to have an estimate of how many of the positively co-occurring interactions coincide with their predictions. As the authors state, it's hard to judge novel interactions without orthogonal validation, but a large overlap between predictions and the results presented here might instill confidence in the novel findings.

We thank the reviewer for the comment and now label in green, in Fig 4a, the positively co-occurring interactions that are also predicted by Omnipath, NicheNet or CellTalkDB.

Fig 4D: It's hard to judge very small p-values on this plot, might be better to plot -log10(pval).

We have now changed this plot to display the differential co-occurrence score, calculated as FDRinflamed - FDRnon-inflamed.

The axes on some of the plots should be better defined in the figure legends (e.g. Fig 4D, 5C)

We have included better axis descriptions.

I'm not an expert in inflammation or IBD biology, so I will defer that to other reviewers more suited to comment on this.

Reviewer #1 (Significance):

The proposed method provides a reasonable framework for studying co-occurences of cell types and transcripts (particularly ligand-receptor pairs), which are currently questions of great interest to the community applying novel spatial transcriptomics technologies in many different domains of life sciences. The manuscript is very well written, and provides a clear and consistent logical flow. The manuscript can be easily read and understood both by specialized users as well as biologist/clinical end-users wanting to apply the proposed technique. The addition of experimental data using an orthogonal technology to validate computational predictions illustrates nicely the power of the proposed approach.

Although the presented approach is methodologically rather simple (which is not necessarily a disadvantage), it is novel in the field as far as I know and a good implementation is likely to see great adoption by the field, especially if it's well documented, maintained and integrated into existing data processing workflows. The authors should however compare their approach fairly with the rest of the available packages in order to convince the reader.

Although the presented data seems convincing to me, the authors should take greater care of defining good practice statistical reporting of their findings. Even though these tools are often hypothesis-generating and predictions should always be experimentally validated, some end-users might interpret p-values literally. As such, proper multiple-testing correction and analysis of critical confounding factors should be carried out as to set an example.

I'm a computational biologist with expertise in method development (machine learning and statistical modelling) for spatial multi-omics assays. I'm not an expert in inflammation or IBD biology, so I will defer that to other reviewers more suited to comment on this.

Reviewer #2 (Evidence, reproducibility and clarity):

The authors developed ISCHIA to study co-occurence of cell types and transcript species. This work was further extended to study cell-cell interactions based on ligand-receptor co-expression. The observation by ISCHIA was further validated using hybridization based spatial transcriptomics approaches. ISCHIA was applied to study healthy and inflamed human colons.

Referees cross-commenting

As the reviewer #1 pointed, there is no description about existing methods. The reviewer #1 only asked stating qualitative differences.

If the manuscript is mainly for IBD and ISCHIA is the bioinformatics steps they followed, I would agree with the reviewer #1. However, the authors wanted to say that it is a new software. I still think that full benchmarking is needed in this circumstance.

We thank the reviewer for the remark and we have compared the analysis performed by ISCHIA with other state-of-the-art tools. The main difference between ISCHIA with respect to other methods such as Spacemake (Sztanka-Toth et al. 2022), Squidpy (Palla et al. 2022) and Giotto (Del Rossi et al. 2022), is that ISCHIA computes cell-type and LR co-occurrence within individual spots, not between neighboring spots. We include here an extensive comparison with other methods for this reviewer, and now discuss the differences between ISCHIA and other tools in the manuscript.

For neighborhood analysis, Spacemake and Squidpy use spatial coordinates of spots to identify neighbors among them (neighborhood sets are defined as a fixed number of adjacent spots in a square or hexagonal grid). Squipdy computes co-occurrence of clusters in spatial dimensions, however it uses the coordinates of spots and clusters to calculate co-occurrence of entire clusters of spots, not of cell-types within spots. This approach ignores the missing data between spots, as well as the multicellular nature of each spot. Similarly to Squidpy, Giotto assigns a score of a cell type to each spot upon deconvolution, to further identify the spatial patterns of the major cell taxonomies across all the spots on the tissue. For image-based spatial technologies with single cell resolution, Giotto creates a neighborhood graph of the single-cells to study gene expression patterns. This is similar to the approach we used to validate our predictions from Visium data in the Resolve dataset.

Tangram (Biancalani et al. 2021) is a deep learning approach to harmonize sc/snRNA-seq data with in situ, histological, and anatomical data, toward a high-resolution, integrated atlas. Tangram focuses on learning spatial gene-expression maps transcriptome-wide at single-cell resolution, and relating those to histological and anatomical information from the same specimens. However, it does not address cell-cell and ligand receptor interactions, nor co-occurrences from spatial data. Therefore, Tangram can be used to improve the deconvolution step of ISCHIA, to improve the definition of cellular composition in multicellular spatial spots.

Starfysh (He et al. 2022) is a computational toolbox for joint modeling of ST and histology data, dissection of refined cell states, and systematic integration of multiple ST datasets from complex tissues. It uses an auxiliary deep generative model that incorporates archetypal analysis and any known cell state markers to avoid the need for a single-cell-resolution reference. Starfysh also clusters spots based on cell type composition, and terms group of spots with similar composition “spatial hubs”. They use spatial hubs to integrate multiple samples, and to uncover regions with varying composition of cell states. As we propose in ISCHIA, the Starfysh authors also suggest that finding inter-sample commonalities using spatial hubs is easier compared to finding common clusters between samples. Starfysh addresses the co-localization of cell states by calculating the spatial correlation index (SCI) within a certain hub and penalizing the calculated correlation with a weight matrix τ in a way that : τ_(between two spots i,j)=1 if the coordinate distance of spot i and spot j was less than √3 else τ_(between two spots i,j)=0 . While this approach provides a measure of cell state co-localization across a spatial hub, it looks at the problem from an inter-spot perspective, similar to Squipdy and Giotto. Again, this is different from ISCHIA that calculates co-occurrence within spots.

In conclusion, none of the current approaches focuses on addressing the co-occurrence of cell types and molecules within individual Visium spots. As the analysis is fundamentally different, we did not perform a full quantitative benchmark. We agree, however, that these differences need to be addressed in the manuscripts. To illustrate the different results obtained, we ran ISCHIA on a Visium slide of a coronal section of the mouse brain, which was also analyzed using Squidpy (https://squidpy.readthedocs.io/en/stable/notebooks/tutorials/tutorial_visium_hne.html) and Giotto (https://rubd.github.io/Giotto_site/articles/mouse_visium_brain_201226.html#part-9-spatial-network). We clustered the spots based on cell composition and then ran celltype co-occurrence analysis within each composition class (Supplementary Figure 1).

From the Results section:

State-of-the-art analysis tools for Visium data often treat every spot as a single datapoint, and compute co-localization, network or cell-cell interactions analysis between neighboring spots (inter-spot analysis). We hypothesized that CNs would be best reconstructed within individual spots (intra-spot analysis), as their mixed transcriptome contains information about locally occurring cell types, expressed ligands and receptors, and activated signaling pathways. As inferring CNs in each individual spot separately would be noisy, sparse, computationally intensive, and would lack statistical power, ISCHIA first divides the tissue into clusters of spots with similar cellular composition - termed composition classes (CCs) (Fig 1a). CCs are thus groups of spots containing similar mixtures of cells, or cellular communities, e.g, all spots capturing colonic crypts. To achieve the division of the tissue into CCs, spot transcriptomes are deconvoluted, yielding a cell type composition matrix (spot × contribution of each cell type), which is then subjected to dimensionality reduction and k-means clustering. ISCHIA allows for both reference-based deconvolution, with tools such as SPOTlight7 or RCTD8, and reference-free deconvolution9. Upon deconvolution, ISCHIA summarizes spot gene expression data in a cell type presence-absence matrix, where each listed cell type is associated with a probability to be present in a given spot (p > 0.1). Each spot is thus represented as a mixture of cell types, and similar mixtures are then clustered together in CCs. We applied ISCHIA on a publicly available Visium slide of a coronal section of the mouse brain (10x Genomics), using as a reference for deconvolution a scRNA-seq dataset of ~14,000 adult mouse cortical cells from the Allen Institute10. Composition-based clustering of the spots yielded 5 CCs, which broadly reflect the annotated anatomical regions (Supplemental Fig 1). ISCHIA then computes cell type co-occurrence for every CC separately, identifying spatial association of cells in close proximity (Supplemental Fig xx). Intra-spot analysis reconstructs cellular networks with cell types as nodes, and is distinct from inter-spot networks analysis employed by other tools on this sample, in which spots are used as nodes11,12.

We also discuss differences between ISCHIA and other tools in the Discussion:

ISCHIA differs from other analysis tools for Visium data in that it predicts CNs within spots and not across spots. Indeed, spot data from sequencing-based ST methods such as Visium, simultaneously captures information about 1) cell types, 2) expressed LR genes, and 3) associated transcriptional responses at multiple spatially restricted locations. As proximity is a prerequisite for juxtacrine and paracrine cell-cell communication, which in turn constitutes the basis for the coordinated function of CNs, we hypothesized that CNs would best be reconstructed within individual spots, rather than across neighboring spots. To increase robustness, spots are grouped in clusters of similar cellular composition, termed composition classes. Composition-based clustering of the tissue represents a major advantage of this method, and distinguished it from other methods, such as Squidpy11 or Giotto12, that assign an identity to each spot based on marker gene expression or on the most abundant cell type. While preserving the complexity of the cell type composition of the analyzed tissue, composition-based clustering of spots also confers robustness towards variations in expression levels due to batch effects. Indeed, other spatial analysis methods such as Starfysh66 have found that finding inter-sample commonalities using composition-based clusters is easier compared to finding common transcriptome-based clusters between samples. Still, batch analysis and, if needed, correction of the ST data is recommended prior to analysis with ISCHIA. Composition-based clustering of spots allows to restrict downstream analysis to similar mixtures of cells, filtering out transcriptome heterogeneity arising from distinct cellular compositions, which might act as a confounder variable when performing differential gene expression or cell-cell interaction predictions.

To reconstruct CNs, ISCHIA performs co-occurrence analysis of cell types within CCs. Other tools build a neighborhood graph using spatial coordinates of spots and a fixed number of adjacent spots11,12,66, and therefore ignoring the missing data between spots as well as the multicellular nature of each spot, ISCHIA leverages the inherent proximity of mixed transcriptomes within individual spots to infer cellular neighborhoods. Hence, the cell types within the spots, rather than the spots themselves, are the nodes of the CN. This approach allows for reconstruction of much smaller CNs, operating in close spatial proximity, a prerequisite for juxtacrine and paracrine signaling between cells. ISCHIA further predicts LR interaction as edges connecting cell types within spots, not across multiple spots. Finally, by integrating co-occurrence of cell types, co-occurrence of LR pairs, and associated gene signatures, ISCHIA infers CN function.

Reviewer #2 (Significance):

As the authors wanted to introduce ISCHIA as a new tool, discussion and comparison with the previous approaches are essential. The manuscript lacks discussion and the comparison with others. Co-localization has been discussed already in many articles including [PMID:325799]. It does not seem to require additional packages to study co-localization for cell type. There are many cell-cell interaction studies using ligand-receptor co-localization [ref; stLern, SpaGene, and many]. It is not well documented about the relationships with the previous works. Given the advances in algorithms for spatial transcriptomics, it is very uncertain that ISCHIA can provide additional knowledge or contribute to algorithmic development.

We agree with the reviews that there is an increasing body of work addressing co-localization of cell type and cell-cell interactions in spatial transcriptomic data. However, we assert that the introduction of our method holds substantial relevance and adds value to the field, notwithstanding its ostensible simplicity. Our method has been well-received within the scientific community, indicating its applicability and potential significance in deciphering complex cellular ecosystems. We believe our approach offers a distinct conceptual advantage by enabling the analysis of cellular communities within individual Visium spots, rather than solely between them, allowing for a more refined exploration of cellular interactions and co-localizations within specific spatial domains.

Previously, Visium data were generated by Elmentaite et al. (Nture 2021) against healty and IBD samples. what are the new findings of the manuscript?

Visium data generated by Elmentaite et al is from pediatric Crohn's disease, not adult Ulcerative colitis. Spatial analysis of IBD samples has been performed by Nanostring and by CODEX (Garrido-Trigo et al. 2022; Mayer et al. 2023). Publication of our datasets (both Visium and Resolve) will increase the body of patient data available to the community, and should be considered positively.

Here, we use our dataset to demonstrate the ability of ISCHIA to reconstruct cellular networks within Visium spots, and identify a M-cell-fibroblast network in inflamed regions of UC patients. We further identify differentially co-occurring LRs in the inflammatory CC5 centered around EDN1, SEMA3C, and CXCL5. We further reveal inflammation-induced, protective responses from the colonic crypt involving the complement cascade and the immuno-modulator SECTM1. Finally, we apply co-occurrence analysis to an independent mouse Visium dataset and uncover differentially co-occurring LR pairs shared between the inflamed human and murine colon. ISCHIA is an hypothesis generating tool, and its findings should be extensively characterized and validated in larger cohorts.

Reviewer #3 (Evidence, reproducibility and clarity):

Summary

The authors provide a framework to analyze spatial transcriptomics (ST) data in terms of spatial co-occurrence of cell types, and ligand-receptor pairs. The method was applied to an ulcerative colitis sequencing-based data set (10x Visium) and validated using a matched hybridization-based data set (Molecular Cartography).

Major Comments

The Visium data set consisted of a single slide with four samples. The authors should clarify if the current implementation of their method is limited to a single Visium slide.

We thank the reviewer for the remark and now state in the text that ISCHIA can be applied to any Visium, Resolve or other spatial transcriptomic dataset. Visium samples originating from different slides and datasets can be fed into the ISCHIA pipeline, as it is quite robust to batch effects. Indeed, while other methods assign a cell-type identity to spots based on the most abundant cell type detected by deconvolution algorithm, ISCHIA summarizes spot gene expression data in a presence-absence matrix. ISCHIA is therefore robust to variation in expression levels due to batch effects. This was also recently reported in the analytical tool Starfysh (He et al. 2022), which performs similar clustering of spots based on cell type composition. This is included in the Discussion:

While preserving the complexity of the cell type composition of the analyzed tissue, composition-based clustering of spots also confers robustness towards variations in expression levels due to batch effects. Indeed, other spatial analysis methods such as Starfysh66 have found that finding inter-sample commonalities using composition-based clusters is easier compared to finding common transcriptome-based clusters between samples. Still, batch analysis and, if needed, correction of the ST data is recommended prior to analysis with ISCHIA.

In Supplementary Table 1, I think it would be useful to include the minimum number of counts for the Ligand-Receptor genes. Given that the current threshold is 1, I think it warrants a discussion if the minimum number of counts has an effect on whether the ligand-receptor pair is significantly co-occurring (i.e. if ligand-receptor pairs with more counts are more likely to be significant).

We agree with the reviewer that increasing the threshold to >1 will reduce the number of significantly co-occurring LRs, but also increase false negative predictions. We chose to set the threshold for gene count > 0 to account for the sparsity of captured transcripts. Indeed, dropouts due to low capture rate of Visium will lead to false negative predictions. In single cell RNA sequencing analysis, gene expression is analyzed in cell clusters rather than in single cells. Similarly, ISCHIA calculates LR co-occurrence in composition classes, that is clusters of spots of similar cell composition. Aggregating spots in composition classes thus mitigates the effects of low capture rate and consequent false negative predictions. We now include a sentence explaining this concept in the Results section:

The count threshold is a user defined parameter that can be increased to restrict the co-occurrence analysis to highly expressed ligands and receptors. To account for the sparsity of ST data, ISCHIA calculates LR co-occurrence within composition classes, that is clusters of spots with similar cell mixtures. Aggregating spots in composition classes thus mitigates the effects of low transcript capture rate and consequent false negative predictions.

Given the effect of outliers in the Pearson correlation and the nature of the expression values for VIsium data, I think that the Spearman rank correlation is better suited to estimate the correlation between the expression values of the ligand-receptor pairs than the Pearson correlation (the default in R).

We thank the reviewer for the comment and now rank positively co-occurring LR based on Spearman correlation. See Fig 3a and Supplementary Table 1.

In the section titled "Differential co-occurrence identifies niche-specific response programs", it is unclear whether the spatial co-occurrence analysis was done within each CC.

We now specify that we computed co-occurrence analysis of ligands and receptor genes in all spots of our dataset across all CCs. We now include FDR corrected p-values in this analysis. Only after computing this broad co-occurrence analysis, we focus on differential co-occurrence comparing conditions or composition classes.

Minor Comments

I found a few typos in the manuscript

In the Abstract, "tecniquee" instead of "techniques"

On page 10, under "Integration and annotation of scRNASeq data.", "W" instead of "We"

On page 11, there is an equation rendering error: P(lt) = $p_lt

We thank the reviewer for these comments and have now corrected the typos.

Reviewer #3 (Significance):

The method proposed takes advantage of work done in ecology to leverage the spatial context of ST data. Furthermore, the methods proposed goes beyond describing spatial patterns present in the data, but allows for the comparison between two conditions of interest. The method proposed will be of interest to the growing number of researchers generating ST data.

My expertise is in statistical methods for single cell and spatial transcriptomics data. Furthermore, I have extensive experience analyzing single cell and spatial transcriptomics data in the context of liver diseases.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #3

Evidence, reproducibility and clarity

Summary

The authors provide a framework to analyze spatial transcriptomics (ST) data in terms of spatial co-occurrence of cell types, and ligand-receptor pairs. The method was applied to an ulcerative colitis sequencing-based data set (10x Visium) and validated using a matched hybridization-based data set (Molecular Cartography).

Major Comments

The Visium data set consisted of a single slide with four samples. The authors should clarify if the current implementation of their method is limited to a single Visium slide.

In Supplementary Table 1, I think it would be useful to include the minimum number of counts for the Ligand-Receptor genes. Given that the current threshold is 1, I think it warrants a discussion if the minimum number of counts has an effect on whether the ligand-receptor pair is significantly co-occurring (i.e. if ligand-receptor pairs with more counts are more likely to be significant).

Given the effect of outliers in the Pearson correlation and the nature of the expression values for VIsium data, I think that the Spearman rank correlation is better suited to estimate the correlation between the expression values of the ligand-receptor pairs than the Pearson correlation (the default in R).

In the section titled "Differential co-occurrence identifies niche-specific response programs", it is unclear whether the spatial co-occurrence analysis was done within each CC.

Minor Comments

I found a few typos in the manuscript

In the Abstract, "tecniquee" instead of "techniques"

On page 10, under "Integration and annotation of scRNASeq data.", "W" instead of "We"

On page 11, there is an equation rendering error: P(lt) = $p_lt

Significance

The method proposed takes advantage of work done in ecology to leverage the spatial context of ST data. Furthermore, the methods proposed goes beyond describing spatial patterns present in the data, but allows for the comparison between two conditions of interest. The method proposed will be of interest to the growing number of researchers generating ST data.

My expertise is in statistical methods for single cell and spatial transcriptomics data. Furthermore, I have extensive experience analyzing single cell and spatial transcriptomics data in the context of liver diseases.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #2

Evidence, reproducibility and clarity

The authors developed ISCHIA to study co-occurence of cell types and transcript species. This work was further extended to study cell-cell interactions based on ligand-receptor co-expression. The observation by ISCHIA was further validated using hybridization based spatial transcriptomics approaches. ISCHIA was applied to study healthy and inflamed human colons.

Referees cross-commenting<br /> As the reviewer #1 pointed, there is no description about existing methods. The reviewer #1 only asked stating qualitative differences.

If the manuscript is mainly for IBD and ISCHIA is the bioinformatics steps they followed, I would agree with the reviewer #1. However, the authors wanted to say that it is a new software. I still think that full benchmarking is needed in this circumstance.

Significance

As the authors wanted to introduce ISCHIA as a new tool, discussion and comparison with the previous approaches are essential. The manuscript lacks discussion and the comparison with others. Co-localization has been discussed already in many articles including [PMID:325799]. It does not seem to require additional packages to study co-localization for cell type. There are many cell-cell interaction studies using ligand-receptor co-localization [ref; stLern, SpaGene, and many]. It is not well documented about the relationships with the previous works. Given the advances in algorithms for spatial transcriptomics, it is very uncertain that ISCHIA can provide additional knowledge or contribute to algorithmic development.

Previously, Visium data were generated by Elmentaite et al. (Nture 2021) against healty and IBD samples. what are the new findings of the manuscript?

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this manuscript Lafzi et al. present a novel computational framework (ISCHIA) for the analysis of spatial occurrence patterns, be it of cells or transcript species, found in spatial transcriptomics datasets. The authors also show its applications in finding differentially co-occurring ligand-receptor pairs, as well as inter-species analysis to find conserved cell signalling pathways. ISCHIA consists of a well-documented R package and utilizes empirical probabilistic estimations of non-random co-occurrence, as used in the field of ecology, which to my knowledge is novel in the field. The authors also validate their predictions using an orthogonal technology (in situ hybridization-based spatial transcriptomics), which is a nice addition to the computational work presented in the manuscript.

Major:

• When determining the composition classes, the authors discard 4 out of 8 clusters of composition classes, partly due to being highly patient-specific. It's unclear how sensitive ISCHIA is for batch effects which might affect the measured cellular fractions. Given that the presence of batch effects is highly likely with ST methods, due to the sample processing procedures, it would help the reader/potential user to estimate the impact these could have on the resulting output. It would also be useful to plot a version of the UMAP with sample labels as to see if the remaining clusters are properly mixed (at least between replicates of the same condition). Additionally, it would help to illustrate that the biological findings reported in the manuscript are supported across more than 1 biological replicate.
• In the LR analysis, the authors state that ISCHIA's predictions are agnostic to gene expression levels, as the authors model expression as a Boolean (gene count threshold > 0). Wouldn't low expression levels result in increased drop-out due to imperfect sensitivity? This would likely inflate false negative predictions at low expression levels.
• The authors show the enrichment of particular pathways/genesets in differential gene expression comparing interacting vs noninteracting spots (through LR expression) within the same CC. It is however unclear if this enrichment stems from a random sampling of the CC (with possible confounding factors such as batch effect, QC metrics, which might also have a spatial component such as localized tissue degradation) or from the actual interaction. Adding a measure of uncertainty, such as by permuting over interaction-labels to generate a proper null distribution, would help the user to ascertain the robustness of the results. For clarity, it would also be good to add how this is exactly computed to the Methods section.
• It's unclear if the p-values in the manuscript are adjusted for multiple comparisons or not. Given the number of hypotheses being tested here, this is a crucial issue.
• The authors don't really mention any of the existing state-of-the-art methods (e.g. Squidpy, Spacemake, Giotto, ...). This doesn't necessitate a full benchmark, but at least the authors should then state qualitatively what the difference is between the chosen approach and already available packages, with their respective added advantages/disadvantages.

Minor:

• When a priori testing for LR interactions without restricting these interactions to predicted interactions, it would be informative to have an estimate of how many of the positively co-occurring interactions coincide with their predictions. As the authors state, it's hard to judge novel interactions without orthogonal validation, but a large overlap between predictions and the results presented here might instill confidence in the novel findings.
• Fig 4D: It's hard to judge very small p-values on this plot, might be better to plot -log10(pval).
• The axes on some of the plots should be better defined in the figure legends (e.g. Fig 4D, 5C)

I'm not an expert in inflammation or IBD biology, so I will defer that to other reviewers more suited to comment on this.

Significance

The proposed method provides a reasonable framework for studying co-occurences of cell types and transcripts (particularly ligand-receptor pairs), which are currently questions of great interest to the community applying novel spatial transcriptomics technologies in many different domains of life sciences. The manuscript is very well written, and provides a clear and consistent logical flow. The manuscript can be easily read and understood both by specialized users as well as biologist/clinical end-users wanting to apply the proposed technique. The addition of experimental data using an orthogonal technology to validate computational predictions illustrates nicely the power of the proposed approach.

Although the presented approach is methodologically rather simple (which is not necessarily a disadvantage), it is novel in the field as far as I know and a good implementation is likely to see great adoption by the field, especially if it's well documented, maintained and integrated into existing data processing workflows. The authors should however compare their approach fairly with the rest of the available packages in order to convince the reader.

Although the presented data seems convincing to me, the authors should take greater care of defining good practice statistical reporting of their findings. Even though these tools are often hypothesis-generating and predictions should always be experimentally validated, some end-users might interpret p-values literally. As such, proper multiple-testing correction and analysis of critical confounding factors should be carried out as to set an example.

I'm a computational biologist with expertise in method development (machine learning and statistical modelling) for spatial multi-omics assays. I'm not an expert in inflammation or IBD biology, so I will defer that to other reviewers more suited to comment on this.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

Major comments:

• 1/ The model system used in this work is referred to as "organoids", with the premise that organoids, as representatives of the original tissue, can be used to study tissue development. However, the organoids presented in the study are spherical structures, and the paper does not provide any information about how and to what extent these organoids represent the original tissue. Furthermore, it would be difficult to expect these organoids to accurately represent human breast tissue, as they are derived, to the best of the reviewer's understanding (it is not explicitly noted, but rather the text refers the reader to several previous works), from primary human breast epithelial cells that had been cultured for 6 passages in 2D culture. The CD24/CD44 flow cytometry profile of these 2D cultures, as well as the organoids derived from them, shows a unimodal distribution of CD24 and CD44 expression, and does not show distinct cell populations, as typical in primary breast epithelial cells that have not been cultured in 2D

• Response 1:

  We understand the concern raised by the reviewer. Before initiating the study we have carefully characterized our model (See Revision Section part 2.2, for the revisions that have already been carried out).

  In parallel, to further address this point, we plan to perform an extensive characterization of our mammary primary cells as well as our 3D-matrigel embedded culture. This will be achieved through flow cytometry using other multiple lineage-specific surface markers for luminal progenitor cells (such as CD49f−/low/EpCAM+), and basal/myoepithelial cells (such as CD49f+/EpCAM−/low).

  Thus, overall our experiments will confirm that our growth conditions (which we intend to describe in greater details in the methods section) for multipotent mammary stem cells can generate multi-lineage organoids.

2/ There appears to be confusion between concepts: primarily confusing a basal phenotype with a stem cell phenotype, and progenitor activity with stem cell activity. Because the 3D organoid model does not display a replication of luminal differentiation, it cannot be used as a proxy for stem cell function. The results from the miRNA screen and the subsequent experiments support two conclusions: 1. miR-160b-3p leads to an enhanced mesenchymal phenotype, manifested by reduced CD24 and enhanced CD44 expression. 2. miR-160b-3p leads to increased organoid formation. The latter may be interpreted, at best, as higher basal progenitor activity, but is not a measure of stem cell activity, as that would require the cells to give rise to more than one lineage, which is not shown here.

• Response 2:

We agree with the reviewer on the confusions between several concepts that we did not discriminate enough. The planned characterization of our cultures will allow us to better define the nature of our primary cell population and avoid any confusion (See Response 1). Indeed, an organoid is a self-organized 3D tissue that typically originates from stem cells (pluripotent, fetal or adult). Therefore, we will replace the term “stem cell activity” through the article by “stem/progenitor activity”.

3/ Overall, the information from figures 1-4 indicate that miR-160b-3p is driving and is associated with mesenchymal differentiation, possibly with EMT.

Response 3:

We agree with the reviewer that our data suggest that miR-160b-3p is driving and is associated with mesenchymal differentiation. As suggested by reviewer, to further evaluate the possible involvement of EMT, we will analysed using RT-qPCR, as we described in Fessart et al, (May 30, 2016 https://doi.org/10.7554/eLife.13887), the expression of the main EMT genes in miR-106a-3p cells. These results will be also confirmed at the protein level.

4/ In contrast to the work in the breast 3D culture model, the experiments with hESCs are interesting and do support a role of this miR in stemness.

Response 4:

We would like to extend our gratitude to the reviewer for their valuable comments on this aspect of our work.

There are several relatively minor comments that cumulatively somewhat undermine the strength of the work:

5/ Abstract: the sentence "organoids can be directly generated from human epithelial cells by only one miRNA, miR-106a-sp" needs better clarification.

• We agree with the reviewer, this sentence will be modified accordingly.

6/ Page 5 line 103: HMECs is a term that generally refers to human mammary epithelial cells, not a specific derivation or subpopulation thereof.

• We will replace HMEC by human primary mammary epithelial cells.

7/ The graph in Fig. 1A is unnecessary, it shows only one bar.

• We will remove this panel in the revised version of the manuscript.

8/ It is unclear if all the HMECs were derived from the same donor, or several donors. There is no information about the donor and how the tissue and cells were derived. In general, it is not entirely clear how the cells were collected, processed, stored, and cultured from the time they were obtained from the donor until their use in the current study.

• We will include in the material section the details information on our primary cells and our culture conditions

9/ The key for Fig. 2D is unclear. The axes read "density" but the text refers to "intensity". Fluorescence intensity in flow cytometry is usually measured on a log scale. Differences on a linear scale are not usually considered meaningful. The authors should clarify why they chose a linear scale for this screen.

• The screening was conducted using a high throughput microscope that measures the relative signal intensity, thus the scale is based on linear signal intensity.

10/ In the miRNA screen, how long were the cells cultured after transfection, and was it enough time for them to shift phenotype?

• The timeline of the screening process is detailed in the Material section. The cells were cultured for 6 days following transfection for the CD44/CD24 staining and 8 days following transfection for the 3D culture, which provides sufficient time for shifting the phenotype.

11/ Page 6 line 154: The authors likely mean z-score, not z factor (two different things).

• We thank the reviewer for pointing this, this will be corrected.

12/ Page 7 line 161: "mir-106a-3p directly promotes the "transdifferentiation" of CD44low/CD24high cells phenotype into CD44high/CD24low cell phenotype" - is an unsupported statement, given that there could be several alternative explanations for the observed change in population ratios, including effects on survival or growth of cells of a certain population.

• Transdifferentiation (also known as lineage reprogramming, or -conversion), is a process in which one mature, specialized cell type changes into another without entering a pluripotent state. This process involves the ectopic expression of transcription factors and/or other stimuli. We agree with the reviewer's observation that we did not fully demonstrate the transdifferentiation process in terms of lineage. Therefore, we will use flow cytometry to analyse multiple lineage-specific surface markers for luminal progenitor cells (such as CD49f−/low/EpCAM+), and basal/myoepithelial cells (such as CD49f+/EpCAM−/low) following miR-106a-3p expression.
• Additionally, we acknowledge that there may be other alternative explanations; so we have already assessed the impact of mir-106a-3p on the population doubling time in culture to determine whether it affects the cells' survival or growth of the cells (See Section Part 2.2 for a description of experiments already carried out).

13/ There is need for quantification of the phenotypes described in Fig. 3C

• We thank the reviewer for this valuable suggestion and we will indeed characterize the 3D structure using flow cytometry.

14/ Figures 3 D-F it is not clear if the graphs display percentage or mean number (there is discrepancy between figure text and figure legend text), and when percentage, not clear for figure 3F out of what.

• We appreciate the reviewer's suggestion, and we have replaced the term 'axis' with 'Organoid Formation Capacity (OFC),' which is the commonly used nomenclature for this measure. OFC corresponds to the percentage of organoids per cell seeded, as explained in the legend.

15/ Fig. 5B, what is the statistical significance of the enrichment?

• In Figure 5B, the statistical significance of the enrichment is an FDR value of 0.024 and it is based on the multiple rotation gene-set testing (mroast) from the Limma R package (https://doi.org/10.1093/bioinformatics/btq401). This information will be included in the figure legend.

Reviewer #2 (Evidence, reproducibility and clarity):

In this work, Robert et al. utilize mammary organoid culture as an in vitro model of stem cell renewal and maintenance. Authors show that a putative mammary stem cell population, characterized as CD44high CD24low, is enriched in organoid culture relative to 2D monolayer culture. They conduct a microRNA (miR) screen and identify miR-106a-3p as a miRNA that enriches for stem cell (CD44high CD24low) and organoid formation capacity, confirming these findings using miR-106a-3p overexpressing cells. Authors also show that CBX7 overexpression achieves a similar enrichment in CD44high CD24low and organoid formation capacity in an miR-106a-3p dependent manner, though the rationale behind the connection between CBX7 and miR-106a-3p is not well defined. Finally, the manuscript conducts a transcriptomic analysis on miR-106a-3p-OE cells and aims to functionally validate its role in embryonic stem cells (ESCs) as a model that is versatile for renewal and differentiation studies. How this relates to mammary adult stem cells (ASCs) is not entirely clear. Overall, the manuscript contains significant technical and conceptual limitations, and the data presented do not support the major conclusions of the study. There are two overarching issues that question the validity of most of the findings in this study. Firstly, there is no clear demonstration that the structures reported as 3D organoids are indeed driven by multipotent stem cells (in all data and experimental approaches associated with this). Secondly, there is a lack of evidence to support the claim that CD44high CD24low cells are stem cells with an exclusive or enhanced capacity to generate multi-lineage organoids.

In addition to these general points, there are several specific major issues summarized below:

1/ Authors base their findings about mammary ASC renewal using a poorly defined organoid culture system that they claim is driven by ASC renewal and differentiation. These organoid growth conditions were originally developed to support growth of bronchial epithelial cells, and the authors have not presented data to objectively assess the validity of this extrapolation to expansion of mammary organoids. The authors did not present data to support the notion that these growth conditions generate multi-lineage organoids that expand from multipotent mammary stem cells.

• Response 1:
• This concern has been also raised by the 1st reviewer (See Response 12 from Reviewer 1).
• Our experiments will confirm that our growth conditions (which will be more comprehensively described in the methods section) can generate multi-lineage organoids from multipotent mammary stem cells. We will include these characterizations in the new version of the manuscript.

2/ Authors claim that miR-106a-3p downregulates stem cell differentiation and utilize 'organoid' culture to track the temporal expression of OCT4, SOX2 and NANOG during phases of organoid renewal and differentiation. However, mammary stem cell differentiation is associated with the emergence of luminal progenitor, mature luminal and myoepithelial lineages that are characterized by the expression of a well-defined set of markers. The authors did not investigate the expression of these markers in their 'differentiation' settings. Furthermore, modulators of major pathways reported to be critical for mammary ASC maintenance are lacking, including modulators of WNT, TGF/BMP, Notch and other pathways. As such, it is difficult to ascertain that organoids emerging under such culture conditions are the result of stem cell renewal and differentiation, as opposed to lineage-restricted proliferative non-ASCs, thus questioning the validity of many of the findings in this work.

• Response 2:

• As suggested by the reviewer, we will investigate the expression of emergence of luminal progenitor, mature luminal and myoepithelial lineages in our 'differentiation' settings using flow cytometry. This analysis will involve the examination of multiple lineage-specific surface markers, including CD49f−/low/EpCAM+ for luminal progenitor cells and CD49f+/EpCAM−/low for basal/myoepithelial cells in the 3D context.

• Secondly, we agree with the reviewer that major pathways such as Wnt, TGFb/BMP and Notch have been reported to be critical for mammary ASC maintenance. As suggested by the reviewer, to further evaluate the possible involvement of these pathways, we have analyzed our transcriptomic data, using PROGENy pathway, to investigate which pathways are regulated. The major pathways are depicted in a new figure and will be included in the manuscript (See Revision Section part 2.2, for the revisions that have already been carried out).

• There was significant enrichment indicating down-regulation of TGFβ, MAPK, WNT, PI3K genes along with gene sets representing other oncogenic pathways, are up-regulated such as the hypoxia response, JAK/STAT pathway, and p53 pathway activity (Figure A and B). Stem cells possess self-renewal activities and multipotency, characteristics that tend to be maintained under hypoxic microenvironments [1], thus this is not surprising to observe an up-regulation of Hypoxia pathway and activation of HIF1α transcription factor. In mammary stem cells, it has also been shown that p53 is critical to control the maintenance of a constant number of stem cells pool [2, 3]. Remarkably, it has been shown that cell sorting of the cells with a putative cancer stem cell phenotype (CD44+/CD24 low) express a constitutive activation of Jak-STAT pathway [4], which we observed as up-regulated along with STAT1 and STAT2 transcription factors. In parallel, we observe a down-regulation of Wnt signaling as well as PI3K pathways. Wnt is known to play important role in the maintenance of stem cells; its inhibition has been shown to lead to the inactivation of PI3 kinase signaling pathways to ensures a balance control of stem cell renewal [5]. Moreover, we observe a down-regulation of SMAD4 and SMAD3 transcription factors correlated with a down-regulation of TGFβ pathway. Signals mediated by TGF-β family members have been implicated in the maintenance and differentiation of various types of somatic stem cells [6].

• References

• Semenza GL. Dynamic regulation of stem cell specification and maintenance by hypoxia-inducible factors. Molecular aspects of medicine2016 Feb-Mar;47-48:15-23.

• Solozobova V, Blattner C. p53 in stem cells. World journal of biological chemistry2011 Sep 26;2(9):202-14.
• Cicalese A, Bonizzi G, Pasi CE, Faretta M, Ronzoni S, Giulini B et al. The tumor suppressor p53 regulates polarity of self-renewing divisions in mammary stem cells. Cell2009 Sep 18;138(6):1083-95.
• Hernandez-Vargas H, Ouzounova M, Le Calvez-Kelm F, Lambert MP, McKay-Chopin S, Tavtigian SV et al. Methylome analysis reveals Jak-STAT pathway deregulation in putative breast cancer stem cells. Epigenetics2011 Apr;6(4):428-39.
• He XC, Zhang J, Tong WG, Tawfik O, Ross J, Scoville DH et al. BMP signaling inhibits intestinal stem cell self-renewal through suppression of Wnt-beta-catenin signaling. Nature genetics2004 Oct;36(10):1117-21.
• Watabe T, Miyazono K. Roles of TGF-beta family signaling in stem cell renewal and differentiation. Cell research2009 Jan;19(1):103-15.

3/ The authors base their work on the claim that CD44high CD24low cells represent bona fide mammary ASCs. This claim is not support by functional work to show that organoid generation is exclusive to or enhanced in CD44high CD24low cells relative to other cells. The claim of differentiation of this stem cell population in vitro is not supported by data to show emergence of luminal progenitor, mature luminal and myoepithelial lineages that are characterized by the expression of a well-defined set of reported markers as abovementioned. Although miR-106a-3p is claimed to downregulate stem cell differentiation based on a gene set enrichment analysis, authors did not investigate the expression of mammary differentiation markers or the association of miR-106a-3p-transfected HMECs with gene set pathways involved in mammary gland differentiation.

• Response 3:
• This concern has been also raised by the 1st reviewer (See Response 1 from Reviewer 1). As explained in response 1, to address this point, we will perform an extensive characterization of our mammary primary cells as well as our 3D-matrigel embedded cultures using flow cytometry This analysis will involve multiple lineage-specific surface markers, including luminal alveolar progenitor (such as CD49f−/low/EpCAM+) and basal/myoepithelial cells (such as CD49f+/EpCAM−/low).
• Secondly, as suggested by the reviewer, we will investigate the expression of mammary differentiation markers or the association of miR-106a-3p-transfected human mammary epithelial cells with gene set pathways involved in mammary gland differentiation by bioinformatics using our transcriptomic data.

4/ Line 129: "Together, these results indicate that cells grown as organoids acquired a CD44high / CD24low expression pattern similar to that of stem/progenitor cells, which suggests that 3D organoids can be used to enrich breast stem cell markers for further screening". This conclusion is not supported by the data presented. It is not clear if the expression of these markers was acquired upon 3D culture. It could be that 3D culture better maintained and expanded already existing CD44high CD24low cells in 2D culture. It is also unclear if these cells are indeed organoid-forming. Authors have not isolated and tested the organoid-forming capacity of CD44high CD24low cells relative to other cells. Along the same line, the conclusion that " mir-106a-3p directly promotes the "transdifferentiation" of CD44low/CD24high cell phenotype into CD44high/CD24low cell phenotype" isn't justified. Experiments required to conclude that 'transdifferentiation' is involved are lacking. miR-106a-3p overexpression could be creating conditions that are permissive for expansion of already existing CD44high CD24low cells as opposed to 'transdifferentiation' of other cell types into this phenotype. The authors could FACS-isolate CD44low CD24low cells and treat these with control or miR-106a-3p to conclusively establish 'transdifferentiation'.

• Response 4:
• This concern has been also raised by the 1st reviewer (See Response 12 from Reviewer 1). Transdifferentiation (lineage reprogramming, or -conversion), is a process in which one mature, specialized cell type changes into another without entering a pluripotent state. This process involves the ectopic expression of transcription factors and/or other stimuli. We agree with the reviewer that we did not demonstrate the transdifferentiation process in terms of lineage. Therefore, we will use flow cytometry to analyze multiple lineage-specific surface markers for luminal progenitor (CD49f−/low/EpCAM+), and basal/myoepithelial cells (CD49f+/EpCAM−/low) following miR-106a-3p expression.
• Additionally, we cannot exclude the possibility that the 3D culture better maintained and expanded the pre-existing CD44high CD24low cells, as suggested by the reviewer. The reviewer recommends FACS-isolating CD44low CD24low cells. We apologise for any lack of clarity in our description. Technically, our primary cells have a low percentage of colony-forming efficiency (CFE) in 3D and not enough cells for FACS isolation of CD44low and CD24low cells. Therefore, we leveraged our knowledge that the expression of CBX7 potentiates the growth of 3D structures. We decided to FACS-isolate the different subpopulations of CD44 and CD24 cells to further elucidate the expression of miR-106a-3p in the different CD44/CD24 cell subpopulations. CBX7-transfected human mammary epithelial cells showed enrichment in CD44high/CD24low cells as compared to empty vector-transfected human mammary epithelial cells (Figure 4A-B). Subsequently, we separated the CD44high/CD24low (green) population from the CD44high/CD24high cell populations (blue) using flow cytometry to analyze the role of the endogenous expression of miR-106a-3p. The CD44high/CD24low population was the only one to exhibit endogenous expression of miR-106a-3p (Figure 4C). Blocking the endogenous expression of miR-106a-3p with LNA-anti-miR-106a-3p or LNA-control (Figure 4D) impacted organoid generation (Figure 4E-F). We will modify the text accordingly to include this point and this figure will be moved in the supplemental figure.

5/ Line 242: "These data demonstrate that miR-106a-3p is involved in the early cell differentiation process into the three germ layers ". The authors have not conducted functional or mechanistic work to show that miR-106a-3p is involved in morphological or transcriptomic differentiation changes in ESCs. This and other data would be necessary to substantiate this conclusion.

• Response 5:
• Indeed, we agree that we cannot conclude that the miR-106a-3p is involved in the early cell differentiation process into the three germ layers without demonstrating the differentiation changes in ESCs through transcriptomic analysis. To clarify the take-home message, we did not include the transcriptomic characterization of the differentiation changes in ESCs. Instead, we focused on assessing the impact on Oct4, Sox2, and Nanog expression. However, to further understand the impact of miR-106a-3p depletion on hESCs differentiation, we have also monitored the expression of specific genes upon induction of the three embryonic germ layers (See Section Part 2.2 for a description of the experiments already carried out).

Selected technical points:

6) Figure 1A: The Y-axis label is misleading and suggests multiple organoids seeded per cell? Organoid Formation Efficiency (OFE) or Organoid Formation Capacity (OFC) are the commonly used nomenclature for this.

• Response 6:
• Since reviewer 1 found that the graph in Fig. 1A to be unnecessary, we have decided to remove this panel in the revised version of the manuscript. Furthermore, as suggested, we will replace the axis “Number of organoids per cell seeded” with “Organoid Formation Capacity” (OFC), which is the commonly used nomenclature for this measure in the article.

7) Figure 2G: X-axis unclear. Is this meant to investigate the percentage of cells expressing CD44/CD24 as double high, double low, high/low and low/high?

• Response 7:
• We thank the reviewer for this careful examination of the manuscript. We apologize for this error, and will make the corresponding adjustment in Figure 2G.

8) Figure 4C: In HMEC-CBX7 cells, it is unclear whether the high miR-106a-3p levels in CD44high CD24low cells are due to CBX7 expression. An important control, HMEC-Control Vector, is missing.

• Response 8:
• This control has been done (see Section 3, for details on experiments that have been carried out).

Reviewer #3 (Evidence, reproducibility and clarity):

In this study the authors identify miR-106a-3p as a potent inducer of organoid formation from HMECs. Overexpression of miR-106a-3p induced formation of more organoids, increased the number of stem/progenitor cells and overall positively affected the stemness properties of the organoids, likely by affecting SOx2, Oct4 and Nanog expression.

Major comments:

1/ the flow of the paper is confusing, it appears that the authors are trying to combine several non-completed studies into one paper. It is not immediately evident how is the rationale or the conclusion supported by published data. For example, is there published evidence that Sox2/Oct4/Nanog are expressed in healthy mammary gland stem cells in vivo, or whether they have a role in establishment of stem cell population?

• Response 1:
• There is still a controversy about the existence of unipotent, bipotent or multipotent stem cells in mammary gland tissue [7-9]. Notably, OCT4, SOX2, and NANOG collectively form the core transcriptional network responsible for maintaining pluripotency in embryonic stem cells [10]. Evidence has accumulated over the past few years, accumulating evidence has supported the presence of stem cells in both mouse and human mammary [11]. Various strategies have been used to identify and isolate human breast stem/progenitor cells, including FACS sorting based on cell surface antigen expression. In addition, an in vitro cell culture system has been described allowing the propagation of human mammary epithelial cells in an undifferentiated state through their ability to proliferate in suspension as non-adherent mammospheres [12].. Simoe et al have demonstrated that stem cells isolated from both normal human breast and breast tumor cells display an increased expression of the embryonic stem cell genes NANOG, OCT4 and SOX2 [13]. Moreover, they have shown that the ectopic expression of any one of these factors, but in particular NANOG and SOX2, in breast cancer cells increases the pool of stem cells and enhances the cells' ability to form mammospheres. They observed higher expression of NANOG, OCT4, and SOX2 in the stem cell populations CD44+CD24−/low and EMA+CALLA+ compared to the rest of the sample population. Cells overexpressing these factors displayed an increase in the stem cell populations, thereby confirming the role of Nanog, Oct4, and Sox2 in the maintenance of human mammary stem cells. We will include this rationale in the manuscript.

• References

• Van Keymeulen A, Rocha AS, Ousset M, Beck B, Bouvencourt G, Rock J et al. Distinct stem cells contribute to mammary gland development and maintenance. Nature2011 Oct 9;479(7372):189-93.

• Deome KB, Faulkin LJ, Jr., Bern HA, Blair PB. Development of mammary tumors from hyperplastic alveolar nodules transplanted into gland-free mammary fat pads of female C3H mice. Cancer research1959 Jun;19(5):515-20.
• Shackleton M, Vaillant F, Simpson KJ, Stingl J, Smyth GK, Asselin-Labat ML et al. Generation of a functional mammary gland from a single stem cell. Nature2006 Jan 5;439(7072):84-8.
• Boyer LA, Lee TI, Cole MF, Johnstone SE, Levine SS, Zucker JP et al. Core transcriptional regulatory circuitry in human embryonic stem cells. Cell2005 Sep 23;122(6):947-56.
• LaMarca HL, Rosen JM. Minireview: hormones and mammary cell fate--what will I become when I grow up? Endocrinology2008 Sep;149(9):4317-21.
• Dontu G, Abdallah WM, Foley JM, Jackson KW, Clarke MF, Kawamura MJ et al. In vitro propagation and transcriptional profiling of human mammary stem/progenitor cells. Genes & development2003 May 15;17(10):1253-70.
• Simoes BM, Piva M, Iriondo O, Comaills V, Lopez-Ruiz JA, Zabalza I et al. Effects of estrogen on the proportion of stem cells in the breast. Breast cancer research and treatment2011 Aug;129(1):23-35.

2/ The organoids are all spherical, while mammary gland is characterized by branching. Therefore, the organoids are not recapitulating the gland morphology and the validation should include wider range of molecular markers.

• Response 2:
• This concern has been also raised by both reviewers 1 and 2. As explained in response 1 to reviewer 1, we will perform a comprehensive characterization of our mammary primary cells as well as our 3D-matrigel embedded culture culture using flow cytometry to examine multiple lineage-specific surface markers, including luminal alveolar progenitor (such as CD49f−/low/EpCAM+), and basal/myoepithelial cells (such as CD49f+/EpCAM−/low). As a result, our experiments will collectively confirm that our growth conditions (which will be more thoroughly described in the methods section) for multipotent mammary stem cells can indeed generate multi-lineage organoids.

3/ The choice of control is not clear. For example, why was miR-106a-5p chosen as a control? And why choose miR-106a-5p when a better candidate would be miR-106b-3p, that produces very high number of organoids as well? And how did the other miRNAs affected the CD44/24 profile?

• Response 3:
• We apologize for any lack of clarity in our description. It's important to note that miRNAs consist of two strands, -5p and -3p, and both strands can coexist and play distinct roles. For example, paired species of members in the let-7 and mir-126 families coexist and have different regulatory functions in reprogramming and differentiation of embryonic stem cells [1]. Several deep sequencing studies have demonstrated the coexistence of 5p/3p pairs in approximately half of the miRNA populations analyzed [2, 3]. Therefore, to determine whether the biological effect specifically resulted from the -3p strand, we also assessed the -5p strand.

• References

• Koh W, Sheng CT, Tan B, Lee QY, Kuznetsov V, Kiang LS et al. Analysis of deep sequencing microRNA expression profile from human embryonic stem cells derived mesenchymal stem cells reveals possible role of let-7 microRNA family in downstream targeting of hepatic nuclear factor 4 alpha. BMC genomics2010 Feb 10;11 Suppl 1(Suppl 1):S6.

• Jagadeeswaran G, Zheng Y, Sumathipala N, Jiang H, Arrese EL, Soulages JL et al. Deep sequencing of small RNA libraries reveals dynamic regulation of conserved and novel microRNAs and microRNA-stars during silkworm development. BMC genomics2010 Jan 20;11:52.
• Kuchenbauer F, Mah SM, Heuser M, McPherson A, Ruschmann J, Rouhi A et al. Comprehensive analysis of mammalian miRNA* species and their role in myeloid cells. Blood2011 Sep 22;118(12):3350-8.

4/ What is the CD44/CD24 profile in the organoid cultures from Figure 7?

• Response 4:
• The CD44/CD24 profile from the organoid cultures from Figure 7 will be assessed in the revised manuscript.

Part 2.2 Following the experiment that have been already carried out that will be included in the manuscript after revisions.

Reviewer #1 (Evidence, reproducibility and clarity):

Major comments:

The model system used in this work is referred to as "organoids", with the premise that organoids, as representatives of the original tissue, can be used to study tissue development. However, the organoids presented in the study are spherical structures, and the paper does not provide any information about how and to what extent these organoids represent the original tissue. Furthermore, it would be difficult to expect these organoids to accurately represent human breast tissue, as they are derived, to the best of the reviewer's understanding (it is not explicitly noted, but rather the text refers the reader to several previous works), from primary human breast epithelial cells that had been cultured for 6 passages in 2D culture. The CD24/CD44 flow cytometry profile of these 2D cultures, as well as the organoids derived from them, shows a unimodal distribution of CD24 and CD44 expression, and does not show distinct cell populations, as typical in primary breast epithelial cells that have not been cultured in 2D.

• Response:
• First, we assessed the aldehyde dehydrogenase activity (ALDH) [14] in our primary cells to demonstrate that these culture conditions preserve stem/progenitor properties (See Figure A, below). Additionally, we conducted immuno-staining of primary cells in 2D culture for lineage-specific luminal markers (CK18, MUC1) and basal markers (CK14, CK5), which revealed the heterogeneity of our population (See Figure B, below).

FIGURE FOR REVIEWERS

• Reference

• Ginestier C, Hur MH, Charafe-Jauffret E, Monville F, Dutcher J, Brown M et al. ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome. Cell stem cell2007 Nov;1(5):555-67.

• For the characterization of the 3D culture, we have presented early time points of 3D cell growth, which are mainly spherical (until Day 8). However, differentiation occurs in a stepwise manner, where single stem cells proliferate to form small spheroids before undergoing multilineage differentiation into organoids that initiate branching (Day 10). Subsequently, ducts undergo budding and lobule formation (Day 20). We have included a time-course representation of this 3D growth to illustrate the differentiation process (See Figure 1C).

• Under these culture conditions, the 3D structure retains its self-renewal activity and the ability to reseed and regenerate secondary tissues. We have also assessed the self-renewal capacity of our 3D structure (See Figure 1D).

FIGURE FOR REVIEWERS<br />

Page 7 line 161: "mir-106a-3p directly promotes the "transdifferentiation" of CD44low/CD24high cells phenotype into CD44high/CD24low cell phenotype" - is an unsupported statement, given that there could be several alternative explanations for the observed change in population ratios, including effects on survival or growth of cells of a certain population.

• Indeed, we cannot exclude the possibility of other alternative explanations. Therefore, we have already assessed the impact of miR-106a-3p on population doubling in culture to determine whether it has an effect on the survival or growth of the cells. We observed that miR-V cells stopped growing after approximately 15 population doublings, indicating their limited proliferative potential. In contrast, we found that miR-106a extended the lifespan of the cells, suggesting an effect on cell survival (Figure below). We will include this information in the manuscript.

FIGURE FOR REVIEWERS

• Why was GATA3 not included in the last analysis depicted in Fig. 7?

• We apologize for any lack of clarity in our description. It's important to note that GATA3 was not included in Figure 7. As explained in the text, GATA3 plays a role in regulating Nanog expression. However, it's worth noting that the cells did not form organoids when GATA3 was depleted, as illustrated in the results below. We will include these results in the revised version of the manuscript.

FIGURE FOR REVIEWERS

Reviewer #2:

2/ Authors claim that miR-106a-3p downregulates stem cell differentiation and utilize 'organoid' culture to track the temporal expression of OCT4, SOX2 and NANOG during phases of organoid renewal and differentiation. However, mammary stem cell differentiation is associated with the emergence of luminal progenitor, mature luminal and myoepithelial lineages that are characterized by the expression of a well-defined set of markers. The authors did not investigate the expression of these markers in their 'differentiation' settings. Furthermore, modulators of major pathways reported to be critical for mammary ASC maintenance are lacking, including modulators of WNT, TGF/BMP, Notch and other pathways. As such, it is difficult to ascertain that organoids emerging under such culture conditions are the result of stem cell renewal and differentiation, as opposed to lineage-restricted proliferative non-ASCs, thus questioning the validity of many of the findings in this work.

• We agree with the reviewer that major pathways such as Wnt, TGFβ/BMP and Notch have been reported to be critical for mammary ASC maintenance. As suggested by the reviewer, to further evaluate the possible involvement of these pathways, we have analyzed our transcriptomic data, using the PROGENy R package (version 1.16.0) (https://doi.org/10.1038/s41467-017-02391-6) and DoRothEA (version 1.6.0)-decoupleR (version 2.1.6) computational pipeline (https://doi.org/10.1101/gr.240663.118, https://doi.org/10.1093/bioadv/vbac016), to investigate the differential activation of major signaling pathways and transcriptional factors. The major pathways are depicted in the figure below and will be included in the manuscript.

FIGURE FOR REVIEWERS

• There was significant enrichment indicating down-regulation of TGFβ, MAPK, WNT, PI3K genes along with gene sets representing other oncogenic pathways, are up-regulated such as the hypoxia response, JAK/STAT pathway, and p53 pathway activity (Figure A and B). Stem cells possess self-renewal activities and multipotency, characteristics that tend to be maintained under hypoxic microenvironments [15], thus this is not surprising to observe an up-regulation of Hypoxia pathway and activation of HIF1α transcription factor. In mammary stem cells, it has also been shown that p53 is critical to control the maintenance of a constant number of stem cells pool [16,17]. Remarkably, it has been shown that cell sorting of the cells with a putative cancer stem cell phenotype (CD44+/CD24 low) express a constitutive activation of Jak-STAT pathway [18], which we observed as up-regulated along with STAT1 and STAT2 transcription factors. In parallel, we observe a down-regulation of Wnt signaling as well as PI3K pathways. Wnt is known to play important role in the maintenance of stem cells; its inhibition has been shown to lead to the inactivation of PI3 kinase signaling pathways to ensure a balance control of stem cell renewal [19]. Moreover, we observe a down-regulation of SMAD4 and SMAD3 transcription factors correlated with a down-regulation of TGFβ pathway. Signals mediated by TGF-β family members have been implicated in the maintenance and differentiation of various types of somatic stem cells [20].

• References

• Semenza GL. Dynamic regulation of stem cell specification and maintenance by hypoxia-inducible factors. Molecular aspects of medicine2016 Feb-Mar;47-48:15-23.

• Solozobova V, Blattner C. p53 in stem cells. World journal of biological chemistry2011 Sep 26;2(9):202-14.
• Cicalese A, Bonizzi G, Pasi CE, Faretta M, Ronzoni S, Giulini B et al. The tumor suppressor p53 regulates polarity of self-renewing divisions in mammary stem cells. Cell2009 Sep 18;138(6):1083-95.

• Hernandez-Vargas H, Ouzounova M, Le Calvez-Kelm F, Lambert MP, McKay-Chopin S, Tavtigian SV et al. Methylome analysis reveals Jak-STAT pathway deregulation in putative breast cancer stem cells. Epigenetics2011 Apr;6(4):428-39.

• He XC, Zhang J, Tong WG, Tawfik O, Ross J, Scoville DH et al. BMP signaling inhibits intestinal stem cell self-renewal through suppression of Wnt-beta-catenin signaling. Nature genetics2004 Oct;36(10):1117-21.

• Watabe T, Miyazono K. Roles of TGF-beta family signaling in stem cell renewal and differentiation. Cell research2009 Jan;19(1):103-15.

5) Line 242: "These data demonstrate that miR-106a-3p is involved in the early cell differentiation process into the three germ layers ". The authors have not conducted functional or mechanistic work to show that miR-106a-3p is involved in morphological or transcriptomic differentiation changes in ESCs. This and other data would be necessary to substantiate this conclusion.

• Response:
• Indeed, we cannot conclude that miR-106a-3p is directly involved in the early cell differentiation process into the three germ layers without demonstrating the differentiation changes in ESCs through transcriptomic analysis. To clarify the take-home message, it's important to note that we did not include the transcriptomic characterization of differentiation changes in ESCs. Instead, we focused on assessing the impact of miR-106a-3p depletion on the expression of Oct4, Sox2, and Nanog. However, to gain a deeper understanding of the effects of miR-106a-3p depletion on hESCs differentiation, we also monitored the expression of specific genes upon the induction of the three embryonic germ layers (see Figure A, B, and C below). We observed that the expression of endodermal genes was not or only weakly affected by the level of miR-106a-3p expression (Figure A), whereas the expression of mesoderm- and ectoderm-specific genes increased upon miR-106a-3p down-regulation (Figure B and C). We will include these findings in the manuscript.

FIGURE FOR REVIEWERS

Reviewer 2 (Minor points)

3) Figure 4C: In HMEC-CBX7 cells, it is unclear whether the high miR-106a-3p levels in CD44high CD24low cells are due to CBX7 expression. An important control, HMEC-Control Vector, is missing.

• Response
• The control HMEC-Vector is shown in panel Figure 4A for the FACS analysis. In Figure 4C, we did not include the control since there is no expression of miR-106a-3p, but it's important to note that this control was included in the experiment. As illustrated, there is no miR-106a-3p expression in the HMEC control cells. We intend to include this panel in Figure 4 of the manuscript.

FIGURE FOR REVIEWERS

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Referee #3

Evidence, reproducibility and clarity

In this study the authors identify miR-106a-3p as a potent inducer of organoid formation from HMECs. Overexpression of miR-106a-3p induced formation of more organoids, increased the number of stem/progenitor cells and overall positively affected the stemness properties of the organoids, likely by affecting SOx2, Oct4 and Nanog expression.

Major comments: the flow of the paper is confusing, it appears that the authors are trying to combine several non-completed studies into one paper. It is not immediately evident how is the rationale or the conclusion supported by published data. For example, is there published evidence that Sox2/Oct4/Nanog are expressed in healthy mammary gland stem cells in vivo, or whether they have a role in establishment of stem cell population?<br /> The organoids are all spherical, while mammary gland is characterized by branching. Therefore, the organoids are not recapitulating the gland morphology and the validation should include wider range of molecular markers.<br /> The choice of control is not clear. For example, why was miR-106a-5p chosen as a control? And why choose miR-106a-5p when a better candidate would be miR-106b-3p, that produces very high number of organoids as well? And how did the other miRNAs affected the CD44/24 profile?<br /> What is the CD44/CD24 profile in the organoid cultures from Figure 7?

Significance

The study provides a novel methodology to enrich for the mammary stem cells. However, many of the experiments need further clarification.

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Referee #2

Evidence, reproducibility and clarity

In this work, Robert et al. utilize mammary organoid culture as an in vitro model of stem cell renewal and maintenance. Authors show that a putative mammary stem cell population, characterized as CD44high CD24low, is enriched in organoid culture relative to 2D monolayer culture. They conduct a microRNA (miR) screen and identify miR-106a-3p as a miRNA that enriches for stem cell (CD44high CD24low) and organoid formation capacity, confirming these findings using miR-106a-3p overexpressing cells. Authors also show that CBX7 overexpression achieves a similar enrichment in CD44high CD24low and organoid formation capacity in an miR-106a-3p dependent manner, though the rationale behind the connection between CBX7 and miR-106a-3p is not well defined. Finally, the manuscript conducts a transcriptomic analysis on miR-106a-3p-OE cells and aims to functionally validate its role in embryonic stem cells (ESCs) as a model that is versatile for renewal and differentiation studies. How this relates to mammary adult stem cells (ASCs) is not entirely clear.

Overall, the manuscript contains significant technical and conceptual limitations, and the data presented do not support the major conclusions of the study. There are two overarching issues that question the validity of most of the findings in this study. Firstly, there is no clear demonstration that the structures reported as 3D organoids are indeed driven by multipotent stem cells (in all data and experimental approaches associated with this). Secondly, there is a lack of evidence to support the claim that CD44high CD24low cells are stem cells with an exclusive or enhanced capacity to generate multi-lineage organoids.

In addition to these general points, there are several specific major issues summarized below:

1. Authors base their findings about mammary ASC renewal using a poorly defined organoid culture system that they claim is driven by ASC renewal and differentiation. These organoid growth conditions were originally developed to support growth of bronchial epithelial cells, and the authors have not presented data to objectively assess the validity of this extrapolation to expansion of mammary organoids. The authors did not present data to support the notion that these growth conditions generate multi-lineage organoids that expand from multipotent mammary stem cells.
2. Authors claim that miR-106a-3p downregulates stem cell differentiation and utilize 'organoid' culture to track the temporal expression of OCT4, SOX2 and NANOG during phases of organoid renewal and differentiation. However, mammary stem cell differentiation is associated with the emergence of luminal progenitor, mature luminal and myoepithelial lineages that are characterized by the expression of a well-defined set of markers. The authors did not investigate the expression of these markers in their 'differentiation' settings. Furthermore, modulators of major pathways reported to be critical for mammary ASC maintenance are lacking, including modulators of WNT, TGF/BMP, Notch and other pathways. As such, it is difficult to ascertain that organoids emerging under such culture conditions are the result of stem cell renewal and differentiation, as opposed to lineage-restricted proliferative non-ASCs, thus questioning the validity of many of the findings in this work.
3. The authors base their work on the claim that CD44high CD24low cells represent bona fide mammary ASCs. This claim is not support by functional work to show that organoid generation is exclusive to or enhanced in CD44high CD24low cells relative to other cells. The claim of differentiation of this stem cell population in vitro is not supported by data to show emergence of luminal progenitor, mature luminal and myoepithelial lineages that are characterized by the expression of a well-defined set of reported markers as abovementioned. Although miR-106a-3p is claimed to downregulate stem cell differentiation based on a gene set enrichment analysis, authors did not investigate the expression of mammary differentiation markers or the association of miR-106a-3p-transfected HMECs with gene set pathways involved in mammary gland differentiation.
4. Line 129: "Together, these results indicate that cells grown as organoids acquired a CD44high / CD24low expression pattern similar to that of stem/progenitor cells, which suggests that 3D organoids can be used to enrich breast stem cell markers for further screening".

This conclusion is not supported by the data presented. It is not clear if the expression of these markers was acquired upon 3D culture. It could be that 3D culture better maintained and expanded already existing CD44high CD24low cells in 2D culture. It is also unclear if these cells are indeed organoid-forming. Authors have not isolated and tested the organoid-forming capacity of CD44high CD24low cells relative to other cells. Along the same line, the conclusion that " mir-106a-3p directly promotes the "transdifferentiation" of CD44low/CD24high cell phenotype into CD44high/CD24low cell phenotype" isn't justified. Experiments required to conclude that 'transdifferentiation' is involved are lacking. miR-106a-3p overexpression could be creating conditions that are permissive for expansion of already existing CD44high CD24low cells as opposed to 'transdifferentiation' of other cell types into this phenotype. The authors could FACS-isolate CD44low CD24low cells and treat these with control or miR-106a-3p to conclusively establish 'transdifferentiation'.<br /> 5. Line 242: "These data demonstrate that miR-106a-3p is involved in the early cell differentiation process into the three germ layers "

The authors have not conducted functional or mechanistic work to show that miR-106a-3p is involved in morphological or transcriptomic differentiation changes in ESCs. This and other data would be necessary to substantiate this conclusion.

Selected technical points:

1. Figure 1A: The Y-axis label is misleading and suggests multiple organoids seeded per cell? Organoid Formation Efficiency (OFE) or Organoid Formation Capacity (OFC) are the commonly used nomenclature for this.
2. Figure 2G: X-axis unclear. Is this meant to investigate the percentage of cells expressing CD44/CD24 as double high, double low, high/low and low/high?
3. Figure 4C: In HMEC-CBX7 cells, it is unclear whether the high miR-106a-3p levels in CD44high CD24low cells are due to CBX7 expression. An important control, HMEC-Control Vector, is missing.

Significance

The findings in this report do not represent a sufficient conceptual advance in mammary stem cell biology. There are some interesting data on the role of miR-106a-3p in differentiation and regulation of pluripotency-inducing factors OCT4, SOX2 and NANOG. This is, however, more relevant to ESC biology. The data presented do not sufficiently explain the proposed transcriptional and molecular influences of miR-106a-3p on ESC maintenance and differentiation. Furthermore, there are multiple instances in the manuscript of overstating conclusions in a manner that is not supported by the data presented, as well as several instances of a conceptually premature extrapolation of various aspects of ESC biology to mammary ASC biology. It is not clear what literature or experimental findings serve as the basis for the author's connection between these two developmentally and temporally distinct aspects of tissue biology.

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Referee #1

Evidence, reproducibility and clarity

Major comments:

• The model system used in this work is referred to as "organoids", with the premise that organoids, as representatives of the original tissue, can be used to study tissue development. However, the organoids presented in the study are spherical structures, and the paper does not provide any information about how and to what extent these organoids represent the original tissue. Furthermore, it would be difficult to expect these organoids to accurately represent human breast tissue, as they are derived, to the best of the reviewer's understanding (it is not explicitly noted, but rather the text refers the reader to several previous works), from primary human breast epithelial cells that had been cultured for 6 passages in 2D culture. The CD24/CD44 flow cytometry profile of these 2D cultures, as well as the organoids derived from them, shows a unimodal distribution of CD24 and CD44 expression, and does not show distinct cell populations, as typical in primary breast epithelial cells that have not been cultured in 2D.
• There appears to be confusion between concepts: primarily confusing a basal phenotype with a stem cell phenotype, and progenitor activity with stem cell activity. Because the 3D organoid model does not display a replication of luminal differentiation, it cannot be used as a proxy for stem cell function. The results from the miRNA screen and the subsequent experiments support two conclusions: 1. miR-160b-3p leads to an enhanced mesenchymal phenotype, manifested by reduced CD24 and enhanced CD44 expression. 2. miR-160b-3p leads to increased organoid formation. The latter may be interpreted, at best, as higher basal progenitor activity, but is not a measure of stem cell activity, as that would require the cells to give rise to more than one lineage, which is not shown here.
• Overall, the information from figures 1-4 indicate that miR-160b-3p is driving and is associated with mesenchymal differentiation, possibly with EMT.
• In contrast to the work in the breast 3D culture model, the experiments with hESCs are interesting and do support a role of this miR in stemness.<br /> There are several relatively minor comments, that cumulatively somewhat undermine the strength of the work:
• Abstract: the sentence "organoids can be directly generated from human epithelial cells by only one miRNA, miR-106a-sp" needs better clarification.
• Page 5 line 103: HMECs is a term that generally refers to human mammary epithelial cells, not a specific derivation or subpopulation thereof.
• The graph in Fig. 1A is unnecessary, it shows only one bar.
• It is unclear if all the HMECs were derived from the same donor, or several donors. There is no information about the donor and how the tissue and cells were derived. In general, it is not entirely clear how the cells were collected, processed, stored, and cultured from the time they were obtained from the donor until their use in the current study.
• The key for Fig. 2D is unclear. The axes read "density" but the text refers to "intensity". Fluorescence intensity in flow cytometry is usually measured on a log scale. Differences on a linear scale are not usually considered meaningful. The authors should clarify why they chose a linear scale for this screen.
• In the miRNA screen, how long were the cells cultured after transfection, and was it enough time for them to shift phenotype?
• Page 6 line 154: The authors likely mean z-score, not z factor (two different things).
• Page 7 line 161: "mir-106a-3p directly promotes the "transdifferentiation" of CD44low/CD24high cells phenotype into CD44high/CD24low cell phenotype" - is an unsupported statement, given that there could be several alternative explanations for the observed change in population ratios, including effects on survival or growth of cells of a certain population.
• There is need for quantification of the phenotypes described in Fig. 3C
• Figures 3 D-F it is not clear if the graphs display percentage or mean number (there is discrepancy between figure text and figure legend text), and when percentage, not clear for figure 3F out of what.
• Fig. 5B, what is the statistical significance of the enrichment?
• Why was GATA3 not included in the last analysis depicted in Fig. 7?

Significance

This manuscript describes experiments that aim to explore the role of miR-160b-3p in stem cells. It uses primarily a model of breast epithelial cells in 3D Matrigel culture, termed organoids.

The first part of the paper describes a screen that identified miR-160b-3p as changing the expression profile of the two surface markers CD24 and CD44 in breast epithelial cells, which the authors refer to as a stem cell population, and enhancing organoid formation capability, which the authors interpret as stem cell capacity. In the second part of the paper, the experiments use another cell model - human embryonic stem cells (ESCs). In the second part, the authors link the expression of miR-160b-3p to the expression of Nanog, Sox2, and Oct4, which are key transcription factors that play essential roles in maintaining pluripotency and self-renewal of ESCs.<br /> The premise of the work is strong, namely that genetic screens that use an organoid model have the potential to uncover genes and pathways that direct tissue development and regulate stem cell fate and function. Several issues make it difficult to draw conclusions about stem cell function from the first part of the paper, namely the experiments with breast epithelial organoids. The second part of the paper is stronger and more convincing of the main claim, which is that miR-160b-3p has a role in stem cell maintenance.

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Reply to the reviewers

'The authors do not wish to provide a response at this time.' The response has been included in a PDF

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Referee #3

Evidence, reproducibility and clarity

Summary:

This manuscript attempts to answer how the maternal and paternal chromosomes are organized, and probe the determinants of this organization.

The authors use two divergent strains of worms - Bristol(N2) and Hawaiian - and hybrids progeny to study this as there are large regions of sequence variants between chromosome V in the two strains, making it an ideal candidate to design specific FISH probes. The authors build on their previously published work and optimize a protocol to trace chromosomes by using a multiple-probe FISH approach to investigate chromosome architecture. This approach is well illustrated in Figure 1.

Overall, this manuscript does a good job of describing a potentially useful technique with wide application. The claims about differences (and similarities) require statistical analysis to be appreciated, and much work is necessary to make the analysis approachable to readers outside the immediate field.

Major comments:

Nearly all claims regarding the organization, compactness and pair-wise distances of chromosomes lack any statistical measures of significance. This is particularly important for the clustering and scaling analysis. This makes interpretation of the claims made in the text impossible. For example, claims such as "the step size remained virtually unchanged" or "the paternal chromosomes adopt the maternal conformation in hybrids" cannot be currently analyzed.

Throughout the manuscript (including in the abstract), the authors use the term "sister chromosomes" to (presumably) refer to the maternal and paternal chromosomes. This is a confusing term, since "sister chromatids" usually refers to the identical products of DNA replication, and "homologous chromosomes" is usually used to describe the parental chromosomes. The term would ideally be changed, and at the very least, it should be clearly defined.

Presentation: The manuscript in its current state (excluding figure one) is essentially impossible to interpret by readers who are unfamiliar with this subfield. The authors could include a blurb on the methodology behind each data type to help the manuscript reach a larger audience. The pipeline, meaning, and potential caveats of the clustering analysis should also be explicated.

Other suggestions:

The use of Hi-C-like heatmaps is good, since they are commonly used and are clear and easy to understand. However, it would be best to explain how the FISH data were used to construct the maps. The implications of the map could also be better explained (e.g., that the red cluster means relatively looser regions, and the blue means more tightly compacted ones).

The last sentence in the Introduction does not make clear sense. How does the similarity between N2 & HI open up the possibility of interrogating inheritance effects?

Several of the additional analysis that could improve the paper are: 1. Measure the nucleus diameter in N2/HI hybrid and HI/N2 hybrid. 2. Normalize the spatial distance to the nucleus size, rather than directly using the distance. 3. Explore some of the patterns in the spatial distance plots (e.g., red/blue lines and boxes). Are the sequences that are in them any different between N2 and HI in a way that might be able to account for these patterns?

Significance

The manuscript introduces a technical advance in the study of chromosomal territories - an important area of study that benefitted from recent advances in microscopy and in development of FISH approaches. However, it lacks mechanistic analysis and remains almost purely descriptive. It is also not clear what motivated the work beyond the technical feasibility. These issues make it impossible to assign biological significance to the seemingly minor differences that are documented.

However, as a report of a technical advance, it could be useful to many chromosome biologists who might apply it to diverse organisms and biological questions.

I am a chromosome biologist working on worms. However, my research does not directly deal with parental effects or with the development of novel FISH methodologies, so I did not examine claims regarding these specific points.

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Referee #2

Evidence, reproducibility and clarity

Building on their previous work using sequential fluorescent in situ hybridization (FISH) to follow the path of entire chromosomes in C. elegans (Sawh et al. 2020; Sawh and Mango 2020), the authors developed and then applied a method that distinguishes maternal and paternal homologs. In particular, they designed probes specific for ChrV in two strains, N2 and HI, and then demonstrated their effectiveness in homozygotes and hybrids. They found that HI ChrV is more compact in HI homozygotes as compared to N2 homozygotes, but decompacts in the F1 of N2 hermaphrodites x HI males. A different outcome was observed with respect to decompaction in the reverse cross, where both N2p and HIm ChrV chromosomes (p=paternal, m=maternal) exhibit decompaction. Through unsupervised clustering, the authors further found both dominant and minor patterns of chromosome-wide organization, with maternal chromosomes similar in terms of their dominant clusters regardless of the direction of the cross, but paternal chromosomes less so. Finally, the authors measured the degree to which homologous chromosomes interact, concluding that, although homologs overlap quite frequently, they rarely align (pair).

In sum, the significance of the authors' work lies in its chromosome-level observations and the application of computationally designed probes that distinguish homologs by targeting strain-specific insertions. While homolog distinction by FISH has been previously demonstrated, this study is the first to demonstrate this approach in C. elegans as well as implement it via insertions in a chromosome-wide manner. As such, the manuscripts should be of interest to a broad range of researchers, especially those in the fields of genetics, genomics, and 3D genome organization. That said, the study falls short in several ways, and we recommend the authors i) present a more thorough summary of what is known about homolog positioning across species, ii) describe how homologs have been previously distinguished by FISH and, thus, more clearly elucidate the specific advances they have enabled, iii) ground their work in more rigorous quantitation, and iii) provide a better description of the technologies (strengths as well as limitations) carried over from their previous studies so that readers can better evaluate the current study. We detail our suggestions and questions, below:

Major Comments:

1. Page 1: We suggest the authors broaden the reach of their introduction to include well-known examples outside of C. elegans of the impact of parent-of-origin on 3D genome organization. Such examples would include X-inactivation, selective silencing of paternal genomes, the physical elimination of paternal chromosomes, and the like.
2. Page 1: We also suggest the authors consider including mention of observations from the following research publications and review:

Mayer W et al. Spatial separation of parental genomes in preimplantation mouse embryos. 2003 PMC2169371

Reichmann J et al. Dual-spindle formation in zygotes keeps parental genomes apart in early mammalian embryos. 2018 PMID: 30002254

Nagele R, Freeman T, McMorrow L, Lee HY. Precise spatial positioning of chromosomes during prometaphase: evidence for chromosomal order. 1995 PMID: 8525379

Hua LL et al. Mitotic antipairing of homologous and sex chromosomes via spatial restriction of two haploid sets. 2018 PMID: 30530674

Hua LL, Casas CJ, Mikawa T. Mitotic antipairing of homologous chromosomes. 2022 PMC9731508 3. Page 5: The authors designed their strain-specific probes to target 172 insertions that are over 1 kb in size and distributed across ChrV. We ask the authors to describe these insertions in greater detail, especially as, later in the manuscript, the authors touch on the possibility that sequence differences between the two strains may account for differences in chromatin architecture. How many insertions were on the N2 and HI chromosomes, respectively? What is the range and distribution of insertion sizes for all insertions as well as specifically for the N2 and HI ChrV chromosome? Do the insertions contain repetitive sequences, or are they predominantly composed of unique sequences? What is their distribution with respect to genes, active regions, TADs? Is there an explanation for why some are clustered? If they contain genes, are the genes enriched in certain GO categories? Do the insertions differ in their characteristics across the different chromosomes? This information could be included as graphs and/or tables. 4. Page 5: Given that N2 and HI ChrV chromosomes differ by the number of insertions and, ultimately, probes, how might these differences have skewed the authors' results, especially with respect to overlap between the homologs? Here, simulations of different ratios of insertions between N2 and HI could be clarifying. 5. What difficulties were encountered when tracing the paths of overlapping homologs, and how were these difficulties accounted for and/or solved? Did the different numbers and distributions of insertions between N2 and HI exacerbate the challenge? What confidence levels accompanied their findings? 6. Page 4: It would be helpful if the authors to put their insertion-based method into the context of other studies that have developed and used FISH to distinguish homologs. 7. Pag 6: It would clarifying if the authors provided details about the mis-annotation of the Thompson genome and how it is pertains to probe design. 8. Page 6: The authors state that "...N2 and HI...harbor sequence differences, some of which are predicted to affect chromatin architecture" and that HI lacks ppw-1. We ask the authors to provide a more thorough discussion. To what extent might such predictions rest on the insertional differences between the strains? 9. Page 7: How much smaller is the HI genome and what percent of this difference is due to insertions (deletions)? Related to this, how much smaller is the HI ChrV chromosome as compared to N2? 10. Page 7 and throughout: For those readers who have not read Sawh and Mango 2020 and Sawh et al. 2020, or who are unfamiliar with the broader category of imaging technologies that support the current study, we ask the authors to provide much more background and citations to key methods. Without this information, many readers will neither sufficiently understand the strategies used for imaging acquisition, processing, and analysis nor grasp the relevance of terms such as step size, polymer step size, power-law fitting, etc. and therefore be less able to assess the authors' data and conclusions. 11. General statement: When the authors infer similarity or differences between power-law fittings, scaling exponents, step sizes, etc. what is the statistical significance of those comparisons? 12. Experiments in general: We suggest the authors provide considerably more quantitation. For example, we urge them to provide the number of trials, sample sizes, numbers of embryos examined for all experiments. Equally important would be information regarding the stage of embryos examined and, where more than one embryonic stage was involved, the number of embryos for each stage. Are there stage-specific changes? We are also concerned about the impact of mixed populations of embryos on studies using unsupervised clustering. In other words, what was the contribution of developmental stage to the outcome of the clustering? Furthermore, if not all nuclei in an embryo were captured, we ask the authors to give the percent of nuclei captured from an embryo and reasons why only a subset of nuclei were included in the analysis. 13. With regard to cluster analyses both here and elsewhere, will the authors please include statements of statistical significance whenever they note differences and/or similarities? 14. Page 8: It would be helpful if the authors explained how they implemented the nearest-neighbor approach, including caveats and limitations (success rates, drop-out rates, etc.) and providing statistical assessment wherever possible. 15. Page 8: "In some instances (6-8% of traces), traces were ambiguous and excluded from further analysis". We ask the authors to provide more detail. For example, what does ambiguous mean and, with respect to the 6-8% value, what was the total number of traces? What was the distribution of all traces (prior to filtering) with respect to percent of targets detected? Also, how coincident were the two homologs of a nucleus to each other in terms of capturing all the targets? 16. Page 8: "...we classified traces into N2 or HI based on whether the trace was located closest to a strain marking volume for N2 or HI." Will the authors please quantify "closest" and explain what this means, whether there was a cut-off and, if there had been, how it was determined and implemented? What percent of cells were problematic, and were there traces that did not overlap the strain marking volumes at all? As stated in the Materials and Methods, only a subset of traced chromosomes were analyzed for overlap - why were only a subset analyzed for overlap, how was the subset selected, and how many/what percentage did these traces represent? It would also be helpful if the authors provided a quantitative summary of the traces. 17. Page 8: Did the authors account for chromatic aberration and, if so, what protocol did they use? 18. Page 8: "...counting how often more than two N2 or HI traces were detected in one nucleus." This is puzzling, and we suggest that the authors include explanations for how this might have happened. Did the nuclei not contain signal from the other strain marking probe at all? Was there a bias for this to happen with N2 or HI chromosomes? Could this have been a consequence of biology or of the algorithm for tracing? The authors' observations are reminiscent of the many implications raised by Jia et al. (2023; A spatial genome aligner for resolving chromatin architectures from multiplexed DNA FISH. PMID: 36593410), and we ask the authors to comment on the relevance of their observations to those in this recent publication. 19. Page 8: "We found only a minority of 2% of HI traces and 7% of N2 traces were mis-assigned and excluded these from downstream analysis." 2% and 7% of what total? 20. Page 8: How do pairwise distances remain almost identical between N2 and HI and yet generate different scaling exponents? 21. Page 9: Figure 1F shows images of embryos derived from N2 hermaphrodites x HI males. It would be helpful if the authors added analogous images from the reciprocal cross as a supplementary figure. 22. Page 2, 9, 9, and 12: The authors make several comments regarding the action of factors in trans: "... factors from the mother impact chromosome folding in trans (p. 2)"; "... the HIp decompacts when subjected to the N2m environment and implies that the paternal chromosome is influenced by the maternal environment in trans (p. 9)"; "...N2 chromosomes influence HI chromosomes in trans, while N2 chromosome structure seems to be resistant to influences by the HI chromosomes (p. 9)"; and "...implicating maternal factors that act in trans (p. 12). While provocative, these statements call for more concrete consideration. Are the authors using "in trans" in lieu of "indirectly", or are they alluding to factors, such as ppw-1, or direct physical contact? Without further substantiation or argument, mention of in trans activity might best be reserved for the Discussion. 23. Page 10: "While HIp subpopulations were characterized by folding of one or the other chromosome arm, N2p clusters were more open and a subpopulation with a highly folded right arm was not present" (Figure 5CD). Was there a significant correlation between left vs. right arm folding and overall genome organization and function? 24. Page 11: Will the authors please provide a more explicit definition of alignment as well as a more detailed description of how alignment is quantified in the main text? 25. Page 11: With respect to direct physical contact, the authors mention transvection, which they conclude in the abstract is unlikely because pairing between homologs was observed to be rare. As transvection and pairing can both be short-lived, and the data are not compelling, the statement may need to be toned down considerably and/or be moved to the discussion. 26. Page 11: The authors draw a distinction between % territory overlap and physical distances between homologs. In particular, the differences between % overlap across different stages is quite interesting and potentially suggests embryo stage-specific changes. Could the authors explore this further by breaking down mean pairwise distances into different embryo stages (Figure 6B and C)? 27. Pages 2, 11-13: When the authors use "sisters", do they mean "homologs"? If the latter, we recommend they use "homologs", only, as "sisters" refers to the sister chromatids after replication. If, however, the authors are using "sisters" to mean sister chromatids, will they please explain how their data can distinguish sisters? 28. Discussion: We encourage the authors to speculate further regarding the basis of decompaction. Is it a hybrid-specific phenomenon?

Minor comments:

1. Page 1: Although the introduction focuses on C. elegans, the genome length that is mentioned (2 meters) is more aligned with that of mammalian species. The authors could cite a range of lengths or make more clear which species is being discussed.
2. Page 5: As the figures label the strains as Hawaiian and Bristol, the authors might wish to include this nomenclature in the main text. Curiously, the authors use several different spellings for the Hawaiian/Hawai'ian/Hawaiin/Hawaii strain.
3. Page 5: Will the authors please explain why the common whole-chromosome tracing probes have only one tail, while the strain-specific probes have two tails, as shown in Figure 1D?
4. Figures in general: The axes of a number graphs and heat maps need to be labeled.
5. Materials and Methods: The section on "Cluster analysis" is missing units for the resolution.
6. Page 8: Will the authors please give a reference for and explain watershed segmentation?

Significance

In sum, the significance of the authors' work lies in its chromosome-level observations and the application of computationally designed probes that distinguish homologs by targeting strain-specific insertions. While homolog distinction by FISH has been previously demonstrated, this study is the first to demonstrate this approach in C. elegans as well as implement it via insertions in a chromosome-wide manner. As such, the manuscript should be of interest to a broad range of researchers, especially those in the fields of genetics, genomics, and 3D genome organization. That said, the study falls short in several ways, and we recommend the authors i) present a more thorough summary of what is known about homolog positioning across species, ii) describe how homologs have been previously distinguished by FISH and, thus, more clearly elucidate the specific advances they have enabled, iii) ground their work in more rigorous quantitation, and iii) provide a better description of the technologies (strengths as well as limitations) carried over from their previous studies so that readers can better evaluate the current study.

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Referee #1

Evidence, reproducibility and clarity

The authors designed an elegant series of FISH probes taking advantage of insertions that are divergent between HI and N2 strains of C. elegans to identify the maternal versus paternal chromosomes in hybrid embryos. Overall, the conclusions are well supported by the data. I only have minor comments, which are numbered below in relation to each figure.

In figure 1 they demonstrate that the probes can specifically recognize their corresponding chromosome in hybrid embryos

In figure 2 they demonstrate that overall chromosome 5 adopts a similar shape from both strains.

In figure 3, they demonstrate that in the hybrids, the chromosomes are generally the same as in the homozygous embryos. However, when the HI chromosome is brought in paternally in the hybrids, it is more decompacted. In this cross, the maternal N2 chromosome is normal. In figure 5, when the N2 chromosome is now brought in paternally, it is similarly decompacted. However, in this cross, the HI chromosome that is brought in maternally is also decompacted. This appears to be the biggest difference, but is somewhat mitigated by a decrease in the scaling component, which I believe means it takes a straighter path? This is in contrast to the reciprocal cross where the maternal N2 chromosome is normal.

1. In figures 2-4, it is important to note what stages of embryos are being analyzed and whether any analysis was done to determine if the chromosomes varied with embryonic stage?
2. The authors need to clearly define chromosomal step size, scaling coefficient and pair wise distance and then describe the difference between these measurements. For example, it would be nice in relation to figure 4F if it was described exactly what it means to have an increase in step size along with a decrease in scaling exponent. This will enable the reader to more easily interpret the results.
3. Is there any way to determine if changes in the step size and scaling are significant? It would be good to know if the changes are actually significant.

In figure 5, the authors examine sub-clusters of where individual chromosomes locate. 4. In figure 5 (and maybe in earlier figures as well), it would also be helpful to mark the different clusters in the figures to show what is meant by a folder arm or a compacted central domain and refer to every panel in the text (only some descriptions in the text reference specific panels).

In figure 6, the authors determine whether the two chromosomes 5's overlap in nuclear territory and whether they the align along the length of the chromosome. From this analysis, they conclude that the chromosomes overlap a fair amount of time, but do not align. This makes it unlikely that transvection might occur. 5. In figure 6, the authors need to do a better job of describing how the data is being presented. The % overlap is being graphed by density, but density of what? Also, the text mentions the total number of nuclei that overlap, but how is that number derived from the presented data? Finally, the data are broken down by embryonic stage, but there is no mention of this in the text. It is not mentioned until the discussion. Overall, this makes it very difficult to determine what the data are showing. 6. In the discussion, the authors perhaps should spend more time interpreting their results in light of others work on the maternal and paternal inheritance of chromatin in C. elegans. For example, Arico et al 2011 Plos Genetics from the Kelly Lab. In addition to examining chromatin in the early embryo, in this paper the authors examine translocations, which might be interesting to look at using the technique presented here. Also, a number of recent papers from the Strome lab have examined chromatin inheritance from sperm. It would be interesting to interpret the finding that paternal chromosomes are influenced by the maternal environment, in light of this work.

Significance

These studies are the important extension of the elegant chromosome tracing that the Mango lab has pioneered. The authors have clearly demonstrated that the technique works well to identify individual chromosomes in a hybrid background. This provides the opportunity for the system to be used in numerous different ways, not just in C. elegans. This is the most significant advance of this paper and should be of interest to a fairly broad audience. However, using this technique, the authors also provide the initial characterization of sister chromosomes in C. elegans embryos and draw important initial conclusions, such as finding that the chromosomes do not pair, as they do in Drosophila. This makes the paper of interest to the C. elegans audience, as well as the general field of chromosomal organization. My expertise is in C. elegans biology and chromatin biology in general. I also am familiar with the field of chromosome biology. As a result, I believe I am capable of judging the significance of this paper in these areas.

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Reply to the reviewers

Point-by-point response to reviewers, including our plans for the revision:

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

* Summary: In this manuscript by the Sanson group, Lye and colleagues try to definitively answer the question of whether pulling forces from the ventral mesoderm have significant effects on convergent extension in the Drosophila germband (germband extension). While germband extension does occur in mutant embryos lacking mesoderm invagination, it has long been an open question in the field as to whether ventral pulling forces from the mesoderm have significant effects (positive or negative) on cell intercalation during germband extension. To definitely address this question, Lye and colleagues generated high-quality, directly comparable datasets from wild-type and twist mutant embryos, and then systematically assessed nearly all aspects of cell intercalation, myosin recruitment, and tissue elongation over time. They demonstrate that pulling forces from the ventral mesoderm have negligible impacts on the course of germband extension. While there are indeed some interesting differences between wild-type and twist embryos with respect to cell intercalation and myosin recruitment, such differences are relatively minor. They conclude that the events of germband extension neither require nor are strongly affected by external forces from the mesoderm. While this is largely a negative results paper, I believe that it should be published and that it will be an impactful paper within the field. Namely, it will settle once and for all the question of whether mesoderm invagination is required for optimal germband extension in the early Drosophila embryo, and it suggests that tissues are largely autonomous developmental units that are buffered from outside mechanical inputs.*

• * *Major comments: *

* It seems to me that the one obvious omission from this paper is a general measure of convergent extension over time. I think it would be useful to the reader to include some measure of change in tissue aspect ratio over time between wild-type and twist embryos. This could be included in Figure 5 or 6. *

• *

We are happy to include a graph with what we call “tissue strain rate”, which measures the deformation of the germ-band in the direction of extension (along AP) over time, and propose to add it as a panel in Supplementary Figure 6. Note that in our measures, the “tissue” strain rate is decomposed into contributions from two cell behaviors, the “cell intercalation” strain rate and the “cell shape” strain rate (Blanchard et al., 2009). “Tissue” and “cell shape” strain rate are directly measured, and “cell intercalation” strain rate is what remains when “cell shape” strain rate is removed from “tissue” strain rate. The “cell intercalation” strain rate calculated in that way is a “continuous” measure of cell intercalation, measuring the progressive shearing of cells during convergent extension. We also use a “discrete” measure of cell intercalation, which measures the number of cell neighbor exchanges, also called T1 swaps. We found that both “continuous” and “discrete” measures of cell intercalation are unchanged in twist mutant compared to wild-type embryos (Fig. 6F and 6E, respectively). In contrast, we find that the “cell shape” strain rate is increased in twist mutants (Fig. 5B and Fig. 5S1A). Consistent with this finding, the “tissue” strain rate is also increased in twist mutants (see graph below).

Otherwise, I have no major comments on the experimental approach or the findings of this manuscript. It seems to me a straightforward and systematic approach for determining whether mesoderm invagination affects germband extension. I do have several minor comments that should be addressed prior to publication (below).

*Minor comments: *

*I understand why cells would initially stretch more along the DV axis in wild-type embryos compared with twist embryos, but why do cells become so much more stretched along the AP axis (and become smaller apically) after 10 minutes of GBE in wild type compared with twist (Figure 2C and E). *

*I think this is an interesting and non-intuitive result that would warrant a bit of explanation/conjecture. *

This is not what Fig. 2C and E show, and we realize now that our schematics on the graphs might have been confusing. We will work on those to improve their clarity (or remove them), and also review our text.

Figure 2C shows how cells deform along DV (cell shape strain rate projected onto the DV axis). So the graph does not show that the cells are elongating in AP, as only the DV component of the strain rate is shown in this figure. In the wild type, the DV strain rate is positive (the cells are elongating in DV) at developmental times when the mesoderm invaginate (from about -10 minutes to until 7.5 minutes). The DV strain shows an acceleration until about 5 mins, then decelerates, crossing the x-axis to become negative at 7.5 minutes. From this timepoint and until the end of GBE, the DV strain rate is negative (the cells are contracting along DV). Mirroring the positive section of the curve, the DV contraction of the cells accelerate until about 12 mins and then slows down. The strong rate of DV contraction between 7.5 and 20 mins could in part be due to the endoderm invagination pulling in the orthogonal direction (AP) and helping the cells regaining a more isotropic shape. We could add a mention about this in the discussion.

In Figure 2E, the rate of change in cell area follows a similar time course in the wild type, showing that the cells are increasing their areas until about 10 mins (positive values) and then reduce their areas again until the end of GBE (negative values). Note that the graph does not show raw (instantaneous) cell areas as suggested by the comment, but rather a rate of change.

So in wild type, the cells get stretched by the invaginating mesoderm, and once the mesoderm is not pulling anymore, the cells appear to relax back. As there is no stretching in twist mutants, there is no equivalent relaxation of the cells along DV. Note that in twist, there is a milder increase in cell area in the first 15 mins of GBE (Fig. 2E). This could again be caused by the pull from endoderm invagination stretching the cells along AP, which, as we have shown before, increases both cell shape strain rates along AP and cell areas (Butler et al., 2009). So the pull from endoderm invagination (along AP) will have an impact on cell area rates of change and possibly also, indirectly, on DV cell shape strain rates, in both twist and wild type embryos, during most of GBE. Therefore cell area and DV cell shape strain rates are affected by more than one process during GBE. In this paper, we are focusing on the impact of mesoderm invagination, which happens around the start of GBE, so have focused our analysis of the graphs in the results section to this period, and the differences between wildtype and *twist. *

*I don't understand how you are defining cell orientation in Figure 2G. How are you choosing the cell axis that you are then comparing with the body axis? Is it the long axis, or something more complicated than that? I think you should briefly provide this information in the results section. If it is included in the methods, I wasn't able to locate it. *

Yes, it is the orientation of the long axis of the cell relative to the antero-posterior embryonic axis. We will clarify this in the text, in particular in the Methods, and also try improve our schematics.

Figure 2: Since you have the space, it might help the reader if you simply wrote out "strain rate" for panels B, D, and F, rather that used the abbreviation "SR." Thank you for this suggestion, we will reduce use of abbreviations where space permits.

*Please ensure that all axis labels are fully visible in the final figures. In several figures, the Y-axis labels were cut off (e.g., Fig 2I, 4A, 4D, 6B, 6C). *

These were visible to us in our submitted version, but of course we will ensure everything is visible on the final version.

*Where space permits, I would suggest using fewer abbreviations in axis labels to increase readability of the figures (e.g., in Figures 3H or 4D). *

Thank you for this suggestion, will do.

* In Figure 7, I would move the wild-type panels to the left and the twist panels to the right. I think it is more conventional to describe the normal wild-type scenarios first, and then contrast the mutant state.*

Will do.

To be consistent with the literature, "wildtype" should be hyphenated (wild-type) when used as an adjective, or two separate words (wild type) when used as a noun. Thank you, we will change this.

Review*er #1 (Significance (Required)): *

* Advance: The advances in this manuscript are largely methodological, but the experiments and analyses are quite rigorous and allow the authors to make strong conclusions concerning their hypotheses. Their findings are based on a high-quality collection of movies from control and twist mutant embryos expressing a cell membrane marker and knock-in GFP-tagged myosin. Importantly, I think the researchers were correct in choosing to analyze twist single-mutant embryos (as opposed to snail or twist, snail double-mutant embryos), as the overall embryo geometry of these mutants is fairly similar to wild-type embryos, allowing the researchers to directly compare cell behaviors and myosin dynamics during germband extension. This approach also allows them to avoid indirect effects on the germband due to a completely non-internalized mesoderm. *

*

Audience: The primary audience for this article will be basic science researchers working in the early Drosophila embryo who are interested in the interplay between the germband and neighboring tissues. Secondary audiences will include developmental biologists more broadly who are interested in biomechanical coupling (or in this case decoupling) of neighboring tissues. *

*

Describe your expertise: I have been a Drosophila developmental geneticist for over twenty years, and I have been working directly on Drosophila germband extension for over a decade. I have published numerous papers and reviews in this field, and I am very familiar with the genetic backgrounds and types of experimental analyses used in this manuscript. Therefore, I believe I am highly qualified to serve as a reviewer for this manuscript.*

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Review____er #2 (Evidence, reproducibility and clarity (Required)):

*

In the present manuscript, Lye et al. describe a highly detailed quantification of cell shape changes during germband extension in Drosophila melanogaster early embryo. During this process, ectodermal tissue contracts along the dorso-ventral axis, simultaneously expanding along the perpendicular antero-posterior direction, migrating from the ventral to the dorsal surface of the embryo as it extends. This important morphogenetic event is preceded by ventral furrow formation when mesodermal tissue (located in the ventral part of the embryo) contracts along the dorso-ventral axis and invaginates into the embryonic interior. The study compares cell shape dynamics in the wildtype Drosophila with that in the twist mutant, which largely lacks mesoderm and does not form ventral furrow. The major motivation of the study is to examine whether cellular behaviors and myosin recruitment in the ectoderm is cell autonomous, or if those cellular behaviors depend on mechanical interactions between mesoderm and ectoderm.*

• The authors first examine whether transcriptional patterning of key genes involved in germband extension is different between the wildtype and the twist mutant and find no significant difference. Next, the authors thoroughly quantify cellular behaviors and patterns of myosin recruitment in the two genetic backgrounds. A number of different measures are investigated, notably the rate of change in the degree of cellular asymmetry, rate of cell area change, rate of change of cell orientation, differences in myosin recruitment to cell edges of various orientation, as well as the rates of growth, shrinkage, and re-orientation of the various cellular interfaces. It is thoroughly documented how these quantities change as a function of developmental timing and spatial position within the embryo. These data serve basis for quantitative comparison between cellular dynamics in the two genetic backgrounds considered.*

• Overall, the study shows that cellular behaviors observed in the ectoderm are largely the same during the period of time following ventral furrow formation, as would be expected if those cellular behaviors were predominantly cell autonomous and not dependent on stresses generated in the mesoderm.*

• The data presented in the manuscript are of excellent quality and presentation is very clear.

Minor comments: none *

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

* I find that the study provides a thorough quantification of cell behaviors in a widely studied important model of morphogenesis. The work may be of particular interest for future model-to-data comparison, perhaps providing a basis for future modeling work. I therefore certainly think that this work warrants publication.*

• However, the results of the study largely parallel previous findings and do not appear novel or surprising. It is well established that in snail mutant that lack mesoderm entirely, germband extension proceeds largely normally. This well-established fact suggests that since tissue dynamics in complete absence of mesoderm are largely unaffected, behaviors of individual cells are likely to not be affected either*.

*The work is pretty much entirely observational, and for most part provides a more detailed documentation/quantification of previous findings. I do not think it is appropriate for high profile publication. *

We are not sure which evidence the reviewer is referring to here specifically. We agree that the single mutants twist or snail, or the double twist snail mutants do extend their germ-band. However, the question we are asking here, is how well do they extend their germband and to answer this question, quantitation is needed. The first quantitation of GBE were performed by (Irvine and Wieschaus, 1994). While they quantified GBE in various mutant contexts, they did not perform quantitation for snail, twist, or twist snail mutants. Instead, they refer to these mutants once in p839, with the following sentence: ”Additionally, twist and snail mutant embryos, which lack mesoderm, extend their germbands almost normally (Leptin and Grunewald, 1990; Simpson, 1983)*.” *

Following these earlier qualitative observations, various studies have quantified different aspects of GBE in mesoderm invagination mutants, with contradictory results. For example, some studies, including from our own lab, report a reduction in cell intercalation in the absence of mesoderm invagination (Butler et al., 2009; Wang et al., 2020), but there have also been reports that tissue extension and T1-transistions occur normally (Farrell et al., 2017)(see also introduction of our manuscript). These contradictory results have motivated our present study, and we have implemented rigorous comparison between wild type and mesoderm invagination mutants, being careful i) to check that the regions analyzed were comparable in terms of cell fate, and ii) to control for any confounding effects between experiments (see also response to reviewer 4, main question 2). We have also considered which mesoderm invagination mutants to use. We rejected snail or twist snail mutants because the absence of snail means that the mesodermal cells do not contract and thus stay at the surface of the embryo, which changes the spatial configuration of the embryo considerably and would make a fair quantitative comparison very difficult. Instead, we decided to use twist mutants, as in those, cell contractions still happen so the cells do not take as much space at the surface of the embryo, but the contractions are uncoordinated which means that there is no invagination (and we demonstrate here, no significant pulling on the ectoderm). We note that reviewer 1 highlights the merit of settling the question of the impact of mesoderm invagination on GBE and the pertinence of choosing twist mutants versus the alternatives (see also response to reviewer 4, suggestion 1).

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__Review____er #3 (Evidence, reproducibility and clarity (Required)): __

During morphogenesis, the final shape of the tissue is not only dictated by mechanical forces generated within the tissue but can also be impacted by mechanical contributions from surrounding tissues. The way and extent to which tissue deformation is influenced by tissue-extrinsic forces are not well understood. In this work, Lye et al. investigated the potential influence of Drosophila mesoderm invagination on germband extension (GBE), an epithelial convergent extension process occurring during gastrulation. Drosophila GBE is genetically controlled by the AP patterning system, which determines planar polarized enrichment of non-muscle myosin II along the DV-oriented adherens junctions. Myosin contractions drive shrinking of DV-oriented junctions into 4-way vertices, followed by formation of new, AP-oriented junctions. This process results in cell intercalation, which causes tissue convergence along the DV-axis and extension along the AP-axis. In addition, GBE is facilitated by tissue-extrinsic pulling forces produced by invagination of the posterior endoderm. Interestingly, some recent studies suggest that the invagination of the mesoderm, which occurs immediately prior to GBE, also facilitates GBE. In the proposed mechanism, invaginating mesoderm pulls on the germband tissue along the DV-axis; the resulting strain of the germband cells generates a mechanotransduction effect that promotes myosin II recruitment to the DV-oriented junctions, thereby facilitating cell intercalation. Here, the authors revisited this proposed mechanotransduction effect using quantitative live imaging approaches. By comparing the wildtype embryos with twist mutants that fail to undergo mesoderm invagination, the authors show that although the DV-oriented strain of the germband cells was greatly reduced in the absence of mesoderm pulling, this defect had a negligible impact on junctional myosin density, myosin planar polarity, the rate of junction shrinkage or the rate of cell intercalation during GBE. A mild increase in the rate of new junction extension and a slight defect in cell orientation were observed in twist mutants, but these differences did not cause obvious defects in cell intercalation. The authors conclude that myosin II-mediated cell intercalation during GBE is robust to the extrinsic mechanical forces generated by mesoderm pulling.

• * *Overall, I found that the results described here are very interesting and of high quality. The data acquisition and analyses were elegantly performed, statistics were appropriately used, and the manuscript was clearly written. However, there are a few points where some further explanation or clarification is necessary, as detailed below: *

• The main conclusion of the manuscript relies on appropriate quantification of myosin intensity at cell junctions. It is therefore important that the methods of quantification are well justified. Below are a few questions regarding the methods used in the analyses:*

• -For myosin quantification, the authors state that "Background signal was subtracted by setting pixels of intensities up to 5 percentile set to zero for each timepoint" [Line826]. The rationale for selecting 5 percentile as the threshold for background should be explained. Also, how does this background value change over time? *

• *

For our normalization method, we stretched the intensity histogram of images to use the full dynamic range for quantification and enable meaningful comparison of intensities between different movies. The 5th percentile was chosen to set to zero intensity as this removed background signal without removing any structured Myosin signal (i.e., non-uniform, low level fluorescence - this was assessed by eye). We will provide some before and after normalization images at different timepoints to illustrate this (See reviewer 3, minor point 4 below). Since the cytoplasmic signal is uniform, it is difficult to discern from true ‘background’, therefore some cytoplasmic signal might be set to zero with this method, but all medial and junctional Myosin structures will still be visible and have none-zero intensity values. However, since cytoplasm takes up a large majority of pixels in the image, and we only set 5% of pixels to zero, the majority of the cytoplasm will have non-zero pixel values. ‘Background’ changes increases slightly as Myosin II levels increase in general over time, as expected from the embryo accumulating Myosin II as they develop.

-The authors mention that "Intensities varied slightly between experiments due to differences in laser intensity and therefore histograms of pixel intensities were stretched" [Line828]. The method of intensity justification should be justified. For example, does this normalization result in similar cytoplasmic myosin intensity between control and twist mutant embryos?

• *

As stated above, we stretched the intensity histogram of images to enable meaningful comparison of intensities between different movies, as stretching the histograms would bring Myosin II structures of similar intensities into the same pixel value range. We chose to stretch histograms using a reference timepoint (30 minutes, the latest timepoint analyzed), rather than on a per timepoint basis, because we saw a general increase in Myosin II over time, and we wanted to ensure that this increase was preserved in our analysis.

• *

Note that we quantify Myosin from 2 µm above to 2 µm below the level of the adherens junctions (see Methods), not throughout the entire cell, and therefore we have no true measure of cytoplasmic Myosin. However, we can plot non-membrane Myosin from this same apicobasal position in the cell. Non-membrane Myosin will include both the cytoplasmic signal and the Myosin II medial web (see above). When plotting these, we find that Myosin II intensities in this pool are similar in wildtype and twist (see graph below, dotted lines show standard deviations), confirming that that we are not inappropriately brightening one set of images compared to the other (e.g., twist versus wildtype).

Finally, our observations of rate of junction shrinkage and intercalation are consistent with our Myosin II quantification results (see Figures 4A, 4D and 6F). This further validates our methods.

• *

• *

- A previous study demonstrates that the accumulation of junctional myosin is substantially reduced in twist mutant embryos compared to the wild type (Gustafson et al., 2022). In that work, junctional myosin was quantified as (I_junction - I_cytoplasm)/I_cytoplasm. In contrast, the cytoplasmic myosin intensity does not appear to be subtracted from the quantification in this study. How much of the difference in the conclusions of the two studies can be explained by this difference in myosin quantification?

        As explained above, we choose to normalize our data by stretching histograms, rather than subtracting and dividing intensities between different pools of Myosin. The setting pixels of intensities up to 5 percentiles set to zero for each will have a similar effect to subtracting a small fraction of the cytoplasmic pool. We note that the intensity measurements in (Gustafson et al., 2022) are in the apical-top 5µm of the cell, and therefore their ‘cytoplasmic’ signal is likely to also include the apical medial web of Myosin. Also, after subtraction they use division by the cytoplasmic intensity in an attempt to bring pixel intensities between different movies into a comparable range, whereas we do this by stretching the histograms themselves (see above).  We carefully designed our method to preserve the increase in Myosin levels that we see over time in our post-normalization data. This is something that their method of normalization would not be predicted to capture, if their ‘cytoplasmic’ signal increase over time as well as their junctional signal.  Indeed, in FigS6D of their paper, Myosin II levels do not appear to increase over time in these (presumably normalized) images.

Additionally, we note that in (Gustafson et al., 2022), not all Myosin II is fluorescently tagged since they use a sqhGFP transgene located on the balancer chromosome. This means that the line they use will have a pool of exogeneous Myosin tagged with GFP (expressed from the CyO balancer) and a pool of endogenous Myosin (expressed from the sqh gene on the X chromosome. It is not known whether endogenous and exogeneous GFP-tagged Myosin II will be recruited equally to cell junctions when in competition with each other. Therefore, in their genetic background, the ratio of junctional/cytoplasmic sqhGFP might not reflect the true ratio. To avoid this potential caveat, in our study we have used a new knock-in of Myosin, which tags the sqh gene at the endogenous locus (Proag et al., 2019). The line is homozygous viable and thus all the molecules of Myosin II Regulatory Light Chain (encoded by sqh), and thus the Myosin II mini-filaments, are labelled with GFP.

Additionally, we note that when comparing their images of Myosin II in wildtype and twist (Figure 5D and D’), the overall Myosin signal appears reduced in twist mutants (including in the head and posterior midgut, which is outside the area that they are claiming Myosin II is recruited in response to mesoderm invagination). This suggests that Myosin II is generally reduced in their twist mutants (or images thereof), which is not expected and might indicate issues with their methods.

Therefore differences in the methods may explain the discrepancies between studies. Importantly, we have quantified junctional shrinkage rates and intercalation, and our analysis of these rates is consistent with our Myosin II quantification results (see above).

-The authors used the tissue flow data to register the myosin channel and the membrane channel, which were acquired at slightly different times. The accuracy of this channel registration should be demonstrated.

As stated in our methods: “the channel registration was corrected post-acquisition in order that information on the position of interfaces in the Gap43 channel could be used to locate them in the Myosin channel. Therefore the local flow of cell centroids between successive pairs of time frames in the Gap43 channel is used to give each interface/vertex pixel a predicted flow between frames. A fraction of this flow is applied, equal to the Myosin II to Gap43 channel time offset, divided by the frame interval. Because cells deform as well as flow, the focal cell’s cell shape strain rate is also applied, in the same fractional manner as above.”

The images in Figure 3C and C’ show the Myosin II, with quantified membrane Myosin superimposed on the image as a color-code. Images in Figure 3B and B’ show the (normalized) Myosin II. Comparison of these images demonstrates that the channel registration is accurate. We will add a reference to these images in the methods.

• The authors show that cell intercalation is not influenced in twist mutant embryos. However, a previous study demonstrates that the speed of GBE is substantially reduced in twist mutants (Gustafson et al., 2022). It would be interesting to see whether a similar reduction in the speed of GBE was observed in this study. *

We do not see a reduction in the speed of GBE as reported by (Gustafson et al., 2022), we will add “tissue strain rate” graphs to demonstrate this. On the contrary, we find a slight increase in the “tissue strain rate”, because there is a slight increase in the “cell shape strain rate” contributing to extension (while “cell intercalation strain rate” is unchanged). See also response to Reviewer 1 (major comment) .

• It has been previously shown that contractions of medioapical myosin in germband cells also contribute to cell intercalation. The authors should explain why medioapical myosin was not included in the comparison between wildtype and twist mutant embryos. *

• *

Indeed, it has been shown that there is a flow of medial Myosin towards the junctions (Rauzi et al., 2010). However, and as described in that paper, this flow ‘feeds’ the enrichment of Myosin II at shrinking junctions, and thus the junctional Myosin II can be taken as a readout of polarized Myosin II behavior. Additionally, medial flows are more technically challenging to quantify, especially when quantification is required in a large number of cells as is the case for our study.

Importantly, our junctional Myosin II and junctional shrinkage rate results are consistent with each other, therefore it is very unlikely that analyzing medial Myosin II would lead us to form a different conclusion. We will add a sentence to explain why we chose to quantify junctional, and not medial, Myosin II.

*Minor points: *

1. * Fig. 1-S1 panel C: the number of cyan cells changes non-monotonically. It first decreases from -10 min to 10 min, then increases from 10 min to 20 min. This is confusing since in theory the number of tracked cells should not increase over time if the cells are tracked from the beginning of the movie. *
2. *

The cyan cells highlight tracked mesodermal and mesectodermal cells, which are not included in the analysis. The low number of mesodermal cells highlighted at 10mins germband extension is because mesodermal and mesectodermal cells are not always tracked successfully at this time. Note that the legend includes a note that ‘”Unmarked cells are poorly tracked and excluded from the analysis”. Also see Methods: “Note on number of cells in movies, for notes on changes to the number of tracked ectodermal cells throughout the timecourse of the movies.”

• Fig. 1-S2: the vnd band in panel A appears to be much narrower than in panel B. *

• *

These are fixed embryos, therefore this could be (at least partially) due to slight differences in exact developmental age of the embryo. Note that we wanted to check that vnd and ind are expressed in the correct places in the ectoderm. We were motivated to check this because the width of mesoderm is reduced in twist, so we thought it was important to verify that there is not a population of ‘ectodermal’ cells with a strange fate (i.e., negative for both vnd and ind). Our experiments show that vnd abuts the mesoderm/mesectoderm in twist as in wildtype, and that the cells immediately lateral to the vnd cell population express ind as expected.

It is possible that there is a slight difference in the number of vnd cells in twist mutants compared to wildtype, but we see no differences in Myosin II bipolarity that would coincide with the vnd/ind boundary (Fig3-S1). Therefore, this would not change the interpretation of our results. Counting the number of rows of vnd cells prior to any cell intercalation (the number of rows will reduce as cells intercalate) would be technically challenging as the lateral border of vnd expression is hard to discern at this time due to lower levels of vnd expression laterally within the vnd expression domain.

• The schematic in Fig. 2J suggests that at the onset of mesoderm pulling the germband cells have a uniform angle of rotation (towards bottom right). Is this the case?*

• *

No, this schematic is purely supposed to show that as cells stretch, they also reorient. Note that we will review our schematics in Fig. 2 to increase clarity (see response to reviewer 1, first minor comment).

• The description of myosin intensity normalization in the Methods section is somewhat difficult to follow [Line 829 - 832]. It would be helpful if the authors can show one or two images before and after intensity normalization as examples. *

We will add some examples of before and after normalization images to this section. We will also review the Methods to improve the text’s clarity.

• Line 704: "Z-stacks for each channel were collected sequentially" - the step size in Z-axis should be reported. *

Thank you for this, the step size was 1µm. We will add this information.

• Fig. 4C: what are the thin, black lines in the image? *

This image is a 2D representation of the Gap43Cherry signal at the level of the adherens junctions extracted for tracking, not a simple confocal z-slice. When viewing these representations, you can see lines showing borders between where information from different z-stacks was used for the tracking layer. Unfortunately, our software does not allow us to remove these lines, but they do not affect tracking, quantification etc.

Reviewer #3 (Significance (Required)):

While most previous work on tissue mechanics and morphogenesis focuses on tissue-intrinsic mechanical input, recent studies have started to emphasize the contribution of tissue-extrinsic forces. An important challenge in understanding the function of tissue-extrinsic forces lies in the difficulties in properly comparing the wild type and the mutant samples that disrupt extrinsic forces, in particular when cell fate specification is altered in the mutants. In this work, the authors addressed this challenge by employing a number of approaches to warrant a parallel comparison between genotypes, including examining the AP- and DV-patterning of the tissue, selecting sample regions with comparable cell fate for analysis, and carefully aligning the stage of the movies. With these approaches, the authors provide compelling evidence to support their main conclusions. By teasing apart the role of the intrinsic genetic program and the extrinsic tissue forces, the work provides important clarifications on the function of mesoderm pulling in GBE and adds new insights into this well-studied tissue morphogenetic process. This work should be of interest to the broad audience of epithelial morphogenesis, tissue mechanics and myosin mechanobiology.

• *

Review____er #4 (Evidence, reproducibility and clarity (Required)):

*Lye and colleagues investigate the impact of tissue-tissue interactions on morphogenesis. Specifically, they ask how disrupting mesoderm internalization affects convergence and extension of the ectoderm (germband) in Drosophila embryos. Using twi mutants in which mesoderm invagination fails, the authors find that the invagination of the mesoderm deforms germband cells, but does not significantly contribute to patterning, cell alignment, myosin polarization and cell-cell contact disassembly (which drive germband convergence). The authors find modest effects of mesoderm invagination on new junction formation and orientation (which drive extension), but these changes do not have a significant effect on germband elongation. The authors conclude that germband extension is robust to external forces from the invagination of the mesoderm. *

*MAIN 1. The authors clearly show that myosin density is not different in wild-type and twi mutant embryos, and subsequently argue that the pulling force from the mesoderm does not elicit a mechanosensitive response in early germband extension. But if the cell density is constant, doesn't that mean that the longer, DV-oriented interfaces in the wild type accumulate more total myosin than their shorter counterparts in twi mutants? Assuming that the total number of myosin molecules per cell is not greater in the wild type, wouldn't increased total myosin at the membrane suggest a response to the increased deformation? Certainly the cells are able to maintain the same cell density despite the pulling force from the mesoderm, so can the authors rule out a mechanosensing mechanism? *

• *

We do not rule out a mechanosensing mechanism. We agree the total Myosin at stretched interfaces is higher than at unstretched interfaces and proposed a homeostatic mechanism to maintain Myosin II density on the cortex upon rapid stretching (summarized in Fig. 7). Indeed it is possible that this mechanism could itself be due to mechanosensitive recruitment of Myosin II (though there are also other possibilities). We have tried to address this in our discussion (under “Mechanisms regulating Myosin II density at the cortex and consequences for cell intercalation” and “Restoration of DV cell length after being stretched by mesoderm invagination”), but we will amend the wording the make the possibility of mechanosensitive recruitment of Myosin II to maintain cortical density more explicit.

*What happens to the Gap43mCherry signal? From Figure 2A, it seem to be diluted ventrally in the wild type as compared to twi mutants? Comparing myosin and Gap43 dynamics may shed light on whether myosin accumulates more or less than one would expect simply on the basis of having longer contacts. *

We quantify the density of Myosin, rather than the total amount. Therefore, the length of the contact should not matter. The suggestion of comparing Myosin density to Gap43Cherry density is in principle a good one, as it would allow us to compare a protein which is not diluted as cell contact length increases (Myosin) to one which appears to be (Gap43). However, it is not essential for the conclusions that we make. However, in practice quantifying the Gap43Cherry signal would not be straightforward on our existing movies due to the imaging parameters used. We capture the Gap43Cherry channel (but not the Myosin channel) with a ‘spot noise reducer’ tuned on in the camera software, due to very occasional bright spot noise, which confuses the tracking software. Therefore, our Gap43Cherry signal is manipulated during acquisition and to quantify from these images would not be appropriate. Therefore, we would have to acquire, track and quantify some new movies, which is not possible within the timeframe of a revision.

In summary, we think that we have sufficient evidence from our analysis that Myosin II is not diluted upon junctional stretching without comparing to quantification of Gap43Cherry, and the time investment required to quantify the Gap43Cherry would not be worthwhile as it would require more data to be acquired and processed.

• The authors previously argued that mesoderm invagination was required for the fast phase of cell intercalation [Butler et al., 2009]. However, here the authors interpret that loss of twi does not significantly slow down interface contraction, but accelerates the elongation of junctions and cells along the AP axis, which overall would mean that mesoderm invagination is (slightly) detrimental for axis elongation. The discrepancy between their previous and current results should be discussed. *

We are happy to add more information about these discrepancies in the discussion. In a nutshell, we think that these discrepancies arise from the challenges of comparing wildtype and twist mutant embryos relative to each other, and as a consequence we have made various improvements to our methods since (Butler et al., 2009). These improvements included using markers that would be expressed at the same levels in wildtype and twist embryos. Additionally, we did not use overexpressed cadherin-FPs (namely, the ubi-CadGFP transgene), which may have confounding effects, and we used a knock-in sqhGFP to ensure we could all Myosin II molecules were labelled by GFP. We also carefully controlled the temperature at which we acquired the movies, standardized the level at which to track cells and quantify Myosin between movies, as well as improving the accuracy of our image segmentation and cell type identification since our previous study (Butler et al., 2009). See also response to reviewer 2.

• Related to the previous point, it is surprising that the differences shown in Figure 4A-B are not significant. This is particularly troubling when in Figure 5B the authors claim a significant difference in cell elongation rate, which is higher in twi mutants (but only in very short time intervals and actually switches sign at the end of germband extension). These are just two examples, but I think the analysis of significance on a per-time point basis is problematic. *

*Have the authors considered analyzing their results as time series rather than comparing individual time points? Or perhaps integrating the different metrics over the duration of germband extension (e.g. using areas under the curve)? That way they would not have to arbitrarily decide if significant differences in a few time points should or not be interpreted as significant overall differences. *

• *

For graphs plotted against time of germband extension, we do not think it is appropriate to analyze as a time series rather than comparing individual time points, since different developmental events (such as mesoderm invagination) occur at different times. For graphs plotted against time to/from cell neighbor swap, these can also change over time (e.g., ctrd-ctrd orientation, Fig6D). Therefore we do not feel that it appropriate to run statistical analyses as a timeseries for these comparisons either. Statistically cut-offs are by their nature arbitrary. We have tried to highlight non-significant trends throughout the text (including for Fig4A&B), in addition to stating where we see significant differences to highlight where there may be minor (but not significant) differences.

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• While the number of cells analyzed is impressive, the number of embryos is relatively low, particularly for the wild type (only four embryos analyzed). If I understood correctly (if not, please clarify) the authors ran their statistics using cells and not embryos as their measurement unit. But I could not find any evidence that cells from the same embryo can be considered as independent measurements. This could be easily done by demonstrating that the variance of any of the measurements (e.g. elongation, area change rate, etc.) for cells in an embryo is comparable to that calculated when mixing cells from different embryos. *

• *

We do not simply use the number of cells as an n for our experiments. We use a mixed effects model for our statistics as previously (Butler et al., 2009; Finegan et al., 2019; Lye et al., 2015; Sharrock et al., 2022; Tetley et al., 2016). This estimates the P value associated with a fixed effect of differences between genotypes, allowing for random effects contributed by differences between embryos within a given genotype. We will make sure that this is clear in the Methods.

MINOR 1. Figure 4D: the authors show no difference in the proportion of neighbor swaps per minute between wild-type and twi- mutant embryos. But how about the absolute number of neighbour swaps per minute? Does that change in twi mutants (and if so, why?).

The number of interfaces involved in a T1 swap are expressed as a proportion of the total number of DV-oriented interfaces for all tracked ectodermal germband cells, to take account of differences in the number of tracked cells between different timepoints and different movies. Presenting the absolute number of swaps per minute could lead to misleading interpretations.

• I was a bit confused about the reason why in Figure 4A the authors measure the rate of interface contraction in units of “proportion/min”, but in Figure 5A they measure interface elongation in units of “um/min”. Unless there is a good reason not to, these two metrics should be reported using the same units. Is there a difference in the rate of interface contraction when measured in absolute units (um/min)? *

Thank you, we will amend so that both measures are expressed in the same units.

• The discussion of previous work on cell deformation within the mesoderm (page 16, first paragraph) should probably include recent work from Adam Martin's lab (e.g. [Heer et al., 2017]; or [Denk-Lobnig et al., 2021]). *

Thank you, and apologies for this oversight, we will add these references__.__

SUGGESTIONS 1. While I appreciate the arguments that the authors provide to use twi mutants rather than sna mutants or twi sna double mutants, as the authors indicate, in twi mutants there is still contractility in the mesoderm (albeit not ratcheted). Therefore, it is possible that contractile pulses from the mesoderm in twi mutants could still facilitate cell alignment and polarization of myosin in the germband. Given the previous results from the Zallen lab using twi sna double mutants (see above) this is unlikely to be the case, but the findings in this manuscript would be significantly stronger if they included similar analysis in the double mutants.

We had concerns about using sna or twi sna double mutants due to the large amount of space the un-internalized mesoderm takes up on the exterior of the embryo. This concern is also shared by reviewer 1 “Importantly, I think the researchers were correct in choosing to analyze twist single-mutant embryos (as opposed to snail or twist, snail double-mutant embryos), as the overall embryo geometry of these mutants is fairly similar to wild-type embryos, allowing the researchers to directly compare cell behaviors and myosin dynamics during germband extension. This approach also allows them to avoid indirect effects on the germband due to a completely non-internalized mesoderm.” * In addition to this concern, imaging of snail or twist snail* embryos by confocal imaging to include the ventral midline (which is required to define embryonic axes) is problematic as the un-constricted mesodermal cells occupy virtually all the field of view, leaving very few ectodermal cells to analyze.

Whilst we acknowledge that there are some (un-ratcheted) contractions of mesodermal cells in twist mutants, we have clearly shown that there is no DV stretch and very little reorientation of cells. Therefore, any residual contractile activity in the mesodermal cells of twist mutants does not appear to have a mechanical impact on the ectoderm. We cannot exclude the possibility that there is some transmission of forces between contracting cells of the mesoderm and the ectoderm in twist mutants. However, our evidence suggests that the large tissue scale force that transmits to the ectoderm from the invaginating mesoderm is missing in twist mutants, and it was the effects of that force that we wished to investigate (See also response to reviewer 2).

Review*er #4 (Significance (Required)): *

*This is an interesting study, with careful quantitative analysis of cellular and subcellular dynamics. The results follow previous findings from Jennifer Zallen and the authors themselves. The Zallen lab showed that cell alignment, myosin polarization and germband extension are normal in sna twi mutants [Fernandez-Gonzalez et al., 2009], a result that the authors fail to cite. The results in the present manuscript are similar, but the analysis is much more in depth here, so the findings by Lye and colleagues certainly warrant publication. *

We did not specifically cite this result from (Fernandez-Gonzalez et al., 2009), because the subject of their study is the formation of multicellular rosettes, not whether a pull from mesoderm affects Myosin II polarity and cell intercalation. The formation of multicellular rosettes occurs later in germband extension, and therefore these results are not directly relevant to our study. Additionally, their measures of alignment are defined as linkage to other approximately DV oriented interfaces, rather than directly measuring orientation compared to the embryonic axes as we do here, as a different question is being addressed. Specifically, the quoted sna twi experiment is interpreted as extrinsic forces from the mesoderm not being required for linkage of Myosin enriched DV-oriented interfaces together. Myosin II quantification is more rudimentary with edges being assigned as Myosin positive or Myosin negative, as opposed to quantifying the density of Myosin on each interface and we cannot see any comparison of Myosin II quantification between wildtype and twist embryos.­

So, although the results are consistent with each other, they are not directly comparable due to methods used and we are happy that the reviewer acknowledges that our analysis is more in depth, which was necessary to address the specific questions that we investigate in our study.

        In general, there have been inconsistencies in results between previous studies, leading reviewer one to recognize that *“…it should be published and that it will be an impactful paper within the field. Namely, it will settle once and for all the question of whether mesoderm invagination is required for optimal germband extension in the early Drosophila embryo.”  *The high amount of conflicting information in the literature led us to not exhaustively describe individual findings, but we will ensure the results from the Zallen lab are appropriately cited.

However, there are a number of experimental points that I think need to be addressed to solidify the manuscript, particularly in terms of statistical analysis.

Please see more details above (main points 3 and 4) regarding specific concerns about experimental points and statistics. Additionally, we note that reviewer 3 states “statistics were appropriately used”, and our statistical methods are the same as we have used in previous studies comparing live imaging data (Butler et al., 2009; Finegan et al., 2019; Lye et al., 2015; Sharrock et al., 2022; Tetley et al., 2016).

• *

__REFERENCES

__

Blanchard, G. B., Kabla, A. J., Schultz, N. L., Butler, L. C., Sanson, B., Gorfinkiel, N., Mahadevan, L. and Adams, R. J. (2009). Tissue tectonics: morphogenetic strain rates, cell shape change and intercalation. Nat Methods 6, 458-464.

Butler, L. C., Blanchard, G. B., Kabla, A. J., Lawrence, N. J., Welchman, D. P., Mahadevan, L., Adams, R. J. and Sanson, B. (2009). Cell shape changes indicate a role for extrinsic tensile forces in Drosophila germ-band extension. Nat Cell Biol 11, 859-864.

Farrell, D. L., Weitz, O., Magnasco, M. O. and Zallen, J. A. (2017). SEGGA: a toolset for rapid automated analysis of epithelial cell polarity and dynamics. Development 144, 1725-1734.

Fernandez-Gonzalez, R., Simoes Sde, M., Roper, J. C., Eaton, S. and Zallen, J. A. (2009). Myosin II dynamics are regulated by tension in intercalating cells. Dev Cell 17, 736-743.

Finegan, T. M., Hervieux, N., Nestor-Bergmann, A., Fletcher, A. G., Blanchard, G. B. and Sanson, B. (2019). The tricellular vertex-specific adhesion molecule Sidekick facilitates polarised cell intercalation during Drosophila axis extension. PLoS Biol 17, e3000522.

Gustafson, H. J., Claussen, N., De Renzis, S. and Streichan, S. J. (2022). Patterned mechanical feedback establishes a global myosin gradient. Nat Commun 13, 7050.

Irvine, K. D. and Wieschaus, E. (1994). Cell intercalation during Drosophila germband extension and its regulation by pair-rule segmentation genes. Development 120, 827-841.

Leptin, M. and Grunewald, B. (1990). Cell shape changes during gastrulation in Drosophila. Development 110, 73-84.

Lye, C. M., Blanchard, G. B., Naylor, H. W., Muresan, L., Huisken, J., Adams, R. J. and Sanson, B. (2015). Mechanical Coupling between Endoderm Invagination and Axis Extension in Drosophila. PLoS Biol 13, e1002292.

Proag, A., Monier, B. and Suzanne, M. (2019). Physical and functional cell-matrix uncoupling in a developing tissue under tension. Development 146.

Rauzi, M., Lenne, P. F. and Lecuit, T. (2010). Planar polarized actomyosin contractile flows control epithelial junction remodelling. Nature 468, 1110-1114.

Sharrock, T. E., Evans, J., Blanchard, G. B. and Sanson, B. (2022). Different temporal requirements for tartan and wingless in the formation of contractile interfaces at compartmental boundaries. Development 149.

Simpson, P. (1983). Maternal-Zygotic Gene Interactions during Formation of the Dorsoventral Pattern in Drosophila Embryos. Genetics 105, 615-632.

Tetley, R. J., Blanchard, G. B., Fletcher, A. G., Adams, R. J. and Sanson, B. (2016). Unipolar distributions of junctional Myosin II identify cell stripe boundaries that drive cell intercalation throughout Drosophila axis extension. Elife 5.

Wang, X., Merkel, M., Sutter, L. B., Erdemci-Tandogan, G., Manning, M. L. and Kasza, K. E. (2020). Anisotropy links cell shapes to tissue flow during convergent extension. Proc Natl Acad Sci U S A 117, 13541-13551.

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Referee #4

Evidence, reproducibility and clarity

Lye and colleagues investigate the impact of tissue-tissue interactions on morphogenesis. Specifically, they ask how disrupting mesoderm internalization affects convergence and extension of the ectoderm (germband) in Drosophila embryos. Using twi mutants in which mesoderm invagination fails, the authors find that the invagination of the mesoderm deforms germband cells, but does not significantly contribute to patterning, cell alignment, myosin polarization and cell-cell contact disassembly (which drive germband convergence). The authors find modest effects of mesoderm invagination on new junction formation and orientation (which drive extension), but these changes do not have a significant effect on germband elongation. The authors conclude that germband extension is robust to external forces from the invagination of the mesoderm.

Main

1. The authors clearly show that myosin density is not different in wild-type and twi mutant embryos, and subsequently argue that the pulling force from the mesoderm does not elicit a mechanosensitive response in early germband extension. But if the cell density is constant, doesn't that mean that the longer, DV-oriented interfaces in the wild type accumulate more total myosin than their shorter counterparts in twi mutants? Assuming that the total number of myosin molecules per cell is not greater in the wild type, wouldn't increased total myosin at the membrane suggest a response to the increased deformation? Certainly the cells are able to maintain the same cell density despite the pulling force from the mesoderm, so can the authors rule out a mechanosensing mechanism? What happens to the Gap43mCherry signal? From Figure 2A, it seem to be diluted ventrally in the wild type as compared to twi mutants? Comparing myosin and Gap43 dynamics may shed light on whether myosin accumulates more or less than one would expect simply on the basis of having longer contacts.
2. The authors previously argued that mesoderm invagination was required for the fast phase of cell intercalation [Butler et al., 2009]. However, here the authors interpret that loss of twi does not significantly slow down interface contraction, but accelerates the elongation of junctions and cells along the AP axis, which overall would mean that mesoderm invagination is (slightly) detrimental for axis elongation. The discrepancy between their previous and current results should be discussed.
3. Related to the previous point, it is surprising that the differences shown in Figure 4A-B are not significant. This is particularly troubling when in Figure 5B the authors claim a significant difference in cell elongation rate, which is higher in twi mutants (but only in very short time intervals and actually switches sign at the end of germband extension). These are just two examples, but I think the analysis of significance on a per-time point basis is problematic. Have the authors considered analyzing their results as time series rather than comparing individual time points? Or perhaps integrating the different metrics over the duration of germband extension (e.g. using areas under the curve)? That way they would not have to arbitrarily decide if significant differences in a few time points should or not be interpreted as significant overall differences.
4. While the number of cells analyzed is impressive, the number of embryos is relatively low, particularly for the wild type (only four embryos analyzed). If I understood correctly (if not, please clarify) the authors ran their statistics using cells and not embryos as their measurement unit. But I could not find any evidence that cells from the same embryo can be considered as independent measurements. This could be easily done by demonstrating that the variance of any of the measurements (e.g. elongation, area change rate, etc.) for cells in an embryo is comparable to that calculated when mixing cells from different embryos.

Minor

1. Figure 4D: the authors show no difference in the proportion of neighbor swaps per minute between wild-type and twi-mutant embryos. But how about the absolute number of neighbour swaps per minute? Does that change in twi mutants (and if so, why?).
2. I was a bit confused about the reason why in Figure 4A the authors measure the rate of interface contraction in units of "proportion/min", but in Figure 5A they measure interface elongation in units of "um/min". Unless there is a good reason not to, these two metrics should be reported using the same units. Is there a difference in the rate of interface contraction when measured in absolute units (um/min)?
3. The discussion of previous work on cell deformation within the mesoderm (page 16, first paragraph) should probably include recent work from Adam Martin's lab (e.g. [Heer et al., 2017]; or [Denk-Lobnig et al., 2021]).

Suggestions

1. While I appreciate the arguments that the authors provide to use twi mutants rather than sna mutants or twi sna double mutants, as the authors indicate, in twi mutants there is still contractility in the mesoderm (albeit not ratcheted). Therefore, it is possible that contractile pulses from the mesoderm in twi mutants could still facilitate cell alignment and polarization of myosin in the germband. Given the previous results from the Zallen lab using twi sna double mutants (see above) this is unlikely to be the case, but the findings in this manuscript would be significantly stronger if they included similar analysis in the double mutants.

Significance

This is an interesting study, with careful quantitative analysis of cellular and subcellular dynamics. The results follow previous findings from Jennifer Zallen and the authors themselves. The Zallen lab showed that cell alignment, myosin polarization and germband extension are normal in sna twi mutants [Fernandez-Gonzalez et al., 2009], a result that the authors fail to cite. The results in the present manuscript are similar, but the analysis is much more in depth here, so the findings by Lye and colleagues certainly warrant publication. However, there are a number of experimental points that I think need to be addressed to solidify the manuscript, particularly in terms of statistical analysis.

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Referee #3

Evidence, reproducibility and clarity

During morphogenesis, the final shape of the tissue is not only dictated by mechanical forces generated within the tissue but can also be impacted by mechanical contributions from surrounding tissues. The way and extent to which tissue deformation is influenced by tissue-extrinsic forces are not well understood. In this work, Lye et al. investigated the potential influence of Drosophila mesoderm invagination on germband extension (GBE), an epithelial convergent extension process occurring during gastrulation. Drosophila GBE is genetically controlled by the AP patterning system, which determines planar polarized enrichment of non-muscle myosin II along the DV-oriented adherens junctions. Myosin contractions drive shrinking of DV-oriented junctions into 4-way vertices, followed by formation of new, AP-oriented junctions. This process results in cell intercalation, which causes tissue convergence along the DV-axis and extension along the AP-axis. In addition, GBE is facilitated by tissue-extrinsic pulling forces produced by invagination of the posterior endoderm. Interestingly, some recent studies suggest that the invagination of the mesoderm, which occurs immediately prior to GBE, also facilitates GBE. In the proposed mechanism, invaginating mesoderm pulls on the germband tissue along the DV-axis; the resulting strain of the germband cells generates a mechanotransduction effect that promotes myosin II recruitment to the DV-oriented junctions, thereby facilitating cell intercalation. Here, the authors revisited this proposed mechanotransduction effect using quantitative live imaging approaches. By comparing the wildtype embryos with twist mutants that fail to undergo mesoderm invagination, the authors show that although the DV-oriented strain of the germband cells was greatly reduced in the absence of mesoderm pulling, this defect had a negligible impact on junctional myosin density, myosin planar polarity, the rate of junction shrinkage or the rate of cell intercalation during GBE. A mild increase in the rate of new junction extension and a slight defect in cell orientation were observed in twist mutants, but these differences did not cause obvious defects in cell intercalation. The authors conclude that myosin II-mediated cell intercalation during GBE is robust to the extrinsic mechanical forces generated by mesoderm pulling.

Overall, I found that the results described here are very interesting and of high quality. The data acquisition and analyses were elegantly performed, statistics were appropriately used, and the manuscript was clearly written. However, there are a few points where some further explanation or clarification is necessary, as detailed below:

1. The main conclusion of the manuscript relies on appropriate quantification of myosin intensity at cell junctions. It is therefore important that the methods of quantification are well justified. Below are a few questions regarding the methods used in the analyses:
   • For myosin quantification, the authors state that "Background signal was subtracted by setting pixels of intensities up to 5 percentile set to zero for each timepoint" [Line826]. The rationale for selecting 5 percentile as the threshold for background should be explained. Also, how does this background value change over time?
   • The authors mention that "Intensities varied slightly between experiments due to differences in laser intensity and therefore histograms of pixel intensities were stretched" [Line828]. The method of intensity justification should be justified. For example, does this normalization result in similar cytoplasmic myosin intensity between control and twist mutant embryos?
   • A previous study demonstrates that the accumulation of junctional myosin is substantially reduced in twist mutant embryos compared to the wild type (Gustafson et al., 2022). In that work, junctional myosin was quantified as (I_junction - I_cytoplasm)/I_cytoplasm. In contrast, the cytoplasmic myosin intensity does not appear to be subtracted from the quantification in this study. How much of the difference in the conclusions of the two studies can be explained by this difference in myosin quantification?
   • The authors used the tissue flow data to register the myosin channel and the membrane channel, which were acquired at slightly different times. The accuracy of this channel registration should be demonstrated.
2. The authors show that cell intercalation is not influenced in twist mutant embryos. However, a previous study demonstrates that the speed of GBE is substantially reduced in twist mutants (Gustafson et al., 2022). It would be interesting to see whether a similar reduction in the speed of GBE was observed in this study.
3. It has been previously shown that contractions of medioapical myosin in germband cells also contribute to cell intercalation. The authors should explain why medioapical myosin was not included in the comparison between wildtype and twist mutant embryos.

Minor points:

1. Fig. 1-S1 panel C: the number of cyan cells changes non-monotonically. It first decreases from -10 min to 10 min, then increases from 10 min to 20 min. This is confusing since in theory the number of tracked cells should not increase over time if the cells are tracked from the beginning of the movie.
2. Fig. 1-S2: the vnd band in panel A appears to be much narrower than in panel B.
3. The schematic in Fig. 2J suggests that at the onset of mesoderm pulling the germband cells have a uniform angle of rotation (towards bottom right). Is this the case?
4. The description of myosin intensity normalization in the Methods section is somewhat difficult to follow [Line 829 - 832]. It would be helpful if the authors can show one or two images before and after intensity normalization as examples.
5. Line 704: "Z-stacks for each channel were collected sequentially" - the step size in Z-axis should be reported.
6. Fig. 4C: what are the thin, black lines in the image?

Significance

While most previous work on tissue mechanics and morphogenesis focuses on tissue-intrinsic mechanical input, recent studies have started to emphasize the contribution of tissue-extrinsic forces. An important challenge in understanding the function of tissue-extrinsic forces lies in the difficulties in properly comparing the wild type and the mutant samples that disrupt extrinsic forces, in particular when cell fate specification is altered in the mutants. In this work, the authors addressed this challenge by employing a number of approaches to warrant a parallel comparison between genotypes, including examining the AP- and DV-patterning of the tissue, selecting sample regions with comparable cell fate for analysis, and carefully aligning the stage of the movies. With these approaches, the authors provide compelling evidence to support their main conclusions. By teasing apart the role of the intrinsic genetic program and the extrinsic tissue forces, the work provides important clarifications on the function of mesoderm pulling in GBE and adds new insights into this well-studied tissue morphogenetic process. This work should be of interest to the broad audience of epithelial morphogenesis, tissue mechanics and myosin mechanobiology.

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Referee #2

Evidence, reproducibility and clarity

In the present manuscript, Lye et al. describe a highly detailed quantification of cell shape changes during germband extension in Drosophila melanogaster early embryo. During this process, ectodermal tissue contracts along the dorso-ventral axis, simultaneously expanding along the perpendicular antero-posterior direction, migrating from the ventral to the dorsal surface of the embryo as it extends. This important morphogenetic event is preceded by ventral furrow formation when mesodermal tissue (located in the ventral part of the embryo) contracts along the dorso-ventral axis and invaginates into the embryonic interior. The study compares cell shape dynamics in the wildtype Drosophila with that in the twist mutant, which largely lacks mesoderm and does not form ventral furrow. The major motivation of the study is to examine whether cellular behaviors and myosin recruitment in the ectoderm is cell autonomous, or if those cellular behaviors depend on mechanical interactions between mesoderm and ectoderm. The authors first examine whether transcriptional patterning of key genes involved in germband extension is different between the wildtype and the twist mutant and find no significant difference. Next, the authors thoroughly quantify cellular behaviors and patterns of myosin recruitment in the two genetic backgrounds. A number of different measures are investigated, notably the rate of change in the degree of cellular asymmetry, rate of cell area change, rate of change of cell orientation, differences in myosin recruitment to cell edges of various orientation, as well as the rates of growth, shrinkage, and re-orientation of the various cellular interfaces. It is thoroughly documented how these quantities change as a function of developmental timing and spatial position within the embryo. These data serve basis for quantitative comparison between cellular dynamics in the two genetic backgrounds considered. Overall, the study shows that cellular behaviors observed in the ectoderm are largely the same during the period of time following ventral furrow formation, as would be expected if those cellular behaviors were predominantly cell autonomous and not dependent on stresses generated in the mesoderm.

The data presented in the manuscript are of excellent quality and presentation is very clear.

Minor comments: none

Significance

I find that the study provides a thorough quantification of cell behaviors in a widely studied important model of morphogenesis. The work may be of particular interest for future model-to-data comparison, perhaps providing a basis for future modeling work. I therefore certainly think that this work warrants publication. However, the results of the study largely parallel previous findings and do not appear novel or surprising. It is well established that in snail mutant that lack mesoderm entirely, germband extension proceeds largely normally. This well-established fact suggests that since tissue dynamics in complete absence of mesoderm are largely unaffected, behaviors of individual cells are likely to not be affected either. The work is pretty much entirely observational, and for most part provides a more detailed documentation/quantification of previous findings. I do not think it is appropriate for high profile publication.

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Referee #1

Evidence, reproducibility and clarity

Summary: In this manuscript by the Sanson group, Lye and colleagues try to definitively answer the question of whether pulling forces from the ventral mesoderm have significant effects on convergent extension in the Drosophila germband (germband extension). While germband extension does occur in mutant embryos lacking mesoderm invagination, it has long been an open question in the field as to whether ventral pulling forces from the mesoderm have significant effects (positive or negative) on cell intercalation during germband extension. To definitely address this question, Lye and colleagues generated high-quality, directly comparable datasets from wild-type and twist mutant embryos, and then systematically assessed nearly all aspects of cell intercalation, myosin recruitment, and tissue elongation over time. They demonstrate that pulling forces from the ventral mesoderm have negligible impacts on the course of germband extension. While there are indeed some interesting differences between wild-type and twist embryos with respect to cell intercalation and myosin recruitment, such differences are relatively minor. They conclude that the events of germband extension neither require nor are strongly affected by external forces from the mesoderm. While this is largely a negative results paper, I believe that it should be published and that it will be an impactful paper within the field. Namely, it will settle once and for all the question of whether mesoderm invagination is required for optimal germband extension in the early Drosophila embryo, and it suggests that tissues are largely autonomous developmental units that are buffered from outside mechanical inputs.

Major comments:

It seems to me that the one obvious omission from this paper is a general measure of convergent extension over time. I think it would be useful to the reader to include some measure of change in tissue aspect ratio over time between wild-type and twist embryos. This could be included in Figure 5 or 6.

Otherwise, I have no major comments on the experimental approach or the findings of this manuscript. It seems to me a straightforward and systematic approach for determining whether mesoderm invagination affects germband extension. I do have several minor comments that should be addressed prior to publication (below).

Minor comments:

I understand why cells would initially stretch more along the DV axis in wild-type embryos compared with twist embryos, but why do cells become so much more stretched along the AP axis (and become smaller apically) after 10 minutes of GBE in wild type compared with twist (Figure 2C and E). I think this is an interesting and non-intuitive result that would warrant a bit of explanation/conjecture.

I don't understand how you are defining cell orientation in Figure 2G. How are you choosing the cell axis that you are then comparing with the body axis? Is it the long axis, or something more complicated than that? I think you should briefly provide this information in the results section. If it is included in the methods, I wasn't able to locate it.

Figure 2: Since you have the space, it might help the reader if you simply wrote out "strain rate" for panels B, D, and F, rather that used the abbreviation "SR."

Please ensure that all axis labels are fully visible in the final figures. In several figures, the Y-axis labels were cut off (e.g., Fig 2I, 4A, 4D, 6B, 6C).

Where space permits, I would suggest using fewer abbreviations in axis labels to increase readability of the figures (e.g., in Figures 3H or 4D).

In Figure 7, I would move the wild-type panels to the left and the twist panels to the right. I think it is more conventional to describe the normal wild-type scenarios first, and then contrast the mutant state.

To be consistent with the literature, "wildtype" should be hyphenated (wild-type) when used as an adjective, or two separate words (wild type) when used as a noun.

Significance

Advance: The advances in this manuscript are largely methodological, but the experiments and analyses are quite rigorous and allow the authors to make strong conclusions concerning their hypotheses. Their findings are based on a high-quality collection of movies from control and twist mutant embryos expressing a cell membrane marker and knock-in GFP-tagged myosin. Importantly, I think the researchers were correct in choosing to analyze twist single-mutant embryos (as opposed to snail or twist, snail double-mutant embryos), as the overall embryo geometry of these mutants is fairly similar to wild-type embryos, allowing the researchers to directly compare cell behaviors and myosin dynamics during germband extension. This approach also allows them to avoid indirect effects on the germband due to a completely non-internalized mesoderm.

Audience: The primary audience for this article will be basic science researchers working in the early Drosophila embryo who are interested in the interplay between the germband and neighboring tissues. Secondary audiences will include developmental biologists more broadly who are interested in biomechanical coupling (or in this case decoupling) of neighboring tissues.

Describe your expertise: I have been a Drosophila developmental geneticist for over twenty years, and I have been working directly on Drosophila germband extension for over a decade. I have published numerous papers and reviews in this field, and I am very familiar with the genetic backgrounds and types of experimental analyses used in this manuscript. Therefore, I believe I am highly qualified to serve as a reviewer for this manuscript.

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Reply to the reviewers

1. General Statements

We thank the editors for sending our manuscript for peer review and the reviewers for careful reading and their critical comments to improve the manuscript. Below, we describe the experiments that have been carried out in response to the reviewers and incorporated in the preliminary revision. We also describe our plan for the revisions that will address the remaining comments of the reviewers. Most of the comments are addressable with additional experiments (some of which are already ongoing) and these experiments will surely strengthen the study reported in this manuscript without affecting the fundamental findings. We would require up to 4-6 weeks to complete these experiments.

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

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

­Summary: The authors used a conditional transgenic mouse model to demonstrate that deletion of serum response factor (SRF) from adult astrocytes provides neuroprotection in various insult/ diseases contexts without promoting any obvious phenotypic deficiencies. The work builds on the group’s previous study where SRF was embryonically deleted from astrocytes and their precursor cells. Given the role of SRF in promoting glial cell differentiation, the adult conditional KO used in the current study was designed to circumvent the limitations of the previous approach. The authors used a variety of complementary approaches (including immunohistochemistry, electrophysiology, transcriptomics, and behavior) to demonstrate the therapeutic potential of their approach. However, I have questions regarding the validity of the behavioral analyses as well as some of the imaging results that dampen my overall enthusiasm.

Major Comment #1

The synaptogenic factors probed in Figure 3C (e.g. glypicans, thrombospondins, etc.) are not likely to play major roles in the adult brain in a non-injury context, so I do not know that these analyses provide any significant insight into potential functional changes in the mutant mice. Along the same lines, the analysis of synapse count (Figure 3D-E) seems inconsequential given that SRF was knocked out well after the period of developmental synaptogenesis. It would have been much more interesting to have performed these analyses following insult (such as the kainate injury model used by the authors) or in one of the disease models presented later in the manuscript. As it stands, I don't think they add very much to the study.

Response: We are grateful to the reviewer for the careful reading of the manuscript. Astrocytes are known to regulate the formation, maintenance, and elimination of synapses. It has been previously shown that LPS-induced reactive astrocytes exhibit reduced expression of several synaptogenic factors, were unable to promote synapse formation and showed reduced phagocytic activity (PMID: 28099414). We wanted to determine whether the SRF-deficient reactive-like astrocytes were likely compromised in their ability to produce pro-synaptogenic factors and/or adversely affect synapse maintenance. We agree with the reviewer that analysis of synapses in the adult brain may not address the role of these mutant astrocytes in synaptogenesis. But our results indicate that the mutant astrocytes are likely not affecting synapse maintenance or exhibit altered phagocytotic activity that would result in increased or decreased synapse numbers. We will make this clearer in the revised manuscript.

Minor Comment #2:

The authors should note that the use of GluA1 as a postsynaptic marker will not identify silent synapses (i.e. structurally "normal" but functionally inert).

Response: We agree with the reviewer that GluA1 will not identify silent synapses. To study silent vs functional synapses, we will stain for Piccolo (presynaptic) and NMDA receptor NR1 subunit (post-synaptic) to label all synapses and compare this with Piccolo/GluA1 co-localized synapses to identify the functional synapses.

Reviewer #2 (Significance (Required):

The manuscript addresses the important area of the cellular mechanisms that underlie neuroprotection. The ms adds to our understanding of genetic control of neuroprotection and should be of significant interest to others in the field. The experimental approach systematic and the data presented are generally of high quality and believable. While the ms presents quite a bit of overall cellular data that underlies various areas of neuronal and brain function that may be affected by loss of SRF, it is still somewhat descriptive. It is unclear what aspect of astrocyte reactivity is determinative, how mechanistically in normal cells SRF suppresses reactivity, and how SRF -negative reactive astrocytes confer such broad neuroprotection. While the latter is well beyond the scope of this study, the authors do propose SRF may be involved in regulating oxidative stress and amyloid plaque clearance as a potential pathway to account for SRF's role, however a more systematic discussion based on the gene expression data and known pathways would be welcome. Overall, this is a high quality ms that should be of interest to the field that identifies a SRF as a novel player in neuroprotection.

Response: We thank the reviewer for the careful reading of the manuscript and for the positive comments. We will include a more detailed discussion on the genes and pathways based on our gene expression data that may provide insights into how SRF may regulate astrocyte reactivity and neuroprotection.

Additional considerations:

1. Quantification of the extent of SRF loss in astrocytes in conditional tamoxifen knockout would strengthen the quality of the data.

Response: We will provide this data in the revised manuscript.

While the authos did use a Sholl analysis to show hypertophic changes in SRF negative astrocytes, given that SRF is an important regulator of actin and other cytoskeletal related proteins in other cell types, and that cytoskeletal components can play an important role in cell signaling, it is somewhat surprising that the gene array analysis did not include actin and other cytoskeletal proteins, nor did the authors consider a more careful analysis of intracellular cytoskeletal changes and the potential mechanistic implications of this for observed reactivity and neuroprotection.

Response: We agree with the reviewer that SRF is a well-established regulator of actin cytoskeleton. However, we did not any significant changes in gene expression for actin or actin-regulatory proteins. We would have expected a decrease in astrocyte morphology similar to the neurite/axon defects exhibited by SRF-deficient neurons. It is unclear whether the hypertrophic morphology is due to transcriptional regulation of actin/actin-binding proteins or due to astrocyte reactivity. This would be a very interesting question and we will investigate these aspects in future studies.

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

Summary: The study by Thumu et al., suggests that astrocytic specific deletion of SRF in mice results in morphological changes in these cells that does not affect neuronal survival, synapse number, plasticity or cognition. However, in in vivo mouse models of excitotoxic damage and neurodegenerative disease, deletion of SRF reduced neurotoxicity. The authors provide sufficient evidence to suggest that astrocytic SRF contributes to neurotoxicity in various models however some claims are made that are currently not supported by evidence.

Major comments:

2) The authors claim that SRF KO astrocytes undergo hypertrophy. However, the quantification of the number of intersections gives information about morphology rather than hypertrophy. Quantification of cell size (area of S100B staining) should be provided.

Response: We will provide the data suggested by the reviewer.

6) For the RNAseq of isolated astrocytes did the authors confirm that other cell types (e.g microglia) did not contaminate their samples?

Response: We will provide the information requested by the reviewer.

Reviewer #3:

Minor comments:

1) The authors say that in Figure 1B many astrocytes did not show any SRF expression. However, overall averages of SRF intensity are plotted in Figure 1C. It would support their claim to instead to calculate the percentage of SRF expressing cells above a certain threshold in each condition, rather than plotting the mean intensity. As a control for their method of quantifying SRF intensity in Figure 1B, demonstrating no change in SRF in neurons would provide confidence for the specificity of the knockout.

Response: We will provide the quantification of the extent of SRF loss in astrocytes (percent astrocytes that are deleted for SRF) as suggested by Reviewer 2. We will also provide SRF intensity from neurons as suggested by the reviewer.

2) The authors use the term "reactivation" throughout the manuscript. This could be misconstrued as re-activation and so I would suggest using the terms "reactivity" or "reactive transformation". Furthermore, only one region is quantified in Figure 1C while in later figures multiple regions are quantified. The authors should justify this decision or update the figures with data from missing regions.

Response: We will make this change in using the term “reactivity” as suggested by the reviewer.

3) In Figure S2 the authors should provide a positive control for their staining.

Response: We will provide the positive control data for this experiment.

4) Can the authors explain the large amount of variability in number of synapses in 15 mpi in Figure 3E?

Response: We will perform more immunostainings and update the data presented in this figure.

5) Images in Figure 2C are poorly visible and should be improved in terms of either quality or magnification.

Response: We will provide better quality image for Figure 2C.

8) The authors should provide a list of differentially expressed genes from RNAseq of SRF KO mice. No information is currently given in the text about the number of differentially expressed genes in the conditional knockout.

Response: We will include this information in the revised manuscript.

9) In figure 5A data would be better illustrated as a volcano plot (similar to Fig. S7C).

Response: We will provide this in the revised manuscript.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

Reviewer #1: Major Comment #2

There is considerable variability in the behavioral results, particularly the fear conditioning and Barnes maze tasks (Figures 4F-G). Given the extremely low sample size for mouse behavior (n=5 in on group, n=7 in the other), it is highly likely that the behavioral tests were done with a single cohort of animals (which would be far from ideal) and that these experiments are significantly underpowered. Furthermore, it does not appear that the fear conditioning task was properly optimized. For example, in the control mice in context A, there were two animals that were at or very close to 0 percent freezing; these were likely outliers, or even an indication that the foot shock conditioning protocol was not working as it should. The highest percent freezing of either group was ~70%, which would have been an ideal starting place as an average for the control group. In addition, sex of the animals was not reported for these experiments. If the authors combined sexes as they did in other analyses in this paper, it is possible that they missed reaching the appropriate reaction threshold for the foot shock for some of the animals, as sex differences have previously been demonstrated in mice (DOI: 10.1037/bne0000248). Given the age at which the animals are assessed with these tasks, these specific revisions would require greater than 6 months to complete. However, as currently presented, there simply are not enough data points to make any conclusions regarding behavior.

Response: We have performed the behavioural experiments with an additional cohort of animals for both control and mutant groups and reanalysed the data. We now have n=11 for control and n=9 for mutant group. Only males were used for the behaviour experiments, and we do not see any significant difference in behaviour between the two groups. These results are included in revised Figure 4E-G in the Preliminary Revision of the manuscript. However, we are waiting to perform the remote recall memory for the fear conditioning experiment and will include this date in the revised manuscript.

Minor Comment #1:

The representative GFAP images (Figure 1 E/G) do not appear to have been taken at the same magnification. This was particularly apparent in the comparison between the control and CKO hippocampus at 12mpi. It is difficult to say with certainty, due to the lack of fiducial markers in many of the images. Inclusion of a nuclear stain (DAPI) would be highly beneficial to allow the reader to make a more informed comparison.

Response: These images were taken at the same magnification. We have included the DAPI staining for these images in Suppl. Figure 2 in the Preliminary Revision of the manuscript.

**Referees cross-commenting**

After reading the comments of the other reviewer, I think we're in agreement that the cellular and molecular data, while descriptive, is of mostly excellent quality. Moreover, the significance of the study is high, and the potential readership broad. However, I stand by my initial assessment of the behavioral data and find the manuscript quite lacking in this regard. Proper revisions would take at least half a year or more, so the authors may be disinclined to go this route. That being said, if the behavioral data were to be excised, I would be happy to sign off on the rest of the manuscript provided that the other major criticisms are addressed.

Response: We thank the reviewer for the appreciation of our work. We have increased the number of animals in the behavioural experiments and do not see any significant difference between the two groups. These results are included in revised Figure 4E-G in the Preliminary Revision of the manuscript.

In response cross-comment of Rev 2:

Agreed that if properly conducted and presented, the behavioral data would indeed provide a nice functional correlate to the cellular work. In its current state, I'm afraid that it is instead a hindrance to the study and I would recommend that they just remove it if they choose not to address my concerns with the quality (particularly the extreme variability and the complete lack of freezing by several of the animals, especially in the controls).

Response: We hope that the revised behaviour data would provide a strong functional correlate to the other findings in the study.

Additional cross-comments:

I agree with the added criticisms raised by Reviewer #3, and I think that the manuscript would be greatly improved by revisions that address those and the original criticisms from myself and Reviewer #2. I still think that the behavioral data should be omitted, provided that the authors are not capable or willing to appropriately address those concerns within a reasonable time frame.

Response: We will address all the concerns raised by the reviewers with the required experiments to further strengthen the findings in this study.

Reviewer #3

Major Comment

3) In Figure S1 the authors provide evidence showing lack of B-gal in cell types other than astrocytes (neurons/OPCs). However, microglia are missing, which could be important as later they show that microglia undergo changes in the SRF knockout model. This staining should be provided.

Response: We have performed double immunostaining for b-gal and IbaI and do not see any overlap between IbaI and b-gal, suggesting that there is no Cre expression in microglia. We have included this data in revised Figure S1F in the Preliminary Revision of the manuscript.

5) The authors claim in the text that microglia have thicker processes and an amoeboid shape however no evidence of this is provided in Figure S5.

Response: We have provided data to show larger microglia area and morphology in revised Figure S5 in the Preliminary Revision of the manuscript.

7) In the text "Enrichment analysis of Gene Ontology terms for Biological Process (GO BP) revealed that Srf deficient astrocytes showed enrichment of pathways related to cellular response to beta amyloid and beta-amyloid clearance." This is not shown in fig 5. It would be more accurate to say that there is a downregulation of genes involved in B amyloid metabolic process.

Response: We apologize for the omission in showing that this data was presented in Suppl. Fig. S8E. We have now indicated this in the main text.

Minor Comments:

4) Figure 1E is missing body weight data noted in the figure legend.

Response: We apologize for this oversight. This data was actually included in Suppl. Figure S3E and not in Figure 1. We have made the appropriate correction to Figure legend 1.

6) In Figure 2B figure labels are missing.

Response: We thank the reviewer for pointing out this omission. We have added the missing labels.

7) Details of houskeeping gene normalisation are missing from qPCR data.

Response: We apologize for not providing this information. We have included this in the revised Methods section.

4. Description of analyses that authors prefer not to carry out

Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

Reviewer #3, Major Comment 1:

1) The title of the manuscript is "SRF-deficient astrocytes provide neuroprotection in mouse models of excitotoxicity and neurodegeneration". It would be more accurate to say that SRF is involved in neurotoxicity in these models. To make a comment on the role of SRF in neuroprotection, experiments should be performed in spinal cord injury or ischaemia, where deficiency of SRF would be hypothesised to worsen recovery.

Response: We disagree with the reviewer with this assessment. There is no evidence to suggest that SRF is involved in neurotoxicity. What our data suggests is that SRF deficiency results in a reactive astrocyte state that is neuroprotective in these models. We hypothesize that in injury/infection/disease conditions that would result in generation of neuroprotective astrocytes, SRF expression or function may be negatively regulated. It would be interesting to see whether the SRF-deficient astrocytes alleviate or exacerbate pathology and recovery following spinal cord injury and ischaemia.

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Referee #3

Evidence, reproducibility and clarity

Summary: The study by Thumu et al., suggests that astrocytic specific deletion of SRF in mice results in morphological changes in these cells that does not affect neuronal survival, synapse number, plasticity or cognition. However, in in vivo mouse models of excitotoxic damage and neurodegenerative disease, deletion of SRF reduced neurotoxicity. The authors provide sufficient evidence to suggest that astrocytic SRF contributes to neurotoxicity in various models however some claims are made that are currently not supported by evidence.

Major comments: The title of the manuscript is "SRF-deficient astrocytes provide neuroprotection in mouse models of excitotoxicity and neurodegeneration". It would be more accurate to say that SRF is involved in neurotoxicity in these models. To make a comment on the role of SRF in neuroprotection, experiments should be performed in spinal cord injury or ischaemia, where deficiency of SRF would be hypothesised to worsen recovery.

The authors claim that SRF KO astrocytes undergo hypertrophy. However, the quantification of the number of intersections gives information about morphology rather than hypertrophy. Quantification of cell size (area of S100B staining) should be provided.

In Figure S1 the authors provide evidence showing lack of B-gal in cell types other than astrocytes (neurons/OPCs). However, microglia are missing, which could be important as later they show that microglia undergo changes in the SRF knockout model. This staining should be provided.

Can the authors explain the large amount of variability in number of synapses in 15 mpi in Figure 3E?

The authors claim in the text that microglia have thicker processes and an amoeboid shape however no evidence of this is provided in Figure S5.

For the RNAseq of isolated astrocytes did the authors confirm that other cell types (e.g microglia) did not contaminate their samples?

In the text "Enrichment analysis of Gene Ontology terms for Biological Process (GO BP) revealed that Srf deficient astrocytes showed enrichment of pathways related to cellular response to betaamyloid and beta-amyloid clearance." This is not shown in fig 5. It would be more accurate to say that there is a downregulation of genes involved in B-amyloid metabolic process.

OPTIONAL: Figure 6 would be greatly strengthened by functional in vivo experiments showing reversal of motor/ cognitive phenotypes.

OPTIONAL: The study would be improved by isolating astrocytes from the models used in figure 6 and performing RNAseq to provide information about how SRF knockout affects astrocyte reactivity in these models.

Minor comments: The authors say that in Figure 1B many astrocytes did not show any SRF expression. However, overall averages of SRF intensity are plotted in Figure 1C. It would support their claim to instead to calculate the percentage of SRF expressing cells above a certain threshold in each condition, rather than plotting the mean intensity. As a control for their method of quantifying SRF intensity in Figure 1B, demonstrating no change in SRF in neurons would provide confidence for the specificity of the knockout.

The authors use the term "reactivation" throughout the manuscript. This could be misconstrued as re-activation and so I would suggest using the terms "reactivity" or "reactive transformation".

Furthermore, only one region is quantified in Figure 1C while in later figures multiple regions are quantified. The authors should justify this decision or update the figures with data from missing regions.

In Figure S2 the authors should provide a positive control for their staining.

Figure 1E is missing body weight data noted in the figure legend.

Images in Figure 2C are poorly visible and should be improved in terms of either quality or magnification.

In Figure 2B figure labels are missing.

Details of houskeeping gene normalisation are missing from qPCR data.

The authors should provide a list of differentially expressed genes from RNAseq of SRF KO mice. No information is currently given in the text about the number of differentially expressed genes in the conditional knockout. In figure 5A data would be better illustrated as a volcano plot (similar to Fig. S7C).

Significance

The strength of the manuscript is that the authors demonstrate in more than one model that astrocyte specific knockout of SRF rescues neuronal death, implicating SRF in astrocyte mediated neurotoxicity. The limitations of the study are that the mechanism by which SRF deletion reduces excitotoxicity is not addressed and there is no supporting data beyond neuronal survival in the excitotoxicity/OHDA models or plaque density in the APP/PS1 model.

This study adds SRF to an expanding understanding of the neurotoxic capacity of astrocytes in certain reactive states. It will be of broad interest to the astrocyte reactivity field.

My field of expertise is in astrocyte and microglia interactions in neurodegenerative diseases.

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Referee #2

Evidence, reproducibility and clarity

The manuscript, "SRF-deficient astrocytes provide neuroprotection in mouse models of excitotoxicity and neurodegeneration" by Thumu et al., describes the observation astrocyte specfic SRF deficient mice exhibit neuroprotection against a broad range of brain pathologies. The current ms follows up on previous work done by the corresponding author Ramanan and colleagues in which they showed that astrocyte-specific deletion of SRF early during mouse development resulted in persistent reactive-like astrocytes throughout the postnatal mouse brain. In the current ms the authors present data that adult astrocyte specific conditional deletion of serum response factor results in reactive-like hypertrophic astrocytes that localize throughout the mouse brain. They further show that SRF deficient astrocytes do not affect neuron survival, synapse numbers, synaptic plasticity or learning and memory. Strikingly, they further show that brains of Srf knockout mice exhibit protection against neurodegenerative disease related pathologies including induced excitotoxic cell death and that SRF-deficient astrocytes abrogate dopaminergic neuron death and reduce beta-amyloid plaques in mouse models of Parkinson's and Alzheimer's disease. Based on their results, the authors proposes that SRF is a key molecular switch for the generation of reactive astrocytes with neuroprotective functions can attenuate neuronal injury in the setting of neurodegenerative diseases.

Referees cross-commenting

Reviewer #1 raises an important concern regarding the quality of the behavioral studies. I would also agree that the ms is still strong and the findings are significant without them, although they do extend the functional dimensions of the overall study.

Significance

The manuscript addresses the important area of the cellular mechanisms that underlie neuroprotection. The ms adds to our understanding of genetic control of neuroprotection and should be of significant interest to others in the field. The experimental approach systematic and the data presented are generally of high quality and believable. While the ms presents quite a bit of overall cellular data that underlies various areas of neuronal and brain function that may be affected by loss of SRF, it is still somewhat descriptive. It is unclear what aspect of astrocyte reactivity is determinative, how mechanistically in normal cells SRF suppresses reactivity, and how SRF -negative reactive astrocytes confer such broad neuroprotection. While the latter is well beyond the scope of this study, the authors do propose SRF may be involved in regulating oxidative stress and amyloid plaque clearance as a potential pathway to account for SRF's role, however a more systematic discussion based on the gene expression data and known pathways would be welcome. Overall, this is a high quality ms that should be of interest to the field that identifies a SRF as a novel player in neuroprotection.

Additional considerations:

1. Quantification of the extent of SRF loss in astrocytes in conditional tamoxifen knockout would strengthen the quality of the data.
2. While the authos did use a Sholl analysis to show hypertophic changes in SRF negative astrocytes, given that SRF is an important regulator of actin and other cytoskeletal related proteins in other cell types, and that cytoskeletal components can play an important role in cell signaling, it is somewhat surprising that the gene array analysis did not include actin and other cytoskeletal proteins, nor did the authors consider a more careful analysis of intracellular cytoskeletal changes and the potential mechanistic implications of this for observed reactivity and neuroprotection.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #1

Evidence, reproducibility and clarity

Summary: The authors used a conditional transgenic mouse model to demonstrate that deletion of serum response factor (SRF) from adult astrocytes provides neuroprotection in various insult/diseases contexts without promoting any obvious phenotypic deficiencies. The work builds on the group's previous study where SRF was embryonically deleted from astrocytes and their precursor cells. Given the role of SRF in promoting glial cell differentiation, the adult conditional KO used in the current study was designed to circumvent the limitations of the previous approach. The authors used a variety of complementary approaches (including immunohistochemistry, electrophysiology, transcriptomics, and behavior) to demonstrate the therapeutic potential of their approach. However, I have questions regarding the validity of the behavioral analyses as well as some of the imaging results that dampen my overall enthusiasm.

Major Comments:

1. The synaptogenic factors probed in Figure 3C (e.g. glypicans, thrombospondins, etc.) are not likely to play major roles in the adult brain in a non-injury context, so I do not know that these analyses provide any significant insight into potential functional changes in the mutant mice. Along the same lines, the analysis of synapse count (Figure 3D-E) seems inconsequential given that SRF was knocked out well after the period of developmental synaptogenesis. It would have been much more interesting to have performed these analyses following insult (such as the kainate injury model used by the authors) or in one of the disease models presented later in the manuscript. As it stands, I don't think they add very much to the study.
2. There is considerable variability in the behavioral results, particularly the fear conditioning and Barnes maze tasks (Figures 4F-G). Given the extremely low sample size for mouse behavior (n=5 in on group, n=7 in the other), it is highly likely that the behavioral tests were done with a single cohort of animals (which would be far from ideal) and that these experiments are significantly underpowered. Furthermore, it does not appear that the fear conditioning task was properly optimized. For example, in the control mice in context A, there were two animals that were at or very close to 0 percent freezing; these were likely outliers, or even an indication that the foot shock conditioning protocol was not working as it should. The highest percent freezing of either group was ~70%, which would have been an ideal starting place as an average for the control group. In addition, sex of the animals was not reported for these experiments. If the authors combined sexes as they did in other analyses in this paper, it is possible that they missed reaching the appropriate reaction threshold for the foot shock for some of the animals, as sex differences have previously been demonstrated in mice (DOI: 10.1037/bne0000248). Given the age at which the animals are assessed with these tasks, these specific revisions would require greater than 6 months to complete. However, as currently presented, there simply are not enough data points to make any conclusions regarding behavior.

Minor Comments:

1. The representative GFAP images (Figure 1 E/G) do not appear to have been taken at the same magnification. This was particularly apparent in the comparison between the control and CKO hippocampus at 12mpi. It is difficult to say with certainty, due to the lack of fiducial markers in many of the images. Inclusion of a nuclear stain (DAPI) would be highly beneficial to allow the reader to make a more informed comparison.
2. The authors should note that the use of GluA1 as a postsynaptic marker will not identify silent synapses (i.e. structurally "normal" but functionally inert).

Referees cross-commenting

After reading the comments of the other reviewer, I think we're in agreement that the cellular and molecular data, while descriptive, is of mostly excellent quality. Moreover, the significance of the study is high, and the potential readership broad. However, I stand by my initial assessment of the behavioral data and find the manuscript quite lacking in this regard. Proper revisions would take at least half a year or more, so the authors may be disinclined to go this route. That being said, if the behavioral data were to be excised, I would be happy to sign off on the rest of the manuscript provided that the other major criticisms are addressed.

In response cross-comment of Rev 2:

Agreed that if properly conducted and presented, the behavioral data would indeed provide a nice functional correlate to the cellular work. In its current state, I'm afraid that it is instead a hindrance to the study and I would recommend that they just remove it if they choose not to address my concerns with the quality (particularly the extreme variability and the complete lack of freezing by several of the animals, especially in the controls).

Additional cross-comments:

I agree with the added criticisms raised by Reviewer #3, and I think that the manuscript would be greatly improved by revisions that address those and the original criticisms from myself and Reviewer #2. I still think that the behavioral data should be omitted, provided that the authors are not capable or willing to appropriately address those concerns within a reasonable time frame.

Significance

General assessment: Overall strengths of this study are the implications of SRF as a broad spectrum anti-neurodegeneration agent and the variety of techniques used. Limitations of this study include a lack of meaningful synaptic comparisons and underpowered behavioral assays.

Advance: Provided the above limitations are addressed, this study would provide a meaningful advance in our understanding of controlled reactive astrogliosis as a potential therapeutic strategy for neuroprotection.

Audience: This study would be of interest to a wide audience, particularly neuro- and gliobiologists as well as clinicians who deal with brain disorders and injury.

Expertise: imaging; behavior; synaptic development

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Reply to the reviewers

We appreciate the valuable suggestions and the overall highly positive review of our manuscript. We have now included many suggestions provided by the reviewers, which have made our manuscript much stronger and more rigorous. One reviewer acknowledged, “This study uncovers sex-dependent mechanisms underlying cold sensitivity between male and female mice. The detailed IHC analysis of MHCII expression in DRG neurons is a clear strength of this study and supports flow cytometry results as well as existing literature. The specificity of MHCII expression on small diameter is well characterized and supported by conditional knockout mouse models of MHCII in TRVPV1-lineage neurons.”

R1: It is not, yet, possible to conclude that all experiments are adequately powered as N's for some studies are not provided.

All experiments include N’s both in the text and in the figure legend.

R1: It is unclear what is meant by "novel" expression, used throughout the manuscript.

MHCII is traditionally thought to be constitutively expressed on antigen-presenting cells (APCs) and induced by inflammation on some non-APCs, including endothelial, epithelial, and glial cells (van Velzen et al., 2009). RNA seq data sets (Nguyen et al., 2021, Tavares- Ferreira et al., 2022, Usoskin et al., 2015, Lopes et al., 2017) demonstrate that mouse and human DRG neurons express transcripts for MHCII and MHCII-associated genes. However, there are no reports to date that demonstrate MHCII protein expression in terminally differentiated neurons. To the best of our knowledge, we are the first group to show that MHCII protein is expressed in DRG neurons.

R1: The statement at the end of the abstract, "and that neuronal MHCII may also contribute to many other neurological disorders" seems premature, beyond the scope of the present study.

We agree with the reviewer’s comment and have changed the sentence to the following: “Collectively, our results demonstrate expression of MHCII on DRG neurons and a functional role during homeostasis and inflammation” (pg. 1).

R1: While cold allodynia (hypersensitivity) is a clinically important feature of CIPN, especially in CIPN associated with the platinum based chemotherapeutic agents, it is less so taxane CIPN. Do 60% of patients with PTX CIPN express cold allodynia or does that number refer to CIPN in general?

This statistic is based on a study that conducted a meta-analysis of CIPN incidence and prevalence with paclitaxel, bortezomib, cisplatin, oxaliplatin, vincristine or thalidomide. However, we now include another reference (PMID: 15082135) that demonstrates patients receiving PTX experience cold hypersensitivity (pg.3).

R1: Again, the future direction of expanding studies of the role of MHCII in other aspects of the CIPN phenotype might bear mention.

We have included future directions regarding other aspects of CIPN phenotype in the discussion. We state, “Reducing the expression of MHCII in TRPV1-lineage neurons exacerbated PTXinduced cold hypersensitivity in both male and female mice. Future studies will evaluate the role of MHCII in PTX-induced mechanical hypersensitivity, another prominent feature of CIPN” (pg. 29).

R1: Is there any evidence that IL-4 and/or IL-10 influence cold sensitivity?

IL-10 and IL-4 have been shown to suppress spontaneous activity from sensitized nociceptors (Krukowski et al., 2016; Laumet et al., 2020; Chen et al., 2020) and to reduce neuronal hyperexcitability (Li et al., 2018), respectively. In addition, IL-10 has been shown to reduce mechanical hypersensitivity (Krukowski et al., 2016); however, cold sensitivity has not been evaluated. IL-4 KO mice do not have an increase in tactile allodynia or cold sensitivity after CCI; however, there is an increase in anti-inflammatory cytokines, specifically IL-10, and opioid receptors, which may be a compensatory mechanism that protects against enhanced pain after injury (Nurcan Üçeyler et al. 2011).

R1: Are these experiments run blinded?

Yes, this is discussed in the materials and methods section (pg. 31).

R1: The term "directly contacts" is unclear. No synaptic structure is identified. It might be more accurate to estimate the actual proximity between the two cells, especially as direct contact would not be necessary for the type of intercellular communication they are studying. This is not an EM study.

We agree with the reviewer’s comment and have changed the wording to “in close proximity” (pgs. 1,5, 7, 27).

R1: Two abbreviations are used for immunohistochemistry, ICC and IHC.

IHC refers to immunohistochemistry, and ICC refers to immunocytochemistry. We accidently wrote ICC in the immunohistochemistry section in the materials and methods section. We have now corrected it to say IHC (pg. 32).

R1: In some figure, group sizes are not indicated (e.g., Fig. 4D).

All group sizes are indicated in the text and figure legends.

R1: "small non-nociceptive neurons" - seems to refer to TRPV1+ neurons. There are, however, TRPV1-nociceptors. "Therefore, the majority of MHCII+ neurons in the DRG of naïve female mice were not TRPV1- lineage neurons but non-nociceptive C-LTMRs." Could use some clarification here. Are the authors suggesting that being TRPV1- defines a neuron a non-nociceptive?

We never said small non-nociceptive neurons are TRPV1+ neurons. We crossed TRPV1 lineage mice with td-tomato to label TRPV1 lineage neurons, which include TRPV1 neurons, IB4, and a subset of Aẟ neurons. We found that TRPV1 lineage neurons comprise about 65% of small diameter neurons, so 35% of small diameter neurons are not TRPV1 lineage cells. These non- TRPV1 lineage small diameter neurons are non-nociceptive LTMRs, most likely TH and MrgB4 neurons.

R2: The most pressing concern regarding this study is a lack of a vehicle control group. It is not appropriate to be comparing paclitaxel treated mice to naïve mice. Please include a vehicle treatment (cremophor:ethanol 1:1 diluted 1:3 in PBS) group for all experiments involving paclitaxel.

We believe the most appropriate control to paclitaxel treatment is the naïve control because clinically, paclitaxel is always administered to the patient in a formulation of 50% Cremophor and 50% ethanol. In clinical studies, the controls are healthy no-pain individuals and patients receiving paclitaxel without pain. However, the percentage of patients receiving paclitaxel that do not develop CIPN is low, emphasizing the need for healthy individuals not taking paclitaxel.

R2: Figure 1A only includes representative images of a small number of T cells in presumable contact with DRG neurons in female Day 14 paclitaxel mice but does not include images from other groups. Similarly, B-D show a single CD4+ T cell in contact with DRG neurons in Day 14 paclitaxel and naïve female mice. Please include quantification of the frequency of CD4+ T cells interacting with DRG neurons in the different experimental groups utilized in this study.

We have now quantified the number of CD4+ T cells per mm2 of DRG tissue, which is found in the text (pg. 5) and figures (Fig. S1 and Fig. 1A). We plan to add the quantification of CD4+ T cells per mm2 of DRG tissue for naïve and day 14 PTX-treated male mice. This data will be included in the text (pg. 5) and in Fig. S1.

R2: Please include entire blot for Figure 2A (or at least more of the blot). There is plenty of space in the figure and as it currently appears is not free from apparent manipulation.

We included a larger area of the western blot in Fig 2A (pg. 9).

R2: The authors conclude that MHCII helps to suppress chemotherapy-induced peripheral neuropathy, resolving cold allodynia following paclitaxel treatment. To support this conclusion, I think it is necessary to include a time-course experiment highlighting whether cKO of MHCII in TRPV1 neurons indeed increases the duration for cold hypersensitivity to resolve following paclitaxel treatment.

We conclude that neuronal MHCII suppresses cold hypersensitivity in naïve male mice and reduces the severity of PTX-induced cold hypersensitivity at the peak of the response (day 6) (pg. 1-2). In addition, knocking out one copy of MHCII in male TRPV1-lineage mice reduced total neuronal MHCII in naïve and PTX-treated mice (day 7 and 14) (pgs. 21-22; Fig.7). Moreover, knocking out one copy of MHCII in female TRPV1-lineage mice reduced surface- MHCII in female 7 days post-PTX (pgs. 19-20; Fig.6). Future studies will investigate the distinct roles of surface and intracellular neuronal MHCII and the contribution of MHCII to the resolution of CIPN.

R2: The graphical abstract is misleading. The authors suggest paclitaxel is acting exclusively via TLR4 and that signaling is resolved at Day 14 which their data does not support. Please adjust to reflect findings from the experiments included in this study.

We have removed TLR4 from our graphical abstract as we do not investigate the role of TLR4 in this manuscript. However, we do not suggest paclitaxel is acting exclusively through TLR4. We modified our wording to indicate both pro-inflammatory cytokines and PTX act on neurons to induce hyperexcitability and neurotoxicity: “Pro-inflammatory cytokines and PTX act on DRG neurons inducing hyperexcitability (Li et al., 2018, Boehmerle et al., 2006, Li et al., 2017) and neurotoxicity (Goshima et al., 2010, Flatters and Bennett, 2006), which manifests as pain, tingling, and numbness in a stocking and glove distribution (Rowinsky et al., 1993)” (pg. 9).

R2: Figure 4 and 6 MHCII labelling is oversaturated in most of the images, creating a blurry hue in the representative images. This should be fixed.

The signal intensity of immune cell MHCII is >5 times greater than neuronal MHCII; therefore, in order to visualize neuronal MHCII, the immune cell MHCII is oversaturated. We reference this in the discussion (pg. 26).

R2: The effects of the PTX cHET group are very mild in both the male and female cohorts, and specific to 1 trial. R3: Furthermore, the behavioral effect is seemingly variable, with only one of the three trials being significantly different between groups. This variable response needs to be discussed further.

This behavioral assay was developed by the UNE COBRE Behavior Core, under the guidance of Dr. Tamara King, who has extensive experience in using learning and memory measures to determine changes in pain such as development of thermal hypersensitivity (1-3, King et al, Nat Neuro 2009). Methodologically, the process is as follows: In the temperature placed preference assay, mice are placed on the reference plate (25 °C) to begin each 3-minute trial. For the habituation trial, both the test and reference plates are set to 25 °C, and the mice are allowed to explore for 3 minutes. The following 3 trials are the acquisition trials where the reference plate is set to 25 °C and the test plate to 20 °C. If the animals have cold hypersensitivity, modeling cold allodynia, then they will demonstrate faster acquisition of a learned avoidance response compared to the WT controls. For the results, we will clarify our findings, which are outlined below: 1) We will change the axis labels to better distinguish BL/habituation trial from reference trials in the graphs. 2) We will add graphs comparing naïve versus PTX for male and female WT mice. 3) The changes in the graphs will now reflect 3 key findings: First, we note that PTX-treated mice learn to avoid the cold test plate faster than the naive controls in the WT mice reflecting PTX-induced cold hypersensitivity. Of interest, both males and females demonstrate learned avoidance by trial 2 and that the percent of time on the cold plate continued to decline only in the PTX-treated mice. We had not graphed this in the original figure and plan to add graphs for both male and female WT mice. These graphs are important to include as it validates that this TPP can capture the expected PTX-induced cold hypersensitivity in WT mice. Second, in terms of the naïve cHET mice, these data show that both female and male cHET mice demonstrate faster learning to avoid the cold (20 °C) plate compared to the WT mice (Fig. 8A, B. We note that the males demonstrate a more robust effect, (faster learned avoidance of the cold plate) with significant avoidance to the cold plate emerging in the cHET mice by trial 3 compared to trial 4 in the females (sig diff compared to BL trial). Third, we observed that cHET mice treated with PTX demonstrate even more accelerated learning to avoid the cold plate compared to WT mice treated with PTX. This observation suggests that PTX-treated cHET mice have heightened cold allodynia compared to the WT mice.

R2: The statistical analysis (for the behavior) should also have been a mixed-effects repeated measures between groups ANOVA.

We agree and re-analyzed our behavior data using repeated measures mixed-effects model (REML) with Dunnett’s multiple comparison test comparing trials 2-4 to trial 1 within same group, and Sidak’s multiple tests for significance between groups at the same trial (pgs. 23-25; Fig. 8)

R3: Presented in Figure 3, the authors present data to show surface expression of MHCII, along with the ability of MHCII to present OVA peptide, on naïve and PTX-treated DRG neurons. These data are probably the most relevant in terms of expression as they look at the surface expression of MHCII along with the potential of MHCII to function; therefore, it is unclear why the authors only conducted this analysis on female neurons, and not both male and female neurons. Given the claims of the paper in terms of sex differences for MHC expression, I strongly suggest this is done in order to put the other observations into context.

We completely agree and have added male mice data in Figs. 2 and 3. By western blot, we show that PTX increased the amount of MHCII protein 14 days post-PTX in DRG neurons from female mice, but there’s no change in MHCII protein after PTX in male mice (Fig. 2). In agreement with the western blot, surface-MHCII determined by flow cytometry did not increase after PTX on DRG neurons from male mice (Fig. 3B). Moreover, the frequency of DRG neurons from male mice with surface-MHCII (determined by ICC) and OVA peptide did not change after PTX treatment (Fig. 3D, E). However, the percent area with polarized MHCII on DRG neurons from male mice increased 14 days post-PTX, indicating a modest PTX-induced response in males (Fig. 3F). We have now included the frequency of surface-MHCII on DRG neurons from male and female mice after PTX treatment, and again there was no change in surface-MHCII in male mice (Fig. 6). Collectively, our data demonstrates that neuronal MHCII in male mice is not strongly regulated by PTX treatment.

R3: Given the data presented in Figure 3, it is not clear what the relevance of investigating the subcellular puncta expression of MHCII neurons is, particularly when considering the sex differences observed, and how this was not been performed for surface expression.

We now include surface and total MHCII quantification for male and female WT and cHET mice (Figs. 6,7). In the text, we describe the significance of surface versus endosomal MHCII. “While endosomal MHCII can promote TLR signaling events(Liu et al., 2011), expression of MHCII on the cell surface is required to activate CD4+ T cells.” (pg. 10). “Although the major role for surface MHCII is to activate CD4+ T cells, cAMP/PKC signaling occurs in the MHCII-expressing cell(Harton, 2019). In addition, it has recently been shown that endosomal MHCII plays an important role in promoting TLR responses(Liu et al., 2011), and since DRG neurons are known to express TLRs (Lopes et al., 2017, Wang et al., 2020, Cameron et al., 2007, Barajon et al., 2009, Xu et al., 2015, Zhang et al., 2018), this suggests the potential for T-independent responses in MHCII+ neurons. Knocking out one copy of MHCII in TRPV1- lineage neurons (cHET) from female mice did not change total MHCII 7 days post-PTX but reduced surface-MHCII. Accordingly, PTX-treated cHET female mice were more hypersensitive to cold than PTX-treated WT female mice, suggesting a role for neuronal MHCII in CD4+ T cell activation and/or neuronal cAMP/PKC signaling. In contrast, knocking out one copy of MHCII in TRPV1-lineage neurons (cHET) from male mice did not change surface-MHCII in naïve or PTX-treated mice but reduced total MHCII, indicating endosomal MHCII and potentially a role in TLR signaling. Future studies are required to delineate MHCII surface and endosomal signaling mechanisms in naïve and PTX-treated female and male mice.” (pg. 28).

R3: Furthermore, the authors should provide details of what the abundant non-neuronal structures are within the DRG images that appear positive for MHCII staining.

We now include an image of the high MHCII+ cells in mouse DRG co-stained with macrophage and dendritic cell markers (CD11b/c), indicating the presence of immune cells (Fig. S6).

R3: The behavioral data presented in Figure 7 is somewhat confusing. Can the authors confirm how many alleles of MHCII were knocked out from the Trpv1-lineage neurons for these experiments? In Figure 7, it states cKO Het, which suggests that only one allele was deleted within the Trpv1 population. If this is the case, this needs to be clearly outlined within the results section and not simply referred to as "knocking out MHCII in Trpv1-lineage neurons". In addition, an explanation as to why heterozygous cKO were used rather than homozygous cKO needs to be provided. This is particularly relevant when discussing potential sex differences.

The mouse behavior is performed in wild type and TRPV1lin MHCII+/- heterozygote mice (Fig 8). Instead of saying we knocked out MHCII, we changed the text to “knocking out one copy of MHCII in TRPV1-lineage neurons” (pgs. 23, 29). In the methods section, we state that “cHET×MHCIIfl/fl crosses only yielded 8% cKO mice (4% per sex) instead of the predicted 25% (12.5% per sex) based on normal Mendelian genetics. Thus, cKO mice were only used to validate MHCII protein in small nociceptive neurons” (pg. 30) (Fig 7).

R3: A significant gap in the current manuscript is the functional assessment of MHCII protein expressed on DRG neurons in terms of T cell activity. I would suggest the authors consider performing a co-culture DRG-T cell (i.e. Treg) assay where anti-inflammatory cytokine release can be measured in the presence and absence of MHCII on DRG neurons.

The functional implication of MHCII protein on DRG neurons in terms of T cell activity is out of the scope of this manuscript. We currently have another manuscript in progress investigating CD4+ T cell signaling and cytokine production when co-cultured with DRG neurons. R3: Within the first paragraph of the results section, the authors reference Goode et al, 2022, stating that they have previously shown that CD4+ T cells in the DRG secrete anti-inflammatory cytokines. I have read this paper and could not find any data that showed increased secretion of cytokines, only that there is an increase in T-cell populations that contain anti-inflammatory markers. Please consider rewording to reflect the observations made in the original paper. We have changed “secrete” to “produce” (pg. 5) because we detected anti-inflammatory cytokines (IL-10 and IL-4) within CD4+ T cells using intracellular staining and multi-color flow cytometry.

R3: Figure 1A states that it is "day 14 PTX", however, there is no reference to this in the corresponding text - please state what Figure 1A is showing in the main text and legend regarding PTX treatment.

We have now included text and Fig. 1. legend that states that the images in Fig1A are of DRG tissue collected from female mice 14 days after PTX treatment (pg. 5).

R3: Throughout the results section (Figure 3-Figure 6), the authors provide percentage changes in observed difference in expression, however, in addition to this, it would be valuable to have the actual number of neurons analysed for each group and sex.

We now report in the materials and methods section the number of neurons that were analyzed (pg. 33).

R3: For Figure 5, can the authors confirm whether this was performed on tissue sections or dissociated cell culture?

This analysis was performed in DRG tissue sections. The legend now states, “Gaussian distribution of the diameter of MHCII+ DRG neurons in DRG tissue from naïve (pink), day 7 (orange) and day 14 PTX-treated (blue) (A) female and (E) male mice (n=8/sex, pooled neurons).”

R3: Can the authors comment on why surface expression for MHCII was not performed on the these reporter neurons?

In the future, we plan to delineate which subsets of neurons express MHCII by co-staining for MHCII and specific neuronal markers. However, these studies are beyond the scope of the current manuscript.

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Referee #3

Evidence, reproducibility and clarity

This manuscript sets out to investigate the mechanism by which the previously reported infiltration CD4+ T cells into the DRG parenchyma can mediate analgesia in the paclitaxel (PTX) model of chemotherapy-induced neuropathic pain (CIPN). The authors provide good rationale for the purpose of the study and make a number of interesting observations, including the expression of MHCII on DRG neurons, the effect of PTX on MHCII expression on DRG neurons, and the effect of targeted deletion of MHCII on Trpv1-expressing putative nociceptive neurons in exacerbating the effect of PTX-induced cold hypersensitivity. These data culminate to a hypothesis that MHCII expression on DRG neurons may drive T-cell mediated anti-inflammatory effects (and analgesia) in models where their recruitment is notable. Overall I enjoyed reading the manuscript, however, I believe there are a number of points that need to be considered further.

Major comments.

• Presented in Figure 3, the authors present data to show surface expression of MHCII, along with the ability of MHCII to present OVA peptide, on naïve and PTX-treated DRG neurons. These data are probably the most relevant in terms of expression as they look at the surface expression of MHCII along with the potential of MHCII to function; therefore, it is unclear why the authors only conducted this analysis on female neurons, and not both male and female neurons. Given the claims of the paper in terms of sex differences for MHC expression, I strongly suggest this is done in order to put the other observations into context.
• Given the data presented in Figure 3, it is not clear what the relevance of investigating the subcellular puncta expression of MHCII neurons is, particularly when considering the sex differences observed, and how this was not been performed for surface expression. Furthermore, the authors should provide details of what the abundant non-neuronal structures are within the DRG images that appear positive for MHCII staining.
• The behavioural data presented in Figure 7 is somewhat confusing. Can the authors confirm how many alleles of MHCII were knocked out from the Trpv1-lineage neurons for these experiments? In Figure 7, it states cKO Het, which suggests that only one allele was deleted within the Trpv1 population. If this is the case, this needs to be clearly outlined within the results section and not simply referred to as "knocking out MHCII in Trpv1-lineage neurons". In addition, an explanation as to why heterozygous cKO were used rather than homozygous cKO needs to be provided. This is particularly relevant when discussing potential sex differences. Furthermore, the behavioural effect is seemingly variable, with only one of the three trials being significantly different between groups. This variable response needs to be discussed further.
• A significant gap in the current manuscript is the functional assessment of MHCII protein expressed on DRG neurons in terms of T cell activity. I would suggest the authors consider performing a co-culture DRG-T cell (i.e. Treg) assay where anti-inflammatory cytokine release can be measured in the presence and absence of MHCII on DRG neurons.

Minor comments.

• Within the first paragraph of the results section, the authors reference Goode et al, 2022, stating that they have previously shown that CD4+ T cells in the DRG secrete anti-inflammatory cytokines. I have read this paper and could not find any data that showed increased secretion of cytokines, only that there is an increase in T-cell populations that contain anti-inflammatory markers. Please consider rewording to reflect the observations made in the original paper.
• Figure 1A states that it is "day 14 PTX", however, there is no reference to this in the corresponding text - please state what Figure 1A is showing in the main text and legend regarding PTX treatment.
• Throughout the results section (Figure 3-Figure 6), the authors provide percentage changes in observed difference in expression, however, in addition to this, it would be valuable to have the actual number of neurons analysed for each group and sex.
• For Figure 5, can the authors confirm whether this was performed on tissue sections or dissociated cell culture? In addition, can the authors comment on whey surface expression for MHCII was not performed on the these reporter neurons?

Significance

This paper presents interesting data on the expression of MHCII on DRG neurons, which corroborates existing and published RNA expression data from the literature. In addition, this paper builds on our current understanding of how T-cells may be able to interact with DRG neurons in order to modulate their responses in instances of nerve injury. However, there are significant gaps in the data presented which prevent a more informative conclusion being drawn regarding the role of MHCII in modulating neuronal responses following PTX-induced CIPN.

Audience: I would suggest basic scientists working within the field of pain and neuroimmunology would be interested in this work.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Whitaker EE and co-authors implicate MHCII expression in DRG neurons in the resolution of pain following paclitaxel treatment. The authors demonstrate that CD4 T cells closely interact with DRG neurons, which also express MHCII proteins. They further characterize neuronal MHCII expression in naïve and paclitaxel treated mice in small diameter TRPV1+ neurons. Utilizing genetic animal models with MHCII knockout in TRPV1-lineage neurons, the authors highlight that loss of MHCII in TRPV1 neurons exaggerates cold sensitivity in naïve male mice, and in both sexes following paclitaxel treatment.

Major concerns:

The most pressing concern regarding this study is a lack of a vehicle control group. It is not appropriate to be comparing paclitaxel treated mice to naïve mice. Please include a vehicle treatment (cremophor:ethanol 1:1 diluted 1:3 in PBS) group for all experiments involving paclitaxel. This would also improve statistics as unpaired T tests comparing naïve vs paclitaxel is not convincing.

Figure 1A only includes representative images of a small number of T cells in presumable contact with DRG neurons in female Day 14 paclitaxel mice, but does not include images from other groups. Similarly, B-D show a single CD4+ T cell in contact with DRG neurons in Day 14 paclitaxel and naïve female mice. Please include quantification of the frequency of CD4+ T cells interacting with DRG neurons in the different experimental groups utilized in this study.

Please include entire blot for Figure 2A (or at least more of the blot). There is plenty of space in the figure and as it currently appears is not free from apparent manipulation.

The authors conclude that MHCII helps to suppress chemotherapy-induced peripheral neuropathy, resolving cold allodynia following paclitaxel treatment. To support this conclusion, I think it is necessary to include a time-course experiment highlighting whether cKO of MHCII in TRPV1 neurons indeed increases the duration for cold hypersensitivity to resolve following paclitaxel treatment.

Minor concerns:

The graphical abstract is misleading. The authors suggest paclitaxel is acting exclusively via TLR4 and that signaling is resolved at Day 14 which their data does not support. Please adjust to reflect findings from the experiments included in this study.

Figure 4 and 6 MHCII labelling is oversaturated in most of the images, creating a blurry hue in the representative images. This should be fixed

The effects of the PTX cHET group are very mild in both the male and female cohorts, and specific to 1 trial. I believe these assessments were conducted at Day 6 post injection. Why was this timepoint chosen considering differences in MHCII expression in small neurons was only present at Day 14 relative to naïve? The statistical analysis should also have been a mixed-effects repeated measures between groups ANOVA.

Significance

This study uncovers sex-dependent mechanisms underlying cold sensitivity between male and female mice. The detailed IHC analysis of MHCII expression in DRG neurons is a clear strength of this study, and supports flow cytometry results as well as existing literature. The specificity of MHCII expression on small diameter is well characterized and supported by conditional knockout mouse models of MHCII in TRVPV1-lineage neurons. The clear limitations of this study is the lack of a vehicle control group and limited behavioral analysis. They undermine the conclusions made by the author, and in extension, the significance of this study.

This study adds to the understanding of neuro-immune signaling in peripheral neuropathic pain. As far as I am aware, this is the first study to investigate MHCII expression in DRGs in relation to development of chemotherapy-induced peripheral neuropathy. Thus this study provides an incremental advance in neuroimmune mechanisms contributing to the development of chemotherapy-induced peripheral neuropathy in mice.

This study would be of interest to basic researchers interested in neuropathic pain, with particularly researchers with a focus on neuroimmunology and chemotherapy-induced peripheral neuropathy models. The sex differences observed in naïve mice would also be of interest to basic researchers within the wider pain field. Given the preliminary nature of the findings, I do not think this would be of interest to broader neuroimmunology or clinical audiences.

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Referee #1

Evidence, reproducibility and clarity

Section A:

The key conclusions of this study are quite robust and compelling.

While no claims need qualification clarification of some conclusions could improve the impact of this study.

Additional experiments are not essential to support the claims of this study.

Sufficient details are provided to allow reproduction of the key findings of this study.

It is not, yet, possible to conclude that all experiments are adequately powered as N's for some studies are not provided.

Significance

Section B:

• State what audience might be interested in and influenced by the reported findings.

This study should be of broad interest not only in the field of the neurobiology of pain but in broader issues related to neuroimmunology.<br /> - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

I am a clinician-scientist with clinical responsibilities in immunologic disorders and a basic scientist with expertise in the area of pain, including chemotherapy-induced painful peripheral neuropathies and neuroimmune mechanisms.<br /> - Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.<br /> - Place the work in the context of the existing literature (provide references, where appropriate).

The authors provide compelling evidence that MHCII expression of MHCII in primary sensory neurons, is regulated in painful chemotherapy-induced peripheral neuropathy (CIPN) induced by the commonly used taxane class of chemotherapy drugs, paclitaxel (PTX).

The present studies build on recent literature demonstrating that PTX CD4+ T cells in DRG. This is key to their hypothesis as T cells and anti-inflammatory cytokines protect against CIPN. In the present study these investigators studied how CD4+ T cells are activated role of cytokines released from these cells on CIPN. To key findings of the present study: the expression of functional MHCII protein in DRG neurons and the proximity of the DRG neurons and CD4+ T cells. While the MHCII protein was expressed in small-diameter, nociceptive, DRG neurons, in male mice, in females it was induced by PTX. Compatible with the hypothesis that the anti-inflammatory CD4+ T cells attenuate CIPN. Finally, in support of the contribution of this mechanism to CIPN pain, they demonstrated that attenuation of MHCII protein from nociceptors produced the predicted increase in cold hypersensitivity. Taken together their findings support suppression of CIPN by MHCII

While the experiments are well designed and executed and the results clearly presented, I have some relatively minor concerns that, if addressed, might improve the ability of a general scientific audience to appreciate the impact of the findings presented (possibly a penultimate paragraph covering caveats and limitations of the present study).

It is unclear what is meant by "novel" expression, used throughout the manuscript.

The statement at the end of the abstract, "and that neuronal MHCII may also contribute to many other neurological disorders" seems premature, beyond the scope of the present study.

While cold allodynia (hypersensitivity) is a clinically important feature of CIPN, especially in CIPN associated with the platinum based chemotherapeutic agents, it is less so taxane CIPN. Do 60% of patients with PTX CIPN express cold allodynia or does that number refer to CIPN in general? Again, the future direction of expanding studies of the role of MHCII in other aspects of the CIPN phenotype might bear mention. Is there any evidence that IL-4 and/or IL-10 influence cold sensitivity? Are these experiments run blinded?

The term "directly contacts" is unclear. No synaptic structure is identified. It might be more accurate to estimate the actual proximity between the two cells, especially as direct contact would not be necessary for the type of intercellular communication they are studying. This is not an EM study.

Two abbreviations are used for immunohistochemistry, ICC and IHC.

In some figure, group sizes are not indicated (e.g., Fig. 4D).

"small non-nociceptive neurons" - seems to refer to TRPV1+ neurons. There are, however, TRPV1-nociceptors.

"Therefore, the majority of MHCII+ neurons in the DRG of naïve female mice were not TRPV1-lineage neurons but non-nociceptive C-LTMRs." Could use some clarification here. Are the authors suggesting that being TRPV1- defines a neuron a non-nociceptive?

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Reply to the reviewers

We would like to thank all reviewers for their highly valuable comments, reviewing the article, and suggesting changes to improve its overall structure, clarity, and comprehension. Please find below our point-by-point responses to each reviewer’s comments. Lines, figures, and tables we refer to in the responses correspond to the clean copy of the revised manuscript.

Reviewer #1:

1) In the introduction the authors list 4 databases of ultra-conserved non-coding elements, and choose to work with the UCNEbase that detects UCNE regions by comparing human and chicken, as they state that it is one of the most comprehensive resources. Can the authors comment on the extent of overlap of UCNE regions identified in this database compared to other databases. It would also be helpful to add an explanation in the Introduction on the advantage of comparing the human genome to the chicken genome, e.g., is chicken the most distant vertebrate with a high quality genome, or another reason? And can the authors comment on the extent of conservation of the UCNEs compared to other species - in the UCNEbase paper, orthologues for the UCNEs are identified in 18 vertebrate species including reptiles, amphibians and fish.

We have addressed these comments and incorporated them in the introduction (lines 46-47; 49-52). As stated, it is not straightforward to evaluate to which extent UCNE regions overlap with those collected in other databases due to the different scopes and methods of these resources. We clarified why we selected this database, which was precisely based on the comments mentioned by the reviewer, i.e., sufficient evolutionary distance to identify functional regions confidently and high quality genome assemblies. Regarding the extent to which UCNEs are conserved in other species, the UCNEdatabase indeed provides additional information with respect to UCNE orthologues in other vertebrate species, including reptiles, amphibians, and fish. As we consider that this comparison is beyond the scope of the present study, it was not included in the main manuscript. However, to address this interesting point raised by the reviewer, we evaluated the proportion of UCNEs found in different species with respect to those annotated for human-chicken. As show in the figure below, UCNEs are conserved to a larger extent up to Xenopus tropicalis, for which most of the UCNEs annotated for human-chicken have corresponding orthologues in this species. Although to a minor extent, UCNEs are also conserved across more distant species (e.g., fish), for which approximately half of the UCNEs annotated for human-chicken have orthologues.

2) The authors briefly describe how potential target genes were assigned to UCNEs in the Method section (section begins on page 21, line 407 and on lines 379-381). They note that they used the Genomic Regions Enrichment of Annotations Tool (GREAT). Can the authors provide additional details on what this method does and also provide a high level sentence in the Results section on page 8, lines 144-146, on how target genes were assigned to UCNEs, as well as in the Discussion on page 16, line 289. Based on the Methods description on lines 409-414, a UCNE was associated with any gene within 1Mb of the UNCE that was expressed in retina and whose curated regulatory domains overlapped the UNCE. Is this correct? How were the regulatory domains curated (line 411-412)? Can the authors please clarify this important point.

Additional details about the GREAT algorithm have been included in the Methods section (lines 410-424) as well as a high-level sentences in the Results (lines 140-141) and Discussion (lines 276-280). It is correct that a UCNE was associated with any gene within 1Mb of the UCNE that was expressed in retina and whose curated regulatory domains overlapped the UCNE. As stated now (lines 420-424), these regulatory domains are supported by experimental evidence demonstrating that a gene is directly regulated by an element located beyond of its putative regulatory domain. We specified these domains for the utilized version of GREAT as well.

3) There are other approaches for linking putative transcriptionally active genomic regions with target genes in specific tissues or cell types through correlation analysis of ATAC-seq peaks (chromatin accessibility) and gene expression in RNA-seq data. A commonly used peak-to-gene linkage method is implemented in ArchR (Granja et al, 2021, PMID: 33633365). The authors note that they used scATAC-seq gene scores and scRNA-seq gene expression to further characterize the proposed target genes of UCNEs. It is not clear what the authors mean by "scATAC-seq gene scores". Please define "scATAC-seq gene score" and add a reference. Can the authors compare the target gene mapping to UCNEs using GREAT and gene expression filtering to that using peak-to-gene linkage based on retina tissue or single cell ATAC-seq and RNA-seq data?

We have defined explicitly what scATAC-seq gene scores are in the Methods section (lines 436-437) along with its reference. We have also addressed this important point and compared the overlap between the set of target genes predicted by GREAT and those assigned by the peak-to-gene linkage method implemented by ArchR. Details of this analysis, its results, interpretation are included in the Methods (lines 441-446), Results (lines 158-163; Supplementary Table 4), and Discussion (lines 280-282) sections.

4) For gene expression filtering (line 419) the authors quantify transcript expression of retina from FASTQ samples using the Kallisto method, and then note that they did the filtering on gene expression levels (TPM<0.5). Please add details on how you went from transcript expression levels to gene expression levels for the filtering, or was the filtering performed at the transcript level?

We have added details on how transcript-level quantification estimates were summarized at gene level, for which the filtering was performed (lines 429-430).

5) The authors use the words "active UCNE", first mentioned in the Results on line 144. Can the authors define what they mean by "active UCNE". What information/evidence is used to ascertain that a UCNE is indeed active. Overlap of a UCNE with a chromatin accessibility region from ATAC-seq or DNAase-Seq would only suggest that the UCNE may be active. Intersection with enhancer activity measured with in vivo enhancer reporter assays in transgenic mice from the VISTA enhancer browser provides stronger evidence of transcriptional activity. The authors might want to distinguish between putatively actively and active based on the functional support.

We thank the reviewer for this relevant comment to address the nuances of defining active UCNEs. The reviewer’s assumptions are correct and hence these terms were clarified throughout the entire text. The term functional is now only used when referring to UCNEs for which there is functional support (e.g., PAX6-associated UCNE in line 193) .

6) The authors assessed the significance of depletion of common variation (MAF>1%) in the UCNE regions compared to a background of randomly selected genomic regions. In generating the random distribution of regions, did the authors match on the distribution of distances of the UNCEs to the TSS of genes in the randomly selected regions? This may be a confounder. Also, in the legend of Figure 3, lines 191-192, it is stated: "Variant population frequencies within putative retinal UCNEs normalized to a background composed of randomly selected sequences (see Methods).", but we did not find a description of this analysis in the Methods section.

Evaluating the potential confounding effect of the genomic background was indeed a very important point. We have now incorporated the details showing the suitability of such background well as a detail description of how such background was generated (lines 479-481; Figure S1). Additionally, to further support our analysis demonstrating the depletion of common variation within UCNEs, we have included an evaluation of the distribution of genome-wide residual variation intolerance score (gwRVIS) values (PMID: 33686085) compared to this background of randomly selected genomic regions in a human reference cohort (lines 173-178; 487-495; Fig. 3C).

7) In regards to intersecting UCNEs with epigenetic marks that detect active or repressed enhancers in retina, the authors used data from Aldiri et al 2017 that measured epigenetic changes during retinal development. Did this dataset contain epigenetic measurements in adult retina? The authors might want to consider using the epigenetic marks/ChIP-seq data from adult human retina in Cherry et al. PNAS 2020 (PMID: 32265282)

We have incorporated the adult-stage data suggested by the reviewer to provide a more comprehensive characterization. Details about the integration of this dataset as well as the results and their corresponding interpretation have been incorporated in the Methods (lines 372-374), Results (lines 115-117; Supplementary Table 2), and Discussion (lines 271-273) sections accordingly.

8) With respect to the examination of rare variants that may be associated with rare eye disease in retina active UCNEs, for the interpretation of the results, it would be helpful to get more information on the distribution of rare variants found in UCNEs associated in this study to known IRD genes in all affected individuals in families, if this information is available in the 100,000 Genomes Project.

Although it is indeed a relevant point, this information cannot be retrieved in the 100,000 Genomes Project. As it is a restricted research environment, we are only allowed to query sequencing data corresponding to participants enrolled within the framework of our specific sub-domain, namely “Hearing and Sight”, and therefore evaluating the distribution of rare variants in all affected individuals is not feasible.

9) In the Methods section on lines 450-451, the authors mention that they performed variant screening of retinal disease genes, referencing the Genomics England Retinal Disorder panel and Martin et al., 2019. Can the authors add to the Methods and Results sections how many retinal disease genes were initially tested. Also, to get a sense of the specificity of the overlap of rare variants in the 100k Genome Project cohort with UNCE regions, it would be informative to show a distribution of the number of rare variants <0.5% that passed the filtering in gnomAD per eye disease gene before the overlap with UNCEs.

We specified the number of retinal genes that were tested in the Method section (line 471). In addition, as suggested by the reviewer, we generated allele frequency distributions for all variants retrieved within a selection of 25 disease-gene associated loci and their corresponding UCNEs in order to assess the specificity of the overlap between rare variants and UCNE regions (lines 181-182, 496-501; Figure S2).

10) The authors found "an ultrarare SNV (chr11:31968001T>C) within a candidate cis-regulatory UCNE located ~150kb upstream of PAX6. This variant was found in four individuals of a family segregating autosomal dominant foveal abnormalities". They tested the functional effect of this element with a reporter assay in zebrafish and found that the UNCE affects expression in the eye, forebrain, and neural tube. It would add further value if the authors were to test the effect of this SNV in the UCNE on the reporter expression pattern, using CRISPR/cas9?

That is a very relevant point. We have tested the effect of this SNV in the UCNE on the reported expression pattern using the same experimental setup that we used for testing the wild-type construct, namely transgenic enhancer zebrafish assays. However, we could not obtain conclusive results, most likely due to the limitations posed by testing these regions outside their native genomic context. Therefore, additional experimental work (e.g., CRISPR-based) should be performed to address this question. This is, however, beyond the scope of the present study, for which the main focus was the identification and functional annotation of ultraconserved cCREs. We have incorporated the details, results, and interpretation of the assays performed mutant construct in the Methods (lines 525-527; Supplementary Table 12), Results (lines 235-238; Supplementary Table 10), and Discussion (lines 350-353) sections.

11) The authors found rare variants in UCNEs linked to 45 IRD genes. Can the authors provide additional information on the functional genomic annotations of these UCNEs and distance to the target genes. The UCNEs were characterized with respect to their genic features in the original paper (UCNEbase, Dimitrieva et al., NAR, 2013), e.g., intergenic, intronic and 3'/5' UTR. Also, it would be useful for clinical applications to provide the start and end positions of the UCNEs that contain the rare variants associated with their 45 IRD genes in Supplementary Table 6.

Additional functional genomic annotations, genic features following those of the original UCNE paper, and the distance to the TSS of these 45 target disease-associated genes have been incorporated in (new) Supplementary Table 5. The start and end positions of the UCNEs that contain the rare variants have also been indicated in new Supplementary Table 7.

12) A total of 724 target genes were assigned to 1,487 UCNEs that displayed candidate cis-regulatory activity. Given the interest in using UCNEs to help identify potential pathogenic mutations that lead to IRDs, can the authors note in the Results section how many of the 724 target genes are IRDs.

We thank the reviewer for this important point. From the total of 724, a total of 27 genes are annotated as IRD genes, of which (interestingly) 23 were kept as found to be expressed in the retina. This has been clarified in the Results section (line 166-168).

13) In the Discussion on page 15 line 259, can the authors clarify if variation found in UCNEs were only associated with rare disease or also with common diseases.

We have clarified that variation found in UCNEs has only been associated with rare diseases (line 247).

Minor edits:

1) In abstract, the authors might consider changing the words "rare eye diseases" on lines 20 and 22 to "rare retina degeneration diseases", and on lines 88-89.

We thank the reviewer for this comment. However, we consider that rare eye diseases is a more suitable term for our purpose as it includes diseases primarily characterized by stationary and non-progressive phenotypes such as North Carolina Macular Dystrophy and fovea hypoplasia.

2) In the Introduction on line 49, there seems to be a typo in the number of UCNE regions reported. 4,135 UCNE regions is supposed to be 4,351, based on the original paper (https://academic.oup.com/nar/article/41/D1/D101/1057253).

We have corrected this typo accordingly.

3) In the introduction on lines 75-76, these references: Lyu et al., Cell Reports 2021, PMID: 34788628, and Zhang et al., Trends in Genetics 2023, PMID: 37423870, could be added to the following sentence to provide additional: "This cellular complexity is the result of spatiotemporally controlled gene expression programs during retinal development”.

We have now included these relevant references.

4) On lines 77 and 84, I would write IRD as plural: IRDs.

This has been amended in the new version.

5) In introduction on lines 89-90, it can be further added that you provide experimental support for an ultra rare SNV in a cis regulatory UCNE affecting PAX6.

We have explicitly stated that we provided functional evidence for this UCNE.

6) On line 98, the authors refer to Figure 1A when noting that the integration of UCNEs with multi-omics data in human retina allows to evaluate the regulatory capacity of UCNEs across the major developmental stages of human retina. However panel A in figure 1 does not seem to show this point. It shows the comparison of elements across species. Please make appropriate changes to the main text and figure legend.

We have made the appropriate changes and located the reference to this figure in a more relevant part of the text (line 87).

7) Please explain what the names appended to the gene symbols in the first column "UCNE ID" in Supplementary Tables 1 and 2 refer to.

We have clarified what these refer to.

8) On line 145, can the authors clarify what they mean by "active gene expression in the retina". Is this just another way of referring to genes found to be expressed in retina? If so, it might be clearer just to write: "We annotated the identified active UCNEs to assign them potential target genes and thus assess their association with genes expressed in the retina"

We indeed meant genes found to be expressed in retina. As this phrasing might not be completely clear, we have now changed to the wording suggested by the reviewer.

9) One line 156, I would write "regulation of transcription" as listed in the gene ontology terms in Figure 2C, instead of "regulation of gene expression". The authors might want to add this to the Discussion. Can the authors include the full gene set enrichment results from Enrichr in a supplementary table at Padj<0.05 since only the top gene sets are shown in Fig. 2C (at P<1E-13)?

We changed the term to “regulation of transcription” to keep the nomenclature consistent to that of Figure 2C. We have also provided a full gene set enrichment from Enrichr as well (Supplementary Table 3).

10) On page 12, line 214, what does "EH38E1530321" Stand for? It seems to specify a distal enhancer-like signatures in bipolar neurons, but I couldn't find this ID in any database.

This refers to the identifier of ENCODE:

https://screen-v2.wenglab.org/search/?q=EH38E1530321&assembly=GRCh38

Additionally, when mentioning a specific UCNE, VISTA enhancer, or ENCODE cCRE (as in this case), we have explicitly included its corresponding identifier.

11) In the Methods section on lines 391-392, can the authors give some high-level explanation of the unconstrained integration method: "Single-nucleus RNA-seq of the same tissue and timepoints (GSE183684) were integrated using the unconstrained integration method". Also, can they comment on how retinal cell class identities were assigned (line 393). Was it based on known markers or on previous identification of cell classes and highly variable genes between clusters?

We have included a high-level explanation of the unconstrained method in the Methods section (lines 387-392). We also clarified that the assignment of cell class identities was based on known markers (line 394).

12) In the integration of UCNEs with bulk and single cell ATAC-seq and Dnase hypersentitivity regions, can the authors note in the Methods section (lines 400-404) what peak width was used to test for overlap with the UCNEs.

We have specified the peak widths that were used for the overlap with UCNEs (lines 397 and 403).

13) On line 436, the word 'and' is missing between "(SNVs, and indels < 50bp)" and "large structural variants".

This has been corrected.

14) On lines 443-444, please provide references to the computational tools listed. Please note if default settings/parameters were used.

We have specified that default parameters were used in the analysis (line 464).

15) In the following sentence in the Methods section on lines 447-449, it is not clear in the Results section how this was used in the flow of the analysis, and how many cases showed such a similarity in phenotype: "For each candidate variant, we compared the similarities between the participant phenotype (HPO terms) and the ones known for its target gene through literature search and clinical assessment by the recruiting clinician when possible." Can the authors add more detail to the Results section.

As the evaluation of the candidate variants was essentially performed on a case-by-case basis, we opted to include a rather general description of the workflow, which indeed included a comparison of the reported phenotypes with those associated with the putative target gene. An example of such comparison has been included in the Results (lines 186-187) section regarding the cases for which a NCMD-like phenotype was suspected.

16) It would be helpful to have a table that describes the different omics datasets used in the paper, with some basic annotations (tissue type, sample size, reference).

This has now been incorporated in Supplementary Table 11.

17) Can the authors add references to their sentence in the Discussion on page 17 lines 299-301: "As has been shown before, the phenotype caused by a coding mutation of a developmental gene can be different from the phenotype caused by a mutation in a CRE controlling spatiotemporal expression of this gene."

We clarified that this phrase referred to the case of PRDM13, for which bi-allelic coding pathogenic variants are linked to hypogonadotropic hypogonadism and perinatal brainstem dysfunction in combination with cerebellar hypoplasia (Whittaker et al., 2021), while non-coding variants within its regulatory regions are associated with NCMD.

Reviewer #2:

Minor discretionary suggestions for improving the presentation:

1) Wherever a specific UCNE, Vista enhancer or ENCODE cCRE is mentioned, the element should be identified by name or accession code: For instance (Iine 212): "this variant is located within a UCNE (PAX6_Veronica) that is catalogued as a cCRE in ENCODE (EH38E1530321)". UCNE names are particularly important, because they are systematically used as identifiers in the supplemental Tables and thus would enable the reader to easily find additional information about the element mentioned in the main text.

We have now explicitly included all corresponding identifiers throughout the text.

2) I also recommend inclusion of the UCNE, Vista and ENCODE cCRE tracks in all genome browser screen shots. The UCNE track is currently included only in Figure 1. Vista and ENCODE cCRE tracks are missing in all browser views.

We have now included UCNE, VISTA, and ENCODE cCREs tracks in the main genome browser figures (Figures 1 and 4).

3) Supplementary Table 6: It would be useful to indicate for each variant, the type of ophthalmological disorder (Table S5, column C) it is associated with.

We agree this is a relevant point. However, due to limitations in the (bulk) export of clinical information from the protected Research Environment of Genomics England, inclusion of this type of information is not feasible.

4) Fig S2 and supplementary Table 3 are not referred to in the main Text.

We have corrected this and updated the figure and table accordingly.

5) Supplementary Table 8: The Table caption should be expanded.

The contents of each column should be explained. For instance, column F: what means Homo_sapiens|M01298_1.94d|Zoo_01|2337? Where does this information come from, what data and software resources were used?

We have expanded the caption of this table to clarify this output, which is derived from the QBiC-Pred tool, a software used for predicting quantitative TF binding changes due to nucleotide variants.

6) Line 401 probable typo: 103-105 days (103-125?)

Indeed, this typo has now been corrected.

Reviewer #3:

1) Given that UCNE only accounts for a small fraction of gene regulatory elements, this study is likely with low sensitivity in terms of identifying potential regulatory mutations. Although one would expect that variants in UCNE are more likely to be pathogenic, it is hard to extrapolate from the results to assess the contribution of gene regulatory variant to the disease.

We agree that restricting our analysis to these particular regions is one of the limitations of the study, as stated in the Discussion section (line 304-311). However, one of our main aims was to provide a strategy to reduce the search space for pathogenic variants with a potential regulatory effect. Given the substantial body of literature supporting a regulatory role for these regions and, particularly, the availability of already-existing functional data, we considered that this set of regions could represent a suitable target for such analysis. Indeed, the features evaluated, and the methods presented in this study could be extrapolated and applied in other settings involving other candidate regulatory regions and/or tissues of interest, and their associated disease-phenotypes, for which, in any case, the overall contribution of regulatory pathogenic variation to disease might vary greatly.

2) I am wondering how many UCNE overlaps with open chromatin regions specific to the fetal retina and how many UCNE overlaps with adult only. Are UCNEs enriched for developmental genes? If so, how many patients are due to developmental defect?

We have now integrated into our analysis epigenetic measurements in adult retina, in particular the candidate cis-regulatory elements nominated by Cherry et al. PNAS 2020 (PMID: 32265282) based on accessible chromatin and enrichment for active enhancer-related histone modifications in adult human retina. Details about the integration of this dataset as well as the results and their corresponding interpretation have been incorporated in the Methods (lines 372-374), Results (115-117; Supplementary Table 2), and Discussion (lines 271-273) sections accordingly. In particular, out of the 111 UCNEs identified to display the active enhancer mark H3K27ac, 33 were found to maintain this signature at adult stage. Regarding the specific question from the reviewer, the majority of UCNEs overlapping with open chromatin regions are specific to the fetal retina (1,346), with only 7 UCNEs overlapping with open chromatin regions exclusively in adult state. This indeed further supports the major role of the identified candidate cis-regulatory UCNEs in the regulation of developmental gene expression programs, which was already suggested by the Gene Ontology enrichment analysis performed using the set of UCNE target genes as input (Figure 2C; new Supplementary Table 3). As far as the number of patients with development defects that were included in this study, these included: corneal abnormalities (n=62), Leber congenital amaurosis (n=142), ocular coloboma (n=111), developmental foveal and macular dystrophy (n=230), developmental glaucoma (n=94), anophthalmia or microphthalmia (n=120).

3) I am wondering if the 431 ultrarare variants found in the UCNEs is higher than expected. This can be tested by comparing patients without visual disorders.

Although it is indeed a relevant point, retrieving sequencing data from patients without visual disorders is not feasible for us. As it is a restricted research environment, we are only allowed to query sequencing data corresponding to participants enrolled within the framework of our specific sub-domain, namely “Hearing and Sight”, and therefore evaluating additional patients from other sub-domains is not doable. Based on previous studies and our observations, common variants are precisely the ones depleted within UCNEs, while ultrarare variation seems to occur at levels comparable to those observed elsewhere in the genome. Therefore, it is reasonable to speculate that this amount of ultrarare variants is not higher than expected as compared to patients without visual disorders. To further demonstrate the high intolerance of UCNEs to common variation, we have included an evaluation of the distribution of genome-wide residual variation intolerance score (gwRVIS) values compared to a set of randomly selected genomic regions in a human reference cohort (lines 173-178; 487-495; Fig. 3C). Additionally, to address this question further, we have also generated allele frequency distributions for all variants retrieved within a selection of 25 disease-gene associated loci and their corresponding UCNEs in order to assess the specificity of the overlap between rare variants and UCNE regions (lines 181-182, 496-501; Figure S2).

4) It seems that the ultrarare variants listed in sup table 6 are more abundant in a small number of genes. Is this due to the number/size of UCNEs is larger in these genes?

Indeed, the clustering of UCNEs in genomic regions containing genes coding for transcription factors and developmental regulators (e.g., OTX2, PAX6, ZEB2) seems to be one of their intrinsic properties, hence the observed enrichment for a small number of genes. One reason can be that these neighboring UCNEs cooperate to achieve higher degrees of tissue-specific regulatory accuracy needed for these genes.

5) The variant in the putative Pax6 gene regulatory element is intriguing. It would be much more informative if the enhancer with and without the variant is tested in parallel in fish.

That is a very relevant point. We have now tested the effect of this SNV in the UCNE on the reported expression pattern using the same experimental setup that we used for testing the wild-type construct, namely transgenic enhancer zebrafish assays. However, we could not obtain conclusive results, most likely due to the limitations posed by testing these regions outside their native genomic context. We have incorporated the details, results, and interpretation of the assays performed mutant construct in the Methods (lines 525-527; Supplementary Table 12), Results (lines 235-238; Supplementary Table 10), and Discussion (lines 350-353) sections.

6) (optional) it would be quite interesting to check the phenotype in fish or mice with the element repressed via technique such as CRISPRi.

Indeed, we fully agree that CRISPR-based techniques would be the ideal experimental approaches to further validate the functionality of the PAX6-associated UCNE and the identified variant in their native genomic context. Conducting these detailed and focused mechanistic studies is, however, beyond the scope of the present work, for which the main focus was the identification and functional annotation of ultraconserved cCREs.

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Referee #3

Evidence, reproducibility and clarity

In the report, the authors combined bulk and single cell multi-omics data from the retina to identify ultraconserved noncoding elements (UCNE) that might function as regulatory elements based on chromatin openness, DNAse sensitive regions, and histone marks. The candidate UCNEs are intersected with whole genome sequencing data from 3.220 patients with rare eye disease to identify potential rare variants that might affect the activity of UNCE. The goal of the project is intriguing.

My comments are listed below:

1. Given that UCNE only accounts for a small fraction of gene regulatory elements, this study is likely with low sensitivity in terms of identifying potential regulatory mutations. Although one would expect that variants in UCNE are more likely to be pathogenic, it is hard to extrapolate from the results to assess the contribution of gene regulatory variant to the disease.
2. I am wondering how many UCNE overlaps with open chromatin regions specific to the fetal retina and how many UCNE overlaps with adult only. Are UCNEs enriched for developmental genes? If so, how many patients are due to developmental defect?
3. I am wondering if the 431 ultrarare variants found in the UCNEs is higher than expected. This can be tested by comparing patients without visual disorders.
4. It seems that the ultrarare variants listed in sup table 6 are more abundant in a small number of genes. Is this due to the number/size of UCNEs is larger in these genes?
5. The variant in the putative Pax6 gene regulatory element is intriguing. It would be much more informative if the enhancer with and without the variant is tested in parallel in fish.
6. (optional) it would be quite interesting to check the phenotype in fish or mice with the element repressed via technique such as CRISPRi.

Significance

Given that UCNE only accounts for a small fraction of gene regulatory elements, this study is likely with low sensitivity in terms of identifying potential regulatory mutations. Although one would expect that variants in UCNE are more likely to be pathogenic, it is hard to extrapolate from the results to assess the contribution of gene regulatory variant to the disease.

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Referee #2

Evidence, reproducibility and clarity

Starting with a comprehensive analysis of multi-omics data, the authors identify a subset of ultra-conserved non-coding elements (UCNEs) likely to play a role in retinal development. Restricting subsequent analyses to these genomic regions, they identify ultra-rare mutations associated with eye disease, using data from the 100k genome project. They then follow-up on one newly discovered, disease associated UCNE and a presumably causal mutation within this UCNE. The follow-up experiments involve fine mapping of the spatiotemporal expression pattern of a UCNE-driven reporter gene in zebra fish, as well as a re-examination of the disease phenotype in carriers of the mutation.

The computational pipeline used for prioritization of UCNEs is sound. The evidence supporting the claim that the identified ultra-rare SNV is causal is highly convincing. This study also constitutes proof of concept for a novel methodology to search for causal disease associated mutations in the nowadays still under-investigated non-coding part of the genome.

The paper is clearly written. Methods are described in enough detail to allow for reproduction of the results. Overall, this study is of high scientific quality, self-contained, and complete. Publication in a peer-reviewed journal should not be delayed by additional, perhaps interesting but non-essential experiments.

However, if the authors intend to undertake similar studies in the future, I would recommend to carry out the reporter-gene assays in zebrafish with both the wild-type and the mutant version of the UCNE. Comparison of the spatiotemporal expression patterns of the two alleles could provide valuable insights into the mechanism of action of the deleterious mutation under investigation.

Minor discretionary suggestions for improving the presentation:

Wherever a specific UCNE, Vista enhancer or ENCODE cCRE is mentioned, the element should be identified by name or accession code: For instance (Iine 212):

"this variant is located within a UCNE (PAX6_Veronica) that is catalogued as a cCRE in ENCODE (EH38E1530321)"

UCNE names are particularly important, because they are systematically used as identifiers in the supplemental Tables and thus would enable the reader to easily find additional information about the element mentioned in the main text.

I also recommend inclusion of the UCNE, Vista and ENCODE cCRE tracks in all genome browser screen shots. The UCNE track is currently included only in Figure 1. Vista and ENCODE cCRE tracks are missing in all browser views.

Supplementary Table 6: It would be useful to indicate for each variant, the type of ophthalmological disorder (Table S5, column C) it is associated with.

Fig S2 and supplementary Table 3 are not referred to in the main Text.

Supplementary Table 8: The Table caption should be expanded. The contents of each column should be explained. For instance, column F: what means Homo_sapiens|M01298_1.94d|Zoo_01|2337? Where does this information come from, what data and software resources were used?

Line 401 probable typo: 103-105 days (103-125?)

Significance

This work is of interest to different research communities: Biomedical researchers working on neural disorders, human geneticists engaged in GWAS studies, computational biologists trying to make sense out of omics data, molecular biologists exploring the "dark matter" of the genome, and finally the small community tackling the enigma of UCNEs. As with many omics papers, the most valuable parts of this study are in the supplemental tables, in particular tables 1,4, and 6. It can be hoped that some prospective readers will follow up on the leads presented in these tables.

The detailed computational and experimental characterization of a likely causal ultra-rare disease associated mutation may serve as a guiding and motivating example for medical geneticists working on other syndromes.

Back to UCNEs: They are enigmatic entities, which so far have largely resisted molecular and physiological characterization. It took 10 years to finally uncover a phenotype in ko mice missing one or several UCNEs, after the surprising initial observation that such mice were viable and fertile. The difficulties in studying the function of UCNEs may be due to their conjectured pleiotropic activity in different cell types at different developmental stages, their apparent cooperative interactions with many other control elements (limiting the power of reporter gene assays with single elements), and their putative involvement in morphogenetic processes (minimizing the relevance of epigenetic data collected from cell lines). In view of these considerations, I consider UCNE research starting from human disease phenotypes more promising, than ab initio approaches using reverse genetics in model organisms.

The impact of this paper is potentially very high. Note the following statement in the paper:

"For each instance for which only the UCNE variant remained as candidate, we placed a clinical collaboration request with Genomics England."

We thus can expect more exiting stories from the same team. The strategy and computational pipeline introduced here are of course applicable to other congenital diseases, and it can reasonably be hoped that researchers inspired by this study will apply components of the methodology in other contexts. The prioritization of UCNEs in studying the "dark matter" of the genome and the "missing heredity" would likely lead to new insights into the function of these enigmatic elements and the reasons for their extreme conservation.

My background: I'm a bioinformatician with first training in molecular genetics. My research focus is on gene regulation: promoters, enhancers, transcription factor binding sites. I also made some contributions to the UCNE field, having co-developed UNCEbase with Slavica Dimitrieva. On the other hand, I don't claim to be an expert in medical genetics, and more specifically, I know very little about eye diseases and retinal development.

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Referee #1

Evidence, reproducibility and clarity

In the manuscript entitled "Multi-omics analysis in human retina uncovers ultraconserved cis-regulatory elements at rare eye disease loci", Soriano et al. explore the role of ultraconserved noncoding elements (UCNEs) as candidate cis-regulators (cCREs) in developing and adult human retina by integrating UCNEs with multi-omic data from fetal and adult human retina. They further examine the potential contribution of UCNEs to rare eye diseases by testing for rare variants that fall in UCNEs that are potentially active in retina in patients with inherited retina degeneration diseases (IRDs), using whole genome sequencing (WGS) data from Genomics England. This work is based on the assumption that genomic regions under strong evolutionary constrain are good predictors of important functionality. The authors use 4,135 UCNEs identified in Dimitrieva and Bucher, 2013, defined as sequences with more than 95% identity between human and chicken with >200bp in length. By integrating bulk tissue RNA-Seq, single cell (sc)RNA-Seq, DNAase-Seq, scATAC-Seq and ChIP-Seq from retinal tissues from different developmental stages and adulthood with the 4,135 UCNEs, they identified one third of UCNEs (1,487) that may be associated with various retinal development stages. Using bioinformatic approaches they identify potential target genes for 724 UCNEs that may be regulated in cis. Based on ATAC-Seq, the authors show that 834 UCNEs fall in open chromatin regions, and of these 111 have active histone markers, supporting an active functional role for the identified UCNEs. The authors demonstrate an application of this approach in identifying potential pathogenic noncoding variants for rare eye diseases. Using WGS from 100,000 Genomes project the authors identify 45 UCNEs that were bioinformatically associated with mendelian IRD genes that contain rare variants, proposing potential solutions for unsolved cases. They demonstrate the utility of their analysis by highlighting an ultra-rare SNP they identified in a UCNE candidate CRE located upstream of PAX6 that belongs to a family of genes with foveal abnormalities. They confirm the effect of the UCNE 150k upstream of PAX6 on gene expression in the eye, forebrain and neuronal tube using an enhancer reporter assay in Zebrafish.

The authors demonstrate a strategy for narrowing down the genomic space to putative functional noncoding regions involved in development and that may contribute to rare disease. This work that integrates various genomic modalities is of broad interest and can be applied to other diseases and tissues. There are several points that should be addressed to assess the robustness of the results and strengthen the conclusions of the paper, and in some places to better clarify the analyses conducted.

Major edits:

1. In the introduction the authors list 4 databases of ultra-conserved non-coding elements, and choose to work with the UCNEbase that detects UCNE regions by comparing human and chicken, as they state that it is one of the most comprehensive resources. Can the authors comment on the extent of overlap of UCNE regions identified in this database compared to other databases. It would also be helpful to add an explanation in the Introduction on the advantage of comparing the human genome to the chicken genome, e.g., is chicken the most distant vertebrate with a high quality genome, or another reason? And can the authors comment on the extent of conservation of the UCNEs compared to other species - in the UCNEbase paper, orthologues for the UCNEs are identified in 18 vertebrate species including reptiles, amphibians and fish.
2. The authors briefly describe how potential target genes were assigned to UCNEs in the Method section (section begins on page 21, line 407 and on lines 379-381). They note that they used the Genomic Regions Enrichment of Annotations Tool (GREAT). Can the authors provide additional details on what this method does and also provide a high level sentence in the Results section on page 8, lines 144-146, on how target genes were assigned to UCNEs, as well as in the Discussion on page 16, line 289. Based on the Methods description on lines 409-414, a UCNE was associated with any gene within 1Mb of the UNCE that was expressed in retina and whose curated regulatory domains overlapped the UNCE. Is this correct? How were the regulatory domains curated (line 411-412)? Can the authors please clarify this important point.

There are other approaches for linking putative transcriptionally active genomic regions with target genes in specific tissues or cell types through correlation analysis of ATAC-seq peaks (chromatin accessibility) and gene expression in RNA-seq data. A commonly used peak-to-gene linkage method is implemented in ArchR (Granja et al, 2021, PMID: 33633365). The authors note that they used scATAC-seq gene scores and scRNA-seq gene expression to further characterize the proposed target genes of UCNEs. It is not clear what the authors mean by "scATAC-seq gene scores". Please define "scATAC-seq gene score" and add a reference. Can the authors compare the target gene mapping to UCNEs using GREAT and gene expression filtering to that using peak-to-gene linkage based on retina tissue or single cell ATAC-seq and RNA-seq data?

For gene expression filtering (line 419) the authors quantify transcript expression of retina from FASTQ samples using the Kallisto method, and then note that they did the filtering on gene expression levels (TPM<0.5). Please add details on how you went from transcript expression levels to gene expression levels for the filtering, or was the filtering performed at the transcript level?<br /> 3. The authors use the words "active UCNE", first mentioned in the Results on line 144. Can the authors define what they mean by "active UCNE". What information/evidence is used to ascertain that a UCNE is indeed active. Overlap of a UCNE with a chromatin accessibility region from ATAC-seq or DNAase-Seq would only suggest that the UCNE may be active. Intersection with enhancer activity measured with in vivo enhancer reporter assays in transgenic mice from the VISTA enhancer browser provides stronger evidence of transcriptional activity. The authors might want to distinguish between putatively actively and active based on the functional support.<br /> 4. The authors assessed the significance of depletion of common variation (MAF>1%) in the UCNE regions compared to a background of randomly selected genomic regions. In generating the random distribution of regions, did the authors match on the distribution of distances of the UNCEs to the TSS of genes in the randomly selected regions? This may be a confounder.

Also, in the legend of Figure 3, lines 191-192, it is stated: "Variant population frequencies within putative retinal UCNEs normalized to a background composed of randomly selected sequences (see Methods).", but we did not find a description of this analysis in the Methods section.<br /> 5. In regards to intersecting UCNEs with epigenetic marks that detect active or repressed enhancers in retina, the authors used data from Aldiri et al 217 that measured epigenetic changes during retinal development. Did this dataset contain epigenetic measurements in adult retina? The authors might want to consider using the epigenetic marks/ChIP-seq data from adult human retina in Cherry et al. PNAS 2020 (PMID: 32265282)<br /> 6. With respect to the examination of rare variants that may be associated with rare eye disease in retina active UCNEs, for the interpretation of the results, it would be helpful to get more information on the distribution of rare variants found in UCNEs associated in this study to known IRD genes in all affected individuals in families, if this information is available in the 100,000 Genomes Project.<br /> 7. In the Methods section on lines 450-451, the authors mention that they performed variant screening of retinal disease genes, referencing the Genomics England Retinal Disorder panel and Martin et al., 2019. Can the authors add to the Methods and Results sections how many retinal disease genes were initially tested. Also, to get a sense of the specificity of the overlap of rare variants in the 100k Genome Project cohort with UNCE regions, it would be informative to show a distribution of the number of rare variants <0.5% that passed the filtering in gnomAD per eye disease gene before the overlap with UNCEs.<br /> 8. The authors found "an ultrarare SNV (chr11:31968001T>C) within a candidate cis-regulatory UCNE located ~150kb upstream of PAX6. This variant was found in four individuals of a family segregating autosomal dominant foveal abnormalities". They tested the functional effect of this element with a reporter assay in zebrafish and found that the UNCE affects expression in the eye, forebrain, and neural tube. It would add further value if the authors were to test the effect of this SNV in the UCNE on the reporter expression pattern, using CRISPR/cas9?<br /> 9. The authors found rare variants in UCNEs linked to 45 IRD genes. Can the authors provide additional information on the functional genomic annotations of these UCNEs and distance to the target genes. The UCNEs were characterized with respect to their genic features in the original paper (UCNEbase, Dimitrieva et al., NAR, 2013), e.g., intergenic, intronic and 3'/5' UTR. Also, it would be useful for clinical applications to provide the start and end positions of the UCNEs that contain the rare variants associated with their 45 IRD genes in Supplementary Table 6.<br /> 10. A total of 724 target genes were assigned to 1,487 UCNEs that displayed candidate cis-regulatory activity. Given the interest in using UNCEs to help identify potential pathogenic mutations that lead to IRDs, can the authors note in the Results section how many of the 724 target genes are IRDs.<br /> 11. In the Discussion on page 15 line 259, can the authors clarify if variation found in UNCEs were only associated with rare disease or also with common diseases.

Minor edits:

1. In abstract, the authors might consider changing the words "rare eye diseases" on lines 20 and 22 to "rare retina degeneration diseases", and on lines 88-89.
2. In the Introduction on line 49, there seems to be a typo in the number of UCNE regions reported. 4,135 UCNE regions is supposed to be 4,351, based on the original paper (https://academic.oup.com/nar/article/41/D1/D101/1057253).
3. In the introduction on lines 75-76, these references: Lyu et al., Cell Reports 2021, PMID: 34788628, and Zhang et al., Trends in Genetics 2023, PMID: 37423870, could be added to the following sentence to provide additional: "This cellular complexity is the result of spatiotemporally controlled gene expression programs during retinal development,"
4. On lines 77 and 84, I would write IRD as plural: IRDs.
5. In introduction on lines 89-90, it can be further added that you provide experimental support for an ultra rare SNV in a cis regulatory UCNE affecting PAX6.
6. On line 98, the authors refer to Figure 1A when noting that the integration of UCNEs with multi-omics data in human retina allows to evaluate the regulatory capacity of UCNEs across the major developmental stages of human retina. However panel A in figure 1 does not seem to show this point. It shows the comparison of elements across species. Please make appropriate changes to the main text and figure legend.
7. Please explain what the names appended to the gene symbols in the first column "UCNE ID" in Supplementary Tables 1 and 2 refer to.
8. On line 145, can the authors clarify what they mean by "active gene expression in the retina". Is this just another way of referring to genes found to be expressed in retina? If so, it might be clearer just to write: "We annotated the identified active UCNEs to assign them potential target genes and thus assess their association with genes expressed in the retina"
9. One line 156, I would write "regulation of transcription" as listed in the gene ontology terms in Figure 2C, instead of "regulation of gene expression". The authors might want to add this to the Discussion. Can the authors include the full gene set enrichment results from Enrichr in a supplementary table at Padj<0.05 since only the top gene sets are shown in Fig. 2C (at P<1E-13)?
10. On page 12, line 214, what does "EH38E1530321" Stand for? It seems to specify a distal enhancer-like signatures in bipolar neurons, but I couldn't find this ID in any database.
11. In the Methods section on lines 391-392, can the authors give some high-level explanation of the unconstrained integration method: "Single-nucleus RNA-seq of the same tissue and timepoints (GSE183684) were integrated using the unconstrained integration method". Also, can they comment on how retinal cell class identities were assigned (line 393). Was it based on known markers or on previous identification of cell classes and highly variable genes between clusters?
12. In the integration of UCNEs with bulk and single cell ATAC-seq and DNase hypersentitivity regions, can the authors note in the Methods section (lines 400-404) what peak width was used to test for overlap with the UCNEs.
13. On line 436, the word 'and' is missing between "(SNVs, and indels < 50bp)" and "large structural variants".
14. On lines 443-444, please provide references to the computational tools listed. Please note if default settings/parameters were used.
15. In the following sentence in the Methods section on lines 447-449, it is not clear in the Results section how this was used in the flow of the analysis, and how many cases showed such a similarity in phenotype: "For each candidate variant, we compared the similarities between the participant phenotype (HPO terms) and the ones known for its target gene through literature search and clinical assessment by the recruiting clinician when possible." Can the authors add more detail to the Results section.
16. It would be helpful to have a table that describes the different omics datasets used in the paper, with some basic annotations (tissue type, sample size, reference)
17. Can the authors add references to their sentence in the Discussion on page 17 lines 299-301: "As has been shown before, the phenotype caused by a coding mutation of a developmental gene can be different from the phenotype caused by a mutation in a CRE controlling spatiotemporal expression of this gene."

Significance

General assessment: This is the first study to our knowledge to integrate ultraconserved noncoding elements (UNCEs) with a range of multi-omic data to identify UNCEs that may be active and their target genes in a specific tissue, in this case human retina. They also experimentally test one of the UNCE predicted to affect the expression of a given gene in retina (and potentially other tissues) in Zebrafish and confirm its transcriptional activity in the eye, to provide some functional support to their strategy. This study also demonstrates the potential value of inspecting UCNEs in prioritizing pathogenic mutations for rare disease. One limitation is the lack of statistical significance assessment of the potential causal effect of a rare variant found in a UCNE on the expression of its predicted target gene, which is a rare eye disease gene, since the linkage of the UCNE to the disease gene was performed based on bioinformatic analysis of multi-omic data. Experimental testing of the effect of some of these mutations on their target gene expression could provide additional support.

Advance: This study addresses an important unsolved problem in the field of human genetics and rare diseases, namely the challenge of identifying pathogenic mutations that lead to rare Mendelian diseases, in particular, in noncoding regions in unsolved cases. This is the first study to consider UCNEs together with tissue or cell type specific expression, epigenetics and chromatin accessibility in detecting pathogenic mutations for retina degeneration diseases. See for example Ellingford et al., Genome Medicine 2023 (PMID: 35850704). Their demonstration of rare variants in UCNE associated with inherited retinal degeneration diseases in patients with IRD in the 100k Genomics Project suggests that the role of UCNEs in disease should be further investigated and functionally tested. This work could have important clinical implications, and also proposes a strategy for integrating UCNEs with multi-omic and genomic data.

Audience: This work will be of interest to the human genetics and genomics community, in particular to researchers interested in uncovering the genetic basis and causal mechanisms of rare diseases, and to scientists interested in clinical applications of genetic variation. This work will also be of interest to scientists interested more broadly in understanding the regulatory effects of the noncoding regions in the genome.

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Reply to the reviewers

Reviewer #1:

1. If doable, image dynein and dynactin simultaneously in the Halo-DYNC1H1/DCTN4-SNAP iNeurons. Co-movement of dynein and dynactin towards the somatodendritic compartment and their separate movement in the anterograde direction along the axon would provide the most convincing evidence for the key claims of the manuscript.

Please see the planned revision section for our response

Reviewer #2:

Major comment (requires additional experimentation)

1. While the data presented do certainly suggest that dynein and Lis1 are transported anterogradely on separate vesicular cargoes from dynactin and Ndel1, the study would be much stronger if supported by dual imaging of dynein and dynactin to prove that these proteins do indeed move in association with separate vesicular populations. I would like to see dual-color kymograph traces showing that the proteins move independently. The authors should be able to accomplish this using their dual Halo-DYNC1H1/DCTN4-SNAP hESC line. To acquire and analyze this data might take several months, but it would greatly strengthen this paper. If the authors do this experiment, they may also be able to address the mechanism of reversal of anterograde cargoes which they speculate about in the Discussion, which would add even more interest and insight.

Please see the planned revision section for our response

Minor comments (addressable without additional experimentation)

1. The authors deduce that 1-4 Halo fluorochromes corresponds to 1-2 dynein molecules. This implies that the cells are homozygous for the Halo tag, but I do not see this addressed explicitly. The authors should state explicitly whether the lines generated for their study are heterozygous or homozygous for the tag. If the cells are heterozygous, which would seem most likely, then they may be underestimating the number of dyneins per spot and should take this into account.

We have added whether lines are homozygous or heterozygous to the manuscript. We also include a new Supplementary Figure panel (Fig S6) showing the genotyping data. In summary, all lines are homozygous except for PAFAH1B1-Halo (hESCs) which is heterozygous.

1. Why are the moving spots lower in intensity than the NEM-treated static spots. It appears to suggest that they may be associated with different structures. This should be clarified and discussed.

Our data suggest that the fast-moving spots have fewer dyneins than NEM treated static spots. We suggest this is because the fast-moving cargos are smaller than the average cargo and therefore have fewer dyneins on them. This is also supported by the smaller number of dyneins reported previously on endosomes as compared to the large lysosomes. We have clarified this in the discussion (page 7-8).

1. The authors state in the Results that most of the dynein spots were diffusing, often along microtubules, but they do not visualize microtubules so how do they know this? They may need to remove the phrase "often along microtubules".

This has been removed.

1. At the end of the Introduction the authors state that their data "allow us to understand how the dynein machinery drives long-range transport in the axon". This is an overstatement. The "how" in this sentence is not addressed in this study.

We have softened the sentence by adding the phrase “better understand”.

1. The conclusion that dynein binds to cargos stably throughout their transport along the axon is based on measurements of the fastest moving cargoes but the authors do not provide data on the distribution of velocities for the entire population of retrograde cargoes. It is not valid to extrapolate the behavior of a small number of cargoes to the entire population. The average may be much slower than the fastest cargoes. Moreover, even for the fastest organelles the authors cannot say that the dynein is stably bound because they did not track single cargoes and thus do not know that the cargoes moved continuously in one single bout of movement for 500 µm; it is possible that the cargoes moved in multiple consecutive bouts interrupted by brief pauses and dynein motors may have exchanged between bouts.

We have added a section to the discussion to highlight that other cargos may behave differently from the fastest ones (page 7). We have also clarified the assumptions that lead us to expect a slower arrival time of the first signal (page 5).

1. The authors say that "it is clear that at least some dyneins remain on cargoes throughout their transport along the axon". As explained above, the data do not prove this so this statement should be removed.

We have softened this sentence from “it is clear” to “our results suggest” and explained in more detail why we make this conclusion

1. The authors note that most of the dynein spots were not moving processively and state that this is consistent with prior studies showing that only a subset of dynein is actively involved in transport. However, as they note elsewhere, dynein is both motor and cargo and most axonal dynein is transported at slow average velocities so maybe they should be more explicit about what they mean by "involved in transport".

We have clarified we mean fast axonal transport and thank the reviewer for highlighting this point.

1. When the authors note that most of the dynein in axons is transported in the slow component of axonal transport, they should also cite the work of Pfister and colleagues who were the first to show this (PMID 8824315 and 8552592).

This was an omission on our part. The references have now been added.

1. The authors propose that dynein and Lis1 are transported together but there were significantly fewer anterogradely transported Lis1 particles than dynein particles. This should be discussed.

We have added more information to the discussion. Although we cannot rule out this effect being due to the heterozygous tagging of our LIS1 cell line, we do not witness the same decrease in events in the retrograde direction. Therefore, we believe there is a subset of anterogradely moving dynein lacking LIS1. As discussed in the manuscript, this subset may already be bound to dynactin and therefore not require LIS1.

1. For the statistical analysis, the authors should provide p values in the legends for the comparisons that are judged to be "not significant". The authors should also be consistent in how they label differences that are not significant - they mark them as "ns" in Fig. 1, but in the other figures they do not, leaving some ambiguity about whether particular comparisons were not tested or were found to be not significant. For example, in Fig. 4C the average speed of the dynactin is about 0.5 µm/s greater than for the other proteins and the spread in the data suggest that this could be significant, but no significance is indicated on the plot, implying p>0.05. It is not clear how confident we can be that there is no difference.

We have now included all p values in the figure legends and have removed the “ns” in Fig 1D. In our revised manuscript, only significant differences are highlighted in the figures.

Reviewer #3:

• if I look at the kymographs, trajectories appear rather complex, pausing, standing still, moving and everything mixed. The explanation of how actual trajectories are extracted and on what basis is very short, too short for me. I think the authors should expand this. Furthermore, I think it would be good if the authors would present, in their kymographs examples of the tracked (and also the not included) tracks. Maybe in supplementary info.

The analysis of this data used the Trackmate Fiji plugin. This tracks spots frame to frame in a movie and then outputs the data of the tracks. No data was extracted from kymographs but they were used as a graphical illustration of the moving spots. To better explain our analysis pipeline, we have expanded our methods section and have added an example of a tracked movie (Video 15) as well as highlighted the tracked spots in one kymograph example (Figure 7S).

• I found 'velocity' ill defined. I get the impression, judging from the number of points (compared to the other parameters) that the authors determine the average velocity of each individual trajectory. That is an important parameter (but should indeed be called 'trajectory averaged' velocity), but might not be the only one useful to learn from the data, where trajectories do not always appear to have constant speeds (pausing, etc.). Why do the authors not determine point-to-point velocities and plot histograms of those for all the trajectories (simply plot histograms of all the displacements between subsequent data points in trajectories)? This might provide great insight into the actual maximum velocity and the fraction of pausing or moving in opposite direction etc., providing much more molecular detail than currently extracted from the data.

The reviewer is correct. We have measured the average velocity of the spots from the beginning of the track to the end. We have clarified this in the text. Furthermore, as stated above in the revision plan, we are currently doing the additional analysis and will include it in the final revision

• I was a bit surprised to read that the authors have gone to the effort to create a dual-color labeled cell line, but did not do actual correlative two-color measurements (or at least show them). It would be so insightful to see dynein and dynactin move separately in the anterograde direction.

Please see the planned revision section for our response.

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Referee #3

Evidence, reproducibility and clarity

Fellows and coauthors present a signle-molecule study toward dynein regulation in axons. They observe that dynein in vivo makes very long runs and that regulators LIS1 and NDEL1 cotransport with dynein all the (retrograde way). Remarkably, different components of the dynein complex appear to be transported in different ways/velocities in the antorograde direction. Overall experiments are well conducted, I only have a couple of important questions regarding data analysis. Some aspects should be explained better, more steps should be shown and here and there I think the authors could, with minimal effort, obtain much more out of their data (see below). Nevertheless, I think this is an important study, on of the first single-molecule efforts to understand axonal transport in the cell (see below). Key findings are important for our understanding of dynein regulation.

My concerns:

• if I look at the kymographs, trajectories appear rather complex, pausing, standing still, moving and everything mixed. The explanation of how actual trajectories are extracted and on what basis is very short, too short for me. I think the authors should expand this. Furthermore, I think it would be good if the authors would present, in their kymographs examples of the tracked (and also the not included) tracks. Maybe in supplementary info.
• I found 'velocity' ill defined. I get the impression, judging from the number of points (compared to the other parameters) that the authors determine the average velocity of each individual trajectory. That is an important parameter (but should indeed be called 'trajectory averaged' velocity), but might not be the only one useful to learn from the data, where trajectories do not always appear to have constant speeds (pausing, etc.). Why do the authors not determine point-to-point velocities and plot histograms of those for all the trajectories (simply plot histograms of all the displacements between subsequent data points in trajectories)? This might provide great insight into the actual maximum velocity and the fraction of pausing or moving in opposite direction etc., providing much more molecular detail than currently extracted from the data.
• I was a bit surprised to read that the authors have gone to the effort to create a dual-color labeled cell line, but did not do actual correlative two-color measurements (or at least show them). It would be so insightful to see dynein and dynactin move separately in the anterograde direction.

Referee Cross-Commenting

I think we agree on the key points:<br /> - in principle, great study<br /> - quantification / tracking could go a bit further and should be explained better<br /> - manuscript / conclusions would be strengthened substantially if the authors could do some 2-color experiments to correlated dynein / dynactin movements in anterograde vs retrograde direction.

Significance

I think this is an important and exciting manuscript. As an in vivo single-molecule biophysicist with great interest in intracellular transport, I have been astonished in the lack of people trying to take single-molecule data on the motor involved, in particular neurons. I believe this is the only way to find out how transport actually works and what role motors play. Mutants is not enough, bulk data is not enough, in vitro is not enough. This is what the field needs (and many in the field do not seem to be aware of this...). Great that Fellows and coauthors took on this task and show some really exciting data. I am not an expert on their stem-cell labeling approach so cannot judge on that. The imaging seems to be done well. As discussed above, I think there might be much more in the data than the authors now get out, so I would encourage them to do some additional analysis. But overall, this effort is important and I think the conclusions will stand and provide important new insights in dynein regulation in the cell.

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Referee #2

Evidence, reproducibility and clarity

Summary - The authors use a CRISPR knock-in gene editing strategy to label endogenous dynein, dynactin (p62 or Arp11) and dynein regulators (Ndel1 and Lis1) with Halo or SNAP tags. They do this in human iPSC and ESC cell lines engineered to express doxycycline-inducible NGN2 cloned into a "safe harbor" site of the genome. They induce the cells to differentiate into iNeurons using doxycycline and image the tagged proteins in axons with single molecule sensitivity using HILO illumination. The paper is clearly written, the description of the methods is thorough, and the data and figures (including the videos) are of good quality. The use of gene editing to knock the tags into the endogenous gene loci is a superior strategy to classic overexpression strategies. The authors also make effective use of microfluidic chambers to ensure the axons are uniformly orientated and coaligned over a distance of 500µm.

Major comment (requires additional experimentation)

1. While the data presented do certainly suggest that dynein and Lis1 are transported anterogradely on separate vesicular cargoes from dynactin and Ndel1, the study would be much stronger if supported by dual imaging of dynein and dynactin to prove that these proteins do indeed move in association with separate vesicular populations. I would like to see dual-color kymograph traces showing that the proteins move independently. The authors should be able to accomplish this using their dual Halo-DYNC1H1/DCTN4-SNAP hESC line. To acquire and analyze this data might take several months, but it would greatly strengthen this paper. If the authors do this experiment, they may also be able to address the mechanism of reversal of anterograde cargoes which they speculate about in the Discussion, which would add even more interest and insight.

Minor comments (addressable without additional experimentation)

1. The authors deduce that 1-4 Halo fluorochromes corresponds to 1-2 dynein molecules. This implies that the cells are homozygous for the Halo tag, but I do not see this addressed explicitly. The authors should state explicitly whether the lines generated for their study are heterozygous or homozygous for the tag. If the cells are heterozygous, which would seem most likely, then they may be underestimating the number of dyneins per spot and should take this into account.
2. Why are the moving spots lower in intensity than the NEM-treated static spots. It appears to suggest that they may be associated with different structures. This should be clarified and discussed.
3. The authors state in the Results that most of the dynein spots were diffusing, often along microtubules, but they do not visualize microtubules so how do they know this? They may need to remove the phrase "often along microtubules".
4. At the end of the Introduction the authors state that their data "allow us to understand how the dynein machinery drives long-range transport in the axon". This is an overstatement. The "how" in this sentence is not addressed in this study.
5. The conclusion that dynein binds to cargos stably throughout their transport along the axon is based on measurements of the fastest moving cargoes but the authors do not provide data on the distribution of velocities for the entire population of retrograde cargoes. It is not valid to extrapolate the behavior of a small number of cargoes to the entire population. The average may be much slower than the fastest cargoes. Moreover, even for the fastest organelles the authors cannot say that the dynein is stably bound because they did not track single cargoes and thus do not know that the cargoes moved continuously in one single bout of movement for 500 µm; it is possible that the cargoes moved in multiple consecutive bouts interrupted by brief pauses and dynein motors may have exchanged between bouts.
6. The authors say that "it is clear that at least some dyneins remain on cargoes throughout their transport along the axon". As explained above, the data do not prove this so this statement should be removed.
7. The authors note that most of the dynein spots were not moving processively and state that this is consistent with prior studies showing that only a subset of dynein is actively involved in transport. However, as they note elsewhere, dynein is both motor and cargo and most axonal dynein is transported at slow average velocities so maybe they should be more explicit about what they mean by "involved in transport".
8. When the authors note that most of the dynein in axons is transported in the slow component of axonal transport, they should also cite the work of Pfister and colleagues who were the first to show this (PMID 8824315 and 8552592).
9. The authors propose that dynein and Lis1 are transported together but there were significantly fewer anterogradely transported Lis1 particles than dynein particles. This should be discussed.
10. For the statistical analysis, the authors should provide p values in the legends for the comparisons that are judged to be "not significant". The authors should also be consistent in how they label differences that are not significant - they mark them as "ns" in Fig. 1, but in the other figures they do not, leaving some ambiguity about whether particular comparisons were not tested or were found to be not significant. For example, in Fig. 4C the average speed of the dynactin is about 0.5 µm/s greater than for the other proteins and the spread in the data suggest that this could be significant, but no significance is indicated on the plot, implying p>0.05. It is not clear how confident we can be that there is no difference.

Referee Cross-Commenting

There seems to be agreement among all three reviewers that the authors should perform dual imaging of dynein and dynactin to prove that these proteins do indeed move together in the retrograde direction but separately in the anterograde direction. This would strengthen the study greatly.

Significance

General assessment - There are now multiple papers that have analyzed axonal transport of cargoes in iPSC-derived neurons, but this one appears to be the first to do it by tagging dynein motors and with single-molecule sensitivity. The principal conclusions are (1) that dynein is capable of long-range movement in axons and (2) that dynein moves dynein/Lis1 complexes are transported anterogradely in association with distinct cargoes from dynactin/Ndel1 complexes. The former is a modest conclusion and is entirely expected so not very impactful, but the latter is interesting and novel. The difference between the average velocities for the four proteins in the anterograde and retrograde directions is striking. All four move at similar velocities in the retrograde direction but in the anterograde direction, dynein and Lis1 move significantly faster than dynactin and Ndel1. Given these data, it is reasonable to infer that these proteins are being transported in two separate sets of cargoes. As the authors note in their Discussion, this is important because it could provide a mechanism for transporting dynein components anterogradely in a less active form that could then be activated when the components come together in the distal axon. However, I feel that one critical experiment is missing, which is to perform dual labeling of anterogradely transported dynein and dynactin in the same cells (see major comment). Without this experiment, the evidence is indirect.

Audience - If confirmed by the dual labeling experiment, the authors' conclusions would represent a conceptual and mechanistic insight into the mechanism of bidirectional transport in axons that would be of broad interest to neuronal cell biologists studying neuronal trafficking.

Expertise - This reviewer has expertise in the neuronal cytoskeleton, live imaging and axonal transport and has some experience working with iPSC-derived neurons.

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Referee #1

Evidence, reproducibility and clarity

To image dynein in the axon at a single-molecule level, Fellows et al. used neuron-inducible human stem cell lines to Halo/SNAP tag endogenous dynein components by gene editing, and visualized fluorescently labeled protein molecules in differentiated neurons in microfluidic chambers by HILO microscopy-based live imaging. Using those cutting edge technologies, the authors demonstrate that in the axon, not only dynein and dynactin but also the dynein regulators LIS1 and NDEL1 can move long distance retrogradely towards the somatodendritic compartment. They also show that dynein /LIS1 move faster than dynactin/NDEL1 in the anterograde direction, suggesting that they are delivered separately to the distal end of the axon. The approach to study subcellular motility of endogenous dynein/dynactin is creative, the data are solid. I would like to suggest one experiment to support more strongly the authors' conclusions:<br /> If doable, image dynein and dynactin simultaneously in the Halo-DYNC1H1/DCTN4-SNAP iNeurons. Comovement of dynein and dynactin towards the somatodendritic compartment and their separate movement in the anterograde direction along the axon would provide the most convincing evidence for the key claims of the manuscript.

Referee Cross-Commenting

I agree with Reviewer 2 that the authors should clarify whether the knockin lines for dynein are homozygous. I also agree with both Reviewers 2 and 3 that the authors should do more analysis of the kymographs to obtain more information.

Significance

This is an elegant study on dynein motility and transport in vivo. The experimental approaches and findings presented in this manuscript are very valuable contributions to the field of dynein/dynactin and axonal transport. The results showing that dynein/dynactin can move long-range retrogradely in the axon are in good agreement with previous findings that dynein-driven cargo transport is highly processive, and the data suggesting that dynein and dynactin/NDEL1 are trafficked separately to the distal tip of the axon provide new insights into the regulatory mechanisms for the subcellular distribution and activity of molecular motors. Together these findings provide conceptual advances for understanding axonal transport. They will be of great interest to not only scientists in the field of intracellular transport but also those in cellular neurobiology.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

REVIEW COMMENT

The article titled "The tRNA thiolation-mediated translational control is essential for plant immunity" by Zheng et al. highlights the critical role of tRNA thiolation in Arabidopsis plant immunity through comprehensive analysis, including genetics, transcriptional, translational, and proteomic approaches. Through their investigation, the authors identified a cbp mutant, resulting in the knockout of ROL5, and discovered that ROL5 and CTU2 form a complex responsible for catalyzing the mcm5s2U modification, which plays a pivotal role in immune regulation. The findings from this study unveil a novel regulatory mechanism for plant defense. Undoubtedly, this discovery is innovative and holds significant potential impact. However, before considering publication, it is necessary for the authors to address the various questions raised in the manuscript concerning the experiments and analysis to ensure the reliability of the study's conclusions.

Response: Thank you very much for your support and suggestions!

Here is Comments:

Line 64-65:

The author mentioned that 'While NPR1 is a positive regulator of SA signaling, NPR3 and NPR4 are negative regulators.' However, several recent discoveries are suggesting that it may not be a definitive fact that NPR3 and NPR4 are negative regulators. Therefore, I recommend the authors to review this section in light of the findings from recent papers and make necessary edits to reflect the most current understanding.

Response: Thank you for your feedback. Since we mainly focused on NPR1 in this study, we removed this sentence to avoid confusion. We provided additional information about NPR1 in the Introduction section to emphasize the importance of NPR1 (Line 64-68).

Line 182- & Figure 4:

The author conducted RNA-seq, Ribo-seq, and proteome analysis. Describing the analysis as "transcriptional and translational" using RNA-seq and proteome data seems not entirely accurate. Proteome data compared with RNA-seq not only reflects translational changes but may also encompass post-translational regulations that contribute to the observed differences. To maintain precision, the title of this section should either be modified to "transcriptional and protein analysis" or, alternatively, compare RNA-seq and Ribo-seq data to demonstrate the transcriptional and translational changes more explicitly.

Responses: Thank you for your suggestions. We agree with you and thus change the description accordingly throughout the manuscript.

Line 229-235 and Figure 5C:

The interpretation of Figure 5C's polysome profiling results is inconclusive. There does not seem to be a noticeable difference in polysomal fractions between the cab mutant and CAM. The observed differences in the overlay of multiple polysome fractions between cab and COM could be primarily influenced by baseline variations rather than a significant decrease in the polynomial fractions in cpg. Therefore, it is necessary to carefully review other relevant papers that discuss polysome fraction data and their analysis. By doing so, the authors can make the appropriate corrections to ensure accurate interpretations.

Responses: Thank you for your comments. We agree that the difference between cgb and COM was not dramatic visually. This is a common feature of polysome profiling assay (e.g. Extended Data Fig. 1f in Nature 545: 487–490; Fig. 1c in Nature Plants, 9: 289–301). In our case, the difference between polysome fractions was unlikely due to the baseline variation for two reasons. First, baseline variation affects monosome and polysome fractions in the same way. However, our results showed the monosome fraction of cgb is higher than that of COM, whereas the polysome fraction of cgb is lower than that of COM. Second, this result was repeatedly detected. For better visualization, we adjusted the scale of Y axis in the revised manuscript (Figure 5D).

Line 482 Ion Leakage assay:

I could not find the ion leakage assay in this manuscript, so I wonder why it is mentioned.

Response: We are sorry for the mistake. The Ion leakage data were included in previous visions of the manuscript. We removed the data but forgot to remove the corresponding method in the present version.

Materials and Methods:

To enhance the reproducibility of the study, the authors should provide a more detailed description of the materials and methods, especially for critical experiments like the Yeast-two-hybrid assays. Clear documentation of specific reagents, strains, and protocols used, along with information on controls, will bolster the validity of the results and facilitate future research in this area.

Response: Thank you for your suggestions. We provided more details in the methods. For yeast two-hybrid assays, the vector information was included in “Vector constructions” section.

Minor Point:

Line 61: There is a space between ')' and '.', which needs to be edited.

Response: The space was deleted.

Reviewer #1 (Significance): This study holds significant importance within the field of plant immunity research. The authors have made valuable contributions through their comprehensive analysis, encompassing genetics, transcriptional, translational, and proteomic approaches, to elucidate the critical role of tRNA thiolation in plant immunity. One of the major strengths of this study lies in its ability to shed light on a previously unknown regulatory mechanism for plant defense. By identifying the cbp mutant and investigating the role of ROL5 and CTU2 in catalyzing the mcm5s2U modification, the authors have unveiled a novel aspect of plant immune regulation. This innovative discovery provides a deeper understanding of the intricate molecular processes governing immunity in plants.

Moreover, the study's findings are not limited to the immediate field of plant immunity but also have broader implications for the scientific community. By employing diverse methodologies, the authors have demonstrated how tRNA thiolation exerts control over both transcriptional and translational reprogramming, revealing intricate links between these processes. This integrative approach sets a precedent for future research in the field of plant molecular biology and opens up new avenues for investigating other aspects of immune regulation.

In terms of its relevance, the study's findings have the potential to captivate researchers across various disciplines, such as plant biology, molecular genetics, and translational research. The insights gained from this study may inspire researchers to explore further the role of tRNA in other regulation.

Response: Thank you very much for your positive comments and support!

Reviewer #2 (Evidence, reproducibility and clarity): The authors presented an intriguing and previously unknown mechanism that the tRNA mcm5s2U modification regulates plant immunity through the SA signaling pathway, specifically by controlling NPR1 translation. The manuscript was well-written and logically structured, allowing for a clear understanding of the research. The authors provided strong and persuasive data to support their key claims. However, further improvement is required to strengthen the conclusion that mcm5s2U regulates plant immunity by controlling NPR1 translation.

Response: Thank you very much for your positive comments and support!

Major comments:

1. NPR1 translation should be examined to verify the Mass Spec (Figure 5B) and polysome profiling data (Figure 5D) by checking the NPR1 protein and mRNA level using antibodies and qPCR, respectively, in the cgb mutant background to establish a concrete confirmation of CGB regulation in NPR1 translation.

Response: This is a very constructive suggestion. We performed these experiments and found that the transcription levels of NPR1 were similar between COM and cgb both before and after PsmES4326 infection (Figure S2), which is consistent with RNA-Seq data. Consistent with the Mass Spec and polysome profiling data, the NPR1 protein level was much higher in COM than that in cgb(Figure 5C) after Psm ES4326 infection. Together, these data further supported our conclusion that translation of NPR1 is impaired in cgb.

1. Analyzing the genetic epistasis of CGB and NPR1 to check if CGB regulates plant immunity through the NPR1-dependent SA signal pathway. If the authors' claim is valid, I would expect no addictive effect on bacterial growth in the cgb/npr1 double mutant compared to the single mutants. Due to the broad impact of CGB on plant signaling (Figures 4E and 4F), the SA protection assay, which concentrates on the SA signal pathway, needs to be tested in WT, cgb and npr1 plants as an alternative assay to the genetic epistasis analysis. I expect that the SA-mediated protection is also compromised in cgb mutant background.

Response: Thank you for your suggestions. We did examine the growth of Psm ES4326 in the cgb npr1_double mutant and found that _cgb npr1 was significantly more susceptible than npr1 and cgb (Figure below). Although the additive effects were observed, this result was not against our conclusion for the following reasons. First, the translation of NPR1 was reduced rather than completely blocked in cgb. In other words, NPR1 still has some function in cgb. But in the cgb npr1 double mutant, the function of NPR1 is completely abolished, which explains why cgb npr1 was more susceptible than cgb. Second, in addition to NPR1, some other immune regulators (such as PAD4, EDS5, and SAG101) were also compromised in cgb(Figure 5B), which explained why cgb npr1 was more susceptible than npr1. Since the result of the genetic analysis was not intuitive, we decided not to include it in the manuscript.

SA signaling is known to regulate both basal resistance and systemic acquired resistance (SA-mediated protection). We have shown that cgb is defective in the defect of basal resistance, which cgb is sufficient to support our conclusion that the tRNA thiolation is essential for plant immunity. We agree that it is expected that the SA-mediated protection is also compromised in cgb. We will test this in the future study.

1. Could the authors comment on why using COM instead of WT as a control to perform the majority of the experiments?

Response: Thank you for your comments. In addition to ROL5, the cgb mutant may have other mutations compared with WT.COM is a complementation line in the cgb background. Therefore, the genetic background between COM and cgb may be more similar than that of WT and cgb.

1. In Figure 5E, why does ACTIN2 have an enhanced translation while NPR1 shows a compromised one in cgb mutant? How does the mcm5s2U distinguish NPR1 and ACTIN2 codons? Does mcm5s2U modification have both positive and negative roles in regulating protein translation?

Response: Thank you for raising this question. As previously reported, loss of the mcm5s2U modification causes ribosome pausing at AAA and CAA codons. Therefore, the translation of the mRNAs with more GAA/CAA/AAA codons (called s2 codon) is likely to be affected more dramatically in cgb. We have analyzed the percentage of s2 codon at whole-genome level (Figure below). The average percentage is 8.5%, while NPR1 contains 10.1% s2 codon and actin contains only 4.5% s2 codon. When fewer ribosomes are used for translation of the mRNAs with high s2 codon percentage, more ribosomes are available for translation of the mRNAs with low s2 codon percentage, which may account for the enhanced translation efficiency. To focus on NPR1 and to avoid confusion, we removed the ACTIN data in the revised manuscript.

1. Specify the protein amount used for the in vitro pull-down assay and agrobacteria concentration used for the tobacco Co-IP assay in the protocol section.

Response: We added this information in Method section in the revised manuscript.

4. Delete the SA quantification and Ion leakage assay in the protocol, which are not used in the study.

Response: We are sorry for the mistake. The SA quantification and ion leakage data were included in previous visions of the manuscript. We removed the data but forgot to remove the corresponding method in the present version. We deleted them in the revised manuscript.

1. The strain Pst DC3000 avrRPT2 was not used in this study. Please remove it.

Response: We are sorry for the mistake. The strain Pst DC3000 avrRPT2 was used for ion leakage assay in previous visions of the manuscript. We deleted it in the revised manuscript.

1. In Figure 5F, did the 59 genes tested overlap with the 366 attenuated proteins in the cgb mutant? Were the 59 genes translationally regulated?

Response: Thank you for your suggestion. Venn diagram analysis revealed that 12 genes (about 20%) are also attenuated proteins, suggesting that the mcm5s2U modification regulates the translation of some SA-responsive genes.

Reviewer #2 (Significance): The authors' study is significant as it establishes the first connection between tRNA mcm5s2U modification and plant immunity, specifically by regulating NPR1 protein translation. This research expands our understanding of the biological role of tRNA mcm5s2U modification and highlights the importance of translational control in plant immunity. It is likely to captivate scientists working in this field.

Response: Thank you very much for your positive comments and support!

Reviewer #3 (Evidence, reproducibility and clarity):

In this manuscript, the authors identified a cgb mutant that carries a mutation in the ROL5 gene Both the cgb mutant and the newly created rol5-c mutant are susceptible to the bacterial pathogen Psm. The authors showed that ROL5 interacts with CTU2, the Arabidopsis homologous protein of the yeast tRNA thiolation enzyme NCS2. A ctu2-1 mutant is also susceptible to Psm, suggesting the tRNA thiolation may play a role in plant immunity. Indeed, tRNA mcm5S2U levels are undetectable in rol5-c and ctu2-1 mutants. The authors found that the cgb mutation significantly attenuated basal and Psm-induced transcriptome and proteome changes. Furthermore, it was found that the translation efficiency of a group of SA signaling-related proteins including NPR1 is compromised.

The manuscript provides solid evidence for the involvement of ROL5 and CTU2 in plant immunity using the rol5 and ctu2 mutants. The authors may consider the following suggestions and comments to improve the manuscript.

Response: Thank you very much for your support and suggestions!

1. The function of the Elongator complex in tRNA modification/thiolation has been extensively studied. In Arabidopsis Elongator mutants, mcm5S2U levels are very low, similar to the levels in the rol5 and ctu2 mutants (Mehlgarten et al., 2010, Mol Microbiology, 76, 1082-1094; Leitner et al., 2015 Cell Rep). In elp mutants, the PIN protein levels are reduced without reduced mRNA levels (Leitner et al., 2015), indicating that Elongator-mediated tRNA modification is involved in translation regulation. The Elongator complex plays an important role in plant immunity, though the reduced mcm5S2U levels in elp mutants were not proposed as the exclusive cause of the immune phenotypes. In fact, it would be difficult to establish a cause-effect relationship between tRNA modification and immunity. These results should be discussed in the manuscript.

Response: Thank you very much for your insightful comment on the role of the ELP complex in tRNA modification and plant immunity. We added a paragraph discussing the ELP complex in the revised manuscript (Line 280-295).

In addition to tRNA modification, the ELP complex has several other distinct activities including histone acetylation, α-tubulin acetylation, and DNA demethylation. Therefore, it is difficult to dissect which activity of the ELP complex contributes to plant immunity. However, the only known activity of ROL5 and CTU2 is to catalyze tRNA thiolation. Considering that the elp, rol5, and ctu2 mutants are all defective in tRNA thiolation, it is likely the tRNA modification activity of the ELP complex underlies its function in plant immunity.

1. The interaction between CTU2 and ROL5 in Y2H has previously been reported (Philipp et al., 2014). The same report also showed reduced tRNA thiolation in the ctu2-2 mutant using polyacrylamide gel. These results should be mentioned/discussed in the manuscript.

Response: Thank you for pointing them out. We added this information in the revised version (Line 146-147).

1. tRNA modification unlikely plays a unique role in plant immunity. It can be inferred that mutations affecting tRNA modification (rol5, ctu2, elp, etc.) would delay both internal and external stimulus-induced signaling including immune signaling.

Response: We agree with you that tRNA modification has other roles in addition to plant immunity. In the Discussion section, we have mentioned that “it was found that tRNA thiolation is required for heat stress tolerance (Xu et al., 2020). ……It will also be interesting to test whether tRNA thiolation is required for responses to other stresses such as drought, salinity, and cold.” (Line276-279).

1. It would be interesting to conduct statistical analyses on the genetic codons used in the CDSs whose translation was attenuated as described in the manuscript. Do these genes including NPR1 use more than average levels of AAA, CAA, and GAA codons? If not, why their translation is impaired?

Response: Thank you for your suggestion. We called GAA/CAA/AAA codons s2 codon. We have analyzed the percentage of s2 codon at whole-genome level (Figure below). NPR1 does contain more s2 codon (10.1%) than the average level (8.5%). We are preparing another manuscript, which will report the relationship between s2 codon and translation.

Referees cross-commenting

It is important to put current research in the context of available knowledge in the field. The digram in Figure 3C shows that the Elongator complex functions upstream of ROL5 & CTU2 in modifying tRNA. The function of Elongator in plant immunity has been well established. The similarities and differences should be discussed. Additionally, it may no be a good idea to claim that the results are novel.

Response: Thank you for your comments. We added a paragraph discussing the ELP complex in the revised manuscript (Line 280-295). The ELP complex catalyzes the cm5U modification, which is the precursor of mcm5s2U catalyzed by ROL5 and CTU2. In addition to tRNA modification, the ELP complex has several other distinct activities including histone acetylation, α-tubulin acetylation, and DNA demethylation. Therefore, it is difficult to dissect which activity of the ELP complex contributes to plant immunity. However, the only known activity of ROL5 and CTU2 is to catalyze tRNA thiolation. Considering that the elp, rol5, and ctu2 mutants are all defective in tRNA thiolation, it is likely the tRNA modification activity of the ELP complex underlies its function in plant immunity. Therefore, our study improved our understanding of the ELP complex in plant immunity. We have deleted the words “new” and “novel” throughout the manuscript.

Reviewer #3 (Significance): The manuscript provides solid evidence for the involvement of ROL5 and CTU2 in plant immunity. However, the authors did not acknowledge the existing results about the Elongator complex that functions in the same pathway in modifying tRNA. The involvement of Elongator in plant immunity has been well established. The cause-effect relationship between tRNA modification and plant immunity is difficult to demonstrate.

Response: We think that the cause-effect relationship between the activities of the ELP complex and plant immunity is difficult to demonstrate because the ELP complex has several distinct activities other than tRNA modification. However, since the only known activity of ROL5 and CTU2 is to catalyze tRNA thiolation, the cause-effect relationship between tRNA thiolation and plant immunity is clear, which indicated that the tRNA modification activity of the ELP complex contributes to plant immunity.