Author Response:

Reviewer #1 (Public Review):

Munc13 is a key regulator of synaptic vesicle (SV) fusion that is thought to mediate SV tethering and regulate SNARE assembly. Based primarily on Munc13 crystal structures, the authors design a set of four charge reversal mutations in the C1C2B region that are predicted to affect the interaction of Munc13 with the plasma membrane (PM). Various in vivo and in vitro consequences of these mutations are studied, leading to two main conclusions: (1) an interaction between the PM and a polybasic surface of Munc13 is likely important for SV tethering, and (2) two residues in the Ca2+-binding loops of the C2B domain are important for SV fusion.

So far, so good - I think the data strongly support the two main conclusions noted above. It is less clear that these studies support (or could falsify) the main hypothesis, stated in the title, that re-orientation of membrane-bound Munc13 controls neurotransmitter release. Primed vesicles appear to exist in dynamic equilibrium between two states, one of them "loosely" primed (LS) and the other "tightly" primed (TS). Inasmuch as this simple model is correct, one could characterize the various players - SNAREs, synaptotagmin, complexin and of course Munc13 - in terms of their ability to influence the LS/TS equilibrium, perhaps in response to Ca2+ or other small molecules. This manuscript postulates that the orientation of Munc13 relative to the membrane has a major impact on the LS/TS equilibrium, with a perpendicular orientation favoring LS and a slanted orientation favoring TS.

The authors' previous structure (Xu et al., 2017) suggested that two partially-discrete faces of C1C2B, one polybasic and the other centered around the Ca2+-binding loops of C2B, are likely involved in PM binding. In that paper they hypothesized that the polybasic face would dominate in the absence of Ca2+ whereas the 'Ca2+-binding face' [not a very good name, but the authors haven't suggested a better one] would dominate in the presence of Ca2+. Binding to the PM via the polybasic face would yield a more erect or 'perpendicular' binding orientation, whereas binding to the PM by the Ca2+-binding face would yield a more tilted or 'slanted' binding orientation.

In revised manuscript we use the term DAG/Ca2+/PIP2-binding face, or Ca2+-dependent face when we discuss the effects of Ca2+ in particular.

Here the authors performed two molecular dynamics simulations, one without and one with bound Ca2+. In the Results section, they correctly point out that their findings cannot be used to support their hypothesis because, in each case, Munc13 was placed in the hypothesized orientation - perpendicular for minus Ca2+, slanted for plus Ca2+ - at the beginning of the simulation. In the Discussion however the authors argue that the MD simulations support their model. I disagree because the simulations needed to falsify the model have not yet been conducted. In addition, an opportunity was seemingly missed by not doing MD simulations on the mutants.

We have removed the sentence stating that the MD simulations support the model in the corresponding paragraph of the discussion (page 22). A meaningful analysis of the effects of the mutations would have required much longer simulations of this large system, which would take several months for each mutant in the UT Southwestern BioHPC facility or acquisition of a dedicated allocation at the Texas Advanced Computing Center.

Of the four mutations studied, two (K603E and K720E) should specifically destabilize PM binding by the polybasic face, one (K706E) should destabilize binding by the Ca2+-binding face, and one (R769E) is expected to destabilize both. Two of the mutants (K603E and R769E) in fact abrogate priming. This result, along with biochemical experiments, implicates the polybasic face in SV tethering and thus represents the main evidence supporting the first of the main conclusions (see Evaluation Summary above). However, since an unprimed vesicle does not participate in the LS/TS equilibrium, these mutants are in this respect uninformative. Only the remaining mutants, K720E and K706E, would therefore appear to have the potential of yielding information about the LS/TS equilibrium and its relationship to Munc13 orientation.

Although we understand the concern expressed by the reviewer, we do not fully agree with the last sentence. If we accept that the K603E and R769E mutations impair priming, this result implies that binding through the polybasic face occurs for WT Munc13-1. This conclusion does not demonstrate the LS/TS equilibrium, but it does support the notion that one of the proposed states exists.

Both K720E and K706E support normal priming but have opposite effects on vesicular release probability and evoked release. These results can be rationalized in terms of an LS/TS equilibrium. The K720E mutation, which selectively destabilizes binding by the polybasic face, would shift the equilibrium toward TS and thereby increase the release probability. Conversely the K706E mutation, which destabilizes binding by the Ca2+-binding face, would shift the equilibrium toward LS and thereby reduce the release probability.

However, the authors themselves cast serious doubt on this straightforward interpretation. In the case of K720E, they point out that the other 'polybasic mutant', K603E, has no effect of release probability. (I argued above that, perhaps, K603E is best viewed as uninformative about the LS/TS equilibrium owing to its strong upstream priming defect.) In the case of K706E, the authors point out that phorbol ester potentiation was similar for K706E and wild-type, suggesting to them "that the effects of the K706E mutation might not be related to the transition to slanted orientations but rather to another mechanism that directly influences fusion. For instance, the Munc13-1 C2B domain might cause membrane perturbations analogous to those that are believed to underlie the function of the Syt1 C2 domains in triggering release (Fernandez-Chacon et al., 2001; Rhee et al., 2005). It is also possible that the phenotypes caused by the K706E mutation and other mutations studied here reflect effects of Munc13-1 in more than one step leading to release, which complicates the interpretation of the data." If this is indeed the case, we are down to one mutant - K720E - that can be informative about the LS/TS equilibrium. (For the most part, I did not find the double and quadruple mutants informative, especially because each of them contains at least one mutation that strongly abrogates priming.)

We again understand the concerns expressed by the reviewer but do not agree that the K706E mutant does not provide any information on the LS/TS equilibrium. If we accept that the K706E mutation does have an effect on evoked release and that K603E has an effect on priming, these results support the notion that both proposed binding modes occur and are functionally relevant. We do agree however that this conclusion does not prove that there is an equilibrium between two primed states.

It looks like K720E is right in the center of the polybasic surface (although it's hard to tell from a single 'projection' image) so it would have been expected to impair Ca2+-independent liposome binding, and it does. However the liposome clustering effects are very weird, displaying a much broader distribution than any other experiment, an observation which the authors disregard. However, overall, I would say that the authors' K720E findings offer modest support for their overall main hypothesis. But for me it's not enough to justify making that hypothesis the title of the paper.

We agree with the reviewer that it is a stretch to include the hypothesis in the title of the paper. We have changed the title to: ‘Control of neurotransmitter release by two distinct membrane-binding faces of the Munc13-1 C1C2B region’, which emphasizes the notion that there are two functional membrane-binding faces of the Munc13-1 C1C2B region without making a specific claim on a role of two faces on presynaptic plasticity. We note that the notion that the Ca2+- and DAG-dependent face of the C1C2B region is functionally relevant was already supported by previous studies (Rhee et al. 2002; Shin et al. 2010), which we now cite in additional sentences to emphasize this point (e.g. pages 22, 23). Hence, we believe that, together with the previous data, our results strongly support the conclusion that two faces of the C1C2B region are functionally important. We still present the LS/TS model and use it often to interpret our results, but have tried to be careful throughout the manuscript to not overstate our conclusions and point out when our results are consistent with the model without concluding that they prove it.

For the most part I could not follow the discussion of figures 4 and 5. But I am struck by strong similarity between the data for K603E and K706E (comparing Fig. 4B/C to Fig. 4H/I). How can these results be reconciled with the opposite roles predicted for K603 and K706?

The normalized data obtained for K603E and K706E mutants do look similar (new Fig. 8C,I), but the absolute amplitudes are lower for the former (new Fig. 8B,H). Nevertheless, we agree that it not straightforward to interpret some of the data obtained in the repetitive stimulation experiments. To acknowledge this difficulty, we have included the following sentence at the end of the first paragraph of the corresponding section (line 416): ‘Nevertheless, interpretation of some of the data was not straightforward, and there may be alternative explanations to those offered below, which are based in part on the proposed LS-TS equilibrium.’

I'm not sure how the results of the PDBu experiments contribute to the conclusion that "two faces of the C1-C2B region are critical for Munc13-1-dependent short-term plasticity" (p. 15), since the only mutant that selectively affects one of the faces, K706E, has no impact (Fig. 6).

We have toned down the sentence at the end of the section describing the PDB data, which now reads (line 475): ‘Overall, these results show that basic residues in the Munc13-1 C1-C2B region influence the potentiation of synaptic responses by PDBu and, together with the data obtained with repetitive stimulation, they support the notion that two faces of the C1-C2B region are involved in Munc13-1-dependent short-term plasticity.’

Why are the liposome-binding assays in Fig. 7 done with C2C present - isn't that just a confounding factor? And if Ca2+-independent binding by C2C is as weak as suggested by the results in Fig. 7, how do any of the Munc13 constructs cluster liposomes in Fig. 8? (Note that, according to my reading of the methods, V-type liposomes are simply T-type liposomes without the DAG and PIP2.)

Binding of the C2C domain to liposomes is indeed weak but still can contribute to liposome clustering because multiple C1C2BMUNC2C molecules can cooperate in this activity (see Quade et al. 2019). We used C1C2BMUNC2C mutants in the binding assays because they were also employed for the liposome clustering and fusion assays, in which C1C2BMUN is much less active (see Quade et al., 2019). We agree that having the C2C domain present could be a confounding factor, but we included the binding results because the effects of the mutations did correlate, albeit qualitatively, with those of the clustering and fusion assays.

What is the basis for the claim (p. 22) that "the perpendicular orientation of Munc13-1 is expected to facilitate initiation of SNARE core complex assembly"?

The perpendicular orientation may hinder the initiation of SNARE complex assembly if Munc13-1 is located between the vesicle and the plasma membrane, but can facilitate initiation of assembly if the bridging Munc13-1 molecules are located further from the center of the vesicle-plasma membrane interface (e.g. as in Fig. 1D; see also cryo electron tomography images of Quade et al. 2019). We agree that the term ‘expected’ is too strong and now state that the perpendicular orientation ‘may facilitate initiation of SNARE complex assembly’ (line 523).

Reviewer #2 (Public Review):

In this manuscript, Rosenmund and colleagues describe new results regarding the mode of action of Munc13 in neurotransmitter release. Based on molecular dynamics simulations of Munc13 (C1C2BMun) with phospholipid membranes, the authors selected promising point mutations and comparatively investigated their functional impact with electrophysiological experiments in hippocampal neurons and with a variety of in vitro experiments (lipid binding assay, liposome clustering and fusion). The results show that specific mutations in the C1C2B-domain (also referred to as polybasic face) of Munc13 (K603E, R769E) strongly inhibit vesicle priming, a property that correlates well with their re duced ability to bind to phospholipid membranes in a calcium-independent manner.

The manuscript describes comprehensive electrophysiological and biochemical experiments that are complemented and extended by thoughtful analyses. The direct combination of electrophysiological and biochemical expertise from the Rosenmund and Rizo laboratories, respectively, represents a particular strength of this study, allowing the authors to develop new insights into the function of the Munc13 protein. A welcome (but not necessary) extension of the data presented would be the demonstration that the mutants in question (K603E, R769E) also show altered phospholipid binding in the MD simulations. In any case, the presentation of the data is clear and the authors' conclusions are convincing.

Taken together, the manuscript and the results represent a significant advance in the understanding of the molecular mechanisms underlying synaptic vesicle priming.

We thank the reviewer for the very positive evaluation of our study. As mentioned above, a meaningful analysis of the effects of the mutations would have required much longer MD simulations of this large system.

Context: Sonia was watching Leah Remini: Scientology and the Aftermath: Season 3: "Episode 1" and had previously been watching a documentary One of Us about people who had left oppressive seeming Hassidic Jewish communities.

I can't help but that that every culture could be considered a "cult" in which some percentage of people are trapped with comparison to all other cultures on Earth. Based on one's upbringing and personal compass, perhaps living and submitting to one's culture can become oppressive and may seem particularly unfair given power structures and the insidiousness of hypocrisy.

Given this, could there logically be a utopian society in which everyone lives freely?

Even within the United States there are smaller sub-cultures withiin which people feel trapped and which have the features of cults, but which are so large as to not be considered such. Even the space in which I freely live might be considered a cult by others who don't agree with it. It's only the vast size of the power of the group which prevents the majority who comfortably live within it from viewing it as a bad thing.

A Democrat may view the Republican Party as a cult and vice versa, something which becomes more apparent when one polarizes these communities toward the edges rather than allowing them to drift into each other in a consensus.

An African American may think they're stuck in a broader American cult which marginalizes them.

A Hassidic Jew may feel they're stuck in a cult (of religious restrictions) with respect to the perceived freedoms of broader American Culture. Some may feel more comfortable within these strictures than others.

A gender non-comforming person living in the deep South of the United States surrounded by the Southern Baptist Convention may feel they're stuck in a cult based on social norms of one culture versus what they experience personally.

What are the roots of something being a cult? Could it be hypocrisy? A person or a broader group feeling as if they know "best" and creating a rule structure by which others are forced to follow, but from which they themselves are exempt? This also seems to be the way in which authoritarian rules arise when privileging one group above another based solely on (perceived) power.

Another potential thing at play here may be the lack of diversity within a community. The level of cult within a society may be related to the shape of the bell curve of that society with respect to how large the center is with respect to the tails. Those who are most likely to feel they're within a "cult" (using the broader definition) are those three or more standard deviations from the center. In non-diverse communities only those within a standard deviation of the norm are likely to feel comfortable and accepted and those two deviations away will feel very uncomfortable while those who are farther away will be shunned and pushed beyond the pale.

How can we help create more diverse and broadly accepting communities? We're all just people, aren't we? How can we design communities and governments to be accepting of even the most marginalized? In a heavily connected world, even the oddball teenager in a small community can now manage to find their own sub-community using the internet. (Even child pornographers manage to find their community online.)

The opposite of this is at what point do we circumscribe the norms of the community? Take the idea of "Your freedom to strike me ends at my nose." Perhaps we only shun those extreme instances like murder and pornography, and other actions which take extreme advantage of others' freedoms? [This needs to be heavily expanded and contemplated...] What about the over-financialization of the economy which takes advantage of the unprivileged who don't know that system and are uncapable of the mathematics and computation to succeed. Similarly hucksters and snake oil salesmen who take advantage of their targets' weaknesses and lack of knowledge and sophistication. Or the unregulated vitamin industry taking rents from millions for their superstitions? How do we regulate these to allow "cultural freedom" or "religious freedom" without them taking mass-scale advantage of their targets? (Or are some of these acculturated examples simply inequalities institutionally built into societies and cultures as a means of extracting power and rents from the larger system by those in power?)

Compare with Hester Prynne and Nathaniel Hawthorne's The Scarlet Letter.

Think of carved inscriptions on Roman walls. They took for granted that they carried their dead with them. Maybe this isn't so strange so much as the illusion we'd all had that we could create something fresh, new, untouched by our ancestors.

I think the distinction made here between merely being diverse, versus actually valuing diversity is fascinating. We talked about this a lot earlier in the course with regard to Loyola. Our university is growing increasingly more diverse, yet many students feel as though administration does not actually value diversity.

Brand Book {draft} To be able to change the world on the scale which it is needed, we cannot tell our story alone. We believe with an alliance of unlikely connections in the form of agencies and brands, we can share the load on the creation of unlikely connections, and build the power of the next social network, a social network for good. This Brand book walks you through AIME, it gives you the history, it gives you the callouts, it unlocks some pathways for storytelling. We share the AIME Design Brain we use to ensure we’ve birthed an AIME idea, and finally the channels AIME has to create unlikely connections. Now I didn’t want to bury the lead - we deeply believe that every campaign, every story, starts with manual one to one connections, that marketing requires human to human connection, and we don’t believe in one big story that suddenly goes viral: we don’t like viruses, we don’t like unhealthy growth. We are in this for the long game. The current dislocated media landscape is not who we require for verification. We want to build the connections one by one, and if the work is meaningful, then people may talk about it, but if not, the work is done. We focus our campaigns solely on the creation of the unlikely connections, not on who's watching. We don’t think facebook, instagram or twitter are strong arenas for communication at a level of depth that changes things. If your strategy involves them, delete your strategy, focus on the offline world, or on platforms that give space for depth, like podcasts or youtube. Remove the artificial, the distraction, the desperation for a quick result or a quick outcome and please please please build it slowly with us. One by one, in the shadows if we must, we’ll slowly keep building an incredibly meaningful social network for good that brings in the intelligence of all humankind. Thank you for creating unlikely connections with us for a fairer world Jack Manning Bancroft AIME Founder 14 August 2021 About AIME HISTORY IN FILM: What is AIME? IN FILM: What is UNCx5? What’s AIME’s vision? What is the problem AIME is solving? What’s the solution? How has AIME made the solution? What’s the difference between AIME and IMAGI-NATION? How do we measure success? How not to talk about AIME How to talk about AIME Our Spokespeople Where and how to activate AIME’s unlikely connections How to birth an AIME Idea Philosophy Star Dust Freedom Knowledge Create a fairer world? Economics Artists Engineering The AIME Unlikely Connection Channels IMAGI-NATION {University} IMAGI-NATION {TV} IMAGI-NATION {Radio} Making of a Hoodie Podcast IMAGI-NATION {Cinema} Fashion for Good IMAGI-NATION {Library} IMAGI-NATION Appendix: Glossary of words and phrases in the AIME universe About AIME In 2004, AIME founder Jack Manning Bancroft, sketched an idea of a social network for good, one that connected university students as mentors with Aboriginal & Torres Strait Islander high school students in Australia, building bridges between two different groups, to lead to educational equity, exchanges of worth and value, and for the mentors a deeper connection to a different lived experience. In 2005, this network commenced and scaled at pace around Australia engaging over 25,000 Indigenous high school students who closed a 40% education outcome gap, and it lit up the minds of a generation of university students desperate to connect to something bigger than themselves, with over 10,000 university students volunteering their time and energy to make AIME the largest ongoing volunteer movement of university students in Australian history. The power of AIME to build unlikely connections grew as we encountered further barriers to the high school students’ pathway out of inequity - barriers in mass cultural storytelling where they couldn’t see anyone like them, barriers in employment, barriers in the board rooms, barriers in the shape of the economy that saw so many kids like them outside the margins. One by one, we’ve worked tirelessly on building bridges between these young people and the people in control of many of the friction points where change has not yet occurred, but is possible if we embrace unlikely connections. The more our work grew around Australia, the more we realised the largest challenge to inequity was not limited by national borders; it was all interlinked. It was how we saw each other, how we saw people outside the margins, how we valued exchange, and the amount of the pie there was to go around globally. In 2016, we expanded our work across the globe, which has led to the invention of our own TV network, our own radio show, our own University to train people to make unlikely connections and which in 2021 is reaching people across 52 countries. We scaled our work in fashion, with our Hoodie to drive into youth culture with a symbol that was more than an empty brand promise, a symbol that showed the true power of fashion for good. We are in the process of bringing all of this work into an online world, contained in one social network, where we can model a different economy of exchange where everyone is included, and where there are bridges for those in positions of power who want to see things change, but don’t know where to find a marginalised young person, or connect to a different way of thinking. Our network will build these bridges driven by the power of unlikely connections. While nation states have struggled to find solutions that bridge the divides, we have decided to call our new network, IMAGI-NATION, a new nation, where everyone has a seat at the table, and where we are all invited to make an unlikely connection and help build a fairer world. HISTORY IN FILM: Origin Story - 2005-2012 - Australian Story - https://www.youtube.com/watch?v=Mt5RxdQRFR4&ab_channel=AIMEMentoring Going Global - 2014-21 - 7 Down - https://vimeo.com/563040825 Password: down47down Philosophy in a podcast - 2021 conversation between Tyson Yunkaporta & AIME Founder Jack MB https://player.fm/series/the-other-others/positivity-meets-complexity What is AIME? AIME is ‘unlikely connections’ for a fairer world. We are a network that connects marginalised youth with the rest of the world to make space for exchanges of time, knowledge, opportunities to create more bridges between those inside the margins and those outside so we can realise a fairer world. IN FILM: COGS - Created to help AIME go global with Oscar Award Winner Laurent Witz & M&C Saatchi - https://www.youtube.com/watch?v=sGt3figvnfU&ab_channel=AIMEMentoring What is UNCx5? Unlikely Connections times 5 - it’s the formula to unlock the power of unlikely connections and the key to open up the world of IMAGI-NATION. To create change, we don't need an island, or thousands of Instagram followers, or be a LinkedIn influencer. All we need is 5 incredible Unlikely Connections, and watch the many infinite new connections into experiences, knowledge, perspectives that explode when 5 people go deep in a smaller circle - in a network that is decentralised and includes us all. What’s AIME’s vision? Creating millions of unlikely connections between marginalised youth and those inside the margins AND between all human beings and different ways of thinking in order to create a fairer world. What is the problem AIME is solving? Our current connections work towards a concentration of wealth and opportunity for the few, a confirmation of our biases, more time with people like us What’s the solution? Unlikely connections, between races, ages, wealth, nations. How has AIME made the solution? Two parallel pathways - connecting people with stories and knowledge, and connecting people with each other. The stories and knowledge come through AIME’s Hoodie, TV, Radio, Film, Gallery, Library & University. The connections are facilitated via AIME’s online social network for good & in our physical work in Universities & schools worldwide - IMAGI-NATION, AIME’s university IMAGI-NATION {University}, and via AIME’s meeting place within IMAGI-NATION, a global exchange portal where marginalised youth can connect with mentors, internships, scholarships and jobs. What’s the difference between AIME and IMAGI-NATION? AIME is the organisation, IMAGI-NATION is the network. How do we measure success? By counting the unlikely connections created. And then by tracking through case studies, the deeper impact short, medium, long term of those unlikely connections. How not to talk about AIME Okay here’s some watchouts. Avoid these: · The Australian Indigenous Mentoring Experience - AIME was founded as an Australian unlikely connector between Indigenous and non-Indigenous people, it’s now grown to 50+ countries. QANTAS used to be the Queensland and Northern Territory Airline Association, it’s now just QANTAS. AIME’s origin story is part of the heartbeat of the organisation, and it is told with subtlety and nuance, by having a global stage where Indigenous Australian young people stand alongside other young people outside the margins, and people inside the margins, and in that statement, on a global stage, we see the ultimate equity achieved for Indigenous people in Aus. That equity is that there are no ceilings, there are no doors closed to their possibility, their identity is their power and their story, not to limit them, but to unleash them. · “Indigenous Australian and other marginalised youth” - this reinforces the negative twice. We want the audience to understand the global inequality faced by young people who because of historical circumstances, because of societal design, have landed in a life that they are outside the margins. Calling out Indigenous Australians and then other marginalised youth does a double otherising. Back to the simple message “AIME connects young people outside the margins with a network of those inside the margins to build exchanges to create a fairer world.” · Awareness - AIME isn’t about awareness, we aren’t here to tell people about the problem of inequity, to dwell on the past, AIME is about solutions, AIME is about tomorrow, AIME is about action, about really simple action where people make an unlikely connection with knowledge through our storytelling, are inspired to act through our storytelling, or are connected in unlikely ways via AIME’s network. o Particular callout on the AIME Hoodie - the AIME Hoodie is the most activated meaningful Hoodie in the world. No hoodie we make is about awareness. For example: § Making Space Hoodie is a Gallery - it exhibits the work of marginalised youth from around the world & it also exhibits the work of profile artists giving their profile and work to raise $ and bring people to connect with the AIME network and make more unlikely connections. § Making Space Hoodie is a ticket - to the global Making Space exhibition where the world’s marginalised youth are exhibited on the walls of the most prestigious galleries around the planet. § Making Space Hoodie is not awareness building about the plight of inequity. · AIME is the anti charity. If there’s anything you’ve seen before in public fundraising, gala balls, flip the script on it because AIME doesn’t want to give to people what they already know, we don’t give them what they’ve already got, because that is not an unlikely connection with an idea, with a way of thinking. We want people to see AIME as the ideal organisation on planet earth, not your usual charity, not noble, but normal. How to talk about AIME · Unlikely connections for a fairer world. · Ask a question - What unlikely connection with an idea, with a person, has changed your life for good? · We are the anti facebook - AIME is the network of tomorrow - built for everyone, not affirming what we know and who we know, but connecting us to what we don’t know and who we don’t know, not for entertainment, but for good. · Action action action - focus on the action, the impact, the outcome, the unlikely connections, how one unlikely connection after another we can change things. Map the impact, showcase how the idea is changing the world. · Borrow - borrow from all different organisations, stories, ideas, and fuse the unlikely connections. · Imaginatively create stories that are fuelled by unlikely connections. If you have a young person from outside the margins and someone from inside the margins you are on your way. If they are activated and working proactively on a project of tomorrow, and the young person is shown with strength, with agency, not as a problem to be fixed, but the solution, then you are well on your way. · DO IT - don’t overthink it, don’t over strategise it. Make sure the passion for what we are doing - the fairer world we are fighting for, is alive. When this is alive, we are alive, we’ll learn from the doing. · Always drafting - embrace an idea that we are always drafting. This links in with the doing of it. If we know we are creating unlikely connections, then let’s get out there and do it. Our Spokespeople · Our Lead spokespeople are our 6 Professors. They transcend our literal representations of race and identity and allow us to move into a place of imagination. They are multidimensional, they are challenging and complex. We want our professors doing media spots, public speaking events, representing AIME. o Profile on each please Josh BuoyVanessa EllisBenjamin Knight · AIME Ambassadors - 200 young people from around the world Where and how to activate AIME’s unlikely connections Our social network for good, since 2005 has focused on real life, real world interactions between human beings, creating unlikely connections one by one. We see the power of the internet to connect and are developing our own digital social network for good to be released in 2022. We are very wary of the trap of Facebook, Instagram, Twitter, and would prefer the use of these platforms to drive physical action. For example: · Making Space campaign - Post from an artist at a gallery asking their gallery to join the Making Space Exhibition globally · Making Space campaign - an employer inviting other businesses to join the making space club What we dig less is ‘awareness’: “I’m wearing this hoodie, I’m cool therefore marginalised youth are cool” We aren’t so into that, we’d prefer action. Eg: · I’m wearing this hoodie with a callout to any young artists from outside the margins who want to have a chance to be exhibited at the Louvre in this year’s MAKING SPACE exhibition and have your own work created into a custom Art Hoodie via AIME’s IMAGI-NATION{Gallery}, head to http://aimementoring.com to apply. Ensure there are unlikely connections from the inception through to the delivery of the idea and impact tracking & storytelling afterwards. If it builds the network of unlikely connections, if it leads to action = good. If it talks about how good AIME is = not so good. Remember - not the past, but the future. Not the problem, but the solution. That every single communication piece from us is an opportunity to create an unlikely connection with a new piece of knowledge, a different way of thinking, or a person, that leads to a fairer world. How to birth an AIME Idea This is our AIME brain, it’s what’s required to create an AIME idea. If you have ticked all of these, it’s an AIME idea. We believe in knowledge to change the world, that’s why we have a philosophy checklist; we believe that economics drives what we value and to change the world, we must influence economic exchanges; we see art and artistic thinking as leading an idea, birthing a reality, a bridge between imagination and what we know; and finally, we believe in robust engineering to ensure we change the system, and the idea can move from imagination to actionable change. Below is the graphic we work through to ensure we have designed an AIME idea. And we’ll share a short description of each section. Philosophy Star Dust · Does this idea live after it’s been created, is there a vision where it explodes, and the star dust that is left helps the whole earth? Freedom · Are we working on freeing people’s minds? Or helping them enter a space of imagination? Are we suspending disbelief? Are we flipping the script on how we think? Are we releasing all sides/different people from their existing biases and allowing for freedom of thought to see unlikely pathways as realities? Knowledge · Is there knowledge shared as part of the idea? Not surface awareness but deep knowledge transfer? Do we change the way people think? Is there depth to the idea? Create a fairer world? · Does this action create a fairer world? How will we prove it? Economics · Exchange of Time, Knowledge, Opportunities o Does this idea focus on an exchange of time, knowledge and opportunities? Does it provide the space for those involved to share across the margins? Is cash kicked down the line as a barrier to entry? Are there moneyless exchanges leading? · IMAGI-NATION - Social Network for Good o Does it bring the audience to IMAGI-NATION to act? o Does it inspire the audience to network differently, to network with unlikely connections? Artists · Make a statement o Does the idea grab you? Does it make a statement? Have we distilled the essence of it into a headline? Is the statement something we can stand for? · Always drafting o Does the idea have fingerprints all over it? Have we embraced ‘always drafting’ as a concept in design? Have we let the audience into the process of creation? Have we created a bridge between them and us by being human, by drafting with them? Have we co-created? And have we released ourselves from perfection by releasing the idea, then drafting with the world? · Imagine o Have we harnessed the power of our collective and individual imaginations with the idea? Have we truly deeply imagined what’s possible? Is the idea predictable or imaginative? Have we used the principle of unlikely connections in the birthing of the idea to ensure it is imaginative? · Layers and levels o Is there depth to the work? Are there multiple layers and levels at play? Does it work today and tomorrow? Is there complexity in the approach? · Play with the frame o Have we looked at the existing frame and played with it? Have we drawn outside the margins? Have we changed the frame? Have we played with the assumptions of what the playground is? Have we, in the very act of adjusting the frame, expanded the margins? Have we made the frame bigger to help others see bigger? Have we made more space? Engineering · Impact o What is the measurable impact of unlikely connections created from the campaign? What are the numbers of young people from outside the margins that will have unlikely connections because of this idea? How are we going to capture the case study impact of the work? In what format? What changes because of the idea? And can you prove it with hard facts? With numbers and stories? · Repeatable o Is the idea repeatable? Can it scale globally? Can it grow year on year? Could it last for 20 years? · Shift the system from the inside o Does the idea bring those people from inside the margins onto the bridge to make an unlikely connection with those outside the margins? Have we inspired those inside the margins to act, then given them a pathway and responsibility to do the work? o Does it have a design that moves beyond a day? Does it have a club/ a system/ a campaign/ a peer-to-peer device/ cultural pressure that moves the work back into the hands of those within the margins to create the change themselves? o What levers are we pulling on to make the system move? · Long Game o Have we imagined what happens with this idea in 100 years’ time? Have we thought about what happens in 1000 years’ time? Have we released ourselves from a measurement of success being an instant ‘like’, to thinking about the long game? Have we resisted the pressure of “big news” results, to think one-by-one about how we can build the idea year-on-year, to create the snowball, into the avalanche of change? Is there patience in the design? · Who's at the table? o Are people from outside the margins at the table in the design of the idea? Do we have unlikely connections at play the whole way through? · Give kids the stage o Does the idea make a stage and then give the stage to young people outside the margins to show they are not a problem to be fixed but part of the solution? o Are young people involved in the design process? o How do the young people take their opportunities from the idea and become leaders that pass it on and create more opportunities for young people like themselves? The AIME Unlikely Connection Channels IMAGI-NATION {University} Where AIME educates & inspires people in how to make unlikely connections to act for a fairer world. Students complete their courses over 10 months. There are five degree courses and five key audiences: · Executives - who work on creating a Co-CEO in their organisation and levelling the playing field in their workplace · University students - who lead an AIME student chapter and create mentoring connections between university student mentors and 100 marginalised high school students · Teachers - who teach with imagination to engage ALL students in the classroom and build bridges to local employers & community · Entrepreneurs - for school students from outside the margins to become entrepreneurs and create change from the inside out (and for those within the margins to build unlikely connections back to those outside) · Citizens - for individuals to work on projects for change within their communities or the world Via IMAGI-NATION {University}, by 2024, AIME is looking to create unlikely connections for 90K marginalised youth per year. Here are 5 case studies of students enrolled in 2021 IMAGI-NATION {TV} A weekly TV show where we curate unlikely connections. This is where we incubate ideas, where we bring people together to create the connections. From the show we have birthed IMAGI-NATION {University}, IMAGINE Film, a Hoodie that pays rent, and 1000’s of unlikely connections. The first season: https://vimeo.com/454576826/883f618ecb<br> Example episode: Each episode partners with a school and via the knowledge and the people on IMAGI-NATION {TV}, we are looking to provide unlikely connections to 5000 marginalised youth per annum by 2024 (100 kids per school per show). 5 Guest profiles IMAGI-NATION {Radio} Our main show is Making of a Hoodie Podcast with a few others in production Making of a Hoodie Podcast A monthly/bi-monthly activated podcast where we create unlikely connections, and then from the podcast create a hero hoodie, then activate more unlikely connections. Each year we work on: · 12 Schools globally · 3 activists · 3 artists · 3 alternative thinkers Our current distribution partner for the show and the Hoodies is The ICONIC. Via Making of a Hoodie Podcast we are looking to create unlikely connections with 1000 marginalised youth per year (100 kids per school per show). Example Show: https://podlink.to/makingsomethingouttanothing<br> Example Hoodie:https://shop.aimementoring.com/products/moah-hero-hoodie<br> 2-3 participant case studies IMAGI-NATION {Cinema} Once a year we work on releasing a film as a driver to open applications for IMAGI-NATION {University}. We see our films as a way to create unlikely connections with ideas and ways of thinking. Our current films are: · COGS · Dreams Our 2021 Film is: · 7 Down Our 2022 Film is: · IMAGINE Film We are also working on ways to tell the story of our Professors of IMAGI-NATION {University} to the world. Fashion for Good We can create activated Hoodies to amplify any campaign or idea or story. Current Hoodie campaigns we are running: · Kindness Hoodie · Making Space Hoodie · Hero Hoodies via Making of a Hoodie Podcast IMAGI-NATION {Library} This is our legacy to the world for the next 60,000+ years of human existence. This is where we keep developing all of our books, and mentor tools. The key development in this area is the creation of Mentor Class - a variety of videos and lessons from Mentors.

Mentor Class example IMAGI-NATION In 2022 we will have our own digital social network for people to be able to enter and exchange and engage with each other's time, knowledge, and opportunities. Designed with unlikely connections between some of the world’s most interesting organisations and human beings, IMAGI-NATION models a different economy and is a home for kids pushed outside the margins to walk across a bridge into knowledge and opportunities, and a space for citizens of our earth to work out how to live and design a world more equitably Appendix: Glossary of words and phrases in the AIME universe 18 values A set of values that infuse everything we do. Everyone enrolled in IMAGI-NATION {University} is trained in our 18 values. These are hope, change, freedom, rebelliousness, listening, empathy, BRAVE goals, no shame, initiative, yes and, forgiveness, kindness, gift of time, failure, asking questions, hard work/discipline, know yourself, mentors not saviours. 365-day Goal Station An ingredient of pop-up IMAGI-NATION {Factory} days. Where mentees write on post-it notes their goals for the year and place them on a sign where they can be seen. 6 knowledge fields The 6 key areas of knowledge and experience we have gained over the years that form the basis for everything we do and that we teach at IMAGI-NATION {University}. These are Imagination, Mentoring, Organising Change, Building Bridges, Flipping the Script and Hoodie Economics. AIME A global network that connects youth from marginalised backgrounds with the rest of the world to make space for exchanges of time, knowledge and opportunities between them. AIME Time Machine (AimeTM) An ingredient of pop-up IMAGI-NATION {Factory} days. Where mentees ‘deposit’ the baggage they are going to leave behind before they enter the IMAGI-NATION {Factory}. Always drafting We are always drafting. We have released ourselves from perfection and embraced the idea that our work is always a draft. It’s never finished, never perfect. Asking questions One of our 18 values. Asking questions allows us to move from what we already know to what we don’t yet know. Asterix Professor of Hoodie Economics at IMAGI-NATION {University}. Asterix is a philosopher combined with an economics major in pursuit of what makes life worth living. She’s asking some very big questions through her research to redefine how we think about adding value in our world – pursuing an exchange of time and experience instead of just money. Blue Professor of Flipping the Script at IMAGI-NATION {University}. Blue knows that self-authorship and an entrepreneurial mindset are integral in order to move oneself outside the dominant narrative. His fundamental lesson in flipping the script: “I don’t have to play a part in someone else’s story”. Blue wants to help write a curriculum for all students to see themselves in a new light at IMAGI-NATION {University}. BRAVE goals One of our 18 values. AIME embodies BRAVE (BIG, RISKY, AUDACIOUS, VISIONARY, ENDLESS) goals. Building bridges One the 6 knowledge fields taught at IMAGI-NATION {University}. Creating connections across nations, cultures, races, ages, socio-economic differences. Cellular network Our organisational structure at AIME—a living, evolving, decentralised system of intermingling cells. Change One of our 18 values. Change is the only constant! Co-CEO The Co-CEO program looks at levelling the playing field and making boardrooms more diverse and inclusive. Executives recruit a young person aged 18-30 from a background that has historically experienced marginalisation who will shadow them for 6-12 months and absorb all the learning available to those who get a seat at the decision-making table. Empathy One of our 18 values. Empathy is feeling with the heart of another person. Einstein Professor of Building Bridges at IMAGI-NATION {University}. Einstein wrote a paper as a sociology grad student in the 1970’s based on a phenomenon she gathered from her research: instead of going from A to B and B to C, what if we just built a bridge from A to C? Now she’s getting recruited by leaders around the world to talk about building bridges. The problem is: she’s not sure it’s going to work... Energy Professor of Mentoring at IMAGI-NATION {University}. Energy is on a lifelong quest to pass on knowledge. She earned her PhD in mentoring; she loves the Plato/Socrates relationship; she’s obsessed with seeing knowledge passed down from generation to generation. If she wants to tell you something, she’ll tell you a parable. She knows that if you want to change the world, you need to connect with people through stories. Failure One of our 18 values. When we fail, we learn. When we learn, we grow. Failure Time An ingredient of pop-up IMAGI-NATION {Factory} days. A confidence and resilience-building session where kids try out new things and learn it’s ok to fail. Flipping the script One the 6 knowledge fields taught at IMAGI-NATION {University}. Shifting the dominant narrative from a lens of problem to solution. Forgiveness One of our 18 values. Forgiveness gives us the power to move beyond a certain circumstance or person and not let it define us. Freedom One of our 18 values. Freedom is about casting off the chains that come from ourselves, history, society. GAIME of Life An ingredient of pop-up IMAGI-NATION {Factory} days. An interactive writers’ room and role play game where kids get to write and bring to life a story that can inspire kids like themselves. Gift of time One of our 18 values. At AIME, we believe the greatest gift we can give is the gift of our time by turning up for others. Hard work/discipline One of our 18 values. Hard work and discipline are the gears behind change. It’s not always pretty but it’s completely necessary. Hoodie The AIME Hoodie is the most meaningful Hoodie in the world. Since 2010, it has been the currency of IMAGI-NATION and our device for change. We ask people to act, to stand up and create change, and in exchange, we give them a hoodie to say “Thank you for fighting for a fairer world.” Hoodie Economics One the 6 knowledge fields taught at IMAGI-NATION {University}. The economy that underpins AIME: elevating the exchange of time, knowledge and opportunity above money. Hope One of our 18 values. Hope is believing in a better future and working to make it happen Hope Professor of Imagination at IMAGI-NATION {University}. Hope knows that hope doesn’t come easy: it’s always a struggle. He’s trying as hard as possible to build that bridge between reality and unreality. Hope feels the heaviness of hope; he carries this giant burden and responsibility – all the while thinking: “Don’t make me carry this alone!” Hope is the epitome of hard work: he knows you don’t get to opt out of the work if you want to change the world. Imagination One the 6 knowledge fields taught at IMAGI-NATION {University}. Imagination is the beginning of human thought and action. IMAGI-NATION An online world where we can model how society can work differently, where everyone has a seat at the table, where people enter and engage with each other and exchange time, knowledge, and opportunities, and where we are all invited to make an unlikely connection and help build a fairer world. IMAGI-NATION {Ambassadors} One of the options of the {Entrepreneurs} course at IMAGI-NATION {University}. A 100-day challenge for school students to use IMAGI-NATION to create a fairer world through a change mission of their choice. IMAGI-NATION {Artists} One of the options of the {Entrepreneurs} course at IMAGI-NATION {University}. A three-month residency for young artists to be mentored by a team of artists and have their work featured on IMAGI-NATION {TV} and in our IMAGI-NATION {Gallery}. IMAGI-NATION {CEO4Good} One of the options of the {Entrepreneurs} degree course at IMAGI-NATION {University}. A 100-day challenge for school students from inside the margins to mobilise their networks to share wealth, knowledge, opportunities with kids being left behind. IMAGI-NATION {Cinema} Our films and series use story to create unlikely connections with ideas and ways of thinking. Currently: Cogs (film, 2017), Dreams (film, 2018), The Professors (series, 2021), 7 Down (film, 2021), IMAGINE (film, coming in 2022), The Professors’ House (series, coming in 2022). IMAGI-NATION {Citizens} One of the degree courses at IMAGI-NATION {University}. For individuals to lead projects that drive meaningful change in their community and the world using the tools of IMAGI-NATION. IMAGI-NATION {Classrooms} A digital mentoring and tutoring session delivered via online meeting platforms to support mentees academically. IMAGI-NATION {Curriculum} A suite of mentoring tools and activities for school students based around our 18 values delivered through our pop-up IMAGI-NATION {Factory} days, IMAGI-NATION {Teachers} and partnerships with educators. Includes the Magic Maker, the Purple Carpet, the 365-day Goal Station, Sacrifice Planes, AIME Time Machine, Failure Time, GAIME of Life, Keys to the City, the Hoodie, books, films and more. IMAGI-NATION {Entrepreneurs} One of the degree courses at IMAGI-NATION {University}. School students become entrepreneurs for good and gain hands-on experience in leading change for themselves, for others or for the planet, including by using their artistic talents to have their voice and other voices heard. Includes {Ambassadors}, {Artists}, {CEO4Good}, {Filmmakers}, {Writers}. IMAGI-NATION {Executives} One of the degree courses at IMAGI-NATION {University}. For executives wanting to transform the leadership culture of their organisations, level the playing field for young people from outside the margins and bring diverse young talent into the boardroom. IMAGI-NATION {Factory} An immersive theatre experience delivered on school and university campuses to help kids develop confidence, unlock imagination and brave thought, and free their potential to create change in their world and in the wider world. This is how the IMAGI-NATION {Curriculum} is delivered to mentees. IMAGI-NATION {Filmmakers} One of the options of the {Entrepreneurs} degree course at IMAGI-NATION {University}. School students are mentored by professionals from the TV and film industry in how to tell stories and make films. IMAGI-NATION {Gallery} A virtual space and physical spaces (including the Hoodie) where AIME artists can display and sell their artwork and connect with established artists, galleries and opportunities. IMAGI-NATION {Library} A resource library of free IMAGI-NATION knowledge and tools to live on forever, for humanity. Available to everyone, both within and outside of IMAGI-NATION {University}. IMAGI-NATION {Presidents} One of the degree courses at IMAGI-NATION {University}. {Presidents} are university students who lead an AIME student chapter on their campus and create mentoring connections between university student mentors and 100 high school kids who have been pushed outside the margins. IMAGI-NATION {Radio} Where we host our Making of a Hoodie podcast. Each episode invites a different school to give the stage for youngsters to create unlikely connections and co-design an exclusive AIME hoodie sold on ICONIC apparel. IMAGI-NATION {Teachers} One of the degree courses at IMAGI-NATION {University}. Teachers wanting to teach with IMAGI-NATION to engage all students in the classroom and build bridges to local employers and community. IMAGI-NATION {TV} A weekly TV show on YouTube where we curate unlikely connections. Hosted by AIME school students, {Presidents} and partner schools. People from all walks of life around the world come together to connect with young people pushed outside the margins and share knowledge and ideas on ways to actively create a fairer world. IMAGI-NATION {University} A free online university where we educate and inspire people in how to make unlikely connections to act for a fairer world. With courses for school students, university students, teachers, executives and everyday citizens. IMAGI-NATION {Writers} One of the options of the {Entrepreneurs} degree course at IMAGI-NATION {University}. School students are mentored by established writers in how to write and tell stories. IMAGINE film The first crowd-written, crowd-produced feature-length film. To create the stage for kids from marginalised backgrounds to join household names in film and television for a live creation mentoring experiment in filmmaking. Impact Real-life change in someone’s mind, in their life, in their community, in the world because of an unlikely connection with an idea, a way of thinking, a piece of knowledge, a person, and that leads to a fairer world in the short, long or medium term. Initiative One of our 18 values. Initiative is about saying ‘If not me then who? If not, now then when?’ Keys to the City An ingredient of pop-up IMAGI-NATION {Factory} days. A moment where kids step to the front, take the stage and get the chance to shine in any way they choose. Kindness One of our 18 values. Kindness is in the AIME DNA, we LOVE to trade in the currency of kindness. Kindness Hoodie A hoodie designed to inspire kindness throughout the year. Every week, 20 people around the world each wear a kindness hoodie for a week, spread as much kindness as they can in whichever way they choose, then pass the hoodie on to the next 20 people. Know yourself One of our 18 values. Knowing yourself means increasing your awareness of the emotions, motivations, desires, abilities, fears and aspirations that form the self. Lionelcorn Professor of Organising Change at IMAGI-NATION {University}. Lionelcorn is fascinated by organizational systems that exist in the wild. (He’s obsessed with mycelium.) All of his organizational theories are based in natural regeneration. He thinks it’s bullshit that the world isn’t changing like nature. Chaos is natural. He’s pushing for chaos and change. Listening One of our 18 values. Listening is the gateway to empathy and connection, and is often a more powerful action than speaking. Magic Maker An ingredient of pop-up IMAGI-NATION {Factory} days. A question mark wand with 1,000 questions on it that work as a ‘get to know you’ conversation starter to bring mentors and mentees together. Making of a Hoodie A monthly/bi-monthly activated podcast that connects schools with activists, artists and alternative thinkers through ideas, knowledge and perspectives and sees them create a school hoodie together. Making Space A global art initiative that sees the work of youth from marginalised backgrounds exhibited in the IMAGI-NATION {Gallery}, on custom AIME hoodies, alongside profile artists and through their networks, and in galleries around the world. Meeting Place A global exchange portal where youth from marginalised backgrounds can connect with mentors, internships, scholarships and jobs. Mentoring One the 6 knowledge fields taught at IMAGI-NATION {University}. Mentoring is the key to sharing knowledge and wisdom across generations. Mentors in Residence Knowledge-holders from different walks of life who mentor 20 of our key leaders at AIME over a 3-month term twice a year. Mentors, not saviours One of our 18 values. We are not here to ‘save’ the youth that we work with. We are here to mentor kids so that they are stronger without us. Mycelium We are inspired by mycelium, a magical and intelligent web of life above and beneath the soil and a vital force of life on Earth. Mycelium is the oldest continuously surviving multi-cellular network in the world and helps trees talk to each other underground. No shame (at AIME) One of our 18 values. No shame at AIME is one of our earliest catchphrases. There’s zero tolerance at AIME for casting shame on others for expressing themselves or for being who they are. Organising change One the 6 knowledge fields taught at IMAGI-NATION {University}. Organising change is about how we can change things to be fairer. Professors The six non-human and complex academics who are the founders of IMAGI-NATION {University} and AIME’s lead spokespeople: Professor Asterix, Professor Blue, Professor Einstein, Professor Energy, Professor Hope, Professor Lionelcorn. Purple Carpet An ingredient of pop-up IMAGI-NATION {Factory} days. Inspired by world class, theatrical architecture and the red carpet, a device that signals to the mentees they are stepping into the world of IMAGI-NATION. Rebelliousness One of our 18 values. We’re not going to change anything by accepting the status quo. Sometimes a little rebellion is necessary to create change. Sacrifice Planes An ingredient of pop-up IMAGI-NATION {Factory} days. Where mentees write on paper planes the sacrifices they will make to achieve their goals and send them out into the world. Social Network for Good IMAGI-NATION: our social network founded in 2005 that focuses on meaningful human interactions anchored in real life and in the real world, and forges unlikely connections between people to create a fairer world. Sunday Kindness Our weekly newsletter delivering a dose of kindness to the world every Sunday. This Hoodie Pays Rent An initiative launched in April 2020 where we split profits from ‘This Hoodie Pays Rent’ hoodie sales with those struggling to pay their rent because of COVID-19, to help keep a roof over their heads. Unlikely connections Unlikely connections is connecting people with other people and with stories, experiences, knowledge, ideas, perspectives and opportunities they wouldn’t ordinarily connect with, across races, ages, wealth and nations. Yes, and One of our 18 values. At AIME, YES AND means encouraging a collaborative environment and cherishing ideas before burning them.

How does race factor into how the European characters interact with Othello?

Hmmm. I have been thinking about Earth having a fever in response to a pathogen. Foreign bodies, viruses, known as corporations have infected the minds of their host organisms, using legal systems to reprogram their syntropic nature as living organisms with a compulsion to replace themselves with entropy machines. By assuming personhood, corporations are consuming and monopolizing the time, energy, and resources of their hosts so that they have achieved a level of control and domination over nature such that they can change the climate and reversing the process of biological and cultural evolution.

“I think I can feel the future.”

https://world.builderscollective.org/awake/

I think this is an important paragraph because it points out that academic English can often times act as a barrier to acknowledging the true ability of our students. We may focus on the grammar or misspellings and mistake that for a lack of intelligence or ability when it's simply the hegemonic norm we continue to enforce.

Idk what it is, but I get a weird feeling about how explicit the lines are being drawn around casual and committed relationships. I think it's related to the earlier comment about feeling like the text is overemphasizing the DisCOs agency in defining the relationship.

I think part of it is also the fact that the casualization of labor is a really sore point for a lot of workers right now, as we emerge as a new "precariat" class. While its understandable that a lot of this work may be considered volunteer work and thus unpaid, continually noting that this is a casual relationship with no obligation for pay definitely makes me recall toxic work relationships in my past and I imagine will do the same for other workers.

On this read, I'm putting myself in the shoes of someone that is interested in working as a contributor. These sentences feel way more relevant to the DisCO itself. Not to say this doesn't belong in this page, but I think we may need to work on shaping the tone of this document to more clearly delineate who. the audience is at each point of the text.

High level, I think it may make more sense to lead with "TL;DR"s rather then ending with them so that as people are browsing through the wiki, they can quickly decide if they're on the right page or not.

I don't know that this is inherent to DisCOs as individual organizations! I think that this might appear to be the case given that we're still building out the core of the philosophy while also trying to create organizations that work by these ideals while existing as an explicitly counter-hegemonic endeavor. All that to say, I think you may be selling DisCOs short by including this in there, but understand why it's included right now. My main reason for bringing it up is, we don't want to give people the impression that this is the hardest path possible because it will likely result in slower adoption of these principles.

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

Learn more at Review Commons

Reply to the reviewers

Manuscript number: RC-2021-01041 Corresponding author(s): Gregory P. Way, PhD

1. General Statements

On behalf of the authors, I’d like to thank the Review Commons team for sending our manuscript out for review. I’d also like to thank the three anonymous reviewers for providing valuable feedback that will improve the clarity, focus, and analysis interpretation presented in our manuscript.

To prompt the editorial team, our paper provides two well-controlled innovations:

We are the first to train variational autoencoders (VAEs) on classical image features extracted from Cell Painting images. VAEs are commonplace in, and have contributed major discoveries to, other biomedical data types (e.g. transcriptomics), but they have been underexplored in morphology data. In our paper, we trained and optimized three different VAE variants using Cell Painting readouts and compared these variants against shuffled data, against PCA (a nonlinear dimensionality reduction algorithm commonly used as a VAE control), and against L1000 (mRNA) readouts from the same perturbations. We found that cell morphology VAEs train with different settings than gene expression data, and that they generate interpretable latent spaces that depend on the chosen VAE variant.

We tested special VAE properties to predict polypharmacology cell states in a novel way. Polypharmacology is a major reason why drugs fail to reach the bedside. Off-target effects cause unintended toxicity, and lead to adverse clinical events. In our paper, we used VAE latent space arithmetic (LSA) to predict polypharmacology cell states; in other words, what cells might look like if we perturbed them with a compound that had two mechanisms of action (MOA). We compared our results to shuffled data, PCA, and to LSA performed with VAEs trained using L1000 readouts. We found that cell morphology and gene expression provide complementary information, and that we could predict some polypharmacology cell states robustly, while others were more difficult to predict.

We found value in all of the reviewer comments. We intend to conduct all but four of the proposed analyses to supplement our aforementioned innovations.

In the following revision plan, we include all reviewer comments exactly as they were written. The reviewers often had overlapping suggestions. In these cases, we grouped together similar reviewer comments and responded to them once.

We include three sections: 1) A description of the revisions we plan to conduct in the near future; 2) A description of changes we have already made; and 3) A description and rationale of changes we will not pursue.

Lastly, we would like to highlight that all reviewers provided positive feedback in their reviews. They discussed our paper as “conceptually and technically unique” and were positive about our methods section, stating that we did a “good job making everything available and reproducible”. Our methods section is complete, and we provide a fully reproducible and versioned github repository. We will release a second version of our github repository when we complete our revision plan to maintain clarity for our submitted version and the peer-reviewed version.

1. Description of the planned revisions

2.1. Address UMAP interpretability to provide a deeper description of MOA performance

Reviewer 1: Instead of using UMAP embedding, it would be better to compare reconstruction error or show a reconstructed image with the original image to claim that models reliably approximate the underlying morphology data.

Reviewer 1: Rather than just stating that the VAE's did not span the original data distribution and saying beta-VAE performed best by eye, some simple metrics can be drawn to analyze the overlap in data for a more direct and quantified comparison. Researchers should also explain what part of the data is not being captured here. Some analysis of what the original uncaptured UMAP represents is important in understanding the limitations of the VAEs' capacity.

Reviewer 2: The authors compare generation performance based on UMAP. In the UMAP space, data tend to cluster together even though they might be far from each other in the feature space. I would like to see more quantitive metrics on how well these methods capture morphology distributions. You can compute metrics like MMD distance, kullback leibler (KL), earthmoving distance, or a simple classifier trained on actual MoA classes tested on generated data.

We agree with the reviewers that evaluating reconstruction loss in addition to providing the UMAP coordinates would improve understanding of VAE limitations and enable a better comparison of VAE performance. We will analyze reconstruction loss across models and include these data as a new supplementary figure, which will enable direct comparisons across models and across different MOAs.

We also agree that UMAP interpretation can be misleading. While currently state-of-the-art, UMAP has mathematical limitations that prevent interpretation of global data structures. However, there are emerging tools, including a new dimensionality reduction algorithm, called PaCMAP, which aims to preserve both local and global structure (Wang et al, 2021). We will explore this tool to determine, both mathematically and empirically, which is most appropriate for our dataset by cross-referencing the visualization with our added supplementary figure describing per-MOA reconstruction loss.

We would also like to emphasize that we trained our VAEs using CellProfiler readouts from Cell Painting images and not the raw Cell Painting images themselves. As this was one of our primary innovations, this detail is extremely important. Therefore, we have improved clarity and added emphasis to this point in the manuscript introduction and discussion (see section 3).

2.2. More specific comparisons of MOA predictions to shuffled data and improved description of MOA label accuracy

Reviewer 1: It is difficult to know the clear threshold for successful performance is on figures like Figure 7 and SFigure 9, but by and large, it appears that the majority of predicted combination MOAs were not successful. Without the ability to either A) adequately predict most all combinations from individual profiles that were used in training or B) an explanation prior to analysis of which combination will be able to predict, it is difficult to see this method being used since the combinatorial predictions are more likely not informative.

Reviewer 1: The researchers justify the poor performance compared to shuffled data, by saying that A) MOA annotations are noisy and unreliable and B) they MOAs may only manifest in other modalities like what was seen in the L1000 vs morphology predictability. While these might be true, knowing this the researchers should make an effort to clean and de-noise their data and select MOAs that are well-known and reliable, as well as, selecting MOAs for which we have a known morphological or genetic reaction.

Reviewer 3: Figure 6 is missing error bars (standard deviation of the L2 distance) and, as such, is hard to draw conclusions from.

We thank the reviewers for raising this concern. We agree that it is critical, and we appreciate the opportunity to address it.

All three of these comments relate to being unable to draw conclusions from our results when most A∩B predictions appear to have no difference from shuffled controls. Therefore, to address this comment, we will update our LSA evaluation to compare each MOA to a matched set of randomly shuffled data. Specifically, in our existing comparison, we realized a methodological fallacy in how we're displaying these data shuffles. We should be comparing specific MOA combinations to their corresponding shuffled results instead of comparing all to all, which will artificially decrease performance when there are polypharmacology predictions that fail to recapitulate the ground truth cell states.

We have connected with Paul Clemons, the senior director Director of Computational Chemical Biology Research at the Broad Institute of MIT and Harvard, who has informed us that the Drug Repurposing Hub annotations are among the most well documented. Therefore, while we know that biological annotations are often incomplete, our original text overemphasized the amount of noise contributed by inaccurate labels. We therefore added the following sentence to the discussion to clarify this important point:

“However, the Drug Repurposing Hub MOA annotations are among the most well-documented resources, so other factors like different dose concentration and non-additive effects contribute to weak LSA performance for some compound combinations (Corsello et al, 2017).”

We will also update our supplementary figure to account for specific MOA shuffling and include additional text comparing Cell Painting and L1000 showing which MOAs perform best in which modality.

2.3. More detailed evaluation of MOA performance across drug variance and drug classes

Reviewer 1: With the small number of combinations that are successfully predicted, to build confidence in the performance, it would be necessary to explain the reason for the differences in performance. Further experimentations should be done looking into any relationship between the type of MOAs (and their features) and the resulting A|B predictability. Looking at Figure 7, the top-performing combinations are comprised entirely of inhibitor MOAs. If the noisiness of the data is a factor, there should be some measurable correlation between feature noisiness and variation and the resulting A|B predictability from LSA.

We agree with the reviewer that further experimentation would be helpful to gain confidence in our LSA performance. We plan to perform two different analyses to address this question. First, we will compare profile reproducibility (median pairwise correlations among MOAs) to MOA predictability. This will provide insight to determine the relationship between MOA measurement variance and performance. Second, we will split MOAs by category (e.g. inhibitor, activator) and test if there are significant performance differences between categories across VAE models in both L1000 and Cell Painting data. This will tell us if there are certain trends in the type of MOAs we’re able to predict. If there is, this would be useful knowledge since it could suggest that certain types of MOAs are associated with a more consistent cell state.

2.4. Higher confidence in LSA overfitting assessment

Reviewer 1: To show that the methodology works well on unseen data, researchers withheld the top 5 performing A|B MOAs (SFig 9) and showed they were still well predicted. This is not the most compelling demonstration since the data to be held out was selected with bias as the top-performing samples. It would be much more interesting to withhold an MOA that was near or only somewhat above the margin of acceptability and see how many holdouts affected the predictability of those more susceptible data points. From my best interpretation, the hold-out experiment also only held out the combination MOA groups from training. It would be better if single MOAs (for example A) which were a part of a combination of MOA (A|B) were also held out to see if predictability suffered as a result and if generalizability did extend to cells with unseen MOAs (not just cells which had already highly performing combinations of seen MOAs).

We believe our original analysis was extremely compelling. Even if we removed the top MOAs from training, we were still able to capture their combination polypharmacology cell states through LSA. We find this similar to removing all pictures of sunglasses in an image corpus of human faces, but still being able to reliably infer pictures of people wearing sunglasses. Specifically, this tells us that our model is learning some fundamental data generating function that our top performing MOAs tap into regardless of if they are present or not in training.

However, we agree with the reviewer that withholding intermediate-performing MOAs would also be informative, but for a separate reason. Unlike the best predicted MOAs, the intermediate MOAs are likely more susceptible to changes in the training data, so it would be interesting to determine if intermediate MOAs’ performance is a result of overfitting instead of truly learning aspects of the data generating function. We plan to perform this new analysis and add the results to Supplementary Figure 8 as a subpanel and add a full description of the approach to the appropriate methods subsection.

2.5. Additional metrics to evaluate LSA predictions to provide more confident interpretation

Reviewer 2: The predictions are evaluated using L2 distances, which I find not that informative. I would like to see other metrics (correlation or L1 or distribution distances in previous comments)

We agree with the reviewer that using more than one metric would be helpful because oftentimes a single metric does not tell a complete story. We will add a panel to the LSA supplementary figure (Supplementary Figure 7), using Pearson correlation instead. While L2 distances will tell us how close predictions are to ground truth, Pearson correlations will tell us how consistent, on average, we are able to predict feature direction.

2.6. Adding a performance-driven feature level analysis to categorize per-feature modeling ability

Reviewer 2: I would like to see feature-level analysis, which features are well predicted and which ones are more challenging to predict?

We agree with the reviewer that feature level analysis would be interesting to study. We believe that understanding which features are easy and hard to model could give insight into why certain MOAs (which could be associated with more signal in certain Cell Painting features) are predicted better than others.

However, we are concerned that it is difficult to have an objective measurement of which features are easier to model because features that have less variation might be easier to model. So, we will analyze the correlation between individual feature reconstruction loss vs. feature variance across profiles. We will color-code the points to represent feature groups or channels. This analysis will not only demonstrate the relationship between feature variance and modeling ability, but also provide insight into the difficulty of modeling individual CellProfiler features.

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

3.1. Documenting positive feedback as provided by the three reviewers

Reviewer 1: With access to the dataset, the posted GitHub, and documentation in the paper, I believe that the experiments are reproducible.

Reviewer 1: The experiments are adequately replicated statistically for conventions of deep learning.

Reviewer 1: This paper proposes a conceptually and technically unique proposal in terms of application, taking existing technologies of VAEs and LSA and, and as far as I know, uses them in a novel area of application (predicting and simulating combination MOAs for compound treatments). If this work is shown to work more broadly and effectively, is seen through to it completion, and is eventually successfully implemented, it will help to evaluate the effects of drugs used in combination on gene expression and cell morphology. An audience in the realm of biological deep learning applications as well as an audience working in the compound and drug testing would be interested in the results of this paper. Authors successfully place their work within the context of existing literature, referencing the numerous VAE applications that they build off of and fit into the field of (Lafarge et al, 2018; Ternes et al, 2021, etc...), citing the applications of LSA in the computer vision community (Radford et al, 2015, Goldsborough et al, 2017), and discussing the biological context that they are working in (Chandrasekaran et al, 2021).

Reviewer 2: The main novelty of the work is applying VAEs on cell painting data to predict drug perturbations. The final use case could be guiding experimental design by predicting unseen data. However, the authors do not show such an example and use case which is understandable due to the need for doing further experiments to validate computational results and maybe not the main focus of this paper. The authors did a good job of citing existing methods and relevant. The potential audience could be the computational biology and applied machine learning community.

Reviewer 3: The manuscript is beautifully written in a crystal clear manner. The authors have made a visible effort towards making their work understandable. The methods section is clear and comprehensive. All experiments are rigorously conducted and the validation procedures are sound. The conclusions of the paper are convincing and most of them are well supported by the data. Both the data and the code required to reproduce this work are freely available. Overall, the article is of high quality and relevance to several scientific communities.

We thank the reviewers for their encouraging remarks and overall positive sentiment. As early-career researchers, we feel empowered by these words.

3.2. Moved Figure 2 to supplement and removed Figure 5

Reviewer 1: Fig 2 is not informative so it can go to supplementary.

Reviewer 2: I liked the paper's GitHub repo, the authors did a good job making everything available and reproducible. As a suggestion, you can move the learning curves in two the sup figures cause they might not be the most exciting piece of info for the non-technical reader.

Reviewer 3: I would suggest removing Figure 5 (or moving it to the supplementary) as it revisits the content of Figure 1 and does not bring much extra information.

We agree that Figure 2 might not be informative to a non-technical reader, so we have accepted this suggestion by both reviewers 1 and 2, and we have moved Figure 2 to supplementary.

We agree with the reviewer and have removed Figure 5.

3.3. Clarified our data source as CellProfiler readouts, not raw Cell Painting images

Reviewer 1: In Fig 4, it would be useful to show a few sample representative images with respect to CellProfiler feature groups.

Reviewer 1: Figure 6, what does it means original input space? Does it mean raw pixel image? As researchers extracted CellProfiler feature groups already, it would be interesting to compare mean L2 distance based on CellProfiler features so that whether VAE improves performance or not (compared to handcrafted features) as a baseline.

Reviewer 3: While what "morphological readouts" concretely mean becomes clearer later on in the paper, it would be useful to give a couple of examples early on when introducing the considered datasets.

We thank the reviewer for these suggestions, which bring to light a common source of confusion, which we must alleviate. We are working with CellProfiler readouts (features extracted using classical algorithms) of the Cell Painting images and not the images themselves. We have made several edits throughout the manuscript to improve clarity and remove this confusion, including the introduction, in which we clearly state our model input data:

“Because of the success of VAEs on these various datasets, we sought to determine if VAEs could also be trained using cell morphology readouts (rather than directly on images), and further, to carry out arithmetic to predict novel treatment outcomes. We derive the cell morphology readouts using CellProfiler (McQuin et al, 2018), which measures the size, structure, texture, and intensity of cells, and use these readouts to train all models.”

This decision comes with tradeoffs: The benefit of using CellProfiler readouts instead of images is that they are more manageable but we might lose some information. We more thoroughly discuss this important tradeoff in the discussion section:

“We determined that VAEs can be trained on cell morphology readouts rather than directly using the cell images from which they were derived. This decision comes with various trade-offs. Compared to cell images, cell morphology readouts as extracted by image analysis tools (e.g. CellProfiler) are a more manageable data type; the data are smaller, easier to distribute, substantially less expensive to analyze and store, and faster to train (McQuin et al, 2018). However, it is likely some biological information is lost, because these tools might fail to measure all morphology signals. The so-called image-based profiling pipeline also loses information, by nature of aggregating inherently single-cell data to bulk consensus signatures (Caicedo et al, 2017).”

3.4. Clarified future directions to infer cell health readouts from simulated polypharmacology cell states

Reviewer 1: Authors also make the claim that they can infer toxicity and simulate the mechanism of how two compounds might react. This is a claim that would not be supported even if the method were able to successfully predict morphology or gene profiles. Drug interaction and toxicity are quite complex and goes beyond just morphology and expression. VAEs predicting a small set of features would not be able to capture information beyond the readouts, especially when dealing with potentially unseen compounds for which toxicity is not yet known. For example, two compounds might produce a morphology that appears similar to other safe compounds but has other factors that contribute to toxicity. Further, here they show no evidence of toxicity or interaction analysis.

The reviewer is correct that such a claim is unsupported by our research. Our message was actually that inferring toxicity could be a potential future application of our work. Specifically, for example, we can apply orthogonal models of cell toxicity that we previously derived using other data (Way et al, 2021a) to our inferred polypharmacology cell states. We thank this reviewer for noticing our lack of clarity, and we have made changes in the discussion to make it clear that inferring toxicity is something we may do in the future and is not something that is discussed in the manuscript:

“In the future, by predicting cell states of inferred polypharmacology, we can also infer toxicity using orthogonal models (e.g. Way et al. 2021) and simulate the mechanisms of how two compounds might interact.”

3.5. Clarified our method of splitting data, and noting how a future analysis will answer overfitting extent

Reviewer 2: Could authors outline detailed data splits? Which MoA are in train and which are held out from training? As I understood, there were samples from MoAs that were supposed to be predicted in the calculation of LSA? Generally, the predicted MoA should not be seen during training and not in LSA calculation.

We now more explicitly detail how we split our data in the methods:

“As input into our machine learning models, we split the data into an 80% training, 10% validation, and 10% test set, stratified by plate for Cell Painting and stratified by cell line for L1000. In effect, this procedure evenly distributes compounds and MOAs across data splits.”

We also thank the reviewer for this comment, because they express an important concern about making sure that we are not overfitting to the data. We have explained in the manuscript that because of lack of data, MOAs were repeated in training and LSA. However, we believe overfitting is not playing a large role in model performance. Through our hold 5 out experiment, we are able to show that our models are able to predict the same MOAs irrespective of whether they were in the training data, indicating that we did not overfit to the distribution of certain MOAs.

Reviewer 1 also suggested that we do the hold 5 out experiment on A∩Bs that were barely predicted. After we do that, we will explicitly demonstrate the extent of overfitting.

3.6. Introduced acronyms when they first appear in the manuscript

Reviewer 3: The Kullback-Leibler divergence is properly introduced in the methods part, but not at all in the introduction (it directly appears as "the KL divergence"). To enhance readability, it would be better to fully spell it before using the acronym, and maybe give a one-sentence intuition of what it is about before pointing out to the methods part for more details.

We thank the reviewer for bringing this to our attention. We have carefully reviewed the entire manuscript and have corrected such instances of clear introductions to acronyms.

3.7. Fixed minor text changes

Reviewer 3: In Figure 1, I would recommend changing "compression algorithms" to "dimension reduction algorithm" or "embedding algorithm". In a compression setting, I would expect the focus to be on the number of bits of information each method requires (or the dimension of the resulting embedding) to encode the data while guaranteeing a certain quality threshold. This is obviously not the case here as the dimension of the embedding is fixed and the focus is on exploring how the embedding is constructed (eg how much it decorrelates the different features, etc) - which may be misleading.

Reviewer 3: I recommend using "A n B" or "A & B" or "(A, B)" to denote the combination of two independent modes of action A and B. The current notation "A | B" overloads the statistical "A given B" which appears in the VAE loss and is therefore misleading.

We agree with the reviewer, and aim to minimize all sources of potential confusion. We have made the change in the figure.

We also agree that our current notation can be confusing. We have updated all instances of “A|B” with “A ∩ B”.

3.8. Added hypothesis of MMD-VAE oscillations to supplementary figure legend

Reviewer 3: Do the authors have a hypothesis of what may be causing MMD-VAE to oscillate during validation when data are shuffled? This seems to be the case on two of the three considered datasets (Figure 2 and SuppFigure 1) and is not observed for the other models. Including a few sentences on that in the text would be interesting.

We believe a big reason for this is because of the fact that the optimal MMD-VAE had a much higher regularization term, which puts a greater emphasis on forming normal latent distributions, than the optimal Beta or Vanilla VAE. Forcing the VAE to encode a shuffled distribution into a normally distributed latent distribution would be difficult to do consistently across different randomly shuffled data subsets, and therefore might cause oscillations in the training curve across epochs when the penalty for that term is high. As these observations may be interesting to a certain population of readers, we have incorporated this explanation into the supplementary figure legend (which is where this figure is shown):

“Forcing the VAE to consistently encode a shuffled distribution into a normally distributed latent distribution would be difficult, and therefore might cause oscillations in the training curve across epochs.”

3.9. Explained our selection of VAE variants

Reviewer 3: The different types of considered VAE and their differences are very clearly introduced. It may however be good to motivate a bit more the focus on beta-VAE and MMD-VAE among all the possible VAE models. This is partly done through examples in the second paragraph of page 2, but could be elaborated further.

We thank the author for their encouraging remarks. We have made edits to the manuscript’s introduction, explaining why we chose these two variants out of all the possible choices:

“We trained vanilla-VAEs, β-VAEs, and MMD-VAEs only, and not other VAE variants and other generative model architectures, such as generative adversarial networks (GANs), because these VAE variants are known to facilitate latent space interpretability.”

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

4.1. We will not explore additional latent space dimensions in more detail, as this is out of scope

Reviewer 1: As both reconstructed and simulated data did not span the full original data distribution, it might be better to look at reconstruction error and increase the dimension of latent space.

We thank the reviewer for bringing up this important point. Our VAE loss function consists of the sum of reconstruction error and some form of KL divergence. Specifically, this reviewer is suggesting that if we only minimize reconstruction error (or focus more on reconstruction over KLD by lowering beta), a higher latent dimension would result in better overall reconstruction. This is true, but doing so would have negative consequences. While we would perhaps get the UMAPs to show the full data distribution, the UMAPs are not our focus; predicting polypharmacology through LSA is. We found that when we have a higher focus on the reconstruction term, we have more feature entanglement, as indicated by lower performance when simulating data and overlapping feature contribution per latent feature. The fact that simulating data would logically require less disentanglement than performing LSA shows that we require higher regularization (and hence lower focus on reconstruction) than the one we got from simulating data.

Essentially, while the reviewer's comments would improve reconstruction and allow us to improve the UMAPs, doing so would likely worsen LSA performance, which is the main focus of the project. Also, increasing the latent dimension without changing beta would likely have caused little to no change because since beta is encouraging disentanglement, it would cause the newly added dimension to have little variation and encode little new information that wasn’t already encoded before.

We have also previously explored the concept of toggling the latent dimensions in a separate project (Way et al, 2020). We are very interested in this area of research in general, and any additional analyses (beyond hyperparameter optimization) deserves a much deeper dive than what we can provide in this paper.

Lastly, we intend to include a deeper description and analysis of reconstruction loss across models, datasets, and MOAs as was suggested by a previous reviewer comment (see section 2.1 above)

4.2. We will not review Gaussian distribution assumptions of the VAE as we feel it is not informative

Reviewer 1: By looking at SFigure 6, I am wondering whether latent distribution actually met gaussian distribution (assumption of VAE). It may show skew distribution as some of latent features shows low contribution.

This reviewer’s comment is interesting, but we do not believe it would change the findings of our study. Suppose we find that the latent dimensions aren’t normally distributed. This wouldn’t change much; a gaussian distribution isn’t the most critical to perform LSA. We need the latent code to be disentangled, but having normally distributed latent features doesn't necessarily mean that we have good disentanglement (see https://towardsdatascience.com/what-a-disentangled-net-we-weave-representation-learning-in-vaes-pt-1-9e5dbc205bd1)

4.3. In this paper, we will not train or compare conditional VAEs nor cycle GANs

Reviewer 2: While authors provided a comparison between vanilla VAE and MMD-VAE, B-VEA, there are other methods capable of doing similar tasks (data simulation, counterfactual predictions ), I would like to see a comparison with those methods such as conditional VAE( https://papers.nips.cc/paper/2015/hash/8d55a249e6baa5c06772297520da2051-Abstract.html, CVAE + MMD : https://academic.oup.com/bioinformatics/article/36/Supplement_2/i610/6055927?login=true) or cycle GANs(https://arxiv.org/abs/1703.10593 ).

While such comparisons would be interesting, they are not the main focus of the manuscript, which is to benchmark the use of VAEs in cell morphology readouts and to predict polypharmacology.

We think that CVAE would not be appropriate for our study. In a CVAE, the encoder and decoder are both conditioned to some variable. In our situation where we are predicting the cell states of different MOAs, it would make most sense to condition on the MOA. However, because we’re using the MOA labels in our LSA experiment, conditioning on them is likely to bias our results and not be effective for MOAs outside the conditioning.

For cycle GANs, we have found that training using these data, in a separate study in our lab, is extremely difficult. Our lab has not published this yet, but once we are able to better understand cycleGAN behavior in these data, it will require a separate paper in which we compare performance and dissect model properties in much greater detail.

Nevertheless, we have added citations to multi-modal approaches like cycle GANs (see section 4.4) as they will point a reader to useful resources for future directions.

4.4. We will not be comparing with multi-modal integration, but we clarified our focus on Cell Painting VAE novelty and added multi-modal citations

Reviewer 1: Researchers found that the optimal VAE architectures were very different between morphology and gene expression, suggesting that the lessons learned training gene expression VAEs might not necessarily translate to morphology. It would be interesting to compare the result with multimodal integration as baseline (i.e., Seurat).

Our focus in this paper was to train and benchmark different variational autoencoder (VAE) architectures using Cell Painting data and to demonstrate an important, unsolved application in predicting polypharmacology that we show is now possible for a subset of compounds. It was a natural and useful extension to compare Cell Painting VAE performance with L1000 VAE performance especially since our data set contained equivalent drug perturbations. We feel that any extension including multi-modal data integration will distract focus away from the Cell Painting VAE novelty, and requires a much deeper dive beyond scope of our current manuscript.

Additionally, there have been other, more in-depth and very recent multi-modal data integration efforts using the same or similar datasets (Caicedo et al, 2021; Haghighi et al, 2021). In a separate paper that we just recently submitted, we also dive much deeper to answer the question of how the two modalities complement one another in various ways and for various tasks (Way et al, 2021b). These two papers already provide a deeper and more informative exploration of Cell Painting and L1000 data integration.

Therefore, because multi-modal data integration, while certainly interesting, will distract from the Cell Painting VAE novelty and is redundant with other recent publications, we feel it is beyond scope of this current paper.

Nevertheless, multi-modal data integration is important to mention, so we add it to the discussion. Specifically, we discuss how multi-modal data integration might help with predicting polypharmacology in the future and include pertinent citations so that we, or another reader, might be able to follow-up in the future. The new section reads:

“Because we had access to the same perturbations with L1000 readouts, we were able to compare cell morphology and gene expression results. We found that both models capture complementary information when predicting polypharmacology, which is a similar observation to recent work comparing the different technologies’ information content (Way et al, 2021). We did not explore multi-modal data integration in this project; this has been explored in more detail in other recent publications (Caicedo et al, 2021; Haghighi et al, 2021). However, using multi-modal data integration with models like CycleGAN or other style transfer algorithms might provide more confidence in our ability to predict polypharmacology in the future (Zhu et al, 2017).”

1. References

Caicedo JC, Cooper S, Heigwer F, Warchal S, Qiu P, Molnar C, Vasilevich AS, Barry JD, Bansal HS, Kraus O, et al (2017) Data-analysis strategies for image-based cell profiling. Nat Methods 14: 849–863

Caicedo JC, Moshkov N, Becker T, Yang K, Horvath P, Dancik V, Wagner BK, Clemons PA, Singh S & Carpenter AE (2021) Predicting compound activity from phenotypic profiles and chemical structures. bioRxiv: 2020.12.15.422887

Corsello SM, Bittker JA, Liu Z, Gould J, McCarren P, Hirschman JE, Johnston SE, Vrcic A, Wong B, Khan M, et al (2017) The Drug Repurposing Hub: a next-generation drug library and information resource. Nat Med 23: 405–408

Haghighi M, Singh S, Caicedo J & Carpenter A (2021) High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations. bioRxiv: 2021.09.08.459417

McQuin C, Goodman A, Chernyshev V, Kamentsky L, Cimini BA, Karhohs KW, Doan M, Ding L, Rafelski SM, Thirstrup D, et al (2018) CellProfiler 3.0: Next-generation image processing for biology. PLoS Biol 16: e2005970

Wang Y, Huang H, Rudin C & Shaposhnik Y (2021) Understanding how dimension reduction tools work: an empirical approach to deciphering t-SNE, UMAP, TriMAP, and PaCMAP for data visualization. J Mach Learn Res 22: 1–73

Way GP, Kost-Alimova M, Shibue T, Harrington WF, Gill S, Piccioni F, Becker T, Shafqat-Abbasi H, Hahn WC, Carpenter AE, et al (2021a) Predicting cell health phenotypes using image-based morphology profiling. Mol Biol Cell 32: 995–1005

Way GP, Natoli T, Adeboye A, Litichevskiy L, Yang A, Lu X, Caicedo JC, Cimini BA, Karhohs K, Logan DJ, et al (2021b) Morphology and gene expression profiling provide complementary information for mapping cell state. bioRxiv: 2021.10.21.465335

Way GP, Zietz M, Rubinetti V, Himmelstein DS & Greene CS (2020) Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations. Genome Biol 21: 109

Zhu J-Y, Park T, Isola P & Efros AA (2017) Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. arXiv [csCV]

both women are anxious and feel uncomfortable in the situation

Aristotle brilliantly acknowledges that when we perceive something there are many factors that are apart of it. The conscious, the subconscious, the present, the feeling of "likeness" and how we think we perceive something may not always be the case. The mind is complex, this part of the writing provokes self thought about "Can I trust my own sense/perception?"

This statement made by Knox really stood out to me given our current "clickbait" styled media production. It seems to me that Stillwater is an example of a media outlet using a highly sought after story, person or current even to gain attention on their given piece of content.

I'm reminded of YouTube videos that use current happenings of the world as a thumbnail and/or subject matter in hopes of gaining more followers, listeners or viewers to their channel.

Similarly, Stillwater (although I haven't seen the film) seems like Hollywood using Knox's name and story to gain views, clicks and dollars.

I think of the saying "all publicity is good publicity." However in this situation, for someone like Knox who wishes to be left alone; all publicity, even the good is considered to be bad.

I didn't know anything about Amanda Knox, but was familiar with the name. After reading the article and doing some research on my own I came to remember I had seen a Netflix Documentary titled Amanda Knox on my feed a while back. Looking forward to diving into that in the near future.

I'd like to conclude that we could all learn from the way this story is handled. Just because covering a story may receive a lot of interaction, traction and/or popularity doesn't necessarily make it right to do so. We as proper media literate students must be held to a higher standard and lead by example and take all accounts into consideration before moving forward with something. We must also admit when we're wrong, apologize for our mistakes and right our wrongs to the best of our abilities. We must learn by experience from Amanda Knox's past history so that we can be proactive in not allowing it to occur in the future.

Author Response:

Reviewer #1 (Public Review):

This study sought to systematically identify the components and driving forces of transcriptome evolution in fungi that exhibit complex multicellularity (CM). The authors examined a series of parameters or expression signatures (i.e. natural antisense transcripts, allele-specific expression, RNA-editing) concluding that the best predictor of a gene behavior in the CM transcriptome was evolutionary age.

Thus, the transcriptomes of fruiting bodies showed a distinct gene-age-related stratification, where it was possible to sort out genes related to general sexual processes from those likely linked to morphogenetic aspects of the CM fruiting bodies. Notably, their results did not support a developmental hourglass, which is the rather predominant hypothesis in metazoans, including some analysis in fungi.

The studies involved analyses of new transcriptomic datasets for different developmental stages (and tissue types in some cases) of Pleurotus ostreatus and Pterula gracilis, as well as the analyses of existing datasets for other fungi.

There are diverse interesting observations such as ones regarding Allele Specific Expression (ASE), suggesting that in P. ostreatus ASE mainly occurs due to cis-regulatory allele divergence, possibly in fast evolving genes that are not under strong selection constraints, such as ones grouped in youngest gene ages categories. In addition, a large number of conserved unannotated genes among CM-specific orthogroups highlights the rather cryptic nature of CM in fungi and raises as an important area for future research.

Some of the key aspects of the analyses would need to be better exemplified such as:

– Providing a better description of the developmentally expressed TFs only in CM species

– Providing clear examples of the promoter divergence that could be the underlying mechanism behind ASE. In particular, for some cases, there may be enough information in the literature/databases to predict the appearance or disappearance of relevant cis-elements in the promoters showing the highest divergence in genes depicting the highest levels of ASE.

We appreciate the constructive comments of the Reviewer and have revised the ms in accordance with the suggestions. In particular, we link different parts of the ms better to each other, provided a more detailed discussion of developmentally expressed TFs (lines 615-621). We also provide case studies of ASE genes with cis-regulatory divergence (Figure 5 and see below), although we note that these analyses are based on inferred and not directly determined motifs, so they should be considered as preliminary.

We had considered using TF binding motifs previously, and now we gave a try to analyzing potential transcription factor binding sites in divergent promoters. We find that there are no P. ostreatus transcription factors for which motifs based on direct evidence are available; rather, all P. ostreatus motifs are based on extrapolations from experimentally determined motifs (typically in Neurospora crassa). Therefore, to avoid too general motifs, we used only those where at least 5 nucleotides show at least 80% expected frequency in the PWM-s. This left us with 158 motifs (126 excluded). High motif binding score (>=4) and self-rate (>=0.9) were also required to ignore false positive hits. Different binding ability and lack of binding in one of the parental genomes were counted for each promoter. We found that genes with allele specific expression (ASE S2 and S4) show significantly higher differences in motif binding (lacking motifs, or different binding ability) than non-ASE genes (Fig. A1). These observations show that, not only promoter divergence, but differential predicted TF binding ability is also more common among ASE genes than among non-ASE genes. This supports our conjecture that ASE arises from cis-regulatory divergence.

*Fig A1: The left plot below shows the number of cases when the promoter of one allele of an allele pair in the two parent genomes has, but the other lacks a motif. The right plot shows the same in terms of difference in binding score.*

We could find examples, such as the allele specific expression of PleosPC15_2_1031042, a Hemerythrin-like (IPR012312) protein which might be regulated by the conserved c2h2 transcription factor, containing zinc finger domain of the C2H2 type (Fig. A2). C2h2 has already been proved to be important during the initiation of primordia formation with targeted gene inactivation (Ohm et al 2011, https://pubmed.ncbi.nlm.nih.gov/21815946/). A binding site of c2h2 was detected in the upstream region of PleosPC15_2_1031042. There is a mismatch in the inferred binding motif which causes a reduced binding score in PC15 (Fig. A2/c). Indeed the PC9 nuclei contribute better to the total expression of this gene.

Despite this, and other (not shown) examples that we have found, we were not convinced about the reliability of this approach. There are many assumptions in this analysis, the positional weight matrices (PWM) that we used, are all based on indirect evidence, high number of loci these PWMs identify, uncertainty in the position of binding site from transcriptional start site, relation of difference in binding motif and expressional changes. We consider these factors to potentially contribute too much noise to the analyses for these to be robust, therefore, we are hesitant to include these results in the ms.

*Fig A2: An example for promoter divergence a) expression of c2h2 transcription factor (TF) in P. ostreatus. b) allele-specific expression pattern of PleosPC15_2_1031042 from the two parental genomes. c) inferred binding motif of c2h2 TF and a detected potential binding site in the upstream region of PleosPC15_2_1031042 gene.*

Reviewer #2 (Public Review):

The evolution of complex multicellularity represents a major developmental reprogramming, and comparing related species which differ in multicellular structures may shed light on the mechanisms involved. Here, the authors compare species of Basidiomycete fungi and focus on analyzing developmental transcriptomes to identify patterns across species. Deep RNA-Seq data is generated for two species, P. ostreatus and Pt. gracilis, sampling different developmental stages. The authors report conflicting evidence for a "developmental hourglass" using a weighted transcription index vs gene age categories. There is substantial allele-specific expression in P. ostreatus, and these genes tend to have a more recent origin, have more divergent upstream regions and coding sequences, and are enriched for developmentally regulated transcripts. Antisense transcripts have low overlap with coding regions and low conservation, and a subset show a positive or negative correlation with the overlapping gene. Comparison to a species without complex multicellular development is used to further classify the developmental program.

Overall the new transcriptional data and extensive analysis provide a thorough view of the types of transcripts that appear differentially regulated, their age, and associated gene function enrichment. The gene sets identified from this analysis as well as the potential to re-analyze this data will be useful to the community studying multicellularity in fungi. The primary insights drawn in this study relate to the dating of the developmental transcriptome, however some patterns observed with young genes and noncoding transcripts are primarily reflective of expected patterns of evolutionary time.

We appreciate the Reviewer’s nice words on our ms, we think the revised version has substantial improvements in many aspects listed above.

Reviewer #3 (Public Review):

Fungi are unique in forming complex 3D multicellular reproductive structures from 2D mycelium filaments, a property used in this paper to study the genetic changes associated with the evolution of complex 3D multicellularity. The manuscript by Merenyi et al. investigates the evolution of gene expression and genome regulation during the formation of reproductive structures (fruiting bodies) in the Agaricomycetes lineage of Fungi. Transcriptome and multicellularity evolution are very exciting fundamental questions in biology that only become accessible with recent technological developments and the appropriate analysis framework. Important perspectives include understanding how genes acquire new functions and what role plays transcriptional regulation in adaptation. The study gathers a very useful dataset to this end, and relies on generally relevant hypotheses-driven analyses.

Analysis of fruiting body transcriptome in nine species revealed that prediction from the development hourglass model (that young genes are expressed in early and late but not intermediate phases of development) verified only in a few species, including Pleurotus ostreatus. An allele-specific expression (ASE) analysis in P. ostreatus showed that young genes frequently show ASE during fruiting body development. A comparative analysis with C. neoformans, which reproduces sexually without forming fruiting body, indicates that young and old (but not intermediate) genes are likely involved specifically in fruiting body morphogenesis. A number of underlying hypothesis could be presented better, and importantly the connection between the various analyses did not appear obvious to me. Some hypotheses and reasoning therefore need clarification. Some important results from the analyses are not provided and not commented, although they are required to fully meet the manuscript's objectives.

We appreciate the Reviewer’s suggestions and have revised the ms as explained below.

1. I do not clearly see the connection between the developmental hourglass model studied in the first part of the ms, and the allele-specific expression patterns in the second half of the ms. Which "phase" of the hourglass is expected to contain true CM-related genes (by contrast to general sexual processes)? Was P. ostreatus chosen for the ASE analysis because evidence for a developmental hourglass pattern was detected in this species? The conclusion that "evolutionary age predicts, to a large extent, the behaviour of a gene in the CM transcriptome" was established thanks to ASE in P. ostreatus, which was also found to be rather an exception for conforming to the hourglass model of developmental evolution. To what extent is this conclusion transferable to other Agaricomycete/fungal species?

We chose P. ostreatus because this is the only species for which the genomes of both parental strains (PC9 and PC15) are available. Although the hourglass concept is indeed a central hypothesis in animal developmental biology (though see recent critiques some (Piasecka et al 2013), our results suggest that it simply does not generally apply to fungal development. This may be due to the unique developmental mechanisms of fungi, or the independent origin(s) of CM in fungi. Our ms might have been misleading in this respect, in the revision we clarify that the ASE and hourglass analyses are independent of each other. Our interpretation of the hourglass results is that this model is not or hardly applicable for fungal development and the fact that P. ostreatus was the only species that in fact showed an hourglass did not drive our selection of this species. We inserted a note on this in the ms.

1. The authors acknowledge that fruiting body-expressed genes may relate either to CM or to more general sexual functions, and that disentangling these functions is a major challenge in their study. An overview of which gene was assigned to which function is not explicit in the ms (proposed to be described in a separate publication). Do these functional gene classes show distinct transcriptome evolution patterns (hourglass model, ASE...)?

We made accessible the complete list of CM-related genes and genes with more general sexual functions in Table S2/b-c. Due to length restrictions, we do not discuss many or each of these genes here, but provided gene ontology-based overviews (Fig 8/c-d, from lines 631). To answer the question whether CM vs shared genes show distinct transcriptomic patterns, we analyzed ASE, NATs and the hourglass model separately for CM-specific and shared genes. as follows:

-hourglass: We calculated and visualised the TAI for CM-specific and Shared gene sets of P. ostreatus separately. The average value of TAI decreased a lot in Shared genes possibly due to the overrepresentation of ancient genes here, but the patterns remained similar to the original, which imply that not simply one or the other gene set drives these patterns (Fig A3).

*Fig A3: Transcriptome Age Index for CM-specific and Shared gene sets of P. ostreatus separately*

-ASE: As we detailed in the ms, allele specific expression occurs mainly in young genes. Indeed, only 13.1% of ASE genes belong to the conserved gene sets (CMspecific: 200 and Shared: 144). Although there are more ASE genes (>2FC) among CM-specific genes, they are still underrepresented compared to young genes that are neither shared, nor CM-specific. This indicates that ASE is generally a feature of non-conserved genes and is not particularly characteristic for either conserved or CM-specific genes.

-NAT: We found that 17.3% of CM-specific (141 genes) and 18.3% of Shared genes (165 genes) overlap with antisense transcripts. Since these numbers don't differ substantially from 17.6%, which is the proportion of NATs corresponding to all protein coding genes, it implies an independent occurrence between NATs and these gene conservation groups.

3.) As far as I understand, major functions of the fruiting body transcriptome are either CM or general sexual functions. Could these genes, notably those showing ASE, play a role in general processes other than sexual development (hyphal growth, environment sensing, cell homeostasis, pathogenicity)?

Certainly, ASE might also occur in genes related to these processes. However, the processes mentioned by the Reviewer are likely associated with very conserved genes (except pathogenicity, which we can’t examine here) and our results suggest that ASE is more typical of young genes that are under weak selection. We detected ASE in 931/343 (S2/S4 genes) genes expressed in the vegetative mycelium stage of P. ostreatus. We also note that by the definition of developmentally regulated genes, we do not expect very basic fungal processes, like hyphal growth to be among the functions of the genes we identified. Genes related to such basic (housekeeping) processes usually (exceptions exist) show flat expression profiles (because they are equally important in mycelia and all fruiting body stages) and will not be picked up by our pipelines for identifying shared developmentally regulated genes.

1. As stated by the authors, "the goal of this study was to systematically tease apart the components and driving forces of transcriptome evolution in CM fungi". What drives the interesting ASE pattern discovered however remains an open question at the end of the ms. The authors appropriately discuss that these patterns could be either adaptive or neutral but there is no direct evidence for any scenario in P. ostreatus. Is the expression of (some of) the young genes showing ASE required for CM? one or two case studies would allow providing support for such scenarios.

We respectfully disagree. We provide evidence that the driving force of ASE is promoter divergence (and consequently differential transcription factor binding) in genes in which it is tolerated (see conclusions, lines 708-712). Our results suggest that ASE is mostly a neutrally arising phenomenon. To get to the mechanistic bases of how promoter divergence can cause ASE (following the suggestion of Reviewer 1), we analysed putative, inferred transcription factor binding motifs in P. ostreatus and found that ASE genes had more divergence in putative TF binding sites. However, it is important to emphasise that all TF motifs we analyzed are inferred motifs and therefore these results are indicative at best.

Reviewer #4 (Public Review):

This work develops a comparative framework to test genes which support complex morphological structures and complex multicelluarity. This expands beyond simple gene sharing and phylogenomics by incorporating comparison of gene expression profiling of development of multicellular structures during sexual reproduction. This approach tests the hypothesis that genes underlying sexual reproductive structure formation are homologous and the molecular evolutionary processes that control transcriptome evolution which underlie complex multicellularity.

The approaches used are appropriate and employ modern comparative and transcriptome analyses to example allele specific expression, and evaluate an age of the evolutionary ages of genes. This work produced additional new RNAseq to examine developmental processes and combined it with existing published data to contrast fungi with either complex morphologies or yeast forms.

The strengths of work are well selected comparison organisms and efforts to have developmental stages which are appropriate comparisons.

We appreciate the Reviewer’s positive comments.

Weakness could be pointed to in how the NAT descriptions are interesting it isn't clear how they link directly to morphology variation or development. I am unclear if these are arising from new de novo promotors, are ferried by transposable elements, or if any other understanding of their genesis indicates they are more than very recent gains in a species for the most part and not part of any conserved developmental process (outside a few exemplars).

Originally, we assayed natural antisense transcripts (NAT) based on the assumption that they regulate developmental processes (e.g. Kim et al 2018 https://doi.org/10.1128/mBio.01292-18). Our analyses showed that although NATs are abundant in CM transcriptomes of fungi, they show no homology across species and so are unlikely to drive conserved developmental processes, which we are after in this ms. Rather, our data are compatible with most (but likely not all) NATs being transcriptional noise, arising from novel or random promoters. We therefore shortened this section and moved much of it to the Appendix 1.

The impact of this work will reside in how gene age intersects with variability and relative importance in CM. it will be interesting to see future work examine the functions of these genes and test how allele specific expression and specific alleles are contributing to the formation of these tissues and growth forms. I am still not sure if molecular mechanisms of how high variability in gene expression is still producing relatively uniform morphologies, or if it isn't quantification of morphological variation would be nice to link to whether ASE underlie that.

We agree that allele specific expression could influence morphologies significantly, but investigating that is beyond the scope of the current work (it would require a population genomics project). More direct evidence on allelic differences can be seen in monokaryon phenotypes, which only express one of the parental alleles. Phenotypic differences are obvious in the mycelium of the two parental monokaryons : the mycelium of PC9 is more fluffy and grows faster than that of PC15. This was reported recently by Lee et al 2021 (https://doi.org/10.1093/g3journal/jkaa008). We agree with the Reviewer that this is a very exciting future research direction.

To my read of the work, the authors achieved their goals and confirmed hypothesis about the age of genes and the variability of gene expression. I still feel there is some clarity lost in whether the findings across the large number of species compared here help inform predictions or classifications of types of genes which either have ASE or are implicated in CM. This is really work for the future as the authors have provided a detailed analysis and approach that can fuel further direction in this research area.

To address this issue we reworked the ms to make connections between ASE and CM clearer. Because ASE appears based on our results to (mostly) arise neutrally, predictions for other species are expected to be hard. On the other hand, we think we can make confident predictions on what types of genes are implicated in CM in other species, at least for conserved aspects of fruiting body development.

I don't know if Howard is trying to give us a nudge and wink here, but if so then the subsequent retelling that Doug was "by his own story very influenced by Vannevar Bush's As We May Think article" is certainly relevant to what Howard is suggesting, given that in Doug's own letter to Bush in 1962, he claims not to have even had Bush's ideas on his radar (no pun intended) until having rediscovered the Atlantic article after already having his own serious pursuits underway at SRI (and getting on at SRI was no small feat according to Howard's telling in Tools For Thought).

See https://web.stanford.edu/dept/HPS/sloanconference/papers/lenoir/STIMPresentations/Presentation/LetterToVBush.html

Author Response:

Reviewer #1:

This study aims to find the genetic mechanisms underlying sex-ratio distortion through male-killing in Drosophila melanogaster flies infected with the endosymbiont Wolbachia. The endosymbiont carries the prophage WO, which is in the center of interested in this study. The key result of this study is that a synonymous mutation in a prophage gene can explain the differences between sex-ratio distorting and not distorting symbionts. The study uses transgene technology to modify phage genes and to investigate which changes in the gene is involved in the phenotype. The finding, that a synonymous SNP plays a key role is not entirely novel in biology, but there are only few examples known of this type of genotype - phenotype associations. The study does not include experiments to show that the main finding is not limited to one particular background of the fly line used. An experiment including multiple genotypes would be needed to show this.

We agree that recapitulating the results in other backgrounds is intriguing and important for establishing a broader role of these findings. We thank the Reviewers and Editor for allowing us to pursue this line of investigation separately from this work, and we now discuss what experiments can be completed to answer these and other questions. We also edited the manuscript to tone down any conclusions that would imply generalizability of the findings at this point. For example:

"For example, we cannot conclude that the particular codon tested here is responsible for phenotype alterations in other host genetic backgrounds or species. It is possible that this codon plays a functional role only in a singular host genetic context. Here, we changed wmk sequences while holding the host genetic background fixed, but the reverse is required to conclude whether or not the particular codon plays a general role in other genotypes or natural contexts. Second, due to possible coevolution, various codons may or may not yield similar functional effects across different host backgrounds, and additional synonymous sites may contribute to the male-killing phenotype. Thus, the results here illuminate a previously unrecognized need for future research on the functional impacts of synonymous substitutions in endosymbionts. Future work may focus on determining if there is one specific synonymous codon that affects the male-killing function in all cases, if a more general feature exists where alteration of any or a subset of N-terminal or other wmk codons affects function, or if the effect of synonymous changes is specific to this background.”

Text summarizing the 06/21/2021 query to the Editor and Reviewers for further clarification: We believe there are several reasons why the results can stand on their own, while appropriately acknowledging caveats. First, we note the lack of genetic background testing on previous transgene experiments driving the major discoveries of Wolbachia genes involved in reproductive parasitism. This requirement would therefore hold the current work to a novel bar not previously applied by the field. In addition, the genetic background here is the same as used in previous work on these phenotypes, making it the most pertinent to test and inform previous and ongoing studies by many research groups. Second, the results shown here would still stand no matter the results of genetic background testing and would demonstrate that it is possible for synonymous changes to have functional relevance in the transgenic wmk phenotype. The major findings are still novel in the field, relevant to ongoing studies of reproductive parasitism, and informative regarding one of the most common genetic backgrounds. Finally, we note that two different lines with unique synonymous codon changes (the final experiment) independently created the same result that a synonymous codon change ablates phenotype, providing additional robustness to our findings. Doing additional experiments would be logistically difficult. Barriers include the relocation of the first author of the work to another lab for a postdoctoral position, completion of the funding for the project, remaining institutional COVID-19 restrictions, and lack of replacement personnel in the lab to continue the work. Notably, there is also the non-trivial requirement to create and test new transgene lines that would be costly and take nearly a year to complete (the experiments in the manuscript already took several years and the new fly lines would cost thousands to make).

The study is mostly clear and easy to follow, but requires a lot of attention. The authors choose to build up the story as I guess it was carried out in the lab. Thus, the reader is guided through every step of the process. While I see that this is appealing from the way the study was carried out, it results in a very long manuscript with a lot of material that would be much better placed in a supplement.

We thank the reviewer for pointing this out. We shortened the manuscript by removing redundant information and transferring some parts of the results to the supplement. We also removed about three pages of text from the discussion (before adding in new sections as requested by reviewers).

The introduction seems unfocused. It meanders around, jumping from topic to topic and does not give the reader a sense of where things will go.

We added a few topics into the Introduction as recommended in other comments, and we edited various portions of the Introduction to connect the ideas together more clearly. We hope the changes are now satisfactory, and we are of course happy to consider further feedback.

Fig. 1 gives an overview about the different aspects addressed here, but it is not used to guide the reader through the different lines of thought addressed in the introduction. If Fig. 1 will stay (I actually think it is not needed) it should be introduced earlier and used as a road map for the paper. Alternatively, the introduction could stay more general and only in the last paragraph the different ways the system is studied will be summarized.

We edited the final paragraph of the Introduction to more comprehensively cover the content of the figure and full direction of the paper. For readers not familiar with the biological system or questions, we believe this figure will serve as a gateway to the genetic alterations conducted in the experiments.

Along these lines, it would be good to have a better reasoning for the combination of experiments conducted. It is left to the reader to understand why certain types of experiments have been done.

It was not clear to us at the outset of these experiments what results would ultimately emerge and what follow-up experiments would be necessary as our initial hypotheses were proven wrong with many of the surprises from the work. So, there was no a priori reasoning for why experiments were done until we had the results of the previous experiments. We agree that this makes the reading a bit confusing. As such, we clarified the logic flow in the results section as the narrative progresses from experiment to experiment, and we reorganized some of the introduction to improve transition statements and offer a roadmap to readers earlier on.

On the other hand, the introduction misses a section on the biology of the phage and its interaction with the host(s). It is hard to understand the biology of the system without getting an understanding of the insect - Wolbachia - phage interactions. For non-specialist, understanding the role of the three players is essential for the system.

Thank you for the suggestion. We now add a section introducing phage WO and its relevance to the phenotypes tested here.

“The wmk gene and two cytoplasmic incompatibility factor (cif) genes that underlie cytoplasmic incompatibility (a parasitism phenotype whereby offspring die in crosses between infected males and uninfected females) occur in the eukaryotic association module (EAM) of prophage WO, which refers to the phage WO genome that is inserted into the bacterial chromosome. The EAM is common in WO phages across several Wolbachia strains and is rich in genes that are homologous to eukaryotic genes or annotated with eukaryotic functions. As such, the expression of reproductive parasitism genes from the EAM and tripartite interactions between phage WO, Wolbachia, and eukaryotic hosts are central to Wolbachia’s ability to interact with and modify host reproduction.”

The result section could be easily shortened by focusing on the essential experiments. Experiments that do not contribute to the final result can go into the supplement.

We removed redundant sentences and made some figures supplemental.

Also the discussion is much too long. I suggest to reduce it to half and focus on the important points and the take-home messages. Currently the discussion follows the way the results are presented in the result section. However, this is not needed. The important finding should be discussed first. Findings that are important in the development of the project, may not be important for the biology of the system overall. And they may not be important for the reader.

We reordered the discussion to cover the biggest findings first, and removed about a third of the original writing in the discussion.

Reviewer #2:

This study aims to unravel the genomic basis to wmk-induced male killing by transgenically expressing homologs of varying relatedness, with synonymous nucleotide changes, and predicted alternative start codons in D. melanogaster flies. The study builds on previous work showing that expression of wmk in fly embryos recapitulates several aspects of male killing. While more distantly related homologs did not induce male killing when expressed in D. melanogaster, more closely related wmk homologs induce either killing of both sexes or male killing only. However, the male-killing phenotype was not due to amino acid differences, but associated with RNA structural differences of the different wmk homologs. In addition, only one synonymous nucleotide change was sufficient to ablate the killing phenotype. These findings suggests that minor and even silent nucleotide differences impact on the expression of male killing in D. melanogaster. It is concluded that a new model incorporating the impacts of RNA structure and post-transcriptional processes in wmk-induced male killing needs to be developed.

The strength of the study lies in the systematic and carefully controlled approach to quantify the phenotypic effects of both sequence and structural changes to various wmk homologs for inducing the male-killing phenotype. Detailed dissection of the phenotypic impact of minor changes to the wmk homologs including sequence variation, silent nucleotide changes, and RNA structural differences was quantified. This approach reveals a complex genotype-phenotype relationship, but highlights the importance of including post-translational processes. The data is novel in that previous work have largely ignored structural changes and assumed that synonymous differences in codons has no effect on protein function, whereas the current study based on updated codon optimization algorithms reveal that this assumption is incorrect. The finding highlights the importance of considering also structural genetic variation for phenotypic expression differences. This suggestion is further corroborated by the lack of difference in wmk homologue expression levels, indicating that the functional differences are due to post-translational effects.

We thank the reviewer for the thoughtful comments.

There are limitations to the findings of this complex genotype-phenotype relationship. The current study only examined the phenotypic impact by expressing the different homologs in one D. melanogaster genetic background. Given the variability of the phenotypic pattern revealed based on minor changes to the wmk homologs, it will be critical to repeat some of the main findings in other D. melanogaster genotypes to determine the importance of the variation in the wmk homologs more generally. It is entirely plausible that the observed changes in the effect and strength of killing is due to an interaction between host and wmk genotype. This has implications for unravelling the underlying genetic basis to the male-killing phenotype more widely. It is as yet to be demonstrated whether wmk is involved in male killing in natural population, and to what extent there are shared patterns and mechanisms of male killing induced by other bacterial endosymbionts such as Spiroplasma.

We addressed this point in more detail above in the first response to the comments from Reviewer 1.

Author Response:

Reviewer #3 (Public Review):

INaR is related to an alternative inactivation mode of voltage activated sodium channels. It was suggested that an intracellular charged particle blocks the sodium channel alpha subunit from the intracellular space in addition to the canonical fast inactivation pathway. Putative particles revealed were sodium channel beta4 subunit and Fibroblast growth factor 14. However, abolishing the expression of neither protein does eliminate INaR. Therefore as recently suggested by several authors it is conceivable that INaR is not mediated by a particle driven mechanism at all. Instead, these and other proteins might bind to the pore forming alpha subunit and endow it with an alternative inactivation pathway as envisioned in this paper by the authors.

The main experimental findings were (1) The amplitude of INaR is independent of the voltage of the preceding step. (2) The peak amplitudes of INaR are dependent on the time of the depolarizing step but independent of the sodium driving force. (3) INaT and INaR are differential sensitive to recovery from inactivation. According to their experimental data the authors put forward a kinetic scheme that was fitted to their voltage-clamp patch-clamp recordings of freshly isolated Purkinje cells. The kinetic model proposed here has one open state and three inactivated states, two states related to fast inactivation (IF1, IF2) and one state related to a slower process (IS). Notably IS and IF are not linked directly in the kinetic scheme.

In my humble opinion, the proposed kinetic model fails to explain important experimental aspects and falls short to be related to the molecular machinery of sodium channels as outlined below. Still it is due time to advance the concepts of INaR. The new experimental findings of the authors are important in this respect and some ideas of the new model might be integrated in future kinetics schemes. In addition, the framework of INaR is not easy to get hold on with lots of experimental findings in the literature. Likely, my review falls also short in some aspects. Discussion is much needed and appreciated.

INaT & INaR decay The authors stated that decay speed of INaT and INaR is different and hence different mechanisms are involved. However at a given voltage (-45 mV) they have nicely illustrated (Fig. 2D and in the simulation Fig. 3H) that this is not the case. This statement is also not compatible with the used Markov model. That is because (at a given voltage) the decay of both current identities proceed from the same open state. Apparent inactivation time constants might be different, though, due to the transition to the on state.

We apologize that the language used was confusing. Our suggestion that there is more than one pathway for inactivation (from an open/conducting state) is the observation that the decay of INaT being biexponential at steady-state voltages. In the revised manuscript, we point out (lines 546-549) that, at some voltages, the slower of the two decay time constants (of INaT) is identical to the time constant of INaR decay. We also discuss how this observation was previously (Raman and Bean, 2001) interpreted.

Accumulation in the IS state after INaT inactivation in IF1 and IF2 has to proceed through closed states. How is this compatible with current NaV models? The authors have addressed this issue in the discussion. The arguments they have brought forward are not convincing for me since toxins and mutations are grossly impairing channel function.

Thank you for this comment. We would like to point out that, in our Markov model, Nav channels may accumulate in IS through either the closed state or open state. This requires, of course, that Nav channels can recover from inactivation prior to deactivation. While we agree that toxins and mutations can grossly impair channel function, we think these studies remain crucial in revealing the potential gating mechanisms of Nav channel pore-forming subunits, and how these mechanisms may vary across cell types that express different combinations of accessory proteins.

Fast inactivation - parallel inactivation pathways Related to the comment above the motivation to introduce a second fast-inactivated state IF2 is not clear. Using three states for inactivation would imply three inactivation time constants (O->IF1, IF1->IF2, O->IS) which are indeed partially visible in the simulation (Fig. 3). However, experimental data of INaT inactivation seldom require more than one time constant for fast inactivation. Importantly the authors do not provide data on INaT inactivation of the model in Fig. 3. Fast Inactivation is mapped to the binding of the IFM particle. In this model at slightly negative potential IF1 and IF2 reverse from absorbing states to dissipating states. How is this compatible with the IFM mechanism? Additionally, the statements in the discussion are not helpful, either a second time constants is required for IF (two distinct states, with two time constants) or not.

We thank this Reviewer for this comment. We tried to developed the model based on previous data on Nav channel inactivation. Indeed, much experimental data exists for the fast inactivation pathway (O -> IF1). As we noted in the discussion, without the inclusion of the IF2 state, we were unable to fully reproduce our experimental data, which led us to add the IF2 state. As with all model development, we balanced the need to faithfully reproduce the experimental data with efforts to limit the complexity of the model structure. In addition, as noted in the Methods section, our routine is an automatic parameter optimization routine that seeks to minimize the error between simulation and experiments. We can never be sure that we have found an absolute minimum, or that the optimization got stuck at a local minimum when simulating without inclusion of IF2. In other words, there may be a parameter set that sufficiently fits the data without inclusion of IF2, but we were unable to find it. As a safeguard against local minima, we used multistarts of the optimization routine with different initial parameter sets. In each case, we were unable to find a sufficiently acceptable parameter set.

We agree with this Reviewer that at slightly negative potentials (compared to strong depolarizations), channels exit the IF1 state at different rates, although we would point out that channels dissipate from the IF1 state (accumulating into IS1) under both conditions (see Figure 8B-C). This requires the binding and unbinding of the IFM motif to occur with some voltagesensitivity. We believe this to be a possibility in light of evidence that suggests IFM binding (and fast-inactivation) is an allosteric effect (Yan et al., 2017) and evidence showing that mutations in the pore-lining S6 segments can give rise to shifts of the voltage-dependence of fast inactivation without correlated shifts in the voltage-dependence of activation (Cervenka et al., 2018). However, it remains unclear how voltage-sensing in the Nav channel interact with fast- and slow-inactivation processes.

Due to space constraints in Figure 3, we did not show a plot of INaT voltage dependence. However, below, please find the experimental data (points), and simulated (line) INaT in our model.

Differential recovery of INaT & INaR Different kinetics for INaR and INaR are a very interesting finding. In my opinion, this data is not compatible with the proposed Markov model (and the authors do not provide data on the simulation). If INaT1 and INaT2 (Fig. 5 A) have the same amplitude the occupancy of the open state must be the same. I think there is no way to proceed differentially to the open state of INaR in subsequent steps unless e.g. slow inactivated states are introduced.

Thank you for bringing up this important point. The differential recovery of INaT and INaR indicates there are distinct Nav channel populations underlying the Nav currents in Purkinje neurons. We make this point on lines 632-635 of the revised manuscript. Because our Markov model is used to simulate a single channel population, we do not expect the model to reproduce the results shown in Figure 5. We have now added this point to the Discussion section on lines 637-640.

Kinetic scheme Comparison with the Raman-Bean model is a bit unfair unless the parameters are fitted to the same dataset used in this study. However, the authors have an important point in stating that this model could not reproduce all aspects of INaR. A more detailed discussion (and maybe analysis) of the states required for the models would be ideal including recent literature (e.g., J Physiol. 2020 Jan;598(2):381-40). Could the Raman-Bean model perform better if an additional inactivated state is introduced? Are alternative connections possible in the proposed model? How ambiguous is the model? Is given my statements above a second open state required? Finally, a better link of the introduced states to NaV structure-function relationship would be beneficial.

These are all excellent points. We absolutely agree; it was/is not our intention to “prove” that the Raman-Bean model does not fit our dataset (as you mention, with proper refinement of the parameters, some of the data may be well fit). In fact, qualitatively we found the Raman-Bean model quite consistent with our dataset (which is an excellent validation of both the model, and our data). It was our intention to show (in Figure 7) that there is good agreement between the Raman-Bean model and our experimental data for steady state inactivation (C), availability (D), and recovery from inactivation (E). While we find the magnitude of the resurgent current (F) to be markedly different than the Raman-Bean data, we now note this to likely be due to the large differences in the extracellular Na+ concentrations used in voltage-clamp experiments (lines 440-444). Our models, however, specifically differ in our parallel fast and slow inactivation pathways (Figure 7H). As seen in the Raman-Bean model, in response to a prolonged depolarizing holding potential, there is negligible inactivation, as the OB state remains absorbent until the channel is repolarized. This is primarily because the channel must transit through the Open state on repolarization. We find distinctly different behavior in our data. As seen in the experimental data shown in 7H, despite a prolonged depolarization, Nav channels begin to inactivate and accumulate in the slow inactivated state without prerequisite channel opening. This behavior is impossible to fit in the Raman-Bean model, given the topological constraint of the model requiring a single pathway through the open state from the OB state.

To that point, it is also unlikely that the addition of inactivated states to the Raman-Bean model would help fit this new dataset. Indeed, the Raman-Bean model contains 7 inactivated states. If there were a connection between OB ->I6, it is possible that direct inactivation (bypassing the O state) may help. Again, however, it is not our intention to discredit the Raman-Bean model, nor is it our intention to improve the Raman-Bean model. With new datasets, a fresh look at model topology was undertaken, which is how we developed our proposed model.

This Reviewer astutely points out a known limitation of Markov (state-chain) modeling; it is impossible to tell uniqueness, or ambiguity of the model (both with parameters as well as model topology). Following the results of Menon et al. 2009 (PNAS vol. 106 / #39 / 16829 – 16834), in which they used a state mutating genetic algorithm to vary topologies of a Markov model, our group (Mangold et al. 2021, PLoS Comp Bio) recently published an algorithm to distinctly enumerate all possible model structures using rooted graph theory (e.g. all possible combinations of models, rooted around a single open state). What we found (which is not entirely surprising) is that there are many model structures and parameter sets that adequately fit certain datasets (e.g., cardiac Nav channels).

Therefore, the goal is never to find the model (indeed we don’t propose that we have done so), but rather to find a model with acceptable fits to the data and then use that model to hypothesize why that model structure works, as well as to hypothesize higher dimensional dynamics. We make these points in the revised manuscript (lines 591-597).

We did not specifically explore the impact of a second open state in our modeling and simulation studies, but we would certainly agree that a model with a second open state may recapitulate the dataset.

Author Response:

Reviewer #2:

The manuscript by Podinovskaia focuses on a new method to visualize and measure endosome maturation in common cell lines by enlarging early endosomes. This was achieved by producing acute insult to the cells by ionophore treatment, leading to budding of abnormally large post Golgi vesicles that fuse with early endosomes. Endosome maturation of these enlarged endosomes containing Golgi-derived cargo (GalT) proceeding with apparently normal kinetics, ultimately leading to lysosomal delivery. Taking advantage of this assay, the authors investigate Rab5-to-Rab7 conversion, acquisition and loss of PI3P, acquisition and loss of Snx1 on apparent endosomal subdomains, interaction of early and late endosomes with Rab11-positive recycling endosomes, and lumenal pH changes. The new maturation model presented here will likely be quite useful to the field with continuing impact. The current state of the endosome field in many ways remains fragmentary, with various processes studied extensively in isolation, but with little information on their relative timing and potential interactions as endosomes mature. This new assay should help understand the relationships between these processes, some of which are investigated in this manuscript.

Concerns:

1) The data and conclusions related to Rab11 interaction with early endosomes in Fig 8 are not convincing. There are simply too many Rab11 endosomes in the cell to know if their short term proximity indicates meaningful interaction with the early endosomes, or if the data simply reflects random collisions of small recycling endosomes with the enlarged early endosomes. No data is presented to show that the interactions are meaningful, e.g. that recycling cargo transfer occurs during these interactions. Conclusions from this analysis are overstated.

We now provide more evidence for the interaction of Rab11 vesicles with the enlarged endosomes. We made movies with shorter intervals (2 sec instead of 1 min) between the individual frames. These data clearly show that this is not an accidental bumping into an endosome but rather that Rab11 vesicles can circle around endosomes and stay for several minutes (Video Fig. 8A, supplement 2 and 3).

In addition, we imaged TfR-GFP together with mApple-Rab5. These data show that TfR-GFP positive vesicles bud off from mApple-Rab5 positive endosomes and that the GFP fluorescence intensity goes down over time in enlarged endosomes. These data are consistent with recycling of TfR to the plasma membrane. Moreover, CDMPR-GFP, which cycles between the TGN and endosomes was found to be present on Rab5 negative enlarged structure, which then turned Rab5 positive, and subsequently lost the CDMPR signal. Importantly those endosomes could regain CDMPR, which we interpret as acquisition from the TGN. These data may indicate that the TGN-endosome shuttle is intact after nigericin washout (Fig. 9).

That the TfR and CDMPR are really transported out of the enlarged endosome is also contrasted by our finding that GalT-GFP stayed in the enlarged endosome and the signal intensity did not significantly drop.

2) Lack of information on endocytic cargo acquisition by the enlarged early endosomes: to really establish this endosome maturation model the authors would need to establish if the enlarged endosomes contain endocytosed cargo, as opposed to Golgi-derived cargo, and determine how long it takes to acquire such cargo. This could be accomplished using Tf, EGF, or perhaps dextran at early timepoints after nigericin washout.

As described above, we now show that TfR-GFP is present in enlarged endosomes and is lost from these endosomes over time (Fig. 9A,D,G).

Additionally, we performed experiments with dextran-Alexa647 and nanobody-tagged surface TfR to show that endocytosed material from the plasma membrane indeed reached the enlarged endosomes (Fig. 3, figure supplement 1 and 2). Quantification of TfR signal at the enlarged endosomes demonstrates that TfR acquisition by the enlarged endosome takes place as soon as the enlarged compartment becomes Rab5-positive. This was also observed with the nanobody-tagged surface TfR and endocytosed Dextran-AF647, representative examples of which are provided (Fig 3, figure supplement 1 and 2). The quantification for the latter experiments was not carried out due to the very short time range during which asynchronous Rab5 recruitment events needed to be captured after addition of nanobody/Dextran pulse-and-chase.

3) Figure 7 - It was not convincing that data in panels F and G are different from each other.

We agree with the reviewer that the difference between the data presented in panel F and G is not very big. These panels represent the average of many endosomes and with the averaging the differences from the individual traces get cancelled out. The process is asynchronous and thus in this case the individual traces are more telling than the averaged traces. Nevertheless, we decided to keep the average traces in the manuscript because the highlight the asynchronous nature of the process. We modified the text to make this point clear.

4) Figure 11 - it is unclear how we can interpret this as connected to Rab conversion when even the labeled compartments at the earliest time point in the czz1 knockout have abnormally high pH, and during the time-course even the last timepoint for czz1 KO is higher than that of the earliest timepoint for WT.

We agree that the ccz1 KO cells display higher endosomal pH than WT cells throughout the time-course.

However, the cells in which we express the rescue plasmid of Ccz1 also have apparently less acidified endosomes, even though Ccz1 can still drive Rab conversion, and the pH dropped at an intermediate rate, when comparing rescued cells to control and ccz1 KO cells. Even in ccz1 KO cells endosomal traffic down the degradation pathway is not completely blocked, similarly to what we observed for sand-1 (-/-) in C. elegans and Mon1a/b knockdown in mammalian cells (Poteryaev et al. 2010). Acidification eventually will occur, but it is massively slowed down; the molecular basis of which is still under investigation in our lab.

We think that in the absence of Ccz1, a condition under which Rab conversion is severely impaired, acidification cannot occur at normal rate. As pointed out by reviewers 1 and 2, the pH is already higher in the ccz1 KO cells than in the control condition. However, in the rescue condition, the YFP/CFP ratio is not that different from the knockout and yet acidification can occur at an intermediate rate. Why under rescue conditions, the YFP/CFP ratio is at a similar level compared to the KO is not entirely clear. It is conceivable that too much Ccz1 has also a negative effect. Moreover, recently it has been shown that ccz1 KO cells accumulate free cholesterol in the enlarged endosomes (Van den Boomen Nat Comm., 2020). The transient expression might be not sufficient to rescue this accumulation phenotype or other secondary effects. Nevertheless, the v-ATPase appears to maintain its function because lysosomes can acidify in ccz1 KO cells, albeit with a delay (Figure 13).

5) Figure 12 - The criteria used to determine which GalT structures are Golgi or lysosomes seems questionable. Morphology alone is not sufficient to identify the compartments with high accuracy, especially after perturbation. Also, it is unclear to what extent GalT-CFP labels lysosomes without nigericin treatment.

To address these issues, we co-labelled cells with lysotracker. GalT-CFP (pHlemon) and lysotracker showed a very high degree of co-localization. These data are included in the manuscript (Fig. 10B).

Author Response:

Reviewer #1:

In this manuscript, the authors make use of next-generation sequencing to provide a preliminary inventory of tribe Metriorrhynchini, a hyperdiverse group of beetles with intricate systematics mainly due to likely morphological convergence of their Millerian rings. The authors provide an admirable sampling within Africa, Asia and Oceania, with about 700 successfully sampled localities and thousands of specimens.

The main result of the manuscript is the curated database of Metriorrhynchini that will be useful in future research. In addition, different statistical methods are used to provide an idea of the undescribed species within the tribe, the astonishing species richness in New Guinea or the use of phylogenomic data to explore major phylogenetic relationships. However, some of the author's claims should be questioned:

• Surprisingly, the authors rely on a very low threshold to identify mOTUs (2% in the manuscript). The authors refer to Hebert et al. (2003) and Eberle et al. (2020) to justify the threshold, but still, they are likely overestimating the number of mOTUs and thus, considering putative species what it may be different populations. Figure S17 provide estimates of mOTUs with different thresholds (1 to 10%), which rapidly decrease their estimates (a decrease of 25% mOTUs is found when 6% was considered). Still, an overwhelming sampling effort but a more realistic estimate.

• I think the phylogenomic tree did not receive the required attention (for example, the FcLM analysis is barely mentioned).

• It is not clear why should be important to mention the "person-months of focused field research" across the manuscript. Each study group has a unique sampling technique (also not found in the manuscript), preferred localities or traits, which make comparisons impossible. The authors' effort is remarkable, but it is not an important result/finding to be highlighted all over the manuscript.

Many thanks for all comments and suggestions that pointed to the weak parts of our argumentation. We modified the manuscript accordingly and added some references that can be used for the justification of some claims.

We addressed the question of thresholds for species number estimations. Now, two thresholds are considered in the manuscript as relevant for discussion: 2% and 5%. We added further information on our previous studies dealing with integrative species delimitation in Metriorrhynchini. Some of them were not referred in the earlier version (to avoid self-citations) and we also expanded information on the evidence given in the study which we have already referenced (Bocek et al. 2019). The earlier comparison of nextRAd, mtDNA and moprhology-based delimitation of species in Eniclases (the trichaline clade in the present study) showed that many well defined species (nextRAD and morphology) have highly similar mtDNA and they split only recently, eventually some introgresion or incomplete lineage sorting affect mtDNA signal. If we apply 5% threshold for this group, we would delimit as a single species two entities which differ in the body size, coloration and the relative size of male eyes (diurnal and nocturnal activity in putative sister species). In such a way, we would decrease the number of species in our analyzed sample of Eniclases by 40% in clear contrast with the number based on morphology and nextRADs. We found similar rapid morphological diversification also in other metriorrhynchines (Jiruskova et al., 2019,; Kalousova & Bocak, 2017) and other not referenced taxonomic studies that have shown that closely related species have well diversified male genitalia and often belong to different mimetic rings). To limit our discussion, we do not reference our earlier nextRAD study showing the speciation in other subfamily of net-winged beetles within a single mountain range (Bray & Bocak 2016). Also this study supports morphological differentiation in species with highly similar mtDNA. Now, we noted in the manuscript that before taxonomic revisions are produced, our claim is provisional and therefore we modified the text as proposed and present the lower numbers of species as a realistic possibility.

Phylogenetic relationships: We added additional information on the congruence with earlier studies to Results and Discussion, but we still do not describe details. The main reason is that morphology must be studied to delimit and formally name new taxa and that the morphology is out of scope of this work (except some information provided in Supplementary Text – description of delimited generic groups and subtribes). The FcLM analysis addressed only the relative position of the leptotrichaline and procautirine clade. Both clades are monophyletic, morphologically distinct and no conclusion is based on their relative position. We noted that without further data we are unable to robustly solve their positions. Provisionally, we prefer the deeper postion of the leptotrichalines (61%, a not very convincing phylogenenomic signal).

Quantification of sampling effort: As proposed, we excluded the consideration of person months as a measure of relative collecting effort in various regions and add justification for field research methods.

Reviewer #2:

Conservation efforts must be evidence-based, so rapid and economically feasible methods should be used to quantify diversity and distribution patterns. The principal objective of this study is to demonstrate how biodiversity information for a hyperdiverse tropical group can be rapidly expanded via targeted field research and large-scale sequencing. The authors have attempted to overcome current impediments to the gathering of biodiversity data by using integrative phylogenomic and three mtDNA fragment analyses. As a model, they sequenced the Metriorrhynchini beetle fauna, sampled from ~700 localities in three continents. The species-rich dataset included ~6,500 terminals, >2,300 putative species, more than a half of them unknown to science. It is an amazing finding. Their information and phylogenetic hypotheses can be a resource for higher-level phylogenetics, population genetics, phylogeographic studies, and biodiversity estimation. At the same time, they want to show how limited the taxonomical knowledge is and how this lack is hindering biodiversity research and management.

Thanks for your comments on our study. We agree with your specific recommendations and modify the manuscript accordingly.

It seems like when we go online we are hardly actors anymore in the information we "seek". In many ways, it is already paved out for us, as described here. We are studied, and our lives are made easier because of it. I never get completely irrelevant advertisements anymore when on social media, and specifically on YouTube, where I remember a time only some companies were learning to tap into the monetization of producers' content and the same, few ads would always play. It seems that technology knows me better now than I know myself, and even though it makes me frightened, I haven't changed my interaction and consumption of technology in the slightest, and I don't think many other people have either, which is something I would be curious to know more about.

Minor: I think it's good to discuss the issues you're interested in from your area of expertise with neuroscience. You even state outright that you'll be framing things from that perspective. However, the both the intro and that outright statement suggest the paper is written for a more general audience. That conflicts somewhat with the framing here, which seems to be geared towards neuroscientists familiar with the features of neurodata. Not a major concern though, this is just a question of whether you want to maintain a consistent audience throughout the paper. Arguably reconciled by substituting "We" with "neuroscientists". Same comment applies to other areas where this may pop up

I think this bring up point about priorities, control, and what we see as valuable. In order to give children the time and listening they deserve, it is at the expense of other things - other children who at the time may be doing something that we want to observe, other tasks that the teacher may have planned, other things the adult wants to get on to for themselves, etc.

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

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)): **Summary:** The manuscript submitted by Djekidel et al entitled: "CovidExpress: an interactive portal for intuitive investigation on SARS-CoV-2 related transcriptomes" reports on a new web portal to search and analyze RNAseq data related to SARS-CoV-2 infections. The authors downloaded and reprocessed data of more than 40 different studies, which is available on the web portal along with all available meta data. The web portal allows to perform numerous differential expression and gene set enrichment analyses on the data and provides publication ready figures. Because of batch effects that could not be removed, the authors do not recommend to analyze data across studies at this point. The authors conclude that the web portal is unique and will allow scientists to rapidly analyze gene expression signatures related to SARS-CoV-2 infections with the potential to make new discoveries. **Major comments:** Based on the scientific literature, the web portal seems to be an unprecedented resource to search and analyze SARS-CoV-2-related RNAseq data and as such would certainly be a useful resource for the SARS-CoV-2 scientific community. The authors argue that new discoveries are possible by using their web portal in providing use cases. However, the section detailing the analyses the authors did to generate new hypotheses about genes potentially relevant in SARS-CoV-2 infections are very difficult to follow and without more guidance very difficult to reproduce with the web portal. It would require substantial expert knowledge in RNAseq data analysis without more information being provided. It also seems that key candidate genes identified by their analyses have all been studied or identified to be related to SARS-CoV-2 infections, so it is somewhat unclear whether new hypotheses can be generated by the reanalysis of RNAseq datasets, especially because combining the data from different studies is currently not recommended by the authors. The manuscript would benefit from providing fewer use cases but for each of them providing more information on how the portal and which studies were used to generate them and which findings were not described in the publication of the used studies. Some observations in the manuscript are not substantiated with significance calculations (see below). At times, the English writing (grammar) should be improved.

We thank the reviewer for the positive comments. We suppose the reviewer conclude it need substantial expert knowledge in RNAseq data analysis were due to lacking Video Tutorial. We have now put up several Video Tutorials and more tutorials would be added along later along with users’ feedbacks. We believed this would help ease reviewers’ concern.

In response to whether new hypothesis can be generated. Sorry if it’s not clear, for all the case studies and our “CovidExpress Reveals Insights and Potential Discoveries”, our portal has provided information not reported by their original publications, as listed below:

1. Case study #1: The original publication employed a multiomics approach to find the predictor genes between ICU and non-ICU patient. But it’s not obviously to know which genes were mainly due to expression level, which might be due to other data they included (e.g. mass spectrometry data). Our portal allow user to quickly check their expression level and find SESN2 does not have strong expression differences.
2. Case study #2: We replace this case study with bacterial-susceptibility genes to show such questions could be quickly asked and answered using our portal. Such investigation has not been reported before.
3. FURIN’s function have been well related to SARS-CoV-2. However, for all reports we could find, they focused on Furin cleavage sites of SARS-CoV-2 or whether FURIN were expressed in the SARS-CoV-2 sensitive tissues. SARS-CoV-2 infection could up-regulate FURIN expression have never been reported before. The study published the data didn’t mentioned FURIN at all. We have made this discovery simply by using CovidExpress portal to find the differential expressed genes and overlap with the literature-based gene list (Supplementary Table S2), we believe more discoveries could be made by users by selecting different data.
4. If we search OASL AND " SARS-CoV-2" on pubmed, only 5 results shown up indicated it’s under-studied. And none of them indicated OASL could be up-regulated both by SARS-CoV-2 infected lung and Rhinovirus-infected nasal in human. It is not clear to us if we might misunderstand reviewers’ suggestion as “fewer use cases”. Thus, we haven’t removed any use cases, instead we provided more details to help users understand what and how did we made those discoveries not reported by their original studies using CovidExpress.

At last, we have gone through substantial scientific editing to improve the grammar. **Minor comments:** Page 6 last sentence: The statement of this sentence is very much what one would expect. It remains unclear whether the authors mean this as a result to validate the processing of the RNAseq data or as a new discovery. Please, clarify.

We apologize for the confusion. We intended this statement to be a result confirming what we had expected. We have now amended the text to make this point clearer.

Figure 3A: The violin plots are so tiny that it is impossible to see any trends. It is also difficult to understand which categories one should compare with each other. If there is anything significant to observe, please, add a statistical test and better guide the reader.

We agree with the reviewer; therefore, we have removed this figure from the paper. The goal of this figure was to demonstrate how to use violin plots for exploratory analysis; however, in this case, the violin plot did not show a clear trend. By using more filtering and other plots (e.g., Figure 3B-C), we believe we now provide better insight.

Figure 3C: A legend for the color scale is missing. The signal (I guess expression amounts) for SESN2 seems very weak and the same between ICU and non-ICU samples. What is the significance for assigning this gene to the group of genes being upregulated in ICU samples? Also contrary to what the authors state on page 8, SESN2 does not seem to be highly expressed in ICU samples, however, without knowing what the colors represent (fold changes or absolute expression values?) this is somewhat speculative.

We thank the reviewer for bringing this to our attention. We have now added a legend for the color scale in the revised figure. In Figures 3A-C, we are showcasing how an exploratory analysis can be performed using CovidExpress. As an example, we investigated the expression of the top 20 genes identified by the random forest classifier of Overmyer et al., 2021, as predictors of ICU and non-ICU cases. In the original Overmyer et al. paper, only the general performance metrics of the models are presented (Fig. 6c-g), but the authors do not show the expression patterns of the top predictors. Hence, we demonstrate how CovidExpress can be used to further investigate some questions not explored in the original paper. SESN2 was listed as a top predictor; however, its expression did not vary between ICU and non-ICU samples, as was also observed by the reviewer. We suspect SESN2 was a top predictor due to other data the Overmyer et al. paper included, such as mass spectrometry data. Our statement about SESN2 was not accurately reflected in the figure; therefore, we have rewritten this section to make it clearer.

Page 9 first sentence: Please, specify what you mean by "starting list". Furthermore, in this paragraph, how do your results compare to the results from the study that you re-analyze here?

We thank the reviewer for the question. By “starting list,” we meant the top genes from the Overmyer et al., 2021, article as predictors of ICU and non-ICU cases. We have now rewritten this section to make it clearer. We did not expect our results to differ from their data. Our goal was to ask which of their top predictors (by multi-omics data) show a difference in gene expression. When we downloaded their TPM values from their GEO records, the values were very similar overall (see below).

Figure 3F: Please add labels to your axes and is there a particular reason why in a correlation plot like this one, the y and x axis are not shown with the same range and why does the y axis not start at 0?

We thank the reviewer for this helpful comment. Our reasoning for presenting the figure in this way is that different genes can have very different expression levels but still be correlated. For example, if gene A expressed 1, 5, and 10 in samples 1,2, and 3, while gene B expressed 100, 500, and 1000 for samples 1, 2, and 3, then their range would be very different but still perfectly correlated (see panel A below). If we draw the x- and y-axes using the same range, this correlation will not be visually obvious (see panel B below).

This comparison is different from the correlation plots that compare the expression of one gene in different samples. We apologize for the confusion and to avoid misleading readers, we have enlarged the gene names in the Figure labels to ensure that readers notice their differences. We have also added an option to the correlation plot on our portal so that users can choose the optimal format (see below).

Page 9 second last sentence: It remains unclear which kind of analysis the authors intend to do here and what the starting question is. Please, try to rewrite with less technical terms (i.e. what do you mean by "precalculated contrasts"). In line with this, it remains unclear what Figure 3I is supposed to show. Please, provide some more information to readers who are not RNAseq analysis experts.

We thank the reviewer for this suggestion. To avoid any misleading claims, we followed Reviewer #2’s suggestion and replaced the coagulation gene list with a filtered gene list from the “Coronavirus disease - COVID-19” KEGG pathway (hsa05171) to showcase how to identify experiments in which this gene signature is enriched or depleted. We also replaced the related figures and text with new results and rewrote this section to avoid using technical terms.

Figure 3J is somewhat confusing. Why is the mean expression range indicated from 0 to 1 and why are all genes apparently having a mean expression of 1?

We thank the reviewer for this question. Because the levels of expression of different genes can vary greatly, in Figure 3J (new Figure 3A and 3I), we normalized the mean expression levels of the genes to their maximum values across groups to improve the visualization. We have now made this clearer in the figure, legend, and text.

Page 10 line 5-6. Are you referring to coagulation markers here or general expression patterns? In case of the latter, how does this statement fit to the paragraph about analyzing expression patterns of coagulation markers? Please, specify. And in line with this, are the highlighted genes in Figure 3K coagulation markers? If not, what is the relevance of these to make the point that one can use the portal to investigate the role of coagulation markers in SARS-CoV-2 infections?

As mentioned above, to avoid any misleading claims, we followed Reviewer #2’s suggestion and replaced the coagulation gene list with a filtered gene list from the “Coronavirus disease - COVID-19” KEGG pathway (hsa05171). This revision enables us to show how to identify experiments in which this gene signature is enriched or depleted. We have now replaced these figures and text with new results.

The appearance of describing batch effects and attempts to remove them from the studies was somewhat surprising on page 10 as I would expect this kind of results rather earlier in the results section before describing use cases of the data. You may consider changing the order of your results for a better flow.

We apologize for the confusion. However, we want to make it clear that the analysis before page 10 did not involve “batch effect”; all analyses were performed within each study. Thus, it is not necessary to change the order in which the results are presented. Also, based on Reviewer #2’s comments, we did not accurately use the term “batch effect,” because “batch effects are purely due to technical differences.” We have now revised the corresponding text to make this point clearer.

Page 11, second paragraph. Please, explain briefly what the silhouette score is supposed to reflect and thus how Figure S4G should be interpreted. The difference of both bars in Figure S4G is very marginal and thus, does not seem to support the statement of the authors that the ssGSEA scores-based projection is better unless you perform a significance test or I misunderstood. Please, clarify.

We thank the reviewer for this suggestion. We have now added an explanation of the silhouette score in the manuscript. Briefly, a silhouette score is a metric of the degree of separability of gene clusters from the nearest cluster. For a given sample, lets be the mean intra-cluster distance, and be the mean distance to the nearest cluster. The silhouette score (sil) will be calculated as follows

The silhouette score ranges between -1 and 1. A value near 1 means that the clusters are well separated, and a value near -1 means that the clusters are intermingled. Using a Wilcoxon rank test, we showed that using ssGSEA scores significantly improves the separability of global GTEx tissues (in Figure S4G; p=8.75e-26).

Page 11, third paragraph: Figure 4B, to the best of my understanding, does not support the claim that samples clustered less according to study cohorts using the ssGSEA approach. Please, quantify the effect and test for significance or better explain.

We apologize for the confusion. We quantified the separability between cohorts (GSE ids) by using the silhouette score. In Figure S4H (panel A below), we show that the TPM-based PCA leads to more separation by studies than does the Covid contrast ssGSEA scores in which the separation between studies is less prominent (p-value=0.0045, paired Wilcoxon test).

For the analyses described starting on page 12 it remains largely unclear whether they were conducted across studies or within studies and which studies were used. This section until the end of the results would especially benefit from providing more information on how the analyses were performed, either in the results or in the methods section.

We apologize for the confusion. The goal of the analysis on page 12 and the corresponding Figure 4G was to identify genes whose expression increased in both the SARS-CoV-2 infection lung and rhinovirus-infected nasal tissue. Hence, we did a log2(fold-change) vs log2(fold-change) comparison. The log2(fold-change) values were independently calculated for each study. Because we compared values by using the same ranking metric, the cross-samples comparison was possible, as shown in Figure 4G. We have now added more details to the Methods section to clarify this point.

Figures 4J and 4K miss axis labels and since we look at correlations, the figures could be redrawn using the same ranges on x and y axis.

We thank the reviewer for this suggestion. We have now added axes labels to the new figures. However, we have not used the same range on the x and y axes because they depict expression levels of different genes. For example, if gene A is expressed 1, 5, and 10 in samples 1, 2, and 3, while gene B is expressed 100, 500 and 1000 for samples 1, 2, and 3, their range would be very different but still perfectly correlated (panel A below). If we draw x and y axes using the same range, this correlation will not be visually obvious (panel B below).

This comparison is different from the correlation plots that compare the expression of one gene in different samples. We apologize for the confusion and to avoid misleading readers, we have enlarged the gene names in Figure labels to ensure that readers notice they are different genes. We have also added an option to the correlation plot on our portal so that users can choose the optimal format (see below).

Page 14 line 5: Is this the right figure reference here to Figure 4G? If yes, then it is unclear how Figure 4G supports the statement in this sentence. Please, clarify.

We apologize for the confusion. In Figure 4G, we labeled several important genes and used different colors to indicate whether the gene was regulated by SARS-CoV-2 only (purple), Rhinovirus only (black), or both(red). FURIN was the gene that is only significantly upregulated by SARS-CoV-2. The data in Figure 4G were from GSE160435(“SARS-CoV-2 infection of primary human lung epithelium for COVID-19 modeling and drug discovery”); that study used lung organoid alveolar type 2 (AT2) cells as the model. We think this confusion was caused by our failure to provide the details about the GSE160435 study. We have now amended the manuscript to include these details in the Methods section to avoid confusion. We also enlarged the gene labels in the figure to make them more visible. In the manuscript, we have changed from “our results found FURIN gene was also upregulated in SARS-CoV-2–infected lung organoid alveolar type 2 cells (Figure 4G, Supplementary Table S3).” to “We found that FURIN was upregulated in SARS-CoV-2-infected lung organoid alveolar type 2 cells (Figure 4G, Supplementary Table S4) (Mulay, Konda et al., 2021), it has reported that TGF-β signaling could also regulates FURIN (Blanchette, Rivard et al., 2001). Our gene enrichment analysis also found TGF-β signaling enriched only for up-regulated genes in SARS-CoV-2-infected lung cells (FDR correct p=7.58E-05, Supplementary Table S4), these observations implicated a positive feedback mechanism only for SARS-CoV-2-infected lung but not RV-infected nasal cells.”

Figure 2 is of too low resolution. Many details cannot be read. Please, provide a higher resolution figure.

We apologize for the inconvenience. However, we did not expect the reader to read the details on Figure 2, as it is just an overview of the CovidExpress portal. The aim is give the reader an impression about what functions CovidExpress could offer.

Reviewer #1 (Significance (Required)):

Providing a single platform for the analysis of SARS-CoV-2-related RNAseq data is certainly of high value to the scientific community. However, as the portal and manuscript are currently presented, for scientists that are not RNAseq analysis specialists, more guidance would be required to understand and use correctly the functionalities of the portal. Unfortunately, because batch effects could not be removed from the studies, the authors, correctly, do not recommend to combine data from different studies for analyses, however, this likely will also limit the potential of the resource to make new discoveries beyond what the original studies have already published. As indicated above, the authors could support their claim by comparing their findings with findings published from the studies they reanalyzed. The portal is only of use to scientists studying SARS-CoV-2. I am not an expert in RNAseq data analysis and thus cannot comment on the technicalities, especially the processing of the RNAseq datasets. We thank the reviewer for the positive comments. We apologize for the confusion and acknowledge that we should not describe our effort using the term “batch effect.” As described by Reviewer #2 (and we agree), batch effect should be used only to indicate a purely technical difference in the same biological system; for example, differences in experiments performed on different days or by different lab personnel. Thus, we cannot correct for “batch effect” by using CovidExpress. We hope that the reviewer realizes that what we did was correct for the effect caused by differences in software and parameters across the studies. For example, in our approach, the DEGs from GSE155518 and GSE160435 (both primary lung alveolar AT2 cells (both from Mulay et al., Cell Report, 2021) were significantly correlated (panel A below; p = 1.36e-24, F-test). However, when we downloaded the TPM values from their GEO records, GSE155518 appeared to have a genome-wide decrease in the expression of SARS-CoV-2–infected samples (panel B below). We suspect that this is because in their data processing, the expression of virus themselves were also considered. Thus, using the proceed data directly without careful reviewing the method might lead to false hypothesis.

At last, researchers can make new discoveries, such as our OASL and FURIN findings, by using many other features that CovidExpress provides.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): Djekidel and colleagues describe a web portal to explore several SARS-CoV-2 related datasets. The authors applied a uniform reprocessing pipeline to the diverse RNA-seq datasets and integrated them into a cellxgene-based interface. The major strengths of the manuscript are the scale of the compiled data, with over one thousand samples included, and the data portal itself, which has useful visualization and analysis functions, including GSEA and DEG analysis. My primary concerns with the study are centered on the analysis examples that are presented and their interpretation, as well as the user interface for the data portal. **Major Comments:**

1. The literature analysis feels out of place and is not informative (Fig 1E), as the conclusions that can be drawn from literature mining are minimal. In evidence of this, the authors highlight that CRP is a top-studied "gene" and later voice their interest in how CRP is not a differentially expressed gene (pg6). This illustrates the problems with the literature-based analysis, since in the context of COVID-19, CRP is a common blood laboratory measurement that is used as a general marker of inflammation. Transcription of CRP is essentially exclusively in hepatocytes as an acute phase reactant (see GTEx portal for helpful reference), and would therefore not be expected to be found in the various datasets collected by the authors. The one exception might be liver RNA-seq samples from COVID-19 patients, but I do not think these are available in the current collection. I would therefore suggest to remove the literature analysis parts from the manuscript.

We thank the reviewer for sharing knowledge about CRP. As discussed in our manuscript, we agree that not all top genes from literature-based analysis were expected to be included in RNA-seq analysis. We apologize for the confusion, and we have amended our description to make this point clearer. However, we still believe that literature-based analyses are very useful in the following aspects:

1. This type of analysis bridges the gap between data-driven research and hypothesis-driven research. For example, we found many genes in our meta-analysis, but it is not feasible to describe the functions of all of them. Thus, in Figure 1F, we color-coded genes in red if they also appeared as top genes in the literature-based analysis and read related manuscripts to build confidence that the meta-analysis is useful. Then we expanded our review to more top genes and found more interesting evidence (Supplementary Table S2, “TopGenesbyDifferentialAnalysis” tab).
2. Literature-based analyses also reduce the time researchers spend prioritizing their investigations. For example, in our comparison of SARS-CoV-2–infected lung and Rhinovirus-infected nasal tissue, we found >2000 genes upregulated only in SARS-CoV-2–infected lung but not in Rhinovirus-infected nasal cells. It is not easy to derive a hypothesis from so many genes. When we overlapped the gene list with literature-based analysis, FURIN popped up as the most well-studied gene, and we did not find any report that mentioned that SARS-CoV-2 can regulate FURIN This raised our interest and led to a suggested mechanism in which SARS-CoV-2 could evolve to induce FURIN expression and gain superior infectivity. FURIN’s upregulation is significant but not among the top genes, in terms of fold change (>2-fold change, FDR p th by fold change). Thus, without the literature-based analysis, this observation could have easily been neglected.
3. Such analyses help researchers to prime their hypotheses for novel findings. For example, in our comparison between SARS-CoV-2–infected lung and Rhinovirus-infected nasal tissues (Figure 4G, Supplementary Figure 5D and E), we found many upregulated genes, but OASL was not in our literature-based analysis, which indicated that it is under-studied and worth highlighting. We hope the reviewer will agree that we should retain the literature-based analysis in our paper. These analyses were not meant to be conclusive but rather a way to prioritize investigations. Finally, we removed CRP from Fig 1E and the main text to avoid confusion.

1. The data portal, implemented through cellxgene, is accessible for non-programmers to use. However, it is very easy to end up with an "Unexpected HTTP response 400, BAD REQUEST" error, with essentially no description of the cause of the error or how to rectify it. When this occurs (and in my experience it occurs very frequently), this also forces the user to refresh the page entirely, losing any progress they may have made. I see that the authors describe this error in their FAQ page, but their answer is not very intuitive and I was unsure of what they meant: "This happens because the samples you selected doesn't contain all "Group by" you want compare for each "Split by" group. You could confirm using the "Diff. groups" buttons.".

We apologize for the confusion. This excellent point made by the reviewer required an improvement in the software engineering, which we have now completed. We have figured out how to avoid this error and have run thorough tests to ensure that it does not appear anymore. We also added a gitter chat channel to our landing page, so that users can report if they encounter this or other errors.

I would therefore ask that the authors provide more detailed tutorials (ideally step-by-step) on common analyses that users will want to perform, hopefully minimizing the amount of frustration that users will encounter.

We thank the reviewer for this suggestion. We have uploaded several video tutorials to our landing page and will gradually add more. We also added a gitter chat channel, so users can ask questions, report bugs, or suggest new studies to include in the portal.

1. Selection of samples is not very quick or intuitive. If I wanted to select only the samples from one specific GEO accession, I had to resort to individually checking the boxes of the sample IDs that I wanted. If I instead selected the GEO accession under the samples source ID, then used the "Subset to currently selected samples" button, I invariable got the HTTP error 400 message. Of course, this may simply reflect my lack of familiarity with cellxgene; I would nevertheless encourage the authors to improve the FAQ to include a step-by-step example for how to do common analyses/procedures.

We apologize for the confusion. To select an individual GEO accession, users can simply tick the box beside “Samples Source ID.”

Then all boxes would be clear for “Samples Source ID” that allow you to select only the one you want. We also have uploaded video tutorials to help users learn how to navigate the portal.

We apologize for the “HTTP error 400” messages. We figured out that users would encounter that message frequently after they encounter it once due to a back-end cache mechanism. We have now improved the portal from the software-engineering side. In our recent tests of the latest version, this error does not appear anymore. We also added a gitter chat channel on our landing page so that users can report encountering this or other errors.

1. The second case study, centered on coagulation genes, is misguided. Alteration of coagulation lab values in severe COVID-19 patients is reflecting the general inflammatory state of these patients, and would not be expected to manifest on the transcriptional level in infected cells/tissues. Coagulation labs are measuring the functional status of the coagulation cascade, which is far-removed from the direct transcription of the corresponding genes - proteolytic processing of clotting factors, etc. As with CRP (see above comment), most clotting factors are transcribed almost exclusively in the liver (check GTEx portal); I would not expect upregulation of coagulation factors in lung cell lines/organoids/cultures etc after infection with SARS-CoV-2. I would recommend the authors to pick a different gene ontology set for a case study, as the current one focusing on coagulation is confusing in a pathophysiologic sense.

We thank the reviewer for this suggestion. To avoid any misleading claims, we have replaced the coagulation gene list with a filtered gene list from the “Coronavirus disease - COVID-19” KEGG pathway (hsa05171) to showcase how to identify experiments in which this gene signature is enriched or depleted. We also replaced Figures 3G-J with new results.

1. The two large clusters of blood-derived samples vs other tissues is not surprising and the authors' interpretation is confusing. The authors write that "the COVID-19 signature was not able to overcome the tissue specificity and that immune cells might respond to SARS-CoV-2 differently." This should be immediately obvious given the pathophysiology of COVID-19 infection; the cell types that are directly infected by SARS-CoV-2 will of course have a distinct response compared to the circulating blood cells of COVID-19 patients, which are responding by mounting an immune response. There is no reason to expect a priori that the DEGs in the directly infected lung cells would be similar to that of immune cells that are mounting a response against the virus.

We thank the reviewer for these comments. We agree that it should be obvious that directly infected lung cells would differ from immune cells. However, this has never been shown in a large dataset. Also, it is not obviously whether all other different tissues would respond to SARS-CoV-2 differently. Thus, we believe it is important to present this overview. We have amended the description to deliver clearer message as “This confirmed immune cells respond to SARS-CoV-2 differently from other tissues also suggested the response of most other tissues might sharing similar features.”.

1. The authors devote considerable space in the manuscript to exploring "batch effects" and trying to minimize them (pg10-11 Fig 4A-D, Fig S4). However, given that the compiled datasets are from entirely different experimental and biological systems (e.g. in vitro infection vs patient infection, different cell lines, timepoints after virus exposure, diverse tissues, varying disease severity), it is inappropriate to simply refer to all of these differences as "batch effects" alone. Usually, the term "batch effect" would refer to the same biological experiment/system (i.e. A549 cells infected with CoV vs control), but performed on different days or by different lab personnel - in other words, batch effects are purely due to technical differences. This term clearly does not apply when comparing samples from entirely different cell lines, or tissues, etc, and the authors should not keep describing these differences as batch effects that should be "corrected" out.

We thank the reviewer for the insight. We apologize for the confusion caused by using the phrase “batch effect correction” to describe our approach. We agree that the difference between studies should not be referred to as a “batch effect correction” and have now amended the descriptions to avoid confusion.

Indeed, the authors themselves state that the main point of their "batch effect correction" efforts is only for PCA visualization. I therefore feel this section contributes very little to the overall manuscript, especially given the authors' own recommendation that all analyses should be performed on individual datasets (which I certainly agree with). I assume that the authors were required to provide some sort of dimensional reduction projection for the cellxgene browser, but this is more a quirk in their choice of platform for the web portal. Thus, this section of the manuscript should be deemphasized.

We thank the reviewer for these comments and again apologize for the confusion caused by our use of the term “batch effect correction” to describe our approach. However, we believe these parts of the paper should be retained for the following reasons:

• In practice, sample mislabeling can happen. PCA or simple clustering approaches are very useful for helping raise researchers’ attention, so they could further check the possibility of sample mislabeling.
• Even within a study, one sample can be an outlier due to low or unequal sample quality. Removing outliers would help boost the significance of real findings. Without our approach, it would be harder for users to notice and remove outliers from their investigations.
• Finally, these efforts are useful for generating hypotheses. For example, although we collected a lot of data, it is not feasible for us to read all the details in all the manuscripts published. We observed a similarity between SARS-CoV-2–infected lung samples and Rhinovirus–infected nasal samples by exploring our portal’s capabilities (Figure 3E-F). Then we read the manuscripts in which those data were published and found that our discovery was consistent with the original studies’ results. We believe these efforts are essential to help researchers generate or refine their hypotheses. As we update the database with more samples, this approach will become increasingly powerful.

  1. Given the limitations of any combined multi-dataset analyses, one very useful feature would be to conduct "meta-analyses" across multiple datasets. For instance, it would be informative to find which genes are commonly DEGs in user-selected comparisons, calculated separately for each dataset and then cross-referenced across the relevant/user-selected datasets.

We thank the reviewer for this comment. Indeed, we agree that “meta-analyses” are useful and have now compiled Supplementary Table S2 and Figure 1F to demonstrate the commonly regulated genes. To enable user-selected comparisons across studies on our portal, we need to design a thoughtful user interface. Otherwise, the results from our portal could easily cause fatal misinterpretation. For example, GSE154613 includes samples like DMSO, Drug, SARS-CoV-2, and DMSO+SARS-CoV-2. If a user simply selected to compare SARS-CoV-2 versus Control, the results would be SARS-CoV-2 and DMSO+SARS-CoV-2 versus DMSO and Drug. Such functions need time to design and implement; therefore, we will consider this suggestion for further development of our portal.

**Minor comments:**

1. Fig S1G, color legend should be added (I understand that these colors are the same from S1H).

We thank the reviewer for the comment. We have now added information about the colors in the figure legend.

1. Mouseover text for trackPlot on the data portal is incorrect (it says the heatmap text instead).

We thank the reviewer for this comment. We have now corrected this bug.

1. Abstract should be revised to describe only the 1093 final remaining RNA-seq samples after filtering/QC steps.

We thank the reviewer for this comment. We have now amended the Abstract to include this information.

1. Text in many figures is too small to be legible. I would suggest pt 6 font minimum for all figure text, including the various statistics in the figure panels.

We thank the reviewer for this comment. We have now amended the font sizes and will provide high-resolution figures in revision.

1. Are the DE analyses in Fig 1F specifically limited to control vs SARS-CoV-2/COVID-19 comparisons? Many of the samples included in this study are from other respiratory infections (labeled "other" in Fig 1B).

We thank the reviewer for the question. Figure 1F was not originally limited to control vs SARS-CoV-2/COVID-19 comparisons, because we thought control vs virus, drug vs mock, or difference between time points would also be interesting. If we narrow the analysis to contrasts only between control vs SARS-CoV-2/COVID-19, Figure 1F would be still look similar (as below) because the genes in that comparison comprise the largest share of genes included in the original graphic.

In the end, we replaced Figure 1F to avoid confusion and added more details in the Methods.

1. The word cloud format is not conducive for understanding or interpretation. It would be much more informative to simply have a barplot or similar to clearly indicate the relative "abnudance" of a given gene among all 315 DE analyses.

We thank the reviewer for this comment but respectfully disagree with this point. Visualization of the relative “abundance” of genes with word clouds is a relatively novel concept in computational biology. However, we believe, that in this case, it has certain advantages over visualization using traditional bar plots for example. The word cloud format allows us to highlight genes relative to their importance, with the word “importance” being used here in the sense of combined metrics from DEGs, as shown in Figure 1F, or the frequency with which genes are mentioned/discussed in various literature sources, as shown in Figure 1E. For this purpose, the exact values will most likely not be important for most users/readers. Be presenting a word cloud visualization, readers can easily discern the top genes and use them in the exploration of their own data or the CovidExpress portal. However, if users want to analyze raw values, we provide in Supplementary Table S3 a full list of all genes and gene sets that can be download from our landing page (section “CovidExpress Expression Data Download”) in GMT format. Also, when we visualized the ranks of genes by using bar plots as the reviewer suggested, the results were much harder to read (as shown in the bar graph below) than simply looking at the raw data in supplementary tables.

1. Claims of increased/decreased dataset separability should have statistical analysis on the silhouette score boxplots (Fig S4G-I).

We thank the reviewer for the reminder. We have added statistical tests to referred silhouette score boxplots (Wilcoxon rank test)

1. Regarding Fig 4E-F - what are the key genes that contribute to PC1, and how do they relate to the DEGs in Fig 4G?

We thank the reviewer for this question and apologize for the confusion. In Figure 4E-F, the PCA were based on ssGSEA score, as each gene set would have a score for a sample, not individual genes. Thus, the top contributed to PC1 were gene sets upregulated or down-regulated in certain contrasts. We provided on the portal’s landing page detailed results for top gene sets (for the ssGSEA approach) and genes (for the TPM approach) that contributed to various PCs (“Clustering Results for Reviewing and Download” section). This allows users to download and further explore these data.

1. Statistics describing the relation between OASL And TNF/PPARGC1A should be included to justify the author's statements. This could be correlation, mutual information, regression, etc.

We thank the reviewer for this suggestion, and we have updated Figures 4J-K to show the correlation values and corresponding F-statistics. The Pearson correlation between OASL and TNF was significant (Pearson Correlation=0.75 and p-value = 6.85e-72), but the correlation between OASL and PPARGC1A had a negative slope and showed a moderately significant p-value (Pearson Correlation=-0.08 and p-value=0.12), confirming to a certain degree our statement. We have now updated the corresponding text in the manuscript.

1. There are several studies now that have performed scRNA-seq on the lung resident and peripheral immune cells of COVID-19 patients. To more definitively tie in their analyses in Fig 4J-K/Fig S5D-E (to affirm "its important role in the innate immune response in lungs"), the authors should assess whether OASL is upregulated in the lung macrophages of COVID-19 patients vs controls.

We thank the reviewer for this suggestion. Indeed, Liao, et al. recently reported “BALFs of patients with severe/critical COVID-19 infection contained higher proportions of macrophages and neutrophils and lower proportions of mDCs, pDCs, and T cells than those with moderate infection.” (Nature Medicine, 2020, https://doi.org/10.1038/s41591-020-0901-9). They further refined macrophage data into subclusters and reported top enriched GO terms as “response to virus” (group 1), “type I interferon signaling pathway” (group 2), “neutrophile degranulation” (group 3), and “cytoplasmic translational initiation” (group 4). When we investigated their data, we found that group1 and group2 both identified OASL as a marker gene, indicated OASL might response to virus and help type I interferon signaling. Furthermore, another data set (from Ren et al., Cell, 2021, https://dx.doi.org/10.1016%2Fj.cell.2021.01.053) showed several clusters in patients with severe COVID-19 (left panel below) that were enriched for OASL expression(right panel below).

We have now added these observations to strengthen our hypothesis about the role of OASL.

1. The visualization and analysis functions in the data portal appear to work reasonably well out of the box. However, the download buttons for plots did not work in my hands. I realized that a workaround is to right click -> "Save image as" (which then downloads a .svg file), but this is not ideal and should be fixed to improve usability. I had tested the data portal on both Firefox and Edge browsers, using a Windows 10 PC.

We agree with the reviewer. Due to some technical issues with the figure javascript plugin, the download feature does not work unless the figure is saved as a file on the server side. To avoid any security issues, we tried to minimize new file generations, hence, for the moment we have disabled this feature. Users can still download high-resolution .svg figures by using the right-click -> “save image as.” This information is now included in the FAQ section on the portal’s landing page.

Reviewer #2 (Significance (Required)): The data portal appears to have useful analysis and visualization features, and the data collection appears to be quite comprehensive. I would strongly encourage the authors to continue collecting datasets as they become available and further improving the usability of the portal. As noted in the above comments, I think there is potential for their cellxgene-based browser to be useful to non-computational biologists, but at present, the data portal is not as simple to use as it should be. With further efforts to developing step-by-step tutorials for common analysis/visualization tasks, more informative case studies, and the other revisions suggested above, this study could be a valuable resource for the community. Of note, this review is written from the perspective of a primary wet-lab biologist with extensive bioinformatics experience but limited web development expertise.

We thank the reviewer for the positive comments. We understand the importance of data updating. Our plan is to complete quarterly updates once this manuscript has been accepted or when 10 new studies have been either collected by us or suggested by users. This information is also now included in the FAQs of the portal’s landing page. We have also uploaded several tutorials videos to the landing page and will gradually add more. We also added a gitter chat channel, so users can ask questions, report bugs, or suggest new studies to add to the database.

**Referee Cross-commenting** I agree with the comments of the other reviewers. Reviewer #3 (Evidence, reproducibility and clarity (Required)): **Summary:** The ongoing COVID-19 pandemic is a big threat to human health. The researchers have conducted studies to explore the gene expression regulations of human cells responding to COVID-19 infection. A website that integrating those datasets and providing user-friendly tools for gene expression analysis is a valuable resource for the COVID-19 study community. The authors collected published RNASeq datasets and developed a database and an interactive portal for users to investigate the gene expression of SARS-CoV-2 related samples. This website would be of great value for the SARS-CoV-2 research community if the batch normalization problems are solved. **Major comments:** 1) The major concern of CovidExpress is the batch effects from different studies. As the authors have shown and mentioned in their discussion that "For the current release, we strongly suggest investigators to perform gene expression comparison within individual study." This limits the usage of CovidExpress as integrating analysis from multiple datasets of different studies is the key value and purpose of CovidExpress.

We thank the reviewer for the comment. Reviewer #2 reminded us, and we agree, that differences between studies should not be considered “batch effects.” We apologize for the confusion. The GSEA function provided in the portal does not suffer from batch effect, because all the pre-ranked lists of genes are based on contrasts from the same studies. Although we cannot correct for the differences between studies, we did correct for effect caused by differences in software and parameters used. For example, in our approach, the DEGs from GSE155518 and GSE160435 (both studies of primary lung alveolar AT2 cells from Mulay et al., Cell Report, 2021) were significantly correlated (below panel A, p-value = 1.36e-24, F-test). However, if we simply download the TPM values from their GEO records, GSE155518 appears to show a genome-wide decrease in expression in SARS-CoV-2–infected samples (below panel B). These errors might lead to false hypotheses.

2) The authors should include experimental protocols as one key parameter in the description and further integrating analysis of different datasets. As the authors showed that QuantSeq is a 3' sequencing protocol of RNA sequencing. However, it is not convincing to me that simply excluding QuantSeq samples is the ideal solution for downstream integrating analysis as QuantSeq has been shown that it has pretty good correlations with normal RNASeq methods in gene quantifications. It is interesting that there are 21.2% of samples were biased toward intronic reads. What protocol differences or experimental variations would explain the biases?

We thank the reviewer for the comment and apologized for not being clearer. One of our main goals re-processing all samples is to correct for pipeline processing–related batch effects. We tried to reduce those effects introduced by using different software or parameters. QuantSeq or similar protocols are heavily bias to 3’ UTR; thus, the software and parameters used for RNA-seq data will not be suitable. In contrast, we agree that the downstream results from QuantSeq have good correlation to RNA-seq (we observed a correlation of ~0.75, when compared to the log2 fold-change from Quant-Seq to RNA-seq). However, we could not reconcile QuantSeq always correlated well with RNA-seq, in terms of individual quantification. For example, Jarvis et al. recently reported only ~0.35 correlation between QuantSeq and RNA-seq (https://doi.org/10.3389/fgene.2020.562445). Theoretically, the correlation would be weaker for genes with a small 3’ UTR. Thus, we will not include QuantSeq data in this portal. However, if we collect enough studies in the future, we will consider uploading a separate portal just for QuantSeq using a pipeline optimized for protocol bias to 3’ UTR.

For the 21.2% samples that were biased towards intronic reads, we believe they reflect differences in the kits used. For example, of the 162 samples “BASE_INTRON (%)” >30% (Supplementary Table S1) that passed QC, 76 samples were total RNA obtained using the SMARTer kit and 36 were total RNA obtained using the Trio kit. Given that we have 105 samples of total RNA derived using the SMARTer kit and 38 samples of total RNA derived using the Trio kit, we conclude that the Trio kit was more biased toward introns, and the SMARTer kit was also strongly biased. This finding is consistent with those of others who have reported the bias of the SMARTer kit (Song et al., https://doi.org/10.1186/s12864-018-5066-2). Users can find these results in our Supplementary Table S1. We have also uploaded the protocol information to our portal.

3) How do the authors plan to update and maintain CovidExpress?

We thank the reviewer for this question. We understand the importance of data updating. Our plan is to update the database quarterly once this manuscript has been accepted or when 10 new studies have been collected by us or suggested by users. We have added this information to the FAQs on the portal’s landing page. We also understand the importance of maintaining the service for a feasible amount of time for research. Therefore, we will keep the server activated for at least 2 years after the WHO announces that COVID-19 is no longer a global pandemic. We will also ensure that, even after we take down the server , scientists with programming skills will be able to create local servers based on the data provided on CovidExpress.

**Minor comments:** 1) Some texts in figures are not readable. For example, Fig2B, 2C, 2D, 2E.

We thank the reviewer for this comment. We have now increased the font sizes and provided high-resolution figures in revision.

2) The authors could use Videos to demonstrate how to use CovidExpress on the website as they have shown in Fig3.

We thank the reviewer for this suggestion. We have uploaded several video tutorials to the landing page and will gradually add more. We also added a gitter chat channel so that users can ask questions, report bugs, or suggest new studies to include in the database.

Reviewer #3 (Significance (Required)): The ongoing COVID-19 pandemic is a big threat to human health. Many molecular and cellular questions related to COVID-19 pathophysiology remain unclear and many researchers have conducted studies to explore the gene expression regulations of human cells responding to COVID-19 infection. However, there is no database/website that integrating all RNASeq data to provide user-friendly tools for gene expression analysis for COVID-19 researchers. The authors collected the published RNASeq datasets and developed a database and an interactive portal, named CovidExpress, to allow users to investigate the gene expressions response to COVID-19 infection. CovidExpress is a valuable resource for the COVID-19 study community once the batch normalization problems are solved. The users who came up with ideas about the regulation of COVID-19 response could use the system to test their hypothesis, without experience in bioinformatics and RNASeq data analysis. This will be more important when more RNASeq data from samples with different tissues, cell lines, and conditions are integrated into the database.

We thank the reviewer for the positive comments. We apologize for the confusion and acknowledge that we should not describe our effort using the term “batch effect.” As described by Reviewer #2 (and we agree), batch effect should be used only to indicate a purely technical difference in the same biological system; for example, differences in experiments performed on different days or by different lab personnel. Thus, we cannot correct for “batch effect” by using CovidExpress. We hope that the reviewer realizes that what we did was correct for the effect caused by differences in software and parameters across the studies. For example, in our approach, the DEGs from GSE155518 and GSE160435 (both primary lung alveolar AT2 cells (both from Mulay et al., Cell Report, 2021) were significantly correlated (panel A below; p = 1.36e-24, F-test). However, when we downloaded the TPM values from their GEO records, GSE155518 appeared to have a genome-wide decrease in the expression of SARS-CoV-2–infected samples (panel B below).

Thus, using the proceed data directly without careful reviewing the method might lead to false hypothesis. At last, researchers can make new discoveries, such as our OASL and FURIN findings, by using many other features that CovidExpress provides.

This is an interesting classification of "open" and "closed", depending on whether the living organism has uniform functionality or specialized, and centralized structures respectively.

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

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Reply to the reviewers

FULL REVISION

Manuscript number: RC-2021-00934

Corresponding author(s): Seiya, Mizuno

General Statements

We would like to thank all the reviewers for their comments on improving the manuscript. We are encouraged by the overall positive responses from the reviewers. According to the reviewers’ comments, we have further refined our manuscript. We are confident that we have addressed all the reviewers’ comments and suggestions by incorporating them into the revised manuscript. We highlighted the changed text in the manuscript in red. The point-by-point responses to all comments follow.

Point-by-point description of the revisions

Reviewer 1:

The study by Akihiro and colleagues describe the generation of multiplex genotyping method for detecting CRISPR gene editing alleles using nanopore sequencing and a machine learning program. The method is based on long-range PCR amplification of intended targeted loci from gene edited animals followed by nanopore sequencing. A PCR-index is introduced to the sample pooling system before sequencing, thus allow sequencing up to 100 sample in one flowcell. The study developed a machine learning program for allele binning, analysis, and presentation. To demonstrate the applicability of the method, the study has validated their methods for detection of point mutations, deletion, and flox insertion. The study has in principal provided sufficient investigation and data to demonstrate the validity of the method. All the figures are very nicely and clearly presented. However, there is a few concerns that it should be taken in to consideration.

We appreciate the constructive and important comments from the reviewer.

Reviewer 1_Comment #1:

Many previous reported unintended structure variations caused by CRISPR off-targets are typically much larger deletion/insertion/insertion/translocation occurred outside the target sites. The current study is more for targeted allele genotyping. The use of structure variable (SV) in the whole study should be considered to revise thoroughly.

SV is typically referred to genomic variation of approximately 1kb and above. What the study describe in this study is still within indel types instead. Thus, comparing the DAJIN with NanoSV and Sniffles on reads with 50, 100 and 200 bases deletions is not appropriate.

The detection of SV alleles in the whole study is most likely a cause of minor indel alleles and sequencing errors. Figure 2b, BC32, WT mice also contains a proportion of SV allele, which is apparently caused by sequencing error. Such SV which is not related to CRISPR gene editing is also seen in other genotyping results e.g. Figure 3a. Figure 4b, Figure 5c, Figure 6b.

Another co-factor that contributes to the SVs is the PCR-error from the method.

Thank you very much for your comments. We agree that structural variation traditionally referred to genomic alterations that are larger than 1 kb in length. Although the application of sequencing technology has expanded the spectrum of structural variation to include smaller events >50 bp in length (PMID: 21358748, PMID: 26432246), there are no common understanding on the definition of the name of genomic rearrangements >50 bp in length through genome editing. We changed the name of the unexpected mutation reads more than approximately 50 bp in length “Large rearrangements (LAR)”. We changed description on the name of reads that DAJIN annotates in the Methods (Page 6, Line 205) and Results section (Page8, Line 249) as well as all other parts throughout the manuscript.

Although we believe most of the LAR alleles are the real alleles generated through genomic rearrangements (Fig. 3b&3c, S12, and S16), we recognize that minor fractions of the LAR alleles, including those observed in WT mice, are composed of reads with high sequencing error rate. Visualized BAM files and consensus sequences can be indicators of the annotation results, providing information to the users of DAJIN that minor alleles that are similar in proportion to the one in the WT sample can be artificial alleles. We also cannot exclude the possibility that LAR alleles include those generated through PCR error. ‘Pseudo-LoxP’ alleles could be generated if the PCR products, which included one-side LoxP but not another-side LoxP, worked as a PCR primer to anneal WT allele in the next PCR step (Page 12, Line 425-427). Recently developed methods may address these limitations. We added description in the Discussion section (Page 17-18, Line 608-620).

Reviewer 1_Comment #2:

The reason that current method detect more than two alleles from one animal is probably due to the chimerism of the animal. However, when looking at the BAM file and figures presented in Figure 1b, 2c, 3b, 3d, 4c, as well as those in the Supplementary figures, there seems to be more than one allele (indels reads with different size) presented in one category.

For example, Figure 2C, mice BC12, it is not fully aligned between the all alleles and the allele1 and allele 2 presented. For allele 1, which is called SV, there are reads with different size of indels. For allele 2, which is called intended PM, some reads are a hybrid of deletion and intended substitution.

Thank you for checking the data in detail. As the reviewer pointed out, some of the reads in each allele showed indels with different sizes. We think these indel mutations are due to nanopore sequencing errors. Although the error rate of nanopore sequencing has improved, it has been reported that an error rate of 5% occurs in 1D sequencing of R9.4 flow cells that is the same flow cells used in our study (DOI: 10.1002/wfs2.1323). In this study, DAJIN mitigated the nanopore sequencing errors by calculating the MIDS score (Fig. S7), but the visualization using the BAM file showed the raw reads including the sequence errors. For this reason, the one allele seems to include different indel alleles.

To evaluate the point, we performed Sanger sequencing and found that there were no hybrid sequences containing indel mutations, but only intended point mutation in BC12 allele 2 (Fig. 2d). The results of Sanger sequencing suggested that the indel mutations visualized by the BAM file were due to nanopore sequencing errors. To clarify the points, we updated the description in the Discussion section (Page 15-16, Line 528-548).

Reviewer 1_Comment #3:

What is the advantage of the current method as compared to the one reported by Bi et al., 2020, genome biology, previously?

Thank you for pointing it out. We believe that one of the advantages of IDM-seq developed by Bi et al. is performing quantitative analysis by correcting PCR bias via Unique Molecular Identifiers (UMIs). However, when multiple samples are processed simultaneously, it is impractical in terms of cost and workability to prepare primers for the UMIs. While IDM-seq has the advantage to quantify the precise amount of each allele in a single sample, DAJIN is more suitable for primary and comprehensive analysis of multiple genome-edited samples. We have described these points in the Discussion section (Page 15, Line 509-513).

Reviewer 1_Comment #4:

The report machine learning method is developed for calling the different alleles. Has the authors compare DAJIN with e.g. NanoCaller, which is developed for SNPs and small indels calling based on DNN.

We are thankful to the referee for bringing the comparison with NanoCaller to our attention. We conducted NanoCaller and found it performed better to detect the point mutation than Medaka and Clair. However, because NanoCaller could not detect the LAR (formerly labelled as “SV”) alleles, it incorrectly reported the genotype of BC25 as 'point mutation', not 'LAR with point mutation'. We added the results of NanoCaller in Table S9 and described these points in the Results section (Page 10, Line338-339).

Reviewer 1_Comment #5:

Apart from genotyping, many CRISPR studies performed in cells are focusing on profiling the indel profiles in a pool of edited cells. It would broaden the applicability of the method for detecting different indels types in such samples and conditions. Current methods, such as TIDE/ICE, NGS-based amplicon sequencing, IDAA can only detect smaller indels. DAJIN will add the advantage of detecting longer indels for such application.

Thank you very much for your comments. We added description on application of DAJIN in the Discussion section (Page 17, Line 592-596).

Reviewer #1 Significance :

Although similar methods are reported for genotyping of the CRISPR editing outcome, the current study introduce the PCR barcoding and particularly the bioinformatic tool box for allele binning and calculation contribute with useful tool to the filed. The study has demonstrated with multiple applications demonstrating the broad applicability of it.

Reviewer 2:

CRISPR nucleases typically generate DNA double strand breaks (DSBs) at target site, which typically generate small insertion and deletion (indel) enabling precise gene knockout or knock-in. However, accompanied DNA DSBs often induce unwanted large deletions or chromosomal translocation. Thus, to assess such large variations as well as small indels is crucial in the genome editing field. In this manuscript, the authors developed a long-range assessment tool, named Determine Allele mutations and Judge Intended genotype by Nanopore sequencer (DAJIN), using a long-read sequencer, Nanopore sequencing. Overall, the topic will be interesting for broad readers and this tool looks technologically sound. I would suggest a few comments that may strengthen this manuscript, as follows.

We are grateful for the referee’s valuable suggestions to improve our manuscript.

Reviewer 2_Comment #1:

Another key study is missed in this manuscript. Recently, a tool with similar concept to DAJIN was published in Nat Methods, which uses also long-read sequencers, Nanopore or PacBio [PMID: 33432244]. It is necessary to describe the benefits of DAJIN against the previous study.

Thank you for pointing this out. Our method has an advantage over those utilizing unique molecular identifiers (UMIs) in its automatic identification and classification of genomic rearrangements including unexpected mutations in multiple samples obtained under different editing conditions (different target loci). As per our response to the Reviewer #1_Comment #3, one of the disadvantages of UMIs is the cost. More accessible methods of routine assessment of on-target genome editing outcomes are required, as well as unbiased assessment of editing products (PMID: 32643177). We showed in the manuscript that the machine-learning-based model could bypass molecular tagging to provide a feasible approach for routine assessment of genome editing outcomes. DAJIN will make a very significant contribution to speeding up and improving the accuracy of this experimental process.

We agree that the approach reported by Karst et al. has certainly contributed to generation of highly accurate single-molecule consensus sequences. Analysis of small portion of samples using UMI-based methods may compensate for the limitations of DAJIN such as PCR bias and/or PCR-mediated recombination as you described in your comment #6. We added description in the Discussion section (Page 15, Line 509-513; Page 17, Line 615-618).

Reviewer 2_Comment #2:

In Figure 1a, the authors used Barcoding but details information is not present in the main text. The length and context information are necessary to be described in the main text.

We thank the reviewer for these comments. According to the comments, we illustrated the process of PCR-based barcoding in Fig. 1a. Besides, we described the length of barcodes at "Library preparation and nanopore sequencing" in the Methods section (Page 4, Line 137 & 140).

Reviewer 2_Comment #3:

The term "SV (structural variation)" over "Single-nucleotide variant (SNV)" seems ambiguous. Does "SV" include large deletion and chromosomal translocation? In this manuscript, I guess that SNV indicates small indels, whereas SV indicates large indels. The detailed definition is needed for better understanding.

Thank you very much for your comments. We intended to classify and label large genomic rearrangements including large deletion and chromosomal translocation as “SV (structural variation)”. We agree that structural variation traditionally referred to genomic alterations that are larger than 1 kb in length. Although the application of sequencing technology has expanded the spectrum of structural variation to include smaller events >50 bp in length (PMID: 21358748, PMID: 26432246), there are no common understanding on the definition of the name of genomic rearrangements >50 bp in length through genome editing. We changed the name of the unexpected mutation reads more than approximately 50 bp in length “Large rearrangements (LAR)”. We changed description on the name of reads that DAJIN annotates in the Methods (Page 6, Line 205) and Results section (Page8, Line 249) as well as all other parts throughout the manuscript.

Reviewer 2_Comment #4:

In Figure 2, IGV exhibits several SNVs (i.e., random errors) in each query sequence, which might be due to the low accuracy of Nanopore sequencing. I understand that DAJIN makes consensus sequence based on those long-read sequences. But I wonder how DAJIN pinpoint the point mutation (PM) so exactly?

Thank you for pointing it out. As you mentioned, the low accuracy of Nanopore long-read sequencing made PM detection difficult. We tackled the issue and partly solved it by (i) calculation of MIDS score (Fig. S7), (ii) reducing data's dimension by principal component analysis (PCA), and (iii) setting proper parameters of HDMSCAN.

DAJIN converts ACGT nucleotide information to MIDS (Match, Insertion, Deletion, and Substitution) (Fig. S6). Subsequently, DAJIN subtracts the relative frequency of MIDS between a control and a sample. We called the subtracted relative frequency 'MIDS score' (Fig. S7). The subtraction mitigates the sequencing errors because the error patterns are similar between a sample and a control. We next perform clustering using the MIDS score. DAJIN compresses the score by PCA and extracts five dimensions. The dimension reduction may be effective to mitigate sequencing errors because the sequencing errors have lower scores than true mutations. Subsequently, DAJIN performs HDBSCAN, a density-based clustering method. The HDBSCAN have a parameter of 'min_cluster_size' that indicates a minimum number of samples in a cluster. DAJIN finds the parameter returning the most frequent cluster numbers by searching the value in the range of 10% to 40% of reads. It means DAJIN ignores minor clusters that contain less than 10% of reads. We set the criteria because sequencing errors often made such minor clusters.

In summary, we consider the MIDS score, PCA and the parameter setting of HDBSCAN support DAJIN's accurate PM detection. To clarify the point, we updated the description in the Methods section (Page 7, Line 217-225).

Reviewer 2_Comment #5:

In page 9, the authors also used next-generation sequencing (NGS). I guess this NGS indicates illumine-based short-read sequencing. Clearer definition is necessary here.

We thank the referee for bringing this unclarity to our attention. According to the reviewer's comment, we updated the words 'NGS' to the 'illumina-based short-read next-generation sequencing' or 'short-read NGS' in the whole text.

Reviewer 2_Comment #5-1:

Whereas DAJIN could reported SVs, PM, and WT, the NGS could not capture SVs. Could you write the reason here? I guess that the short-read sequences including SVs might be discarded during the alignment process, which means that it is because of software limitation, rather than the NGS itself.

Thank you for pointing this out. In this study, we performed the short-read NGS analysis by paired-end sequencing (2 x 151 bases) for PCR amplicons of about 200 bp length. We consider the main reason that NGS could not capture LAR (formerly labelled as “SV”) is due to the PCR process. The allele 2 in BC20, BC25, and BC26 of Tyr point mutation had a large deletion including primer annealing sites, which makes it impossible to obtain the PCR amplicon of this allele. Besides, allele 1 in BC25 had about 60-70 bp insertions. The insertion might make it difficult to amplify the whole length of this allele because of the limited number of cycles in short-read NGS.

To examine whether the short-read sequencing reads were discarded during the alignment process, we calculated the mapping percentages of BC20, BC25, and BC26 and found that 97-99% of reads were successfully aligned to the mm10 reference genome. We think this result can support our hypothesis. We added the results in Table S10 and described the points in the Results section (Page 10, Line 329-332).

Reviewer 2_Comment #6:

Basically, DAJIN amplify the target region using PCR, thus PCR bias (e.g. unequal amplification according to different lengths) should be considered. The authors should address it. Moreover, it is better to describe the limitation of current DAJIN in the discussion section.

Thank you very much for your comments. PCR amplification of genomic DNA is essential in our method described in the manuscript. As we have described in a paragraph in the Discussion section (Page 17, Line 597-601), we recognize there is an unavoidable limitation with PCR bias. We also cannot exclude the possibility that large rearrangements (‘LAR’, formerly labeled as ‘SV’) include alleles generated through PCR and/or sequencing error. ‘Pseudo-LoxP’ alleles could be generated if the PCR products, which included one-side LoxP but not another-side LoxP, worked as a PCR primer to anneal WT allele in the next PCR step (Page 17, Line 608-613). We recognize that minor fractions of the ‘LAR’ alleles, including those observed in WT mice, are composed of reads with high sequencing error rate. Recently developed methods including the one you kindly mentioned in the comment #1 may address these limitations. We added description in the Discussion section (Page 17-18, Line 615-618).

Reviewer #2 Significance:

Overall, the topic will be interesting for broad readers

SciScore for 10.1101/2021.10.18.21265145: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: Thank you for sharing your code.

Our study has limitations, which must be considered. The SNP heritability estimates for both measures of Covid-19, while >0, were small. Second is that genetic correlations, while being robust against environmental confounders, can still suffer from genetic sources of confounding (i.e., even with genetic correlations, correlation is not always causation). To this point, we think it is highly unlikely that not being breastfed as a baby and eating less cheese cause ASB. In fact, we chose these dietary traits to illustrate this very point. Rather, the shared genetic architecture that these have with education years, verbal reasoning, and average income are the more plausibly causal phenomena. Third, we cannot determine the direction of causality with genetic correlations alone. For much of the discussion above, we tacitly presumed plausible directions of effect (e.g., ASB causing Covid-19 versus Covid-19 causing ASB). But with all the traits in our matrix, the prevailing direction of effect could be the opposite and/or some level of bi-directional causation may exist15,24. These uncertainties are avenues for future research. Specifically, at present MR cannot be leveraged to test whether ASB causes any of the traits investigated, since few genome-wide significant signals have been found for ASB. But once they are found, bi-directional MR can be used to decipher the prevailing directions. A fourth limitation is that our findings are limited to those of European ancestry. The limi...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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Results from scite Reference Check: We found no unreliable references.

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A lot of the literature and shorter articles out there treat many of these systems as recent or "new inventions". Many reference "innovators" like Ryan Holiday or Niklas Luhmann. They patently are not. They've grown out of the Western commonplace book tradition which were traditionally written into books underneath thematic headings (tags/categories in modern digital parlance) until it became cheap enough to mass manufacture Carl Linnaeus' earlier innovation of the index cards in the early 1900s. Then one could more easily rerarrange their ideas with these cards. Luhmann allowed uniquely addressing his cards which made things easier to link. Now there are about thirty different groups working on creating digital tools to do this work, some under the heading of creating "digital gardens".

Often I think it may be easier to go back to some of the books of Erasmus, Melanchthon, or Agricola in the 1500s which described these systems for use in education. Sadly Western culture seems to have lost these traditions and we now find ourselves spending an inordinate amount of time reinventing them.

I'd love to hear your experience in re-introducing it to students in modern educational settings.

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

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Reply to the reviewers

Manuscript number: #RC-2021-00992

Corresponding author(s): Parisa Kakanj and Maria Leptin

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

In this study, the authors use the fruit fly as a model to understand the role and regulation of autophagy in epidermal integrity during development and wound healing. They discover that hyper activation of autophagy via overexpression of Atg1 leads to disruption of epithelial organization, junctional protein localization, and syncytium formation. In addition, these epidermal defects were found to be dependent on TORC1 as knockdown or inhibition of TORC1 antagonists resulted in similar epidermal defects which could be rescued by knockdown of Atg1 or Atg5. Wound healing in fruit fly epidermis is known to induce cell fusion and here the authors show that syncytium formation is dependent on autophagy. GFP-Atg8a autophagosomes were found to accumulate in cells adjacent to the wound site, but Atg1-induced syncytium formation was dispensable for wound repair. However, the authors found that hyper activation of autophagy prior to injury slowed wound closure. This may be due to defects in actomyosin organization or another developmental defect the authors observed in the epidermis. Overall, the key conclusions of this study are convincing, but the experiments would be strengthened by validation of all the RNAi strains used as well as demonstration that epidermal barrier remains intact as described.

**Major Comments**

1. This study uses a number of UAS-RNAi strains as well as dominant negative and overexpression transgenes. There is no validation that these genetic perturbations work as expected.

   Almost all of the lines we use have been extensively used and validated by others as documented in the literature. We append a table (below, page 14) with these references. It would be close to impossible for us to show their tissue specific efficacy in the larval epidermis because it is extremely difficult to obtain clean dissections of epidermis without contamination from other tissues (muscles, nerves, etc.), and we believe we can rely on the known validation of most of the lines. It is true that some of the lines are less well characterised, and we comment on those below, and will eliminate our speculation on their effects in the manuscript.

In fact, the authors state on pg 5 that RNAi to Atg6, Atg7, and Atg12 may be less effective, but do not verify the knockdown efficiency to the gene of interest (i.e. Atg5 RNAi knock downs Atg5 transcript or protein).

Atg12 and Atg7 have been shown (PMID: 25882046) by quantitative RT-PCR to effectively reduce RNA levels in the midgut during larval to pupal transition. We will therefore have to eliminate our speculation that the weak effect in the epidermis may be due to ineffective knock-down. Rather, it seems that these components are accessory but not necessarily essential for the completion of autophagy, as also observed by others (PMID: 25882046, PMID: 1805642, PMID: 23599123, PMID: 15296714, PMID: 23873149, PMID: 23406899)

This is particularly important as authors use a single UAS-rictor RNAi strain to conclude that autophagy is dependent on TORC1 and not TORC2. If rictor RNAi is also weak or ineffective than this conclusion would be erroneous.

The function of rictor has been validated by classic genetics: Animals homozygous for deletions of rictor show no defects throughout their normal life cycle (Hietakangas and Cohen, 2007). We have also shown that epidermis of homozygous rictor∆1 larvae (marked with Src-GFP, DsNuc-Red2) shows no abnormalities in cell shapes or cell membranes. We include an image here.

Figure A __| Effect of rictor deletion on the epidermis. a,b, Fluorescence micrographs of larval epidermis expressing the indicated markers in a larva homozygous for a rictor deletion (rictorEY08986 , also named rictor∆1). a, Lower magnification showing the entire width of larval segments A3 or A4. n=16-18 larvae each genotype. Scale bars: a 50 μm; b,__ 20 µm.

A major conclusion of this study is that autophagy remodels the lateral cell membranes and not the basal or apical, so the membrane integrity remains intact. This is described and shown in Fig S3a, but it is hard to see that the apical membrane is intact. It would be helpful if authors could show a true membrane marker, such as UAS-CD8mGFP to see if there is a continuous membrane.

We will include new experiments with this marker.

Alternatively, is there a barrier assay that could help demonstrate that syncytium formation does not disrupt epithelial integrity?

This follows from the fluorescence recovery we performed (Supplementary Video 13), where we observe rapid diffusion between areas in the epidermis, but never any leakage of fluorescence in the y-axis into the body cavity. We will emphasize this more clearly in the text.

This could be performed in the fly gut, using the smurf assay (Rera M et al. 2011), since the authors also describe (pg 9) a similar role for autophagy in disruption of epithelial lateral membranes.

We had done a smurf assay, and observed no leakage from the gut, but didn’t document this at the time because of difficulties during the period of Covid restrictions of accessing a dissecting scope/camera set up in a lab outside our own. We will try to repeat this now in the hope that with current reduced restrictions we can record the result.

Is autophagy dependent syncytium formation cell autonomous?

Our clonal analysis in wound healing addresses this point (Figure 2; text page 5 and 6). Clones of GFP-expressing cells neighbouring a wound share their cytoplasmic contents with their neighbours during wound closure. However, a clonal cell that is Atg5-deficient in a wild-type background does not share its content with the neighbouring cells. This shows that for a cell to participate in syncytium formation, that every cell itself has to be competent to perform autophagy. We will expand the explanation of this point in the text.

The A58-Gal is not cell-type specific as authors describe (pg 9) similar effects in trachea, salivary glands, and intestine and it is unclear if effects are due to disruption of autophagy in epidermal cells or general disruption in fly's physiology. The authors should determine, using a more restrictive Gal driver, whether syncytium formation is due to activation of autophagy in the epidermal cells or another cell type (trachea, salivary glands, or intestine).

We apologize if our phrasing of ‘ectodermal’ led to the impression that A58-Gal4 is cell-type specific. A58 also drives expression in the tracheal system, as all other available epidermal drivers do. A58 expression in the salivary gland is presumably due to the origin of the Gla4 construct, which like many other Gal4 drivers (e.g. NP1-Gal4) includes salivary gland specific enhancers (PMID: 8223268 and PMID: 12324947). A58 is not active in the gut, and for the experiments in the gut we used the NP1 driver. We will rephrase the text in the paper to avoid confusion. There is no driver that is absolutely restricted to the epidermis.

Alternatively, if no other Gal4 is available for the larval epidermis then authors could at least show using enterocytes driver (NP1-Gal4) that overexpression of Atg1 is sufficient to induce syncytium formation and its effect on gut barrier integrity.

We did do this experiment but didn’t include the images because of the large number of figures we already had. We now show them here. As mentioned above, barrier integrity is not compromised. We can also provide images of the phenotype in tracheal cells.

Figure B __| Effect__ of uncontrolled autophagy on enterocytes and salivary glands. Larval gut or salivary glands expressing the indicated markers and overexpression (Tsc1,2 or Atg1S) or RNAi (raptori) constructs using the NP1-Gal4 driver. Images are from live imaging of gut or salivary gland of 6 to 11 larvae for each genotype. Scale bars, 20 µm.

In Fig 8, authors nicely show that Atg1 RNAi can rescue Tor RNAi and raptor RNAi, but, what about the reverse. Is overexpression of Tor sufficient to inhibit the overexpression Atg1 and reduce autophagy-induced syncytium formation?

Overexpression of Tor would affect both TORC1 and TORC2. We have done this experiment using UAS-Torwt construct but found that it leads to excessive autophagy rather than suppression, consistent with similar results reported by others (PMID: 12324961 and PMID: 15186745). This approach can therefore not be used to do the proposed experiment. Instead, one could use downregulation of the Tor inhibitor TSC1, which acts on TORC1, and we have shown to reduce autophagosome formation in wound healing (Fig. 1d). Another option is to overexpress the TORC1-specific activator Rheb (PMID: 12893813, PMID: 17208179 and PMID: 31422886). We will set up the experiments with these constructs in the hope that they will yield interpretable results.

**Minor comments:**

1. Check spelling of abbreviations, Sqh is often misspelled Shq in figures

   We will correct them. Thanks for alerting us.

The order of images in Figure 3 should match the description in the text (pg. 6).

We would prefer to retain the current order because it is then consistent with all the other figures. Re-writing the text to reflect this order would make it less clear.

AtgW is described in text, but not shown in Fig 3a-c. Also, upstream activators of TORC1 are described first, but shown last in this Figure making it difficult to follow.

We will now only mention Atg1W later in the text where we also show it in a figure.

Fig7a should show junctional effect of Atg1W alone and in combination with Atg5i which is used in 7b.

We had left this out to save space, but we will now include these data.

It is unclear why authors switched to this weak overexpression for this photobleaching assay when Atg1S was predominantly used in the rest of the study.

The reason we used Atg1W in this particular experiment is that we had it on a chromosome where it was recombined with GFP which made it genetically much easier to use for FLIP experiments. However, perhaps these constructs merit some discussion. Atg1W and Atg1S were originally called “weak” and “strong” based on studies in other tissues and other stages (PMID: 33253201). However, we found that in the epidermis their effects are practically indistinguishable, as judged by TEM (Fig.3d,e) (Fig 5e,f) (Suppl. Fig. 5a,b and Suppl. Fig. 6b,c), and all markers we used in confocal analyses (which we will include them). Thus, to avoid confusion, we will change the nomenclature we use on our paper to the neutral Atg1GS and Atg16B.

Reviewer #1 (Significance (Required)):

This study elucidates the role and regulation of TORC1 and autophagy in epithelial membrane remodeling. This is important work that is significant to both developmental and wound healing research. Many cell types become multinucleate during differentiation, aging, and wound healing and here the authors find a novel role for authophagy in remodeling lateral cellular junctions to facilitate syncytium formation.

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

In their present manuscript Kakanj and colleagues show that during epithelial wound healing autophagy pathway controls plasma membrane integrity and homeostasis. Furthermore, elevated autophagic activity is sufficient to induce syncytium formation, which is essential for wound closure and healing. Authors used the epidermis of fruit fly larvae as model to study wound healing and video microscopy to examine this process. The methodology is well established, since authors already published a related study in 2016 using similar tools.

The findings presented here are interesting and promising, the quality of most experiments are satisfactory, the confocal images/videos are excellent and I truly appreciate that authors used electron microscopy to support some of their claims. Findings are well presented and the text is well written and easy to read.

Overall, my opinion is very positive about this manuscript.

I believe most of the findings are very well supported, but I have some suggestions, which may can help strengthen the authors' points.

1) Authors used GFP-Atg8a reporter to follow autophagy during wound healing. While I also believe that, the appearing GFP-Atg8a dots represent autophagic vesicles after wounding but GFP-Atg8a has some certain limitations. First: Atg8a (or LC3 in mammals) is removed from the outer surface of autophagosomes by Atg4 and the Atg8a trapped inside the autophagosomes will be degraded in the autolysosomal lumen. Thus Atg8a not always localizes to autolysosomes, actually Atg8a immunostaining mostly labels autophagosomes (and phagophores) but not autolysosomes in insect cells. Accordingly, GFP-Atg8a reporter is also subject of autolysosomal degradation and furthermore most of the GFP signal is quenched in the acidic lumen of autolysosomes, since at lower pH GFP loses fluorescence. Nevertheless, if lysosomal degradation proceeds normally, GFP-Atg8 will be degraded completely. Thus, some of the autolysosomes cannot be detected using this reporter, for this mCherry-Atg8a reporters can be used, since mCherry is more resistant than GFP and thus accumulate inside lysosomes, and retains its fluorescence in acidic environments.

This is a good suggestion and we had done these experiments. However, the red fluorophores have a serious problem in that they all tend to form small aggregates or puncta – not in all tissues and at all stages, but this is a very wide-spread phenomenon, and is even observed in in vitro experiments (own observations). This makes quantification of vesicles or other small structures such as autophagosomes complete impossible. Nevertheless, here are a few figures from our analyses. While some of the spots clearly appear to be autophagosomes, as judged by their positions, they cannot be objectively distinguished from the other spots.

Figure C __| Autophagy during epidermal wound healing. Time-lapse series of single-cell wound healing in larva expressing mCherry-Atg8a (black) to mark autophagosomes and autolysosomes (A58>mCherry-Atg8a). a, z-projections of a time-lapse series. b, Higher magnification of the areas marked by magenta boxes in (a). n=11 larvae. Each frame is a merge of 57 planes spaced 0.28 μm apart. Scale bars: a 20 μm; b,__ 10 µm.

However, I still believe that for video microscopy GFP-Atg8a was a perfect choice, I just suggest to confirm the appearance of autophagosomes after wounding by other means: for instance, immunostaining of the epidermis after wounding (120 min) against Atg8a should confirm the presence of autophagosomes. There are a few specific available antibodies working in flies which are listed in the reviews of Nagy (PMID: 25481477) or more recently in Lorincz (PMID: 28704946)

This is technically a huge challenge. We would have to induce a single cell wound, then filet and fix the epidermis, during which it rolls up and often destroys the area of interest. If it doesn’t, then the prep can be flattened out, but it still can be very difficult to find the wound in the prep.

2) One of the major claims of the authors is that elevated autophagy leads to the breakdown or removal of lateral plasma membranes to promote syncytium formation. It is clearly seen on the confocal or EM images that lateral membranes disappear after wounding. However, it is also suggested that the lateral plasma membrane material is incorporated into autophagosomes or plasma membrane is a potential membrane source of autophagosome formation. I believe this is the least supported claim of the manuscript since no direct evidence for this is presented. This is based on BodyPy staining only, that BodyPy positive vesicles accumulate inside the cells. If this is indeed the case plasma membrane components should be detected in autophagic vesicles. Thus, I recommend co-staining membrane components with autophagic markers.

This is indeed the clear next step, and we did a number of experiments along those lines, but they were once again compromised by the problem with the mCherry aggregates. This made the interpretation in the unwounded epidermis with artificially upregulated autophagy impossible. However, experiments with naturally upregulated autophagy in wound healing yielded results that are consistent with plasma membrane components being associated with autophagosomes (with the caveat that not every red dot we see represents an autophagosome). We have just repeated some of these using the septate junction marker FasIII and have obtained some beautiful movies that show FasIII labelled membrane (green) being surrounded by mCherry spots, and as the membrane begins to dissociate, the mCherry spots turn from red to yellow. We have included stills from results of these analyses here and will include them in a new figure in the revised manuscript.

Figure D __| Colocalization of Atg8a and the septate junction component FasIII during epidermal wound healing. a, Time-lapse series of single-cell wound healing in a larva expressing mCherry-Atg8a (red) (A58>mCherry-Atg8a) and endogenously tagged FasIII (GFP gene trap; green), a transmembrane component of septate junctions. b, Higher magnification of the time-lapse marked by magenta boxes in (a). n=11 larvae. a,b, Each frame is a merge of 68 planes spaced 0.28 μm apart. Scale bars: a,b __20 μm.

However if authors observe no colocalization of plasma membrane components with autophagy markers I still believe this study worth to be published. I would like to recommend the review of Ungermann and Reggiori (PMID: 29966469) in which the trafficking of Atg9 is discussed,

Yes, indeed. And there is in fact now a further paper that goes in a similar direction (PMID: 34257406). We had left this out because we did not have direct data on Atg9, but will be happy to include it in the discussion in which we cite the paper that shows that Drosophila Atg9 is localized on the lateral plasma membrane in nurse cells, and loss of it leads to syncytium formation.

since the source of autophagosomal Atg9 is in part the plasma membrane in mammalian cells. Therefore, these findings may strengthen the authors' claims.

**Minor points:**

Figure 2A: I believe authors wanted to use the word 'maintaining' not mating in their scheme.

Indeed. Thanks for alerting us.

Discussion: Authors suggest that: another function of autophagy in the cells surrounding the wound may be to clear up debris as in planarian and other cell types autophagy is activated in healthy cells, which simultaneously phagocytose cell debris. Honestly, I do not believe that this is the case here. Some of the Atg proteins are indeed required for phagocytosis during LC3-assiciated phagocytosis (LAP) (see: PMID: 30787029), but LAP is independent form Atg1

Good point, we will include this in the discussion.

and if LAP happened in the cells, surrounding the wound then GFP-Atg8a positive phagosomes would appear in those cells. However, it is clearly not the case here.

Reviewer #2 (Significance (Required)):

I highly recommend this manuscript to be uploaded to a relevant journal and I believe the findings presented here will be interesting for biologists specialized in regeneration and readers from the autophagy fields alike.

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

**Summary:**

The larval epidermis of Drosophila is a prime model for studying wound healing by combining live imaging with cellular, genetic and molecular analysis of the processes involved. Autophagy is known to be activated and necessary for efficient wound healing in animal models through secretion of cytokines and clearance of bacteria. This manuscript implicates autophagy in cellular syncytium formation during wound healing. Live imaging demonstrates autophagy activation in cells surrounding the wound. Inhibition of autophagy by RNAi against atg1 or atg5, required for autophagy initiation and autophagosome formation had no effect on the rate of constriction and closing of the wound site. However, elegant live imaging demonstrates that autophagy is required cell autonomously for cell fusion, a normal process during wound healing in flies. Autophagy can also be instructive for cell fusion. Strong induction of autophagy by TORC1 inhibition, TSC1/2 overexpression or Atg1 overexpression induce cell fusion that is genetically dependent on atg5, a gene acting downstream of atg1 in autophagosome formation. As Chloroquine treatment, a chemical inhibiting autophagosome fusion to the lysosome and lysosomal breakdown showed no effect, the authors suggest that later steps of autophagy are not involved. Live imaging with a selection of cellular fluorescently tagged markers of apical, lateral and basolateral membrane domains, combined with electron microscopy show clearly that lateral membrane are disrupted and removed within the epithelium. During this process, membranous large vesicles "drift" away from the plasma membrane. If these vesicles relate to autophagy is not addressed. In addition to the effect on cell fusion, strong autophagy induction also leads to autophagy within the nucleus, chromatin condensation and distortion of the nuclear membrane. The manuscript is well written and easy to follow. Figure panels and data are clearly presented. All experiments are well described throughout and skillfully executed with appropriate controls and statistical analysis. It remains unknown what induces autophagy in response to wounding. It also remains unclear whether autophagy deconstructs or engulfs parts of the plasma membrane, or if parts of the autophagy machinery has additional roles in plasma membrane fusion.

**Major comments:**

• Are the key conclusions convincing? -Conclusions are generally balanced and convincing.

-I have seldom seen a paper so well written, presented and balanced by first pass. Hence my experimental suggestions are few.

• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? -Claims are well founded.

• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary to evaluate the paper as it is, and do not ask authors to open new lines of experimentation.

  -The inhibition of autophagy is performed using knockdown of two genes acting in autophagy initiation (atg1, a part of the ULK1 kinase complex) and atg5, required for autophagosome formation. Later acting genes in the autophagy process such as autophagosome closure, fusion with the lysosome or degradation were not analyzed. In the abstract, the authors state "Proper functioning of TORC1 is needed to prevent autophagy from destroying the larval epidermis which depends on membrane isolation and phagophore expansion, but not fusion of autophagosomes to lysosomes". As far as I can see, the last statement on fusion derives from experiments with Chloroquine. Although frequently used for qualitative experiments, CQ is not suited for conclusive experiments. Without genetic experiments targeting genes for autophagosome-lysosome fusion such as snap29,stx17,vamp7 this statement is in my mind not well supported.

We agree this would strengthen our findings, and we had indeed ordered these strains from the Bloomington stock collection. However, they were dead on arrival and both our labs in Heidelberg and Cologne currently have major problems with shipments from Bloomington and German customs. Other colleagues whom we asked did not have them available either. We will continue to search for appropriate constructs, but even if we find them and they arrive alive, and are processed by customs within a reasonable time, it will take many weeks to establish and then expand them and subsequently do the multi-generation crosses to obtain the stocks with all the relevant drivers and markers to set up the experiment. Three months is the absolute lower limit provided everything works according to plan, and first time round 6 months is a more realistic assumption. We hope that the referees and the editors agree that while this is a desirable experiment, it is not essential for the publication of the other results we present.

• Are the suggested experiments realistic for the authors? It would help if you could add an estimated cost and time investment for substantial experiments. -Given the expertise of the authors, these experiments should be easy to perform within 3 months.

  • Are the data and the methods presented in such a way that they can be reproduced?

  • The manuscript is well written and an excellent example of how how methods and experiments should be presented. Methods, tools and experiments are all well described.

  • Are the experiments adequately replicated and statistical analysis adequate? -Replicates and statistics are adequate and custom for the type of analysis performed.

  **Minor comments:**

• Specific experimental issues that are easily addressable. Figure 3 h. The live imaging documents the striking disappearance of lateral cell membranes using SRC-GFP. In 3h, large vesicle formation and movement towards the cell interior is shown. How frequent is this?

This can only be seen clearly in experiments with time-controlled (Gal80ts) induction of authophagy where we can observe the process unfolding. We see these structures very frequently, but great variability in morphology and the structures are not always captured clearly in the plane of imaging. We here provide further examples.

Figure E __| Autophagy in unwounded epidermis. a-c, Three additional examples showing apparent extrusions from lateral membranes after induction of autophagy (same experiment asn Figure 3h).__ Time-lapse series of epidermal cells expressing Src-GFP and Atg1S. Transgene expression is induced at the end of the second larval instar, live imaging started 6 h later (t=0) and continued for an additional 6 hours. a-c, Src-GFP containing material appears to be taken out of and eventually detached from lateral cell membranes (arrows).

Is this believed to be the mechanism of lateral membrane removal?

We would of course like to believe that, but we have no proof, and would therefore only be able to speculate.

If so, is it dependent on the autophagy machinery. Are these vesicle positive for autophagy markers?

Some autophagy markers have indeed been reported to be associated with the plasma membrane (e.g. Atg9, Atg16), but a conclusive study, while highly desirable, in our view goes beyond the scope of this study.

Resolving this issue may lift the conclusions of the paper. Using 3xCherry-Atg8 together with SRC-GFP, this should be possible.

We are intrigued by this suggestion and will be setting up the necessary crosses to do the experiments. However, it will take a long time to generate the necessary stocks (see genetics described below), and we will then again encounter the problem with the mCherry aggregates (see response to referees # 2). We are curious about the outcome, but we do not think it will be reasonable to promise as part of this revision that we will be able to provide conclusive results in the foreseeable future. Along with the many other things to do, this may just have to become part of a future paper, especially if there turn out to be other problems to be solved along the way. Like, for example, having to make an infrared (like mIFP or mKate, with which we have had much better experience in other contexts) Atg8 construct.

Using CQ, the authors should be able to detect plasma membrane and junctional components in autophagosomes or autolysosomes (by confocal and electron microscopy) as degradation is inhibited. This should help to distinguish whether lateral membranes are engulfed and digested or if cells simply fuse, by using a part of the autophagy machiney.

We have many interesting EM images on which we have had extensive discussions with the Paolo Ronchi and Yannick Schwab at the EMBL (whom we embarrassingly forgot to acknowledge in our manuscript, which will now be corrected), and one of the authors of this paper (BM) is an expert in EM imaging of the larval epidermis. It was agreed that some structures could indeed be interpreted as autophagosomes with content resembling junctional material. However, in the absence of absolute proof, we did not include them in the paper. We now show them here.

Figure F __| Autophagosomes with junctional material in unwounded epidermis.__ Transmission electron micrographs of sections through the epidermis of a larva with elevated autophagy (A58>Atg1S) at two different magnifications. Arrows mark the autophagosomal membrane with content resembling junctional material.

The authors, state that strong autophagy activation also leads to syncytium formation of tracheal cells, salivary glands and gut EC cells. Representative images in a supplementary figure would be useful for future reference.

See response to other comments above (response to referees # 1). We have added some images in this document (Figure B) and will be happy to add additional ones in the revised manuscript.

• Are prior studies referenced appropriately? -Yes. Key literature and findings are cited and discussed.

• Are the text and figures clear and accurate? -Yes

  • Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

-See suggested experiments above.

Reviewer #3 (Significance (Required)):

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. -The findings clearly documents a role of autophagy in syncytium formation in the physiological process of wounding. This has parallels to muscle syncytium formation, but has to my knowledge not been demonstrated in any other cell type to be performed by autophagy. Moreover, the authors show that strong autophagy induction can lead to fusion of epithelial cells. This may have relevance for processes and diseases where polyploidy are observed.

• Place the work in the context of the existing literature (provide references, where appropriate).

• State what audience might be interested in and influenced by the reported findings. -The data are very strong and the demonstration that autophagy controls syncytium formation outside of muscle development is surprising and significant. It is of interest to the field of cell biology and development in general and the autophagy field in particular. It will also be of interest for the medical field that deals with multinuclear phenotypes, such as cancer.

• Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. -Development, cell signaling, autophagy, vesicle trafficking.

Table 2 | Fly stocks used in experiments

Transgenes

Stock ID

Source

Publications using this construct

Reference

UAS-GFP-Kuk

(UAS-GFP-KukEY07696(w+))

Jörg Großhans

PMID: 16421189

https://flybase.org/reports/FBal0161312

29

UAS-Atg1i

(UAS-Atg1RNAi)

V # 16133

(GD7149)

PMID: 19363474

PMID: 31995752

PMID: 32032548

PMID: 32915229

https://flybase.org/reports/FBtp0034071.html

UAS-Atg5i

(UAS-Atg5RNAi)

V # 104461

(KK108904)

PMID: 31995752

PMID: 32032548

https://flybase.org/reports/FBtp0046851.html

UAS-Atg6i

(UAS-Atg6RNAi)

V # 110197

(KK102460)

PMID: 28581519

PMID: 23599123

PMID: 27542914

PMID: 25644700

Dissertation of Philipp Trachte, Abb. 23. https://refubium.fu-berlin.de/handle/fub188/27709

Dissertation of Sirena Soriano Rodríguez. https://roderic.uv.es/bitstream/handle/10550/50749/Tesis%20SSoriano.pdf?sequence=1

UAS-Atg7i

(UAS-Atg7RNAi)

V # 45558

(GD11671)

PMID: 25882046

PMID: 31995752

PMID: 32032548

PMID: 23599123

https://flybase.org/reports/FBtp0025106.html

UAS-Atg12i

(UAS-Atg12RNAi)

V # 29791

(GD15230)

PMID: 25882046

PMID: 17568747

PMID: 31995752

https://flybase.org/reports/FBtp0027770.html

UAS-TSC1,2

(UAS-TSC1, AUS-TSC2)

Iswar K. Hariharan

PMID: 15296714

PMID: 11348592

64

UAS-TSC1i

(UAS-TSC1RNAi)

V # 22252

(GD11836)

PMID: 23144631

PMID: 29144896

PMID: 29456138

https://flybase.org/reports/FBtp0025266.html

UAS-Tori

(UAS-TorRNAi)

BL # 33951

Nobert Perrimon

PMID: 25882046

PMID: 26395483

https://flybase.org/reports/FBtp0065159.html

65

UAS-TORDN

(UAS-TORTED)

BL # 7013

Thomas P. Neufeld

PMID: 15296714

PMID: 29144896

https://flybase.org/reports/FBtp0016360.html

66

UAS-raptori

(UAS-raptorRNAi)

BL # 34814

Nobert Perrimon

PMID: 25882046

PMID: 31048465

https://flybase.org/reports/FBtp0068814.html

65

UAS-raptori-2

(UAS-raptorRNAi)

BL # 41912

Nobert Perrimon

PMID: 32097403

https://flybase.org/reports/FBtp0081336.html

65

UAS-rictori

(UAS-rictorRNAi)

BL # 36699

Nobert Perrimon

PMID: 25882046

https://flybase.org/reports/FBtp0070835.html

65

UAS-Atg1S

(UAS-Atg16B)

Thomas P. Neufeld

PMID: 33253201

https://flybase.org/reports/FBtp0041043.html

67

UAS-Atg1W, UAS-GFP

(UAS-Atg1GS10797)

Thomas P. Neufeld

PMID: 33253201

https://flybase.org/reports/FBal0216676.html

67

UAS-S6Ki

(UAS-S6KRNAi)

BL # 41895

Nobert Perrimon

PMID: 25284370

https://flybase.org/reports/FBtp0080798.html

65

UAS-SqaKA

(UAS-SqaT279A/CyO)

Guang-Chao Chen

PMID: 21169990

https://flybase.org/reports/FBtp0071419

30

UAS-RhoAi

(UAS-RhoARNAi)

V # 12734

(GD4726)

PMID: 23853710

PMID: 33789114

https://flybase.org/reports/FBtp0031970.html

UAS-Roki

(UAS-RokRNAi)

V # 104675

(KK107802)

PMID: 24995985

PMID: 33789114

https://flybase.org/reports/FBtp0046110.html

UAS-RhebAV4

BL # 9690

Fuyuhiko Tamanoi

PMID: 31909714

PMID: 28829944

https://flybase.org/reports/FBal0141561.html

69

This part of the lecture, which explains the importance of the telegraph in society during its prime stages, is something that I feel like it truly significant to recognize as a progressor in an environment. When we mention the invention and use of the telegraph in today's world when discussing the past, the true role it played for the different branches of society is always overlooked as it was an element in which many depended on due to its transmission speed. Before this discussion, I looked at the telegraph as a simple device of skinny wires that communicates information from one end to another, mainly helping news reporters. However, as the lecture mentions, the telegraph was more than this, it was heavily depended on in crucial areas of society such as the military, the economy, and the railroad, aspects that are all signs of progression towards a better life. If the telegraph ever ran into any mechanical problems, the flow of life would be heavily disturbed due to its importance, as "trains would stop running, businesses with branch offices couldn't function, news papers couldn't cover events far away..." (Networking Nature Lecture). Personally, I think it is interesting, and also important to note, that the telegraph is not only just an aspect meant for communicating information across the world, but also a necessity to continue improving the economy and keeping the public informed and motivated. Even if people know the importance of it, they ignore the fact how if one piece of society stops working, being the telegraph, many other parts of society "come to a halt" as well and not just one minimal aspect, something truly interesting. While it may not be considered an important item by some, it seems as if the telegraph is one of the first major inventions that heavily impacted the change and improvement of society along with the railroad, as it was often used by many, those being reporters and businessmen, and any issues would stop progression and hurt those who depend on its functions.

I think this essay expands on what sublime really comes down to. In Cole's writing he is more appreciating the nature, but in Emerson's he is justifying that nature is all around us and makes the world up as we know it. Even beyond that point he goes to talk about our reasoning for existence a lot saying that, "All science has one aim, namely, to find a theory of nature." I think this may have been true in his time with underdeveloped sciences but in today's world there a scientists who have a theory on why nature was created yet still pursue science. Why would a scientist do this if still only trying to pursue the theory of nature that we already have discovered.

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

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Reply to the reviewers

1. General Statements [optional]

We thank the reviewers for their critical comments and suggestions. We are glad that the reviewers appreciated the quality of the data and the novel findings connecting the secretory trafficking machinery with extracellular matrix-related signaling.

2. Description of the planned revisions

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

The manuscript by Jung et al reports on an interesting finding that focal adhesion signaling regulates the expression of Sec23A and thereby regulates COPII-dependent trafficking. The data presented a mostly solid and the finding itself is highly novel, as it tackles an area of secretory trafficking that remains poorly understood, namely the connection between the ECM and secretion.

I will list below all comments that I have mixing both technical and conceptual topics:

\*Technical issues:***

1-The authors should provide a better description of how the designed this siRNA library. What were the inclusion criteria for these 378 genes? I might have missed it, but I could not find this information easily.

Reply: The library has been designed in-house based on gene annotations and literature to include cytoskeleton structural proteins, motor proteins, and other associated and regulatory proteins. We will add this information in the Materials and Methods section.

2-Figure 2: I know this is challenging for EM images, but is there a way the authors could quantify these data? How many images were looked at? What was the average width of ER cisterne?

Reply: We will provide image quantifications and statistics

3-Figure 4: I think that the characterization of the FA phenotype is a bit underdeveloped. There is no quantification of these data. Is the size of FA changing? Is the number of FA per cell changing? Is the length of FAs changing? I think that more work is needed to increase the confidence in these data.

I could also not easily see what type of cells these are. A better description of this experiment is also required. Also, how many cells were analyzed. I think it is important that this experiment is done with a sufficient number of cells to increase the confidence in the data.

Reply: We agree with the reviewer that our observations regarding the focal adhesion (FA) phenotype will benefit from image quantification and we intend to include this in the revised manuscript. All FA experiments were performed on HeLa cells. We will update the materials and methods sections to better describe this experiment.

\*Conceptual issues:***

1-The finding that focal adhesion signaling negatively affects ER-export is surprising, because cancer cells that grow on stiff substrates have more focal adhesions and are more invasive and migratory. Both migration and invasion are expected to depend on ER-export. Although the authors did not formally test Sec23A expression under different stiffnesses, I would expect that stiff substrates would lower Sec23A expression and thereby negatively affect ER-export. It would certainly increase the breadth of this work to include data like this and to also discuss this highly surprising finding. However, it is of course the decision of the authors and the editors to decide whether such an experiment would benefit the entire story.

Reply: In this work, we have shown that cells plated on ECM or matrigel have decreased SEC23A expression compared to control cells. We have also shown that inhibition of FA kinase leads to an increase in SEC23A expression (Figure 5). Whether this translates into a change in ER transport, is a fair point that we will address in the revision. Regarding stiffness, we have done a preliminary experiment that shows that cells plated on a soft synthetic substrate have less SEC23A than cells plated on plastic.This goes in line with our ECM experiments because Matrigel and fibroblast-derived ECM are softer than plastic.

2-The authors postulate that this novel mechanism could be part of a feedback loop. If this were the case one would expect the acute effect of FA to increase ER-export (or secretion) and the negative feedback will then reduce secretion. However, the acute effect of FA is not addressed in this manuscript. In order to postulate a feedback loop, the authors would need to test the individual nodes of this loop.

Reply: The question appears to be whether an acute effect on FA would affect the expression of SEC23A and therefore ER transport. If by the acute effect the reviewer means a pharmacological manipulation, we have shown that upon treatment with the FAK inhibitor the expression of SEC23A increases (Fig 5A). Whether this increase in SEC23A expression translates into a corresponding increase in ER transport remains to be seen. This will be tested in our revised manuscript as mentioned above in reply to point # 1.

Our data encouraged us to propose a hypothetical feedback loop that would connect the deposition of ECM through the expression of SEC23A. We will have more data to support (or reject) this idea once we do the transport experiments as mentioned above. However, we think that a full characterization of this hypothetical loop by testing individual nodes is beyond the scope of this manuscript

Reviewer #1 (Significance (Required)):

I think that the basic finding of this manuscript is highly novel, by showing the impact of the ECM and focal adhesions on COPII-dependent trafficking. I think that this will not only appeal to people from the trafficking community, but also to people working on cell migration and on mechanobiology. The work in its current form does not require much extra efforts (max. 3 month). However, if the authors would decide to increase the breadth of data, they would require 3-6 months.

Reply: We thank reviewer #1 for the comments. We also believe that this story will appeal to a broader audience and would help to bridge the gap between membrane trafficking and mechanobiology communities.

\*Referees cross-commenting***

I went through the comments of the two other reviewers and agree with their verdict. Some extra work on the characterization of the early secretory pathway would be good. Both reviewers provided a nice catalogue of possible experiments to choose from.

Reply: We have characterized the early secretory pathway in terms of ER exit sites, Beta-COP, and Golgi morphology (FIG. 2B-H and S1A-B). Together, these data strongly characterize the nature of ER-block. Moreover, the finding that our interactors affect the expression of SEC23A allows us to explain mechanistically why an ER transport block occurs. This is further strengthened by the rescue experiments (FIG. 3F). We believe that further characterization of the secretory pathway will not contribute substantially to the main message of this manuscript.

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

The manuscript by Jung et al which based on a targeted siRNA screen, demonstrates regulation of SEC23A (component of the SEC23 complex of the COP coat) levels at transcriptional level downstream of focal adhesion signaling. By regulating siRNA mediated downregulation, the authors were able to identify proteins which either increased or decreased traffic of VSVG through the secretory pathway when combined with downregulation in the levels of with either SEC23A or SEC23B. Authors have focused on a group of SEC23B functional interactors, downregulation of which shows them increased size of focal adhesions which also downregulate SEC23A levels, thus providing an explanation for reduced secretory traffic. Authors further show that plating cells on fibronectin or Matrigel, which activate Focal adhesion kinase signaling also results in downregulation of SEC23A transcript levels. The screen is conducted in a well-controlled manner for most parts with a clear explanation of the analysis routines and the data presentation if of very good quality. Most important results have been validated by more than one experimental strategy which lends substantial confidence to the findings. The results also open further avenues for understanding the transcriptional regulation in different physiological and disease contexts.

There are certain issues, which the authors should address with regards to controls and some conflicting observations with published results with respect the phenotypes associated with downregulating proteins on focal adhesions size. Additionally, authors don't tie the ends by monitoring secretory traffic in cells grown on different matrices but include it in the model. Addressing/explaining these issues could improve this manuscript and the model may have to be tweaked a bit.

\*Major comments:***

1)I wonder why the authors only used siRNA control in their screen when the effects are scored in context of double knockdown fashion in combination with mild knockdown of SEC23A and SEC23B to get functional interactors. Control siRNA in combination with SEC23A and SEC23B should have been two ideal negative controls in the screen. Nevertheless, in data presented Figure 1E and whole of Figure 2, using control siRNA in combination with SEC23B siRNA would have been ideal control to show that the combination does not induce any trafficking defects which could impact the findings of the study. Hence, a few of the data presented from some of these figures should have sicontrol+SEC23B siRNA combination as a control.

Reply: There seems to be a misunderstanding. In the screen, the negative controls are only used as a reference as the scoring is based on a 5X5 matrix centered on the siRNA of interest. This is done to overcome possible plate effects and to normalize data across different biological replicas. As seen in figure 1B, the negative controls (Control siRNA or Control siRNA + SEC23A siRNA or Control siRNA + SEC23B siRNA are very close to 0 (but not exactly 0) as they were not used in the normalization process. It is important to mention that all single knockdowns also contain our control siRNA to keep the same final siRNA concentration in single and double knockdowns. In Fig 1E we will include the images from Control + SEC23A siRNAs and Control + SEC23B siRNA as a reference. For Figure 2 all except 2A and 2H have the single knockdowns as controls.

2)What is the identity of post-ER structures which authors refer to in Figure 2A? Could the images represent VSVG concentrated at ER exit sites? Authors should stain with markers for ERES to see if the VSVG puncta colocalize with it.

Reply: We have done the experiment, and indeed these structures colocalize with an ER exit site marker (SEC31A). We intend to include this data into the revised manuscript. Our observations are in agreement with what is known in the literature about VSVG transport.

3)Based on RNA sequencing results, authors chose to follow up on SEC23A levels in background of siRNA knockdown of components (like MACF1, ROCK1, FERMT2 etc.) which regulate Focal adhesions in cells and show that there is a reduction in both transcript and protein levels of SEC23A. In images shown in Figure 2B and Figure 2C, levels or SEC31A and β-Cop1 are reduced. Authors should test using qPCR and western blots whether there is a downregulation SEC31A, β-Cop1 and SEC23B in siRNA knockdowns of MACF1, ROCK1, FERMT2 etc. It would provide new insights if there were a co-regulation of secretory machinery to modulate the secretory traffic in response to Focal Adhesion based signaling.

Reply: Our transcriptomics data (FIG 3C and Table 5) shows that SEC31A and COPB1 mRNAs are not altered upon any of the knockdowns. For SEC23B, we observed only a slight decrease in ROCK1 knockdown. This data suggests that a co-regulation of the secretory machinery might not be present. Instead, the curation of secretory pathway genes in our transcriptome data shows that SEC23A is the only commonly differentially expressed gene.

4)Most major concern in this manuscript surrounds around results presented in Figure 4C. Authors show that in response to all the knockdowns, they see more focal adhesions as monitored by Vinculin staining and this along with the experiments with cells plated on Matrigel and Fibronectin arrive at the conclusion that increased Focal adhesion signaling downregulates SEC23A levels which presumably modulates secretory traffic. I am not an expert on Focal adhesions but based on my understanding of the literature on that topic, downregulation of ROCK1, FEMRT2 disrupts focal adhesions. (See: Theodosiou et. al., Elife, 2016 or Lock et. al., Plos One, 2012 for example). How do authors explain their results in siRNA knockdown of ROCK1 and FEMRT2 which leads to an increased size of focal adhesions which seems contradictory to the published results? To clarify these results authors should test phosphorylation of FAK in their siRNA backgrounds which is another read out of focal adhesion signaling.

The experiments from cells grown on Fibronectin and Matrigel favor the argument which authors put forth, but authors may have to tweak the model a bit based on FAK phosphorylation and FAK signaling in context of above-mentioned knockdowns.

Reply: Based on the images for vinculin staining, in our current manuscript we propose that changes in FAs occur upon knocking down our interactors. In our revised manuscript we will provide a more robust quantitative assessment of those changes (change in number, size, or intensity) as mentioned in our reply to Reviewer #1.

As for the discrepancies in the relation of FA phenotype upon depletion of ROCK1 and FERMT2, we want to point out that this effect depends on the cell type used. For instance, the papers listed by the reviewer here use fibroblasts and keratinocytes respectively while we have used Hela Kyoto cells which are epithelial in nature. Another example is that while in fibroblasts depletion of FERMT2 leads to a rounded morphology and almost an absence of FAs (Theodosiou et. al., Elife, 2016), in podocytes (Qu et al JCS, 2011), it leads to fewer FAs but an increase in their size. Nonetheless, this is a very keen observation from the reviewer and we will address this point in our revised manuscript discussion.

5)What happens to VSVG traffic or RUSH-Cadherin traffic when cells are plated on Matrigel and Fibronectin? Reduction in secretory traffic of these is an important experiment which is missing to close the loop and validate the model presented. Authors must test these experiments either with cells grown on matrix alone or in combination with siRNA to SEC23B. Authors should also monitor ERES and transport carriers in this background.

Reply: We agree with the reviewer and intend to perform these experiments.

6)This is not such a major issue, but it would be good to see a comparison in SEC23A levels in siRNA knockdown condition in comparison to those when cells are grown on different substrates and in ROCK1, FEMRT2 knockdowns (blots of which authors already have in this manuscript).

Reply: We will assess the level of SEC23A at the protein level for cells plated on matrigel or Fibroblast-derived ECM.

\*Minor comments:***

1)Scale bars are missing in EM images in Figure 2H.

Reply: We will add the scales in our EM images

2)Show molecular weight markers in Western blots in main figure 3E and supplementary figure S1E.

Reply: We will add molecular weight markers in our Western-Blots

Reviewer #2 (Significance (Required)):

I have looked at the manuscript from through the lens of a cell biologist as that is predominantly my area of expertise. In that respect I find the screen conducted by authors particularly interesting as they aim to connect how extracellular cues regulate the secretory pathway. A screen seems justified as there is no comprehensive understanding linking the two above-mentioned processes. Authors have done a functional interaction screen and analyzed a lot of images to identify candidates which either increase or decrease secretory traffic in combination with SEC23A and SEC23B. Such a functional screen has helped authors identify candidates which were otherwise missed in single siRNA knockdowns in their previous work from 2012. This definitely opens up interesting avenues to test the candidates identified in the screen in different physiological contexts and in disease as also the transcriptional program connecting Focal adhesion signaling with the regulation of components governing secretion. Such functional interaction screens could also be employed to identify crosstalk of different cellular processes with the regulation secretory pathway at ER as well as at the Golgi apparatus.

Reply: We thank reviewer #2 for the comments. As we mentioned in our reply to reviewer #1, we strongly believe that these results will encourage further research at the crossroads of membrane trafficking and mechanobiology.

\*Referees cross-commenting***

I agree with the comments from both the referees that the manuscript is very interesting, most experiments are well controlled, but the quantification of focal adhesion phenotype in knockdowns need to be done in an extensive manner and secretion phenotypes need to measured upon plating cells on different matrix to validate the model presented.

Reply: These two experiments will be included in our revision

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

\*Summary***

The authors use a synchronized cargo release assay following codepletion of either Sec23 paralog with cytoskeletal and associated proteins to identify potential functional interactions between COPII trafficking and the cytoskeleton. This screen yields a number of Sec23b functionally interacting molecules that stall cargo trafficking to various degrees within the secretory pathway upon codepletion, and in the case of MACF1 reduce ERES number despite not physically interacting. Depletion of the majority of the identified Sec23b functional interactors alone surprisingly caused the downregulation of Sec23a at the mRNA and protein levels, and cargo trafficking could be partially or fully rescued by Sec23a overexpression depending on the codepleted cytoskeletal factor. RNA-seq enrichment analysis and imaging of a focal adhesion marker suggest that genes involved in cell adhesion were differentially regulated following depletion of the cytoskeletal functional interactors. Finally, the authors show that Sec23a expression levels are reduced when cells are cultured on dishes with high amounts of ECM to induce focal adhesions, and that inhibition of focal adhesion kinase can rescue Sec23a expression levels.

\*Major comments***

#1 The authors successfully implicate a group of cytoskeletal proteins and their actions at focal adhesions in negatively regulating Sec23a expression levels and COPII trafficking. This description of a shared, novel mode of COPII transcriptional regulation by cytoskeletal factors is convincingly shown to be at least a contributor to the delayed trafficking in the presence of focal adhesions. In general, the data are reproducible and use appropriate statistical analysis. However, a more robust description of the architecture of early secretory pathway would be beneficial, especially in the case of MACF1 codepletion which cannot be fully rescued by Sec23a-YFP overexpression. In contrast, trafficking during codepletion of FERMT2 is fully rescued by Sec23a-YFP despite both MACF1 and FERMT2 showing similar loss of Sec23a mRNA levels upon codepletion. This data suggests that while the trafficking delay in FERMT2 codepletion might be exclusively due to reduced Sec23a expression levels, there are likely additional causes for the trafficking delay observed in MACF1 codepletion.

Reply: We thank the reviewer for the appreciation of our results and the importance they might bear for the field. The reviewer has very neatly highlighted that each of our interactor hits might have roles in the secretory pathway beyond the ER or independent of the expression levels of SEC23A. This phenomenon could also explain the differential rescue of the arrival of VSVG at the plasma membrane upon SEC23A overexpression in FERMT2 and MACF1 knockdowns (FIG 3F). For instance, MACF1 has been involved in Golgi to Plasma Membrane transport as well (Kakinuma et al. Exp. Cell Res. 2004, Burgo et al. Dev. Cell 2012). So a possibility is that SEC23A overexpression rescues only ER to Golgi transport but the lack of rescue in the compartment between Golgi and plasma membrane independent of SEC23A expression levels would result in reduced rescue In the case of MACF1 compared to FERMT2. To support this, in our revised manuscript, we will provide example images from the experiment.

Nonetheless, we agree that these are very important observations from Reviewer #3 and warrant a detailed discussion in the light of other interactors as well, which we intend to highlight in our revised manuscript.

#2 While there is indeed a reduction in the number of ERESs following MACF1 codepletion, the authors report an even more dramatic reduction in 'transport intermediates / cell' as marked by COPI. However, as recent cyro-EM analysis of ERESs has definitively show, COPI exists stably at ERGIC membranes (1). Thus, an alternative possibility for the more dramatic reduction of COPI sites compared to Sec31a sites in Figures 2B-E is that ERGIC membranes are destabilized following MACF1 codepletion in a manner independent of Sec23a expression, and this destabilization compounds with reduced ERES number to ultimately delay trafficking. To more directly determine whether ERGIC membranes stability is regulated by MACF1, the authors should compare COPI and ERGIC-53 staining among MACF1 codepleted and FERMT2 codepleted cells with and without Sec23a-YFP overexpressed to levels that rescue cargo trafficking. If Sec23a-YFP restores the number of ERGIC puntae marked by these stains in FERMT2 but not MACF1 codepleted cells, it would suggest a role for MACF1 in forming or stabilizing ERGIC membranes which are known to associate with microtubules and WHAMM, an actin nucleator. Additionally, it would be useful to costain COPII with COPI or ERGIC-53 in control, MACF1 depleted, MACF1 codepleted, and MACF1 codepleted and Sec23a-YFP rescued cells to determine their colocalization. COPII and ERGIC membranes should be almost entirely coupled and juxtaposed in control cells and may be decoupled upon loss of MACF if plays a role in ERGIC membrane localization and stability. These proposed experiments are relevant because ERGIC membranes are sites of COPII cargo delivery and changes in ERGIC stability or localization would suggest an additional mechanism for cytoskeletal regulation of COPII trafficking. These immunofluorescence studies should be straightforward and completed in a few weeks.

Reply: Although a possible additional role of MACF1 in the organisation of early secretory pathway, stability of ERES, etc., independent of the expression of SEC23A is interesting on its own, we believe that an extensive characterization of these possible roles/ pathways as proposed by the reviewer is beyond the scope this manuscript.

#3 The choice to use VSVG and E-Cadherin for the synchronized release assays unfortunately convolutes interpreting the 'transport ratios' used by the authors to compare the effects of the various codepletions. Each protein progresses beyond the Golgi during secretion, and the authors choose to calculate the ratio of cargo intensity at the plasma membrane normalized to the total cellular cargo. This means that the synchronized release assays and calculated 'transport ratios' assay not only ER to Golgi trafficking, but also trafficking from the Golgi to the plasma membrane. In instances where Sec23a-YFP overexpression does not fully rescue the codepletion, it is possible that additional trafficking delays occur during Golgi to plasma membrane trafficking that cause the 'transport score' to decrease. Thus, the 'transport score' as the authors calculate it is needlessly nonspecific to COPII trafficking and should not be used to compare the codepletions for COPII functional interactors.

Reply: We agree that the “transport score” used here and in our previous genome-wide screen (Simpson et. al Nat. Cell Biol. 2012) does not allow us to distinguish between the individual transport substeps in the transport of VSVG from the ER to the plasma membrane. However, as we see in Fig 1E, the proteins that we have decided to follow in more detail in this study do have a clear ER transport block phenotype (except for CRKL). So for 6 out of 7 of these proteins, the images clearly show that the decrease in the “transport score” is due to a decreased ER to Golgi transport.

#4 To mitigate unwanted contributions of post-COPII trafficking events from altering 'transport scores,' the authors should use a cargo for synchronized release assays that does not progress past the Golgi such as α-Mannosidase II and quantify a ratio of the perinuclear cargo signal to whole cell signal. Ideally, the screen would be repeated with a more appropriate cargo generating new 'transport scores' for the full list of cytoskeletal proteins. However, this may not be feasible, and as such 'transport scores' based on a Golgi resident protein should at least be produced for the 7 Sec23b functional interactors featured in this manuscript. These Golgi 'transport scores' would add much needed quantification of ER to Golgi transport delays that currently can only be inferred from the representative images in Figure 1E, which unfortunately show significant heterogeneity among cells from the same image. The authors should also explicitly state that any 'transport score' from a synchronous release assay using a cargo destined for the plasma membrane will take into account trafficking rate changes due not only to COPII, but also COPI from the ERGIC to the Golgi, and transport carriers departing from the TGN. These synchronized release assays would likely take between a few weeks to a few months depending on their ability to automate image analysis.

Reply: We consider that having a “Golgi transport score” won't add any new information as the proteins that we have chosen to follow are the ones that show a strong ER-block phenotype. However, we agree that such a “Golgi score” would indeed be useful if one would like to study other interactors, for instance, the ones that induce transport acceleration.

Also, we don't expect all cells to behave similarly as the level of knockdown might be slightly different or because of the cell to cell variability. Even in control conditions (no knockdown), this heterogeneity is evident. As suggested by the reviewer, in our revised manuscript we will explicitly state that a change in the transport scores could mean a change in any sub-step of the transport from the ER to the PM in our assay.

\*Minor comments***

It would be useful for the authors to quantify the number of focal adhesions present from Vinculin stains from Figure 4C and 5C instead of just showing representative images. It would be interesting to determine if there is a meaningful relationship between focal adhesion number induced by the codepletions or tissue culture coating and Sec23a expression levels like in Figure 3D. Generally, the figures, text, and references were appropriate.

Reply: As also pointed out by the other reviewers we will quantify the FA changes

Reviewer #3 (Significance (Required)):

In recent years, significant effort has been devoted to elucidating mechanisms by which COPII trafficking is modulated in response to cellular cues. These studies have revealed that changes in nutrient availability, growth factors, ER stress, autophagy, and T-cell activation all cause changes in COPII trafficking via unique gene expression, splicing, or post-translational control (2-7). This work elucidates a novel mechanism of transcriptional control driven by focal adhesions. Additionally, it provides a number of potentially useful Sec23a and Sec23b functional interactors among cytoskeletal factors for further study. These unexplored factors may have unique mechanism of COPII regulation that could contribute to our understanding ER export modulation. Altogether, this and similar works are building an increasingly complex set of regulatory pathways that when integrated ultimately dictate COPII trafficking kinetics.

The reported findings are not only relevant to those who study COPII trafficking, but also other fields where secretion is studied in the context of the ECM. This work would suggest that secretion of factors involved in crosstalk between cells, including in tumors, is likely to be controlled by the interactions of cells with ECM.

Reply: We thank reviewer #3 for the comments and insightful discussion about the limitations of our assay that we will highlight in the revised manuscript and in general for the insight into the early secretory pathway regulation. Furthermore their explicit summary of how our study could bridge COPII trafficking, ECM signaling and the relevance to various pathophysiologies is highly appreciated.

Expertise keywords: cell biology, light microscopy, membrane trafficking

References

1.Weigel A V., Chang CL, Shtengel G, Xu CS, Hoffman DP, Freeman M, et al. ER-to-Golgi protein delivery through an interwoven, tubular network extending from ER. Cell. 2021 Apr;184(9):2412-2429.e16.

2.Farhan, H., Wendeler, M. W., Mitrovic, S., Fava, E., Silberberg, Y., Sharan, R., Zerial, M., & Hauri, H. P. (2010). **MAPK signaling to the early secretory pathway revealed by kinase/phosphatase functional screening. Journal of Cell Biology, 189(6), 997-1011.

3.Zacharogianni, M., Kondylis, V., Tang, Y., Farhan, H., Xanthakis, D., Fuchs, F., Boutros, M., & Rabouille, C. (2011). ERK7 is a negative regulator of protein secretion in response to amino-acid starvation by modulating Sec16 membrane association. **EMBO Journal, 30(18), 3684-3700.

4.Lillmann, K.D., V. Reiterer, F. Baschieri, J. Hoffmann, V. Millarte, M.A. Hauser, A. Mazza, N. Atias, D.F. Legler, R. Sharan, et al 2015. **Regulation of Sec16 levels and dynamics links proliferation and secretion. J. Cell Sci. 128:670-682.

5.Liu, L., Cai, J., Wang, H., Liang, X., Zhou, Q., Ding, C., Zhu, Y., Fu, T., Guo, Q., Xu, Z., Xiao, L., Liu, J., Yin, Y., Fang, L., Xue, B., Wang, Y., Meng, Z. X., He, A., Li, J. L., ... Gan, Z. (2019). Coupling of COPII vesicle trafficking to nutrient availability by the IRE1α-XBP1s axis. Proceedings of the National Academy of Sciences of the United States of America, 116(24), 11776-11785.

6.Jeong, Y.-T., Simoneschi, D., Keegan, S., Melville, D., Adler, N. S., Saraf, A., Florens, L., Washburn, M. P., Cavasotto, C. N., Fenyö, D., Cuervo, A. M., Rossi, M., & Pagano, M. (2018). The ULK1-FBXW5-SEC23B nexus controls autophagy. ELife, 1-25.

7.Wilhelmi, I., Kanski, R., Neumann, A., Herdt, O., Hoff, F., Jacob, R., Preußner, M., & Heyd, F. (2016). Sec16 alternative splicing dynamically controls COPII transport efficiency. Nature Communications, 7, 12347. https://doi.org/10.1038/ncomms12347

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

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

Reviewer #3 suggested to robustly characterise the early secretory pathway, in response to the depletion of our interactors, for instance, the role of MACF1 in the organization and the stability of ERES. This view is also supported by reviewer #1. However, in our revised manuscript we would like to focus more on the novel aspect of our study (as highlighted by all the reviewers), namely how ECM signaling and changes in FAs affect SEC23A and possibly ER transport. For this, we would like to present a more quantitative outlook of the FA phenotype and concentrate on the transport experiments. The reason for not dwelling into a more extensive characterization of the early secretory pathway is that these experiments are very interesting on their own, and merit a separate study that would deconvolve in detail the individual trafficking steps, and their relation to SEC23A expression, ERES stability, and ECM signaling.

Reviewer #2 suggested that to better characterize the FA phenotype and solve the apparent discrepancies between our data and the literature, we could test FAK phosphorylation. As we mentioned in our reply to this point, we think that most of the discrepancies arise from the different cell types used. Nevertheless, we agree that a quantitative approach is needed for a better characterisation of FA phenotype, therefore we intend to perform quantification of the vinculin stainings.

Author Response:

Reviewer #1:

In this manuscript titled "The LRRK2 G2019S mutation alters astrocyte-to-neuron communication via extracellular vesicles and induces neuron atrophy in a human iPSC-derived model of Parkinson's disease", Jacquet and colleagues investigated the role of Parkinsonism gene mutation LRRK2 G2019S in hiPSC-differentiated astrocytes. By isolating extracellular vesicles from ACM and examining astrocytes with various electron microscopy techniques, the authors found that LRRK2 G2019S affects the morphology and distribution of MVBs and the morphology of secreted EVs in hiPSC-differentiated astrocytes. Furthermore, the authors observed that astrocyte-derived EVs can be internalized by dopaminergic neurons and such EVs support neuronal survival. However, LRRK2 G2019S EVs lost the ability of promoting neuronal survival. This is an interesting study showing a non-cell autonomous contribution to dopaminergic neuron loss in PD.

The proposed idea of how LRRK2 G2019S dysregulates EV-mediated astrocyte-to-neuron communication is novel and exciting. However, the authors present some conflicting data that is not addressed during the discussion: they first conclude upregulated exosome biogenesis by RNAseq in G2019S vs WT astrocytes, but later show a decrease in the number of <120nm particles in G2019S mutants suggesting a decrease in the classical exosome-sized vesicle secreted compared to WT. Lastly, their MVB images show less CD63 gold particles in G2019S compared to WT control (though this was not quantified). Do the authors suggest and increase or decrease in exosome biogenesis in G2019S mutants? How do they reconcile these seemingly contradicting data? Several experiments, controls and additional analyses are needed to fully demonstrate the validity of the proposed mechanism.

The RNA-sequencing data of LRRK2 G2019S astrocytes showed an enrichment in genes associated with the “extracellular exosome” gene ontology term but not with the MVB/EV trafficking or secretion pathways. While we found CD82 and Rab27b to be upregulated, the classical biogenesis markers of MVB/EV trafficking and secretion (e.g. VTA1, VPS4, ALIX) were not dysregulated. Instead, the gene list shows an overwhelming dysregulation of genes coding for EV-enclosed proteins which do not have known roles in MVB/EV biogenesis or function (we now discuss this point in the main text, see highlights in italics below). As a result, we do not believe that exosome biogenesis is upregulated but instead propose the working hypothesis that the EV pathway may contribute to LRRK2 G2019S astrocyte dysfunction. To complement the sequencing data, our study provides a characterization of this pathway by (i) describing the cellular distribution of CD63+ structures in astrocytes, (ii) measuring the size of secreted EVs, and (iii) analyzing the neurotrophic potential of control and LRRK2 G2019S astrocyte-secreted EVs. We have not characterized the cellular biology of exosome/EV biogenesis in depth, and we do not propose a mechanism by which the LRRK2 G2019S mutation dysregulates these pathways. These questions are beyond the scope of our study, which is focused on the role of astrocytes in neurodegeneration.

The reviewer also referred to the CD63 immunogold staining used in Figures 4C and 6A to localize MVBs. After careful quantification of the number of CD63 gold particles in WT and LRRK2 G2019S MVBs, we conclude that there are no differences between the two genotypes and we apologize for selecting non-representative images. We have now replaced these with representative images. Regarding the shift in the size of WT vs. LRRK2 G2019S vesicles, we complemented our cryo-EM analysis with new data generated using Nanoparticle Tracking Analysis (NTA) (Figure 3C,D). The NTA analysis enabled the quantification of a greater number of particles, and we found that both WT and LRRK2 G2019S astrocytes secrete a significant number of particles in the 0-120 nm range. The cryo-EM data suggested that mutant astrocytes secreted fewer particles in this size range, but this is not observed in the NTA analysis. This discrepancy could be explained by the following: (i) in contrast to cryo-EM, NTA does not distinguish EVs from cell debris, which could bias the quantification and increase the number of small particles quantified (Noble et al., 2020), and (ii) studies showed that the size distributions between NTA and cryo-EM differ, the latter enabling the identification of larger particles (Noble et al., 2020). These two techniques are therefore complementary in the study of secreted EV and our manuscript now presents data generated using these two approaches (Figure 3C-G) (see italicised text below).

Results

Expression of exosome components in iPSC-derived astrocytes is altered by the LRRK2 G2019S mutation Gene ontology (GO) analysis revealed that components of the extracellular compartment are up-regulated in LRRK2 G2019S astrocytes – these include GO terms corresponding to the extracellular region, extracellular matrix and extracellular exosomes (Figure 1D,F). The exosome component is one of the most significantly up-regulated GO terms in both isogenic and non-isogenic astrocytes, and is comprised of a total of 67 (isogenic pair) or 95 (non isogenic pair) genes (Supplementary Tables 1 and 2). The large majority (~ 98 %) of these gene products are described to be enclosed in exosomes (e.g. CBR1) but do not perform specific functions related to EV formation or secretion. Only a few genes are associated with exosome biogenesis (e.g. CD82) and trafficking (e.g. Rab27b) (Andreu & Yanez-Mo, 2014; Chiasserini et al., 2014; Ostrowski et al., 2010) and we did not detect differences in the expression of canonical factors that regulate MVB formation (e.g. VTA1, VPS4 or ALIX).

Profiling WT and LRRK2 G2019S EVs secreted by iPSC-derived astrocytes

The astrocyte-derived EV pellet is enriched in exosomes, as demonstrated by the expression of 8 exosomal markers and the absence of cellular contamination (Supplementary Figure 3D). NTA quantification showed that the number of secreted EVs does not differ between LRRK2 G2019S and isogenic control (Figure 3C), and it appears that LRRK2 G2019S particles have a slightly different size distribution compared to WT particles (Figure 3D). It should be noted that TEM and NTA are methods traditionally used to estimate the size distribution of EVs, but their accuracy is often challenged by sample processing artifacts and technical biases (Pegtel & Gould, 2019). To overcome these limitations, we complemented the NTA results with cryo-EM analysis of the size of EVs secreted by WT and LRRK2 G2019S isogenic astrocytes. EVs mostly displayed a circular morphology (as opposed to the cup-shaped morphology observed by TEM) (Figure 3E), but a variety of other shapes were also observed (Supplementary Figure 3E). Cryo-EM analysis confirmed that WT astrocyte-secreted EVs display a large range of sizes, from 80 nm to greater than 600 nm in diameter, with differences between WT and mutant populations (Figure 3F). The cryo-EM data suggested that mutant astrocytes secreted fewer particles in the 0-120 nm size range, and the discrepancy with the NTA results could be explained by the following: (i) in contrast to cryo-EM, NTA does not discriminate EVs from cell debris, which could bias the quantification and increase the number of small particles quantified (Noble et al., 2020), and (ii) studies showed that the size distributions between NTA and cryo EM differ, the latter enabling the identification of larger particles (Noble et al., 2020). However, cryo-EM is a low throughput methodology that limits data collection to a small sample size and has therefore a lower statistical power than NTA. Quantification of the number of simple vs. multiple EV structures did not reveal differences between the two lines, and represent up to 16% of the EV population (Figure 3G). We then sought to complement our EV profiling experiments with an analysis of secreted CD63+ particles, which form one of the known exosomal sub-populations. We previously showed that WT and LRRK2 G2019S MVBs contain similar levels of the CD63 tetraspanin (Figure 2E, Supplementary Figure 3A,B), and an ELISAbased quantification confirmed that the number of CD63+EVs remained unchanged between the two genotypes (Supplementary Figure 3F). We conclude from these results that the total number and morphology of EVs produced by WT and LRRK2 G2019S astrocytes are similar, but mutant EVs may have a different size distribution compared to WT vesicles.

Major concerns:

1) In figure 1 A authors demonstrate iPSC-derived astrocytes characterization. Since there is no one unified and validated method for astrocytes differentiation, there is a need for more accurate characterization of iPSC-derived astrocytes. Authors should demonstrate the percentage of cells positive to astrocytic markers and to prove that obtained astrocytes are functional (able to promote synaptogenesis and uptake glutamate). I would also recommend analyzing the iPSC-derived astrocyte cultures for expression of more specific astrocytic markers as GLT1, SOX9 in addition to those which have been analyzed. Moreover, it is highly important to know what is the proportion of astrocytes derived from LRRK2 G2019S line and its isogenic control in order to be able to compare their effect on neurons.

We thank the reviewer for these suggestions. It is true that there exist many different astrocyte differentiation protocols, and this study uses a protocol developed by TCW et al. that has been further optimized by our lab to derive astrocytes from a midbrain-patterned population of neural progenitor cells (NPCs) (de Rus Jacquet, 2019; Tcw et al., 2017). The protocol is published, and shows that these astrocytes are functional – they respond to inflammatory factors and alter secretion of the IL-6 cytokine. Furthermore, Supplementary Figure 2D shows a whole transcriptome analysis (by RNA-seq) of the cell populations produced for this study and demonstrates that iPSC-derived astrocytes cluster with human primary midbrain astrocytes and away from iPSCs or NPCs in an unsupervised cluster analysis. However, we agree that in-depth characterization of iPSC-derived astrocytes is essential, and the updated manuscript now shows that (i) the astrocyte differentiation protocol yields 100 % GFAP+ cells with both WT and mutant lines (Supplementary Figure 2B), (ii) expression of six astrocyte markers (GLT1, SOX9, APOE, BHLHE41, CD44, GLUD1) (Supplementary Figure 2Aii, B), as well as (iii) transient intracellular calcium signaling (Supplementary Figure 2E), and (iv) synaptosome uptake (Supplementary Figure 2F) in both WT and LRRK2 G2019S astrocytes. We also updated the text as follows (italicised):

Results section

Midbrain-patterned NPCs carrying the LRRK2 G2019S mutation or its isogenic control were differentiated into astrocytes as described previously (de Rus Jacquet, 2019; Tcw et al., 2017). As expected, astrocytes expressed the markers GFAP, vimentin, and CD44 as demonstrated by immunofluorescence (Figure 1A) and flow cytometry analyses (Supplementary Figure 2A). Differentiation was equally effective in WT and LRRK2 G2019S cells, with 100 % of the differentiated astrocytes expressing GFAP (Supplementary Figure 2Bi). To further demonstrate the successful differentiation of iPSCs into astrocytes, we analyzed gene expression using RNA-sequencing analysis (RNA-seq), including primary human midbrain astrocyte samples in the RNA-seq study to serve as a positive control for human astrocyte identity. iPSC-derived and human midbrain astrocytes expressed similar levels of genes markers of astrocyte identity, including SOX9 and GLUT1 (Supplementary Figure 2B). In addition, principal component and unsupervised cluster analyses separated undifferentiated iPSCs, iPSC-derived NPCs and iPSC-derived astrocytes into independent clusters, demonstrating that our differentiation strategy produces distinct cell types (Supplementary Figure 2C-D). Importantly, the transcriptome of iPSC-derived astrocytes showed more similarities to fetal human midbrain astrocytes than to NPCs or iPSCs, further validating their astrocyte identity (Supplementary Figure 2D). Lastly, control and LRRK2 G2019S astrocytes showed classic astrocytic functional phenotypes such as spontaneeous and transient calcium signaling and synaptosome uptake (Supplementary Figure 2E-F).

2) In Figure 1, the authors found a significant upregulation of exosome components in astrocytes, demonstrating an important role of LRRK2 G2019S in EV signaling pathway. In the discussion, the authors briefly mentioned 'sub-populations of CD63- EVs may be differentially secreted in mutant astrocytes'. Since the authors have obtained the RNA-seq data, it would be nice to dig deep into the data and comment on potential EV sub-populations which can be differentially secreted. This information can be very beneficial for follow-up studies in the PD and LRRK2 field. Furthermore, the authors should assess the expression of Rab27a and CD82 in WT and LRRK2 G2019S astrocytes by western blots to verify RT-qPCR data. Furthermore, the authors should present specifically exosome biogenesis or secretion genes are altered to provide further insight into the stage of exosome biogenesis that is affected (ESCRT0-3, VPS4, ALIX, etc).

In the first comment, the reviewer refers to the observation that the number of total and CD63-positive EVs secreted by astrocytes is unchanged between the WT and LRRK2 G2019S genotypes. The classification of different EV sub-populations based on marker proteins is an evolving field of research, and an important study by Kowal et al. defined generic and sub population-specific EV markers (Kowal et al., 2016). Our RNA-seq dataset revealed five upregulated genes identified in the Kowal study, namely actin, GAPDH, actinin, complement and fibronectin, but unfortunately there is no clear pattern correlated with specific EV sub populations. For example, actin and GAPDH are two upregulated proteins that can be found in multiple types of EVs, actinin is enriched in large and medium-sized EVs, and complement and fibronectin are enriched in high density but small EVs (Kowal et al., 2016). The majority of dysregulated genes identified in our sequencing experiment are not proteins classically used to categorize EVs, so unfortunately our data does not allow us to address the reviewer’s question. To make sure that the data is readily accessible to the scientific community, we have prepared a supplementary table with a list of extracellular exosome-related genes identified in the RNA sequencing study. To respond to the reviewer’s comment on a specific stage of EV biogenesis/secretion altered in LRRK2 G2019S, the sequencing data presented in this manuscript does not allow to conclude that there is such a dysregulation. Our gene list corresponding to the “extracellular exosome” gene ontology term contains a large majority of genes coding for proteins enclosed within EVs that do not play a role in biogenesis/secretion. For example, the gene list does not contain ESCRT0-3, VPS4, ALIX or other classical markers involved in EV biogenesis and we cannot conclude anything about the alteration of MVB/EV biogenesis or defects in specific stages of MVB trafficking or EV secretion. In addition, we thank the reviewer for suggesting the validation of RT-qPCR data by western blot. The purpose of the RT-qPCR experiment was to validate the gene expression data collected by RNA-seq. Given that our objective was to confirm gene expression levels, and that we do not further study CD82 and Rab27b, we think that collecting protein expression levels is not necessary in the context of this study.

We agree with the reviewer’s suggestion, and we added images showing the subcellular localization of CD63 in both WT and LRRK2 G2019S MVBs by immunogold staining (Figure 2E). Validation experiments available in Figure 1A and Supplementary Figure 2A and 2B confirm that our astrocytes express CD44 as well as markers of mature astrocytes (BHLHE41, SOX9, GLUT1, APOE, GLUD1). The reason for showing CD44 instead of a more mature marker such as GFAP in Figure 2 of the manuscript is because CD44 is a membrane marker, and it therefore enables a clear visualization of the astrocyte surface area. We also note that, as shown in Supplementary Figure 2Aii, iPSC-derived and human fetal astrocytes express CD44, but iPSCs and NPCs do not significantly express this marker gene. In addition, as suggested by the reviewer, we added information related to MVB maturation in the introduction and the new text reads as follows (changes in italics):

Introduction section The sorting and loading of exosome cargo is an active and regulated process (Temoche-Diaz et al., 2019), and the regulatory factors involved in EV/exosome biogenesis are just beginning to be identified. Among the well-known factors, Rab proteins are essential mediators of MVB trafficking and they regulate endosomal MVB formation/maturation as well as microvesicle budding directly from the plasma membrane (Pegtel & Gould, 2019; T. Wang et al., 2014). In addition, membrane remodeling is an essential aspect of MVB/EV formation that appears to be regulated, at least in part, by the endosomal sorting complex required for transport (ESCRT) machinery (Pegtel & Gould, 2019; Schoneberg, Lee, Iwasa, & Hurley, 2017).

3) In Figure 2A and B, data shows that both WT and LRRK2 G2019S astrocytes produce MVBs and MVBs in LRRK2 G2019S astrocytes is smaller than in WT astrocytes. In Figure 2E, the authors showed the abundance of CD63 localized within MVBs in WT astrocytes but did not show the CD63 localization in MVBs in G2019S astrocytes. However, it is important to show CD63 localization in MVBs in G2019S astrocytes to fully support the conclusion that CE63+ MVBs are present in LRRK2 G2019S astrocytes. In addition, CD44 is a marker for astrocyte-restricted precursor cells. Although CD44+ positive cells are committed to give rise to astrocytes, it is crucial to include another astrocyte marker to ensure these cells are indeed mature astrocytes. -Related, authors should consider citing some of the MVB maturation literature to guide the readers.

We agree with the reviewer’s suggestion, and we added images showing the subcellular localization of CD63 in both WT and LRRK2 G2019S MVBs by immunogold staining (Figure 2E). Validation experiments available in Figure 1A and Supplementary Figure 2A and 2B confirm that our astrocytes express CD44 as well as markers of mature astrocytes (BHLHE41, SOX9, GLUT1, APOE, GLUD1). The reason for showing CD44 instead of a more mature marker such as GFAP in Figure 2 of the manuscript is because CD44 is a membrane marker, and it therefore enables a clear visualization of the astrocyte surface area. We also note that, as shown in Supplementary Figure 2Aii, iPSC-derived and human fetal astrocytes express CD44, but iPSCs and NPCs do not significantly express this marker gene. In addition, as suggested by the reviewer, we added information related to MVB maturation in the introduction and the new text reads as follows (changes in italics):

Introduction section

The sorting and loading of exosome cargo is an active and regulated process (Temoche-Diaz et al., 2019), and the regulatory factors involved in EV/exosome biogenesis are just beginning to be identified. Among the well-known factors, Rab proteins are essential mediators of MVB trafficking and they regulate endosomal MVB formation/maturation as well as microvesicle budding directly from the plasma membrane (Pegtel & Gould, 2019; T. Wang et al., 2014). In addition, membrane remodeling is an essential aspect of MVB/EV formation that appears to be regulated, at least in part, by the endosomal sorting complex required for transport (ESCRT) machinery (Pegtel & Gould, 2019; Schoneberg, Lee, Iwasa, & Hurley, 2017).

4) In Figure 3, it is impressive that the authors are able to image EVs using cyro-EM approach and analyze their sizes. The authors also observed different shapes of EVs. Is there any shape difference between WT EVs and G2019S EVs? Is there a way that the authors could categorize these shapes and do a detailed analysis in EV shapes? Also, In Figure 3D, both WT EV and G2019S EV images should present side by side for comparison. -Related, the size frequencies of EVs presented suggest a difference in the types of EV's released. Interestingly, exosomes are classically known to range from ~50-120nm and this population is significantly decreased in G2019S compared to WT. What does this suggest?

As suggested by the reviewer, we classified the two main EV shapes as “simple” and “multiple” EVs, and found no quantitative differences between WT and LRRK2 G2019S. This new data and side-by-side images of WT and LRRK2 G2019S EV images are available in Figure 3E-G, and the text has been updated accordingly (see text in italics below). One of the observations of Figure 3 is that there exist genotype-specific differences in the size distribution of EVs, which suggests that different classes of vesicles may be preferably produced by WT vs. LRRK2 G2019S astrocytes. This could be the result of differences in dynamics related to cargo loading, or a shift from MVB-released exosomes to membrane budding and microvesicle production. These observations are of great interest and we added a short discussion (in italics below) but they are beyond the scope of this study focused on EV neurotrophic properties, and we do not currently have evidence to support these hypotheses.

Results - LRRK2 G2019S affects the size of EVs secreted by iPSC-derived astrocytes

EVs mostly displayed a circular morphology (as opposed to the cup-shaped morphology observed by TEM) (Figure 3E), but a variety of other shapes were also observed (Supplementary Figure 3C). (…) Quantification of the number of simple vs. multiple EV structures did not differ between the two lines, and represent up to 16 % of the EV population (Figure 3G).

Discussion – Dysregulation of iPSC-derived astrocyte-mediated EV biogenesis in Parkinson’s disease

The observation that LRRK2 G2019S MVBs are less frequently located in the perinuclear area suggests that they may spend less time loading cargo at the Trans-Golgi network, which could in turn produce smaller MVBs and EVs with a different size range compared to WT (Edgar, Eden, & Futter, 2014; Pegtel & Gould, 2019). We did not observe a difference in the number of secreted EVs (total and CD63+ subpopulation) between WT and LRRK2 G2019S astrocytes (Figure 3C,H), suggesting that the secretion of at least one population of EVs is independent of the astrocyte genotype.

5) In figure 3c, SBI ELISA claims to quantify CD63+ vesicles, the authors should present more standardized particle quantification data (either by CD63 FACs for isolated EVs in WT vs G2019S or ZetaView/QNano particle tracking). The authors should also directly quantify the total number of EVs secreted in WT vs G2019S conditions (not only CD63+).

The updated manuscript now contains the NTA analysis of WT and LRRK2 G2019S EVs (Figure 3C,D) which provides the total number of EVs secreted by WT and LRRK2 G2019S astrocytes.

6) In Figure 4, the authors quantify LRRK2+/CD63+ particles by imaging. Importantly, it appears that there are less CD63 "large gold" particles in MVB of G2019S compared to control. This CD63 baseline quantification in MVB of WT vs. G2019S should be presented in this figure. These data are not convincing and should be quantified by FACS in secreted EV. Supplementary figure 3 should be brought into this figure.

As suggested by the reviewer, we quantified the number of CD63 large gold particles per MVB in WT and LRRK2 G2019S lines (Supplementary Figure 3A,B), and we re-introduced Supplementary Figure 3 into the main text (Figure 4E). We also updated the text (see in italics below). Additionally, we present extensive quantification of LRRK2 levels in MVBs and secreted EVs via imaging and biochemical analysis (ELISA), two different but complementary analytical methods.

Results - LRRK2 G2019S affects the size of MVBs in iPSC-derived astrocytes

Tetraspanins are transmembrane proteins, and the tetraspanin CD63 is enriched in exosomes and widely used as an exosomal marker (Escola et al., 1998; Men et al., 2019). However, cell type specificities in the expression of exosomal markers such as CD63 have been documented (Jorgensen et al., 2013; Yoshioka et al., 2013). We therefore confirmed the presence of CD63- positive MVBs in iPSC-derived isogenic astrocytes by immunofluorescence (Figure 2D) and immunogold electron microscopy (IEM) (Figure 2E). Analysis of IEM images showed an abundance and similar levels of CD63 localized within MVBs in WT and LRRK2 G2019S astrocytes (Figure 2E, Supplementary Figure 3A,B), confirming that CD63 can be used as a marker of MVBs and exosomes in iPSC-derived astrocytes.

7) In Figure 5, using CD63 as a MVB marker is not the most accurate approach. ESCRT markers should be co-stained with these experiments to truly show MVB localization (CD63 can localize to MVBs but is known to have a wider distribution throughout the cell compared to TSG1010 or other ESCRT complex proteins). Additionally, the authors must show their Supplemental Figure 3 ELISA quantification of p-aSyn in this main figure, and comment on why they conclude higher p-aSyn content in MVBs based on their IEM but then find no differences in aSyn in secreted EVs in WT vs. G2019S by ELISA.

We thank the reviewer for the suggestion to use ESCRT proteins as MVB markers. We decided to use CD63 because it is recognized in the literature as an MVB and EV marker (Beatty, 2008; Edgar et al., 2014), and we now refer to these two studies in the manuscript to support this choice (see text in italics below). Using ESCRT complex proteins as MVB markers is an interesting alternative, but we note that proteins associated with this complex are also found to regulate other biological processes such as autophagy (Takahashi et al., 2018) and plasma membrane repair (Jimenez et al., 2014), and so they can co-localize to non-MVB structures (e.g. autophagosomes or plasma membrane). Similarly, TSG101 can also localize to non-MVB structures such as the nucleus and Golgi complex (Xie, Li, & Cohen, 1998), and also lipid droplet (LD) membranes where it promotes LD-mitochondria contact (J. Wang et al., 2021). As suggested by the reviewer, Supplemental Figure 3 has been re-introduced into the main text (Figure 6C). Regarding αSyn, the immunogold staining specifically detects the phosphorylated form of αSyn (p-αSyn), while the ELISA detects all forms of αSyn (total αSyn). We observed increased p-αSyn in LRRK2 G2019S MVBs, but similar levels of total αSyn in WT vs LRRK2 G2019S EVs. This observation suggests that the phosphorylated form of αSyn, but not the total amount of αSyn, is affected by the experimental conditions. The text has been updated and reads as follows (changes in italics).

Results - LRRK2 is associated with MVBs and EVs in iPSC-derived astrocytes

In light of our observations that mutations in LRRK2 result in altered astrocytic MVB and EV phenotypes, we asked if LRRK2 is directly associated with MVBs in astrocytes and if this association is altered by the LRRK2 G2019S mutation. We analyzed and quantified the co localization of LRRK2 with CD63 (Figure 4A), a marker for MVBs (Beatty, 2008; Edgar et al., 2014), and found that the proportion of LRRK2+ /CD63+ structures remains unchanged between WT and LRRK2 G2019S isogenic astrocytes (Figure 4B).

Results - The LRRK2 G2019S mutation increases the amount of phosphorylated alpha synuclein (Ser129) in MVBs

Since the MVB/EV secretion pathway is altered in our LRRK2 G2019S model of PD, we reasoned that mutant astrocytes might produce αSyn-enriched EVs by accumulating the protein in its native or phosphorylated form in MVBs or EVs. IEM analysis revealed an abundance of p-αSyn (small gold) inside and in the vicinity of MVBs of LRRK2 G2019S iPSC-derived astrocytes, but not isogenic control astrocytes (Figure 6A). We observed that 55 % of the CD63+ (large gold) MVBs in LRRK2 G2019S astrocytes are also p-αSyn+ (small gold), compared to only 16 % in WT MVBs. LRRK2 G2019S astrocytes contained on average 1.3 p-αSyn small gold particles per MVB compared to only 0.16 small gold particles in isogenic control astrocytes, and MVB populations containing more than 3 p-αSyn small gold particles were only observed in LRRK2 G2019S astrocytes (Figure 6B). When we analyzed the content of EVs by ELISA, we found that total αSyn levels (phosphorylated and non-phosphorylated) in EV-enriched fractions are similar between isogenic controls and LRRK2 G2019S (Figure 6C). These results suggest that astrocytes secrete αSyn-containing EVs, and the LRRK2 G2019S mutation appears to alter the ratio of p-αSyn/total αSyn in MVB-related astrocyte secretory pathways.

8) In figure 6, it is even more clear that there is a stark difference between the CD63 presence in/near MVBs between WT and G2019S conditions. Since the authors normalize several pieces of data to CD63 (MVB localization, LRRK2 co-localization, etc), it is critical to quantify the number of baseline CD63 gold particles in MVBs in WT vs G2019S.

After careful quantification of the number of CD63 gold particles in WT and LRRK2 G2019S MVBs (available in Supplementary Figure 3A,B), we conclude that there are no significant differences between the two genotypes, and the MVB images initially selected in Figure 6 are not representative. We therefore replaced Figure 6A with new images.

9) In Figure 7, the authors used the co-culture of astrocytes and neurons to assess astrocyte-derived EV uptake by dopaminergic neurons. Although 3D reconstitution of neurons and exosomes can be precise, the data may not be 100% clean. It would be better if the authors collect ACM containing EV fraction from WT astrocyte and G2019S astrocytes and then incubate dopaminergic neurons with ACM containing EV fraction. In this way, only dopaminergic neurons are in the culture and there will be no CD63-GFP expressed astrocytes to contaminate the CD63-GFP signal in neurons.

We understand the concerns raised by the reviewer, and we can ensure that state-of-the-art imaging technologies and image post-processing techniques have been used to prevent astrocytic CD63 signal from contaminating the neuronal signal. We performed confocal microscopy with a 63X oil objective lens (numerical aperture = 1.4), and the images were processed with a Gaussian Filter (0.18 μm filter width) to reduce background noise in the MAP2 channel, and deconvolved (10 iteration) to enhance confocal image resolution in the CD63 channel. Furthermore, CD63-positive structures were detected with background subtraction enabled.

10) In Figure 9, the authors must show their ACM control. They show untreated, EV-free, and EV-rich ACM, but do not show unmanipulated ACM control.

The results of dendrite length analysis for unmanipulated ACM was initially available in Figures 8E and 8F. For clarity, we prepared a new Figure 9 that shows treatment with unmanipulated ACM, EV-free ACM, and EV-enriched fractions.

Reviewer #2:

In this manuscript by de Rus Jacquet et al., authors present an interesting study to detect changes in extracellular vesicles in human PD patient derived iPSC-derived astrocytes carrying the LRRK2 G2019S mutation. Isogenic gene corrected iPSCs were used as controls in all experiments. Authors first performed RNA-Seq for global gene expression changes between G2019S and "WT" gene corrected astrocytes. GO analysis showed an upregulation of extracellular compartments (including exosome compartments) in LRRK2 astrocytes. Subsequent experiments focusing on extracellular vesicles (EVs) and multivesicular bodies (MVBs), showed specific differences of MVB area and the size of secreted EVs. Secreted EVs from G2019S astrocytes also contained more LRRK2 particles and G2019S EVs contained more phosphorylated aSyn particles. Co-culture of LRRK2 astrocytes with human dopamine neurons showed accumulation of CD63+ exosomes in neurites, compared to co-culture with WT astrocytes. Co-culture with LRRK2 astrocytes decreased viability of TH+ neurons and LRRK2 dendrites/neurites were also shorter. These co-culture findings were replicated using EV-enriched conditioned media. Finally, authors showed that the trophic effect of astrocytes on neurons was due both to soluble factors released into the media, and production and release of EVs. Overall, this is a well-written and systematically performed study. This reviewer has several comments as detailed below.

1) Based on their data, authors conclude that astrocyte-to-neuron signaling and trophic support mediated by EVs is disrupted in LRRK2 G2019S astrocytes. Have authors measured the differences in trophic factors released by LRRK2 astrocytes in EVs and in conditioned media?

This is an important question, and we have not measured the levels of various neurotrophic factors in the medium. We concluded that LRRK2 G2019S astrocytes failed to secrete neurotrophic factors based on the neuron viability data. Healthy neurons cultured with disease astrocytes displayed dendrite shortening equivalent to that of neurons cultured in basal medium lacking neurotrophic factors. Furthermore, the morphological alterations occurred over a long period of time (2 weeks) and did not recapitulate the rapid and high level of neuron death and neurite fragmentation typically observed as a result of exposure to neurotoxins (Liddelow et al., 2017). However, we performed a new analysis of our RNA-seq data and identified dysregulated trophic processes of interest in LRRK2 G2019S astrocytes.

2) Authors differentiate cells (astrocytes and neurons) from midbrain lineage NPCs. The data show convincing effects of the LRRK2 derived astrocytes on neurons, but one question is whether this is specific to dopaminergic cells. Would this genotype specific effect also be expected in other lineages, e.g. cortical neurons? Authors should discuss this point.

The reviewer is making an excellent point. We prepared mouse primary midbrain cultures, and co-cultured WT midbrain neurons with WT or LRRK2 G2019S astrocytes. We found that the survival of WT midbrain dopaminergic neurons was significantly affected by LRRK2 G2019S astrocytes, but the viability of non-dopaminergic midbrain neurons was not changed when co cultured with WT or disease astrocytes. A previous study by di Domenico et al. also showed that dopaminergic neurons are more sensitive to the effect of LRRK2 G2019S astrocytes compared to non-dopaminergic cell types (di Domenico et al., 2019).

3) Prior work has demonstrated reductions in neurite length in neurons derived from LRRK2 G2019S iPSCs (not specific to dopaminergic neurons in LRRK2 cells) (for example Reinhard et al 2013). It is curious that the LRRK2 G2019S mutation itself can cause such a phenotype in neurons mono-cultures, and as shown in the current study, that LRRK2 G2019S astrocytes also induce a similar effect on WT neurons in co-culture. Can authors expand on this point in the Discussion?

We thank the reviewer for this question, and we added a new point of discussion in our manuscript, which reads as follows (changes in italics):

Evidence from this study and previous reports indicates that the LRRK2 G2019S mutation affects neurons through a variety of mechanisms. Here, we show a non-cell autonomous effect on neuronal viability via impairment of essential astrocyte-to-neuron trophic signaling, but the LRRK2 G2019S mutation can also mediate cell-autonomous dopaminergic neurodegeneration (Reinhardt et al., 2013). These observations support the idea that the LRRK2 kinase may be involved in a large number of pathways essential to maintain cellular function, cell-cell communication and brain homeostasis, and disruption of LRRK2 in one cell type has cascading effects on other neighboring cell types. In conclusion, our study suggests a novel effect of the PD-related mutation LRRK2 G2019S in astrocytes, and in their ability to support dopaminergic neurons. This study supports a model of astrocyte-to-neuron signaling and trophic support mediated by EVs, and dysregulation of this pathway contributes to LRRK2 G2019S astrocyte mediated dopaminergic neuron atrophy.

4) Authors should provide data on % dopaminergic neurons generated in the cultures.

We agree that this is important information, and we updated the latest version of the manuscript with this information (see below in italics). We estimate that the neuron cultures consist of 50 to 70 % dopaminergic neurons, and they are depleted of non-neuronal cells as explained in Material and Methods.

Material and Methods - Preparation and culture of iPSC-derived NPCs, dopaminergic neurons and astrocytes

To isolate a pure neuronal population, the cells were harvested in Accumax medium, diluted to a density of 1 × 106 cells in 100 µl MACS buffer (HBSS, 1 % v/v sodium pyruvate, 1 % GlutaMAX, 100 U/ml penicillin/streptomycin, 1 % HEPES, 0.5 % bovine serum albumin) supplemented with CD133 antibody (5 % v/v, BD Biosciences, San Jose, CA, cat. # 566596), and the CD133+ NPCs were depleted by magnetic-activated cell sorting (MACS) using an LD depletion column (Miltenyi Biotech, San Diego, CA), as described previously (de Rus Jacquet, 2019). The final cultures are depleted of non-neuronal cells and contain approximately 70 % dopaminergic neurons, the remaining neurons consisting of uncharacterized non-dopaminergic populations.

5) p7. Authors refer to phosphorylated a-synuclein as accelerating PD pathogenesis, but the references cited do not show this. In fact, Gorbatyuk et al 2008, showed that overexpression of S129 with constitutive phosphorylation eliminated a-synuclein induced nigrostriatal degeneration. The Fujiwara et al 2002 reference showed the presence of phospho a-syunclein in Lewy bodies and neurites. Authors should revise their statement that phospho a-synuclein is associated with accelerated pathology.

The reviewer is correct. We meant to highlight that there is a correlation between phosphorylated αSyn levels and PD pathogenesis, not that phosphorylated αSyn causes an acceleration of PD pathogenesis. We rephrased the sentence as follows, and replaced the study by Gorbatyuk et al. with a study by Anderson et al. that shows presence of phosphorylated αSyn in Lewy bodies (new text in italics):

EVs isolated from the biofluids of PD patients exhibit accumulation of αSyn (Lamontagne Proulx et al., 2019; Shi et al., 2014; Zhao et al., 2018), a hallmark protein whose phosphorylation at the serine residue 129 (p-αSyn) is correlated with PD pathogenesis (Anderson et al., 2006; Fujiwara et al., 2002).

6) Please provide details on the number of iPSC lines used for these experiments.

Experiments in the first version of this manuscript were performed using a single LRRK2 G2019S iPSC line and its gene-corrected control. The manuscript now presents the results collected using a second, independent non-isogenic iPSC line, as well as mouse primary cultures.

7) Clarify whether the WT neurons used for co-culture were derived from the isogenic human neurons?

We confirm that the WT neurons used for co-culture experiments were derived from isogenic controls. We added subtitles to our figures to clarify when data show results from isogenic or non-isogenic iPSC-derived cells.

Wow, I hate this. How is the work of interpretation discouraged? Giving vague description to something doesn't preclude better description; to encourage people to express the idea that there's something coherent about, well, something is to create the space for further interpretation. A vibe is a term for a fetal stage, something emergent still emerging. If you already had a better name for it you'd use that. Articulating that you think there's a there there is a meaningful step! (this is where I would make a joke about attention mechanisms in deep learning if I were committed to the author's schtick) We can analyze whether cottagecore is fashy because people recognized a vibe and nurtured it into a whole... thing. (A thing that is sometimes fashy)

This manuscript is in revision at eLife

The decision letter after peer review, sent to the authors on June 30 2020, follows:

Summary:

This paper addresses a critical issue in neuroscience: what's the question, and can we answer it? The questions the author proposes are ones that have been considered, in one form or another, reasonably often by experimentalists. And the author shows rigorously that there's a reasonable chance that they are simply not answerable.

Essential Revisions:

We believe that this is an extremely important issue, and the approach the paper takes is a reasonable one for addressing it. Our main worry, though, is that mainstream neuroscientists will ignore it, for two reasons. One is that it's not a message they want to hear. Second, the example circuits are sufficiently abstract that they can be dismissed as yet another musing by your typical uninformed theorists. (That is not, we should emphasize, our view, but it's not an uncommon one in the field.) Our goal, therefore, is to fix these potential problems, so that people will have to pay attention.

The premise of the paper is that if you understand a neural circuit, there are certain questions about it that you should be able to answer. The author proposes six such questions, and then shows that in the worst case they are exponentially (in the number of neurons) hard to answer.

The success of this program hinges on two things: a sensible set of questions, and a demonstration that answering those questions is hard. We're not ecstatic about the questions, but we believe that's not an insurmountable issue (more on that below). More problematic is the result that the questions are hard to answer. What's really shown is that there is at least one circuit for which, in the worst case, answering the questions is exponentially hard. While this is certainly correct, it doesn't make a convincing case that answering these questions will be hard in the brain. First, the worst case may not be the typical case. The 3 SAT problem, for instance, is NP complete, but is hard to solve only for a narrow range of parameters. Second, answering the questions for actual circuits found in the brain may not even be exponentially hard in the worst case.

This brings us to two critical comments. First, it needs to be crystal clear that this paper does not demonstrate that answering the proposed questions is guaranteed to be exponentially hard, only that it might be. This was stated in the manuscript, but not emphasized. For instance, on lines 138-140, it says

"Using techniques from Computational Complexity Theory, we ask what is the smallest number of experiments necessary, in general, in order to answer these questions, in typical experimental settings."

Here "in general" means worst-case. For neuroscientists, though, "in general" means "most of the time". It should be clear that you mean worst case, and that the typical case may be very different.

In fact, this needs an expanded discussion. Whether or not it will take an exponential number of experiments to answer the questions depends on the circuit. We might get lucky, and only a small number of experiments are needed. Or we might get unlucky, and a large number are needed. This analysis can't tell us that, and this should be clear in the paper.

Second, what's really needed is the analysis of a more realistic circuit, ideally with both positive examples (for which it is possible to answer the questions) and negative examples (for which it isn't). This is hard, but we have a few suggestions, some of which can probably be done without a huge amount of work.

a. Linear network, y=Ax+noise. For this (and possibly in all realistic) situations, "perform the task" needs to be replaced by "achieve a certain level of performance". For instance, if there's a true mapping y=f(x), then "perform the task" would be replaced with "<(y-y*)^2> below some threshold". The questions should be answerable in polynomial time for this network; otherwise, one should worry.

b. In 2000, Hopfield and Brody came up with a simple circuit which we think of as "understandable" (Hopfield and Brody, PNAS 97:25, 13919-13924, 2000). It would be nice to determine whether the questions can be answered in polynomial time for this circuit. Again, if they can't, one should worry.

c. Deep networks. Again, "perform the task" would have to be replaced with "performance is above some particular threshold". Here we suspect that the questions are not answerable; if that could be shown, it would be a huge step forward.

d. A made-up model of a deep network. Assume that in a deep network, whenever you delete a neuron, performance drops. That's probably not so far from the truth -- and also not so far from what we think would happen in the brain. (With some exceptions; occasionally I hear talks where performance is better when two areas are ablated rather than just one, but let's ignore that.) How much performance drops depends, of course, on which neurons are deleted, so there's not a simple mapping between performance and which neurons are present in the circuit. Can the questions be answered in this case? This sounds like a problem computer scientists have considered, so possibly rigorous analysis could be done.

We believe it's critical to consider a case that is not far from what one might find in the brain. Otherwise, it will be too easy to dismiss this work as irrelevant to real neuroscience. The above are only possibilities, and a and d may be pretty easy, but the author is welcome to come up with his own examples. Note that rigor is not absolutely necessary here, since there's already one rigorous example. Plausible arguments would be fine.

Finally, "understand" needs further discussion. That's partly because the approach here is a little non-standard. Most people try to directly define "understanding". Instead, the statement is "if you understand a circuit, you should be able to answer these questions". This has to be made crystal clear -- especially since people aren't expecting it. In addition, a discussion of the more standard approach, a direct definition, should be included. The usual definition is something like "A short description of what is being computed, along with a description of an algorithm for computing it." It should be clear how this, more standard, definition compares to the one in the paper. For instance, under the standard definition it may be possible to understand a circuit without being able to answer any of the questions. For instance, I believe we can "understand" (by the more standard definition) the synfire chain circuit. This doesn't mean that one definition is better than the other, but their differences should be acknowledged.

I live and teach in Qatar (Qatar was previously known as a small oil-rich country in the Middle East but is now famous as the host country for FIFA World Cup 2022). At my school, we have many Indian and South Asian students who I am sure would totally understand and appreciate the use of “Namaste” as a concept for a workshop classroom.

An equivalent to Namaste would be “Salaam” for my teaching context in Qatar. Salaam is a common greeting in Muslim countries. It means wishing “peace” and it carries intentional respect and kindness toward the other person. For a writing workshop classroom, Salaam can be understood as a peace-based concept that encourages safe, healthy, respectful dialogue and that discourages verbal or written “attack” on a student’s writing. If I were to do a writing workshop with my students in the future, I may consider replacing Namaste with Salaam for we have a very high Muslim student population in our school. That said, I am actually reminded of a Bollywood movie titled “Salaam Namaste.” Isn’t that interesting? Salaam Namaste would mean “peaceful greetings of respect and kindness.”

As long as we are creating and maintaining the kindness culture in our classrooms, I think we can safely play with the word choice, depending on our location, culture, teaching situation, students and other preferences.

Author Response:

Reviewer #2 (Public Review):

Valentini et al. explore the contribution of inexperienced homing pigeons in a pair, while finding the most efficient route back home. My comments below mostly concern the need of broadening the scope of the introduction and discussion by discussing and citing literature beyond homing pigeons as at the moment the manuscript could be characterized as too specific for the readership.

We thank the reviewer for their suggestions which allowed us to expand the focus of our manuscript. Our answers to the reviewer’s comments are reported below together with modifications done on the revised manuscript.

The authors use and present transfer entropy methods which regard the transmission of information from one individual to the other and effect of this information on behaviour. I haven't used such methods myself, but I think the methodology is nicely explained and easy to follow as it's written here. However, I would still encourage the authors to avoid jargon and un-introduced terms while first presenting their methods and results in the introduction and results sections. I also think that the paragraph in the introduction (L92-104) that refers to transfer entropy (TE) has to be extended and also direct readers to reviews such as [1] that attempt to make TE accessible to a broad audience of non-physicists. Behavioural ecologists and primatologists that study leadership and influence in animals, using less data hungry methods than TE, will probably be interested in reading this manuscript. Because eLife is a journal that attracts a very broad audience I would suggest investing more on better introducing TE to biologist and anthropologists.

We thank the reviewer for their suggestions. In the revised version of our manuscript, we clarified the meaning of symbols and unintroduced terms and extended the introduction paragraph about transfer entropy to provide more information. We now discuss data requirements of information-theoretic approaches, point the reader towards recent literature reviews aimed at introducing these (and similar) approaches to the community of behavioural ecology, and better introduce the advantages of transfer entropy with respect to methods based on models of alignment, attraction, and repulsion.

“Leader–follower interactions of this sort can be accurately captured using information-theoretic measures that quantify causal relations in terms of predictive information (Butail, Mwaffo, and Porfiri 2016; Kim et al. 2018; Crosato et al. 2018; Ray et al. 2019; Valentini et al. 2020). This methodological approach, which generally requires large amounts of data (but see (Porfiri and Ruiz Marín 2020)), is gaining popularity among behavioural ecologists (Strandburg-Peshkin et al. 2018; Pilkiewicz et al. 2020) as tools for automatic monitoring and extraction of the necessary volumes of behavioural data become increasingly available (Egnor and Branson 2016). One of these measures, transfer entropy, quantifies information about the future behaviour of a focal individual that can be obtained exclusively from knowledge of the present behaviour of another subject (Schreiber 2000). Transfer entropy measures information transferred from the present of the sender to the future of the receiver (Lizier and Prokopenko 2010). It explicitly accounts for autocorrelations characteristic of individual birds’ trajectories (Mitchell et al. 2019) by discounting predictive information available from the sender’s present that is already included in the receiver’s past (see Figure 1). Furthermore, it does not require a model of how sender and receiver interact, and it is well suited to study social interactions both over space and time (Lizier, Prokopenko, and Zomaya 2008; Strandburg- Peshkin et al. 2018). This aspect of transfer entropy encompasses traditional methods to quantify collective movement that are based on modelling an individual’s behaviour as a combination of three motional tendencies (Couzin et al. 2002) – alignment of direction to nearby group members, attraction towards sufficiently distant members, and repulsion from sufficiently close members – that allow an individual to maintain proximity to the group. In this context, transfer entropy is advantageous as it can capture causal interactions due not only to alignment forces (Nagy et al. 2010) but also to attraction and repulsion forces that result in temporarily unaligned states (Pettit, Perna, et al. 2013).”

A thought I had while reviewing this work regards the theory of the wisdom of the crowd [2]. This indicates that when a group or a collective averages the different estimates of its members, they reach a more accurate collective estimate. Studies have also shown that animals can average their movement directions to resolve conflicts of interest [3,4]. The current manuscript also shows that pooling infomration leads to better movement decisions. Would it thus make sense for this manuscript to discuss how its findings may support the wisdom of the crowd theory?

We thank the reviewer for the suggestion. In the revised version of out manuscript, we included a new paragraph where we discuss a possible connection with the phenomenon of the wisdom of crowds as well as how our results might generalize to flocks of larger size.

“The ability of groups to outperform single individuals by pooling information across their members is an aspect of collective intelligence that has long intrigued researchers. One potential mechanism underlying this phenomenon, popularly known as the wisdom of crowds (Surowiecki 2005), is averaging many individuals’ estimates independent from each other. Averaging individual decisions is expected to provide a more accurate group estimate than any individuals’ guess. Previous studies have also shown that animals can average their movement decisions to reach a compromise (Biro et al. 2006; Strandburg-Peshkin et al. 2015). Although the mechanisms by which experienced and naïve individuals pool information during route development remain unknown, our study points to the importance of naïve group members within the information-pooling process. Moreover, the wisdom of crowds is known to require personal information to be independent among group members (Couzin 2018) otherwise group performance can degrade quickly for increasing group size (Kao and Couzin 2014). Experimental pairs could thus benefit from pooling information with naïve individuals that, at least at the beginning of each generation, likely provide a source of information independent from that of the experienced bird. The potentially deleterious effects of losing independence may provide another pressure to shift over time from innovative exploration to route6 preserving exploitation. It remains to be explored how our results generalize to larger flock sizes. Previous experiments without generational replacement showed that, even in larger flocks, birds flying ahead of the flock had a tendency to assume leadership positions (Nagy et al. 2010). However, the repeated introduction of naïve individuals into larger flocks might complicate the dichotomy between leaders and followers by inducing turnover dynamics between the front and the back of the flock.”

As briefly mentioned earlier, I think that the cited literature in this manuscript (especially in L58-138 and throughout the discussion) includes mostly studies on homing pigeons whereas relevant studies to the current manuscript have been performed on other species and by discussing and citing relevant studies on various species the manuscript would become more attractive to a broader audience and wouldn't read as homing-pigeon specific.

We thank the reviewer for pointing us towards additional literature related to our study. We included the suggestions from the reviewer as well as further references to a broader literature to expand the scope of our manuscript.

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Reply to the reviewers

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1. General Statements

We want to thank all three reviewers for their positive feedback, constructive comments, and suggestions for clarity and improvement. We are delighted to find their consensus that the manuscript represents a contribution to the field.

Accordingly, we made changes in the text (all highlighted in blue in the revised manuscript) and added a new figure as detailed in the point-by-point response.

2. Point-by-point description of the revisions

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

The authors describe results of the comprehensive analysis of the prevalence and functionality of intrinsically disordered regions of the pathogen-encoded signaling receptor Tir, which serves as an illustrative example of the bacterial effector proteins secreted by Attaching and Effacing (A/E) pathogens. This is an interesting and important study that represents an impressive amount of data generated computationally and using a broad spectrum of biophysical techniques. The work serves as a model of the well-designed and perfectly conducted study, where intriguing conclusions are based on the results of the comprehensive experiments. The manuscript is well-written and concise, and I have a real pleasure reading it. The text and figures are clear and accurate.

We thank the Reviewer for these positive comments on our work.

Although, in general, prior studies are referenced appropriately, the authors should mention that the pre-formed structural elements they found in Tir are in line with the concept of "PreSMos" (pre-structured motifs) previously introduced and described in several important studies from the laboratory of Kyou-Hoon Han.

We thank the Reviewer for this suggestion. We have added a sentence to acknowledge the presence of “PreSMos” in the target-free state of Tir as putative signatures for target-binding, referring to a review article summarizing several local structural elements in unbound IDPs:

“This supports the presence of pre-structured motifs (PreSMos) as pre-existing signatures for target binding and function within target-free Tir (72)**.”

Please, note that we decided to keep this discussion to a minimum, as we cannot rule out the contribution of the induced fit model to the binding mechanism (i.e., disorder-to-order transition upon binding).

Reviewer #1 (Significance (Required)):

Solid evidence is provided that structural disorder and short linear motifs represent common features of A/E pathogen effectors. In fact, using a set of bioinformatics tools, the authors first show that although prokaryotic proteins typically contain significantly less intrinsic disorder than eukaryotic proteins, A/E pathogen effectors are as disordered as eukaryotic proteins. Using the translocated intimin receptor (Tir) as a subject of focused study, the authors then utilized a number of biophysical techniques to draw an impressive picture of disorder-based functionality. This study clearly represents a major advancement in the field of functional intrinsic disorder in general and in disorder-based functionality of proteins expressed by pathogenic bacteria. This was adds significantly to the field and will have a noticeable impact.

Again, reading this manuscript was a real joy. Finally, this work perfectly fits in the area of my expertise, since for the past 25 years or so I am working on the different aspects of intrinsically disordered proteins.

Thank you for this encouraging assessment.

**Referee Cross-commenting**

I agree with the amended recommendation of reviewer #3 to add in the manuscript EPEC O127.

According to the suggestion of Reviewer #3, we have now included EPEC O127:H6 in the manuscript.

I completely agree with comments of reviewer #2 and partially agree with reviewer #3. In my view, comparison of various strains as references for EPEC represents an interesting but independent project. It can be recommended to the authors as one of the potential future developments of their work.

Thanks for the suggestion. We are pursuing that line of research.

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

The general impression is that this is an excellent study that establishes

The C-terminal intracellular region of Tir called C-Tir spanning residues 338 to 550 is largely disordered, however, observe helical structural elements involved with lipid interactions; multi-phosphorylation. The intracellular N-terminal part of Tir called N-Tir spanning residues 1 to 233 is also partially disordered but include a folded domain that is shown to assemble into a dimer

The only major concern is that no SDS-PAGE gels or size exclusion chromatograms have been included to verify purity and monodispersed of the various constructs worked on. In particular, the SAXS and CD measurement is highly sensitive to purity, and the level of degradation as IDPs are notorious for being difficult to handle in solution. it would strengthen the arguments made based that

We produced N-Tir and C-Tir as fusion proteins with a cleavable N-terminal thioredoxin tag (Trx-His6) and C-terminal Strep-tag. The latter allowed us to purify them via Strep-tag affinity chromatography as indicated by SDS-PAGE (please see Fig. S1).

We agree with the Reviewer that even small amounts of impurities (i.e., higher oligomers/degradation) can interfere with the data analysis and make interpretation of the resulting data difficult and potentially misleading. So, to avoid such problems, all samples were purified in monodispersed forms by size-exclusion chromatography (SEC) before any biophysical study.

Following the Reviewer's suggestion, we added a new supplementary figure (Fig. S5) showing the SEC-SAXS chromatogram profiles of C-Tir, N-Tir, and NS-Tir. Briefly, in the inline SEC-SAXS experiment, the sample eluates from an HPLC system directly and continuously into a BioSAXS flow cell for subsequent X-ray interrogation. Under our experimental conditions, C-Tir elutes as a single peak with Rg-values and mass compatible with a disordered monomeric protein, providing an excellent fit to the experimental SAXS curves. For N-Tir and NS-Tir, by SEC-SAXS, we separated the dimer from small amounts of high-order oligomers to yield the experimental SAXS curves of the pure dimers.

“Fig. S5. SEC-SAXS chromatograms of (A) C-Tir, (B) N-Tir, and (C) NS-Tir. Each plane shows normalized total scattering intensity I(s), over the entire s range, from each frame acquired along elution volume and respective Rg-value (black circles). The flat variation of Rg reflects a pure monodisperse sample. The column type for size exclusion chromatography and sample concentrations are on the top left of each panel. For reference, the retention volume for monomeric BSA (66.4 kDa) is displayed by red triangles.”

**Minor Comments**

Read through the manuscript to remove passages with spoken language

We thank the Reviewer for this suggestion. We went through the manuscript and improved the writing to reduce passages with spoken language.

Line 263, "To do so", should be removed

Line 290 "Our data thus" replaced with "this"

We have amended the manuscript accordingly.

Line 292 "lipid bilayers that might potentially fine-tune Tir's activity in the host cell." Weak sentence and the word fine-tune is slang. Rewrite the sentence. The interaction with lipids is fascinating!

Thanks for the suggestion. The sentence has now been changed to “**This shows that C-Tir can undergo multivalent and tunable electrostatic interaction with lipid bilayers via pre-structured elements, suggesting that membrane-protein interplay at the intracellular side might control the activity and interactions of Tir in host cells.**”

We also reinforce this fascinating message in the abstract by adding the sentence: “Membrane affinity is residue-specific and modulated by lipid composition, suggesting a previously unrecognized mechanism for interaction with the host.”

Line 192 "In figure Fig. 3A," remove the Fig

Fixed.

Line 326, "In a similar fashion," is redundant. Rewrite the sentences below.

We have modified the sentence as follows: “We evaluated whether the N-terminal cytosolic region of Tir (N-Tir; Fig S1) was also intrinsically disordered ...”

Line 342 add spaces between digit and SI unit "52kDa" there are more cases of this.

Thank you for pointing this out. This has now been corrected to 52 kDa.

Reviewer #2 (Significance (Required)):

I expect this study to have broad relevance to microbiologists working with the intimin and translocated intimin receptor, in particular the lipid interaction is likely to be followed up by the community.

We thank the reviewer for this comment. Indeed, we believe that further studies on Tir's lipid-binding ability as a novel molecular strategy in host-pathogen interactions, will potentially provide new insights on virulence, transmembrane signaling in general, and disorder-mediated functions.

**Referee Cross-commenting**

What reviewer 3 suggested in the comments sounds like added value and should be included.

I agree with reviewer 1, that the strain comparison potentially is beyond the scope presented in this manuscript.

We have now included EPEC O127:H6 in the manuscript.

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

**Summary:**

This interesting manuscript look at the structure of the Nter and Cter of the effector Tir from enteropathogenic E. coli. The authors confirmed previous study highlighting the "disordered" part of the Cter. However, the extended experimental work (NMR, Small-angle X-ray scattering and CD spectroscopy) from this study also reveals the connection between different area of Tir and its implication during Tir phosphorylation and its interactions with SH2 domain.

We thank the Reviewer for this positive remark. Indeed, in our work, we highlight the structural features of the SH2-mediated interaction between Tir and host SHP-1 protein, and we also show that C-Tir is capable of lipid interaction via pre-structured motifs and that N-Tir is disordered but assembled into a dimer. Overall, we provide an updated and wide picture of Tir's intracellular side that goes beyond the scrutiny of previously described disorder features.

**Major Comments:**

The authors used E2348/69 (O127:H7) strain as a reference for EPEC. However, this strain are the least effectors of all the EPEC sequences and may over estimated the PDR in EPEC. It would be wiser to use a strain like B171 as a reference for EPEC to be able to conclude "Disordered Proteins (PDR) with long disordered regions occur in EPEC effectors similar to the human proteome". I believe that the PDR in EPEC is similar to EHEC and CR. I do not have any major concern for the rest of the work.

We thank the Reviewer for this comment. So, to clarify, we amended “EPEC” with “EPEC O127:H6” in text and figures.

We also added a paragraph at the beginning of the Discussion section to acknowledge that our prediction analysis concerns EPEC O127:H6 and two additional representative A/E bacteria strains:

“Among the enteropathogenic Escherichia coli strains EPEC O127:H6 (E2348/69) is commonly used as a prototype strain to study EPEC biology, genetics, and virulence (69). Here, we have determined the structural disorder propensity of EPEC O127:H6 sequences and two additional representatives of A/E bacteria: EHEC O157:H7 and CR ICC168.”

Finally, the Reviewer suggests to include EPEC strain B171 (serotype O111:NM) in our analysis. We agree that considering additional strains would be of value, however we believe that this is beyond the scope of this manuscript, which mainly focuses on the characterization of the structural features of the E2348/69 Tir effector. We are currently working on a broader comparative analysis among different Escherichia coli pathogenic strains, including B171, and we hope to share our findings with the community in the near future.

**Minor comments**

Statistic problem: Mann Whitney U Test (Wilcoxon Rank Sum Test) is a comparison of two independent samples with the underlying assumption is normally distributed or that the samples were sufficiently large. It is not certain that any of this assumption is correct. In addition, the effector are part of the whole proteome. Can it be then considered that both groups are independent?

We thank the Reviewer for this remark, which allows us to clarify the choice of this particular test. Indeed the Mann Whitney U-test is a non-parametric test to compare two samples with the alternative hypothesis being that one of the two samples is stochastically greater than the other. As it is a nonparametric test samples are not required to be normally distributed, as it is for the Student t-test.

Regarding the independence of the samples, when comparing the effectors collections to their corresponding proteomes, we did exclude the effectors sequences from the latter. We have clarified this point in the Supplementary Material and Methods section.

Line 120 and 442: O127 not H127

Thank you for pointing this out. It has now been corrected to O127.

Line 212: positions 409 or 405?

Yes, it should be 405. Thank you.

Reviewer #3 (Significance (Required)):

**Nature and significance:**

Tir plays a major role during EPEC infection. It is a signalling platform that has been reported to interact with multiple proteins. Whereas the extracellular part has been well characterised and crystallised, the intracellular part has been proven so far to be difficult to study. Over the last decade, no progress has been made to explain how Tir works. This manuscript provides interesting information that shade some light on how the protein could work.

**Existing literature:**

The last research manuscript trying to highlight the structural function of Tir dates from 2007 (PMC1896257). This study is far more extended and in depth than any other previous work done.

**Audience:**

the Audience may probably limited to researcher working on the field of cellular microbiology and the function associated with bacterial effector in the host. This study could be also a useful tool to identify new effectors base on their "disorder".

We thank the Reviewer for recognizing the importance of this study. We agree that our work highlights the pivotal role of disordered regions in bacterial effectors, thus enabling a better understanding of the molecular mechanisms used by pathogens to subvert the host-cell processes. We indeed believe that our work can stimulate further research on the characterization of intrinsically disordered effectors, and also beyond the cellular microbiology field, in order to gain a broader knowledge on the molecular dialogue at the host-pathogen interface, which is essential to design better therapeutic strategies.

**Expertise:**

I have been working on A/E pathogens for the last 15 years with a particular interest in Tir signalling. My domain of expertise is more in relation to cell signalling than crystallography or structural study.

**Referee Cross-commenting**

I agree with both reviewers. My comment about EPEC is more about the conclusion for some of the figures. I don't think they should conclude for the whole EPEC. The Tir variation among EHEC O157:H7 is low, but it is far more diverse for EPEC. Simply adding in the manuscript EPEC O127 should be enough.

We thank the Reviewer for this comment. As mentioned above, we now state in the manuscript, in both Results and Discussion sections, that we used E2348/69 as a representative strain for EPEC.

Author Response:

Reviewer #1 (Public Review):

Summary: In " Rapid and Sensitive Detection of SARS-CoV-2 Infection Using Quantitative Peptide Enrichment 1 LC-MS/MS Analysis" Hober, A. et al. describe the addition of peptide immunoprecipitation by means of SISCAPA technology to the Sars-Cov2 mass spectrometry-based diagnostics toolbox. The work shows in a straightforward way that this is a huge improvement and of great importance to the field. It shows beyond any doubt that mass spectrometry can become a clinically applied diagnostic instrument to detect (viral) infection.

Overall remark: The main concern is the reported number of 83% sensitivity. This is not because the number is too low, but because the number is misleading. In line with "CLSI EP 12-A2 User Protocol for Evaluation of Qualitative Test Performance guidance" a summary of the sample analysis results are shown in a 2x2 contingency table. Unfortunately, I oppose to this representation of the results at this stage for three reasons: (i) reporting a percentage shouldn't be done on less than 100 samples because of the weight of a few misannotated samples on these numbers, be it in the qPCR or the MS results; (ii) because both assays are imperfect, it is impossible to assess the ground truth for calling patients and thus assess sensitivity and specificity; (iii) the authors still only target a single peptide, which is not conventional in MS-based assays that targets proteins.

We have changed to PPA and NPA in the new version of the manuscript. We have also included 264 RT-PCR negative samples collected in the same study. We agree that protein quantification should not be done using only one single peptide. We have updated the manuscript to clarify that we do not perform protein quantification, but rather peptide quantification.

Rather than the proposed confusion matrix, which assumes that the ground truth is known to call it e.g. "false negatives", the authors could refer to it as an agreement matrix and not be tempted to calculate threshold values like sensitivity, which have too much of an impact on the clinical readership that is used to seeing this value in a more controlled context. This is in line with the recent Lancet manuscript from Fitzpatrick, M. et al (2021), proposing percent positive agreement (PPA) and percent negative agreement (PNA) instead (Fitzpatrick et al., 2021).

We have decided to keep the confusion matrix but we are referring to it as PPA and NPA and rephrased sensitivity to “estimated sensitivity” based on PPA.

More specifically, as we and others have shown, qPCR Ct values rarely agree in two (consecutive) analyses, even within accredited settings (personal communication NHS). Above Ct30, patients regularly turned negative in our hands (https://doi.org/10.1021/jacsau.1c00048), even with an assay that had proven detectability of 1 plasmid at Ct40. Furthermore, we suspect that freeze-thaw cycles further inflate this uncertainty, two of which the current samples were subjected to. Undetected mRNA would then classify these patient samples as "false positives" if they did yield signal in the LCMS results. By chance, this did not happen in this manuscript, yet this could very well be the reason for the highest signal reported in Figure 3 as a green dot at log2 MRM response of -6 (see minor remarks).

The authors already distinguished the patients in a High Pool of Ct <30, a Low Pool 30{less than or equal to}Ct<33 and the negative samples (Ct>40). It is clear from the gap (no 34<Ct<39) that finding patients between Ct33 and Ct39 is challenging. Indeed, qPCR has its own "diagnostic grey zone" of LOQ negative and LOQ positive that rarely is being referenced. Thus, a "sensitivity" of 95% for patients <Ct30, despite the low number of samples and considering the uncertainties in qPCR (just above or below Ct30) at least limits the comparison to samples that are positive beyond any doubt. But again, we would be thresholding against a trembling metric, in turn making the claim from the authors dangerous that "the estimated LLOQ is 3 amol/μL approximates to Ct {less than or equal to}30". Rather, the Ct30 threshold should be set for a different reason, if one is chosen at all.

What is needed is good thresholding for clinical diagnostics, as is done in qPCR. In the public hospital in Belgium that provided us with patient samples, the positive threshold is set to Ct33 on the first measurement and practitioners use higher Ct values only in the context of physical symptoms of the disease to come to a final conclusion. For MS, we now need to measure >1000 samples in order to decide what log2 MRM response for a given set of peptides corresponds to an LOQ positive from - say - Ct27 to Ct30 and an LOQ Negative from Ct31 to Ct33. In other words, the linearity of the correlation between qPCR and MS illustrates the intrinsic value of MS; the point up until which we can provide clinically relevant information remains to be determined on large patient cohorts. In turn, these large patient cohorts can allow to sort (clinically) validated patients according to signal intensity and set a log2 threshold at which e.g. 2% or 5% negatives are expected, in line with False Discovery calculations for target decoy strategies. At this stage however, it might be most straightforward to conclude with percent positive agreement (PPA) and percent negative agreement (PNA), as is recommended for laminar flow tests validated on <100 samples.

Finally, realizing the importance of this pivotal moment in the implementation of MS in the clinic, I find it somewhat tricky to only focus on one peptide. In fact, the authors perform the qPCR on two genes (three genes being even more common) because of the drop-outs that can occur. I feel like the use of peptide IP with MRM for detecting pathogens has not yet matured enough to rely solely on one peptide. Still, I understand that asking for a second peptide would mean repeating all the measurements, so that is most probably not realistic. Yet, I do consider this to be yet another reason not to report % sensitivity and specificity in the current manuscript and the potential to gain robustness with more peptides should clearly be emphasized at every stage of the manuscript.

We agree that the method would be much improved by adding another peptide to the repertoire. The method was developed using the most sensitive antibody-peptide pair and the most promising pair was used in the downstream process. We have highlighted the limitations of using only one peptide and emphasized that this is a proof-of-principle study.

In conclusion, because patient batches in the thousands are currently unavailable to MS-oriented diagnostic labs and because of all the reasons mentioned above, we cannot report the numbers of sensitivity and specificity in this manuscript, as they are misleading and do not quantify what they are intended to do.

Fitzpatrick, M. C. et al. (2021) 'Buyer beware: inflated claims of sensitivity for rapid COVID-19 tests', The Lancet. Lancet Publishing Group, pp. 24-25. doi: 10.1016/S0140-6736(20)32635-0.

We agree and have changed to PPA and NPA for this reason.

Major remarks: P3L250: "on-column amount of 60 amol." Because of the enrichment procedure, could the authors specify what initial conditions they spiked into the dilution series prior to enrichment. This would allow recalculation and avoid confusion about the correctness of the 60 amol on column claim (which in our hands is still detectable).

We made changes to this in the updated version of the manuscript.

P8L181: "50 μL elution buffer (0.5 % 180 formic acid, 0.03% CHAPS, 1X PBS) and incubated for 5 min at room temperature." This minor sentence is placed under major remarks, because in our understanding the elution buffer needs to be acidic and adding PBS will reduce acidity. If this is a typo, please correct. If this is not, could the authors try and use H2O instead and see if their results improve?

The access to the raw data was denied.

The raw data is accessible through the provided Panorama link and can be accessed under the tab “Raw Data”. The entry in ProteomeXchange, however, is only a reserved data set identifier for now, but the data will be made available through this link after the review process.

Reviewer #2 (Public Review):

MS-based proteomics is currently discussed as a method for detection of viruses from clinical samples. Several studies have already shown the potential of this method on the example of the detection of SARS-CoV-2 from respiratory specimens. However, one of the major drawbacks still remains the low sensitivity of MS-based virus detection compared to real-time PCR, which is the gold-standard method. In their manuscript Hober and colleagues apply specific antibody-based enrichment of SARS-CoV-2 peptides from upper airway samples to concentrate the analyte prior to analysis by targeted MS (MRM). The authors determined the dynamic range of the method for four different SARS-CoV-2 NCAP peptides using a calibration curve. On the example of the SARS-CoV-2 NCAP peptide AYNVTQAFGR a correlation between the MS result and the cT value is shown. Furthermore, using stable isotope labelled (SIL) peptides as internal reference, a quantitative MS measurement was achieved. The presented approach is able to distinguish real-time PCR SARS-CoV-2 positive samples from negative samples in the used set of 88 samples from asymptomatic patients. Combined with a specificity of 100 % and sensitivities of up to 94.7 % for samples with cT values {less than or equal to} 30 the authors conclude that the method could be an alternative to real-time PCR.

Strengths of the manuscript:

I think the applied technique (SISCAPA) is highly interesting in the context of virus proteomics. This is because virus proteins are often underrepresented in relation to the host proteins, especially during early time points of infection, hampering their detection. Recently, the application of SISCAPA for SARS-CoV-2 diagnostics has been suggested in the discussion of a manuscript from Van Puyvelde and colleagues. The manuscript from Hober and colleagues presents the first study demonstrating that this technique can be applied to enrich, detect and quantify SARS-CoV-2 peptides from upper airway samples. The manuscript is clearly arranged, the data is sound and supports the main conclusions.

Weaknesses of the manuscript:

I think the manuscript in some points underestimates the PCR and vice versa overemphasizes the proteomics approach. For example, I don't agree that real-time PCR generally suffers from technical problems, degraded probes or non-specific amplification. Vice versa I think the LC-MS/MS approach is not inherently absolute specific and does not outperform PCR in terms of specificity. Further, LC-MS/MS does not eliminate the problem of false positives, which could be introduced during sample preparation or by inter-run contaminations. Although in real-time PCR no internal standards analogous to isotopically labelled peptides are used there are internal controls used to assure the quality of the extraction and the PCR reaction itself. The method presented by Hober and colleagues is clearly beneficial for the field of proteomics-based virus detection, but I suggest a more balanced discussion also including also the potential drawbacks of the method.

Another point I like to raise is that the authors conclude at the end of the results section that patient samples were collected at an infectious stage.

We have made changes to the manuscript accordingly and removed the claim that the samples were collected in an infectious stage since this cannot be confirmed. The patients did not show any symptoms when sampled, which has been highlighted in the new version.

However, an assessment of the infectivity cannot be drawn from the presented data. The analysis of real-time PCR results in the manuscript is based on cT values. But to draw the conclusion, that the analysed samples contained infectious virus particles, the number of viral genome equivalents has to be determined, which in turn can be correlated to infectivity.

We have removed this section since we cannot make any claim on infectious virus-particles.

The detection of viral proteins itself does not proof that samples were collected at an infectious stage and there is currently no correlate of the amount of NCAP protein and infectivity. Since viral proteins are likely more stable than viral RNA, they could even be detectable for a more prolonged time in patient samples.

Reviewer #3 (Public Review):

Major comments

P2, l245, Figure 2: It is not completely clear to me what is represented in panels A and B. Is this the pure SIL peptide of the endogenous peptide in a complex matrix? This may make a large difference for the determination of the LLOQ. Panel B shows a calibration curve and as these are curves for which the signal is detected based on known input amounts of sample, I assume that the input is pure SIL peptide here?

In panel A, what does '3 amol/ul' in the middle chromatogram exactly mean? Is this the endogenous peptide that was calculated to be present at 3 amol/ul based on a known concentration of spiked-in SIL peptide?

P4, l276: The authors need to explain the details of data imputation. It is unclear which data were imputed and how this was done. In Figure 3 the grey data points represent "not detected" or "inconclusively identified" samples by LC-MS, while some of the data points seem to have a higher 'response' values than others. Please explain.

In Figure 3, how is 'response' defined? I don't understand the following sentence (p4, l277): "… for the LC-MS results the lowest response divided by three was used, mimicking….". Which variable does the data point size reflect? There seem to be clear differences in ball sizes. Please explain. For clarity, it would be advisable to keep the y-axes for panels A and B identical. Also, how could RT-PCR values be not obtained, apparently leading to missing Ct values (p5, l278)?

Assuming that all collected samples from individuals in the test group in this study are visualized in Figure 3, the majority was tested positive for SARS-CoV-2. This is very different from the percentages oberserved in regular testing facilities. How was the study group composed? Were these individuals who were already admitted to the hospital?

We have specified that the sampels were selected based on RT-PCR result and have included more negative samples in the new version of the mansucript. We have also speciied how individuals were enrolled into the study.

It would be interesting to include more negatively tested individuals to see the distribution of 'MRM response' values in this group, since some of the negatively tested individuals (green data points) show higher than expected MRM response values if no viral protein is present at all. Related to this, I do not understand how a specificity score of 100 % (p5, l292) was obtained while some green data points (negative by RT-PCR) have higher associated MRM response values than some of the blue (positive by RT-PCR) samples. Can the authors explain this?

The negative samples that show a stronger MRM response do not have the required qualifying ions, thereby failing the QC parameter of the assay. This has been clarified in the new version of the manuscript.

I find the text from p6, l298 ("However…") onward more suited for the Discussion section, since this is about the interpretation of the results presented here and the use of the described methodology in diagnostics; no results are shown in this part.

Reviewer #3 (Public Review): 

Constant et al describe a study investigating an important issue - are judgements of agency metacognitive in nature? While this topic has received a lot of theoretical attention, empirically the issue is underexplored, partly due to a lack of appropriate frameworks and tools. Here the authors suggest the issue can be tackled by thinking more precisely about the computations involved in both judging agency over an outcome and in forming a (metacognitive) confidence report. This focus on constituent computations is an important conceptual strength of the paper. 

The authors choose to operationalise metacognitive computations as those where agents have "second order access to sensory noise" and design two similar tasks - a confidence judgement task and an agency judgement task - where observers report their experience of controlling a virtual hand that can move synchronously or be delayed. Crucially, the uncertainty of the incoming sensory signals is varied, and the authors explore whether agency and confidence judgements are influenced by this sensory noise, and which kind of computational processes can best explain how. While the authors find empirically noise has an effect on both kinds of judgements, computational modelling suggests that agency judgements are best explained by a 'rescaling' model which does not include an explicit representation of the noise, whereas confidence judgements are better explained by a 'Bayesian' model which does represent noise. 

There is lots to enjoy about this paper. It is particularly inspired to have an agency and confidence task that are so similar, making them more directly comparable. Indeed, they are compared in the paper with basically identical computational models, something which to my knowledge has never been achieved in this field of work. The models themselves all seem well chosen given certain design assumptions, though I suspect the more general insight of generating explicit computational models of agency-like judgements is one that could inspire other researchers in this field, and charts a route to progress on thorny issues on this and related topics. 

However, while this approach is intriguing, I think the main weakness of this study relates to the core experimental manipulation: introducing temporal delays between actions and outcomes to influence ratings of control. While this is a popular approach in the field, recent authors (e.g., Wen, 2020, Consciousness and Cognition) have suggested that this manipulation may be problematic for a number of reasons. In similar types of paradigm, Wen (2020) notes that agents are able to accurately judge their control over action outcomes that are substantially delayed (e.g., well over 1000 ms) and thus it is possible that 'delay manipulation' designs actually introduce response biases, where participants are somewhat artificially reporting variance in the delays they experience rather than their actual experience/belief about what they can and cannot control. Indeed, in the methods of this present paper, the authors note participants were asked to "focus specifically on the timing of the movement" of the virtual hand, which may make this concern particularly apposite. 

Because of this manipulation, all of the computational modelling (naturally) assumes that agents are engaged in a task where they have to detect the delay and compare this to some criterion value. Indeed, there is nothing else they could be doing in these tasks. The report of "agency" is thus generated directly from this internal variable that encodes "did I detect a delay?", and any confidence report is a metacognitive judgement about that decision. 

This raises an important issue of conceptual validity: is a judgement of agency equivalent to judging whether an outcome was delayed or not? Many results (see review by Wen, 2020) suggest that agents can simultaneously tell an action outcome was delayed, but still judge themselves to be the agent, suggesting that an equivalence along these lines is unlikely. If so, this would mean acknowledging the generalisability of these is conclusions is potentially limited: rather than concluding that agency judgements in general are non-metacognitive, the conclusion would be the sensorimotor delay judgements in particular are non-metacognitive. The latter conclusion is by no means uninteresting, but has a somewhat narrower theoretical significance for the key debate used to frame this paper ("do agency judgements monitor uncertainty in a metacognitive way?") 

A second important issue relates to what exactly makes a computation 'metacognitive'. For example, the authors argue their Bayesian model is a metacognitive one, because it requires the observer to have second-order access to an estimate of their own sensory noise. I am not completely sure this follows: the Bayesian model in this paper clearly incorporates an estimate of the noise/uncertainty in the signal, but not all representations of noise are second-order or metacognitive. For example, Shea (2012) has noted that in precision-weighted Bayesian inference models throughout neuroscience (e.g., Bayesian cue combination, also discussed in this paper) the models contain noise estimates but the models are not metacognitive in nature. For example, when we combine a noisy visual estimate and a noisy auditory estimate, the Bayesian solution requires you account for the noise in the unimodal signals. But - as Shea argues - the precision parameters in these models do not necessarily refer to uncertainty in the agent's perceptions or beliefs, but uncertainty in the outside world. It seems a similar argument is relevant to the Bayesian model of agency offered by the authors in the present paper. It is not clear to me why we should think the uncertainty parameter in the Bayesian model is something metacognitive (e.g., about the agent's internal comparator representations) rather than something about the outside world too (e.g., the sensory environment is noisy). 

References:

Shea (2012) Reward prediction error signals are meta-representational. Nous, DOI: 10.1111/j.1468-0068.2012.00863.x 

Wen (2020). Does delay in feedback diminish sense of agency? A Review. Consciousness and Cognition, DOI: 10.1016/j.concog.2019.05.007

Author Response:

Reviewer #1 (Public Review):

The authors provide evidence for the following key points:

• that low and likely biologically relevant levels of oxidized phospholipids (OxPLs) can induce macrophage death and interleukin-1-beta release
• that the pro-inflammatory activities of OxPLs can be tempered by acyloxyacyl hydrolase (AOAH) which deacylates oxPLs in vitro
• that AOAH deficient mice exhibit exacerbated inflammation in vivo in response to exogenously delivered OxPLs, but interestingly, also in response to HCl, which presumably induces the release of endogenous OxPLs

In general the data are a nice combination of in vitro and in vivo observations and are supportive of the conclusions. A few points should be addressed:

• how do the authors reconcile their results with others' apparently contradictory results in the field?

We thank the reviewer for raising this important question. We think the oxPL species used and their concentrations, the routes of MAMP and oxPL delivery, and the order of addition of MAMP and oxPLs may contribute to the observations made in different laboratories. We have added a paragraph in the Discussion and another in the Methods, lines 447-474 and lines 495-506 (highlighted).

• which inflammasome is activated by OxPLs?

We found that NLRP3 specific inhibitor MCC950 reduced PGPC or LPC-induced inflammasome activation and IL-1β release. To our surprise, using inhibitors we found that in addition to caspase 1, caspase 8 was also indispensable, suggesting that caspase 8 may cleave caspase 1 and activated caspase 1 cleaves pro-IL-1β (Chi et al., 2014; Philip et al., 2014). Please see lines 94-105, new Fig. 1E, F and new Fig. 3B, C.

• can the possible effects of AOAH on the priming stimulus (Pam) be more cleanly distinguished from its effects on OxPLs?

Because AOAH does not regulate acute responses to LPS (Lu et al., 2008) or Pam3 (Fig. 4C, IL-6) in vitro or in vivo (Lu et al., 2008; Zou et al., 2017), we do not expect AOAH to modulate the priming effects of Pam3 or LPS. To exclude this possibility, we tested CpG, which can also prime macrophages for oxPL-induced inflammasome activation. We found that when AOAH WT and KO macrophages were primed with CpG, PGPC induced more cell death and IL-1β release from AOAH KO macrophages. Please see lines 220-225 and new Fig. 4E.

• a few other experimental controls could be provided

We have added actin controls to all Western blots.

Reviewer #2 (Public Review):

Zou et al. investigated the function of acyloxyacyl hydrolase (AOAH) in inflammation caused by oxidised lipids. Using cell culture models (murine BMDs) the authors first show that oxidised lipids such as oxPAPC, POVPC and PGPC induce inflammasome activation. Focusing on AOAH, they then demonstrate that AOAH, which can act as a phospholipase A1/2 or B, can remove sn-2 oxidised fatty acyl chains and sn-1 palmitate from pro inflammatory oxidised lipids thereby modulation their ability to activate inflammasome and induce cell death inflammation (IL-1b production). Release of sn-2 acyl chains from PGPC or POVPC results in the formation of LPC (lysophophatidylcholine) which has also pro-inflammatory properties. The author demonstrate that LPC also activated inflammasomes, and that that LPS, or PGPC or POVPC-induced inflammasome activation is enhanced in BMDMs from AOAH-deficient mice. Moving to mouse models of inflammation the author find that AOAH-deficient mice have higher level of lung inflammation and injury after nasal instillation of LPS+oxPLs, and that AOAH regulates inflammation after nasal instillation of HCl.

The conclusions of this paper are mostly well supported by data, but some aspects need to be clarified and extended.

1) what inflammasome/s is/are activated by PGPC, POVPC and LPC?

Zanoni et al found that PGPC or POVPC but not oxPAPC can induce IL-1β release from primed bone marrow derived macrophages (BMDM) in a NLRP3-, Caspase 1/Caspase 11-dependent manner (Zanoni et al., 2017). Yeon et al also found that POVPC induced IL-1β and processed caspase 1 release from primed BMDM, which required NLRP3 (Yeon et al., 2017). In contrast, Muri et al., found that caspase 8 but not caspase 1 or NLRP3 was required for cyclo-epoxycyclopentenone-induced IL-1β release in primed bone marrow-derived dendritic cells or macrophages We found that NLRP3 specific inhibitor MCC950 reduced PGPC or LPC-induced inflammasome activation and IL-1β release. Using other inhibitors we found that in addition to caspase 1, caspase 8 was also indispensable, suggesting that caspase 8 may cleave caspase 1 and activated caspase 1 cleaves pro-IL-1β (Chi et al., 2014; Philip et al., 2014). Please see lines 94-105, new Fig. 1E, F and new Fig. 3B, C.

2) how does AOAH affect the anti-inflammatory functions of oxPLs which have previously been reported (PMID:29520027, 32234476 )

It is a very intriguing question. In this study, we focus on studying the role that AOAH plays in preventing oxPL-induced inflammasome activation. We will study whether AOAH alters the anti-inflammatory functions of oxPLs in the future. We have added a sentence in Discussion, lines 471 - 474.

3) additional controls need to be provided to increase confidence into the immunoblot analysis

Thanks. We have added actin loading controls.

4) experimental procedures need to be better explained and justified

dPGPC/dPOVPC means PGPC/POVPC treated with AOAH. AOAH can release both sn-2 and sn-1 fatty acyl chains from PGPC/POVPC. In addition, AOAH deacylates LPC. Please see Fig. 2A, B and Fig. 3A. We have clarified the definition of dPGPC/dPOVPC, line 144. The samples were frozen after treatment. Freezing in the absence of glycerol inactivates AOAH. We added a sentence to make it clear, lines 568, 569.

Author Response

Reviewer #2 (Public Review): Osteoblasts are highly anabolic cells that display a high proliferation rate and secrete ample amounts of extracellular matrix, indicating that these cells have a specific metabolic profile. Here, using a set of in vivo and in vitro experiments, Sharma et al describe that SLC1A5-mediated glutamine and asparagine uptake is critical to sustain osteoblast anabolism. While the experimental setup is robust, this concept has already been put forth, questioning therefore the novelty of the results. In addition, some of the author's claims are insufficiently supported by the presented data. Especially the metabolic role of asparagine in regulating osteoblast differentiation remains enigmatic. The main concerns are detailed below.

1. Based on their data, the authors propose that the main mechanism whereby SLC1A5 regulates osteoblast proliferation and differentiation is via glutamine uptake, while asparagine only contributes to a lesser extent. Importantly, the concept that glutamine metabolism regulates proliferation and differentiation of osteogenic cells by sustaining anabolic processes has already been described recently, even by the same research group (Yu Y. Cell Metab. 2019; Stegen S. JBMR 2021), questioning the novelty of the present study. Moreover, no metabolic rescue experiments were performed to unequivocally demonstrate that the defect in amino acid/protein synthesis in SLC1A5-deficient cells was causing the decrease in osteoblast proliferation and differentiation.

We appreciate the reviewer’s thorough and thoughtful review and we thank the reviewer for helping us to improve this manuscript. To address this, we evaluated proliferation or osteoblast marker genes in Slc1a5 deficient cells cultured in media supplemented with 10 times the normal concentration of the reduced amino acids (excluding Gln and Asn, Fig. 4B). There was no effect on EDU incorporation, however exogenous amino acids did rescue the induction of Ibsp and Bglap to a lesser extent (Fig. S6D-E). Interpretation of these types of experiments are tricky as the uptake of NEAA may be inherently limited in osteoblasts and due to time constraints, we were unable to quantify intracellular amino acid levels in the rescued cells. Regardless, we interpret these data as affirming the necessity of Slc1a5 to provide Gln and Asn used to synthesize amino acids for osteoblast differentiation. In addition, these data indicate other metabolites (e.g. alpha-ketoglutarate, glutathione, nucleotides etc) derived from Gln and/or Asn are required for proliferation. We have modified the discussion to address this uncertainty.

In addition, Gln and Asn tracing (carbon and nitrogen) in SLC1A5-deficient cells would confirm that Gln and Asn uptake via SLC1A5 is important for osteoblast functioning.

We did not perform tracing experiments in the Slc1a5 deficient cells. We directly evaluated amino acid uptake using radiolabeled amino acids in Slc1a5 deficient cells (Figure 4). Slc1a5 ablation reduced the uptake of Gln and Asn. To test if Gln and Asn uptake was important for osteoblast function we directly compared the cellular effects of Slc1a5 ablation to Gln or Asn withdrawal. From these experiments we concluded that Gln and Asn uptake is essential for osteoblast differentiation.

1. Using isotopic labeling experiments, the authors demonstrate that asparagine-derived carbon and nitrogen label several amino acids that are critical for protein synthesis, albeit at a lower level compared to glutamine. Based on these observations, they claim that the decrease in osteoblast differentiation upon asparagine depletion also occurs via a defect in protein synthesis. However, proliferation, EIF2a phosphorylation and COL1A1 levels were not affected in asparagine-deprived conditions, questioning that the decrease in differentiation is resulting from impaired protein synthesis. Further experiments to decipher the metabolic role of extracellular asparagine are therefore warranted to avoid overinterpretation of the data, including protein/matrix synthesis, analysis of amino acid levels in Asn-deprived conditions and rescue with Asn-derived metabolites.

Again, the reviewer raises a very important point. Our data indicates that Asn does contribute to amino acid biosynthesis, chiefly Asp, however, we did not evaluate the requirement of Asn for protein synthesis directly. We think it is probable that asparagine contribution to osteoblast differentiation is multifaceted. Thus, we have softened the conclusions about asparagine and the regulation of protein synthesis to reflect this uncertainty.

1. To inactivate SLC1A5 in vivo, the authors use the Tet-off Osx-GFP::Cre mouse line. Importantly, newborn Osx-Cre mice display severe craniofacial abnormalities, which may complicate correct interpretation of the in vivo data, especially when analyzing at embryonic stages. Do the authors observe a similar defect in osteoblast function when SLC1A5 was deleted postnatally? This might be especially relevant because the phenotype seems to wane off over time, as knockout mice at P0 only display a craniofacial phenotype, whereas long bones appear to be normal.

The reviewer raises a very important point regarding the Sp7tTA;tetoCre line we used in this study. As mentioned, the Sp7tTA;tetoCre mice do have a partially penetrant craniofacial bone phenotype. To control for this, we only use Sp7tTA;tetoCre as “wild type” controls. In addition to the early embryonic endochondral ossification and persistent calvarial phenotypes, the Sp7tTA;tetoCre;Slc1a5^fl/fl have additional bone phenotypes compared to the Sp7tTA;tetoCre controls. This included a calvarial phenotype at both birth and 2 months of age (Figures 1 and S2). Likewise, we observe similar changes in osteoblast differentiation and bone development in the developing limbs at birth and in femurs at 2 months of age (Figure S4). Due to time constraints, we have not been able to generate sufficient numbers of mice with postnatal deletion of SLC1A5 to include here. These experiments are ongoing and will be published later.

Reviewer #3 (Public Review): This work by Sharma et al studied the role of aa transporter, ASCT2, encoded by Slc1a5 gene, that transports mostly Glmn and Asn, in osteoblasts (OB). They use gene targeting in vitro and in vivo using Sp7-Cre driven cKO. They found that ASCT2 deletion impairs OB differentiation in vitro as well as mostly intramembranous ossification in vivo by interfering with proliferation and protein synthesis. Mechanistically, they show that Glmn uptake via ASCT2 is important for aa synthesis in OBs. This group has shown before that Glmn is essential for OB metabolism. The current work further investigates this phenomenon and identifies ASCT2 as the key mechanism of Glmn uptake into OBs. The work is logically structured and carefully done with appropriate in vivo and in vitro controls. A variety of methods is used to confirm their findings, such as in vivo immunodetection and in situ hybridization and in vitro metabolic tracing. The conclusions are well justified by the data. Minor comments are: -MicroCT methodology is not adequately described and needs to be expanded

We appreciate this positive review of our work. We have modified the methods to adequately describe µCT methodology. We modified the methods as follows:

“Micro computed tomography (µCT) (VivaCT80, Scanco Medical AG) was used for three-dimensional reconstruction and analysis of bone parameters. Calvariae were harvested from either newborn mice or 2-month-old mice. All muscle and extemporaneous tissue were removed and the isolated calvariae were washed in PBS, fixed overnight in 10%NBF and dehydrated in 70% ethanol. The calvariae were immobilized in 2% agarose in PBS for scanning. A fixed volume surrounding the skull was used for 3D reconstructions. In newborn calvariae, bone volume was quantified from a fixed number of slices in the occipital lobe. The threshold was set at 280. For quantification of bone mass in the long bone, 2-month-old femurs were isolated, fixed, immobilized and scanned. Bone parameters were quantified from 200 slices directly underneath the growth plate with the threshold set at 333.”

The authors discussed the implications of moving away from elimination or hard elimination strategy to a softer containment strategy. At present, elimination strategy is about zero tolerance towards new cases, not so much as tha the total number of cases in the country will be 0. This is also impossible given the emergence of delta variant. The authors argue that to do that, the country first needs to vaccinate every eligible individual. This will shift the "risk perception" of people such that even if deaths occur and infections continue, there will be less anxiety and urgency to act in a way that is strict and hard, rather, covid19 related hospitalisations and deaths would be viewed as "unavoidable" but risks associated with normal life, much as we think about these things in case of lnfluenza. They also argue that once universal vaccination for covid19 is in place, then the government, in order to sustain a zero tolerance policy towards death from covid19 need to implement three things:

• Electronic device based contact tracing and tracking
• Issuing vaccine passports to the fully vaccinated and providing facilities to vaccine passport holders that will not allowed to the non-passport holders
• Mass testing of people for covid19 using saliva and other tests (such as rapid antigen testing but they have not mentioned that either)

Beyond this, they think that the health system and relationship of the government with public health and businesses need to be "overhauled" to some extent. Their advices include:

• Delay an urgent health reform
• create a closer partnership with the businesses such that along the lines of biosecurity and primary industries in a way that businesses are registered that will have implemented minimising covid19
• Increase workforce capacity so that as the country is dependent on overseas health care workers, fast track residency applications and retrain or arrange for training of existing workforces and retired but capable workers
• They have not mentioned but reasonable to understand that this is about minimising attrition of health workforce
• Develop purpose built MIQs rather than depend on ad-hoc MIQ facilities
• Develop a specific Pandemic controlling agency

The authors make several assumptions but not quite clear from what they have written how they address them, although several of their desiderata and suggestions will have bearing on those assumptions. First of all, this is now established that covid19 is largely airborne, and therefore, ventilation and masking have a more prominent role in non-pharmacological intervention that what was considered earlier. But in order to enforce or enable this, individuals need to change their behaviours about precautionary practices and businesses will need to adjust their practices such as ventilation on shop floor and how many people to allow; restaurants may consider to open more open spaced. Second, while they have considered endemicity of covid19, this is open to debate. Endemicity of covid19 implies that the authors have assumed that COVID19 will have reinfections; so far, there is little evidence of re-infections as a dominant mode of transmission of this virus. Besides, as it is now known that extant vaccines do not confer "sterilising immunity", the discussions around "booster vaccinations" are in order. If so, the question of an endemic infection and thereby the premise that there will be a "shift in public risk tolerance" needs careful consideration. Third, the authors have not discussed the potential issues around "long covid", and the need to study the implications of long covid. Albeit, we may be in the throe of the birth of a new speciality in public health and medicine devoted to the study of the causes and consequences of Covid. What the implications of long COVID19 will be is open to speculations.

I think here he is trying to establish a sense of security. Mainly because the same dialogue is repeated especially in this highlighted section but also throughout the text. He seems to represent love and not harm. He really explains in this section how Powhatan, did not keep his promise and peace amongst everyone. The captain here shows that he is not trying to take anyone's food at all.

This may be more report-focused thinking than anything else, but I wanted to include a sort of "Call to Action" in the version I wrote. Like, "we recommend fixing [Critical] findings as soon as possible."

(the ones I wrote I don't think are perfect, but it seemed like another good way to indicate Severity)

Author response

_______________________

We thank the researchers within the ASAPbio community for taking the time to provide valuable feedback on our manuscript and also Iratxe Puebla for both facilitating this review of our preprint and for consolidating the comments we received. Here we provide comments to some of the points raised by the reviewers.

In regards to the reviewers’ comment that our “work focuses on the nwk mutant”, we note that Figures 3 and 4 show the unexpected EV cargo depletion phenotype for mutants of numerous components of the clathrin-mediated endocytic machinery. We chose the nwk mutant for our in-depth analysis because it best shows separability of functions in synaptic vesicle (mild defect) vs extracellular vesicle traffic (severe defect), and also produces null mutant viable adult flies for our APP functional studies. However, our work indicates that EV cargo regulation is a broader function for the endocytic machinery and raises the possibility that previously identified neuronal phenotypes for many endocytic mutants could be due to loss of EV cargoes from synapses. Related to this, in reference to the comment that the nwk mutant “affects EV release” we also wanted to highlight that while the EV phenotype we observed for nwk and other endocytic mutants shows both pre- and postsynaptic depletion of EV cargoes, our retromer(vps35);nwk double mutant result suggests that endocytic machinery such as Nwk is not directly regulating release of EV cargoes. Instead, we conclude that the reduction of postsynaptic EV cargoes is a secondary consequence of presynaptic depletion due to defective intracellular traffic. Your helpful feedback has alerted us that we could make these points more clear in the writing and organization of our manuscript.

In response to the points that “...the question arises as to how specific this pathway is to EVs” we should clarify that our findings seem to be specific to cargoes for which sorting to extracellular vesicles is at least a major trajectory (ie, Syt4 and hAPP, of which 30-50% of the synaptic complement is in EVs). We agree that they both have an intracellular component (either en route to EVs or for intracellular signaling functions, which have been well-documented for APP). In response to the comment that “other cargoes that undergo clathrin-dependent endocytosis and are not packaged into EVs would need to be tested”, indeed both Syt1 or Tkv require CME machinery for their traffic (PMID12795692, PMID16459302, PMID18498733), but we find that they are not detectable in EVs and are not depleted at CME machinery mutant synapses. This indicates that local synaptic depletion is specific to cargoes for which least a significant portion of their total pool is normally packaged in EVs.

The reviewers commented that APP and perhaps Syt4 also have intracellular itineraries and functions that may be affected by their depletion at synapses - we agree that our results have implications for both extracellular vesicle and intracellular functions of these cargoes. We fully agree that “the [Figure 2] results might not be specific to EV functions of Syt4 or hAPP” and that a more general statement (such as was suggested in the comments) here would make this possibility more explicit. Our results at least indicate that reduction of these cargoes in presynaptic terminals (but not axons, cell bodies, or dendrites) is sufficient to abrogate their functions. It will be critical in the future to identify trafficking mutants that specifically disrupt EV release without impacting levels in the donor cell, in order to directly query the physiological functions of EV sorting.

To “provide some more information on how the [postsynaptic ɑ-HRP puncta intensity] quantification was done”, we selected an intensity threshold sufficient to distinguish postsynaptic puncta from background muscle fluorescence. We did not directly select puncta manually. Puncta with brightness above this intensity threshold were measured within a 3 μm region around the neuronal HRP. Puncta brightness was not normalized to neuronal HRP brightness, but instead was normalized to the neuronal HRP volume. This analysis was not blinded as many endocytic mutants exhibit synaptic overgrowth phenotypes that are easily visible, thus complicating the blinding process. Using a complementary automated analysis for presynaptic Syt4-GFP, we found very similar results to our manual thresholding analysis. We were however unable to successfully automate the postsynaptic signal measurements due to signal-to-noise-ratio heterogeneity, especially for HRP. Here we’d also like to clarify that in regards to “postsynaptic objects smaller than 0.015 μm were excluded”, we meant postsynaptic objects smaller than 0.015 μm3.

In response to the comment that “...saying this trafficking opposes retromer complex sorting appears to extend beyond the results” we would like to clarify that while direct opposition of endocytic machinery to retromer on endosomes is one possible interpretation, it is not the one we favor in the discussion. We agree that endocytosis and retromer are more likely to oppose each other more indirectly by regulating overall flux through the recycling pathway. We intended to convey opposition as a genetic rather than a mechanistic argument, and we think this conclusion is supported by our data. However based on this feedback we see that we could make this more clear in our manuscript.

We thank the reviewers for pointing out that “In Figure 4B clc depletion does not yield a significant difference in pre-synaptic Syt4 levels. However, Figure 4D the levels of Syt4 are significantly lower in clc both pre- and postsynaptically”. One possibility is that this just reflects variance in the assay, and that the subtle Syt4 phenotype in the clc mutant reached our arbitrary threshold of significance in one experiment but did not in another. There is however also a potentially interesting biological explanation. The 4B clc experiment was conducted at 25℃ while the 4D experiment was conducted at 20℃, since we found that at the lower temperature we were able to recover more clc; nwk double mutant third instar larvae. Endocytosis is well-known as a temperature-dependent process, and perhaps there is some residual endocytosis at this lower temperature in the clc mutant, making it more similar to slowed endocytosis in the endocytic accessory protein mutants (see PMID16269341), compared to a more complete block in the chc or clc 25º condition. This would suggest that slow endocytosis drives cargo into the degradative pathway, fast endocytosis into the rapidly recycling and EV pathway, while no endocytosis traps cargo in unproductive membrane cisternae. Proving this would likely require more quantitative endocytosis assays than are currently available.

We are also appreciative of reviewers comments that will help to make our manuscript more clear, such as suggestions to present the plots consistently, to mention that individual points represent individual NMJs, and to report that C155 is a neuron-specific driver, among other helpful points.

SciScore for 10.1101/2021.10.01.21264412: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Our study has a few notable limitations. Although this is one of the first studies to connect viral load with increased risk of mortality, the number of in-hospital deaths in our study population was low, and larger studies are needed to assess the role of viral load in the outpatient setting and after adjustments for potential confounding factors. The data on hospital (and ICU) admission of our study population were collected from the two large teaching hospitals our region that largely cover the adherence area of the Regional Public Health Laboratory Kennemerland (where the tests were performed). It can however not be excluded that (ICU)hospitalization data of some of the included patients were missed when they were admitted to other hospitals in adjacent regions. However, we do not think that this will have influenced our main results as the chance of admission to a hospital in another region is not likely to be related to the initial SARS-CoV-2 viral load of a particular patient and would only have resulted in nondifferential misclassification of our outcome measurement. And finally, including only patients who were able to have themselves tested at Public Health Service testing facilities may have resulted in a healthy selection of all SARS-CoV-2 positive patients, as patients were able to make an appointment and go to the public health care facility. Even though this generally took place after a mean of 2 days, patients who got very ill, or needed to be admitted to the ...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

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I think looking at patterns of activation rather than specific brain regions may reveal more insights. As we know different regions perform multiple tasks and pin pointing one specific behavior is difficult.

Author Response:

Reviewer #1:

A role for integrins in lowering the threshold for B cell activation was first observed over 15 years ago, but the mechanism has remained elusive. In this paper, Wang et al. investigate the role of LFA-1:ICAM-1 ligation in B cell synapse formation using live-cell super-resolution fluorescence microscopy in both primary B cells and the A20 B cell line. The use of super-resolution imaging is critical to the investigation as it reveals a level of organisation of the actomyosin network that is not visible with conventional microscopy approaches such as TIRF microscopy. They find that LFA-1:ICAM-1 ligation promotes the formation of actomyosin arcs that regulate various activities in the B cell synapse including BCR signalling, BCR:antigen microcluster transport, and the centralisation of antigen. In agreement with earlier studies, they show that LFA-1:ICAM-1 ligation is required for B cells to centralise antigen that is present at very low density. They also demonstrate that myosin IIa contractility is required for the formation of the actomyosin arcs and promotes the exertion of strong traction forces on the antigen- and ICAM-1-presenting substrate. Using a small molecule inhibitor of formin activity in combination with miRNA knockdown of the formin mDia1, the authors show that the actomyosin arcs originate at the outer edge of the synapse and that their generation is formin dependent. These data provide a much-needed advance to our understanding of the role LFA-1 plays in the earliest events in B cell responses to antigen.

The conclusions of the paper are mostly well supported by the data, but there are a few points that would need to be clarified.

1) The requirement for LFA-1:ICAM-1 ligation in the formation of the actomyosin arcs is not clear. The authors observe that ~30% of B cells form actomyosin arcs with anti-IgM stimulation only (Figure 1). Does LFA-1:ICAM-1 ligation simply stabilise the arcs and therefore make their appearance more likely, or does it promote the formation of a distinct actomyosin network with unique functions? The images and videos selected to represent cells stimulated with anti-IgM only (Figure 1; Movies 1A and 1B) seem form a highly branched actin network throughout the synapse, but it would be informative to see cells having the actomyosin arcs for comparison. Since B cells stimulated with anti-IgM alone are capable of signalling and centralising antigen, it would be interesting to know whether and how these two populations (with and without arcs) differ.

We thank the reviewers for their questions regarding this central aspect of our study. In response to the reviewers’ statement “The requirement for LFA-1:ICAM-1 ligation in the formation of the actomyosin arcs is not clear”, our results state that “Consistently, scoring B cells for the presence of a discernable actin arc network showed that the addition of ICAM-1 increases the percentage of such cells from ~30% to ~70% (Fig. 1G).” Importantly, we then state that “dynamic imaging showed that the arcs in cells engaged with anti-IgM alone are typically sparse and transient (Movies 1A and 1B), while those in cells engaged with both anti-IgM and ICAM-1 are dense and persistent (Movies 2A and 2B).” To emphasize this point, which we think is clear when comparing Movies 1A/1B to Movies 2A/2B, we have now added the following two sentences to the text: “In other words, when B cells receiving only anti-IgM stimulation do form discernable arcs (see, for example, those marked by magenta arrows in Fig. 1A and 1B), they are much sparser and less robust than those formed by cells also receiving ICAM-1 stimulation. Moreover, we never saw even one B cell receiving anti-IgM stimulation alone that possessed a robust actin arc network.” Please note that the magenta arrows in Fig. 1A and 1B were added upon revision. In summary, the cell shown in Fig. 1E, which lacks discernable arcs, is representative of ~70% of anti-IgM stimulated cells, while the cell shown in Fig. 1F, which possesses a robust arc network, is representative of ~70% of anti-IgM+ICAM-1 stimulated cells.

We would also like to address what we think is a misunderstanding regarding our images in Figure 1, as reflected in reviewer 1’s statement: “The images and videos selected to represent cells stimulated with anti-IgM only (Figure 1; Movies 1A and 1B) seem form a highly branched actin network throughout the synapse”. The outer, Arp2/3-generated, branched network comprising the dSMAC/lamellipodium in primary B cells is really quite thin under both stimulation conditions (please see Fig. 1, E1, E2, F1 and F2). In other words, we would not characterize the region between this thin, outer, canonical branched actin network and the central actin hypodense area (i.e. the region corresponding to the pSMAC) in B cells engaged with anti-IgM alone as “a highly branched actin network throughout”. We described it in the text as “a highly disorganized mixture of short actin filaments/fibers and actin foci”. While it likely contains some branched filaments, it is not a canonical branched actin network like the one comprising the dSMAC. Indeed, it is a lot like the mixture of actin asters, actin foci, branched actin and linear filaments described in Hela cells using the same imaging technique ((Fritzsche et al., 2017); we have now cited this paper). Of note, A20 B cells make a much bigger branched actin/dSMAC/lamellipodium than do primary B cells (compare the image of the representative A20 B cell in Fig. 1J to the various images of primary B cells in this figure). Interestingly, this difference between immortalized cells and primary cells is conserved in T cells, as Jurkat T cells make a much bigger branched actin/dSMAC/lamellipodium than do primary T cells (Murugesan et al, JCB 2016).

Although the reviewers did not specifically comment on why only ~70% of primary B cells engaged with both anti-IgM and ICAM-1 make actomyosin arcs, we note that this is also the case for both Jurkat T cells and primary T cells (Murugesan et al, JCB 2016). We do not know why the number does not go to 100%, but the ~70% limit is the case for both B cells and T cells. Of note, in unpublished work we see that LFA-1 ligation also promotes actomyosin arc formation in T cells.

With regard to the reviewers’ question “Does LFA-1:ICAM-1 ligation simply stabilize the arcs and therefore make their appearance more likely, or does it promote the formation of a distinct actomyosin network with unique functions?”, we think that ICAM-1 engagement likely leads to the strong activation of RhoA, which then serves to drive both the formation of actin arcs by recruiting, unfolding, and activating mDia at the plasma membrane, and the stabilization and concentric organization of these arcs by activating myosin 2A filament assembly and contractility. In other words, we think ICAM-1 engagement leads simultaneously to the creation and stabilization/organization of the arcs. While it is true that BCR stimulation alone activates RhoA signaling to some extent (see Saci and Carpenter, Mol Cell 2005 and Caloca et al, J Biol Chem 2008), and that this may account for the sparse actin arcs seen in cells stimulated with anti-IgM alone, it is likely that RhoA signaling is more robust with the addition of integrin co-stimulation (Lawson & Burridge, 2014) and that this would promote the creation of the actomyosin arcs seen in these cells. That said, without independent measures of the creation and stabilization/turnover of the arcs, we cannot gauge the relative significance of creation versus stabilization/turnover in determining the steady state amount of arcs. To address this limitation, we have added the following sentence to the section of the Discussion dealing with integrin-dependent signaling pathways leading to actomyosin arc formation: “Finally, future studies should also seek to clarify the extent to which integrin ligation promotes the formation of actomyosin arcs by driving their creation versus stabilizing them once created.

With regard to the reviewers’ comment that “B cells stimulated with anti-IgM alone are capable of signalling and centralising antigen” we would like to emphasize that our study focuses on B cell immune synapse formation under limiting antigen conditions, where a previous study (Carrasco et al. Immunity 2004) and our data in Fig. S5 show that the impairments in BCR signaling and antigen centralization seen under this condition are rescued by integrin co-stimulation. We expand upon these findings by showing in Figures 5 and 6 that this integrin-dependent rescue of antigen centralization and BCR signaling requires actomyosin. In other words, the actomyosin arc network described here is required for integrin co-stimulation to promote antigen centralization and signaling under limiting antigen conditions. We agree with the reviewer that under non-limiting antigen conditions B cells can signal and centralize antigen in the absence of ICAM-1. That said, these high levels of BCR stimulation are probably not as physiological as limiting BCR stimulation. Finally, our data in Figure S7 shows that antigen centralization in primary B cells receiving non-limiting anti-IgM stimulation alone is also significantly impaired when myosin is inhibited. This suggests that cells receiving high levels of BCR stimulation employ myosin in some fashion to drive antigen centralization. We now close the section describing these results with the following statement: “That said, additional experiments should help define exactly how myosin contributes to antigen centralization in B cells receiving only strong anti-IgM stimulation."

Finally, and most generally, we avoided the use of the word “requirement” as in the reviewer’s statement “the requirement for LFA-1:ICAM-1 ligation in the formation of the actomyosin arcs is not clear”. Given that some B cells receiving only anti-IgM stimulation create arcs (albeit sparse and transient), we were careful to say throughout the text that ICAM-1 engagement “promotes” actomyosin arc formation. We think our evidence for this is compelling.

2) The authors propose that the contractile actomyosin network formed in the presence of LFA-1:ICAM-1 interactions promotes B cell activation especially at low antigen concentrations; however, their data focus only on early signalling (pCD79a and pCD19) and it would be helpful to know whether LFA-1:ICAM-1 interactions impact signalling further downstream.

We thank the reviewer for this important suggestion, which we will address in a future study.

3) The observation that some GC B cells centralise antigen is very interesting, but there are a few aspects of this investigation that should be expanded upon. The authors show that with LFA-1:ICAM-1 interactions, GC B cells are about equally likely to organise BCR:antigen complexes into peripheral clusters and centralised clusters. It would be informative to have, in the same study (Figure 7), a comparison with GC B cells stimulated with antigen alone. The reason is that other studies investigating GC B cell synapse architecture did not quantify antigen organisation in this way, so it is difficult to make comparisons with previous work. It would also be very useful to see how the actomyosin network is organised in GC B cells exhibiting different synaptic architectures (i.e. peripheral versus central clusters), especially given the critical role of myosin IIa activity in GC B cell antigen affinity discrimination. Additionally, while it is a very interesting observation that LFA-1:ICAM-1 interactions may affect GC B cell synapse organisation, it is not clear whether this has an impact on cellular function. For instance, does antigen and actomyosin organisation in GC B cell synapses contribute to differences in signalling or traction force generation? In the introduction the authors suggest that actomyosin arcs contribute to antibody affinity maturation (line 87-88), but without functional studies to support this claim I think it is too speculative.

We thank the reviewer for their comments and suggestions regarding our GC data. Our sole purpose in performing the experiments in Figure 7 was to see if GC B cells can also make actomyosin arcs. We did this because recent papers and reviews state that the organization and dynamics of actin at GC B cell synapses are completely different from the organization and dynamics of actin at naive B cells synapses. As such, these initial observations are meant to add to previous work on GC B cells rather than generate direct comparisons. The reviewers appear to agree that the data in Figure 7 shows convincingly that a subset of GC B cells can make actomyosin arcs that are indistinguishable in appearance from those formed by naive B cells (so the specific claim we are making does not “require additional supporting data”). Rather, the reviewers request that we expand on the data in Figure 7 in several ways, some of which we had already mentioned in the Discussion (“While additional work is required to prove that the subset of GC B cells with actomyosin arcs are the ones that centralize antigen, this seems likely given our evidence here that actomyosin arcs drive antigen centralization in naïve B cells.”, and “Future work will also be required to understand why GC B cells vary with regard to actomyosin organization and the ability to centralize antigen 18 (e.g. dark zone versus light zone GCs)”). In addition to these statements, we now end the section describing the results in Figure 7 with the following statement: “We note, however, that our conclusions regarding actomyosin arcs in GC B cells require additional supporting data that include testing the ICAM-1 dependence of actomyosin arc formation and quantitating the contributions that this contractile structure makes to GC B cell traction force, signaling, and antigen centralization.”

With regard to the reviewers concerns indicated by their comment “In the introduction the authors suggest that actomyosin arcs contribute to antibody affinity maturation (line 87-88), but without functional studies to support this claim I think it is too speculative”, we have changed the relevant sentence to “Finally, we show that germinal center (GC) B cells can also create this actomyosin structure, suggesting that it may contribute to the functions of GC B cells as well”.

Reviewer #2:

The manuscript utilizes elegant imaging tools to describe the contractile actomyosin arcs, induced by integrin-ligation, and their involvement in antigen gathering in B cells. The findings are important and have the potential to make a considerable impact in the field. The main conclusions are well supported by strong data and the manuscript convincingly brings across the need of integrin-ligation to induce generation of the arc network and the role of this structure in antigen gathering. The methods and the quality of imaging are state-of-the-art and provide an important example for future studies in B cell immune synapse. Some aspects of the study would benefit from clarification and extended experimentation or analysis.

1) In addition to cultured B cells, the work includes naïve primary B cells as well as isolated germinal center B cells. While the use of primary cells adds value to the study, in most cases the cells are activated first with LPS prior to transfection with F-Tractin constructs. Such a treatment is likely to alter the cytoskeletal features of the naïve B cells and, thus, it would be informative to provide an analysis of this effect.

We thank the reviewer for commenting on this. To clarify, we treated primary B cells with LPS to promote cell survival during the harsh nucleofection/electroporation conditions that otherwise kill these fragile cells. Moreover, the cells were rested for 24 hours post-nucleofection in the absence of LPS to promote return to a resting state, as previously described (see(Freeman et al., 2011)). Moreover, only those primary B cells used for live cell imaging of the F-actin using the F-actin reporter F-Tractin were LPS treated. The majority of our experiments employed non-treated ex vivo B cells that were fixed, stained and imaged for quantitation. Importantly, under conditions of ICAM-1 co-stimulation, the actomyosin arcs formed by ex vivo B cells and by LPS-activated cells were indistinguishable. For example, compare the F-Tractin-expressing cell in Fig. 3A to the non-treated cells in Fig. 3D and Fig. 7A. To summarize, then, only live-cell imaging experiments that required F-Tractin to visualize F-actin dynamics were performed using LPS-activated B cells. Finally, we clarified in the Methods that we refer to all primary B cells as “naïve” B cells because they had not been previously activated by antigen at the time of antigen stimulation.

Reviewer #3:

The work 'A B cell actomyosin arc network couples integrin co-stimulation to mechanical force-dependent immune synapse formation' by Wang et al. describes the importance of integrin mediated B-cell co-stimulation for IS formation in B-cells by fostering the formation of myosin II A driven actin arcs that are essential in the transport of IgM clusters towards the IS center.

The work presented here, i.e. experiments and analysis, is very thoroughly done and includes tests and controls using different labelling strategies and constructs of myosin II A, multiple cell types including primary cells and a range of chemical inhibitors to rule out artefacts.

The authors claim that the observation of actin arcs in B-cells co-stimulated by ICAM-1 - LFA-1 interaction is important for the efficient activation of B-cells in the presence of limiting levels of anti-IgM and this is very well supported by the experiments. However, it was a bit surprising that the paper did not draw much of parallels between the observed phenomenon and the reported actin arcs in activated T-cells even though some of the authors were very much involved in such work on T-cells. If there is a good reason to believe there is no ground to draw comparisons, this would then also need to be highlighted by the authors.

We thank the reviewer for their comments. We have now added the following two sentences to the Discussion: “It is also important to note that the contractile actomyosin arcs described here in B cells and the actomyosin arcs described previously in T cells (Murugesan et al., 2016) share much in common as regards formation, organization and dynamics (Hammer et al., 2019; Wang & Hammer, 2020). Going forward, it will be vital to define how these two immune cell types harness the same contractile synaptic structure to accomplish different goals (i.e. antibody production by B cells and target cell killing by T cells).”

The work on establishing the drivers of actin arc formation and dynamics is well done, but it is important to note that previous work has analyzed actin arc formation in other cell types. Work by Bershadsky has already established many 'ground rules' for the formation of actin arcs and the role of integrin adhesion, formin activity and myosin II in the process (Tee YH, Shemesh T, Thiagarajan V, Hariadi RF, Anderson KL, Page C, Volkmann N, Hanein D, Sivaramakrishnan S, Kozlov MM, Bershadsky AD. 2015. Cellular chirality arising from the self-organization of the actin cytoskeleton. Nat Cell Biol 17:445-457. doi:10.1038/ncb3137). It might be very instructive if the authors could put their findings in relation to this work.

The formation of actin arcs is also well studied in U2OS cells and the results presented here could highlight interesting general features of this process observed in very different cell types (Tojkander S, Gateva G, Husain A, Krishnan R, Lappalainen P. 2015. Generation of contractile actomyosin bundles depends on mechanosensitive actin filament assembly and disassembly. Elife 4:1-28. doi:10.7554/eLife.06126; Bur-nette DT, Shao L, Ott C, Pasapera AM, Fischer RS, Baird MA, Der Loughian C, Delanoe-Ayari H, Paszek MJ, Davidson MW, Betzig E, Lippincott-Schwartz J. 2014. A contractile and counterbalancing adhesion system controls the 3D shape of crawling cells. J Cell Biol 205:83-96. doi:10.1083/jcb.201311104).

In this regard, the findings about the importance of myosin II A activity, integrin adhesion and mDia1 in the formation of actin arcs is not that surprising and the authors might rather highlight the important role of these newly studied structures for co-stimulation in B-cells as this is the more novel and insightful bit of the work.

We thank the reviewer for their comments. Indeed, our prior work in T cells (Murugesan et al., 2016; Yi et al., 2012) also linked formin activity and myosin 2 contractility to the formation of actin arcs and the generation of integrin-based adhesion. We now cite the papers highlighted by the reviewer using the following sentence in the revised Discussion: “It is important to note here that several earlier studies performed using other cell types have also linked formin activity and myosin 2 contractility to the formation of actin arcs and the generation of integrin-based adhesions (Burnette et al., 2014; Tee et al., 2015; Tojkander et al., 2015).” As for highlighting the relevance of our results for the B cell field, we think we have done that by demonstrating the existence of this contractile network in B cells, and by showing that it provides mechanistic insight into how integrin co-stimulation promotes synapse formation and B cell activation when antigen is limiting. Given that many recent studies of actin cytoskeletal dynamics in B cells were performed in the absence of LFA-1 ligation, we think our findings invite a critical “reset” for the way in which future B cell studies should be approached by highlighting the need for integrin co-stimulation when examining the roles of actin and myosin in B cell activation.

I can't begin to imagine how this pandemic affected those who have already existing mental health conditions. And the fact that individuals, and children especially, were not getting the right amount of exercise is definitely a concerning factor to think about.

I think the essay will be about how rituals have provided the author peace in difficult times, and why they may do the same for others. The author is trying to create sadness and empathy. They are also using pathos.

Reviewer #1 (Public Review): 

Overall, this manuscript presents a careful study of sea star larval nervous system regeneration using new transgenic tools for marking and following cells involved in regeneration. The authors provide a nice, well-written introduction to their study in the Abstract and Introduction sections. I do have one major issue with the wording they are using for describing what can be done with the transgenic tools they have developed. 

They mention in the third paragraph of the Introduction that "Only cell tracking can definitively establish the origin and trajectory of cells during regeneration and resolve the debate as to the role of stem cells versus cellular reprogramming in echinoderms." And then in the final paragraph they state that "We establish a novel cell lineage tracking system to determine the cellular origin of these regenerated neurons." 

The system they develop does mark individual sox2 and sox4 expressing cells but I object to it being called a "cell lineage tracking system" as this is a very specific term used for a set of methods that allow for tracing the fate of individual cells and all of their progeny, traditionally through development or with stem cells. In essence cell lineage tracking/tracing provides the identification of ALL progeny of a single cell. According to a Primer on Lineage Tracing by Kretzschmar and Watt (2012) https://www.cell.com/fulltext/S0092-8674(12)00003-700003-7)<br> "For any lineage tracer, the key features are that it should not change the properties of the marked cell, its progeny, and its neighbors. The label must be passed on to all progeny of the founder cell, should be retained over time, and should never be transferred to unrelated, neighboring cells." 

I strongly believe that the BAC-reporters developed in this manuscript do not fit that definition of a cell lineage tracing/tracking system and new verbiage should be used to describe these tools. These could very simply be referred to as fluorescent BAC-reporters and describe specifically how they are used to mark and follow the fate of cells expressing the Sox2 and Sox4 genes. The only way the language of a cell lineage tracing/tracking system could be used is if they had created a BAC-reporter for a gene that was expressed constitutively throughout a cell lineage as it progresses or if the protein expression (the tracer) was passed along to all progeny of the cell expressing that gene. My understanding is that the gene expression of Sox2 and Sox4 is highly dynamic and thus the label, by definition, is not going to be passed on to all progeny of the founder cell. I do think this is a powerful system, I just object to how the authors have chosen to describe it in the manuscript. Careful rewording can still make the reader aware of the limitations and advantages of this system and will avoid misunderstanding. 

Therefore, all mentions throughout the manuscript of "a lineage tracing system" would need to be removed and replaced with wording that accurately reflects the true nature of these reporters, simply as photoconvertible expression reporters that can show Sox2 or Sox4 expressing cells. This includes text in the Results and Discussion section, e.g. "To our knowledge, this is the first time that any cell lineage tracing studies have been performed in echinoderm regeneration." 

Results: 

The authors nicely present their larval regeneration system and highlight the timeline of when serotonergic neurons regenerate over a period of 21 days. They then demonstrate that embryonic neurogenesis pathways are recapitulated during larval regeneration. Then, they present results from their photoconvertible expression reporters and demonstrate three populations of cells in decapitated larvae. The green-only sox4+ cells are the most interesting population - these are cells that are induced to express sox4 only after decapitation. Comparing embryogenesis and larval development demonstrated that the wound response in larvae involves specifying new sox4+ cells, something that had ended by 4dpf in normally developing larvae. 

The co-injected double BAC recombinant larvae showed colocalization of sox2+ and sox4+ in regenerating larvae. De novo sox2 expression following bisection together with colocalization with sox4 expression nicely shows that these new sox2+ cells contribute to the neural lineage. Considering that the colocalization appeared to be a rare-ish event (only observed in 4 out of 15 larvae), it would be nice if the authors could comment on why this may be. Is it just a truly rare event to catch or could it have anything to do with the reporters themselves? 

Same question about the sox2+ cells that do not express sox4:Cardinal by 3dpb. Can the authors comment specifically on whether they think there are multiple subpopulations of sox2+ cells and why some get specified to the neural fate while others do not? 

The final experiment using cell division inhibitor Aphidicolin was very clever and nicely demonstrates that cells that did not previously express sox2 can be induced in the absence of cell division. It would be helpful if the authors could indicate how many larvae showed this pattern as they did for the previous colocalization experiment. 

Discussion:

In the final paragraph of the Discussion, the authors discuss a dichotomy between the use of stem cells versus de- or trans-differentiation in different model systems of regeneration. They describe the planarian system in the following way: "For example, the freshwater planarian, Schmidtea mediterranea, utilizes a population of heterogeneous, pluripotent somatic stem cells, called neoblasts, to proliferate and differentiate to replace body parts (Sánchez Alvarado, 2006)" and contrast this with Hydra and axolotl, saying "Conversely species such as Hydra and axolotl, refate differentiated cells either through dedifferentiation or transdifferentiation (Gerber et al., 2018)." I think this oversimplifies the current understanding of these systems. For example, a recent paper by Raz et al. 2021 (Cell Stem Cell 28(7): 1307-1322.e5) makes the case that Schmidtea mediterranea is capable of having specialized neoblasts undergo fate-switching and "propose a non-hierarchical lineage model for neoblasts, in which a neoblast can specify one of a diverse set of possible fates in the course of a single division and specialized neoblasts can divide to generate neoblasts that can specify different fates." In essence, this could be considered something more flexible and complicated than what the authors described - just using pluripotent neoblasts to proliferate and differentiate to replace body parts. And although Hydra is known to use trans-differentiation during regeneration, this organism also employs stem cells in the process of regeneration. Please see Siebert et al. 2008 (Developmental Biology 313(1): 13-24) for a discussion of how both mechanisms are employed in this regeneration model. Therefore, I think it is an oversimplification to characterize these regeneration models as either using stem cells OR using de- or trans-differentiation. I think in these systems, there is not a simple dichotomy and more flexibility has been demonstrated in how regeneration is accomplished than the authors describe here and the text would need to be revised accordingly.

I like this comparison about the brush and the path. It really gives us that imagery that helps us understand what learning does to our brains.

Author Response:

Reviewer #1:

In this study, the authors use CyTOF-based analysis to characterise spike-specific T cell responses following mRNA vaccination. They seek to understand both the breadth of responses to 'wildtype'-like and variant spikes, as well as the differences between T cell responses from convalescent and previously uninfected subjects. Consistent with other studies, they find that spike-specific T cell responses are similar across different variants, both in frequency and phenotype. In contrast, however, they identify several phenotypic differences in the T cell response elicited by infection, vaccination, or vaccination following infection.

Despite a somewhat limited sample size, they clearly identify changes in memory phenotype and chemokine receptor expression that may affect T cell trafficking to mucosal tissues across infection and vaccination. While inclusion of additional chemokine receptors (such as CXCR3) in the CyTOF panel would have aided in characterising these cells, this data highlights how infection and vaccination may elicit distinct T cell responses.

In fact CXCR3 and CCR4 were chemokine receptors that were considered for the panel, but could not be included as antibodies against these antigens do not stain properly on cells fixed with paraformaldehyde (PFA), and for logistical and biosafety reasons the specimens analyzed in this study had to be PFA-fixed before CyTOF staining. Although we have previously analyzed expression of CXCR3 and CCR4 on T cells by CyTOF (Cavrois et al, Cell Reports 2017 20(4):984 PMID: 28746881; Xie et al, Cell Reports 2021 35(4):109038 PMID: 33910003), those studies were exclusively performed on viable cells, and not on COVID-19 patient specimens. All our prior CyTOF phenotyping studies using COVID-19 patient specimens (Neidleman et al, Cell Reports Medicine 2020 1(6):100081 PMID: 32839763; Neidleman et al, Cell Reports 2021 36(3):109414 PMID: 34260965; Ma et al, J Immunol 207(5):1344, PMID 34389625), as well as some of our non-COVID-19 studies (Ma et al, Elife 9:e55487 PMID: 32452381; Neidleman et al, Elife 2020 9:e60933 PMID: 32990219), were performed on fixed cells, where CXCR3 and CCR4 unfortunately could not be included as parameters analyzed.

Future studies will be required to better assess the functional impacts of these phenotypic differences on T cell recall and contribution to protective immunity.

We absolutely agree that future studies should be pursued to better assess the functional impacts of the phenotypic differences on T cell recall, and on contribution to protective immunity. Such studies will most certainly require use of animal models, and in fact are studies that we have just begun (mouse model) or will soon begin (non-human primate model). To fully acknowledge the need for such functional studies, we have now added to multiple sections of the Discussion the need for future studies to incorporate animal models (Line 472 and Lines 488-491), including the statement “Such follow-up studies should also examine the functional outcomes of the discoveries made here (e.g., effect of chemokine receptor expression on homing of infection- and vaccine-elicited SARS-CoV-2-specific T cells), including in animal models of SARS-CoV-2 infection.”

Reviewer #2:

The authors address an important question, whether it people who have had Covid19 and are then vaccinated with one mRNA Spike vaccines made better immune responses than those who had not previously been infected and have two shots of the vaccine. They also compare responses to different virus variants and find extensive cross reactions and no differences between the groups - an important result.Their main finding is a difference in the quality of the CD4+ T cells in the 'Covid-vaccinees' compared to the 'naive double vaccines'. They suggest that T cells in the former may home better to the respiratory tract and persist longer.

The major strengths are:

• The methodology used, based on Cytof multiparameter analysis of antigen responding CD4 and CD8 T cells.

• Demonstration that the second vaccine dose in the naive group 'improves' the T cell response.

• Demonstration that a second vaccination in the Covid19 group does not improve the T cells.

We thank the Reviewer for the nice summary and for the positive comments.

Weaknesses:

Fully (and commendably) acknowledged in the manuscript:

• The study groups are small

• The antigen specific T cells are stimulated in vitro so may be distorted, nevertheless there were still differences

We agree with the Reviewer about the listed weaknesses of the study. We note that we had in our original manuscript acknowledged all these weaknesses within our “Limitations” section, including the fact that we had to stimulate our samples to identify and characterize the SARS- CoV-2-specific T cells. We have now expanded the part about our having stimulated the samples, by proposing that future studies should take advantage of tetramer technology to characterize cells in their baseline (non-stimulated) states, whilst acknowledging that such studies would for the most part be limited to CD8+ T cell responses as tetramer reagents for CD4+ T cells are less robust (Lines 500-506).

Not acknowledged but possibly outside the scope of this study:

• The reader will wonder how this affects the antibody response which ultimately is the main protector from reinfection and also how the T cell responses might impact on disease severity after post vaccination (re)-inrfection

Serological assays were not performed in this study; however we fully agree with the importance of associating the in-depth phenotypes of vaccine-elicited SARS-CoV-2-specific T cells with the antibody response. In fact, just as we went very “deep” into the phenotypes of SARS-CoV-2-specific T cells in this study, we are at the moment optimizing techniques to, in an analogous fashion, deeply characterize the serological response to vaccination. This entails optimizing a flow cytometry-based approach we recently introduced and implemented on a small number of specimens (Ma et al, J Immunol 207(5):1344, PMID 34389625), to be able to simultaneously assess the levels of IgA1, IgA2, IgE, IgG1, IgG2, IgG3, IgG4, and IgM against the S1, S2, and RBD domains of the SARS-CoV-2 spike protein in a large number of patient specimens. Once we’ve optimized the assay and applied it on the vaccine specimens, we plan to associate the resulting 24-parameter serological datasets (8 isotypes of antibodies each against 3 antigens = 24 parameters total) with the high-dimensional SARS-CoV-2-specific T cell datasets from this study, but that will be its own separate (and large) study and beyond the scope of this current one. As generating such serological data will take at least 3-6 months to complete, and the focus of this study is on SARS-CoV-2-specific T cells (and all conclusions we drew were based only on the T cell data), we think it appropriate that we limit this study to deep-phenotyping of the T cells. We have now brought up in the last part of our “Limitations” section the lack of serological analysis in this current study as a limitation, and how follow-up studies should associate serological responses with the T cell responses characterized here. (Lines 506-511: “A final limitation is that serological analyses were not performed in this study. As coordination between the humoral and cellular arms of immunity are likely key to effectively controlling viral replication, future studies should assess to what extent the breadth, isotypes, and functional features of spike-specific antibodies elicited by vaccination associate with the herein described phenotypic features of vaccine-elicited SARS-CoV-2-specific T cells.”)

With regards to how T cell responses might impact disease severity and breakthrough infections, this is an aspect we are very interested in investigating, as detailed in our final response further below.

I think it is important to recognize that the original article that motivated much of the early research into Hypertext, including that of Ted Nelson, who coined the term "Hypertext," assumed that "associative links" would, in fact, exist "outside" the document. That article was, of course, Vannevar Bush's "As We May Think," published in the July 1945 Atlantic Monthly.

Given that Bush assumed that documents were stored primarily as immutable microfilm images, he had no choice but to assume that links would stored be external to the documents which were their subjects. It simply wasn't possible to embed links in documents as we do today with HTML and with other formats that support embedded links.

Thus, it can be said that the idea of external associative links, or annotations, was actually the original idea for how Hypertext would be implemented. It was only later, long after 1945, that we found that it was convenient to support links embedded in content.

It is also important to note the external links allow us to more easily do things that can't be done with the more common internal links. For instance, if you're reading a document with internal links, you can easily answer the question "What does this document link to?" However, it is much harder to answer the question: "What documents link to this one?" This is because internal links are only "one-way links. However, external links, which are "two-way links," establish a relationship between documents can exist independent of the documents themselves. Thus, if you have a collection of external links, you can answer the question: "What links to this document?" That question can't be easily answered in today's web unless you've got a web-crawling system like Google's that is capable of reading all documents on the web and then deducing all the back-links.

In fact, one of the key elements of a Web annotation system is the ability to pass the URL/URI for some web resource to the annotation system and say: "Show me what links (annotations) exist for this resource!" Of course, each of the annotations which contains the external links would itself have a unique identifier and should thus be something that can be annotated or linked to. In this way, we can have annotations of annotations as well as the same kind of forking "associative trails" that present alternative paths through the document space that Vannevar Bush imagined in his 1945 Atlantic Monthly article. In other words, when we allow for external links, we elevate the "link" or "annotation" to something which is a first-class object on the Web instead of leaving it as a mere attribute of some web resource.

To a great extent, raising links to the status of first class objects "completes" a large part of the journey from idea to implementation that began with Vannevar Bush's 1945 vision. We should understand that supporting annotations doesn't just provide a "nice new feature." It provides the foundation for what is today a very unfamiliar method for interacting with the web as a record of human experience and knowledge. The idea is very old. Nonetheless, we have yet to begin to have much experience with its use and implications.

I think that this is important because the cases in the voluntary section talked about how the suspect has to confess of their own free will and without intimidation. So we need to see if this was the case with Lav.

there may be cultural differences - something we should think about as we apply this to students in the US

I think this recap should be the introductory paragraph, and should be expanded

Reviewer #3 (Public Review):

This paper is based on digital reconstruction of a serial EM stack of a larva of the annelid Platynereis and presents a complete 3D map of all desmosomes between somatic muscle cells and their attachment partners, including muscle cells, glia, ciliary band cells, epidermal cells and specialized epidermal cells that anchor cuticular chaetae (circumchaetal cells) and aciculae (circumacicular cells). The rationale is that the spatial patterning of desmosomes determines the direction of forces exerted by muscular contraction on the body wall and its appendages will determine movement of these structures, which in turn results in propulsion of the body as part of specific behaviors.

To go a step further, if connecting this desmosome connectome with the (previously published) synaptic connectome, one may gain insight into how a specific spatio-temporal pattern of motor neuron activity will lead, via a resulting pattern of forces caused by muscles, to a specific behavior. In the authors' words: "By combining desmosomal and synaptic connectomes we can infer the impact of motoneuron activation on tissue movements". This is an interesting idea which has the potential to make progress towards understanding in a "holistic" way how a complex neural circuitry controls an equally complex behavior. The analysis of the EM data appears solid; the authors can show convincingly that desmosomes can be resolved in their EM dataset; and the technology used to plot and analyze the data is clearly up to the task. My main concern is with the way in which the desmosome pattern is entered in the analysis, which I think makes it very difficult to extract enough relevant information from the analysis that would reach the stated goal.

1.The context of how different structures of the Platynereis larval body, by changing their position, move the body needs much more introduction than the short paragraph given at the end of the Introduction.<br> -My understanding is that the larval body is segmented, and contraction of the segments can cause a certain type crawling or swimming: does it? Do the longitudinal muscles, for example, insert at segment boundaries, and alternating contraction left-right cause some sort of "wiggling" or peristalsis?<br> -In addition, there are segmental processes (parapodia, neuropodia), and embedded in them are long chitinous hairs (Chaetae, Acicula). Do certain types of the muscles described in the study insert at the base of the parapodia/neuropodia (coming from different angles), such that contraction would move the entire process, including the chaetae/acicula embedded in their tips?<br> -Or is it that only these chaetae/acicula move, by means of muscles inserting at their base (the latter is clearly part of the story)? Or does both happen at the same time: parapodium moves relative to the trunk, and chaeta/acicula moves relative to the parapodium? How would these movements lead to different kind of behaviors?<br> -Diagrams should be provided that shed light on these issues.

2.The main problem I have with the analysis is the way a muscle cell is treated, namely as a "one dimensional" node, rather than a vector.<br> -In the current state of the analysis, the authors have mapped all desmosomes of a given muscle cell to its attached "target" cell. But how is that helpful? The principal way a muscle cell acts is by contracting, thereby pulling the cells it attaches to at its two end closer together. As the authors state (p.4) "...desmosomes..are enriched at the ends of muscle cells indicating that these adhesive structures transmit force upon muscle-cell contraction."<br> -for that reason, the desmosomes at the muscle tips have to be treated as (2) special sets. Aside from these tip desmosomes there are other desmosomes (inbetween muscles, for example), but they (I would presume) have a very different function; maybe to coordinate muscle fiber contraction? Augment the force caused by contraction?<br> -As far as I understand for (all of) the desmosome connectome plots, there is no differentiation made between desmosome subsets located at different positions within the muscle fiber. I therefore don't see how the plots are helpful to shed light on how the multiplicity of muscles represented in the graphs cause specific types of neurons.<br> -As it stands these plots "merely" help to classify muscles, based on their position and what cell type they target: but that (certainly useful) map could have probably also be achieved by light microscopic analysis.

3.Section "Local connectivity and modular structure of the desmosomal connectome" p.4-7" undertakes an analysis of the structure of the desmosome network, comparing it with other networks.<br> -What is the rationale here? How do the conclusions help to understand how the spatial pattern of muscles and their contraction move the body?<br> -Isn't, on the one hand (given that position of the desmosome was apparently not considered), the finding that desmosome networks stand out (from random networks) by their high level of connectivity ("with all cells only connecting to cells in their immediate neighbourhood forming local cliques") completely expected?<br> -On the other hand, does this reflect the reality, given that (many?) muscle cells are quite long, connecting for example the anterior border of a segment with the posterior border.

4.In the section "Acicular movements and the unit muscle contractions that drive them" the authors record movement of the acicula and correlate it with activity (Ca imaging) of specific muscle types. This study gives insightful data, and could be extended to all movements of the larva.<br> -The fact that a certain muscle is active when the acicula moves in a certain direction can be explained (in part) by the "connectivity": as shown in Fig.7L, the muscle inserts at a circumacicular cell on the one side, and to an epithelial (epidermal?) cell and the basal lamina on the other side. But how meaningful is a description at this "cell type level" of resolution? The direction of acicula deflection depends on where (relative to the acicula base) the epithelial cell (or point in the basal lamina) is located. This information is not given in the part of the connectome network shown in Fig.7L, or any of the other graphs.

Reading vast amounts of information can lead one to think they know a lot, but retention is not good. To increase retention one should write, converse or otherwise engage with the information.

Author Response:

Reviewer #1:

The authors address an interesting but neglected issue in pigment cell biology, concerning the developmental origin of red erythrophores, especially in relationship to yellow xanthophores, and the genetic basis for their differing pigmentation. Red-yellow colouration in vertebrates usually arises from accumulation of dietary carotenoids, and often has significant behavioural importance, e.g. as an honest signal of individual quality. This and the biochemistry of carotenoid colour variation is nicely covered in the Introduction, providing helpful background to a broad audience.

The authors document the widespread presence of erythrophore in Danio, highlighting the unusual nature of Zebrafish within the genus as lacking them. They then develop some quantitative and objective measures of the xanthophores and erythrophores based upon Hue and Red:Green autofluorescence ratios, allowing clear distinction of the mature cell-types, and note the often binucleate nature of the erythrophores.

The authors then use a variety of tools to assess, with differing degrees of certainty, the lineage relationships of the erythrophores; together these provide a consistent and convincing picture of shared lineage between the two cell-types. This is consistent with the observed gradual shift in properties of proximal cells from xanthophore-like to erythrophore. A more direct test of the conversion of early xanthophores to erythrophores comes from the clonal analysis of aox5:nucEosFP cells (Fig. 4). They then use a fin regeneration assay to assess the plasticity of these cells in the mature adult. This is a neat experiment, but I am struggling with the interpretation of Figure 5A: which cells are being used as landmarks to justify the conclusion that the cells shown are clonally-derived form that single cell in the 5 dpa image? It may be that the full series of images could be provided in a supplementary figure and might make this clear, but the current images do not seem convincing to me. The experiment in Fig. 5B is convincing, so conclusion seems sound.

We added a supplementary figure (Figure 5—figure supplement 1) to show more context and nearby landmarks, including the amputation plane. We additionally swapped out the images in Figure 5A with an example that more clearly makes our point that cells seem to both lose red coloration and increase in number. Cells of both the original and the new example are visible in the new supplemental figure. Given the concern expressed we additionally modified the salient portion of the text, to make it clearer that the brightfield-only analyses were intended merely to see if a transformation is plausible, based on overt cell colors and behaviors in the absence of formal clonal analysis. The revised text reads:

“We first assessed the possibility that transfating occurs by repeatedly imaging individual fish in brightfield, to learn whether cells near the amputation plane might lose their red color during regenerate outgrowth. Individual erythrophores could often be reidentified using other cells as well as distinctive features of fin ray bones and joints as landmarks (Figure 5A; Fig- ure 5—figure supplement 1). As regeneration proceeded, small groups of cells having paler red or orange coloration, were sometimes observable where individual cells of deep red col- oration had been found, suggestive of proliferation and dilution of pre-existing pigments. Later, only yellow cells were found in these same locations. These observations were con- sistent with the possibility of erythrophore → xanthophore conversion, and so to test this idea directly we marked nucEosFP+ erythrophores by photoconversion prior to amputation (Figure 5B; Figure 5—figure supplement 2A). ”

The authors then use a transcriptomic comparison to identify candidate genes influencing erythrophore v xanthophore differentiation. They study 3 with mutant phenotypes affecting these cell-types, identifying likely roles of 3 erythrophore genes. Whilst most of this analysis is beautifully presented, I am confused by Fig. 7 in which I think panel D and F as described in the legend are inverted.

We fixed the relative ordering of panels and legends. We also changed the Y axis label in Figure 7F to indicate cells per 40 μm2 rather than density, which might be misinterpreted to mean cells per mm.

As is expected form this lab, the manuscript is generally very carefully and clearly written and includes thorough data presentation and statistical analysis. Conclusions drawn are appropriately nuanced, and justified by data presented. The manuscript provides an important first step in understanding the developmental relationship of erythrophores to xanthophores, and a number of genetic resources for the further exploration of this question.

This opening sequence is interesting, but quite frankly, very confusing! I am trying to picture the actions of that actors but many of them just seem far-fetched for the stage, or requiring makeup and special effects to simulate the blood or makeup. I do have the understanding that Brecht's Epic Theatre is extremely stripped down, so this would not be required. It would be up to the actor to show the audience with their actions what is happening to them in this sequence. I absolutely see the value in striping down the entire play to have the audience accept this world as it is. We may not see this bald spot, but through dialogue and action we will certainly know it is there. I think I am having a difficult time reading this after all of the realism and naturalism we have been reading. Directing a Brecht play seems like a monster, but a fun one.

Author Response:

Reviewer #2 (Public Review):

This manuscript details an investigation into whether blinding NIH grant reviewers to the name and institution may affect their review scores. They demonstrate that unblinded grants lead to slightly higher scores for white applicants than blacks, however, a deeper dive demonstrates that grantsmanship and history of prior funding can be even greater predictors of scores regardless of race.

Overall the manuscript touches on a presently vogue topic and that is of equality in outcomes and systemic racism. The major limitations of the study however, are ironically demonstrating the very topic that the manuscript tries to address. There are no considerations in the manuscript or mention of applications from Asian, Hispanic or Native American applicants, as the authors distill the problem literally down to only Black and White.

We now incorporate more of this perspective.

We rewrote the introduction, adding information about funding rates for Hispanic and Asian PIs (the 2 largest groups of minority applicants), and provided a stronger explanation for why this study focused on Black-White differences only (lines 86-95) Our aim was to provide a broader context while keeping the intro reasonably focused. Demographic differences in patterns of application numbers, review outcomes, and funding success is a complex topic, not easily presented concisely. More importantly, we think that this information, while no doubt of interest to some, is not relevant background to the experiment at hand. We tried to strike a balance between context and focus.

Author Response:

Reviewer #1:

This work demonstrates that functional KATP channels exist in most neuronal cell types in the mouse somatosensory cortex. While the transcriptomic profiling of electrophysiologically characterized neurons is only indicative of the existence of the Kir6.2/SUR1 KATP channel, the acute slice pharmacological/electrophysiological experiments convincingly supports this notion.

The uncertainty of single-cell RT-PCR is likely due to a small amount of starting material inherent to the sample collection method. As the authors discuss, low copy numbers of target transcripts may also have contributed to the negative/uncertain results.

We fully agree that scRT-PCR analysis underdetected Kir6.2 (kcnj11) and SUR1 (abcc8) mRNAs. This is likely due to their low abundance at the single-cell level, the sample collection method and the low efficiency of the reverse transcription (RT).

As requested by reviewer 2 we now report the low detection rate of these subunits in neurons responsive to diazoxide and tolbutamide and acknowledge the limitation of scRT- PCR (pages 7,8, lines, 34,1-6).

We have also improved the discussion by providing the copy number of these mRNAs detected by single cell RNAseq (Zeisel et al. 2015, DOI: 10.1126/science.aaa1934, data available online https://linnarssonlab.org/cortex/) and the estimated sensitivity limit of the scRT-PCR (page 13, lines 29-33).

Next, the authors demonstrate that lactate is taken up by neurons and elevates the discharge rate via an increased ATP production due to the oxidative metabolism downstream of lactate, which is in line with earlier studies including Ivanov et al. (2011, doi: 10.3389/fnene.2011.00002).

We thank the reviewer for pointing out this reference that we have added in the discussion (page 17, line 16).

The authors showed this by introducing 15 mM lactate, and discuss a possibility that extracellular lactate can be elevated by a systemic increase of lactate. However, such an increase is likely more modest in the brain (Carrard et al., 2018, doi: 10.1038/mp.2016.179). So, the lactate-enhanced firing might occur in extreme conditions such as during anoxia or ischemia; however, intracellular ATP would most probably decrease and hence KATP channels would open in this case. A discussion on extracellular lactate levels in physiological conditions would be helpful.

We have improved the discussion on the physiological extracellular level of lactate which can be as high as 5 mM at rest. Since during neuronal activity lactate levels are almost doubled (i.e. up to 10 mM), lactate-enhanced firing might occur under physiological conditions (page 18, lines 9-13). We agree that a systemic lactate increase modestly elevates its extracellular concentration to a level with little or no effect on firing rate. Accordingly we now also quote references reporting this observation. Nonetheless, peripheral lactate could represent an additional source facilitating lactate-sensing when both the brain and the body are active, as during physical exercise (page 18, lines 13-19).

Overall, this is a rigorous study that confirms the existence of functional KATP and dominant oxidative metabolism in most types of juvenile somatosensory cortical neurons.

Thank you.

Reviewer #2:

The authors present an impressive array of experiments testing the effect of lactate on a number of neocortical cell types. They uncover a mechanism by which lactate might enhance neuronal firing although direct physiological relevance needs further support for CSF lactate concentrations. Most of the experiments are sound and interesting and the remaining experiments have limitations inherent to the methodology and presented accordingly in the discussion. The results are convincing, however a number of specific points need to be addressed.

We thank the reviewer for the specific points raised that helped us to improve and clarify the manuscript.

Specific points:

• Page 6 line 21 onwards. The authors state consistent expression of Kir6.2 and SUR1 in various cortical cell types. Data presented in Fig1 challenge this statement showing that Kir6.2 and/or SUR1 was expressed in the minority of cells tested regardless of cell type. For example, out of the 10 intrinsically bursting cells shown in the Ward cluster plot on Fig1A-B, only two was positive for Kir6.2 according to Fig1D. Surprisingly, Fig1F shows that 10% of intrinsically bursting cells express Kir6.2 which is clearly not the case (it is 20%).

We thank the reviewer for pointing out this apparent incoherence. Indeed, Fig. 1D showed two intrinsically bursting cells that appeared positive for Kir6.2. However, one of them was also positive for genomic control and was discarded from the calculation of detection rate, as already discussed (pages 13,14 lines 34,1-5). For the sake of clarity Fig. 1D now depicts potential Kir6.2 false positive as shaded colored rectangles.

Amplification was used for the detection of mRNAs by the authors, thus it is unlikely that detection threshold plays a role in having Kir6.2 or SUR1 negative cells.

We agree that PCR amplification can detect a single DNA molecule (e.g. Li et al 1988, DOI: 10.1038/335414a0). However, the low reverse transcription (RT) efficiency is an important limiting factor for the mRNA detection by scRT-PCR. In addition, dendritic mRNAs are almost inaccessible to the harvesting from a somatic patch pipette, thereby decreasing the detection rate. Similar issues of mRNA detection by scRT-PCR have been reported for neuropeptide receptors despite a functional expression in a majority of recorded pyramidal cells (Gallopin et al. 2006, DOI: 10.1093/cercor/bhj081). scRT-PCR detection limit was estimated to be around 25 molecules of mRNA in a previous study quantifying at the single-cell level AMPA receptor mRNAs harvested in the patch pipette (Tsuzuki et al. 2001, DOI: 10.1046/j.1471-4159.2001.00388.x).

We have now improved the discussion by providing the copy number of Kir6.2 (kcnj11) and SUR1 (abcc8) mRNAs detected by RNAseq from single isolated cells (Zeisel et al. 2015, data available online https://linnarssonlab.org/cortex/). The estimated sensitivity limit of the scRT-PCR is also now provided (page 13, lines 29-33).

Along the same vein, amplification makes it difficult to understand what the authors mean by "low copy number at single cell level". Specifically, the sentence (p6l22-25) is self-conflicting suggesting reliable detection of KATP subunits yet downplaying the significance of moderate single cell detection rates.

Since the point on the "low copy number" is now discussed in more detail the sentence has been removed from the results section. To avoid confusion between detection and expression we now use only "detection" for scRT-PCR data and "expression" for functional data. Accordingly, in Figures 1F, 3B, 6A and S5, "Occurrence" was changed to "Detection rate".

I think a moderate statement with percentages of expression would adequately describe the findings with an emphasis on potential variability between individual cells regardless of cell type. Throughout the text, the authors should avoid the use of uniform expression of KATP channels in neurons.

• Page 6 line 30. The authors conclude co-expression of Kir6.2 and SUR1 subunits. Fig1D shows that out of approximately n=71 Kir6.2 positive cells and n=28 SUR1 positive cells only n=16 expresses Kir6.2 and SUR1 together and the evidence presented shows that n=83 cells do not co-express Kir6.2 and SUR1. Again, the conclusion in the manuscript seems biased towards the minority of cases and does not reflect the overall dataset. Accordingly, the suggestion that neurons and beta cells use the same KATP channel is not supported (p6l32).

The statement has been mitigated as follows (page 6, lines 21-27): "Apart from a single Adapting NPY neuron (Figure 1D), where Kir6.1 mRNA was observed, only the Kir6.2 and SUR1 subunits were detected in cortical neurons (in 25%, n=63 of 248 neurons; and in 10%, n=28 of 277 of neurons; respectively). The single-cell detection rate was similar between the different neuronal subtypes (Figure 1F). We also codetected Kir6.2 and SUR1 in cortical neurons (n=14 of 248, Figure 1D) suggesting the expression of functional KATP channels."

We have also avoided the use of uniform expression throughout the text and do not refer anymore to pancreatic beta-cell like KATP channels in the results section.

• KATP channel presence in neurons. With respect to the points above, it would be helpful to see in the results section and possibly on Fig2 whether there is an electrophysiological indication of pharmacologically unresponsive cells. This would help in assessing the relative sensitivity of the two approaches. Fig.2G is helpful here, however signal to noise is hard to assess in the current version in individual experiments. Please state if single cell PCR was performed on any pharmacologically examined cells.

We now clearly report that all neurons pharmacologically analyzed in voltage clamp were responsive to diazoxide and tolbutamide. We also mention the range of the effects of these KATP channel modulators on membrane resistance and whole-cell current (page 7, lines 12-15).

We thank the reviewer for suggesting to state if scRT-PCR was performed on pharmacologically examined cells, which helps to evaluate the relative sensitivity of scRT- PCR and pharmacological/electrophysiological experiments. We now report the number of neurons pharmacologically characterized and successfully analyzed by scRT-PCR (pages 7,8, lines 34,1-6). All these neurons were found to express functional KATP channels, but Kir6.2 and SUR1 subunits were detected in only a minority of them. We thus conclude that scRT-PCR underdetects these mRNAs.

Fig3B recapitulates the results of Fig1 that only a small fraction of RS cells express Kir6.2 and SUR1.

Since scRT-PCR is less sensitive than electrophysiological investigations, as just discussed above, the absence of detection of mRNAs does not mean an absence of functional expression of KATP channels. The absence of outward ATP-washout current in Kir6.2 KO neurons, in marked contrast with neurons from wild-type mice, supports the notion of a widespread functional expression of Kir6.2-containing KATP channels in cortical neurons. To avoid the confusion between detection and expression, we have reformulated the sentence (page 8, lines 11-12) as follows: "We first verified that Kir6.2 and SUR1 subunits can be detected in pyramidal cells from wild type mice by scRT-PCR".

In spite having a clever pharmacological design, due to limitations inherent to spatially nonspecific drug application methods, one cannot exclude that the results measured on individual cells could also reflect network interactions with astrocytes and/or neurons and should be discussed.

We agree with the reviewer that bath applications of drugs can induce network effects leading to potential confounding results. However, the kinetics and biophysical properties of the whole-cell currents recorded during pharmacological manipulations do not support such a network effect. This possibility, nonetheless, is now discussed page 13, lines 18- 23.

We have also discussed the possibility that the blockade of lactate transport by 4-CIN could reflect an impairment of lactate uptake by neurons but also of lactate release by astrocytes. However, under our conditions the contribution of astrocyte-derived lactate is expected to be negligible (page 16, lines 10-18).

• Lactate concentration in blood vs CSF. As the authors point out, there is a discrepancy in glucose concentration between the blood and CSF, yet they use lactate concentrations measured in the blood (and not in the CSF) during exercise in their experiments. The physiological relevance of these experiments is unclear unless there is evidence that lactate concentration in the CSF is indeed in the range found effective here.

We thank the reviewer for pointing out the discrepancies between plasma and extracellular levels of glucose vs. lactate. Although surprising at first, and in contrast to glucose, extracellular lactate level is higher than its plasma level. Such a difference, most likely reflects the ability of the brain produce lactate and not glucose.

As also requested by reviewer 1 we have improved the discussion on the physiological extracellular level which can be as high as 5 mM at rest. Since during neuronal activity lactate levels are almost doubled (i.e. up to 10 mM), we believe that lactate-enhanced firing might occur under physiological conditions (page 18, lines 9-13).

We have improved the rationale of the lactate concentration used which is an isoenergetic condition to 10 mM glucose for having the same number of carbon atoms (page 10, lines 4-5).

We also discuss the possibility, that peripheral lactate could represent an additional source facilitating lactate-sensing when both the brain and the body are active, as during physical exercise (page 18, lines 13-19).

• MCT1 and MCT2 expression and widespread lactate effects. Here, the authors admit that relatively low single cell detection rates were observed for MCT1 (19%) and MCT2 (28%). It seems consistent (and a bit worrisome) throughout the manuscript that expression of mRNAs additionally tested functionally have a limited range of PCR detection yet (again) ubiquitous presence was found when tested pharmacologically.

Similar to KATP channels subunits and as reported by single cell RNAseq data (Zeisel et al. 2015, DOI: 10.1126/science.aaa1934, data available online https://linnarssonlab.org/cortex/), MCT1 (slc16a1) and MCT2 (slc16a7) are expressed in cortical neurons at a copy number below the detection limit of scRT-PCR.

We have now discussed the discrepancy between MCT1 and MCT2 detection and the widespread lactate effects which are most likely due to their low abundance at the single cell level (pages 15,16, lines 32-34, 1-6). We also provide a counter example with LDH subunits which are expressed at higher single-cell levels, and for which a higher scRT- PCR detection rate was found to match the functional data (page 16, lines 6-9).

Author Response:

Reviewer #2:

This paper investigates cell size-dependent regulation of G1/S cell cycle transition in budding yeast, with a focus on the relationship between the activator Cln3 and the inhibitor Whi5. A prominent 2015 paper proposed that cell growth dilutes the inhibitor Whi5 while Cln3 levels remain constant. This 'inhibitor dilution' model has been challenged by several recent papers. In the present paper, Sommer et al. perform a series of quantitative western blots of whole cell extracts from synchronized cell cultures. They show that Cln3 concentration increases 10-fold before bud emergence (i.e. G1/S) but Whi5 concentration is largely constant, at least in rich media. Similar results were obtained in poor carbon media with a smaller increase in Cln3. These data argue against the inhibitor-dilution model and indicate that Cln3 levels are tuned by carbon availability and cell growth rate. Interestingly, Cln3 increases are not dependent on actin-based growth or bud emergence, but rather depend on membrane trafficking and TORC-SGK signaling. A series of experiments altering ceramide synthesis identify a link with Cln3 synthesis, although it remains unclear how directly this ceramide-Cln3 connection occurs.

The combination of results in this paper represent a significant contribution to the field. Major strengths include the careful quantitation of Whi5/Cln3 levels, and the clear effects on Cln3 from membrane trafficking events. I also appreciated the balanced tone of the text, which describes the strengths and weaknesses of each experiment and interpretation. I have a series of comments/concerns that could be addressed to strengthen the paper, as described below.

1) I understand why cells were pre-grown in poor carbon media for these experiments, but it seems important to know how Cln3 and Whi5 levels change for cells pre-grown in rich media. Otherwise, each paper reporting different results for Cln3/Whi5 could be dismissed as using a unique set of growth conditions. Along these lines, it would be ideal for the authors to test Cln3/Whi5 levels in their western blot assay using the same strain background and media as the Schmoller paper. It would be very interesting if the inhibitor-dilution model were observed under these conditions, whereas alternative mechanisms like Cln3 accumulation were observed under other conditions.

We attempted to grow cells in YPD, isolate small unbudded cells, and then release the cells back into YPD. However, we found that it was not possible to isolate a uniform population of small unbudded cells under these conditions. The problem is that very little growth occurs in G1 phase in YPD so that newly born cells are nearly the same size as mother cells (PMID: 28939614). This, combined with the normal variation in cell size observed in wild type yeast, means that elutriation yields a mix of unbudded and budded cells. Others have faced the same problem (PMID: 31685990, 10728640). The fact that so little growth occurs in G1 phase in YPD is an additional argument against the idea that dilution of Whi5 plays a substantial and general role in cell size control.

As an alternative, we grew cells in complete synthetic medium (CSM) containing 2% glucose. Under these conditions, cells grow more slowly and are smaller because CSM is limiting for nutrients other than glucose. We isolated small unbudded cells and released them into the same medium so that there would not be shift in carbon source. We found that Cln3 levels increased 3-fold, while Whi5 levels did no change substantially, similar to the effects observed in YP medium containing poor carbon. These data are shown in a new figure (Figure 1 – figure supplement 2). In addition, we have included new text to highlight these issues and how they can influence interpretation of the results.

We agree that it could be interesting to see how Cln3 and Whi5 behave in the mutant background and media conditions used by Schmoller et al. However, we were concerned that any behavior observed only in the bck2∆ background would say more about the effects of bck2∆ on accumulation of Whi5/Cln3 than it would about how cell size control works in wild type cells. Therefore, to limit the number of time-intensive elutriation experiments that we needed to complete the manuscript we would prefer to leave this experiment for others to complete if they are interested.

2) The authors over-express WHI5 to test the inhibitor-dilution. Their results dovetail with a recent study from the Murray lab (Barber et al., PNAS) suggesting that cells are not very sensitive to Whi5 levels. However, one can envision mechanisms (e.g. PTMs) that inhibit Whi5 molecules when expressed beyond their physiological concentration. Instead, it would be interesting to know what happens in WHI5/whi5 heterozygous diploid mutants that cut Whi5 levels in half. Perhaps this experiment exists in the literature, but it would be an ideal setting for the authors to perturb the inhibitor-activator ratio, and test Cln3/Whi5 protein levels along with cell size in synchronized cultures.

We were not able to find an analysis of the size of WHI5/whi5∆ cells in the published literature. We carried out the analysis and the data are shown in a new figure panel (Figure 3C). The effect is small – deletion of one copy of WHI5 in a diploid strain caused only a 0.9% decrease in median cell size. These data nicely complement the data showing little effect of 2xWHI5 on cell size. We were surprised that we did not think to do this simple experiment, and we were also surprised that we couldn’t find it in the literature. We thank the reviewer for suggesting the experiment. Since the heterozygous WHI5/whi5∆ cells showed minimal size defects, we have not elutriated the strain to test for changes in the Cln3/Whi5 ratio.

3) I found the result in Figure 5E very correlative and hard to interpret. For example, Ypk1 phosphorylation is lost at 2.5 min, but Cln3 levels seem unaffected at this timepoint and the next (?). I would suggest softening the (already soft) tone of explaining these results. In general, the connection between ceramide synthesis and Cln3 levels remains quite unclear to me.

We agree that our interpretation of the data in Figure 6E was confusing in the original version. Part of the confusion may arise from a lack of clarity in our writing and in the literature about the different phosphorylation inputs into Ypk1/2. The literature suggests that changes in the electrophoretic mobility of Ypk1 could be due largely to the Fpk1/2 kinases. TORC2 also influences Ypk1/2 phosphorylation, as detected by a phosphospecific antibody, but it remains unclear whether TORC2 also influences the electrophoretic mobility of Ypk1/2. The data suggest that the phosphorylation of Ypk1/2 that can be detected via electrophoretic mobility shifts is correlated with Cln3 levels, while TORC2-dependent phosphorylation with a phosphospecific antibody is not well correlated with Cln3 levels. We have edited the manuscript to make this more clear and to clarify what can and cannot be concluded from the data.

4) The text would need to describe a potential role for protein localization in this pathway. All the results come from cell extracts, whereas local protein concentration in the nucleus could be changing and impact the pathway.

The last three paragraphs of the Discussion include a discussion of potential roles for protein localization in the context of data from our work and previous studies that point to a potential role for localization of Ypk1/2 and Cln3 to the endoplasmic reticulum. In addition, we added the following sentence to the Results section to highlight potential localization issues: "Population level analysis of Cln3 and Whi5 protein levels by western blotting could miss changes in Whi5 or Cln3 concentration driven by changes in localization to specific subcellular compartments.”

Author Response:

Reviewer #2:

In this study, the authors develop a novel method, called MCGA, extending from their previous gene-based methods, to detect gene-trait association removing redundant signal. They further leverage expression QTL into their model to improve the resolution of gene-trait association. The overall structure is clear, and data is presented well. I am concerned about the simulation methods, and would like the authors to present some clarifications.

1) When comparing MCGA-eQTL and MCGA-sQTL, the authors simulate a single isoform-trait association, and the simulated gene expression is averaged among isoforms, which is kind of unfair for MCGA-eQTL model. Hormozdiari et al reveal that sQTL contributes few to traits after conditioning on eQTL (Hormozdiari et al., 2018, doi: 10.1038/s41588-018-0148-2). I would suggest to simulating a case that gene-trait association is mediated by overall expression, instead of a single isoform (transcript);

We thank Reviewer #2 overall for the numerous insightful and helpful suggestions and comments. Thanks for pointing out this problem! We agree with the reviewer that the gene-trait association can be mediated by the overall expression instead of a single isoform. However, we think that, mathematically, the two scenarios are equivalent. We also added a scenario in which gene-trait association is mediated by the overall expression of multiple susceptibility isoforms, and its power is similar to the scenario of single isoform-trait association (see Table 1 in the revised manuscript). In the real data analysis, we did observe that MCGA based on the isoform-level eQTLs detected more significant genes than that based on the gene-level eQTLs. Besides, we noticed that the sQTL (splicing QTL) in Hormozdiari et al. is different from the isoform-level eQTL used in our manuscript.

2) When comparing MCGA-eQTL and MCGA-sQTL, only power is considered. The authors should include the analysis to demonstrate the performance in control for false positive;

We thank the reviewer for this comment and suggestion. In the revised manuscript, we reported the results for controlling the false positive. Please refer to Essential Revisions point 2 (see line 261-262 in the revised manuscript).

3) When choosing a favorable exponent value c (1.432 chosen in the study), the authors found that the c value is robust to trait type, sample size or variant size, but the authors didn't explain what factors affect the choosing of c. Considering the potential application of MCGA method in other studies, the authors should explain what factor affects c value, and provide the guidance how to choose an optimal c;

We thank the reviewer for this comment and suggestion. Please refer to Question A and B of Essential Revisions point 3.

A: "Motived from the boundary of chi-square correlation, we adopted simulation studies to empirically choose c for controlling the type I error of the effective chi-square test. Besides the correlation of chi-square statistics, the choosing of c for the effective chi-square test may also be affected by the approximated non-negative solutions. However, the correlation of chi-square statistics is the major factor. Our simulation showed that the derived boundary and influence trend of LD on chi-square statistics were also applicable to the effective chi-square test. In the revised manuscript, we showed that the correlation of chi-square statistics is affected by the non-centrality parameter of chi-square statistics (see lines 640-655 in the revised manuscript)."

B: "As the optimal c for controlling the type I error of the effective chi-square test would be affected by the non-centrality parameter of chi-square statistics which are generally unknown in practice, we have to resort to a grid search algorithm to explore an empirically optimal c. In our last manuscript, we mixed the methods of choosing optimal c with the introduction of new effective chi-squared statistics. We wrote a new subsection in Materials and Methods to describe the procedure of choosing the optimal c in the revised manuscript (see lines 610-628 in the revised manuscript)."

4) The mediation analysis result in Yao et al. estimates that 11% of trait heritability is mediated by gene expression (Yao et al., 2020, doi: 10.1038/s41588-020-0625-2), while in simulation section of this study, 100% of trait heritability is mediated by gene expression. Simulations mimicking real scenarios should be used;

We thank the reviewer for this comment and suggestion and apologize for the confusion here. To our knowledge, the estimation by Yao et al. was for the entire genome. Note that many contributing variants of a trait may be far away from gene regions and beyond the scope of our approach. It is possible that some genes may have larger trait heritability (>11%) mediated by gene expression. Certainly, we agree with the reviewer that it is also necessary to mimic the scenario in which the gene expression mediates part of trait heritability. In the revised manuscript, we also added the scenario that part of trait heritability is mediated by the gene expression (see Table 1 in the revised manuscript). As expected, when the majority is mediated by other factors (except the gene expression), using all variants could be more powerful than only using eQTLs (see lines 247-279 in the revised manuscript).

5) It is important to choose a background gene set when conducting GO enrichment analysis. It is not clear what kind of genes are used as control when evaluating significance;

We thank the reviewer for this comment and apologize for the confusion here. We used the g:Profiler, a web server for functional enrichment analysis, to perform GO enrichment analyses. The conventional GO enrichment analysis took all annotated human protein-coding genes as a background in the present study (see lines 739-743 in the revised manuscript).

6) GTEx v8 contains samples from diverse populations, and it is crucial to handle the issue of population structure. Based on the description on https://pmg-lab-docs.readthedocs.io/en/latest/KGGSEE_doc/KGGSEE.html#id18, it seems that eQTL/isoQTL were detected ignoring population structure. The authors should explain why they applied a pipeline like that, and show that their conclusion wouldn't be affected by the choice.

We thank the reviewer for this comment. Indeed, in the original manuscript, we estimated the gene-level and isoform-level eQTLs without considering the population structure in GTEx v8. One reason is that though GTEx v8 contains samples from diverse populations, the majority (~85%) of the subjects are Europeans. Another reason is that the article of the GTEx consortium (https://www.science.org/doi/abs/10.1126/science.aaz1776) pointed that only 178 population-biased cis-eQTLs (pb-eQTLs) for 141 unique eGenes (FDR ≤ 25%) were identified across 31 tissues, which suggested that pb-eQTLs are hard to find at current sample sizes.

In the revised manuscript, to avoid the potential population structure issues, we only used the expression profiles and genotype data of the Europeans for the eQTLs identification (see lines 788-801 in the revised manuscript).

Reviewer #3:

The manuscript, "MCGA: a multi-strategy conditional gene-based association framework integrating with isoform-level expression profiles reveals new susceptible and druggable candidate genes of schizophrenia", describes an approach to conduct gene-level association testing in GWAS data with integration of gene expression data. The authors have conducted comprehensive simulation studies for main modules involved in this framework, demonstrating the advantages of the MCGA strategy compared to established similar work. The method has also been applied to the analysis of schizophrenia GWAS, with several interesting discoveries. All methods proposed are implemented in the KGGSEE package, a command tool written in Java with good documentation, data resource and examples for the type of analysis proposed in this work.

Overall, the framework is solid and the analyses performed are thorough. In particular, the simulation study and real data demonstration of advantages of isoQTL over conventional eQTL is novel and interesting. With the user friendly software available, I can envisage that MCGA will receive interest from the community and be adopted to many projects.

My major reservation on the methods is the component using conditional analysis to identify gene specific signals. Even though the MCGA framework is as solid as the methods it is based on, alternative methods are available for gene-level association analysis that takes into consideration of contribution from multiple SNPs and the LD without having to rely on conditional analysis. For example, fine-mapping approach such as SuSiE (https://github.com/stephenslab/susieR) uses summary statistics and LD, and can produces gene-level evidence of association in terms of Bayes Factor, when a gene region is analyzed. Such an approach does not have a potential type I error issue, is efficient enough to analyze multiple genes in LD with each other. Most importantly it provides inferences directly for multiple genes accounting for LD, without having to rely on conditional analysis. Conditional analysis, as a greedy algorithm, suffers an obvious limitation: suppose genes A and B are two causal genes in weak LD with each other. A non-causal gene C physically in between A and B are correlated with both A and B. Then C may have a stronger marginal signal than either A or B. A conditional analysis may identify C, and conditional on C, association signals of the true causal genes A and B will become weaker. I therefore am not convinced that a conditional analysis such as ECS is the best approach on which MCGA should be based.

We thank Reviewer #3 overall for the numerous insightful and helpful suggestions. We are happy that the reviewer found that our work will receive interest from the community and be adopted to many projects. To the best of our knowledge, MCGA had different application scenarios from SuSiE. The former worked with summary statistics, while the latter can only perform fine-mapping analysis with individual-level genotypes and phenotypes. Besides, MCGA can also be suitable for the three-gene case supposed by the reviewer. For example, if A and B are two causal genes, they may have larger selective expression scores than gene C in the phenotype-associated tissue. In the conditional analysis, A and B will enter the conditional procedure prior to gene C, which will make gene C not to be significant when conditioning on gene A and B.

Those 'circumstances' may be associated with demographic and governance (Think Like a Futurist.) What I see is the disparity between haves and have nots growing and eventually the haves nots will increase lawlessness out of desperation, childhood trauma, generational domestic violence, mental illness, and many other myriad of factors. The haves will no longer want to associate with the have nots and legislatively create a caste system. Even the have nots will form enclaves of safe places. I'm going dystopian here...BUT that's a silver lining, too. It's taxing being a have. Have nots have the opportunity to be closer in tune with natural forces and nature. Simply not wanting what the haves have relieves the burden of capitalism. Now i'm going Buddhist....

There is no single way of being effective. Rather one must discern when a fast loop or a slow loop is necessary. If you act fast when a slow loop is required, you may outpace yourself and waste precious resources (touching upon Think Like a Futurist.)

Author Response:

Reviewer #1 (Public Review):

This study is well-written and well-presented. The conclusions are clear and robustly supported by the data. The figures provide useful visualizations for the major findings. Virophage are an important and underappreciated component of global viral diversity, and they likely play important roles in eukaryotic genome evolution; this work is therefore quite timely. Relatively few studies focus on virophage or giant viruses compared to other viral lineages, so studies like this are highly valuable.

Strengths of this work include the high quality of the reference genomes, which were constructed using both short-read and long-read sequencing, as well as the diverse locations and isolation times of the host genomes.

We thank the reviewer for his encouraging and constructive comments!

I found no major weaknesses in this study. One minor issue is that the details of how EMALEs were delineated and initially detected seem a bit unclear to me. Based on my reading I am curious if some divergent or degraded EMALEs could have been missed. This may be important for assessing the consequences of possible retrotransposition-mediated EMALE inactivation.

Thank you for pointing this out. We added two sections to Materials and Methods called “Detection and annotation of EMALEs” and “Detection of Ngaro retrotransposons” where we describe the procedure in detail.

Based on our approach of visually screening the entire genome assemblies for GC anomalies, combined with blast searches of Cafeteria genomes using as input manually annotated EMALEs as well as databases of all available virophage sequences, we are quite confident that we have not missed any obvious virophage genomes. We would only have missed putative virophage sequences if their GC-contents were similar to that of the host (~70% GC) and if these sequences bore no detectable similarity to known virophage genes/proteins.

In contrast, our sequencing and assembly strategy probably did not result in a complete account of all EMALEs in these host genomes, as is evident from the large number of partially assembled EMALEs. However, partial does not equal degraded, but simply means that contig assembly stopped somewhere within the EMALE, resulting in an artificially truncated sequence. We therefore do not think that our approach introduced any relevant bias towards addressing the question whether retrotransposon insertion may lead to EMALE inactivation.

These points are now included in the discussion.

Author Response:

Reviewer #1 (Public Review):

The key question that the authors were addressing was how ethnicity differentially affects the microbiota of subjects living in a particular area (in this case East Asians and Caucasians living in San Francisco that have been enrolled in an 'Inflammation, Diabetes, Ethnicity and Obesity cohort - although inflammatory disease was apparently excluded in these subjects).

The existence of differences between different populations allows potential discrimination of the underlying factors - such as host genetics, diet, lifestyle, physiological parameters, body habitus or other environmental influences. In this case body habitus has been selected as a stratification factor between the two ethnicities. Immigration potentially allows distinction of environmental and host genetical influences.

The strength of the study is in the level of robust analysis of the microbiotas by a very experienced group of researchers, distinguishing the microbiota differences, especially in lean subject, with analysis of associations that may be driving the differences. It is interesting that diet is not one of the apparent associations in this study, yet the relationship of microbiota diversity to body habitus is strong in Caucasian subjects. These associations cannot easily be extrapolated to causation or mechanism - a fact well recognized in the paper - but remain important observations that rationalize in vivo modeling with experimental animals or in vitro analyses of microbial interactions between different taxa simulating the context of differences in the intestinal milieu. The paper includes work showing that differences of the microbiota can be recapitulated after transfer to germ-free mice, at least over the short term: this is important to provide tools to model the reasons for differences in consortial composition.

A very large amount of work required to assemble the samples and the clinical phenotypic metadata set making the data an important and definitive contribution for the subjects studied. Of course, this is one sample of extremely variable human conditions and lifestyles that will help build the overall picture of how differences in our genetics and environment shape our intestinal microbiota.

We appreciate the reviewers' positive summary of our manuscript and agree with the reviewer’s assessment of the need for both mechanistic follow-on studies and extensions to larger and more diverse cohorts.

Reviewer #2 (Public Review):

The study's primary aims are to test for the differences in the microbiome between self-identified East Asian and White subjects from the San Francisco area in the new IDEO cohort. The study builds on an growing literature which describes variations among ethnic groups. The major conclusion of "emphasize the utility of studying diverse ethnic groups" is not novel to the literature.

It was not our intention to imply that our study is novel in studying two distinct ethnic groups, but rather to emphasize that differences exist between ethnicities with regard to the gut microbiome and to provide a systematic analysis of this including gnotobiotic mouse models along a key health disparity in Asian Americans. We include references of prior examples of this work in our introduction (including several references in our introductory paragraph). We have modified our abstract to clarify this point further:

“Taken together, our findings add to the growing body of literature describing variation between ethnicities and provide a starting point for defining the mechanisms through which the microbiome may shape disparate health outcomes in East Asians.”

Overall, the strength of the results is that they confirm patterns from different cohorts/studies and demonstrate that ethnic-related differences are common. The results are subject to sample size concerns that may underpin some of the conflicting or lack of significant results. For instance, there is no overlap in highlighted species-level taxonomy differences between 16S and metagenomic analyses, which precludes a clear interpretation of the meaning of those differences and whether taxa should be highlighted in the abstract; there are low AUC values for the random forest modelling; and there is a lack of significance in correlations between BMI and East Asian subjects in F4a where there may be a correlation. While a minor point, it serves to highlight the sample sizes as the range of the variation in East Asian subjects is not as substantial as the White subjects because there are fewer East Asian data points above a 30 BMI (~N=5) relative to those of White subjects (~N=11).

We agree that our study was limited by sample size and that future studies increasing sample size would be valuable to assess the intersection of metabolic health in colocalized EA and W subjects. We include this in our discussion:

“Due to the investment of resources into ensuring a high level of phenotypic information on each cohort member, and due to its restricted geographical catchment area, the IDEO cohort was relatively small at the time of this analysis (n=46 individuals). This study only focused on two of the major ethnicities in the San Francisco Bay Area; as IDEO continues to expand and diversify its membership, we hope to study a sufficient number of participants from other ethnic groups in the future.”

The microbiome transfers from humans to mice also demonstrate that certain features of interpersonal or ethnic-related differences can be established in mice. This is useful for future studies, but it is not unexpected in and of itself given the robustness of transferring microbiome differences in other human-to-mouse studies. If the phenotype data were more compelling, then the utility of these transfers could be valuable.

We respectfully disagree with this point. To our knowledge, this is the first study demonstrating that ethnicity-associated differences in the gut microbiota are stable following transplantation, which is certainly not guaranteed given the marked and currently unpredictable variations between donor and recipient microbiotas shown here and in prior studies by us (Nayak et al., 2021; Turnbaugh et al., 2009b) and others (Walter et al., 2020).

We state this rationale in our results section:

“Taken together, our results support the hypothesis that there are stable ethnicity-associated signatures within the gut microbiota of lean EA vs. W individuals that are independent of diet. To experimentally test this hypothesis, we transplanted the gut microbiotas of two representative lean W and lean EA individuals into germ-free male C57BL/6J mice…Next, we sought to assess the reproducibility of these findings across multiple donors and in the context of a distinctive dietary pressure. We fed 20 germ-free male mice a high-fat, high-sugar (HFHS) diet for 4 weeks prior to colonization with a gut microbiota from one of 5 W and 5 EA donors....”

Furthermore, while the phenotypic data may not be as dramatic as the reviewer had hoped, this is to our knowledge the first demonstration that ethnicity-associated differences in the gut microbiota play a causal role in host phenotypes, as highlighted in our discussion:

“Our results in humans and mouse models support the broad potential for downstream consequences of ethnicity-associated differences in the gut microbiome for metabolic syndrome and potentially other disease areas. However, the causal relationships and how they can be understood in the context of the broader differences in host phenotype between ethnicities require further study.”

However, in the current state, I am concerned with the experimental design since the LFPP experiments used N=1 donor per ethnicity for establishing the mice colonies and are resultantly confounded by mice pseudo-replication with recipient mice derived from one donor of each ethnicity. This concern is relevant to interpreting results back to interpersonal or interethnic variation. Are phenotypic differences due to individual differences or ethnic differences? It's not clear.

We presented our data in summary form integrating the results from 3 independent experiments across two figures. To account for pseudoreplication as the reviewer suggests, we have restricted permutational space to account for one donor for multiple recipient mice using the parameters outlined in the adonis software package. Analyzing our results from 3 separate experiments, our results are statistically significant, which we mention in the revised text:

“In a pooled analysis of all gnotobiotic experiments accounting for one donor for multiple recipient mice, ethnicity and diet were both significantly associated with variations in the gut microbiota (Fig. S9), consistent with the extensive published data demonstrating the rapid and reproducible impact of a HFHS diet on the mouse and human gut microbiota (Bisanz et al., 2019).”

Figure S9. Combined analysis of recipient mice reveals significant associations with donor ethnicity and recipient diet. A PhILR PCoA is plotted based on 16S-Seq data from all gnotobiotic experiments. Individual mice are colored by (A) donor ethnicity or (B) the recipient’s diet. Both ethnicity and diet were statistically significant contributors to variance (ADONIS p-values and estimated variance displayed using blocks restricted by donor identifiers to account for one donor going to multiple recipient mice). We also observed a trend for interaction between diet and ethnicity in this model (p=0.068, R2=0.047, ADONIS).

The HFHS experiment also used N=5 donors that somewhat mitigates these concerns, but mixed sexes were used here and there can be sex-specific human microbiome differences.

Our study was designed to evaluate ethnicity and metabolic health. As we report in our original and updated analysis, we found no significant associations between the gut microbiota and biological sex (Figs. 2E and S4) in the IDEO cohort, perhaps due to the small effect size of sex reported in prior studies by other groups (Arumugam et al., 2011; Ding and Schloss, 2014; Schnorr et al., 2014; Zhang et al., 2021) coupled to the limited size of the current IDEO cohort.

The Turnbaugh and Koliwad labs use mixed sexes as donors for studies in conventionally raised and gnotobiotic mice due to our active funding from the NIH, which has clear guidelines meant to prevent continued discrimination against studies in females. The following link has additional information for your consideration: https://orwh.od.nih.gov/sex-gender/nih-policy-sex-biological-variable.

Importantly, our study was not confounded by sex due to the use of similar numbers of male and female donors (2 male and 2 females in the LFPP experiments and 3 female and 2 males for both ethnicities in the HFHS experiment). All of our recipient mice were male, as specified in our methods section and our revised main text:

“To experimentally test this hypothesis, we transplanted the gut microbiotas of two representative lean W and lean EA individuals into germ-free male C57BL/6J mice…Next, we sought to assess the reproducibility of these findings across multiple donors and in the context of a distinctive dietary pressure. We fed 20 germ-free male mice a high-fat, high-sugar (HFHS) diet for 4 weeks prior to colonization with a gut microbiota from one of 5 W and 5 EA donors....”

To further investigate any potential sex-specific signal we have stratified our analysis for the HFHS experiment by the gender of the donors (Reviewer Figure 2). This reveals that the significance between ethnicity in the microbiota transplantation experiments is preserved in mice that received stool from male donors (Reviewer Fig. 2A) but not female donors (Reviewer Fig. 2B). In Reviewer Fig. 1 above, LFPP1 and LFPP2 were conducted using different donors of different biological sex. Splitting our LFPP experiments up revealed the consistent signal for ethnicity in microbial community composition that we report above. The small sample sizes in this stratified analysis makes it difficult to conclude that there are reproducible sex-specific differences in the microbiome transplant experiments, but we agree with the reviewer that this question should be more thoroughly explored in future work.

We have added a brief note to the discussion to emphasize this important point:

“...differences between the human donor and recipient mouse microbiotas inherent to gnotobiotic transplantation warrant further investigation as do differences in the stability of the gut microbiotas of male versus female donors”

Reviewer Figure 2. (A,B) Principal coordinate analysis of PhILR Euclidean distances of stool from germ-free recipient mice transplanted with stool microbial communities from (A) male (n=2 EA and n=2 W donors) or (B) female (n=3 EA and n=3 W) donors of either ethnicity and fed a HFHS diet. Significance was assessed by ADONIS. Pairs of germ-free mice receiving the same donor sample are connected by a dashed line (n=2 recipient mice per donor). Experimental designs are shown in Fig. S7.

Finally, experimental results are not always consistent and sometimes show opposite trends that may be related to the sampling sizes. For instance, fat and lean mass increased and decreased respectively in LFPP, but there were no statistically-similar differences in HFHS. Moreover, the metabolic fat mass outcomes in mice do not match the expected human donor data. For instance, in LFPP1, White subjects had lower fat mass in humans but recipient mice on average gained more fat. It is difficult to reconcile these differences to a biological or sampling scheme reason.

We wholeheartedly agree with this point and were also surprised that the recipient mouse phenotypes did not match our original hypothesis based upon the observed health disparities between EA and W individuals. These surprising and perhaps counter-intuitive results demand further study and mechanistic dissection. We have tried to capture potential explanations for these findings while highlighting the limitations of our current study in our expanded discussion. With respect to the glucose tolerance data, the lack of a microbiome-driven phenotype might be due to the use of genetically identical mice that are not prone to metabolic illness without significant perturbation. If we had used mice prone to metabolic disease, such as non-obese diabetic (NOD) germ free recipient mice where the microbiome is known to impact the development of diabetes, we may have seen between ethnic differences in glucose tolerance.

Our revised discussion, with key points underlined is copied below for your convenience:

“Our results in humans and mouse models support the broad potential for downstream consequences of ethnicity-associated differences in the gut microbiome for metabolic syndrome and potentially other disease areas. However, the causal relationships and how they can be understood in the context of the broader differences in host phenotype between ethnicities require further study. While these data are consistent with our general hypothesis that ethnicity-associated differences in the gut microbiome are a source of differences in host metabolic disease risk, we were surprised by both the nature of the microbiome shifts and their directionality. Based upon observations in the IDEO (Alba et al., 2018) and other cohorts (Gu et al., 2006; Zheng et al., 2011), we anticipated that the gut microbiomes of lean EA individuals would promote obesity or other features of metabolic syndrome. In humans, we did find multiple signals that have been previously linked to obesity and its associated metabolic diseases in EA individuals, including increased Firmicutes (Basolo et al., 2020; Bisanz et al., 2019), decreased A. muciniphila (Depommier et al., 2019; Plovier et al., 2017), decreased diversity (Turnbaugh et al., 2009a), and increased acetate (Perry et al., 2016; Turnbaugh et al., 2006). Yet EA subjects also had higher levels of Bacteroidota and Bacteroides, which have been linked to improved metabolic health (Johnson et al., 2017). More importantly, our microbiome transplantations demonstrated that the recipients of the lean EA gut microbiome had less body fat despite consuming the same diet. These seemingly contradictory findings may suggest that the recipient mice lost some of the microbial features of ethnicity relevant to host metabolic disease or alternatively that the microbiome acts in a beneficial manner to counteract other ethnicity-associated factors driving disease.

EA subjects also had elevated levels of the short-chain fatty acids propionate and isobutyrate. The consequences of elevated intestinal propionate levels are unclear given the seemingly conflicting evidence in the literature that propionate may either exacerbate (Tirosh et al., 2019) or protect from (Lu et al., 2016) aspects of metabolic syndrome. Clinical data suggests that circulating propionate may be more relevant for disease than fecal levels (Müller et al., 2019), emphasizing the importance of considering both the specific microbial metabolites produced, their intestinal absorption, and their distribution throughout the body. Isobutyrate is even less well-characterized, with prior links to dietary intake (Berding and Donovan, 2018) but no association with obesity (Kim et al., 2019). Unlike SCFAs, we did not identify consistent differences in BCAAs, potentially due to differences in both extraction and standardization techniques inherent to GC-MS and NMR analysis (Cai et al., 2016; Lynch and Adams, 2014; Qin et al., 2012).

There are multiple limitations of this study. Due to the investment of resources into ensuring a high level of phenotypic information on each cohort member coupled to the restricted geographical catchment area, the IDEO cohort was relatively small at the time of this analysis (n=46 individuals). The current study only focused on two of the major ethnicities in the San Francisco Bay Area. As IDEO continues to expand and diversify its membership, we hope to study a sufficient number of participants from other ethnic groups. Stool samples were collected at a single time point and analyzed in a cross-sectional manner. While we used validated tools from the field of nutrition to monitor dietary intake, we cannot fully exclude subtle dietary differences between ethnicities (Johnson et al., 2019), which could be interrogated through controlled feeding studies (Basolo et al., 2020). Our mouse experiments were all performed in wild-type adult males. The use of a microbiome-dependent transgenic mouse model of diabetes (Brown et al., 2016) would be useful to test the effects of inter-ethnic differences in the microbiome on insulin and glucose tolerance. Additional experiments are warranted using the same donor inocula to colonize germ-free mice prior to concomitant feeding of multiple diets, allowing a more explicit test of the hypothesis that diet can disrupt ethnicity-associated microbial signatures. These studies, coupled to controlled experimentation with individual strains or more complex synthetic communities, would help to elucidate the mechanisms responsible for ethnicity-associated changes in host physiology and their relevance to disease.”

Reviewer #3 (Public Review):

The authors aimed to characterise how gut microbiota changes between different ethnic group for bacterial richness and community structure. They also wanted to address how this is associated with ethnic group within a defined geographical location. They have started to their story by comparing the fecal microbiota of relatively small cohort consisting of 46 lean and obese East Asian and White participants living in the San Francisco Bay Area. For that reason they used 16S and shotgun metagenomics. They demonstrated that ethnicity-associated differences in the gut microbiota are stronger in lean individuals and obese did not have a clear difference in the gut microbiota profile between ethnic groups, either suggesting that established obesity or its associated dietary patterns can overwrite long-lasting microbial signatures or alternatively that there is a shared ethnicity-independent microbiome type that predisposes individuals to obesity. The authors did also show the metabolic differences between these ethnic groups and the major differences were in the branched chain amino acid and the short-chain fatty acids. To prove their point, at this stage they have also used different metabolomic methodology. Although some aspects of the work are not very novel, the work does provide additional insights into the effect(s) of ethnicity, current living location and diet on shaping microbiota. Honestly, while reading through the manuscript, I have several questions where I believed that clarification was needed. But somehow, I felt like the authors have been reading my mind every step of the way. At the end of each section whatever I questioned was addressed in the next paragraph There are, however, a few points that I think would like to hear the authors' clarification.

• The authors pursued the story using 16S data. However, they have shotgun Metagenomics data which gives more power and resolution to microbiota profile. Is there any specific reason why the story was not build with shotgun Metagenomic data? However, if this is the case it will be nice to justify in the text or legend which figure was built with what dataset exactly?

As discussed above, 16S rRNA gene and metagenomic sequencing both have strengths and weaknesses. For example, 16S-seq is inexpensive and allows analysis of low abundance species, whereas metagenomics permits analysis of gene and pathway abundances of abundant taxa. As requested, we have now expanded Figure 2 (metagenomics) to better match Figure 1 (16S-seq). The type of technology is defined within each legend and the relevant text within our results.

• Even though the authors mentioned in the discussion that they have not used the same inocula from a donor to different diet, it will be nice if the authors further comments whether they would expect the same results or slightly different results which each different inocula.

As requested, we have modified the text in our discussion to include these comments:

“Additional experiments are warranted using the same donor inocula to colonize germ-free mice prior to concomitant feeding of multiple diets, allowing a more explicit test of the hypothesis that diet can disrupt ethnicity-associated microbial signatures. These studies, coupled to controlled experimentation with individual strains or more complex synthetic communities, would help to elucidate the mechanisms responsible for ethnicity-associated changes in host physiology and their relevance to disease.”

Overall, the study is well executed and claims and conclusions seem relatively well justified by the provided evidence. The findings are interesting for a broad audience of biologists. The findings are interesting for a broad audience of biologists.

This review reflects comments and contributions by Ankita Jha, Zara Weinberg, Julia Grzymkowski, Julien Berro, Karen Lange, Sónia Gomes Pereira, Arthur Molines, Jacob Herman, and Manoj Yadav. Review synthesized by Jacob Herman.

The work by Joseph Varberg and colleagues uses super resolution microscopy to better characterize the non-random distribution of nuclear pore complexes within the nuclei of the fission yeast Schizosaccharomyces pombe. This work also confirms findings in other organisms that nuclear pore complexes exist in multiple compositions. In addition to better documenting this phenomenon, this work begins to characterize the mechanisms by which nuclear pore position is regulated. Specifically, the authors show that clustering centromeres at the spindle pole body excludes nuclear pore complexes from the spindle pole body, and when these two complexes are forcibly dimerized mitotic defects result in decreased fitness.

The commenters were overall quite impressed with the imaging technique. The major conclusions and message of the preprint were generally well received, the comments or questions below relate to very specific text and experiments.

A few key themes mentioned in specific comments were:

• A desire for more consistent statistical analysis of data.
• Suggestions for additional data for some statements or toning down of the claims. NPC clustering is commonly discussed but there were questions as to how this phenotype was being measured.

Specific comments

Introduction

“perhaps explaining links between changes in NPC density and cancer” - The statement could note whether the correlation between NPC density and cancer is positive or negative.

“for example, emerin is enriched at pore-free regions of the NE in cultured cells (44). In budding yeast, NPC density is increased in the region of the NE near the spindle pole body (SPB),” - Does the SPB contain LEM domain proteins or is this a different possible mechanism for the non-random regulation of NPC density?

“Using S. pombe as a model system”- Why not S. cerevisiae, which is discussed earlier to have significant prior art in this regard? I'm sure there is a good reason, I think it could be outlined a bit more in the intro.

“We quantify NPC number under a range of conditions” - It would be useful to mention briefly at this point what types of conditions this refers to.

“Additionally, NPCs are excluded from the NE region surrounding the SPBs by Lem2 and other factors.” - Could the authors clarify if it is also something that is conserved or it is a new finding?

Results

Subheader “3D-SIM image analysis pipeline for NPC quantitation” - Worth mentioning the conclusion that NPC density is independent of cell cycle stage since that is the major conclusion from this section.

“This approach provides a roughly two-fold increase in resolution as compared to conventional light microscopy” (Figure 1A) - For those who have never imaged NPCs it would be really informative to see a confocal or wide field image to better understand how SIM imaging made this project possible.

Figure 1 legend “E) Mean number of NPCs, nuclear surface area and NPC density measurements from four independent replicates. Significant differences (*) determined using Wilcoxon rank sum tests. ns, not significant.”

• Each one of the coloured dots on the graph appear to represent the mean NPC number for each replicate. If so, then this information should be added to the figure legend, as it is not immediately obvious for a broader audience. If not, please clarify what each dot represents. Same in Figure S1E.
• Were these tests conducted pairwise? Are the reported p values corrected for multiple hypothesis testing? Having a dedicated "Statistics" section in the methods would be helpful for reporting this.

“We observed that the number of NPCs also increases through interphase to maintain a constant NPC density (Table S1, Fig. 1D-E)”- There are no cell-cycle markers used to determine cell-cycle progression but only a visual assessment from cell size and nucleus shape. It would be good to show the three plots in figure 1E as scatter plots with the X axis being cell size for cells that are not yet in mitosis (1 nucleus). Then do a correlation analysis between cell size and NPC number, Surface Area, NPC density.

“We observed occasional differences in NPC density between mother and daughter nuclei produced by the symmetric mitotic division in S. pombe, reminiscent of the elevated NPC density observed for daughter nuclei produced by the asymmetric mitosis in S. cerevisiae”

• It is not clear what the authors mean by "occasional", is it 1%, 5%,10%? It would be better to replace this with a specific number/% of events. Additionally, the question arises as to what happens afterwards in the daughter cells, do they retain the NPC density asymmetry? Or do they eventually achieve similar densities?
• Some of these points are addressed later on in the paragraph and Fig S1A. Some edits to this sentence should address this and provide clarity.

“Despite the improved lateral resolution offered by SIM, clustering of NPCs and the comparatively reduced axial resolution likely leads to undercounting of NPCs using 3D-SIM” - This is useful context for Figure 1C - it would be worth mentioning in that section how the automated counting handles clustered NPCs, or placing the paragraph earlier with a short description of the methods.

“Similarly, a constant NPC density was maintained when nuclear size was reduced in mitotic cells using a temperature-sensitive allele of Wee1 kinase (wee1.50)” - The NPC density does not change with temperature in the wee1 mutant but the NPC density in Fig 2B is lower than in a WT in Fig 1. The Nup tagged in the two figures are different, so this could be an explanation (as shown in Fig S1 E) but it could be good to make sure that the wee1 background does not have a different NPC density. I don't see a quantification of the NPC density using Nup37 in the WT elsewhere. In fact, Nup37 seems to be used only in wee1 background and in Sup Fig. 1 B.

“The increase in NPC number was dependent on NE membrane expansion during arrest, as chemical inhibition of fatty acid synthesis by treatment with cerulenin blocked nuclear growth while NPC density was maintained (36°C + Cerulenin = 6.8 ± 1.5 NPCs/μm2, n=110) (Fig. 2C)” -The effect of the Cerulenin drug on a WT background is not shown, was that control experiment done? It would also be helpful to include statistics in this section.

“Yeast lacking core components of the autophagy machinery (atg8Δ or atg1Δ) (75) that targets NPCs for degradation during nutrient deprivation do not show increased NPC density compared to wild-type cells, suggesting that autophagy is not used to remove NPCs to maintain NPC density (Fig. 2D)” - It is unclear that this experiment alone tells us much about the regulation of NPC density. If atg8 and atg1 are known to regulate NPC removal only in response to nutrient deprivation then consider performing these experiments under that condition or revisiting whether they fit here.

“NPC density is maintained by a mechanism that restricts the assembly of new NPCs in the absence of increased available NE surface area.” - This conclusion is indicative of a mechanism where NPC assembly is maintained by restricting the assembly, however all the data above is indicative of the mechanism where NPC assembly is correlated with NE surface area, for increase there must be an additive mechanism and for a decrease in the NE, there must be a mechanism of removal. This suggests that the NE surface area regulation mechanism could be tied to NPC density. One way to clearly show that could be a correlation plot of NE surface area and NPCS density, color coded for all the different conditions tested.

Figure 2- It appears as though no comparative statistical analysis was done with the quantitative data displayed in Figure 2, yet it is stated that e.g., "treatment with cerulenin blocked nuclear growth while NPC density was maintained" or "yeast lacking the autophagy machinery do not show increased NPC density". These conclusions would be strengthened if statistics were run on the data similar to Figure 1.

“NPC clustering is common phenotype in different cell types and in mutants defective in NPC assembly.” - Does this mean that NPC clustering is higher in mutants defective in NPC assembly? Would suggest including references for this. Also, this paragraph needs an introduction to why NPC clustering matters? Does it have any connection with the NPC distribution?

“3D-SIM images revealed the presence of multiple smaller clusters distributed throughout the NE (Fig. 3A).” - It is unclear (also not mentioned in the Methods section) how clusters are identified. The images show rings but it is hard to tell how many clusters compose that ring structure. It will be beneficial to show how clusters are quantified. Can that be resolved with 3DSIM?

“We frequently observed NPC clusters organized in a ring-like structure with diameters ranging from 250-300 nm (Fig. 3B)” - Is it possible to report what was the frequency of the ring structures in nup132-deleted and wt cells?

“Clustering increased in aged nup132Δ cells grown on plates (Fig. 3C)”- The figure depicts the NPC ring like structure, does this mean that the ring increased or the clustering has increased. Does increase in clustering make the rings more continuous?

“NPC clusters were frequently enriched in the anaphase bridge, along with excess membrane (Fig. 3E)” - Providing a quantification of NPC cluster enrichment in the anaphase bridge would be helpful.

“Following completion of nuclear division, the resulting daughter nuclei had normal NE morphologies and NPC densities equivalent to wild-type nuclei (Fig. S2). This suggests that nem1Δ nuclei can remove excess NE membrane and NPCs during mitosis via the anaphase bridge.”

• This implies that prior to mitosis nem1∆ cells have abnormal morphology and NPC densities but the latter is not measured.
• The NPC density reported in Figure S2 for the WT and the Nem1 mutant are different from the NPC density reported for the WT in figure 1 and figure S1 yet it is done using the same tagged Nup, Nsp1. It would be helpful to have an explanation. If the NPC density is “a constant” in the WT it should not be different from one figure to another. If the nem1 mutant has a density of 4 NPC/micron^2 then it is different from the WT. Also, the NPC density in the nem1 mutant in Figure S2 seems almost bimodal. Increasing the number of nem1-delta cells analyzed could help identify if it is bimodal or if it is due to under-sampling.
• For the nem1 mutant the clustering is not quantified.

“In contrast, NPC clusters in nup132Δ nuclei coalesced into larger clusters that preferentially localized to the SPBs in mitosis (Fig. 3G)”

• An overlay image could be included to support this statement.
• Fig. 3F could be referenced here too because otherwise it is not referenced until the discussion; at which point it is used to reference the data that is referenced here as Fig. 3G.

“We observed a clear reduction in NPC density over the nucleolus” - Is this referring to where the yellow and magenta staining meet in Fig. 4? It is not immediately obvious as to where "over the nucleolus" is in those slices. Can the regions that are being compared (NPC staining at NE vs. NPC staining over nucleolus) be highlighted/specified in some way so as to better understand the quantification method?

Figure 4

• 4C is gorgeous - really conveys the point well!
• In this figure the authors at first show a 3D-SIM image, but perform the intensity analysis on the confocal slice. What is the reason for it? Analysis of the 3D-SIM data could provide more information on the characteristics (number, spatial distribution) of NPS density reduction.

Figure 5

• Very minor comment -- the scale bar is very hard to see in Fig 5A.
• Statistics for Fig. 5B would strengthen the conclusion that the exclusion was cell-cycle independent.

Figure 6

• Figure 6D - Looking at the insets, the exclusion area in the lem2(delta)C-off appears to be the smallest one and closer to the exclusion area shown for the lem2(delta) in panel B. However, this is not represented in the quantification/results. I wonder if there is another image that would more closely represent the quantification outcome? Or if the insets might have been mislabeled, for instance lem2(delta)C-off could indeed represent the lem(delta)N-on and vice-versa?
• Figure 6E - This is listed as F in the legend.

“Tethering did not affect microtubule nucleation at the SPB, including the formation of cytoplasmic microtubules.” - Please provide evidence supporting this statement.

Discussion

“In nem1Δ mutants, both excess nuclear membrane material and NPCs are segregated into the anaphase bridge region during nuclear division” - This would benefit from some analysis - are there too many NPCs? Is it specifically the clustered NPCs? Currently the data supporting this is snapshots from a single movie.

“The ability for 3D-SIM to resolve and quantify individual NPCs labelled with multiple fluorescent proteins at endogenous levels provides tools to begin to interrogate__ how altered NPC compositions may allow for functional specialization of NPC function at distinct regions of the NE.” - The high resolution images are really beautiful! Great job in showing the power of 3D-SIM to help answer these types of biological questions.

Methods

“Images were acquired overa6 μm volume with 0.3 μm z-spacing for 45 min at 2 min intervals.” - For dynamic measurements a 2-minute interval is big and it would be interesting to see a few time-series imaging with smaller intervals to capture the fast changes.

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Reply to the reviewers

1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

We thank the reviewers for their constructive and helpful comments on our manuscript and are delighted to find their consensus that the manuscript represents an important contribution to the field. We provide a detailed response to specific points below. In addition, we propose to include new data showing that our method can be applied to experimentally infected lung tissue. Namely, we show highly sensitive detection of SARS-CoV-2 RNA in infected hamster lung section.

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

Reviewer #1 **Major comments:**

The authors used approaches provided in FISH-quant (Mueller et al, Nat Methods 2013) and big-fish. However, these tools to analyze RNA aggregates were not designed and validated for such massive aggregations as observed by SARS-Cov-2. They were developed for cases such as transcription sites with much smaller aggregations, with a few tens to a hundred molecules. With a regular spot detection approach, usually a few thousand spots can be detected in a cell (e.g. King et al, J Virol 2018), but this depends also on the used microscope and the available cellular volume. Higher RNA concentrations cannot be resolved with a standard approach, because RNA spots start to overlap. Decomposing RNA aggregations can help but will not work reliably for the high RNA densities observed for SARS-Cov-2, especially at later infection time-points. The tools will then not provide accurate estimates anymore. To my knowledge, there is currently not accurate quantification method for such massive RNA levels in smFISH. What has been done in the past, is using cellular intensity as an approximation and perform calibrations with cells having lower and thus still resolvable RNA counts (Raj et al., PLO Biology; https://doi.org/10.1371/journal.pbio.0040309.sg003). The authors proposed three expression regimes (partially resistant, permissive, and super permissive). My concerns here apply mainly to the category super-permissive, where an accurate estimation can't be performed. Here a more cautious quantification should be applied. __To a lesser extent, this will also apply to some of quantifications of gRNAs per factory, with counts exceeding 100s of molecules. As mentioned above, this does not affect any of the conclusions, but would reflect more accurately what kind of reliable information can be drawn from such experiments.__

We agree with the reviewer that approaches like FISH-quant and Big-FISH cannot reliably quantify RNA spots with high spatial density such as our examples of “super-permissive” cells. Single molecule quantitation of such cases is likely to underestimate RNA expression as noted by us and King et al 2018 (doi: 10.1128/JVI.02241-17). Therefore, we integrated the combined smFISH signal intensity within entire cellular volumes and compared to the median intensity of single molecules in cells with lower infection density. We will (i) revise the methods and results sections to explain more carefully and explicitly the quantification of RNA in super-permissive cells. (ii) Provide a calibration plot for the quantitation as previously reported (Raj et al 2006, doi: 10.1371/journal.pbio.0040309).

We agree that high local RNA density has the potential to interfere with quantification of gRNAs within viral factories. We have used the “cluster.decomposition()” function of Big-FISH to quantify viral factories, which is conceptually similar to the “Integrated intensity” mode of FISH-quant. Applying this algorithm to non-super permissive cells allows us to use the mean intensity of a reference single-molecule spot to estimate the number of molecules in a cluster. We are confident such estimates are reliable in the majority of viral factories, which contain less than or equal to 200 single gRNA molecules. We will revise the methods section to clarify this method of analysis.

Reviewer #1 __**Minor comments:**__

1.Page 6; the authors state that "smFISH identifies ... cellular distribution .... within ER-like membranous structures". However, the authors didn't directly show such a localization, could they provide an experiment with an ER stain?

This text was based on previous light microscopy and EM studies that reported SARS-CoV-2 RNA in ER-derived membranes (termed Double Membrane Vesicles - DMVs) or co-localisation of anti-dsRNA (J2) with ER-markers (Cortese et al 2020; Hackstadt et al 202; Mendonca et al 2021)*. We propose to clarify the text on page 6 including the citation of these publications and to tone down our claim that the virus is located in ER-like membranous structures.

*Cortese et al 2020, doi: 10.1016/j.chom.2020.11.003

Hackstadt et al 2021, doi: 10.3390/v13091798

Mendonca et al 2021, doi: 10.1038/s41467-021-24887-y

2.It might be worthwhile pointing out that the probe-sets can be used in different host organisms (Vero - African green monkey; human cell lines).

We propose to revise the text to emphasise more clearly the applicability of SARS-CoV-2 probes for the study of many different host organisms.

3.I really liked the experiment, where the authors showed absence of signal when infecting with another virus & elegant control with the J2 AB. Maybe the authors could explain more clearly that the used a different coronavirus & that based on their sequence alignment no/little signal would be expected.

Thank you for this supportive comment. We plan to follow the reviewer’s suggestion and expand our explanation of the rationale of this experiment in the text.

7.The experiment with the isolated virions shows nicely that the smFISH approach has single-virus sensitivity. Did the authors compare the intensity of these isolated virions with the signal in Fig 1B? This might be a question of personal taste, but to me, this section might actually fit better in the first paragraph of page 4/5, where the authors describe single virions in cells.

Thank you for the interesting question. We have not performed a direct comparison of the spot intensities of intracellular genomic RNA molecules and those from the isolated virions, because isolated SARS-CoV-2 requires poly-L-lysine coating for the coverslip attachment while our infection strategy utilises cells growing on uncoated glass. Nonetheless, the isolated virion spot intensities follow a unimodal distribution, and their shape approximates to the point-spread function of the microscope. Since spots at 2 hpi are largely derived from non-replicative viral genomes and they are measured in the intracellular environment with the same background (autofluorescence), they are a better ‘single RNA molecule’ reference.

We also thank the reviewer for suggesting rearranging the text section. To address this point we plan to move the relevant text to the second paragraph of the Results section.

8.Page 6. The authors state "+ORF-N and +ORF-S single labelled spots, corresponding to sgRNAs, were more uniformly distributed throughout the cytoplasm than dual labelled gRNA". This is difficult to appreciate from the image. Is this something the authors could quantify, e.g. with the metrics proposed by Stueland et al, Scientific Reports 2019?

To address this point, we plan to: (i) present an alternative image illustrating a clearer example of differential spatial localisation of gRNA and sgRNA, and (ii) perform quantification of spatial dispersion indices for gRNA and sgRNA using the suggested method for our revision.

9.Page 6. The authors perform a FISH/IF experiment including a co-localization analysis, where a "limited overlap" with sgRNAs was observed. I was wondering if this overlap could actually be simply due to rather high density of the sgRNAs. Maybe a control analysis by slightly changing the RNA positions could provide insight here, and give a threshold for what's to be expected randomly at a given RNA density.

The reviewer’s comment is correct, in that a high density of sgRNAs and nucleocapsid protein could lead to signal overlap due to chance. This is why we excluded “super-permissive” cells from this analysis. Our co-localisation data showed that gRNA spots had a bimodal nucleocapsid immunofluorescence intensity distribution (data not shown), suggesting nucleocapsid-associated and “free” gRNAs, providing a threshold for this analysis. Nevertheless, we agree with the reviewer that the analysis of randomly positioned transcripts of the same density would provide a valuable control. In our revised MS we will include: (i) a random distribution analysis comparing the overlap between sgRNA and nucleocapsid in the “Observed” and a “Randomised” simulation, and (ii) a plot showing a full distribution of co-localised nucleocapsid immunofluorescence intensity for both genomic and sub-genomic viral RNAs.

10.I don't fully follow the argument about stability on page 8. The authors also see an increase in the RNA levels. Couldn't this increase compensate for loss of RNA due to degradation? Would it be possible to perform an experiment at a very high REMDESIVIR concentrations which would blocks transcription?

Remdesivir is a nucleoside analogue that inhibits viral RNA polymerase activity. While this drug inhibits viral replication, the inhibition is incomplete and using higher concentrations results in cellular toxicity. At the present time there are no stronger polymerase inhibitors available, so these experiments are the best approximation possible to assess viral RNA stability. We propose to revise the text to discuss the limitations of Remdesivir for modelling RNA stability.

12.How did the authors define/detect replication factories? I couldn't find information about this in the methods.

This is a good point raised by both the reviewers. Please see [Reviewer 2 General comment #1] for our response.

Reviewer #2 **General comments:**

1.The authors' definition of viral factories, in part as foci with at least 4 gRNA molecules, comes across as arbitrary. Perhaps a clearer explanation of this cutoff would be helpful to the readers' understanding of this definition. Additionally, confirmation of the functionality of such factories by immunofluorescence with anti-RdRp, for example, in addition to identifying staining of gRNAs and (-) sense viral RNAs at each focus could provide valuable support to the authors' conclusions.

We thank both reviewers for requesting further information on our explanation of viral factories. We defined viral factories as smFISH signals with spatially extended foci that exceed the size of the point spread function of the microscope and the intensity of a reference single molecule. We then filtered these candidate factories based on the radius of the signal foci with EM-measured radii of double-membrane vesicles and single-membrane vesicles formed by SARS-CoV-2 (150 nm pre-8hpi and 200 nm post-8hpi) (Cortese et al 2020; Mendoca et al 2021). Our terminology encompasses both replication and viral assembly sites. The threshold of 4 genomic RNA molecules was selected as a technical threshold to limit an over-estimation of viral factories at later timepoints. For our spinning-disk confocal imaging system, we found the threshold of 3-7 RNA molecules provided satisfactory results. We propose to revise both the Results and Methods sections to clarify our rationale for defining and quantifying viral factories.

As the reviewer mentioned, we have shown a partial overlap of positive sense genomic RNAs with negative sense genomic RNAs (Figure 2D, S2C), suggesting these viral factories represent double membrane vesicles. The use of antibodies against the viral polymerase (nsp12) is also a possibility to detect replication centres. However, replication centres are not the only ‘viral factories’ as there are also double-membrane structures where viral particles assemble (Mendoca et al 2021) and they, in principle, lack negative sense RNA and replication machinery, so neither smFISH probes against the negative strand nor a nsp12 antibody will comprehensively detect viral factories. We appreciate the valuable suggestion, but the classification of viral factories into replication and assembly sites would be challenging due to reagent availability and is beyond the scope of this manuscript.

2.The random distribution of super-permissive cells in each cell line was demonstrated early in the infection, primarily at 8 hpi. The authors do not show how this pattern changes over time (8, 10, 12, 16, 24 hpi, for example). Do clusters of super-permissive cells appear at later time points, or does the pattern of 'highly' infected cells remain random for each virus? Any strain-specific differences identified from such patterns may be important for understanding infection progression. Finally, the authors do acknowledge this point, but it cannot be overstated that these data were taken from cell culture systems that have limited similarities to the human respiratory epithelium. A better model for such studies might be primary cultured human bronchial epithelial cells, but of course, these cells are not as readily accessible as the cell lines used in this manuscript.

We share the same view that the presence and the spatial distribution of “super-permissive” cells can provide unique insights into SARS-CoV-2 infection dynamics. Our findings suggest that even at 24 hours post infection (hpi), not all cells become “super-permissive” and the culture maintains a heterogenous population of “partially resistant”, “permissive” and “super-permissive” cells (Figure 3C, S3C-D). We agree with the reviewer that the spatial distribution of “super-permissive” cells at later timepoints is of interest. To address this point, we plan to: (i) analyse the spatial distribution of “super-permissive” cells at 24 hpi, and (ii) compare the distribution of “super-permissive” cells at 24 hpi between VIC and B.1.1.7 strains.

We appreciate the comment on the limitations of the cell culture systems to the human respiratory tract. However, Calu-3 and A549-ACE2 lung epithelial cells have been used in many studies over the last year and we feel it is important to publish single cell quantitation with these models to enable comparison with the published literature. We believe our results provide valuable information on the intrinsic nature of host cell susceptibility to support viral replication. During the review of this manuscript, we applied our smFISH probes to detect SARS-CoV-2 RNA in infected Golden Syrian hamster lung sections, which show an uneven distribution of infected cells. While the identification and spatial characterisation of susceptible cell types in the lung are beyond the scope of this manuscript, we are excited to include this data in our revised paper to demonstrate the utility of this sensitive approach to track spatiotemporal viral infection dynamics.

3.The difference in early replication kinetics between the VIC and B.1.1.7 strains is an exciting finding that may have implications for clinical outcomes and transmissibility of these viruses. However, the authors did not clearly demonstrate how these differences in RNA production correlate to infectious viral load released from these cells (in bulk) at each time point. An explanation of this omission would be helpful.

We will provide data on the level of infectious virus secreted from VIC and B.1.1.7 infected cells at all time points in the revised paper.

In my opinion, findings related to specific cell lines are of much less importance (and are much less biologically relevant) that identification of replicative differences among strains. Such differences could be used, in part, to aid prediction of the transmissibility of VOC, for example. I think this point gets a bit 'lost in the weeds' of the rest of the paper.

To address this comment, we will revise text on the differential replication kinetics of the SARS-CoV-2 strains to make this more prominent in our paper.

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

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

Reviewer #1 __**Minor comments:**__

4.I might have missed this, but they authors could also mention the positive control data about but Calu3 infected with SARS-COv2. One thing I was wondering: why did the authors use two different cell lines for this experiment?

To address this point, we have added a sentence about a positive control visualising SARS-CoV-2 in Calu-3 cells using our probe set (page 5 – line 17).

The experiments with HCoV-229E were done in Huh-7.5 cells because SARS-CoV-2 and HCoV-229E have distinct cell preferences. Using the J2 antibody we show that the levels of the dsRNA derived from viral replication are similar in the two cell lines and with the two viruses. Therefore, the lack of smFISH signal in HCoV-229E infected cells supports the high specificity of the probe set.

5.Fig 1E. Would be nice to have the intensity scale for all time-points to permit a comparison of image intensities along the different time-points.

6.Fig 3B. Would be important to have intensity scale bars to judge the signal intensities across the different time-points.

The fluorescence intensity scale in Figure 1E is applicable to all timepoints, except for the lower panel at 24 hpi, which was intended to show wider dynamic contrast range. To address this point, we have provided intensity scales for all time-points studied in this figure and also Figure 3B.

11.Fig 3C. maybe indicate the two groups with dashed lines.

We have added a dashed line at the 102 mark in Figure 3C to visually differentiate “partially resistant” and “permissive” cells.

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

Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

Reviewer #3 (Public Review): 

The paper contains a substantial amount of novel experimental work, the experiments appear well done, and the analysis of the data makes sense. Raw data and analysis scripts have been made fully available. 

I have two specific comments: 

- While the paper talks extensively about deep mutational scanning, I don't think this is a deep mutational scanning study. In deep mutational scanning, we usually make every possible single-point mutation in a protein. This is not what was done here, as far as I can tell. 

- For the analysis of epistasis vs distance (Fig 4d, e, f), it would be better to look at side-chain distances rather than C_alpha distances. In covariation analyses, it can be seen that C_alpha distances are not a good predictor of pairwise interactions. Similar patterns may be observable here. 

See e.g.: A. J. Hockenberry, C. O. Wilke (2019). Evolutionary couplings detect side-chain interactions. PeerJ 7:e7280.

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Reply to the reviewers

We want to thank all three reviewers for their positive and constructive comments and suggestions for improvement. We have now thoroughly revised the manuscript including new analysis, extra figures, and new material in the wiki. The manuscript has significantly improved because of the reviewers input. Detailed responses to questions and comments are given below.

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

Lange et al. have developed an automatic feeding system for zebrafish facilities. The system is open-source and relatively easy to implement. The authors propose to systems, one that delivers the same amount of food for each aquarium (ZAF) and a second (ZAF+) that can adjust the amount of delivered food to each aquarium. The authors show no difference in fish weight, spawning and water quality, when fed using the automatic system or manually.

In my opinion, the ZAF and ZAF+ are an excellent first approach to solve the complex problem of automatizing feeding in fish facilities. So far, only one company offers this option which is extremely expensive and demands a lot of maintenance.

The manuscript is very well written and easy to follow. The supplementary material is very well detailed. It is clear that the authors intended to facilitate the implementation of the ZAF by potential users.

We appreciate the supportive comments from Reviewer 1 and address all comments below:

I just have a few comments regarding the system:

1) The authors do not indicate how the system is cleaned. the system drains itself, but will any deposits of food remain in the tubes ? Why is the system not flushed with clear water after each feeding? do the tubes get clogged ?

We agree that the cleaning process was not clearly explained in the manuscript. We added clear sentences in ‘Box 1’ to describe the first cleaning step (see text and figure). Indeed, after each feeding we flush water and then air into the tubes. Moreover, we explain in ‘Box 2’ that we have a second level of cleaning in the form of a special cleaning program that is run at least once a day with no food distribution (i.e same program as used for feeding but without actual food mixed, we flush lots of clean water and then air in the system). Finally, in the discussion we clarify the different cleaning steps by adding extra explanations in the first paragraph.

All these procedures and programs are very effective in preventing system clogging and in reducing the accumulation of debris and algae. After more than 19 months of ZAF and ZAF+ feeding in our facility we never experienced any tube clogging.

2) How long the system was tested for?

ZAF has run in the facility for 9 months and ZAF+ for 10 months since September. We added a sentence about the testing time in the discussion. We never experienced any major problems, only a few minor malfunctions, reported in the new troubleshooting table added to the wiki (suggested by the reviewer 2).

3) The ZAFs were used to feed 16 aquariums. For such a small rack, manually feeding takes less than 5 min. The authors should highlight that, at least for such small systems, the ZAFs will be especially very useful for feeding during weekends and holidays. Still, adding 16 commercially available small automatic feeders to each aquarium, could be simpler to implement.

As noticed by the reviewer, ZAFs are very useful when staff are not present (week end, vacation, etc..). To emphasize on this particular point we added a sentence in the discussion's first paragraph. The small automatic feeders available commercially are usually very difficult to attach to zebrafish facilities . Indeed they can’t adapt to conventional lab aquatic facility racks because they are designed for pet aquariums. They also have less features compared to the ZAFs (difficult to adapt the food quantity, more food waste, cumbersome...). Additionally, by multiplying the number of devices (you need one small feeder per tank), one increases the risk of possible malfunction as well as the maintenance time required for food filling, cleaning etc...

Thus, usage of small automatic feeders in laboratory aquatic housing racks is complex to adapt, a source of feeding error, is more cumbersome, and potentially more time consuming etc… They are simply not designed for professional aquaculture systems. Whereas ZAFs can be easily adapted to all the commercially available aquatic facilities. The fact that ZAFs simply ‘interfaces’ via tubes to fish facility racks makes them very versatile and unintrusive.

4) How do authors envisage implementing the ZAFs in much larger facilities (from 100 to 1000 tanks) ? Implementing a specific ZAF for each rack containing ~20 tanks may not be realistic.

Indeed building multiple ZAFs will be complex and resource consuming. Thus, we designed ZAFs to be adaptable and modular, so one ZAF ( or ZAF+) can easily be scaled to handle bigger facilities. The supplementary information and the wiki describe all the steps required to build a ZAF for 16 tanks and a ZAF+ for 30 tanks and many tips to scale up these devices without major modifications (up to 80 tanks for ZAF no restrictions for ZAF+). Of course, we do think that for truly large facilities, there is probably a sweet spot that balances the number of individual devices and the per-device capability. Having a single device feeding 1000 tanks is probably not wise, perhaps 5 devices for 200 tanks each (ZAF+) would be the best. Please note that the hardware cost and complexity scales roughly linearly with the number of tanks, no surprises here. Moreover, in the case of ZAF+ it is possible to use splitters to feed even more tanks from the same line (ZAF+).

We added pages in the ZAF/ZAF+ wiki, to help the users extend the feeding capacities of their desired ZAFs (see in the wiki “tips to scale up ZAF “- “tips to scale up ZAF+”). We also mentioned in the discussion the possibility of distributing food to more tanks with one device by increasing the outputs and referenced the wiki accordingly.

Having said this, we did not primarily design ZAFs for super large fish facilities, instead we designed the ZAF systems to facilitate adoption of fish models by many small and medium sized labs. We hope that our system will lower the bar for labs with moderate ressources to get started with aquatic models, or labs that just want to ‘try’ a new aquatic model organism ‘on-the-side’.

5) how the length of the tubes influences the efficiency of feeding ? For feeding many tanks with the same ZAF it is necessary that the tubes will be of the same length. In that case, the system will become very cumbersome. Longer tubes will probably need stronger pumps. What's the maximal length of tubes tested ? That will limit the number of aquariums a ZAF can feed.

how the length of the tubes influences the efficiency of feeding ? For ZAF the size of the tubes is very important because its design assumes homogeneous food distribution. In contrast, ZAF+ distributes the entire amount of water and food mix to each tank sequentially, so the tube length is not an issue. To make sure that tube length or tube layout is not affecting feeding efficiency we evaluated the weight of fish coming from tanks housed on two different rows (top and bottom). This was not clearly explained in the methods section -- we changed the text to reflect that. Additionally, at the end of each ZAF+ run, the washing sequence runs a relatively large quantity of water to ensure that all food gets flushed out to the right tanks. We did not evaluate the precise amount of food delivered. However after each feeding and cleaning all tubes are empty (see last sentences of the Box 2).

For feeding many tanks with the same ZAF it is necessary that the tubes will be of the same length. In that case, the system will become very cumbersome. This is a fair concern. However, with a good design and with the help of cable tie it is very easy to organise the tubing, and avoid ‘tube-hell’. We added a sentence to clarify the organisation in the wiki (see ZAF>Hardware>Tubing in wiki) .

Longer tubes will probably need stronger pumps. What's the maximal length of tubes tested ? That will limit the number of aquariums a ZAF can feed. We never precisely measured that because the generic pumps we use are very powerful and their running time can be adjusted in the software by changing the constants in the code source (see troubleshooting new supplementary table). Therefore the length of tubes should not be a limiting factor. Even stronger pumps (more amps) can be readily sourced on Amazon if really needed -- although we doubt that this is necessary. Regarding the number of tanks that ZAF can feed, we simply recommend adding more pumps to increase its capacity (see previous comments or “tips to scale up ZAF” in the wiki).

Despite these comments, this is an excellent first approach, and the fact that the authors made it open-source and open access, make the ZAFs a very important contribution to the community. I have no doubt that some fish facilities will implement it and the community will help to improve it. Thank you. We do think that the main benefit of an open source project is the community around it. We are currently collecting a growing list of interested labs and we are interested in organising an online workshop to discuss ZAF and ZAF+, with some talks, QAs, and more to help people getting started.

Reviewer #1 (Significance (Required)):

This is the first open-source open-access automatic feeding system ever published.

It is the first but very important step to the automation of research fish facilities.

**Referee Cross-commenting**

I agree with all the other reviewers.

We also have to take into account that the system is a first prototype and although not ideal, it is open source. This will allow other labs to develop and improve their own models based on the ZAF.

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

**Summary**

The manuscript proposes an open source automated feeder for zebrafish facilities, although it would be amenable to other species. Overall, the manuscript is clearly written and easy to understand, the wiki is well sourced and clear. The commitment to open source is commendable.

I have some questions regarding the long-term sustainability of this setup, as well as some discrepancies in the methods. Finally, as this aims to be useful to people with no engineering/electronics competence, I feel that it is not yet at a level that is accessible enough.

We are very pleased to see that the Reviewer appreciates our manuscript and our commitment to open access. We thanks the Reviewer for his comments, in particular the comments about accessibility, and address them bellow:

**Major comments**

It would be useful to have a centralized list of parts and components, which would make it easier for users to order all that is needed to assemble the ZAF or ZAF+, at the moment the information is distributed through the wiki as hyperlinks.

Extremely important! This was clearly an oversight on our part. We agree that a table listing all the components would help for constructing ZAF and ZAF+. We have added two tables in the wiki, one for ZAF and another for ZAF+, with all the necessary parts and components required to build both devices, with articles number, supplier and cost in dollars. Thanks to the reviewer for this excellent suggestion.

A troubleshooting guide for the common problems the team ran into (if any) would be useful for newcomers, even just as issues on the GitHub. The team may also consider some form of chat/forum/google group to allow discussions between users and experts.

The reviewer raised an important point so we added to the ZAF wiki a troubleshooting guide to help users by listing the minor malfunctions that we observed. Additionally, users will be able to ask questions or report bugs on the ZAF GitHub using issues. Github issues will allow discussion and to track ideas and feedback within the ZAF user community. Finally, we just created a Gitter room: https://gitter.im/ZAF-Zebrafish-Automatic-Feeder to enable more interactive discussion.

Did the author observe any algal or bacterial growth in the feeding tubes over the 60 days? Do they have an estimate on how long the tubes stay "clean" enough? The authors mention tube changing every 10 weeks, can they explain the rationale, and did they assess the bacterial/algal contamination over that time? Do the splitter panel and food mixing flask also need replacing regularly?

After several weeks of usage we indeed observed algal and bacterial growth in the tubes. In order to report and justify the need to change the tubes, we made a new supplementary figure illustrating the tube cleanliness over time, mainly algal and bacterial (see Suppl. Fig 3). We realised that 12 weeks is actually the optimal tubing renewing period in our facility. Algal and bacterial growth depends on the facility environment characteristics such as light intensity, water and air temperature, as well as feeding frequency and therefore might be adapted to the users facility specs. The splitter tubing can be changed based on user observations; we now mention this in the ZAF tubing supplementary material and on the wiki.

The authors mention that the tubing needs to be of similar length to ensure similar resistance and food distribution, did they compare the body weight of fish in racks at the top or at the bottom of their system? There are no overall differences, but maybe the bottom racks would received slightly more food? Furthermore, did they quantify the differences in food/water delivery as a function of length differences?

The requirement for similar length is only necessary for ZAF because its accessible design assumes homogeneous distribution of the water-food mix through a passive splitter system which is susceptible to variable fluid resistance. In contrast, ZAF+ distributes the water-food mix one tank at a time -- ensuring that the correct amount of food is entirely flushed through any required tube length (the pumps are strong enough and we flush enough water). In the eventuality that the tube length is too long the user can adjust the pump running time by changing constants in the code (see troubleshooting table in the wiki and corresponding links).

We thank the reviewer for suggesting to evaluate the fish weight on fish from two extremal heights. Although we did not explicitly report this in the first version of the manuscript, we had actually anticipated this potential issue and therefore we did collect data for ZAF and ZAF+ for tanks housed on the top and bottom rows. We added a clear description of the weighting process in the material and method, highlighting the housing condition of the tanks tested.

Finally, after each feeding run the tubes have been fully flushed and are empty without food debris or pellets remaining, irrespective of their sizes. So we did not find it relevant to evaluate the precise amount of food effectively delivered as we control that already upstream.

Methods fish weight: The methods mention different amounts of food than the wiki, the rationale in the wiki is also different from the 5% of body weight outlined in the methods (which then matches the food amount of the methods). Which is the correct amount?

We thank the reviewer for noticing the inconsistency. The method numbers are the correct one so we changed the wiki, we made a mistake when editing the figures. We wrote some sections of the wiki early during the development of the hardware. We unfortunately forgot to correct the inconsistencies.

The code is decently commented for scientific software with clear variable names, but I wonder how flexible it is if users cannot get access to the specific hardware (especially the pumps) used in ZAF/ZAF+? Can the authors briefly comment on this point?

The pumps are just built from 12V motors, you can find a large variety of such pumps online (Amazon, etc…), we have ourselves tried several, but there is no need to have the exact same model. We added a note to the tubing section of the ZAF and ZAF+ about that.

The only components that cannot be easily exchanged are the arduino and Raspberry PI, but that is not an issue as these are very easily sourced components.

The wiki could use more pictures or, to borrow the Proust Madeleine allusion, schematics akin to LEGO with more intermediary steps clearly outlined. Some pictures are also a bit small/busy (such as 2D and 2E in the frame section, or the magnet pictures), they may benefit from cartoons/schematics to clarify what is done. Alternatively, videos/timelapses may help with better visualising the assembly.

We appreciate the reviewer comments and added new pictures, schematic and extra legends in the wiki to help potential ZAFs builders. In the wiki for ZAF hardware we increased the size of all the pictures for all the different steps and added new legends to clarify the assembly. There are also now more pictures illustrating the construction steps (i.e in “frame”, “pumps and valve”) and we added a simple schematic for “servo and food container”. Picture sizes have been increased in “ZAF electronics” and added to the “Raspberry Pi and Servo Hat” section. We increased the picture sizes and added more legends to the ZAF+- Hardware “Pumps & Valve'. Moreover, we added more photos to the “tubing” section and the “ZAF+ Electronics” section.

We agree that videos or gifs would have been great to visualize the assembly. Unfortunately, we did not record such videos during the construction. We created ZAF as an open source project and clearly hope to generate a community that will share assembly pro-tips and may be constructions videos on the github.

Our institute is expanding on zebrafish research so we will build additional ZAFs and will use this opportunity to prepare nice videos to add to the wiki. We envision that the wiki will be improved over time with better material, some of it contributed, as well as perhaps newer and better versions of ZAF.

The main question that would affect if this approach were taken up would be how reliable it is in the long run. Have the authors experienced any issue over the 2 months test? Is this system still being used currently? If so, could the authors update the water quality logs?

The reviewer suggests that the key question is to see if using ZAFs all year long is possible. We can reply yes, it is actually possible! We have used ZAF for 9 months, and now ZAF+ for the past 10 months in our fish facility, with great success. We never experienced major malfunctions and the minor issues we encountered are reported in the troubleshooting table. Since ZAF and ZAF+ have been used daily for months with logs recorded every day we have updated the water logs quality to 3 months. We have been using the ZAFs in full autonomy for a total of 19 months, frankly invaluable.

Getting a sense of how long it can run without problems, how much troubleshooting is involved per month would be very useful in answering those questions.

Except manual cleaning and tube replacement, there is no other big maintenance on ZAF. Of course, the food reserve needs to be changed at least once per week. We listed the malfunctions in the troubleshooting guide in the wiki. In our facility ZAFs require an average of 1 hour of maintenance per month. And if any hardware part fails you can just immediately replace it because all the parts are cheap and easily replaceable. Actually, we recommend keeping spare parts of all the key components (pumps, valves, arduino, Raspberry Pi, tubes, ...).

**Minor comments**

• Main text page 3: Fig. Supp. 2 instead of Supp. Fig. 2. Furthermore, would the authors have similar data for the manual feeding? If so, it could be useful to add here for comparison (although that is not necessary if the data is unavailable).

We changed the text but we don’t have data available for the water logs with manual feeding.

Main text page 3: it would be useful to add how long it takes to change all the tubing after 10 weeks?

This is really dependent on ZAF tubing and the fish facility, in our hand for about one hour. We mentioned it in the results section, ZAF paragraph.

Methods fish weight: The phrasing as it stands make it unclear the same method was used for ZAF and ZAF+, the authors may consider to start with the description of the common weighting method, then the specifics of ZAF+.

Thank you, we changed the text accordingly.

Supp.Fig.1a: "Waste water drain pipe"

Thank you, we changed the text accordingly.

Acknowledgments: "...for their help..."

Thank you, we changed the text accordingly.

ZAF - Servo Hat connection: "to control the pumps"

Thank you, we changed the text accordingly.

ZAF - Installation: the dependencies should be listed as they are in ZAF+, or the two sections merged, unless the GUI is not functional (see below).

Thank you, we now list the dependencies in the wiki.

ZAF - How to use: there is no mention of the GUI, is it not yet implemented? If not, is the touch screen needed?

The standard ZAF hardware is controlled by a very simple python-based program that works with a command line interface. Therefore to interact with the Raspberry Pi for installation and configuration we strongly recommend building ZAF with a screen, and the touch screen is an easy way to be able to quickly point and click in the absence of a mouse -- which can be cumbersome when no clean horizontal surfaces are available in a lab environment.

ZAF+ - soldering: "A 12V power supply (at least 10A best 20A) provides power to the electronics, except the Raspberry Pi and the two Arduino Megas." It seems the sentence is incomplete, or at least I cannot make sense of it.

Changed to “A 12V power supply (at least 10A, but ideally 20A) provides power to the electronics, except for the Raspberry Pi and the two Arduino Megas that are powered by the Raspberry Pi 5V GPIOs.”

Reviewer #2 (Significance (Required)):

This manuscript provides a significant technical advance to the zebrafish field. The proposed automated feeder would be a very useful option for smaller labs, to ensure the consistency of feeding, and to remove one of the routine aspects of fish husbandry.

As the authors state, there is certainly interest in the zebrafish community [9,10] for automation of feeding. I am not aware of other DIY fully automated feeding system, commercial systems do exist, but are expensive.

The manuscript, and proposed automated feeder, would certainly be of interest within the zebrafish community, as well as other researchers using aquatic models that can rely on dry food. How many in the community would embrace this method will depend on how confident they are in the long-term stability.

I am neither electronics, nor husbandry expert. As such I am not qualified to comment on any long-term approach this may prove, if any, for fish health. My expertise lies in image and data analysis, as well as microscopy.

**Referee Cross-commenting**

I think the major points are shared by all reviewers, I think the other reviews are fair in their content and I have nothing specific to comment on.

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

**Summary:**

This technical report describes an open-source fully automated feeding system for husbandry of zebrafish (and potentially other aquatic organisms). It provides detailed instructions for assembling individual components into two different feeding systems of varying adaptability, as well as their operation. Links to relevant control software are also provided. The characterization of the systems' performance appears somewhat limited (e.g. only maintenance of adult fish over a period of 8 weeks and use of dry food is documented). These systems could be of use for husbandry in a large number of research labs, and, in

addition, for automated reward delivery in large-scale associative conditioning assays.

We thank the Reviewer for his encouraging comments and appreciate his helpful suggestions. We answer to the Reviewer comments bellow:

**Major comments:**

Providing food to large numbers of tanks in aquatic animal facilities in a regular fashion is a time- and resource-consuming process. Some automated feeding systems for large numbers of tanks are commercially available, but these feeder robots are expensive and are restricted to systems of specific vendors. Therefore, an adaptable automated system that can be assembled from off-the-shelf components is a very attractive option for many research labs to both save resources and standardize the feeding process.

The instructions for assembly provided by the authors appear quite detailed and sufficient to allow non-experts the assembly and operation of the automated feeder systems. The design of the system appears appropriate for the task.

While additional experiments are not required to support the claims of the article, I feel that it would be significantly improved by the provision of additional information. My suggestions in that regard include:

Description of the washing procedure of the system (which solvents, how often, how long?). The authors mention that an exchange of the tubing is required every 10 weeks, but since the tubing transports liquid food mixture, it is easily conceivable that microbial growth will occur rapidly in the system without thorough hygiene / washing procedures. Also could the authors provide some information, which type of tubing material they are using (Silicone, Tygon etc.)?

Description of the washing procedure of the system (which solvents, how often, how long?).

We agree that the cleaning procedure must be clarified. So we added a more clear description of the process in the first paragraph of the discussion and clarified the explanation about cleaning in Box 1 and Box 2 (suggested also by the reviewer1). To summarise there are two levels of cleaning, the first one happens just after a food distribution program by flushing water and air in the system (Box1). Additionally at least once a day, we run an entire program without food, to rinse/clean the system (Box2). This last step is programmable using ZAFs software.

The authors mention that an exchange of the tubing is required every 10 weeks, but since the tubing transports liquid food mixture, it is easily conceivable that microbial growth will occur rapidly in the system without thorough hygiene / washing procedures

Following all reviewers' comments we added an extra supplementary figure justifying the need of changing the tubes every 12 weeks (updated based on our latest observations). We monitored the cleanliness (algal/microbial growth) of the tubes and realized that it becomes necessary to replace the tubes every 12 weeks (supp figure 3). Interestingly, we remarked that the microbial and algal growth depends on the facility specificities such as light intensity and temperature.

Also could the authors provide some information, which type of tubing material they are using (Silicone, Tygon etc.)?

For ZAF we used silicone based tubing then we changed to PVC based tubes for ZAF+ because they are cost effective and have similar specifications for our usage. We added a note about the tubing material in the wiki ZAF tubing and ZAF+ tubing.

In a related point, I was left wondering how long the food is being mixed in the mixing flask before being applied to the animals? Too long mixing might lead to a loss of nutrients into the solution (through diffusion). Could the authors comment on that, please? Do the food pellets remain more or less integral so that the majority of delivered food is actually ingested by the fish?

• In a related point, I was left wondering how long the food is being mixed in the mixing flask before being applied to the animals? Too long mixing might lead to a loss of nutrients into the solution (through diffusion). Could the authors comment on that, please? Very relevant point, indeed it is very important for the food to not be mixed too long in water to avoid pellet dissolution in water and loss of nutrients. The food manufacturer website mentioned: “duration of “wet” feeding should be kept short” (https://zebrafish.skrettingusa.com/pages/faq). Therefore we adapted our feeding program to keep the “wet” feeding extremely short. For ZAF and ZAF+, the software is designed to deliver the mix of food and water to tank(s) within 3 minutes at most. To clarify this, we added in the Box describing the feeding, a sentence : “Overall, they share many common features, like the quick distribution of food and water mix, to avoid pellet dissolution in water and loss of nutrients.”

• Do the food pellets remain more or less integral so that the majority of delivered food is actually ingested by the fish? We manually evaluated the integrity of food pellets in the early phase of development, these parameters being difficult to quantify, we decided to record the fish weight as a readout of good food delivery and general effectiveness. However, we clearly understand the reviewer's remarks and therefore added to the manuscript a supplementary video that shows the distribution of the food pellets and their integrity once they reach the tanks.

In yet another related point, I was left wondering, whether the authors observed any negative impact of feeder usage on water quality (besides pH and conductivity, which they report)? Especially, with regards to ammonia that might arise from the decomposition of uneaten food items?

Ammonia toxicity is mentioned to induce clinical and microscopic changes that reduce growth and increase susceptibility to pathogens according to aquaculture textbooks as summarized here: https://zebrafish.org/wiki/health/disease_manual/water_quality_problems#ammonia_toxicity). However, we never experienced such abnormal phenotypes in our facility and our regular aquatic PCR health monitoring profiles have always been negative for pathogens. Additionally, high ammonia is influenced by husbandry conditions, such as important fish density or inappropriate water circulation, characteristics that are not present in our fish facility. Therefore we did not find relevant to test for ammonia levels.

The authors only tested the feeder on adult fish, but discuss that it would easily be transferable to a system that is used for raising fish fry. In that context, could the authors comment, on whether the system of using water as the carrier for the dry food (after mixing) would work as well for the smaller pellets required in feeding fish fry (e.g. 75 or 100 um pellet size as compared to the 500 um pellet size they use)? With smaller pellets, break-down of the dry food during the mixing process seems to be an even larger problem, I could imagine.

We appreciate the reviewer's comment about using different food pellets sizes, a very important point for ZAFs adoption beyond adult fish. During ZAFs testing we actually tested different food sizes (from 100uM pellets to 500uM) and did not observe differences in pellet distribution. Most of the industrial aquatic food pellets are oily and designed for automatic distribution (for large farming environments). Therefore they keep their integrity and are not easily broken. Besides, during food distribution, as mentioned previously, the duration of wet food (water and food mix) is relatively short, which helps maintain pellet integrity.

**Minor comments:**

(1) the average weight of animals is given as lying in the range of 5 to 6g. That seems very high. The "standard" weight range of adult zebrafish is more around 1g [see, for example: Clark, T. S., Pandolfo, L. M., Marshall, C. M., Mitra, A. K. & Schech, J. M. Body Condition Scoring for Adult Zebrafish (Danio rerio). j am assoc lab anim sci (2018)]. Could the authors comment on that discrepancy?

Good observation by the reviewer. We did make a mistake during figure preparation and our legends were actually not reflecting the exact weight of the fish. The scale bars of the figures have been changed to reflect the real weight of the fish (below 1g). We thank the reviewer for noticing the mistakes.

(2) The authors state that spawning success is not negatively affected by the automated feeding, and they quantify the number of successful crosses. Could the authors briefly confirm or state, that or whether the clutch size was also unaffected?

We never precisely quantified the clutch size/quality but we are now using ZAFs for the feeding of our facility for 19months and never observed any problem with our clutch. Our lab is working on early development and crucially relies on clutch quality.

(3) The manual feeding procedure / regime that is used to compare husbandry success against the automated feeding regime is not described in any detail. That seems important given the topic of the article.

We agreed and added a brief description of the protocol in the Methods section (“Animal and husbandry”).

(4) The authors cite two recent papers that describe semi-automatic feeding systems for zebrafish in the introduction. The authors might want to consider discussing some key differences between their system and these semi-automatic systems in the discussion.

The two published semi-automatic feeding systems are completely different from the devices presented in our paper. They are also open access but they are devices that need to be manually operated by facility staff. In contrast, our solutions are fully automatic and do not require the human hand during operation. We mention these two solutions during our brief literature overview in the introduction. However, since these are in a different category, we did not judge it necessary to comment on them in the discussion.

(5) What do the error bars in Fig. 1c signify (s.d., s.e.m.)? Please state in Figure legend.

We thank the reviewer for their attention to details and explain in the figure that we mean standard error of the mean by s.e.m.

(6) I do think that the system could be of particular interest to researchers that study learning and that use food rewards in automated associative conditioning experiments. While this might be obvious to researchers with such an interest, this aspect is not at all discussed in the paper. Mentioning it might further underscore the versatility of the feeder system.

We agree with the reviewer that ZAF can be adapted to experimental conditions such as behavioral conditioning, nutritions and drug delivery. Any experiment requiring the automatic delivery of solid pellets or liquid can benefit from ZAF. We revised our text and mentioned it in the discussion.

(7) A list of all required equipment with vendors and price estimates (e.g. in the Supplement) would make this paper an even more readily accessible resource.

This is a very important point already suggested by another reviewer. We added two extra tables in the wiki with the necessary parts and components, listing models, references, and prices.

Reviewer #3 (Significance (Required)):

Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

This article signifies a purely technical advance in that it provides a characterization of an open-source, scalable automated feeder for aquatic facilities. As such, it presents a significant advance in the field of aquatic animal husbandry. In addition, this system could also be useful for automated large- or medium-scale associative conditioning paradigms, in which food rewards are given as positive reinforcers.

Place the work in the context of the existing literature (provide references, where appropriate).

The authors refer to previously published semi-automatic feeder systems. Regardless of the advantages or disadvantages of all these systems, the field will benefit from a broad(er) choice of automatic feeding systems that are described in sufficient detail to be easily assembled in the laboratory.

State what audience might be interested in and influenced by the reported findings.

This study is of interest for any research laboratory working with zebrafish or other aquatic model organisms. Thus, the audience for this article is very broad. Specific interest might also arise in researchers that are performing learning studies in zebrafish (see above).

Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

Zebrafish, neural circuits, sensory systems.

**Referee Cross-commenting**

Many of the major points are shared by all three reviewers. Beyond these shared points, I agree with the other reviews; they raise important questions. All reviews are fair, in my opinion.

Author Response:

Reviewer #1:

Strengths

This study is a technical and analytical tour de force. The evolution experiments with barcoded lineages involved an immense amount of work and clever design, and the scale of the data challenged the authors to develop new statistical summaries. The figures are clear and results easy to interpret, even outside the evolution-experiment bubble. While the essential findings are not especially surprising, the robustness enabled by this level of replication is appreciated.

Weaknesses

I'm not exactly sure what I learned. I'm biased to like this work and while I'm confident that if I studied these findings more I would learn more, it wasn't obvious. For example - I want to know more about the effects of ploidy on pleiotropy, and while there are some differences e.g in Figure 4A, I don't know what these PCs actually are saying. If particular phenotypes associate with PC's, it'd be helpful to "load" them on these axes.

To more clearly show general trends and variation in pleiotropy, we have added a summary of the changes in fitness across all populations in Figure 2B and Figure 2– figure supplements 2–5. We have also expanded our consideration of these trends, including the effects of ploidy on pleiotropy. To supplement Figure 4, we have included the contribution of each assay environment to the principal components (Figure 4–figure supplement 5), as suggested.

Also, do some treatments lead to faster or more complete diminishing returns than others, and does this influence pleiotropy?

To compare changes in fitness across evolution environments and over time, we have computed the change in fitness for each population over the first 400 generations and the last 400 generations. This is plotted in Figure 2-figure supplement 6A. To assess the statistical significance of apparent diminishing returns, we compared the mean change in fitness over these time intervals using a t-test and provided the resulting p-values in Figure 2-figure supplement 6B. Overall, we see that different treatments lead to different extents of declining adaptability and note this in the Results. This declining adaptability may certainly influence pleiotropic outcomes, but unfortunately it is difficult to disentangle any potential such effects from other differences between environments (or assign any causality to correlations in the strengths of diminishing returns and differences in pleiotropy between replicates in the same environment), so we refrain from drawing any conclusions about this possibility.

In total I think this manuscript can be improved by being presented / read by others, which is the job of peer review but here I think it's also to broaden its implications.

Even though the poem starts with saying love is not all, it ends with love is all we need. I think this is interesting and relatable among teenagers that they want to be loved and feel love, but at the same time, they act as they don't really care about not being loved.

In these last few lines Millay sort of contradicts herself as even though she compares love in a way that is less valuable, and could be said unnecessary, she still acknowledges that it does have a value that she wouldn't trade. However I think it remains in saying that love is not all, as it is not a necessity but something she thinks shed rather have. Love is not all that we need but we think it is.

I really like the idea. Just like the old saying goes: two heads are better than one. Triggered brainwalking can be very helpful. I think that is also why we have to talk other classmates about our wicked problem.

In this passage, Poulydamas is trying to convince Hector during a Trojan advance on Achaian positions that it is necessary for the horses to be left behind due to the presence of a large ditch with sharp stakes in front of the Achaian fortifications. Attacking well defended fortifications on foot is more dangerous than on a chariot due to a loss in mobility, therefore Poulydamas has to inspire confidence in Hector to act on his plan. A method that he uses to convince Hector in the passage is by claiming that the odds are in their favour. He mentions that Zeus is “sending aid to the Trojans”. Zeus is the most powerful God and therefore his support in a bloody conflict is a strong sign of success in war. In fact, throughout the entire poem, the support of the Gods always played an instrumental role in whichever side succeeds in battle. Therefore, Zeus’s support is a compelling reason to take such a military risk. Additionally, he tries to bolster Hector’s confidence by claiming that “the bonds of death are fastened upon them”. In this case, the bonds of death are fastened upon the Achaians by Zeus, and indirectly, by Achilles, who is the reason Zeus supports the Trojans. Finally, Poulydamous proposes to Hector that all of his men “follow Hector in mass formation”. This proposal reveals that Poulydamous has a lot of trust in his commander, enough to follow him into the heat of a battle, and contributes to Homer’s image in the poem as a brave, heroic warrior.

Author Response:

Reviewer #2 (Public Review):

In this work, authors investigated the versatility of the beta-proteobacterium Cupriavidus necator from the proteome perspective. For this purpose, they cultivated the microorganism in a chemostat using different limiting substrates (fructose, fructose with limited ammonia, formate and succinate) and under different dilution rates. Integration of experimental proteomic data with a resource balance analysis model allowed to understand the relation between enzyme abundances and metabolic fluxes in the central metabolism. Moreover, the use of a transposon mutant library and competition experiments, could add insights regarding the essentiality of the genes studied. This shed light on the (under)utilization of metabolic enzymes, including some interpretations and speculations regarding C. necator's physiological readiness to changes in nutrients within its environmental niche. However, several parts of C. necator metabolism are not yet well analyzed (PHB biosynthesis and photorespiration) and some conclusions are not well reported.

Strengths:

1) The manuscript is well written, easily understandable also for (pure) experimentalists, and adds a novel layer of comprehension in the physiology and metabolism of this biotechnologically relevant microorganism. Therefore, it is likely to raise attention and be well-cited among the metabolic engineering community of this organisms.

2) More generally, the scope of the study is broad enough to potentially attract experts in the wider-field of autotrophic/mixotrophic metabolism, especially regarding the metabolic difference in the transition from heterotrophic to autotrophic growth modes and vice versa.

3) Findings from different experimental techniques (chemostat cultivation, proteomics, modelling, mutant libraries) complement each other and increase the level of understanding. Consistency of the results from these different angles increases the roundness of the study.

Weaknesses:

1) A main conclusion of this paper is that it concludes that the CCB cycle operation in heterotrophic conditions (fructose and succinate) is not useful for the biomass growth. However, Shimizu et al., 2015 claim that the CBB cycle has a benefit for at least PHB production is increased, in the presence of the CCB cycle (as demonstrated by a decrease in PHB production when Rubisco or cbbR are knocked out). In this work the authors do not analyze PHB production, but they do analyze fitness in mutant libraries. They claim not see this benefit in this study, however in their data (Figure 5 F) also small fitness drops are seen for cbbR mutants on fructose, as well as on succinate. So I think the authors have to revisit this conclusion. The type of modelling they use (RBA/FBA) may not explain such re-assimilation as 'a theoretically efficient' route, as this type of modelling assumes ' stochiometric' metabolic efficiency with setting a maximum growth objective, which is not what seems to happen in reality fully.

We agree that a minor decrease in fitness is visible for cbbR transposon mutants in heterotrophic conditions (Figure 5F). However, we have noticed that small changes in fitness can occur -particularly at a late stage of cultivation- as an artifact of the sequencing method (fast growing mutants displacing slow-growing ones). A replication of the experiment with pulsed instead of continuous feed showed a slightly increased instead of decreased fitness on succinate for cbbR (Figure 5-figure supplement 1). We therefore conclude that the resolution of the transposon library experiments is not sufficient to decide if the cbbR KO mutant conveys a small fitness benefit or loss. As the reviewer correctly points out, Shimizu et al. do not show a general fitness benefit but only increased PHB yield from CO2-refixation. We have rewritten our conclusions to account for the fact that our results do not contradict the findings from Shimizu et al., but that both increased PHB production and slightly decreased fitness (= growth rate) is possible at the same time. We also toned down our conclusions such that the question of a potential small fitness burden/benefit of the CBB cycle in heterotrophic conditions remains open.

2) The authors focus a lot on readiness as a rational, but actually cannot really prove readiness as an explanation of the expression of 'unutilized' proteome, in the manuscript they also mention that it maybe a non-optimized, recent evolutionary trait, especially for the Calvin Cycle (especially because of the observed responsiveness to PEP of the cbbR regulator). The authors should discuss and not present as if readiness is the proven hypothesis. It would be interesting (and challenging) if the authors can come up with some further suggestions how to research and potentially proof readiness or ' evolutionary inefficiency'.

We rephrased the respective sections to highlight readiness as one potential explanation among others. We added a suggestion for an experimental strategy to test this hypothesis (laboratory evolution of lean-proteome strains).

3) C. necator is well-known for the production of the storage polymer polyhydroxybutyrate (PHB) under nutrient-limited conditions, such as nitrogen of phosphate starvation. Even though the authors looked at such a nitrogen-limited condition ("ammonia") they do not report on the enzymes involved in this metabolism (phABC), which can be typically very abundant under these conditions. This should be discussed and ideally also analyzed. The formation of storage polymers is hard to incorporate in the flux balance analyze with growth as objective, however in real life C. necator can incorporate over 50% of carbon in PHB rather than biomass, so I suggest the authors discuss this and ideally develop a framework to analyze this, specifically for the ammonia-limited condition

As mentioned above to Reviewer 1, we have now performed nitrogen-limited chemostat cultivations in order to disentangle the formation of biomass and PHB. We have updated our model by incorporating separate fluxes 1) to biomass, and 2) to PHB according to the experimental results. We have also analyzed the enzyme abundance and utilization for phaA (in the model reaction ACACT1r), phaB (AACOAR) and phaC (PHAS). The first two enzymes showed high abundance that increased with degree of limitation for all substrates. PHAS showed a different pattern with much lower, constant expression. All enzymes were expressed regardless of N- or C-limitation, but the model did only show utilization during N-limitation where PHB production was enforced. These results were summarized in the new Figure 3-figure supplement 2.

4) The authors extensively discuss the CCB cycle and its proteome abundance. However during autotrophic growth also typically photorespiration/phosphoglycolate salvage pathways are required to deal with the oxygenase side-activity of Rubisco. The authors have not discuss the abundance of the enzymes involved in that key process. Recently, a publication in PNAS on C. necator showed by transcriptomics and knockout that the glycerate pathway on hydrogen and low CO2 is highly abundant (10.1073/pnas.2012288117). Would be good to include these enzymes and the oxygenase side-activity in the modelling, proteome analysis and fitness analysis. An issue with the growth on formate is that the real CO2 concentration in the cells cannot be determined well, but not feeding additional CO2, likely results in substantial oxygenase activity

C. necator has several pathways for 2-phosphoglycolate (2-PGly) salvage, as the reviewer points out. The key enzymes for the universal 'upper part' of 2-PGly salvage, 2-PGly-phosphatase (cbbZ2, cbbZP) and glycolate dehydrogenase GDH (GlcDEF), were all quantified in our proteomics experiments. The cbbZ isoenzymes showed identical expression compared to the other cbb enzymes: highest on formate, lowest on succinate (Figure 1-figure supplement 2D). The GDH subunits encoded by GlcDEF showed no significant trend between growth rates or substrates, and were more than 10-fold lower abundant than 2-PGly-phosphatase. This is in line with the findings from Claassens et al., PNAS, 2020, that showed only a 2.5-fold upregulation of GDH transcripts in a low versus high CO2 comparison (changes on protein level are often less extreme than transcript). The same study demonstrated that the glycerate pathway is the dominant route for 2-PGly salvage and found four enzymes extremely upregulated in low CO2: glyoxylate carboligase GLXCL (H16_A3598), hydroxypyruvate isomerase HPYRI (H16_A3599), tartronate semialdehyde reductase TRSARr (H16_A3600), and glycerate kinase GLYCK (H16_B0612). Here, these enzymes showed only slightly higher abundance on formate compared to the other conditions we tested (~2-fold). The increase was much lower than what the transcriptional upregulation in Classens et al. would suggest; It is therefore difficult to say if 2-PGly salvage plays a role during formatotrophic growth. Moreover, we also investigated conditional essentiality and found that none of the 2-PGly salvage mutants showed impaired growth on formate (see Figure R1 below).

Unfortunately there is -to our knowledge- no data available on the rate of Rubisco's oxygenation reaction during formatotrophic growth, and our bioreactor setup does not support measurement of pCO2. It is known though that only 25% of the CO2 from formic acid oxidation is consumed for biomass (Grunwald et al., Microb Biotech, 2015, http://dx.doi.org/10.1111/1751-7915.12149), effectively creating an excess intracellular CO2 supply. Further, the substrate specificity of the C. necator Rubisco for CO2 over O2 is very high, about twice that of cyanobacteria (Horken & Tabita, Arch Biochem Biophys, 1999, https://pubmed.ncbi.nlm.nih.gov/9882445/). This indirect evidence suggests that flux through this pathway is most likely marginal. We therefore decided to omit it from model simulations. We have added a paragraph summarizing our findings regarding phosphoglycolate salvage to the results section.

Figure R1: Fitness of 2-phosphoglycolate salvage mutants during growth on three different carbon sources, fructose, formate, and succinate. Four genes essential for growth on formate were included for comparison (soluble formate dehydrogenase fdsABDG). Fitness scores are mean and standard deviation of four biological replicates.