Perhaps this is true during the time of Plato, but I am of the belief that children have accumulated enough literacy to be able to be told most stories.

Imitation is the best form of flattery, but if done correctly, imitation is by no means inferior. An iteration upon a pre-existing piece can lead to something even greater being created.

He views the form made by God/nature as being of one almighty form. If there were 2 one would have to appear to be the best. There must be a best form and only one best form.

Art is only an imitation of reality it cannot fully replicate it, only in image

Author response:

Reviewer #1 (Public review):

Summary:

Dong et al. present an in-depth analysis of mutant phenotypes of the Rab GTPases Rab5, Rab7, and Rab11 in Drosophila second-order olfactory neuron development. These three Rab GTPases are amongst the best-characterized Rab GTPases in eukaryotes and have been associated with major roles in early endosomes, late endosomes, and recycling endosomes, respectively. All three have been investigated in Drosophila neurons before; however, this study provides the most detailed characterization and comparison of mutant phenotypes for axonal and dendritic development of fly projection neurons to date. In addition, the authors provide excellent high-resolution data on the distribution of each of the three Rabs in developmental analyses.

Strengths:

The strength of the work lies in the detailed characterization and comparison of the different Rab mutants on projection neuron development, with clear differences for the three Rabs and by inference for the early, late, and recycling endosomal functions executed by each.

We would like to thank Reviewer #1 for their appreciation of our characterization of distinct Rab mutants.

Weaknesses:

Some weakness derives from the fact that Rab5, Rab7, and Rab11 are, as acknowledged by the authors, somewhat pleiotropic, and their actual roles in projection neuron development are not addressed beyond the characterization of (mostly adult) mutant phenotypes and developmental expression.

Prior to mid-pupal stage (around 48 hours after puparium formation), glomeruli in the antennal lobe have not yet assumed their stereotyped positions, which complicates analyses and interpretation; thus, many of our analyses are conducted at the adult stage. For Rab11 mutants we did perform many developmental analyses to evaluate the origins of the axonal development (Figure 6—figure supplement 1) and dendrite elaboration phenotypes (Figure 5 J–L) we observed at the adult stage. We realize that the development axonal analyses are in supplemental material where they could be missed. Given the reviewer’s comments, we will move these data to the main figures.

Further, we will extend our Rab5 analyses to evaluate the function of this protein during development in experiments we will add to the revised manuscript.

Reviewer #2 (Public review):

Summary:

This study by Dong et al. characterizes the roles of highly-expressed Rab GTPases Rab5, Rab7, and Rab11 in the development and wiring of olfactory projection neurons in Drosophila. This convincing descriptive study provides complementary approaches to Rab expression and localization profiling, conventional dominant-negative mutants, and clonal loss-of-function mutants to address the roles of different endosomal trafficking pathways across circuit development. They show distinct distributions and phenotypes for different Rabs. Overall, the study sets the stage for future mechanistic studies in this well-defined central neuron.

We appreciate Reviewer #2’s analysis of our work and thank them for their suggestions to improve the clarity of our manuscript.

Strengths:

Beautiful imaging in central neurons demonstrates differential roles of 3 key Rab proteins in neuronal morphogenesis, as well as interesting patterns of subcellular endosome distribution. These descriptions will be critical for future mechanistic studies. The cell biology is well-written and explanatory, very accessible to a wide audience without sacrificing technical accuracy.

Weaknesses:

The Drosophila manipulations require more explanation in the main text to reach a wide audience.

In our revised manuscript we will clarify the fly-specific manipulations and terminology to make our work more accessible to a broader audience.  

Reviewer #3 (Public review):

Summary:

The authors aimed at a comprehensive phenotypic characterization of the roles of all Rab proteins expressed in PN neurons in the developing Drosophila olfactory system. Important data are shown for a number of these Rabs with small/no phenotypes (in the Supplements) as well as the main endosomal Rabs, Rab5, 7, and 11 in the main figures.

We appreciate Reviewer #3’s assessment and appreciation of our work.

Strengths:

The mosaic analysis is a great strength, allowing visualization of small clones or single neuron morphologies. This also allows some assessment of the cell autonomy of the observed phenotypes. The impact of the work lies in the comprehensiveness of the experiments. The rescue experiments are a strength.

Weaknesses:

The main weakness is that the experiments do not address the mechanisms that are affected by the loss of these Rab proteins, especially in terms of the most significant cargos. The insights thus do not extend far beyond what is already known from other work in many systems.

We understand this critique and are also interested in the specific cargos regulated by each Rab during development. We attempted to use antibodies to evaluate changes in cell-surface protein localization in response to disrupting individual Rabs but were unable to reliably distinguish(?) shifts in association with specific endosomal compartments. Many available antibodies label cell-surface proteins expressed in antennal lobe cells beyond projection neurons (such as olfactory receptor neurons, glia, or local interneurons) which complicates analyses. Further, although we have produced multiple ‘flp-on’ tags for PN cell-surface proteins, they cannot be used with the MARCM system. This prevents us from simultaneously perturbing individual Rabs and tracking corresponding changes in surface-protein localization with single cell resolution. Moreover, for proteins that are not highly endocytosed, it is difficult to separate plasma-membrane from endosomal localization, and we currently do not know which cell-surface proteins are most robustly endocytosed. Thus, while we share the reviewer’s interest in identifying candidate cargos, technological limitations make it difficult to achieve this goal within the scope of the current study.

Motherhood is a noble identity, and her mention of her child being sold underscores her low social standing, a situation that white people have never experienced, which relates to the theory of crossover. All of this illustrates the consequences of gender oppression and racial violence.

Reviewer #1 (Public review):

Summary:

The authors show that targeted inhibition can turn on and off different sections of networks that produce sequential activity. These network sections may overlap under random assumptions, with the percent of gated neurons being the key parameter explored. The networks produce sequences of activity through drifting bump attractor dynamics embedded in 1D ring attractors or in 2D spaces. Derivations of eigenvalue spectra of the masked connectivity matrix are supported by simulations that include rate and spiking models. The paper is of interest to neuroscientists interested in sequences of activity and their relationship to neural manifolds and gating.

Strengths:

(1) The study convincingly shows preservation and switching of single sequences under inhibitory gating. It also explores overlap across stored subspaces.

(2) The paper deals with fast switching of cortical dynamics, on the scale of 10ms, which is commonly observed in experimental data, but rarely addressed in theoretical work.

(3) The introduction of winner-take-all dynamics is a good illustration of how such a mechanism could be leveraged for computations.

(4) The progression from simple 1D rate to 2D spiking models carries over well the intuitions.

(5) The derivations are clear, and the simulations support them. Code is publicly available.

Weaknesses:

(1) The inhibitory mechanism is mostly orthogonal to sequences: beyond showing that bump attractors survive partial silencing, the paper adds nothing on observed sequence properties or biological implications of these silenced sequences. The references clump together very different experimental sequences (from the mouse olfactory bulb to turtle spinal chord or rat hippocampus) with strongly varying spiking statistics and little evidence of targeted inhibitory gating. The study would benefit from focusing on fewer cases of sequences in more detail and what their mechanism would mean there.

(2) The paper does not address the simultaneous expression of sequences either in the results or the discussion. This seems biologically relevant (e.g., Dechery & MacLean, 2017) and potentially critical to the proposed mechanism as it could lead to severe interference and decoding limitations.

(3) The authors describe the mechanism as "rotating a neuronal space". In reality, it is not a rotation but a projection: a lossy transformation that skews the manifold. The two terms (rotation and projection) are used interchangeably in the text, which is misleading. It is also misrepresented in Figure 1de. Beyond being mathematically imprecise in the Results, this is a missed opportunity in the Discussion: could rotational dynamics in the data actually be projections introduced by inhibitory gating?

(4) The authors also refer to their mechanism as "blanket of inhibition with holes". That term typically refers to disinhibitory mechanisms (the holes; for instance, VIP-SOM interactions in Karnani et al, 2014). In reality, the inhibition in the paper targets the excitatory neurons (all schematics), which makes the terminology and links to SOM-VIP incorrect. Other terms like "clustered" and "selective" inhibition are also used extensively and interchangeably, but have many connotations in neuroscience (clustered synapses, feature selectivity). The paper would benefit from a single, consistent term for its targeted inhibition mechanism.

(5) Discussion of this mechanism in relation to theoretical work on gating of propagating signals (e.g., Vogels & Abbott 2009, among others) seems highly relevant but is missing.

(6) Schematics throughout give the wrong intuition about the network model: Colors and arrows suggest single E/I neurons that follow Dale's rule and have no autapses. None of this is true (Figure 2b W). Autapses are actually required for the eigenvalue derivation (Equation 11).

Reviewer #2 (Public review):

Summary:

In "Spatially heterogeneous inhibition projects sequential activity onto unique neural subspaces", Lehr et al. address the question of how neural circuits generate distinct low-dimensional, sequential neural dynamics that can shift to different neural subspaces on fast, behaviorally relevant timescales.

Lehr et al. propose a circuit architecture in which spatially heterogeneous inhibition constrains network dynamics to sequential activity on distinct neural subspaces and allows top-down sequence selection on fast timescales. Two types of inhibitory interneurons play separate roles. One class of interneuron balances excitation and contributes to sequence propagation. The second class of interneuron forms spatially heterogeneous, clustered inhibition that projects onto the sequence-generating portion of the circuit and suppresses all but a subset of the sequential activity, thus driving sequence selection. Due to the random nature of the inhibitory projections from each inhibitory cluster, the selected sequences exist on well-separated neural subspaces, provided the 'selection' inhibition is sufficiently dense. Lehr et al. use mathematical analysis and computational modeling to study this type of circuit mechanism in two contexts: a 1D ring network and a 2D, locally connected, spiking network. This work connects to previous literature, which considers the role of selective inhibition in shaping and restructuring sequential dynamics.

Strengths:

(1) This study makes testable predictions about the connectivity patterns for the two types of interneurons contributing to sequence generation and sequence selection.

(2) This study proposes a relatively simple circuit motif that can generate many distinct, low-dimensional neural sequences that can vary dynamically on fast, behaviorally relevant timescales. The authors make a clear analytical argument for the stability and structure of the dynamics of the sub-sequences.

(3) This study applies the inhibitory selection mechanisms in two different model network contexts: a 1D rate model and a 2D spiking model. Both settings have local connectivity patterns and two inhibitory pools but differ in several significant ways, which supports the generality of the proposed mechanism.

Weaknesses:

(1) Scaling synaptic weights to match the original sequence dynamics is a complex requirement for this mechanism. In the 2D network, the solution to this scaling issue is the saturation of single-unit firing rates. It is unclear if this is in a biologically relevant dynamical regime or to what degree the saturation dynamics of the sequences themselves are altered by the density of selective inhibition.

(2) In the 2D model, although the sequence-generating circuit is quite general, the heterogenous interneuron population requires a tuned connectivity structure paired with matched external inputs. In particular, the requirement that inhibitory pools project to shared but random excitatory neurons would benefit from a discussion about the biological feasibility of this architecture.

Reviewer #1 (Public review):

Summary:

This manuscript by Wang et al. describes the development of an optimized soluble ACE2-Fc fusion protein, B5-D3, for intranasal prophylaxis against SARS-CoV-2. As shown, B5-D3 conferred protection not only by acting as a neutralizing decoy, but also by redirecting virus-decoy complexes to phagocytic cells for lysosomal degradation. The authors showed complete in vivo protection in K18-hACE2 mice and investigated the underlying mechanism by a combination of Fc-mutant controls, transcriptomics, biodistribution studies, and in vitro assays.

Strengths:

The major strength of this work is the identification of a novel antiviral approach with broad-spectrum and beyond simple neutralization. Mutant ACE2 enables broad and potent binding activity with the S proteins of SARS-CoV-2 variants, while the fused Fc part mediates phagocytosis to clear the viral particles. The conceptual advance of this ACE2-Fc combination is convincingly validated by in vivo protection data and by the completely abrogated protection of Fc LALA mutant.

Weaknesses:

Some aspects could be further modified.

(1) A previously reported ACE2 decamer (DOI: 10.1080/22221751.2023.2275598) needs to be mentioned and compared in the Discussion part.

(2) Limitations of this study, such as off-target binding and potential immunogenicity, should also be discussed.

Reviewer #1 (Public review):

Summary:

This study investigated the immunogenicity of a novel bivalent EABR mRNA vaccine for SARS-CoV-2 that expresses enveloped virus-like particles in pre-immune mice as a model for boosting the population that is already pre-immune to SARS-CoV-2. The study builds on promising data showing a monovalent EABR mRNA vaccine induced substantially higher antibody responses than a standard S mRNA vaccine in naïve mice. In pre-immune mice, the EABR booster increased the breadth and magnitude of the antibody response, but the effects were modest and often not statistically significant.

Strengths:

Evaluating a novel SARS-CoV-2 vaccine that was substantially superior in naive mice in pre-immune mice as a model for its potential in the pre-immune population.

Weaknesses:

(1) Overall, immune responses against Omicron variants were substantially lower than against the ancestral Wu-1 strain that the mice were primed with. The authors speculate this is evidence of immune imprinting, but don't have the appropriate controls (mice immunized 3 times with just the bivalent EABR vaccine) to discern this. Without this control, it's not clear if the lower immune responses to Omicron are due to immune imprinting (or original antigenic sin) or because the Omicron S immunogen is just inherently more poorly immunogenic than the S protein from the ancestral Wu-1 strain.

(2) The authors reported a statistically significant increase in antibody responses with the bivalent EABR vaccine booster when compared to the monovalent S mRNA vaccine, but consistently failed to show significantly higher responses when compared to the bivalent S mRNA vaccine, suggesting that in pre-immune mice, the EABR vaccine has no apparent advantage over the bivalent S mRNA vaccine which is the current standard. There were, however, some trends indicating the group sizes were insufficiently powered to see a difference. This is mostly glossed over throughout the manuscript. The discussion section needs to better acknowledge these limitations of their studies and the limited benefits of the EABR strategy in pre-immune mice vs the standard bivalent mRNA vaccine.

(3) The discussion would benefit from additional explanation about why they think the EABR S mRNA vaccine was substantially superior in naïve mice vs the standard S mRNA vaccine in their previously published work, but here, there is not much difference in pre-immune mice.

Reviewer #2 (Public review):

Summary:

In this manuscript, Fan, Cohen, and Dam et al. conducted a follow-up study to their prior work on the ESCRT- and ALIX-binding region (EABR) mRNA vaccine platform that they developed. They tested in mice whether vaccines made in this format will have improved binding/neutralization antibody capacity over conventional antigens when used as a booster. The authors tested this in both monovalent (Wu1 only) or bivalent (Wu1 + BA.5) designs. The authors found that across both monovalent and bivalent designs, the EABR antigens had improved antibody titers than conventional antigens, although they observed dampened titers against Omicron variants, likely due to immune imprinting. Deep mutational scanning experiments suggested that the improvement of the EABR format may be due to a more diversified antibody response. Finally, the authors demonstrate that co-expression of multiple spike proteins within a single cell can result in the formation of heterotrimers, which may have potential further usage as an antigen.

Strengths:

(1) The experiments are conducted well and are appropriate to address the questions at hand. Given the significant time that is needed for testing of pre-existing immunity, due to the requirement of pre-vaccinated animals, it is a strength that the authors have conducted a thorough experiment with appropriate groups.

(2) The improvement in titers associated with EABR antigens bodes well for its potential use as a vaccine platform.

Weaknesses:

As noted above, this type of study requires quite a bit of initial time, so the authors cannot be blamed for this, but unfortunately, the vaccine designs that were tested are quite outdated. BA.5 has long been replaced by other variants, and importantly, bivalent vaccines are no longer used. Testing of contemporaneous strains as well as monovalent variant vaccines would be desirable to support the study.

If section exceeds model's max_seq_length they encode its paragraphs separately instead. But they don't check again if these paragraphs exceed the max_seq_length, or whether they are under the min_seq_length threshold.

What about those who love thy neighbor and who do Christlike behaviors, moreso than those who proclaim to be christian and who really believe jesus is God, maybe those who commit violence in the name of christianity were not real christian but that does not negate the harm done in the name of christianity, and as a christian, I do not believe that a Muslim's christlike actions, for instance, Mosques act of giving a woman baby formula and not rejecting her, is less christlike just because they are not christian

Conversely the man who doesn't use the ring would be praised openly but talked ill of behind doors for wasting the power.

It is cool that he was not only a geographer, but a poet from Valencia, Spain. It is also quite a journey that he made by ship and land to all of those different cities.

I find this interesting. I do not know what exactly their hygiene was, but it seems like it may be very bad according to ibn Fadlan. I am going to look up the Rusiyyah to learn more about them.

I think it depends on the person. If the person is being harassed because they did something wrong, like saying something morally wrong on the internet, maybe they are worth some punishment. But people shouldn't harass others online just because of personal conflicts or having different opinions.

I think due to how interconnected society is because of social media, it allows for more harassment to occur. For example "cancel culture" could be considered a form of harassment as it includes dogpiling and public shaming. I find it interesting how easy it is to get canceled because there are so many things that can be deemed offensive to so many different people.

It is scary how fast a crowd can turn into a weapon online. Sometimes one angry post turns into hundreds of people attacking someone they've never met. What surprised me most is how small groups even bots can make it like everyone is against a person. It made me realize how easily social media can amplify harm when people stop thinking for themselves and just join in.

I've seen many instances where people on social media will ban together to harass individuals or businesses. While often it's because these people/ businesses did something to provoke it (such as go something offensive, or offend a costumer), sometimes it can also purely be because a person online posted a video, story, tweet, etc. to tell people to go harass that person, and people than ban wagon together to do so. For example, an influencer may see someone has posted something critiquing them online, and send their fans on harass that person. While usually this isn't something where one side is completely in the right, I think online harassment as a whole is morally wrong.

Yes I believe that crowd harassment can be justified in certain situations. Here it defines that crowd harassment can be riots, mob violence, revolts, revolution. Not all of these will be justified all the time, but there are scenarios that require riots, that require revolution. If a group of people are being mistreated, protests and harassment can become necessary to get justice and stop the mistreatment. A revolution may be necessary to fix an issue, one may be necessary soon in our country. This is why I think that somer crowd harassment can be justified and is not always a negative thing. I notice in the comment above me it says it can never be justified because no violence can be justified. Crowd harassment does not have to include violence, not all riots and revolutions result in violence, harassment is not a term that only covers violence. A crowd can harass with their words too.

This chapter make me see harassment very different than before. I used to think it is only about big, obvious things like death threats, but the puddle example shows how many small actions together can still really hurt someone. Online it’s even worse, because people can pretend every single comment is “not that serious” while the target already feel scared and tired. I still don’t fully know where the line should be between free speech and moderation, but now it’s harder to say “it’s just the internet, just ignore it,” because clearly it’s not that simple.

This moment is actually very delicate. The doctor is clearly going to do something that will cause you more pain, but because you said "I agree", the force that was previously intimidating suddenly becomes no longer terrifying, and even becomes beneficial assistance to you. It seems that freedom is not something you protect on your own; rather, when you are willing to hand over the power to make decisions to others, it becomes clearer. In other words, we are not giving up freedom; rather, we are choosing who can touch our pain when necessary - and making that intrusion worthwhile and legitimate.

For the bibliography, I was really interested in [q16] by Marwick about “morally motivated networked harassment.” I think this idea is scary, because it means harassers actually feel like they are good people, defending the community rules. When I look at some Twitter dogpiles, it really feels like that: everyone thinks they are “doing justice,” but the result is just one person getting destroyed. It make me wonder how we can critic somebody’s bad behavior without turning it into this huge mob that push them completely out of the conversation.

This article made me realize how serious crown harassment can become. It's one thing for people to argue online, but exposing someone'e private information turns it into a real-world danger. What surprised me is how quickly doxing can spread once a crowd gets involved, even people who didn't start it can end up sharing the information without any thinking. It shows how important it is for platforms to react quickly before things get out of control.

After looking at the Wired article by Roni Jacobson, one thing that really stuck with me was how long-term and personal online harassment can get. The chapter talks about dogpiling and harassment in a kind of “big picture” way, but her story makes it feel way more real. She explains how a random person online basically followed her for years, posting rumors about her and trying to mess with her life even as she grew up.

What hit me the most was that she didn’t even do anything to “cause” it — she was literally a kid when it started. It shows how the internet gives people this power to fixate on someone and keep attacking them from behind a screen, and there’s not always an easy way to stop it.

It made me realize that harassment isn’t just about one bad moment online — sometimes it becomes a whole pattern that affects someone’s safety, their mental health, and how they see the internet in general. The chapter talks about vulnerability and marginalized groups, but this article adds another layer: sometimes it’s not even about identity, sometimes people get targeted for no reason at all. And that randomness honestly makes the internet feel a little more dangerous than I thought.

Reading this article makes one realize that cyber violence is not an abstract "big problem", but rather starts from a specific individual. At the age of 12, which should be a time for a child to learn how to stand on their own in the world, someone, relying on the anonymous power behind the screen, turned her growth into a process of being monitored and intimidated. You suddenly realize: The so-called "freedom in the online space" in the eyes of some people is actually a weapon that can hurt at any time, and they don't even need to pay any price for such harm. This story makes it very difficult for us to regard cyber bullying as some dramatic "topic" anymore, because it has deeply changed a real person's understanding of security in their lifetime.

This article reveals that there is a forum called 4chan, where people talk about illegal contents and offensive opinions that are not allowed on mainstream social media on. As these illegal contents become more and more on 4chan, they appear on other social media such as YouTube, and people use large tech companies name to replace some illegal words, which brings these contents to mainstream. But as more people realize the existence of 4chan, more control is put on this platform, and the illegal contents on 4chan are getting less.

In Roni Jacobson’s article “I’ve Had a Cyberstalker Since I Was 12”, she describes how online harassment followed her for years and years and how hard it was to get support from platforms or authorities. What stood out to me is how unprepared social media platforms were to handle long term, targeted harassment. It connects to the chapter because it shows how design choices, like weak reporting systems or slow responses can make harmful behavior spread even more. It made me realize that platforms have a responsibility not just to react, but to design systems that prevent this level of harm in the first place.

This article shares the story of a woman who has had an internet stalker since the age of 12. She recounts the way she first met her stalker in her youth when they formed a friendship at summer camp. She then goes on to explain how she pulled away from their relationship after they communicated online for a few years, but he continued to reach out to her via social media and text message. His messages became more threatening and resulted in him making active efforts to interfere with her social and professional life. She accounts that despite her efforts to get law enforcement involved, they were little to no help.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In the manuscript entitled "CIP2A Mediates the Recruitment of the SLX4-MUS81-XPF Tri-Nuclease Complex in Mitosis and Protects Against Replication Stress" by Meroni et al the authors have characterized localization of the CIP2A-TopBP1 complex as well as some aspects of its function in U2OS and DLD1 cell lines exposed to different types of stress. They find that replication stress due to BRCA2 KO, APH or ATRi results in increased focus formation of the CIP2A-TopBP1 complex in mitotic cells. Moreover, the authors find significant decrease in EdU incorporartion in mitotic cells when disrupting CIP2A in (i) U2OS exposed to ATRi or Aph; (ii) in DLD1 BRCA2 KO; (iii) in one clone of DLD1 with Cip2A KO, and a non significant decrease the other DLD1 with Cip2A KO that they tested. Thus, under most of the tested conditions CIP2A is facilitating MiDAS. However, the authors find that expression of a previously characterised fragment of TopBP1 called B6L, which disrupts CIP2A-TopBP1 interaction, does not inhibit MiDAS in DLD1 cells.

Major comments:

It is convincing but not surprising that CIP2A-TopBP1 form more foci in mitotic cells after replication stress. The authors statement in the abstract: "We demonstrate that in the absence of CIP2A, cells fail to recruit the SLX4-MUS81-XPF (SMX) tri-nuclease complex to sites of under-replicated DNA in mitosis, resulting in a high incidence of lagging chromosomes during anaphase and subsequent micronuclei formation" is not supported by experiments. The authors indeed show that absence of CIP2A leads to lagging chromosomes during anaphase and subsequent micronuclei formation (which has previously been shown) but they have not shown that it is the failure to recruit the SMX complex that results in the phenotypes they mention. The authors should rephrase or remove this claim.

There is a discrepancy between the B6L-mediated disruption of TopBP1-CIP2A interaction having no effect on MiDAS in DLD1 cells (fig. 4F) whereas knockout of CIP2A in DLD1 cells seem to have an effect (fig 3E). The most obvious explanation for this observation is that the B6L peptide does not fully abolish TopBP1-CIP2A interaction and can still allow for some SLX4-MUS81 recruitment that is not visible as foci but still sufficient to induce MiDAS. To understand whether MiDAS in DLD1 expressing B6L is dependent on the fraction of TopBP1 that can still form foci (according to Fig 4D) the authors must co-stain for TopBP1 together with EdU detection to address whether they observe any colocalization of TopBP1 with MiDAS.

Many of the experiments are only performed with two independent replicates. The authors must perform 3 independent replicates. Also, it is not clear how many cells were analysed for each replicate. This should be clearly stated and the mean of each replicate should always be shown. Statistical analyses should be carried out using the means of the replicates. The authors must provide data showing the efficiency of CIP2A knockdown and CIP2A expression in the complementation assay (Fig. 2G)

Minor comments:

The authors should change "U-2 OS" in the figures to "U2OS" for consistency.

In figure 4D - is the increase with APH and S1 significant compared to S1 alone?

Figure 3 B and C. It is worrying that there is a huge difference in the EdU foci/mitotic cell in untreated condition from panel B to pabel C.

Fig 3F - is the increase in EdU incorporation after complementation significant?

For figure 3I representative images should be added

Significance

The data presented in the manuscript is of high quality but unfortunately does not present a big advance compared to current knowledge. Nevertheless, it is useful to have side-by-side comparison of different cell lines and conditions and IF localization studies. Given the therapeutic interest in the CIP2A-TopBP1 pathway it is important to get all the details right and researches with interest in DNA repair during mitosis will have interest in this work.

Moreover, in this manuscript the authors demonstrate that the impact of CIP2A disruption on MiDAS is variable across different cell lines-and even between individual clones. The concept of MiDAS is still clouded by considerable ambiguity, possibly due to earlier studies overstating the consequences of knockdown or knockout. It is therefore great that this manuscript presents clear, unbiased observations, highlighting both inter-cell line differences and the partial nature of the effects. This kind of nuanced reporting is valuable for the field.

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Referee #1

Evidence, reproducibility and clarity

Summary and Significance

This is a timely and exciting study that provides us with some new molecular insights into mitotic DNA repair. It builds on previous studies that identified the CIP2A-TOPBP1 complex as a molecular tether that connects broken DNA ends that get transmitted from interphase into mitosis (PMID: 30898438, 35842428, 35842428). The results are also largely complementary with those of Martin et al. (BioRxiv preprint at https://doi.org/10.1101/2024.11.12.621593) and de Haan et al. (BioRxiv preprint at https://www.biorxiv.org/content/10.1101/2025.04.03.647079v1).

The authors report three main findings, as summarized below.

1) The CIP2A oncoprotein is involved in the cellular response to replication stress in mitosis.

2) CIP2A is required for the recruitment of SLX4, MUS81, and XPF into foci during mitosis. SLX4 is a well-established protein scaffold for multiple DNA repair factors, including three structure-selective endonucleases called SLX1, MUS81-EME1, and XPF-ERCC1 that together, form the SMX tri-nuclease that removes DNA repair intermediates and chromosome entanglements during mitosis. In some cell lines, the SMX complex is required for mitotic DNA synthesis at sites of under-replicated DNA, thus ensuring complete DNA replication prior to cell division.

3) The role(s) of CIP2A in MiDAS are cell line-dependent/context-dependent.

In general, this is a solid body of microscopy-based work that includes appropriate cell models and experimental controls. The manuscript is well-written, and the data is presented coherently. The main findings will have important implications for researchers interested in mitotic DNA damage, genome stability, and cancer biology. After addressing the points below, I believe this manuscript will be suitable for publication.

Major comments

1) Figure 1C: The CIP2A-TOPBP1 PLA experiments are lacking critical controls, namely cells lacking or depleted of CIP2A and TOPBP1. These controls are necessary to provide confidence for the results presented in Figure 1C. If these controls are too expensive or time-demanding for the manuscript, then I recommend removing the PLA data from Figure 1C.

2) In Figure 2, the authors conclude that the loss of SLX4, XPF, and MUS81 foci in CIP2A depleted cells is synonymous with the loss of recruitment to DNA lesions. However, I can think of many other reasons that could explain the loss of foci. For example, do the authors know that the proteins are expressed to similar levels in cells with and without CIP2A (this should be tested by a simple western blot). Along the same vein, a biochemical fractionation and western blot of the soluble vs chromatin-bound fraction would complement and substantiate their microscopy-based assays in Figure 2. If the fractionation is not possible, then the text should be adjusted accordingly.

3) The experimental set-up in Figure 2 probes whether CIP2A mediates the recruitment of SMX subunits - SLX4, XPF, MUS81 - but not the SMX complex per se, which would require the study of SLX4 point mutants that selectively ablate the interactions with XPF or MUS81 (but not CIP2A). As such, I suggest that they rephrase their wording appropriately.

4) Western blots must be provided to substantiate the experiments performed with siRNA (Figure 1G-J, Figure 2A-E and 2H, Figure 3A-D, Figure 5B-D). Similarly, the authors should provide western blots to confirm the BRCA2 and CIP2A statuses in their KO cell lines, as well as the complementation cell lines. In the absence of this information, it is difficult for someone to make an independent and meaningful interpretation of their data.

5) Most of the data presented in this manuscript is derived from n = 2 biological replicates. All of the experiments reported in the study should be repeated for n = 3 biological replicates.

6) Since the authors report the median of their data, they should also report the interquartile range or confidence interval to display the uncertainty.

Minor comments

1) The references can be improved by acknowledging some of the foundational papers on SLX4 and the SMX tri-nuclease.

1.a) Page 3: Neither Minocherhomji et al. 2015 nor Pedersen et al. 2015 were the first to describe SLX4 as a scaffold for structure-selective endonucleases. The founding papers were published in 2009 (Svendsen et al. 2009, Munoz et al. 2009, Fekairi et al. 2009, Andersen et al. 2009) with important mechanistic studies on nuclease activation reported in 2013 (Wyatt et al. 2013, Castor et al. 2013) and 2017 (Wyatt et al. 2017).

1.b) Page 6: The authors should cite Wyatt et al. 2013, alongside Castor et al. 2013 and Garner et al. 2013 since these 3 articles were published at similar times. They may also want to acknowledge previous work from the Hickson and Rosselli labs showing that XPF-ERCC1 and MUS81-EME1 are recruited to fragile sites in mitosis.

2) To improve broad readability, the authors should remove the following abbreviations: Aph and WT.

3) In several figures, the authors show that a given treatment causes a very small change in the number of foci observed per mitotic cell. Although the values may be statistically different, it is important that they discuss the biological significance of these small effects - for example, I am not convinced that a difference of 2-3 foci per cell is sufficient to induce a robust cellular response.

4) The methods could be expanded to ensure reproducibility, particularly with respect to the drug treatments (e.g., timing, washes, etc.).

Significance

This is a timely and exciting study that provides us with some new molecular insights into mitotic DNA repair. It builds on previous studies that identified the CIP2A-TOPBP1 complex as a molecular tether that connects broken DNA ends that get transmitted from interphase into mitosis (PMID: 30898438, 35842428, 35842428). The results are also largely complementary with those of Martin et al. (BioRxiv preprint at https://doi.org/10.1101/2024.11.12.621593) and de Haan et al. (BioRxiv preprint at https://www.biorxiv.org/content/10.1101/2025.04.03.647079v1).

With the rise of new AI generation software such as SoraAI or google Veo3, I wonder how these forms of harassment will be amplified, as I have already seen AI videos of celebrities, especially right when Sora was launched. I know there is watermarks, but surely there will be software that can edit that out.

Personally, I never experienced harassment on social media, but based on my observation, harassment happens a lot to influencers on social media, especially for females. Influencers usually get bullied when they do something wrong( or there are some people telling rumors about bad things influencers did), as some people start doing this, others might trust them and follow up bullying influencers. And for females influencers, revenge porn and deep-fake porn happen to them, and people tend to believe these porns to be real because that's what they want to see. These harassments will bring very huge damage to the influencers and their families.

We know Lizzy belongs there, but she has to earn that / mature in order to be able to arrive there.

Mystery!

Honestly, reading this section made me think about how different identities stack on top of each other and affect the way people get treated, especially online. I always knew certain groups got more hate, but the numbers about Black women getting harassed way more than anyone else are kinda wild. It makes the internet feel less like an “equal place for everyone” and more like a mirror of real-life problems, just amplified.

It also made me realize how easy it is for people to miss intersectionality in general. Like with the résumé example — people might think, “oh good, no more sexism and no more racism,” but they don’t even notice a system can still be unfair to someone who fits both categories. I’ve seen people say stuff like “everyone gets bullied online,” but clearly that’s not true for everyone in the same way. It makes me think about how many other systems look “fine” on the surface but actually hide deeper problems.

I guess my question is: if most of this harassment is happening on platforms that can track everything, why don’t they do more to prevent it? It feels like social media companies have the data, they just don’t take responsibility for the people being targeted the most.

Author response:

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

Reviewer #1 (Public review):

Summary

This work provides important new evidence of the cognitive and neural mechanisms that give rise to feelings of shame and guilt, as well as their transformation into compensatory behavior. The authors use a well-designed interpersonal task to manipulate responsibility and harm, eliciting varying levels of shame and guilt in participants. The study combines behavioral, computational, and neuroimaging approaches to offer a comprehensive account of how these emotions are experienced and acted upon. Notably, the findings reveal distinct patterns in how harm and responsibility contribute to guilt and shame and how these factors are integrated into compensatory decision-making.

Strengths

(1) Investigating both guilt and shame in a single experimental framework allows for a direct comparison of their behavioral and neural effects while minimizing confounds.

(2) The study provides a novel contribution to the literature by exploring the neural bases underlying the conversion of shame into behavior.

(3) The task is creative and ecologically valid, simulating a realistic social situation while retaining experimental control.

(4) Computational modeling and fMRI analysis yield converging evidence for a quotient-based integration of harm and responsibility in guiding compensatory behavior.

We are grateful for your thoughtful summary of our work’s strengths and greatly appreciate these positive words.

We would like to note that, in accordance with the journal’s requirements, we have uploaded both a clean version of the revised manuscript and a version with all modifications highlighted in blue.

Weakness

(1) Post-experimental self-reports rely both on memory and on the understanding of the conceptual difference between the two emotions. Additionally, it is unclear whether the 16 scenarios were presented in random order; sequential presentation could have introduced contrast effects or demand characteristics.

Thank you for pointing out the two limitations of the experimental paradigm. We fully agree with your point. Participants recalled and reported their feelings of guilt and shame immediately after completing the task, which likely ensured reasonably accurate state reports. We acknowledge, however, that in-task assessments might provide greater precision. We opted against them to examine altruistic decision-making in a more natural context, as in-task assessments could have heightened participants’ awareness of guilt and shame and biased their altruistic decisions. Post-task assessments also reduced fMRI scanning time, minimizing discomfort from prolonged immobility and thereby preserving data quality.

In the present study, assessing guilt and shame required participants to distinguish conceptually between the two emotions. Most research with adult participants has adopted this approach, relying on direct self-reports of emotional intensity under the assumption that adults can differentiate between guilt and shame (Michl et al., 2014; Wagner et al., 2011; Zhu et al., 2019). However, we acknowledge that this approach may be less suitable for studies involving children, who may not yet have a clear understanding of the distinction between guilt and shame.

The limitations have been added into the Discussion section (Page 47): “This research has several limitations. First, post-task assessments of guilt and shame, unlike in-task assessments, rely on memory and may thus be less precise, although in-task assessments could have heightened participants’ awareness of these emotions and biased their decisions. Second, our measures of guilt and shame depend on participants’ conceptual understanding of the two emotions. While this is common practice in studies with adult participants (Michl et al., 2014; Wagner et al., 2011; Zhu et al., 2019), it may be less appropriate for research involving children.”

We apologize for the confusion. The 16 scenarios were presented in a random order. We have clarified this in the revised manuscript (Page 13): “After the interpersonal game, the outcomes of the experimental trials were re-presented in a random order.”

(2) In the neural analysis of emotion sensitivity, the authors identify brain regions correlated with responsibility-driven shame sensitivity and then use those brain regions as masks to test whether they were more involved in the responsibility-driven shame sensitivity than the other types of emotion sensitivity. I wonder if this is biasing the results. Would it be better to use a cross-validation approach? A similar issue might arise in "Activation analysis (neural basis of compensatory sensitivity)." 

Thank you for this valuable comment. We replaced the original analyses with a leave-one-subject-out (LOSO) cross-validation approach, which minimizes bias in secondary tests due to non-independence (Esterman et al., 2010). The findings were largely consistent with the original results, except that two previously significant effects became marginally significant (one effect changed from P = 0.012 to P = 0.053; the other from P = 0.044 to P = 0.062). Although we believe the new results do not alter our main conclusions, marginally significant findings should be interpreted with caution. We have noted this point in the Discussion section (Page 48): “… marginally significant results should be viewed cautiously and warrant further examination in future studies with larger sample sizes.”

In the revised manuscript, we have described the cross-validation procedure in detail and reported the corresponding results. Please see the Method section, Page 23: “The results showed that the neural responses in the temporoparietal junction/superior temporal sulcus (TPJ/STS) and precentral cortex/postcentral cortex/supplementary motor area (PRC/POC/SMA) were negatively correlated with the responsibility-driven shame sensitivity. To test whether these regions were more involved in responsibilitydriven shame sensitivity than in other types of emotion sensitivity, we implemented a leave-one-subject-out (LOSO) cross-validation procedure (e.g., Esterman et al., 2010). In each fold, clusters in the TPJ/STS and PRC/POC/SMA showing significant correlations with responsibility-driven shame sensitivity were identified at the group level based on N-1 participants. These clusters, defined as regions of interest (ROI), were then applied to the left-out participant, from whom we extracted the mean parameter estimates (i.e., neural response values). If, in a given fold, no suprathreshold cluster was detected within the TPJ/STS or PRC/POC/SMA after correction, or if the two regions merged into a single cluster that could not be separated, the corresponding value was coded as missing. Repeating this procedure across all folds yielded an independent set of ROI-based estimates for each participant. In the LOSO crossvalidation procedure, the TPJ/STS and PRC/POC/SMA merged into a single inseparable cluster in two folds, and no suprathreshold cluster was detected within the TPJ/STS in one fold. These instances were coded as missing, resulting in valid data from 39 participants for the TPJ/STS and 40 participants for the PRC/POC/SMA. We then correlated these estimates with all four types of emotion sensitivities and compared the correlation with responsibility-driven shame sensitivity against those with the other sensitivities using Z tests (Pearson and Filon's Z).” and Page 24: “To directly test whether these regions were more involved in one of the two types of compensatory sensitivity, we applied the same LOSO cross-validation procedure described above. In this procedure, no suprathreshold cluster was detected within the LPFC in one fold and within the TP in 27 folds. These cases were coded as missing, resulting in valid data from 42 participants for the bilateral IPL, 41 participants for the LPFC, and 15 participants for the TP. The limited sample size for the TP likely reflects that its effect was only marginally above the correction threshold, such that the reduced power in cross-validation often rendered it nonsignificant. Because the sample size for the TP was too small and the results may therefore be unreliable, we did not pursue further analyses for this region. The independent ROI-based estimates were then correlated with both guilt-driven and shame-driven compensatory sensitivities, and the strength of the correlations was compared using Z tests (Pearson and Filon's Z).”

Please see the Results section, Pages 34 and 35: “To assess whether these brain regions were specifically involved in responsibility-driven shame sensitivity, we compared the Pearson correlations between their activity and all types of emotion sensitivities. The results demonstrated the domain specificity of these regions, by revealing that the TPJ/STS cluster had significantly stronger negative responses to responsibility-driven shame sensitivity than to responsibility-driven guilt sensitivity (Z = 2.44, P = 0.015) and harm-driven shame sensitivity (Z = 3.38, P < 0.001), and a marginally stronger negative response to harm-driven guilt sensitivity (Z = 1.87, P = 0.062) (Figure 4C; Supplementary Table 14). In addition, the sensorimotor areas (i.e., precentral cortex (PRC), postcentral cortex (POC), and supplementary motor area (SMA)) exhibited the similar activation pattern as the TPJ/STS (Figure 4B and 4C; Supplementary Tables 13 and 14).” and Page 35: “The results revealed that the left LPFC was more engaged in shame-driven compensatory sensitivity (Z = 1.93, P = 0.053), as its activity showed a marginally stronger positive correlation with shamedriven sensitivity than with guilt-driven sensitivity (Figure 5C). No significant difference was found in the Pearson correlations between the activity of the bilateral IPL and the two types of sensitivities (Supplementary Table 16). For the TP, the effective sample size was too small to yield reliable results (see Methods).”

(1) Regarding the traits of guilt and shame, I appreciate using the scores from the subscales (evaluations and action tendencies) separately for the analyses (instead of a composite score). An issue with using the actions subscales when measuring guilt and shame proneness is that the behavioral tendencies for each emotion get conflated with their definitions, risking circularity. It is reassuring that the behavior evaluation subscale was significantly correlated with compensatory behavior (not only the action tendencies subscale). However, the absence of significant neural correlates for the behavior evaluation subscale raises questions: Do the authors have thoughts on why this might be the case, and any implications?

We are grateful for this important comment. According to the Guilt and Shame Proneness Scale, trait guilt comprises two dimensions: negative behavior evaluations and repair action tendencies (Cohen et al., 2011). Behaviorally, both dimensions were significantly correlated with participants’ compensatory behavior (negative behavior evaluations: R = 0.39, P = 0.010; repair action tendencies: R = 0.33, P = 0.030). Neurally, while repair action tendencies were significantly associated with activity in the aMCC and other brain areas, negative behavior evaluations showed no significant neural correlates. The absence of significant neural correlates for negative behavior evaluations may be due to several factors. In addition to common explanations (e.g., limited sample size reducing the power to detect weak neural correlates or subtle effects obscured by fMRI noise), another possibility is that this dimension influences neural responses indirectly through intermediate processes not captured in our study (e.g., specific motivational states). We have added a discussion of the non-significant result to the revised manuscript (Page 47): “However, the neural correlates of negative behavior evaluations (another dimension of trait guilt) were absent. The reasons underlying the non-significant neural finding may be multifaceted. One possibility is that negative behavior evaluations influence neural responses indirectly through intermediate processes not captured in our study (e.g., specific motivational states).”

In addition, to avoid misunderstanding, the revised manuscript specifies at the appropriate places that the neural findings pertain to repair action tendencies rather than to trait guilt in general. For instance, see Pages 46 and 47: “Furthermore, we found neural responses in the aMCC mediated the relationship between repair action tendencies (one dimension of trait guilt) and compensation… Accordingly, our fMRI findings suggest that individuals with stronger tendency to engage in compensation across various moral violation scenarios (indicated by their repair action tendencies) are more sensitive to the severity of the violation and therefore engage in greater compensatory behavior.”

(2) Regarding the computational model finding that participants seem to disregard selfinterest, do the authors believe it may reflect the relatively small endowment at stake? Do the authors believe this behavior would persist if the stakes were higher?

Additionally, might the type of harm inflicted (e.g., electric shock vs. less stigmatized/less ethically charged harm like placing a hand in ice-cold water) influence the weight of self-interest in decision-making?

Taken together, the conclusions of the paper are well supported by the data. It would be valuable for future studies to validate these findings using alternative tasks or paradigms to ensure the robustness and generalizability of the observed behavioral and neural mechanisms.

Thank you for these important questions. As you suggested, we believe that the relatively small personal stakes in our task (a maximum loss of 5 Chinese yuan) likely explain why the computational model indicated that participants disregarded selfinterest. We also agree that when the harm to others is less morally charged, people may be more inclined to consider self-interest in compensatory decision-making. Overall, the more stigmatized the harm and the smaller the personal stakes, the more likely individuals are to disregard self-interest and focus solely on making appropriate compensation.

We have added the following passage to the Discussion section (Page 42): “Notably, in many computational models of social decision-making, self-interest plays a crucial role (e.g., Wu et al., 2024). However, our computational findings suggest that participants disregarded self-interest during compensatory decision-making. A possible explanation is that the personal stakes in our task were relatively small (a maximum loss of 5 Chinese yuan), whereas the harm inflicted on the receiver was highly stigmatized (i.e., an electric shock). Under conditions where the harm is highly salient and the cost of compensation is low, participants may be inclined to disregard selfinterest and focus solely on making appropriate compensation.”

Reviewer #2 (Public review):

Summary

The authors combined behavioral experiments, computational modeling, and functional magnetic resonance imaging (fMRI) to investigate the psychological and neural mechanisms underlying guilt, shame, and the altruistic behaviors driven by these emotions. The results revealed that guilt is more strongly associated with harm, whereas shame is more closely linked to responsibility. Compared to shame, guilt elicited a higher level of altruistic behavior. Computational modeling demonstrated how individuals integrate information about harm and responsibility. The fMRI findings identified a set of brain regions involved in representing harm and responsibility, transforming responsibility into feelings of shame, converting guilt and shame into altruistic actions, and mediating the effect of trait guilt on compensatory behavior.

Strengths

This study offers a significant contribution to the literature on social emotions by moving beyond prior research that typically focused on isolated aspects of guilt and shame. The study presents a comprehensive examination of these emotions, encompassing their cognitive antecedents, affective experiences, behavioral consequences, trait-level characteristics, and neural correlates. The authors have introduced a novel experimental task that enables such a systematic investigation and holds strong potential for future research applications. The computational modeling procedures were implemented in accordance with current field standards. The findings are rich and offer meaningful theoretical insights. The manuscript is well written, and the results are clearly and logically presented.

We are thankful for your considerate acknowledgment of our work’s strengths and truly value your positive comments.

We would like to note that, in accordance with the journal’s requirements, we have uploaded both a clean version of the revised manuscript and a version with all modifications highlighted in blue.

Weakness

In this study, participants' feelings of guilt and shame were assessed retrospectively, after they had completed all altruistic decision-making tasks. This reliance on memorybased self-reports may introduce recall bias, potentially compromising the accuracy of the emotion measurements.

Thank you for this crucial comment. We fully agree that measuring guilt and shame after the task may affect accuracy to some extent. However, because participants reported their emotions immediately after completing the task, we believe their recollections were reasonably accurate. In designing the experiment, we considered intask assessments, but this approach risked heightening participants’ awareness of guilt and shame and thereby interfering with compensatory decisions. After careful consideration, we ultimately chose post-task assessments of these emotions. A similar approach has been adopted in prior research on gratitude, where post-task assessments were also used (Yu et al., 2018).

In the revised manuscript, we have specified the limitations of both post-task and intask assessments of guilt and shame (Page 47): “… post-task assessments of guilt and shame, unlike in-task assessments, rely on memory and may thus be less precise, although in-task assessments could have heightened participants’ awareness of these emotions and biased their decisions.”.

In many behavioral economic models, self-interest plays a central role in shaping individual decision-making, including moral decisions. However, the model comparison results in this study suggest that models without a self-interest component (such as Model 1.3) outperform those that incorporate it (such as Model 1.1 and Model 1.2). The authors have not provided a satisfactory explanation for this counterintuitive finding. 

Thank you for this important comment. In the revised manuscript, we have provided a possible explanation (Page 42): “Notably, in many computational models of social decision-making, self-interest plays a crucial role (e.g., Wu et al., 2024). However, our computational findings suggest that participants disregarded self-interest during compensatory decision-making. A possible explanation is that the personal stakes in our task were relatively small (a maximum loss of 5 Chinese yuan), whereas the harm inflicted on the receiver was highly stigmatized (i.e., an electric shock). Under conditions where the harm is highly salient and the cost of compensation is low, participants may be inclined to disregard self-interest and focus solely on making appropriate compensation.”

The phrases "individuals integrate harm and responsibility in the form of a quotient" and "harm and responsibility are integrated in the form of a quotient" appear in the Abstract and Discussion sections. However, based on the results of the computational modeling, it is more accurate to state that "harm and the number of wrongdoers are integrated in the form of a quotient." The current phrasing misleadingly suggests that participants represent information as harm divided by responsibility, which does not align with the modeling results. This potentially confusing expression should be revised for clarity and accuracy.

We sincerely thank you for this helpful suggestion and apologize for the confusion caused. We have removed expressions such as “harm and responsibility are integrated in the form of a quotient” from the manuscript. Instead, we now state more precisely that “harm and the number of wrongdoers are integrated in the form of a quotient.”

However, in certain contexts we continue to discuss harm and responsibility. Introducing “the number of wrongdoers” in these places would appear abrupt, so we have opted for alternative phrasing. For example, on Page 3, we now write:

“Computational modeling results indicated that the integration of harm and responsibility by individuals is consistent with the phenomenon of responsibility diffusion.” Similarly, on Page 49, we state: “Notably, harm and responsibility are integrated in a manner consistent with responsibility diffusion prior to influencing guilt-driven and shame-driven compensation.”

In the Discussion, the authors state: "Since no brain region associated with social cognition showed significant responses to harm or responsibility, it appears that the human brain encodes a unified measure integrating harm and responsibility (i.e., the quotient) rather than processing them as separate entities when both are relevant to subsequent emotional experience and decision-making." However, this interpretation overstates the implications of the null fMRI findings. The absence of significant activation in response to harm or responsibility does not necessarily imply that the brain does not represent these dimensions separately. Null results can arise from various factors, including limitations in the sensitivity of fMRI. It is possible that more finegrained techniques, such as intracranial electrophysiological recordings, could reveal distinct neural representations of harm and responsibility. The interpretation of these null findings should be made with greater caution.

Thank you for this reminder. In the revised manuscript, we have provided a more cautious interpretation of the results (Page 43): “Although the fMRI findings revealed that no brain region associated with social cognition showed significant responses to harm or responsibility, this does not suggest that the human brain encodes only a unified measure integrating harm and responsibility and does not process them as separate entities. Using more fine-grained techniques, such as intracranial electrophysiological recordings, it may still be possible to observe independent neural representations of harm and responsibility.”

Reviewer #3 (Public review):

Summary

Zhu et al. set out to elucidate how the moral emotions of guilt and shame emerge from specific cognitive antecedents - harm and responsibility - and how these emotions subsequently drive compensatory behavior. Consistent with their prediction derived from functionalist theories of emotion, their behavioral findings indicate that guilt is more influenced by harm, whereas shame is more influenced by responsibility. In line with previous research, their results also demonstrate that guilt has a stronger facilitating effect on compensatory behavior than shame. Furthermore, computational modeling and neuroimaging results suggest that individuals integrate harm and responsibility information into a composite representation of the individual's share of the harm caused. Brain areas such as the striatum, insula, temporoparietal junction, lateral prefrontal cortex, and cingulate cortex were implicated in distinct stages of the processing of guilt and/or shame. In general, this work makes an important contribution to the field of moral emotions. Its impact could be further enhanced by clarifying methodological details, offering a more nuanced interpretation of the findings, and discussing their potential practical implications in greater depth.

Strengths

First, this work conceptualizes guilt and shame as processes unfolding across distinct stages (cognitive appraisal, emotional experience, and behavioral response) and investigates the psychological and neural characteristics associated with their transitions from one stage to the next.

Second, the well-designed experiment effectively manipulates harm and responsibility - two critical antecedents of guilt and shame.

Third, the findings deepen our understanding of the mechanisms underlying guilt and shame beyond what has been established in previous research.

We truly appreciate your acknowledgment of our work’s strengths and your encouraging feedback.

We would like to note that, in accordance with the journal’s requirements, we have uploaded both a clean version of the revised manuscript and a version with all modifications highlighted in blue.

Weakness

Over the course of the task, participants may gradually become aware of their high error rate in the dot estimation task. This could lead them to discount their own judgments and become inclined to rely on the choices of other deciders. It is unclear whether participants in the experiment had the opportunity to observe or inquire about others' choices. This point is important, as the compensatory decision-making process may differ depending on whether choices are made independently or influenced by external input.

Thank you for pointing this out. We apologize for not making the experimental procedure sufficiently clear. Participants (as deciders) were informed that each decider performed the dot estimation independently and was unaware of the estimations made by the other deciders. We now have clarified this point in the revised manuscript (Pages 10 and 11): “Each decider indicated whether the number of dots was more than or less than 20 based on their own estimation by pressing a corresponding button (dots estimation period, < 2.5 s) and was unaware of the estimations made by other deciders”.

Given the inherent complexity of human decision-making, it is crucial to acknowledge that, although the authors compared eight candidate models, other plausible alternatives may exist. As such, caution is warranted when interpreting the computational modeling results.

Thank you for this comment. We fully agree with your opinion. Although we tried to build a conceptually comprehensive model space based on prior research and our own understanding, we did not include all plausible models, nor would it be feasible to do so. We acknowledge it as a limitation in the revised manuscript (Page 47): “... although we aimed to construct a conceptually comprehensive computational model space informed by prior research and our own understanding, it does not encompass all plausible models. Future research is encouraged to explore additional possibilities.”

I do not agree with the authors' claim that "computational modeling results indicated that individuals integrate harm and responsibility in the form of a quotient" (i.e., harm/responsibility). Rather, the findings appear to suggest that individuals may form a composite representation of the harm attributable to each individual (i.e., harm/the number of people involved). The explanation of the modeling results ought to be precise.

We appreciate your comment and apologize for the imprecise description. In the revised manuscript, we now use the expressions “… integrate harm and the number of wrongdoers in the form of a quotient.” and “… the integration of harm and responsibility by individuals is consistent with the phenomenon of responsibility diffusion.” For example, on Page 19, we state: “It assumes that individuals neglect their self-interest, have a compensatory baseline, and integrate harm and the number of wrongdoers in the form of a quotient.” On Page 3, we state: “Computational modeling results indicated that the integration of harm and responsibility by individuals is consistent with the phenomenon of responsibility diffusion.”

Many studies have reported positive associations between trait gratitude, social value orientation, and altruistic behavior. It would be helpful if the authors could provide an explanation about why this study failed to replicate these associations.

Thanks a lot for this important comment. We have now added an explanation into the revised manuscript (Page 47): “Although previous research has found that trait gratitude and SVO are significantly associated with altruistic behavior in contexts such as donation (Van Lange et al., 2007; Yost-Dubrow & Dunham, 2018) and reciprocity (Ma et al., 2017; Yost-Dubrow & Dunham, 2018), their associations with compensatory decisions in the present study were not significant. This suggests that the effects of trait gratitude and SVO on altruistic behavior are context-dependent and may not predict all forms of altruistic behavior.”

As the authors noted, guilt and shame are closely linked to various psychiatric disorders. It would be valuable to discuss whether this study has any implications for understanding or even informing the treatment of these disorders.

We are grateful for this advice. Although our study did not directly examine patients with psychological disorders, the findings offer insights into the regulation of guilt and shame. As these emotions are closely linked to various disorders, improving their regulation may help alleviate related symptoms. Accordingly, we have added a paragraph highlighting the potential clinical relevance (Pages 48 and 49): “Our study has potential practical implications. The behavioral findings may help counselors understand how cognitive interventions targeting perceptions of harm and responsibility could influence experiences of guilt and shame. The neural findings highlight specific brain regions (e.g., TPJ) as potential intervention targets for regulating these emotions. Given the close links between guilt, shame, and various psychological disorders (e.g., Kim et al., 2011; Lee et al., 2001; Schuster et al., 2021), strategies to regulate these emotions may contribute to symptom alleviation. Nevertheless, because this study was conducted with healthy adults, caution is warranted when considering applications to other populations.”

Reviewer #1 (Recommendations for the authors):

(1) Would it be interesting to explore other categories of behavior apart from compensatory behavior?

Thanks a lot for this insightful question. We focused on a classic form of altruistic behavior, compensation. Future studies are encouraged to adapt our paradigm to examine other behaviors associated with guilt and/or shame, such as donation (Xu, 2022), avoidance (Shen et al., 2023), or aggression (Velotti et al., 2014). Please see Page 48: “Future research could combine this paradigm with other cognitive neuroscience methods, such as electroencephalography (EEG) or magnetoencephalography (MEG), and adapt it to investigate additional behaviors linked to guilt and shame, including donation (Xu, 2022), avoidance (Shen et al., 2023), and aggression (Velotti et al., 2014).”

(2) Did the computational model account for the position of the block (slider) at the start of each decision-making response (when participants had to decide how to divide the endowment)? Or are anchoring effects not relevant/ not a concern?

Thank you for this interesting question. In our task, the initial position of the slider was randomized across trials, and participants were explicitly informed of this in the instructions. This design minimized stable anchoring effects across trials, as participants could not rely on a consistent starting point. Although anchoring might still have influenced individual trial responses, we believe it is unlikely that such effects systematically biased our results, since randomization would tend to cancel them out across trials. Additionally, prior research has shown that when multiple anchors are presented, anchoring effects are reduced if the anchors contradict each other (Switzer

III & Sniezek, 1991). Therefore, we did not attempt to model potential anchoring effects. Nevertheless, future research could systematically manipulate slider starting positions to directly examine possible anchoring influences. In the revised manuscript, we have added a brief clarification (Page 11): “The initial position of the block was randomized across trials, which helped minimize stable anchoring effects across trials.”

(3) Was there a real receiver who experienced the shocks and received compensation? I think it is not completely clear in the paper.

We are sorry for not making this clear enough. The receiver was fictitious and did not actually exist. We have supplemented the Methods section with the following description (Page 12): “We told the participant a cover story that the receiver was played by another college student who was not present in the laboratory at the time. … In fact, the receiver did not actually exist.”.

(4) What was the rationale behind not having participants meet the receiver?

Thank you for this question. Having participants meet the receiver (i.e., the victim), played by a confederate, might have intensified their guilt and shame and produced a ceiling effect. In addition, the current approach simplified the experimental procedure and removed the need to recruit an additional confederate. These reasons have been added to the Methods section (Page 12): “Not having participants meet the receiver helped prevent excessive guilt and shame that might produce a ceiling effect, while also eliminating the need to recruit an additional confederate.”

Minor edits:

(1) Line 49: "the cognitive assessment triggers them", I think a word is missing.

(2) Line 227: says 'Slide' instead of 'Slider'.

(3) Lines 867/868: "No brain response showed significant correlation with responsibility-driven guilt sensitivity, harm-driven shame sensitivity, or responsibilitydriven shame sensitivity." I think it should be harm-driven guilt sensitivity, responsibility-driven guilt sensitivity, and harm-driven shame sensitivity.

(4) Supplementary Information Line 12: I think there is a typo ( 'severs' instead of 'serves')

We sincerely thank you for patiently pointing out these typos. We have corrected them accordingly. 

(1) “the cognitive assessment triggers them” has been revised to “the cognitive antecedents that trigger them” (Page 2).

(2) “SVO Slide Measure” has been revised to “SVO Slider Measure” (Page 8).

(3) “No brain response showed significant correlation with responsibility-driven guilt sensitivity, harm-driven shame sensitivity, or responsibility-driven shame sensitivity." has been revised to “No brain response showed significant correlation with harm-driven guilt sensitivity, responsibility-driven guilt sensitivity, and harm-driven shame sensitivity.” (Page 35).

(4) “severs” has been revised to “serves” (see Supplementary Information). In addition, we have carefully checked the entire manuscript to correct any remaining typographical errors.

Reviewer #2 (Recommendations for the authors):

The statement that trait gratitude and SVO were measured "for exploratory purposes" would benefit from further clarification regarding the specific questions being explored.

Thank you for this valuable suggestion. In the revised manuscript, we have illustrated the exploratory purposes (Page 9): “We measured trait gratitude and SVO for exploratory purposes. Previous research has shown that both are linked to altruistic behavior, particularly in donation contexts (Van Lange et al., 2007; Yost-Dubrow & Dunham, 2018) and reciprocity contexts (Ma et al., 2017; Yost-Dubrow & Dunham, 2018). Here, we explored whether they also exert significant effects in a compensatory context.”

In the Methods section, the authors state: "To confirm the relationships between κ and guilt-driven and shame-driven compensatory sensitivities, we calculated the Pearson correlations between them." However, the Results section reports linear regression results rather than Pearson correlation coefficients, suggesting a possible inconsistency. The authors are advised to carefully check and clarify the analysis approach used.

We thank you for the careful reviewing and apologize for this mistake. We used a linear mixed-effects regression instead of Pearson correlations for the analysis. The mistake has been revised (Page 25): “To confirm the relationships between κ and guiltdriven and shame-driven compensatory sensitivities, we conducted a linear mixedeffects regression. κ was regressed onto guilt-driven and shame-driven compensatory sensitivities, with participant-specific random intercepts and random slopes for each fixed effect included as random effects.”

A more detailed discussion of how the current findings inform the regulation of guilt and shame would further strengthen the contribution of this study.

Thank you for this suggestion. We have added a paragraph discussing the implications for the regulation of guilt and shame (Pages 48 and 49): “Our study has potential practical implications. The behavioral findings may help counselors understand how cognitive interventions targeting perceptions of harm and responsibility could influence experiences of guilt and shame. The neural findings highlight specific brain regions (e.g., TPJ) as potential intervention targets for regulating these emotions. Given the close links between guilt, shame, and various psychological disorders (e.g., Kim et al., 2011; Lee et al., 2001; Schuster et al., 2021), strategies to regulate these emotions may contribute to symptom alleviation. Nevertheless, because this study was conducted with healthy adults, caution is warranted when considering applications to other populations.”

As fMRI provides only correlational evidence, establishing a causal link between neural activity and guilt- or shame-related cognition and behavior would require brain stimulation or other intervention-based methods. This may represent a promising direction for future research.

Thank you for this advice. We also agree that it is important for future research to establish the causal relationships between the observed brain activity, psychological processes, and behavior. We have added a corresponding discussion in the revised manuscript (Pages 47 and 48): “… fMRI cannot establish causality. Future studies using brain stimulation techniques (e.g., transcranial magnetic stimulation) are needed to clarify the causal role of brain regions in guilt-driven and shame-driven altruistic behavior.”

Reviewer #3 (Recommendations for the authors):

It was mentioned that emotions beyond guilt and shame, such as indebtedness, may also drive compensation. Were any additional types of emotion measured in the study?

Thank you for this question. We did not explicitly measure emotions other than guilt and shame. However, the parameter κ from our winning computational model captures the combined influence of various psychological processes on compensation, which may reflect the impact of emotions beyond guilt and shame (e.g., indebtedness). We acknowledge that measuring other emotions similar to guilt and shame may help to better understand their distinct contributions. This point has been added into the revised manuscript (Page 48): “… we did not explicitly measure emotions similar to guilt and shame (e.g., indebtedness), which would have been helpful for understanding their distinct contributions.”

The experimental task is complicated, raising the question of whether participants fully understood the instructions. For instance, one participant's compensation amount was zero. Could this reflect a misunderstanding of the task instructions?

Thanks a lot for this question. In our study, after reading the instructions, participants were required to complete a comprehension test on the experimental rules. If they made any mistakes, the experimenter provided additional explanations. Only after participants fully understood the rules and correctly answered all comprehension questions did they proceed to the main experimental task. We have clarified this procedure in the revised manuscript (Page 13): “Participants did not proceed to the interpersonal game until they had fully understood the experimental rules and passed a comprehension test.”

Making identical choices across different trials does not necessarily indicate that participants misunderstood the rules. Similar patterns, where participants made the same choices across trials, have also been observed in previous studies (Zhong et al., 2016; Zhu et al., 2021).

Reference

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Esterman, M., Tamber-Rosenau, B. J., Chiu, Y. C., & Yantis, S. (2010). Avoiding nonindependence in fMRI data analysis: Leave one subject out. NeuroImage, 50(2), 572–576. https://doi.org/10.1016/j.neuroimage.2009.10.092

Kim, S., Thibodeau, R., & Jorgensen, R. S. (2011). Shame, guilt, and depressive symptoms: A meta-analytic review. Psychological Bulletin, 137(1), 68. https://doi.org/10.1037/a0021466

Lee, D. A., Scragg, P., & Turner, S. (2001). The role of shame and guilt in traumatic events: A clinical model of shame-based and guilt-based PTSD. British Journal of Medical Psychology, 74(4), 451–466. https://doi.org/10.1348/000711201161109

Ma, L. K., Tunney, R. J., & Ferguson, E. (2017). Does gratitude enhance prosociality?: A meta-analytic review. Psychological Bulletin, 143(6), 601–635. https://doi.org/10.1037/bul0000103

Michl, P., Meindl, T., Meister, F., Born, C., Engel, R. R., Reiser, M., & Hennig-Fast, K. (2014). Neurobiological underpinnings of shame and guilt: A pilot fMRI study. Social Cognitive and Affective Neuroscience, 9(2), 150–157.

Schuster, P., Beutel, M. E., Hoyer, J., Leibing, E., Nolting, B., Salzer, S., Strauss, B., Wiltink, J., Steinert, C., & Leichsenring, F. (2021). The role of shame and guilt in social anxiety disorder. Journal of Affective Disorders Reports, 6, 100208. https://doi.org/10.1016/j.jadr.2021.100208

Shen, B., Chen, Y., He, Z., Li, W., Yu, H., & Zhou, X. (2023). The competition dynamics of approach and avoidance motivations following interpersonal transgression. Proceedings of the National Academy of Sciences, 120(40), e2302484120. https://doi.org/10.1073/pnas.230248412

Switzer III, F. S., & Sniezek, J. A. (1991). Judgment processes in motivation: Anchoring and adjustment effects on judgment and behavior. Organizational Behavior and Human Decision Processes, 49(2), 208–229. https://doi.org/10.1016/0749-5978(91)90049-Y

Van Lange, P. A. M., Bekkers, R., Schuyt, T. N. M., & Van Vugt, M. (2007). From games to giving: Social value orientation predicts donations to noble causes. Basic and Applied Social Psychology, 29(4), 375–384. https://doi.org/10.1080/01973530701665223

Velotti, P., Elison, J., & Garofalo, C. (2014). Shame and aggression: Different trajectories and implications. Aggression and Violent Behavior, 19(4), 454–461. https://doi.org/10.1016/j.avb.2014.04.011

Wagner, U., N’Diaye, K., Ethofer, T., & Vuilleumier, P. (2011). Guilt-specific processing in the prefrontal cortex. Cerebral Cortex, 21(11), 2461–2470. https://doi.org/10.1093/cercor/bhr016

Wu, X., Ren, X., Liu, C., & Zhang, H. (2024). The motive cocktail in altruistic behaviors. Nature Computational Science, 4, 659–676. https://doi.org/10.1038/s43588-024-00685-6

Xu, J. (2022). The impact of guilt and shame in charity advertising: The role of self- construal. Journal of Philanthropy and Marketing, 27(1). https://doi.org/10.1002/nvsm.1709

Yost-Dubrow, R., & Dunham, Y. (2018). Evidence for a relationship between trait gratitude and prosocial behaviour. Cognition and Emotion, 32(2), 397–403. https://doi.org/10.1080/02699931.2017.1289153

Yu, H., Gao, X., Zhou, Y., & Zhou, X. (2018). Decomposing gratitude: Representation and integration of cognitive antecedents of gratitude in the brain. Journal of Neuroscience, 38(21), 4886–4898. https://doi.org/10.1523/JNEUROSCI.2944-17.2018

Zhong, S., Chark, R., Hsu, M., & Chew, S. H. (2016). Computational substrates of social norm enforcement by unaffected third parties. NeuroImage, 129, 95–104. https://doi.org/10.1016/j.neuroimage.2016.01.040

Zhu, R., Feng, C., Zhang, S., Mai, X., & Liu, C. (2019). Differentiating guilt and shame in an interpersonal context with univariate activation and multivariate pattern analyses. NeuroImage, 186, 476486. https://doi.org/10.1016/j.neuroimage.2018.11.012

Zhu, R., Xu, Z., Su, S., Feng, C., Luo, Y., Tang, H., Zhang, S., Wu, X., Mai, X., & Liu, C. (2021). From gratitude to injustice: Neurocomputational mechanisms of gratitude-induced injustice. NeuroImage, 245, 118730. https://doi.org/10.1016/j.neuroimage.2021.118730

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the amarican revolution was started for hardly a reason at all .Every system has some friction, and maybe that friction even does enough good to make up for the harm. In any case, it would be a big mistake to cause an uproar over something so small. but when the friction makes its own system and crime and robbery are organized let us have such a system no longer because when a sixth of the population has taken refuge under liberty yet are slaves and a nation is invaded and subject to military law i think it is not to soon to rebel and revolutionize.

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Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this review, the author covered several aspects of the inflammation response, mainly focusing on the mechanisms controlling leukocyte extravasation and inflammation resolution.

Strengths:

This review is based on an impressive number of sources, trying to comprehensively present a very broad and complex topic.

Weaknesses:

(1) This reviewer feels that, despite the title, this review is quite broad and not centred on the role of the extracellular matrix.

Since this review focuses on the whole extravasation journey of leukocyte, this topic is definitely quite broad and covers several related fields. The article highlights the involvement of extracellular matrices (ECM), which are important regulators in multiple phases of the process, as a common theme to thread together these related topics. In the revised manuscript, we have made further emphasis on the role of specific ECM where appropriate (see point 2 below) and reorganized the last section to fit to this theme (see point 3 below).

(2) The review will benefit from a stronger focus on the specific roles of matrix components and dynamics, with more informative subheadings.

ECM may exert their roles either as a collective structure or as individual components. In the latter case, though the concerned ECM are specifically named throughout the manuscript, they may not be sufficiently obvious since they were often not mentioned in subheadings. For sections discussing functions of a specific ECM protein or at least a specific class of ECM proteins, we have now included their names in the subheadings as well for clarity (section 5 and 8). For other sections discussing functions that involve ECM as a macrostructure, either in form of vascular basement membrane to enable force generation or contributing to the overall tissue stiffness to provide biophysical cues (section 7, 9-10), we have included the specific processes regulated in the subheadings like that in section 4.

In the newly added discussion about the effects of matrikines on lymphocytes, we have also focused on the roles of specific ECM (PGP and versican; line 396-408). We hope these measures have made the subheadings more informative and provided better clarity of the roles of specific ECM components.

(3) The macrophage phenotype section doesn't seem well integrated with the rest of the review (and is not linked to the ECM).

Section 10-11 concerns how macrophage phenotypes affect the tissue fate following inflammation, that is, either to resolve inflammation and regenerate damages incurred or to sustain inflammation. This fate decision is an important aspect of this review: By furthering our understanding on the processes and mechanisms involved, we hope to gain the capability to properly control tissue outcomes in inflammatory diseases.

In section 10, an emphasis is put on macrophage efferocytosis, for its documented efficiency to resolve tissue inflammation. Specific ECM components (type-V collagens and 𝑎2-laminins) could directly promote macrophage efferocytosis (line 494-499). On the other hand, changes in tissue stiffness, as a result of ECM turnover regulated by activities of leukocytes or other cell types like fibroblasts as described in section 9, also affects efferocytosis (line 504-507).

We acknowledge that section 11 does not integrate well to the rest of the review, this section is now restructured. First, we describe how the ECM-regulated efferocytosis may be leveraged in disease modulation (line 522-529) and the need for a unified system to describe macrophage states for disease modulation (line 527-533) such that the responsible cell states for producing ECM regulators / effectors can be clarified (line 533-535). Given means to control macrophage cell states, this clarification will be useful to modulate pathologies involving ECM malfunctioning, that might be hinted by emergence or expansion of those responsible macrophage states in pathology (line 577-579, 581-585). Next, we provide historic background of efforts to establish such a unified descriptive platform for macrophage states (line 538-548) and describe the recent solution offered by MIKA. MIKA is a pan-tissue archive for tissue macrophage cell states based on meta-analysis of published single-macrophage transcriptomes, we have described the establishment, the latest development (Supplementary Data 1-4) and how the complex tissue macrophage states are segmented to core and tissue-specific identities under this framework (line 548-560, Figure 5A). Under this identity framework, expression of different ECM regulators discussed in this review (either the ECM per se, fibroblastic growth factors or proteases or protease inhibitors that regulate ECM turnover or matrikine production) are examined and linked to specific macrophage identities to offer insights of their potential relevance in pathologies (line 561-586, Figure 5B).

(4) Table 1 is difficult to follow. It could be reformatted to facilitate reading and understanding

We apologize for the complex setup. Table 1 is now reformatted to horizontal orientation to have enough space for the columns and reorganized for much easier comprehension.

(5) Figure 2 appears very complex and broad.

The original Figure 2 is now split to 2 separate figures (Figure 3-4). Since many processes of diverse natures influence tissue decision of resolution/inflammation, Figure 3 serves to outline and summarise these processes. Figure 4 now focuses on the regulation and tissue-resolving roles of macrophage efferocytosis, which specific ECM components (type-V collagens and 2-laminins) or tissue stiffness contribute to acquisition of this cell state. We hope this split can better focus the messages and ease understanding.

(6) Spelling and grammar should be thoroughly checked to improve the readability.

The manuscript is now proofread again, with corrections made throughout the text.

Reviewer #2 (Public review):

Summary:

The manuscript is a timely and comprehensive review of how the extracellular matrix (ECM), particularly the vascular basement membrane, regulates leukocyte extravasation, migration, and downstream immune function. It integrates molecular, mechanical, and spatial aspects of ECM biology in the context of inflammation, drawing from recent advances. The framing of ECM as an active instructor of immune cell fate is a conceptual strength.

Strengths:

(1) Comprehensive synthesis of ECM functions across leukocyte extravasation and post-transmigration activity.

(2) Incorporation of recent high-impact findings alongside classical literature.

(3) Conceptually novel framing of ECM as an active regulator of immune function.

(4) Effective integration of molecular, mechanical, and spatial perspectives.

Weaknesses:

(1) Insufficient narrative linkage between the vascular phase (Sections 2-6) and the in-tissue phase (Sections 7-10).

A transition paragraph between these two phases is now added between Section 6 and Section 7 to provide a narrative that ECM interaction events during extravasation affect downstream leukocyte functions (line 300-307).

(2) Underrepresentation of lymphocyte biology despite mention in early sections.

Although lymphocytes follow a similar extravasation principle as described in earlier sections, their in-tissue activities differ much from innate leukocytes. Discussion of crosstalk amongst T cells, innate leukocytes and matrikines is now incorporated into section 8 (line 396-408). Functional effects of tissue stiffness on different T cell subsets are now discussed in section 9 (line 456-469).

(3) The MIKA macrophage identity framework is only loosely tied to ECM mechanisms.

The involved section 11 is now restructured to better integrate to the ECM topics with the associated Figure 3 changed to Figure 5. Specifically, under the MIKA framework, we have now linked specific macrophage identities to expression / production of ECM functional effectors or regulators discussed in this review to highlight their regulatory roles and potential relevance in pathologies. Reviewer #1 and #3 also have raised this issue, please refer to the response to point (3) of reviewer #1 for detailed description.

(4) Limited discussion of translational implications and therapeutic strategies.

Besides translational implications or therapeutic strategies included in the original manuscript (line 291-298, 375-377, 421-424, 427-429, 508-511, 512-516 of the current manuscript), we have now included additional discussion to enrich these aspects (line 356-358, line 396-398, 402-403, 428, 436-439, 467-469, 523-536, 579-586).

(5) Overly dense figure insets and underdeveloped links between ECM carryover and downstream immune phenotypes.

The original Figure 1 containing the insets is now split to Figure 1-2 to avoid too dense information fitting to a single figure and to better focus the message in each figure. To resolve the issue of overly dense insets, insets in Figure 1 are redrawn/ reorganized. The original Figure 1C is moved to Figure 2A. The inset showing platelet plugging, together with the issue of diapedesis overloading described in the original Figure 1B, is reorganized to Figure 2B. In this way, Figure 1 focuses on the vascular barrier organization, overview of extravasation, and the force related events during endothelial junctional remodelling. Figure 2 focuses on the low expression regions, and junctional sealing processes after diapedesis.

We have now expanded discussion on ECM carryovers and their reported or implicated effects on downstream leukocyte functions (line 329-335).

(6) Acronyms and some mechanistic details may limit accessibility for a broader readership.

A glossary explaining specialized terms that may be confusing to readers of different fields is now included as Appendix 1 to broaden accessibility (line 977).

Reviewer #3 (Public review):

Summary & Strengths:

This review by Yu-Tung Li sheds new light on the processes involved in leukocyte extravasation, with a focus on the interaction between leukocytes and the extracellular matrix. In doing so, it presents a fresh perspective on the topic of leukocyte extravasation, which has been extensively covered in numerous excellent reviews. Notably, the role of the extracellular matrix in leukocyte extravasation has received relatively little attention until recently, with a few exceptions, such as a study focusing on the central nervous system (J Inflamm 21, 53 (2024) doi.org/10.1186/s12950-024-00426-6) and another on transmigration hotspots (J Cell Sci (2025) 138 (11): jcs263862 doi.org/10.1242/jcs.263862). This review synthesizes the substantial knowledge accumulated over the past two decades in a novel and compelling manner.

The author dedicates two sections to discussing the relevant barriers, namely, endothelial cell-cell junctions and the basement membrane. The following three paragraphs address how leukocytes interact with and transmigrate through endothelial junctions, the mechanisms supporting extravasation, and how minimal plasma leakage is achieved during this process. The subsequent question of whether the extravasation process affects leukocyte differentiation and properties is original and thought-provoking, having received limited consideration thus far. The consequences of the interaction between leukocytes and the extracellular matrix, particularly regarding efferocytosis, macrophage polarization, and the outcome of inflammation, are explored in the subsequent three chapters. The review concludes by examining tissue-specific states of macrophage identity.

Weaknesses:

Firstly, the first ten sections provide a comprehensive overview of the topic, presenting logical and well-formulated arguments that are easily accessible to a general audience. In stark contrast, the final section (Chapter 11) fails to connect coherently with the preceding review and is nearly incomprehensible without prior knowledge of the author's recent publication in Cell. Mol. Life Sci. CMLS 772 82, 14 (2024). This chapter requires significantly more background information for the general reader, including an introduction to the Macrophage Identity Kinetics Archive (MIKA), which is not even introduced in this review, its basis (meta-analysis of published scRNA-seq data), its significance (identification of major populations), and the reasons behind the revision of the proposed macrophage states and their further development.

The issue of section 11 being not well-integrated to the rest of the review has also been pointed out by other reviewers. In response, this section and the associated Figure 3 are now restructured for better integration to the theme of ECM. In brief, we have now discussed the regulatory roles of specific macrophage identities under the MIKA framework on the ECM regulators described in this review. Please refer to the response to point (3) of reviewer #1 for further details.

Regarding the difficulties in understanding the MIKA framework without prior knowledge of our previous work, first, we thank the reviewer for pointing out this issue and for making suggestion to better introduce the framework in a way easy to comprehend. Accordingly, in the current structure of section 11, we have described the rationales behind the needs of a common descriptive platform for tissue macrophage states (line 523-536), previous historic efforts (line 538-548), have introduced MIKA with mentions of the establishment and significance (line 548-555), and also have explained the rationales behind further development (line 555-560).

Secondly, while the attempt to integrate a vast amount of information into fewer figures is commendable, it results in figures that resemble a complex puzzle. The author may consider increasing the number of figures and providing additional, larger "zoom-in" panels, particularly for the topics of clot formation at transmigration hotspots and the interaction between ECM/ECM fragments and integrins. Specifically, the color coding (purple for leukocyte α6-integrins, blue for interacting laminins, also blue for EC α6 integrins, and red for interacting 5-1-1 laminins) is confusing, and the structures are small and difficult to recognize.

We apologize for the figures being too dense. Other reviewers have also raised this issue (see response to point (5) of reviewer #2 and response to point (5) of reviewer #1). The original Figure 1 and 2 are now reorganized to Figure 1-2 and 3-4 respectively, with insets also redrawn / expanded. Figure 1 now focuses on the vascular barrier organization, overview of extravasation, and the force related events during endothelial junctional remodelling. Figure 2 focuses on the low expression regions, and junctional sealing processes after diapedesis. Figure 3 serves to outline and summarise the diverse processes influencing tissue decision of resolution/inflammation. Figure 4 focuses on the regulation and tissue-resolving roles of macrophage efferocytosis. The original Figure 3, mainly concerning the methodological aspects of update of MIKA, is now integrated to Supplementary Data 1. This figure is now replaced as Figure 5 concerning the specific macrophage identities producing ECM effectors / regulators discussed in this review.

The concerned colour-coding issue is now in Figure 2A. All integrins are now in sky blue and all laminins in red. VE-Cad is also in red but has a different size and shape than laminins. We hope these modifications have improved the figures avoiding confusion.

Recommendations for the authors:

As you will see, the reviewers thought your manuscript was interesting and timely. However, as part 11 and its corresponding Figure 3 seem somewhat detached from the rest of the manuscript, one recommendation would be to remove this part for improved clarity. Other recommendations can be found in the comments below.

Reviewer #2 (Recommendations for the authors):

(1) Improve narrative linkage between vascular extravasation (Sections 2-6) and in-tissue leukocyte activities (Sections 7-10) by adding explicit transition text that connects ECM changes during transmigration to downstream immune cell phenotypes.

A transition paragraph is now added between section 6 and 7 (line 300-307).

(2) Expand discussion of lymphocyte-ECM interactions, either within existing sections or as a dedicated subsection.

We have now added discussion of the effects of matrikine on in vivo T cell traffic (line 396-409) and how T cell functions are regulated by tissue stiffness (line 457-466).

(3) Strengthen integration of the MIKA macrophage identity framework with ECM-specific drivers (e.g., stiffness, matrikines) and reduce methodological detail in Fig. 3 to focus on biological relevance.

We thank the reviewer for this recommendation and have adopted accordingly. First, the methodological details in the original Fig.3 is now integrated to Supplementary Data 1. This figure is now replaced as Fig.5 serving to examine different macrophage identities’ contribution to ECM effectors / regulators (specifically, ECM per se, growth factors for ECM-producing fibroblasts, proteases and protease inhibitors) discussed in earlier sections. Relevant texts are on line 561-586.

(4) Consider adding a glossary of key terms (e.g., matrikines, efferocytosis) to aid accessibility.

A glossary explaining selected terms that may be confusing to the general readership is now added as Appendix 1 (line 977).

Reviewer #3 (Recommendations for the authors):

The discussion of fibrosis as a significant consequence of inflammatory activity is currently limited to skin keloids and bleomycin-induced lung fibrosis. Considering the substantial clinical relevance, it would be beneficial to include a mention of the various forms of liver fibrosis resulting from chronic inflammation.

Liver cirrhosis is now mentioned as further examples of stiffening tissues on line 428, 436-439.

While the manuscript is generally well-written, there are several minor language issues that could be easily addressed by a native speaker during revisions. Some examples are listed below:

We thank the reviewer for these very helpful suggestions. They are adopted with the relevant line number in the revised manuscript indicated below. In addition, the manuscript is proofread again, with other grammatical mistakes corrected throughout the text.

(1) Line 40: ... proliferative pathogen, can be timely eliminated.

line 40

(2) Line 79: It may be worthwhile pointing out that while Claudin 5 expression is highest in the BBB, it is also relevant in the BRB and expressed at lower levels in peripheral ECs. Similarly, ZO-1 is widely found to be expressed in peripheral endothelial cells.

Thanks for indicating this caution, it is now mentioned on line 79-82.

(3) Line 82: affects leukocyte traffic and...

line 84

(4) Line 125: ..., both neutrophil and lymphocyte extravasation were reduced by ~60%

line 125-126

5) Line 128: The term "paracellular endothelial junction" is odd, as junctions are per se paracellular, i.e., between cells.

line 129

(6) Line 147: ... VE-Cadherin, in which the FRET signal vanishes.

line 148

(7) Line 186: "activation by direct leukocyte pressing" might be rephrased to be clearer, e.g. "it might as well be activated by mechanical force exerted by leukocytes like it is the case for Piezo-1."

line 185-186

(8) Line 216: The phrasing "knockout analogy" is somewhat unfortunate. I would suggest "...a4 ko mice consequently largely lack a5 low expression regions and the resulting reduction in leukocyte extravasation confirms the facilitating role of the low a5 expression regions."

line 217-218

(9) Line 219: ...how the low expression regions form / are formed in the first place... The term construction implies active planning.

line 220

(10) Line 278: ... thrombocytopenic mice ...

line 279

(11) Line 294: ... use platelets as a drug delivery vehicle ...

line 295

(12) Line 304: instead of "could have changed", use "might change"

line 315

(13) Line 320: at the level of the monocyte

line 336-337

(14) Line 324: ... consistent with ...

line 340

(15) Line 335: ... progenitors

line 351

(16) Line 432: ... a considerable number of apoptotic neutrophils has (been) accumulated

line 480

(17) Line 442: ..., which promote killing responses, cross activate other leukocytes ..., or reduce tissue availability...

line 490-491

(18) Line 453: ...This macrophage is responsive to BMP...

This sentence is now rephrased on line 500-501.

(19) Line 454: ...involved in forming S1 macrophages.

line 502

(20) Line 476: ...numerous pathologies...

Points (20-22) concerns Section 11, which is now restructured (line 523-586).

21) Line 492: ...macrophages acquiring phenotypes specific to their residence tissue.

(22) Line 498: ...either - the tissue macrophage is of heterogeneous nature... or - tissue macrophages are of heterogeneous nature...

Reviewer #4 (Public review):

Summary:

This work investigates whether a citation to a referee made by a paper is associated with a more positive evaluation by that referee for that paper. It provides evidence supporting this hypothesis. The work also investigates the role of self-citations by referees where the referee would ask authors to cite the referee's paper.

Strengths:

This is an important problem: referees for scientific papers must provide their impartial opinions rooted in core scientific principles. Any undue influence due to the role of citations breaks this requirement. This work studies the possible presence and extent of this.

The methods are solid and well done. The work uses a matched pair design which controls for article-level confounding and further investigates robustness to other potential confounds.

Weaknesses:

The authors have addressed most concerns in the initial review. The only remaining concern is the asymmetric reporting and highlighting of version 1 (null result) versus version 2 (rejecting null). For example the abstract says "We find that reviewers who were cited in the article under review were more likely to recommend approval, but only after the first version (odds ratio = 1.61; adjusted 99.4% CI: 1.16 to 2.23)" instead of a symmetric sentence "We find ... in version 1 and ... in version 2"

Author response:

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

Public Reviews:

Reviewer #1 (Public review)::

Summary:

The work used open peer reviews and followed them through a succession of reviews and author revisions. It assessed whether a reviewer had requested the author include additional citations and references to the reviewers' work. It then assessed whether the author had followed these suggestions and what the probability of acceptance was based on the authors decision.

Strengths and weaknesses:

The work's strengths are the in-depth and thorough statistical analysis it contains and the very large dataset it uses. The methods are robust and reported in detail. However, this is also a weakness of the work. Such thorough analysis makes it very hard to read! It's a very interesting paper with some excellent and thought provoking references but it needs to be careful not to overstate the results and improve the readability so it can be disseminated widely. It should also discuss more alternative explanations for the findings and, where possible, dismiss them.

I have toned down the language including a more neutral title. To help focus on the main results, I have moved four paragraphs from the methods to the supplement. These are the sample size, the two sensitivity analyses on including co-reviewers and confounding by reviewers’ characteristics, and the analysis examining potential bias for the reviewers with no OpenAlex record.

Reviewer #2 (Public review):

Summary:

This article examines reviewer coercion in the form of requesting citations to the reviewer's own work as a possible trade for acceptance and shows that, under certain conditions, this happens.

Strengths:

The methods are well done and the results support the conclusions that some reviewers "request" self-citations and may be making acceptance decisions based on whether an author fulfills that request.

Weaknesses:

The author needs to be more clear on the fact that, in some instances, requests for selfcitations by reviewers is important and valuable.

This is a key point. I have included a new text analysis to examine this issue and have addressed this in the updated discussion.

Reviewer #3 (Public review):

Summary:

In this article, Barnett examines a pressing question regarding citing behavior of authors during the peer review process. In particular, the author studies the interaction between reviewers and authors, focusing on the odds of acceptance, and how this may be affected by whether or not the authors cited the reviewers' prior work, whether the reviewer requested such citations be added, and whether the authors complied/how that affected the reviewer decision-making.

Strengths:

The author uses a clever analytical design, examining four journals that use the same open peer review system, in which the identities of the authors and reviewers are both available and linkable to structured data. Categorical information about the approval is also available as structured data. This design allows a large scale investigation of this question.

Weaknesses:

My concerns pertain to the interpretability of the data as presented and the overly terse writing style.

Regarding interpretability, it is often unclear what subset of the data are being used both in the prose and figures. For example, the descriptive statistics show many more Version 1 articles than Version 2+. How are the data subset among the different possible methods?

I have now included the number of articles and reviews in the legends of each plot. There are more version 1 articles because some are “approved” at this stage and hence a second version is never submitted (I’ve now specifically mentioned this in the discussion).

Likewise, the methods indicate that a matching procedure was used comparing two reviewers for the same manuscript in order to control for potential confounds. However, the number of reviews is less than double the number of Version 1 articles, making it unclear which data were used in the final analysis. The methods also state that data were stratified by version. This raises a question about which articles/reviews were included in each of the analyses. I suggest spending more space describing how the data are subset and stratified. This should include any conditional subsetting as in the analysis on the 441 reviews where the reviewer was not cited in Version 1 but requested a citation for Version 2. Each of the figures and tables, as well as statistics provided in the text should provide this information, which would make this paper much more accessible to the reader.

[Note from editor: Please see "Editorial feedback" for more on this]

The numbers are now given in every figure legend, and show the larger sample size for the first versions.

The analysis of the 441 reviews was an unplanned analysis that is separate to the planned models. The sample size is much smaller than the main models due to the multiple conditions applied to the reviewers: i) reviewed both versions, ii) not cited in first version, iii) requested a self-citation in their first review.

Finally, I would caution against imputing motivations to the reviewers, despite the important findings provided here. This is because the data as presented suggest a more nuanced interpretation is warranted. First, the author observes similar patterns of accept/reject decisions whether the suggested citation is a citation to the reviewer or not (Figs 3 and 4). Second, much of the observed reviewer behavior disappears or has much lower effect sizes depending on whether "Accept with Reservations" is considered an Accept or a Reject. This is acknowledged in the results text, but largely left out of the discussion. The conditional analysis on the 441 reviews mentioned above does support a more cautious version of the conclusion drawn here, especially when considered alongside the specific comments left by reviewers that were mentioned in the results and information in Table S.3. However, I recommend toning the language down to match the strength of the data.

I have used more cautious language throughout, including a new title. The new text analysis presented in the updated version also supports a more cautious approach.

Reviewer #4 (Public review):

Summary:

This work investigates whether a citation to a referee made by a paper is associated with a more positive evaluation by that referee for that paper. It provides evidence supporting this hypothesis. The work also investigates the role of self citations by referees where the referee would ask authors to cite the referee's paper.

Strengths:

This is an important problem: referees for scientific papers must provide their impartial opinions rooted in core scientific principles. Any undue influence due to the role of citations breaks this requirement. This work studies the possible presence and extent of this.

Barring a few issues discussed below, the methods are solid and well done. The work uses a matched pair design which controls for article-level confounding and further investigates robustness to other potential confounds.

It is surprising that even in these investigated journals where referee names are public, there is prevalence of such citation-related behaviors.

Weaknesses:

Some overall claims are questionable:

"Reviewers who were cited were more likely to approve the article, but only after version 1" It also appears that referees who were cited were less likely to approve the article in version 1. This null or slightly negative effect undermines the broad claim of citations swaying referees. The paper highlights only the positive results while not including the absence (and even reversal) of the effect in version 1 in its narrative.

The reversed effect for version 1 is interesting, but the adjusted 99.4% confidence interval includes 1 and hence it’s hard to be confident that this is genuinely in the reverse direction. However, it is certainly far from the strongly positive association for versions 2+.

"To the best of our knowledge, this is the first analysis to use a matched design when examining reviewer citations" Does not appear to be a valid claim based on the literature reference [18]

This previous paper used a matched design but then did not used a matched analysis. Hence, I’ve changed the text in my paper to “first analysis to use a matched design and analysis”. This may seem a minor claim of novelty, but not using a matched analysis for matched data could discard much of the benefits of the matching.

It will be useful to have a control group in the analysis associated to Figure 5 where the control group comprises matched reviews that did not ask for a self citation. This will help demarcate words associated with approval under self citation (as compared to when there is no self citation). The current narrative appears to suggest an association of the use of these words with self citations but without any control.

Thanks for this useful suggestion. I have added a control group of reviewers who requested citations to articles other than their own. The words requested were very similar to the previous analysis, hence I’ve needed to reinterpret the results from the text analysis as “please” and “need” are not exclusively used by those requesting selfcitations. I also fixed a minor error in the text analysis concerning the exclusion of abstracts of shorter than 100 characters.

More discussion on the recommendations will help:

For the suggestion that "the reviewers initially see a version of the article with all references blinded and no reference list" the paper says "this involves more administrative work and demands more from peer reviewers". I am afraid this can also degrade the quality of peer review, given that the research cannot be contextualized properly by referees. Referees may not revert back to all their thoughts and evaluations when references are released afterwards.

This is an interesting point, but I don’t think it’s certain that this would happen. For example, revisiting the review may provide a fresh perspective and new ideas; this sometimes happens for me when I review the second version of an article. Ideally an experiment is needed to test this approach, as it is difficult to predict how authors and reviewers will react.

Recommendations for the Authors:

Editorial feedback:

I wonder if the article would benefit from a shorter title, such as the one suggested below. However, please feel free to not change the title if you prefer.

[i] Are peer reviewers influenced by their work being cited (or not)?

I like the slightly simpler: “Are peer reviewers influenced by their work being cited?”

[ii] To better reflect the findings in the article, please revise the abstract along the following lines:

Peer reviewers for journals sometimes write that one or more of their own articles should have been cited in the article under review. In some cases such comments are justified, but in other cases they are not. Here, using a sample of more than 37000 peer reviews for four journals that use open peer review and make all article versions available, we use a matched study design to explore this and other phenomena related to citations in the peer review process. We find that reviewers who were cited in the article under review were less likely to approve the original version of an article compared with reviewers who were not cited (odds ratio = 0.84; adjusted 99.4% CI: 0.69-1.03), but were more likely to approve a revised article in which they were cited (odds ratio = 1.61; adjusted 99.4% CI: 1.16-2.23). Moreover, for all versions of an article, reviewers who asked for their own articles to be cited were much less likely to approve the article compared with reviewers who did not do this (odds ratio = 0.15; adjusted 99.4% CI: 0.08-0.30). However, reviewers who had asked for their own articles to be cited were much more likely to approve a revised article that cited their own articles compared to a revised article that did not (odds ratio = 3.5; 95% CI: 2.0-6.1).

I have re-written the abstract along the lines suggested. I have not included the finding that cited reviewers were less likely to approve the article due to the adjusted 99.4% interval including 1.

[iii] The use of the phrase "self-citation" to describe an author citing an article by one of the reviewers is potentially confusing, and I suggest you avoid this phrase if possible.

I have removed “self-citation” everywhere and instead used “citations to their own articles”.

[iv] I think the captions for figures 2, 3 and 4 from benefit from rewording to more clearly describe what is being shown in the figure. Please consider revising the caption for figure 2 as follows, and revising the captions for figures 3 and 4 along similar lines. Please also consider replotting some of the panels so that the values on the horizontal axes of the top panel align with the values on the bottom panel.

I have aligned the odds and probability axes as suggested which better highlights the important differences. I have updated the figure captions as outlined.

Figure 2: Odds ratios and probabilities for reviewers giving a more or less favourable recommendation depending on whether they were cited in the article.

Top left: Odds ratios for reviewers giving a more favourable (Approved) or less favourable (Reservations or Not approved) recommendation depending on whether they were cited in the article. Reviewers who were cited in version 1 of the article (green) were less likely to make a favourable recommendation (odds ratio = 0.84; adjusted 99.4% CI: 0.691.03), but they were more likely to make a favourable recommendation (odds ratio = 1.61; adjusted 99.4% CI: 1.16-2.23) if they were cited in a subsequent version (blue). Top right: Same data as top left displayed in terms of probabilities. From the top, the lines show the probability of a reviewer approving: a version 1 article in which they are not cited (please give mean value and CI); a version 1 article in which they are cited (mean value and CI); a version 2 (or higher) article in which they are not cited (mean value and CI); and a version 2 (or higher) article in which they are cited (mean value and CI).

Bottom left: Same data as top left except that more favourable is now defined as Approved or Reservations, and less favourable is defined as Not approved. Again, reviewers who were cited in version 1 were less likely to make a favourable recommendation (odds ratio = 0.84; adjusted 99.4% CI: 0.57-1.23),and reviewers who were cited in subsequent versions were more likely to make a favourable recommendation (odds ratio = 1.12; adjusted 99.4% CI: 0.59-2.13).

Bottom right: Same data as bottom left displayed in terms of probabilities. From the top, the lines show the probability of a reviewer approving: a version 1 article in which they are not cited (please give mean value and CI); a version 1 article in which they are cited (mean value and CI); a version 2 (or higher) article in which they are not cited (mean value and CI); and a version 2 (or higher) article in which they are cited (mean value and CI).

This figure is based on an analysis of [Please state how many articles, reviewers, reviews etc are included in this analysis].

In all the panels a dot represents a mean, and a horizontal line represents an adjusted 99.4% confidence interval.

Reviewer #1 (Recommendations for the Authors):

A big recommendation to the author would be to consider putting a lot of the statistical analysis in an appendix and describing the methods and results in more accessible terms in the main text. This would help more readers see the baby through the bath water

I have moved four paragraphs from the methods to the supplement. These are the sample size, the two sensitivity analyses on including co-reviewers and confounding by reviewers’ characteristics, and the analysis examining potential bias for the reviewers with no OpenAlex record.

One possibility, that may have been accounted for, but it is hard to say given the density of the analysis, is the possibility that an author who follows the recommendations to cite the reviewer has also followed all the other reviewer requests. This could account for the much higher likelihood of acceptance. Conversely an author who has rejected the request to cite the reviewer may be more likely to have rejected many of the other suggestions leading to a rejection. I couldn't discern whether the analysis had accounted for this possibility. If it has it need to be said more prominently, if it hasn't this possibility at least needs to be discussed. It would be good to see other alternative explanations for the results discussed (and if possible dismissed) in the discussion section too.

This is an interesting idea. It’s also possible that authors more often accept and include any citation requests as it gives them more license to push back on other more involved changes that they would prefer not to make, e.g., running a new analysis. To examine this would require an analysis of the authors’ responses to the reviewers, and I have now added this as a limitation.

I hope this paper will have an impact on scientific publishing but I fear that it won't. This is no reflection on the paper but a more a reflection on the science publishing system.

I do not have any additional references (written by myself or others!) I would like the author to include

Thanks. I appreciate that extra thought is needed when peer reviewing papers on peer review. I do not know the reviewers’ names! I have added one additional reference suggested by the reviewers which had relevant results on previous surveys of coercive citations for the section on “Related research”.

Reviewer #2 (Recommendations for the Authors):

(1) Would it be possible for the author to control for academic discipline? Some disciplines cite at different rates and have different citation sub-cultures; for example, Wilhite and Fong (2012) show that editorial coercive citation differs among the social science and business disciplines. Is it possible that reviewers from different disciplines just take a totally different view of requesting self-citations?

Wilhite, A.W., & Fong, E.A. 2012. Coercive citation in academic publishing. Science, 335: 542-543.

This is an interesting idea, but the number of disciplines would need to be relatively broad to keep a sufficient sample size. The Catch-22 is then whether broad disciplines are different enough to show cultural differences. Overall, this is an idea for future work.

(2) I would like the author to be much more clear about their results in the discussion section. In line 214, they state that "Reviewers who requested a self-citation were much less likely to approve the article for all versions." Maybe in the discussion some language along the lines of "Although reviewers who requested self-citation were actually much less likely to approve an article, my more detailed analyses show that this was not the case when reviewers requested a self-citation without reason or with the inclusion of coercive language such as 'need' or 'please'." Again, word it as you like, but I think it should be made clear that requests for self-citation alone is not a problem. In fact, I would argue that what the author says in lines 250 to 255 in the discussion reflects that reviewers who request self-citations (maybe for good reasons) are more likely to be the real experts in the area and why those who did not request a self-cite did not notice the omission. It is my understanding that editors are trying to get warm bodies to review and thus reviewers are not all equally qualified. Could it be that requesting self-citations for a good reason is a proxy for someone who actually knows the literature better? I'm not saying this is s fact, but it is a possibility. I get this is said in the abstract, but worth fleshing out in the discussion.

I have updated the discussion after a new text analysis and have addressed this important question of whether self-citations are different from citations to other articles. The idea that some self-citers are more aware of the relevant literature is interesting, although this is very hard to test because they could also just be more aware of their own work. The question of whether self-citations are justified is a key question and one that I’ve tried to address in an updated discussion.

Reviewer #3 (Recommendations for the Authors):

Data and code availablility are in good shape. At a high level, I recommend:

Toning down the interpretation of reviewers' motivation, especially since some of this is mitigated by findings presented in the paper.

I have reworded the discussion and included a warning on the observational study design.

Devote more time detailing exactly what data are being presented in each figure/table and results section as described in more detail in the main review (n, selection criteria, conditional subsetting, etc.).

I agree and have provided more details in each figure legend.

Reviewer #4 (Recommendations for the Authors):

A few aspects of the paper are not clear:

I did not follow Figure 4. Are the "self citation" labels supposed to be "citation to other research"?

Thanks for picking up this error which has now been fixed.

I did not understand how to parse the left column of Figure 2

As per the editor’s suggestion, the figure legend has been updated.

Table 3: Please use different markers for the different curves so that it is clearly demarcated even in grayscale print

I presume you meant Figure 3 not Table 3. I’ve varied the symbols in all three odds ratio plots.

Supplementary S3: Typo "Approvep" Fixed, thanks.

OTHER CHANGES: As well as the four reviews, my paper was reviewed by an AI-reviewer which provided some useful suggestions. I have mentioned this review in the acknowledgements. I have reversed the order of figure 5 to show the probability of “Approved” as this is simpler to interpret.

很直觀想：「把所有剩下的資料直接往前搬，把 slot 0~4（最前端）都填回來，這樣空間就都可重用。」

但這有超重的負擔：如果 queue 還有 1000 個資料，每個 dequeue 都要把 1000 個元素整個搬來搬去，非常慢！

queue 最期待的就是 "dequeue 很快"（O(1)），但如果每次都要 copy 很多資料，就變成 O(n)，效率低、設計不合格。

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

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Reply to the reviewers

We thank the reviewers for their detailed comments, which have already helped us improve our manuscript. The responses below detail changes we have already made as part of the Review Commons revision plan, and further changes we expect to make in a longer revision period.

__Reviewer #1 __

Major points __ It is mentioned throughout the manuscript that 3 plates were evaluated per line. I believe these are independently differentiated plates. This detail is critical concerning rigor and reproducibility. This should be clearly stated in the Methods section and in the first description of the experimental system in the Results section for Figure 1.__

These experimental details have now been clarified. Unless otherwise stated, all findings were confirmed in three independently differentiated plates from the same line or at least one differentiation from each of three lines.

For the patient-specific lines - how many lines were derived per patient?

This has now been clarified in the methods. Microfluidic reprogramming of a small number of amniocytes produces one line per patient representing a pool of clones. Subcloning from individual cells would not be possible within the timeframe of a pregnancy.

Methods: For patient-specific iPSC lines, one independent iPSC line was obtained per patient following microfluidic mmRNA reprogramming.

Was the Vangl2 variant introduced by prime editing? Base editing? The details of the methods are sparse.

We have now expanded these details:

Methods: VANGL2 knock-in lines were generated using CRSIPR-Cas9 homology directed repair editing by Synthego (SO-9291367-1). The guide sequence was AUGAGCGAAGGGUGCGCAAG and the donor sequence was CAATGAGTACTACTATGAGGAGGCTGAGCATGAGCGA AGGGTGTGCAAGAGGAGGGCCAGGTGGGTCCCTGGGGGAGAAGAGGAGAG. Sequence modification was confirmed by Sanger sequencing before delivery of the modified clones, and Sanger sequencing was repeated after expansion of the lines (Supplementary Figure 5) as well as SNP arrays (Illumina iScan, not shown) confirming genomic stability.

Some additional suggestions for improvement. __ The abstract could be more clearly written to effectively convey the study's importance. Here are some suggestions.__

Line 26: Insert "apicobasal" before "elongation" - the way it is written, I initially interpreted it as anterior-posterior elongation.

Line 29: Please specify that the lines refer to 3 different established parent iPSC lines with distinct origins and established using different reprogramming methods, plus 2 control patient-derived lines. - The reproducibility of the cell behaviors is impressive, but this is not captured in the abstract.

Line 32: add that this mutation was introduced by CRISPR-Cas9 base/prime editing.

The last sentence of the abstract states that the study only links apical constriction to human NTDs, but also reveals that neural differentiation and apical-basal elongation were found. __ The introduction could also use some editing. __ Line 71: insert "that pulls actin filaments together" after "power strokes" __ Line 73: "apically localized," do you mean "mediolaterally" or "radially"? __ Line 75: Can you specify that PCP components promote "mediolaterally orientated" apical constriction __ Lines 127: Specify that NE functions include apical basal elongation and neurodifferentiation are disrupted in patient-derived models__

These text changes have all been made.

Reviewer #2:____ __ __Major comments: __ 1. Figure 1. The authors use F-actin to segment cell areas. Perhaps this could be done more accurately with ZO-1, as F-actin cables can cross the surface of a single cell. In any case, the authors need to show a measure of segmentation precision: segmented image vs. raw image plus a nuclear marker (DAPI, H2B-GFP), so we can check that the number of segmented cells matches the number of nuclei.__

We used ZO-1 to quantify apical areas of the VANGL2-konckin lines in Figure 3. Segmentation of neuroepithelial apical areas based on F-actin staining is commonplace in the field (e.g. Fig 9 of Bogart & Brooks 2025 as a recent example), and is generally robust because the cell junctions are much brighter than any apical fibres not associated with the apical cortex. However, we accept that at earlier stages of differentiation there may be more apical fibres when cells are cuboidal. We have therefore repeated our analysis of apical area using ZO-1 staining as suggested, shown in the new Supplementary Figure 1, analysing a more temporally-detailed time course in one iPSC line. This new analysis confirms our finding of lack of apical area change between days 2-4 of differentiation, then progressive reduction of apical area between days 4-8, further validating our system. Including nuclear images is not helpful because of the high nuclear index of pseudostratified epithelia (e.g. see Supplementary Figure 7) which means that nuclei overlap along the apicobasal axis. Individual nuclei cannot be related to their apical surface in projected images.

__2.Lines 156-166. The authors claim that changes in gene expression precede morphological changes. I am not convinced this is supported by their data. Fig. 1g (epithelial thickness) and Fig. 1k (PAX6 expression) seem to have similar dynamics. The authors can perform a cross-correlation between the two plots to see which Δt gives maximum correlation. If Δt __We are happy to do this analysis fully in revision. __Our initial analysis performing cross-correlation between apical area and CDH2 protein in one line shows the highest cross-correlation at Δt = -1, suggesting neuroepithelial CDH2 increases before apical area decreases. In contrast, the same analysis comparing apical area versus PAX6 shows Δt = 0, suggesting concurrence. This analysis will be expanded to include the other markers we quantified and the manuscript text amended accordingly. We are keen to undertake additional experiments to test whether these cells swap their key cadherins - CDH1 and CDH2 - before they begin to undergo morphological changes (see the response to Reviewer 3's minor comment 1 immediately below).

3. Figure 2d. The laser ablation experiment in the presence of ROCK inhibitor is clear, as I can easily see the cell outlines before and after the experiment. In the absence of ROCK inhibitor, the cell edges are blurry, and I am not convinced the outline that the authors drew is really the cell boundary. Perhaps the authors can try to ablate a larger cell patch so that the change in area is more defined.

The outlines on these images are not intended to show cell boundaries, but rather link landmarks visible at both timepoints to calculate cluster (not cell) change in area. This is as previously shown in Galea et al Nat Commun 2021 and Butler et al J Cell Sci 2019. We have now amended the visualisation of retraction in Figure 2 to make representation of differences between conditions more intuitive.

4. Figure 2d. Do the cells become thicker after recoil?

This is unlikely because the ablated surface remains in the focal plane. Unfortunately, we are unable to image perpendicularly to the direction of ablation to test whether their apical surface moves in Z even by a very small amount. This has now been clarified in the results:

Results: The ablated surface remained within the focal plane after ablation, indicating minimal movement along the apical-basal axis.

5. Figure 3. The authors mention their previous study in which they show that Vangl2 is not cell-autonomously required for neural closure. It will be interesting to study whether this also the case in the present human model by using mosaic cultures.

We agree with the reviewer that this is one of the exciting potential future applications of our model, which will first require us to generate stable fluorescently-tagged lines (to identify those cells which lack VANGL2). We will also need to extensively analyze controls to validate that mixing fluo-tagged and untagged lines does not alter the homogeneity of differentiation, or apical constriction, independently of VANGL2 deletion. As such, the reviewer is suggesting an altogether new project which carries considerable risk and will require us to secure dedicated funding to undertake.

6. Lines 403-415. The authors report poor neural induction and neuronal differentiation in GOSB2. As far as I understand, this phenotype does not represent the in vivo situation. Thus, it is not clear to what extent the in vitro 2D model describes the human patient.

The GOSB2 iPSC line we describe does represent the in vivo situation in Med24 knockout mouse embryos, but is clearly less severe because we are still able to detect MED24 protein expressed in this line. We do not have detailed clinical data of the patient from which this line was obtained to determine whether their neurological development is normal. However, it is well established that some individuals who have spina bifida also have abnormalities in supratentorial brain development. It is therefore likely that abnormalities in neuron differentiation/maturation are concomitant with spina bifida. Our findings in the GOSB2 line complement earlier studies which also identified deficiencies in the ability of patient-derived lines to form neurons, but were unable to functionally assess neuroepithelial cell behaviours we studied. This has now been clarified in the discussion:

Discussion: *Neuroepithelial cells of the GOSB2 line described here, which has partial loss of MED24, similarly produces a thinner neuroepithelium with larger apical areas. Although apical areas were not analysed in mouse models of Med24 deletion, these embryos also have shorter and non-pseudostratified neuroepithelium. *

Our GOSB2 line - which retains readily detectable MED24 protein - is clearly less severe than the mouse global knockout, and the clinical features of the patient from which this line was derived are milder than the phenotype of Med24 knockout embryos68. Mouse embryos lacking one of Med24's interaction partners in the mediator complex, Med1, also have thinner neuroepithelium and diminished neuronal differentiation but successfully close their neural tube85.

7.The experimental feat to derive cell lines from amniotic fluid and to perform experiments before birth is, in my view, heroic. However, I do not feel I learned much from the in vitro assays. There are many genetic changes that may cause the in vivo phenotype in the patient. The authors focus on MED24, but there is not enough convincing evidence that this is the key gene. I would like to suggest overexpression of MED24 as a rescue experiment, but I am not sure this is a single-gene phenotype. In addition, the fact that one patient line does not differentiate properly leads me to think that the patient lines do not strengthen the manuscript, and that perhaps additional clean mutations might contribute more.

We thank the reviewer for their praise of our personalised medicine approach and fully agree that neural tube defects are rarely monogenic. The patient lines we studied were not intended to provide mechanistic insight, but rather to demonstrate the future applicability of our approach to patient care. Our vision is that every patient referred for fetal surgery of spina bifida will have amniocytes (collected as part of routine cystocentesis required before surgery) reprogrammed and differentiated into neuroepithelial cells, then neural progenitors, to help stratify their post-natal care. One could also picture these cells becoming an autologous source for future cell-based therapies if they pass our reproducible analysis pipeline as functional quality control. This has now been clarified in the discussion:

Discussion____: The multi-genic nature of neural tube defect susceptibility, compounded by uncontrolled environmental risk factors (including maternal age and parity102), mean that patient-derived iPSC models are unlikely to provide mechanistic insight. They do provide personalised disease models which we anticipate will enable functional validation of genetic diagnoses for patients and their parents' recurrence risk in future pregnancies, and may eventually stratify patients' postnatal care. We also envision this model will enable quality control of patient-derived cells intended for future autologous cell replacement therapies, as is being developed in post-natal spinal cord injury103.

Minor comments: __ 1.Figure 1c. Text is cropped at the edge of the image.__

This image has been corrected.

Reviewer #2 (Significance (Required)): __ ...In addition, the model was unsuccessful in one of the two patient-derived lines, which limits generalizability and weakens claims of patient-specific predictive value.__

We disagree with the reviewer that "the model was unsuccessful in one of the two patient-derived lines". The GOSB1 line demonstrated deficiency of neuron differentiation independently of neuroepithelial biomechanical function, whereas the GOSB2 line showed earlier failure of neuroepithelial function. We also do not, at this stage, make patient-specific predictive claims: this will require longer-term matching of cell model findings with patient phenotypes over the next 5-10 years.

Reviewer #3: Major comments __ 1) One of my few concerns with this work is that the relative constriction of the apical surface with respect to the basal surface is not directly quantified for any of the experiments. This worry is slightly compounded by the 3D reconstructions Figure 1h, and the observation that overall cell volume is reduced and cell height increased simultaneously to area loss. Additionally, the net impact of apical constriction in tissues in vivo is to create local or global curvature change, but all the images in the paper suggest that the differentiated neural tissues are an uncurved monolayer even missing local buckles. I understand that these cells are grown on flat adherent surfaces limiting global curvature change, but is there evidence of localized buckling in the monolayer? While I believe-along with the authors-that their phenotypes are likely failures in apical constriction, I think they should work to strengthen this conclusion. I think the easiest way (and hopefully using data they already have) would be to directly compare apical area to basal area on a cell wise basis for some number of cells. Given the heterogeneity of cells, perhaps 30-50 cells per condition/line/mutant would be good? I am open to other approaches; this just seems like it may not require additional experiments.__

As the reviewer observes, our cultures cannot bend because they are adhered on a rigid surface. The apical and basal lengths of the cultures will therefore necessarily be roughly equal in length. Some inwards bending of the epithelium is expected at the edges of the dish, but these cannot be imaged. The live imaging we show in Figure 2 illustrates that, just as happens in vivo, apical constriction is asynchronous. This means not all cells will have 'bottle' shapes in the same culture. We now illustrate the evolution of these shapes in more detail in Supplementary Figure 1 (shown in point 2.1 above).

Additionally, the reviewer's comment motivated us to investigate local buckles in the apical surface of our cultures when their apical surfaces are dilated by ROCK inhibition. We hypothesised that the very straight apical surface in normal cultures is achieved by a balance of apical cell size and tension with pressure differences at the cell-liquid interface. Consistent with our expectation, the apical surface of ROCK-inhibited cultures becomes wrinkled (new Supplementary Figure 3). The VANGL2-KI lines do not develop this tortuous apical surface (as shown in Figure 3), which is to be expected given their modification is present throughout differentiation unlike the acute dilation caused by ROCK inhibition.

This new data complements our visualisation of apical constriction in live imaging, apical accumulation of phospho-myosin, and quantification of ROCK-dependent apical tension as independent lines of evidence that our cultures undergo apical constriction.

2) Another slight experimental concern I have regards the difference in laser ablation experiments detailed in Figure 3h-i from those of Figure 2d-e. It seems like WT recoil values in 3h-I are more variable and of a lower average than the earlier experiments and given that it appears significance is reached mainly by impact of the lower values, can the authors explain if this variability is expected to be due to heterogeneity in the tissue, i.e. some areas have higher local tension? If so, would that correspond with more local apical constriction?

There is no significant difference in recoil between the control lines in Figures 2 and 3, albeit the data in Figure 3 is more variable (necessitating more replicates: none were excluded). We also showed laser ablation recoil data in Supplementary Figure 10, in which we did identify a graphing error (now corrected, also no significant difference in recoil from the other control groups).

Minor comments __ 1) There seems to be a critical window at day 5 of the differentiation protocol, both in terms of cell morphology and the marker panel presented in Figure 1i. Do the authors have any data spanning the hours from day 5 to 6? If not, I don't think they need to generate any, but do I think this is a very interesting window worthy of further discussion for a couple of reasons. First, several studies of mouse neural tube closure have shown that various aspects of cell remodeling are temporally separable. For example, between Grego-Bessa et al 2016 and Brooks et al 2020 we can infer that apicobasal elongation rapidly increases starting at E8.5, whereas apical surface area reduction and constriction are apparent somewhat earlier at E8.0. I think it would be interesting to see if this separability is conserved in humans. Second, is there a sense of how the temporal correlation between the pluripotent and early neural fate marker data presented here corroborate or contradict the emerging set of temporally resolved RNA seq data sets of mouse development at equivalent early neural stages?__

Cell shape analysis between days 5 and 6 has now been added (see the response to point 2.1 below). As the reviewer predicted, this is a transition point when apical area begins to decrease and apicobasal elongation begins to increase.

We also thank the reviewer for this prompt to more closely compare our data to the previous mouse publications, which we have added to the discussion. The Grego-Bessa 2016 paper appears to show an increase in thickness between E7.75 and E8.5, but these are not statistically compared. Previous studies showed rapid apicobasal elongation during the period of neural fold elevation, when neuroepithelial cells apically constrict. This has now been added to the discussion:

Discussion In mice, neuroepithelial apicobasal thickness is spatially-patterned, with shorter cells at the midline under the influence of SHH signalling14,77,78. Apicobasal thickness of the cranial neural folds increases from ~25 µm at E7.75 to ~50 µm at E8.579: closely paralleling the elongation between days 2 and 8 of differentiation in our protocol. The rate of thickening is non-uniform, with the greatest increase occurring during elevation of the neural folds80, paralleled in our model by the rapid increase in thickness between days 4-6 as apical areas decrease. Elevation requires neuroepithelial apical constriction and these cells' apical area also decreases between E7.75 and E8.5 in mice79, but we and others have recently shown that this reduction is both region and sex-specific14,81. Specifically, apical constriction occurs in the lateral (future dorsal) neuroepithelium: this corresponds with the identity of the cells generated by the dual SMAD inhibition model we use56. More recently, Brooks et al82 showed that the rapid reduction in apical area from E8-E8.5 is associated with cadherin switching from CDH1 (E-cadherin) to CDH2 (N-cadherin). This is also directly paralleled in our human system, which shows low-level co-expression of CDH1 and CDH2 at day 4 of differentiation, immediately before apical area shrinks and apicobasal thickness increases.

Prompted by the in vivo data in Brooks et al (2025)82, we are keen to further explore the timing of CDH1/CDH2 switching versus apical constriction with new experimental data in revisions.

2) Can the authors elaborate a bit more on what is known regarding apicobasal thickening and pseudo-stratification and how their work fits into the current understanding in the discussion? This is a very interesting and less well studied mechanism critical to closure, which their model is well suited to directly address. I am thinking mainly of the Grego-Bessa at al., 2016 work on PTEN, though interestingly the work of Ohmura et al., 2012 on the NUAK kinases also shows reduced tissue thickening (and apical constriction) and I am sure I have missed others. Given that the authors identify MED24 as a likely candidate for the lack of apicobasal thickening in one of their patient derived lines, is there any evidence that it interacts with any of the known players?

We have now added further discussion on the mechanisms by which the neuroepithelium undergoes apicobasal elongation. Nuclear compaction is likely to be necessary to allow pseudostratification and apicobasal elongation. The reviewer's comment has led us to realise that diminished chromatin compaction is a potential outcome of MED24 down-regulation in our GOSB2 patient-derived line. Figure 4D suggests the nuclei of our MED24 deficient patient-derived line are less compacted than control equivalents and we propose to quantify nuclear volume in more detail to explore this possibility.

Additionally, we have already expanded our discussion as suggested by the reviewer:

Discussion: *Mechanistic separability of apical constriction and apicobasal elongation is consistent with biomechanical modelling of Xenopus neural tube closure showing that both are independently required for tissue bending61. Nonetheless, neuroepithelial apical constriction and apicobasal elongation are co-regulated in mouse models: for example, deletion of Nuak1/283, Cfl184, and Pten79 all produce shorter neuroepithelium with larger apical areas. Neuroepithelial cells of the GOSB2 line described here, which has partial loss of MED24, similarly produces a thinner neuroepithelium with larger apical areas. Although apical areas were not analysed in mouse models of Med24 deletion, these embryos also have shorter and non-pseudostratified neuroepithelium. *

Our GOSB2 line - which retains readily detectable MED24 protein - is clearly less severe than the mouse global knockout, and the clinical features of the patient from which this line was derived are milder than the phenotype of Med24 knockout embryos68. Mouse embryos lacking one of Med24's interaction partners in the mediator complex, Med1, also have thinner neuroepithelium and diminished neuronal differentiation but successfully close their neural tube85. As general regulators of polymerase activity, MED proteins have the potential to alter the timing or level of expression of many other genes, including those already known to influence pseudostratification or apicobasal elongation. MED depletion also causes redistribution of cohesion complexes86 which may impact chromatin compaction, reducing nuclear volume during differentiation.

3) Is there any indication that Vangl2 is weakly or locally planar polarized in this system? Figure 2F seems to suggest not, but Supplementary Figure 5 does show at least more supracellular cable like structures that may have some polarity. I ask because polarization seems to be one of the properties that differs along the anteroposterior axis of the neural plate, and I wonder if this offers some insight into the position along the axis that this system most closely models?

VANGL2 does not appear to be planar polarised in this system. This is similar to the mouse spinal neuroepithelium, in which apical VANGL2 is homogenous but F-actin is planar polarised (Galea et al Disease Models and Mechanisms 2018). We do observe local supracellular cable-like enrichments of F-actin in the apical surface of iPSC-derived neuroepithelial cells. _We propose to compare the length of F-actin cables and coherency of their orientation at the start and end of neuroepithelial differentiation, and in wild-type versus VANGL2-mutant epithelia._

4) I think some of the commentary on the strengths and limitations of the model found in the Results section should be collated and moved to the discussion in a single paragraph. For example ' This could also briefly touch on/compare to some of the other models utilizing hiPSCs (These are mentioned briefly in the intro, but this comparison could be elaborated on a bit after seeing all the great data in this work).

These changes have now been made:

__Discussion: __Some of these limitations, potentially including inclusion of environmental risk factors, can be addressed by using alternative iPSC-derived models93,94. For example, if patients have suspected causative mutations in genes specific to the surface (non-neural) ectoderm, such as GRHL2/3, 3D models described by Karzbrun et al49 or Huang et al95 may be informative. Characterisation of surface ectoderm behaviours in those models is currently lacking. These models are particularly useful for high-throughput screens of induced mutations95, but their reproducibility between cell lines, necessary to compare patient samples to non-congenic controls, remains to be validated. Spinal cell identities can be generated in human spinal cord organoids, although these have highly variable morphologies96,97. As such, each iPSC model presents limitations and opportunities, to which this study contributes a reductionist and highly reproducible system in which to quantitatively compare multiple neuroepithelial functions.

5) While the authors are generally good about labeling figures by the day post smad inhibition, in some figures it is not clear either from the images or the legend text. I believe this includes supplemental figures 2,5,6,8, and 10 (apologies if I simply missed it in one or more of them)

These have now been added.

6) The legend for Figure 2 refers to a panel that is not present and the remaining panel descriptions are off by a letter. I'm guessing this is a versioning error as the text itself seems largely correct, but it may be good to check for any other similar errors that snuck in

This has now been corrected.

7) The cell outlines in Figure 3d are a bit hard to see both in print and on the screen, perhaps increase the displayed intensity?

This has now been corrected.

8) The authors show a fascinating piece of data in Supplementary Figure 1, demonstrating that nuclear volume is halved by day 8. Do they have any indication if the DNA content remains constant (e.g., integrated DAPI density)? I suppose it must, and this is a minor point in the grand scheme, but this represents a significant nuclear remodeling and may impact the overall DNA accessibility.

We agree with the reviewer that the reduction in nuclear volume is important data both because it informs understanding of the reduction in total cell volume, and because it suggests active chromatin compaction during differentiation. Unfortunately, the thicker epithelium and superimposition of nuclei in the differentiated condition means the laser light path is substantially different, making direct comparisons of intensity uninterpretable. Additionally, the apical-most nuclei will mostly be in G2/M phase due to interkinetic nuclear migration. As such, the comparison of DAPI integrated density between epithelial morphologies would not be informative.

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Referee #3

Evidence, reproducibility and clarity

This manuscript by Ampartzidis et al., significantly extends the human induced pluripotent stem cell system originally characterized by the same group as a tool for examining cellular remodeling during differentiation stages consistent with those of human neural tube closure (Ampartzidis et al., 2023). Given that there are no direct ways to analyze cellular activity in human neural tube closure in vivo, this model represents an important platform for investigating neural tube defects which are a common and deleterious human developmental disease. Here, the authors carefully test whether this system is robust and reproducible when using hiPSC cells from different donors and pluripotency induction methods and find that despite all these variables the cellular remodeling programs that occur during early neural differentiation are statistically equivalent, suggesting that this system is a useful experimental substrate. Additionally, the carefully selected donor populations suggest these aspects of human neural tube closure are likely to be robust to sexual dimorphism and to reasonable levels of human genetic background variation, though more fully testing that proposition would require significant effort and be beyond the scope of the current work. Subsequent to this careful characterization, the authors next tested whether this system could be used to derive specific insights into cell remodeling during early neural differentiation. First, they used a reverse genetics approach to knock in a human point mutation in the critical regulator of planar cell polarity and apical constriction, Vangl2. Despite being identified in a patient, this R353C variant has not been directly functionally tested in a human system. The authors find that this variant, despite showing normal expression and phospho-regulation, leads to defects consistent with a failure in apical constriction, a key cell behavior required to drive curvature change during cranial closure. Finally, the authors test the utility of their hiPSC platform to understand human patient-specific defects by differentiating cells derived from two clinical spina bifida patients. The authors identify that one of these patients is likely to have a significant defect in fully establishing early proneural identity as well as defects in apicobasal thickening. While early remodeling occurs normally in the other patient, the authors observe significant defects in later neuronal induction and maturation. In addition, using whole exome sequencing the authors identify candidate variant loci that could underly these defects.

Major comments

1) One of my few concerns with this work is that the relative constriction of the apical surface with respect to the basal surface is not directly quantified for any of the experiments. This worry is slightly compounded by the 3D reconstructions Figure 1h, and the observation that overall cell volume is reduced and cell height increased simultaneously to area loss. Additionally, the net impact of apical constriction in tissues in vivo is to create local or global curvature change, but all the images in the paper suggest that the differentiated neural tissues are an uncurved monolayer even missing local buckles. I understand that these cells are grown on flat adherent surfaces limiting global curvature change, but is there evidence of localized buckling in the monolayer? While I believe-along with the authors-that their phenotypes are likely failures in apical constriction, I think they should work to strengthen this conclusion. I think the easiest way (and hopefully using data they already have) would be to directly compare apical area to basal area on a cell wise basis for some number of cells. Given the heterogeneity of cells, perhaps 30-50 cells per condition/line/mutant would be good? I am open to other approaches; this just seems like it may not require additional experiments.

2) Another slight experimental concern I have regards the difference in laser ablation experiments detailed in Figure 3h-i from those of Figure 2d-e. It seems like WT recoil values in 3h-I are more variable and of a lower average than the earlier experiments and given that it appears significance is reached mainly by impact of the lower values, can the authors explain if this variability is expected to be due to heterogeneity in the tissue, i.e. some areas have higher local tension? If so, would that correspond with more local apical constriction?

Minor comments

1) There seems to be a critical window at day 5 of the differentiation protocol, both in terms of cell morphology and the marker panel presented in Figure 1i. Do the authors have any data spanning the hours from day 5 to 6? If not, I don't think they need to generate any, but do I think this is a very interesting window worthy of further discussion for a couple of reasons. First, several studies of mouse neural tube closure have shown that various aspects of cell remodeling are temporally separable. For example, between Grego-Bessa et al 2016 and Brooks et al 2020 we can infer that apicobasal elongation rapidly increases starting at E8.5, whereas apical surface area reduction and constriction are apparent somewhat earlier at E8.0. I think it would be interesting to see if this separability is conserved in humans. Second, is there a sense of how the temporal correlation between the pluripotent and early neural fate marker data presented here corroborate or contradict the emerging set of temporally resolved RNA seq data sets of mouse development at equivalent early neural stages?

2) Can the authors elaborate a bit more on what is known regarding apicobasal thickening and pseudo-stratification and how their work fits into the current understanding in the discussion? This is a very interesting and less well studied mechanism critical to closure, which their model is well suited to directly address. I am thinking mainly of the Grego-Bessa at al., 2016 work on PTEN, though interestingly the work of Ohmura et al., 2012 on the NUAK kinases also shows reduced tissue thickening (and apical constriction) and I am sure I have missed others. Given that the authors identify MED24 as a likely candidate for the lack of apicobasal thickening in one of their patient derived lines, is there any evidence that it interacts with any of the known players?

3) Is there any indication that Vangl2 is weakly or locally planar polarized in this system? Figure 2F seems to suggest not, but Supplementary Figure 5 does show at least more supracellular cable like structures that may have some polarity. I ask because polarization seems to be one of the properties that differs along the anteroposterior axis of the neural plate, and I wonder if this offers some insight into the position along the axis that this system most closely models?

4) I think some of the commentary on the strengths and limitations of the model found in the Results section should be collated and moved to the discussion in a single paragraph. For example ' This could also briefly touch on/compare to some of the other models utilizing hiPSCs (These are mentioned briefly in the intro, but this comparison could be elaborated on a bit after seeing all the great data in this work).

5) While the authors are generally good about labeling figures by the day post smad inhibition, in some figures it is not clear either from the images or the legend text. I believe this includes supplemental figures 2,5,6,8, and 10 (apologies if I simply missed it in one or more of them)

6) The legend for Figure 2 refers to a panel that is not present and the remaining panel descriptions are off by a letter. I'm guessing this is a versioning error as the text itself seems largely correct, but it may be good to check for any other similar errors that snuck in

7) The cell outlines in Figure 3d are a bit hard to see both in print and on the screen, perhaps increase the displayed intensity?

8) The authors show a fascinating piece of data in Supplementary Figure 1, demonstrating that nuclear volume is halved by day 8. Do they have any indication if the DNA content remains constant (e.g., integrated DAPI density)? I suppose it must, and this is a minor point in the grand scheme, but this represents a significant nuclear remodeling and may impact the overall DNA accessibility.

Significance

Overall, I am enthusiastic about this work and believe it represents a significant step forward in the effort to establish precision medicine approaches for diagnoses of the patient-specific causative cellular defects underlying human neural tube closure defects. This work systematizes an important and novel tool to examine the cellular basis of neural tube defects. While other hiPSC models of neural tube closure capture some tissue level dynamics, which this model does not, they require complex microfluidic approaches and have limited accessibility to direct imaging of cell remodeling. Comparatively, the relative simplicity of the reported model and the work demonstrating its tractability as a patient-specific and reverse genetic platform make it unique and attractive. This work will be of interest to a broad cross section of basic scientists interested in the cellular basis of tissue remodeling and/or the early events of nervous system development as well as clinical scientists interested in modeling the consequences of patient specific human genetic deficits identified in neural tube defect pregnancies.

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Referee #2

Evidence, reproducibility and clarity

The authors' work focuses on studying cell morphological changes during differentiation of hPSCs into neural progenitors in a 2D monolayer setting. The authors use genetic mutations in VANGL2 and patient-derived iPSCs to show that (1) human phenotypes can be captured in the 2D differentiation assay, and (2) VANGL2 in humans is required for neural contraction, which is consistent with previous studies in animal models. The results are solid and convincing, the data are quantitative, and the manuscript is well written. The 2D model they present successfully addresses the questions posed in the manuscript. However, the broad impact of the model may be limited, as it does not contain NNE cells and does not exhibit tissue folding or tube closure, as seen in neural tube formation. Patient-derived lines are derived from amniotic fluid cells, and the experiments are performed before birth, which I find to be a remarkable achievement, showing the future of precision medicine.

Major comments:

1.Figure 1. The authors use F-actin to segment cell areas. Perhaps this could be done more accurately with ZO-1, as F-actin cables can cross the surface of a single cell. In any case, the authors need to show a measure of segmentation precision: segmented image vs. raw image plus a nuclear marker (DAPI, H2B-GFP), so we can check that the number of segmented cells matches the number of nuclei. 2.Lines 156-166. The authors claim that changes in gene expression precede morphological changes. I am not convinced this is supported by their data. Fig. 1g (epithelial thickness) and Fig. 1k (PAX6 expression) seem to have similar dynamics. The authors can perform a cross-correlation between the two plots to see which Δt gives maximum correlation. If Δt < 0, then it would suggest that gene expression precedes morphology, as they claim. Fig. 1j shows that NANOG drops before the morphological changes, but loss of NANOG is not specific to neural differentiation and therefore should not be related to the observed morphological changes. 3.Figure 2d. The laser ablation experiment in the presence of ROCK inhibitor is clear, as I can easily see the cell outlines before and after the experiment. In the absence of ROCK inhibitor, the cell edges are blurry, and I am not convinced the outline that the authors drew is really the cell boundary. Perhaps the authors can try to ablate a larger cell patch so that the change in area is more defined. 4.Figure 2d. Do the cells become thicker after recoil? 5.Figure 3. The authors mention their previous study in which they show that Vangl2 is not cell-autonomously required for neural closure. It will be interesting to study whether this also the case in the present human model by using mosaic cultures. 6.Lines 403-415. The authors report poor neural induction and neuronal differentiation in GOSB2. As far as I understand, this phenotype does not represent the in vivo situation. Thus, it is not clear to what extent the in vitro 2D model describes the human patient. 7.The experimental feat to derive cell lines from amniotic fluid and to perform experiments before birth is, in my view, heroic. However, I do not feel I learned much from the in vitro assays. There are many genetic changes that may cause the in vivo phenotype in the patient. The authors focus on MED24, but there is not enough convincing evidence that this is the key gene. I would like to suggest overexpression of MED24 as a rescue experiment, but I am not sure this is a single-gene phenotype. In addition, the fact that one patient line does not differentiate properly leads me to think that the patient lines do not strengthen the manuscript, and that perhaps additional clean mutations might contribute more.

Minor comments:

1.Figure 1c. Text is cropped at the edge of the image.

Significance

This study establishes a quantitative, reproducible 2D human iPSC-to-neural-progenitor platform for analyzing cell-shape dynamics during differentiation. Using VANGL2 mutations and patient-derived iPSCs, the work shows that (1) human phenotypes can be captured in a 2D differentiation assay and (2) VANGL2 is required for neural contraction (apical constriction), consistent with animal studies. The results are solid, the data are quantitative, and the manuscript is well written. Although the planar system lacks non-neural ectoderm and does not exhibit tissue folding or tube closure, it provides a tractable baseline for mechanistic dissection and genotype-phenotype mapping. The derivation of patient lines from amniotic fluid and execution of experiments before birth is a remarkable demonstration that points toward precision-medicine applications, while motivating rescue strategies and additional clean genetic models. However, overall I did not learn anything substantively new from this manuscript; the conclusions largely corroborate prior observations rather than extend them. In addition, the model was unsuccessful in one of the two patient-derived lines, which limits generalizability and weakens claims of patient-specific predictive value.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Ampartzidis et al. report the establishment of an iPSC-derived neuroepithelial model to examine how mutations from spina bifida patients disrupt fundamental cellular properties that underlie neural tube closure. The authors utilize an adherent neural induction protocol that relies on dual SMAD inhibition to differentiate three previously established iPSC lines with different origins and reprogramming methods. The analysis is comprehensive and outstanding, demonstrating reproducible differentiation, apical-basal elongation, and apical constriction over an 8-day period among the 3 lines. In inhibitor studies, it is shown that apical constriction is dependent on ROCK and generates tension, which can be measured using an annular laser ablation assay. Since this pathway is dependent on PCP signaling, which is also implicated in neural tube defects, the authors investigated whether VANGL2 is required by generating 2 lines with a pathogenic patient-derived sequence variant. Both lines showed reduced apical constriction and reduced tension in the laser ablation assays. The authors then established lines obtained from amniocentesis, including 2 control and 2 spina bifida patient-derived lines. These remarkably exhibited different defects. One line showed defects in apical-basal elongation, while the other showed defects in neural differentiation. Both lines were sequenced to identify candidate variants in genes implicated in NTDs. While no smoking gun was found in the line that disrupts neural differentiation (as is often the case with NTDs), compound heterozygous MED24 variants were found in the patient whose cells were defective in apical-basal elongation. Since MED24 has been linked to this phenotype, this finding is especially significant.

Some details are missing regarding the method to evaluate the rigor and reproducibility of the study.

Major points

It is mentioned throughout the manuscript that 3 plates were evaluated per line. I believe these are independently differentiated plates. This detail is critical concerning rigor and reproducibility. This should be clearly stated in the Methods section and in the first description of the experimental system in the Results section for Figure 1. For the patient-specific lines - how many lines were derived per patient? Was the Vangl2 variant introduced by prime editing? Base editing? The details of the methods are sparse.<br /> Some additional suggestions for improvement.<br /> The abstract could be more clearly written to effectively convey the study's importance. Here are some suggestions Line 26: Insert "apicobasal" before "elongation" - the way it is written, I initially interpreted it as anterior-posterior elongation. Line 29: Please specify that the lines refer to 3 different established parent iPSC lines with distinct origins and established using different reprogramming methods, plus 2 control patient-derived lines. - The reproducibility of the cell behaviors is impressive, but this is not captured in the abstract. Line 32: add that this mutation was introduced by CRISPR-Cas9 base/prime editing The last sentence of the abstract states that the study only links apical constriction to human NTDs, but also reveals that neural differentiation and apical-basal elongation were found. The introduction could also use some editing. Line 71: insert "that pulls actin filaments together" after "power strokes" Line 73: "apically localized," do you mean "mediolaterally" or "radially"? Line 75: Can you specify that PCP components promote "mediolaterally orientated" apical constriction Lines 127: Specify that NE functions include apical basal elongation and neurodifferentiation are disrupted in patient-derived models

Significance

This paper is significant not only for verifying the cell behaviors necessary for neural tube closure in a human iPSC model, but also for establishing a robust assay for the functional testing of NTD-associated sequence variants. This will not only demonstrate that sequence variants result in loss of function but also determine which cellular behaviors are disrupted.

Reviewer #1 (Public review):

Overall, the manuscript reveals the role for actin polymerization to drive fusion of myoblasts during adult muscle regeneration. This pathway regulates fusion in many contexts, but whether it was conserved in adult muscle regeneration remained unknown. Robust genetic tools and histological analyses were used to convincingly support the claims.

This got me confused at first.

It does not mean that we don't know who cast a vote.

What it means is that each vote has equal weight.

Since everybody knows voter->vote it may affect how others vote. E.g., some agents may vote as another agent voted.

This cannot be solved by tech, as they may communicate out-of-band to coordinate.

But a futile attempt may look like: establish a blinded shared secret (DKG, every member knows public key, in order to restore secret key they need to cooperate).

Encrypt your vote + nonce with public key, producing a "blinded" vote.

Once everybody casted their blinded vote, cooperate so all are able to unblind the shared secret (private key) - everybody can unblind all votes.

This, however, requires that everybody casts their vote. As it's unclear whether vote has passed for any subset of blinded votes. So not only it's futile in its effort but also limits usability down to none, as any 1 uncooperative agent will block voting de-facto.

Reviewer #1 (Public review):

The revised manuscript addresses several reviewer concerns, and the study continues to provide useful insights into how ZIP10 regulates zinc homeostasis and zinc sparks during fertilization in mice. The authors have improved the clarity of the figures, shifted emphasis in the abstract more clearly to ZIP10, and added brief discussion of ZIP6/ZIP10 interactions and ZIP10's role in zinc spark-calcium oscillation decoupling. However, some critical issues remain only partially addressed.

(1) Oocyte health confound: The use of Gdf9-Cre deletes ZIP10 during oocyte growth, meaning observed defects could result from earlier disruptions in zinc signaling rather than solely from the absence of zinc sparks at fertilization. The authors acknowledge this and propose transcriptome profiling as a future direction. However, since mRNA levels often do not accurately reflect protein levels and activity in oocytes, transcriptomics may not be particularly informative in this context. Proteomic approaches that directly assess the molecular effects of ZIP10 loss seem more promising. Although current sensitivity limitations make proteomics from small oocyte samples challenging, ongoing improvements in this area may soon allow for more detailed mechanistic insights.

(2) ZIP6 context and focus: The authors clarified the abstract to emphasize ZIP10, enhancing narrative clarity. This revision is appropriate and appreciated.

(3) Follicular development effects: The biological consequences of ZIP6 and ZIP10 knockout during folliculogenesis are still unknown. The authors now say these effects will be studied in the future, but this still leaves a major mechanistic gap unaddressed in the current version.

(4) Zinc spark imaging and probe limitations: The addition of calcium imaging enhances the clarity of Figure 3. However, zinc fluorescence remains inadequate, and the authors depend solely on FluoZin-3AM, a dye known for artifacts and limited ability to detect subcellular labile zinc. The suggestion that C57BL/6J mice may differ from CD1 in vesicle appearance is plausible but does not fully address concerns about probe specificity and resolution. As the authors acknowledge, future studies with more selective probes would increase confidence in both the spatial and quantitative analysis of zinc dynamics.

(5) Mechanistic insight remains limited: The revised discussion now recognizes the lack of detailed mechanistic understanding but does not significantly expand on potential signaling pathways or downstream targets of ZIP10. The descriptive data are useful, but the inability to pinpoint how ZIP10 mediates zinc spark regulation remains a key limitation. Again, proteomic profiling would probably be more informative than transcriptomic analysis for identifying ZIP10-dependent pathways once technical barriers to low-input proteomics are overcome.

Overall, the authors have reasonably revised and clarified key points raised by reviewers, and the manuscript now reads more clearly. However, the main limitation, lack of mechanistic insight and the inability to distinguish between developmental and fertilization-stage roles of ZIP10, remains unresolved. These should be explicitly acknowledged when framing the conclusions.

Comments on revisions: I have no further comments to add to this review.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

The revised manuscript addresses several reviewer concerns, and the study continues to provide useful insights into how ZIP10 regulates zinc homeostasis and zinc sparks during fertilization in mice. The authors have improved the clarity of the figures, shifted emphasis in the abstract more clearly to ZIP10, and added brief discussion of ZIP6/ZIP10 interactions and ZIP10's role in zinc spark-calcium oscillation decoupling. However, some critical issues remain only partially addressed. 

Thank you for your valuable inputs. We plan to address the issues that could not be clarified in this report going forward.

(1) Oocyte health confound: The use of Gdf9-Cre deletes ZIP10 during oocyte growth, meaning observed defects could result from earlier disruptions in zinc signaling rather than solely from the absence of zinc sparks at fertilization. The authors acknowledge this and propose transcriptome profiling as a future direction. However, since mRNA levels often do not accurately reflect protein levels and activity in oocytes, transcriptomics may not be particularly informative in this context. Proteomic approaches that directly assess the molecular effects of ZIP10 loss seem more promising. Although current sensitivity limitations make proteomics from small oocyte samples challenging, ongoing improvements in this area may soon allow for more detailed mechanistic insights.

Thank you for your suggestions. We will keep that in mind for the future.

(2) ZIP6 context and focus: The authors clarified the abstract to emphasize ZIP10, enhancing narrative clarity. This revision is appropriate and appreciated. 

Thanks to your feedback, my paper has improved. Thank you for your evaluation.

(3) Follicular development effects: The biological consequences of ZIP6 and ZIP10 knockout during folliculogenesis are still unknown. The authors now say these effects will be studied in the future, but this still leaves a major mechanistic gap unaddressed in the current version. 

As you mentioned, we have not been able to clarify the effects of ZIP6 and ZIP10 knockout on follicle formation. The effects of ZIP6 and ZIP10 knockout on follicle formation will be discussed in the future.

(4) Zinc spark imaging and probe limitations: The addition of calcium imaging enhances the clarity of Figure 3. However, zinc fluorescence remains inadequate, and the authors depend solely on FluoZin-3AM, a dye known for artifacts and limited ability to detect subcellular labile zinc. The suggestion that C57BL/6J mice may differ from CD1 in vesicle appearance is plausible but does not fully address concerns about probe specificity and resolution. As the authors acknowledge, future studies with more selective probes would increase confidence in both the spatial and quantitative analysis of zinc dynamics. 

Thank you for your comment. Moving forward, we plan to conduct spatial and quantitative analyses of zinc dynamics using various other zinc probes.

(5) Mechanistic insight remains limited: The revised discussion now recognizes the lack of detailed mechanistic understanding but does not significantly expand on potential signaling pathways or downstream targets of ZIP10. The descriptive data are useful, but the inability to pinpoint how ZIP10 mediates zinc spark regulation remains a key limitation. Again, proteomic profiling would probably be more informative than transcriptomic analysis for identifying ZIP10-dependent pathways once technical barriers to low-input proteomics are overcome. 

Thank you for your helpful advice. I'll use it as a reference for future analysis.

Future studies should assess the transcriptomic or proteomic profile of Zip10^d/d mouse oocytes (P.11 Line 349-350).

Overall, the authors have reasonably revised and clarified key points raised by reviewers, and the manuscript now reads more clearly. However, the main limitation, lack of mechanistic insight and the inability to distinguish between developmental and fertilization-stage roles of ZIP10, remains unresolved. These should be explicitly acknowledged when framing the conclusions.

We have added the two limitations you pointed out to the conclusion section of the main text.

However, the role of ZIP6 remained uncertain. Additionally, the absence of mechanistic insight for zinc spark and the inability to distinguish between the developmental and fertilization stage roles of ZIP10 remain unresolved. These challenges necessitate further investigation (P.11-12 Line 354-357).

eLife Assessment

This important study addresses a topic that is frequently discussed in the literature but is under-assessed, namely correlations among genome size, repeat content, and pathogenicity in fungi. Contrary to previous assertions, the authors found that repeat content is not associated with pathogenicity. Rather, pathogenic lifestyle was found to be better explained by the number of protein-coding genes, with other genomic features associated with insect association status. The results are considered solid, although there remain concerns about potential biases stemming from the underlying data quality of the analyzed genomes.

Reviewer #2 (Public review):

Summary:

In this paper, the authors report on the genomic correlates of the transition to the pathogenic lifestyle in Sordariomycetes. The pathogenic lifestyle was found to be better explained by the number of genes, and in particular effectors and tRNAs, but this was modulated by the type of interacting host (insect or not insect) and the ability to be vectored by insects.

Strengths:

The main strengths of this study lie in (i) the size of the dataset, and the potentially high number of lifestyle transitions in Sordariomycetes, (ii) the quality of the analyses and the quality of the presentation of the results, (iii) the importance of the authors' findings.

Weaknesses:

The weakness is a common issue in most comparative genomics studies in fungi, but it remains important and valid to highlight it. Defining lifestyles is complex because many fungi go through different lifestyles during their life cycles (for instance, symbiotic phases interspersed with saprotrophic phases). In many fungi, the lifestyle referenced in the literature is merely the sampling substrate (such as wood or dung), which does not necessarily mean that this substrate is a key part of the life cycle. The authors discuss this issue, but they do not eliminate the underlying uncertainties.

[Editors' note: this version was assessed by the editors, without involving the reviewers again.]

Joint Public Review:

Summary:

Sha K et al aimed at identifying mechanism of response and resistance to castration in the Pten knock out GEM model. They found elevated levels of TNF overexpressed in castrated tumors associated to an expansion of basal-like stem cells during recurrence, which they show occurring in prostate cancer cells in culture upon enzalutamide treatment. Further, the authors carry on timed dependent analysis of the role of TNF in regression and recurrence to show that TNF regulates both processes. Similarly, CCL2, which the authors had proposed as a chemokine secreted upon TNF induction following enzalutamide treatment, is also shown elevated during recurrence and associate it to the remodeling of an immunosuppressive microenvironment through depletion of T cells and recruitment of TAMs.

Strengths:

The paper exploits a well stablished GEM model to interrogate mechanisms of response to standard of care treatment. This of utmost importance since prostate cancer recurrence after ADT or ARSi marks the onset of an incurable disease stage for which limited treatments exist. The work is relevant in the confirmation that recurrent prostate cancer is mostly an immunologically "cold" tumor with an immunosuppressive immune microenvironment.

Comments on revised version:

The Reviewing Editor has reviewed the response letter and revised manuscript and has the following recommendations (all text revisions) prior to the Version of Record.

More information for Panel 4A:

For the most part, the authors have addressed the statistical concerns raised in the initial review through inclusion of p values in the relevant figure legends. One important exception is Fig 4A which includes some of the most impactful data in the paper. The response letter and the new Fig4A legend refers to statistical in Supp Table 3. I could not find this in the package. Because this is such an important panel, I would urge the authors to include the statistics in the main figure. The display should include a fourth panel with castration alone, as requested by at least one reviewer.

I would also urge the authors to place a schema of the experimental design at the top of the figure to clarify the timing of anti-TNF therapy and the fact that it is administered continuously rather than as a single dose (I was confused by this upon first reading). Last, it is hard to reconcile the curves in the day +3 panel with the conclusion that there is no effect (the red curve in particular).

Include a model cartoon of the TNF switch:

A key concept in the report is the concept of a "TNF switch". I recommend the authors include a model cartoon that lays out this out visually in an easily understandable format. The cartoon in Supp Fig 8 touches on this but is more biochemically focused and does not easily convey the "switch" concept.

Add a "study limitations" paragraph at the end of the discussion:

The authors noted that several other concerns expressed by the reviewers were considered beyond the scope of this report. These include the inclusion of additional tumor response endpoints beyond US-guided assessment of tumor volume (e.g., histology, proliferation markers, etc.) and the purely correlative association of macrophage and T cell infiltration with recurrence, in the absence of immune cell depletion experiments. To this point, the subheading "Immune suppression is a key consequence of increased tumor cell stemness" in the Discussion is too strongly worded.

Similarly, there is no experimental proof that CCL2 from stroma (vs from tumor cell) is required for late relapse. Prior to formal publication, I suggest the authors include a "limitations of the study" paragraph at the end of the discussions that delineates several of these points.

Other points:

For concerns that several reviewers raised about basal versus luminal cells and stemness, the authors have modified the text to soften the conclusions and not assign specific lineage identities.

The answer to the question regarding timing of castration (based on tumor size, not age) needs more detail. This is particularly relevant for the Hi-MYC model that is exquisitely castration sensitive and not known to relapse, except perhaps at very late time points (9-12 months). Surely the authors can include some information on the age range of the mice.

Author response:

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

Reviewer #1 (Public Review):

Summary:

Sha K et al aimed at identifying the mechanism of response and resistance to castration in the Pten knockout GEM model. They found elevated levels of TNF overexpressed in castrated tumors associated with an expansion of basal-like stem cells during recurrence, which they show occurring in prostate cancer cells in culture upon enzalutamide treatment. Further, the authors carry on a timed dependent analysis of the role of TNF in regression and recurrence to show that TNF regulates both processes. Similarly, CCL2, which the authors had proposed as a chemokine secreted upon TNF induction following enzalutamide treatment, is also shown to be elevated during recurrence and associated with the remodeling of an immunosuppressive microenvironment through depletion of T cells and recruitment of TAMs.

Strengths:

The paper exploits a well-established GEM model to interrogate mechanisms of response to standard-of-care treatment. This is of utmost importance since prostate cancer recurrence after ADT or ARSi marks the onset of an incurable disease stage for which limited treatments exist. The work is relevant in the confirmation that recurrent prostate cancer is mostly an immunologically "cold" tumor with an immunosuppressive immune microenvironment

Weaknesses:

While the data is consistent and the conclusions are mostly supported and justified, the findings overall are incremental and of limited novelty. The role of TNF and NF-kB signaling in tumor progression and the role of the CCL2-CCR2 in shaping the immunosuppressive microenvironment are well established.

We contend there is novelty in: the experimental design; our finding of a TNF signaling ‘switch’ and the role of androgen-deprivation induced immunosuppression.    

On the other hand, it is unclear why the authors decided to focus on the basal compartment when there is a wealth of literature suggesting that luminal cells are if not exclusively, surely one of the cells of origin of prostate cancer and responsible for recurrence upon antiandrogen treatment. As a result, most of the later shown data has to be taken with caution as it is not known if the same phenomena occur in the luminal compartment.

While we appreciate the reviewer’s interest in the cancer stem cell biology occurring in the tumor in response to androgen deprivation, our focus in this report is identifying mechanisms that account for a switch in TNF signaling.  Specifically, our previous studies showed a rapid increase in TNF mRNA following castration (in the normal murine prostate) but in the current report we also observe an increase in TNF at late times post-castration (in a murine prostate cancer model).  We propose that the increase in TNF at late times is due to plasticity (increased stemness) in the tumor cell population, rather than - for example - a change in signal-driven TNF mRNA transcription.  While a possible mechanism is expansion of a recurrent tumor stem-cell population, a careful investigation is beyond the scope of this report.  Therefore, in the revised manuscript, we have altered the text in multiple places to indicate a suggestive, rather than definitive, role for tumor stem cells.  Indeed, we did include caveats regarding the role of tumor stem cells in the original discussion (lines 425-429 in the revised manuscript), and this is now made more explicit in the revised manuscript.   

Reviewer #2 (Public Review):

Summary:

In this study, Sha and Zhang et al. reported that androgen deprivation therapy (ADT) induces a switch to a basal-stemness status, driven by the TNF-CCL2-CCR2 axis. Their results also reveal that enhanced CCL2 coincides with increased macrophages and decreased CD8 T cells, suggesting that ADT resistance may be related to the TNF/CCL2/CCR2-dependent immunosuppressive tumor microenvironment (TME). Overall, this is a very interesting study with a significant amount of data.

Strengths:

The strengths of the study include various clinically relevant models, cutting-edge technology (such as single-cell RNA-seq), translational potential (TNF and CCR2 inhibitors), and novel insights connecting stemness lineage switch to an immunosuppressive TME. Thus, I believe this work would be of significant interest to the field of prostate cancer and journal readership.

Weaknesses:

(1) One of the key conclusions/findings of this study is the ADT-induced basal-stemness lineage switch driving ADT resistance. However, most of the presented evidence supporting this conclusion only selects a couple of marker genes. What exacerbates this issue is that different basal-stemness markers were often selected with different results. For example, Figure S1A uses CD166/EZH2 as markers, while Figure S1B uses ITGb1/EZH2. In contrast, Figure 1D uses Sca1/CD49, and Figure 2B-C uses CD49/CD166. Since many basal-stemness lineage gene signatures have been previously established, the study should examine various basal-stemness gene signatures rather than a couple of selected markers. Moreover, why were none of the stemness/basal-gene signatures significantly changed in the GO enrichment analysis in Figure 6A/B?

Mice and human cells express similar but also partially distinct prostate stem cell markers.  For example, Sca1 is predominantly used as a stem cell marker in mice but not in human prostate epithelial cells.  CD166 and CD49f are expressed in both human and murine prostate epithelium and therefore we used these in both sets of studies.  Also see the response to R1-2.

(2) A related weakness is the lack of functional results supporting the stemness lineage switch. Although the authors present colony formation assay results, these could be influenced simply by promoted cell proliferation, which is not a convincing indicator of stemness. To support this key conclusion, widely accepted stemness assays, such as the prostasphere formation assay (in vitro) and Extreme Limiting Dilution Analysis (ELDA) xenograft assay (in vivo), should be carried out.

See the response to R1-2 and R2-1, above.

(3) Another significant concern is that this study uses concurrency to demonstrate a causal relationship in many key results, which is entirely different. For example, Figure S4A and S4B only show increased CCL2 and TNF secretion simultaneously, which cannot support that CCL2 is dependent on TNF. Similarly, Figure 5A only shows that CCL2 increased coincidently with a rise in TNF, which cannot support a causal relationship. To support the causal relationship of this conclusion, it is necessary to show that TNF-KO/KD would abolish the increased CCL2 secretion.

Regarding Fig. S4A and S4B: We previously demonstrated (Sha et al, 2015; reference 10) that CCL2 secretion is dependent on TNF, in the same cell lines.  We have added additional data (new Fig. S4B) in this report to confirm this dependency.  

Regarding Fig 5: In Fig 5B we demonstrated that the increase in CCL2-staining cells in recurrent tumors from castrated animals (the equivalent of human CRPC in our model) was significantly inhibited in animals receiving etanercept, demonstrating TNF dependency for CCL2 in this context.  

While the use of TNF KO cell lines and animals could provide additional insights, the creation of such cell lines and tumor models is arduous.  Moreover, we previously demonstrated that administration of anti-TNF drugs such as etanercept are as effective as the KO phenotypes (Davis et al 2011; ref. 11).  

(4) Some of the selective data presentations are not explained and are difficult to understand. For example, why does CD49 staining in Figure S3A have data for all four time points, while CD166 in Figure S3D only has data for the last time point (day 21)? Similarly, although several TNF_UP gene signatures were highlighted in Figure 4B, several TNF_DN signatures were also enriched in the same table, such as RUAN_RESPONSE_TO_TNF_DN. What is the explanation for these contrasting results?

Regarding Fig. S3A and S3D: The cell-staining studies in Fig. S3 are confirmatory of the FACS studies in Figs. 2 and 3.  We were not able to stain all of the CD166 time-points for technical reasons (difficulty optimizing the automated staining protocol) but we were able to successfully stain key late time-points, so we have included this data in the supplementary figure.  There was no attempt to selectively present data; this was just a practical limitation of the time and funds that we could devote to confirmatory studies.   

Regarding Fig 4B: The highlighting identifies a common (i.e., identical) group of gene sets in the two GSEA analyses, demonstrating that these very same gene sets are all up-regulated in one instance, and down-regulated in the other.  The ‘TNF DN’ genes were not identical in the two GSEA analyses and so we cannot draw any conclusions about these.  Note that we are scoring the TNF-related genes sets with the 10 largest (positive or negative) normalized enrichment scores (NES), and are not relying on DN or UP designations in the gene set name (identifier).  In this analysis up- and down-regulation refers to the sign and magnitude of the NES, not the gene set names.  

Reviewer #3 (Public Review):

Summary:

The current manuscript evaluates the role of TNF in promoting AR targeted therapy regression and subsequent resistance through CCL2 and TAMs. The current evidence supports a correlative role for TNF in promoting cancer cell progression following AR inhibition. Weaknesses include a lack of descriptive methodology of the pre-clinical GEM model experiments and it is not well defined which cell types are impacted in this pre-clinical model which will be quite heterogenous with regards to cancer, normal, and microenvironment cells.

Strengths:

(1) Appropriate use of pre-clinical models and GEM models to address the scientific questions.

(2) Novel finding of TNF and interplay of TAMs in promoting cancer cell progression following AR inhibition.

(3) Potential for developing novel therapeutic strategies to overcome resistance to AR blockade.

Weaknesses:

(1) There is a lack of description regarding the GEM model experiments - the age at which mice experiments are started.

Table S1 in the supplementary data summarizes the salient characteristics of the GEM models.  Note that as described in the M&M, we selected animals for experimental groups based on the tumor volume (determined by HFUS) and not based on the age of the mouse, since there is some variability in the kinetics of tumor growth in genetically identical mice, as shown by our HFUS observations of hundreds of mice harboring the genetic changes (PTEN loss, MYC gain) in the models we have studied most extensively.  Although admittedly an imperfect criteria, we reasoned that tumor volume would be the best surrogate criteria for tumor biology.  

(2) Tumor volume measurements are provided but in this context, there is no discussion on how the mixed cancer and normal epithelial and microenvironment is impacted by AR therapy which could lead to the subtle changes in tumor volume.

The reviewer’s criticism is well-founded - most of our studies involved bulk analysis, which makes it difficult to probe the cellular interactions within the TME.  Future studies - beyond the scope of this report - using single cell technical approaches - are needed to investigate these subtle changes.  We have added a statement to this effect to the manuscript (lines 464-468).

(3) There are no readouts for target inhibition across the therapeutic pre-clinical trials or dosing time courses.

The reviewer’s criticism is well-founded, since we cannot be 100% certain of drug delivery in the TNF and CCL2 blockade experiments.  Two points in this regard.  First, with the assistance of institutional veterinarian staff, we have had good success in training multiple scientists (PhD student, technicians) to deliver both biological and small molecule drugs i.p.  Second, the observation that the drugs did ‘work’ in most animals in well-defined experimental protocols strongly suggests that the delivery methodology is reliable.  If sporadic delivery failures do occur, this would tend to underestimate the magnitude of the ‘positive’ (i.e., blocking) effects rather than leading to false negatives.   

(4) The terminology of regression and resistance appears arbitrary. The data seems to demonstrate a persistence of significant disease that progresses, rather than a robust response with minimal residual disease that recurs within the primary tumor.

We explain our rationale for the criteria defining regression and recurrence in the M&M and in the legend to Table S2.  In the revised version of the manuscript, we now explicitly reference these descriptions in the relevant RESULTS section (lines 222-223).  Note that we use the term ‘recurrence’ rather than ‘resistance’ as the former does not necessarily imply a particular biological mechanism.  

(5) It is unclear if the increase in basal-like stem cells is from normal basal cells or cancer cells with a basal stem-like property.

See the response to R1-2 and R2-1.

(6) In the Hi-MYC model, MYC expression is regulated by AR inhibition and is profoundly ARi responsive at early time points.

We agree that this is the likely mechanism of castration-induced regression (so-called ‘MYC addiction’) but it is unclear what the reviewer’s concern is vis-a-vis our manuscript.  

Reviewer #4 (Public Review):

In this manuscript by Sha et al. the authors test the role of TNFa in modulating tumor regression/recurrence under therapeutic pressure from castration (or enzalutamide) in both in vitro and in vivo models of prostate cancer. Using the PTEN-null genetic mouse model, they compare the effect of a TNFα ligand trap, etanercept, at various points pre- and post-castration. Their most interesting findings from this experiment were that etanercept given 3 days prior to castration prevented tumor regression, which is a common phenotype seen in these models after castration, but etanercept given 1 day prior to castration prevented prostate cancer recurrence after castration. They go on to perform RNA sequencing on tumors isolated from either sham or castrate mice from two time points post-castration to study acute and delayed transcriptional responses to androgen deprivation. They found enrichment of gene sets containing TNF-targets which initially decrease post-castration but are elevated by 35 days, the time at which tumors recur. The authors conduct a similar set of experiments using human prostate cancer cell lines treated with the androgen receptor inhibitor enzalutamide and observe that drug treatment leads to cells with basal stem-like features that express high levels of TNF. They noticed that CCL2 levels correlate with changes in TNF levels raising the possibility that CCL2 might be a critical downstream effector for disease recurrence. To this end, they treated PTEN-null and hi-MYC castrated mice with a CCR2-antagonist (CCR2a) because CCR2 is one receptor of CCL2 and monitors tumor growth dynamics. Interestingly, upon treatment with CCR2a, tumors did not recur according to their measurements. They go on to demonstrate that the tumors pre-treated with CCR2a had reduced levels of putative TAMs and increased CTLs in the context of TNF or CCR2 inhibition providing a cellular context associated with disease regression. Lastly, they perform single-cell RNA sequencing to further characterize the tumor microenvironment post-castration and report that the ratio of CTLs to TAMs is lower in a recurrent tumor.

While the concepts behind the study have merit, the data are incomplete and do not fully support the authors' conclusions. The author's definition of recurrence is subjective given that the amount of disease regression after castration is both variable (Figure 8) and relatively limited

See the response to R3-4, above.

particularly in the PTEN loss model. Critical controls are missing. For example, both drug experiments were completed without treating non-castrate plus drug controls

In these experiments, we are investigating the effect of anti-TNF or anti-CCL2 therapy on the response to the castration.  The appropriate controls are castrated mice which received vehicle or no treatment.  The response of intact animals (with tumors still increasing in size) is not only irrelevant to the question we are asking, but also impractical, as the tumor size would be too large for mouse viability. 

which raises the question of how specific these findings are to castration resistance. No validation was performed to ensure that either the TNF ligand trap or the CCR2 agonist was acting on target. 

See the response to R3-3, above.

The single-cell sequencing experiments were done without replicates which raises concern about its interpretation. 

The goal in these experiments is to address a relatively narrow question concerning changes in a few key TAM-associated transcripts versus changes in a few CTL-associated transcripts.  This is not meant to provide rigorous single cell transcriptomic analysis that is required - for example - to definitely assess the levels of various cell populations.   As noted in R3-2 (and in the DISCUSSION , lines 467-468) future single cell analysis is ongoing, but beyond the scope of this manuscript.

At a conceptual level, the authors say that a major cause of disease recurrence in the immunosuppressive TME, but provide little functional data that macrophages and T cells are directly responsible for this phenotype.   

The requirement for CCL2-CCR2 signaling for recurrence suggests that TAMs drive recurrence, presumably due to immunosuppression in the TME.  However, CCR2 is expressed by other cell types.  Therefore, in future studies we will need to examine the response to additional inhibitors and also employ single cell ‘omics to more thoroughly characterize the changes in the cellular components of the tumor immune microenvironment.  Functional analysis of T-cell subsets is an even more formidable experimental challenge.  

Statistical analyses were performed on only select experiments. 

See the response to R1-3, below.

In summary, further work is recommended to support the conclusions of this story.

Reviewer #1 (Recommendations For The Authors):

I suggest the authors address the following:

(1) Throughout the figures, statistical analysis needs to be made clear including n numbers, replicates, and whether or not differences shown are statistically significant. These includes Figure 1c, and d,; Figure 2 A and B, Figure 3A; Figure 4A; Figure 5A, C and D; Figure 7B.

We thank the reviewer for identifying these issues and we have inserted statistical analyses into the text as follows: 

Figure 1C-D: Statistical analysis added to the legend of Fig. 1.  

FIgure 2A: Statistical analysis added to the legend of Fig. 2.

Figures 2B: These are representative FACS scatter plots –  the corresponding statistical analysis is shown in Fig. 2C (left panel).  

Figure 3A: Statistical comparisons are not relevant to this figure – the data is presented to document the cell sorting enrichment process.

Figure 4A and Figure 5C-D:  For the small n, categorical data sets related to the studies using GEM prostate cancer models shown in Figures 4A, 5C and 5D, we employed the exact binomial test to determine the Clopper-Pearson confidence interval for the proportion and Fisher’s exact test to determine the p-values and now present these analyses in a new Supplementary Table 3.  We have included this information in the M&M section and edited the Figure legends to direct the reader to the new Supplementary Table.  

We would like to emphasize that the reported p-values are exact probabilities from Fisher’s exact test. Given the small sample sizes and the discrete nature of the distribution, these values should not be interpreted as if they strictly conform to conventional thresholds such as p<0.05. Instead, they represent the exact probability of observing data as extreme as (or more extreme than) what we obtained under the null hypothesis.

Figure 5A: The legend of Fig. 5A was edited to clarify the statistical analysis.  

Figure 7B: The differences in CD8+ T cells and F4/80 macrophages due to CCR2a-35d treatment were not statistically different (p>0.05) - we have now stated this explicitly in the figure legend.  

(2) Several experiments either lack appropriate controls or the choice of data presentation is confusing. In Figure 4A vehicle controls should 

We have not observed any effect of IP administration of vehicle in any experiments across multiple published studies employing these GEMMs, and so we conclude that the injection of vehicle is very unlikely to modify the outcome of these experiments.

be included in the graphs and for ease of interpretation perhaps average tumor growth should be shown with individual tumor growth can be shown in the supplement. In Figure 5 the vehicle control is missing and in Figure 5D 4 out of 5 CX+vehicle tumors are said to have recurred but the trend line in the graph shows otherwise.

We thank the reviewer for noting this issue - the color designations were inadvertently reversed in the legend text.  This error has been corrected in the revised version of the manuscript.  

In Figure 8B flow cytometry would actually be more convincing than scRNAseq. If scRNAseq is chosen, a higher quality UMAP or t_SNE plot is needed with a broader color palette.

We did consider the FACS approach suggested by the reviewer, but decided against it as we could not readily identify and validate a TAM-specific antibody to allow such measurements. 

Reviewer #3 (Recommendations For The Authors):

(1)  A clear description of the GEM model experiments will be helpful in interpreting the data as it is unclear what age the PTEN or MYC mice were when therapy was started. PTEN are generally intrinsically resistant to ARi whereas MYC are robustly sensitive.

(2) Prostate organoid technology of the GEM prostate cell, and normal prostate cells may allow for a better evaluation of which basal stem-like cells are expressing TNF - dissecting out normal basal from cancer with basal-like properties.

(3) Experiments to demonstrate targeting inhibition should be performed for AR and TNF inhibition. Especially across the spectrum of TNF blockade timing given the differences in proposed responsiveness over an acute change in dosing schedule.

(4) Detailed histology and pathologic evaluation should be provided to characterize the impact on cancer and TME as well as normal prostate mixed in these tumors.

(5) Prostate organoid development with genetic manipulation (PTEN ko) and transplant back into immunocompetent mice may provide experiments to prove causality and address the impact on the immune microenvironment.

(6) The descriptive of regression and recurrence need to be defined as based on the kinetics and presented data this seems to be associated with minimal responsiveness and progression from a substantial volume of persistent cells.

(7) The authors should also explore the impact of TNF inhibition on the cancer cell directly and evaluate downstream PI3K signaling.

Responding to this set of recommendations:  A number of these recommendations (R3-7, -9, -12) are similar or identical to those already noted in Reviewer 3’s public review and have been addressed above.  The remaining recommendations (R3-8, -10, -11; organoids, histological approaches to the TME, etc.) are potentially interesting experimental approaches but beyond the scope of the current manuscript.  

Reviewer #4 (Recommendations For The Authors):

Major comments:

(1) Figure 1A-B: While the decrease in tumor growth post-castration is apparent, the increase in tumor growth that has been designated as the point of androgen-independence is a mild increase from the 28 measurements and would benefit from statistical support. Further time points demonstrating that the tumors continue to increase in size would better support the claim that these tumors appropriately model disease recurrence.

This data meets our criteria for recurrence (outlined in the M&M and in the legend to Table S2).

(2) Figure 2A: Statistical analysis should be performed and why is this figure shown twice (also in the S2A right panel)?

We added statistical analysis to the legend of Fig. 2A.  The data from Fig 2 (C4-2 cell line) is replicated in Supplementary Fig S2 to allow the reader to directly compare the response of the C4-2 cell line with the response of the LNCaP cell line.   

(3) Figure 4A: Non-castrate + etan control is needed here. Also, the data should be statistically assessed.

Regarding non-castrate controls, see our response to R4-2.  Statistical analysis has been added - see Supplementary Table S3.   

(4) It appears that at least two of the mice shown in Figure 5C have the same level of disease recurrence as was demonstrated in Figure 1B, yet the analysis defines recurrence in 0/6 mice.

Again, similar to R4-7, None of the mice in Figure 5C meet our criteria for recurrence (outlined in the M&M and in the legend to Table S2).

(5) The text for Figure 5D states that vehicle-treated tumors (red) regress then recur while mice pre-treated with a CCR2 antagonist (blue) don't recur, but in the figure, these groups appear to be reversed. In addition, it would be good to have noncastrate + CCR2a control for Figure 5C and 5D.

We corrected the labeling error in the legend to Figure 5.

(6) It would be good to validate major RNAseq findings using orthogonal approaches.

We agree that it is valuable to validate our findings but these experiments are beyond the scope of the manuscript

(7) Figure 7B is quite puzzling. It appears to show the opposite of what was written.

We thank the reviewer for bringing this error to our attention.  Our internal review of previous versions of the manuscript showed that the corresponding author (JJK) inadvertently mis-edited this figure when preparing the BioRxiv submission.  Figure 7B has been corrected and now aligns with the Results text. We have also appended a PDF documenting the editing error/ mistake.  

(8) Figure 8: This experiment appears to have been done without replicates making the current interpretation questionable.

A more detailed scRNAseq analysis of the GEMM response to castration (with replicated) is already underway.  The analysis in Fig. 8 includes 1000’s of cells, capturing the variation in mRNA levels.  However, it does not capture animal-to-animal variation.  Given the supporting role of this data in this manuscript, we believe that the single animal approach is adequate in this case.  

(9) The level of detail included in the mechanism described in Figure S8 is not supported by the work shown.

Fig. S8 is not presented as a summary of our findings but as a model that is consistent with our data - since it is by definition somewhat speculative, we present it in the supplementary data.   

Minor Comments:

(1) Figure 6S title is written incorrectly.

We thank the reviewer for noticing this - we have corrected this in the revised manuscript.

(2) Images shown in Figure S7C need scale bars.

These images are at 40X magnification - this has been added to the legend.

Reviewer #1 (Public review):

Summary:

This paper investigates the physical basis of epithelial invagination in the morphogenesis of the ascidian siphon tube. The authors observe changes in actin and myosin distribution during siphon tube morphogenesis using fixed specimens and immunohistochemistry. They discover that there is a biphasic change in the actomyosin localization that correlates with changes in cell shapes. Initially, there is the well-known relocation of actomyosin from the lateral sides to the apical surface of cells that will invaginate, accompanied by a concomitant lengthening of the central cells within the invagination, but not a lot of invagination. Coincident with a second, more rapid, phase of invagination, the authors see a relocalization of actomyosin back to the lateral sides of the cells. This 2nd "bidirectional" relocation of actin appears to be important because optogenetic inhibition of myosin in the lateral domain after the initial invaginations phase resulted in a block of further invagination. Although not noted in the paper, that the second phase of siphon invagination is dependent on actomyosin is interesting and important because it has been shown that during Drosophila mesoderm invagination that a second "folding" phase of invagination is independent of actomyosin contraction (Guo et al. elife 2022), so there appear to be important differences between the Drosophila mesoderm system and the ascidian siphon tube systems.

Using the experimental data, the authors create a vertex model of the invagination, and simulations reveal a coupled mechanism of apicobasal tension imbalance and lateral contraction that creates the invagination. The resultant model appears to recapitulate many aspects of the observed cell behaviors, although there are some caveats to consider (described below).

Strengths:

The studies and presented results are well done and provide important insights into the physical forces of epithelial invagination, which is important because invaginations are how a large fraction of organs in multicellular organisms are formed.

Weaknesses:

(1) This reviewer has concerns about two aspects of the computational model. First, the model in Figure 5D shows a simulation of a flat epithelial sheet creating an invagination. However, the actual invagination is occurring in a small embryo that has significant curvature, such that nine or so cells occupy a 90-degree arc of the 360-degree circle that defines the embryo's cross-section (e.g., see Figure 1A). This curvature could have important effects on cell behavior.

(2) The second concern about the model is that Figure 5 D shows the vertex model developing significant "puckering" (bulging) surrounding the invagination. Such "puckering" is not seen in the in vivo invagination (Figure 1A, 2A). This issue is not discussed in the text, so it is unclear how big an issue this is for the developed model, but the model does not recapitulate all aspects of the siphon invagination system.

(3) In Figure 2A, Top View, and the schematic in Figure 2C, the developing invagination is surrounded by a ring of aligned cell edges characteristic of a "purse string" type actomyosin cable that would create pressure on the invaginating cells, which has been documented in multiple systems. Notably, the schematic in Figure 2C shows myosin II localizing to aligned "purse string" edges, suggesting the purse string is actively compressing the more central cells. If the purse string consistently appears during siphon invagination, a complete understanding of siphon invagination will require understanding the contributions of the purse string to the invagination process.

(4) The introduction and discussion put the work in the context of work on physical forces in invagination, but there is not much discussion of how the modeling fits into the literature.

Author response:

Reviewing Editor Comments:

Based on the feedback from the reviewers, a focus on the following major points has the potential to improve the overall assessment of the significance of the findings and the strength of the evidence:

(1) It would be helpful to clearly articulate how these findings advance the field beyond what has already been demonstrated or suggested in other systems.

We will revise the Introduction and Discussion to better contextualize our findings. We will provide a careful comparison of the Ciona atrial siphon invagination with the other established systems to elucidate the unique aspects of our model. Highlighting our discovery of a novel bidirectional "lateral-apical-lateral" contractility as a distinct mechanical paradigm for sequential morphogenesis.

(2) It would be helpful to clarify the meaning of "translocation" and more explicitly describe the temporal and spatial patterns of active myosin localization during the two steps of invagination.

We will replace “translocation” with the more accurate and conservative term “redistribution” throughout the manuscript, including in the title. We will also revise the text in Result and Discussion sections to avoid overinterpretation. To provide a more explicit description of the spatiotemporal patterns, we will add new quantitative analyses of active myosin intensity from earlier time points (13-14 hpf) to rigorously support the initial lateral-to-apical redistribution phase. Then, we will add high-resolution top-view images to unambiguously show the ring-like localization of myosin at the apical cell-cell junctions during the initial stage. Finally, we will correct the schematic in Figure 2C to accurately reflect the predominant localization of active myosin at the apical cell-cell borders.

(3) It would be helpful to explain how the optogenetic data support the conclusion that "redistribution of myosin contractility from the apical to lateral regions is essential for the development of invagination".

We acknowledge the limitation of the original global inhibition experiment. We will perform additional experiments that combine optogenetic inhibition with subsequent immunostaining of the active myosin. By quantitatively comparing the distribution of actomyosin in light-stimulated versus dark-control embryos, we will be able to demonstrate whether the inhibition prevents the establishment of the lateral contractility domain. This will allow us to refine our conclusion.

(4) It would be helpful to describe how the modeling work fits within the existing literature on modeling epithelial folding and to address discrepancies between the model and the actual biological observations, such as tissue curvature, limited invagination depth in the model, and the "puckering" surrounding the invagination. In addition, certain descriptions of the modeling results should be clarified, as suggested by Reviewer #3.

We fully agree that we should discuss the existing theoretical work on epithelial folding more clearly. Clarifying how physical forces contribute to invagination is central to interprete the underlying mechanisms, and we appreciate the opportunity to better connect our framework to existing studies. In the revision, we will expand the Introduction and Discussion to place our model in the appropriate theoretical context and highlight how it relates to and differs from previous approaches. At the same time, we will extend the model to a curved geometric framework to more accurately reproduce the experimental observations, which will improve its predictive value. We will also revise the descriptions and schematic representations of the modeling results to enhance clarity and better align them with the biological data.

(5) It would be helpful to elaborate on the methods for quantitative image analysis and statistical tests.

We will thoroughly expand the Methods section to provide a detailed step-by-step description of image quantification procedures, including precise definitions of the apical, lateral, and basal domains used for intensity measurements and the measurement of cell surface areas and invagination depths.

Reviewer #1 (Public review):

Summary:

This paper investigates the physical basis of epithelial invagination in the morphogenesis of the ascidian siphon tube. The authors observe changes in actin and myosin distribution during siphon tube morphogenesis using fixed specimens and immunohistochemistry. They discover that there is a biphasic change in the actomyosin localization that correlates with changes in cell shapes. Initially, there is the well-known relocation of actomyosin from the lateral sides to the apical surface of cells that will invaginate, accompanied by a concomitant lengthening of the central cells within the invagination, but not a lot of invagination. Coincident with a second, more rapid, phase of invagination, the authors see a relocalization of actomyosin back to the lateral sides of the cells. This 2nd "bidirectional" relocation of actin appears to be important because optogenetic inhibition of myosin in the lateral domain after the initial invaginations phase resulted in a block of further invagination. Although not noted in the paper, that the second phase of siphon invagination is dependent on actomyosin is interesting and important because it has been shown that during Drosophila mesoderm invagination that a second "folding" phase of invagination is independent of actomyosin contraction (Guo et al. elife 2022), so there appear to be important differences between the Drosophila mesoderm system and the ascidian siphon tube systems.

Using the experimental data, the authors create a vertex model of the invagination, and simulations reveal a coupled mechanism of apicobasal tension imbalance and lateral contraction that creates the invagination. The resultant model appears to recapitulate many aspects of the observed cell behaviors, although there are some caveats to consider (described below).

We sincerely thank you for this insightful comment and for bringing the important study by Guo et al. (2022) to our attention. We fully agree that a direct comparison between these two mechanisms is important of our findings. As you astutely point out, the fundamental difference lies in the autonomy and driving force of the second, rapid invagination phase. To highlight this important conceptual advance, we will add a dedicated paragraph in the Discussion section to explicitly discuss this point.

Strengths:

The studies and presented results are well done and provide important insights into the physical forces of epithelial invagination, which is important because invaginations are how a large fraction of organs in multicellular organisms are formed.

Thank you for this positive assessment and for recognizing the significance of our work in elucidating the physical mechanisms underlying fundamental morphogenetic processes. We have striven to provide a comprehensive and rigorous analysis, and are grateful for this encouraging feedback.

Weaknesses:

(1) This reviewer has concerns about two aspects of the computational model. First, the model in Figure 5D shows a simulation of a flat epithelial sheet creating an invagination. However, the actual invagination is occurring in a small embryo that has significant curvature, such that nine or so cells occupy a 90-degree arc of the 360-degree circle that defines the embryo's cross-section (e.g., see Figure 1A). This curvature could have important effects on cell behavior.

Thank you for bringing up the issue of tissue curvature. In this initial version of the model, we treated the tissue as flat because although the anterior epidermis indeed has significant curvature, the region that actually undergoes invagination occupies only a small arc of the embryo's cross-section—roughly 30-degree arc of the 360-degree circle. In addition, the embryo elongates anisotropically, and by 16.5 hpf the curvature has largely diminished (Fig.1A), leaving this local region effectively flattened. We agree that this simplification may overlook contributions from early curvature, and we will examine curvature changes more carefully in the data and incorporate curved geometry into the model to evaluate their impact.

(2) The second concern about the model is that Figure 5 D shows the vertex model developing significant "puckering" (bulging) surrounding the invagination. Such "puckering" is not seen in the in vivo invagination (Figure 1A, 2A). This issue is not discussed in the text, so it is unclear how big an issue this is for the developed model, but the model does not recapitulate all aspects of the siphon invagination system.

Thank you for pointing out the issue regarding the accuracy of the deformation pattern in our simulations. We do observe a mild puckering in vivo around 17 hpf (Fig. 1A), but it is clearly less pronounced than in the current model. The presence of such deformation suggests that bending stiffness of the epithelial sheet contributes to the mechanics of the invagination, which is included in our current model. While the discrepancy reflects limitations in our mechanical assumptions and geometric simplifications, including oversimplified interactions between the apical cell layer and the underlying basal cells, as well as the omission of tissue curvature. We will refine these aspects in the revised model to better reproduce the deformation patterns observed in vivo.

(3) In Figure 2A, Top View, and the schematic in Figure 2C, the developing invagination is surrounded by a ring of aligned cell edges characteristic of a "purse string" type actomyosin cable that would create pressure on the invaginating cells, which has been documented in multiple systems. Notably, the schematic in Figure 2C shows myosin II localizing to aligned "purse string" edges, suggesting the purse string is actively compressing the more central cells. If the purse string consistently appears during siphon invagination, a complete understanding of siphon invagination will require understanding the contributions of the purse string to the invagination process.

Thank you for this excellent observation. We agree that the ring-like actomyosin structure is a prominent feature during the initial stages of invagination, and its potential role warrants discussion. We carefully re-examined our data. Our analysis confirms that this myosin ring is most pronounced during the early initial invagination stage (approximately 13-14 hpf). This inward compression from the periphery would work in concert with apical constriction to help shape the initial invagination. However, this ring-like myosin pattern significantly diminishes in the accelerated invagination stage. We feel that the purse string may play a collaborative role in the early phase, however, its dissolution at the accelerated invagination stage indicates that Ciona atrial siphon invagination does not entirely rely on the sustained compression from the purse string of surrounding cells. These data will be included in the supplementary materials.

(4) The introduction and discussion put the work in the context of work on physical forces in invagination, but there is not much discussion of how the modeling fits into the literature.

We apologize for not providing sufficient context on how our theoretical framework relates to prior work on the mechanics of invagination. You are absolutely right that the Introduction and Discussion sessions should more clearly situate our model within the existing literature, including the classical formulations it builds upon and the more recent models that address similar morphogenetic processes. In the revision, we will expand this section to acknowledge relevant work, clarify how our approach connects to and differs from previous models, and explicitly discuss the strengths and limitations of our framework. We appreciate this helpful suggestion and will make these connections much clearer.

Reviewer #2 (Public review):

Summary:

The authors propose that bidirectional translocation of actomyosin drives tissue invagination in Ciona siphon tube formation. They suggest a two-stage model where actomyosin first accumulates apically to drive a slow initial invagination, followed by translocation to lateral domains to accelerate the invagination process through cell shortening. They have shown that actomyosin activity is important for invagination - modulation of myosin activity through expression of myosin mutants altered the timing and speed of invagination; furthermore, optogenetic inhibition of myosin during the transition of the slow and fast stages disrupted invagination. The authors further developed a vertex model to validate the relationship between contractile force distribution and epithelial invagination.

Thank you for your thoughtful and accurate summary of our work and for your constructive critique.

Strengths:

(1) The authors employed various techniques to address the research question, including optogenetics, the use of MRLC mutants, and vertex modelling.

(2) The authors provide quantitative analyses for a substantial portion of their imaging data, including cell and tissue geometry parameters as well as actin and myosin distributions. The sample sizes used in these analyses appear appropriate.

(3) The authors combined experimental measurements with computer modeling to test the proposed mechanical models, which represents a strength of the study. It provides a framework to explore the mechanical principles underlying the observed morphogenesis.

We are grateful for your positive assessment of the multidisciplinary approaches, quantitative analyses, and the integration of modeling with experiments.

Weaknesses:

(1) The concept of coordinated and sequential action of apical and lateral actomyosin in support of epithelial folding has been documented through a combination of experimental and modeling approaches in other contexts, such as ascidian endoderm invagination (PMID: 20691592) and gastrulation in Drosophila (PMIDs: 21127270, 22511944, 31273212). While the manuscript addresses an important question, related findings have been reported in these previous studies. This overlap reduces the degree of novelty, and it remains to be clarified how their work advances beyond these prior contributions.

We thank you for raising this important point regarding the novelty of our work and for directing us to the key literature on ascidian endoderm invagination (PMID: 20691592) and Drosophila gastrulation (PMIDs: 21127270, 22511944, 31273212). We agree with the reviewer that the sequential activation of contractility in different cellular domains is a fundamental mechanism driving epithelial morphogenesis, as elegantly demonstrated in these prior studies. Our work builds upon this foundational concept. However, we believe we reveals a novel and distinct mechanical model: The ascidian endoderm and the atrial siphon involve a sequential shift of actomyosin contractility. However, the spatial pattern and functional outcomes are fundamentally different. In the ascidian endoderm (PMID: 20691592), the transition is from apical constriction to basolateral contraction. Basolateral contraction works in concert with a persistent circumferential to overcome tissue resistance and drive invagination. In contrast, our study of the atrial siphon reveals a bidirectional actomyosin redistribution between the apical and lateral domains. The basal domain in our system appears to play a more passive, structural role. While, Drosophila gastrulation also involves apical and lateral myosin, the mechanisms and dependencies differ. As supported by recent work (Guo et al. elife 2022), ventral furrow invagination can proceed even when lateral contractility is compromised, indicating that it is not an absolute requirement. In our system, however, optogenetic inhibition and our vertex model strongly suggest that the acquisition of lateral contractility is essential for the accelerated invagination stage. We will revise the text to better articulate these points of distinction and novelty in the Introduction and Discussion sections.

(2) One of the central statements made by the authors is that the translocation of actomyosin between the apical and lateral domains mediates invagination. The use of the term "translocation" infers that the same actomyosin structures physically move from one location to another location, which is not demonstrated by the data. Given the time scale of the process (several hours), it is also possible that the observed spatiotemporal patterns of actomyosin intensity result from sequential activation/assembly and inactivation/disassembly at specific locations on the cell cortex, rather than from the physical translocation of actomyosin structures over time.

Your critique regarding the term "translocation" was well-founded. We will replace “translocation” with the more accurate and conservative term “redistribution” throughout the manuscript, including in the title. We will also revise the text in the Results and Discussion sections to avoid overinterpretation.

(3) Some aspects of the data on actomyosin localization require further clarification. (1) The authors state that actomyosin translocation is bidirectional, first moving from the lateral domain to the apical domain; however, the reduction of the lateral actomyosin at this step was not rigorously tested. (2) During the slow invagination stage, it is unclear whether myosin consistently localizes to the apical cell-cell borders or instead relocalizes to the medioapical domain, as suggested by the schematic illustration presented in Figure 2C. (3) It is unclear how many cells along the axis orthogonal to the furrow accumulate apical and lateral myosin.

Thank you for your insightful comments, which will help us significantly improve the clarity and rigor of our actomyosin localization analysis. To address the points raised, we will undertake several key revisions: First, we will add new quantitative analyses of active myosin intensity from earlier time points (13-14 hpf) to rigorously support the initial lateral-to-apical redistribution phase. Second, we will correct the schematic in Figure 2C to accurately reflect the predominant localization of active myosin at the apical cell-cell borders. Finally, we will clarify that the actomyosin redistribution occurs within a broader domain of approximately 15-20 cells in the invagination primordium, not being restricted to the single central cell on which our quantitative measurements were focused.

(4) The overexpression of MRLC mutants appears to be rather patchy in some cases (e.g., in Figure 3A, 17.0 hpf, only cells located at the right side of the furrow appeared to express MRLC T18ES19E). It is unclear how such patchy expression would impact the phenotype.

Thank you for your observation. We acknowledge that mosaic expression is common in Ciona electroporation. For all quantitative analyses, we only selected embryos in which the central cell, along with more than half of the surrounding cells in the primordium, showed clear expression of the plasmid.

(5) In the optogenetic experiment, it appears that after one hour of light stimulation, the apical side of the tissue underwent relaxation (comparing 17 hpf and 16 hpf in Figure 4B). It is therefore unclear whether the observed defect in invagination is due to apical relaxation or lack of lateral contractility, or both. Therefore, the phenotype is not sufficient to support the authors' statement that "redistribution of myosin contractility from the apical to lateral regions is essential for the development of invagination".

We agree that our optogenetic inhibition experiment does not distinguish between apical and lateral roles. To directly address this point, we will perform additional experiments in which we conduct the optogenetic inhibition and subsequently fix and stain the embryos for active myosin and F-actin. This will allow us to quantitatively compare the distribution of actomyosin in the light-stimulated experimental group versus the dark control group. We expect that light activation will have a more pronounced inhibitory effect on the lateral domains than on the apical domain, as the latter is naturally undergoing a reduction in contractility at this stage.

(6) The vertex model is designed to explore how apical and lateral tensions contribute to distinct morphological outcomes. While the authors raise several interesting predictions, these are not further tested, making it unclear to what extent the model provides new insights that can be validated experimentally. In addition, modeling the epithelium as a flat sheet and not accounting for cell curvature is a simplification that may limit the model's accuracy. Finally, the model does not fully recapitulate the deeply invaginated furrow configuration as observed in a real embryo (comparing 18 hpf in Figure 5D and 18 hpf in Figure 1A) and does not fully capture certain mutant phenotypes (comparing 18 hpf in Figure 5F and 18 hpf in Figure 3B right panel).

Thank you for raising these important points. We agree that several model predictions require stronger experimental grounding, and that the flat-sheet assumption is an oversimplification that likely contributes to the model not fully capturing certain morphological features. Our current simulations of myosin perturbation are largely consistent with the optogenetic experiments and the behavior of the myosin mutant. However, the predictions obtained by theoretically decoupling apical and lateral tension are difficult to validate experimentally, given the challenges of selectively manipulating these two components in vivo. Based on your helpful suggestions, we will extend the model to incorporate tissue curvature and examine how initial bending influences the mechanics of invagination, which we expect will improve the accuracy of the model’s morphological predictions.

Reviewer #3 (Public review):

Summary:

In this manuscript by Qiao et al., the authors seek to uncover force and contractility dynamics that drive tissue morphogenesis, using the Ciona atrial siphon primordium as a model. Specifically, the authors perform a detailed examination of epithelial folding dynamics. Generally, the authors' claims were supported by their data, and the conceptual advances may have broader implications for other epithelial morphogenesis processes in other systems.

Thank you for your positive summary and for recognizing the broader implications of our work.

Strengths:

The strengths of this manuscript include the variety of experimental and theoretical methods, including generally rigorous imaging and quantitative analyses of actomyosin dynamics during this epithelial folding process, and the derivation of a mathematical model based on their empirical data, which they perturb in order to gain novel insights into the process of epithelial morphogenesis.

Thank you for highlighting the strengths of our multidisciplinary methodology.

Weaknesses:

There are concerns related to wording and interpretations of results, as well as some missing descriptions and details regarding experimental methods.

We will revise the manuscript to address your concerns regarding wording and methodological details. Your feedback led us to improve clarity, precision, and the depth of methodological description throughout the text.

Reviewer #1 (Public review):

Summary:

In their paper, Shimizu and Baron describe the signaling potential of cancer gain-of-function Notch alleles using the Drosophila Notch transfected in S2 cells. These cells do not express Notch or the ligand Dl or Dx, which are all transfected. With this simple cellular system, the authors have previously shown that it is possible to measure Notch signaling levels by using a reporter for the 3 main types of signaling outputs, basal signaling, ligand-induced signaling and ligand-independent signaling regulated by deltex. The authors proceed to test 22 cancer mutations for the above-mentioned 3 outputs. The mutation is considered a cluster in the negative regulatory region (NRR) that is composed of 3 LNR repeats wrapping around the HD domain. This arrangement shields the S2 cleavage site that starts the activation reaction.

The main findings are:

(1) Figure 1: the cell system can recapture ectopic activation of 3 existing Drosophila alleles validated in vivo.

(2) Figure 2: Some of the HD mutants do show ectopic activation that is not induced by Dl or Dx, arguing that these mutations fully expose the S2 site. Some of the HD mutants do not show ectopic activation in this system, a fact that is suggested to be related to retention in the secretory pathway.

(3) Figure 3: Some of the LNR mutants do show ectopic activation that is induced by Dl or Dx, arguing that these might partially expose the S2 site.

(4) Figure 4-6: 3 sites of the LNR3 on the surface that are involved in receptor heterodimerization, if mutated to A, are found to cause ectopic activation that is induced by Dl or Dx. This is not due to changes in their dimerization ability, and these mutants are found to be expressed at a higher level than WT, possibly due to decreased levels of protein degradation.

Strengths and Weaknesses:

The paper is very clearly written, and the experiments are robust, complete, and controlled. It is somewhat limited in scope, considering that Figure 1 and 5 could be supplementary data (setup of the system and negative data). However, the comparative approach and the controlled and well-known system allow the extraction of meaningful information in a field that has struggled to find specific anticancer approaches. In this sense, the authors contribute limited but highly valuable information.

Author response:

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

Reviewer #1 (Public review)

Summary: 

In the paper, the authors investigate how the availability of genomic information and the timing of vaccine strain selection influence the accuracy of influenza A/H3N2 forecasting. The manuscript presents three key findings: 

(1) Using real and simulated data, the authors demonstrate that shortening the forecasting horizon and reducing submission delays for sharing genomic data improve the accuracy of virus forecasting. 

(2) Reducing submission delays also enhances estimates of current clade frequencies. 

(3) Shorter forecasting horizons, for example, allowed by the proposed use of "faster" vaccine platforms such as mRNA, resulting in the most significant improvements in forecasting accuracy. 

Strengths: 

The authors present a robust analysis, using statistical methods based on previously published genetic-based techniques to forecast influenza evolution. Optimizing prediction methods is crucial from both scientific and public health perspectives. The use of simulated as well as real genetic data (collected between April 1, 2005, and October 1, 2019) to assess the effects of shorter forecasting horizons and reduced submission delays is valuable and provides a comprehensive dataset. Moreover, the accompanying code is openly available on GitHub and is well-documented. 

Thank you for this summary! We worked hard to make this analysis robust, reproducible, and open source.

Weaknesses: 

While the study addresses a critical public health issue related to vaccine strain selection and explores potential improvements, its impact is somewhat constrained by its exclusive reliance on predictive methods using genomic information, without incorporating phenotypic data. The analysis remains at a high level, lacking a detailed exploration of factors such as the genetic distance of antigenic sites.

We are glad to see this acknowledgment of the critical public health issue we've addressed in this project. The goal for this study was to test effects of counterfactual scenarios with realistic public health interventions and not to introduce methodological improvements to forecasting methods. The final forecasting model we analyzed in this study (lines 301-330 and Figure 6) was effectively an "oracle" model that produced the optimal forecast for each given current and future timepoint. We expect any methodological improvements to forecasting models to converge toward the patterns we observed in this final section of the results.

We've addressed the reviewer's concerns in more detail in response to their numbered comments 4 and 5 below.

Another limitation is the subsampling of the available dataset, which reduces several tens of thousands of sequences to just 90 sequences per month with even sampling across regions. This approach, possibly due to computational constraints, might overlook potential effects of regional biases in clade distribution that could be significant. The effect of dataset sampling on presented findings remains unexplored. Although the authors acknowledge limitations in their discussion section, the depth of the analysis could be improved to provide a more comprehensive understanding of the underlying dynamics and their effects.

We have addressed this comment in the numbered comment 1 below.

Suggestions to enhance the depth of the manuscript: 

Thank you again for these thoughtful suggestions. They have encouraged us to revisit aspects of this project that we had overlooked by being too close to it and have helped us improve the paper's quality.

(1) Subsampling and Sampling Strategies: It would be valuable to comment on the rationale behind the strong subsampling of the available GISAID data. A discussion of the potential effects of different sampling strategies is necessary. Additionally, assessing the stability of the results under alternative sequence sampling strategies would strengthen the robustness of the conclusions. 

We agree with the reviewer's point that our subsampled sequences only represent a fraction of those available in the GISAID EpiFlu database and that a more complete representation would be ideal. We designed the subsampling approach we used in this study for two primary reasons.

(1) First, we sought to minimize known regional and temporal biases in sequence availability. For example, North America and Europe are strongly overrepresented in the GISAID EpiFlu database, while Africa and Asia are underrepresented (Figure 1A). Additionally, the number of sequences in the database has increased every year since 2010, causing later years in this study period to be overrepresented compared to earlier years. A major limitation of our original forecasting model from Huddleston et al. 2020 is its inability to explicitly estimate geographic-specific clade fitnesses. Because of this limitation, we trained that original model on evenly subsampled sequences across space and time. We used the same approach in this study to allow us to reuse that previously trained forecasting model. Despite this strong subsampling approach, we still selected an average of 50% of all available sequences across all 10 regions and the entire study period (Figure 1B). Europe and North America were most strongly downsampled with only 7% and 8% of their total sequences selected for the study, respectively. In contrast, we selected 91% of all sequences from Southeast Asia.

(2) Second, our forecasting model relies on the inference of time-scaled phylogenetic trees which are computationally intensive to infer. While new methods like CMAPLE (Ly-Trong et al. 2024) would allow us to rapidly infer divergence trees, methods to infer time trees still do not scale well to more than ~20,000 samples. The subsampling approach we used in this study allowed us to build the 35 six-year H3N2 HA trees we needed to test our forecasting model in a reasonable amount of time.

We have expanded our description of this rationale for our subsampling approach in the discussion and described the potential effects of geographic and temporal biases on forecasting model predictions (lines 360-376). Our original discussion read:

"Another immediate improvement would be to develop models that can use all available data in a way that properly accounts for geographic and temporal biases. Current models based on phylogenetic trees need to evenly sample the diversity of currently circulating viruses to produce unbiased trees in a reasonable amount of time. Models that could estimate sample fitness and compare predicted and future populations without trees could use more available sequence data and reduce the uncertainty in current and future clade frequencies."

The section now reads:

"Another immediate improvement would be to develop models that can use all available data in a way that properly accounts for geographic and temporal biases. For example, virus samples from North America and Europe are overrepresented in the GISAID EpiFlu database, while samples from Africa and Asia are underrepresented (McCarron et al. 2022). As new H3N2 epidemics often originate from East and Southeast Asia and burn out in North America and Europe (Bedford et al. 2015), models that do not account for this geographic bias are more likely to incorrectly predict the success of lower fitness variants circulating in overrepresented regions and miss higher fitness variants emerging from underrepresented regions. Additionally, the number of H3N2 HA sequences per year in the GISAID EpiFlu database has increased consistently since 2010, creating a temporal bias where any given season a model forecasts to will have more sequences available than the season from which forecasts occur. The model we used in this study does not explicitly account for geographic variability of viral fitness and relies on time-scaled phylogenetic trees which can be computationally costly to infer for large sample sizes. As a result, we needed to evenly sample the diversity of currently circulating viruses to produce unbiased trees in a reasonable amount of time. Models that could estimate viral fitness per geographic region without inferring trees could use more available sequence data and reduce the uncertainty in current and future clade frequencies."

We also added a brief explanation of our subsampling method to the corresponding section of the methods (lines 411-415). These lines read:

"This sampling approach accounts for known regional biases in sequence availability through time (McCarron et al. 2022) and makes inference of divergence and time trees computationally tractable. This approach also exactly matches our previous study where we first trained the forecast models used in this study (Huddleston et al. 2020), allowing us to reuse those previously trained models."

Although our forecast model is limited to a small proportion of sequences that we evenly sample across regions and time, we agree that we could improve the robustness of our conclusions by repeating our analysis for different subsets of the available data. To assess the stability of the results under alternative sequence sampling strategies, we ran a second replicate of our entire analysis of natural H3N2 populations with three times as many sequences per month (270) than our original replicate. With this approach, we selected between 17% (Europe) and 97% (Southeast Asia) of all sequences per region with an average of 72% and median of 83% (Figure 1C). We compared the effects of realistic interventions for this high-density subsampling analysis with the effects from the original subsampling analysis (Figure 6). We have added the results from this analysis to the main text (lines 313-321) which now reads:

"For natural A/H3N2 populations, the average improvement of the vaccine intervention was 1.1 AAs and the improvement of the surveillance intervention was 0.27 AAs or approximately 25% of the vaccine intervention. The average improvement of both interventions was only slightly less than additive at 1.28 AAs. To verify the robustness of these results, we replicated our entire analysis of A/H3N2 populations using a subsampling scheme that tripled the number of viruses selected per month from 90 to 270 (Figure 1—figure supplement 4C). We found the same pattern with this replication analysis, with average improvements of 0.93 AAs for the vaccine intervention, 0.21 AAs for the surveillance intervention, and 1.14 AAs for both interventions (Figure 6—figure supplement 2)."

We updated our revised manuscript to include the summary of sequences available and subsampled as Figure 1—figure supplement 4 and the effects of interventions with the high-density analysis as Figure 6—figure supplement 2. For reference, we have included Figure 2 showing both the original Figure 6 (original subsampling) and Figure 6—figure supplement 2 (high-density subsampling).

(2) Time-Dependent Effects: Are there time-dependent patterns in the findings? For example, do the effects of submission lag or forecasting horizon differ across time periods, such as [2005-2010, +2010-2015,2015-2018]? This analysis could be particularly interesting given the emergence of co-circulation of clades 3c.2 and 3c.3 around 2012, which marked a shift to less "linear" evolutionary patterns over many years in influenza A/H3N2. 

This is an interesting question that we overlooked by focusing on the broader trends in the predictability of A/H3N2 evolution. The effects of realistic interventions that we report in Figure 6 span future timepoints of 2012-04-01 to 2019-10-01. Since H1N1pdm emerged in 2009 and 3c3 started cocirculating with 3c2 in 2012, we can't inspect effects for the specific epochs mentioned above. However, there have been many periods during this time span where the number of cocirculating clades varied in ways that could affect forecast accuracy. The streamgraph, Author response image 1, shows the variation in clade frequencies from the "full tree" that we used to define clades for A/H3N2 populations.

Author response image 1.

Streamgraph of clade frequencies for A/H3N2 populations demonstrating variability of clade cocirculation through time.

We might expect that forecasting models would struggle to accurately predict future timepoints with higher clade diversity, since much of that diversity would not have existed at the time of the forecast. We might also expect faster surveillance to improve our ability to detect that future variation by detecting those variants at low frequency instead of missing them completely.

To test this hypothesis, we calculated the Shannon entropy of clade frequencies per future timepoint represented in Figure 6 (under no submission lag) and plotted the change in optimal distance to the predicted future by the entropy per timepoint. If there was an effect of future clade complexity on forecast accuracy, we expected greater improvements from interventions to be associated with higher future entropy.

There was a trend for some of the greatest improvements per intervention to occur at higher future clade entropy timepoints, but we didn’t find a strong relationship between clade entropy and improvement in forecast accuracy by any intervention (Figure 4). The highest correlation was for improved surveillance (Pearson r=0.24).

We have added this figure to the revised manuscript as Figure 6—figure supplement 3 and updated the results (lines 321-323) to reflect the patterns we described above. The updated results (which partially includes our response to the next reviewer comment) read:

"These effects of realistic interventions appeared consistent across the range of genetic diversity at future timepoints (Figure 6—figure supplement 3) and for future seasons occurring in both Northern and Southern Hemispheres (Figure 6—figure supplement 4)."

(3) Hemisphere-Specific Forecasting: Do submission lags or forecasting horizons show different performance when predicting Northern versus Southern Hemisphere viral populations? Exploring this distinction could add significant value to the analysis, given the seasonal differences in influenza circulation.

Similar to the question above, we can replot the improvements in optimal distances to the future for the realistic interventions, grouping values by the hemisphere that has an active season in each future timepoint. Much like we expected forecasts to be less accurate when predicting into a highly diverse season, we might also expect forecasts to be less accurate when predicting into a season for a more densely populated hemisphere. Specifically, we expected that realistic interventions would improve forecast accuracy more for Northern Hemisphere seasons than Southern Hemisphere seasons. For this analysis, we labeled future timepoints that occurred in October or January as "Northern" and those that occurred in April or July as "Southern". We plotted effects of interventions on optimal distances to the future by intervention and hemisphere.

In contrast to our original expectation, we found a slightly higher median improvement for the Southern Hemisphere seasons under both of the interventions that improved the vaccine timeline (Figure 5). The median improvement for the combined intervention was 1.42 AAs in the Southern Hemisphere and 0.93 AAs in the Northern Hemisphere. Similarly, the improvement with the "improved vaccine" intervention was 1.03 AAs in the South and 0.74 AAs in the North. However, the range of improvements per intervention was greater for the Northern Hemisphere across all interventions. The median increase in forecast accuracy was similar for both hemispheres in the improved surveillance intervention, with a single Northern Hemisphere season showing an unusually greater improvement that was also associated with higher clade entropy (Figure 4). These results suggest that both an improved vaccine development timeline and more timely sequence submissions would most improve forecast accuracy for Southern Hemisphere seasons compared to Northern Hemisphere seasons.

We have added this figure to the revised manuscript as Figure 6—figure supplement 4 and updated the results (lines 321-326) to reflect the patterns we described above. The new lines in the results read:

"These effects of realistic interventions appeared consistent across the range of genetic diversity at future timepoints (Figure 6—figure supplement 3) and for future seasons occurring in both Northern and Southern Hemispheres (Figure 6—figure supplement 4). We noted a slightly greater median improvement in forecast accuracy associated with both improved vaccine interventions for the Southern Hemisphere seasons (1.03 and 1.42 AAs) compared to the Northern Hemisphere seasons (0.74 and 0.93 AAs)."

(4) Antigenic Sites and Submission Delays: It would be interesting to investigate whether incorporating antigenic site information in the distance metric amplifies or diminishes the observed effects of submission delays. Such an analysis could provide a first glance at how antigenic evolution interacts with forecasting timelines. 

This would be an interesting area to explore. One hypothesis along these lines would be that if 1) viruses with more substitutions at antigenic sites are more likely to represent the future population and 2) viruses with more antigenic substitutions originate in specific geographic locations and 3) submissions of sequences for those viruses are more likely to be lagged due to their geographic origin, then 4) decreasing submission lags should improve our forecasting accuracy by detecting antigenically-important sequences earlier. If there is not a direct link between viruses that are more likely to represent the future and higher submission lags, we would not expect to see any additional effect of reducing submission lags for antigenic sites. Based on our work in Huddleston et al. 2020, it is also not clear that assumption 1 above is consistently true, since the specific antigenic sites associated with high fitness change over time. In that earlier work, we found that models based on these antigenic (or "epitope") sites could only accurately predict the future when the relevant sites for viral success were known in advance. This result was shown by our "oracle" model which accurately predicted the future during the model validation period when it knew which sites were associated with success and failed to predict the future in the test period when the relevant sites for success had changed (Figure 6).

To test the hypothesis above, we would need sequences to have submission lags that reflect their geographic origin. For this current study, we intentionally decoupled submission lags from geographic origin to allow inclusion of historical A/H3N2 HA sequences that were originally submitted as part of scientific publications and not as part of modern routine surveillance. As a result, the original submission dates for many sequences are unrealistically lagged compared to surveillance sequences.

(5) Incorporation of Phenotypic Data: The authors should provide a rationale for their choice of a genetic-information-only approach, rather than a model that integrates phenotypic data. Previous studies, such as Huddleston et al. (2020, eLife), demonstrate that models combining genetic and phenotypic data improve forecasts of seasonal influenza A/H3N2 evolution. It would be interesting to probe the here observed effects in a more recent model.

The primary goal of this study was not to test methodological improvements to forecasting models but to test the effects of realistic public health policy changes that could alter forecast horizons and sequence availability. Most influenza collaborating centers use a "sequence-first" approach where they sequence viral isolates first and use those sequences to prioritize viruses for phenotypic characterization (Hampson et al. 2017). The additional lag in availability of phenotypic data means that a forecasting model based on genetic and phenotypic data will necessarily have a greater lag in data availability than a model based on genetic data only. Since the policy changes we're testing in this study only affect the availability of sequence data and not phenotypic data, we chose to test the relative effects of policy changes on sequence-based forecasting models.

We have updated the abstract (lines 18-26 and 30-32), introduction (lines 87-88), and discussion (lines 332-334) to emphasize the focus of this study on effects of policy changes. The updated abstract lines read as follows with new content in bold:

"Despite continued methodological improvements to long-term forecasting models, these constraints of a 12-month forecast horizon and 3-month average submission lags impose an upper bound on any model's accuracy. The global response to the SARS-CoV-2 pandemic revealed that the adoption of modern vaccine technology like mRNA vaccines can reduce how far we need to forecast into the future to 6 months or less and that expanded support for sequencing can reduce submission lags to GISAID to 1 month on average. To determine whether these public health policy changes could improve long-term forecasts for seasonal influenza, we quantified the effects of reducing forecast horizons and submission lags on the accuracy of forecasts for A/H3N2 populations. We found that reducing forecast horizons from 12 months to 6 or 3 months reduced average absolute forecasting errors to 25% and 50% of the 12-month average, respectively. Reducing submission lags provided little improvement to forecasting accuracy but decreased the uncertainty in current clade frequencies by 50%. These results show the potential to substantially improve the accuracy of existing influenza forecasting models through the public health policy changes of modernizing influenza vaccine development and increasing global sequencing capacity."

The updated introduction now reads:

"These technological and public health policy changes in response to SARS-CoV-2 suggest that we could realistically expect the same outcomes for seasonal influenza."

The updated discussion now reads:

"In this work, we showed that realistic public health policy changes that decrease the time to develop new vaccines for seasonal influenza A/H3N2 and decrease submission lags of HA sequences to public databases could improve our estimates of future and current populations, respectively."

We have also updated the introduction (lines 57-65) and the discussion (lines 345-348) to specifically address the use of sequence-based models instead of sequence-and-phenotype models. The updated introduction now reads:

"For this reason, the decision process is partially informed by computational models that attempt to predict the genetic composition of seasonal influenza populations 12 months in the future (Morris et al. 2018). The earliest of these models predicted future influenza populations from HA sequences alone (Luksza and Lassig 2014, Neher et al. 2014, Steinbruck et al. 2014). Recent models include phenotypic data from serological experiments (Morris et al. 2018, Huddleston et al. 2020, Meijers et al. 2023, Meijers et al. 2025). Since most serological experiments occur after genetic sequencing (Hampson et al. 2017) and all forecasting models depend on HA sequences to determine the viruses circulating at the time of a forecast, sequence availability is the initial limiting factor for any influenza forecasts."

The updated discussion now reads:

"Since all models to date rely on currently available HA sequences to determine the clades to be forecasted, we expect that decreasing forecast horizons and submission lags will have similar relative effect sizes across all forecasting models including those that integrate phenotypic and genetic data."

Reviewer #2 (Public review): 

Summary: 

The authors have examined the effects of two parameters that could improve their clade forecasting predictions for A(H3N2) seasonal influenza viruses based solely on analysis of haemagglutinin gene sequences deposited on the GISAID Epiflu database. Sequences were analysed from viruses collected between April 1, 2005 and October 1, 2019. The parameters they investigated were various lag periods (0, 1, 3 months) for sequences to be deposited in GISAID from the time the viruses were sequenced. The second parameter was the time the forecast was accurate over projecting forward (for 3,6,9,12 months). Their conclusion (not surprisingly) was that "the single most valuable intervention we could make to improve forecast accuracy would be to reduce the forecast horizon to 6 months or less through more rapid vaccine development". This is not practical using conventional influenza vaccine production and regulatory procedures. Nevertheless, this study does identify some practical steps that could improve the accuracy and utility of forecasting such as a few suggested modifications by the authors such as "..... changing the start and end times of our long-term forecasts. We could change our forecasting target from the middle of the next season to the beginning of the season, reducing the forecast horizon from 12 to 9 months.' 

Strengths: 

The authors are very familiar with the type of forecasting tools used in this analysis (LBI and mutational load models) and the processes used currently for influenza vaccine virus selection by the WHO committees having participated in a number of WHO Influenza Vaccine Consultation meetings for both the Southern and Northern Hemispheres. 

Weaknesses: 

The conclusion of limiting the forecasting to 6 months would only be achievable from the current influenza vaccine production platforms with mRNA. However, there are no currently approved mRNA influenza vaccines, and mRNA influenza vaccines have also yet to demonstrate their real-world efficacy, longevity, and cost-effectiveness and therefore are only a potential platform for a future influenza vaccine. Hence other avenues to improve the forecasting should be investigated. 

We recognize that there are no approved mRNA influenza vaccines right now. However, multiple mRNA vaccines have completed phase 3 trials indicating that these vaccines could realistically become available in the next few years. A primary goal of our study was to quantify the effects of switching to a vaccine platform with a shorter timeline than the status quo. Our results should further motivate the adoption of any modern vaccine platform that can produce safe and effective vaccines more quickly than the egg-passaged standard. We have updated the introduction (lines 88-91) to note the mRNA vaccines that have completed phase 3 trials. The new sentence in the introduction reads:

"Work on mRNA vaccines for influenza viruses dates back over a decade (Petsch et al. 2012, Brazzoli et al. 2016, Pardi et al. 2018, Feldman et al. 2019), and multiple vaccines have completed phase 3 trials by early 2025 (Soens et al. 2025, Pfizer 2022)."

While it is inevitable that more influenza HA sequences will become available over time a better understanding of where new influenza variants emerge would enable a higher weighting to be used for those countries rather than giving an equal weighting to all HA sequences. 

This is definitely an important point to consider. The best estimates to date (Russell et al. 2008, Bedford et al. 2015) suggest that most successful variants emerge from East or Southeast Asia. In contrast, most available HA sequence data comes from Europe and North America (Figure 1A). Our subsampling method explicitly tries to address this regional bias in data availability by evenly sampling sequences from 10 different regions including four distinct East Asian regions (China, Japan/Korea, South Asia, and Southeast Asia). Instead of weighting all HA sequences equally, this sampling approach ensures that HA sequences from important distinct regions appear in our analysis.

We have updated our methods (lines 411-423) to better describe the motivation of our subsampling approach and proportions of regions sampled with our original approach (90 viruses per month) and a second high-density sampling approach (270 viruses per month). These new lines read:

"This sampling approach accounts for known regional biases in sequence availability through time (McCarron et al. 2022) and makes inference of divergence and time trees computationally tractable. This approach also exactly matches our previous study where we first trained the forecast models used in this study (Huddleston et al. 2020), allowing us to reuse those previously trained models. With this subsampling approach, we selected between 7% (Europe) and 91% (Southeast Asia) of all available sequences per region across the entire study period with an average of 50% and median of 52% across all 10 regions (Figure 1—figure Supplement 4). To verify the reproducibility and robustness of our results, we reran the full forecasting analysis with a high-density subsampling scheme that selected 270 sequences per month with the same even sampling across regions and time as the original scheme. With this approach, we selected between 17% (Europe) and 97% (Southeast Asia) of all available sequences per region with an average of 72% sampled and a median of 83% (Figure 1—figure Supplement 4C)."

We added Figure 1—figure Supplement 4 to document the regional biases in sequence availability and the proportions of sequences we selected per region and year.

Also, other groups are considering neuraminidase sequences and how these contribute to the emergence of new or potentially predominant clades.

We agree that accounting for antigenic evolution of neuraminidase is a promising path to improving forecasting models. We chose to focus on hemagglutinin sequences for several reasons, though. First, hemagglutinin is the only protein whose content is standardized in the influenza vaccine (Yamayoshi and Kawaoka 2019), so vaccine strain selection does not account for a specific neuraminidase. Additionally, as we noted in response to Reviewer 1 above, the goal of this study was to test effects of counterfactual scenarios with realistic public health interventions and not to introduce methodological improvements to forecasting models like the inclusion of neuraminidase sequences.

We have updated the introduction to provide the additional context about hemagglutinin's outsized role in the current vaccine development process (lines 40-44):

"The dominant influenza vaccine platform is an inactivated whole virus vaccine grown in chicken eggs (Wong and Webby, 2013) which takes 6 to 8 months to develop, contains a single representative vaccine virus per seasonal influenza subtype including A/H1N1pdm, A/H3N2, and B/Victoria (Morris et al., 2018), and for which only the HA protein content is standardized (Yamayoshi and Kawaoka, 2019)."

We have updated the abstract (lines 18-26 and 30-32), introduction (lines 87-88), and discussion (lines 332-334) to emphasize our goal of testing effects of public health policy changes on forecasting accuracy rather than methodological changes. The updated abstract lines read as follows with new content in bold:

"Despite continued methodological improvements to long-term forecasting models, these constraints of a 12-month forecast horizon and 3-month average submission lags impose an upper bound on any model's accuracy. The global response to the SARS-CoV-2 pandemic revealed that the adoption of modern vaccine technology like mRNA vaccines can reduce how far we need to forecast into the future to 6 months or less and that expanded support for sequencing can reduce submission lags to GISAID to 1 month on average. To determine whether these public health policy changes could improve long-term forecasts for seasonal influenza, we quantified the effects of reducing forecast horizons and submission lags on the accuracy of forecasts for A/H3N2 populations. We found that reducing forecast horizons from 12 months to 6 or 3 months reduced average absolute forecasting errors to 25% and 50% of the 12-month average, respectively. Reducing submission lags provided little improvement to forecasting accuracy but decreased the uncertainty in current clade frequencies by 50%. These results show the potential to substantially improve the accuracy of existing influenza forecasting models through the public health policy changes of modernizing influenza vaccine development and increasing global sequencing capacity."

The updated introduction now reads:

"These technological and public health policy changes in response to SARS-CoV-2 suggest that we could realistically expect the same outcomes for seasonal influenza."

The updated discussion now reads:

"In this work, we showed that realistic public health policy changes that decrease the time to develop new vaccines for seasonal influenza A/H3N2 and decrease submission lags of HA sequences to public databases could improve our estimates of future and current populations, respectively."

Figure 1a. I don't understand why the orange dot 1-month lag appears to be on the same scale as the 3-month/ideal timeline. 

We apologize for the confusion with this figure. Our original goal was to show how the two factors in our study design (forecast horizons and sequence submission lags) interact with each other by showing an example of 3-month forecasts made with no lag (blue), ideal lag (orange), and realistic lag (green). To clarify these two factors, we have removed the two lines at the 3-month forecast horizon for the ideal and realistic lags and have updated the caption to reflect this simplification. The new figure looks like this:

The authors should expand on the line "The finding of even a few sequences with a potentially important antigenic substitution could be enough to inform choices of vaccine candidate viruses." While people familiar with the VCM process will understand the implications of this statement the average reader will not fully understand the implications of this statement. Not only will it inform but it will allow the early production of vaccine seeds and reassortants that can be used in conventional vaccine production platforms if these early predictions were consolidated by the time of the VCM. This is because of the time it takes to isolate viruses, make reassortants and test them - usually a month or more is needed at a minimum. 

Thank you for pointing out this unclear section of the discussion. We have rewritten this section, dropping the mention of prospective measurements of antigenic escape which now feels off-topic and moving the point about early detection of important antigenic substitutions to immediately follow the description of the candidate vaccine development timeline. This new placement should clarify the direct causal relationship between early detection and better choices of vaccine candidates. The original discussion section read:

"For example, virologists must choose potential vaccine candidates from the diversity of circulating clades well in advance of vaccine composition meetings to have time to grow virus in cells and eggs and measure antigenic drift with serological assays (Morris et al., 2018; Loes et al., 2024). Similarly, prospective measurements of antigenic escape from human sera allow researchers to predict substitutions that could escape global immunity (Lee et al., 2019; Greaney et al., 2022; Welsh et al., 2023). The finding of even a few sequences with a potentially important antigenic substitution could be enough to inform choices of vaccine candidate viruses."

The new section (lines 386-391) now reads:

"For example, virologists must choose potential vaccine candidates from the diversity of circulating clades months in advance of vaccine composition meetings to have time to grow virus in cells and eggs and measure antigenic drift with serological assays (Morris et al. 2018; Loes et al. 2024). Earlier detection of viral sequences with important antigenic substitutions could determine whether corresponding vaccine candidates are available at the time of the vaccine selection meeting or not."

A few lines in the discussion on current approaches being used to add to just the HA sequence analysis of H3N2 viruses (ferret/human sera reactivity) would be welcome.

We have added the following sentences to the last paragraph (lines 391-397) to note recent methodological advances in estimating influenza fitness and the relationship these advances have to timely genomic surveillance.

"Newer methods to estimate influenza fitness use experimental measurements of viral escape from human sera (Lee et al., 2019; Welsh et al., 2024; Meijers et al., 2025; Kikawa et al., 2025), measurements of viral stability and cell entry (Yu et al., 2025), or sequences from neuraminidase, the other primary surface protein associated with antigenic drift (Meijers et al., 2025). These methodological improvements all depend fundamentally on timely genomic surveillance efforts and the GISAID EpiFlu database to identify relevant influenza variants to include in their experiments."

This statement is really important, because it clarifies what does a multicultural literacy means. Each culture should be respected and valued, and this helps students to create a sense that all kinds of people around them should be respected and valued, which fosters students to be kind and with empathy. They won't simply judge a person based on their outlook and they won't live with prejudice or discrimination toward a minor group. This is what schools should teach students: not sorely academic knowledge, but also the way to behave and think inclusively and multi-culturally, basically respect other.

Author response:

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

Reviewer #1 (Public review):

Summary:

van der Linden et al. report on the development of a new green-fluorescent sensor for calcium, following a novel rational design strategy based on the modification of the cyan-emissive sensor mTq2-CaFLITS. Through a mutational strategy similar to the one used to convert EGFP into EYFP, coupled with optimization of strategic amino acids located in proximity of the chromophore, they identify a novel sensor, GCaFLITS. Through a careful characterization of the photophysical properties in vitro and the expression level in cell cultures, the authors demonstrate that G-CaFLITS combines a large lifetime response with a good brightness in both the bound and unbound states. This relative independence of the brightness on calcium binding, compared with existing sensors that often feature at least one very dim form, is an interesting feature of this new type of sensors, which allows for a more robust usage in fluorescence lifetime imaging. Furthermore, the authors evaluate the performance of G-CaFLITS in different subcellular compartments and under two-photon excitation in Drosophila. While the data appears robust and the characterization thorough, the interpretation of the results in some cases appears less solid, and alternative explanations cannot be excluded.

Strengths:

The approach is innovative and extends the excellent photophysical properties of the mTq2-based to more red-shifted variants. While the spectral shift might appear relatively minor, as the authors correctly point out, it has interesting practical implications, such as the possibility to perform FLIM imaging of calcium using widely available laser wavelengths, or to reduce background autofluorescence, which can be a significant problem in FLIM.

The screening was simple and rationally guided, demonstrating that, at least for this class of sensors, a careful choice of screening positions is an excellent strategy to obtain variants with large FLIM responses without the need of high-throughput screening.

The description of the methodologies is very complete and accurate, greatly facilitating the reproduction of the results by others, or the adoption of similar methods. This is particularly true for the description of the experimental conditions for optimal screening of sensor variants in lysed bacterial cultures.

The photophysical characterization is very thorough and complete, and the vast amount of data reported in the supporting information is a valuable reference for other researchers willing to attempt a similar sensor development strategy. Particularly well done is the characterization of the brightness in cells, and the comparison on multiple parameters with existing sensors.

Overall, G-CaFLITS displays excellent properties for a FLIM sensor: very large lifetime change, bright emission in both forms and independence from pH in the physiological range.

Weaknesses:

The paper demonstrates the application of G-CaFLITS in various cellular subcompartments without providing direct evidence that the sensor's response is not affected by the targeting. Showing at least that the lifetime values in the saturated state are similar in all compartments would improve the robustness of the claims.

In some cases, the interpretation of the results is not fully convincing, leaving alternative hypotheses as a possibility. This is particularly the case for the claim of the origin of the strongly reduced brightness of G-CaFLITS in Drosophila. The explanation of the intensity changes of G-CaFLITS also shows some inconsistency with the basic photophysical characterization.

While the claims generally appear robust, in some cases they are conveyed with a lack of precision. Several sentences in the introduction and discussion could be improved in this regard. Furthermore, the use of the signal-to-noise ratio as a means of comparison between sensors appears to be imprecise, since it is dependent on experimental conditions.

We thank the reviewer for a thorough evaluation and for suggestions to improve our manuscript. We are happy with the recognition of the strengths of this work. The list with weaknesses has several valid points which will be addressed in a point-by-point reply and a revision.

Reviewer #2 (Public review):

Summary:

Van der Linden et al. describe the addition of the T203Y mutation to their previously described fluorescence lifetime calcium sensor Tq-Ca-FLITS to shift the fluorescence to green emission. This mutation was previously described to similarly red-shift the emission of green and cyan FPs. Tq-Ca-FLITS_T203Y behaves as a green calcium sensor with opposite polarity compared with the original (lifetime goes down upon calcium binding instead of up). They then screen a library of variants at

two linker positions and identify a variant with slightly improved lifetime contrast (TqCa-FLITS_T203Y_V27A_N271D, named G-Ca-FLITS). The authors then characterize the performance of G-Ca-FLITS relative to Tq-Ca-FLITS in purified protein samples, in cultured cells, and in the brains of fruit flies.

Strengths:

This work is interesting as it extends their prior work generating a calcium indicator scaffold for fluorescent protein-based lifetime sensors with large contrast at a single wavelength, which is already being adopted by the community for production of other FLIM biosensors. This work effectively extends that from cyan to green fluorescence. While the cyan and green sensors are not spectrally distinct enough (~20-30nm shift) to easily multiplex together, it at least shifts the spectra to wavelengths that are more commonly available on commercial microscopes.

The observations of organellar calcium concentrations were interesting and could potentially lead to new biological insight if followed up.

Weaknesses:

(1) The new G-Ca-FLITS sensor doesn't appear to be significantly improved in performance over the original Tq-Ca-FLITS, no specific benefits are demonstrated.

(2) Although it was admirable to attempt in vivo demonstration in Drosophila with these sensors, depolarizing the whole brain with high potassium is not a terribly interesting or physiological stimulus and doesn't really highlight any advantages of their sensors; G-Ca-FLITS appears to be quite dim in the flies.

We thank the reviewer for a thorough evaluation and for suggestions to improve our manuscript. Although the spectral shift of the green variant is modest, we have added new data (figure 7) to the manuscript that demonstrates multiplex imaging of G-Ca-FLITS and Tq-Ca-FLITS.

As for the listed weaknesses we respond here:

(1) Although we agree that the performance in terms of dynamic range is not improved, the advantage of the green sensor over the cyan version is that the brightness is high in both states.

(2) We agree that the performance of G-Ca-FLITS is disappointing in Drosophila. We feel that this is important data to report, and it makes it clear that Tq-Ca-FLITS is a better choice for this system. Depolarization of the entire brain was done to measure the maximal lifetime contrast.

Reviewer #3 (Public review):

Summary:

The authours present a variant of a previously described fluorescence lifetime sensor for calcium. Much of the manuscript describes the process of developing appropriate assays for screening sensor variants, and thorough characterization of those variants (inherent fluorescence characteristics, response to calcium and pH, comparisons to other calcium sensors). The final two figures show how the sensor performs in cultured cells and in vivo drosophila brains.

Strengths:

The work is presented clearly and the conclusion (this is a new calcium sensor that could be useful in some circumstances) is supported by the data.

Weaknesses:

There are probably few circumstances where this sensor would facilitate experiments (calcium measurements) that other sensors would prove insufficient.

We thank the reviewer for the evaluation of our manuscript. As for the indicated weakness, we agree that the main application of genetically encoded calcium biosensors is to measure qualitative changes in calcium. However, it can be argued that due to a lack of tools the absolute quantification has been very challenging. Now, thanks to large contrast lifetime biosensors the quantitative measurements are simplified, there are new opportunities, and the probe reported here is an improvement over existing probes as it remains bright in both states, further improving quantitative calcium measurements.

Reviewer #1 (Recommendations for the authors):

While the science in the paper appears solid, the methods well grounded and excellently documented, the manuscript would benefit from a revision to improve the clarity of the exposition. In particular:

Part of the introduction appears like a patchwork of information with poor logical consequentiality. The authors rapidly pass from the impact of brightness on FLIM accuracy, to mitochondrial calcium in pathology, to the importance of the sensor's affinity, to a sentence on sensor's kinetics, to fluorescent dyes and bioluminescence, to conclude that sensors should be stable at mitochondrial pH. I highly recommend rewriting this part.

We thank the referee for the comment and we have adjusted to introduction to better connect the parts and increase the logic. The updated introduction addresses all the feedback by the reviewers on different aspects of the introductory text, and we have removed the section on dyes and bioluminescence. We feel that the introduction is better structured now.

The reference to particular amino acid positions would greatly benefit from including images of the protein structure in which the positions are highlighted, similar to what the same authors do in their fluorescent protein development papers. While in the case of sensors a crystal structure might be lacking, highlighting the positions with respect to an AlphaFold-generated structure or the structure of mTq2 might still be helpful.

We appreciate this remark and we have added a sequence alignment of the FLITS probes to supplemental Figure S4. This shows the residues with number, and we have also highlighted the different domains, linkers and mutations. We think that this linear representation works better than a 3D structure (one issue is that alphafold fails to display the chromophore and it has usually poor confidence for linker residues).

The use of SNR, as defined by the authors (mean of the lifetime divided by standard deviation) appears a poorly suited parameter to compare sensors, as it depends on the total number of collected photons and on the strength of the algorithms used to retrieve the lifetime value. In an extreme example, if one would collect uniform images with millions of photons per pixel, most likely SNR would be extremely good for all sensors in all states, irrespective of the fact that some states are dimmer (within reasonable limits). On the other hand, if the same comparison would be performed at a level of thousands or hundreds of photons per pixel, the effect of different brightness on the SNR would be much more dramatic. While in general I fully agree with the core concept of the paper, i.e. that avoiding low-brightness forms leads more easily to experiments with higher SNR, I would suggest to stick to comparing the sensors in terms of brightness and refer to SNR (if needed) only when describing the consequences on measurements.

The reviewer is right that in absolute terms the SNR is not meaningful. In addition to acquisition time, it depends on expression levels. Yet, it is possible to compare the change in SNR between the apo- and saturated states, and that is what is shown in figure 5. We have added text to better explain that the change in SNR is relevant here:

“The absolute SNR is not relevant here, as it will depend on the expression level and acquisition time. But since we have measured the two extremes in the same cells, we can evaluate how the SNR changes between these states for each separate probe”

Some statements from the authors or aspects of the paper appear problematic:

(1) "Additionally, the fluorescence of most sensors is a non-linear function of calcium concentration, usually with Hill coefficients between 2 and 3. This is ideal when the probe is used as a binary detector for increases in Ca2+ concentrations, but it makes robust quantification of low, or even intermediate, calcium concentrations extremely challenging."

To the best of my knowledge, for all sensors the fluorescence response is a nonlinear function of calcium concentrations. If the authors have specific examples in mind in which this is not true, they should cite them specifically. Furthermore, the Hill coefficient defines the range of concentrations in which the sensor operates, while the fact that "low concentrations" might be hard to detect depends only on the dim fluorescence of some sensors in the unbound form.

We agree with the reviewer that this part is not clearly written and confusing, as the sentence “Additionally, the fluorescence of most sensors is a non-linear function of calcium concentration, usually with Hill coefficients between 2 and 3” was not relevant in this section and so we removed it. Now it reads:

“Many GECIs harboring a single fluorescent protein (FP), like GCaMPs, are optimized for a large intensity change, and have a (very) dim state when calcium levels are below the KD of the probe (Akerboom et al., 2013; Dana et al., 2019; Shen et al., 2018; Zhang et al., 2023; Zhao et al., 2011). This is ideal when the probe is used as a binary detector for increases in Ca2+ concentrations, but it makes robust quantification of low, or even intermediate, calcium concentrations extremely challenging”

(2) "The affinity of a sensor is of major importance: a low KD can underestimate high concentrations and vice versa."

It is not clear to me why the concentrations would be underestimated, rather than just being less precise. Also, if a calibration curve is plotted in linear scale rather than logarithmic scale, it appears that the precision problem is much more severe near saturation (where low lifetime changes result in large concentration changes) than near zero (where low concentration changes produce large lifetime changes).

We agree that this could be better explained, what we meant to say that concentrations that are ~10x lower or higher than the KD cannot be precisely measured. See also our reply to the next comment.

(3) "Differences can also arise due to the method of calibration, i.e. when the absolute minimum and maximum signal are not reached in the calibration procedure (Fernandez-Sanz et al., 2019)."

Unless better explained, this appears obvious and not worth mentioning.

What may be obvious to the reviewer (and to us) may not be obvious to the reader, and that’s why this is included. To make it clearer we rephrased this part as a list of four items:

“Accurate determination of the affinity of a sensor is important and there are several issues that need to be considered during the calibration and the measurements: (i) the concentrations can only be measured with sufficient precision when it is in the range between 10x K_D and 1/10x K_D, (ii) the calibration is only valid when the two extremes are reached during the calibration procedure (Fernandez-Sanz et al., 2019), (iii) the sensor’s kinetics should be sufficiently fast enough to be able to track the calcium changes, and (iv) the biosensor should be compatible with the high mitochondrial pH of 8 (Cano Abad et al., 2004; Llopis et al., 1998).”

(4) In the experiments depicted in Figure 6C the underlying assumption is that the sensor behaves in the same way independently of the compartment to which it is targeted. This is not necessarily the case. It would be valuable to see the plots of Figure 6C and D discussed in terms of lifetime. Is the saturating lifetime value the same in all compartments?

This is a valid point and we have now included a plot with the actual lifetime data for each of the organelles (figure S15). 

We have also added text to discuss this point: “We note that the underlying assumption of the quantification of organellar calcium concentrations is that the lifetime contrast is the same. This is broadly true for most of the measurements (Figure S15). Yet, there are also differences. It is currently unclear whether the discrepancies are due to differences in the physicochemical properties of the compartments, or whether there is a technical reason (the efficiency of ionomycin for saturating the biosensor in the different compartments is unknown, as far as we know). This is something that is worth revisiting. A related issue that deserves attention is the level of agreement between in vitro and in vivo calibrations.”

(5) A similar problem arises for the observation of different calcium levels in peripheral mitochondria. In figure S11b, the values of the two lifetime components of a biexponential fit are displayed. Both the long and short components seem to be different. This is an interesting observation, as in an ideal sensor (in which the "long lifetime conformation" is the same whether the sensor is bound to the analyte or not, and similarly for the short lifetime one) those values should be identical. While it is entirely possible that this is not the case for G-CaFLITS, since the authors have conducted a calibration experiment using time-domain FLIM, could they show the behavior of the lifetimes and preamplitudes? Are the trends consistent with their interpretation of a different calcium level in the two mitochondrial populations?

We have analyzed the calibration data from TCSPC experiments done with the Leica Stellaris. From these data (acquired at high photon counts as it is purified protein in solution), we infer that both the short and long lifetime do change as a function of calcium concentration. In particular the long lifetime shows a substantial change, which we cannot explain at this moment. We agree that this is interesting and may potentially give insight in the conformation changes that give rise to the lifetime change.

The lifetime data of the mitochondria has been acquired with a different FLIM setup, but the trend is consistent, both the long and short lifetime decrease in the peripheral mitochondria that have a higher calcium concentration.

Author response image 1

(6) "The lifetime response of Tq-Ca-FLITS and the ΔF/F response of jGCaMP7f resembled each other, with both signals gradually increasing over the span of 3-4 minutes after we increased external [K+]; the two signals then hit a plateau for ~1 min, followed by a return to baseline and often additional plateaus (Figure 8B-C). By comparison, G-Ca-FLITS responses were more variable, typically exhibiting a smaller ramping phase and seconds-long spikes of activity rather than minutes-long plateaus (Figure 8C)."

This statement does not appear fully consistent with the data in Figure 8. While in figure 8B it looks like GCaMP and mTq-CaFLITS have very similar profiles, these curves come from one single experiment out of a very variable dataset (see Figure 8C). If one would for example choose the second curve of GCaMP in Figure 8C, it would look very similar to the response of G-CaFLITS in figure 8B, and the argument would be reversed. How do the averages look like?

Indeed, the dynamics of the responses are very variable and we do not want to draw attention to these differences in the dynamics, so we have removed the comparison. Instead, the difference in intensity change and lifetime contrast are of importance here. To answer the question of the reviewer, we have added a new panel (D) which shows the average responses for each of the GECIs.  

(7) "Although the calibration is equipment independent under ideal conditions, and only needs to be performed once, we prefer to repeat the calibration for different setups to account for differences in temperature or pulse frequency."

While I generally agree with the statement, it is imprecise. A change in temperature is generally expected to affect the Kd, so rather than "preferring to repeat", it is a requirement for accurate quantification at different concentrations. I am not sure I understand what the pulse frequency is in this context, and how it affects the Kd.

We thank the referee for pointing out that our text is imprecise and confusing. What we meant to say is that we see differences between different set-ups and we have clarified this by changing the text. We have also added that it is “necessary” to repeat the calibration:

“Although the calibration is equipment independent under ideal conditions, and only needs to be performed once, we do see differences between different set-ups. Therefore, it is necessary to repeat the calibration for different set-ups.”

(8) "A recent effort to generate a green emitting lifetime biosensor used a GFP variant as a template (Koveal et al., 2022), and the resulting biosensor was pH sensitive in the physiological range. On the other hand, biosensors with a CFP-like chromophore are largely pH insensitive (van der Linden et al., 2021; Zhong et al., 2024)."

The dismissal of the use of T-Sapphire as a pH independent template is inaccurate. The same group has previously reported other sensors (SweetieTS for glucose and Peredox for redox ratio) that are not pH sensitive. Furthermore, in Koveal et al. also many of the mTq2-based variants showed a pH response, suggesting that the pHdependence for the Lilac sensor might be more complex. Still, G-CaFLITS present advantages in terms of the possibility to excite at longer wavelengths, which could be mentioned instead.

We only want to make the point that adding the T203Y mutation to Turquoise-based lifetime biosensors may be a good approach for generating pH insensitive green biosensors. There is no point in dismissing other green biosensors and we have changed the text to: “Since biosensors with a CFP-like chromophore are largely pH insensitive (van der Linden et al., 2021; Zhong et al., 2024), and we show here that the pH independence is retained for the Green Ca-FLITS, we expect that adding the T203Y mutation to a cyan sensor is a good approach for generating pH-insensitive green lifetime-based sensors.”

(9) "Usually, a higher QY results in a higher intensity; however, in G-Ca-FLITS the open state has a differential shaped excitation spectrum which leads to a decreased intensity. These effects combined have resulted in a sensor where the two different states have a similar intensity despite displaying a large QY and lifetime contrast."

This statement does not seem to reflect the excitation spectra of Figure 1. If this explanation would be true, wouldn't there be an isoemissive point in the excitation spectrum (i.e. an excitation wavelength at which emission intensity would not change)?

The excitation spectra in figure 1 are not ideal for the interpretation as these are not normalized. The normalized spectra are shown in figure S10, but for clarity we show the normalized spectra here below as well. For the FD-FLIM experiments we used a 446 nm LED that excites the calcium bound state more efficiently. Therefore, the lower brightness due to a lower QY of the calcium bound state is compensated by increased excitation. So the limited change in intensity is excitation wavelength dependent. We have added a sentence to the discussion to stress this:

“The smallest intensity change is obtained when the calcium-bound state is preferably excited (i.e. near 450 nm) and the effect is less pronounced when the probe is excited near its peak at 474 nm”   

(10) "We evaluated the use of Tq-Ca-FLITS and G-Ca-FLITS for 2P-FLIM and observed a surprisingly low brightness of the green variant in an intact fly brain. This result is consistent with a study finding that red-shifted fluorescent-protein variants that are much brighter under one-photon excitation are, surprisingly, dimmer than their blue cousins in multi-photon microscopy (Molina et al., 2017). The responses of both probes were in line with their properties in single photon FLIM, but given the low brightness of G-Ca-FLITS under 2-photon excitation, the Tq-Ca-FLITS may be a better choice for 2P-FLIM experiments."

The differences appear strikingly high, and it seems improbable that a reduction in two-photon absorption coefficient might be the sole cause. How can the authors rule out a problem in expression (possibly organism-specific)?

The reviewers are correct that the changes in brightness between G-Ca-FLITS and Tq-Ca-FLITS may arise from changes in expression levels. It is difficult to calibrate for these changes explicitly without a stable reference fluorophore. However, both the G-Ca-FLITS and Tq-Ca-FLITS transgenic flies produced used the same plasmid backbone (the Janelia 20x-UAS-IVS plasmid), landed in the same insertion site (VK00005) of the same genetic background and were crossed to the same Janelia driver line (R60D05-Gal4), so at the level of the transcriptional machinery or genetic regulatory landscape the two lines are probably identical except for the few base pair differences between the G-Ca-FLITS and Tq-Ca-FLITS sequence. But the same level of transcription may not correspond to the same amount of stable protein in the ellipsoid body. So, we cannot rule out any organism-specific problems in expression. To examine the 2P excitation efficiency relative to 1P excitation efficiency, we have measured the fluorescence intensity of purified G-Ca-FLITS and Tq-Ca-FLITS on beads. See also response to reviewer 3 and supplemental figure S14

Suggestions

(1) The underlying assumption of any experiment using a biosensor is that the concentration of the biosensor should be roughly 2 orders of magnitude lower than the concentration of the analyte, otherwise the calibration equations do not hold. When measuring nM concentrations of calcium, this problem can be in principle very significant, as the concentration of the sensor in cells is likely in the low micromolar range. Calcium regulation by the cell should compensate for the problem, and the equations should hold. However, this might not hold true during experimental conditions that would disrupt this tight regulation. It might be a good thing to add a sentence to inform users about the limitations in interpreting calcium concentration data under such conditions.

Good point. We have added this to the discussion: “All calcium indicators also act as buffers, and this limits the accuracy of the absolute measurements, especially for the lower calcium concentrations (Rose et al., 2014), as the expression of the biosensor is usually in the low micromolar range.”

(2) Different methods of lifetime "averaging", such as intensity or amplitude-weighted lifetime in time domain FLIM or phase and modulation in frequency domain might lead to different Kd in the same calibration experiment. This is an underappreciated factor that might lead to errors by users. Since the authors conducted calibrations using both frequency and time-domain, it would be useful to mention this fact and maybe add a table in the Supporting Information with the minima, maxima and Kds calculated using different lifetime averaging methods.

To avoid biases due to fitting we prefer to use the phasor plot, this can be used for both frequency and time-domain methods and we added a sentence to the discussion to highlight this: “We prefer to use the phasor analysis (which can be used for both frequency- and time-domain FLIM), as it makes no assumptions about the underlying decay kinetics.”

(3) The origin of the redshift observed in G-CaFLITS is likely pi-stacking, similar to the EGFP-to-EYFP case. While previous studies suggest that for mTq2 based sensors a change in rigidity would lead to a change in the non-radiative rate, which would result in similar changes in quantum yield and (amplitude-weighted average) lifetime. If pi-stacking plays a role, there could be an additional change in the radiative rate (as suggested also by the change in absorption spectra). Could this play a role in the relation between brightness and lifetime in G-CaFLITS? Given the extensive data collected by the authors, it should be possible to comment on these mechanistical aspects, which would be useful to guide future design.

We do appreciate this suggestion, but we currently do not have the data to answer this question. The inverted response that we observe, solely due to the introduction of the tyrosine is puzzling. Perhaps introduction of the mutation that causes the redshift in other cyan probes will provide more insight.

Reviewer #2 (Recommendations for the authors):

Specific points:

The first section of Results is basically a description of how they chose the lysis conditions for screening in bacteria. I didn't see anything particularly novel or interesting about this, anyone working with protein expression in bacteria likely needs to optimize growth, lysis, purification, etc. This section should be moved to the Methods.

As reviewer 1 lists the thorough documentation of this approach as one of the strengths, we prefer to keep it like this. We see this section as method development, rather than purely a method. When this section would be moved to methods, it remains largely invisible and we think that’s a shame. Readers that are not interested can easily skip this section.

In the Results section Characterization of G-Ca-FLITS, the authors state "Here, the calcium affinity was KD = 339 nM, higher compared to the calibration at 37{degree sign}C. This is in line with the notion that binding strength generally increases with decreasing temperature." However, the opposite appears to be true - at 37C they measured a KD of 209 nM which would represent higher binding strength at higher temperature.

Thanks for catching this, we’ve made a mistake. We rephrase this to “higher compared to the calibration at 37 ˚C. This is unexpected as it not in line with the notion that binding strength generally increases with decreasing temperature.”

In Figure 8c, there should be a visual indicator showing the onset of application of high potassium, as there is in 8b.

This is a good suggestion; a grey box is added to indicates time when high K+ saline was perfused.

Reviewer #3 (Recommendations for the authors):

I think the science of the manuscript is sound and the presentation is logical and clear. I have some stylistic recommendations.

Supp Fig 1: The figure requires a bit of "eyeballing" to decide which conditions are best, and figuring out which spectra matched the final conditions took a little effort. Is there a way to quantify the fluorescence yield to better show why the one set of conditions was chosen? If it was subjective, then at least highlight the final conditions with a box around the spectra, making it a different colour, or something to make it stand out.

Thanks for the comment; we added a green box.

Supp Fig 3: Similar suggestion. Highlight the final variant that was carried forward (T203Y). The subtle differences in spectra are hard to discern when they are presented separately. How would it look if they were plotted all on one graph? Or if each mutant were presented as a point on a graph of Peak Em vs Peak Ex? Would T203Y be in the top right?

We have added a light blue box for reference to make the differences clearer.

Supp Fig 4 & Fig 1: Too much of the graph show the uninteresting tails of the spectra and condenses the interesting part. Plotting from 400 nm to 600 nm would be more informative.

We appreciate the suggestion but disagree. We prefer to show the spectra in its entirety, including the tails. The data will be available so other plots can be made by anyone.

Fig 3a: People who are not experts in lifetime analysis are probably not very familiar with the phase/modulation polar plot. There should be an additional sentence or two in the main text that _briefly_ describes the basis for making the polar plot and the transformation to the fractional saturation plot in 3B. I can't think of a good way to transform Eq 3 from Supp Info into a sentence, but that's what I think is needed to make this transformation clearer.

We appreciate the suggestion and feel that it is well explained here:

"The two extreme values (zero calcium and 39 μM free calcium) are located on different coordinates in the polar plot and all intermediate concentrations are located on a straight line between these two extremes. Based on the position in the polar plot, we determined the fraction of sensor in the calcium-bound state, while considering the intensity contribution of both states"  

Fig 4: The figure is great, and I love the comparison of different calcium sensors. But where is Tq-Ca-FLITS? I get that this is a figure of green calcium sensors, but it would be nice to see Tq-Ca-FLITS in there as well. The G-Ca-FLITS is compared to Tq-Ca-FLITS in Fig 5. Maybe I'm just missing why the bottom panel of Fig 5 cannot be replotted and included in Fig 4.

The point is that we compare all the data with identical filter sets, i.e. for green FPs.using these ex/em settings, the Tq probe would seriously underperform. Note that the data in fig. 5 is not normalized to a reference RFP and can therefore not be compared with data presented in figure 4.

Fig 6: The BOEC data could easily be moved to Supp Figs. It doesn't contribute much relevant info.

We are not keen of moving data to supplemental, as too often the supplemental data is ignored. Moreover, we think that the BOEC data is valuable (as BOEC are primary cells and therefore a good model of a healthy human cell) and deserves a place in the main manuscript.

2P FLIM / Fig 8 / Fig S4: The lack of brightness of G-Ca-FLITS in the 2P FLIM of fruit fly brain could have been predicted with a 2P cross section of the purified protein. If the equipment to perform such measurements is available, it could be incorporated into Fig S4.

Unfortunately, we do not have access to equipment that measures the 2P cross section. As an alternative, we compared the 2P excitation efficiency with 1P excitation efficiency. To this end, we have used beads that were loaded with purified G-Ca-FLITS or Tq-Ca-FLITS. We have evaluated the fluorescence intensity of the beads using 1P (460 nm) and 2P (920 nm) excitation. Although the absolute intensity cannot be compared (the G-Ca-FLITS beads have a lower protein concentration), we can compare the relative intensities when changing from 1P to 2P. The 2P excitation efficiency of G-Ca-FLITS is comparable (if not better) to that of Tq-Ca-FLITS. This excludes the option that the G-Ca-FLITS has poor 2P excitability. We will include this data as figure S12.

We also have added text to the results: “We evaluated the relative brightness of purified Tq-Ca-FLITS and G-Ca-FLITS on beads by either 1-Photon Excitation (1PE) (at 460 nm) or 2-Photon Excitation (2PE) (at 920 nm) and observed a similar brightness between the two modes of excitations (figure S14). This shows that the two probes have similar efficiencies in 2PE and suggest that the low brightness of GCa-FLITS in Drosophila is due to lower expression or poor folding.” and discussion: “The responses of both probes were in line with their properties in single photon FLIM, but given the low brightness of G-Ca-FLITS under 2-photon excitation in Drosphila, the Tq-Ca-FLITS is a better choice in this system. Yet, the brightness of G-Ca-FLITS with 2PE at 920 nm is comparable to Tq-Ca-FLITS, so we expect that 2P-FLIM with G-Ca-FLITS is possible in tissues that express it well.”

Sometimes people say or think because it feels more acceptable since rich people or person who has great influence can handle the negative comment. But for me, even then, it still feels wrong. Criticism is fine, and people with power should be held responsible, but harassment crosses a line. It becomes more about punishing someone instead of having a real conversation or mentioning the problem.

In my perspective, people often feel less bad about crowd harassment when the target is rich, famous, or powerful, because they think those people can handle it because of their social status. But harassment is still wrong. If someone did something harmful, people may feel the harassment is more understandable, but it still doesn’t make it okay.

This occurrence demonstrates the complexities of cancel culture and how it can be ineffective. In instances like this, people often see morally wrong doings occur and want to find ways to create justice for the situation, a noble act. However, sending mass hate or negativity to an individual usually does not result in them adjusting their beliefs or changing their lifestyle. It often results in insincere apologies issued to save face or stop hate. In this example, Dr. Palmer was not truly apologetic for his actions; he just wanted to end the hate coming his way. He did not learn from the experience, as he did the same thing 5 years after his first incident.

synthesizing harmonizes categories

A synthesis is two or more summaries that turns into a thesis.

Not to take away from the quilt, but this statement seems applicable to any medium that is digitized. What is lost when something is digitally archived? What new narratives are gained? Relating this concept to the Rodney footage and the K-town'92 archive, profound connections emerge with an arrangement and description that allows a viewer to choose and make their own narrative. Digitizing and making work accessible to the public creates awareness and allows for people to experience these histories in new ways. But physically experiencing something does seem to create its own unique narrative.The AIDS Memorial Quilt

This signifies the importance of proper oral care

It's fascinating how far his imagination could stretch in terms of computation, but maintain rigidity in terms of future job roles.

great analogy

The author explains that Hong Kong’s shift from British colonial influence to a more global identity has changed how English is learned and taught. This connects to the topic because it shows that Standard English evolves as communities evolve.

Async (Cooperative Multitasking) The Model: There is one thread (one worker).

The Control: The code decides when to switch tasks.

Mechanism: When your code hits await (or yield), it voluntarily hands control back to the Event Loop.

Analogy: A single chess master playing against 50 opponents simultaneously. The master makes a move on Board 1, then immediately walks to Board 2. There is only one person moving pieces, but 50 games are progressing.

Multithreading (Preemptive Multitasking) The Model: There are multiple threads (multiple workers).

The Control: The Operating System decides when to switch tasks.

Mechanism: The OS slices time into tiny chunks. It runs Thread A for 10ms, then forcibly pauses it (interrupts) to run Thread B for 10ms. Thread A has no say in this.

Analogy: 50 novice chess players playing 50 games. They play at the same time, but they crowd the room, bump into each other, and consume more resources (space/food).

Very sad but very true at the same time.

Suggests I'm in an always high sleep pressure, with excellent LTP, but excessively and chronically elevated intracellular Cl. This creates string suspicion that Cl mediated lysosome acidification is broken. This is supported by LSD features, immunodeficiency features, acidosis features, HAGMA features. ..... Perhaps high intracellular cellular Cl causes extended Ca influx, thus causing excessive ATP efflux, thus causing ATP deficiency, thus causing ion saturation, swelling, microglia activation, ....

The sentence means that being a celebrity has both positives and negatives. It’s good because many people know who you are, but it’s bad because you might be famous for reasons that aren’t meaningful or important.

The early meaning of “celebrity” was religious and ceremonial. It is very different from today’s meaning. Interesting how the word changed so much.

tow boys mad a large change in the Townhead numbers so they had to take of there number because they would change the mean by to much

some things are more restricted to verbal like divvnt but some can be both like come, or went

the paper aims to studly how stander English effects schooling

The phrase says that one word can mean different things to different people at the same time these mixed meanings matter when we talk about important issues, because misunderstandings can cause arguments knowing this helps everyone listen better and share ideas more clearly with others.

Fucking yikes.

Would you have done anything different then, or sit back and let the Hoovervilles gentrify themselves? [eyeroll]

Author Response:

Reviewer #2 (Public Review):

The manuscript by Li et al describes the development of styrylpyridines as cell permeant fluorescent sensors of SARM1 activity. This work is significant because SARM1 activity is increased during neuron damage and SARM1 knockout mice are protected from neuronal degeneration caused by a variety of physical and chemical insults. Thus, SARM1 is a key player in neuronal degeneration and a novel therapeutic target. SARM1 is an NAD+ hydrolase that cleaves NAD+ to form nicotinamide and ADP ribose (and to a small extent cyclic ADP ribose) via a reactive oxocarbenium intermediate. Notably, this intermediate can either react with water (hydrolysis), the adenosine ring (cyclization to cADPR), or with a pyridine containing molecule in a 'base-exchange reaction'. The styrylpyridines described by Li et al exploit this base-exchange reaction; the styrylpyridines react with the intermediate to form a fluorescent product. Notably, the best probe (PC6) can be used to monitor SARM1 activity in vitro and in cells. Upon validating the utility of PC6, the authors use this compound to perform a high throughput screen of the Approved Drug Library (L1000) from TargetMol and identify nisoldipine as a hit. Further studies revealed that a minor metabolite, dehydronitrosonisoldipine (dHNN), is the true inhibitor, acting with single digit micromolar potency. The authors provide structural and proteomic data suggesting that dHNN inhibits SARM1 activity via the covalent modification of C311 which stabilizes the enzyme in the autoinhibited state.

Thanks to the positive comments and suggestions from Reviewer #2 !

Key strengths of the manuscript include the probe design and the authors demonstration that they can be used to monitor SARM1 activity in vitro in an HTS format and in cells. The identification of C311 as potential reactive cysteine that could be targeted for drug development is an important and significant insight.

Key weaknesses include the fact that dHNN is a highly reactive molecule and the authors note that it modifies multiple sites on the protein (they mentioned 8 but MS2 spectra for only 5 are provided). As such, the compound appears to be a non-specific alkylator that will have limited utility as a SARM1 inhibitor. Additionally, no information is provided on the proteome-wide selectivity of the compound.

Although dHNN may react with cysteines in general, our results indicate it does target specifically Cys311. Quantification of cysteine-containing peptides of other proteins showed no dHNN modification. So, we conclude that dHNN shows significant specificity to the Cys311 of SARM1. Some other SH-reactive agents we tested show little inhibition on SARM1. The evidence for Cys311 being dominant includes quantification of the intensity of the modified peptides and normalizing with that of the corresponding total peptides, with or without modification, showing that the modification is mainly on Cys311 (Figure 5—figure supplement 1). The dominant role of Cys311 is also confirmed by our mutagenesis and structural studies. Our result strongly suggested that the C311 is a druggable site for designing allosteric inhibitors against SARM1 activation.

dHNN is effective in inhibiting SARM1 activation and AxD at low micromolar range, making it a useful inhibitor. Considering that the neuroprotective effect of NSDP, an approved drug, may well be due to dHNN, labeling it as inhibitor of SARM1 serves focus more attentions.

Revision has been made in Discussion.

An additional key weakness is the lack of any mechanistic insights into how the adducts are generated. Moreover, it is not clear how the proposed sulphonamide and thiohydroxylamine adducts are formed.

From the images presented, it is unclear whether there is sufficient 'density' in the cryoEM maps to accurately predict the sites of modification.

Please refer to Fig . 5 F, in which we show the close up view of dHNN in the ARM domain. dHNN ( purple ) linked to the residue C311 and formed the hydrophobic interactions with surrounding residues E264, L268, R307, F308, and A315. The extra electron densities near the residue C311 fit the shape of dHNN and were shown as grey mesh.

Finally, the authors do not show whether the conversion of PC6 to PAD6 is stable or if PAD6 can also be hydrolyzed to form ADPR.

PAD6 is stable and cannot be hydrolyzed by the activated SARM1, as shown in the following figure. The reactions contain 10μM PAD6, 100 μM NMN, 2.65 μg/mL SARM1 or blank as a control. The PAD6 fluorescence was monitored for one hour and did not change in both groups.

Author Response:

Reviewer #1 (Public Review):

The work by Wang et al. examined how task-irrelevant, high-order rhythmic context could rescue the attentional blink effect via reorganizing items into different temporal chunks, as well as the neural correlates. In a series of behavioral experiments with several controls, they demonstrated that the detection performance of T2 was higher when occurring in different chunks from T1, compared to when T1 and T2 were in the same chunk. In EEG recordings, they further revealed that the chunk-related entrainment was significantly correlated with the behavioral effect, and the alpha-band power for T2 and its coupling to the low-frequency oscillation were also related to behavioral effect. They propose that the rhythmic context implements a second-order temporal structure to the first-order regularities posited in dynamic attention theory.

Overall, I find the results interesting and convincing, particularly the behavioral part. The manuscript is clearly written and the methods are sound. My major concerns are about the neural part, i.e., whether the work provides new scientific insights to our understanding of dynamic attention and its neural underpinnings.

1) A general concern is whether the observed behavioral related neural index, e.g., alpha-band power, cross-frequency coupling, could be simply explained in terms of ERP response for T2. For example, when the ERP response for T2 is larger for between-chunk condition compared to within-chunk condition, the alpha-power for T2 would be also larger for between-chunk condition. Likewise, this might also explain the cross-frequency coupling results. The authors should do more control analyses to address the possibility, e.g., plotting the ERP response for the two conditions and regressing them out from the oscillatory index.

Many thanks for the comment. In short, the enhancement in alpha power and cross-frequency coupling results in the between-cycle condition compared with those in the within-cycle condition cannot be accounted for by the ERP responses for T2.

In general, the rhythmic stimulation in the AB paradigm prevents EEG signals from returning to the baseline. Therefore, we cannot observe typical ERP components purely related to individual items, except for the P1 and N1 components related to the stream onset, which reveals no difference between the two conditions and are trailed by steady-state responses (SSRs) resonating at the stimulus rate (Fig. R1).

Fig. R1. ERPs aligned to stream onset. EEG signals were filtered between 1–30 Hz, baseline-corrected (-200 to 0 ms before stream onset) and averaged across the electrodes in left parieto-occipital area where 10-Hz alpha power showed attentional modulation effect.

To further inspect the potential differences in the target-related ERP signals between the within- and between-cycle conditions, we plotted the target-aligned waveforms for these experimental conditions. As shown in Fig. R2, a drop of ERP amplitude occurred for both conditions around T2 onset, and the difference between these two conditions was not significant (paired t-test estimated on mean amplitude every 20 ms from 0 to 700 ms relative to T1 onset, p > .05, FDR-corrected).

Fig. R2. ERPs aligned to T1 onset. EEG signals were filtered between 1–30 Hz, and baseline-corrected using signals -100 to 0 ms before T1 onset. The two dash lines indicate the onset of T1 and T2, respectively.

Since there is a trend of enhanced ERP response for the between-cycle relative to the within-cycle condition during the period of 0 to 100 ms after T2 onset (paired t-test on mean amplitude, p =.065, uncorrected), we then directly examined whether such post-T2 responses contribute to the behavioral attentional modulation effect and behavior-related neural indices. Crucially, we did not find any significant correlation of such T2-related ERP enhancement with the behavioral modulation index (BMI), or with the reported effects of alpha power and cross-frequency coupling (PAC). Furthermore, after controlling for the T2-related ERP responses, there still remains a significant correlation between the delta-alpha PAC and the BMI (rpartial = .596, p = .019), which is not surprising given that the PAC is calculated based on an 800-ms time window covering more pre-T2 than post-T2 periods (see the response to point #4 for details) rather than around the T2 onset. Taken together, these results clearly suggest that the T2-related ERP responses cannot explain the attentional modulation effect and the observed behavior-related neural indices.

2) The alpha-band increase for T2 is indeed contradictory to the well known inhibitory function of alpha-band in attention. How could a target that is better discriminated elicit stronger inhibitory response? Related to the above point, the observed enhancement in alpha-band power and its coupling to low-frequency oscillation might derive from an enhanced ERP response for T2 target.

Many thanks for the comment. We have briefly discussed this point in the revised manuscript (page 18, line 477).

A widely accepted function of alpha activity in attention is that alpha oscillations suppress irrelevant visual information during spatial selection (Kelly et al., 2006; Thut et al., 2006; Worden et al., 2000). However, it becomes a controversial issue when there exists rhythmic sensory stimulation at alpha-band, just like the situation in the current study where both the visual stream and the contextual auditory rhythm were emitted at 10 Hz. In such a case, alpha-band neural responses at the stimulation frequency can be interpreted as either passively evoked steady-state responses (SSR) or actively synchronized intrinsic brain rhythms. From the former perspective (i.e., the SSR view), an increase in the amplitude or power at the stimulus frequency may indicate an enhanced attentional allocation to the stimulus stream that may result in better target detection (Janson et al., 2014; Keil et al., 2006; Müller & Hübner, 2002). Conversely, the latter view of the inhibitory function of intrinsic alpha oscillations would produce the opposite prediction. In a previous AB study, Janson and colleagues (2014) investigated this issue by separating the stimulus-evoked activity at 12 Hz (using the same power analysis method as ours) from the endogenous alpha oscillations ranging from 10.35 to 11.25 Hz (as indexed by individual alpha frequency, IAF). Interestingly, they found a dissociation between these two alpha-band neural responses, showing that the RSVP frequency power was higher in non-AB trials (T2 detected) than in AB trials (T2 undetected) while the IAF power exhibited the opposite pattern. According to these findings, the currently observed increase in alpha power for the between-cycle condition may reflect more of the stimulus-driven processes related to attentional enhancement. However, we don’t negate the effect of intrinsic alpha oscillations in our study, as the current design is not sufficient to distinguish between these two processes. We have discussed this point in the revised manuscript (page 18, line 477). Also, we have to admit that “alpha power” may not be the most precise term to describe our findings of the stimulus-related results. Thus, we have specified it as “neural responses to first-order rhythms at 10 Hz” and “10-Hz alpha power” in the revised manuscript (see page 12 in the Results section and page 18 in the Discussion section).

As for the contribution of T2-related ERP response to the observed effect of 10 Hz power and cross-frequency coupling, please refer to our response to point #1.

References:

Janson, J., De Vos, M., Thorne, J. D., & Kranczioch, C. (2014). Endogenous and Rapid Serial Visual Presentation-induced Alpha Band Oscillations in the Attentional Blink. Journal of Cognitive Neuroscience, 26(7), 1454–1468. https://doi.org/10.1162/jocn_a_00551

Keil, A., Ihssen, N., & Heim, S. (2006). Early cortical facilitation for emotionally arousing targets during the attentional blink. BMC Biology, 4(1), 23. https://doi.org/10.1186/1741-7007-4-23

Kelly, S. P., Lalor, E. C., Reilly, R. B., & Foxe, J. J. (2006). Increases in Alpha Oscillatory Power Reflect an Active Retinotopic Mechanism for Distracter Suppression During Sustained Visuospatial Attention. Journal of Neurophysiology, 95(6), 3844–3851. https://doi.org/10.1152/jn.01234.2005

Müller, M. M., & Hübner, R. (2002). Can the Spotlight of Attention Be Shaped Like a Doughnut? Evidence From Steady-State Visual Evoked Potentials. Psychological Science, 13(2), 119–124. https://doi.org/10.1111/1467-9280.00422

Thut, G., Nietzel, A., Brandt, S., & Pascual-Leone, A. (2006). Alpha-band electroencephalographic activity over occipital cortex indexes visuospatial attention bias and predicts visual target detection. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 26(37), 9494–9502. https://doi.org/10.1523/JNEUROSCI.0875-06.2006

Worden, M. S., Foxe, J. J., Wang, N., & Simpson, G. V. (2000). Anticipatory Biasing of Visuospatial Attention Indexed by Retinotopically Specific α-Bank Electroencephalography Increases over Occipital Cortex. Journal of Neuroscience, 20(6), RC63–RC63. https://doi.org/10.1523/JNEUROSCI.20-06-j0002.2000

3) To support that it is the context-induced entrainment that leads to the modulation in AB effect, the authors could examine pre-T2 response, e.g., alpha-power, and cross-frequency coupling, as well as its relationship to behavioral performance. I think the pre-stimulus response might be more convincing to support the authors' claim.

Many thanks for the insightful suggestion. We have conducted additional analyses.

Following this suggestion, we have examined the 10-Hz alpha power within the time window of -100–0 ms before T2 onset and found stronger activity for the between-cycle condition than for the within-cycle condition. This pre-T2 response is similar to the post-T2 response except that it is more restricted to the left parieto-occipital cluster (CP3, CP5, P3, P5, PO3, PO5, POZ, O1, OZ, t(15) = 2.774, p = .007), which partially overlaps with the cluster that exhibits a delta-alpha coupling effect significantly correlated with the BMI. We have incorporated these findings into the main text (page 12, line 315) and the Fig. 5A of the revised manuscript.

As for the coupling results reported in our manuscript, the coupling index (PAC) was calculated based on the activity during the second and third cycles (i.e., 400 to 1200 ms from stream onset) of the contextual rhythm, most of which covers the pre-T2 period as T2 always appeared in the third cycle for both conditions. Together, these results on pre-T2 10-Hz alpha power and cross-frequency coupling, as well as its relationship to behavioral performance, jointly suggest that the observed modulation effect is caused by the context-induced entrainment rather than being a by-product of post-T2 processing.

4) About the entrainment to rhythmic context and its relation to behavioral modulation index. Previous studies (e.g., Ding et al) have demonstrated the hierarchical temporal structure in speech signals, e.g., emergence of word-level entrainment introduced by language experience. Therefore, it is well expected that imposing a second-order structure on a visual stream would elicit the corresponding steady-state response. I understand that the new part and main focus here are the AB effects. The authors should add more texts explaining how their findings contribute new understandings to the neural mechanism for the intriguing phenomena.

Many thanks for the suggestion. We have provided more discussion in the revised manuscript (page 17, line 447).

We have provided more discussion on this important issue in the revised manuscript (page 17, line 447). In brief, our study demonstrates how cortical tracking of feature-based hierarchical structure reframes the deployment of attentional resources over visual streams. This effect, distinct from the hierarchical entrainment to speech signals (Ding et al., 2016; Gross et al., 2013), does not rely on previously acquired knowledge about the structured information and can be established automatically even when the higher-order structure comes from a task-irrelevant and cross-modal contextual rhythm. On the other hand, our finding sheds fresh light on the adaptive value of the structure-based entrainment effect by expanding its role from rhythmic information (e.g., speech) perception to temporal attention deployment. To our knowledge, few studies have tackled this issue in visual or speech processing.

References:

Ding, N., Melloni, L., Zhang, H., Tian, X., & Poeppel, D. (2016). Cortical tracking of hierarchical linguistic structures in connected speech. Nature Neuroscience, 19(1), 158–164. https://doi.org/10.1038/nn.4186

Gross, J., Hoogenboom, N., Thut, G., Schyns, P., Panzeri, S., Belin, P., & Garrod, S. (2013). Speech Rhythms and Multiplexed Oscillatory Sensory Coding in the Human Brain. PLoS Biol, 11(12). https://doi.org/10.1371/journal.pbio.1001752

Reviewer #2 (Public Review):

In cognitive neuroscience, a large number of studies proposed that neural entrainment, i.e., synchronization of neural activity and low-frequency external rhythms, is a key mechanism for temporal attention. In psychology and especially in vision, attentional blink is the most established paradigm to study temporal attention. Nevertheless, as far as I know, few studies try to link neural entrainment in the cognitive neuroscience literature with attentional blink in the psychology literature. The current study, however, bridges this gap.

The study provides new evidence for the dynamic attending theory using the attentional blink paradigm. Furthermore, it is shown that neural entrainment to the sensory rhythm, measured by EEG, is related to the attentional blink effect. The authors also show that event/chunk boundaries are not enough to modulate the attentional blink effect, and suggest that strict rhythmicity is required to modulate attention in time.

In general, I enjoyed reading the manuscript and only have a few relatively minor concerns.

1) Details about EEG analysis.

. First, each epoch is from -600 ms before the stimulus onset to 1600 ms after the stimulus onset. Therefore, the epoch is 2200 s in duration. However, zero-padding is needed to make the epoch duration 2000 s (for 0.5-Hz resolution). This is confusing. Furthermore, for a more conservative analysis, I recommend to also analyze the response between 400 ms and 1600 ms, to avoid the onset response, and show the results in a supplementary figure. The short duration reduces the frequency resolution but still allows seeing a 2.5-Hz response.

Thanks for the comments. Each epoch was indeed segmented from -600 to 1600 ms relative to the stimulus onset, but in the spectrum analysis, we only used EEG signals from stream onset (i.e., time point 0) to 1600 ms (see the Materials and Methods section) to investigate the oscillatory characteristics of the neural responses purely elicited by rhythmic stimuli. The 1.6-s signals were zero-padded into a 2-s duration to achieve a frequency resolution of 0.5 Hz.

According to the reviewer’s suggestion, we analyzed the EEG signals from 400 ms to 1600 ms relative to stream onset to avoid potential influence of the onset response, and showed the results in Figure 4. Basically, we can still observe spectral peaks at the stimulus frequencies of 2.5, 5 (the harmonic of 2.5 Hz), and 10 Hz for both power and ITPC spectrum. However, the peak magnitudes were much weaker than those of 1.6-s signals especially for 2.5 Hz, and the 2.5-Hz power did not survive the multiple comparisons correction across frequencies (FDR threshold of p < .05), which might be due to the relatively low signal-to-noise ratio for the analysis based on the 1.2-s epochs (only three cycles to estimate the activity at 2.5 Hz). Importantly, we did identify a significant cluster for 2.5 Hz ITPC in the left parieto-occipital region showing a positive correlation with the individuals’ BMI (Fig. R3; CP5, TP7, P5, P7, PO5, PO7, O1; r = .538, p = .016), which is consistent with the findings based on the longer epochs.

Fig. R3. Neural entrainment to contextual rhythms during the period of 400–1600 ms from stream onset. (A) The spectrum for inter-trial phase coherence (ITPC) of EEG signals from 400 to 1600 ms after the stimulus onset. Shaded areas indicate standard errors of the mean. (B) The 2.5-Hz ITPC was significantly correlated with the behavioral modulation index (BMI) in a parieto-occipital cluster, as indicated by orange stars in the scalp topographic map.

Second, "The preprocessed EEG signals were first corrected by subtracting the average activity of the entire stream for each epoch, and then averaged across trials for each condition, each participant, and each electrode." I have several concerns about this procedure.

(A) What is the entire stream? It's the average over time?

Yes, as for the power spectrum analysis, EEG signals were first demeaned by subtracting the average signals of the entire stream over time from onset to offset (i.e., from 0 to 1600 ms) before further analysis. We performed this procedure following previous studies on the entrainment to visual rhythms (Spaak et al., 2014). We have clarified this point in the “Power analysis” part of the Materials and Methods section (page 25, line 677).

References:

Spaak, E., Lange, F. P. de, & Jensen, O. (2014). Local Entrainment of Alpha Oscillations by Visual Stimuli Causes Cyclic Modulation of Perception. The Journal of Neuroscience, 34(10), 3536–3544. https://doi.org/10.1523/JNEUROSCI.4385-13.2014

(B) I suggest to do the Fourier transform first and average the spectrum over participants and electrodes. Averaging the EEG waveforms require the assumption that all electrodes/participants have the same response phase, which is not necessarily true.

Thanks for the suggestion. In an AB paradigm, the evoked neural responses are sufficiently time-locked to the periodic stimulation, so it is reasonable to quantify power estimate with spectral decomposition performed on trial-averaged EEG signals (i.e., evoked power). Moreover, our results of inter-trial phase coherence (ITPC), which estimated the phase-locking value across trials based on single-trial decomposed phase values, also provided supporting evidence that the EEG waveforms were temporally locked across trials to the 2.5-Hz temporal structure in the context session.

Nevertheless, we also took the reviewer’s suggestion seriously and analyzed the power spectrum on the average of single-trial spectral transforms, i.e., the induced power, which puts emphasis on the intrinsic non-phase-locked activities. In line with the results of evoked power and ITPC, the induced power spectrum in context session also peaked at 2.5 Hz and was significantly stronger than that in baseline session at 2.5 Hz (t(15) = 4.186, p < .001, FDR-corrected with a p value threshold < .001). Importantly, Person correlation analysis also revealed a positive cluster in the left parieto-occipital region, indicating the induced power at 2.5 Hz also had strong relevance with the attentional modulation effect (P7, PO7, PO5, PO3; r = .606, p = .006). We have added these additional findings to the revised manuscript (page 11, line 288; see also Figure 4—figure supplement 1).

2) The sequences are short, only containing 16 items and 4 cycles. Furthermore, the targets are presented in the 2nd or 3rd cycle. I suspect that a stronger effect may be observed if the sequence are longer, since attention may not well entrain to the external stimulus until a few cycles. In the first trial of the experiment, they participant may not have a chance to realize that the task-irrelevant auditory/visual stimulus has a cyclic nature and it is not likely that their attention will entrain to such cycles. As the experiment precedes, they learns that the stimulus is cyclic and may allocate their attention rhythmically. Therefore, I feel that the participants do not just rely on the rhythmic information within a trial but also rely on the stimulus history. Please discuss why short sequences are used and whether it is possible to see buildup of the effect over trials or over cycles within a trial.

Thanks for the comments. Typically, to induce a classic pattern of AB effect, the RSVP stream should contain 3–7 distractors before the first target (T1), with varying lengths of distractors (0–7) between two targets and at least 2 items after the second target (T2). In our study, we created the RSVP streams following these rules, which allowed us to observe the typical AB effect that T2 performance was deteriorated at Lag 2 relative to that at Lag 8. Nevertheless, we agree with the reviewer that longer streams would be better for building up the attentional entrainment effect, as we did observe the attentional modulation effect ramped up as the stream proceeded over cycles, consistent with the reviewer’s speculation. In Experiments 1a (using auditory context) and 2a (using color-defined visual context), we adopted two sets of target positions—an early one where T2 appeared at the 6th or 8th position (in the 2nd cycle) of the visual stream, and a late one where T2 appeared at the 10th or 12th position (in the 3rd cycle) of the visual stream. In the manuscript, we reported T2 performance with all the target positions combined, as no significant interaction was found between the target positions and the experimental conditions (ps. > .1). However, additional analysis demonstrated a trend toward an increase of the attentional modulation effect over cycles, from the early to the late positions. As shown in Fig. R4, the modulation effect went stronger and reached significance for the late positions (for Experiment 1a, t(15) = 2.83, p = .013, Cohen’s d = 0.707; for Experiment 2a, t(15) = 3.656, p = .002, Cohen’s d = 0.914) but showed a weaker trend for the early positions (for Experiment 1a, t(15) = 1.049, p = .311, Cohen’s d = 0.262; for Experiment 2a, t(15) = .606, p = .553, Cohen’s d = 0.152).

Fig. R4. Attentional modulation effect built up over cycles in Experiments 1a & 2a. Error bars represent 1 SEM; * p<0.05, ** p<0.01.

However, we did not observe an obvious buildup effect across trials in our study. The modulation effect of contextual rhythms seems to be a quick process that the effect is evident in the first quarter of trials in Experiment 1a (for, t(15) = 2.703, p = .016, Cohen’s d = 0.676) and in the second quarter of trials in Experiment 2a (for, t(15) = 2.478, p = .026, Cohen’s d = 0.620.

3) The term "cycle" is used without definition in Results. Please define and mention that it's an abstract term and does not require the stimulus to have "cycles".

Thanks for the suggestion. By its definition, the term “cycle” refers to “an interval of time during which a sequence of a recurring succession of events or phenomena is completed” or “a course or series of events or operations that recur regularly and usually lead back to the starting point” (Merriam-Webster dictionary). In the current study, we stuck to the recurrent and regular nature of “cycle” in general while defined the specific meaning of “cycle” by feature-based periodic changes of the contextual stimuli in each experiment (page 5, line 101; also refer to Procedures in the Materials and Methods section for details). For example, in Experiment 1a, the background tone sequence changed its pitch value from high to low or vice versa isochronously at a rate of 2.5 Hz, thus forming a rhythmic context with structure-based cycles of 400 ms. Note that we did not use the more general term “chunk”, because arbitrary chunks without the regularity of cycles are insufficient to trigger the attentional modulation effect in the current study. Indeed, the effect was eliminated when we replaced the rhythmic cycles with irregular chunks (Experiments 1d & 1e).

4) Entrainment of attention is not necessarily related to neural entrainment to sensory stimulus, and there is considerable debate about whether neural entrainment to sensory stimulus should be called entrainment. Too much emphasis on terminology is of course counterproductive but a short discussion on these issues is probably necessary.

Thanks for the comments. As commonly accepted, entrainment is defined as the alignment of intrinsic neuronal activity to the temporal structure of external rhythmic inputs (Lakatos et al., 2019; Obleser & Kayser, 2019). Here, we are interested in the functional roles of cortical entrainment to the higher-order temporal structure imposed on first-order sensory stimulation, and used the term entrainment to describe the phase-locking neural responses to such hierarchical structure following literature on auditory and visual perception (Brookshire et al., 2017; Doelling & Poeppel, 2015). In our study, the consistent results of power and ITPC have provided strong evidence that neural entrainment at the structure level (2.5 Hz) is significantly correlated with the observed attentional modulation effect. However, this does not mean that the entrainment of attention is necessarily associated with neural entrainment to sensory stimulus in a broader context, as attention may also be guided by predictions based on non-isochronous temporal regularity without requiring stimulus-based oscillatory entrainment (Breska & Deouell, 2017; Morillon et al._2016).

On the other hand, there has been a debate about whether the neural alignment to rhythmic stimulation reflects active entrainment of endogenous oscillatory processes (i.e., induced activity) or a series of passively evoked steady-state responses (Keitel et al., 2019; Notbohm et al., 2016; Zoefel et al., 2018). The latter process is also referred to as “entrainment in a broad sense” by Obleser & Kayser (2019). Given that a presented rhythm always evokes event-related potentials, a better question might be whether the observed alignment reflects the entrainment of endogenous oscillations in addition to evoked steady-state responses. Here we attempted to tackle this issue by measuring the induced power, which emphasizes the intrinsic non-phase-locked activity, in addition to the phase-locked evoked power. Specifically, we quantified these two kinds of activities with the average of single-trial EEG power spectra and the power spectra of trial-averaged EEG signals, respectively, according to Keitel et al. (2019). In addition to the observation of evoked responses to the contextual structure, we also demonstrated an attention-related neural tracking of the higher-order temporal structure based on the induced power at 2.5 Hz (see Figure 4—figure supplement 1), suggesting that the observed attentional modulation effect is at least partially derived from the entrainment of intrinsic oscillatory brain activity. We have briefly discussed this point in the revised manuscript (page 17, line 460).

References:

Breska, A., & Deouell, L. Y. (2017). Neural mechanisms of rhythm-based temporal prediction: Delta phase-locking reflects temporal predictability but not rhythmic entrainment. PLOS Biology, 15(2), e2001665. https://doi.org/10.1371/journal.pbio.2001665

Brookshire, G., Lu, J., Nusbaum, H. C., Goldin-Meadow, S., & Casasanto, D. (2017). Visual cortex entrains to sign language. Proceedings of the National Academy of Sciences, 114(24), 6352–6357. https://doi.org/10.1073/pnas.1620350114

Doelling, K. B., & Poeppel, D. (2015). Cortical entrainment to music and its modulation by expertise. Proceedings of the National Academy of Sciences, 112(45), E6233–E6242. https://doi.org/10.1073/pnas.1508431112

Henry, M. J., Herrmann, B., & Obleser, J. (2014). Entrained neural oscillations in multiple frequency bands comodulate behavior. Proceedings of the National Academy of Sciences, 111(41), 14935–14940. https://doi.org/10.1073/pnas.1408741111

Keitel, C., Keitel, A., Benwell, C. S. Y., Daube, C., Thut, G., & Gross, J. (2019). Stimulus-Driven Brain Rhythms within the Alpha Band: The Attentional-Modulation Conundrum. The Journal of Neuroscience, 39(16), 3119–3129. https://doi.org/10.1523/JNEUROSCI.1633-18.2019

Lakatos, P., Gross, J., & Thut, G. (2019). A New Unifying Account of the Roles of Neuronal Entrainment. Current Biology, 29(18), R890–R905. https://doi.org/10.1016/j.cub.2019.07.075

Morillon, B., Schroeder, C. E., Wyart, V., & Arnal, L. H. (2016). Temporal Prediction in lieu of Periodic Stimulation. Journal of Neuroscience, 36(8), 2342–2347. https://doi.org/10.1523/JNEUROSCI.0836-15.2016

Notbohm, A., Kurths, J., & Herrmann, C. S. (2016). Modification of Brain Oscillations via Rhythmic Light Stimulation Provides Evidence for Entrainment but Not for Superposition of Event-Related Responses. Frontiers in Human Neuroscience, 10. https://doi.org/10.3389/fnhum.2016.00010

Obleser, J., & Kayser, C. (2019). Neural Entrainment and Attentional Selection in the Listening Brain. Trends in Cognitive Sciences, 23(11), 913–926. https://doi.org/10.1016/j.tics.2019.08.004

Zoefel, B., ten Oever, S., & Sack, A. T. (2018). The Involvement of Endogenous Neural Oscillations in the Processing of Rhythmic Input: More Than a Regular Repetition of Evoked Neural Responses. Frontiers in Neuroscience, 12. https://doi.org/10.3389/fnins.2018.00095

Reviewer #3 (Public Review):

The current experiment tests whether the attentional blink is affected by higher-order regularity based on rhythmic organization of contextual features (pitch, color, or motion). The results show that this is indeed the case: the AB effect is smaller when two targets appeared in two adjacent cycles (between-cycle condition) than within the same cycle defined by the background sounds. Experiment 2 shows that this also holds for temporal regularities in the visual domain and Experiment 3 for motion. Additional EEG analysis indicated that the findings obtained can be explained by cortical entrainment to the higher-order contextual structure. Critically feature-based structure of contextual rhythms at 2.5 Hz was correlated with the strength of the attentional modulation effect.

This is an intriguing and exciting finding. It is a clever and innovative approach to reduce the attention blink by presenting a rhythmic higher-order regularity. It is convincing that this pulling out of the AB is driven by cortical entrainment. Overall, the paper is clear, well written and provides adequate control conditions. There is a lot to like about this paper. Yet, there are particular concerns that need to be addressed. Below I outline these concerns:

1) The most pressing concern is the behavioral data. We have to ensure that we are dealing here with a attentional blink. The way the data is presented is not the typical way this is done. Typically in AB designs one see the T2 performance when T1 is ignored relative to when T1 has to be detected. This data is not provided. I am not sure whether this data is collected but if so the reader should see this.

Many thanks for the suggestion. We appreciate the reviewer for his/her thoughtful comments. To demonstrate the AB effect, we did include two T2 lag conditions in our study (Experiments 1a, 1b, 2a, and 2b)—a short-SOA condition where T2 was located at the second lag of T1 (i.e., SOA = 200 ms), and a long-SOA condition where T2 appeared at the 8th lag of T1 (i.e., SOA = 800 ms). In a typical AB effect, T2 performance at short lags is remarkably impaired compared with that at long lags. In our study, we consistently replicated this effect across the experiments, as reported in the Results section of Experiment 1 (page 5, line 106). Overall, the T2 detection accuracy conditioned on correct T1 response was significantly impaired in the short-SOA condition relative to that in the long-SOA condition (mean accuracy > 0.9 for all experiments), during both the context session and the baseline session. More crucially, when looking into the magnitude of the AB effect as measured by (ACClong-SOA - ACCshort-SOA)/ACClong-SOA, we still obtained a significant attentional modulation effect (for Experiment 1a, t(15) = -2.729, p = .016, Cohen’s d = 0.682; for Experiment 2a, t(15) = -4.143, p <.001, Cohen’s d = 1.036) similar to that reflected by the short-SOA condition alone, further confirming that cortical entrainment effectively influences the AB effect.

Although we included both the long- and short-SOA conditions in the current study, we focused on T2 performance in the short-SOA condition rather than along the whole AB curve for the following reasons. Firstly, for the long-SOA conditions, the T2 performance is at ceiling level, making it an inappropriate baseline to probe the attentional modulation effect. We focused on Lag 2 because previous research has identified a robust AB effect around the second lag (Raymond et al., 1992), which provides a reasonable and sensitive baseline to probe the potential modulation effect of the contextual auditory and visual rhythms. Note that instead of using multiple lags, we varied the length of the rhythmic cycles (i.e., a cycle of 300 ms, 400 ms, and 500 ms corresponding to a rhythm frequency of 3.3 Hz, 2.5 Hz, and 2 Hz, respectively, all within the delta band), and showed that the attentional modulation effect could be generalized to these different delta-band rhythmic contexts, regardless of the absolute positions of the targets within the rhythmic cycles.

As to the T1 performance, the overall accuracy was very high, ranging from 0.907 to 0.972, in all of our experiments. The corresponding results have been added to the Results section of the revised manuscript (page 5, line 103). Notably, we did not find T1-T2 trade-offs in most of our experiments, except in Experiment 2a where T1 performance showed a moderate decrease in the between-cycle condition relative to that in the within-cycle condition (mean ± SE: 0.888 ± 0.026 vs. 0.933 ± 0.016, respectively; t(15) = -2.217, p = .043). However, by examining the relationship between the modulation effects (i.e., the difference between the two experimental conditions) on T1 and T2, we did not find any significant correlation (p = .403), suggesting that the better performance for T2 was not simply due to the worse performance in detecting T1.

Finally, previous studies have shown that ignoring T1 would lead to ceiling-level T2 performance (Raymond et al., 1992). Therefore, we did not include such manipulation in the current study, as in that case, it would be almost impossible for us to detect any contextual modulation effect.

References:

Raymond, J. E., Shapiro, K. L., & Arnell, K. M. (1992). Temporary suppression of visual processing in an RSVP task: An attentional blink? Journal of Experimental Psychology: Human Perception and Performance, 18(3), 849–860. https://doi.org/10.1037/0096-1523.18.3.849

2) Also, there is only one lag tested. The ensure that we are dealing here with a true AB I would like to see that more than one lag is tested. In the ideal situation a full AB curve should be presented that includes several lags. This should be done for at least for one of the experiments. It would be informative as we can see how cortical entrainment affects the whole AB curve.

Many thanks for the suggestion. Please refer to our response to the point #1 for “Reviewer #3 (Public Review)”. In short, we did include two T2 lag conditions in our study (Experiments 1a, 1b, 2a and 2b), and the results replicated the typical AB effect. We have clarified this point in the revised manuscript (page 5, line 106).

3) Also, there is no data regarding T1 performance. It is important to show that this the better performance for T2 is not due to worse performance in detecting T1. So also please provide this data.

Many thanks for the suggestion. Please refer to our response to the point #1 or “Reviewer #3 (Public Review)”. We have reported the T1 performance in the revised manuscript (page 5, line 103), and the results didn’t show obvious T1-T2 trade-offs.

4) The authors identify the oscillatory characteristics of EEG signals in response to stimulus rhythms, by examined the FFT spectral peaks by subtracting the mean power of two nearest neighboring frequencies from the power at the stimulus frequency. I am not familiar with this procedure and would like to see some justification for using this technique.

According to previous studies (Nozaradan, 2011; Lenc e al., 2018), the procedure to subtract the average amplitude of neighboring frequency bins can remove unrelated background noise, like muscle activity or eye movement. If there were no EEG oscillatory responses characteristic of stimulus rhythms, the amplitude at a given frequency bin should be similar to the average of its neighbors, and thus no significant peaks could be observed in the subtracted spectrum.

References:

Lenc, T., Keller, P. E., Varlet, M., & Nozaradan, S. (2018). Neural tracking of the musical beat is enhanced by low-frequency sounds. Proceedings of the National Academy of Sciences, 115(32), 8221–8226. https://doi.org/10.1073/pnas.1801421115

Nozaradan, S., Peretz, I., Missal, M., & Mouraux, A. (2011). Tagging the Neuronal Entrainment to Beat and Meter. The Journal of Neuroscience, 31(28), 10234–10240. https://doi.org/10.1523/JNEUROSCI.0411-11.2011

Author Response:

Reviewer #1 (Public Review):

The manuscript by Chakraborty focuses on methods to direct dsDNA to specific cell types within an intact multicellular organism, with the ultimate goal of targeting DNA-based nanodevices, often as biosensors within endosomes and lysosomes. Taking advantage of the endogenous SID-2 dsRNA receptor expressed in C. elegans intestinal cells, the authors show that dsDNA conjugated to dsRNA can be taken into the intestinal endosomal system via feeding and apical endocytosis, while dsDNA alone is not an efficient endocytic cargo from the gut lumen. Since most cells do not express a dsRNA receptor, the authors sought to develop a more generalizable approach. Via phage display screening they identified a novel camelid antibody 9E that recognizes a short specific DNA sequence that can be included at the 3' end of synthesized dsDNAs. The authors then showed that this antibody can direct binding, and in some cases endocytosis, of such DNAs when 9E was expressed as a fusion with transmembrane protein SNB-1. This approach was successful in targeting microinjected dsDNA pan-neuronally when expressed via the snb-1 promoter, and to specific neuronal subsets when expressed via other promoters. Endocytosed dsDNA appeared in puncta moving in neuronal processes, suggesting entry into endosomes. Plasma membrane targeting appeared feasible using 9E fusion to ODR-2.

The major strength of the paper is in the identification and testing of the 9E camelid antibody as part of a generalizable dsDNA targeting system. This aspect of the paper will likely be of wide interest and potentially high impact, since it could be applied in any intact animal system subject to transgene expression. A weakness of the paper is the choice of "nanodevice". It was not clear what utility was present in the DNAs used, such as D38, that made them "devices", aside from their fluorescent tag that allowed tracking their localization.

We used a DNA nanodevice, denoted pHlava-9E, that uses pHrodo as a pH-sensitive dye. pHlava-9E is designed to provide a digital output of compartmentalization i.e., its pH profile is such that even if it is internalized into a mildly acidic vesicle, the pH readout is as high as one would observe with a lysosome. This gives an unambiguous readout of surface-immobilized probe to endocytosed probe.

Another potential weakness is that the delivered DNA is limited to the cell surface or the lumen of endomembrane compartments without access to the cytoplasm or nucleus. In general the data appeared to be of high quality and was well controlled, supporting the authors conclusions.

We completely agree that we cannot target DNA nanodevices to sub-cellular locations such as the cytoplasm or the nucleus with this strategy. However, we do not see this as a “weakness”, but rather, as a limitation of the current capabilities of DNA nanotechnology. It must be mentioned that though fluorescent proteins were first described in 1962, it was 30 years before others targeted them to the endoplasmic reticulum (1992) or the nucleus (1993)(Brini et al., 1993; Kendall et al., 1992). Probe technologies undergo stage-wise improvements/expansions. We have therefore added a small section in the conclusions section outlining the future challenges in sub-cellular targeting of DNA-nanodevices.

Reviewer #2 (Public Review):

The authors demonstrate the tissue-specific and cell-specific targeting of double-stranded DNA (dsDNA) using C. elegans as a model host animal. The authors focused on two distinct tissues and delivery routes: feeding dsDNA to target a class of organelles within intestinal cells, and injecting dsDNA to target presynaptic endocytic structures in neurons. To achieve efficient intestinal targeting, the authors leveraged dsRNA uptake via endogenous intestinal SID-2 receptors by fusing dsRNA to a fluorophore-labeled dsDNA probe. In contrast, neuronal endosome/synaptic vesicle (SV) targeting was achieved by designing a nanobody that specifically binds a short dsDNA motif fused to the fluorophore-labeled dsDNA probe. Combining dsDNA probe injection with nanobody neuronal expression (fused to a neuronal vSNARE to achieve synaptic targeting), the authors demonstrated that the injected dsDNA could be taken up by a variety of distinct neuronal subtypes.

Strengths:

While nanodevices built on dsDNA platforms have been shown to be taken up by scavenger receptors in C. elegans (including previous work from several of these authors), this strategy will not work in many tissue types lacking these receptors. The authors successfully circumvented this limitation using distinct strategies for two cell types in the worm, thereby providing a more general approach for future efforts. The approaches are creative, and the nanobody development in particular allows for endocytic delivery in any cell type. The authors exploited quantitative imaging approaches to examine the subcellular targeting of dsDNA probes in living animals and manipulated endogenous receptors to demonstrate the mechanism of dsRNA-based dsDNA uptake in intestinal cells.

Weaknesses:

To validate successful delivery of a functional nanodevice, one would ideally demonstrate the function of a particular nanodevice in at least one of the examples provided in this work. The authors have successfully used a variety of custom-designed dsDNA probes in living worms in numerous past studies, so this would not be a technical hurdle. In the current study, the reader has no means of assessing whether the dsDNA is intact and functional within its intracellular compartment.

We now demonstrate the use of a functional nanodevice to detect pH profiles of a given microenvironment. This functional nanodevice contains two fluorescent reporter dyes, each attached to one of the strands of a DNA duplex. In order to obtain pH readouts, the device integrity is essential for ratiometric sensing.

Coelomocytes are cells known for their scavenging and degradative lysosomal machinery. Previous studies of the stability of variously structured DNA nanodevices in coelomocytes, have shown that DNA devices based on 38 bp DNA duplexes have a half life of >8 hours in actively scavenging cells such as coelomocytes (Chakraborty et al., 2017; Surana et al., 2013) Given that our sensing in the gut as well as in the neuron are performed in <1 hour post feeding or injection, pHlava-9E is >97% intact.

Another minor weakness is the lack of a quantitative assessment of colocalization in intestinal cells or neurons in an otherwise nicely quantitative study. Since characterization of the targeting described here is an essential part of evaluating the method, a stronger demonstration of colocalization would significantly buttress the authors' claims.

We have now quantified colocalization in each cellular system. Please see Figure R1 below (Figure 1 Supplementary figure 1 and Figure 4 Supplementary figure 2 of the revised manuscript).

Figure R1: a) Pearson’s correlation coefficient (PCC) calculated for the colocalization between R50D38 (red) and lysosomal markers LMP-1 or GLO-1 (green) in the indicated transgenic worms. b) & d) Representative images of nanodevice nD647 uptake (red) in transgenics expressing both prab-3::gfp::rab-3 (green) and psnb-1:snb-1::9E c - e) Normalized line intensity profiles across the indicated lines in b and d; f) Percentage colocalization of nD647 (red) with RAB3:GFP (green). Error bar represents the standard deviation between two data sets.

While somewhat incomplete, this study represents a step forward in the development of a general targeting approach amenable to nanodevice delivery in animal models.

Author Response

Summary:

This work is of interest because it increases our understanding of the molecular mechanisms that distinguish subtypes of VIP interneurons in the cerebral cortex and because of the multiple ways in which the authors address the role of Prox1 in regulating synaptic function in these cells.

The authors would like to thank the reviewers for their constructive comments. In response, we would like to clarify a number of issues, as well as outline how we plan to resolve major concerns.

Reviewer #1:

Stachiak and colleagues examine the physiological effects of removing the homeobox TF Prox1 from two subtypes of VIP neurons, defined on the basis of their bipolar vs. multipolar morphology.

The results will be of interest to those in the field, since it is known from prior work that VIP interneurons are not a uniform class and that Prox1 is important for their development.

The authors first show that selective removal of a conditional Prox1 allele using a VIP cre driver line results in a change in paired pulse ratio of presumptive excitatory synaptic responses in multipolar but not bipolar VIP interneurons. The authors then use RNA-seq to identify differentially expressed genes that might contribute and highlight a roughly two-fold reduction in the expression of a transcript encoding a trans-synaptic protein Elfn1 known to contribute to reduced glutamate release in Sst+ interneurons. They then test the potential contribution of Elfn1 to the phenotype by examining whether loss of one allele of Elfn1 globally alters facilitation. They find that facilitation is reduced both by this genetic manipulation and by a pharmacological blockade of presynaptic mGluRs known to interact with Elfn1.

Although the results are interesting, and the authors have worked hard to make their case, the results are not definitive for several reasons:

1) The global reduction of Elfn1 may act cell autonomously, or may have other actions in other cell types. The pharmacological manipulation is less subject to this interpretation, but these results are not as convincing as they could be because the multipolar Prox1 KO cells (Fig. 3 J) still show substantial facilitation comparable, for example to the multipolar control cells in the Elfn1 Het experiment (controls in Fig. 3E). This raises a concern about control for multiple comparisons. Instead of comparing the 6 conditions in Fig 3 with individual t-tests, it may be more appropriate to use ANOVA with posthoc tests controlled for multiple comparisons.

The reviewer’s concerns regarding non-cell-autonomous actions of global Elfn1 KO are well founded. Significant phenotypic alterations have previously been reported, both in the physiology of SST neurons as well in the animals’ behavior (Stachniak, Sylwestrak, Scheiffele, Hall, & Ghosh, 2019; Tomioka et al., 2014). The homozygous Elfn1 KO mouse displays a hyperactive phenotype and epileptic activity after 3 months of age, suggesting generalcortical activity differences exist (Dolan & Mitchell, 2013; Tomioka et al., 2014). Nevertheless, we have not observed such changes in P17-21 Elfn1 heterozygous (Het) animals.

Comparing across different experimental animal lines, for example the multipolar Prox1 KO cells (Fig. 3 J) to the multipolar control cells in the Elfn1 Het experiment (controls in Fig. 3E), is in our view not advisable. There is a plethora of examples in the literature on the effect of mouse strain on even the most basic cellular functions and hence it is always expected that researchers use the correct control animals for their experiments, which in the best case scenario are littermate controls. For these reasons, we would argue that statistical comparisons across mouse lines is not ideal for our study. Elfn1 Het and MSOP data are presented side by side to illustrate that Elfn1 Hets (3C,E) phenocopy the effects of Prox1 deletion (3G,H,I,J). (See also point 3) MSOP effect sizes, however, do show significant differences by ANOVA with Bonferroni post-hoc (normalized change in EPSC amplitude; multipolar prox1 control: +12.1 ± 3.8%, KO: -8.4 ± 4.3%, bipolar prox1 control: -5.2 ± 4.3%, KO: -3.4 ± 4.7%, cell type x genotype interaction, p= 0.02, two way ANOVA).

2) The isolation of glutamatergic currents is not described. Were GABA antagonists present to block GABAergic currents? Especially with the Cs-based internal solutions used, chloride reversal potentials can be somewhat depolarized relative to the -65 mV holding potential. If IPSCs were included it would complicate the analysis.

No, in fact GABA antagonists were not present in these experiments. The holding voltage in our evoked synaptic experiments is -70 mV, which combined with low internal [Cl-] makes it highly unlikely that the excitatory synaptic responses we study are contaminated by GABA-mediated ones, even with a Cs MeSO4-based solution. Nevertheless, we have now performed additional experiments where glutamate receptor blockers were applied in bath and we observe a complete blockade of the synaptic events at -70mV proving that they are AMPA/NMDA receptor mediated. When holding the cell at 0mV with these blockers present, outward currents were clearly visible, suggesting intact GABA-mediated events.

3) The assumption that protein levels of Elfn1 are reduced to half in the het is untested. Synaptic proteins can be controlled at the level of translation and trafficking and WT may not have twice the level of this protein.

We thank reviewer for pointing this out. Our rationale for using the Elfn1 heterozygous animals is rather that transcript levels are reduced by half in heterozygous animals, to match the reduction we found in the mRNA levels of VIP Prox1 KO cells (Fig 2). The principle purpose of the Elfn1 KO experiment was to determine whether the change in Elfn1 transcript levels could be sufficient to explain the synaptic deficit observed in VIP Prox1 KO cells. As the reviewer notes, translational regulation and protein trafficking could ultimately result in even larger changes than 0.5x protein levels at the synapse. This may ultimately explain the observed multipolar/bipolar disparity, which cannot be explained by transcriptional regulation alone (Fig 4).

4) The authors are to be commended for checking whether Elfn1 is regulated by Prox1 only in the multipolar neurons, but unfortunately it is not. The authors speculate that the selective effects reflect a selective distribution of MgluR7, but without additional evidence it is hard to know how likely this explanation is.

Additional experiments are underway to better understand this mechanism.

Reviewer #2:

Stachniak et al., provide an interesting manuscript on the postnatal role of the critical transcription factor, Prox1, which has been shown to be important for many developmental aspects of CGE-derived interneurons. Using a combination of genetic mouse lines, electrophysiology, FACS + RNAseq and molecular imaging, the authors provide evidence that Prox1 is genetically upstream of Elfn1. Moreover, they go on to show that loss of Prox1 in VIP+ cells preferentially impacts those that are multipolar but not the bipolar subgroup characterized by the expression of calretinin. This latter finding is very interesting, as the field is still uncovering how these distinct subgroups emerge but are at a loss of good molecular tools to fully uncover these questions. Overall, this is a great combination of data that uses several different approaches to come to the conclusions presented. I have suggestions that I think would strengthen the manuscript:

1) Can the authors add a supplemental table showing the top 20-30 genes up and down regulated in their Prox1 KOS? This would make these, and additional, data more tenable to readers.

We would be happy to provide supplementary tables with candidate genes at both P8 and P12.

2) It is interesting that loss of Prox1 or Elfn1 leads to phenotypes in multipolar but are not present or mild in bipolar VIP+ cells. The authors test different hypotheses, which they are able to refute and discuss some ideas for how multipolar cells may be more affected by loss of Elfn1, even when the transcript is lost in both multipolar and bipolar after Prox1 deletion. If there is any way to expand upon these ideas experimentally, I believe it would greatly strengthen the manuscript. I understand there is no perfect experiment due to a lack of tools and reagents but if there is a way to develop one of the following ideas or something similar, it would be beneficial:

We thank the reviewer for the note.

a) Would it be possible to co-fill VIPCre labeled cells with biocytin and a retroviral tracer? Then, after the retroviral tracer had time to label a presynaptic cell, assess whether these were preferentially different between bipolar and multipolar cell types, the latter morphology determined by the biocytin fill? This would test whether each VIP+ subtype is differentially targeted.

Although this is a very elegant experiment and we would be excited to do it, we do feel that single-cell rabies virus tracing is technically very challenging and will take many months to troubleshoot before being able to acquire good data. Hence, we think it is beyond the scope of this study.

b) Another biocytin possibility would be to trace filled VIP+ cells and assess whether the dendrites of multipolar and bipolar cells differentially targeted distinct cortical lamina and whether these lamina, in the same section or parallel, were enriched for mGluR7+ afferents.

We thank the reviewer for their suggestion and we are planning on doing these kinds of experiments.

Reviewer #3:

In this work Stachiak and colleagues investigate the role of Prox1 on the development of VIP cells. Prox1 is expressed by the majority of GABAergic derived from the caudal ganglionic eminence (CGE), and as mentioned by the authors, Prox1 has been shown to be necessary for the differentiation, circuit integration, and maintenance of CGE-derived GABAergic cells. Here, Stachiak and colleagues show that removal of Prox1 in VIP cells leads to suppression of synaptic release probability onto cortical multipolar VIP cells in a mechanism dependent on Elfn1. This work is of interest for the field because it increases our understanding of differential synaptic maturation of VIP cells. The results are noteworthy, however the relevance of this manuscript would potentially be increased by addressing the following suggestions:

1) Include histology to show when exactly Prox1 is removed from multipolar and bipolar VIP-expressing cells by using the VIP-Cre mouse driver.

We can address this by performing an in-situ hybridization against Prox1 from P3 onwards (when Cre becomes active).

2) Clarify if the statistical analysis is done using n (number of cells) or N (number of animals). The analysis between control and mutants (both Prox1 and Elfn1) need to be done across animals and not cells.

Statistics for physiology were done across n (number of cells) while statistics for ISH are done across number of slices. We will clarify this point in the text and update the methods.

Regarding the statistics for the ISH, these have been done across n (number of slices) for control versus KO tissue (N = 3 and N = 2 animals, respectively). We will add more animals to this analysis to compare by animal instead, although we do not expect any change in the results.

Regarding the physiology, we would provide a two-pronged answer. We first of all feel that averaging synaptic responses for each animal would hide a good deal of the biological variability in PPR present in different cells (response Fig 1), the characterization of which is integral to the central findings of the paper. Secondly, to perform such analysis asked by the reviewer one would need to obtain recordings from ~10 animals or so per condition for each condition, which, to our knowledge, is something that is not standard when utilizing in vitro electrophysiological recordings from single cells. For example, in these very recent studies that have performed in vitro electrophysiological recordings all the statistics are performed using “n” number of cells and not the average of all the cells recorded per animal collapsed into a single data point. (Udakis, Pedrosa, Chamberlain, Clopath, & Mellor, 2020) https://www.nature.com/articles/s41467-020-18074-8

(Horvath, Piazza, Monteggia, & Kavalali, 2020) https://elifesciences.org/articles/52852

(Haas et al., 2018) https://elifesciences.org/articles/31755

Nevertheless, we have now re-run the analysis grouping the cells and averaging the values we get per animal, since we have obtained our data from many animals. The results are more or less indistinguishable from the ones presented in the original submission, except for on p value that rose to 0.07 from 0.03 due to the lack of the required number of animals. We hope that the new plots and statistics presented herein address the concern put forward by the reviewer.

Response Fig 1: A comparison of cell wise versus animal-wise analysis of synaptic physiology. Some cell to cell variability is hidden, and the reduction in numbers impacts the P values.

(A) PPR of multipolar Prox1 Control for 14 cells from 9 animals (n/N=14/9) under baseline conditions and with MSOP, cell-wise comparison p = 0.02 , t = 2.74 and (B) animal-wise comparisons (p = 0.04, t stat = 2.45). Statistics: paired t-test.

(C) PPR of multipolar Prox1 KO cells (n/N=9/8) under baseline conditions and with MSOP, cell-wise comparison p = 0.2, t = 1.33 and (D) animal-wise comparisons (p = 0.2, t stat = 1.56). Statistics: paired t-test. Comparisons for PPR of bipolar Prox1 Control (n/N=8/8) and KO cells (n/N=9/9) did not change.

(E) PPR for Prox1 control (n/N=18/11) and KO (n/N=13/11) bipolar VIP cells, cell-wise comparison p = 0.3, t = 1.1 and (F) animal-wise comparisons (p = 0.4, t stat = 0.93). Statistics: t-test.

(G) PPR of Elfn1 Control (n/N=12/4) and Het (n/N=12/4) bipolar VIP cells, cell-wise comparison p = 0.3, t = 1.06 and (H) animal-wise comparisons (p = 0.4, t stat = 0.93)

(I) PPR of Prox1 control (n/N=33/18) and KO (n/N=19/14) multipolar VIP cells, cell-wise comparison p = 0.03, t = 2.17. and (J) animal-wise comparisons (p = 0.07, t stat = 1.99).

(K) PPR of Elfn1 Control (n/N=14/6) and Het (n/N=20/8) multipolar VIP cells, cell-wise comparison p = 0.008, t = 2.84 and (L) animal-wise comparisons (p = 0.007, t stat = 3.23).

3) Clarify what are the parameters used to identify bipolar vs multipolar VIP cells. VIP cells comprise a wide variety of transcriptomic subtypes, and in the absence of using specific genetic markers for the different VIP subtypes, the authors should either include the reconstructions of all recorded cells or clarify if other methods were used.

We thank the reviewer for this comment. The cell parameter criteria will be amended in the methods: “Cell type was classified as bipolar vs. multipolar based on cell body morphology (ovoid vs. round) and number and orientation of dendritic processes emanating from it (2 or 3 dendrites perpendicular to pia (for bipolar) vs. 3 or more processes in diverse orientations (for multipolar). In addition, the laminar localization of the two populations differs, with multipolar cells found primarily in the upper layer 2, while bipolar cells are found throughout layers 2 and 3. Initial determination of cell classification was made prior to patching fluorescent-labelled cells, but whenever possible this initial assessment was confirmed with post-hoc verification of biocytin filled cells.”

Reference:

Dolan, J., & Mitchell, K. J. (2013). Mutation of Elfn1 in Mice Causes Seizures and Hyperactivity. PLOS ONE, 8(11), e80491. Retrieved from https://doi.org/10.1371/journal.pone.0080491

Haas, K. T., Compans, B., Letellier, M., Bartol, T. M., Grillo-Bosch, D., Sejnowski, T. J., … Hosy, E. (2018). Pre-post synaptic alignment through neuroligin-1 tunes synaptic transmission efficiency. ELife, 7, e31755. https://doi.org/10.7554/eLife.31755

Horvath, P. M., Piazza, M. K., Monteggia, L. M., & Kavalali, E. T. (2020). Spontaneous and evoked neurotransmission are partially segregated at inhibitory synapses. ELife, 9, e52852. https://doi.org/10.7554/eLife.52852

Stachniak, T. J., Sylwestrak, E. L., Scheiffele, P., Hall, B. J., & Ghosh, A. (2019). Elfn1-Induced Constitutive Activation of mGluR7 Determines Frequency-Dependent Recruitment of Somatostatin Interneurons. The Journal of Neuroscience, 39(23), 4461 LP – 4474. https://doi.org/10.1523/JNEUROSCI.2276-18.2019

Tomioka, N. H., Yasuda, H., Miyamoto, H., Hatayama, M., Morimura, N., Matsumoto, Y., … Aruga, J. (2014). Elfn1 recruits presynaptic mGluR7 in trans and its loss results in seizures. Nature Communications. https://doi.org/10.1038/ncomms5501

Udakis, M., Pedrosa, V., Chamberlain, S. E. L., Clopath, C., & Mellor, J. R. (2020). Interneuron-specific plasticity at parvalbumin and somatostatin inhibitory synapses onto CA1 pyramidal neurons shapes hippocampal output. Nature Communications, 11(1), 4395. https://doi.org/10.1038/s41467-020-18074-8

Author Response:

Evaluation Summary:

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

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

Reviewer #1 (Public Review):

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

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

Major Comments:

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

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

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

Author Response

Reviewer #1:

The Lambowitz group has developed thermostable group II intron reverse transcriptases (TGIRTs) that strand switch and also have trans-lesion activity to provide a much wider view of RNA species analyzed by massively parallel RNA sequencing. In this manuscript they use several improvements to their methodology to identify RNA biotypes in human plasma pooled from several healthy individuals. Additionally, they implicate binding by proteins (RBPs) and nuclease-resistant structures to explain a fraction of the RNAs observed in plasma. Generally I find the study fascinating and argue that the collection of plasma RNAs described is an important tool for those interested in extracellular RNAs. I think the possibility that RNPs are protecting RNA fragments in circulation is exciting and fits with elegant studies of insects and plants where RNAs are protected by this mechanism and are transmitted between species.

I have one major comment for the authors to consider. In my view the use of pooled plasma samples prevented the important opportunity to provide a glimpse on human variation in plasma RNA biotypes. This significantly limits the use of this information to begin addressing RNA biotypes as biomarkers. While I realize that data from multiple individuals represents a significant undertaking and may be beyond the scope of this manuscript, I urge the authors to do two things: (1) downplay the significance of the current study on the development of biomarkers in the current manuscript (e.g., in the abstract and discussion - e.g., "The ability of TGIRT-seq to simultaneously profile a wide variety of RNA biotypes in human plasma, including structured RNAs that are intractable to retroviral RTs, may be advantageous for identifying optimal combinations of coding and non-coding RNA biomarkers for human diseases."). (2) Carry out an analysis in multiple individuals - including racially diverse individuals - very important information will come of this - similar to C. Burge's important study in Nature ~2008 where it was clear that there is important individual variation in alternative splicing decisions - very likely genetically determined. This second suggestion could be added here or constitute a future manuscript.

The identification of biomarkers in human plasma is an important application of this study, as was noted by reviewer 3 -- "Overall, this study provided a robust dataset and expanded picture of RNA biotypes one can detect in human plasma. This is valuable because the findings may have implications in biomarker identification in disease contexts." The present manuscript lays the foundation for such applications, which we have been carrying out in parallel. In one such study in collaboration with Dr. Naoto Ueno (MD Anderson), we used TGIRT-seq to identify combinations of mRNA and non-coding RNA biomarkers in FFPE-tumor slices, PBMCs and plasma from inflammatory breast cancer patients compared to non-IBC breast cancer patients and healthy controls (manuscript in preparation; data presented publicly in seminars), and in another, we explored the potential of using full-length excised intron (FLEXI) RNAs as biomarkers. In the latter study, we identified >8,000 FLEXI RNAs in different human cell lines and tissues and found that they are expressed in a cell-type specific manner, including hundreds of differences between matched tumor and healthy tissues from breast cancer patients and cell lines. A manuscript describing the latter findings was submitted for publication after this one and has been uploaded as a pertinent related manuscript. This new manuscript follows directly from the last sentence of the present manuscript and fully references the BioRxiv preprint currently under review for eLife.

Reviewer #2:

Yao et al used thermostable group II intron reverse transcriptase sequencing (TGIRT-seq) to study apheresis plasma samples. The first interesting discovery is that they had identified a number of mRNA reads with putative binding sites of RNA-binding proteins. A second interesting discovery from this work is the detection of full-length excised intron RNAs.

I have the following comments:

1) One doubt that I have is how representative is apheresis plasma when compared with plasma that one obtains through routine centrifugation of blood. The authors have reported the comparison of apheresis plasma versus a single male plasma in a previous publication. I think that to address this important question, a much increased number of samples would be necessary.

Detailed comparison of plasma prepared by apheresis to that prepared by centrifugation would require a separate large-scale study, preferably by multiple laboratories using different methods to prepare plasma. However, our impression both from our findings and from the literature (Valbonesi et al. 2001, cited in the manuscript) is that apheresis-prepared plasma has very low levels of cellular contamination (required to meet clinical standards) compared to plasma prepared by centrifugation, even with protocols designed to minimize contamination from intact 4 or broken cell (e.g., preparing plasma from freshly drawn blood, centrifugation into a Ficoll cushion to minimize cell breakage, and carefully avoiding contamination from sedimented cells).

We do have additional information about the degree of variation in protein-coding gene transcripts detected by TGIRT-seq in plasma samples prepared by centrifugation from five healthy females controls in our collaborative study with Dr. Naoto Ueno (M.D. Anderson; see above), and we have added it to the manuscript citing a manuscript in preparation with permission from Dr. Ueno (p. 10, beginning line 6 from bottom) as follows:

“The identities and relative abundances of different protein-coding gene transcripts in the apheresis-prepared plasma were broadly similar to those in the previous TGIRT analysis of plasma prepared by Ficoll-cushion sedimentation of blood from a healthy male individual (Qin et al., 2016) (r = 0.62-0.80; Figure 3C) and between high quality plasma samples similarly prepared from five healthy females in a collaborative study with Dr. Naoto Ueno, M.D. Anderson (r = 0.53-0.67; manuscript in preparation).” See Author Response Image below.

2) For the important conclusion of the presence of binding sites of RNA-binding proteins in a proportion of apheresis plasma mRNA molecules, the authors need to explore whether there is any systemic difference in terms of mapping quality (i.e. mapping quality scores in alignment results) between RBP binding sites and non-RBP binding sites, so that any artifacts of peaks caused by the alignment issues occurring in RNA-seq analysis could be revealed and solved subsequently. Furthermore, it would be prudent to perform immunoprecipitation experiments to confirm this conclusion in at least a proportion of the mRNA.

We have added a figure panel comparing MAPQ scores for reads from peaks containing RBP-binding site to other long RNA reads (Figure 4–figure supplement 2A) and have added further details about the methods used to obtain peaks with high quality reads, including the following (p. 13, beginning line 3 from the bottom).

“After further filtering to remove read alignments with MAPQ <30 (a cutoff that eliminates reads mapping equally well at more than one locus) or ≥5 mismatches from the mapped locus, we were left with 950 high confidence peaks ranging in size from 59 to 1,207 nt with ≥5 high quality read alignments at the peak maximum (Supplementary File).”

3) In Fig. 2D, one can observe that there are clearly more RNA reads in TGIRT-seq located in the 1st exon of ACTB, compared with SMART-seq. Is there any explanation? Will this signal be called as a peak (a potential RBP binding site) in the peak calling analysis (MACS2)? Is ACTB supposed to be bound by a certain RBP?

The higher coverage of the ACTB 5'-exon in the TGIRT-seq datasets reflects in part the more uniform 5' to 3' coverage of mRNA sequences by TGIRT-seq compared to SMART-seq, which is biased for 3'-mRNA sequences that have poly(A) tails (current Figure 3F). The signal in the first exon of ACTB was in fact called as a peak by MACS2 (peak ID#893, Supplementary file), which overlapped an annotated binding site for SERBP1 (see Supplementary File).

4) For Fig 2A, it would be informative for the comparison of RNA yield and RNA size profile among different protocols if the author also added the results of TGIRT-seq.

Figure 3D (previously Figure 2A) shows a bioanalyzer trace of PCR amplified cDNAs obtained by SMART-Seq. These cDNAs correspond to 3' mRNA sequences that have poly(A) tails and are not comparable to the bioanalyzer profiles of plasma RNA (Figure 1–figure supplement 1) or read span distributions in the TGIRT-seq datasets (Figure 1B), which are dominated by sncRNAs. The coverage plots for protein-coding gene transcripts show that TGIRT-seq captures mRNA fragments irrespective of length that span the entire mRNA sequence, whereas SMART-seq is biased for 3' sequences linked to poly(A) (Figure 3F). We also note that coverage plots and mRNAs detected by TGIRT-seq remain similar, even if the plasma RNA is chemically fragmented prior to TGIRT-seq library construction (Figure 3F and Figure 3–figure supplement 2).

5) As shown in Figure 4 C (the track of RBP binding sites), it seems quite pervasive in some gene regions. How many RBP binding sites from public eCLIP-seq results are used for overlapping peaks present in TGIRT-seq of plasma RNA? What percentage of plasma RNA reads have fallen within RBP binding sites? Are those peaks present in TGRIT-seq significantly enriched in RBPs binding regions?

Some of these points are addressed under Reviewer 1-comment #4. Additionally, we noted that 109 RBP-binding sites were searched in the original analysis, and we have now added further analyses for 150 RBPs currently available in ENCODE eCLIP datasets with and without irreproducible discovery rate (IDR) analysis (Figure 6 and Figure 6–figure supplement 1). We have also added a tab to the Supplementary File identifying the 109 and 150 RBPs whose binding sites were searched. The requested statistical analysis has been added in Figure 4–figure supplement 2C. The analysis shows that enrichment of RBP-binding site sequences in the 467 called peaks was statistically significant (p<0.001) (p. 14, para. 3, last sentence).

6) Since there is a considerable portion of TGIRT-seq reads related to simple repeat, one possible reason is likely the high abundance of endogenous repeat-related RNA species in plasma. Nonetheless, have authors studied whether the ligation steps in TGIRT-seq have any biases (e.g. GC content) when analyzing human reference RNAs and spike ins (page 4, paragraph 2)?

We have added a note to the manuscript indicating that although repeat RNAs constitute a high proportion of the called peaks, they do not constitute a similarly high proportion of the total RNA reads (Figure 1C; p. 18, para. 2, first sentence). The TGIRT-seq analysis of human reference RNAs and spike-ins showed that TGIRT-seq recapitulates the relative abundance of human transcripts and spike-in comparably to non-strand-specific TruSeq v2 and better than strand-specific TruSeq v3 (Nottingham et al. RNA 2016). Subsequently, we used miRNA reference sets for detailed analysis of TGIRT-seq biases, including developing a computer algorithm for bias correction based on a random forest regression model that provides insight into different factors that contribute to these biases (Xu et al. Sci. Report. 2019). Overall GC content does not make a significant contribution to TGIRT-seq biases (Figure 9 of Xu et al. Sci. Report, 2017). Instead, biases in TGIRT-seq are largely confined to the first three nucleotides at the 5'-end (due to bias of the thermostable 5' App DNA ligase used for 5' RNA-seq adapter addition) and the 3' nucleotide (due to TGIRT-template switching). These end biases are not expected to significantly impact the quantitation of repeat RNAs.

7) As described in Figure 2 legend, there are 0.25 million deduplicated reads for TGIRT-seq reads assigned to protein-coding genes transcripts which are far less than 2.18 million reads for SMART-seq. The authors need to discuss whether the current protocol of TGIRT-seq would cause potential dropouts in mRNA analysis, compared with SMART-seq?

We have added the following to the manuscript (p. 11, para. 1, line 15).

“The larger number of mRNA reads compared to TGIRT-seq (0.28 million) largely reflects that SMART-seq selectively profiles polyadenylated mRNAs, while TGIRT-seq profiles mRNAs together with other more abundant RNA biotypes. In addition, ultra low input SMART-Seq is not strand-specific, resulting in redundant sense and antisense strand reads (Figure 3–figure supplement 1).”

The manuscript contains the following statement regarding potential drop outs (p. 11, para. 2, line 1).

“A scatter plot comparing the relative abundance of transcripts originating from different genes showed that most of the polyadenylated mRNAs detected in DNase I-treated plasma RNA by ultra low input SMART-Seq were also detected by TGIRT-seq at similar TPM values when normalized for protein-coding gene reads (r=0.61), but with some, mostly lower abundance mRNAs undetected either by TGIRT-seq or SMART-Seq, and with SMART-seq unable to detect non-polyadenylated histone mRNAs, which are relatively abundant in plasma (Figure 3E and Figure 3–figure supplement 1).”

8) While scientific thought-provoking, the practical implication of the current work is still unclear. The authors have suggested that their work might have applications for biomarker development. Is it possible to provide one experimental example in the manuscript?

We addressed the relevance of the manuscript to biomarker identification and noted parallel studies that supports this application in the response to reviewer 1--comment 1. We have also modified the final paragraph of the Discussion (p. 30, para. 2).

“The ability of TGIRT-seq to simultaneously profile a wide variety of RNA biotypes in human plasma, including structured RNAs that are intractable to retroviral RTs, may be advantageous for identifying optimal combinations of coding and non-coding RNA biomarkers that could then be incorporated in target RNA panels for diagnosis and routine monitoring of disease progression and response to treatment. The finding that some mRNAs fragments persist in discrete called peaks suggests a strategy for identifying relatively stable mRNA regions that may be more reliably detected than other more labile regions in targeted liquid biopsies. Finally, we note that in addition to their biological and evolutionary interest, short full-length excised intron RNAs and intron RNA fragments, such as those identified here, may be uniquely well suited to serve as stable RNA biomarkers, whose expression is linked to that of numerous protein-coding genes."

Reviewer #3:

In this work, Yao and colleagues described transcriptome profiling of human plasma from healthy individuals by TGIRT-seq. TGIRT is a thermostable group II intron reverse transcriptase that offers improved fidelity, processivity and strand-displacement activity, as compared to standard retroviral RT, so that it can read through highly structured regions. Similar analysis was performed previously (ref. 20), but this study incorporated several improvements in library preparation including optimization of template switching condition and modified adapters to reduce primer dimer and introduce UMI. In their analysis, the authors detected a variety of structural RNA biotypes, as well as reads from protein-coding mRNAs, although the latter is in low abundance. Compared to SMART-Seq, TGIRT-seq also achieved more uniform read coverage across gene bodies. One novel aspect of this study is the peak analysis of TGIRT-seq reads, which revealed ~900 peaks over background. The authors found that these peaks frequently overlap with RBP binding sites, while others tend to have stable predicted secondary structures, which explains why these regions are protected from degradation in plasma. Overall, this study provided a robust dataset and expanded picture of RNA biotypes one can detect in human plasma. This is valuable because the findings may have implications in biomarker identification in disease contexts. On the other hand, the manuscript, in the current form, is relatively descriptive, and can be improved with a clearer message of specific knowledge that can be extracted from the data.

Specific points:

1) Several aspects of bioinformatics analysis can be clarified in more detail. For example, it is unclear how sequencing errors in UMI affect their de-duplication procedure. This is important for their peak analysis, so it should be explained clearly.

We have added details of the procedure used for de-duplication to the following paragraph in Materials and methods (p. 35, para. 2).

“Deduplication of mapped reads was done by UMI, CIGAR string, and genome coordinates (Quinlan, 2014). To accommodate base-calling and PCR errors and non-templated nucleotides that may have been added to the 3' ends of cDNAs during TGIRT-seq library preparation, one mismatch in the UMI was allowed during deduplication, and fragments with the same CIGAR string, genomic coordinates (chromosome start and end positions), and UMI or UMIs that differed by one nucleotide were collapsed into a single fragment. The counts for each read were readjusted to overcome potential UMI saturation for highly-expressed genes by implementing the algorithm described in (Fu et al., 2011), using sequencing tools (https://github.com/wckdouglas/sequencing_tools ).”

Also, it is not described how exon junction reads (when mapped to the genome) are handled in peak calling, although the authors did perform complementary analysis by mapping reads to the reference transcriptome.

We have added this to first sentence of the paragraph describing peak calling against the transcriptome reference (p. 16, line 4), which now reads as follows:

"Peak calling against the human genome reference sequence might miss RBP-binding sites that are close to or overlap exon junctions, as such reads were treated by MACS2 as long reads that span the intervening intron."

2) Overall, the authors provided convincing data that TGIRT-seq has advantages in detecting a wide range of RNA biotypes, especially structured RNAs, compared to other protocols, but these data are more confirmatory, rather than completely new findings (e.g., compared to ref. 20).

As indicated in the response to Reviewer 1, comment 2, we modified the first paragraph of the Discussion to explicitly describe what is added by the present manuscript compared to Qin et al. RNA 2016 (p. 24, para. 2). Additionally, further analysis in response to the reviewers' comments resulted in the interesting finding that stress granule proteins comprised a high proportion of the RBPs whose binding sites were enriched in plasma RNAs (to our knowledge a completely new finding), consistent with a previously suggested link between RNP granules, EV packing, and RNA export (p. 16, last sentence; data shown in Figure 6 and Figure 6–figure supplement 1). Also highlighted in the Discussion p. 26, last sentence, continuing on p. 27).

3) The peak analysis is more novel. The authors observed that 50% of peaks in long RNAs overlap with eCLIP peaks. However, there is no statistical analysis to show whether this overlap is significant or simply due to the pervasive distribution of eCLIP peaks. In fact, it was reported by the original authors that eCLIP peaks cover 20% of the transcriptome.

We have added statistical analysis, which shows that the enrichment of RBP-binding sites in the 467 called peaks is statistically significant at p<0.001 (p. 14, para. 3, last sentence; Figure 4–Figure supplement 2C), as well as scatter plots identifying proteins whose binding sites were more highly represented in plasma than cellular RNAs or vice versa (p. 16, last two sentences; Figure 6 and Figure 6-figure supplement 1).

Similarly, the authors found that a high proportion of remaining peaks can fold into stable secondary structures, but this claim is not backed up by statistics either.

First, near the beginning of the paragraph describing these findings, we added the following to provide a guide as to what can and can't be concluded by RNAfold (p. 17, line 6 from the bottom).

"To evaluate whether these peaks contained RNAs that could potentially fold into stable secondary structures, we used RNAfold, a tool that is widely used for this purpose with the understanding that the predicted structures remain to be validated and could differ under physiological conditions or due to interactions with proteins."

Second, at the end of the same paragraph, we have added the requested statistics (p. 18, para. 1, last sentence).

"Subject to the caveats above regarding conclusions drawn from RNAfold, simulations using peaks randomly generated from long RNA gene sequences indicated that enrichment of RNAs with more stable secondary structures (lower MFEs) in the called RNA peaks was statistically significant (p≤0.019; Figure 4–figure supplement 2D)."

4) Ranking of RBPs depends on the total number of RBP binding sites detected by eCLIP, which is determined by CLIP library complexity and sequencing depth. This issue should be at least discussed.

We have added scatter plots in Figure 6 and Figure 6–figure supplement 1, which show that the relative abundance of different RBP-binding sites detected in plasma differs markedly from that for cellular RNAs in the eCLIP datasets (both for the 109 RBPs searched initially and for 150 RBPs with or without irreproducible discovery rate (IDR) analysis from the ENCODE web site,) As mentioned in comments above, this analysis identified a number of RBP-binding sites that were substantially enriched in plasma RNAs compared to cellular RNAs or vice versa and led to what we think is the important new finding that plasma RNAs are enriched binding sites for a number of stress granule proteins (Figure 6 and Figure 6–figures supplement 1). We thank the reviewers for this and related comments that led to this additional analysis.

5) Enrichment of RBP binding sites and structured RNA in TGIRT-seq data is certainly consistent with one's expectation. However, the paper can be greatly improved if the authors can make a clearer case of what is new that can be learned, as compared to eCLIP data or other related techniques that purify and sequence RNA fragments crosslinked to proteins. What is the additional, independent evidence to show the predicted secondary structures are real?

Compared to CLIP and related methods, peak calling enables more facile identification of candidate RBPs and putatively structured RNAs for further analysis and may be particularly useful for the vanishingly small amounts of RNA present in plasma and other bodily fluids. New findings resulting from peak calling in the present manuscript include that plasma RNAs are enriched in binding sites for stress granule proteins (see above) and the discovery of a variety of novel RNAs, including the full-length excised intron RNAs first identified here and subsequently studied in cellular RNAs in the Yao et al. pertinent submitted manuscript. We also note that peak calling enables the identification of protein-protected and structured mRNA regions that are relatively stable in plasma and may be more reliably detected in targeted liquid biopsy assays than are more labile mRNA regions (p. 17, para. 1, last sentence; and p. 30, para. 2, beginning on line 5).

6) The authors should probably discuss how alignment errors can potentially affect detection of repetitive regions.

In the Empirical Bayes method that we used for the analysis of repeats, repeat sequences were quantified by aggregate counts irrespective of the genomic locus to which they mapped (Materials and methods, p. 38, para. 2, line 5), which should not be affected by alignment errors.

7) Many figures are IGV screenshots, which can be difficult to follow. Some of them can probably be summarized to deliver the message better.

Some IGV-based figures are crucial for showing key features of the RNAs that are called as peaks (e.g., the predicted secondary structures of the full-length excised intron RNAs and intron RNA fragments). However, in the process of reformatting, we have switched in and added non-IGV main text figures including Figure 2 (microbiome analysis), Figure 3 (TGIRT-seq versus SMART-Seq), Figure 4 (repeats), and Figure 6 (new figure comparing relative abundance of RBP-binding sites in plasma versus cells).

Author Response:

Reviewer #1 (Public Review):

Strengths:

1) The model structure is appropriate for the scientific question.

2) The paper addresses a critical feature of SARS-CoV-2 epidemiology which is its much higher prevalence in Hispanic or Latino and Black populations. In this sense, the paper has the potential to serve as a tool to enhance social justice.

3) Generally speaking, the analysis supports the conclusions.

Other considerations:

1) The clean distinction between susceptibility and exposure models described in the paper is conceptually useful but is unlikely to capture reality. Rather, susceptibility to infection is likely to vary more by age whereas exposure is more likely to vary by ethnic group / race. While age cohort are not explicitly distinguished in the model, the authors would do well to at least vary susceptibility across ethnic groups according to different age cohort structure within these groups. This would allow a more precise estimate of the true effect of variability in exposures. Alternatively, this could be mentioned as a limitation of the the current model.

We agree that this would be an important extension for future work and have indicated this in the Discussion, along with the types of data necessary to fit such models:

“Fourth, due to data availability, we have only considered variability in exposure due to one demographic characteristic; models should ideally strive to also account for the effects of age on susceptibility and exposure within strata of race and ethnicity and other relevant demographics, such as socioeconomic status and occupation \cite{Mulberry2021-tc}. These models could be fit using representative serological studies with detailed cross-tabulated seropositivity estimates.”

2) I appreciated that the authors maintained an agnostic stance on the actual value of HIT (across the population & within ethnic groups) based on the results of their model. If there was available data, then it might be possible to arrive at a slightly more precise estimate by fitting the model to serial incidence data (particularly sorted by ethnic group) over time in NYC & Long Island. First, this would give some sense of R_effective. Second, if successive waves were modeled, then the shift in relative incidence & CI among these groups that is predicted in Figure 3 & Sup fig 8 may be observed in the actual data (this fits anecdotally with what I have seen in several states). Third, it may (or may not) be possible to estimate values of critical model parameters such as epsilon. It would be helpful to mention this as possible future work with the model.

Caveats about the impossibility of truly measuring HIT would still apply (due to new variants, shifting use & effective of NPIs, etc….). However, as is, the estimates of possible values for HIT are so wide as to make the underlying data used to train the model almost irrelevant. This makes the potential to leverage the model for policy decisions more limited.

We have highlighted this important limitation in the Discussion:

“Finally, we have estimated model parameters using a single cross-sectional serosurvey. To improve estimates and the ability to distinguish between model structures, future studies should use longitudinal serosurveys or case data stratified by race and ethnicity and corrected for underreporting; the challenge will be ensuring that such data are systematically collected and made publicly available, which has been a persistent barrier to research efforts \cite{Krieger2020-ss}. Addressing these data barriers will also be key for translating these and similar models into actionable policy proposals on vaccine distribution and non-pharmaceutical interventions.”

3) I think the range of R0 in the figures should be extended to go as as low as 1. Much of the pandemic in the US has been defined by local Re that varies between 0.8 & 1.2 (likely based on shifts in the degree of social distancing). I therefore think lower HIT thresholds should be considered and it would be nice to know how the extent of assortative mixing effects estimates at these lower R_e values.

We agree this would be of interest and have extended the range of R0 values. Figure 1 has been updated accordingly (see below); we also updated the text with new findings: “After fitting the models across a range of $\epsilon$ values, we observed that as $\epsilon$ increases, HITs and epidemic final sizes shifted higher back towards the homogeneous case (Figure \ref{fig:model2}, Figure 1-figure supplement 4); this effect was less pronounced for $R_0$ values close to 1.”

Figure 1: Incorporating assortativity in variable exposure models results in increased HITs across a range of $R_0$ values. Variable exposure models were fitted to NYC and Long Island serosurvey data.

4) line 274: I feel like this point needs to be considered in much more detail, either with a thoughtful discussion or with even with some simple additions to the model. How should these results make policy makers consider race and ethnicity when thinking about the key issues in the field right now such as vaccine allocation, masking, and new variants. I think to achieve the maximal impact, the authors should be very specific about how model results could impact policy making, and how we might lower the tragic discrepancies associated with COVID. If the model / data is insufficient for this purpose at this stage, then what type of data could be gathered that would allow more precise and targeted policy interventions?

We have conducted additional analyses exploring the important suggestion by the reviewers that social distancing could affect these conclusions. The text and figures have been updated accordingly:

“Finally, we assessed how robust these findings were to the impact of social distancing and other non- pharmaceutical interventions (NPIs). We modeled these mitigation measures by scaling the transmission

rate by a factor $\alpha$ beginning when 5\% cumulative incidence in the population was reached. Setting the duration of distancing to be 50 days and allowing $\alpha$ to be either 0.3 or 0.6 (i.e. a 70\% or 40\% reduction in transmission rates, respectively), we assessed how the $R_0$ versus HIT and final epidemic size relationships changed. We found that the $R_0$ versus HIT relationship was similar to in the unmitigated epidemic (Figure 1-figure supplement 5). In contrast, final epidemic sizes depended on the intensity of mitigation measures, though qualitative trends across models (e.g. increased assortativity leads to greater final sizes) remained true (Figure 1-figure supplement 6). To explore this further, we systematically varied $\alpha$ and the duration of NPIs while holding $R_0$ constant at 3. We found again that the HIT was consistent, whereas final epidemic sizes were substantially affected by the choice of mitigation parameters (Figure 1-figure supplement 7); the distribution of cumulative incidence at the point of HIT was also comparable with and without mitigation measures (Figure 2-figure supplement 8). The most stringent NPI intensities did not necessarily lead to the smallest epidemic final sizes, an idea which has been explored in studies analyzing optimal control measures \cite{Neuwirth2020- nb,Handel2007-ee}. Longitudinal changes in incidence rate ratios also were affected by NPIs, but qualitative trends in the ordering of racial and ethnic groups over time remained consistent (Figure 3- figure supplement 3).

Figure 1-figure supplement 6: Final epidemic sizes versus $R_0$ in variable exposure models with mitigation measures for $\alpha = 0.3$ (top) and $\alpha = 0.6$ (bottom). NPIs were initiated when cumulative incidence reached 5\% in all models and continued for 50 days. Models were fitted to NYC and Long Island serosurvey data.

Figure 1-figure supplement 7: Sensitivity analysis on the impact of intensity and duration of NPIs on final epidemic sizes. HIT values for the same mitigation parameters were 46.4 $\pm$ 0.5\% (range). The smallest final size, corresponding to $\alpha = 0.6$ and duration = 100, was 51\%. Census-informed assortativity models were fit to Long Island seroprevalence data. NPIs were initiated when cumulative incidence reached 5\% in all models.

See points 1 and 2 above for examples of additional data required.

Minor issues:

-This is subjective but I found the words "active" and "high activity" to describe increases in contacts per day to be confusing. I would just say more contacts per day. It might help to change "contacts" to "exposure contacts" to emphasize that not all contacts are high risk.

To clarify this, we have replaced instances of “activity level” (and similar) with “total contact rate”, indicating the total number of contacts per unit time per individual; e.g. “The estimated total contact rate ratios indicate higher contacts for minority groups such as Hispanics or Latinos and non-Hispanic Black people, which is in line with studies using cell phone mobility data \cite{Chang2020-in}; however, the magnitudes of the ratios are substantially higher than we expected given the findings from those studies.”

We have also clarified our definition of contacts: “We define contacts to be interactions between individuals that allow for transmission of SARS-CoV-2 with some non-zero probability.”

-The abstract has too much jargon for a generalist journal. I would avoid words like "proportionate mixing" & "assortative" which are very unique to modeling of infectious diseases unless they are first defined in very basic language.

We have revised the abstract to convey these same concepts in a more accessible manner: “A simple model where interactions occur proportionally to contact rates reduced the HIT, but more realistic models of preferential mixing within groups increased the threshold toward the value observed in homogeneous populations.”

-I would cite some of the STD models which have used similar matrices to capture assortative mixing.

We have added a reference in the assortative mixing section to a review of heterogeneous STD models: “Finally, under the \textit{assortative mixing} assumption, we extended this model by partitioning a fraction $\epsilon$ of contacts to be exclusively within-group and distributed the rest of the contacts according to proportionate mixing (with $\delta_{i,j}$ being an indicator variable that is 1 when $i=j$ and 0 otherwise) \cite{Hethcote1996-bf}:”

-Lines 164-5: very good point but I would add that members of ethnic / racial groups are more likely to be essential workers and also to live in multigenerational houses

We have added these helpful examples into the text: “Variable susceptibility to infection across racial and ethnic groups has been less well characterized, and observed disparities in infection rates can already be largely explained by differences in mobility and exposure \cite{Chang2020-in,Zelner2020- mb,Kissler2020-nh}, likely attributable to social factors such as structural racism that have put racial and ethnic minorities in disadvantaged positions (e.g., employment as frontline workers and residence in overcrowded, multigenerational homes) \cite{Henry_Akintobi2020-ld,Thakur2020-tw,Tai2020- ok,Khazanchi2020-xu}.”

-Line 193: "Higher than expected" -> expected by who?

We have clarified this phrase: “The estimated total contact rate ratios indicate higher exposure contacts for minority groups such as Hispanics or Latinos and non-Hispanic Black people, which is in line with studies using cell phone mobility data \cite{Chang2020-in}; however, the magnitudes of the ratios are substantially higher than we expected given the findings from those studies.”

-A limitation that needs further mention is that fact that race & ethnic group, while important, could be sub classified into strata that inform risk even more (such as SES, job type etc….)

We agree and have added this to the Discussion: “Fourth, due to data availability, we have only considered variability in exposure due to one demographic characteristic; models should ideally strive to also account for the effects of age on susceptibility and exposure within strata of race and ethnicity and other relevant demographics, such as socioeconomic status and occupation \cite{Mulberry2021-tc}. These models could be fit using representative serological studies with detailed cross-tabulated seropositivity estimates.”

Reviewer #2 (Public Review):

Overall I think this is a solid and interesting piece that is an important contribution to the literature on COVID-19 disparities, even if it does have some limitations. To this point, most models of SARS-CoV-2 have not included the impact of residential and occupational segregation on differential group-specific covid outcomes. So, the authors are to commended on their rigorous and useful contribution on this valuable topic. I have a few specific questions and concerns, outlined below:

We thank the reviewer for the supportive comments.

1) Does the reliance on serosurvey data collected in public places imply a potential issue with left-censoring, i.e. by not capturing individuals who had died? Can the authors address how survival bias might impact their results? I imagine this could bring the seroprevalence among older people down in a way that could bias their transmission rate estimates.

We have included this important point in the limitations section on potential serosurvey biases: “First, biases in the serosurvey sampling process can substantially affect downstream results; any conclusions drawn depend heavily on the degree to which serosurvey design and post-survey adjustments yield representative samples \cite{Clapham2020-rt}. For instance, because the serosurvey we relied on primarily sampled people at grocery stores, there is both survival bias (cumulative incidence estimates do not account for people who have died) and ascertainment bias (undersampling of at-risk populations that are more likely to self-isolate, such as the elderly) \cite{Rosenberg2020-qw,Accorsi2021-hx}. These biases could affect model estimates if, for instance, the capacity to self-isolate varies by race or ethnicity -- as suggested by associations of neighborhood-level mobility versus demographics \cite{Kishore2020- sy,Kissler2020-nh} -- leading to an overestimate of cumulative incidence and contact rates in whites.”

2) It might be helpful to think in terms of disparities in HITs as well as disparities in contact rates, since the HIT of whites is necessarily dependent on that of Blacks. I'm not really disagreeing with the thrust of what their analysis suggests or even the factual interpretation of it. But I do think it is important to phrase some of the conclusions of the model in ways that are more directly relevant to health equity, i.e. how much infection/vaccination coverage does each group need for members of that group to benefit from indirect protection?

We agree with this important point and indeed this was the goal, in part, of the analyses in Figure 2. We have added additional text to the Discussion highlighting this: “Projecting the epidemic forward indicated that the overall HIT was reached after cumulative incidence had increased disproportionately in minority groups, highlighting the fundamentally inequitable outcome of achieving herd immunity through infection. All of these factors underscore the fact that incorporating heterogeneity in models in a mechanism-free manner can conceal the disparities that underlie changes in epidemic final sizes and HITs. In particular, overall lower HIT and final sizes occur because certain groups suffer not only more infection than average, but more infection than under a homogeneous mixing model; incorporating heterogeneity lowers the HIT but increases it for the highest-risk groups (Figure \ref{fig:hitcomp}).”

For vaccination, see our response to Reviewer #1 point 4.

3) The authors rely on a modified interaction index parameterized directly from their data. It would be helpful if they could explain why they did not rely on any sources of mobility data. Are these just not broken down along the type of race/ethnicity categories that would be necessary to complete this analysis? Integrating some sort of external information on mobility would definitely strengthen the analysis.

This is a great suggestion, but this type of data has generally not been available due to privacy concerns from disaggregating mobility data by race and ethnicity (Kishore et al., 2020). Instead, we modeled NPIs as mentioned in Reviewer #1 point 4, with the caveat that reduction in mobility was assumed to be identical across groups. We added this into the text explicitly as a limitation: “Third, we have assumed the impact of non-pharmaceutical interventions such as stay-at-home policies, closures, and the like to equally affect racial and ethnic groups. Empirical evidence suggests that during periods of lockdown, certain neighborhoods that are disproportionately wealthy and white tend to show greater declines in mobility than others \cite{Kishore2020-sy,Kissler2020-nh}. These simplifying assumptions were made to aid in illustrating the key findings of this model, but for more detailed predictive models, the extent to which activity level differences change could be evaluated using longitudinal contact survey data \cite{Feehan2020-ta}, since granular mobility data are typically not stratified by race and ethnicity due to privacy concerns \cite{Kishore2020-mg}.”

Reviewer #3 (Public Review):

Ma et al investigate the effect of racial and ethnic differences in SARS-CoV-2 infection risk on the herd immunity threshold of each group. Using New York City and Long Island as model settings, they construct a race/ethnicity-structured SEIR model. Differential risk between racial and ethnic groups was parameterized by fitting each model to local seroprevalence data stratified demographically. The authors find that when herd immunity is reached, cumulative incidence varies by more than two fold between ethnic groups, at approximately 75% of Hispanics or Latinos and only 30% of non-Hispanic Whites.

This result was robust to changing assumptions about the source of racial and ethnic disparities. The authors considered differences in disease susceptibility, exposure levels, as well as a census-driven model of assortative mixing. These results show the fundamentally inequitable outcome of achieving herd immunity in an unmitigated epidemic.

The authors have only considered an unmitigated epidemic, without any social distancing, quarantine, masking, or vaccination. If herd immunity is achieved via one of these methods, particularly vaccination, the disparities may be mitigated somewhat but still exist. This will be an important question for epidemiologists and public health officials to consider throughout the vaccine rollout.

We thank the reviewer for the detailed and helpful summary and suggestions.

Author Response

Summary: A major tenet of plant pathogen effector biology has been that effectors from very different pathogens converge on a small number of host targets with central roles in plant immunity. The current work reports that effectors from two very different pathogens, an insect and an oomycete, interact with the same plant protein, SIZ1, previously shown to have a role in plant immunity. Unfortunately, apart from some technical concerns regarding the strength of the data that the effectors and SIZ1 interact in plants, a major limitation of the work is that it is not demonstrated that the effectors alter SIZ1 activity in a meaningful way, nor that SIZ1 is specifically required for action of the effects.

We thank the editor and reviewers for their time to review our manuscript and their helpful and constructive comments. The reviews have helped us focus our attention on additional experiments to test the hypothesis that effectors Mp64 (from an aphid) and CRN83-152 (from an oomycete) indeed alter SIZ1 activity or function. We have revised our manuscript and added the following data:

1) Mp64, but not CRN83-152, stabilizes SIZ1 in planta. (Figure 1 in the revised manuscript).

2) AtSIZ1 ectopic expression in Nicotiana benthamiana triggers cell death from 3-4 days after agroinfiltration. Interestingly CRN83-152_6D10 (a mutant of CRN83-152 that has no cell death activity), but not Mp64, enhances the cell death triggered by AtSIZ1 (Figure 2 in the revised manuscript).

For 1) we have added the following panel to Figure 1 as well as three biological replicates of the stabilisation assays in the Supplementary data (Fig S3):

Figure 1 panel C. Stabilisation of SIZ1 by Mp64. Western blot analyses of protein extracts from agroinfiltrated leaves expressing combinations of GFP-GUS, GFP Mp64 and GFP-CRN83_152_6D10 with AtSIZ1-myc or NbSIZ1-myc. Protein size markers are indicated in kD, and equal protein amounts upon transfer is shown upon ponceau staining (PS) of membranes. Blot is representative of three biological replicates , which are all shown in supplementary Fig. S3. The selected panels shown here are cropped from Rep 1 in supplementary Fig. S3.

For 2) we have added the folllowing new figure (Fig. 2 in the revised manuscript):

Fig. 2. SIZ1-triggered cell death in N. benthamiana is enhanced by CRN83_152_6D10 but not Mp64. (A) Scoring overview of infiltration sites for SIZ1 triggered cell death. Infiltration site were scored for no symptoms (score 0), chlorosis with localized cell death (score 1), less than 50% of the site showing visible cell death (score 2), more than 50% of the site showing cell death (score 3). (B) Bar graph showing the proportions of infiltration sites showing different levels of cell death upon expression of AtSIZ1, NbSIZ1 (both with a C-terminal RFP tag) and an RFP control. Graph represents data from a combination of 3 biological replicates of 11-12 infiltration sites per experiment (n=35). (C) Bar graph showing the proportions of infiltration sites showing different levels of cell death upon expression of SIZ1 (with C-terminal RFP tag) either alone or in combination with aphid effector Mp64 or Phytophthora capsica effector CRN83_152_6D10 (both effectors with GFP tag), or a GFP control. Graph represent data from a combination of 3 biological replicates of 11-12 infiltration sites per experiment (n=35).

Our new data provide further evidence that SIZ1 function is affected by effectors Mp64 (aphid) and CRN83-152 (oomycete), and that SIZ1 likely is a vital virulence target. Our latest results also provide further support for distinct effector activities towards SIZ1 and its variants in other species. SIZ1 is a key immune regulator to biotic stresses (aphids, oomycetes, bacteria and nematodes), on which distinct virulence strategies seem to converge. The mechanism(s) underlying the stabilisation of SIZ1 by Mp64 is yet unclear. However, we hypothesize that increased stability of SIZ1, which functions as an E3 SUMO ligase, leads to increased SUMOylation activity towards its substrates. We surmise that SIZ1 complex formation with other key regulators of plant immunity may underpin these changes. Whether the cell death, triggered by AtSIZ1 upon transient expression in Nicotiana benthamiana, is linked to E3 SUMO ligase activity remains to be investigated. Expression of AtSIZ1 in a plant species other than Arabidopsis may lead to mistargeting of substrates, and subsequent activation of cell death. Dissecting the mechanistic basis of SIZ1 targeting by distinct pathogens and pests will be an important next step in addressing these hypotheses towards understanding plant immunity.

Reviewer #1:

In this manuscript, the authors suggest that SIZ1, an E3 SUMO ligase, is the target of both an aphid effector (Mp64 form M. persicae) and an oomycete effector (CRN83_152 from Phytophthora capsica), based on interaction between SIZ1 and the two effectors in yeast, co-IP from plant cells and colocalization in the nucleus of plant cells. To support their proposal, the authors investigate the effects of SIZ1 inactivation on resistance to aphids and oomycetes in Arabidopsis and N. benthamiana. Surprisingly, resistance is enhanced, which would suggest that the two effectors increase SIZ1 activity.

Unfortunately, not only do we not learn how the effectors might alter SIZ1 activity, there is also no formal demonstration that the effects of the effectors are mediated by SIZ1, such as investigating the effects of Mp64 overexpression in a siz1 mutant. We note, however, that even this experiment might not be entirely conclusive, since SIZ1 is known to regulate many processes, including immunity. Specifically, siz1 mutants present autoimmune phenotype, and general activation of immunity might be sufficient to attenuate the enhanced aphid susceptibility seen in Mp64 overexpressers.

To demonstrate unambiguously that SIZ1 is a bona fide target of Mp64 and CRN83_152 would require assays that demonstrate either enhanced SIZ1 accumulation or altered SIZ1 activity in the presence of Mp64 and CRN83_152.

The enhanced resistance upon knock-down/out of SIZ1 suggests pathogen and pest susceptibility requires SIZ1. We hypothesize that the effectors either enhance SIZ1 activity or that the effectors alter SIZ1 specificity towards substrates rather than enzyme activity itself. To investigate how effectors coopt SIZ1 function would require a comprehensive set of approaches and will be part of our future work. While we agree that this aspect requires further investigation, we think the proposed experiments go beyond the scope of this study.

After receiving reviewer comments, including on the quality of Figure 1, which shows western blots of co-immunoprecipitation experiments, we re-analyzed independent replicates of effector-SIZ1 coexpression/ co-immunoprecipitation experiments. The reviewer rightly pointed out that in the presence of Mp64, SIZ1 protein levels increase when compared to samples in which either the vector control or CRN83-152_6D10 are co-infiltrated. Through carefully designed experiments, we can now affirm that Mp64 co-expression leads to increased SIZ1 protein levels (Figure 1C and Supplementary Figure S3, revised manuscript). Our results offer both an explanation of different SIZ1 levels in the input samples (original submission, Figure 1A/B) as well as tantalizing new clues to the nature of distinct effector activities.

Besides, we were able to confirm a previous preliminary finding not included in the original submission that ectopic expression of AtSIZ1 in Nicotiana benthamiana triggers cell death (3/4 days after infiltration) and that CRN83-152_6D10 (which itself does not trigger cell death) enhances this phenotype.

We have considered overexpression of Mp64 in the siz1 mutant, but share the view that the outcome of such experiments will be far from conclusive.

In summary, we have added new data that further support that SIZ1 is a bonafide target of Mp64 and CRN83-152 (i.e. increased accumulation of SIZ1 in the presence of Mp64, and enhanced SIZ cell death activation in the presence of CRN83-152_6D10).

Reviewer #2:

The study provides evidence that an aphid effector Mp64 and a Phytophthora capsici effector CRN83_152 can both interact with the SIZ1 E3 SUMO-ligase. The authors further show that overexpression of Mp64 in Arabidopsis can enhance susceptibility to aphids and that a loss-of-function mutation in Arabidopsis SIZ1 or silencing of SIZ1 in N. benthamiana plants lead to increased resistance to aphids and P. capsici. On siz1 plants the aphids show altered feeding patterns on phloem, suggestive of increased phloem resistance. While the finding is potentially interesting, the experiments are preliminary and the main conclusions are not supported by the data.

Specific comments:

The suggestion that SIZ1 is a virulence target is an overstatement. Preferable would be knockouts of effector genes in the aphid or oomycete, but even with transgenic overexpression approaches, there are no direct data that the biological function of the effectors requires SIZ1. For example, is SIZ1 required for the enhanced susceptibility to aphid infestation seen when Mp64 is overexpressed? Or does overexpression of SIZ1 enhance Mp64-mediated susceptibility?

What do the effectors do to SIZ1? Do they alter SUMO-ligase activity? Or are perhaps the effectors SUMOylated by SIZ1, changing effector activity?

We agree that having effector gene knock-outs in aphids and oomycetes would be ideal for dissecting effector mediated targeting of SIZ1. Unfortunately, there is no gene knock-out system established in Myzus persicae (our aphid of interest), and CAS9 mediated knock-out of genes in Phytophthora capsici has not been successful in our lab as yet, despite published reports. Moreover, repeated attempts to silence Mp64, other effector and non-effector coding genes, in aphids (both in planta and in vitro) have not been successful thus far, in our hands. As detailed in our response to Reviewer 1, we considered the use of transgenic approaches not appropriate as data interpretation would become muddied by the strong immunity phenotype seen in the siz1-2 mutant.

As stated before, we hypothesize that the effectors either enhance SIZ1 activity or alter SIZ1 substrate specificity. Mp64-induced accumulation of SIZ1 could form the basis of an increase in overall SIZ1 activity. This hypothesis, however, requires testing. The same applies to the enhanced SIZ1 cell death activation in the presence of CRN83-152_6D10.

Whilst our new data support our hypothesis that effectors Mp64 and CRN83-152 affect SIZ1 function, how exactly these effectors trigger susceptibility, requires significant work. Given the substantial effort needed and the research questions involved, we argue that findings emanating from such experiments warrant standalone publication.

While stable transgenic Mp64 overexpressing lines in Arabidopsis showed increased susceptibility to aphids, transient overexpression of Mp64 in N. benthamiana plants did not affect P. capsici susceptibility. The authors conclude that while the aphid and P. capsici effectors both target SIZ1, their activities are distinct. However, not only is it difficult to compare transient expression experiments in N. benthamiana with stable transgenic Arabidopsis plants, but without knowing whether Mp64 has the same effects on SIZ1 in both systems, to claim a difference in activities remains speculative.

We agree that we cannot compare effector activities between different plant species. We carefully considered every statement regarding results obtained on SIZ1 in Arabidopsis and Nicotiana benthamiana. We can, however, compare activities of the two effectors when expressed side by side in the same plant species. In our original submission, we show that expression of CRN83 152 but not Mp64 in Nicotiana benthamiana enhances susceptibility to Phytophthora capsici. In our revised manuscript, we present new data showing distinct effector activities towards SIZ1 with regards to 1) enhanced SIZ1 stability and 2) enhanced SIZ1 triggered cell death. These findings raise questions as to how enhanced SIZ1 stability and cell death activation is relevant to immunity. We aim to address these critical questions by addressing the mechanistic basis of effector-SIZ1 interactions.

The authors emphasize that the increased resistance to aphids and P. capsici in siz1 mutants or SIZ1 silenced plants are independent of SA. This seems to contradict the evidence from the NahG experiments. In Fig. 5B, the effects of siz1 are suppressed by NahG, indicating that the resistance seen in siz1 plants is completely dependent on SA. In Fig 5A, the effects of siz1 are not completely suppressed by NahG, but greatly attenuated. It has been shown before that SIZ1 acts only partly through SNC1, and the results from the double mutant analyses might simply indicate redundancy, also for the combinations with eds1 and pad4 mutants.

We emphasized that siz1-2 increased resistance to aphids is independent of SA, which is supported by our data (Figure 5A). Still, we did not conclude that the same applies to increased resistance to Phytophthora capsici (Figure 5B). In contrast, the siz1-2 enhanced resistance to P. capsici appears entirely dependent on SA levels, with the level of infection on the siz1-2/NahG mutants even slightly higher than on the NahG line and Col-0 plants. We exercise caution in the interpretation of this data given the significant impact SA signalling appears to have on Phytophthora capsici infection.

The reviewer commented on the potential for functional redundancy in the siz1-2 double mutants. Unfortunately, we are unsure what redundancy s/he is referring to. SNC1, EDS1, and PAD4 all are components required for immunity, and their removal from the immune signalling network (using the mutations in the lines we used here) impairs immunity to various plant pathogens. The siz1-2 snc1-11, siz1-2 eds1-2, and siz1-2 pad4-1 double mutants have similar levels of susceptibility to the bacterial pathogen Pseudomonas syringae when compared to the corresponding snc1-11, eds1-2 and pad4-1 controls (at 22oC). These previous observations indicate that siz1 enhanced resistance is dependent on these signalling components (Hammoudi et al., 2018, Plos Genetics).

In contrast to this, we observed a strong siz1 enhanced resistance phenotype in the absence of snc1- 11, eds1 2 and pad4-1. Notably, the siz1-2 snc1-11 mutant does not appear immuno-compromised when compared to siz1-2 in fecundity assays, indicating that the siz1-2 phenotype is independent of SNC1. In our view, these data suggest that signalling components/pathways other than those mediated by SNC1, EDS1, and PAD4 are involved. We consider this to be an exciting finding as our data points to an as of yet unknown SIZ1-dependent signalling pathway that governs immunity to aphids.

How do NahG or Mp64 overexpression affect aphid phloem ingestion? Is it the opposite of the behavior on siz1 mutants?

We have not performed further EPG experiments on additional transgenic lines used in the aphid assay. These experiments are quite challenging and time consuming. Moreover, accommodating an experimental set-up that allows us to compare multiple lines at the same time is not straightforward. Considering that NahG did not affect aphid performance (Figure 5A), we do not expect to see an effect on phloem ingestion.

Author Response

1) Please comment on why many of the June samples failed to provide sufficient sequence information, especially since not all of them had low yields (supp table 2 and supp figure 5).

An extended paragraph about experimental intricacies of our study has been added to the Discussion. It has also been also slightly restructured to give a better and wider overview of how future freshwater monitoring studies using nanopore sequencing can be improved (page 18, lines 343-359).

We wish to highlight that all three MinION sequencing runs here analysed feature substantially higher data throughput than that of any other recent environmental 16S rRNA sequencing study with nanopore technology, as recently reviewed by Latorre-Pérez et al. (Biology Methods and Protocols 2020, doi:10.1093/biomethods/bpaa016). One of this work's sequencing runs has resulted in lower read numbers for water samples collected in June 2018 (~0.7 Million), in comparison to the ones collected in April and August 2018 (~2.1 and ~5.5 Million, respectively). While log-scale variabilities between MinION flow cell throughput have been widely reported for both 16S and shotgun metagenomics approaches (e.g. see Latorre-Pérez et al.), the count of barcode-specific 16S reads is nevertheless expected to be correlated with the barcode-specific amount of input DNA within a given sequencing run. As displayed in Supplementary Figure 7b, we see a positive, possibly logarithmic trend between the DNA concentration after 16S rDNA amplification and number of reads obtained. With few exceptions (April-6, April-9.1 and Apri-9.2), we find that sample pooling with original 16S rDNA concentrations of ≳4 ng/µl also results in the surpassing of the here-set (conservative) minimum read threshold of 37,000 for further analyses. Conversely, all June samples that failed to reach 37,000 reads did not pass the input concentration of 4 ng/µl, despite our attempt to balance their quantity during multiplexing.

We reason that such skews in the final barcode-specific read distribution would mainly arise from small concentration measurement errors, which undergo subsequent amplification during the upscaling with comparably large sample volume pipetting. While this can be compensated for by high overall flow cell throughput (e.g. see August-2, August-9.1, August-9.2), we think that future studies with much higher barcode numbers can circumvent this challenge by leveraging an exciting software solution: real-time selective sequencing via “Read Until”, as developed by Loose et al. (Nature Methods 2016, doi:10.1038/nmeth.3930). In the envisaged framework, incoming 16S read signals would be in situ screened for the sample-barcode which in our workflow is PCR-added to both the 5' and 3' end of each amplicon. Overrepresented barcodes would then be counterbalanced by targeted voltage inversion and pore "rejection" of such reads, until an even balance is reached. Lately, such methods have been computationally optimised, both through the usage of GPUs (Payne et al., bioRxiv 2020, https://doi.org/10.1101/2020.02.03.926956) and raw electrical signals (Kovaka et al., bioRxiv 2020, https://doi.org/10.1101/2020.02.03.931923).

2) It would be helpful if the authors could mention the amount (or proportion) of their sequenced 16S amplicons that provided species-level identification, since this is one of the advantages of nanopore sequencing.

We wish to emphasize that we intentionally refrained from reporting the proportion of 16S rRNA reads that could be classified at species level, since we are wary of any automated species level assignments even if the full-length 16S rRNA gene is being sequenced. While we list the reasons for this below, we appreciate the interest in the theoretical proportion of reads at species level assignment. We therefore re-analyzed our dataset, and now also provide the ratio of reads that could be classified at species level using Minimap2 (pages 16-17, lines 308-314).

To this end, we classified reads at species level if the species entry of the respective SILVA v.132 taxonomic ID was either not empty, or neither uncultured bacterium nor metagenome. Therefore, many unspecified classifications such as uncultured species of some bacterial genus are counted as species-level classifications, rendering our approach lenient towards a higher ratio of species level classifications. Still, the species level classification ratios remain low, on average at 16.2 % across all included river samples (genus-level: 65.6 %, family level: 76.6 %). The mock community, on the other hand, had a much higher species classification rate (>80 % in all three replicates), which is expected for a well-defined, well-referenced and divergent composition of only eight bacterial taxa, and thus re-validates our overall classification workflow.

On a theoretical level, we mainly refrain from automated across-the-board species level assignments because: (1) many species might differ by very few nucleotide differences within the 16S amplicon; distinguishing these from nanopore sequencing errors (here ~8 %) remains challenging (2) reference databases are incomplete and biased with respect to species level resolution, especially regarding certain environmental contexts; it is likely that species assignments would be guided by references available from more thoroughly studied niches than freshwater

Other recent studies have also shown that across-the-board species-level classification is not yet feasible with 16S nanopore sequencing, for example in comparison with Illumina data (Acharya et al., Scientific Reports 2019, doi:10.25405/data.ncl.9693533) which showed that “more reliable information can be obtained at genus and family level”, or in comparison with longer 16S-ITS-23S amplicons (Cusco et al., F1000Research 2019, doi: 10.12688/f1000research.16817.2), which “remarkably improved the taxonomy assignment at the species level”.

3) It is not entirely clear how the authors define their core microbiome. Are they reporting mainly the most abundant taxa (dominant core microbiome), and would this change if you look at a taxonomic rank below the family level? How does the core compare, for example, with other studies of this same river?

The here-presented core microbiome indeed represents the most abundant taxa, with relatively consistent profiles between samples. We used hierarchical clustering (Figure 4a, C2 and C4) on the bacterial family level, together with relative abundance to identify candidate taxa. Filtering these for median abundance > 0.1% across all samples resulted in 27 core microbiome families. To clarify this for the reader, we have added a new paragraph to the Material and Methods (section 2.7; page 29, lines 653-658).

We have also performed the same analysis on the bacterial genus level and now display the top 27 most abundant genera (median abundance > 0.2%), together with their corresponding families and hierarchical clustering analysis in a new Supplementary Figure 4. Overall, high robustness is observed with respect to the families of the core microbiome: out of the top 16 core families (Figure 4b), only the NS11-12 marine group family is not represented by the top 27 most abundant genera (Supplementary Figure 4b). We reason that this is likely because its corresponding genera are composed of relatively poorly resolved references of uncultured bacteria, which could thus not be further classified.

To the best of our knowledge, there are only two other reports that feature metagenomic data of the River Cam and its wastewater influx sources (Rowe et al., Water Science & Technology 2016, doi:10.2166/wst.2015.634; Rowe et al., Journal of Antimicrobial Chemotherapy 2017, doi:10.1093/jac/dkx017). While both of these primarily focus on the diversity and abundance of antimicrobial resistance genes using Illumina shotgun sequencing, they only provide limited taxonomic resolution on the river's core microbiome. Nonetheless, Rowe et al. (2016) specifically highlighted Sphingobium as the most abundant genus in a source location of the river (Ashwell, Hertfordshire). This genus belongs to the family of Sphingomonadaceae, which is also among the five most dominant families identified in our dataset. It thus forms part of what we define as the core microbiome of the River Cam (Figure 4b), and we have therefore highlighted this consistency in our manuscript's Discussion (page 17, lines 316-319).

4) Please consider revising the amount of information in some of the figures (such as figure 2 and figure 3). The resulting images are tiny, the legends become lengthy and the overall impact is reduced. Consider splitting these or moving some information to the supplements.

To follow this advice, we have split Figure 2 into two less compact figures. We have moved more detailed analyses of our classification tool benchmark to the supplement (now Supplementary Figure 1). Supplementary Figure 1 notably also contains a new summary of the systematic computational performance measurements of each classification tool (see minor suggestions).

Moreover, we here suggest that the original Figure 3 may be divided into two figures: one to visualise the sequencing output, data downsampling and distribution of the most abundant families (now Figure 3), and the other featuring the clustering of bacterial families and associated core microbiome (now Figure 4). We think that both the data summary and clustering/core microbiome analyses are of particular interest to the reader, and that they should be kept as part of the main analyses rather than the supplement – however, we are certainly happy to discuss alternative ideas with the reviewers and editors.

5) Given that the authors claim to provide a simple, fast and optimized workflow it would be good to mention how this workflow differs or provides faster and better analysis than previous work using amplicon sequencing with a MinION sequencer.

Data throughput, sequencing error rates and flow cell stability have seen rapid improvements since the commercial release of MinION in 2015. In consequence, bioinformatics community standards regarding raw data processing and integration steps are still lacking, as illustrated by a thorough recent benchmark of fast5 to fastq format "basecalling" methods (Wick et al., Genome Biology 2019, doi: 10.1186/s13059-019-1727-y).

Early on during our analyses, we noticed that a plethora of bespoke pipelines have been reported in recent 16S environmental surveys using MinION (e.g. Kerkhof et al., Microbiome 2017, 10.1186/s40168-017-0336-9; Cusco et al., F1000 Research 2018, 10.12688/f1000research.16817.2; Acharya et al., Scientific Reports 2019, 10.1038/s41598-019-51997-x; Nygaard et al., Scientific Reports 2020, doi: 10.1038/s41598-020-59771-0). This underlines a need for more unified bioinformatics standards of (full-length) 16S amplicon data treatment, while similar benchmarks exist for short-read 16S metagenomics approaches, as well as for nanopore shotgun sequencing (e.g. Ye et al., Cell 2019, doi: 10.1016/j.cell.2019.07.010; Latorre-Pérez et al., Scientific Reports 2020, doi:10.1038/s41598-020-70491-3).

By adding a thorough speed and memory usage summary (new Supplementary Figure 1b), in addition to our (mis)classification performance tests based on both mock and complex microbial community analyses, we provide the reader with a broad overview of existing options. While the widely used Kraken 2 and Centrifuge methods provide exceptional speed, we find that this comes with a noticeable tradeoff in taxonomic assignment accuracy. We reason that Minimap2 alignments provide a solid compromise between speed and classification performance, with the MAPseq software offering a viable alternative should memory usage limitation apply to users.

We intend to extend this benchmarking process to future tools, and to update it on our GitHub page (https://github.com/d-j-k/puntseq). This page notably also hosts a range of easy-to-use scripts for employing downstream 16S analysis and visualization approaches, including ordination, clustering and alignment tests.

The revised Discussion now emphasises the specific advancements of our study with respect to freshwater analysis and more general standardisation of nanopore 16S sequencing, also in contrast to previous amplicon nanopore sequencing approaches in which only one or two bioinformatics workflows were tested (page 16, lines 297-306).

They also mention that nanopore sequencing is an "inexpensive, easily adaptable and scalable framework" The term "inexpensive" doesn't seem appropriate since it is relative. In addition, they should also discuss that although it is technically convenient in some aspects compared to other sequencers, there are still protocol steps that need certain reagents and equipment that is similar or the same to those needed for other sequencing platforms. Common bottlenecks such as DNA extraction methods, sample preservation and the presence of inhibitory compounds should be mentioned.

We agree with the reviewers that “inexpensive” is indeed a relative term, which needs further clarification. We therefore now state that this approach is “cost-effective” and discuss future developments such as the 96-sample barcoding kits and Flongle flow cells for small-scale water diagnostics applications, which will arguably render lower per-sample analysis costs in the future (page 18, lines 361-365).

Other investigators (e.g. Boykin et al., Genes 2019, doi:10.3390/genes10090632; Acharya et al., Water Technology 2020, doi:10.1016/j.watres.2020.116112) have recently shown that the full application of DNA extraction and in-field nanopore sequencing can be achieved at comparably low expense: Boykin et al. studied cassava plant pathogens using barcoded nanopore shotgun sequencing, and estimated costs of ~45 USD per sample, while we calculate ~100 USD per sample in this study. Acharya et al. undertook in situ water monitoring between Birtley, UK and Addis Ababa, Ethiopia, estimated ~75-150 USD per sample and purchased all necessary equipment for ~10,000 GBP – again, we think that this lies roughly within a similar range as our (local) study's total cost of ~3,670 GBP (Supplementary Table 6).

The revised manuscript now mentions the possibility of increasing sequencing yield by improving DNA extraction methods, by taking sample storage and potential inhibitory compounds into account in the planning phase (page 18, lines 348-352).

Minor points:

-Please include a reference to the statement saying that the river Cam is notorious for the "infections such as leptospirosis".

There are indeed several media reports that link leptospirosis risk to the local River Cam (e.g. https://www.cambridge-news.co.uk/news/cambridge-news/weils-disease-river-cam-leptosirosis-14919008 or https://www.bbc.com/news/uk-england-cambridgeshire-29060018). As we, however, did not find a scientific source for this information, we have slightly adjusted the statement in our manuscript from referring to Cambridge to instead referring to the entire United Kingdom. Accordingly, we now cite two reports from Public Health England (PHE) about serial leptospirosis prevalence in the United Kingdom (page 13, lines 226-227).

-Please check figure 7 for consistency across panels, such as shading in violet and labels on the figures that do not seem to correspond with what is stated in the legend. Please mention what the numbers correspond to in outer ring. Check legend, where it says genes is probably genus.

Thank you for pointing this out. We have revised (now labelled) Figure 8 and removed all inconsistencies between the panels. The legend has also been updated, which now includes a description of the number labelling of the tree, and a clearer differentiation between the colour coding of the tree nodes and the background highlighting of individual nanopore reads.

-Page 6. There is a "data not shown" comment in the text: "Benchmarking of the classification tools on one aquatic sample further confirmed Minimap2's reliable performance in a complex bacterial community, although other tools such as SPINGO (Allard, Ryan, Jeffery, & Claesson, 2015), MAPseq (Matias Rodrigues, Schmidt, Tackmann, & von Mering, 2017), or IDTAXA (Murali et al., 2018) also produced highly concordant results despite variations in speed and memory usage (data not shown)." There appears to be no good reason that this data is not shown. In case the speed and memory usage was not recorded, is advisable to rerun the analysis and quantify these variables, rather than mentioning them and not reporting them. Otherwise, provide an explanation for not showing the data please.

This is a valid point, and we agree with the reviewers that it is worth properly following up on this initial observation. To this end, our revised manuscript now entails a systematic characterisation of the twelve tools' runtime and memory usage performance. This has been added as Supplementary Figure 1b and under the new Materials and Methods section 2.2.4 (page 26, lines 556-562), while the corresponding results and their implications are discussed on page 16, lines 301-306. Particularly with respect to the runtime measurements, it is worth noting that these can differ by several orders of magnitude between the classifiers, thus providing an additional clarification on our choice of the - relatively fast - Minimap2 alignments.

-In Figure 4, it would be important to calculate if the family PCA component contribution differences in time are differentially significant. In Panel B, depicted is the most evident variance difference but what about other taxa which might not be very abundant but differ in time? One can use the fitFeatureModel function from the metagenomeSeq R library and a P-adjusted threshold value of 0.05, to validate abundance differences in addition to your analysis.

To assess if the PC component contribution of Figure 5 (previously Figure 4) significantly differed between the three time points, we have applied non-parametric tests to all season-grouped samples except for the mock community controls. We first applied Kruskal-Wallis H-test for independent samples, followed by post-hoc comparisons using two-sided Mann-Whitney U rank tests.

The Kruskal-Wallis test established a significant difference in PC component contributions between the three time points (p = 0.0049), with most of the difference stemming from divergence between April and August samples according to the post-hoc tests (p = 0.0022). The June sampled seemed to be more similar to the August ones (p = 0.66) than to the ones from April (p = 0.11), recapitulating the results of our hierarchical clustering analysis (Figure 4a).

We have followed the reviewers' advice and applied a complementary approach, using the fitFeatureModel of metagenomeSeq to fit a zero-inflated log-normal mixture model of each bacterial taxon against the time points. As only three independent variables can be accounted for by the model (including the intercept), we have chosen to investigate the difference between the spring (April) and summer (June, August) months to capture the previously identified difference between these months. At a nominal P-value threshold of 0.05, this analysis identifies seven families to significantly differ in their relative composition between spring and summer, namely Cyanobiaceae, Armatimonadaceae, Listeriaceae, Carnobacteriaceae, Azospirillaceae, Cryomorphaceae, and Microbacteriaceae. Three out of these seven families were also detected by the PCA component analysis (Carnobacteriacaea, Azospirillaceae, Microbacteriaceae) and two more (Listeriacaea, Armatimonadaceae) occured in the top 15 % of that analysis (out of 357 families).

This approach represents a useful validation of our principal component analysis' capture of likely seasonal divergence, but moreover allows for a direct assessment of differential bacterial composition across time points. We have therefore integrated the analysis into our manuscript (page 10, lines 184-186; Materials and Methods section 2.6, page 29, lines 641-647) – thank you again for this suggestion.

-Page 12-13. In the paragraph: "Using multiple sequence alignments between nanopore reads and pathogenic species references, we further resolved the phylogenies of three common potentially pathogenic genera occurring in our river samples, Legionella, Salmonella and Pseudomonas (Figure 7a-c; Material and Methods). While Legionella and Salmonella diversities presented negligible levels of known harmful species, a cluster of reads in downstream sections indicated a low abundance of the opportunistic, environmental pathogen Pseudomonas aeruginosa (Figure 7c). We also found significant variations in relative abundances of the Leptospira genus, which was recently described to be enriched in wastewater effluents in Germany (Numberger et al., 2019) (Figure 7d)."

Here it is important to mention the relative abundance in the sample. While no further experiments are needed, the authors should mention and discuss that the presence of DNA from pathogens in the sample has to be confirmed by other microbiology methodologies, to validate if there are viable organisms. Definitively, it is a big warning finding pathogen's DNA but also, since it is characterized only at genus level, further investigation using whole metagenome shotgun sequencing or isolation, would be important.

We agree that further microbiological assays, particularly target-specific species isolation and culturing, would be essential to validate the presence of living pathogenic bacteria. Accordingly, our revised Discussion now contains a paragraph that encourages such experiments as part of the design of future studies (with a fully-equipped laboratory infrastructure); page 17, 338-341.

-Page 15: "This might help to establish this family as an indicator for bacterial community shifts along with water temperature fluctuations."

Temperature might not be the main factor for the shift. There could be other factors that were not measured that could contribute to this shift. There are several parameters that are not measured and are related to water quality (COD, organic matter, PO4, etc).

We agree that this was a simplified statement, given our currently limited number of samples, and have therefore slightly expanded on this point (page 17, lines 323-325). It is indeed possible that differential Carnobacteriaceae abundances between the time point measurements may have arisen not as a consequence of temperature fluctuations (alone), but instead as a consequence of the observed hydrochemical changes like e.g. Ca2+, Mg2+, HCO3- (Figure 6b-c) or possible even water flow speed reductions (Supplementary Figure 6d).

-"A number of experimental intricacies should be addressed towards future nanopore freshwater sequencing studies with our approach, mostly by scrutinising water DNA extraction yields, PCR biases and molar imbalances in barcode multiplexing (Figure 3a; Supplementary Figure 5)."

Here you could elaborate more on the challenges, as mentioned previously.

We realise that we had not discussed the challenges in enough detail, and the Discussion now contains a substantially more detailed description of these intricacies (page 18, lines 343-359).

Author Response

Reviewer #1:

Summary:

In this paper, the authors utilize CRISPR-Cas9 to generate two different DMD cell lines. The first is a DMD human myoblast cell line that lacks exon 52 within the dystrophin gene. The second is a DMD patient cell line that is missing miRNA binding sites within the regulatory regions of the utrophin gene, resulting in increased utrophin expression. Then, the authors proceeded to test antisense oligonucleotides and utrophin up-regulators in these cell lines.

Overall opinion (expanded in more detail below).

The paper suffers from the following weaknesses:

1) The protocol used to generate the myoblast cell lines is rather inefficient and is not new.

2) Many of the data figures are of low quality and are missing proper controls (detailed in points 5,7,10, 12, 13,14)

Detailed critiques:

1) The title needs to be changed. The method used by the authors is inefficient. The title should instead focus on the two cell lines generated.

We appreciate the reviewer’s comments: thanks to them, we have realized the focus of the manuscript should be in the new models we described and less in the methodology used to create them.

Originally, we wanted to share the problems we faced when applying new CRISPR/Cas9 edition techniques to myoblasts: our conversations with other researchers in the field confirmed that many were having similar problems. However, the reviewer is right in the fact that there are many ways around this problem. We do describe ours and we are working in a new version of the manuscript with additional data to characterize our new models further and where the method used to create them, although included, is not the main focus of the manuscript. In this new version we will change the title accordingly.

2) Line 104: The authors declare that the efficiency of CRISPR/Cas9 is currently too low to provide therapeutic benefit for DMD in vivo. There are lots of papers that show efficient recovery of dystrophin in small and large animals following CRISPR/Cas9 therapy. The authors should cite them properly.

Thank you for your appreciation. We have reviewed the literature again to include new evidences of efficient dystrophin recovery as well as other studies with lower efficiency.

3) Figures 1, 2,3, and 4 can be merged into one figure.

4) Figure 2A and 2B can be moved to supplementary.

5) Figure 2C and 2D are not clear. Are the duplicates the same? Please invert the black and white colors of the blots.

Thank you for your comments. We have inverted the colors of the blots and changed the marks used in figure 2C and 2D to clarify that duplicates are indeed the same sample, assayed in duplicates. We have also merged figures 1 and 4 and moved figures 2 and 3 to supplementary in this new version.

6) Figure 3: In order to optimize the efficiency of myoblast transfection, the plasmids containing the Cas9 and the sgRNA should have different fluorophores (GFP and mCherry). This approach would increase the percentage of positive edited clones among the clones sorted.

We think the reviewer may have misunderstood our methodology: we are not using a plasmid with the Cas9 and another with the sgRNA, we are using two plasmids, both containing Cas9 and each a different sgRNA. We did try to use two different plasmids, one expressing GFP and one expressing puromycin resistance, but we found out that single GFP positive cell selection plus puromycin selection was too inefficient. We could have tried with two different fluorophores, but we tested the tools we had in our hands first and were successful at obtaining enough clones to continue with their characterization, so we did so instead of a further optimization to our editing protocol.

7) Figure 4A: In the text, the authors state that only 1 clone had the correct genomic edit, but from the PCR genotyping in this figure shows at least 2 positive clones (number 4 and 7).

Thank you for your appreciation. As you said, we got two positive clones (as we also indicate in figure 3B) but we completed the full characterization of one of them (clone number 7= DMD-UTRN-Model). In the new version of the manuscript we explain this further.

8) Figure 4C: The authors should address whether one or both copies of the UTRN gene was edited in their clones.

Thank you for your comment. Both copies of the UTRN gene were edited in our clones. We have included this information both in the text and in the figure 4 legend.

9) Figure 4 B and D: The authors should report the sequence below the electropherograms.

Thank you for this correction, we have included the sequence under the electropherograms.

10) Figure 5B: This western blot is of poor quality. Also, the authors should specify that the samples are differentiated myoblasts. Lastly, a standard protein should be included as a loading control.

Thank you for your comment. Poor quality of dystrophin and utrophin western blots was the main reason to validate a new method in our laboratory to measure these proteins directly in cell culture (1) like an alternative to western blotting. Since then, the myoblot method has been routinely used by us and in collaboration with other groups and companies. We included the western blot as it is sometimes easier for those used to this technique to be able to assess a blot in which there is no dystrophin expression. As you pointed out, our samples were all differentiated myotubes, not myoblasts, and we have modified this accordingly. Thank you very much for pointing out this mistake

On the other hand, as described in the methods, Revert TM 700 Total Protein Stain (Li-Cor) and alpha-actinin were included as standards in dystrophin and utrophin western blots, respectively.

11) Figure 5E: We would like to see triplicates for the level of Utrophin expression.

We thank the reviewer for his/her recommendation, but we do not consider western blotting a good quantitative technique, we have included western blots to show the expression/absence of protein at the same level. We have included many more replicates than needed to show at the level of utrophin by myoblots. We acknowledge that western blotting is the preferred method for some reviewers, so in the new version of our manuscript we clearly indicate the value we give to each technique, being myoblots our choice for quantification.

12) Figure 6: A dystrophin western blot should be included to demonstrate protein recovery following antisense oligonucleotide treatment. Also, the RT-PCR data could be biased as you can have preferential amplification of shorter fragments.

Thank you for your recommendation but as we have explained before, myoblots have been validated in our laboratory to replace western blot for accurate dystrophin quantification in cell culture.

13) Figure 6A: Invert the black and white colors. The authors should also report the control sequences and sequences of the clones under the electropherograms.

Thank you for your suggestion, we have inverted the colors and added the sequences under the electropherograms.

14) Figure 6B: Control myoblasts should be included in figure 5C.

Thank you for this correction, we will include control myoblasts in the new manuscript version.

15) Figure S2A: Invert the black and white colors.

Thank you for your suggestion, we have inverted the colors.

Reviewer #2:

The work from Soblechero-Martín et al reports the generation of a human DMD line deleted for exon 52 using CRISPR technology. In addition, the authors introduced a second mutation that leads to upregulation of utrophin, a protein similar to dystrophin, which has been considered as a therapeutic surrogate. The authors provide a careful description of the methodology used to generate the new cell line and have conducted meticulous evaluations to test the validity of the reagents.

However, if the main purpose of this cell line is to perform drug or small molecule compound screenings, a single line might not be sufficient to draw robust conclusions. The generation of additional DMD lines in different genetic backgrounds using the reagents developed in this study will strengthen the work and will be of interest to the DMD field.

Thank you for your appreciation. We think that a well characterized immortalized culture, like the one we describe is sufficient for compound screening, as described in other recently published studies (2), (3). About the other suggestion, we have indeed used our method to generate other cultures for collaborators, but they will be reported in their own publications, as they are interested in them as tools in their own research projects.

Further, the future use of the edited DMD line with upregulated utrophin is unclear. The utrophin upregulation adds a complexity to this line that might complicate the assessment of screened compounds. In contrast, this line could be used to test if overexpression of utrophin generates myotubes that produce increased force compared to the control DMD line.

We think we may have not explained our screening platform well enough. Our suggestion is to offer our newly generated culture ALONGSIDE the original unedited culture: the original is treated with potential drug candidates, while the new one may or may not be treated, if these drug candidates are thought to act by activating the edited region (see an example in the figure below). In this case, the new culture will be a reliable positive control to the effects that may be reported in the unedited cultures by the drug candidates. We will make this clear in the new version of the manuscript.

Created with BioRender.com

In summary, while there is support and enthusiasm for the techniques and methodological approach of the study, the future use of this single line might be dubious and could be strengthened if additional lines are generated.

We share the reviewer’s enthusiasm for this approach, and we have included in the new version of the manuscript further characterization of this new cell culture that we think would demonstrate its usefulness better.

Author Response

Author Response refers to a revised version of the manuscript, Version 3, which was posted October 23, 2020.

Summary:

Serra-Marques, Martin et al. investigate the individual and cooperative roles of specific kinesins in transporting Rab6 secretory vesicles in HeLa cells using CRISPR and live-cell imaging. They find that both KIF5B and KIF13B cooperate in transporting Rab6 vesicles, but Eg5 and other kinesin-3s (KIF1B and KIF1C) are dispensable for Rab6 vesicle transport. They show that both KIF5B and KIF13B localize to these vesicles and coordinate their activities such that KIF5B is the main driver of the cargos on older, MAP7-decorated microtubules, and KIF13B takes over as the main transporter on freshly-polymerized microtubule ends that are largely devoid of MAP7. Interestingly, their data also indicate that KIF5B is important for controlling Rab6 vesicle size, which KIF13B cannot rescue. By analyzing subpixel localization of the motors, they find that the motors localize to the front of the vesicle when driving transport, but upon directional cargo switching, KIF5B localizes to the back of the vesicle when opposing dynein. Overall, this paper provides substantial insight into motor cooperation of cargo transport and clarifies the contribution of these distinct classes of motors during Rab6 vesicle transport.

We thank the reviewers for their thoughtful and constructive suggestions, and for the positive feedback.

Reviewer #1:

In their manuscript, Serra-Marques, Martin, et al. investigate the individual and cooperative roles of specific kinesins in transporting Rab6 vesicles in HeLa cells using CRISPR and live-cell imaging. They find that both KIF5B and KIF13B cooperate in transporting Rab6 vesicles, but KIF5B is the main driver of transport. In these cells, Eg5 and other kinesin-3s (KIF1B and KIF1C) are dispensable for Rab6 vesicle transport. They find that both KIF5B and KIF13B are present on these vesicles and coordinate their activities such that KIF5B is the main driver of the cargos on older, MAP7-decorated MTs, and KIF13B takes over as the main transporter on freshly-polymerized MT ends that are largely devoid of MAP7. Interestingly, their data also indicate that KIF5B is important for controlling Rab6 vesicle size, which KIF13B cannot rescue. Upon cargo switching from anterograde to retrograde transport, KIF5B, but not KIF13B, engages in mechanical competition with dynein. Overall, this paper provides substantial insight into motor cooperation of cargo transport and clarifies the contribution of these distinct classes of motors during Rab6 vesicle transport. The experiments are well-performed and the data are of very high quality.

Major Comments:

1) In Figure 5, it is very interesting that only KIF5B opposes dynein. It would be informative to determine which kinesin was engaged on the Rab6 vesicle before the switch to the retrograde direction. Can the authors analyze the velocity of the run right before the switch to the retrograde direction? If the velocity corresponds with KIF5B (the one example provided seems to show a slow run prior to the switch), this could indicate that KIF5B opposes dynein more actively because KIF5B was the motor that was engaged at the time of the switch. Or if the velocity corresponds with KIF13B, this could indicate that KIF5B becomes specifically engaged upon a direction reversal. In any case, an analysis of the speed distributions before the switch would provide insight into vesicle movement and motor engagement before the change in direction.

Directional switching was only analyzed in rescue experiments, where the vesicles were driven by either KIF5B alone or by KIF13B alone, and the speeds of vesicles were representative of these motors (please see panels on the right). The number of vesicle runs where two motors were detected simultaneously (KIF5B vs KIF13B in Figure 5G,H,J) were significantly lower, and therefore, unfortunately we could not perform the analysis of their directional switching with sufficient statistical power.

2) One of the most interesting aspects of this paper is the different lattice preferences for KIF5B, which shows runs predominantly on "older" polymerized MTs decorated by MAP7, and for KIF13B, whose runs are predominantly restricted to newly polymerized MTs that lack MAP7. The results in Figure 8 suggest a potential switch from KIF5B to KIF13B motor engagement upon a change in lattice/MAP7 distribution. In general, do the authors observe the fastest runs at the cell periphery, where there should be a larger population of freshly polymerized MTs? For Figure 4E, are example 1 and example 2 in different regions of the cell?

This is indeed a very interesting point and we have considered it carefully. As can be seen in Figure 8B (grey curve), vesicle speed remains relatively constant along the cell radius in control HeLa cells. We note, however, that our previous work has shown that in these cells microtubules are quite stable even at the cell periphery, due to the high activity of the CLASP-containing cortical microtubule stabilization complex (Mimori-Kiyosue et al., 2005, Journal of Cell Biology, PMID: 15631994; van der Vaart et al., 2013, Developmental Cell, PMID: 24120883). We therefore hypothesized that changes in vesicle speed distribution along the cell radius would be more obvious in cells with highly dynamic microtubule networks and performed a preliminary experiment in MRC5 human lung fibroblasts, which have a very sparse and dynamic microtubule cytoskeleton (Splinter et al., 2012, Molecular Biology of the Cell, PMID: 22956769). As shown in the figure below, we indeed found that vesicles move faster at the cell periphery. Even though these data are suggestive, characterization of this additional cell model goes beyond the scope of the current study, and we prefer not to include them in the manuscript.

In Figure 4E, the two examples are from different cells, and were both recorded at the cell periphery. The difference in vesicle speeds reflects general speed variability.

Do the authors think the intermediate speeds are a result of the motors switching roles? Additional discussion would help the reader interpret the results.

Presence of intermediate speeds of cargos driven by multiple motors of two types is most clear in Figure 3F-H, where multiple and different ratios of KIF5B and KIF13B motors are recruited to peroxisomes. As can be seen in Fig. 3G, the kymographs in these conditions are “smooth” and no evidence of motor switching can be detected at this spatiotemporal resolution. On the other hand, it has been previously beautifully shown by the Verhey lab that when artificial cargos are driven by just two motor molecules of different nature, switching does occur (Norris et al., 2014, Journal of Cell Biology, PMID: 25365993). This point is emphasized on page 12 of the revised manuscript. These data suggest that motors working in teams show different properties, and more detailed biophysical analysis will be needed to understand them.

Reviewer #2:

The manuscript by Serra-Marques, Martin, et al provides a tour de force in the analysis of vesicle transport by different kinesin motor proteins. The authors generate cell lines lacking a specific kinesin or combination of kinesins. They analyze the distribution and transport of Rab6 as a marker of most, if not all, secretory vesicles and show that both KIF5B and KIF13B localize to these vesicles and describe the contribution of each motor to vesicle transport. They show that the motors localize to the front of the vesicle when driving transport whereas KIF5B localizes to the back of the vesicle when opposing dynein. They find that KIF5B is the major motor and its action on "old" microtubules is facilitated by MAP7 whereas KIF13B facilitates transport on "new" microtubules to bring vesicles to the cell periphery. The manuscript is well-written, the data are properly controlled and analyzed, and the results are nicely presented. There are a few things the authors could do to tie up loose ends but these would not change the conclusions or impact of the work and I only have a couple of clarifying questions.

In Figure 2E, it seems like about half of the KIF5B events start at or near the Golgi whereas most of the KIF13B events are away from the Golgi? Did the authors find this to be generally true or just apparent in these example images?

We sincerely apologize for the misunderstanding here. To automatically track the vesicles, we had to manually exclude the Golgi area. Moreover, only processive and not complete tracks are shown. Therefore, no conclusions can be made from these data on the vesicle exit from the Golgi. We have indicated this clearly in the Results (page 8) and Discussion (page 21) of the revised manuscript and included more representative images in the revised Figure 2E.

In Figure 8G, the tracks for KIF13B-380 motility are difficult to see, which is surprising as KIF13B has been shown to be a superprocessive motor. Is this construct a dimer? If not, do the authors interpret the data as a high binding affinity of the monomer for new microtubules and if so, do they have any speculation on what could be the molecular mechanism? It appears as if KIF13B-380 and EB3 colocalize at the plus ends for a period of time before both are lost but then quickly replenished. Is this common?

KIF13B-380 construct used here contains a leucine zipper from GCN4 and is therefore dimeric. In the revised version of the paper, we have indicated this more clearly in the Results section on page 17 of the revised manuscript. KIF13B-380 does show processive motility, although this is difficult to see close to the outermost microtubule tip as the motor tends to accumulate there. This does not necessarily correlate with a strong accumulation of EB3, likely because EB3 signal is more sensitive to the dynamic state of the microtubule (it diminishes when microtubule growth rate decreases). We now provide a kymograph in Fig. 8G where the processive motility of KIF13B-380 is clearer.

Reviewer #3:

Serra-Marques and co-authors use CRISPR/Cas9 gene editing and live-cell imaging to dissect the roles of kinesin-1 (KIF5) and kinesin-3 (KIF13) in the transport of Rab6-positive vesicles. They find that both kinesins contribute to the movement of Rab6 vesicles. In the context of recent studies on the effect of MAP7 and doublecortin on kinesin motility, the authors show that MAP7 is enriched on central microtubules corresponding to the preferred localization of constitutively-active KIF5B-560-GFP. In contrast, KIF13 is enriched on dynamic, peripheral microtubules marked by EB3.

The manuscript provides needed insight into how multiple types of kinesin motors coordinate their function to transport vesicles. However, I outline several concerns about the analysis of vesicle and kinesin motility and its interpretation below.

Major concerns:

1) The metrics used to quantify motility are sensitive to tracking errors and uncertainty. The authors quantify the number of runs (Fig. 2D,F; 7C) and the average speed (Fig. 3A,B,D,E,H). The number of runs is sensitive to linking errors in tracking. A single, long trajectory is often misrepresented as multiple shorter trajectories. These linking errors are sensitive to small differences in the signal-to-noise ratio between experiments and conditions, and the set of tracking parameters used. The average speed is reported only for the long, processive runs (tracks>20 frames, segments<6 frames with velocity vector correlation >0.6). For many vesicular cargoes, these long runs represent <10% of the total motility. In the 4X-KO cells, it is expected there is very little processive motility, yet the average speed is higher than in control cells. Frame-to-frame velocities are often over-estimated due to the tracking uncertainty. Metrics like mean-squared displacement are less sensitive to tracking errors, and the velocity of the processive segments can be determined from the mean-squared displacement (see for example Chugh et al., 2018, Biophys. J.). The authors should also report either the average velocity of the entire run (including pauses), or the fraction of time represented by the processive segments to aid in interpreting the velocity data.

Two stages of the described tracking and data processing are responsible for the extraction of processive runs: the “linking” method used during the tracking, and the “trajectory segmentation” method, applied to the obtained tracks. The detection and linking of vesicles have been performed using our previously published tracking method (Chenouard et al., 2014, Nature Methods, PMID: 24441936). Our linking method uses multi-frame data association, taking into account detections from four subsequent image frames in order to extend and create a trajectory at any given time. This allows for dealing with temporal disappearance of particles (missing detections) for 1-2 frames and avoiding creation of breaks in longer trajectories. The method is robust to noise, spurious and missing detections and had been fully evaluated in the aforementioned paper (Chenouard et al., 2014) showing excellent performance compared to other tracking methods.

Having the trajectories describing the behavior of each particle, the track segmentation method had been applied to split each trajectory into a sequence of smaller parts (tracklets) describing processive runs and pieces of undirected (diffusive) motion. The algorithm that we used was validated earlier on an artificial dataset (please see Fig.S2e in Katrukha et al., Nat Commun 2017, PMID: 28322225). The chosen parameters were in the range where the algorithm provided less than 10% of false positives. Since the quantified and reported changes in the number of runs are six-fold (Fig.2D,F), we are quite certain that this estimated error (inherent to all automatic image analysis methods) does not affect our conclusions. Moreover, it is consistent with visual observations and manual analysis of representative movies.

Further, we agree that frame-to-frame velocities are often somewhat over-estimated due to the tracking uncertainty. We are aware of such overestimation which is very difficult to avoid. In our case, we estimated (using a Monte Carlo simulation) that such overestimation will positively bias the average not more than 3-6%. Since we focus not on the absolute values of velocities, but rather on the comparison between different conditions, such biasing will be present in all estimates of average velocity and will not affect the presented conclusions.

The usage of mean square displacement (MSD) to analyze trajectories containing both periods of processive runs and diffusive motion is confusing, since it represents average value over whole trajectories, resulting in the MSD slope which is in the range of 1.5 (i.e. between 1, diffusive and 2, processive; please see Fig.2c in Katrukha et al., 2017, Nature Communications, PMID: 28322225). Therefore, initial segmentation of trajectories is necessary, as it was performed in the paper by Chugh et al (Chugh et al., 2018, Biophysical Journal, PMID: 30021112; please see Fig.2e in that paper), suggested by the reviewer. In this paper the authors used an SCI algorithm, which is very similar to our analysis, relying on temporal correlations of velocities. Indeed, MSD analysis of only processive segments is less sensitive to tracking errors, but it reports an average velocity of the whole population of runs. This method is well suited if one would expect monodisperse velocity distribution (the case in Chugh et al, where single motor trajectories are analyzed). If there are subpopulations with different speeds (as we observed for Rab6 by manual kymograph analysis), this information will be averaged out. Therefore, we used histogram/distribution representations for our speed data, which in our opinion represents these data better.

Finally, we fully agree with the reviewers that the fractions of processive/diffusive motion should be reported. In the revised version, we have added new plots to the revised manuscript (Figure 2G-I, Figure 2 - figure supplement 2G) illustrating these data for different conditions. Our data fully support the reviewer’s statement that processive runs represent less than 10% of total vesicle motility (new Figure 2G). As could be expected, the total time vesicles spent in processive motion and the percentage of trajectories containing processive runs strongly depended on the presence of the motors (new Figure 2H,I). However, within trajectories that did have processive segments, the percentage of processive movement was similar (new Figure 2I).

We note that while our analysis is geared towards identification and characterization of processive runs (which was verified manually), analysis of diffusive movements poses additional challenges and is even more sensitive to linking errors. Therefore, we do not make any strong quantitative conclusions about the exact percentage and the properties of diffusive vesicle movements, and their detailed studies will require additional analytic efforts.

2) The authors show that transient expression of either KIF13B or KIF5B partially rescues Rab6 motility in 4X-KO cells and that knock-out of KIF13B and KIF5B have an additive effect. They also analyze two vesicles where KIF13B and KIF5B co-localize on the same vesicle. The authors conclude that KIF13B and KIF5B cooperate to transport Rab6 vesicles. However, the nature of this cooperation is unclear. Are the motors recruited sequentially to the vesicles, or at the same time? Is there a subset of vesicles enriched for KIF13B and a subset enriched for KIF5B? Is motor recruitment dependent on localization in the cell? These open questions should be addressed in the discussion.

Unfortunately, only fluorescent motors and not the endogenous ones can be detected on vesicles, so we cannot make any strong statements on this issue. Since KIF13B can compensate for the absence of KIF5B, it can be recruited to the vesicle when it emerges from the Golgi apparatus. However, in normal cells, KIF5B likely plays a more prominent role in pulling the vesicles from the Golgi, as Rab6 vesicles generated in the presence of KIF5B are larger (Figure 5I). We show in Figure 1G,H that KIF13B does not exchange on the vesicle and stays on the vesicle until it fuses with the plasma membrane. These data suggest that once recruited, KIF13B stays bound to the vesicle. Obtaining such data for KIF5B is more problematic because fewer copies of this motor are typically recruited to the vesicle (Figure 4B) and its signal is therefore weaker. Further research with endogenously tagged motors and highly sensitive imaging approaches will be needed to address the important open questions raised by the reviewer. We have added these points to the Discussion on pages 19 and 21 of the revised manuscript.

3) The authors suggest that KIF5B transports Rab6 vesicles along centrally-located microtubules while KIF13B drives transport on peripheral microtubules. Is the velocity of Rab6 vesicles different on central and peripheral microtubules in control cells?

As indicated in our answer to Major Comment 2 of Reviewer 1, we show in Figure 8B (grey curve) that vesicle speed remains relatively constant along the cell radius in control HeLa cells. We note, however, that our previous work has shown that in these cells microtubules are quite stable even at the cell periphery, due to the high activity of the CLASP-containing cortical microtubule stabilization complex (Mimori-Kiyosue et al., 2005, Journal of Cell Biology, PMID: 15631994; van der Vaart et al., 2013, Developmental Cell, PMID: 24120883). We therefore hypothesized that changes in vesicle speed distribution along the cell radius would be more obvious in cells with highly dynamic microtubule networks and performed a preliminary experiment in MRC5 human lung fibroblasts, which have a very sparse and dynamic microtubule cytoskeleton (Splinter et al., 2012, Molecular Biology of the Cell, PMID: 22956769). As shown in the figure above, we indeed found that vesicles move faster at the cell periphery.

4) The imaging and tracking of fluorescently-labeled kinesins in cells as shown in Fig. 4 is impressive. This is often challenging as kinesin-3 forms bright accumulations at the cell periphery and there is a large soluble pool of motors, making it difficult to image individual vesicles. The authors should provide additional details on how they addressed these challenges. Control experiments to assess crosstalk between fluorescence images would increase confidence in the colocalization results.

Imaging of vesicle motility was performed using TIRF microscopy focusing on regions where no strong motor accumulation was observed. We have little cross-talk between red and green channels, but channel cross talk in the three-color images shown in Figure 4E was indeed a potential concern. To address this potential issue, we performed the appropriate controls and added a new figure to the revised manuscript (Figure 4 – figure supplement 1). We conclude that we can reliably simultaneously detect blue, green and red channels without significant cross-talk on our microscope setup.

Author Response

Summary

This manuscript examines how N-linked glycosylation regulates the binding of polysaccharide hyaluronan (HA) to cell surface receptor CD44, to conclude that multiple sites exist but are controlled by the nature of the glycosylation. The reviewers appreciated many aspects of the work, but they have raised serious concerns about the experimental and simulation design. The reviewers suggested that the proposed alternative binding site may not be biologically relevant, as the relevant CD44-HA interactions are multivalent and cannot be supported by that site. They also suggested that the findings are not well supported by the NMR experiments, which could have been extended to allow comparisons of the glycosylation patterns hypothesised. Moreover, the MD simulations, despite being considerable in size, were limited in sampling different possibilities without bias from the initial HA placement, and there is not enough data to convince the readers of thorough sampling and reproducibility.

We understand the concerns raised in the review process. However, these concerns can be readily explained and fixed, as we explain below and are briefly introduced here.

• Our data are compatible with the currently accepted multivalent interaction of hyaluronan with several CD44 receptors. The argument that our data goes against it stems from an unfortunate figure provided in the first version of the manuscript. This figure suggested that a bound hyaluronan would not be able to span the length the protein in the upright binding mode. That is not true. We now show another, and more relevant snapshot where the bound hyaluronan indeed spans the length of HABD. Hence, we show that multivalent interaction is not precluded by the upright binding mode.

• We also clarify how our extensive simulation data were designed to avoid any bias. We admit that this was not obvious in the phrasing of our previous version.

• Many of the raised issues stem from the lack of certain critical simulations. We have now added these simulations into the revision.

Below we summarize the main issues raised by the reviewers, accompanied by our responses on how we have fixed them in the revised version of the manuscript.

Reviewer #1

The authors use MD simulations and NMR to study the cell surface adhesion receptor CD44 with the purpose of understanding the binding of carbohydrate polymer, hyaluronan (HA). In particular, this study focuses on the effects of N-glycosylation of the CD44 glycoprotein on potential HA binding. The authors previously proposed two lower affinity HA binding modes as alternatives to the primary mode seen in the crystal structure of the HA binding domain of CD44, driven by different arginine interactions, but overlapping with glycosylation sites that will affect HA binding. This study suggests that, because the canonical site appears blocked by glycans attached to the surface, HA would instead likely bind to an alternate parallel site with lower affinity, thus changing receptor affinity. The authors do not study HA binding to the glycosylated form directly, but undertake simulations of bound glycans to draw their conclusion. They do, however, place HA near the non-glycosylated CD44 in simulations, although it is not clear that MD sampling has been designed to provide unbiased observations of HA binding, or how the simulations help explain the NMR experiments.

To better highlight the message, we left out a significant portion of our total simulation data from the initial version of the manuscript. We have now added e.g. simulations of HA binding to the glycosylated form into our revised manuscript. Furthermore, we are confident that our design of the simulation systems allows unbiased sampling of the binding surface. That is, the hyaluronan hexamers were initially placed several nanometres away from the protein surface. After this, they were allowed to spontaneously sample the space and find their respective binding sites during the course of the simulations. They were not placed into the binding sites manually. However, there was a one system with two HA hexamers from which the other was placed into the canonical binding groove. This was done to test where the freely floating hexamer would bind when the primary binding site is taken. These points are illustrated more clearly in the new version of the manuscript. Finally, all our simulation data is publicly available (see the DOIs provided in the paper).

The data rely on libraries of MD simulation, which are substantial, with several replicas of a microsecond each. But what have these simulations really proved with reliability? Figure 2a shows that, while glycans stay roughly where they started, they are dynamic and cover much of the canonical HA binding site, which may be the case. From this the authors imply that the crystallographic site is significantly obstructed, the lower-affinity upright mode remains most accessible, and that the level of occlusion of the main site depends on the degree of glycosylation and size of the oligosaccharides. However, a full simulation of HA binding to this glycosylated surface was not attempted. It would have been good to see the glycans actually block unbiased simulation of canonical binding to the crystallographic site on long timescales (not being dislodged), but allow alternative binding to the parallel site, without initial placement there.

Commenting both points 1.1 and 1.2, we cropped a large portion of our simulation data from the initial version of the manuscript in order to better highlight the current message. However, we do have extensive simulation data of hyaluronan binding spontaneously to CD44 with different glycosylation patterns. For example, see Figure A below where HA is bound to glycosylated CD44-HABD. These data have been carefully analysed and incorporated into the revised manuscript.

Figure A. A representative binding pose between HA oligomer (dark red) and glycosylated (light blue, yellow, green, pink and purple) CD44-HABD (pale surface) extracted from our simulations.

HA was, however, added to the non-glycosylated CD44-HABD surface in simulations, but no clear data is shown to illustrate the extent of sampling, convergence and reproducibility, beyond some statistical analysis of contacts. It seems a total of 30 microseconds of the non-glycosylated protein with 2 or 3 nearby HA placed was run, leading to contacts. But how well did these 30 simulations sample HA movement and relative binding to sites, if at all? Figure 4 suggests that the HA stay where they have been put. As the MD is the dominant source of data for the paper, the extent of sampling and how the outcomes depend on the initial placement of molecules requires proof. Was any sampling of HA movement, such as between canonical and alternative parallel conformations seen in MD?

It is important to note that, in the non-glycosylated systems, the hyaluronan hexamers were initially placed several nanometres away from the protein surface. After this, they were allowed to spontaneously sample the space and find their respective binding sites during the course of the simulations. That is, they were not manually placed into the binding sites. We have changed the manuscript to better illustrate this key point.

We have also made the simulation data publicly available (see the DOIs provided in the paper). After inspection of the simulations, we are confident that the reviewers will agree that the results are reliable and do not suffer from convergence problems that could compromise the message we provide.

Moreover, we have even more simulation replicas ready with slightly different initial conditions that provide the same qualitative picture, see Figure B below (compare with Figure 4c in the original submission where one of the hyaluronan hexamers was initially placed in the crystallographic binding site). In these simulations, the hexamers have enhanced contacts with the crystallographic and upright mode residues despite being initially placed far from these binding sites. These simulations were already part of the manuscript.

Figure B. Hyaluronate-perturbed residues in the simulations. The colored surface displays the probability of a given residue to be in contact with HA6 in our additional simulations, where three hyaluronan hexamers were placed in solution far from the binding site.

The NMR is suggested to show that a short HA hexamer can bind to non-glycosylated CD44-HABD simultaneously in several modes at distinct binding sites, and that MD "correlates" with this. But is this MD biased by initial choices of where and how many HAs are placed, given HA movement is likely not well sampled?

The hyaluronan hexamers were initially placed several nanometers away from the binding sites. They were not placed into these binding sites manually. During the simulations the hexamers displayed several binding and unbinding events as they were spontaneously sampling the space and finding their respective binding sites during the course of the simulations.

While we saw multiple binding events to the proposed binding sites, the short size of the hyaluronan fragments was likely not enough for stable binding as the fragments often dissociated within few hundreds of nanoseconds. These points are now more clearly presented in the revised manuscript.

No MD seems to have been used to examine the blocking or lack thereof by antibody MEM-85 in glycosylated or non-glycosylated CD44.

This is not feasible using MD simulations, since the structure of the antibody is not available. Fortunately, there is no need for it, as we have direct and reliable experimental evidence using NMR as provided in the manuscript and in our previous work (Skerlova et.al. 2015; doi: 10.1016/j.jsb.2015.06.005). We therefore know where the antibody binds in CD44.

Reviewer #2

This manuscript is focused on understanding how N-linked glycosylation regulates the binding of the (very large) polysaccharide hyaluronan (HA) to its major cell surface receptor CD44, a question relevant, for example to the role of CD44 in mediating leukocyte migration in inflammation. The paper concludes that multiple binding sites for HA exist and that their occupancy is determined by the nature of the glycosylation, a suggestion first made by Teriete et al. (2004). The work is based on atomistic simulations with different glycan compositions and NMR spectroscopy on a non-glycosylated CD44 HA-binding domain (HABD) expressed in E. coli. While the question being researched is interesting and of biological relevance, there are flaws in the work.

The relevance also stems from the increasing applicability of HA in many biomedical devices and treatment strategies, such as tissue scaffolds and HA-coated nanoparticles for targeted drug delivery. However, we respectfully disagree with the proposed flaws. We address these suggested issues point-by-point in sections 2.2–2.5.

The paper describes how the well-established HA-binding site on CD44 (determined by a co-crystal structure; Banerji et al., 2007) is blocked by N-linked glycosylation (principally at N25 with a contribution from glycans at N100 and N110) and how certain glycans favour binding at a completely distinct binding site that lies perpendicular to the canonical 'crystallographic' binding site. This alternative 'upright' binding site, which has been proposed previously by the authors (Vuorio et al., 2017), needs further supporting experimental data.

Indeed, a characterization of the upright mode can be found from (Vuorio et al., 2017. PloS CB. 13:7). This characterization is based on mircoseconds of unbiased MD simulation data as well as extensive free energy calculations. We for example analysed the most important interactions, orientations of the sugar rings, and binding affinities. These data indicate that while the upright binding mode is weaker than the canonical binding mode (Banerji et al., 2007), it has good shape complementarity between the protein, with e.g. most of the sugar rings lying flat on the surface of the protein, indicating that it might have biological relevance.

The supporting experimental data is presented in the current publication. It has been improved and clarified for the revised version of the manuscript.

Firstly, unlike the 'crystallographic' binding site that forms an open-ended shallow groove on the surface of the protein allowing polymeric HA to bind (and multivalent interactions to take place), the 'upright' binding site is closed at one end and can thus only accommodate the reducing end of the polysaccharide (as apparent from Appendix 1 Figure 1). Its configuration means that it would be impossible for this mode of binding to allow multivalent interactions with polymeric HA. This is a major problem since biologically relevant CD44-HA interactions are multivalent where a single HA polymer interacts with a large number of CD44 molecules (e.g. see Wolny et al., 2010 J. Biol. Chem. 285, 30170-30180). So even if this binding site existed, an interaction between a single CD44 molecule on the cell surface with the reducing terminus of an HA polymer would be exceptionally weak.

We have data to show that our proposed secondary binding mode does not preclude multivalent CD44-hyaluronan interactions. This multivalent interaction, where a long hyaluronan binds simultaneously to several CD44 moieties, is important, and our secondary mode is compatible with it, see the new Figure C below. We acknowledge that our Figure 1 in the Appendix 1 was not sufficiently clear on this matter. That figure illustrated a structure of one possible CD44-hyaluronan complex obtained from just one of our simulations. However, we have a number of related CD44-hyaluronan complexes from other simulations where the bound ligand spans the full length of the protein, showing that the binding site can accommodate more than just the reducing end of the polysaccharide, and this is highlighted in the attached Figure C. Therefore, multivalent binding is not precluded by the upright binding mode. Unfortunately, the figure depicted in the SI of the original manuscript was misleading. To avoid this issue, it has been replaced in the revised manuscript.

Figure C. The secondary CD44-hyaluronan binding mode.

Secondly the NMR experiments performed in this study, purporting to provide evidence for multiple modes of binding, are problematic. Why weren't differentially glycosylated proteins used, i.e. where individual sites were mutated (e.g. +/- N25); this would have allowed comparisons of the glycosylation patterns hypothesised (based on the computer simulations) to favour the 'crystallographic' versus 'upright' modes.

Indeed, NMR experiments with glycosylated material would be ideal, but obtaining the required quantities of isotopically labelled protein with a homogeneous glycosylation pattern is not possible even using the state-of-the-art technology. In addition, the substantially increased molecular weight of the glycosylated protein would be out of the experimental window accessible by NMR spectroscopy. We strongly believe that the message of the paper is already sustained by a combination of our observations based on NMR experiments and MD simulation techniques together with the available literature data as detailed in Appendix A (see below).

While being aware of the difficulties of dealing with glycosylated CD44 using NMR, we designed a way to bypass this issue by combining multiple data from different experimental and simulation setups. All the data support the claims and conclusions made in our paper, see appendix A of this rebuttal. The existence of a weaker binding mode promoted upon glycosylation due to the primary binding site being covered is compatible with all available experimental and simulation data.

Furthermore, previous NMR studies have shown that the binding of HA to CD44 causes a considerable number of chemical shift changes due to the induction of a large conformational change in the protein (Teriete et al., 2004; Banerji et al., 2007), making it very difficult to identify amino acids directly involved in HA binding based on the NMR data. Moreover, this conformational change has been fully characterised for mouse CD44 with structures available in the absence and presence of HA (Banerji et al., 2007); this information should have been used to inform the interpretation of the shift mapping. In fact, the way in which the shift mapping data are interpreted is simplistic and doesn't fully take account of the reasons that NMR spectra can exhibit different exchange regimes.

We interpreted the NMR data very carefully. We are aware of the extent of conformational changes induced by HA binding in CD44-HABD, in fact, we identified them as a molecular mechanism underlying the mode of action for the MEM-85 antibody (Skerlova et.al. 2015; doi: 10.1016/j.jsb.2015.06.005). Therefore, we focused on the differential changes in the NMR signal positions of surface exposed residues upon titration with HA and MEM-85. We also observed different exchange regimes that allowed us to discriminate between different HA binding sites. We emphasized these points in the revised manuscript.

Reviewer #3

Vuorio and colleagues combine atomic resolution molecular dynamics simulations and NMR experiments to probe how glycosylation can bias binding of hyaluronan to one of several binding sites/modes on the CD44 hyaluronan binding domain. The results are of interest specifically to the field of CD44 biophysics and more generally to the broad field of glycosylation-dependent protein-ligand binding. The manuscript is clearly written, and the combination of data from computational and experimental methodologies is convincing. I especially commend the authors on the thorough molecular dynamics work, wherein they ran multiple simulations at microsecond timescale and tried different force fields to minimize the likelihood of their findings being an artifact of a particular force field.

The use of multiple force fields was indeed meant to alleviate potential force field specific issues. Likewise, the use of multiple simulation repeats with different starting positions and randomized atom velocities were meant to provide comprehensive statistics, minimizing the chances of over-interpreting any isolated phenomena.

Appendix A: Summary of the logic of the research procedure together with the experimental, simulation and literature results supporting each step.

1) Non-glycosylated CD44 binds HA *(NMR experiments) *

2) Non-glycosylated CD44 also binds HA in the presence of MEM-85 (NMR experiments)

3) Glycosylated CD44s that bind HA do not bind HA in the presence of MEM-85 (from literature [J. Bajorath, B. Greenfield, S. B. Munro, A. J. Day, A. Aruffo, Journal of Biological Chemistry 273, 338 (1998).]).

4) We show the MEM-85 binding site in non-glycosylated CD44 to be far from the canonical crystallographic binding region (NMR experiments). This MEM-85 binding site region is mostly inaccessible to typical N-glycans found in CD44 (MD simulation). Therefore, we expect that MEM-85 binds glycosylated CD44 in the same region. *(Our working hypothesis) *

5) Taken together, the above points indicate that MEM-85 covers at least partially the relevant HA binding mode in glycosylated CD44, which has zero overlap with the crystallographic mode. This supports the idea of an alternative binding mode to the crystallographic mode which must be readily available for glycosylated CD44. (Our finding)

6) Furthermore, heavily glycosylated CD44 variants cover a significant fraction of the crystallographic mode binding region (MD simulation), potentially making it unavailable for HA binding. This explains why non-glycosylated CD44 binds HA in the presence of MEM-85 (i.e., crystallographic mode is free), while glycosylated CD44 does not (i.e., crystallographic mode is covered with N-glycans). The upright region, on the other hand, experiences only minor coverage by the N-glycans in the glycosylated CD44 and is thus free to bind the ligand (MD simulations).

7) Non-glycosylated CD44 binds HA simultaneously with the crystallographic mode and the upright mode when exposed to high concentrations of small hyaluronan hexamers *(NMR titration and MD simulations). *

8) Pinpointing the position of the residues that experience the largest chemical shift during the titration experiments using non-glycosylated CD44 clearly shows the fingerprint of the canonical crystallographic mode but also a region compatible with our proposed upright mode (NMR titration experiments). These results are compatible with our simulations of several hyaluronan hexamers (MD simulation).

9) Upright binding mode is accessible to hyaluronan binding in the glycosylated CD44 (MD simulations shown in this letter that could be included to the paper if deemed necessary).

Glycosylation, and glycoscience in general, is one of the most challenging topics to understand in life sciences. We believe that our paper makes a very significant contribution to this area of research in the context of a central research problem and is exceptionally able to provide an atomic-level description of the HA-CD44 interaction under unambiguously known conditions.

Author Response:

Evaluation Summary:

Since DBS of the habenula is a new treatment, these are the first data of its kind and potentially of high interest to the field. Although the study mostly confirms findings from animal studies rather than bringing up completely new aspects of emotion processing, it certainly closes a knowledge gap. This paper is of interest to neuroscientists studying emotions and clinicians treating psychiatric disorders. Specifically the paper shows that the habenula is involved in processing of negative emotions and that it is synchronized to the prefrontal cortex in the theta band. These are important insights into the electrophysiology of emotion processing in the human brain.

The authors are very grateful for the reviewers’ positive comments on our study. We also thank all the reviewers for the comments which has helped to improve the manuscript.

Reviewer #1 (Public Review):

The study by Huang et al. report on direct recordings (using DBS electrodes) from the human habenula in conjunction with MEG recordings in 9 patients. Participants were shown emotional pictures. The key finding was a transient increase in theta/alpha activity with negative compared to positive stimuli. Furthermore, there was a later increase in oscillatory coupling in the same band. These are important data, as there are few reports of direct recordings from the habenula together with the MEG in humans performing cognitive tasks. The findings do provide novel insight into the network dynamics associated with the processing of emotional stimuli and particular the role of the habenula.

Recommendations:

How can we be sure that the recordings from the habenula are not contaminated by volume conduction; i.e. signals from neighbouring regions? I do understand that bipolar signals were considered for the DBS electrode leads. However, high-frequency power (gamma band and up) is often associated with spiking/MUA and considered less prone to volume conduction. I propose to also investigate that high-frequency gamma band activity recorded from the bipolar DBS electrodes and relate to the emotional faces. This will provide more certainty that the measured activity indeed stems from the habenula.

We thank the reviewer for the comment. As the reviewer pointed out, bipolar macroelectrode can detect locally generated potentials, as demonstrated in the case of recordings from subthalamic nucleus and especially when the macroelectrodes are inside the subthalamic nucleus (Marmor et al., 2017). However, considering the size of the habenula and the size of the DBS electrode contacts, we have to acknowledge that we cannot completely exclude the possibility that the recordings are contaminated by volume conduction of activities from neighbouring areas, as shown in Bertone-Cueto et al. 2019. We have now added extra information about the size of the habenula and acknowledged the potential contamination of activities from neighbouring areas through volume conduction in the ‘Limitation’:

"Another caveat we would like to acknowledge that the human habenula is a small region. Existing data from structural MRI scans reported combined habenula (the sum of the left and right hemispheres) volumes of ~ 30–36 mm3 (Savitz et al., 2011a; Savitz et al., 2011b) which means each habenula has the size of 2~3 mm in each dimension, which may be even smaller than the standard functional MRI voxel size (Lawson et al., 2013). The size of the habenula is also small relative to the standard DBS electrodes (as shown in Fig. 2A). The electrodes used in this study (Medtronic 3389) have electrode diameter of 1.27 mm with each contact length of 1.5 mm, and contact spacing of 0.5 mm. We have tried different ways to confirm the location of the electrode and to select the contacts that is within or closest to the habenula: 1.) the MRI was co-registered with a CT image (General Electric, Waukesha, WI, USA) with the Leksell stereotactic frame to obtain the coordinate values of the tip of the electrode; 2.) Post-operative CT was co-registered to pre-operative T1 MRI using a two-stage linear registration using Lead-DBS software. We used bipolar signals constructed from neighbouring macroelectrode recordings, which have been shown to detect locally generated potentials from subthalamic nucleus and especially when the macroelectrodes are inside the subthalamic nucleus (Marmor et al., 2017). Considering that not all contacts for bipolar LFP construction are in the habenula in this study, as shown in Fig. 2, we cannot exclude the possibility that the activities we measured are contaminated by activities from neighbouring areas through volume conduction. In particular, the human habenula is surrounded by thalamus and adjacent to the posterior end of the medial dorsal thalamus, so we may have captured activities from the medial dorsal thalamus. However, we also showed that those bipolar LFPs from contacts in the habenula tend to have a peak in the theta/alpha band in the power spectra density (PSD); whereas recordings from contacts outside the habenula tend to have extra peak in beta frequency band in the PSD. This supports the habenula origin of the emotional valence related changes in the theta/alpha activities reported here."

We have also looked at gamma band oscillations or high frequency activities in the recordings. However, we didn’t observe any peak in high frequency band in the average power spectral density, or any consistent difference in the high frequency activities induced by the emotional stimuli (Fig. S1). We suspect that high frequency activities related to MUA/spiking are very local and have very small amplitude, so they are not picked up by the bipolar LFPs measured from contacts with both the contact area for each contact and the between-contact space quite large comparative to the size of the habenula.

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Figure S1. (A) Power spectral density of habenula LFPs across all time period when emotional stimuli were presented. The bold blue line and shadowed region indicates the mean ± SEM across all recorded hemispheres and the thin grey lines show measurements from individual hemispheres. (B) Time-frequency representations of the power response relative to pre-stimulus baseline for different conditions showing habenula gamma and high frequency activity are not modulated by emotional

References:

Savitz JB, Bonne O, Nugent AC, Vythilingam M, Bogers W, Charney DS, et al. Habenula volume in post-traumatic stress disorder measured with high-resolution MRI. Biology of Mood & Anxiety Disorders 2011a; 1(1): 7.

Savitz JB, Nugent AC, Bogers W, Roiser JP, Bain EE, Neumeister A, et al. Habenula volume in bipolar disorder and major depressive disorder: a high-resolution magnetic resonance imaging study. Biological Psychiatry 2011b; 69(4): 336-43.

Lawson RP, Drevets WC, Roiser JP. Defining the habenula in human neuroimaging studies. NeuroImage 2013; 64: 722-7.

Marmor O, Valsky D, Joshua M, Bick AS, Arkadir D, Tamir I, et al. Local vs. volume conductance activity of field potentials in the human subthalamic nucleus. Journal of Neurophysiology 2017; 117(6): 2140-51.

Bertone-Cueto NI, Makarova J, Mosqueira A, García-Violini D, Sánchez-Peña R, Herreras O, et al. Volume-Conducted Origin of the Field Potential at the Lateral Habenula. Frontiers in Systems Neuroscience 2019; 13:78.

Figure 3: the alpha/theta band activity is very transient and not band-limited. Why refer to this as oscillatory? Can you exclude that the TFRs of power reflect the spectral power of ERPs rather than modulations of oscillations? I propose to also calculate the ERPs and perform the TFR of power on those. This might result in a re-interpretation of the early effects in theta/alpha band.

We agree with the reviewer that the activity increase in the first time window with short latency after the stimuli onset is very transient and not band-limited. This raise the question that whether this is oscillatory or a transient evoked activity. We have now looked at this initial transient activity in different ways: 1.) We quantified the ERP in LFPs locked to the stimuli onset for each emotional valence condition and for each habenula. We investigated whether there was difference in the amplitude or latency of the ERP for different stimuli emotional valence conditions. As showing in the following figure, there is ERP with stimuli onset with a positive peak at 402 ± 27 ms (neutral stimuli), 407 ± 35 ms (positive stimuli), 399 ± 30 ms (negative stimuli). The flowing figure (Fig. 3–figure supplement 1) will be submitted as figure supplement related to Fig. 3. However, there was no significant difference in ERP latency or amplitude caused by different emotional valence stimuli. 2.) We have quantified the pure non-phase-locked (induced only) power spectra by calculating the time-frequency power spectrogram after subtracting the ERP (the time-domain trial average) from time-domain neural signal on each trial (Kalcher and Pfurtscheller, 1995; Cohen and Donner, 2013). This shows very similar results as we reported in the main manuscript, as shown in Fig. 3–figure supplement 2. These further analyses show that even though there were event related potential changes time locked around the stimuli onset, and this ERP did NOT contribute to the initial broad-band activity increase at the early time window shown in plot A-C in Figure 3. The figures of the new analyses and following have now been added in the main text:

"In addition, we tested whether stimuli-related habenula LFP modulations primarily reflect a modulation of oscillations, which is not phase-locked to stimulus onset, or, alternatively, if they are attributed to evoked event-related potential (ERP). We quantified the ERP for each emotional valence condition for each habenula. There was no significant difference in ERP latency or amplitude caused by different emotional valence stimuli (Fig. 3–figure supplement 1). In addition, when only considering the non phase-locked activity by removing the ERP from the time series before frequency-time decomposition, the emotional valence effect (presented in Fig. 3–figure supplement 2) is very similar to those shown in Fig.3. These additional analyses demonstrated that the emotional valence effect in the LFP signal is more likely to be driven by non-phase-locked (induced only) activity."

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Fig. 3–figure supplement 1. Event-related potential (ERP) in habenula LFP signals in different emotional valence (neutral, positive and negative) conditions. (A) Averaged ERP waveforms across patients for different conditions. (B) Peak latency and amplitude (Mean ± SEM) of the ERP components for different conditions.

Fig. 3–figure supplement 2. Non-phase-locked activity in different emotional valence (neutral, positive and negative) conditions (N = 18). (A) Time-frequency representation of the power changes relative to pre-stimulus baseline for three conditions. Significant clusters (p < 0.05, non-parametric permutation test) are encircled with a solid black line. (B) Time-frequency representation of the power response difference between negative and positive valence stimuli, showing significant increased activity the theta/alpha band (5-10 Hz) at short latency (100-500 ms) and another increased theta activity (4-7 Hz) at long latencies (2700-3300 ms) with negative stimuli (p < 0.05, non-parametric permutation test). (C) Normalized power of the activities at theta/alpha (5-10 Hz) and theta (4-7 Hz) band over time. Significant difference between the negative and positive valence stimuli is marked by a shadowed bar (p < 0.05, corrected for multiple comparison).

References:

Kalcher J, Pfurtscheller G. Discrimination between phase-locked and non-phase-locked event-related EEG activity. Electroencephalography and Clinical Neurophysiology 1995; 94(5): 381-4.

Cohen MX, Donner TH. Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior. Journal of Neurophysiology 2013; 110(12): 2752-63.

Figure 4D: can you exclude that the frontal activity is not due to saccade artifacts? Only eye blink artifacts were reduced by the ICA approach. Trials with saccades should be identified in the MEG traces and rejected prior to further analysis.

We understand and appreciate the reviewer’s concern on the source of the activity modulations shown in Fig. 4D. We tried to minimise the eye movement or saccade in the recording by presenting all figures at the centre of the screen, scaling all presented figures to similar size, and presenting a white cross at the centre of the screen preparing the participants for the onset of the stimuli. Despite this, participants my still make eye movements and saccade in the recording. We used ICA to exclude the low frequency large amplitude artefacts which can be related to either eye blink or other large eye movements. However, this may not be able to exclude artefacts related to miniature saccades. As shown in Fig. 4D, on the sensor level, the sensors with significant difference between the negative vs. positive emotional valence condition clustered around frontal cortex, close to the eye area. However, we think this is not dominated by saccades because of the following two reasons:

1.) The power spectrum of the saccadic spike artifact in MEG is characterized by a broadband peak in the gamma band from roughly 30 to 120 Hz (Yuval-Greenberg et al., 2008; Keren et al., 2010). In this study the activity modulation we observed in the frontal sensors are limited to the theta/alpha frequency band, so it is different from the power spectra of the saccadic spike artefact.

2.) The source of the saccadic spike artefacts in MEG measurement tend to be localized to the region of the extraocular muscles of both eyes (Carl et al., 2012).We used beamforming source localisation to identify the source of the activity modulation reported in Fig. 4D. This beamforming analysis identified the source to be in the Broadmann area 9 and 10 (shown in Fig. 5). This excludes the possibility that the activity modulation in the sensor level reported in Fig. 4D is due to saccades. In addition, Broadman area 9 and 10, have previously been associated with emotional stimulus processing (Bermpohl et al., 2006), Broadman area 9 in the left hemisphere has also been used as the target for repetitive transcranial magnetic stimulation (rTMS) as a treatment for drug-resistant depression (Cash et al., 2020). The source localisation results, together with previous literature on the function of the identified source area suggest that the activity modulation we observed in the frontal cortex is very likely to be related to emotional stimuli processing.

References:

Yuval-Greenberg S, Tomer O, Keren AS, Nelken I, Deouell LY. Transient induced gamma-band response in EEG as a manifestation of miniature saccades. Neuron 2008; 58(3): 429-41.

Keren AS, Yuval-Greenberg S, Deouell LY. Saccadic spike potentials in gamma-band EEG: characterization, detection and suppression. NeuroImage 2010; 49(3): 2248-63.

Carl C, Acik A, Konig P, Engel AK, Hipp JF. The saccadic spike artifact in MEG. NeuroImage 2012; 59(2): 1657-67.

Bermpohl F, Pascual-Leone A, Amedi A, Merabet LB, Fregni F, Gaab N, et al. Attentional modulation of emotional stimulus processing: an fMRI study using emotional expectancy. Human Brain Mapping 2006; 27(8): 662-77.

Cash RFH, Weigand A, Zalesky A, Siddiqi SH, Downar J, Fitzgerald PB, et al. Using Brain Imaging to Improve Spatial Targeting of Transcranial Magnetic Stimulation for Depression. Biological Psychiatry 2020.

The coherence modulations in Fig 5 occur quite late in time compared to the power modulations in Fig 3 and 4. When discussing the results (in e.g. the abstract) it reads as if these findings are reflecting the same process. How can the two effect reflect the same process if the timing is so different?

As the reviewer pointed out correctly, the time window where we observed the coherence modulations happened quite late in time compared to the initial power modulations in the frontal cortex and the habenula (Fig. 4). And there was another increase in the theta band activities in the habenula area even later, at around 3 second after stimuli onset when the emotional figure has already disappeared. Emotional response is composed of a number of factors, two of which are the initial reactivity to an emotional stimulus and the subsequent recovery once the stimulus terminates or ceases to be relevant (Schuyler et al., 2014). We think these neural effects we observed in the three different time windows may reflect different underlying processes. We have discussed this in the ‘Discussion’:

"These activity changes at different time windows may reflect the different neuropsychological processes underlying emotion perception including identification and appraisal of emotional material, production of affective states, and autonomic response regulation and recovery (Phillips et al., 2003a). The later effects of increased theta activities in the habenula when the stimuli disappeared were also supported by other literature showing that, there can be prolonged effects of negative stimuli in the neural structure involved in emotional processing (Haas et al., 2008; Puccetti et al., 2021). In particular, greater sustained patterns of brain activity in the medial prefrontal cortex when responding to blocks of negative facial expressions was associated with higher scores of neuroticism across participants (Haas et al., 2008). Slower amygdala recovery from negative images also predicts greater trait neuroticism, lower levels of likability of a set of social stimuli (neutral faces), and declined day-to-day psychological wellbeing (Schuyler et al., 2014; Puccetti et al., 2021)."

References:

Schuyler BS, Kral TR, Jacquart J, Burghy CA, Weng HY, Perlman DM, et al. Temporal dynamics of emotional responding: amygdala recovery predicts emotional traits. Social Cognitive and Affective Neuroscience 2014; 9(2): 176-81.

Phillips ML, Drevets WC, Rauch SL, Lane R. Neurobiology of emotion perception I: The neural basis of normal emotion perception. Biological Psychiatry 2003a; 54(5): 504-14.

Haas BW, Constable RT, Canli T. Stop the sadness: Neuroticism is associated with sustained medial prefrontal cortex response to emotional facial expressions. NeuroImage 2008; 42(1): 385-92.

Puccetti NA, Schaefer SM, van Reekum CM, Ong AD, Almeida DM, Ryff CD, et al. Linking Amygdala Persistence to Real-World Emotional Experience and Psychological Well-Being. Journal of Neuroscience 2021: JN-RM-1637-20.

Be explicit on the degrees of freedom in the statistical tests given that one subject was excluded from some of the tests.

We thank the reviewers for the comment. The number of samples used for each statistics analysis are stated in the title of the figures. We have now also added the degree of freedom in the main text when parametric statistical tests such as t-test or ANOVAs have been used. When permutation tests (which do not have any degrees of freedom associated with it) are used, we have now added the number of samples for the permutation test.

Reviewer #2 (Public Review):

In this study, Huang and colleagues recorded local field potentials from the lateral habenula in patients with psychiatric disorders who recently underwent surgery for deep brain stimulation (DBS). The authors combined these invasive measurements with non-invasive whole-head MEG recordings to study functional connectivity between the habenula and cortical areas. Since the lateral habenula is believed to be involved in the processing of emotions, and negative emotions in particular, the authors investigated whether brain activity in this region is related to emotional valence. They presented pictures inducing negative and positive emotions to the patients and found that theta and alpha activity in the habenula and frontal cortex increases when patients experience negative emotions. Functional connectivity between the habenula and the cortex was likewise increased in this band. The authors conclude that theta/alpha oscillations in the habenula-cortex network are involved in the processing of negative emotions in humans.

Because DBS of the habenula is a new treatment tested in this cohort in the framework of a clinical trial, these are the first data of its kind. Accordingly, they are of high interest to the field. Although the study mostly confirms findings from animal studies rather than bringing up completely new aspects of emotion processing, it certainly closes a knowledge gap.

In terms of community impact, I see the strengths of this paper in basic science rather than the clinical field. The authors demonstrate the involvement of theta oscillations in the habenula-prefrontal cortex network in emotion processing in the human brain. The potential of theta oscillations to serve as a marker in closed-loop DBS, as put forward by the authors, appears less relevant to me at this stage, given that the clinical effects and side-effects of habenula DBS are not known yet.

We thank the reviewers for the favourable comments about the implication of our study in basic science and about the value of our study in closing a knowledge gap. We agree that further studies would be required to make conclusions about the clinical effects and side-effects of habenula DBS.

Detailed comments:

The group-average MEG power spectrum (Fig. 4B) suggests that negative emotions lead to a sustained theta power increase and a similar effect, though possibly masked by a visual ERP, can be seen in the habenula (Fig. 3C). Yet the statistics identify brief elevations of habenula theta power at around 3s (which is very late), a brief elevation of prefrontal power a time 0 or even before (Fig. 4C) and a brief elevation of Habenula-MEG theta coherence around 1 s. It seems possible that this lack of consistency arises from a low signal-to-noise ratio. The data contain only 27 trails per condition on average and are contaminated by artifacts caused by the extension wires.

With regard to the nature of the activity modulation with short latency after stimuli onset: whether this is an ERP or oscillation? We have now investigated this. In summary, by analysing the ERP and removing the influence of the ERP from the total power spectra, we didn’t observe stimulus emotional valence related modulation in the ERP, and the modulation related to emotional valence in the pure induced (non-phase-locked) power spectra was similar to what we have observed in the total power shown in Fig. 3. Therefore, we argue that the theta/alpha increase with negative emotional stimuli we observed in both habenula and prefrontal cortex 0-500 ms after stimuli onset are not dominated by visual or other ERP.

With regard to the signal-to-noise ratio from only 27 trials per condition on average per participant: We have tried to clean the data by removing the trials with obvious artefacts characterised by increased measurements in the time domain over 5 times the standard deviation and increased activities across all frequency bands in the frequency domain. After removing the trials with artefacts, we have 27 trials per condition per subject on average. We agree that 27 trials per condition on average is not a high number, and increasing the number of trials would further increase the signal-to-noise ratio. However, our studies with EEG recordings and LFP recordings from externalised patients have shown that 30 trials was enough to identify reduction in the amplitude of post-movement beta oscillations at the beginning of visuomotor adaption in the motor cortex and STN (Tan et al., 2014a; Tan et al., 2014b). These results of motor error related modulation in the post-movement beta have been repeated by other studies from other groups. In Tan et al. 2014b, with simultaneous EEG and STN LFP measurements and a similar number of trials (around 30), we also quantified the time-course of STN-motor cortex coherence during voluntary movements. This pattern has also been repeated in a separate study from another group with around 50 trials per participant (Talakoub et al., 2016). In addition, similar behavioural paradigm (passive figure viewing paradigm) has been used in two previous studies with LFP recordings from STN from different patient groups (Brucke et al., 2007; Huebl et al., 2014). In both studies, a similar number of trials per condition around 27 was used. The authors have identified meaningful activity modulation in the STN by emotional stimuli. Therefore, we think the number of trials per condition was sufficient to identify emotional valence induced difference in the LFPs in the paradigm.

We agree that the measurement of coherence can be more susceptible to noise and suffer from the reduced signal-to-noise ratio in MEG recording. In Hirschmann et al. 2013, 5 minutes of resting recording and 5 minutes of movement recording from 10 PD patients were used to quantify movement related changes in STN-cortical coherence and how this was modulated by levodopa (Hirschmann et al., 2013). Litvak et al. (2012) have identified movement-related changes in the coherence between STN LFP and motor cortex with recording with simultaneous STN LFP and MEG recordings from 17 PD patients and 20 trials in average per participant per condition (Litvak et al., 2012). With similar methods, van Wijk et al. (2017) used recordings from 9 patients and around on average in 29 trials per hand per condition, and they identified reduced cortico-pallidal coherence in the low-beta decreases during movement (van Wijk et al., 2017). So the trial number per condition participant we used in this study are comparable to previous studies.

The DBS extension wires do reduce signal-to-noise ratio in the MEG recording. therefore the spatiotemporal Signal Space Separation (tSSS) method (Taulu and Simola, 2006) implemented in the MaxFilter software (Elekta Oy, Helsinki, Finland) has been applied in this study to suppress strong magnetic artifacts caused by extension wires. This method has been proved to work well in de-noising the magnetic artifacts and movement artifacts in MEG data in our previous studies (Cao et al., 2019; Cao et al., 2020). In addition, the beamforming method proposed by several studies (Litvak et al., 2010; Hirschmann et al., 2011; Litvak et al., 2011) has been used in this study. In Litvak et al., 2010, the artifacts caused by DBS extension wires was detailed described and the beamforming was demonstrated to effectively suppress artifacts and thereby enable both localization of cortical sources coherent with the deep brain nucleus. We have now added more details and these references about the data cleaning and the beamforming method in the main text. With the beamforming method, we did observe the standard movement-related modulation in the beta frequency band in the motor cortex with 9 trials of figure pressing movements, shown in the following figure for one patient as an example (Figure 5–figure supplement 1). This suggests that the beamforming method did work well to suppress the artefacts and help to localise the source with a low number of trials. The figure on movement-related modulation in the motor cortex in the MEG signals have now been added as a supplementary figure to demonstrate the effect of the beamforming.

Figure 5–figure supplement 1. (A) Time-frequency maps of MEG activity for right hand button press at sensor level from one participant (Case 8). (B) DICS beamforming source reconstruction of the areas with movement-related oscillation changes in the range of 12-30 Hz. The peak power was located in the left M1 area, MNI coordinate [-37, -12, 43].

References:

Tan H, Jenkinson N, Brown P. Dynamic neural correlates of motor error monitoring and adaptation during trial-to-trial learning. Journal of Neuroscience 2014a; 34(16): 5678-88.

Tan H, Zavala B, Pogosyan A, Ashkan K, Zrinzo L, Foltynie T, et al. Human subthalamic nucleus in movement error detection and its evaluation during visuomotor adaptation. Journal of Neuroscience 2014b; 34(50): 16744-54.

Talakoub O, Neagu B, Udupa K, Tsang E, Chen R, Popovic MR, et al. Time-course of coherence in the human basal ganglia during voluntary movements. Scientific Reports 2016; 6: 34930.

Brucke C, Kupsch A, Schneider GH, Hariz MI, Nuttin B, Kopp U, et al. The subthalamic region is activated during valence-related emotional processing in patients with Parkinson's disease. European Journal of Neuroscience 2007; 26(3): 767-74.

Huebl J, Spitzer B, Brucke C, Schonecker T, Kupsch A, Alesch F, et al. Oscillatory subthalamic nucleus activity is modulated by dopamine during emotional processing in Parkinson's disease. Cortex 2014; 60: 69-81.

Hirschmann J, Ozkurt TE, Butz M, Homburger M, Elben S, Hartmann CJ, et al. Differential modulation of STN-cortical and cortico-muscular coherence by movement and levodopa in Parkinson's disease. NeuroImage 2013; 68: 203-13.

Litvak V, Eusebio A, Jha A, Oostenveld R, Barnes G, Foltynie T, et al. Movement-related changes in local and long-range synchronization in Parkinson's disease revealed by simultaneous magnetoencephalography and intracranial recordings. Journal of Neuroscience 2012; 32(31): 10541-53.

van Wijk BCM, Neumann WJ, Schneider GH, Sander TH, Litvak V, Kuhn AA. Low-beta cortico-pallidal coherence decreases during movement and correlates with overall reaction time. NeuroImage 2017; 159: 1-8.

Taulu S, Simola J. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Physics in Medicine and Biology 2006; 51(7): 1759-68.

Cao C, Huang P, Wang T, Zhan S, Liu W, Pan Y, et al. Cortico-subthalamic Coherence in a Patient With Dystonia Induced by Chorea-Acanthocytosis: A Case Report. Frontiers in Human Neuroscience 2019; 13: 163.

Cao C, Li D, Zhan S, Zhang C, Sun B, Litvak V. L-dopa treatment increases oscillatory power in the motor cortex of Parkinson's disease patients. NeuroImage Clinical 2020; 26: 102255.

Litvak V, Eusebio A, Jha A, Oostenveld R, Barnes GR, Penny WD, et al. Optimized beamforming for simultaneous MEG and intracranial local field potential recordings in deep brain stimulation patients. NeuroImage 2010; 50(4): 1578-88.

Litvak V, Jha A, Eusebio A, Oostenveld R, Foltynie T, Limousin P, et al. Resting oscillatory cortico-subthalamic connectivity in patients with Parkinson's disease. Brain 2011; 134(Pt 2): 359-74.

Hirschmann J, Ozkurt TE, Butz M, Homburger M, Elben S, Hartmann CJ, et al. Distinct oscillatory STN-cortical loops revealed by simultaneous MEG and local field potential recordings in patients with Parkinson's disease. NeuroImage 2011; 55(3): 1159-68.

I doubt that the correlation between habenula power and habenula-MEG coherence (Fig. 6C) is informative of emotion processing. First, power and coherence in close-by time windows are likely to to be correlated irrespective of the task/stimuli. Second, if meaningful, one would expect the strongest correlation for the negative condition, as this is the only condition with an increase of theta coherence and a subsequent increase of theta power in the habenula. This, however, does not appear to be the case.

The authors included the factors valence and arousal in their linear model and found that only valence correlated with electrophysiological effects. I suspect that arousal and valence scores are highly correlated. When fed with informative yet highly correlated variables, the significance of individual input variables becomes difficult to assess in many statistical models. Hence, I am not convinced that valence matters but arousal not.

For the correlation shown in Fig. 6C, we used a linear mixed-effect modelling (‘fitlme’ in Matlab) with different recorded subjects as random effects to investigate the correlations between the habenula power and habenula-MEG coherence at an earlier window, while considering all trials together. Therefore the reported value in the main text and in the figure (k = 0.2434 ± 0.1031, p = 0.0226, R2 = 0.104) show the within subjects correlation that are consistent across all measured subjects. The correlation is likely to be mediated by emotional valence condition, as negative emotional stimuli tend to be associated with both high habenula-MEG coherence and high theta power in the later time window tend to happen in the trials with.

The arousal scores are significantly different for the three valence conditions as shown in Fig. 1B. However, the arousal scores and the valence scores are not monotonically correlated, as shown in the following figure (Fig. S2). The emotional neutral figures have the lowest arousal value, but have the valence value sitting between the negative figures and the positive figures. We have now added the following sentence in the main text:

"This nonlinear and non-monotonic relationship between arousal scores and the emotional valence scores allowed us to differentiate the effect of the valence from arousal."

Table 2 in the main text show the results of the linear mixed-effect modelling with the neural signal as the dependent variable and the valence and arousal scores as independent variables. Because of the non-linear and non-monotonic relationship between the valence and arousal scores, we think the significance of individual input variables is valid in this statistical model. We have now added a new figure (shown below, Fig. 7) with scatter plots showing the relationship between the electrophysiological signal and the arousal and emotional valence scores separately using Spearman’s partial correlation analysis. In each scatter plot, each dot indicates the average measurement from one participant in one emotional valence condition. As shown in the following figure, the electrophysiological measurements linearly correlated with the valence score, but not with the arousal scores. However, the statistics reported in this figure considered all the dots together. The linear mixed effect modelling taking into account the interdependency of the measurements from the same participant. So the results reported in the main text using linear mixed effect modelling are statistically more valid, but supplementary figure here below illustrate the relationship.

Figure S2. Averaged valence and arousal ratings (mean ± SD) for figures of the three emotional condition. (B) Scatter plots showing the relationship between arousal and valence scores for each emotional condition for each participant.

Figure 7. Scatter plots showing how early theta/alpha band power increase in the frontal cortex (A), theta/alpha band frontal cortex-habenula coherence (B) and theta band power increase in habenula stimuli (C) changed with emotional valence (left column) and arousal (right column). Each dot shows the average of one participant in each categorical valence condition, which are also the source data of the multilevel modelling results presented in Table 2. The R and p value in the figure are the results of partial correlation considering all data points together.

Page 8: "The time-varying coherence was calculated for each trial". This is confusing because coherence quantifies the stability of a phase difference over time, i.e. it is a temporal average, not defined for individual trials. It has also been used to describe the phase difference stability over trials rather than time, and I assume this is the method applied here. Typically, the greatest coherence values coincide with event-related power increases, which is why I am surprised to see maximum coherence at 1s rather than immediately post-stimulus.

We thank the reviewer for pointing out this incorrect description. As the reviewer pointed out correctly, the method we used describe the phase difference stability over trials rather than time. We have now clarified how coherence was calculated and added more details in the methods:

"The time-varying cross trial coherence between each MEG sensor and the habenula LFP was first calculated for each emotional valence condition. For this, time-frequency auto- and cross-spectral densities in the theta/alpha frequency band (5-10 Hz) between the habenula LFP and each MEG channel at sensor level were calculated using the wavelet transform-based approach from -2000 to 4000 ms for each trial with 1 Hz steps using the Morlet wavelet and cycle number of 6. Cross-trial coherence spectra for each LFP-MEG channel combination was calculated for each emotional valence condition for each habenula using the function ‘ft_connectivityanalysis’ in Fieldtrip (version 20170628). Stimulus-related changes in coherence were assessed by expressing the time-resolved coherence spectra as a percentage change compared to the average value in the -2000 to -200 ms (pre-stimulus) time window for each frequency."

In the Morlet wavelet analysis we used here, the cycle number (C) determines the temporal resolution and frequency resolution for each frequency (F). The spectral bandwidth at a given frequency F is equal to 2F/C while the wavelet duration is equal to C/F/pi. We used a cycle number of 6. For theta band activities around 5 Hz, we will have the spectral bandwidth of 25/6 = 1.7 Hz and the wavelet duration of 6/5/pi = 0.38s = 380ms.

As the reviewer noticed, we observed increased activities across a wide frequency band in both habenula and the prefrontal cortex within 500 ms after stimuli onset. But the increase of cross-trial coherence starts at around 300 ms. The increase of coherence in a time window without increase of power in either of the two structures indicates a phase difference stability across trials in the oscillatory activities from the two regions, and this phase difference stability across trials was not secondary to power increase.

Reviewer #3 (Public Review):

This paper describes the oscillatory activity of the habenula using local field potentials, both within the region and, through the use of MEG, in connection to the prefrontal cortex. The characteristics of this activity were found to vary with the emotional valence but not with arousal. Sheding light on this is relevant, because the habenula is a promising target for deep brain stimulation.

In general, because I am not much on top of the literature on the habenula, I find difficult to judge about the novelty and the impact of this study. What I can say is that I do find the paper is well-written and very clear; and the methods, although quite basic (which is not bad), are sound and rigourous.

We thank the reviewer for the positive comments about the potential implication of our study and on the methods we used.

On the less positive side, even though I am aware that in this type of studies it is difficult to have high N, the very low N in this case makes me worry about the robustness and replicability of the results. I'm sure I have missed it and it's specified somewhere, but why is N different for the different figures? Is it because only 8 people had MEG? The number of trials seems also a somewhat low. Therefore, I feel the authors perhaps need to make an effort to make up for the short number of subjects in order to add confidence to the results. I would strongly recommend to bootstrap the statistical analysis and extract non-parametric confidence intervals instead of showing parametric standard errors whenever is appropriate. When doing that, it must be taken into account that each two of the habenula belong to the same person; i.e. one bootstraps the subjects not the habenula.

We do understand and appreciate the concern of the reviewer on the low sample numbers due to the strict recruitment criteria for this very early stage clinical trial: 9 patients for bilateral habenula LFPs, and 8 patients with good quality MEGs. Some information to justify the number of trials per condition for each participant has been provided in the reply to the Detailed Comments 1 from Reviewer 2. The sample number used in each analysis was included in the figures and in the main text.

We have used non-parametric cluster-based permutation approach (Maris and Oostenveld, 2007) for all the main results as shown in Fig. 3-5. Once the clusters (time window and frequency band) with significant differences for different emotional valence conditions have been identified, parametric statistical test was applied to the average values of the clusters to show the direction of the difference. These parametric statistics are secondary to the main non-parametric permutation test.

In addition, the DICS beamforming method was applied to localize cortical sources exhibiting stimuli-related power changes and cortical sources coherent with deep brain LFPs for each subject for positive and negative emotional valence conditions respectively. After source analysis, source statistics over subjects was performed. Non-parametric permutation testing with or without cluster-based correction for multiple comparisons was applied to statistically quantify the differences in cortical power source or coherence source between negative and positive emotional stimuli.

References:

Maris E, Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods 2007; 164(1): 177-90.

Related to this point, the results in Figure 6 seem quite noisy, because interactions (i.e. coherence) are harder to estimate and N is low. For example, I have to make an effort of optimism to believe that Fig 6A is not just noise, and the result in Fig 6C is also a bit weak and perhaps driven by the blue point at the bottom. My read is that the authors didn't do permutation testing here, and just a parametric linear-mixed effect testing. I believe the authors should embed this into permutation testing to make sure that the extremes are not driving the current p-value.

We have now quantified the coherence between frontal cortex-habenula and occipital cortex-habenula separately (please see more details in the reply to Reviewer 2 (Recommendations for the authors 6). The new analysis showed that the increase in the theta/alpha band coherence around 1 s after the negative stimuli was only observed between prefrontal cortex-habenula and not between occipital cortex-habenula. This supports the argument that Fig. 6A is not just noise.

Author Response

Reviewer #1:

Köster and colleagues present a brief report in which they study in 9 month-old babies the electrophysiological responses to expected and unexpected events. The major finding is that in addition to a known ERP response, an NC present between 400-600 ms, they observe a differential effect in theta oscillations. The latter is a novel result and it is linked to the known properties of theta oscillations in learning. This is a nice study, with novel results and well presented. My major reservation however concerns the push the authors make for the novelty of the results and their interpretation as reflecting brain dynamics and rhythms. The reason for that is, that any ERP, passed through the lens of a wavelet/FFT etc, will yield a response at a particular frequency. This is especially the case for families of ERP responses related to unexpected event e.g., MMR, and NC, etc. For which there is plenty of literature linking them to responses to surprising event, and in particular in babies; and which given their timing will be reflected in delta/theta oscillations. The reason why I am pressing on this issue, is because there is an old, but still ongoing debate attempting to dissociate intrinsic brain dynamics from simple event related responses. This is by no means trivial and I certainly do not expect the authors to resolve it, yet I would expect the authors to be careful in their interpretation, to warn the reader that the result could just reflect the known ERP, to avoid introducing confusion in the field.

We would like to thank the author for highlighting the novelty of the results. Critically, there is one fundamental difference in investigating the ERP response and the trial-wise oscillatory power, which we have done in the present analysis: when looking at the evoked oscillatory response (i.e., the TF characteristics of the ERP), the signal is averaged over trials first and then subjected to a wavelet transform. However, when looking at the ongoing (or total) oscillatory response, the wavelet transform is applied at the level of the single trial, before the TF response of the single trials is averaged across the trials of one condition trials (for a classical illustration, see Tallon-Baudry & Bertrand, 1999; TICS, Box 2). We have now made this distinction more salient throughout the manuscript.

In the present study, the results did not suggest a relation between the ERP and the ongoing theta activity, because the topography, temporal evolution, and polarity of the ERP and the theta response were very dissimilar: Looking at Figure 2 (A and B) and Figure 3 (B and C), the Nc peaks at central electrodes, but the theta response is more distributed, and the expected versus unexpected difference was specific for the .4 to .6 s time window, but the theta difference lasted the whole trial. Furthermore, the NC was higher for expected versus unexpected, which should (due to the low frequency) rather lead to a higher theta power for unexpected, in contrast to expected events for the time frequency analysis for the Nc. To verify this intuition, we now ran a wavelet analysis on the evoked response (i.e., the ERP) and, for a direct comparison, also plotted the ongoing oscillatory response for the central electrodes (see Additional Figure 1). These additional analyses nicely illustrate that the trial-wise theta response provides a fundamentally different approach to analyze oscillatory brain dynamics.

Because this is likely of interest to many readers, we also report the results of the wavelet analysis of the ERP versus the analysis of the ongoing theta activity at central electrodes and the corresponding statistics in the result section, and have also included the Additional Figure in the supplementary materials, as Figure S2.

Additional Figure 1. Comparison of the topography and time course for the 4 – 5 Hz activity for the evoked (A, B) and the ongoing (C, D) oscillatory response at central electrodes (400 – 600 ms; Cz, C3, C4; baseline: -100 – 0 ms). (A) Topography for the difference between unexpected and expected events in the evoked oscillatory response. (B) The corresponding time course at central electrodes, which did not reveal a significant difference between 400 – 600 ms, t(35) = 1.57, p = .126. (C) Topography for the same contrast in the ongoing oscillatory response and (D) the corresponding time course at central electrodes, which did likewise not reveal a significant difference between 400 – 600 ms, t(35) = -1.26, p = .218. The condition effects (unexpected - expected) were not correlated between the evoked and the ongoing response, r = .23, p = .169.

A second aspect that I would like the authors to comment on is the power of the experimental design to measure surprise. From the methods, I gathered that the same stimulus materials and with the same frequency were presented as expected and unexpected endings. If that is the case, what is the measure of surprise? For once the same materials are shown causing habituation and reducing novelty and second the experiment introduces a long-term expectation of a 50:50 proportion of expected/unexpected events. I might be missing something here, which is likely as the methods are quite sparse in the description of what was actually done.

We have used 4 different stimuli types (variants) in each of the 4 different domains, with either an expected or unexpected outcome. This resulted in 32 distinct stimulus sequences, which we presented twice, resulting in (up to) 64 trials. We have now described this approach and design in more detail and have also included all stimuli as supplementary material (Figure S1). In particular, we have used multiple types in each domain to reduce potential habituation or expectation effects. Still, we agree that one difficulty may be that, over time, infants got used to the fact that expected and unexpected outcomes were to be similarly “expected” (i.e., 50:50). However, if this was the case it would have resulted in a reduction (or disappearance) of the condition effect, and would thus also reduce the condition difference that we found, rather than providing an alternative explanation. We now included this consideration in the method section (p. 7).

Two more comments concerning the analysis choices:

1) The statistics for the ERP and the TF could be reported using a cluster size correction. These are well established statistical methods in the field which would enable to identify the time window/topography that maximally distinguished between the expected and the unexpected condition both for ERP and TF. Along the same lines, the authors could report the spatial correlation of the ERP/TF effects.

For the ERP analysis we used the standard electrodes typically analyzed for the Nc in order to replicate effects found in former research (Langeloh et al., 2020; see also, Kayhan et al., 2019; Reynolds and Richards, 2005; Webb et al., 2005). For the TF analyses we used the most conservative criterion, namely all scalp recorded electrodes and the whole time window from 0 to 2000 ms, such that we did not make any choice regarding time window or the electrodes (i.e., which could be corrected for against other choices). We have now made those choices clearer in the method section, and why we think that, under these condition a multiple comparison correction is not needed/applicable (p. 10). Regarding the spatial correlation of the ERP and TF effects, we explained in response to the first comment the very different nature of the TF decomposition of the ERP and ongoing oscillatory activity and also that these were found to be interdependent (i.e., uncorrelated). We hope that with the additional analysis included in response to this comment that this difference is much clearer now.

2) While I can see the reason why the authors chose to keep the baseline the same between the ERP and the TF analysis, for time frequency analysis it would be advisable to use a baseline amounting to a comparable time to the frequency of interest; and to use a period that does not encroach in the period of interest i.e., with a wavelet = 7 and a baseline -100:0 the authors are well into the period of interested.

The difficulty in choosing the baseline in the present study was two-fold. First, we were interested in the ERP and the change in neural oscillations upon the onset of an outcome picture within a continuous presentation of pictures, forming a sequence. Second, we wanted to use a similar baseline for both analyses, to make them comparable. Because the second picture (the picture before the outcome picture) also elicited both an ERP and an oscillatory response at ~ 4 Hz (see Additional Figure 2), we choose a baseline just before the onset of the outcome stimulus, from -100 to 0 ms. Also we agree that the possibility to take a longer and earlier baseline, in particular for the TF results would have been favorable, but still consider that the -100 to 0 ms is still the best choice for the present analysis. Notably, because we found an increase in theta oscillations and the critical difference relies on a higher theta rhythm in one compared to the other condition, the effects of the increase in theta, if they effected the baseline, this effect would counteract rather than increase the current effect. We now explain this choice in more detail (p.10).

Additional Figure 1. Display of the grand mean signals prior to the -100 to 0 baseline and outcome stimulus. (A) The time-frequency response across all scalp-recorded electrodes, as well as (B) the ERP at the central electrodes (Cz, C3, C4) across both conditions show a similar response to the 2. picture like the outcome picture. Thus a baseline just prior to the stimulus of interest was chosen, consistent for both analyses.

Reviewer #2:

The manuscript reports increases in theta power and lower NC amplitude in response to unexpected (vs. expected) events in 9-month-olds. The authors state that the observed increase in theta power is significant because it is in line with an existing theory that the theta rhythm is involved in learning in mammals. The topic is timely, the results are novel, the sample size is solid, the methods are sound as far as I can tell, and the use of event types spanning multiple domains (e.g. action, number, solidity) is a strength. The manuscript is short, well-written, and easy to follow.

1) The current version of the manuscript states that the reported findings demonstrate that the theta rhythm is involved in processing of prediction error and supports the processing of unexpected events in 9-month-old infants. However, what is strictly shown is that watching at least some types of unexpected events enhance theta rhythm in 9-month-old infants, i.e. an increase in the theta rhythm is associated with processing unexpected events in infants, which suggests that an increase in the theta rhythm is a possible neural correlate of prediction error in this age range. While the present novel findings are certainly suggestive, more data and/or analyses would be needed to corroborate/confirm the role of the observed infant theta rhythm in processing prediction error, or document whether and how this increase in the theta rhythm supports the processing of unexpected events in infants. (As an example, since eye-tracking data were collected, are trial-by-trial variations in theta power increases to unexpected outcomes related to how long individual infants looked to the unexpected outcome pictures?) If it is not possible to further confirm/corroborate the role of the theta rhythm with this dataset, then the discussion, abstract, and title should be revised to more closely reflect what the current data shows (as the wording of the conclusion currently does), and clarify how future research may test the hypothesis that the infant theta rhythm directly supports the processing of prediction error in response to unexpected events.

We would like to thank the reviewer for acknowledging the merit of the present research.

On the one hand, we have revised our manuscript and are now somewhat more careful with our conclusion, in particular with regard to the refinement of basic expectations. On the other hand, we consider the concept of “violation to expectation” (VOE), which is one of the most widely used concepts in infancy research, very closely linked to the concept of a prediction error processing, namely a predictive model is violated. In particular, we have made this conceptual link in a recent theoretical paper (Köster et al., 2020), and based on former theoretical considerations about the link between these two concepts (e.g., see Schubotz 2015; Prediction and Expectation). In particular, in the present study we used a set of four different domains of violation of expectation paradigms, which are among the best established domains of infants core knowledge (e.g., action, solidity, cohesion, number; cf. Spelke & Kinzler, 2007). It was our specific goal not to replicate, for another time, that infants possess expectations (i.e., make predictions) in these domains, but to “flip the coin around” and investigate infants’ prediction error more generally, independent of the specific domain. We have now made the conceptual link between VOE and prediction error processing more explicit in the introduction of the manuscript and also emphasize that we choose a variety of domains to obtain a more general neural marker for infant processing of prediction errors.

Having said this, indeed, we planned to assess and compare both infants gaze behavior and EEG response. Unfortunately, this was not very successful and the concurrent recording only worked for a limited number of infants and trials. This led us to the decision to make the eye-tracking study a companion study and to collect more eye-tracking data in an independent sample of infants after the EEG assessment was completed, such that a match between the two measures was not feasible. We now make this choice more explicit in the method section (p. 7). In addition, contrary to our basic assumption we did not find an effect in the looking time measure. Namely, there was no difference between expected and unexpected outcomes. We assume that this is due to the specificities of the current design that was rather optimized for EEG assessments: We used a high number of repetitions (64), with highly variable domains (4), and restricted the time window for potential looking time effects to 5 seconds, which is highly uncommon in the field and therefore not directly comparable with former studies.

Finally, besides the ample evidence from former studies using VOE paradigms, if it were not the unexpected vs. expected (i.e., unpredicted vs. predicted) condition contrast which explains the differences we found in the ERP and the theta response, there would need to be an alternative explanation for the differential responses in the EEG, which produce the hypothesized effects. (Please also note that there are many studies relying their VOE assumption on ERPs alone, here we have two independent measures suggesting that infants discriminated between those conditions.)

2) The current version of the manuscript states "The ERP effect was somewhat consistent across conditions, but the effect was mainly driven by the differences between expected and unexpected events in the action and the number domain (Figure S1). The results were more consistent across domains for the condition difference in the 4 - 5 Hz activity, with a peak in the unexpected-expected difference falling in the 4 - 5 Hz range across all electrodes (Figure S2)". However, the similarity/dissimilarity of NC and theta activity responses across domains was not quantified or tested. Looking at Figures S1 and S2, it is not that obvious to me that theta responses were more consistent across domains than NC responses. I understand that there were too few trials to formally test for any effect of domain (action, number, solidity, cohesion) on NC and theta responses, either alone or in interaction with outcome (expected, unexpected). It may still be possible to test for correlations of the topography and time-course of the individual average unexpected-expected difference in NC and theta responses across domains at the group level, or to test for an effect of outcome (expected, unexpected) in individual domains for subgroups of infants who contributed enough trials. Alternatively, claims of consistency across domains may be altered throughout, in which case the inability to test whether the theta and/or NC signatures of unexpected event processing found are consistent across domains (vs. driven by some domains) should be acknowledged as a limitation of the present study.

We agree that this statement rather reflected our intuition and would not surpass statistical analysis given the low number of trials. So we are happy to refrain from this claim and simply refer to the supplementary material for the interested reader and also mention this as a perspective for future research in the discussion (p. 12; p. 15).

As outlined in our previous response, it was also not our goal to draw conclusions about each single domain, but rather to present a diversity of stimulus types from different core knowledge domains to gain a more generalized neural marker for infants’ processing of unexpected, i.e., unpredicted events.

Reviewer #3:

General assessment:

In this manuscript, the authors bring up a contemporary and relevant topic in the field, i.e. theta rhythm as a potential biomarker for prediction error in infancy. Currently, the literature is rich on discussions about how, and why, theta oscillations in infancy implement the different cognitive processes to which they have been linked. Investigating the research questions presented in this manuscript could therefore contribute to fill these gaps and improve our understanding of infants' neural oscillations and learning mechanisms. While we appreciate the motivation behind the study and the potential in the authors' research aim, we find that the experimental design, analyses and conclusions based on the results that can be drawn thereafter, lack sufficient novelty and are partly problematic in their description and implementation. Below, we list our major concerns in more detail, and make suggestions for improvements of the current analyses and manuscript.

Summary of major concerns:

1) Novelty:

(a) It is unclear how the study differs from Berger et al., 2006 apart from additional conditions. Please describe this study in more detail and how your study extends beyond it.

We would like to thank the reviewers for emphasizing the timeliness and relevance of the study.

The critical difference between the present study and the study by Berger et al. 2006 was that the authors applied, as far as we understand this from Figure 4 and the method section of their study, the wavelet analysis to the ERP signal. In contrast, in the present study, we applied the wavelet analysis at the level of single trials. We now explain the difference between the two signals in more detail in the revised manuscript and also included an additional comparison between the evoked (i.e., ERP) and the ongoing (i.e., total) oscillatory response (for more details, please see the first response to the first comment of reviewer 1).

(b) Seemingly innovative aspects (as listed below), which could make the study stand out among previous literature, but are ultimately not examined. Consequently, it is also not clear why they are included.

-Relation between Nc component and theta.

-Consistency of the effect across different core knowledge domains.

-Consistency of the effect across the social and non-social domains.

-Link between infants looking at time behavior and theta.

We are thankful for these suggestions, which are closely related to the points raised by reviewer 1 and 2. With regard to the relation between the Nc and the theta response, we have now included a direct comparison of these signals (see Additional Figure 1, i.e., novel Figure S2; for details, please see the first response to the first comment of reviewer 1). Regarding the consistency of effects across domains, we have explained in response to point 1 by reviewer 2 that this was not the specific purpose of the present study, but we aimed at using a diversity of VOE stimuli to obtain a more general neural signature for infants’ prediction error processing, and explain this in more detail in the revised manuscript. Having said this, we agree that the question of consistency of effects between conditions is highly interesting, but we would not consider the data robust enough to confidently test these differences given the limited number of trials available per stimulus category. We now discuss this as a direction for future research (p. 15). Finally, we also agree with regard to the link between looking times and the theta rhythm. As also outlined in response to point 1 by reviewer 2 (paragraph 2), we initially had this plan, but did not succeed in obtaining a satisfactory number of trials in the dual recording of EEG and eye-tracking, which made us change these plans. This is now explained in detail in the method section (p. 7).

(c) The reason to expect (or not) a difference at this age, compared to what is known from adult neural processing, is not adequately explained.

-Potentially because of neural generators in mid/pre-frontal cortex? See Lines 144-146.

The overall aim of the present study was to identify the neural signature for prediction error processing in the infant brain, which has, to the best of our knowledge, not been done this explicitly and with a focus on the ongoing theta activity and across a variety of violations in infants’ core knowledge domains. Because we did not expect a specific topography of this effect, in particular across multiple domains, we included all electrodes in the analyses. We have now clarified this in the method section (p. 10).

(d) The study is not sufficiently embedded in previous developmental literature on the functionality of theta. That is, consider theta's role in error processing, but also the increase of theta over time of an experiment and it's link to cognitive development. See, for example: Braithwaite et al., 2020; Conejero et al., 2018; Adam et al., 2020.

We are thankful that the reviewer indicated these works and have now included them in the introduction and discussion. Closest to the present study is the study by Conejero et al., 2018. However, this study is also based on theta analyses of the ERP, not of the ongoing oscillatory response and it includes considerably older infants (i.e., 16-month-olds instead of 9-month-olds as in the present study).

2) Methodology:

(a) Design: It is unclear what exactly a testing session entails.

-Was the outcome picture always presented for 5secs? The methods section suggests that, but the introduction of the design and Figure 1 do not. This might be misleading. Please change in Figure 1 to 5sec if applicable.

Yes, the final images were shown for 5s in order to simultaneously assess infants’ looking times. However, we included trials in the EEG analysis if infants looked for 2s, so this is the more relevant info for the analysis. We now clarified this in the method section (p. 7) and have also added this info in the figure caption.

-Were infants' eye-movements tracked simultaneously to the EEG recording? If so, please present findings on their looking time and (if possible) pupil size. Also examine the relation to theta power. This would enhance the novelty and tie these findings to the larger looking time literature that the authors refer to in their introduction.

Yes, in response to the second reviewer (comment 1) we explained in more detail why the joint analysis of the EEG and looking time data was not possible: We planned to assess both, infants gaze behavior and EEG response. Unfortunately, this was not very successful and the dual recording only worked for a few infants and trials. This led us to collect more eye-tracking data after the EEG assessment was completed, such that a match between the two measures was not feasible. We now clarified this in the method section (p. 7).

(b) Analysis:

-In terms of extracting theta power information: The baseline of 100ms is extremely short for a comparison in the frequency domain, since it does not even contain half a cycle of the frequency of interest, i.e. 4Hz. We appreciate the thought to keep the baseline the same as in the ERP analysis (which currently is hardly focused on in the manuscript), but it appears problematic for the theta analysis. Also, if we understand the spectral analysis correctly, the window the authors are using to estimate their spectral estimates is largely overlapping between baseline and experimental window. The question arises whether a baseline is even needed here, or if a direct contrast between conditions might be better suited.

Please see our explanation about the choice of the baseline in our response to reviewer 1, comment 2. Because our stimulus sequences were highly variable, likely leading to highly variable overall theta activity, and our specific interest was in the change in theta activity upon the onset of the unexpected versus unpredicted outcome, we still consider it useful to take a baseline here. Also because this makes the study more closely comparable to the existing literature. We now clarified this in the method section (p. 9)

-In terms of statistical testing

-It appears that the authors choose the frequency band that will be entered in the statistical analysis from visual inspection of the differences between conditions. They write: "we found the strongest difference between 4 - 5 Hz (see lower panel of Figure 3). Therefore, and because this is the first study of this kind, we analyzed this frequency range." ll. 277-279). This approach seems extremely problematic since it poses a high risk for 'double-dipping'. This is crucial and needs to be addressed. For instance, the authors could run non-parametric permutation tests on the time-frequency domain using FDR correction or cluster-based permutation tests on the topography.

-Lack of examining time- / topographic specificity.

Please also note the sentence before this citation, which states our initial hypothesis: “While our initial proposal was to look at the difference in the 4 Hz theta rhythm between conditions (Köster et al., 2019), we found the strongest difference between 4 – 5 Hz (see lower panel of Figure 3).” Note that the hypothesis of 4 Hz can be clearly derived from our 2019 study. We would maintain that the center frequency we took for the analysis 4.5Hz (i.e., 4 – 5Hz) is very close to this original hypothesis and, considering that we applied a novel design and analyses in very young infants, could indeed hardly have fallen more closely to this initial proposal. The frequency choice is also underlined, as the reviewer remarks, by the consistency of this peak across domains, peaking at 4Hz (cohesion), 4.5Hz (action), and 5Hz (solidity, number). Importantly, please note that we have chosen the electrodes and time window very conservatively, namely by including the whole time period and all electrodes, which we now explain in more detail on p. 10. Please also see our response to reviewer 1, comment “1)”.

3) Interpretation of results:

(a) The authors interpret the descriptive findings of Figure S1 as illustration of the consistency of the results across the four knowledge domains. While we would partly agree with this interpretation based on column A of that figure (even though also there the peak shifts between domains), columns B and C do not picture a consistent pattern of data. That is, the topography appears very different between domains and so does the temporal course of the 4-5Hz power, with only showing higher power in the action and number domain, not in the other two. Since none of these data were compared statistically, any interpretation remains descriptive. Yet, we would like to invite the authors to critically reconsider their interpretation. You also might want to consider adding domain (action, number etc.) as a covariate to your statistical model.

We agree with the reviewers (reviewer 2 and reviewer 3) that our initial interpretation of the data regarding the consistency of effects across domains may have been too strong. Thus, in the revised version of the manuscript, we do not state that the TF analysis revealed more consistent results. Given that the analysis was based on a different subsample and highly variable in trial numbers, we did not enter them as a covariate in the statistical model.

Both Egyptian hieroglyphics and cuneiform were originally complex writing systems, but hieroglyphics were able to be adapted into a system that is more accessible

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Reply to the reviewers

Manuscript number: RC-2025-03220

Corresponding author(s): Ryusuke Niwa, Yuko Shimada-Niwa, and Wei Sun

Dear Editors,

We are pleased to submit our revised manuscript of RC-2025-03220R. The reviewers’ comments from Review Commons are presented in italic.

For submission of our current revised manuscript, we provide two Word files, which are the “clean” and “Track-and-Change” files. Page and line numbers described below correspond to those of the “clean” file. The “Track-and-Change” file might be helpful for Reviewers to find what we have changed for the current revision.

We hope that the revised version is now suitable for the next stage of evaluation.

Sincerely,

Ryusuke Niwa, Yuko Shimada-Niwa, and Wei Sun

1. General Statements [optional]

We sincerely thank the reviewers for their thoughtful feedback on our initial submission. Experiments that we will conduct and the revisions on the manuscript that have already been incorporated are detailed below in the point-by-point response. For this revised submission, two versions of the manuscript are provided: a clean copy and a tracked-changes file. Page and line numbers mentioned below refer to the clean version, while the tracked-changes file is intended to help reviewers easily identify the revisions made.

In preparing the revision plan, we have included additional data, some of which were generated in collaboration with new contributors. Accordingly, we would like to propose adding Yuichi Shichino and Shintaro Iwasaki as co-authors to acknowledge their contributions.

2. Description of the planned revisions__ __

__

- Also, the authors show that two different RNAi lines for NudC give the same defects - it would be good to know if the RNAi lines target the same or different sequences in the NudC transcripts. Alternatively, it would be equally good to show that trans-allelic combinations of NudC mutants have the same defects in the prothoracic glands and the salivary glands as the RNAi. Instead, they examine only overall body size, developmental delays and lethality in the trans-hetero allelic NudC mutants.

Author response:

In response to the second part of the criticism, we will further validate the observed phenotypes by examining tissue and nuclear size, chromosomal structure, and the levels of Fibrillarin and RpS6 proteins in the prothoracic glands and salivary glands of NudC mutants.

__

- It would be quite helpful to characterize the "5 blob" and "shortened polytene chromosome arm" defects shown in Figure 2 and Figure 6. Are these partially polytenized chromosomes or are large sections of the chromosomes missing or just underreplicated? What do the chromosomes look like if you lyse the nuclei, spread the chromosomes and stain with DAPI or Hoechst - this is a pretty standard practice and would reveal much more about the structure of the polytene chromosomes.

Author response:

To address these structural concerns more clearly, we plan to apply established protocols to obtain higher-resolution images and gather more detailed information on chromosome morphology.

__ - Discussion, line 468. I don't think the authors have provided evidence of DNA damage. With the experiments they have shown, the chromosomes look abnormal - not clear what is abnormal.

Author response:

To further confirm DNA damage in NudC knockdown salivary gland cells, we plan to perform a TUNEL assay, which detects DNA fragmentation associated with damage.

We would like to note that, in the current manuscript, we have shown that depletion of NudC, eIF5, RpLP0-like, or Nopp140 increased γH2Av levels, suggesting activation of the DNA damage response (Figures 6B and 6C).

__

*The authors claim that NudC has a dual role as a cell cycle/cytoskeleton regulator and as a ribosome biogenesis factor. However, because NudC knockdown reduces nuclear size and ploidy (Figures 1F and 2H-2I), the authors cannot exclude that decreased rDNA dosage and nucleolar volume contribute to reduced rRNA signals and that the effects seen are due to a NudC involvement in endoreplication, the rRNA reduction being a consequence of lower polyploidy. Different allelic combinations of NudC induce larval growth defects (Figure S5), consistent with a NudC role in endoreplication. To circumvent this, the authors could genetically modulate endocycle progression (e.g., E2F or Fzr overexpression) in the NudC RNAi background to test whether inducing endoreplication rescues rRNA production and nucleolar volume. This would establish causality between the endocycle state and rRNA output and clarify whether NudC's primary role is in RiBi or endocycle control. *

Author response: In response to Reviewer #2’s suggestion, we plan to genetically modify the progression of the endocycle by inducing continuous expression of Cyclin E (CycE), E2F1, and Fzr in NudC RNAi salivary glands to test whether promoting endoreplication can restore rRNA production and nucleolar volume.

In fact, we have attempted to rescue the developmental arrest in animals with NudC-deficient prothoracic glands (PGs) by inducing continuous expression of CycE. Two constructs, UAS-CycE-1 (BDSC#30725) and UAS-CycE-2 (BDSC#30924), were used. UAS-CycE-1 has previously been shown to rescue developmental arrest in PG-specific TOR loss-of-function animals (Ohhara, Kobayashi, and Yamanaka. PLoS Genetics 13 (1): e1006583, 2017). We introduced each construct into NudC knockdown PGs. However, continuous expression of CycE did not restore development (Figure A as shown below), suggesting that NudC functions in the polyploid cells extend beyond endocycle regulation. We do not currently plan to include the PG data shown in Figure A in the revised manuscript. We will evaluate whether it would be meaningful to present PG data alongside salivary gland results once we have obtained and analyzed data from the salivary gland rescue experiment.

__Figure A. _Survival and developmental progression following continuous expression of CycE._ __Control (phtm>dicer2, +), NudC knockdown (phtm>dicer2, NudC RNAi), and NudC RNAi + CycE (phtm>dicer2, NudC RNAi, CycE) flies were analyzed at 10 days after hatching (10 dAH). Dead indicates dead larvae; L3 denotes third-instar larvae. Sample sizes (number of flies) are shown below each bar.

__

*The conclusion that NudC maintains rRNA levels is derived from salivary gland RNAi phenotypes with strong reductions in ITS1/ITS2 and 18S/28S signals (Figure 4B-4K) and reduced 28S by Northern (Figure 4L), plus corroboration in fat body cells (Figure S7). The authors verified knockdown using two independent RNAi lines for growth phenotypes and NudC::GFP reduction (Figure S2) and generated a UAS-FLAG::NudC transgene (Key Resources), but rRNA measurements were reported for only one RNAi line without rescue. Rescue of the rRNA phenotype by transgenic NudC re-expression, or replication of the rRNA decrease with a second, non-overlapping RNAi, would directly attribute the effect to NudC. In the absence of these standard validation controls, an off-target explanation remains plausible. *

Author response:

We plan to analyze rRNA FISH signals in salivary glands and fat bodies using a second, non-overlapping RNAi strain to confirm the reproducibility of the observed effects.

__ - The authors report in Fig. 2 elevated γH2Av in SG cells upon NudC knockdown and interpret this as evidence of chromosome destabilization. They also state that apoptosis is not observed in Fig S10. However, the increase in γH2Av could reflect transient or early apoptotic events or other stress responses triggered by NudC depletion, rather than direct defects in endoreplication or genome stability. I suggest that the authors clarify this important point, for example, by co-expressing apoptotic inhibitors such as P35, or by using the TUNEL assay, which is more sensitive than anti-Caspase3 or Dcp1 antibodies.

Author response:

We plan to perform a TUNEL assay on salivary gland cells to evaluate apoptosis associated with NudC depletion.

__ - Activation of the JNK pathway is often accompanied by apoptosis. It would strengthen the conclusions if the authors included a positive control to confirm that apoptosis is not induced under these experimental conditions, ensuring that the observed effects are specific to autophagy and not confounded by cell death.

Author response:

We will analyze pJNK and autophagy levels in animals expressing a constitutively-active form of hemipterous (hep) (hep[CA] ) under the control of fkh-GAL4 driver as a positive control. hep encodes the Drosophila JNK kinase, and it is well established that forced expression of hep[CA] induces JNK phosphorylation and activation.

__ - In Figure S1, reduction of NudC in the fat body appears to induce a starvation-like phenotype, suggesting a potential impairment of metabolic or nutrient-sensing pathways. It would be important to determine whether modulation of nutrient-responsive signaling could rescue this phenotype. Specifically, have the authors examined whether activation of the TOR or PI3K pathways mitigates the effects of NudC knockdown? Assessing pathway activity (e.g., via phospho-S6K or phospho-Akt levels) or performing genetic rescue experiments with pathway activators could clarify whether the observed phenotypes are mediated through disrupted nutrient signaling rather than a secondary effect of general cellular stress. Such analyses could also provide a mechanistic explanation for the increased autophagy observed in these cells.

Author response:

1. We will analyze phospho-S6K levels in salivary glands and fat bodies by immunostaining.
2. To activate the TOR pathway in NudC RNAi fat bodies, we will overexpress Rheb, an established upstream activator of the TOR pathway in Drosophila, which has been shown to robustly increase TOR signaling and S6K phosphorylation.

   __ - The current images of autophagic vesicles in the SG in Fig. 8B are not clearly visible and quantified. Considering the large size of these polyploid cells, higher-resolution images or alternative imaging approaches should be presented to better visualize and quantify autophagy. This would make the conclusions regarding enhanced autophagy more convincing. In addition, this data could be further strengthened by expanding the analysis of autophagy to other cell types. For example, examining autophagy in fat body cells, where autophagy plays a primary physiological role associated with rRNA accumulation (Fig. S7), rather than a reduction like in SG (Fig. 4), could provide a useful comparison for the function of NudC between polyploid cells.

Author response:

In response to the second part of the reviewer’s comment, we will conduct additional experiments using anti-Atg8a immunostaining and/or LysoTracker staining to analyze autophagy in NudC RNAi fat bodies and prothoracic glands. These experiments will help further characterize the cellular responses associated with NudC depletion.

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

__

-The title is a bit problematic since they haven't shown that NudC doesn't also affect normal mitotic cells - they only look at polyploid cells, but that doesn't mean normal mitotic cells are not also affected.

Author response:

In response to the suggestion from Reviewer #1, we have revised the title from “NudC moonlights in ribosome biogenesis and homeostasis in Drosophila melanogaster polyploid cells” to “NudC moonlights in ribosome biogenesis and homeostasis in polyploid cells of Drosophila melanogaster” to place greater emphasis on “polyploid cells.”

Regarding mitotic cells, we have added new data in the revised manuscript (Figure S7; lines 249–256 and 417–418) demonstrating that NudC regulates apoptosis and stress responses in mitotic imaginal wing disc cells. However, as the main focus of our study remains polyploid cells, we have chosen to retain the emphasis in the title.

__

- Also, the authors show that two different RNAi lines for NudC give the same defects - it would be good to know if the RNAi lines target the same or different sequences in the NudC transcripts. Alternatively, it would be equally good to show that trans-allelic combinations of NudC mutants have the same defects in the prothoracic glands and the salivary glands as the RNAi. Instead, they examine only overall body size, developmental delays and lethality in the trans-hetero allelic NudC mutants.

Author response:

In response to the first half of criticism, the two RNAi lines used for NudC target distinct sequences. We have added the corresponding RNAi target sites to Figure S4A for clarity.

__

- Results: Lines 261 - 266. Seeing electron dense structures in TEMs and seeing increased Me31B staining by confocal imaging in the cytoplasm is insufficient evidence that the electron dense structures are P-bodies. They could be the P-bodies but they could also be aggregated ribosomes; there is insufficient evidence to "confirm" that they are P-bodies - maybe just say "suggests".

Author response:

In response to Reviewer #1’s suggestion, we have revised lines 261–262 to avoid using the word "confirm." The new sentence reads: “Immunostaining with the P-body marker Me31B reveals numerous cytoplasmic P-bodies in NudC-deficient SG cells,” which appears in lines 293–295.

__

- Abstract, lines 28 - 31. I think this gene has been identified before. The authors probably want to say they have discovered a role for this gene in RiBi.

Author response:

We have followed Reviewer #1’s suggestion and revised the sentence in lines 35–37 to: “In this study, we discovered a role for the gene NudC (nuclear distribution C, dynein complex regulator) in RiBi within polyploid cells of Drosophila melanogaster larvae.”

__

- Introduction, line 66. The protein is imported into the nucleus, where it localizes to the nucleolus - technically the protein is not imported into the nucleolus.

Author response:

To correct the misrepresentation in line 66, we have revised the sentence to: “RP mRNAs are synthesized by RNA polymerase II, and exported to the cytoplasm for translation. Then, RPs are imported into the nucleus, where they localize to the nucleolus.” in lines 70–73.

__ - Introduction, line 70. To be comprehensive in the description of ribosome biogenesis, the authors may want to mention that the 40S and 60S subunits are then exported from the nucleus and form the 80S subunit in the cytoplasm during translation.

Author response:

To improve the representation, we have revised the sentences in lines 73 – 78 as follows: “Within the nucleolus, rRNAs and RPs assemble into pre-40S and pre-60S subunits. immature versions of the small (40S) and large (60S) subunits, respectively, that undergo maturation with numerous ribosome biogenesis factors (RBFs) (Greber, 2016). The 40S and 60S subunits are then transported separately to the cytoplasm, where they combine to form functional 80S ribosomes, capable of sustaining protein synthesis (Pelletier et al., 2018).”

__ - Introduction, line 98. May want to cite paper showing that Minute mutations turn out to be mutations in individual ribosomal protein genes.

Author response:

As Reviewer #1 suggested, we have cited two, Marygold et al. (2007) entitled “The ribosomal protein genes and Minute loci of Drosophila melanogaster” and Recasens-Alvarez et al. (2021) entitled “Ribosomopathy-associated mutations cause proteotoxic stress that is alleviated by TOR inhibition” along with He et al. (2015). The inappropriate citation to Brehme (1939) has been removed.

__ - Results, lines 292. Since they didn't knock down NudC in the fat body cells in this experiment, this comment seems irrelevant.

Author response:

We would like to clarify that the phenotype observed with fkh-GAL4-driven NudC RNAi was specific to salivary glands, and no obvious phenotypes were detected in the surrounding fat body cells, which do not express fkh-GAL4. In this context, the adjacent fat body cells serve as an internal control.

In the revised manuscript, the sentence has been rewritten as: “In contrast, the fat body cells surrounding NudC-deficient SGs did not show this reduction (Figure S9),” in lines 323–324.

__ - Figure 6A. Hoechst is misspelled.

__

- Fig. 2 I - Hoeschest should be Hoescht.

Author response:

We have fixed the error.

__ *- Given that prothoracic gland (PG) size influences ecdysone production, the finding that NudC knockdown alters PG cell size, morphology, and cytoskeletal organization raises the possibility that ecdysone synthesis or signaling may also be affected. This, in turn, could explain the delayed maturation phenotype observed in Figure 1. I recommend testing whether ectopic activation of ecdysone signaling, for instance through 20-hydroxyecdysone (20E) supplementation, can rescue the defects in PG size and developmental timing. Such an experiment would strengthen the link between NudC function, PG morphology, and ecdysone-dependent developmental progression. *

Author response:

We have conducted experiments showing that developmental defects in NudC RNAi animals can be partially rescued by administering 20E. Approximately 32% of NudC RNAi larvae fed with 20E completed pupariation. These new data have been added to Figure S1B and are described in the main text (lines 165-168).

Regarding PG size, our experiments show that PG growth remains inhibited following 20E administration (Figure B as shown below). This observation indicates that treatment with exogenous 20E does not restore PG growth in NudC RNAi animals, suggesting that other factors may be required for normal PG development beyond ecdysone supplementation.

Because this analysis is not the main focus of our manuscript, we currently plan not to include these data in the revised manuscript.

Figure B. Prothoracic gland (PG) size ____after 20E administration.

To assess whether 20E supplementation could restore PG size, control (phtm>dicer2, +) and NudC RNAi (phtm>dicer2, NudC RNAi) larvae were transferred at 60 hours after hatching (hAH) to standard medium containing 20E dissolved in 100% ethanol. Control groups were transferred to medium containing the same volume of 100% ethanol at the same time point. PG size was quantified at the wandering stage. Sample sizes (number of glands) are shown below each bar. Bars represent mean ± SD. **p * *

__ - Additionally, qRT-PCR can be performed to assess the expression levels of ecdysone precursors or target genes in whole larvae, serving as a readout of ecdysone activity, including dilp8, which is usually upregulated when ecdysone levels are reduced.

Author response: To investigate ecdysone biosynthesis, Halloween genes including nvd, spok, sro, phm, dib, and sad were measured by conducting qRT-PCR. In NudC RNAi animals, nvd, sro and phm were suppressed at late L3 stage, indicating that NudC in the PG is required for ecdysone biosynthesis. The new data are described in Figure S1A and in the main text (lines 159-164) in the revised manuscript.

__ - The current images of autophagic vesicles in the SG in Fig. 8B are not clearly visible and quantified. Considering the large size of these polyploid cells, higher-resolution images or alternative imaging approaches should be presented to better visualize and quantify autophagy. This would make the conclusions regarding enhanced autophagy more convincing.

Author response:

Regarding the image quality issue, we have provided improved images of anti-Atg8a immunostaining in the salivary gland mosaic clones (Figure 8B) and included additional data from SG-specific knockdown cells (Supplemental Figures S13A-S13F) to provided quantitative results.

__ - Furthermore, including experiments in other cell types, such as imaginal disc cells, where apoptosis is more readily induced, would help determine whether the effects of NudC knockdown are specific to polyploid cells or are more broadly applicable.

Author response: We found that apoptosis was observed in NudC RNAi wing discs. In the revised manuscript, we have included this data in Figure S7 and referenced it in the main text (lines 249–256).

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

__ - Results, lines 285 to 298. In situs with multiple probes that detect all parts of both the pre-rRNA and processed rRNA indicate that all are down in the SG in NudC knockdowns, but that the 18S and 28S rRNAs are down the internal transcribed spacers go up - can the authors explain or hypothesize how this could happen?

Author response:

As Reviewer #1 indicated, we indeed observed that internal transcribed spacer (ITS) levels decrease in NudC knockdown salivary glands, but increase in knockdown fat bodies. Our hypothesis is that, as noted in the Discussion (lines 529–534), ribosome abundance is typically linked to protein synthesis. Salivary gland cells, which are highly active in protein production, may be particularly sensitive to disruptions in ribosome biogenesis. Therefore, NudC may maintain appropriate levels of rRNA with its impact varying according to the specific regulatory mechanisms of each cell type. We do not have a further explanation for this phenomenon, and therefore we have retained the original sentences without adding new ones.

__ - The data presented in Fig 4 show that NudC knockdown reduces pre-rRNA (ITS1/ITS2) and mature 18S/28S rRNAs in a tissue-specific manner. However, it remains unclear whether these reductions have functional consequences for ribosome assembly and translation. I recommend that the authors perform polysome profiling or an equivalent assay to assess the impact of NudC loss on actively translating ribosomes. This approach would provide a quantitative readout of translation efficiency and clarify whether the observed rRNA defects lead to impaired protein synthesis. Additionally, polysome profiling could help explain the tissue-specific differences observed between salivary glands and fat body cells.

Author response:

We performed ribosome fractionation using wild-type salivary glands and repeated the experiment three times with 56–62 gland pairs per sample. As shown in Figure C, the polyribosome peaks (grey lines) are not prominent, indicating that a much larger number of glands would be required for robust polysome profiling. Given that NudC RNAi salivary glands are significantly smaller than wild-type glands, collecting enough tissue for equivalent profiling would be technically difficult. Therefore, we concluded that obtaining sufficient RNAi samples for polysome profiling is extremely challenging, and these data have not been included in the revised manuscript.

On the other hand, we would like to emphasize that we observed a significant reduction in O-propargyl puromycin (OPP) labeling in NudC-deficient salivary gland cells (Figure 3B), which provides strong evidence for reduced translational activity.

__Figure C. Ribosomal fraction profiles of wild-type salivary glands. __Salivary glands from the late L3 larvae were dissected for analysis. Polyribosome peaks are indicated in grey. The number of salivary gland pairs used for each sample is shown above each bar.

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Referee #2

Evidence, reproducibility and clarity

Summary

In this manuscript, Duoduo Shi and colleagues, propose that NudC, previously known for its role in dynein regulation, has a second role as a critical regulator of ribosome biogenesis (RiBi) in Drosophila melanogaster polyploid cells, where its depletion reduces rRNA levels and ribosome abundance, triggering a compensatory homeostatic response that upregulates ribosomal proteins and biogenesis factors, similar to the response observed upon depletion of established ribosome biogenesis factors.

Strengths

The authors propose a novel role for NudC as a regulator of ribosome biogenesis (RiBi) which is dynein-independent and they provide a detailed homeostatic response to RiBi stress.

Weaknesses

NudC downregulation may be affecting the endocycle and an endoreplication defect may drive rRNA reduction.

Major comments

The authors claim that NudC has a dual role as a cell cycle/cytoskeleton regulator and as a ribosome biogenesis factor. However, because NudC knockdown reduces nuclear size and ploidy (Figures 1F and 2H-2I), the authors cannot exclude that decreased rDNA dosage and nucleolar volume contribute to reduced rRNA signals and that the effects seen are due to a NudC involvement in endoreplication, the rRNA reduction being a consequence of lower polyploidy. Different allelic combinations of NudC induce larval growth defects (Figure S5), consistent with a NudC role in endoreplication. To circumvent this, the authors could genetically modulate endocycle progression (e.g., E2F or Fzr overexpression) in the NudC RNAi background to test whether inducing endoreplication rescues rRNA production and nucleolar volume. This would establish causality between the endocycle state and rRNA output and clarify whether NudC's primary role is in RiBi or endocycle control.

The conclusion that NudC maintains rRNA levels is derived from salivary gland RNAi phenotypes with strong reductions in ITS1/ITS2 and 18S/28S signals (Figure 4B-4K) and reduced 28S by Northern (Figure 4L), plus corroboration in fat body cells (Figure S7). The authors verified knockdown using two independent RNAi lines for growth phenotypes and NudC::GFP reduction (Figure S2) and generated a UAS-FLAG::NudC transgene (Key Resources), but rRNA measurements were reported for only one RNAi line without rescue. Rescue of the rRNA phenotype by transgenic NudC re-expression, or replication of the rRNA decrease with a second, non-overlapping RNAi, would directly attribute the effect to NudC. In the absence of these standard validation controls, an off-target explanation remains plausible.

Minor comments

Fig. 2 I - Hoeschest should be Hoescht

Significance

The findings shown in this manuscript introduce a new player in endoreplication/ribosome biogenesis, a protein previously know as a dynein regulator. The strengths of the work lie on its novelty and thorough analysis of the cellular phenotypes induced by NudC depletion. However, its weaknesses are related to some claims not completely backed by the data, with some uncertainties related with a possible function of NudC in endoreplication.

This basic research work will be of interest to a broad cell and developmental biology community as they provide a novel cellular function of a known protein. It is of specific interest to the specialized field of polyploidy and ribosome biogenesis.

Field of expertise:

Drosophila, morphogenesis, tubulogenesis, cytoskeleton, DNA damage and repair.

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Referee #1

Evidence, reproducibility and clarity

Summary: This manuscript describes evidence for a role for the Nuclear distribution C dynein complex regulator (NudC) in ribosome biogenesis (RiBi) independent of its role in microtubule-associated dynein function.

Evidence: NudC was picked up in a screen for genes affecting ecdysteroid biosynthesis, a process that occurs in the prothoracic gland (PG; an endocrine organ). In the absence of ecdysone, larvae fail to pupate. Consistent with this finding, the authors find that prothoracic RNAi knockdown of NudC results in a failure in pupation and a decrease in total PG size. They also show defects in polytene chromosome architecture and a mild decrease in overall DNA content. They then turn to the salivary gland (SG) to further characterize the phenotypes associated with NudC knockdown. First, they show that an endogenously tagged version of NudC is abundant in the cytosol and has very weak nuclear staining in the region of the nucleolus (marked by the very low levels of DAPI staining). Knockdown of NudC using RNAi results in reduced NudC-GFP staining, a reduction in SG size, and a reduction in nuclear size. They also find that the SG polytene chromosomes are abnormal and that the production of a SG glue protein as measured by Sgs3-GFP levels and electron dense secretory granules is significantly reduced with NudC knockdown. Interestingly, they also observe the presence of abundant virus-like particles in the nucleus (these structures are thought to originate from retrotransposons and are an indicator of stress). Consistent with increased cellular stress, the authors show activation of JNK signalling. Ultrastructural analysis reveals an abnormally organized ER with an apparent loss of ER-associated ribosomes. They do see other electron dense structures in the cytosol, which they provide evidence (see below) of being P-bodies (structures associated with mRNA). They show that, consistent with a decrease in ribosomes, protein translation is reduced. This is supported by FISH experiments where they show significant decreases in ribosomal RNA (rRNA) transcript levels and decreased translation. Seeing the significant decreases in rRNA levels prompted them to look at overall changes in gene expression, where they discovered that both ribosomal protein gene expression as well as expression of other genes involved in ribosome biogenesis (RiBi) are upregulated with knockdown of NudC. They confirm the changes in mRNA for two genes by showing that levels of the corresponding proteins are also upregulated based on immunostaining of SG cells in which NudC is knocked down. Linking NudC function to a response to defects in RiBi, they shown that SG knockdown of several ribosomal biogenesis factors (RBFs) have similar chromosome structural defects and result in an increase in expression of ribosomal protein genes and of NudC itself. Finally, they show that knock down of genes encoding proteins linked to NudC function in microtubule dynamics do not have any of the same phenotypes as knockdown of NudC and RBFs. Altogether, their data support a moonlighting function for NudC in ribosome biogenesis. Moreover, defects in RiBi wherein ribosomal RNAs are decreased seem to result in compensatory changes where both RBFs and ribosomal protein genes are upregulated.

Major issues:

The title is a bit problematic since they haven't shown that NudC doesn't also affect normal mitotic cells - they only look at polyploid cells, but that doesn't mean normal mitotic cells are not also affected.

Also, the authors show that two different RNAi lines for NudC give the same defects - it would be good to know if the RNAi lines target the same or different sequences in the NudC transcripts. Alternatively, it would be equally good to show that trans-allelic combinations of NudC mutants have the same defects in the prothoracic glands and the salivary glands as the RNAi. Instead, they examine only overall body size, developmental delays and lethality in the trans-hetero allelic NudC mutants.

Results: Lines 261 - 266. Seeing electron dense structures in TEMs and seeing increased Me31B staining by confocal imaging in the cytoplasm is insufficient evidence that the electron dense structures are P-bodies. They could be the P-bodies but they could also be aggregated ribosomes; there is insufficient evidence to "confirm" that they are P-bodies - maybe just say "suggests".

It would be quite helpful to characterize the "5 blob" and "shortened polytene chromosome arm" defects shown in Figure 2 and Figure 6. Are these partially polytenized chromosomes or are large sections of the chromosomes missing or just underreplicated? What do the chromosomes look like if you lyse the nuclei, spread the chromosomes and stain with DAPI or Hoechst - this is a pretty standard practice and would reveal much more about the structure of the polytene chromosomes.

Minor points:

Abstract, lines 28 - 31. I think this gene has been identified before. The authors probably want to say they have discovered a role for this gene in RiBi.

Introduction, line 66. The protein is imported into the nucleus, where it localizes to the nucleolus - technically the protein is not imported into the nucleolus.

Introduction, line 70. To be comprehensive in the description of ribosome biogenesis, the authors may want to mention that the 40S and 60S subunits are then exported from the nucleus and form the 80S subunit in the cytoplasm during translation.

Introduction, line 98. May want to cite paper showing that Minute mutations turn out to be mutations in individual ribosomal protein genes.

Results, lines 285 to 298. In situs with multiple probes that detect all parts of both the pre-rRNA and processed rRNA indicate that all are down in the SG in NudC knockdowns, but that the 18S and 28S rRNAs are down the internal transcribed spacers go up - can the authors explain or hypothesize how this could happen?

Results, lines 292. Since they didn't knock down NudC in the fat body cells in this experiment, this comment seems irrelevant.

Discussion, line 468. I don't think the authors have provided evidence of DNA damage. With the experiments they have shown, the chromosomes look abnormal - not clear what is abnormal.

Figure 6A. Hoechst is misspelled.

Referee cross-commenting

I think the other reviewers have valid criticisms. I think among the most critical issues to sort out is (1) what is wrong with the chromosomes, (2) are diploid tissues also affected, (3) are the RIBI phenotypes a primary or secondary consequence of nudC loss. I'm not sure how easy it is to do ribosomal profiling on tissues dissected from larvae as the third reviewer is suggesting.

Significance

It is a novel discovery that a protein regulating microtubule dynamics is moonlighting, presumably in the nucleolus, to regulate rRNA synthesis or stabilization. A little information regarding mechanism of action would make this a much more exciting paper - how does it do it? Right now, it is unclear whether rRNA synthesis or maintenance is being regulated and there are no hypotheses regarding how this protein localizes to nucleoli and exactly what it is doing there. Is it regulating all RNA Pol I-dependent transcription? Is it involved in processing or stabilizing rRNAs? The description of the chromosomal defects also fall short of satisfying. As is, this paper probably of most interest to those who study ribosome biogenesis - an important topic, but without more mechanistic insight, not so interesting to a more general audience.

My expertise

I am an experienced Drosophila biologist who is familiar with the system and who fully understands all of the experiments presented in this manuscript and the relevance of the findings.

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: This manuscript describes evidence for a role for the Nuclear distribution C dynein complex regulator (NudC) in ribosome biogenesis (RiBi) independent of its role in microtubule-associated dynein function.

Evidence: NudC was picked up in a screen for genes affecting ecdysteroid biosynthesis, a process that occurs in the prothoracic gland (PG; an endocrine organ). In the absence of ecdysone, larvae fail to pupate. Consistent with this finding, the authors find that prothoracic RNAi knockdown of NudC results in a failure in pupation and a decrease in total PG size. They also show defects in polytene chromosome architecture and a mild decrease in overall DNA content. They then turn to the salivary gland (SG) to further characterize the phenotypes associated with NudC knockdown. First, they show that an endogenously tagged version of NudC is abundant in the cytosol and has very weak nuclear staining in the region of the nucleolus (marked by the very low levels of DAPI staining). Knockdown of NudC using RNAi results in reduced NudC-GFP staining, a reduction in SG size, and a reduction in nuclear size. They also find that the SG polytene chromosomes are abnormal and that the production of a SG glue protein as measured by Sgs3-GFP levels and electron dense secretory granules is significantly reduced with NudC knockdown. Interestingly, they also observe the presence of abundant virus-like particles in the nucleus (these structures are thought to originate from retrotransposons and are an indicator of stress). Consistent with increased cellular stress, the authors show activation of JNK signalling. Ultrastructural analysis reveals an abnormally organized ER with an apparent loss of ER-associated ribosomes. They do see other electron dense structures in the cytosol, which they provide evidence (see below) of being P-bodies (structures associated with mRNA). They show that, consistent with a decrease in ribosomes, protein translation is reduced. This is supported by FISH experiments where they show significant decreases in ribosomal RNA (rRNA) transcript levels and decreased translation. Seeing the significant decreases in rRNA levels prompted them to look at overall changes in gene expression, where they discovered that both ribosomal protein gene expression as well as expression of other genes involved in ribosome biogenesis (RiBi) are upregulated with knockdown of NudC. They confirm the changes in mRNA for two genes by showing that levels of the corresponding proteins are also upregulated based on immunostaining of SG cells in which NudC is knocked down. Linking NudC function to a response to defects in RiBi, they shown that SG knockdown of several ribosomal biogenesis factors (RBFs) have similar chromosome structural defects and result in an increase in expression of ribosomal protein genes and of NudC itself. Finally, they show that knock down of genes encoding proteins linked to NudC function in microtubule dynamics do not have any of the same phenotypes as knockdown of NudC and RBFs. Altogether, their data support a moonlighting function for NudC in ribosome biogenesis. Moreover, defects in RiBi wherein ribosomal RNAs are decreased seem to result in compensatory changes where both RBFs and ribosomal protein genes are upregulated.

Major issues:

The title is a bit problematic since they haven't shown that NudC doesn't also affect normal mitotic cells - they only look at polyploid cells, but that doesn't mean normal mitotic cells are not also affected.

Also, the authors show that two different RNAi lines for NudC give the same defects - it would be good to know if the RNAi lines target the same or different sequences in the NudC transcripts. Alternatively, it would be equally good to show that trans-allelic combinations of NudC mutants have the same defects in the prothoracic glands and the salivary glands as the RNAi. Instead, they examine only overall body size, developmental delays and lethality in the trans-hetero allelic NudC mutants.

Results: Lines 261 - 266. Seeing electron dense structures in TEMs and seeing increased Me31B staining by confocal imaging in the cytoplasm is insufficient evidence that the electron dense structures are P-bodies. They could be the P-bodies but they could also be aggregated ribosomes; there is insufficient evidence to "confirm" that they are P-bodies - maybe just say "suggests".

It would be quite helpful to characterize the "5 blob" and "shortened polytene chromosome arm" defects shown in Figure 2 and Figure 6. Are these partially polytenized chromosomes or are large sections of the chromosomes missing or just underreplicated? What do the chromosomes look like if you lyse the nuclei, spread the chromosomes and stain with DAPI or Hoechst - this is a pretty standard practice and would reveal much more about the structure of the polytene chromosomes.

Minor points:

Abstract, lines 28 - 31. I think this gene has been identified before. The authors probably want to say they have discovered a role for this gene in RiBi.

Introduction, line 66. The protein is imported into the nucleus, where it localizes to the nucleolus - technically the protein is not imported into the nucleolus.

Introduction, line 70. To be comprehensive in the description of ribosome biogenesis, the authors may want to mention that the 40S and 60S subunits are then exported from the nucleus and form the 80S subunit in the cytoplasm during translation.

Introduction, line 98. May want to cite paper showing that Minute mutations turn out to be mutations in individual ribosomal protein genes.

Results, lines 285 to 298. In situs with multiple probes that detect all parts of both the pre-rRNA and processed rRNA indicate that all are down in the SG in NudC knockdowns, but that the 18S and 28S rRNAs are down the internal transcribed spacers go up - can the authors explain or hypothesize how this could happen?

Results, lines 292. Since they didn't knock down NudC in the fat body cells in this experiment, this comment seems irrelevant.

Discussion, line 468. I don't think the authors have provided evidence of DNA damage. With the experiments they have shown, the chromosomes look abnormal - not clear what is abnormal.

Figure 6A. Hoechst is misspelled.

Referee cross-commenting

I think the other reviewers have valid criticisms. I think among the most critical issues to sort out is (1) what is wrong with the chromosomes, (2) are diploid tissues also affected, (3) are the RIBI phenotypes a primary or secondary consequence of nudC loss. I'm not sure how easy it is to do ribosomal profiling on tissues dissected from larvae as the third reviewer is suggesting.

Significance

It is a novel discovery that a protein regulating microtubule dynamics is moonlighting, presumably in the nucleolus, to regulate rRNA synthesis or stabilization. A little information regarding mechanism of action would make this a much more exciting paper - how does it do it? Right now, it is unclear whether rRNA synthesis or maintenance is being regulated and there are no hypotheses regarding how this protein localizes to nucleoli and exactly what it is doing there. Is it regulating all RNA Pol I-dependent transcription? Is it involved in processing or stabilizing rRNAs? The description of the chromosomal defects also fall short of satisfying. As is, this paper probably of most interest to those who study ribosome biogenesis - an important topic, but without more mechanistic insight, not so interesting to a more general audience.

My expertise

I am an experienced Drosophila biologist who is familiar with the system and who fully understands all of the experiments presented in this manuscript and the relevance of the findings.

Brief summary

Authress avoided downtime during the AWS us-east-1 outage by implementing a multi-region, redundant infrastructure with automated DNS failover using custom health checks, edge-optimized routing, and robust anomaly detection, backed by rigorous testing and incremental deployments to minimize risk and impact. Their system design assumes failure is inevitable and focuses on quick detection, seamless failover, and minimizing single points of failure through automation and continuous validation.

Long summary

• Authress experienced a major AWS us-east-1 outage affecting DynamoDB and other critical AWS services.
• They run infrastructure in us-east-1 due to customer location demands, despite known risks.
• AWS services like CloudFront, Certificate Manager, Lambda@Edge, and IAM control planes are centralized in us-east-1, impacting availability during incidents.
• Aiming for a 5-nines SLA (99.999% uptime) requires more than relying on AWS SLAs alone, which are insufficient.
• Simple single-region architectures fail to meet high reliability due to frequent AWS incidents.
• Authress recognizes "everything fails all the time" and designs systems assuming failure.
• Retry strategies are mathematically analyzed; third-party components must have at least 99.7% reliability to be usable.
• Multi-region redundant infrastructure with DNS failover via AWS Route 53 health checks enables automatic failover.
• Custom health checks validate actual service health beyond default DNS checks.
• Edge-optimized architecture using CloudFront and Lambda@Edge improves latency and provides better failover options.
• DynamoDB Global Tables replicate data across regions to support failover.
• Rigorous testing and validation, including application-level tests, mitigate risks of bugs in production.
• Incremental deployment (customer buckets) limits impact by rolling out changes gradually.
• Asynchronous validation tests check consistency across databases after deployments.
• Anomaly detection is used to identify meaningful incidents impacting business logic, beyond mere HTTP error codes.
• Customer support feedback is integrated into incident detection to catch undetected or gray failures.
• Security measures include rate limiting, AWS WAF with IP reputation lists, and blocking suspicious high-volume requests.
• Resource exhaustion prevention is critical, with rate limiting implemented at multiple infrastructure layers.
• Infrastructure as Code (IaC) deployment differences across regions and edge leads to challenges in consistency.
• Despite all these measures, achieving a true 5-nines SLA is extremely challenging but remains a core commitment.

Summary of HN discussion

https://news.ycombinator.com/item?id=45955565

• The discussion highlights concerns about automation and Infrastructure as Code (IaC) being potential failure points, emphasizing the challenge of safely updating these systems.
• Rollbacks are rarely automatic; often, knowing in advance to avoid certain rollouts is preferable as automated rollbacks can worsen failures.
• Simple, less complex infrastructure changes are preferred to reduce human error, which is the leading cause of incidents.
• There is skepticism about the reliability of Route 53 failover in practice, with concerns about its failure modes and the complexity of multi-cloud DNS failover.
• Some contributors suggest modular IaC approaches (Pulumi, Terragrunt) for safer, repeatable deployments but warn about added complexity.
• Retry logic in failures is criticized; retries may not improve reliability linearly due to correlated failures and overall system overload during outages.
• Latency and client timeout constraints limit the practical number of retries possible.
• DNS is acknowledged as a single point of failure with caching and failover timing challenges.
• Multi-cloud failover at DNS level is complex, costly, and not widely implemented due to infrastructure and coordination requirements.
• Gray failures (where the system reports healthy but customers experience issues) and the difficulty in knowing real incident impact without customer feedback are noted.
• Customer support is critical in incident detection since automated systems cannot catch every failure.
• Detailed monitoring via CloudFront and telemetry helps identify actual service issues during outages.
• Overall, the theme is the difficulty in achieving perfect reliability, the importance of simplicity, and the need for layered detection and response strategies to manage failures.

Author Response

We thank the editors and the reviewers for a number of useful criticisms and suggestions, and for the opportunity given to us, as authors, to publicly reply to the comments. This is a useful exercise, which brings to the attention of the reader lights, but also shadows of the reviewing process, and that we hope will lead in future to develop a better approach to it. Here, we will reply to a number of selected issues which appear to us to be of particular relevance.

Reviewer 1

Reviewer 1 disqualifies our work altogether, based on her/his statement that: “In the paper by Mercurio et al, the authors examine the role of SOX2 in the development of mouse hippocampal dentate gyrus. Using conditionally mutant SOX2 mice the authors show that early, but not late, deletion of SOX2 leads to developmental impairments of the dentate gyrus. A drawback of their study is that these findings have been reported previously by the group (Favaro et al. 2009; Ferri et al. 2013).”

The statement reported in bold is simply not true. In Favaro et al. 2009 (Nat Neurosci 12:1248), we demonstrated that nes-Cre-mediated Sox2 deletion leads to defects in postnatal, but not embryonic, hippocampal neurogenesis. In Ferri et al. 2013 (Development 140:1250), we demonstrated that FoxG1Cre-mediated Sox2 deletion leads to defective development of the VENTRAL forebrain. The presence, at the end of gestation, of hippocampal defects was just mentioned in one sentence: - “the hippocampus, at E18.5, was severely underdeveloped (not shown)” (line 1, page 1253)-, and not analyzed any further. In the present work, we describe in detail, starting from E12.5, up to E18.5, how the hippocampal defect develops, and undertake a detailed study of downstream gene expression and cellular defects arising in mutants.

It is unfortunate that the reviewer further insists on the same misleading, and unfounded statement – see her/his comment 3, highlighted in bold character: “the authors state "...remarkably, in the FoxG1-Cre cKO, the DG appears to be almost absent (Figure 2A).". The question is why this finding is remarkable as it already was published in (Ferri et al. 2013)”. As mentioned above, we only remark, in Ferri et al., that the hippocampus was severely underdeveloped (not shown).

Reviewer 2

Reviewer 2 states, already at the beginning: “I am concerned about a major confounding issue (see below).” ... “The authors rely on Foxg1-Cre for their main evidence that very early deletion of Sox2 leads to near loss of the dentate. However, it doesn't appear that the authors are aware that Foxg1 het mice have a fairly significant dentate phenotype (see this paper).”

The reviewer refers to the fact that, to delete Sox2, we need to express a Cre gene “knocked-in” into the Foxg1 gene; hence, heterozygous and homozygous Sox2 deletions will be accompanied by heterozygous loss of Foxg1. If Foxg1 is important for hippocampus development, the absence of a Foxg1 allele will affect the phenotype.

Unfortunately, the statement of the reviewer is subtly misleading, and leads the reader who has not checked the data reported in the cited paper (Shen et al., 2006) to erroneously believe that heterozygous loss of Foxg1 may be responsible for the effects that we report upon homozygous Sox2 deletion. In contrast to the statement made by the reviewer, the paper cited by the reviewer documents that, while heterozygous loss of Foxg1 leads to important POSTNATAL dentate gyrus abnormalities, the PRENATAL development of the dentate gyrus is essentially normal (Figure 6) (“a subtle and inconsistent defect” of the ventral blade observed in about 50% of the mice at E18.5, according to the authors of that paper). Compare “subtle and inconsistent defect” by Shen et al. with “fairly significant dentate phenotype”, as stated by the reviewer. As our paper is entirely focused on defects seen in PRENATAL development in Foxg1Cre; Sox2 mutants, the subtle and inconsistent defects seen by Shen et al. are in sharp contrast with the deep defects seen in embryonic development in our Foxg1Cre;Sox2-/- mutants, and in agreement with the similarity we observe between wild type and heterozygous Foxg1Cre;Sox2+/- embryos (page 5, lines 140-145, of the version of the Full Submission for publication on August 30). An example showing the comparison between a Wild type, a FoxG1 +/- heterozygote;Sox2+/- heterozygote and a FoxG1 heterozygote;Sox2-/- homozygote is now shown in the accompanying figure.

Obviously the incorrect statement kills our paper by itself. If the reviewer had doubts, we could have provided plenty of additional data demonstrating the lack of significant differences between Foxg1CRE Sox2+/- and wild type (Sox2+/+) embryos, as we stated in our paper.

There is an additional interesting comment by Reviewer 2 (see points 2 and 6). The reviewer argues that “The only two direct targets they find don't seem likely to be important players in the phenotypes they describe”. The Reviewer excludes the Gli3 gene (a direct Sox2 target, see Fig. 6), as a possible important player, in spite of the observation that Gli3 is decreased, at early developmental stages, in the cortical hem (Figure 5). The reviewer says “The Gli3 [mutation] phenotypes that have been published are quite distinct from this”. We object that the Gli3 phenotypes are indeed more severe than the phenotype of our mutant, and include failure to develop a dentate gyrus. However, this observation does not preclude the hypothesis that the decreased expression of Gli3 in our mutant is directly responsible for the phenotype we observe. The more severe phenotype of the Gli3 mutants is in fact due to a germ-line null mutation, whereas, in our Foxg1-Cre Sox2 mutants, we observe only a reduction of Gli3 expression, around E12.5 (Fig. 5), that is compatible with a less severe dentate gyrus phenotype. The Reviewer adds that Wnt3A, based on the phenotype of the knock-out mice, similar to that of our Sox2 deleted mice, is a more relevant gene, but it is not a direct target of Sox2. However, the fact that Wnt3A is apparently not directly regulated by Sox2 is not necessarily to be considered a “minus”; Sox2, being a transcription factor, is expected to directly regulate a multiplicity of genes, whose expression will affect the expression of other genes. Indeed, we presented in Fig 6D the hypothesis that decreased expression of Gli3 may contribute to decreased expression of Wnt3A, as already proposed by Grove et al. (1998) based on the observation that Gli3 null mutants lose the expression of Wnt3A (and other Wnt factors) from the cortical hem. The additional suggestion made by the Reviewer, in the context of the Wnt3A hypothesis, to investigate LEF1, as a potential direct Sox2 target, and its expression, is certainly interesting, but, as stated by the reviewer, LEF1 is downstream to Wnt3A, and, by itself, its hypothetical regulation by Sox2 would not explain the downregulation of Wnt3A. Moreover, we already have evidence that Sox2 does not directly regulate Wnt3A (unpublished).

Reviewer 1 and 2

Both Reviewer 1 and 2 have questions about the timing of Sox2 ablation in the Sox2 mutants obtained with the three different Cre deleters. As we state in the text (pages 4, 6), Foxg1-Cre deletes at E.9.5 (Ferri et al., 2013; Hébert and McConnell, 2000); Emx1-Cre deletes from E10.5 onwards, but not at E9.5 (Gorski et al., 2002; see also Shetty AS et al., PNAS 2013, E4913); Nestin-Cre deletes at later stages, around E12.5 (Favaro et al. 2009).

Reviewer 3

We thank Reviewer 3 for the useful considerations and suggestions, which constructively help to improve the paper.

Evidence that Sox2+/-;FoxG1+/- hippocampi at E18.5 do not significantly differ from wild type (Sox2+/+, FoxG1+/+) controls. In contrast, Sox2-/-;FoxG1+/- hippocampi are severely defective. (A) GFAP immunofluorescence at E18.5 on coronal sections of control and FoxG1-Cre cKO hippocampi (controls n=6, mutants n=4). (B) In situ hybridization at E18.5 for NeuroD (controls n=4, mutants n=3) on coronal sections of control and FoxG1-Cre cKO hippocampi. Arrows indicate dentate gyrus (DG); note the strong decrease of the dentate gyrus, and the radial glia (GFAP) disorganization in cKO.<br /> The Sox2flox/flox genotype corresponds to wild type mice (Sox2+/+). The Sox2+/flox ; FoxG1Cre genotype corresponds to Sox2+/-; FoxG1+/- controls. The Sox2flox/flox ; FoxG1Cre genotype corresponds to Sox2-/-; FoxG1+/- mutants.