I disagree with this, I don't think there will ever be a point where it's "too late" for change to happen, this doesn't mean I think that we should just do nothing right now but it does mean that our future is completely screwed if we do

Wow, that's a thing. Hindering performance at the HARDWARE level. This just takes it up a notch.

It's like limiting max typing speed on a type writter.

Author Response

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

eLife assessment

This study reports important findings regarding the systemic function of hemocytes controlling whole-body responses to oxidative stress. The evidence in support of the requirement for hemocytes in oxidative stress responses as well as the hemocyte single-nuclei analyses in the presence or absence of oxidative stress are convincing. In contrast, the genetic and physiological analyses that link the non-canonical DDR pathway to upd3/JNK expression and high susceptibility, and the inferences regarding the function of hemocytes in systemic metabolic control are incomplete and would benefit from more rigorous approaches. The work will be of interest to cell and developmental biologists working on animal metabolism, immunity, or stress responses.

We would like to thank the editorial team for these positive comments on our manuscript and the constructive suggestions to improve our manuscript. We are now happy to send you our revised manuscript, which we improved according to the suggestions and valuable comments of the referees.

Public Reviews:

Reviewer #1 (Public Review):

The study examines how hemocytes control whole-body responses to oxidative stress. Using single cell sequencing they identify several transcriptionally distinct populations of hemocytes, including one subset that show altered immune and stress gene expression. They also find that knockdown of DNA Damage Response (DDR) genes in hemocytes increases expression of the immune cytokine, upd3, and that both upd3 overexpression in hemocytes and hemocyte knockdown of DDR genes leads to increased lethality upon oxidative stress.

Strengths

1. The single cell analyses provide a clear description of how oxidative stress can cause distinct transcriptional changes in different populations of hemocytes. These results add to the emerging them in the field that there functionally different subpopulations of hemocytes that can control organismal responses to stress.

2. The discovery that DDR genes are required upon oxidative stress to limit cytokine production and lethality provides interesting new insight into the DDR may play non-canonical roles in controlling organismal responses to stress.

We are grateful to referee 1 to point out the importance and novelty of our snRNA-seq data and our findings on the role of DNA damage-modulated cytokine release by hemocytes during oxidative stress. We further extended these analyses in the revised manuscript by looking deeper into the transcriptomic alterations in fat body cells upon oxidative stress (Figure 4, Figure S4). We further provide additional data to support the connection of DNA damage signaling and regulation of upd3 release from hemocytes (Figure 6F). Here we show that upd3-deficiency can abrogate the increased susceptibility of flies with mei41 and tefu knockdown in hemocytes. In line with this finding, we also show that upd3null mutants show a reduced but not abolished susceptibility to oxidative stress overall (Figure 6F), underlining the role of upd3 as a mediator of oxidative stress response.

Weaknesses

1. In some ways the authors interpretation of the data - as indicated, for example, in the title, summary and model figure - don't quite match their data. From the title and model figure, it seems that the authors suggest that the DDR pathway induces JNK and Upd3 and that the upd3 leads to tissue wasting. However, the data suggest that the DDR actually limits upd3 production and susceptibility to death as suggested by several results:

According to the referee’s suggestion, we revised the manuscript and adjusted our title, abstract and graphical summary to be more precise that DNA damage signaling seem to have a modulatory or regulatory effect on upd3 release. Furthermore, we provide now additional data to support the connection between DNA damage signaling and upd3 release. For example, we added several genetic “rescue” experiments to strengthen the epistasis that modulation of DNA damage signaling and the higher susceptibility of the fly is connected to altered upd3 levels (Figure 6F). We now provide additional data showing that the loss of upd3 rescues the susceptibility to oxidative stress in flies, which are deficient for DDR components in hemocytes.

a. PQ normally doesn't induce upd3 but does lead to glycogen and TAG loss, suggesting that upd3 isn't connected to the PQ-induced wasting.

Even though in our systemic gene expression analysis of upd3 expression, we could not detect a significant induction of upd3 upon PQ feeding. However, we found upd3 expression within our snRNAseq data in a distinct cluster of immune-activated hemocytes (Figure 3B, Cluster 6). Upon knockdown of the DNA damage signaling in hemocytes, the levels then increase to a detectable level in the whole fly. This supports our assumption that upd3 is needed upon oxidative stress to induce energy mobilization from the fat body, but needs to be tightly controlled to balance tissue wasting for energy mobilization. Furthermore, we found evidence in our new analysis of the snRNA-seq data of the fat body cells, that indeed we can find Jak/STAT activation in one cell cluster here, which could speak for an interaction of Cluster 6 hemocytes with cluster 6 fat body cells. A hypothesis we aim to explore in future studies.

b. knockdown of DDR upregulates upd3 and leads to increased PQ-induced death. This would suggest that activation of DDR is normally required to limit, rather than serve as the trigger for upd3 production and death.

Our data support the hypothesis that DDR signaling in hemocytes “modulates” upd3 levels upon oxidative stress. We now carefully revised the text and the graphical summary of the manuscript to emphasize that oxidative stress causes DNA damage, which subsequently induces the DNA damage signaling machinery. If this machinery is not sufficiently induced, for example by knockdown of tefu and mei-41, non-canonical DNA damage signaling is altered which induces JNK signaling and induces release of pro-inflammatory cytokines, including upd3. Whereas DNA damage itself is only slightly increase in the used DDR deficient lines (Figure 5C) and hemocytes do not undergo apoptosis (unaltered cell number on PQ (Figure 5B)), we conclude that loss of tefu, mei-41, or nbs1 causes dysregulation of inflammatory signaling cascades via non-canonical DNA damage signaling. However, oxidative stress itself seems to also induce upd3 release and DNA damage signaling in the same cell cluster, as shown by our snRNA-seq data (Figure 3B). Hence, we think that DNA damage signaling is needed as a rate-limiting step for upd3 release.

c. hemocyte knockdown of either JNK activity or upd3 doesn't affect PQ-induced death, suggesting that they don't contribute to oxidative stress-induced death. It’s only when DDR is impaired (with DDR gene knockdown) that an increase in upd3 is seen (although no experiments addressed whether JNK was activated or involved in this induction of upd3), suggesting that DDR activation prevents upd3 induction upon oxidative stress.

Whereas the double knockdown of upd3 or bsk and DDR genes was resulting in insufficient knockdown efficiencies, we added a rescue experiment where we combined upd3null mutants with knockdown of tefu and mei-41 in hemocytes and found a reduced susceptibility of DDR-deficient flies to oxidative stress.

1. The connections between DDR, JNK and upd3 aren't fully developed. The experiments show that susceptibility to oxidative stress-induced death can be caused by a) knockdown of DDR genes, b) genetic overexpression of upd3, c) genetic activation of JNK. But whether these effects are all related and reflect a linear pathway requires a little more work. For example, one prediction of the proposed model is that the increased susceptibility to oxidative stress-induced death in the hemocyte DDR gene knockdowns would be suppressed (perhaps partially) by simultaneous knockdown of upd3 and/or JNK. These types of epistasis experiments would strengthen the model and the paper.

As mentioned before, we had some technical difficulties combining the knockdown of bsk or upd3 with DDR genes. However, we added a new experiment in which we show that upd3null mutation can rescue the higher susceptibility of hemocytes with tefu and mei41 knockdown.

1. The (potential) connections between DDR/JNK/UPD3 and the oxidative stress effects on depletion of nutrient (lipids and glycogen) stores was also not fully developed. However, it may be the case that, in this paper, the authors just want to speculate that the effects of hemocyte DDR/upd3 manipulation on viability upon oxidative stress involve changes in nutrient stores.

In the revised version of the manuscript, we now provide a more thorough snRNA-seq analysis in the fat body upon PQ treatment to give more insights on the changes in the fat body upon PQ treatment. We added additional histological images of the abdominal fat body on control food and PQ food, to demonstrate the elimination of triglycerides from fat body with Oil-Red-O staining (Figure S1). We also analyzed now hemocyte-deficient (crq-Gal80ts>reaper) flies for their levels of triglycerides and carbohydrates during oxidative stress, to support our hypothesis that hemocytes are key players in the regulation of energy mobilization during oxidative stress. Loss of hemocytes (and therefore also their regulatory input on energy mobilization from the fat body) results in increased triglyceride storage in the fat body during steady state with a decreased consumption of these triglycerides on PQ food compared to control flies (Figure 1J). In contrast, glycogen storage and mobilization, which is mostly done in muscle, is not altered in these flies during oxidative stress (Figure 1L). Interestingly, free glucose levels are drastically reduced in hemocyte-deficient flies, which could be due to insufficient energy mobilization from the fat body and subsequently results in a higher susceptibility of these flies on oxidative stress (Figure 1K). Additionally, we aim to point out here that “functional” hemocytes are needed for effective response to oxidative stress, but this response has to be tightly balanced (see also new graphical abstract).

Reviewer #2 (Public Review):

Hersperger et al. investigated the importance of Drosophila immune cells, called hemocytes, in the response to oxidative stress in adult flies. They found that hemocytes are essential in this response, and using state-of-the-art single-cell transcriptomics, they identified expression changes at the level of individual hemocytes. This allowed them to cluster hemocytes into subgroups with different responses, which certainly represents very valuable work. One of the clusters appears to respond directly to oxidative stress and shows a very specific expression response that could be related to the observed systemic metabolic changes and energy mobilization. However, the association of these transcriptional changes in hemocytes with metabolic changes is not well established in this work. Using hemocyte-specific genetic manipulation, the authors convincingly show that the DNA damage response in hemocytes regulates JNK activity and subsequent expression of the JAK/STAT ligand Upd3. Silencing of the DNA damage response or excessive activation of JNK and Upd3 leads to increased susceptibility to oxidative stress. This nicely demonstrates the importance of tight control of JNK-Upd3 signaling in hemocytes during oxidative stress. However, it would have been nice to show here a link to systemic metabolic changes, as the authors conclude that it is tissue wasting caused by excessive Upd3 activation that leads to increased susceptibility, but metabolic changes were not analyzed in the manipulated flies.

We thank the referee for the suggestion to better connect upd3 cytokine levels to energy mobilization from the fat body. We agree that this is an important point to support our hypothesis. First, we added now a detailed analysis of fat body cells in our snRNA-seq data to evaluate the changes induced in the fat body upon oxidative stress. We further added additional metabolic analyses of hemocyte-deficient flies (crq-Gal80ts>reaper) to support our hypothesis that hemocytes are key players in the regulation of energy mobilization during oxidative stress (see also answer to referee 1). Loss of the regulatory role of hemocytes in the energy mobilization and redistribution leads to a decreased consumption of these triglycerides on PQ food compared to control flies (Figure 1J). In contrast, glycogen storage and mobilization from muscle, is not affected in hemocyte-deficient flies during oxidative stress (Figure 1L). Interestingly, free glucose levels are drastically reduced in hemocyte-deficient flies compared to controls, which could be due to insufficient energy mobilization from the fat body resulting in a higher susceptibility to oxidative stress (Figure 1K). This data supports our assumption that “functional” hemocytes are needed for effective response to oxidative stress, but this response has to be tightly balanced (see also new graphical summary).

The overall conclusion of this work, as presented by the authors, is that Upd3 expression in hemocytes under oxidative stress leads to tissue wasting, whereas in fact it has been shown that excessive hemocyte-specific Upd3 activation leads to increased susceptibility to oxidative stress (whether due to increased tissue wasting remains a question). The DNA damage response ensures tight control of JNK-Upd3, which is important. However, what role naturally occurring Upd3 expression plays in a single hemocyte cluster during oxidative stress has not been tested. What if the energy mobilization induced by this naturally occurring Upd3 expression during oxidative stress is actually beneficial, as the authors themselves state in the abstract - for potential tissue repair? It would have been useful to clarify in the manuscript that the observed pathological effects are due to overactivation of Upd3 (an important finding), but this does not necessarily mean that the observed expression of Upd3 in one cluster of hemocytes causes the pathology.

We agree with the referee that the pathological effects and increased susceptibility to oxidative stress are mediated by over-activated hemocytes and enhanced cytokine release, including upd3 during oxidative stress. We edited the revised manuscript accordingly to imply a “regulatory” role of upd3, which we suspect and suggest as an important mediator for inter-organ communication between hemocytes and fat body. Whereas our used model for oxidative stress (15mM Paraquat feeding) is a severe insult from which most of the flies will not recover, we could not account and test how upd3 might influence tissue repair after injury, insults and infection. We believe that this is an important factor, we aim to explore in future studies.

Reviewer #3 (Public Review):

In this study, Kierdorf and colleagues investigated the function of hemocytes in oxidative stress response and found that non-canonical DNA damage response (DDR) is critical for controlling JNK activity and the expression of cytokine unpaired3. Hemocyte-mediated expression of upd3 and JNK determines the susceptibility to oxidative stress and systemic energy metabolism required for animal survival, suggesting a new role for hemocytes in the direct mediation of stress response and animal survival.

Strength of the study:

1. This study demonstrates the role of hemocytes in oxidative stress response in adults and provides novel insights into hemocytes in systemic stress response and animal homeostasis.

2. The single-cell transcriptome profiling of adult hemocytes during Paraquat treatment, compared to controls, would be of broad interest to scientists in the field.

We are grateful to these positive comments on our data and are excited that the referee pointed out the importance of our provided snRNA-seq analysis of hemocytes and other cell types during oxidative stress. In the revised, version we now extended this analysis and looked not only into hemocytes but also highlighted induced changes in the fat body (Figure 4).

Weakness of the study:

1. The authors claim that the non-canonical DNA damage response mechanism in hemocytes controls the susceptibility of animals through JNK and upd3 expression. However, the link between DDR-JNK/upd3 in oxidative stress response is incomplete and some of the descriptions do not match their data.

In the revised manuscript, we aimed to strengthen the weaknesses pointed out by the referee. We now included additional genetic crosses to validate the connection of DDR signaling in hemocytes with upd3 release. For example, we added now survival studies where we show that upd3null mutation can rescue the higher susceptibility of flies with tefu and mei41 knockdown in hemocytes during oxidative stress. Furthermore, we added additional data to highlight the importance of hemocytes themselves as essential regulators of susceptibility to oxidative stress. We analyzed the hemocyte-deficient flies (crq-Gal80ts>reaper) for their triglyceride content and carbohydrate levels during oxidative stress (Figure 1 I-L). As outlined above, loss of hemocytes leads to a decreased consumption of these triglycerides on PQ food compared to control flies (Figure 1J). In contrast, glycogen storage and mobilization from muscle, is not affected in hemocyte-deficient flies during oxidative stress (Figure 1L). Interestingly, free glucose levels are drastically reduced in hemocyte-deficient flies, which could be due to insufficient energy mobilization from the fat body resulting in a higher susceptibility to oxidative stress (Figure 1K).

1. The schematic diagram does not accurately represent the authors' findings and requires further modifications.

We carefully revised the text throughout the manuscript describing our results and edited the graphical abstract to display that upd3 levels and hemocytes are essential to balance and modulate response to oxidative stress.

Reviewer #1 (Recommendations For The Authors):

The summary doesn't say too much about what the specific discoveries and results of the study are. The description is limited to just one sentence saying, "Here we describe the responses of hemocytes in adult Drosophila to oxidative stress and the essential role of non-canonical DNA damage repair activity in direct "responder" hemocytes to control JNK-mediated stress signaling, systemic levels of the cytokine upd3 and subsequently susceptibility to oxidative stress" which doesn't provide sufficient explanation of what the results were.

In the revised version of our manuscript, we now provide further information for the reader to outline the findings of our study in a concise way in the summary.

Reviewer #2 (Recommendations For The Authors):

1. To strengthen the conclusion that the DDR response suppresses JNK, and thus Upd3, rescue of DDR by upd3 null mutation would help (knockdown by Hml>upd3IR might not work, RNAi seems problematic).

We would like to thank the referee for this suggestion and included now a genetic experiment where we combined upd3null mutants with hemocyte-specific knockdown of mei-41 and tefu to test their susceptibility to oxidative stress. Our data indeed provide evidence that loss of upd3 rescues the higher susceptibility of flies with hemocyte-specific knockdown for tefu and mei-41 (Figure 6F). Furthermore, we see that upd3null mutants show a diminished susceptibility to oxidative stress compared to control flies (Figure 6F).

1. To link the observed effects to systemic metabolic changes, it would be useful to measure glycogen and triglycerides in these flies as well:

2. crq-Gal80ts>reaper to see what role hemocytes play in the observed metabolic changes.

3. Hml-Upd3 overexpression and Upd3 null mutant (Upd3 RNAi seems to be problematic, we have similar experiences) to see if Upd3 overexpression leads to even more profound changes as suggested, and if Upd3 mutation at least partially suppresses the observed changes.

We agree with the referee that analyzing the connection of hemocyte activation to metabolic changes should be demonstrated in our manuscript to support our claim that hemocytes are important regulators of energy mobilization during oxidative stress. Hence, we analyzed triglycerides and carbohydrate levels in hemocyte-deficient flies (crq-Gal80ts>reaper) during oxidative stress. Indeed, we found substantial differences in energy mobilization in these flies supporting the assumption that the higher susceptibility of hemocyte-deficient flies could be caused by substantial decrease in free glucose and inefficient lysis of triglycerides from the fat body (Figure 1I-K).

1. To test whether the cause of the increased susceptibility to oxidative stress is due to Upd3 overactivation induced by DDR silencing, the authors should attempt to rescue DDR silencing with an Upd3 null mutation.

The suggestion of the reviewer was included in the revised manuscript and as outlined above we now added this data set to our manuscript (Figure 6F). Indeed, we can now provide evidence that upd3null mutation rescues the higher susceptibility of flies with DDR knockdown in hemocytes.

1. Lethality after PQ treatment varies widely (sometimes from 10 to 90%! as in Figure 5D) - is this normal? In some experiments the variability was much lower. In particular, Figure 5D is very problematic and for example the result with upd3 null mutant compared to control is not very convincing. This could be an important result to test whether Upd3, with normal expression likely coming from cluster 6, actually plays a beneficial role, whereas overexpression with Hml leads to pathology.

We agree with the referee that it would be more convincing if the variation cross of survival experiments would be less. However, we included a lot of flies and vials in many individual experiments to test our hypothesis and variation in these survivals was always the case. These effects can be caused by many factors for example the amount of food intake by the flies, genetic background or inserted transgenes. The n-number is quite high across our survivals; so that we are convinced, the seen effects are valid. This reflects also the power of using Drosophila melanogaster as a model organism for such survivals. The high n-number in our data falls into a normal Gauss distribution with a distinct mean susceptibility between the genotypes analyzed.

1. I like the conclusion at the end of the results: line 413: "We show that this oxidative stressmediated immune activation seems to be controlled by non-canonical DNA damage signaling resulting in JNK activation and subsequent upd3 expression, which can render the adult fly more susceptible to oxidative stress when it is over-activated." This is actually a more appropriate conclusion, but in the summary, introduction and discussion along with the overall schematic illustration, this is not actually stated as such, but rather as Upd3 released from cluster 6 causes the pathology. For example: line 435 "Hence, we postulate that hemocyte-derived upd3, most likely released by the activated plasmatocyte cluster C6 during oxidative stress in vivo and subsequently controlling energy mobilization and subsequent tissue wasting upon oxidative stress."

We thank the referee for this suggestion and edited our manuscript and conclusions accordingly.

Reviewer #3 (Recommendations For The Authors):

1. In Figure 2, the authors claim showed that PQ treatment changes the hemocyte clusters in a way that suppresses the conventional Hml+ or Pxn+ hemocytes (cluster1) while expanding hemocyte clusters enriched with metabolic genes such as Lpin, bmm etc. It is not clear whether these cells are comparable to the fat body and if these clusters express any of previously known hemocyte marker genes to claim that these are bona fide hemocytes.

We now included a new analysis of our snRNA-seq data in Figure S4, where we clearly show that all identified hemocyte clusters do not have a fat body signature and are hemocytes, which seem to undergo metabolic adaptations (Figure S4A). Furthermore, we show that the identified fat body cells have a clear fat body signature (Figure S4B) and do not express specific hemocyte markers (Figure S4C).

1. In Figure 4C, the authors showed that comet assays of isolated hemocytes result in a statistically significant increase in DNA damage in DDR-deficient flies before and after PQ treatment. However, the authors conclude that, in lines 324-328, the higher susceptibility of DDR-deficient flies is not due to an increase in DNA damage. To explicitly conclude that "non-canonical" DNA damage response, without any DNA damage, is specifically upregulated during PQ treatment, the authors require further support to exclude the potential activation of canonical DDR.

The referee is correct that we do not provide direct evidence for non-canonical DNA damage signaling. Therefore, we also decided to tune down our statement here a bit and removed that claim from the title. Increase in DNA damage can of course also increase the non-canonical DNA damage signaling pathway, loss of DNA damage signaling genes such as tefu and mei-41 seem to only have minor impacts on the overall amount of DNA damage acquired in hemocytes by oxidative stress. We therefore concluded that the induction in immune activation is most unlikely only caused by increased DNA damage but might be connected to dysregulation in non-canonical DNA damage signaling. Canonical DNA damage signaling leads essentially to DDR, which could be slow in adult hemocytes because they post-mitotic, or to apoptosis, which we could not observe in the analyzed time window in our experiments. Hemocyte number remained stable over the 24h PQ treatment without reduction in cell number (Figure 1H).

1. From Figure 4D-F, the authors showed that loss of DDR in hemocytes induces the expression of unpaired 2 and 3, Socs36E, which represent the JAK/STAT pathway, and thor, InR, Pepck in the InR pathway, and a JNK readout, puc. These results indicate that the DDR pathway normally inhibits the upd-mediated JAK/STAT activation upon PQ treatment, compared to wild-type animals during PQ treatment in Figure 1B-C, which in turn protects the animal during oxidative stress responses. However, the authors claim that "enhanced DNA damage boosts immune activation and therefore susceptibility to oxidative stress (lines 365-366); we show that this oxidative stress-mediated immune activation seems to be controlled by non-canonical DNA damage signaling resulting in JNK activation and subsequent upd3 expression (line 413-416)". These conclusions are not compatible with the authors' data and may require additional data to support or can be modified.

In the revised manuscript, we carefully revised now the text and our statements that it seems that DNA damage signaling in hemocytes has regulatory or modulatory effect on the immune response during oxidative stress. Accordingly, we also adjusted our graphical summary. We agree with the referee and used the term “non-canonical” DNA damage signaling more carefully throughout the manuscript. The slight increase in DNA damage seen after PQ treatment can contribute to immune activation but seems to be not correlative to the induced cytokine levels or the susceptibility of the flies to oxidative stress.

1. In Fig 1I, the authors showed that genetic ablation of hemocytes using UAS-repear induces susceptibility to PQ treatment. It is possible that inducing cell death in hemocytes itself causes the expression of cytokine upd3 or activates the JNK pathway to enhance the basal level of upd3/JNK even without PQ treatment. If this phenotype is solely mediated by the loss of hemocytes, the results should be repeated by reducing the number of hemocytes with alternative genetic backgrounds.

In the different genotypes analyzed across our manuscript we did not detect cell death of hemocytes or a dramatic reduction in hemocytes number (see Figure 1H, Figure 5B, Figure 6C). The higher susceptibility if hemocyte-deficient flies during oxidative stress is most likely caused by the loss of their regulatory role during energy mobilization. We tested triglyceride levels in hemocyte-deficient flies and found a decreased triglyceride consumption (lipolysis), with reduced levels of circulating glucose levels. This findings support our hypothesis that hemocytes are needed to balance the response to oxidative stress. In contrast, the flies with DDR-deficient hemocytes show higher systemic cytokine levels, which most likely enhance energy mobilization from the fat body and therefore result in a higher susceptibility of the fly to oxidative stress. Hence, we claim that hemocytes and their regulation of systemic cytokine levels are important to balance the response to oxidative stress and guarantee the survival of the organism.

1. Lethality of control animals in PQ treatment is variable and it is hard to estimate the effect of animal susceptibility during 15mM PQ feeding. For example, Fig1A shows that control animals exhibit ~10% death during 15mM PQ which is further enhanced by crq-Gal80>reaper expression to 40% (Fig 1I). However, in Fig 5D-E, the basal lethality of wild-type controls already reaches 40~50%, which makes them hard to compare with other genetic manipulations. Related to this, the authors demonstrated that the expression of upd3 in hemocytes is sufficient to aggravate animal survival upon PQ treatment; however, upd3 null mutants do not rescue the lethality, which indicates that upd3 is not required for hampering animal mortality. These data need to be revisited and analyzed.

As outlined above, we find the variability of susceptibility to oxidative stress across all of our experiments. This could be due to different effects such as food intake but also transgene insertion and genetic background. Crq-gal80ts>reaper flies are healthy, but show a shortened life span on normal food (Kierdorf et al., 2020) due to enhanced loss of proteostasis in muscles. We show in the revised manuscript that these flies have a higher susceptibility to oxidative stress and that this effect could be mediated by defects in energy mobilization and redistribution as shown by less triglyceride lysis from the fat body and decreasing levels in free glucose. This would explain the high mortality rate of these flies at 7 days after eclosion. Paraquat treatment (15mM) is a severe inducer of oxidative stress, which results in death of most flies when they are maintained for longer time windows on PQ food. Hence, it is a model, which is not suitable to examine and monitor recovery from this detrimental insult. upd3null mutants were extensively reexamined in this manuscript, and even though we could not see a full protection of these flies from oxidative stress induced death, we found a reduced susceptibility compared to control flies (Figure 6F). Furthermore, when we combined upd3null mutants with flies deficient for tefu and mei-41 in hemocytes, the increased susceptibility to oxidative stress was rescued.

The idea of Surveillance Capitalism, especially in the context of Meta's practices, really hits close to home for many of us. It's a reminder that our online interactions, which we often consider private, are actually commodities in a larger economic system. The example, where companies target ads based on extremely sensitive or controversial criteria, is not just a privacy violation, but it feels like a deep personal betrayal. It's unnerving to think about how our data – our digital footprints – can be used in ways we never intended or consented to. This reality calls for a more conscientious approach to how we share information online and a demand for greater transparency and ethical practices from these tech giants.

The question of 'serious parody' -- an important point, well-exemplified and wisely mentioned here -- brings to mind the original, in its original function. Art of the Renaissance, especially the Italian Renaissance, was not only a vehicle for expressing faith: it was also a means of demonstrating temporal power and opulence. Think of all those Virgins, with backgrounds of v-v-v-yerry expensive Heavenly blue paint... 120 years ago Louis Bréhier took aim at the likes of (the later, Baroque) Bernini, saying of his sculpture, "la virtuosité, le dilettantisme détruisent le sentiment religieux"; and I can't help but wonder just how faith-full, as opposed to how luscious and Classically decorative, Michelangelo's work is. In Edwards's piece, however, althouygh we have an attractive model as Jesus, there's an actual line of blood coming from the wound -- a reference, however muted, to physical pain and suffering, which was the order of religious art in the centuries prior to Michelangelo and, to a strong degree, in the Northern Renaissance. (I agree that this too could be taken to excess. A side point.) Perhaps the Mother's look to Jesus' face, not his genitals, is a prudish modern misunderstanding of the ideas Steinberg proposes. My take is that it's much more likely that the Church at the time was disavowing a more libidinal motive, much in the way that it was (and to this day is) happy to display Sodoma masterpieces without labelling them with his name, for fear that anyone might actually understand them. ;) Another striking aspect to the photograph is the context. Unlike the statue, which of course would be in a gloriously decorative and figurative church, this scene stands against an implacable black background. Perhaps a touch of Rothko-esque -- hence, trans-religious -- memento mori? Either way, for all of these reasons, I would suggest that it's possible, at least, that this piece could be humbler, and more religious than its original.

reply to u/ethanzanemiller at https://www.reddit.com/r/Zettelkasten/comments/185xmuh/comment/kb778dy/?utm_source=reddit&utm_medium=web2x&context=3

Some of your methodology will certainly depend on what questions you're asking, how well you know your area already, and where you'd like to go. If you're taking notes as part of learning a new area, they'll be different and you'll treat them differently than notes you're collecting on ideas you're actively building on or intriguing facts you're slowly accumulating. Often you'll have specific questions in mind and you'll do a literature review to see what's happing around that area and then read and take notes as a means of moving yourself closer to answering your particular questions.

Take for example, the frequently asked questions (both here in this forum and by note takers across history): how big is an idea? what is an atomic note? or even something related to the question of how small can a fact be? If this is a topic you're interested in addressing, you'll make note of it as you encounter it in various settings and see that various authors use different words to describe these ideas. Over time, you'll be able to tag them with various phrases and terminologies like "atomic notes", "one idea per card", "note size", or "note lengths". I didn't originally set out to answer these questions specifically, but my interest in the related topics across intellectual history allowed such a question to emerge from my work and my notes.

Once you've got a reasonable collection, you can then begin analyzing what various authors say about the topic. Bring them all to "terms" to ensure that they're talking about the same things and then consider what arguments they're making about the topic and write up your own ideas about what is happening to answer those questions you had. Perhaps a new thesis emerges about the idea? Some have called this process having a conversation with the texts and their authors or as Robert Hutchins called it participating in "The Great Conversation".

Almost anyone in the forum here could expound on what an "atomic note" is for a few minutes, but they're likely to barely scratch the surface beyond their own definition. Based on the notes linked above, I've probably got enough of a collection on the idea of the length of a note that I can explore it better than any other ten people here could. My notes would allow me a lot of leverage and power to create some significant subtlety and nuance on this topic. (And it helps that they're all shared publicly so you can see what I mean a bit more clearly; most peoples' notes are private/hidden, so seeing examples are scant and difficult at best.)

Some of the overall process of having and maintaining a zettelkasten for creating material is hard to physically "see". This is some of the benefit of Victor Margolin's video example of how he wrote his book on the history of design. He includes just enough that one can picture what's happening despite his not showing the deep specifics. I wrote a short piece about how I used my notes about delving into S.D. Goitein's work to write a short article a while back and looking at the article, the footnotes, and links to my original notes may be illustrative for some: https://boffosocko.com/2023/01/14/a-note-about-my-article-on-goitein-with-respect-to-zettelkasten-output-processes/. The exercise is a tedious one (though not as tedious as it was to create and hyperlink everything), but spend some time to click on each link to see the original notes and compare them with the final text. Some of the additional benefit of reading it all is that Goitein also had a zettelkasten which he used in his research and in leaving copies of it behind other researchers still actively use his translations and notes to continue on the conversation he started about the contents of the Cairo Geniza. Seeing some of his example, comparing his own notes/cards and his writings may be additionally illustrative as well, though take care as many of his notes are in multiple languages.

Another potentially useful example is this video interview with Kathleen Coleman from the Thesaurus Linguae Latinae. It's in the realm of historical linguistics and lexicography, but she describes researchers collecting masses of data (from texts, inscriptions, coins, graffiti, etc.) on cards which they can then study and arrange to write their own articles about Latin words and their use across time/history. It's an incredibly simple looking example because they're creating a "dictionary", but the work involved was painstaking historical work to be sure.

Again, when you're done, remember to go back and practice for yourself. Read. Ask questions of the texts and sources you're working with. Write them down. Allow your zettelkasten to become a ratchet for your ideas. New ideas and questions will emerge. Write them down! Follow up on them. Hunt down the answers. Make notes on others' attempts to answer similar questions. Then analyze, compare, and contrast them all to see what you might have to say on the topics. Rinse and repeat.

As a further and final (meta) example, some of my answer to your questions has been based on my own experience, but the majority of it is easy to pull up, because I can pose your questions not to my experience, but to my own zettelkasten and then quickly search and pull up a variety of examples I've collected over time. Of course I have far more experience with my own zettelkasten, so it's easier and quicker for me to query it than for you, but you'll build this facility with your own over time.

Good luck. 🗃️

reply to u/ethanzanemiller at https://www.reddit.com/r/Zettelkasten/comments/185xmuh/taking_notes_for_historical_writing/

Rather than spending time theorizing on the subject, particularly since you sound like you're neck-deep already, I would heartily recommend spending some time practicing it heavily within the area you're looking at. Through a bit of time and experience, more of your questions will become imminently clear, especially if you're a practicing historian.

A frequently missing piece to some of this puzzle for practicing academics is upping the level of how you read and having the ability to consult short pieces of books and articles rather than reading them "cover-to-cover" which is often unnecessary for one's work. Of particular help here, try Adler and Van Doren, and specifically their sections on analytical and syntopical reading.

• Adler, Mortimer J., and Charles Van Doren. How to Read a Book: The Classical Guide to Intelligent Reading. Revised and Updated ed. edition. 1940. Reprint, Touchstone, 2011.

In addition to the list of practicing historians I'd provided elsewhere on the topic, you might also appreciate sociologist Beatrice Webb's short appendix C in My Apprenticeship or her longer related text. She spends some time talking about handling dates and the database nature of querying collected facts and ideas to do research and to tell a story.

• Webb, Beatrice Potter. My Apprenticeship. First Edition. New York: Longmans, Green & Co., 1926. https://archive.org/details/myapprenticeship0000beat/page/412/mode/2up.
• Webb, Sidney, and Beatrice Webb. Methods of Social Study. London; New York: Longmans, Green & Co., 1932. http://archive.org/details/b31357891.

Also helpful might be Mill's article which became a chapter in one of his later books:

• Mills, C. Wright. “On Intellectual Craftsmanship (1952).” Society 17, no. 2 (January 1, 1980): 63–70. https://doi.org/10.1007/BF02700062.

Perhaps u/danallosso may have something illuminating to add, or you can skim through his responses on the subject on Reddit or via his previous related writing: https://danallosso.substack.com/.

Enough historians and various other humanists have been practicing these broad methods for centuries to bear out their usefulness in researching and organizing their work. Read a bit, but truly: practice, practice, and more practice is going to be your best friend here.

It's fascinating how politics and athletics coexist. It appears that this has been the case from the beginning of sports and will be so until the very end.

I 100% agree with this. Yeah, it might not be that serious in every case, but I hate this new phenomenon of purposely upsetting kids for tiktok/youtube/etc ... especially because the type of parents to do this likely do it more than just once. Once, it may be funny. Repetitively? It's just kind of bullying. I think posting it online is especially harmful because it publicizes it, and honestly, for the parent, it probably reinforces the behavior. There is a reason a lot of family channels these days get exposed for being abusive, horrible people.

From what I understand this is not the only way people can get “canceled”. Canceling can come from political opinions, culutural and national behaviours and more, it’s not just plain and simple

This concept of "cancel culture" gets taken too far sometimes. Even if someone is reflecting their own opinion which may have some controversy, the individual might be cancelled. It's important to differentiate between holding someone accountable for genuinely harmful actions and penalizing someone for merely expressing a viewpoint that doesn't align with the majority.

It may be just a prank for parents to take away their children's candy, but it can be devastating to a child's young mind and heart, and I don't think it's ethical for adults to use methods used to please adults to be applied to small children

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

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Reply to the reviewers

__Points raised by both reviewers in their cross-comments __

1. “emphasizing the acute nature of the study is important as well as the use of only male rats” RESPONSE: Thank you for pointing this out. It has been clarified throughout the manuscript, including the abstract, limitations section, and conclusions.

“The need for improvement of the presentation cannot be stressed enough”.

RESPONSE: The manuscript has undergone extensive revisions to enhance the clarity of data presentation and discussion, and to highlight its novelty in comparison to our prior studies. We have reduced the use of technical terminology and abbreviations, and when they do appear, they are explained with their first use and in the new Glossary section. The manuscript has been better organized, ensuring a logical flow of data and conclusions.

Reviewer #1

Major comments

“the extensive statistical analysis done for the gene expression would require assistance unless the in-house expertise already existed. If these are in place the work could be reproduced with the details provided.”

RESPONSE: Terms and abbreviations used in statistical and correlation analyses are thoroughly explained in the text and in the newly added Glossary section in the revised manuscript to the extent acceptable in a biological paper.

All statistical codes are accessible in the public GitHub repository at https://github.com/YaromirKo/biostatistics-nms. These codes may be utilized for the purpose of replicating studies.

Minor comments

“It is not clear how the genes that were studied here were picked. It is clearly stated what groups the genes fall into and their relevance to the study but it isn't clear how these were decided upon. Clarifying this would be helpful.”

RESPONSE: There is currently no consensus regarding the classification of genes as related to neuroplasticity. In particular, there is no agreement on lists of genes consistently associated with neuroplasticity across studies, and providers of mRNA analysis platforms do not offer panels of neuroplasticity-related genes. Most companies, such as Thermofisher, Illumina, and Nanostring, provide "Neurological" or "Neuropathology Research" panels that contain genes related to neuroplasticity. However, these panels are not specifically designed for targeted analysis of neuroplasticity-related genes.

The gene selection is arbitrary, and the chosen genes may vary across different studies depending on their objectives. In the present study, genes were selected based on several significant works having determined that these genes were likely related to neuroplasticity. Each gene's selection is justified by citing these works in the "Materials and Methods" section and we made every effort to avoid any bias. We do not assert that the gene set is all-encompassing. This matter is addressed in the Limitations section of the revised manuscript.

“It is not always clear what had been done in the previous work and what is completely new in this work, that could be addressed better.”

RESPONSE: Thank you for emphasizing that. It has been thoroughly addressed in the revised manuscript. While our previous study has discovered a left-sided neuroendocrine system, the current work delves into its organizational principles, which are equally crucial. We have shown that this system is bipartite and mirror asymmetric, and that its left and right counterparts can be targeted differently by pharmacological means. Additionally, we have revealed the left-right side-specific gene regulatory networks that operate in the neuroendocrine system and which activities are laterally coordinated by this system along the neuraxis.

“The text and figures are quite complex and require thorough reading the knowledge of the background to understand, therefore not making this work for a general audience.”

“Given the complexity of the work the reading of the results is quite dense and difficult to maneuver unless you have some prior understanding. My suggestion would be to try to simplify this but I wouldn't know exactly how to go about this.”

RESPONSE: We appreciate the Reviewer’s comments here, and agree that this is a complex work. We have endeavored to find a balance between a comprehensive presentation of the methods and results while also providing a level of simplification that will allow the reader who is not versed in this field to still appreciate this work. However, because of the nature of the experimental designs and of the findings that we report, we believe it to be important to provide a comprehensive explanation of the work and results. We believe that we have struck a balance between simplification and comprehensiveness with this revision. We have simplified the presentation of the results, their statistical analysis, and the analysis of gene regulatory networks for easier understanding. We also provide detailed explanations of technical terms in the newly added Glossary section. Please also refer to our response to point 2.

We believe that the revised manuscript has a level of complexity in data presentation and density similar to that of most combined physiological and molecular studies, complemented with advanced statistical and bioinformatics analysis. See please, for example papers published in Plos Biology (doi.org/10.1371/journal.pbio.3002328; doi.org/10.1371/journal.pbio.3002282; doi.org/10.1371/journal.pbio.3001465) and eLIFE (doi.org/10.7554/eLife.85756; https://doi.org/10.7554/eLife.90511.1).

General assessment

“The limitation would be understanding exactly what was done before and how this work expands on that, often it required the reader to look up references and prior work.”

RESPONSE: The introduction and discussion have been modified accordingly in order to comply with this comment. We have clarified how this study expands upon our previous work. In addition, please see the response to Comment 5 that also addresses this issue.

“The audience would be rather specialized, although it does gear towards clinical translation, this aspect could be highlighted better in the introduction and discussion.”

RESPONSE: Clinical aspects of the findings have been further highlighted in the revised manuscript. In the introduction, we note that the discovered phenomenon could contribute to asymmetrical neurological deficits following stroke and TBI. In the discussion section, we examine mechanical similarities between hindlimb asymmetry in rats and spastic dystonia in patients and hypothesize that the rat asymmetries may model this human neuropathology. In the concluding remarks, we state that it is crucial to examine the balance between neural and endocrine pathways in their contribution to neurological impairments, and to establish pharmacological approaches targeting the neuroendocrine system to restore the disturbed neurohormonal equilibrium.

“Those interested in brain injury/neurodegeneration as well as how signaling of motor control could be affected by not just damage to electrical descending motor tracts but to neuroendocrine signaling would be the specific audience.”

RESPONSE: We agree that the experts in neurotrauma, stroke and motor control may be interested in this study. However, the left-right side-specific neuroendocrine signaling may be a general biological phenomenon essential for regulation of lateralized brain functions, and, in a broader biological perspective, regulation of the body plan along the left-right axis.

Furthermore, the study presents what, to the best of our knowledge, is the first evidence for the existence of the left and right side-specific gene regulatory networks in the CNS. They operate in the neuroendocrine system and its peripheral target, and are coordinated across them via the humoral pathway. This is a novel molecular dimension in asymmetric organization of the generally mirror-symmetric CNS.

We are confident that experts in the establishment of the body plan and functional and molecular brain asymmetries will be interested in the concept formulated in this study.

Reviewer #2

Major comments:

“It should be made clear in the introduction that an acute complete cervical SCI is used and the discussion should be extended to include advantages and disadvantages of the used model and the alternatives.”

RESPONSE: Thank you for your suggestions. The introduction and discussion have been supplemented with the requested information. Specifically, we have noted that hindlimb postural asymmetry, a proxy model for neurological deficits, has enabled the discovery and characterization of the left-right side-specific neuroendocrine system. It is a binary model with two qualitatively different responses generated on either the left or right side. On the other hand, it cannot be used to analyze awake animals, and knowledge of its mechanisms is limited. A role for the neuroendocrine phenomenon in the persistent left-right specific biological and pathophysiological processes requires further investigation. This can be addressed by analyzing the effects of unilateral TBI in subchronic experiments with awake animals whose spinal cords are completely transected to disable neural pathways. The methodology could involve an integrated evaluation of hindlimb function during body weight-supported stepping, utilizing behavioral, electrophysiological, and biomechanical measures.

“A similar concern poses the use of pentobarbital and the interpretation of the results of the deafferentation. Were timing of the application and dosage strictly controlled between the different groups? It's effects on somatosensory afferent transmission through presynaptic inhibition are a concern.”

RESPONSE: Thank you for the remark. We have paid special attention to this issue. The rats were deeply anesthetized with the same dose and timing of anesthesia. These parameters were thoroughly controlled in all of the experiments. The depth of pentobarbital anesthesia was characterized by a barely perceptible corneal reflex and a lack of overall muscle tone. Of note, the side and magnitude of postural asymmetry do not apparently depend on anesthesia and its type; the asymmetry was virtually the same after brain injury in rats under deep pentobarbital or isoflurane anesthesia (this study and Lukoyanov et al., 2021; Watanabe et al., 2020; Watanabe et al., 2021; Zhang et al., 2020) and also in decerebrate unanesthetized rats (Zhang et al., 2020). Similar left-right differences were observed in the rats with left and right brain injury which were deafferentated 3 days later, and then analyzed under isoflurane anesthesia (Zhang et al., 2020). This is discussed in the revised manuscript.

Furthermore, no nociceptive stimulation was applied and tactile stimulation was negligible in the course of the asymmetry analysis; the legs were stretched by pulling the threads glued to nails of the toes. The application of lidocaine to the toes, which were pulled during stretching, had no impact on the formation of asymmetry. After all, the stretch and postural limb reflexes are immediately abolished and remain so for several days, and markedly decreased under anesthesia as it was firmly established in many studies. As these reflexes likely do not play a role in the formation of the asymmetric hindlimb posture, their afferent mechanisms could not be a cause of variations in our experiments.

In summary, three main arguments speak against an interference of pentobarbital with asymmetry formation in rats after rhizotomy. First, a similar asymmetry phenomenon developed in pentobarbital anesthetized rats, isoflurane anesthetized rats, and decerebrate un-anesthetized rats. Second, in rats that underwent rhizotomy, the primary sensory nerve fibers were entirely severed. Thus, the hypothetical link between pentobarbital's impact on asymmetry through its effect on presynaptic inhibition could be eliminated. Third, although there may be some variability in the depth of anesthesia among animals, the probability of such strong and statistically significant differences in the effects of brain injury and deafferentation arising from bias in the depth of anesthesia among groups of animals likely to be negligible.

*“Only two test for the asymmetry of spinal processing were used and the two tests are likely measuring very similar phenomena (tonic flexor over activation). Additional reflex tests could shed light onto underlying mechanisms.” *

RESPONSE: We agree. In previous studies, we also analyzed asymmetry in withdrawal reflexes between the left and right hindlimbs as an indicator of the effects of brain injury (Lukoyanov et al., 2021; Watanabe et al., 2021; Zhang et al., 2020). In the present study, we do not focus on the neurophysiological mechanisms of postural asymmetry. We instead prioritize characterizing the phenomenology and organizational principle of the left-right side-specific neuroendocrine system using the postural asymmetry model as a "black box" and as a robust and reliable readout.

Of note, there are several other equally important issues that remain to be addressed, including the identification of signaling pathways from the injured cortex to the hypothalamic-pituitary system, the identification of signaling molecules in the blood that convey information about the side of the brain injury, and the dissection of encoding and decoding mechanisms in the hypothalamus and spinal cord, respectively. No single study could investigate all of these mechanisms.

Minor comments:

“Figure 3 shows only the magnitude of the postural asymmetry in response to the different opioid receptor antagonists, yet the directionality is of interest, especially in case of the control animals. Pre2 values are missing too.”

RESPONSE: We appreciate the reviewer's comment and apologize for any errors in our previous version. The legend for Figure 3 has been revised and simplified. It is unnecessary to include PAS (Postural Asymmetry Size) in addition to MPA as the direction of PAS in all animals in each group was the same. This is stated in the revised manuscript's Legend for Figure 3. MPA was used to compare the left and right UBI groups, which had positive and negative PAS values, respectively. This comparison could not be carried out with PAS.

“Too many abbreviations are used which makes the text and figures very difficult to read at times.” “Terminology is sometimes inconsistent (e.g., delta vs contrast).”

RESPONSE: The manuscript now features a reduced amount of abbreviations. Technical terms and abbreviations are defined upon their first use and are also included in the newly added Glossary section. Corrections have been made to the use of the term "contrast" and its abbreviation "delta" in Figures. Additionally, the term "deltaW" as the left-right difference is no longer utilized within the manuscript.

“The section "correlation patterns in the hypothalamus and spinal cord" was almost impossible for me to understand and could use rephrasing.”

RESPONSE: We apologize for the previous version, and have simplified the presentation of molecular data. We believe that the level of complexity in the revised manuscript's statistics and data presentation is now comparable to that of many other molecular studies featuring system-level analyses; please see also response to Comment # 6 of the first reviewer.

“Only male rats are used.”

RESPONSE: This limitation has been addressed in the Limitation section. It is important to investigate whether identical or distinct neurohormones are responsible for the outcomes of left and right brain injury in male and female rats. However, this requires prior identification of most hypothalamic neurohormones and neuropeptides that regulate the asymmetric processes. Their number may be considerable, given the constellation of left and right gene regulatory networks in the hypothalamus.

It's a delicate balance between correcting behavior and ensuring the child feels secure in their identity. I've found that reinforcing positive actions and highlighting the opportunity for growth can further encourage a child to choose better behaviors in the future. It's a fascinating aspect of parenting, navigating these early emotional developments and setting a foundation for a healthy understanding of right and wrong.

Exploring shame in childhood development helps us understand the roles of shame and guilt, at least according to certain perspectives. Shame is characterized by the idea that “I am bad,” leading people to retreat or face ostracization from the community. Guilt centers around recognizing a specific action as “bad,” prompting a desire to undo the harm caused by the action. In this framework, a caring parent encountering a child engaging in bad or risky behavior may cause the child to feel shame, often because the child is unable to tell the difference between their actions and who they are. A caring parent can help a child feel less ashamed by reassuring them that it’s the action, not who they are, that’s the problem. This reassurance, along with consistent guidance, helps repair the child’s relationship with them, encouraging a transition from shame to guilt. Over time, the child will learn to identify and address specific behaviors that cause harm, moving away from hiding because of shame and toward a proactive stance of repairing the consequences of the behavior.

Author Response

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

eLife assessment:

This study reports a meta-analysis of published data to address an issue that is topical and potentially useful for understanding how the sites of initiation of DNA replication are specified in human chromosomes. The work focuses on the role of the Origin Recognition Complex (ORC) and the Mini-Chromosome Maintenance (MCM2-7) complex in localizing origins of DNA replication in human cells. While some aspects of the paper are of interest, the analysis of published data is in parts inadequate to allow for the broad conclusion that, in contrast to multiple observations with other species, sites in the human genome for binding sites for ORC and MCM2-7 do not have extensive overlap with the location of origins of DNA replication.

Public Reviews:

Reviewer #1 (Public Review):

In the best genetically and biochemically understood model of eukaryotic DNA replication, the budding yeast, Saccharomyces cerevisiae, the genomic locations at which DNA replication initiates are determined by a specific sequence motif. These motifs, or ARS elements, are bound by the origin recognition complex (ORC). ORC is required for loading of the initially inactive MCM helicase during origin licensing in G1. In human cells, ORC does not have a specific sequence binding domain and origin specification is not specified by a defined motif. There have thus been great efforts over many years to try to understand the determinants of DNA replication initiation in human cells using a variety of approaches, which have gradually become more refined over time.

In this manuscript Tian et al. combine data from multiple previous studies using a range of techniques for identifying sites of replication initiation to identify conserved features of replication origins and to examine the relationship between origins and sites of ORC binding in the human genome. The authors identify a) conserved features of replication origins e.g. association with GC-rich sequences, open chromatin, promoters and CTCF binding sites. These associations have already been described in multiple earlier studies. They also examine the relationship of their determined origins and ORC binding sites and conclude that there is no relationship between sites of ORC binding and DNA replication initiation. While the conclusions concerning genomic features of origins are not novel, if true, a clear lack of colocalization of ORC and origins would be a striking finding.

Response: Thank you. That is where the novelty of the paper lies.

However, the majority of the datasets used do not report replication origins, but rather broad zones in which replication origins fire. Rather than refining the localisation of origins, the approach of combining diverse methods that monitor different objects related to DNA replication leads to a base dataset that is highly flawed and cannot support the conclusions that are drawn, as explained in more detail below.

Response: We are using the narrowly defined SNS-seq peaks as the gold standard origins and making sure to focus in on those that fall within the initiation zones defined by other methods. The objective is to make a list of the most reproducible origins. Unlike what the reviewer states, this actually refines the dataset to focus on the SNS origins that have also been reproduced by the other methods in multiple cell lines. We have changed the last box of Fig. 1A to make this clearer: Shared origins = reproducible SNS-seq origins that are contained in initiation zones defined by Repli-seq, OK-seq and Bubble-seq. This and the Fig. 2B (as it is) will make our strategy clearer.

Methods to determine sites at which DNA replication is initiated can be divided into two groups based on the genomic resolution at which they operate. Techniques such as bubble-seq, ok-seq can localise zones of replication initiation in the range ~50kb. Such zones may contain many replication origins. Conversely, techniques such as SNS-seq and ini-seq can localise replication origins down to less than 1kb. Indeed, the application of these different approaches has led to a degree of controversy in the field about whether human replication does indeed initiate at discrete sites (origins), or whether it initiates randomly in large zones with no recurrent sites being used. However, more recent work has shown that elements of both models are correct i.e. there are recurrent and efficient sites of replication initiation in the human genome, but these tend to be clustered and correspond to the demonstrated initiation zones (Guilbaud et al., 2022).

These different scales and methodologies are important when considering the approach of Tian et al. The premise that combining all available data from five techniques will increase accuracy and confidence in identifying the most important origins is flawed for two principal reasons. First, as noted above, of the different techniques combined in this manuscript, only SNS-seq can actually identify origins rather than initiation zones. It is the former that matters when comparing sites of ORC binding with replication origin sites if a conclusion is to be drawn that the two do not co-localise.

Response: We agree. So the reviewer should agree that our method of finding SNS-seq peaks that fall within initiation zones actually refines the origins to find the most reproducible origins. We are not losing the spatial precision of the SNS-seq peaks.

Second, the authors give equal weight to all datasets. Certainly, in the case of SNS-seq, this is not appropriate. The technique has evolved over the years and some earlier versions have significantly different technical designs that may impact the reliability and/or resolution of the results e.g. in Foulk et al. (Foulk et al., 2015), lambda exonuclease was added to single stranded DNA from a total genomic preparation rather than purified nascent strands), which may lead to significantly different digestion patterns (ie underdigestion). Curiously, the authors do not make the best use of the largest SNS-seq dataset (Akerman et al., 2020) by ignoring these authors separation of core and stochastic origins. By blending all data together any separation of signal and noise is lost. Further, I am surprised that the authors have chosen not to use data and analysis from a recent study that provides subsets of the most highly used and efficient origins in the human genome, at high resolution (Guilbaud et al., 2022).

Response: 1) We are using the data from Akerman et al., 2020: Dataset GSE128477 in Supplemental Table 1. We have now separately examined the core origins defined by the authors to check its overlap with ORC binding (Supplementary Fig. S8b).

2) To take into account the refinement of the SNS-seq methods through the years, we actually included in our study only those SNS-seq studies after 2018, well after the lambda exonuclease method was introduced. Indeed, all 66 of SNS-seq datasets we used were obtained after the lambda exonuclease digestion step. To reiterate, we recognize that there may be many false positives in the individual origin mapping datasets. Our focus is on the True positives, the SNS-seq peaks that have some support from multiple SNS-seq studies AND fall within the initiation zones defined by the independent means of origin mapping (described in Fig. 1A and 2B). These True positives are most likely to be real and reproducible origins and should be expected to be near ORC binding sites.

We have changed the last box of Fig. 1A to make this clearer: Shared origins = reproducible SNS-seq origins that are contained in initiation zones defined by Repli-seq, OK-seq or Bubble-seq.

Ini-seq by Torsten Krude and co-workers (Guillbaud, 2022) does NOT use Lambda exonuclease digestion. So using Ini-seq defined origins is at odds with the suggestion above that we focus only on SNS-seq datasets that use Lambda exonuclease. However, Ini-seq identifies a much smaller subset of SNS-seq origins, so, as requested, we have also done the analysis with just that smaller set of origins, and it does show a better proximity to ORC binding sites, though even then the ORC proximate origins account for only 30% of the Ini-seq2 origins (Supplementary Fig. S8d). Note Ini-seq2 identifies DNA replication initiation sites seen in vitro on isolated nuclei.

References:

Akerman I, Kasaai B, Bazarova A, Sang PB, Peiffer I, Artufel M, Derelle R, Smith G, Rodriguez-Martinez M, Romano M, Kinet S, Tino P, Theillet C, Taylor N, Ballester B, Méchali M (2020) A predictable conserved DNA base composition signature defines human core DNA replication origins. Nat Commun, 11: 4826

Foulk MS, Urban JM, Casella C, Gerbi SA (2015) Characterizing and controlling intrinsic biases of lambda exonuclease in nascent strand sequencing reveals phasing between nucleosomes and G-quadruplex motifs around a subset of human replication origins. Genome Res, 25: 725-735

Guilbaud G, Murat P, Wilkes HS, Lerner LK, Sale JE, Krude T (2022) Determination of human DNA replication origin position and efficiency reveals principles of initiation zone organisation. Nucleic Acids Res, 50: 7436-7450

Reviewer #2 (Public Review):

Tian et al. perform a meta-analysis of 113 genome-wide origin profile datasets in humans to assess the reproducibility of experimental techniques and shared genomics features of origins. Techniques to map DNA replication sites have quickly evolved over the last decade, yet little is known about how these methods fare against each other (pros and cons), nor how consistent their maps are. The authors show that high-confidence origins recapitulate several known features of origins (e.g., correspondence with open chromatin, overlap with transcriptional promoters, CTCF binding sites). However, surprisingly, they find little overlap between ORC/MCM binding sites and origin locations.

Overall, this meta-analysis provides the field with a good assessment of the current state of experimental techniques and their reproducibility, but I am worried about: (a) whether we've learned any new biology from this analysis; (b) how binding sites and origin locations can be so mismatched, in light of numerous studies that suggest otherwise; and (c) some methodological details described below.

Major comments:

• Line 26: "0.27% were reproducibly detected by four techniques" -- what does this mean? Does the fragment need to be detected by ALL FOUR techniques to be deemed reproducible?

Response: If the reproducible SNS-seq peaks are included in the reproducible initiation zones found by the other methods, then we consider it reproducible across datasets. The strategy is to focus our analysis on the most reproducible SNS-seq peaks that happen to be in reproducible initiation zones. It is the best way to confidently identify a very small set of true positive origins. We have re-stated this in the abstract: “only 0.27% were reproducibly obtained in at least 20 independent SNS-seq datasets and contained in initiation zones identified by each of three other techniques (20,250 shared origins),...”

And what if the technique detected the fragment is only 1 of N experiments conducted; does that count as "detected"?

Response: A reproducible SNS-seq origin has been reproduced above a statistical threshold of 20 reproductions of SNS-seq datasets. A threshold of reproduction in 20 datasets out of 66 SNS-seq datasets gives an FDR of <0.1. This is explained in Fig. 2a and Supplementary Fig. S2. For the initiation zones, we considered a Zone even if it appears in only 1 of N experiments, because N is usually small. This relaxed method for selecting the initiation zones gives the best chance of finding SNS-seq peaks that are reproduced by the other methods.

Later in Methods, the authors (line 512) say, "shared origins ... occur in sufficient number of samples" but what does sufficient mean?

Response: “Sufficient” means that SNS-seq origin was reproducibly detected in ≥ 20 datasets and was included in any initiation zone defined by three other techniques.

Then on line 522, they use a threshold of "20" samples, which seems arbitrary to me. How are these parameters set, and how robust are the conclusions to these settings? An alternative to setting these (arbitrary) thresholds and discretizing the data is to analyze the data continuously; i.e., associate with each fragment a continuous confidence score.

Response: We explained Fig. 2a and Supplementary Fig. S2 on line 192 as follows: The occupancy score of each origin defined by SNS-seq (Supplementary Fig. 2a) counts the frequency at which a given origin is detected in the datasets under consideration. For the random background, we assumed that the number of origins confirmed by increasing occupancy scores decreases exponentially (see Methods and Supplementary Table 2). Plotting the number of origins with various occupancy scores when all SNS-seq datasets published after 2018 are considered together (the union origins) shows that the experimental curve deviates from the random background at a given occupancy score (Fig. 2a). The threshold occupancy score of 20 is the point where the observed number of origins deviates from the expected background number (with an FDR < 0.1) (Fig. 2a).

In the Methods: We have revised the section, “Identification of shared origins” to better describe our strategy. The number of observed origins with occupancy score greater than 20 (out of 66 measures) is 10 times more than expected from the background model. This approach is statistically sound and described by us in (Fang et al. 2020).

• Line 20: "50,000 origins" vs "7.5M 300bp chromosomal fragments" -- how do these two numbers relate? How many 300bp fragments would be expected given that there are ~50,000 origins? (i.e., how many fragments are there per origin, on average)? This is an important number to report because it gives some sense of how many of these fragments are likely nonsense/noise. The authors might consider eliminating those fragments significantly above the expected number, since their inclusion may muddle biological interpretation.

Response: We confused the reviewer by the way we wrote the abstract. The 50,000 origins that are mentioned in the abstract is the hypothetical expected number of origins that have to fire to replicate the whole 6x10^9 nt diploid genome based on the average inter-origin distance of 100 kb (as determined by molecular combing). The 7.5M 300 bp fragments are the genomic regions where the 7.5M union SNS-seq-defined origins are located. Clearly, that is a lot of noise, some because of technical noise and some due to the fact that origins fire stochastically. Which is why our paper focuses on a smaller number of reproducible origins, the 20,250 shared origins. Our analysis is on the 20,250 shared origins, and not on all 7.5M union origins. Thus, we are not including the excess of non-reproducible (stochastic?) origins in our analysis.

The revised abstract in the revised paper will say: “Based on experimentally determined average inter-origin distances of ~100 kb, DNA replication initiates from ~50,000 origins on human chromosomes in each cell-cycle. The origins are believed to be specified by binding of factors like the Origin Recognition Complex (ORC) or CTCF or other features like G-quadruplexes. We have performed an integrative analysis of 113 genome-wide human origin profiles (from five different techniques) and 5 ORC-binding site datasets to critically evaluate whether the most reproducible origins are specified by these features. Out of ~7.5 million union origins identified by all the SNS-seq datasets, only 0.27% were reproducibly obtained in at least 20 independent SNS-seq datasets and contained in initiation zones identified by any of three other techniques (20,250 shared origins), suggesting extensive variability in origin usage and identification in different circumstances.”

• Line 143: I'm not terribly convinced by the PCA clustering analysis, since the variance explained by the first 2 PCs is only ~25%. A more robust analysis of whether origins cluster by cell type, year etc is to simply compute the distribution of pairwise correlations of origin profiles within the same group (cell type, year) vs the correlation distribution between groups. Relatedly, the authors should explain what an "origin profile" is (line 141). Is the matrix (to which PCA is applied) of size 7.5M x 113, with a "1" in the (i,j) position if the ith fragment was detected in the jth dataset?

Response: The reviewer is correct about how we did the PCA and have now included the description in the Methods. We have now done the pairwise correlations the way the reviewer suggests, and it is clear that each technique correlates best with itself (though there are some datasets that do not correlate as well as the others even with the same technique) (Supp. Fig. S3). We have also done the PCA by techniques (Fig. 1c), by cell types for all techniques (Supp. Fig. S1c), by cell-types for SNS-seq only (Supp. Fig. S1d), and by year of publication of SNS-seq data (Supp. Fig. S1e). Our conclusions remain the same: in general, origins defined from the same cell lineage are more similar to each other than across lineages, though this similarity within a lineage is more pronounced when we focus on SNS-seq alone. However, even when we look at SNS-seq alone, there is not a perfect overlap of origins determined by different studies on the same lineage. Finally, although we looked only at SNS-seq data after 2018, by which time lamda exonuclease had become the accepted way of defining SNS-seq, there is surprising clustering around each year.

• It's not clear to me what new biology (genomic features) has been learned from this meta-analysis. All the major genomic features analyzed have already been found to be associated with origin sites. For example, the correspondence with TSS has been reported before:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320713/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6547456/

So what new biology has been discovered from this meta-analysis?

Response: The new biology can be summarized as: (a) We can identify a set of reproducible (in multiple datasets and in multiple cell lines) SNS-seq origins that also fall within initiation zones identified by completely independent methods. These may be the best origins to study in the midst of the noise created by stochastic origin firing. (b) The overlap of these Shared origins (True Positive Origins) with known ORC binding sites is tenuous. So either all the origin mapping data, or all the ORC binding data has to be discarded, or this is the new biological reality in mammalian cancer cells: on a genome-wide scale the most reproduced origins are not in close proximity to ORC binding sites, in contrast to the situation in yeast. (c) Several of the features reported to define origins (CTCF binding sites, G quadruplexes etc.) could simply be from the fact that those features also define transcription start sites (TSS), and the origins may prefer to locate to these parts of the genome because of the favorable chromatin state, instead of the sequence or the structural features of CTCF binding sites or G quadruplexes specifically locating the origins.

• Line 250: The most surprising finding is that there is little overlap between ORC/MCM binding sites and origin locations. The authors speculate that the overlap between ORC1 and ORC2 could be low because they come from different cell types. Equally concerning is the lack of overlap with MCM. If true, these are potentially major discoveries that butts heads with numerous other studies that have suggested otherwise. More needs to be done to convince the reader that such a mis-match is true. Some ideas are below:

Idea 1) One explanation given is that the ORC1 and ORC2 data come from different cell types. But there must be a dataset where both are mapped in the same cell type. Can the authors check the overlap here? In Fig S4A, I would expect the circles to not only strongly overlap but to also be of roughly the same size, since both ORC's are required in the complex. So something seems off here.

Response: We agree with the reviewer that there is something “off here”. Either the techniques that report these sites are all wrong, or the biology does not fit into the prevailing hypothesis. As shown in Supplementary Fig. S6C, we do not have ORC1 and ORC2 ChIP-seq data from the same cell-type. We have ORC1 ChIP-seq and SNS-seq data from HeLa cells and ORC2 ChIP seq and origins from K562 cells, and so have now done the overlap of the binding sites to the shared origins in the same cell-type in the new Figure S5e and S5f. Out of 9605 shared origins in K562 cells, 12.8% overlap with ORC2 and 5.4% overlap with MCM3-7 binding sites also defined in K562 cells. Out of 8305 shared origins in HeLa cells, 4.4% overlap with ORC1 binding sites defined in HeLa cells.

There is nothing in the Literature that shows that various ORC subunits ChiP-seq to the same sites, and we have unpublished data that shows very poor overlap in the CHiP binding sites of different ORC subunits. The poor overlap between the binding sites of subunits of the same complex either suggests that the subunits do not always bind to the chromatin as a six-subunit complex or that all the ORC subunit ChIP-seq data in the Literature is suspect. We provide in the supplementary figure S6A examples of true positive complexes (SMARCA4/ARID1A, SMC1A/SMC3, EZH2/SUZ12), whose subunits ChIP-seq to a large fraction of common sites.

Idea 2) Another explanation given is that origins fire stochastically. One way to quantify the role of stochasticity is to quantify the overlap of origin locations performed by the same lab, in the same year, in the same experiment, in the same cell type -- i.e., across replicates -- and then compute the overlap of mapped origins. This would quantify how much mis-match is truly due to stochasticity, and how much may be due to other factors.

Response: A given lab may have superior reproducibility with its own results compared to the entire field, and the finding that origins published in the same year tend to be clustered together could be because a given lab publishes a number of origin sets in a single paper in a given year. But the notion of stochasticity is well accepted in the field because of this observation: the average inter-origin distance measured by single molecule techniques like molecular combing is ~100 kb, but the average inter-origin distance measure on a population of cells (same cell line) is ~30 kb. The only explanation is that in a population of cells many origins can fire, but in a given cell on a given allele, only one-third of those possible origins fire. This is why we did not worry about the lack of reproducibility between cell-lines, labs etc, but instead focused on those SNS-seq origins that are reproducible over multiple techniques and cell lines.

Idea 3) A third explanation is that MCMs are loaded further from origin sites in human than in yeast. Is there any evidence of this? How far away does the evidence suggest, and what if this distance is used to define proximity?

Response: MCMs, of course, have to be loaded at an origin at the time the origin fires because MCMs provide the core of the helicase that starts unwinding the DNA at the origin. Thus, the lack of proximity of MCM binding sites with origins can be because the most detected MCM sites (where MCM spends the most time in a cell-population) does not correspond to where it is first active to initiate origin firing. This has been discussed. MCMs may be loaded far from origin site, but because of their ability to move along the chromatin, they have to move to the origin-site at some point to fire the origin.

Idea 4) How many individual datasets (i.e., those collected and published together) also demonstrate the feature that ORC/MCM binding locations do not correlate with origins? If there are few, then indeed, the integrative analysis performed here is consistent. But if there are many, then why would individual datasets reveal one thing, but integrative analysis reveal something else?

Response: In the revised manuscript we have now discussed Dellino, 2013; Kirstein, 2021; Wang, 2017; Mas, 2023. None of them have addressed what we are addressing, which is whether the small subset of the most reproducible origins proximal to ORC or MCM binding sites, but the discussion is essential.

Idea 5) What if you were much more restrictive when defining "high-confidence" origins / binding sites. Does the overlap between origins and binding sites go up with increasing restriction?

Response: We have made SNS-seq origins more restrictive by selecting those reproduced by 30, 40, or 50 datasets, in addition to the FDR-determined cutoff of 20. The number of origins fall, but when we do not see any significant increase in the % of origins that overlap with or are proximal to with all ORC or MCM binding sites or Shared ORC or MCM binding sites. This analysis is now included in Supp. Fig. S9 and discussed.

Overall, I have the sense that these experimental techniques may be producing a lot of junk. If true, this would be useful for the field to know! But if not, and there are indeed "unexplored mechanisms of origin specification" that would be exciting. But I'm not convinced yet.

• It would be nice in the Discussion for the authors to comment about the trade-offs of different techniques; what are their pros and cons, which should be used when, which should be avoided altogether, and why? This would be a valuable prescription for the field.

Response: Thanks for the suggestion. We have done what the reviewer suggested in the new Supp. Fig. S4.

Among the 20,250 high-confidence shared origins, 9,901 (48.9%) overlapped with SNS-seq origins in K562; 3,872 (19.1%) overlapped with OK-seq IZs; 1,163 (5.7%) overlapped with Repli-seq IZs.

In the reciprocal direction, we asked which method best picks out the highly reproducible shared origins. 2.7% of SNS-seq origins, 17.2% of OK-seq initiation zones and 7.7% of Repli-seq initiation zones overlapped with the 20,250 shared origins

Thus SNS-seq identifies more of the reproducible origins, but it comes with a high false positive rate.

ORC ChIP-seq and MCM ChIP-seq data do not define origins: they define the binding sites of these proteins. Thus we have discussed why the ChIP-seq sites of these protein complexes should not be used to define origins.

Reviewer #3 (Public Review):

Summary: The authors present a thought-provoking and comprehensive re-analysis of previously published human cell genomics data that seeks to understand the relationship between the sites where the Origin Recognition Complex (ORC) binds chromatin, where the replicative helicase (Mcm2-7) is situated on chromatin, and where DNA replication actually beings (origins). The view that these should coincide is influenced by studies in yeast where ORC binds site-specifically to dedicated nucleosome-free origins where Mcm2-7 can be loaded and remains stably positioned for subsequent replication initiation. However, this is most certainly not the case in metazoans where it has already been reported that chromatin bindings sites of ORC, Mcm2-7, and origins do not necessarily overlap, likely because ORC loads the helicase in transcriptionally active regions of the genome and, since Mcm2-7 retains linear mobility (i.e., it can slide), it is displaced from its original position by other chromatin-contextualized processes (for example, see Gros et al., 2015 Mol Cell, Powell et al., 2015 EMBO J, Miotto et al., 2016 PNAS, and Prioleau et al., 2016 G&D amongst others). This study reaches a very similar conclusion: in short, they find a high degree of discordance between ORC, Mcm2-7, and origin positions in human cells.

Strengths: The strength of this work is its comprehensive and unbiased analysis of all relevant genomics datasets. To my knowledge, this is the first attempt to integrate these observations and the analyses employed were suited for the questions under consideration.

Response: Thank you for recognizing the comprehensive and unbiased nature of our analysis. The fact that the major weakness is that the comprehensive view fails to move the field forward, is actually a strength. It should be viewed in the light that we cannot find evidence to support the primary hypothesis: that the most reproducible origins must be near ORC and MCM binding sites. This finding will prevent the unwise adoption of ORC or MCM binding sites as surrogate markers of origins and will stimulate the field to try and improve methods of identifying ORC or MCM binding until the binding sites are found to be proximal to the most reproducible origins. The last possibility is that there are ORC- or MCM-independent modes of defining origins, but we have no evidence of that.

Weaknesses: The major weakness of this paper is that this comprehensive view failed to move the field forward from what was already known. Further, a substantial body of relevant prior genomics literature on the subject was neither cited nor discussed. This omission is important given that this group reaches very similar conclusions as studies published a number of years ago. Further, their study seems to present a unique opportunity to evaluate and shape our confidence in the different genomics techniques compared in this study. This, however, was also not discussed.

Response: We have done what the reviewer suggested: use K562 cell type-specific data where origins have been defined by three methods and reporting the percent of shared origins identified by each method (Supp. Fig. S4). Thanks for the suggestion. We have discussed now that SNS-seq identifies more of the reproducible origins, but it comes with a high false positive rate. ORC ChIP-seq and MCM ChIP-seq data do not define origins: they define the binding sites of these proteins. Thus, we have discussed that the ChIP-seq sites of these protein complexes as we now have them should not be used to define origins.

We do not cite the SNS-seq data before 2018 because of the concerns discussed above about the earlier techniques needing improvement. We have discussed other genomics data that we failed to discuss.

We have cited the papers the reviewer names:

Gros, Mol Cell 2015 and Powell, EMBO J. 2015 discuss the movement of MCM2-7 away from ORC in yeast and flies and will be cited. MCM2-7 binding to sites away from ORC and being loaded in vast excess of ORC was reported earlier on Xenopus chromatin in PMC193934, and will also be cited.

Miotto, PNAS, 2016: publishes ORC2 ChIP-seq sites in HeLa (data we have used in our analysis), but do not measure ORC1 ChIP-seq sites. They say: “ORC1 and ORC2 recognize similar chromatin states and hence are likely to have similar binding profiles.” This is a conclusion based on the fact that the ChIP seq sites in the two studies are in areas with open chromatin, it is not a direct comparison of binding sites of the two proteins.

Prioleau, G&D, 2016: This is a review that compared different techniques of origin identification but has no primary data to say that ORC and MCM binding sites overlap with the most reproducible origins. It has now been referenced in the context of epigenetic marks and origins.

Reviewing Editor:

While there is some disagreement between the reviewers about the analysis performed, there are relevant concerns about the data analyzed (reviewers 1 and 2) and the biological significance of the observation (all three reviewers). There is also concern raised about the ORC ChIP-Seq data and the lack of overlap between published data for ORC1 and ORC2, which, if they were in a complex, the overlap in binding sites should be much better that reported.

Given the high overlap of ChIP-seq data for subunits of three other complexes shown in Supp. Fig. S6A, the most likely explanation is that ORC1 and ORC2 do not necessarily bind to DNA only as part of a complex. In other words, other protein complexes that contain one subunit or the other also bind DNA. This is not entirely unexpected. Biochemically the ORC2-3-4-5 complex is more stable and more abundant than the six subunit ORC.

Reviewer #2 (Recommendations For The Authors):

Minor comments:

• Line 44, missing spaces near references: "origins(Hu". Repeated issue throughout the manuscript.

• Line 82: "Notably any technical biases are uniquely associated with each assay" -- how do you know the biases are unique to each assay and orthogonal to each other?

• Line 135: typo: "using pipeline"

• Line 136: "All the 113 datasets" -> "Each of the 113 datasets"?

• Line 156: "differences among different techniques" -> "different" can be removed.

• Figure 4F: I don't see any difference in 4F amongst shared *. What is the y-axis anyways?

We have addressed these issues in the revised manuscript.

Reviewer #3 (Recommendations For The Authors):

The most significant omission is a contextualization of the results in the discussion and an explanation of why these results matter for the biology of replication, disease, and/or our confidence in the genomic techniques reported on in this study. As written, the discussion simply restates the results without any interpretation towards novel insight. I suggest that the authors revise their discussion to fill this important gap.

A second important, unresolved point is whether replication origins identified by the various methods differ due to technical reasons or because different cell types were analyzed. Given the correlation between TSS and origins (reported in this study but many others too), it is somewhat expected that origins will differ between cell types as each will have a distinct transcriptional program. This critique is partly addressed in Figure S1C. However, given the conclusion that the techniques are only rarely in agreement (only 0.27% origins reproducibly detected by the four techniques), a more in-depth analysis of cell type specific data is warranted. Specifically, I would suggest that cell type-specific data be reported wherever origins have been defined by at least two methods in the same cell type, specifically reporting the percent of shared origins amongst the datasets. This type of analysis may also inform on whether one or more techniques produces the highest (or lowest) quality list of true origins.

We have done what has been suggested: used K562 cell type-specific data because here the origins have been defined by at least two methods in the same cell type, and reported the percent of shared origins amongst the datasets (Supp. Fig. S4).

Other MINOR comments include:

• Line 215: the authors show that shared origins overlap with TF binding hotspots more often than union origins, which they claim suggests "that they are more likely to interact with transcription factors." As written, it sounds like the authors are proposing that ORC may have some direct physical interaction with transcription factors. Is this intended? If so, what support is there for this claim?

The reviewer is correct. We have rephrased because we have no experimental support for this claim.

• In the text, Figure 3G is discussed before Figure 3F. I suggest switching the order of these panels in Figure 3.

Done.

• It's not clear what Figure 5H to Figure 6 accomplishes. What specifically is added to the story by including these data? Is there something unique about the high confidence origins? If there is nothing noteworthy, I would suggest removing these data.

We want to keep them to highlight the small number of origins that meet the hypothesis that ORC and MCM must bind at or near reproducible origins. These would be the origins that the field can focus in on for testing the hypothesis rigorously. They also show the danger of evaluating proximity between ORC or MCM binding sites with origins based on a few browser shots. If we only showed this figure we could conclude that ORC and MCM binding sites are very close to reproducible origins.

• Line 394: "Since ORC is an early factor for initiating DNA replication, we expected that shared human origins will be proximate to the reproducible ORC binding sites." This is only expected if one disbelieves the prior literature that shows that ORC and origins are not, in many cases, proximal. This statement should be revised, or the previous literature should be cited, and an explanation provided about why this prior work may have missed the mark.

We do not know of any genome-wide study in mammalian cell lines where ORC binding sites and MCM binding have been compared to highly reproducible origins, or that show that these binding sites and highly reproducible origins are mostly not proximal to each other. Most studies cherry pick a few origins and show by ChIP-PCR that ORC and/or MCM bind near those sites. Alternatively, studies sometimes show a selected browser shot, without a quantitative measure of the overlap genome wide and without doing a permutation test to determine if the observed overlap or proximity is higher than what would be expected at random with similar numbers of sites of similar lengths. In the revised manuscript we have discussed Dellino, 2013; Kirstein, 2021; Wang, 2017; Mas, 2023. None of them have addressed what we are addressing, is the small subset of the most reproducible origins proximal to ORC or MCM binding sites?

• Line 402-404: given the lack of agreement between ORC binding sites and origins the authors suggest as an explanation that "MCM2-7 loaded at the ORC binding sites move much further away to initiate origins far from the ORC binding sites, or that there are as yet unexplored mechanisms of origin specification in human cancer cells". The first part of this statement has been shown to be true (Mcm2-7 movement) and should be cited. But what do the authors mean by the second suggestion of "unexplored mechanisms"? Please expand.

We have addressed this point in the revised manuscript.

• The authors should better reference and discuss the previous literature that relates to their work, some of these include Gros et al., 2015 Mol Cell, Powell et al., 2015 EMBO J, Miotto et al., 2016 PNAS, but likely there are many others.

We have addressed this point in the revised manuscript.

Reviewer #3 (Public Review):

Summary:<br /> The study investigates the longitudinal changes in hearing threshold, speech recognition behavior, and speech neural responses in 2 years, and how these changes correlate with each other. A slight change in the hearing threshold is observed in 2 years (1.2 dB on average) but the speech recognition performance remains stable. The main conclusion is that there is no significant correlation between longitudinal changes in neural and behavioral measures.

Strengths:<br /> The sample size (N>100) is remarkable, especially for longitudinal studies.

Weaknesses:<br /> The participants are only tracked for 2 years and relatively weak longitudinal changes are observed, limiting how the data may shed light on the relationships between basic auditory function, speech recognition behavior, and speech neural responses.

Suggestions<br /> First, it's not surprising that a 1.2 dB change in hearing threshold does not affect speech recognition, especially for the dichotic listening task and when speech is always presented 50 dB above the hearing threshold. For the same listener, if the speech level is adjusted for 1.2 dB or much more, the performance will not be influenced during the dichotic listening task. Therefore, it is important to mention in the abstract that "sensory acuity" is measured using the hearing threshold and the change in hearing threshold is only 1.2 dB.

Second, the lack of correlation between age-related changes in "neuronal filtering" and behavior may not suggest that they follow independent development trajectories. The index for "neuronal filtering" does not seem to be stable and the correlation between the two tests is only R = 0.21. This low correlation probably indicates low test-retest reliability, instead of a dramatic change in the brain between the two tests. In other words, if the "neuronal filtering" index only very weakly correlates with itself between the two tests, it is not surprising that it does not correlate with other measures in a different test. If the "neuronal filtering" index is measured on two consecutive days and the index remains highly stable, I'm more convinced that it is a reliable measure that just changes a lot within 2 years, and the change is dissociated with the changes in behavior.

The authors attempted to solve the problem in the section entitled "Neural filtering reliably supports listening performance independent of age and hearing status", but I didn't follow the logic. As far as I could tell, the section pooled together the measurements from two tests and did not address the test-retest stability issue.

Third, the behavioral measure that is not correlated with "neuronal filtering" is the response speed. I wonder if the participants are asked to respond as soon as possible (not mentioned in the method). If not, the response speed may strongly reflect general cognitive function or a personal style, which is not correlated with the changes in auditory functions. This can also explain why the hearing threshold affects speech recognition accuracy but not the response speed (lines 263-264).

Good suggestion. I feel it's going to be a mixed bag too. The AI voice overdubbing part is always going to be iffy.

I love this. This is something that resonates with me. This idea, that it’s a courtship, that he’s invested in her. That he values her enough as a person to give her a title towards who she is to him. The disrespect that most young girls receive or have to put up with from other guys in our generation who claim they want to “be with us”. “Situationships” and “hooking up” are just all too common in a way that is demeaning because a lot of these guys don’t know what they want. They don’t value who they’re seeing.

• for: sleep apnea - treatment - vitamin D

• for health - sleep apnea - silent acid reflux connection

• health - sleep apnea - silent reflux connection

  • if you have sleep apnea and you are experiencing post nasal drip, coughing, clearing throat etc, that is the acid coming up from your stomach and obstructing breathing

This is confused. You are every bit as able to do that as with the medium described in As We May Think. What you can't do is take, say, a copy of an issue of The Atlantic, add links to it, and expect them to magically show up in all copies of the original. But then you can't do that with memex, either, and Bush doesn't say otherwise.

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Reply to the reviewers

Response to Referees Letter

We thank the reviewers for their constructive comments and their positive comments that this study provides insights into the non-canonical roles of Bcl-xL in cancer and may lead to therapeutic approaches to repress metastatic capacity. We have carefully read their comments and have extensively revised the manuscript accordingly. The specific points made by each reviewer are addressed below in blue color.

Response to Reviewer #1:

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

Summary In this study the authors build on their previous work that Bcl-xL has a role in metastasis promotion independent of it's function in the mitochondrial apoptotic pathway. They show that Bcl-xL can be found in the nucleus of some human breast cancer cells and through a mass spec approach show that CtBP2 promotes the nuclear translocation of Bcl-xL. Using various knockdown/knockout methods they show that reduced levels of CtBP2 reduces metastasis, because of loss of Bcl-xL translocation to the nucleus. The authors map this interaction and show that this interaction modulates metastasis.

Major comments * Figure 1 - a more comprehsive analysis of nuclear Bcl-xL should be conducted. The data presented only shows 3 different samples, with no quantification. Perhaps the authors could stain a breast cancer TMA or similar?

__Response: __We performed breast cancer TMA staining experiment as suggested. This experiment provides further support to our conclusion. We have included the following information in the revised manuscript.

“We further evaluated human breast specimens in tissue microarrays (TMAs), consisting of 25 non-neoplastic breast tissues, 150 primary breast cancer, 55 lymph node metastases, and 99 metastatic breast cancer at various distant sites, for the expression and localization of Bcl-xL by immunohistochemistry. Compared to normal breast tissues, the intensity of Bcl-xL was significantly higher in breast cancer, including primary tumors, lymph node (LN) metastases, and distant metastases (Table 1a and 1b). The proportion of positive perinuclear/nuclear Bcl-xL cases was significantly increased in human breast cancer tissues compared to normal breast tissues (Table 1c and Figure 1d), and it showed an increasing trend towards metastases (Table 1d, p =0.004).”

* Figure 2 - could the authors show a graph with a representation of the mass spectrometry data, so the reader can get a sense of how many proteins were found to be associated with Bcl-xL?

__Response: __As suggested, we have included the mass spectrometry data in Supplemental Table 1. Forty proteins were commonly immunoprecipitated by anti-HA magnetic beads from all three cell lines overexpressing HA-tagged wt Bcl-xL and two Bcl-xL mutants but not from the parental cells overexpressing the control vector.

* Have the authors tried any other ways to verify the interaction between Bcl-xL and CtBP2? For instance, do they co-localise when imaged? Also, can the reverse IP be performed?

__Response: __We have verified the interaction between Bcl-xL and CtBP2 by several methods, including IP, reverse IP, and co-immunostaining. Please find HA-Bcl-xL IP and Western for endogenous CtBP2 (Figure 2a), co-immunostaining of endogenous Bcl-xL and CtBP2-V5 (Figure 2b and 2c), co-immunostaining of endogenous Bcl-xL and endogenous CtBP2 (Figure 4e), HA-Bcl-xL IP and Western for seven different constructs of V5 tagged CtBP2 (Figure 5b and 5c), and V5-CtBP2 IP and Western for seven different constructs of Myc tagged Bcl-xL (Figure 6b).

* Figure 2C - the authors claim that this data shows that Bcl-xL nuclear translocation is reduced in cells with reduced levels of CtBP2 - however, although they quantify this I simply do not see it from the images presented. I do not think this data supports the conclusion that knockdown of CtBP2 reduces Bcl-xL translocation to the nucleus. Furthermore, this data is only shown with overexpressed Bcl-xL - have the authors tried with endogenous staining of Bcl-xL?

Response: To assist Reviewer #1’s visualization, below are some marked RFP+ cells that responded to Dox-inducible shRNA expression from Figure 2e. Please note that these cells were not sorted by dsRed so that they gave us a unique opportunity to determine whether the knockdown of CtBP2 affected Bcl-xL nuclear localization by comparing subcellular localization of HA-Bcl-xL in the dsRed-positive cells and the neighboring dsRed-negative cells in the same images. The nuclear-to-cytosol ratio of HA-Bcl-xL was reduced in the dsRed-positive shCtBP2 cells compared to the dsRed-negative cells in both shCtBP2 #2260 and #2403 cultures on dox, not in shRLuc #713 control cells on dox.

In addition, we have performed endogenous staining of Bcl-xL and found that CtBP2 knockout reduced the nuclear to cytosol ratio of endogenous Bcl-xL (Figure 4f).

* Figure 2e-f - again these data are in cells with overexpressed Bcl-xL - does the same effect on invasion happen when only CtBP2 levels are reduced, without overexpression of Bcl-xL? What happens when Bcl-xL is knocked down? Also, doxycycline has been shown to affect mitochondrial function, which might confound this data - perhaps another way to knockdown CtBP2 (e.g. CRISPR which is used later in the study) would rule this out

Response: First, we have previously reported that CtBP2 knockdown reduced migration in cells without overexpression of Bcl-xL (Paliwal et al., 2007), and others have shown that siRNA knockdown of Bcl-xL reduces migration and invasion (Trisciuoglio et al., 2017).

Second, to control any effect of doxycycline, we have included the doxycycline-fed control cells that express doxycycline-inducible shRNA against Renilla Luciferase (shRLuc #713) in revised Figure 2g and 2h (original Figure 2e and 2f).

Third, the novelty of this study is that the discovery that Bcl-xL and CtBP2 interact with each other to promote metastasis. Our study showed that CtBP2 controls Bcl-xL in two ways: nuclear translocation and transcription. Because we found that knockout CtBP2 reduced transcription of endogenous Bcl-xL (Figure 4a-c), it will make the interpretation of the migration effect difficult. Using cells overexpressing HA-Bcl-xL, whose transcription is not regulated by CtBP2, we can evaluate whether the invasion effect of HA-Bcl-xL is mediated by CtBP2 when CtBP2 is knocked down. While overexpression of Bcl-xL promotes invasion (Choi et al., 2016), knockdown of CtBP2 can reverse the effect (Figure 2g).

* Figure 3c - these blots are not labelled, but ideally this would be shown with endogenous Bcl-xL, rather that just the overexpressed HA-Bcl-xL. However these data are more convincing than the images presented in Figure 2c

__Response: __We apologize for the missing labels in these blots of Figure 3c when we merged the graphs. We have now added them back.

* Figure 4 - the authors use CRISPR to knockout CtBP2 - logically this data would go with the shRNA data shown before, as it seems to just repeat what has already been shown?

__Response: __In Figure 4, we examined the effect of CtBP2 knockout on the endogenous Bcl-xL. We were pleased to see that CtBP2 knockout reduced the nuclear-to-cytosol ratio of endogenous Bcl-xL. Moreover, we observed that CtBP2 knockout reduced transcription of Bcl-xL. These knockout data (Figure 4) were logically presented after the knockdown data (Figure 2 and 3).

* Figure 4d - what does "SN" refer to? There is no loading control for this part of the fractionation - I assume this is supernatant? If so, why is there no loading control for this (same applies to figure 3c). Also, why are these not on the same blot? If CtBP2 knockdown reduces Bcl-xL mRNA level, does it also reduce Bcl-xL protein levels? We should be able to tell this from the blots in figure 4d, but since they are on different membranes this is impossible to deduce.

__Response: __We apologize for the missing information. We have added “SN: soluble nuclear fraction” in the figure legend of Figure 4d and re-run all the samples on the same blot. No detection of cytoplasmic proteins and chromatin-bound proteins in the soluble nuclear fraction suggested good fractionation as described (Méndez and Stillman, 2000, PMID: 11046155). CtBP2 knockout indeed reduced Bcl-xL protein levels, as shown in Figure 4a.

* Figure 5c - molecular weight markers should be included here.

__Response: __We apologize for the missing labels of the molecular weight markers, and we have added them in the revision.

* Figure 7a - the text says that MM102 treatment "significantly reduced" H3K4me3 levels - where is the quantification of this?

__Response: __We appreciate the suggestion, and we have now added the quantification in Figure 7a.

Minor comments * Some of the figures are not properly labelled * Some of the data are presented in an awkward manner - the authors should consider re-structuring either the manuscript or the figures so there is less "jumping around"

__Response: __We apologize for the missing labels again, and we have now labeled the figures properly. We hope that the revision (with additional data and properly labelled figures) has made the structure of the manuscript sound.

Reviewer #1 (Significance (Required)):

General assessment * Provides new insight into non-canonical roles of Bcl-xL in cancer * Relies heavily on over-expressed proteins to draw conclusions * If the data were stronger and supported the conclusions, this study could be of interest to a broad cancer audience

My expertise Cell biology, cell death, cancer, imaging

__Reviewer #2 (Evidence, reproducibility and clarity (Required)): ____ __ The manuscript describes a large number of experiments each of which describes a small part of the functional cascade of Bcl-xL in nuclear function and metastatic tumor behavior. No one experiment accomplishes a lot, but taken as a total, the story is compelling and fairly complete.

Major: Figure 1 shows Bcl-xL in one primary sample (a) but clearly not in a second one (c). The authors state 3 of 15. Can they make any comment about breast cancer subtype of these 3 or outcomes? This seems fairly thin evidence of Bcl-xL involvement in human tumorigenesis in general - a better survey might be performed with tissue microarrays of more than one cancer subtype. I'm not sure that this figure is compelling or necessary really for the rest of the manuscript. Really, the main weakness of this paper is some proof that this Bcl-xL-mediated pathway is significant in some proportion of human cancer and metastasis. Perhaps some RNASeq datasets on metastatic versus localized cancers could be mined to establish this relvance?

__Response: __We appreciate this suggestion. We have compared the breast cancer subtypes and the outcomes of the cases used in the original immunofluorescent study. No particular cancer subtype or outcome of these cases is associated with the presence of more nuclear Bcl-xL.

As suggested by the reviewer, we used breast cancer TMAs to investigate the involvement of Bcl-xL in human tumorigenesis in general. We have found that the cases positive of peri-nuclear and nuclear Bcl-xL showed an increasing trend of metastases (Table 1d). We have included the following information in the revised manuscript.

“We further evaluated human breast specimens in tissue microarrays (TMAs), consisting of 25 non-neoplastic breast tissues, 150 primary breast cancer, 55 lymph node metastases, and 99 metastatic breast cancer at various distant sites, for the expression and localization of Bcl-xL by immunohistochemistry. Compared to normal breast tissues, the intensity of Bcl-xL was significantly higher in breast cancer, including primary tumors, lymph node (LN) metastases, and distant metastases (Table 1a and 1b). The proportion of positive perinuclear/nuclear Bcl-xL cases was significantly increased in human breast cancer tissues compared to normal breast tissues (Table 1c and Figure 1d), and it showed an increasing trend towards metastases (Table 1d, p =0.004).”

Most other experiments and figures are well explained. The only one I have some trouble with is Figure 8 CUT and RUN data where we are only presented with peaks around six genes. Is there a way to summarize data for the rest of the genome? Or to display a composite of CUT and RUN data on promoters that are not predicted to be targets of Bcl-xL and MLL1 activity (compared to those that are)?

__Response: __We have deposited the entire CUT&RUN-Seq datasets in Gene Expression Omnibus (accession #GSE221629, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE221629), which will become publicly available when the manuscript is published.

It is very challenging to present 1,190 unique H3K4me3 histone modification regions, and we tried our best to present the CUT&RUN-Seq data in the revised manuscript. In addition to the differential H3K4me3 peaks around promoters of six genes, we have included genome browser view, including the whole gene body by zooming out in Supplementary Figure S7 and peaks for 9 regions that are not targets of Bcl-xL and MLL1 activity in Supplementary Figure S8. Furthermore, we used Hypergeometric Optimization of Motif EnRichment (HOMER) to perform motif analysis for the differential H3K4me3 peaks. Enrichment p-values of the motifs were between 1e-12 and 1e-2 (Supplementary Table S5). It is of note that motifs with a p-value of more than 1e-10 or even 1e-12 are likely to be false positives (http://homer.ucsd.edu/homer/introduction/basics.html). The result revealed the limitation to identify motifs around the H3K4me3 CUT&RUN peaks recognized by the nuclear Bcl-xL complex.

Minor: While the main future direction pointed out by the manuscript was made in the last sentence of the Discussion, it could be spelled out in more detail to enforce the manuscript's impact.

__Response: __We appreciate this suggestion and expanded the discussion in the revised manuscript to enforce the impact of this work.

Reviewer #2 (Significance (Required)):

The authors describe nuclear targets and functions of the anti-apoptotic protein TF Bcl-xL, which has long been of research interest to this group. Specifically, this manuscript follows up on Choi 2016 which established that nuclear localization seemed to be critical for promotion of metastatic/invasion properties of Bcl-xL independent of its anti-apoptotic function. Due to the membrane localization in cells, it was unclear how Bcl-xL entered the nucleus, simulating the current paper. Here the authors (i) demonstrate this nuclear localization happens without mutation to the protein, (ii) localization is promoted by binding to CtBP2 in co-precipitations, (iii) enforced loss of CtBP2 expression correlated with lower metastasis, (iii) specific domains within the two proteins are necessary for physical interaction and function (iii) the histone methyltransferase MLL is critical for downstream transcriptomic impacts which include upregulation of the TGFbeta pathway. Description of this pathway and the specific protein domains necessary may lead to therapeutic targets to repress metastatic capacity. This reviewer is an expert as a cancer biologist and epidemiologist.

__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __ Summary Zhang et al. investigated new roles of Bcl-xL and CtBP2 in cancer progression. They previously reported that Bcl-xL is nuclear localized and promotes cancer metastasis by inducing global histone H3 trimethyl Lys4 (H3K4me3) independent of its anti-apoptotic activity. In this study, they found that CtBP2 is a key factor for promoting the nuclear translocation of Bcl-xL. Furthermore, they showed that the binding between Bcl-xL and CtBP2 is required for MLL1 activation. MLL1 mediates the Bcl-xL-induced H3K4me3 activation and upregulation of TGFβ mRNA level. By global analysis of histone H3K4me3, the authors demonstrated that H3K4me3 modifications are enriched in the promoter regions of genes encoding TGFβ and related signaling pathways in cancer cells overexpressing Bcl-xL. Therefore, they concluded that Bcl-xL exerts its metastatic function by interacting with CtBP2 and MLL1. The mechanism for histone modification by Bcl-xL is interesting and this study expanded our current understanding of epigenetic regulation in cancer. However, the mechanism for MLL1 activation induced by Bcl-xL is not fully demonstrated.

Major points 1) Figure 1) The number of primary breast cancer and lymph node specimens is too small. The authors analyzed only two cases of primer breast cancer and one case of lymph node metastasis. They should also present the result of normal breast tissues to show increased nuclear enrichment during disease progression. In addition, quantification of nuclear signals and statistical analysis are necessary. More importantly, the expression of CtBP2 and MLL1 should be evaluated in these clinical samples because they claimed that the interaction of Bcl-xL/CtBP2/MLL1 is important for tumor metastasis in this study.

__Response: __We appreciate this suggestion to increase the number of the clinical samples. We have stained breast cancer TMAs and included normal breast tissues to show increased nuclear enrichment during disease progression (Table 1). We have included the following information in the revised manuscript. Although we would also like to co-stain these breast cancer TMAs with CtBP2 and MLL1, there are no suitable antibodies for co-staining these two proteins with Bcl-xL in these FFPE sections.

“We further evaluated breast cancer specimens in tissue microarrays (TMAs) for the expression and localization of Bcl-xL by immunohistochemistry. Compared to normal breast tissues, the intensity of Bcl-xL was significantly higher in breast cancer, including primary tumors, lymph node (LN) metastases, and distant metastases (Table 1a and 1b). Perinuclear/nuclear Bcl-xL is significantly increased in human breast cancer tissues compared to normal breast tissues (Table 1c and Figure 1d). The proportion of peri-nuclear and nuclear Bcl-xL positive cases showed an increasing trend towards metastasis (Table 1d).”

2) (Figure 2c) In this experiment, the expression of Bcl-xL is mainly observed in the cytoplasm even in the condition of shControl. Therefore, I think that the nuclear localization of Bcl-xL is not convincingly regulated by CtBP2 expression change. Overexpression of CtBP2 is also necessary to show CtBP2-dependent nuclear localization of Bcl-xL.

__Response: __We appreciate this suggestion to overexpress CtBP2. We have performed this experiment by transiently transfecting cells with CtBP2 and found that overexpression of CtBP2 increased the nuclear to cytosol ratio of Bcl-xL (new Figure 2b and 2c) and included the following information in the revised manuscript.

“To determine the role of CtBP2 in mediating Bcl-xL’s nuclear translocation, we employed overexpression and knockdown of CtBP2 approaches. To overexpress CtBP2, we transfected a V5-tagged CtBP2 construct (Paliwal et al., 2006) into 293T cells and performed immunofluorescent staining using anti-V5 and anti-Bcl-xL antibodies. We observed an increased nuclear-to-cytosol ratio of endogenous Bcl-xL in cells overexpressing CtBP2-V5 (Figure 2b and 2c).”

3) (Figure 6d-e) These results are important because the anti-apoptotic activity is not inhibited even if the interaction between CtBP2 and Bcl-xL is lost. I wonder whether the authors analyzed the cellular localization of each mutant protein (particularly, wt, construct #5 and #6) in the presence of CtBP2. In addition, the authors should examine how the histone K4me3 and MLL1 activity is affected by overexpressing construct #5 and #6 to elucidate the metastatic ability by these constructs (Figure 6e). The authors should describe whether wt Bcl-xL is constract #2 or not in the legends.

__Response: __We appreciate that the reviewer pointed out the importance of our finding that even if the interaction between CtBP2 and Bcl-xL is lost, the anti-apoptotic activity of Bcl-xL is not inhibited. As suggested by the reviewer, we described wt Bcl-xL as construct #2 in the manuscript, and we analyzed the subcellular localization of wt HA-Bcl-xL (construct #2, which binds to CtBP2), construct #5 (which binds to CtBP2), and construct #6 (which does not bind to CtBP2), in the presence of endogenous CtBP2 in N134 mouse PNET cells. We found that the nuclear to cytosol ratio of wt HA-Bcl-xL (construct #2) and construct #5 was similar to each other, and we observed a reduction in the nuclear-to-cytosol ratio of construct #6 (Figure 6f and 6g). This is in consistent of the reduction of the metastatic ability of construct #6.

Further, we examined H3K4me3 and MLL1 in these cells and found that H3K4me3 was reduced in construct #6 compared to wt HA-Bcl-xL (construct #2) and construct #5 (Figure 6c). We also found that H3K4me3 levels were reduced in the CtBP2 knockout cells (Supplementary Figure S5b).

Minor points 4) (Figure 2d) Labels for these graphs are lacking.

__Response: __We apologize for the missing labels when we merged the graphs. We have added them back (new Figure 2f).

5) (Figure 2e, f) The authors should label in these graphs whether these results are statistically significant or not.

__Response: __Thanks for the suggestion. We have labeled * for statistically significant (P 6) (Figure 3c) No labels for these blots.

__Response: __We apologize for the missing labels when we merged the graphs. We have added them back.

7) (Figure 3b) They should describe the full spell of n/a in the legends.

__Response: __Thanks for the suggestion. We have described “n/a: non-sorted parental cells” in the legends in the revision.

8) (Figure 4f) The label of Y-axis should be corrected.

__Response: __Thanks for the suggestion. We have corrected the label of Y-axis.

9) (Figure 8c) The location of gene transcriptional start site and ChIP signal level should be shown. In addition, the genome browser view including whole gene body by zooming out should be shown.

__Response: __In addition to the differential peaks around promoters of six genes in Fig. 8, we have included the whole gene body with the location of the gene transcriptional start site in Supplementary Figure S7.

Reviewer #3 (Significance (Required)):

It is interesting that Bcl-xL can be transported to the nucleus and modulate the entire epigenetic condition for promoting metastatic ability. In the previous study, this group highlighted the nuclear function of Bcl-xL in cancer cells. This concept, Bcl-xL functions independent of its anti-apoptotic activity (Choi et al. Nat Commun 2016;7:10384.), is highly original and will bring some impacts on cancer research. In this study, the authors revealed molecular mechanisms to elucidate this nuclear translocation of Bcl-xL and how Bcl-xL regulate the epigenetic condition. However, the authors should present more evidences to demonstrate the mechanism that CtBP2/Bcl-xL interaction with MLL1 regulate global K4me3 levels in the nucleus to promote metastasis. 1) First of all, there are insufficient data to demonstrate how the interaction with Bcl-xL is involved in MLL1 activation. In Figure 7e, the authors analyzed H3K4me3 level by only inhibiting MLL1 expression and activity. However, the authors should investigate whether Bcl-xL and CtBP2 knockdown or overexpression modulate MLL1-mediated histone H3K4me3 regulation.

Response: __We appreciate that Reviewer #3 considered our work to be highly original. As suggested, we investigated whether CtBP2 knockout affected H3K4me3 levels and found that H3K4me3 levels were reduced in the CtBP2 knockout cells (Supplementary Figure S5b). Conversely, we have reported that Bcl-xL overexpression increases H3K4me3 levels (Choi et al., 2016). The main take-home message of this study is the discovery of the nuclear translocation mechanism of Bcl-xL through a novel interaction with CtBP2. We have shown that Bcl-xL or CtBP2 binds to MLL1 only when Bcl-xL and CtB2 bind to each other (__Figure 5b, 5c, and__ 6b__).

2) (Figure 8) The authors should explain why MLL1 activation specifically affect the K4me3 levels of TGFβ signal-associated genes. I wonder whether Bcl-xL/MLL1/CtBP2 functions as cofactors by binding to certain transcription factors. In addition, Bcl-xL, CtBP2 and MLL1 ChIP-seq/CUT & RUN analysis would be preferable.

__Response: __We have tried but have not been able to successfully establish the CUT&RUN conditions using Bcl-xL, CtBP2, and MLL1 antibodies. Whether Bcl-xL/MLL1/CtBP2 functions as cofactors by binding to certain transcription factors is a very interesting question. Additional studies are required to identify the other components of this Bcl-xL/CtBP2/MLL1 protein complex, which is beyond the scope of this work. This is added in the Discussion of the revised manuscript.

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Referee #1

Evidence, reproducibility and clarity

Summary

In this study the authors build on their previous work that Bcl-xL has a role in metastasis promotion independent of it's function in the mitochondrial apoptotic pathway. They show that Bcl-xL can be found in the nucleus of some human breast cancer cells and through a mass spec approach show that CtBP2 promotes the nuclear translocation of Bcl-xL. Using various knockdown/knockout methods they show that reduced levels of CtBP2 reduces metastasis, because of loss of Bcl-xL translocation to the nucleus. The authors map this interaction and show that this interaction modulates metastasis.

Major comments

• Figure 1 - a more comprehsive analysis of nuclear Bcl-xL should be conducted. The data presented only shows 3 different samples, with no quantification. Perhaps the authors could stain a breast cancer TMA or simiilar?
• Figure 2 - could the authors show the a graph with a representation of the mass spectrometry data, so the reader can get a sense of how many proteins were found to be associated with Bcl-xL?
• Have the authors tried any other ways to verify the interaction between Bcl-xL and CtBP2? For instance, do they co-localise when imaged? Also, can the reverse IP be performed?
• Figure 2C - the authors claim that this data shows that Bcl-xL nuclear translocation is reduced in cells with reduced levels of CtBP2 - however, although they quantify this I simply do not see it from the images presented. I do not think this data supports the conclusion that knockdown of CtBP2 reduces Bcl-xL translocation to the nucleus.Furthermore, this data is only shown with overexpressed Bcl-xL - have the authors tried with endogenous staining of Bcl-xL?
• Figure 2e-f - again these data are in cells with overexpressed Bcl-xL - does the same effect on invasion happen when only CtBP2 levels are reduced, without overexpression of Bcl-xL? What happens when Bcl-xL is knocked down? Also, doxycycline has been shown to affect mitochondrial function, which might confound this data - perhaps another way to knockdown CtBP2 (e.g. CRISPR which is used later in the study) would rule this out
• Figure 3c - these blots are not labelled, but ideally this would be shown with endogenous Bcl-xL, rather that just the overexpressed HA-Bcl-xL. However these data are more convincing than the images presented in Figure 2c
• Figure 4 - the authors use CRISPR to knockout CtBP2 - logically this data would go with the shRNA data shown before, as it seems to just repeat what has already been shown?
• Figure 4d - what does "SN" refer to? There is no loading control for this part of the fractionation - I assume this is supernatant? If so, why is there no loading control for this (same applies to figure 3c). Also, why are these not on the same blot? If CtBP2 knockdown reduces Bcl-xL mRNA level, does it also reduce Bcl-xL protein levels? We should be able to tell this from the blots in figure 4d, but since they are on different membranes this is impossible to deduce
• Figure 5c - molecular weight markers should be included here
• Figure 7a - the text says that MM102 treatment "significantly reduced" H3K4me3 levels - where is the quantification of this?

Minor comments

• Some of the figures are not properly labelled
• Some of the data are presented in an awkward manner - the authors should consider re-structuring either the manuscript or the figures so there is less "jumping around"

Significance

General assessment

• Provides new insight into non-canonical roles of Bcl-xL in cancer
• Relies heavily on over-expressed proteins to draw conclusions
• If the data were stronger and supported the conclusions, this study could be of interest to a broad cancer audience

My expertise

Cell biology, cell death, cancer, imaging

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Author Response

Reviewer #1 (Public Review):

The authors present a detailed analysis of a set of molecular dynamics computer simulations of several variants of a T-cell receptor (TCR) in isolation and bound to a Major Histocompatibility Complex with peptide (pMHC), with the aim of improving our understanding of the mechanism T cell activation in immunity. By analyzing simulations of peptide mutants and partially truncated TCRs, the authors find that native peptide agonists lead to a so-called catch-bond response, whereby tensile force applied in the direction of separation between TCR/pMHC appears to strengthen the TCR/pMHC interface, whereas mutated peptides exhibit the more common slip-bond response, in which applied force destabilizes the binding interface. Using various computational metrics and simulation statistics, the authors propose a model in which tensile force preferentially suppresses thermal fluctuations in the variable α domain of the TCR (vs the β domain) in a peptide-dependent manner, which orders and strengthens the binding interface by bringing together the complementarity-determining regions (CDRs) in the TCR variable chains, but only if the peptide is correctly matched to the TCR.

R1-0. The study is detailed and written clearly, and conclusions appear convincing and are supported by the simulation data. However, the actual motions at the molecular or amino-acid level of how the catch-bond vs slip bond response originates remain somewhat unclear, and will probably warrant further investigations. Specific hypotheses that could be testable in experiments, such as predictions of which peptide (or TCR) mutations or which peptides could generate a catch-vs-slip response or activation, would have especially strengthened this study.

Catch bonds have been observed in different αβ TCRs that differ in sequence when paired with their matching pMHC. Thus, there should be a general principle that apply irrespective of particular TCR sequences, as summarized in Fig. 8. The predictive capacity of this model in terms of understanding experiments is explained in our reply R0-3. Here, we discuss about designing specific point mutations to TCR that have not been studied previously. In our simulations, we can identify high-occupancy contacts that are present mainly in the high-load case as target for altering the catch bond behavior. An example is V7-G100 between the peptide and Vβ (Fig. 2C, bottom panel). The V7R mutant peptide is a modified agonist that we have already studied, where R7 forms hydrogen bonds and nonpolar contacts with residues other than βG100, albeit with lower occupancy (page 11, lines 280–282 and page 32, Fig. 5–figure supplement 2B). Instead of the V7R mutation to the peptide, mutating βG100 to other residues may lead to different effects. For example, compared to G100A, mutation to a bulkier residue such as G100F may cause opposing effects: It may induce steric mismatch that destabilizes the interface. Conversely, a stronger hydrophobic effect might increase the baseline bond lifetime. Also, mutating G100 to a polar residue may have even greater effect, leading to a slip bond or absence of measurable binding.

As the reviewer suggested in R1-5, it will also be interesting to crosslink Vα and Cα by a disulfide bond to suppress its motion. Again, there are different possible outcomes. The lack of Vα-Cα motion could stabilize the interface with pMHC, resulting in a longer bond lifetime. Conversely, if the disulfide bond alters the V-C angle, it would have an opposite effect of destabilizing the interface by tilting it relative to the loading direction, similar to the dFG mutant in Appendix 1 (page 24).

To make better predictions, simulations of such mutants should to be performed under different conditions and analyzed, which would be beyond the scope of the present study.

Change made:

• Page 14, Concluding Discussion, lines 395–402: We added a discussion about using simulations for designing and testing point mutants.

Reviewer #2 (Public Review):

In this work, Chang-Gonzalez and co-workers investigate the role of force in peptide recognition by T-cells using a model T-cell/peptide recognition complex. By applying forces through a harmonic restraint on distances, the authors probe the role of mechanical pulling on peptide binding specificity. They point to a role for force in distinguishing the different roles played by agonist and antagonist peptides for which the bound configuration is not clearly distinguishable. Overall, I would consider this work to be extensive and carefully done, and noteworthy for the number of mutant peptides and conditions probed. From the text, I’m not sure how specific these conclusions are to this particular complex, but I do not think this diminishes the specific studies.

I have a couple of specific comments on the methodology and analysis that the authors could consider:

R2-1. 1) It is not explained what is the origin of force on the peptide-MHC complex. Although I do know a bit about this, it’s not clear to me how the force ends up applied across the complex (e.g. is it directional in any way, on what subdomains/residues do we expect it to be applied), and is it constant or stochastic. I think it would be important to add some discussion of this and how it translates into the way the force is applied here (on terminal residues of the complex).

As explained in our reply R0-1, force on the TCRαβ-pMHC complex arises during immune surveillance where the T-cell moves over APC. Generated by the cellular machinery such as actin retrograde flow and actomyosin motility, the applied force fluctuates, which would be on top of spontaneous fluctuation in force by thermal motion. This has been directly measured for the T-cell using a pMHC-coated bead via optical tweezers (see Feng et al., 2017, Fig. 1) and by DNA tension sensors (Liu, et al., 2016, Fig. 4; already cited in the manuscript). The direction of force also fluctuates that is longitudinal on average (see R1-6). How force distributes across the molecule is a great question, for which we plan to develop a computational method to quantify.

Changes made.

• Pages 3–4, newly added Results section ‘Applying loads to TCRαβ-pMHC complexes:’ We included the origin of force and its fluctuating nature, and the question of how loads are distributed across the molecule.

• The reference (Feng et al., 2017) has been added in the above section.

R2-2. 2) In terms of application of the force, I find the use of a harmonic restraint and then determining a distance at which the force has a certain value to be indirect and a bit unphysical. As just mentioned, since the origin of the force is not a harmonic trap, it would be more straightforward to apply a pulling force which has the form -F*d, which would correspond to a constant force (see for example comment articles 10.1021/acs.jpcb.1c10715,10.1021/acs.jpcb.1c06330). While application of a constant force will result in a new average distance, for small forces it does so in a way that does not change the variance of the distance whereas a harmonic force pollutes the variance (see e.g. 10.1021/ct300112v in a different context). A constant force could also shift the system into a different state not commensurate with the original distance, so by applying a harmonic trap, one could be keeping ones’ self from exploring this, which could be important, as in the case of certain catch bond mechanisms. While I certainly wouldn’t expect the authors to redo these extensive simulations, I think they could at least acknowledge this caveat, and they may be interested in considering a comparison of the two ways of applying a force in the future.

Thanks for the suggestions and references. The paper by Stirnemann (2022) is a review including different computational methods of applying forces, mainly constant force and constant pulling velocity (steered molecular dynamics; SMD). The second one by Gomez et al., (2021) is a rather broad review of mechanosensing where discussion about computer simulation was mainly on SMD. In the third one by Pitera and Chodera (2012), potential limitations of using harmonic potentials in sampling nonlinear potential of mean force (PMF) are discussed.

In the above references, loads or restraints are used to study conformational transitions or to sample the PMF, which are different from the use of positional restraints in our work. As explained in R0-1, positional restraint better mimics reality where the terminal ends of TCR and pMHC are anchored on the membranes of respective cells. Also, the concern raised by the reviewer about ruling out different states would be applicable to the case when there are multiple conformational states with local free energy minima at different extensions. Here, we are probing changes in the conformational dynamics (deformation and conformational fluctuation), rather than transitions between well-defined states.

In Pitera and Chodera (2012) and also in other approaches such as umbrella sampling, the spring constant of the harmonic potential should be chosen sufficiently soft so that sampling around the neighborhood of the center of the potential can be made. On the other hand, if the harmonic potential is much stiffer than the local curvature of the PMF, although sampling may suffer, local gradient of the PMF, i.e, the force about the center of the potential, can be made. This has been studied earlier by one of us in Hwang (2007), which forms the basis for using a stiff harmonic potential for measuring the load on the TCRαβ-pMHC complex. The 1-kcal/(mol·˚A2) spring constant used in our study (page 17, line 540) was selected such that the thermally driven positional fluctuation is on the order of 0.8 ˚A. Hence, it is sufficiently stiff considering the much larger size of the TCRαβ-pMHC complex and the flexible added strands.

Changes made:

• Page 4, lines 117–119, newly added Results section ‘Applying loads to TCRαβ-pMHC complexes:’ The above explanation about the use of stiff harmonic restraint for measuring forces is added.

• The 4 references mentioned above have been added to the above section.

R2-3. 3) For the PCA analysis, I believe the authors learn separate PC vectors from different simulations and then take the dot product of those two vectors. Although this might be justified based on the simplified coordinate upon which the PCA is applied, in general, I am not a big fan of running PCA on separate data sets and then comparing the outputs, as the meaning seems opaque to me. To compare the biggest differences between many simulations, it would make more sense to me to perform PCA on all of the data combined, and see if there are certain combinations of quantities that distinguish the different simulations. Alternatively and probably better, one could perform linear discriminant analysis, which is appropriate in this case because one already knows that different simulations are in different states, and hence the LDA will directly give the linear coordinate that best distinguishes classes.

As explained in R0-2, triads and BOC models are assigned to the same TCR across different simulations in identical ways. For the purpose of examining the relative Vα-Vβ and V-C motions, we believe comparing them across different simulations is a valid approach. When the motions are very distinct, it would be possible to combine all data and perform PCA or LDA to classify them. However, when behaviors differ subtly, analysis on the combined data may not capture individual behaviors. By analogy, consider two sets of 2-dimensional data obtained for the same system under different conditions. If each set forms an elliptical shape with the major axis differing slightly in direction, performing PCA separately on the two sets and comparing the angle between the major axes informs the difference between the two sets. If PCA were performed on the combined data (superposition of two ellipses forming an angle), it will be difficult to find the difference. LDA would likewise be difficult to apply without a very clear separation of behaviors.

As also explained in R0-2, PCA is just one of multiple analyses we carried out to establish a coherent picture. The main use of PCA to this end was to compare directions of motion and relative amplitude of the motion among the subdomains.

Changes made:

• Page 6, lines 171–175 and page 8, lines 226–227: The rationale for applying PCA on triads and BOC models in different simulations are explained.

Reviewer #2 (Public Review):

In this work, Chang-Gonzalez and co-workers investigate the role of force in peptide recognition by T-cells using a model T-cell/peptide recognition complex. By applying forces through a harmonic restraint on distances, the authors probe the role of mechanical pulling on peptide binding specificity. They point to a role for force in distinguishing the different roles played by agonist and antagonist peptides for which the bound configuration is not clearly distinguishable. Overall, I would consider this work to be extensive and carefully done, and noteworthy for the number of mutant peptides and conditions probed. From the text, I'm not sure how specific these conclusions are to this particular complex, but I do not think this diminishes the specific studies.

I have a couple of specific comments on the methodology and analysis that the authors could consider:<br /> 1) It is not explained what is the origin of force on the peptide-MHC complex. Although I do know a bit about this, it's not clear to me how the force ends up applied across the complex (e.g. is it directional in any way, on what subdomains/residues do we expect it to be applied), and is it constant or stochastic. I think it would be important to add some discussion of this and how it translates into the way the force is applied here (on terminal residues of the complex).

2) In terms of application of the force, I find the use of a harmonic restraint and then determining a distance at which the force has a certain value to be indirect and a bit unphysical. As just mentioned, since the origin of the force is not a harmonic trap, it would be more straightforward to apply a pulling force which has the form -F*d, which would correspond to a constant force (see for example comment articles 10.1021/acs.jpcb.1c10715, 10.1021/acs.jpcb.1c06330). While application of a constant force will result in a new average distance, for small forces it does so in a way that does not change the variance of the distance whereas a harmonic force pollutes the variance (see e.g. 10.1021/ct300112v in a different context). A constant force could also shift the system into a different state not commensurate with the original distance, so by applying a harmonic trap, one could be keeping ones' self from exploring this, which could be important, as in the case of certain catch bond mechanisms. While I certainly wouldn't expect the authors to redo these extensive simulations, I think they could at least acknowledge this caveat, and they may be interested in considering a comparison of the two ways of applying a force in the future.

3) For the PCA analysis, I believe the authors learn separate PC vectors from different simulations and then take the dot product of those two vectors. Although this might be justified based on the simplified coordinate upon which the PCA is applied, in general, I am not a big fan of running PCA on separate data sets and then comparing the outputs, as the meaning seems opaque to me. To compare the biggest differences between many simulations, it would make more sense to me to perform PCA on all of the data combined, and see if there are certain combinations of quantities that distinguish the different simulations. Alternatively and probably better, one could perform linear discriminant analysis, which is appropriate in this case because one already knows that different simulations are in different states, and hence the LDA will directly give the linear coordinate that best distinguishes classes.

A user on HN writes on the topic of blogging that they've reverted a publishing regime where they "just create github gists now" and "stopped trying to make something fancy". They're not wrong to change their practices, but it's a nonsequitur to give up maintaining control of their own content.

The problem to identify is that they were building thing X—a personal website probably with a traditional (or at least fashionable) workflow centered around a static site generator and maybe even CI/CD—but they never really wanted X, they wanted Y—in this case GitHub Gists (or something like it). Why were they trying to do X in the first place? Probably some memetic notion that this is what it looks like when you do a personal website. Why is that a meme? Who knows!

Consider that if you want a blogging workflow built around a gist-like experience, you can change your setup to work that way instead. In other words, instead of trying to throw up a blog based on some notion that it should look and feel a certain certain blog-like way, you could just go out and literally clone the GitHub Gists product. Along the way, you'll probably realize you don't actually want that, either. How important is it, really, that there's a link to the GitHub API in the footer, for example?

The point is, though, that you shouldn't start with trying to imagine what your work should look like based on trends of people blogging about blogging setups that they never use and then assume that you'll like it. Start with something that you know you like and then ask, "What can I get rid of in a way that dropping means either that my experience doesn't suffer or is actually improved?"

See also: - Blogging vs. blog setups. - New city, new job, new... website?

• for: key insight - shaking up the system - populists
• key insight
  • This is a good observation. The point that Ruben makes is that populist leaders want to shake up the system, they have tapped into the discontent, but they channel it to their own nefarious ends. They are still thoroughly within modernity, however. so don't get to the root problem.

Delegating execution to endorsing peers allows to: 1) Spare the need for clients to be compute-able 2) Gives trust that tx been executed faithfully (if client to entrusted with executed - it can provide false results. Execution by trusted peers would be required to ensure trustful result of a tx. These peers would not necesserily be the system-owned-peers, they can be the stakeholders of blockchain. But here we're back to having endorsment peers, just of a different kind).

Also, is there harm in commiting "wrong" results that's been provided by the issuer? This tx won't be a smartcontract, but it's kinda ain't already, as tx is being issued by some peer, instead of being globally registered.

I think a lot of it lies in the boundaries that the site instills in its platform. If they make their community guidelines clear, detailed, and comprehensive with appropriate consequences, people should be able to report instances of harassment, and from there it's just a matter of the site communicating well with its users.

This is just awful, this just goes to show that the school system fails to help guide students when choosing their college and that it sucks, proving the authors claim that it's the school systems fault.

Time just needs to change as once the flow starts then it will become more and more normal for the want of legalization of marijuana.

SP4: Andrea Fraser addresses the persistent critique that institutional critique has become institutionalized, labeling it as the "zombie argument" that resurfaces repeatedly. She dismisses the notion that institutional critique should remain untouched by institutionalization, deeming it an idealistic view. Fraser recognizes the inevitability of institutionalization over time due to historical and social factor from a materialist’s point of view. Therefore, she advocates for institutional critique to remain site-specific and responsive to counteract this natural institutionalization. Fraser reminds us that even criticisms of institutions can be institutionalized. To prevent institutional critique from being absorbed and assimilated, we need to stay alert and come up with out-of-box ways of thinking and refreshing critical perspectives that go beyond the preconceptions of the institution leaders.

Challenge1: Fournier and Fraser are suggesting the differences in public funding structures between Canada and the United States significantly shape the artistic landscape and artists' relationships with wealth and economics. While Canada indeed has a history of artist-run centers and different dynamics in its art market compared to the US, it's essential to consider that these structural distinctions might not wholly define an artist's approach to their practice or their relationship with the market. While Canada's funding structures are robust and there's less emphasis on the commercial art market, I think artists' decisions to move to the United States and change their bio line might not solely signify a shift in their approach to their practice. It's plausible that the move might reflect a desire for exposure, opportunities, or a different artistic environment rather than solely a change in their relationship with the market. Moreover, even within Canada's supportive funding environment, there might still be pressures or considerations around sustaining an art practice, especially given the rising costs of living and creating art. Therefore, while differences in funding structures certainly impact artists' experiences, their decisions and attitudes towards their practice and money are likely influenced by a complex interplay of factors beyond just the structural differences between the two countries' art scenes.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study aims to further resolve the history of speciation and introgression in Heliconius butterflies. The authors break the data into various partitions and test evolutionary hypotheses using the Bayesian software BPP, which is based on the multispecies coalescent model with introgression. By synthesizing these various analyses, the study pieces together an updated history of Heliconius, including a multitude of introgression events and the sharing of chromosomal inversions.

Strengths:

Full-likelihood methods for estimating introgression can be very computationally expensive, making them challenging to apply to datasets containing many species. This study provides a great example of how to apply these approaches by breaking the data down into a series of smaller inference problems and then piecing the results together. On the empirical side, it further resolves the history of a genus with a famously complex history of speciation and introgression, continuing its role as a great model system for studying the evolutionary consequences of introgression. This is highlighted by a nice Discussion section on the implications of the paper's findings for the evolution of pollen feeding.

Weaknesses:

The analyses in this study make use of a single method, BPP. The analyses are quite thorough so this is okay in my view from a methodological standpoint, but given this singularity, more attention should be paid to the weaknesses of this particular approach.

In the Discussion, we have now added a discussion of the limitations of our approach in the section 'Approaches for estimating species phylogeny with introgression from whole-genome sequence data: advantages and limitations.'

Additionally, little attention is paid to comparable methods such as PhyloNet and their strengths and weaknesses in the Introduction or Discussion.

We have also mentioned other methods (PhyloNet and starBEAST) in our Discussion. Our attempts to obtain usable estimates from PhyloNet were unsuccessful. In another study, the full likelihood version of PhyloNet (comparable in intent to the BPP methodology used here) could run with only small datasets of ~100 loci; see Edelman et al. (2019).

BPP reduces computational burden by fixing certain aspects of the parameter space, such as the species tree topology or set of proposed introgression events. While this approach is statistically powerful, it requires users to make informed choices about which models to test, and these choices can have downstream consequences for subsequent analyses. It also might not be as applicable to systems outside of Heliconius where less previous information is available about the history of speciation and introgression. In general, it is likely that most modelling decisions made in the study are justified, but more attention should be paid to how these decisions are made and what the consequences of them could be, including alternative models.

We agree with the reviewer that inferring the species tree topology and placing introgression events on the species tree, although well justified here, may be challenging in many groups of organisms and may affect downstream analyses. We now discuss this as a limitation of our approach in the Discussion. In general, the initial MSC analysis without gene flow should provide information about possible species trees and introgression events. We can construct multiple introgression models and perform parameter estimation and model comparison to decide which best fits the data. This is summarized in the last paragraph of the section 'Approaches for estimating species phylogeny with introgression from whole-genome sequence data: advantages and limitations.' It would, of course, be nice to have a completely unsupervised method that could work with large phylogenies, but this is currently computationally impossible.

• Co-estimating histories of speciation and introgression remains computationally challenging. To circumvent this in the study, the authors first estimate the history of speciation assuming no gene flow in BPP. While this approach should be robust to incomplete lineage sorting and gene tree estimation, it is still vulnerable to gene flow. This could result in a circular problem where gene flow causes the wrong species tree to be estimated, causing the true species tree to be estimated as a gene flow event.

The goal of this initial analysis is to obtain a list of possible species trees with introgression events. We assume that gene flow results in a topology that is informative about the lineages involved. We also focus on common MAP trees with high posterior probabilities as less frequent trees or trees with low posterior probabilities reflect high uncertainty and are more likely to be erroneous. A difficulty is to decide which tree topology is most likely to be the true species tree. We summarize our approach in the Discussion.

This is a flaw that this approach shares with summary-statistic approaches like the D-statistic, which also require an a-priori species tree.

In a sense, this is true, but BPP is more flexible because it can be used to explore an arbitrary introgression model on any type of tree, while summary methods like D-statistic assume a specific species phylogeny with a particular introgression between nonsister lineages as well as fixed sampling configurations. Furthermore, as shown in the paper, we can compare different assumed trees, and test between them; we do this repeatedly in the paper for difficult branch placement issues. In contrast, summary methods such as the D-statistic works with species quartets only and do not work with either smaller or larger species trees.

Enrichment of particular topologies on the Z chromosome helps resolve the true history in this particular case, but not all datasets will have sex chromosomes or chromosome-level assemblies to test against.

Yes, we have the privilege of having chromosome-level assemblies available for Heliconius. In general, a spatial pattern of species tree estimates across genomic blocks can be informative about possible topologies that could represent the true species relationship. Then these candidate species trees can be tested by fitting different introgression models (as in Figure 1D,E) or by using the recombination rate argument (Figure 1F), which prefers trees common in low recombination rate regions of the genome, although this requires knowing a recombination rate map. In our case, we used a chromosome-level recombination rate per base pair, which is negatively correlated with the chromosome size. We have clarified this in the text. Ultimately, multiple lines of evidence should be examined before deciding on the most likely species tree. We now mention these potential difficulties with applying our methods to other datasets as limitations of our approach in the Discussion.

• The a-priori specification of network models necessarily means that potentially better-fitting models to the data don't get explored. Models containing introgression events are proposed here based on parsimony to explain patterns in gene tree frequencies. This is a reasonable and common assumption, but parsimony is not always the best explanation for a dataset, as we often see with phylogenetic inference. In general, there are no rigorous approaches to estimating the best-fitting number of introgression events in a dataset.

Joint inference of species topologies and possible introgression events remains computationally challenging. PhyloNet implements this joint inference but is limited to small datasets (<100 loci) and we found it to be unreliable.

Likewise, the study estimates both pulse and continuous introgression models for certain partitions, though there is no rigorous way to assess which of these describes the data better.

The Bayes factor can be used to compare different models fitted to the same data, for example, different MSC-I models with different introgression events, or MSC-I models with gene flow in pulses versus MSC-M models with continuous gene flow. We did not attempt this as it was clear to us that a better model would include both modes of gene flow, but such an option is not currently implemented in any software. Rather, we relied on our exploratory analysis (BPP MSC and 3s) and previous knowledge to inform a likely introgression model. In the case of groups that we fitted the MSC-M models, we chose to provide an intuitive justification as to why they might be more realistic than the MSC-I model without formally performing model selection.

• Some aspects of the analyses involving inversions warrant additional consideration. Fewer loci were able to be identified in inverted regions, and such regions also often have reduced rates of recombination. I wonder if this might make inferences of the history of inverted regions vulnerable to the effects of incomplete lineage sorting, even when fitting the MSC model, due to a small # of truly genealogically independent loci.

We agree with the reviewer that it is challenging to infer the history of a small region of the genome, such as the inversions studied here. Indeed, the presence of only a few loci in the 15b inversion means there is only limited information in the data for the species tree, as reflected in the low posterior probabilities for the MAP tree (Figure 3A). The effect of using tightly linked loci in the inversion should be increased uncertainty in the estimates, but not a systematic bias towards any particular species tree topology. Since major patterns of species relationships in each of the 15a, 15b and 15c regions are clear, we do not expect these effects to strongly influence our conclusions.

Additionally, there are several models where introgression events are proposed to explain the loss of segregating inversions in certain species. It is not clear why these scenarios should be proposed over those in which the inversion is lost simply due to drift or selection.

We know that the 15b inversion is absent in most species except for H. numata and H. pardalinus, at least, and that introgression of the inversion occurred between these two species, based on previous studies such as Jay et al (2018) and our own analysis. Polymorphism at this inversion forms a well-known “supergene” that affects mimicry, and is maintained by documented balancing selection in H. numata. Given this information, we propose a few possible scenarios of how the inversion might have originated, and when and where the introgression might have occurred, shown in Figure 3. In particular, the direction of introgression is something we test specifically. One way to test among these scenarios is to date the origin and introgression event of the inversion, but doing so properly is beyond the scope of this work. Nonetheless, we argue that it is at least likely that one difference between H. pardalinus and its sister species H. elevatus is the presence of the 15b inversion. Since other evidence shows that colour patterning loci in H. elevatus originated from an unrelated species, H. melpomene (i.e. the 15b and other non-inverted colour patterning loci), it is indeed likely that the inversion was “swapped out” by an uninverted sequence from H. melpomene during the formation of H. elevatus.

We are aware that hypotheses such as these might appear highly elaborate and unparsimonious. But these are the conclusions where the data lead us. In the melpomene-silvanform clade, many speciation and introgression events occurred in short succession, and wild-caught hybrids prove that occasional hybridizations can occur across all 15 or so species in the group. We now detail how we have looked only for the major introgression patterns using a limited number of key speces. We leave fuller analyses for future work.

In the main text, we have revised our discussion of the four proposed scenarios for 15b to improve clarity. We have also updated the introgression model from the melpomene-cydno clade to H. elevatus to be unidirectional based on the BPP results in Figure S18.

Reviewer #2 (Public Review):

Thawornwattana et al. reconstruct a species tree of the genus Heliconius using the full-likelihood multispecies coalescent, an exciting approach for genera with a history of extensive gene flow and introgression. With this, they obtain a species tree with H. aoede as the earliest diverging lineage, in sync with ecological and morphological characters. They also add resolution to the species relationships of the melpomene-silvaniform clade and quantify introgression events. Finally, they trace the origins of an inversion on chromosome 15 that exists as a polymorphism in H. numata, but is fixed in other species. Overall, obtaining better species tree resolutions and estimates of gene flow in groups with extensive histories of hybridization and introgression is an exciting avenue. Being able to control for ILS and get estimates between sister species are excellent perks. One overall quibble is that the paper seems to be best suited to a Heliconius audience, where past trees are easily recalled, or members of the different clades are well known.

We thank the reviewer for the accurate summary and positive comments. Although our data and some of the discussion are specific to Heliconius, we believe our analysis framework will be useful to study species phylogeny and introgression in other taxa as well.

Overall, applying approaches such as these to gain greater insight into species relationships with extensive gene flow could be of interest to many researchers. However, the conclusions could be strengthened with a bit more clarity on a few points.

1) The biggest point of concern was the choice of species to use for each analysis. In particular the omission of H. ismenius in the resolution of the BNM clade species tree. The analysis of the chromosome 15 inversion seems to rely on the knowledge that H. ismenius is sister to H. numata, so without that demonstrated in the BNM section the resulting conclusions of the origin of that inversion are less interruptible.

The choice of species to be included was mainly based on available high-quality genome resequence data from Edelman et al (2019), which were chosen to cover most of the major lineages within the genus. We agree that inclusion of H. ismenius would strengthen the analysis of the melpomene-silvaniform clade. In particular, it would be interesting to know which of only H. numata or H. numata+H. ismenius are responsible for the main source of genealogical variation across the genome in this group in Figure 2. The reviewer is correct in saying that we do assume that H. ismenius and H. numata are sister species. This relationship is supported by our analysis (Figure 3A) and previous analyses of genomic data, e.g. Zhang et al (2016), Cicconardi et al. (2023) and Rougemont et al. (2023). We made this clearer in the text:

"Although this conclusion assumes that H. numata and H. ismenius are sister species while H. ismenius was not included in our species tree analysis of the melpomene-silvaniform clade (Figure 2), this sister relationship agrees with previous genomic studies of the autosomes and the sex chromosome (Zhang et al. 2016; Cicconardi et al. 2023; Rougemont et al. 2023)."

2) An argument they make in support of the branching scenario where H. aoede is the earliest diverging branch is based on which chromosomes support that scenario and the key observation that less introgression is detected in regions of low recombination. Yet, they go no further to understand the relationship between recombination rate and species trees produced.

We believe Figure 1F does examine this relationship, showing that trees under scenario 2 are more common in regions of the genome with lower recombination rates (i.e. in longer chromosomes). We added more clarification in the text where Figure 1F is mentioned. The relationship between recombination and introgression in Heliconius was earlier discovered and shown using windowed estimated gene trees in Martin et al. (2019) and in Edelman et al. (2019), so we did not re-test this here.

3) How the loci were defined could use more clarity. From the methods, it seems like each loci could vary quite a bit in total bp length and number of informative sites. Understanding the data processing would make this paper a better resource for others looking to apply similar approaches.

We added a new supplemental figure, Figure S20, to illustrate how coding and noncoding loci were extracted from the genome.

Reviewer #3 (Public Review):

The authors use a full-likelihood multispecies coalescent (MSC) approach to identify major introgression events throughout the radiation of Heliconius butterflies, thereby improving estimates of the phylogeny. First, the authors conclude that H. aoede is the likely outgroup relative to other Heliconius species; miocene introgression into the ancestor of H. aoede makes it appear to branch later. Topologies at most loci were not concordant with this scenario, though 'aoede-early' topologies were enriched in regions of the genome where interspecific introgression is expected to be reduced: the Z chromosome and larger autosomes. The revised phylogeny is interesting because it would mean that no extant Heliconius species has reverted to a non-pollen-feeding ancestral state. Second, the authors focus on a particularly challenging clade in which ancient and ongoing gene flow is extensive, concluding that silvaniform species are not monophyletic. Building on these results, a third set of analyses investigates the origin of the P1 inversion, which harbours multiple wing patterning loci, and which is maintained as a balanced polymorphism in H. numata. The authors present data supporting a new scenario in which P1 arises in H. numata or its ancestor and is introduced to the ancestor of H. pardilinus and H. elevatus - introgression in the opposite direction to what has previously been proposed using a smaller set of taxa and different methods.

The analyses were extensive and methodologically sound. Care was taken to control for potential sources of error arising from incorrect genotype calls and the choice of a reference genome. The argument for H. aoede as the earliest-diverging Heliconius lineage was compelling, and analyses of the melpomene-silvaniform clade were thorough.

The discussion is quite short in its current form. In my view, this is a missed opportunity to summarise the level of support and biological significance of key results. This applies to the revised Melpomenesilvaniform phylogeny and, in particular, the proposed H. numata origin of P1. It would be useful to have a brief overview of the relationships that remain unclear, and which data (if any) might improve estimates.

We added a paragraph in the Discussion to summarize our key findings in 'An updated phylogeny of Heliconius', and discuss issues that remain uncertain.

It was good to see the authors reflect on the utility of full-likelihood approaches more generally, though the discussion of their feasibility and superiority was at times somewhat overstated and reductive. Alternative MSC-based methods that use gene tree frequencies or coalescence times can be used to infer the direction and extent of introgression with accuracy that is satisfactory for a wide variety of research questions. In practice, a combination of both approaches has often been successful. Although full-likelihood approaches can certainly provide richer information if specific parameter estimates are of interest, they quickly become intractable in large species complexes where there is extensive gene flow or significant shifts in population size. In such cases, there may be hundreds of potentially important parameters to estimate, and alternate introgression scenarios may be impossible to disentangle. This is particularly challenging in systems, unlike Heliconius where there is little a priori knowledge of reproductive isolation, genome evolution, and the unique life history traits of each species. It would be useful for the authors to expand on their discussion of strategies that can simplify inference problems in such systems, acknowledging the difficulties therein.

We agree that approximate methods based on summary statistics (e.g. gene tree topologies) are computationally much cheaper and are sometimes useful. We now discuss limitations of our approach regarding strategies for constructing possible introgression models, computational cost and analysis of large phylogenies, and modeling assumptions in the MSC framework in the first section of the Discussion.

Reviewer #1 (Recommendations For The Authors):

In addition to the comments raised in the public review, I have some minor suggestions:

• In the Introduction, "Those methods have limited statistical power" implies summary-statistic methods have a high false negative rate for inferring the presence of introgression, which I don't think is true.

We removed 'statistical' as we used the term power loosely to mean ability to estimate more parameters in the model by making a better use of information in the sequence data and not in the sense of a true positive rate.

• When discussing full-likelihood approaches in a general sense, please cite additional methods than just BPP, such as PhyloNet.

We added references for PhyloNet (Wen & Nakhleh, 2018) and starBEAST (Zhang et al., 2018) in the Introduction and Discussion.

• Consider explicitly labelling chromosomal region 21 as the Z chromosome in relevant Figures, for ease of interpretation.

In the main figures, we changed the chromosome label from 21 to Z.

• From reading the main text it's not clear what a "3s analysis" is

The 3s analysis estimates pairwise migration rates between two species by fitting an MSC-withmigration (MSC-M) model, also known as isolation-with-migration (IM), for three species, where gene flow is allowed between the two sister species while the outgroup is used to improve the power but does not involved in gene flow. We changed the text from

"We use estimates of migration rates between each pair of species with a 3s analysis under the IM model of species triplets ..."

to

"We use estimates of migration rates between each pair of species under the the MSC-withmigration (MSC-M or IM) model of species triplets (3s analysis) ..."

• "This agrees with the scenario in which H. elevatus is a result of hybrid speciation between H. pardalinus and the common ancestor of the cydno-melpomene clade [42, 43]." I don't think this model provides any support for hybrid speciation in particular, over a standard post-speciation introgression scenario.

We took the finding that the introgression from the melpomene-cydno clade into H. elevatus occurs almost right after H. elevatus split off from H. pardalinus as evidence for hybrid speciation. We revised the text to make this clearer:

"Our finding that divergence of H. elevatus and introgression from the cydno-melpomene clade occurred almost simultaneously provides evidence for a hybrid speciation origin of H. elevatus resulting from introgression between H. pardalinus and the common ancestor of the cydno-melpomene clade (Rosser et al. 2019; Rosser et al. 2023)."

In particular, the Rosser et al. (2023) paper has now been submitted, and is the main paper to cite for the hybrid speciation hypothesis for H. elevatus.

• "while clustering with H. elevatus would suggest the opposite direction of introgression" careful with terminology here; is this about direction (donor vs. recipient species) or taxa involved (which is not direction)?

This is about the direction of introgression, not the taxa involved. We modified the text to make this clearer:

"By including H. ismenius and H. elevatus, sister species of H. numata and H. pardalinus respectively, different directions of introgression should lead to different gene tree topologies. Clustering of (H. numata with the inversion, H. pardalinus) with H. numata without the inversion would suggest the introgression is H. numata → H. pardalinus while clustering of (H. numata with the inversion, H. pardalinus) with H. elevatus would suggest H. pardalinus → H. numata introgression."

Reviewer #3 (Recommendations For The Authors):

The work is methodologically sound and rigorous but could have been reported and discussed with greater clarity.

It was difficult to assess the level of support for the proposed P1 introgression scenario without digging through the extensive supplementary materials. The discussion would ideally be used to clarify and summarise this.

We have substantially revised the section on the P1 inversion. We also mention in the Results (in the final paragraph of the inversion section) and Discussion that our data provided robust evidence that the introgression of the inversion is from H. numata into H. pardalinus while its precise origin (in which lineage and when it originated) remains uncertain.

The authors may also wish to compare their results to the recent work by Rougemont et al. on introgression between H. hecale and H. ismenius in the discussion.

We now mention Rougemont et al. (2023) in the Discussion as an example of introgression of small regions of the genome involved in wing patterning. We also acknowledge that our updated phylogeny does not include this kind of local introgression.

It was not initially obvious which number corresponded to the Z chromosome in any of the figures, even though this is critical to their interpretation.

We changed the label for chromosome 21 to Z in the main figures.

The supplementary tables should be described in more detail. For example, what is 'log_bf_check' and 'prefer_pred' in supplementary table S11?

We added more details explaning necessary quantities in the table heading in both SI file and in the spreadsheet.

Minor comments:

First paragraph of 'Complex introgression in the 15b inversion region (P locus):' Rephrase "This suggests another introgression between the common...".

We modified the text as follows:

"Another feature of this 15b region is that among the species without the inversion, the cydnomelpomene clade clusters with H. elevatus and is nested within the pardalinus-hecale clade (without H. pardalinus). This is contrary to the expectation based on the topologies in the rest of the genome (Figure 2A, scenarios a–c) that the cydno-melpomene clade would be sister to the pardalinus-hecale clade (without H. pardalinus). One explanation for this pattern is that introgression occurred between the common ancestor of the cydno-melpomene clade and either H. elevatus or the common ancestor of H. elevatus and H. pardalinus together with a total replacement of the non-inverted 15b in H. pardalinus by the P1 inversion from H. numata (Jay et al. 2018). We confirm and quantify this introgression below."

Second paragraph of 'Major Introgression Patterns in the melpomene-silvaniform clade:' "cconclusion" should be "conclusion."

Corrected.

Paragraph preceding discussion: sentences toward the end of the paragraph should be rephrased for clarity. E.g. "different tree topologies are expected under different direction of introgression."

We revised this paragraph. The sentence now says:

"By including H. ismenius and H. elevatus, sister species of H. numata and H. pardalinus respectively, different directions of introgression should lead to different gene tree topologies.<br /> Clustering of (H. numata with the inversion, H. pardalinus) with H. numata without the inversion would suggest the introgression is H. numata → H. pardalinus while clustering of (H. numata with the inversion, H. pardalinus) with H. elevatus would suggest H. pardalinus → H. numata introgression."

I enjoyed reading this paper and I am certain it will generate discussion and future research.

Author Response

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

Re: Revised author response for eLife-RP-RA-2023-90135 (“The white-footed deermouse, an infection-tolerant reservoir for several zoonotic agents, tempers interferon responses to endotoxin in comparison to the mouse and rat” by Milovic, Duong, and Barbour”)

The revised manuscript has taken into account all the comments and questions of the two reviewers. Our responses to each of the comments are detailed below. In brief, the modifications or additional materials for the revision each specifically address a reviewer comment. These modifcations or materials include the following….

• a more in-depth consideration of sample sizes

• a better explanation of what p values signify for a GO term analysis

• a more detailed account of the selection of the normalization procedure for cross-species targeted RNA-seq (including a new supplemental figure)

• several more box plots in supplementary materials to complement the scatterplots and linear regressions of the figures of the primary text

• provision in a public access repository of the complete data for the RNA-seq analyses as well as primary data for figures and tables as new supplementary tables

• the expansion of description of the analysis done for the revision of Borrelia hermsii infection of P. leucopus. This included a new table (Table 10 of the revision) • development of the possible relevance of finding for longevity studies by citing similarities of the findings in P. leucopus with those in the naked mole-rat

• what we think is a better assessment of differences between female and male P. leucopus for this particular study, while still keeping focus on DEGs in common for females and males. This included a new figure (Figure 4 of the revision).

• removal of reference to a “inverse” relationship between Nos2 and Arg1 while still retaining ratios of informative value

We note that in the interval between uploading the original bioRxiv preprint and now we learned of the paper of Gozashti, Feschotte, and Hoekstra (reference 32), which supports our conception of the important place of endogenous retroviruses in the biology and ecology of deermice. This is the only addition or modification that was not a direct response to a reviewer comment or question, but it was germane to one of Reviewer #1’s comments (“Regarding..”).

Reviewer #1:

Supplemental Table 1 only lists genes that passed the authors statistical thresholds. The full list of genes detected in their analysis should be included with read counts, statistics, etc. as supplemental information.

We agree that provision of the entire lists of reference transcripts and the RNA-seq results for each of the 40 animals is merited. These datasets are too large for what the journal’s supplementary materials resource was intended for, so we have deposited them at the Dryad public access repository.

While P. leucopus is a critical reservoir for B. burgdorferi, caution should be taken in directly connecting the data presented here and the Lyme disease spirochete. While it's possible that P. leucopus have a universal mechanism for limiting inflammation in response to PAMPs, B. burgdorferi lack LPS and so it is also possible the mechanisms that enable LPS tolerance and B. burgdorferi tolerance may be highly divergent.

The impetus for the study was the phenomenon of tolerance of infection of P. leucopus by a number of different kinds of pathogens, not just B. burgdorferi. We take the reviewer’s point, though. Certainly, the white-footed deermouse is probably most notable at-large for its role as a reservoir for the Lyme disease agent. We doubt that the species responses to LPS and to the principal agonists of B. burgdorferi are “highly divergent”, though. Other than the TLR itself-TLR4 for LPS vs the heterodimer TLR2/TLR1 for the lipoproteins of these spirochetes--the downstream signaling is generally similar for amounts comparable in their agonist potency.

We had thought that we had addressed this distinction for B. burgdorferi and other Borreliaceae members by referring to the earlier study. But we agree with the reviewer that what was provided on this point was insufficient in the context of the present work. Accordingly, for the revision we have added a new analysis of the data on experimental infection of P. leucopus with Borrelia hermsii, which lacks LPS and for which the TLR agonists eliciting inflammation are lipoproteins. We do this in a format (new Table 6) that aids comparison with the LPS experimental data elsewhere in the article. As the manuscript references, B. burgdorferi infection of P. leucopus elicits comparatively little inflammation in blood even at the height of infection. While this phenomenon with the Lyme disease agent was part of the rationale driving these studies, the better comparison with LPS was 5 days into B. hermsii infection when the animals are spirochetemic.

Statistical significance is binary and p-values should not be used as the primary comparator of groups (e.g. once a p-value crosses the deigned threshold for significance, the magnitude of that p-value no longer provides biological information). For instance, in comparing GO-terms, the reason for using of high p-value cutoffs ("None of these were up-regulated gene GO terms with p values < 1011 for M. musculus.") to compare species is unclear. If the authors wish to compare effect sizes, comparing enrichment between terms that pass a cutoff would likely be the better choice. Similarly, comparing DEG expression by p-value cutoff and effect size is more meaningful than analyses based on exclusively on p-value: "Of the top 100 DEGs for each species by ascending FDR p value." Description in later figures (e.g. Figure 4) is favored.

Effect sizes--in this case, fold-changes--were taken into account for GO term analysis and were specified in the settings that are described. So, any gene that was “counted” for consideration for a particular GO term would have passed that threshold and with a falsediscovery corrected p value of a specified minimum. There is no further scoring of the “hit” based upon the magnitude of the p value beyond that point. It is, as the reviewer writes, binary at that point. We are in agreement on those principles.

As we understand the comment above, though, the p-values referred to are in regard to the GO term analysis itself. The objective was discovery followed by inference. The situation was more like a genome-wide association study (GWAS) study. This is not strictly speaking a hypothesis test, because there was no stated hypothesis ahead of time or one driving the design. The “p value” for something like GO term analysis or GWAS provides an estimate of the strength of the association. It is not binary in that sense. The lower the p value, the greater confidence about the association. In a GWAS of a human population an association of a trait with a particular SNP or indel is usually not taken seriously unless the p value is less than 10^-7 or 10^-8. In the case of GO terms, the p value approximates (but is not equivalent to) the number of genes that are differentially expressed that belong to a GO cluster out of the total number of genes that define that cluster. The higher the proportion of the genes in the cluster that are associated with a treatment (LPS vs. saline), the lower the p value. Thus, it provides information beyond the point at which it would be rightly deemed of little additional value in many hypothesis testing circumstances.

That said, we agree that the original manuscript could have been clearer on this point and have for the revision expanded the description of the GO term analysis in the Methods, including some explanation for a reader on what the p value signifies here. We also refrain from specifying a certain p value for special attention and merely list 20 by ascending p value.

The ability to use of CD45 to normalize data is unclear. Authors should elaborate both on the use of the method and provide some data how the data change when they are normalized. For instance, do correlations between untreated Mus and Peromyscus gene expression improve? The authors seem to imply this should be a standard for interspecies comparison and so it would be helpful to either provide data to support that or, if applicable, use of the technique in literature should be referenced.

The reviewer brings up an important point that we considered addressing in more depth for the original manuscript but in the end deferred to considerations about length and left it out.

But we are glad to address this here, as well as in the revised manuscript.

We did not intend to imply either that this particular normalization approach had been done before by others or that it “should” be a standard. We are not aware of another report on this, and it would be up to others whether it would be useful or not for them. We made no claim about its utility in another model or circumstance. The challenge before us was to do a comparative analysis of transcription in the blood not just for animals of one species under different conditions but animals of two different genera under different conditions. A notable difference between the animals was in their white blood cell counts, as this study documents. White cells would be the source of a majority of transcripts of potential relevance here, but there would also be mRNA for globins, from reticulocytes, from megakaryocytes, and likely cell-free RNA with origins in various tissues. If the white cell numbers differed, but the non-white cell sources of RNA did not, then there could be unacknowledged biases.

It would be like comparing two different kinds of tissues and assuming them to be the same in the types and numbers of cells they contained. Four hours after a dose of LPS the liver cells (or brain cells) would differ in their transcriptional profiles from untreated the livers (or brains) of untreated animals for sure, but there would not be much if any change in the numbers of different kinds of cells in the liver (or brain) within 4 hours. The blood can change a lot in composition within that time frame under these same conditions. Some sort of accounting for differing white cell numbers in the blood in different outbred animals of two species seemed to be called for.

The normalization that was done for the genome-wide analysis was not based on a particular transcript, but instead was based on the total number of reads, the lengths of the reference transcripts, and the distributions of reads matching to the tens of thousands of references for each sample. This was done according to what are standard procedures by now for bulk RNAseq analyses. Because the reference transcript sets for P. leucopus and M. musculus differed in their numbers and completeness of annotation, we did not attempt any cross-species comparison for the same set of genes at that point. That would not be possible because they were not entirely commensurate.

The GO term analysis of those results provided the leads for the more targeted approach, which was roughly analogous to RT-qPCR. For a targeted assay of this sort, it is common to have a “housekeeping gene” or some other presumably stably transcribed gene for normalization. A commonly used one is Gapdh, but we had previously found that Gapdh was a DEG itself in the blood in P. leucopus and M. musculus at the four hour mark after LPS. The aim was to provide for some adjustment so datasets for blood samples differing in white blood cell counts could be compared. Two options were the 12S ribosomal RNA of the mitochondria, which would be in white cells but not mature erythrocytes, and CD45, which has served an approximately similar function for flow cytometry of the blood. As described in what has been added for the revision and the supplementary materials, we compared these different approaches to normalization. Ptprc and 12S rRNA were effectively interchangeable as the denominator with identifying DEGs of P. leucopus and M. musculus and cross-species comparisons.

Regarding the ISG data-is a possible conclusion not that Peromyscus don't upregulate the antiviral response because it's already so high in untreated rodents? It seems untreated Peromyscus have ISG expression roughly equivalent to the LPS mice for some of the genes. This could be compared more clearly if genes were displayed as bar plots/box and whisker plots rather than in scatter plots. It is unclear why the linear regression is the key point here rather than normalized differences in expression.

In answer to the question: yes, that is possible. In the interval between uploading of the manuscript and this revision, we became aware of a study by Gozashti and Hoekstra published this year in Molecular Biology and Evolution (reference 32) and reporting on the “massive invasion” of endogenous retroviruses in P. maniculatus and the defenses deployed in response to achieve silencing. We cite this work and discuss it, including related findings for P. leucopus, in the revision.

We had originally intended to include box plots as well as scatterplots with regressions for the data, but thought it would be too much and possibly considered redundant. But with this encouragement from the reviewer we provide additional box plots in supplementary materials for the revision.

Some sections of the discussion are under supported:

The claim that low inflammation contributes to increased lifespan is stated both in the introduction and discussion. Is there justification to support this? Do aged pathogen-free mice show more inflammation than aged Peromyscus?

We respectively point out that there was not a claim of this sort. We stated a fact about P. leucopus’ longevity. We made no statement connecting longevity and inflammation beyond the suggestion in the introduction that the explanation(s) for infection tolerance might have some bearing for studies on determinants of life span.

But the reviewer’s comment prompted further consideration of this aspect of Peromyscus biology. This led eventually to the literature on the naked mole-rat, which seems to be the rodent with the longest known life span and the subject of considerable study. The discussion section of the revision has an added paragraph on some of the similarities of P. leucopus and the naked mole-rat in terms of neutrophils, expression of nitric oxide synthase 2 in response to LPS, and type 1 interferon responses. While this is far from decisive, it does serve to connect some of the dots and, hopefully, is considered at least partially responsive to the reviewer’s question.

The claim that reduced Peromyscus responsiveness could lead to increased susceptibility to infection is prominently proposed but not supported by any of the literature cited.

There was not this claim. In fact, it was framed as a question, not a statement. Nevertheless, we think we understand what the comment is getting at and acknowledge in the revision that there may be unexamined circumstances in which P. leucopus may be more vulnerable.

References to B. burgdorferi, which do not have LPS, in the discussion need to ensure that the reader understands this and the potential that responses could be very different.

We think we addressed this comment in a response above.

Reviewer #2:

1. How were the number of animals for each experiment selected? Was a power analysis conducted?

A power analysis of any meaning for bulk RNA-seq with tens of thousands of reference transcripts, each with their own variance, and a comparison of animals of two different genera is not straight forward. Furthermore, a specific hypothesis was not being tested. This was a broad, forward screen. But the question about sample sizes is one that deserves more attention than the original manuscript provided. This now provided in added text in two places in Methods ( “RNA-seq” and “Genome-wide different gene expression”) in the revision.

1. The authors conducted a cursory evaluation of sex differences of P. leucopus and reported no difference in response except for Il6 and Il10 expression being higher in the males than the females in the exposed group. The data was not presented in the manuscript. Nor was sex considered for the other two species. A further discussion of the role that sex could play and future studies would be appreciated.

We agree that the limited analysis of sex differences and the undocumented remark about Il6 and Il10 expression in females and males warranted correction. For the revision we removed that analysis of targeted RNA-seq of P. leucopus from the two different studies. For this study we were looking for differences that applied to both species. This was the reason that there were equal numbers of females and males in the samples. We agree that further investigation of differences between sexes in their responses is of interest but is probably best left for “future studies”.

But in revision we do not entirely ignore the question of sex of the animal and provide an additional analysis of the bulk RNA-seq for P. leucopus with regard to differences between females and males. This basically demonstarted an overall commensurability between sexes, at least for the purposes of the GO term analysis and subsequent targeted RNA-seq, but did reveal some exceptions that are candidate genes for those future studies.

In the revision, we also add for the discussion and its “study limitations” section a disclaimer about possibly missing sex associated differences because the groups were mixed sexes.

1. The ratio of Nos2 and Arg1 copies for LPS treated and control P. leucopus and M.musculus in Table 3 show that in P. leucopus there is not a significant difference but in M.musculus there is an increase in Nos2 copies with LPS treatment. The authors then used a targeted RNA-seq analysis to show that in P. leucopus the number of Arg1 reads after LPS treatment is significantly higher than the controls. These results are over oversimplified in the text as an inverse relationship for Nos2/Arg1 in the two species.

We agree. In addition to providing box plots for Arg1 and Nos2, as suggested by Reviewer #1, we also replaced “ratio” in commenting on Arg1 and Nos2, with “differences in Nos2 and Arg1 expresssion” replacing “ratio of Nos2 to Arg1 expression” at one place. At another place we have removed “inverse” with regard to Nos2 and Arg1. But we respectfully decline to remove Nos2/Arg1 from Figure 5 (now Figure 6) or inclusion of Nos2/Arg1 ratios elsewhere. According to our understanding there need not be an inverse relationship for a ratio to have informative value.

Recommendations For the Authors

We thank the two reviewers for their constructive recommendations and suggestions, in some case pointing out errors we totally missed. For the great majority, the recommendations were followed. Where we decline or disagree we explain this in the response.

Reviewer #1 (Recommendations For The Authors):

• How was the FDR < 0.003 cutoff chosen for DEG? All cutoffs are arbitrary but there should be some justification.

We agree and have provided the rationale at that point in the paper (before Figure 3) in R2: "For GO term analysis the absolute fold-change criterion was ≥ 2. Because of the ~3-fold greater number of transcripts for the M. musculus reference set than the P. leucopus reference set, application of the same false-discovery rate (FDR) threshold for both datasets would favor the labeling of transcripts as DEGs in P. leucopus. Accordingly, the FDR p values were arbitrarily set at <5 x 10-5 for P. leucopus and <3 x 10-3 for M. musculus to provide approximately the same number of DEGs for P. leucopus (1154 DEGs) and M. musculus (1266 DEGs) for the GO term comparison."

• It would be helpful to include a figure demonstrating the correlation between CD45 and WBC ("Pearson's continuous and Spearman's ranked correlations between log-transformed total white blood cell counts and normalized reads for Ptprc across 40 animals representing both species, sexes, and treatments were 0.40 (p = 0.01) and 0.34 (p = 0.03), respectively.")

In both the first version of the revision (R1) and in R2 we provide a fuller explanation of the choice of CD45 (Ptprc) for normalization as detailed in the response to Reviewer #1's public comment. In the revision only Pearson's correlation and p value is given. We did not think another figure was justified after there was additional space devoted to this in both R1 and R2.

• Unclear what the following paragraph is referring to-is this from the previous paper? Was this experiment introduced somewhere? "Low transcription of Nos2 and high transcription of Arg1 both in controls and LPS-treated P. leucopus was also observed in the experiment where the dose of LPS was 1 µg/g body mass instead of 10 µg/g and the interval between injection and assessment was 12 h instead of 4 h (Table 4)."

This experiment is described in the Methods in the original and subsequent versions, but we agree that it is not clear whether it was from present study or previous one. Here is the revised text for R2: "Low transcription of Nos2 in both in controls and LPS-treated P. leucopus and an increase in Arg1 with LPS was also observed in another experiment for the present study where the dose of LPS was 1 µg/g body mass instead of 10 µg/g and the interval between injection and assessment was 12 h instead of 4 h (Table 4)."

• Regarding the differences in IFNy between outbred and BALB/c mice-are there any other RNA-seq datasets you can mine where other inbred mice (B/6, C3H, etc) have been injected with LPS and probed roughly the same amount of time later? Do they look like BALB/c or the outbreds?

In both the original and R1 and R2 we cite two papers on the difference of BALB/c mice. While this is of interest for follow-up in the future, we did not think additional content on a subject that mainly pertains to M. musculus was warranted here, where the main focus is Peromyscus.

• Figure 8 and its legend are difficult to follow. The top half of the figure is not well explained and it's unclear what species this is. Decreased use of abbreviations would help. Consider marking each R2 value as Mus or Peromyscus (As done in Fig 9). There are some typographical errors in the legend ("gree," incomplete sentence missing the words LPS or treatment AND Mus: "Co-variation between transcripts for selected PRRs (yellow) and ISGs (gree) in the blood of P. leucopus (P) or (M) with (L") or without (C)."

This is now Figure 9 in both R1 and R2. We revised it for R1 to include references to the box plots in supplementary materials, but agree with Reviewer #1's recommendation to correct the typos and make the legend less confusing. We did not think that further labeling of the R2 values in the scatterplots with the species names was necessary. The data points are not just colors but also different symbols, so it should be fairly easy for readers to distinguish the regression lines by species. For R2 this is the revised legend with additions in response to the recommendation underlined:

"Figure 9. Co-variation between transcripts for selected PRRs and ISGs in the blood of P. leucopus (P) or M. musculus (M) with (L) or without (C) LPS treatment. Top panel: matrix of coefficients of determination (R2) for combined P. leucopus and M. musculus data. PRRs are indicated by yellow fill and ISGs by blue fill on horizontal and vertical axes. Shades of green of the matrix cells correspond to R2 values, where cells with values less than 0.30 have white fill and those of 0.90-1.00 have deepest green fill. Bottom panels: scatter plots of log-transformed normalized Mx2 transcripts on Rigi (left), Ifih1 (center), and Gbp4 (right). The linear regression curves are for each species. For the right-lower graph the result from the General Linear Model (GLM) estimate is also given. Values for analysis are in Table S4; box plots for Gbp4, Irf7, Isg15, Mx2, and Oas1 are provided in Figure S6."

• Discussion section could benefit from editing for clarity. Examples listed: o Unclear what effect is described here "The bacterial infection experiment indicated that the observed effect in P. leucopus was not limited to a TLR4 agonist; the lipoproteins of B. hermsii are agonists for TLR2 (Salazar et al. 2009)."

Both R1 and R2 include the new section on the B. hermsii infection model. This was added in response to Reviewer #1 public comment. So the expanded consideration of this aspect should address the reviewer's recommendation for more clarity and context here. For R2 we modified the text in the discussion of R1:

"The analysis here of the B. hermsii infection experiment also indicated that the phenomenon observed in P. leucopus was not limited to a TLR4 agonist."

o Unclear what the takeaway from this paragraph is: "Reducing the differences between P. leucopus and the murids M. musculus and R. norvegicus to a single all-embracing attribute may be fruitless. But from a perspective that also takes in the 2-3x longer life span of the whitefooted deer mouse compared to the house mouse and the capacity of P. leucopus to serve as disease agent reservoir while maintaining if not increasing its distribution (Moscarella et al. 2019), the feature that seems to best distinguish the deer mouse from either the mouse or rat is its predominantly anti-inflammatory quality. The presentation of this trait likely has a complex, polygenic basis, with environmental (including microbiota) and epigenetic influences. An individual's placement is on a spectrum or, more likely, a landscape rather than in one or another binary or Mendelian category."

We agree that modification, simplication, and clarification was called for. In response to a public comment of Reviewer #1 we had changed that section, leaving out reference to longevity here. Here is the revised text in both R1 and R2:

"Reducing differences between P. leucopus and murids M. musculus and R. norvegicus to a single attribute, such as the documented inactivation of the Fcgr1 gene in P. leucopus (7), may be fruitless. But the feature that may best distinguish the deermouse from the mouse and rat is its predominantly anti-inflammatory quality. This characteristic likely has a complex, polygenic basis, with environmental (including microbiota) and epigenetic influences. An individual’s placement is on a spectrum or, more likely, a landscape rather than in one or another binary or Mendelian category."

Minor comments:

• Use of blue and red in figures as the -only- way to easily distinguish between groups is a poor choice-both in terms of how inclusivity of color-blind researchers and enabling grayscale printing. Most detrimental in Figure 2, but also slightly problematic in Figure 1. Use of color and shape (as done in other figures) is a much better alternative.

We agree. Both figures have been modified to include an additional characteristic for denoting the data point. For Figure 1 it is a black filling, and for Figure 2 it is the size of symbol in additon to the color. This should enable accurate visualization by color blind individuals and printing in gray scale. We have added definitions for the symbols within the graph itself, so there is no need to refer to the legend to interpret what they mean.

• Note the typo where it should read P leucopus: "The differences between P. musculus and M. musculus in the ratios of Nos2/Arg1 and IL12/IL10 were reported before (BalderramaGutierrez et al. 2021),"

We thank the reviewer for pointing this typo out, which also carried over to R1. It has been corrected for R2.

• Optional: Can the relationship between the ratios in figure 5 and macrophage "types" be displayed graphically alongside the graphs? It's a little challenging to go back and forth between the text and the figure to try to understand the biological implication.

We considered something like this but in the end decided that we were not yet comfortable assigning “types” in this fashion for Peromyscus.

Reviewer #2 (Recommendations For The Authors):

• Be consistent with nomenclature for your species/treatment groups in the text, figures, and tables. For example, you go back and forth between "P. leucopus" and "deermouse" in the text. And in figures you use "P," "Peromyscus", or "Pero".

In the Methods section of the original and revisions R1 and R2 we indicate that "deermouse" is synonymous with "Peromyscus leucopus" and "mouse" is synonymous with "Mus musculus" in the context of this paper. We think that some alternation in the terms relieves the text of some of its repetitiveness and that readers should not have a problem with equating one with the other. The use of "deermouse" also reinforces for readers that Peromyscus is not a mouse. With regard to the abbreviations for P. leucopus, those were used to accommodate design and space issues of the figures or tables. In all cases, the abbreviations referred to are defined in the legends of the figures. So, we respectfully decline to follow this recommendation.

• Often the sentence structure and/or word choice is irregular and makes quick/easy comprehension difficult. Several examples are:

o The third paragraph of the introduction

We agree that the first and second sentences are unclear. Here is the revision for R2:

“As a species native to North America, P. leucopus is an advantageous alternative to the Eurasian-origin house mouse for study of natural variation in populations that are readily accessible (9, 53). A disadvantage for the study of any Peromyscus species is the limited reagents and genetic tools of the sorts that are applied for mouse studies.”

o The first line after Figure 5 on page 9.

We agree. The long sentence which we think the reviewer is referring to has been in split into two sentences for R2.

“An ortholog of Ly6C (13), a protein used for typing mouse monocytes and other white cells, has not been identified in Peromyscus or other Cricetidae family members. Therefore, for this study the comparison with Cd14 is with Cd16 or Fcgr3, which deermice and other cricetines do have.”

o The sentence that starts "Our attention was drawn to..." on page 14.

We agree that the sentence was awkward and split into two sentences.

“Our attention was drawn to ERVs by finding in the genome-wide RNA-seq of LPS-treated and control rats. Two of the three highest scoring DEGs by FDR p value and fold-change were a gagpol polyprotein of a leukemia virus with 131x fold-change from controls and a mouse leukmia virus (MLV) envelope (Env) protein with 62x fold-change (Dryad Table D5).”

• For figures with multiple panels, use A), B) etc then indicate which panel you are discussing in your text. This is a very data heavy study and your readers can easily get lost.

We agree and have added pointers in the text to the panels we are referring to. But we prefer to use easily understood descriptors like “left” and “upper” over assigned letters.

• For all the figures, where are the stats from the t-tests? Why didn't you do a two-way ANOVA? Instead of multiple t-tests?

Where we are not hypothesis testing and we are able to show all the data points in box-whisker plots with distributions fully revealed, our default position is not to apply significance tests in a post hoc fashion. If a reader or other investigator wants to do this for other purposes, e.g. a meta-analysis, the data is provided in public repository for them to do this. We are not sure what the reviewer means by "multiple t-tests" for "all figures". Where we do 2-tailed t-tests for presentation of data for many genes in a table for the targeted RNA (where individual values cannot shown in the table), there is always correction for multiple testing, as indicated in Methods. The p values shown as "FDR" are after correction.

• Results paragraph "LPS experiment and hematology studies"

o List the two species for the first description to orient the reader since you eventually include rat data.

We agree that this is warranted and followed this recommendation for R2.

o Not all the mice experienced tachypnea, but the text makes it seem like 100% did.

We are not sure what the reviewer is referring to here. This is what is in the text on tachypnea: "By the experiment’s termination at 4 h, 8 of 10 M. musculus treated with LPS had tachypnea, while only one of ten LPS-treated P. leucopus displayed this sign of the sepsis state (p = 0.005)." The only other mention of "tachypnea" was in Methods.

• Figure 1: Why was the M. musculus outlier excluded? Where any other outliers excluded?

That data point for the mouse was not "excluded" from the graph. It is identified (MM17) for reference with Table 1, and there is the graph for all to see where it is. It was only excluded from the regression curve for control mice. There was no significance testing. There were no other outliers excluded.

• Figure 3: explain the colors and make the scales the same for all the panels or at least for the upregulated DEGs and the downregulated DEGs.

We have modified the legend for Figure 3 to include fuller definitions of the x-axes and a description of the color spectrum. We decline to make the x-axis scale the same for all the panels because the horizontal bars in “transcription down” panels would take up only a small fraction of the space. The x-axes are clearly defined and the colors of the bars also indicate the differences in p-values. We doubt that readers will be misled. Here is the revised legend: “Figure 3. Gene Ontology (GO) term clusters associated with up-regulated genes (upper panels) and down-regulated genes (lower panels) of P. leucopus (left panels) and M. musculus (right panels) treated with LPS in comparison with untreated controls of each species. The scale for the x-axes for the panels was determined by the highest -log10 p values in each of the 4 sets. The horizontal bar color, which ranges from white to dark brown through shades of yellow through orange in between, is a schematic representation of the -log10 p values.”

• Results paragraph "Targeted RNA seq analysis"

o In the third paragraph, an R2 of 0.75 is not close enough to 1 to call it "~1"

What the reviewer is referring to is no longer in either R1 and R2, as detailed in the authors' response to public comments.

o In the 4th paragraph, where are your stats?

We have replaced terms like "substantially" and "marginally" with simple descriptions of relationships in the graphs.

"For the LPS-treated animals there was, as expected for this selected set, higher expression of the majority genes and greater heterogeneity among P. leucopus and M. musculus animals in their responses for represented genes. In contrast to the findings with controls, Ifng and Nos2 had higher transcription in treated mice. In deermice the magnitude of difference in the transcription between controls and LPS-treated was less."

• Figure 4: The colors are hard to see, I suggest making all the up regulated reads one color, the down regulated reads a different color, and the reads that aren't different black or gray.

This is now Figure 5 in R1 and R2. The selected genes that are highlighted in the panels are denoted not only by color but also by type of symbol. We do not think that readers will have a problem telling one from another even if color blind. The purpose of this figure was to provide an overview and a visual representation with calling out of selected genes, some of which will be evaluated in more detail later. We thought that this was necessary before diving deeper into the data of Table 2. We do not think further discriminating between transcripts in the categorical way that the reviewer suggests is warranted at this point. So, we respectfully decline to follow this suggestion.

• Results paragraph " Alternatively- activated macrophages...."

o Include a brief description of Nos2 and Arg1

We have defined what enzymes these are genes for in R2.

o How do you explain the lack of a difference in P. leucopus Arg1? Your text says the RT-qPCR confirms the RNA-seq findings.

There was a difference in P. leucopus Arg1 by RT-qPCR between control and LPS treated by about 3-fold. By both RNA-seq and RT-qPCR Arg1 transcription is higher in P. leucopus than in M. musculus under both conditions. But we have modified the sentence so that does not imply more than what the data and analysis of the table reveal.

"While we could not type single cells using protein markers, we could assess relative transcription of established indicators of different white cell subpopulations in whole blood. The present study, which incorporated outbred M. musculus instead of an inbred strain, confirmed the previous finding of differences in Nos2 and Arg1 expression between M. musculus and P. leucopus (Figure 5; Table 2). Results similar to the RNA-seq findings were obtained with specific RT-qPCR assays for Nos2 and Arg1 transcripts for P. musculus and M. musculus (Table 3)."

• Figure 5: reorganize the panels to make the text description and label with letters, where are the stats?

We thought the figure (now Figure 6) was self-explanatory, but agree that further explanation in the legend was indicated. We prefer to use descriptions of locations (“upper left”) over labels, like “panel C”, which do not obviously indicate the location of the panel. Of course, if the journal’s style mandates the other format we will do so. Our response about “stats” for boxplot figures is the same as what we provided above.

• Results paragraph "Interferon-gamma and interleukin-1 beta..."

o Either add the numbers or direct the viewer to where Ifng is in Table 2. The table is very big and Ifng is all the way at the bottom!

We agree that this table is large, but we thought it better to err on the side of inclusiveness by having a single table, rather than have some genes in the main article and other results in a supplementary table. We thought that it would make it easier for reviewers and readers to find a gene of interest, but we also acknowledge the challenge to locate the genes we highlight. We follow for R2 that reviewer's recommendation to provide some guidance for readers trying to locate a featured gene by pointing relative locations. While adding a column of numbers to already complex table seems more than what is called for, we are depositing an Excel spreadsheet of the table at the Dryad repository to facilitate searching by an interested reader for a particular gene.

• Figure 6: stats? The pink and red are hard to easily distinguish from each other. I also suggest not using red and green together for color blind readers.

With regard to the box-plots and significance testing, please see response above to an earlier recommendation. We have removed an interpretative adjective (i.e. "marked") from the description of the graph. Different symbols as well as colors are used, so we do not think that this will pose a problem for readers, even those with complete red-green color blindness. For what it’s worth, with regard to the "red" and "pink" issue, according to the figure on our displays the colors of the two symbols appear to be red and purple. They are also applied to different species and different conditions for those species.

• Figure 8: In the legend it says "... PRRs (yellow) and ISGs (gree)" which is a typo, but don't you mean blue not green anyways?

See response above to Reviewer #1's recommendation. This has been corrected.

Reviewer #1 (Public Review):

Summary:<br /> This paper examines patterns of diversity and divergence in two closely related sub-species of Zea mays. While the patterns are interesting, the strength of evidence in support of the conclusions drawn from these patterns is weak overall. Most of the main conclusions are not supported by convincing analyses.

Strengths:<br /> The paper presents interesting data from sets of sympatric populations of the two sub-species, maize and teosinte. This sampling offers unique insights into the diversity and divergence between the two, as well as the geographic structure of each.

Weaknesses:<br /> There were issues with many parts of the paper, especially with the strength of conclusions that can be drawn from the analyses. I list the major issues in the order in which they appear in the paper.

1. Gene flow and demography.<br /> The f4 tests of introgression (Figure 1E) are not independent of one another. So how should we interpret these: as gene flow everywhere, or just one event in an ancestral population? More importantly, almost all the significant points involve one population (Crucero Lagunitas), which suggests that the results do not simply represent gene flow between the sub-species. There was also no signal of increased migration between sympatric pairs of populations. Overall, the evidence for gene flow presented here is not convincing. Can some kind of supporting evidence be presented?

The paper also estimates demographic histories (changes in effective population sizes) for each population, and each sub-species together. The text (lines 191-194) says that "all histories estimated a bottleneck that started approximately 10 thousand generations ago" but I do not see this. Figure 2C (not 2E, as cited in the text) shows that teosinte had declines in all populations 10,000 generations ago, but some of these declines were very minimal. Maize has a similar pattern that started more recently, but the overall species history shows no change in effective size at all. There's not a lot of signal in these figures overall.

I am also curious: how does the demographic model inferred by mushi address inbreeding and homozygosity by descent (lines 197-202)? In other words, why does a change in Ne necessarily affect inbreeding, especially when all effective population sizes are above 10,000?

2. Proportion of adaptive mutations.<br /> The paper estimates alpha, the proportion of nonsynonymous substitutions fixed by positive selection, using two different sampling schemes for polymorphism. One uses range-wide polymorphism data and one uses each of the single populations. Because the estimates using these two approaches are similar, the authors conclude that there is little local adaptation. However, this conclusion is not justified.

There is little information as to how the McDonald-Kreitman test is carried out, but it appears that polymorphism within either teosinte or maize (using either sampling scheme) is compared to fixed differences with an outgroup. These species might be Z. luxurians or Z. diploperennis, as both are mentioned as outgroups. Regardless of which is used, this sampling means that almost all the fixed differences in the MK test will be along the ancestral branch leading to the ancestor of maize or teosinte, and on the branch leading to the outgroup. Therefore, it should not be surprising that alpha does not change based on the sampling scheme, as this should barely change the number of fixed differences (no numbers are reported).

The lack of differences in results has little to do with range-wide vs restricted adaptation, and much more to do with how MK tests are constructed. Should we expect an excess of fixed amino acid differences on very short internal branches of each sub-species tree? It makes sense that there is more variation in alpha in teosinte than maize, as these branches are longer, but they all seem quite short (it is hard to know precisely, as no Fst values or similar are reported).

3. Shared and private sweeps.<br /> In order to make biological inferences from the number of shared and private sweeps, there are a number of issues that must be addressed.

One issue is false negatives and false positives. If sweeps occur but are missed, then they will appear to be less shared than they really are. Table S3 reports very high false negative rates across much of the parameter space considered, but is not mentioned in the main text. How can we make strong conclusions about the scale of local adaptation given this? Conversely, while there is information about the false positive rate provided, this information doesn't tell us whether it's higher for population-specific events. It certainly seems likely that it would be. In either case, we should be cautious saying that some sweeps are "locally restricted" if they can be missed more than 85% of the time in a second population or falsely identified more than 25% of the time in a single population.

A second, opposite, issue is shared ancestral events. Maize populations are much more closely related than teosinte (Figure 2B). Because of this, a single, completed sweep in the ancestor of all populations could much more readily show a signal in multiple descendant populations. This is consistent with the data showing more shared events (and possibly more events overall). There also appear to be some very closely (phylogenetically) related teosinte populations. What if there's selection in their shared ancestor? For instance, Los Guajes and Palmar Chico are the two most closely related populations of teosinte and have the fewest unique sweeps (Figure 4B). How do these kinds of ancestrally shared selective events fit into the framework here?

These analyses of shared sweeps are followed by an analysis of sweeps shared by sympatric pairs of teosinte and maize. Because there are not more events shared by these pairs than expected, the paper concludes that geography and local environment are not important. But wouldn't it be better to test for shared sweeps according to the geographic proximity of populations of the same sub-species? A comparison of the two sub-species does not directly address the scale of adaptation of one organism to its environment, and therefore it is hard to know what to conclude from this analysis.

4. Convergent adaptation<br /> My biggest concern involves the apparent main conclusion of the paper about the sources of "convergent adaptations". I believe the authors are misapplying the method of Lee and Coop (2017), and have not seriously considered the confounding factors of this method as applied. I am unconvinced by the conclusions that are made from these analyses.

The method of Lee and Coop (referred to as rdmc) is intended to be applied to a single locus (or very tightly linked loci) that shows adaptation to the same environmental factor in different populations. From their paper: "Geographically separated populations can convergently adapt to the same selection pressure. Convergent evolution at the level of a gene may arise via three distinct modes." However, in the current paper, we are not considering such a restricted case. Instead, genome-wide scans for sweep regions have been made, without regard to similar selection pressures or to whether events are occurring in the same gene. Instead, the method is applied to large genomic regions not associated with known phenotypes or selective pressures.

I think the larger worry here is whether we are truly considering the "same gene" in these analyses. The methods applied here attempt to find shared sweep regions, not shared genes (or mutations). Even then, there are no details that I could find as to what constitutes a shared sweep. The only relevant text (lines 802-803) describes how a single region is called: "We merged outlier regions within 50,000 Kb of one another and treated as a single sweep region." (It probably doesn't mean "50,000 kb", which would be 50 million bases.) However, no information is given about how to identify overlap between populations or sub-species, nor how likely it is that the shared target of selection would be included in anything identified as a shared sweep. Is there a way to gauge whether we are truly identifying the same target of selection in two populations?

The question then is, what does rdmc conclude if we are simply looking at a region that happened to be a sweep in two populations, but was not due to shared selection or similar genes? There is little testing of this application here, especially its accuracy. Testing in Lee and Coop (2017) is all carried out assuming the location of the selected site is known, and even then there is quite a lot of difficulty distinguishing among several of the non-neutral models. This was especially true when standing variation was only polymorphic for a short time, as is estimated here for many cases, and would be confused for migration (see Lee and Coop 2017). Furthermore, the model of Lee and Coop (2017) does not seem to consider a completed ancestral sweep that has signals that persist into current populations (see point 3 above). How would rdmc interpret such a scenario?

Overall, there simply doesn't seem to be enough testing of this method, nor are many caveats raised in relation to the strange distributions of standing variation times (bimodal) or migration rates (opposite between maize and teosinte). It is not clear what inferences can be made with confidence, and certainly the Discussion (and Abstract) makes conclusions about the spread of beneficial alleles via introgression that seem to outstrip the results.

Reviewer #3 (Public Review):

Summary:

The paper points out that non-significance in both the original study and a replication does not ensure that the studies provide evidence for the absence of an effect. Also, it can not be considered a "replication success". The main point of the paper is rather obvious. It may be that both studies are underpowered, in which case their non-significance does not prove anything. The absence of evidence is not evidence of absence! On the other hand, statistical significance is a confusing concept for many, so some extra clarification is always welcome.

One might wonder if the problem that the paper addresses is really a big issue. The authors point to the "Reproducibility Project: Cancer Biology" (RPCB, Errington et al., 2021). They criticize Errington et al. because they "explicitly defined null results in both the original and the replication study as a criterion for replication success." This is true in a literal sense, but it is also a little bit uncharitable. Errington et al. assessed replication success of "null results" with respect to 5 criteria, just one of which was statistical (non-)significance.

It is very hard to decide if a replication was "successful" or not. After all, the original significant result could have been a false positive, and the original null-result a false negative. In light of these difficulties, I found the paper of Errington et al. quite balanced and thoughtful. Replication has been called "the cornerstone of science" but it turns out that it's actually very difficult to define "replication success". I find the paper of Pawel, Heyard, Micheloud, and Held to be a useful addition to the discussion.

Strengths:

This is a clearly written paper that is a useful addition to the important discussion of what constitutes a successful replication.

Weaknesses:

To me, it seems rather obvious that non-significance in both the original study and a replication does not ensure that the studies provide evidence for the absence of an effect. I'm not sure how often this mistake is made.

Author Response

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

Reviewer #1 (Public Review):

The manuscript describes an interesting experiment in which an animal had to judge a duration of an interval and press one of two levers depending on the duration. The Authors recorded activity of neurons in key areas of the basal ganglia (SNr and striatum), and noticed that they can be divided into 4 types.

The data presented in the manuscript is very rich and interesting, however, I am not convinced by the interpretation of these data proposed in the paper. The Authors focus on neurons of types 1 & 2 and propose that their difference encodes the choice the animal makes. However, I would like to offer an alternative interpretation of the data. Looking at the description of task and animal movements seen in Figure 1, it seems to me that there are 4 main "actions" the animals may do in the task: press right lever, press left lever, move left, and move right. It seems to me that the 4 neurons authors observed may correspond to these actions, i.e. Figure 1 shows that Type 1 neurons decrease when right level becomes more likely to be correct, so their decrease may correspond to preparation of pressing right lever - they may be releasing this action from inhibition (analogously Type 2 neurons may be related to pressing left lever). Furthermore, comparing animal movements and timing of activity of neurons of type 3 and 4, it seems to me that type 3 neurons decrease when the animal moves left, while type 4 when the animal moves right.

I suggest Authors analyse if this interpretation is valid, and if so, revise the interpretation in the paper and the model accordingly.

We thank the reviewer for the general appreciation of the study. Regarding to the interpretation of each SNr subtypes, we have compared firing activities of the same SNr neurons in both standard 2-8 s task and reversed 2-8 s task (Figure 2G-R, Figure S4). Type 1 and Type 2 neurons are related to right and left choices respectively in the standard task (Figure 2G, M, N), and this is even more evident in the reversed 2-8 s task (Figure 2J), because when the movement trajectories of the same mice in 8-s trials were reversed from left-then-right in the control task (Figure 2I) to right-then-left in the reversed task (Figure 2L), the Type 1 SNr neurons which showed monotonic decreasing dynamics in the control 2-8 s task (Figure 2M) reversed their neuronal dynamics to a monotonic increase in the reversed 2-8 s task (Figure 2P). The same reversal of neuronal dynamics was also observed in Type 2 SNr neurons in the reversed version of standard task (Figure 2N vs Figure 2Q). Therefore, Type 1 and Type 2 neurons are related to the action selection. Furthermore, Type 3 and Type 4 SNr neurons exhibiting transient change when mice switching either from left to right, or from left to right maintained the same neuronal dynamics in both standard 2-8 s task and reversed 2-8 s task (Figure S4C-F), indicating that Type 3 and Type 4 neurons are related to the switch between choices but not the specific upcoming choice to be made.

Reviewer #1 (Recommendations For The Authors):

Suggest to clarify if SNr neurons recorded just from a single hemisphere or bilaterally.

We have described the recording hemisphere in our Methods (page 46, lines 974-976) as follows “For striatum recording, we implanted 11 mice in the left hemisphere and 8 mice in the right hemisphere. For the SNr recording, we implanted 5 mice in the left hemisphere and 4 mice in the right hemisphere.”

Suggest to analyse if type 1/2/3/4 neurons are preferrably located in hemispheres contra/ipsi lateral to a particular lever or movement.

We have addressed this issue in Figure S3 and Figure S6. In fact, we have implanted electrodes in both left and right hemispheres with mirror M-L coordinates. For striatum recording, we implanted 11 mice in the left hemisphere and 8 mice in the right hemisphere. For the SNr recording, we implanted 5 mice in the left hemisphere and 4 mice in the right hemisphere. We have analyzed the striatal and SNr neuronal activity in left vs. right hemisphere respectively, in relation to action selection. We found that SNr neurons recorded in either left or right hemisphere exhibited the same four types of neural dynamics with similar proportions (Fig. S3). Specially, the Type 1 neurons are dominant in both hemispheres. Similar in striatum, SPNs from left and right hemispheres showed the same four types of neural dynamics with similar proportions (Fig. S6). Therefore, there is no significant difference between hemispheres regarding to the proportion of neuron subtypes.

Suggest to investigate if type 1/2 neurons are involved in preparation for lever press, please investigate if these neurons are also changing their activity during the lever press.

In Figure S1L, we have showed the neuronal activities of example Type 1 and Type 2 SNr neurons to rewarded and non-rewarded lever presses. Type 1 SNr neuron shows higher firing activities when pressing the left lever than pressing the right lever, whereas Type 2 SNr neuron shows higher firing activities when pressing the right lever than pressing the left lever, indicating that Type 1 and Type 2 neurons firing activities are action choice dependent.

Suggest investigating if Type 3/4 neurons are controlling movement from one location to another, please analyse if their activity is correlated with the movement on trial by trial bases.

In Figure S2C-D, we showed firing activities of example Type 3 and Type 4 neurons on trial-by-trial bases. Type 3 neuron showed increased firing activities between 3-4 s during the 8s lever retraction period when the animal switched from left side to right side, whereas Type 4 neuron showed decreased firing activities between 3-4 s during as the animal switching from left to right. We further showed in Figure S4C-F, Type 3 and Type 4 neurons Type 3 and Type 4 neurons are related to the switch between choices but not the specific upcoming choice to be made.

Suggest also performing analogous analyses for striatal neurons.

We showed 4 types of SPNs on the on trial-by-trial bases as follows. Due to the limitation of the number of figures, these data were not included in the manuscript. We have now included these results in Fig. S2(E-H).

Typo: l. 68: "can bidirectionally regulates" -> "can bidirectionally regulate"

Thanks, we have now corrected the typos.

Reviewer #2 (Public Review):

In this valuable manuscript Li & Jin record from the substantial nigra and dorsal striatum to identify subpopulations of neurons with activity that reflects different dynamics during action selection, and then use optogenetics in transgenic mice to selectively inhibit or excite D1- and D2- expressing spiny projection neurons in the striatum, demonstrating a causal role for each in action selection in an opposing manner. They argue that their findings cannot be explained by current models and propose a new 'triple control' model instead, with one direct and two indirect pathways. These findings will be of broad interest to neuroscientists, but lacks some direct evidence for the proposal of the new model.

Overall there are many strengths to this manuscript including the fact that the empirical data in this manuscript is thorough and the experiments are well-designed. The model is well thought through, but I do have some remaining questions and issues with it.

Weaknesses:

1) The nature of 'action selection' as described in this manuscript is a bit ambiguous and implies a level of cognition or choice which I'm not sure is there. It's not integral to the understanding of the paper really, but I would have liked to know whether the actions are under goal-directed/habitual or even Pavlovian control. This is not really possible to differentiate with this task as there are a number of Pavlovian cues (e.g. lever retraction interval, house light offset) that could be used to guide behavior.

Sorry for the confusion of task description in the manuscript. We appreciate reviewer’s deep understanding about the complexity of the 2-8 s task we designed. Indeed, the 2-8 s task can’t be simply categorized as goal-directed/habitual or Pavlovian task. There are several behavioral aspects in this task. Lever retraction is served as a Pavlovian cue for mice to start performing the left-then-right sequential movement, but once levers are retracted, there is no cue available to mice during the lever retraction period, and mice have to make a decision to switch choice solely based on its internal estimation of the passage of time, which is considered as a cognitive process. The house light stays on for the entire training session (2 – 3 hours), and will be turned off when the task is done, so house light will not be used as a guidance for choice behavior. The behavior and neural activities during the lever retraction period is our main focus in this manuscript. The main advantage of such task design is that the animal is engaged in a self-determined, dynamic switch of action selection process, which offers a unique opportunity for investigating the role of various neuronal populations in the basal ganglia pathways during action selection.

2) In a similar manner, the part of the striatum that is being targeted (e.g. Figures 4E,I, and N) is dorsal, but is central with regards to the mediolateral extent. We know that the function of different striatal compartments is highly heterogeneous with regards to action selection (e.g. PMID: 16045504, 16153716, 11312310) so it would have been nice to have some data showing how specific these findings are to this particular part of dorsal striatum.

We thank the reviewer for bringing up this point. We are targeting dorsal-central part of striatum. In Figure S5G-L, we showed the specific location we targeted in striatum. Also as specified in Methods (lines 965-970), the craniotomies for electrode implantation were made at the following coordinates: 0.5 mm rostral to bregma and 1.5 mm laterally, and ~ 2.2 mm from the surface of the brain for dorsal striatum. For the virus injection and optic fiber implantation (lines 997-998), the craniotomies was made bilaterally at 0.5 mm rostral to bregma, 2 mm laterally and ~ 2.2 mm from the surface of the brain.

3) I'm not sure how I feel about the diagrams in Figure 4S. In particular, the co-activation model is shown with D2-SPNs represented as a + sign (which is described as "having a facilitatory effect to selection" in the caption), but the co-activation model still suggests that D2-SPNs are largely inhibitory - just of competing actions rather than directly inhibiting actions. Moreover, I am not sure about these diagrams because they appear to show that D2-SPNs far outnumbers D1-SPNs and we know that this isn't the case. I realize the diagrams are not proportionate, but it still looks a bit misrepresented to me.

We appreciate the reviewer’s comments about the diagram. We borrowed and extended the “center-surround” layout from the receptive field of neurons in the early visual system, as an intuitive analogy in describing the functional interaction among striatal pathways (also see Mink 2003 Archives of Neurology). In the co-activation model, if D2-SPNs inhibit the competing action, then the target action will be more likely to be selected due to the reduced competition, which means D2-SPNs actually facilitate the target action in an indirect way. And this is why we define the effect of D2-SPNs in the co-activation model as facilitatory. The area of each region does not represent the amount of cells but mainly qualitative functional role. To make it clearer, we have now added more explanation in the manuscript (page 17, lines 338-341).

4). There are a number of grammatical and syntax errors that made the manuscript difficult to understand in places.

We have now gone through the text carefully and corrected the typos.

5) I wondered if the authors had read PMID: 32001651 and 33215609 which propose a quite different interpretation of direct/indirect pathway neurons in striatum in action selection. I wonder if the authors considered how their findings might fit within this framework.

We appreciate the reviewer’s comments and suggestion. Miriam Matamales et al. (2020, PMID: 32001651) found that dynamic D2- to D1-SPNs transmodulation across the striatum that is necessary for updating previously learned behavior, which highlights the importance of collateral modulations between D1- and D2-SPNs as an additional layer of behavior control besides the classic direct and indirect pathways. This finding is compatible with our “Triple control” model emphasizing the influence of collateral modulations within striatum on behavior choice. James Peak et al. (2020, PMID: 33215609) demonstrated that D2-SPNs are critical to maintain the flexibility of behavior, which is reflected in our “Triple-control” model that activation of D2-SPNs could trigger the behavioral switch from the current action to another action. Although the two studies mentioned above mainly investigate the roles of striatal D1- and D2-SPNs in action learning and behavioral strategies, their functions in general fit within our new ‘Triple-control’ model of basal ganglia pathways for action selection.

6) There is no direct evidence of two indirect pathways, although perhaps this is beyond the scope of the current manuscript and is a prediction for future studies to test.

As accumulating RNA-seq and physiological data implying the heterogeneity of D2-SPNs, the further investigation of the subtypes of D1- and D2-SPNs and their functionality are likely a direction the field will continue to explore. On the other hand, we have discussed other possible anatomical circuits within basal ganglia circuitry that could fulfill the functional role of a third pathway in our new ‘Triple-control’ model, together with or independent of the second indirect pathway (page 32-33, lines 689-700). We certainly hope that our new model will inspire future work to identify and dissect the additional functional pathways in the basal ganglia circuits for action control.

Reviewer #2 (Recommendations For The Authors):

Suggestions for authors:

1) Consider how specific to the dorso-central striatum these findings are, possibly in the discussion.

We have specified in the Discussion that the study is targeting dorsal-central part of striatum (page 29, lines 609-612).

2) Modify the diagrams in 4S to make them more representative of the model's features.

We have responded this comment above.

3) Consider whether the findings here might fit within the role for direct pathway in excitatory action-outcome learning and the indirect pathway in response flexibility more generally.

The current study is mainly focus on selection and execution of actions. It will definitely be important to continue exploring the functionality of direct vs. indirect pathways in the action learning process.

4) Correct typos and grammatical errors including (but not limited to):

a) Line 62-64 - explain why this is controversial? Is it because we don't know which one applies?

In the “Go/No-go” model, indirect pathway inhibits the desired action and function as gain modulation, while in the “Co-activation” model, indirect pathway inhibits the competing action and in turn facilitates the desired action in an indirect manner, therefore these two existing models disagree with each other on the explanation the function of indirect pathway in its targeting action and the net outcome of behavior.

b) Line 68 - Regulates should be regulate.

This has been corrected in the revised manuscript.

c) Line 86 - should read "there are neuronal populations in either the direct or indirect pathway that are activated..."

This has been corrected in the revised manuscript.

d) Line 146-147 - "these types of neuronal dynamics in Snr only appeared in the correct but not incorrect trials" - It seems the authors are suggesting this only for Types 1 and 2 neurons, but this confused me the first time I read it and I suggest it is made clearer.

Line 146-147 now reads “These four types of neuronal dynamics in SNr only appeared…”

e) Line 346 - significant should be significantly.

This has been corrected in the revised manuscript.

f) Line 360 "contrast" should be "contrasting".

This has been corrected in the revised manuscript.

This is very true. We grew up with devices. A lot of our parents would give us them to distract us. Technology is a key part of our generation, but it didn't ruin us, it just made us. I feel like this argument is often based in the idea that if we hadn't grown up with this much access to technology, we would've turned out better, but it's impossible to separate my generation from technology. It's just a hypothetical, it has no basis in fact.

I like this image of the home speaking through it's history: the history of the speaker's grandmother dying here, the history it had before it became a house and was just wood, before it became "wood" in the building material and was just a tree.

不要为了别人的尊重和羡慕买昂贵的东西

Wikipedia is a good source for obtaining instant information about any topic. However you probably have to do further research to verify the information is accurate. Although it's good that everyone can contribute there needs to be some sort of verification for accuracy.

Big hype for this paragraph.

Reviewer #2 (Public Review):

Summary:<br /> The authors investigated how neuronal activity and metrics of plasticity using local electrical stimulation in the GPi were different between Parkinson's disease and dystonia patients.

Strengths:<br /> The introduction highlights the importance of the work and the fundamental background needed to understand the rest of the paper. It also clearly lays out the novelty (i.e., that the dynamics of plastic effects in GPi between dystonia and PD have not been directly compared).

The methods are clearly described and the results are well organized in the figures.

The results are strong with measurements from a large population of patients for each disease group and with distinct findings for each group.

Weaknesses:<br /> The discussion was hard to follow in several places, making it difficult to fully appreciate how well the authors' claims and conclusions are justified by their data, mostly in relation to the plasticity results. It may help to summarize the relevant findings for each section first and then further expand on the interpretation, comparison with prior work, and broader significance. Currently, it is hard to follow each section without knowing which results are being discussed until the very end of the section. With the current wording in the "Neuronal correlates.." section, it is not always clear which results are from the current manuscript, and where the authors are referring to past work.

Also, I felt that more discussion could be used to highlight the significance of the current results by comparing and/or contrasting them to prior relevant work and mechanisms. The novelty or impact is not very clear as written. Could this be further substantiated in the Discussion?

Some specific comments and questions about the Discussion:<br /> Lines 209-211 - This sentence was hard to understand, could it be clarified?<br /> Lines 211-213 - What do phasic and tonic components mean exactly? Could this be specifically defined? Are there specific timescales (as referred to in Intro)?<br /> Lines 215-217 - It's not clear what was delayed in dystonia, and how the authors are trying to contrast this with the faster time course in PD. I think some of this is explained in the introduction, but could also be re-summarized here as relevant to the results discussed.<br /> Lines 223-224 - I'm not sure I follow the implication that network reorganization leads to delayed functional benefits. Could this be further elaborated?

Could the absence of a relationship between FR and disease in PD be discussed?

It wasn't very clear how the direct pathway can be attributed to plasticity changes if the GPi makes up both the direct and indirect pathways. Could this be further clarified?

The mechanism of short- and long-term plasticity as applied in the protocols used in this work are outlined in reference to previous citations [15, 16, 18]. Because this is a central aspect of the current work and interpreting the results, it was difficult to appreciate how these protocols provide distinct metrics of short and long-term plasticity in GPi without some explanation of how it applies to the current work and the specific mechanisms. It would also help to be able to better link how the results fit with the broader conclusions.

In the Conclusion, it was difficult to understand the sentence about microcircuit interaction (line 232) and how it selectively modulates the efficacy of target synapses. Some further explanation here would be helpful. Also, it was not clear how these investigations (line 237) provide cellular-level support for closed-loop targeting. Could the reference to closed-loop targeting also be further explained?

How is the burst index calculated (Methods)?

Figures and figure captions are missing some details:

Fig. 1 - What does shading represent?

Fig. 2 - Can the stimulation artifact be labeled so as not to be confused with the physiological signal? Is A representing the average of all patients or just one example? Are there confidence intervals for this data as it's not clear if the curves are significantly different or not (may not be important to show if just one example)? Same for D. What is being plotted in E? Is this the exponential fitted on data? Can this be stated in the figure citation directly so readers don't have to find it in the text, where it may not be directly obvious which figure the analyses are being applied towards?

What does shading here represent?

watch out for long, abstract subjects like this one. it's form of sprawl that delays the verb (here, just a weak linking verb) creating a confusing reading experience

I never cared to read the "terms and conditions" because it can seem boring, but it's so important to know what access you're allowing your device to have because we see here that it not only just affects you but your friends, and contacts as well. Making and keeping the internet a safe place is a community effort. Be conscious of how you're affecting others.

But the gods' immortality is real in the canon of the Ring Cycle. Is this author saying that everything is just a metaphor, except for the things that aren't? In this conception, Wotan's construction of Valhalla as the greatest crime against nature... because it's false? Being false hurts Mother Nature?

Objective does not equal good. Just because something is truthful and professional-looking doesn't mean it's without an agenda. The nuclear bomb is a very objective instrument, for instance.

Since 2022, I've added notes like this to GoutPal pages. Sometimes it's just to let you know that you can customize all GoutPal pages with your own thoughts. Other times it's to provide additional information about gout terms or related information.

This note is to suggest that it might also be a way to contact me. Because I believe that I get notified by Hypothesis if anyone replies to any of my notes. But, I won't really know until someone like you tries it.

Crucial concession moment in TUs argument that you could drive a truck through. Ideally we spend most of our time in the ENGL major in the more creative, generative side of learning, but I think it's unavoidable that students need to just plain ol' learn some stuff about literary history, well-trodden warhorses in literary and cultural theory, the kinds of things we all teach in 304, 307, 320, 306, and so on.

This is interesting, especially since I just wrote a comment under the impression that the contributors for sites like Wikipedia were much larger and equally dispersed. It's hard to say if this is inherently detrimental for something like credibility, as on one hand, there's less range of perspective and knowledge, but on the other, the content is more focused and perhaps less hindered by a jumble of external information.

I am observing that General Scott is trying to compare the troops that are staying by him to friends of the Cherokee, hoping that they will listen to the "advice" as a friend would.

I am interpreting this as a form of manipulation to try and move the Cherokee off their land. General Scott is saying that the troops should be looked at as friends, but this statement is almost immediately contradicted by telling them to obey what the troops are saying. "Obeying" is something that friends don usually do with each other, it's what commanders do to lower downs. But by prefixing this with saying that they are friends, I believe he is trying to build a connection to strengthen his argument.

I think this could connect to causality: although I think that Scotts words were an attempt at manipulation, he might have actually been trying to connect with the Cherokee and just not have known the right way to do it. There are many different way to connect with someone as a friend, so maybe this was his attempt at trying to strengthen the bond between the Americans and Natives.

Author Response

Reviewer #1 (Public Review):

This interesting manuscript sets out to develop for the mouse a series of important concepts and models that this group has previously developed for models of monkey brains, where they showed that in a large-scale model, anterior → posterior spatial gradients such as spine density (and thus inferred strength of local coupling) lead to a transition from transient stimulus responses to persistent responses, capable of supporting working memory (WM). No such spine density gradient is found in the mouse. Here, the authors propose and use modeling to explore the idea, that the corresponding gradient may be that of density of inhibitory PV cells in different regions of the brain.

The goal of the study - a large-scale, anatomically-constrained model of WM - is an extremely valuable one, and the authors' efforts in this direction should be supported. That said, some of the main claims in the manuscript were not, at least as currently written, clearly supported by the data, a number of important clarifications need to be made, and some claims of novelty are made in a way that, for a typical reader, may obscure the actual contribution being made.

The biggest issue is that one of the main claims, that together with cell-type specific long-range targeting, "density of cell classes define working memory representations" (abstract), is not terribly clear. For example, Figs. 2D and 2E show that a brain region's hierarchical location tightly predicts its persistent firing rate (2D), but that PV cell fraction has a far weaker correlation (2E). Is hierarchical location sufficient? If PV cell fraction were constant across model brain regions, would we still get persistent activity modes? It seems likely that the answer may be "yes", but the answer, easily within reach of the authors, is surprisingly not in the current version of the manuscript. Figure 3D, for the thalamocortical model, shows no significant correlation of firing rate with PV density.

Given the claim about PV density (in the abstract and the first main point of the discussion), this is a big concern. Yet it seems easily addressable: e.g. if indeed the authors found that hierarchy was sufficient and PV density immaterial, the model would be no less interesting. And if the authors demonstrated clearly that a PV density gradient is required, that would make the claim a solid one. If, within the model, such a causal demonstration is present, this reader at least missed it.

MAJOR CONCERNS:

(1) The model appears to be a model of a single side of the brain. Perhaps each brain region in the model could be considered an amalgam of that region across both sides of the brain. Yet given results like Li et al. Nature 2016, who show that persistent activity is robust to inhibition of one side, but not both sides of ALM, at the very least discussion of the issue is warranted.

The model is indeed a one-hemisphere model, and an expansion to a bihemispheric model is considered for future work. We have added the following sentence in the Discussion section:

“Future versions of the large-scale model may consider different interneuron types to understand their contributions to activity patterns in the cortex (Kim et al,2017; Meng et al., 2023; Tremblay and Rudy, 2016; Nigro et al., 2022), the role of interhemispheric projections in providing robustness for short-term memory encoding (Ni et al., 2016), and the inclusions of populations with tuning to various stimulus features and/or task parameters that would allow for switching across tasks (Yang et al, 2018).”

(2) The authors make an interesting attempt to distinguish core WM regions from other regions such as "readout" regions, defined as showing persistent activity yet not having an effect on persistent activity elsewhere in the network.

However, this definition seemed problematic: for example, consider a network that consists of 20 brain regions, all interconnected to each other, and all equivalent to each other, capable of displaying persistent activity thanks to mutual connectivity. Imagine that inhibition of any one of these regions is not sufficient to significantly perturb persistent activity in the other 19. Then they would all be labeled as "readout". Yet, by construction in this thought experiment, they are all equivalent to each other and are all core areas. Such redundancy may well be present in the brain. How would the authors address this redundancy issue?

We acknowledge the importance of this thought experiment. Although we initially restricted the definition of core area to how a single area contributes to working memory, we proceeded with concurrent inhibition of multiple readout areas (see Essential Revisions response 6 above).

(3) Also important to discuss would be the fact that every brain region in this model is set up as composed of two populations, and when long-range interactions are strong and the attractors strongly coupled, the entire brain is set up as a 1-bit working memory. How would results and the approach be impacted by considering WM for more flexible situations?

We have used a model of two populations as the simplest way to integrate large-scale connectivity and inhibitory gradients. Indeed, future work should consider more realistic connectivity and populations with various degrees of tuning to different task parameters. (see Reviewer 1 response 1 above)

(4) Another concern that is important yet easily addressed is the authors' use of the term "novel cell-type specific graph theory measures". Describing in the abstract and elsewhere the fact that what they mean is to take into account the sign of connections, not just their magnitude, would transmit to readers the essence of the contribution in a manner very simple to understand. Most readers would fail to grasp the essential point of the current labeling, which sounds potentially very vague and complex.

We have reworded the abstract - see also Essential reviews response 2 above.

(5) Finally, the overall significance of the study, and advances over previous work, were not entirely clear. In the discussion, the authors identify three major findings: (1) WM function is shaped by the PV cell density gradient. But as above, further work is required to make it clear that this claim is supported by the model. (2) if local recurrent excitation is insufficient to generate persistent activity, then long-range recurrent excitation is needed to generate it. I had trouble understanding why a model was needed to reach this conclusion - it seems as if it is simply a question of straightforward logic. The discussion states that in this regard, the work here "offers specific predictions to be tested experimentally", but I had trouble identifying what these specific predictions are. (3) Taking into account sign, not only magnitude, of connections, is important. This last point once again seemed a matter of straightforward logic, making its novelty difficult to assess.

We thank the reviewer, we have addressed these issues in the Essential Revisions 3) above.

Reviewer #2 (Public Review):

This paper uses the mouse mesoscale connectome, combined with data on the number and fraction of PV-type interneurons, to build a large-scale model of working memory activity in response to inputs from various sensory modalities. The key claims of the paper are two-fold. First, previous work has shown that there does not appear to be an increase in the number of excitatory inputs (spines) per pyramidal neuron along the cortical hierarchy (and this increase was previously suggested to underlie working memory activity occurring preferentially in higher areas along the cortical hierarchy). Thus, the claim is that a key alternative mechanism in the mouse is the heterogeneity in the fraction of PV interneurons. Second, the authors claim to develop novel cell type-specific graph theory.

I liked seeing the authors put all of the mouse connectomic information into a model to see how it behaved and expect that this will be useful to the community at large as a starting point for other researchers wishing to use and build upon such large-scale models. However, I have significant concerns about both primary scientific claims. With regard to the PV fraction, this does not look like a particularly robust result. First, it's a fairly weak result to start, much smaller than the simple effect of the location of an area along the cortical hierarchy (compare Figs. 2D, 2E; 3C, 3D). Second, the result seems to be heavily dependent upon having subdivided the somatosensory cortex into many separate points and focusing the main figures of the paper (and the only ones showing rates as a function of PV cell fraction) solely on simulations in which the sensory input is provided to the visual cortex. With regards to the claim of novel cell type-specific graph theory, there doesn't appear to be anything particularly novel. The authors simply make sure to assign negative rather than positive weights to inhibitory connections in their graph-theoretic analyses.

Major issues:

1) Weakness of result on effect of PV cell fraction. Comparing Figures 2D and 2E, or 3C and 3D, there is a very clear effect of cortical hierarchy on firing rate during the delay period in Figures 2D and 3C. However, in Figure 2E relating delay period firing rate to PV cell fraction, the result looks far weaker. (And similarly for Figs. 3C, 3D, with the latter result not even significant). Moreover, the PV cell fraction results are dominated by the zero firing rate brain regions (as opposed to being a nice graded set of rates, both for zeros and non-zeros, as with the cortical hierarchy results of Figures 2D), and these zeros are particularly contributed to by subdividing somatosensory (SS) into many subregions, thus contributing many points at the lower right of the graph.

Further, it should be noted that Figure 2E is for visual inputs. In the supplementary Figure 2 - supplement 1, the authors do apply sensory inputs to auditory and somatosensory cortex...but then only show the result that the delay period firing rate increases along the cortical hierarchy (as in Figure 2D for the visual input), but strikingly omit the plots of firing rate versus PV cell fraction. This omission suggests that the result is even weaker for inputs to other sensory modalities, and thus difficult to justify as a defining principle.

We have now made an effort to exhaustively compare the contributions of PV versus hierarchy in defining the firing rate activity patterns in the model - see Essential Revisions response 1 above. Moreover, we included plots of firing rate versus PV cell fraction for other sensory modalities, and the results would still support a common architecture for short-term memory maintenance.

2) Graph theoretic analyses. The main comparison made is between graph-theoretic quantities when the quantities account for or do not account for, PV cells contributing negative connection strengths. This did not seem particularly novel.

See Essential Revisions response 2 above

3) It was not clear to me how much the cell-type specific loop strength results were a result of having inhibitory cell types, versus were a result of the assumption ('counter-stream inhibitory bias') that there is a different ratio of excitation to inhibition in top-down versus bottom-up connections. It seems like the main results were more a function of this assumed asymmetry in top-down vs. bottom-up than it was a function of just using cell-type per se. That is, if one ignored inhibitory neurons but put in the top-down vs. bottom-up asymmetry, would one get the same basic results? And, likewise, if one didn't assume asymmetry in the excitatory vs. inhibitory connectivity in top-down versus bottom-up connections, but kept the Pyramidal and PV cell fraction data, would the basic result go away?

We have addressed the issue of cell-type specific loop strength in Essential Revisions response 2 above.

4) In the Discussion, there is a third 'main finding' claimed: "when local recurrent excitation is not sufficient to sustain persistent activity...distributed working memory must emerge from long-range interactions between parcellated areas". Isn't this essentially true by definition?

We have addressed this important issue in Essential Revisions response 3 above.

5) I don't know if it's even "CIB" that's important or just "any asymmetry (excitatory or inhibitory) between top-down vs. bottom-up directions along the hierarchy". This is worth clarifying and thinking more about, as assigning this to inhibition may be over-attributing a more basic need for asymmetry to a particular mechanism.

We found that this asymmetry is indeed crucial, which may be provided by CIB or, in some regimes, it is sufficient that a PV gradient is present - see Essential Revisions response 1 above.

Other questions:

1) Is it really true that less than 2% of neurons are PV neurons for some areas? Are there higher fractions of other inhibitory interneuron types for these areas, and does this provide a confound for interpreting model results that don't include these other types?

Maybe related to the above, the authors write in the Results that local excitation in the model is proportional to PV interneuron density. However, in the methods, it looks like there are two terms: a constant inhibition term and a term proportional to density. Maybe this former term was used to account for other cell types. Also, is local excitation in the model likewise proportional to pyramidal interneuron density (and, if not, why not?)?

The reviewer is correct in pointing out that the ‘constant inhibition term’, which we interpret as a minimal inhibition, accounts for other cell types. We have added the respective explanation in the Methods section. Future versions of the model may include different interneuron types - see Reviewer 1 Response 1 above.

2) Non-essential areas. The categorization of areas as 'non-essential' as opposed to, e.g. "inputs" is confusing. It seems like the main point is that, since the delay period activity as a whole is bistable, certain areas' contributions may be small enough that, alone, they can't flip the network between its bistable down and up states. However, this does not mean that such areas (such as the purple 'non-essential' area in Figure 5a) are 'non-essential' in the more common sense of the word. Rather, it seems that the purple area is just a 'weaker input' area, and it's confusing to thus label it as 'non-essential' (especially since I'd guess that, whether or not an area flips on/off the bistability may also depend on the assumed strength of the external input signal, i.e. if one made the labeled 'input area' a bit too weak to alone trigger the bistability, then the purple area might become 'essential' to cross the threshold for triggering a bistable-up state).

This is an important point, and a similar point was also raised by Reviewer 1. For simplicity, we have restricted the definition of the function of an area (e.g., input, vs core vs non essential) to how a single area contributes to working memory. The existence of ‘subnetworks’ for any of these functions is indeed plausible - and potentially important, but we have left this for future modeling work. (see Essential Revisions response 6 above). The point that distinguishes ‘input’ and ‘non-essential’ areas is simply whether inhibiting said area during the stimulus period affects stimulus-specific persistent activity. Surely some of the areas that we have classified as ‘non-essential’ have important roles, even for the contents of working memory, however they are not essential to produce the activity pattern we observe here.

3) Relation between 'core areas' and loop strength. The measure underlying 'prediction accuracy = 0.93' in Figure 6D and the associated results seems incomplete by being unidirectional. It captures the direction: 'given high cell-type specific loop strength, then core area' but it does not capture the other direction: 'given a cell is part of a core area, is its predicted cell-type specific loop strength strong?'. It would be good to report statistics for both directions of association between loop strength and core area.

Indeed the prediction accuracy refers to the direction loop strength->core area, for which we estimate how well a continuous variable (loop strength) predicts a binary variable (whether core area or not). A prediction in the reverse direction is not well defined, namely to predict a continuous variable from a binary variable, so the reverse association may be only indirectly inferred from Figure 8D.

4) More justification would be useful on the assumption that the reticular nucleus provides tonic inhibition across the entire thalamus.

Relatively little is known about how specific this inhibition may be. We have included references in the Discussion section that speak to this fact. (Crabtree 2018, Hardinger et al., 2023).

5) Is NMDA/AMPA ratio constant across areas and is this another difference between mice and monkeys? I am aware of early work in the mouse (Myme et al., J. Neurophys., 2003) suggesting no changes at least in comparing two brain regions' layer 2/3, but has more work been performed related to this?

Recent anatomical in-vitro autoradiography work in the macaque shows that NMDA/AMPA ratio (in terms of receptor density) varies across the cortical hierarchy (Klatzmann et al., 2022). Functionally NMDA receptors seem important in PFC L2/3 for persistent activity, while in V1, they contribute relatively little to the stimulus response, which is dominated by AMPA-mediated excitation. This was shown by a recent physiological study in the macaque (Yang et al., 2018). This could indeed point to a species difference, although like-for-like comparisons of equivalent experiments across species are lacking in the literature.. We have included this and other related references in our Discussion - see Essential Revision 4 above.

6) Are bilateral connections between the left and right sides of a given area omitted and could those be important?

These potentially important connections were omitted for simplicity in the model, please see Reviewer 1 Responses 1, 3 above.

Reviewer #3 (Public Review):

Combining dynamical modelling and recent findings of mouse brain anatomy, Ding et al. developed a cell-type-specific connectome-based dynamical model of the mouse brain underlying working memory. The authors find that there is a gradient across the cortex in terms of whether mnemonic information can be sustained persistently or only transiently, and this gradient is negatively correlated to the local density of parvalbumin (PV) positive inhibitory cells but positively correlated with mesoscale-defined cortical hierarchy. In addition, weighing connectivity strength by PV density at target areas provides a more faithful relationship between input strength and delay firing rate. The authors also investigate a model where cortical persistent activity can only be sustained with thalamus input intact, although this result is rather separate from the rest of the study. The authors then use this model to test the causal contributions of different areas to working memory. Although some of the in silico perturbations are consistent with existing experimental data, others are rather surprising and need to be further discussed. Finally, the authors investigate patterns of attractor states as a result of different local and long-range connections and suggest that distinct attractor states could underlie different task demands.

The importance of PV density as a predictor for working memory activity patterns in the mouse brain is in contrast to recent computational findings in the primate brain where the number of spines (excitatory synapses per pyramidal cell) is the key predictor. This finding reveals important species differences and provides complementary mechanisms that can shape distributed patterns of working memory representation across cortical regions. The method of biologically-based near-whole-brain dynamical modeling of a cognitive function is compelling, and the main conclusions are mostly well supported by evidence. However, some aspects of the method, result, and discussion need to be clarified and extended.

1) Based on existing anatomical data, the authors reveal a negative correlation between cortical hierarchy (defined by mesoscale connectivity; this concept needs to be explicitly defined in the Results session, not just in the Method section) and local PV density (Fig. 1). In the dynamical model, the authors find that working memory activity is positively (and strongly) correlated with cortical hierarchy and negatively (and less strongly) correlated with PV cell density (Fig. 2), and conclude that working memory activity depends on both. But could the negative correlation between activity and PV density simply result from the inherent relationship between hierarchy and PV density across regions? To strengthen this result, the authors should quantify the predictive power of local PV density on working memory activity beyond the predictive power of cortical hierarchy.

We have systematically compared the relationship between PV and hierarchy in generating delay-patterns of activity - see Essential Revisions response 1 above.

2) In Fig. 4, the authors find that cell-type-specific graph measures more accurately predict delay-period firing rates. Specifically, the authors weigh connections with a cell-type-projection coefficient, which is smaller when the PV cell fraction is higher in the target area. Considering that local PV cell fraction is already correlated with delay activity patterns, weighing the input with the same feature will naturally result in a better input-output relationship. This result will be strengthened if there is a more independent measure of cell-type-projection coefficient, such as the spine density of PV vs excitatory cells across regions, or even the percentage of inhibitory versus excitatory cells targeted by upstream region (even just for an example set of brain regions).

We have compared different measures of cell-type projection coefficients and how they predict delay-patterns of activity and whether an area is a core area - see Essential Revisions response 2 above.

3) The authors aim to identify a core subnetwork that generates persistent activity across the cortex by characterising delay activity as well as the effects of perturbations during the stimulus and delay period. Consistent with existing data, the model identifies frontal areas and medial orbital areas as core areas. Surprisingly, areas such as the gustatory area are also part of the core areas. These more nuanced predictions from the model should be further discussed. Also surprisingly, the secondary motor cortex (MOs), which has been indicated as a core area for short-term memory and motor planning by many existing studies is classified as a readout area. The authors explain this potential discrepancy as a difference in task demand. The task used in this study is a visual delayed response task, and the task(s) used to support the role of MOs in short-term memory is usually a whisker-based delayed response task or an auditory delay response task. In all these tasks, activity in the delay period is likely a mixture of sensory memory, decision, and motor preparation signals. Therefore, task demand is unlikely the reason for this discrepancy. On the other hand, motor effectors (saccade, lick, reach, orient) could be a potential reason why some areas are recruited as part of the core working-memory network in one task and not in another task. The authors should further discuss both of these points.

We have addressed this important point in Essential Revisions response 5 above.

4) As a non-expert in the field, it is rather difficult to grasp the relationship between the results in Fig. 7 and the rest of the paper. Are all the attractor states related to working memory? If so, why are the core regions for different attractor states so different? And are the core regions identified in Fig. 5 based on arbitrary parameters that happen to identify certain areas as core (PL)? The authors should at least further clarify the method used and discuss these results in the context of previous results in this study.

Attractor states that have a stable baseline are, by definition, related to working memory in that there is a baseline and a memory state associated with the model. Some areas, such as PL are more likely to be associated with different core subnetworks given its position in the hierarchy. In the current version of the manuscript, we provide a motivation for the different attractor states and how they may relate to cognitive function.

Reviewer #2 (Public Review):

This paper uses the mouse mesoscale connectome, combined with data on the number and fraction of PV-type interneurons, to build a large-scale model of working memory activity in response to inputs from various sensory modalities. The key claims of the paper are two-fold. First, previous work has shown that there does not appear to be an increase in the number of excitatory inputs (spines) per pyramidal neuron along the cortical hierarchy (and this increase was previously suggested to underlie working memory activity occurring preferentially in higher areas along the cortical hierarchy). Thus, the claim is that a key alternative mechanism in the mouse is the heterogeneity in the fraction of PV interneurons. Second, the authors claim to develop novel cell type-specific graph theory.

I liked seeing the authors put all of the mouse connectomic information into a model to see how it behaved and expect that this will be useful to the community at large as a starting point for other researchers wishing to use and build upon such large-scale models. However, I have significant concerns about both primary scientific claims. With regard to the PV fraction, this does not look like a particularly robust result. First, it's a fairly weak result to start, much smaller than the simple effect of the location of an area along the cortical hierarchy (compare Figs. 2D, 2E; 3C, 3D). Second, the result seems to be heavily dependent upon having subdivided the somatosensory cortex into many separate points and focusing the main figures of the paper (and the only ones showing rates as a function of PV cell fraction) solely on simulations in which the sensory input is provided to the visual cortex. With regards to the claim of novel cell type-specific graph theory, there doesn't appear to be anything particularly novel. The authors simply make sure to assign negative rather than positive weights to inhibitory connections in their graph-theoretic analyses.

Major issues:<br /> 1) Weakness of result on effect of PV cell fraction. Comparing Figures 2D and 2E, or 3C and 3D, there is a very clear effect of cortical hierarchy on firing rate during the delay period in Figures 2D and 3C. However, in Figure 2E relating delay period firing rate to PV cell fraction, the result looks far weaker. (And similarly for Figs. 3C, 3D, with the latter result not even significant). Moreover, the PV cell fraction results are dominated by the zero firing rate brain regions (as opposed to being a nice graded set of rates, both for zeros and non-zeros, as with the cortical hierarchy results of Figures 2D), and these zeros are particularly contributed to by subdividing somatosensory (SS) into many subregions, thus contributing many points at the lower right of the graph.<br /> Further, it should be noted that Figure 2E is for visual inputs. In the supplementary Figure 2 - supplement 1, the authors do apply sensory inputs to auditory and somatosensory cortex...but then only show the result that the delay period firing rate increases along the cortical hierarchy (as in Figure 2D for the visual input), but strikingly omit the plots of firing rate versus PV cell fraction. This omission suggests that the result is even weaker for inputs to other sensory modalities, and thus difficult to justify as a defining principle.

2) Graph theoretic analyses. The main comparison made is between graph-theoretic quantities when the quantities account for or do not account for, PV cells contributing negative connection strengths. This did not seem particularly novel.

3) It was not clear to me how much the cell-type specific loop strength results were a result of having inhibitory cell types, versus were a result of the assumption ('counter-stream inhibitory bias') that there is a different ratio of excitation to inhibition in top-down versus bottom-up connections. It seems like the main results were more a function of this assumed asymmetry in top-down vs. bottom-up than it was a function of just using cell-type per se. That is, if one ignored inhibitory neurons but put in the top-down vs. bottom-up asymmetry, would one get the same basic results? And, likewise, if one didn't assume asymmetry in the excitatory vs. inhibitory connectivity in top-down versus bottom-up connections, but kept the Pyramidal and PV cell fraction data, would the basic result go away?

4) In the Discussion, there is a third 'main finding' claimed: "when local recurrent excitation is not sufficient to sustain persistent activity...distributed working memory must emerge from long-range interactions between parcellated areas". Isn't this essentially true by definition?

5) I don't know if it's even "CIB" that's important or just "any asymmetry (excitatory or inhibitory) between top-down vs. bottom-up directions along the hierarchy". This is worth clarifying and thinking more about, as assigning this to inhibition may be over-attributing a more basic need for asymmetry to a particular mechanism.

Other questions:<br /> 1) Is it really true that less than 2% of neurons are PV neurons for some areas? Are there higher fractions of other inhibitory interneuron types for these areas, and does this provide a confound for interpreting model results that don't include these other types?<br /> Maybe related to the above, the authors write in the Results that local excitation in the model is proportional to PV interneuron density. However, in the methods, it looks like there are two terms: a constant inhibition term and a term proportional to density. Maybe this former term was used to account for other cell types. Also, is local excitation in the model likewise proportional to pyramidal interneuron density (and, if not, why not?)?

2) Non-essential areas. The categorization of areas as 'non-essential' as opposed to, e.g. "inputs" is confusing. It seems like the main point is that, since the delay period activity as a whole is bistable, certain areas' contributions may be small enough that, alone, they can't flip the network between its bistable down and up states. However, this does not mean that such areas (such as the purple 'non-essential' area in Figure 5a) are 'non-essential' in the more common sense of the word. Rather, it seems that the purple area is just a 'weaker input' area, and it's confusing to thus label it as 'non-essential' (especially since I'd guess that, whether or not an area flips on/off the bistability may also depend on the assumed strength of the external input signal, i.e. if one made the labeled 'input area' a bit too weak to alone trigger the bistability, then the purple area might become 'essential' to cross the threshold for triggering a bistable-up state).

3) Relation between 'core areas' and loop strength. The measure underlying 'prediction accuracy = 0.93' in Figure 6D and the associated results seems incomplete by being unidirectional. It captures the direction: 'given high cell-type specific loop strength, then core area' but it does not capture the other direction: 'given a cell is part of a core area, is its predicted cell-type specific loop strength strong?'. It would be good to report statistics for both directions of association between loop strength and core area.

4) More justification would be useful on the assumption that the reticular nucleus provides tonic inhibition across the entire thalamus.

5) Is NMDA/AMPA ratio constant across areas and is this another difference between mice and monkeys? I am aware of early work in the mouse (Myme et al., J. Neurophys., 2003) suggesting no changes at least in comparing two brain regions' layer 2/3, but has more work been performed related to this?

6) Are bilateral connections between the left and right sides of a given area omitted and could those be important?

Author Response

Reviewer #1 (Public Review):

Due complicated and often unpredictable idiosyncratic differences, comparing fMRI topography between subjects typically would require extra expensive scan time and extra laborious analyzing steps to examine with specific functional localizer scan runs that contrast fMRI responses of every subject to different stimulus categories. To overcome this challenge, hyperaligning tools have recently been developed (e.g., Guntupalli et al., 2016; Haxby et al., 2011) based on aligning in a high-dimensional space of voxels of subjects' fMRI responses to watching a given movie. In the present study, Jiahui and colleagues propose a significantly improved version of hyperaligning functional brain topography between individuals. This new version, based on fMRI connectivity, works robustly on datasets when subjects watched different movies and were scanned with different parameters/scanners at different MRI centers.

Robustness is the major strength of this study. Despite the fact that datasets from different subjects watching different movies at different MRI centers with different scan parameters were used, the results of functional brain topography from between-subject hyperalignment based on fMRI connectivity were comparable to the golden standard of within-subject functional localizations, and significantly better than regular surface anatomical alignments. These results also support the claim that the present approach is a useful improvement from previous hyperalignments based on time-locked fMRI voxel responses, which would require normative samples of subjects watching a same movie.

We thank the reviewer for the appreciation of our work.

Given the robustness, this new version of hyperalignment would provide much stronger statistical power for group-level comparisons with less costs of time and efforts to collect and analyze data from large sample size according to the current stringent standard, likely being useful to the whole research community of functional neuroimaging. That said, more discussions of the limit of the present hyperalignment approach would be helpful to potential eLife readers. For example, to what extend the present hyperalignment approach would be applicable to individuals with atypical functional brain topography such as brain lesion patients with e.g., acquired prosopagnosia? Even in typical populations, while bilateral fusiform face areas can be identified in the majority through functional localizer scans, the left fusiform face area sometimes cannot be found. Moreover, many top-down factors are known to modulate functional brain topography. Due to these factors, brain responses and functional connectivity may be different even when a same subject watched a same movie twice (e.g., Cui et al., 2021).

We thank the reviewer for the suggestion and agree that it would be fascinating if the predictions can be made with high fidelity in neuropsychological populations. Although we are optimistic that our algorithm is able to generalize across diverse populations, to date, no previous literature has provided empirical evidence to illustrate the effectiveness, including optimizations and special applications beyond typical brains. Besides the neuropsychological population, it would also be valuable to study the generalization across a broad age range, for example, from infants to the elderly. The brain changes across age both anatomically and functionally, so it is a challenge to predict functional topographies based on a normative group that only includes young participants. With all these potential applications in mind, future research is needed to illustrate the efficacy, build the pipeline, and construct the representative normative groups to meet the requirements of accurate individualized predictions in diverse populations.

In typical populations, although participants have great individual variabilities in their functional topographies, for instance, some participants have distinguishable patches of activations in their left ventral temporal cortex while some participants don’t, our algorithms successfully captured these individualized differences in the prediction. The figure below shows, as an example, the face-selective topographies of two individuals that have markedly different face-selective topographies on the left ventral temporal cortex. The left participant has prominent face-selective areas on the left ventral temporal cortex that are in similar sizes as the right side, while the right participant only has a few scattered small face-selective spots on the left side. No matter what their face-selective areas look like, our algorithm accurately recovered the individualized locations, shapes, and sizes, retaining the individual variability in the functional topographies.

Functional connectivity profiles based on naturalistic stimuli are very stable across the cortex, even when participants watch different movies. In Figure 4-figure supplement 9, the mean correlations of fine-scaled connectome for most searchlights (r = 15mm) when participants watched The Grand Budapest Hotel and the Raiders of the Lost Ark were generally around 0.8. The mean correlations were about 0.9 between the first and second half of the same movie although the stimuli contents were different between the two halves. Thus, the fine-grained functional connectivity profiles remain highly stable and reliable across movie contents, which contributes to the robustness of cross-movie, time, and other parameters (e.g., scanner models, scanning parameter) predictions using our algorithms.

We added a paragraph in the discuss section to address the concerns (page 18-19):

“This study successfully illustrated that accurate individualized predictions are both robust and applicable across a variety of conditions, including movie types, languages, scanning parameters, and scanner models. Importantly, the intricate connectivity profiles remain consistent even when participants view entirely different movies, as evidenced by Figure 4-figure supplement 9, reinforcing the prediction's stability in various scenarios. However, all four datasets in this study only included typical participants with anatomically intact brains. An unanswered question is whether individualized topographies of neuropsychological populations with atypical cortical function (e.g., developmental prosopagnosics) or with lesioned brains (e.g., acquired prosopagnosics) could also be accurately predicted using the hyperalignment-based methods. Up to now, as far as we know, no previous literature has investigated this question. Beyond neuropsychological groups, it is also valuable to investigate how well the predictions will be across a wide range of age, from infants to the elderly. Future research is essential to adapt our algorithms to diverse populations.”

Reviewer #2 (Public Review):

Guo and her colleagues develop a new approach to map the category-selective functional topographies in individual participants based on their movie-viewing fMRI data and functional localizer data from a normative sample. The connectivity hyperalignment are used to derived the transformation matrices between the participants according to their functional connectomes during movies watching. The transformation matrices are then used to project the localizer data from the normative sample into the new participant and create the idiosyncratic cortical topography for the participant. The authors demonstrate that a target participant's individualized category-selective topography can be accurately estimated using connectivity hyperalignment, regardless of whether different movies are used to calculate the connectome and regardless of other data collection parameters. The new approach allows researchers to estimate a broad range of functional topographies based on naturalistic movies and a normative database, making it possible to integrate datasets from laboratories worldwide to map functional areas for individuals. The topic is of broad interest for neuroimaging community; the rationale of the study is straightforward and the experiments were well designed; the results are comprehensive. I have some concerns that the authors may want to address, particularly on the details of the pipeline used to map individual category-selective functional topographies.

We thank the reviewer for the encouragement.

1) How does the length of the scan-length of movie-viewing fMRI affect the accuracy in predicting the idiosyncratic cortical topography? Similarly, how does the number of participants in the normative database affect the prediction of the category-selective topography? This information is important for the researchers who are interested in using the approach in their studies.

To investigate the influence of movie-viewing data length and the number of participants in the normative database on prediction performance, we systematically varied these parameters. Specifically, we altered the number of runs utilized in the analysis for both the normative and target data and experimented with varying the number of participants in the normative dataset using the Budapest and the Sraiders datasets. We have included a new Figure 4-figure supplement 5 to present a summary of these findings.

The results reveal that both within-dataset and between-dataset prediction performances are positively correlated with the length of movie-viewing fMRI data used for both the normative and target groups. A similar trend was observed with respect to the number of participants included in the normative dataset. It is important to highlight, though, that, even when analyzing as little as one run of movie-viewing data—roughly 10-15 minutes, our hyperalignment-based prediction performance was significantly higher than that achieved using traditional surface alignment. This held true even when the normative dataset included as few as five participants.

In summary, our results show that prediction performance generally improves with longer movie-viewing sessions and larger normative datasets. However, it is noteworthy that even with minimal data—10 minutes of movie-viewing and a small number of participants in the normative dataset—our algorithm still outperforms traditional surface alignment methods significantly.

We also added sentences in the discussion section (page 15):

“We investigated the influence of naturalistic movie length and the size of the training group on the prediction accuracy of individualized functional topographies. By incrementally increasing both the number of movie runs in the training and target dataset and the participants in the training group in the Budapest and Sraiders dataset, we observed enhanced prediction accuracy (Figure 4-figure supplement 5). Notably, even with just one movie run in the training or target dataset, or with a mere five participants in the training group, our prediction performance (Pearson r) ranged from about 0.6 to 0.7. This accuracy significantly outperformed results obtained using surface-based alignment.”

2) The data show that category-selective topography can be accurately estimated using connectivity hyperalignment, regardless of whether different movies are used to calculate the connectome and regardless of other data collection parameters. I'm wondering whether the functional connectome from resting state fMRI can do the same job as the movie-watching fMRI. If it is yes, it will expand the approach to broader data.

We agree with the reviewer that demonstrating the applicability of the resting state data will expand the application scenarios of this approach. Most previous findings on resting state connectivity, including the comparison between the naturalistic and the resting state paradigms, focused on the macro-scale similarities and differences (e.g., Samara et al., 2023). Very few studies have investigated the fine-scaled connectome based on resting state data. The study on connectivity hyperalignment (Guntupalli et al., 2018) demonstrated a shared fine-scale connectivity structure among individuals that co-exists with the common coarse-scale structure and built the algorithm to successfully hyperalign individuals to the shared fine-scaled space. Another study from our lab (Feilong et al., 2021) revealed that the fine-scaled connectivity profiles in both resting and task states are highly predictive of general intelligence, indicating reliable and biologically relevant fine-scaled resting state connectome structures. Thus, it is highly plausible that our approach is able to be generalized to the resting state data, generating significantly better predictions of individualized functional topographies than traditional surface alignment. However, due to the limitations of the current datasets, we do not have resting state data available in the current datasets to perform this analysis. We are in the process of collecting new data to explore this hypothesis in future work.

We added sentences to the discussion section to discuss this idea (page 18):

“Studies comparing movie-viewing and resting state functional connectivity have shown that both paradigms yield overlapping macroscale cortical organizations (29), though naturalistic viewing introduces unique modality-specific hierarchical gradients. However, there remains a gap in research comparing the fine-scaled connectomes of naturalistic and resting state paradigms. Guntupalli and colleagues (14) revealed a shared fine-scale structure that coexists with the coarse-scale structure, and connectivity hyperalignment successfully improved intersubject correlations across a wide variety of tasks. Feilong et al. (13) noted that the fine-scaled connectivity profiles in both resting and task states are highly predictive of general intelligence. This suggests a reliable and biologically relevant fine-scale resting state connectivity structure among individuals. Therefore, it is plausible that individualized functional topography could be effectively estimated using resting state functional connectivity, expanding the applicability of our approach. Future studies are needed to explore this direction.”

3) The authors averaged the hyper-aligned functional localizer data from all of subjects to predict individual category-selective topographies. As there are large spatial variability in the functional areas across subjects, averaging the data from many subjects may blur boundaries of the functional areas. A better solution might be to average those subjects who show highly similar connectome to the target subjects.

We appreciate the reviewer’s insightful question about optimizing prediction performance by selecting participants most similar in functional connectivity to the target individuals. This is a promising direction and difficult problem as well. Our approach is based on fine-scale connectome to hyperalign participants, thus different groups of participants may be similar to the target participant in different searchlights. In addition, based on results discussed in the response to Q2, the more participants included in the normative dataset, the better the prediction performance. Thus, there is a trade-off between the number of participants included in the normative dataset for the prediction and the overall similarity of those participants to the target participant.

To quantitatively explore this idea, we used a searchlight in the right ventral temporal cortex, roughly at the location of posterior fusiform face area (pFFA).We sorted participants by their connectome similarity to each target participant and then examined prediction performance based on either the top nine most similar participants or the bottom nine least similar participants. Our results, presented in Figure 4-figure supplement 8, reveal that hyperalignment consistently outperforms surface alignment regardless of the subset of participants used. Notably, using the nine most similar participants did not significantly alter prediction performance (Tukey Test, z = -0.09, p = 0.996), while using the least similar participants did negatively impact it (Tukey Test, z = 2.492, p = 0.034). Interestingly, the stability of hyperalignment-based predictions remained high even when only a subset of participants was used, contrasting with the variability observed in surface-alignment-based predictions.

Overall, these findings suggest that while selecting functionally similar participants is a promising avenue for future optimization, the process will require nuanced, searchlight-specific criteria. Each searchlight may necessitate its own set of optimal participants to balance between the performance boost from having more participants and the fidelity gained from participant similarity.

We added the following to the discussion in the manuscript (page 16):

“In our study, we used fine-scale connectomes, noting that some participants are more similar to the target participant in specific searchlights. It is an interesting question whether predictions could be enhanced by exclusively selecting those more similar participants for the target participant. To explore this option, we examined a searchlight in the right ventral temporal cortex that was roughly at the location of the posterior fusiform area (pFFA) using the top and bottom nine participants similar to each target participant measured by their fine-scale connectome similarities in the budapest dataset. Generally, using all or part of the participants for the prediction generated similar results (Figure 4-figure supplement 8). Compared to using all the participants, using only the top nine participants who are the most similar to the target participants did not significantly improve the prediction (Tukey Test, z = -0.09, p = 0.996), but using only the bottom nine participants generated significantly lower prediction accuracies (Tukey Test, z = 2.492, p = 0.034). This suggests a trade-off between the number of participants included in the prediction and the similarity of the participants. Future studies are needed to explore the optimal threshold for the number of participants included for each searchlight to refine the algorithm.”

4) It is good to see that predictions made with hyperalignment were close to and sometimes even exceeded the reliability values measured by Cronbach's alpha. But, please clarify how the Cronbach's alpha is calculated.

Cronbach’s alpha calculates the correlation score between localizer-based maps across the runs, and it reflects the amount of noise in maps based on individual localizer runs. Traditionally, the reliability was estimated based on split-half correlations. For example, Guntupalli et al. (2016) used correlations of category-selectivity maps between odd and even localizer runs as the measure of reliability. The odd/even split measure underestimated reliability and necessitated recalculation of correlations between maps for only half the data to provide valid comparisons. In contrast, Cronbach’s alpha involves all localizer runs and provides a more accurate statistical estimate of the reliability of the topographies estimated with localizer runs.

Cronbach’s alpha has been used in many previously published works from our lab (e.g., Feilong et al., 2021; Jiahui et al., 2020, 2023). The code for implementing this metric is publicly accessible on the first author’s Github repository (https://github.com/GUOJiahui/face_DCNN/blob/main/code/cronbach_alpha.py).

We added the detailed explanation above to the Material and Methods section (page 24):

“Cronbach’s alpha calculates the correlation score between localizer-based maps across the runs, and it reflects the amount of noise in maps based on individual localizer runs. Traditionally, the reliability was estimated based on split-half correlations. The common odd/even split measure underestimated reliability and necessitated recalculation of correlations between maps for only half the data to provide valid comparisons. In contrast, Cronbach’s alpha involves all localizer runs and provides a more accurate statistical estimate of the reliability of the topographies estimated with localizer runs.”

5) Which algorithm was used to perform surface-based anatomical alignment? Can the state-ofthe-art Multimodal Surface Matching (MSM) algorithm from HCP achieve better performance?

We preprocessed our datasets using fMRIPrep, which employs algorithms from FreeSurfer’s recon-all for surface-based anatomical alignment. It is worth noting that different alignment methods can yield varying degrees of performance. For instance, a study by Coalson et al. (2018) compared the localization performance of multiple surface-based alignment methods, including Multimodal Surface Matching (MSM) and FreeSurfer. The study found that MSM outperformed FreeSurfer in terms of peak probabilities and spatial clustering, suggesting better overall localization.

Additionally, Guntupalli et al. (2018) evaluated intersubject correlations (ISC) of functional connectivity from movie-viewing data using both Connectivity Hyperalignment (CHA) and MSM-All with the Human Connectome Project (HCP) dataset. The study showed that although MSM-All yielded marginally better ISC than traditional surface alignment, CHA’s performance was significantly superior.

In summary, while using a more advanced alignment algorithm like MSM could marginally improve prediction performance, its advantages may not be substantial when compared to our CHA-based predictions. The combination of MSM and CHA represents an intriguing direction for future research, although it falls outside the scope of our current study.

6) Is it necessary to project to the time course of the functional localizer from the normative sample into the new participants? Does it work if we just project the contrast maps from the normative samples to the new subjects?

It is an interesting question and a practical alternative to researchers to know whether time series of the localizer runs are required to obtain reasonable predictions, as in some scenarios, contrast maps may be the only accessible data in the analysis. To quantitatively explore this possibility, we applied transformation matrices derived from the movie data to training participants’s individual pre-calculated contrast maps of all four categories, and evaluated the predictions. We found nearly similar prediction performance between the two flavors within and across datasets (Figure 4-figure supplement 7). However, it is worth noting that applying transformation matrices directly to contrast maps did not get as much improvement in the interactive steps as the other flavor in the advanced CHA, perhaps due to the scale changes when multiple iterations were implemented and the difficulty to properly normalize the t-maps compared to the regular time series.

Overall, although our algorithm is originally designed to be used on the time course of the functional localizer runs, relatively comparable results can be generated even when the contrast maps are directly projected from the normative group to the target participant. However, to derive the best results with our approach, time series are recommended when the situation permits.

We have also added the contents into the Discussion section (page 16):

“Our original algorithm is designed to apply transformation matrices to the time series of localizer data of training participants before generating contrast maps. To explore whether directly applying these matrices to pre-calculated contrast maps yields comparable results, we conducted an additional analysis across the four categories. Our findings indicate that the prediction outcomes were indeed quite similar between the two approaches for both the within- and across-datasets predictions (Figure 4-figure supplement 7). However, it is worth noting that the improvements observed with enhanced CHA were not as pronounced when applied directly to the contrast maps as opposed to the time series.”

7) Saygin and her colleagues have demonstrated that structural connectivity fingerprints can predict cortical selectivity for multiple visual categories across cortex (Osher DE et al, 2016, Cerebral Cortex; Saygin et al, 2011, Nat. Neurosci). I think there's a connection between those studies and the current study. If the author can discuss the connection between them, it may help us understand why CHA work so well.

We thank the reviewer for raising this point that provides us with the chance of clarifying how our approach differs with methods previously reported in the literature. The computational logic underlying our approach is that we derived the transformation matrices between the training and the target participants in the high-dimensional space based on functional connectivity calculated from the movie data. Then, we applied these transformation matrices to the training participant’s localizer data to accomplish the prediction. On the other hand, Saygin and colleagues directly used diffusion-weighted imaging (DWI) data and predicted participants’ functional responses based on the anatomical-functional correspondence. They evaluated the prediction by calculating the mean absolute errors (AE) of the difference between the actual and predicted contrast responses. Although AE linearly increases with the quality of the prediction, it is difficult to measure the prediction performance of the shape, size, and location of the functional areas precisely using this mean value. With our algorithm, we were able to predict the general location and size of the areas and recover the individualized shapes, generating more powerful predictions. We also used the searchlight analysis to evaluate the performance across the cortex systematically. In addition, Osher et al. (2016) and Saygin et al. (2012) always have a few participants failing to show better predictions based on the connectivity than the group averaged method. Our algorithm is more stable, as all participants across all four datasets had better predicted performance using our algorithm than using the group average. However, although we did not directly use the anatomical-functional correspondence with DWI, the relationships between individual structural connectivity and cortical visual category selectivity could be one of the biological underpinnings that contribute to this robust and accurate prediction.

The Connectivity-Based Shared Response Model (cSRM, Nastase et al., 2020) offers an alternative framework for aligning individuals through functional connectivity. While the overarching aim of cSRM and our methodology converges, substantial differences emerge in the respective implementation and application between the two methods that make our approach the more suitable for predicting individualized topographies. The most significant difference between the two is that, instead of focusing on within-individual connectivity profiles, cSRM used inter-subject functional connectivity (ISFC) in the initial step. This design requires that all participants must have time-locked time series, making the algorithm unusable for cross-content prediction and making it incompatible with resting-state data. Our approach, on the other hand, does not require time-locked stimuli, thereby offering a more flexible framework that permits generalization across different types of stimuli and experimental settings and enables bringing data across laboratories across the world together. Secondly, cSRM predominantly focuses on Region of Interest (ROI) analyses, whereas our model employs searchlight-based analyses designed to comprehensively cover the entire cortical sheet. Whole-brain coverage is needed to generate the topography that reflects the patterns across the cortex. Finally, with the optimized 1step method, our approach directly hyeraligns the training and target participants together, avoiding the accumulation of errors from the intermediate common space. cSRM, with an implementation similar to the classic connectivity hyperalignment, creates and hyperaligns all participants to a shared information space. In summary, while our approach and cSRM share a similar theoretical foundation, our approach has been specifically optimized to address the challenges and complexities in predicting individualized whole-brain functional topographies. Moreover, our approach demonstrates a remarkable ability to generalize across a variety of contexts and stimuli, offering a significant advantage in dealing with diverse experimental settings and datasets.

We have added the contents to the discussion section (page 16-17):

“By leveraging transformation matrices obtained from hyperaligning participants based on movie-viewing data, we successfully mapped these relationships to the training participants’ localizer data, enabling robust predictions. Prior work employing diffusion-weighted imaging (DWI) has underscored the link between anatomical connectivity and category selectivity across diverse visual fields (22, 23) and has established a notable congruence between structural and functional connectivities (24). These findings suggest that the unique anatomical connectivity patterns of individuals may serve as a foundational mechanism, contributing to the stable finescale functional connectome that underpins our approach. The connectivity-based Shared Response Model (cSRM) proposed by Nastase and colleagues (25) used connectivity to functionally align individuals similar to the connectivity hyperalignment algorithm. While both approaches share overarching goals, they diverge considerably in implementation and application. First and most important, cSRM used inter-subject functional connectivity (ISFC) rather than within-subject functional connectivity to initially estimate the connectome. As a result, cSRM requires participants to have time-locked fMRI time series. Therefore, unlike our algorithm, the cSRM approach does not support cross-content applications and also is not suitable for use with resting-state data. Second, cSRM is implemented based on a predefined cortical parcellation rather than the overlapping, regularly-spaced cortical searchlights applied in our method which are not constrained by areal borders. For the application, cSRM has mainly been used to do ROI analysis rather than the estimation of the whole-brain topography that requires broader coverage of the cortex with a searchlight analysis. Third, our method is specifically designed to work in each individual’s space, while cSRM decomposes data across subjects into shared and subjectspecific transformations, focusing on a communal connectivity space. In summary, although cSRM presents a promising alternative for similar aims, its current implementation precludes it from fulfilling the range of applications for which our method is optimized.”

Reviewer #3 (Public Review):

In this paper, Jiahui and colleagues propose a new method for learning individual-specific functional resonance imaging (fMRI) patterns from naturalistic stimuli, extending existing hyperalignment methods. They evaluate this method - enhanced connectivity hyperalignment (CHA) - across four datasets, each comprising between nine (Raiders) and twenty (Budapest, Sraiders) participants.

The work promises to address a significant need in existing functional alignment methods: while hyperalignment and related methods have been increasingly used in the field to compare participants scanned with overlapping stimuli (or lack thereof, in the case of resting state data), their use remains largely tied to naturalistic stimuli. In this case, having non-overlapping stimuli is a significant constraint on application, as many researchers may have access to only partially overlapping stimuli or wish to compare stimuli acquired under different protocols and at different sites.

It is surprising, however, that the authors do not cite a paper that has already successfully demonstrated a functional alignment method that can address exactly this need: a connectivitybased Shared Response Model (cSRM; Nastase et al., 2020, NeuroImage). It would be relevant for the authors to consider the cSRM method in relation to their enhanced CHA method in detail. In particular, both the relative predictive performance as well as associated computational costs would be useful for researchers to understand in considering enhanced CHA for their applications.

We thank the reviewer for raising this point that provides us with the chance of clarifying how our approach differs with methods previously reported in the literature. The computational logic underlying our approach is that we derived the transformation matrices between the training and the target participants in the high-dimensional space based on functional connectivity calculated from the movie data. Then, we applied these transformation matrices to the training participant’s localizer data to accomplish the prediction. On the other hand, Saygin and colleagues directly used diffusion-weighted imaging (DWI) data and predicted participants’ functional responses based on the anatomical-functional correspondence. They evaluated the prediction by calculating the mean absolute errors (AE) of the difference between the actual and predicted contrast responses. Although AE linearly increases with the quality of the prediction, it is difficult to measure the prediction performance of the shape, size, and location of the functional areas precisely using this mean value. With our algorithm, we were able to predict the general location and size of the areas and recover the individualized shapes, generating more powerful predictions. We also used the searchlight analysis to evaluate the performance across the cortex systematically. In addition, Osher et al. (2016) and Saygin et al. (2012) always have a few participants failing to show better predictions based on the connectivity than the group averaged method. Our algorithm is more stable, as all participants across all four datasets had better predicted performance using our algorithm than using the group average. However, although we did not directly use the anatomical-functional correspondence with DWI, the relationships between individual structural connectivity and cortical visual category selectivity could be one of the biological underpinnings that contribute to this robust and accurate prediction.

The Connectivity-Based Shared Response Model (cSRM, Nastase et al., 2020) offers an alternative framework for aligning individuals through functional connectivity. While the overarching aim of cSRM and our methodology converges, substantial differences emerge in the respective implementation and application between the two methods that make our approach the more suitable for predicting individualized topographies. The most significant difference between the two is that, instead of focusing on within-individual connectivity profiles, cSRM used inter-subject functional connectivity (ISFC) in the initial step. This design requires that all participants must have time-locked time series, making the algorithm unusable for cross-content prediction and making it incompatible with resting-state data. Our approach, on the other hand, does not require time-locked stimuli, thereby offering a more flexible framework that permits generalization across different types of stimuli and experimental settings and enables bringing data across laboratories across the world together. Secondly, cSRM predominantly focuses on Region of Interest (ROI) analyses, whereas our model employs searchlight-based analyses designed to comprehensively cover the entire cortical sheet. Whole-brain coverage is needed to generate the topography that reflects the patterns across the cortex. Finally, with the optimized 1step method, our approach directly hyeraligns the training and target participants together, avoiding the accumulation of errors from the intermediate common space. cSRM, with an implementation similar to the classic connectivity hyperalignment, creates and hyperaligns all participants to a shared information space. In summary, while our approach and cSRM share a similar theoretical foundation, our approach has been specifically optimized to address the challenges and complexities in predicting individualized whole-brain functional topographies. Moreover, our approach demonstrates a remarkable ability to generalize across a variety of contexts and stimuli, offering a significant advantage in dealing with diverse experimental settings and datasets.

We have added the contents to the discussion section (page 16-17):

“By leveraging transformation matrices obtained from hyperaligning participants based on movie-viewing data, we successfully mapped these relationships to the training participants’ localizer data, enabling robust predictions. Prior work employing diffusion-weighted imaging (DWI) has underscored the link between anatomical connectivity and category selectivity across diverse visual fields (22, 23) and has established a notable congruence between structural and functional connectivities (24). These findings suggest that the unique anatomical connectivity patterns of individuals may serve as a foundational mechanism, contributing to the stable finescale functional connectome that underpins our approach. The connectivity-based Shared Response Model (cSRM) proposed by Nastase and colleagues (25) used connectivity to functionally align individuals similar to the connectivity hyperalignment algorithm. While both approaches share overarching goals, they diverge considerably in implementation and application. First and most important, cSRM used inter-subject functional connectivity (ISFC) rather than within-subject functional connectivity to initially estimate the connectome. As a result, cSRM requires participants to have time-locked fMRI time series. Therefore, unlike our algorithm, the cSRM approach does not support cross-content applications and also is not suitable for use with resting-state data. Second, cSRM is implemented based on a predefined cortical parcellation rather than the overlapping, regularly-spaced cortical searchlights applied in our method which are not constrained by areal borders. For the application, cSRM has mainly been used to do ROI analysis rather than the estimation of the whole-brain topography that requires broader coverage of the cortex with a searchlight analysis. Third, our method is specifically designed to work in each individual’s space, while cSRM decomposes data across subjects into shared and subjectspecific transformations, focusing on a communal connectivity space. In summary, although cSRM presents a promising alternative for similar aims, its current implementation precludes it from fulfilling the range of applications for which our method is optimized.”

With this in mind, I noted several current weaknesses in the paper:

First, while the enhanced CHA method is a promising update on existing CHA techniques, it is unclear why this particular six step, iterative approach was adopted. That is: why was six steps chosen over any other number? At present, it is not clear if there is an explicit loss function that the authors are minimizing over their iterations. The relative computational cost of six iterations is also likely significant, particularly compared to previous hyperalignment algorithms. A more detailed theoretical understanding of why six iterations are necessary-or if other researchers could adopt a variable number according to the characteristics of their data-would significantly improve the transferability of this method.

In the advanced connectivity hyperalignment implementation, we gradually increased the number of targets. The six steps were not intentionally chosen but were the result of the increase to the maximum number of fine-grained targets, namely single cortical vertices.

Our datasets were resampled to the cortical mesh with 18,742 vertices across both hemispheres (approximately 3 mm vertex spacing; icoorder 5; 20,484 vertices before removing non-cortical vertices). Step 1 was the classic standard connectivity hyperalignment implementation based on the anatomically-aligned data. Since using dense connectivity targets (e.g., using all 18742 vertices on the surface) with anatomically-aligned data generates poor functional correspondence across participants (Busch et al., 2021), we used 1,284 vertices (icoorder 3, before removing the medial wall) as connectivity targets in step 1. However, it is beneficial to include more targets for calculating connectivity patterns after the first iteration of connectivity hyperalignment and repeated iterations to lead to a better solution by gradually aligning the information at finer scales. To better align across participants, we iterated the alignment for another two times (step 2 and step 3) with the same number of 1,284 coarse connectivity targets to ensure improved alignment before increasing the number of targets in the later steps. In step 4, we increased the number of targets to 5,124 (icoorder 4, before removing the medial wall), and iterated with this number of vertices for two times in total (step 4 & step 5) before using all vertices as targets. In the final step (step 6), all vertices were used as connectivity targets.

It is true that the multiple iteration steps largely increased the computational complexity compared to the classic connectivity hyperalignment, but the prediction increase was steady across all datasets and became comparable to response hyperalignment performance which requires time-locked stimuli. We did not use an explicit loss function in the algorithm, but followed the natural progression of the number of potential connectivity targets in the implementation. On the other hand, the difference between the performance of the improved and the classic connectivity hyperalignment was relatively small (difference of r < 0.05), which indicates the effectiveness of our classic algorithm. It is up to the researchers’ own options to adopt the number of iterations and the pace of increasing the number of targets in each step. If computational resources are limited or if a shorter total computational time is the primary priority, using the classic connectivity hyperalignment may be the best option to balance the trade-offs.

The Materials and Methods section had the details of the implementation (page 22-23):

“Using dense connectivity targets (e.g., using all 18742 vertices on the surface) with anatomically-aligned data usually generates poor functional correspondence across participants (33). It is, however, beneficial to include more targets for calculating connectivity patterns after the first iteration of connectivity hyperalignment and repeated iterations to lead to a better solution by gradually aligning the information at finer scales.

We used six steps to further improve the connectivity hyperalignment method. Step 1 was the initial connectivity hyperalignment step as described above that was based on the raw anatomically aligned movie data. The resultant transformation matrices were applied to those movie runs, and the hyperaligned data were then used in step 2 to calculate new connectivity patterns and calculate new transformation matrices. We repeated this procedure iteratively six times and derived transformation matrices for each step. In steps 1, 2, and 3, 642 × 2 (icoorder3, before removing the medial wall) connectivity targets were defined with 13 mm searchlights. In step 4 and 5, 2562 × 2 (icoorder 4, before removing the medial wall) connectivity targets were used with 7 mm searchlights to calculate target mean time series. In the final step 6, all 18742 vertices were included as separate connectivity targets, using each vertex’s time series rather than calculating the mean in a searchlight. Each step of this advanced connectivity hyperalignment algorithm increased the prediction performance (Figure 4-figure supplement 2).”

But to help the readers understand the logic of the advanced connectivity hyperalignment algorithm used in this study, we expanded the discussion section (page 15):

“Because using dense connectivity targets (e.g., using all vertices as connectivity targets) with anatomically-alignment data often leads to suboptimal alignment across participants (33), we started with coarse connectivity targets and gradually increased the number of connectivity targets to form a denser representation of connectivity profiles. The iterations improved the prediction performance step by step, and at the final step (step 6, all vertices were used as connectivity targets) in this analysis, the enhanced CHA generated comparable performance with RHA (Figure 4-figure supplement 4).”

Second, the existing evaluations for enhanced CHA appear to be entirely based on imagederived correlations. That is, the authors compare the predicted image from CHA with the ground-truth image using correlation. While this provides promising initial evidence, correlation-based measures are often difficult to interpret given their sensitivity to image characteristics such as smoothness. Including Cronbach's alpha reliability as a baseline does not address this concern, as it is similarly an image-based statistic. It would be useful to see additional predictive experiments using frameworks such as time-segment classification, intersubject decoding, or encoding models.

We appreciate the reviewer’s concern regarding the stability of local correlations in relation to image characteristics. To address this, we conducted additional analysis using different searchlight sizes (with radii of 10 mm, 15 mm, and 20 mm) to evaluate the predicted categoryselective maps, focusing specifically on the Budapest dataset. The local correlations between the predicted category-selective maps (obtained using enhanced CHA) and participants’ own maps based on classic localizer runs were calculated for each searchlight. We averaged these correlations across participants and plotted the resulting maps, as shown in Figure 4-figure supplement 10. Although using a larger searchlight radius is similar to employing a larger smoothing kernel, the results remained relatively stable across different searchlight sizes, particularly in regions selectively responsive to the specific category. This stability suggests that while the evaluation may be influenced by image-related features, the conclusion would remain consistent under varying parameters.

As for the use of enhanced CHA, it serves as an optimized version of the classic CHA, specifically designed for predicting individualized functional topographies. Evaluating prediction performance in our study is based on t-value contrast maps for each participant. Given this, it's unclear how time-segment classification or other decoding/encoding models could be appropriately implemented for performance evaluation. However, prior research from our lab has already established the effectiveness of classic CHA. Specifically, Guntupalli et al. (2018) showed that classic CHA significantly improved intersubject correlations (ISC) of connectivity profiles across the cortex. They also revealed that CHA captured fine-scale variations in connectivity profiles for nearby cortical nodes across participants and led to improved betweensubject multivariate pattern classification accuracies (bsMVPC) of movie segments. These findings serve as robust evidence for the effectiveness of classic CHA, laying the groundwork for our enhanced CHA approach.

We added Figure 4-figure supplement 10 to the supplementary material:

Addressing these concerns and considering cSRM as a comparison model would significantly strengthen the paper. There are also notable strengths that I would encourage the authors to further pursue. In particular, the authors have access to a unique dataset in which the same Raiders of the Lost Ark stimulus was scanned for participants within the Budapest (SRaiders) dataset as well as non-overlapping participants in the Raiders dataset. Exploring the relative performance for cross-movie prediction within a dataset as compared to a shared movie prediction across datasets is particularly interesting for methods development. I would encourage the authors to explicitly report results in this framework to highlight both this unique testing structure as well as the performance of their enhanced CHA method.

We appreciate the reviewer's suggestion to examine a shared time-series but non-overlapping participants scenario using the Sraiders and Raiders datasets. However, there are significant differences between the two datasets that preclude such direct comparison. These differences include varying scanning parameters, MRI scanners, localizer types, and data collection procedures. Due to these methodological divergences, the datasets cannot be treated as identical time-series.

Firstly, the scanning parameters vary considerably. Sraiders were scanned with TR = 1 s (TR/TE = 1000/33 ms, flip angle = 59 °, resolution = 2.5 mm3 isotropic voxels, matrix size = 96 × 96, FoV = 240 × 240 mm, multiband acceleration factor = 4, and no in-plane acceleration), and Raiders were scanned with TR = 2.5 s (TR = 2.5 s, TE = 35 ms, Flip angle = 90°, 80 × 80 matrix, FOV = 240 mm × 240 mm, resolution = 0.938 mm × 0.938 mm × 1.0 mm).

Secondly, participants in the Sraiders were scanned with a 3 T S Magnetom Prisma MRI scanner with a 32 channel head coil and the Raiders dataset, collected more than 10 years ago, used a 3T Philips Intera Achieva scanner with an eight-channel head coil.

Thirdly, the stimuli presentations were different. In the Sraiders dataset, the movie Raiders of the Lost Ark was split into eight parts (~15 min each), and the first four parts were watched outside of the scanner prior to the scanning (~56 min). The later four parts were watched in the scanner (57 min) with audio. And in the Raiders dataset, the audio-visual movie was split into eight parts (~15 min each). Participants watched all eight parts in the scanner with audio (one part / per run).

Fourthly and critically, the two datasets included two types of localizers. The Sraiders dataset included dynamic localizer runs, and the Raiders dataset only contained a static localizer that was similarly designed as in the Forrest dataset.

With all four points, it is not suitable to treat the two datasets as identical time-series. The difference in the localizer type is a further issue. The topographies generated from the two types of localizers are dissimilar in many ways. For all categories, the dynamic localizer elicited stronger and broader category-selective activations than the static localizer, and the searchlight analysis showed that the dynamic localizer had higher reliabilities across the cortex, especially in regions that were selectively responsive to the target category. Due to these differences, crossdataset predictions yielded lower correlations than within-dataset predictions. This is not indicative of methodological failure but reflects diverging topographies activated by different localizers.

In the manuscript, we have extensively analyzed cross-dataset predictions (Figure 2-figure supplement 1-Figure 4-figure supplement 4 & 6).

● Figure 2-figure supplement 1 demonstrates that, despite the limitations of cross-localizertype evaluation, both R-to-S (Raiders to Sraiders) and S-to-R (Sraiders to Raiders) predictions significantly outperformed surface alignment methods across categories.

● Figure Figure 2-figure supplement 2 confirms that the prediction performance remained stable across individual participants, underscoring the robustness of our methodology.

● Figure 3-figure supplement 1 & Figure 3-figure supplement 2 display contrast maps generated from both native and alternate localizers, revealing that the maps share similar topographies irrespective of the dataset origin.

● Figure 4-figure supplement 1 presents a correlation analysis of local similarities in R-to-S and S-to-R predictions, highlighting particularly strong correlations in the ventral face regions.

● Figure 4-figure supplement 2 employs histograms to showcase performance across major cortices and furnishes additional evidence regarding the influence of localizer types on the results.

● Figure 4-figure supplement 3 offers a searchlight analysis for other categories, enriching the scope of our investigation.

● Figure 4-figure supplement 4 affirms that the advanced CHA is effective in both R-to-S and S-to-R predictions.

● Figure 4-figure supplement 6 compares the efficacy of 1-step vs. 2-step prediction methods for R-to-S and S-to-R, showing a clear advantage for the 1-step approach.

These analyses affirmed that our approach outperforms surface alignment methods. But the inherent limitations in data collection and localizer types preclude a direct exploration of the reviewer’s hypothesis. These complexities necessitate further research to fully validate the proposed scenario.

Overall, I share the authors' enthusiasm for the potential of cross-movie, cross-dataset prediction, and I believe that methods such as enhanced CHA are likely to significantly improve our ability to make these comparisons in the near future. At present, however, I find that the theoretical and experimental support for enhanced CHA is incomplete. It is therefore difficult to assess how enhanced CHA meets its goals or how successfully other researchers would be able to adopt this method in their own experiments.

We hope our new analysis and replies addressed the reviewer’s concerns.

Author Response

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

Reviewer #1 (Public Review):

Soudi, Jahani et al. provide a valuable comparative study of local adaptation in four species of sunflowers and investigate the repeatability of observed genomic signals of adaptation and their link to haploblocks, known to be numerous and important in this system. The study builds on previous work in sunflowers that have investigated haploblocks in those species and on methodologies developed to look at repeated signals of local adaptations. The authors provide solid evidence of both genotype-environment associations (GEA) and genome-wide association study (GWAS), as well as phenotypic correlations with the environment, to show that part of the local adaptation signal is repeatable and significantly co-occur in regions harboring haploblocks. Results also show that part of the signal is species specific and points to high genetic redundancy. The authors rightfully point out the complexities of the adaptation process and that the truth must lie somewhere between two extreme models of evolutionary genetics, i.e. a population genetics view of large effect loci and a quantitative genetics model. The authors take great care in acknowledging and investigating the multiple biases inherent to the used methods (GEA and GWAS) and use a conservative approach to draw their conclusions. The multiplicity of analyses and their interdependence make them slightly hard to understand and the manuscript would benefit from more careful explanations of concepts and logical links throughout. This work will be of interest to evolutionary biologists and population geneticists in particular, and constitutes an additional applied example to the comparative local adaptation literature.

Some thoughts on the last paragraph of the discussion (L481-497): I think it would be fine to have some more thoughts here on the processes that could contribute to the presence/absence of inversions, maybe in an "Ideas and Speculation" subsection. To me, your results point to the fact that though inversions are often presented as important for local adaptation, they seem to be highly contingent on the context of adaptation in each species. First, repeatability results are only at the window/gene level in your results, the specific mutations are not under scrutiny. Is it possible that inversions are only necessary when sets of small effect mutations are used, opposite to a large effect mutation in other species? Additionally, in a model with epistasis, fitness effects of mutations are dependent on the genomic background and it is possible that inversions were necessary in only certain contexts, even for the same mutations, i.e. some adaptive path contingency. Finally, do you have specific demographic history knowledge in this system that maps to the observations of the presence of inversions or not? For example, have the species "using" inversions been subject to more gene flow compared to others?

Thank you for the great suggestions and helpful comments. Regarding the question of demography, each of the species actually harbours quite a large number of haploblocks (13 in H. annuus spanning 326Mb, 6 in H. argophyllus spanning 114 Mb, and 18 in H. petiolaris spanning 467 Mb; see Todesco et al. 2020 for more details) so there does not seem to be any clear association with demography. We agree about the complexities that might underly the evolution of inversions that you outline above, and have refined some of the text where we discuss their evolution in the Discussion.

Reviewer #2 (Public Review):

In this study the authors sought to understand the extent of similarity among species in intraspecific adaptation to environmental heterogeneity at the phenotypic and genetic levels. A particular focus was to evaluate if regions that were associated with adaptation within putative inversions in one species were also candidates for adaptation in another species that lacked those inversions. This study is timely for the field of evolutionary genomics, due to recent interest surrounding how inversions arise and become established in adaptation.

Major strengths

Their study system was well suited to addressing the aims, given that the different species of sunflower all had GWAS data on the same phenotypes from common garden experiments as well as landscape genomic data, and orthologous SNPs could be identified. Organizing a dataset of this magnitude is no small feat. The authors integrate many state-of-the-art statistical methods that they have developed in previous research into a framework for correlating genomic Windows of Repeated Association (WRA, also amalgamated into Clusters of Repeated Association based on LD among windows) with Similarity In Phenotype-Environment Correlation (SIPEC). The WRA/CRA methods are very useful and the authors do an excellent job at outlining the rationale for these methods.

Thank you!

Major weaknesses

The study results rely heavily on the SIPEC measure, but I found the values reported difficult to interpret biologically. For example, in Figure 4 there is a range of SIPEC from 0 to 0.03 for most species pairs, with some pairs only as high as ~0.01. This does not appear to be a high degree of similarity in phenotype-environment correlation. For example, given the equation on line 517 for a single phenotype, if one species has a phenotype-environment correlation of 1.0 and the other has a correlation of 0.02, I would postulate that these two species do not have similar evolutionary responses, but the equation would give a value of (1+0.02)10.02/1 = 0.02 which is pretty typical "higher" value in Figure 4. I also question the logic behind using absolute values of the correlations for the SIPEC, because if a trait increases with an environment in one species but decreases with the environment in another species, I would not predict that the genetic basis of adaptation would be similar (as a side note, I would not question the logic behind using absolute correlations for associations with alleles, due to the arbitrary nature of signing alleles). I might be missing something here, so I look forward to reading the author's responses on these thoughts.

The reviewer makes a very good point about the range of SIPEC, and we have changed our analysis to reflect this, now reporting the maximum value of SIPEC for each environment (across the axes of the PCA on phenotypes that cumulatively explain 95% of the variance), in Figure 4 and Supplementary Figures S2 and S13. For consistency among manuscript versions and to illustrate the effect of this change, we retain the mean SIPEC value in one figure in the supplementary materials (S12), which shows the small effect of this change on the qualitative patterns. Figure 4 now shows that the maximum SIPEC value is regularly quite strong, which should address the reviewer’s concern that this is not being driven by anomalous and small values. We appreciate this point and think this change now more closely reflects how we are trying to estimate the biological feature of interest – that some axis of phenotypic space is strongly (or not) responding to selection from the environmental variable.

With respect to the logic behind using absolute value, we still feel this is justified for traits, because if a trait evolves to be bigger or smaller, it may still use the same genes. For example, flowering time may change to be later or earlier, which would result in opposite correlations with a given environment, but might use the same gene (e.g. FT) for this. As such, we think keeping absolute value is more representative as otherwise species with strong but opposite patterns of adaptation would look like they were very different. We have added a statement on line 584 in the methods section to further clarify the reason for this choice.

An additional potential problem with the analysis is that from the way the analysis is presented, it appears that the 33 environmental variables were essentially treated as independent data points (e.g. in Figure 4, Figure 5). It's not appropriate to treat the environmental variables independently because many of them are highly correlated. For example in Figure 4, many of the high similarity/CRA values tend to be categorized as temperature variables, which are likely to be highly correlated with each other. This seems like a type of pseudo replication and is a major weakness of the framework.

This is a good point and we fully agree. It is for this reason that we didn’t present any p-values or statistical tests of the overall patterns that are shown in these figures (i.e. the linear relationship between SIPEC and number of CRAs in figure 4 and the tendency for most points to fall above the 1:1 line in figure 5). But to make sure this is even more clear, we have added statements to the captions of these figures to remind readers that points are non-independent. We still feel that in the absence of a formal test, the overall patterns are strongly consistent with this interpretation. A smaller number of non-pseudo-replicated points in Figure 4 would still likely show linear patterns. Similarly, there are almost no significant points falling below the 1:1 line in Figure 5, and it seems unlikely that pseudoreplication would generate this pattern.

Below I highlight the main claims from the study and evaluate how well the results support the conclusions.

"We find evidence of significant genome-wide repeatability in signatures of association to phenotypes and environments" (abstract)<br /> Given the questions above about SIPEC, I did not find this conclusion well supported with the way the data are presented in the manuscript.

We have changed the reporting of the SIPEC metric so that it more clearly reflects whichever axis of phenotypic space is most strongly correlated with environment in both species (using max instead of mean). This shows similar qualitative patterns but illustrates that this happens across much higher values of SIPEC, showing that it is in fact driven by high correlations in each species (or non-similar correlations resulting in low values of SIPEC). While we agree about the pseudo-replication problem preventing formal statistical test of this hypothesis, the visual pattern is striking and seems unlikely to be an artefact, so we think this does still support this conclusion.

"We find evidence of significant genome-wide repeatability in signatures of association to phenotypes and environments, which are particularly enriched within regions of the genome harbouring an inversion in one species. " (Abstract) And "increased repeatability found in regions of the genome that harbour inversions" (Discussion)<br /> These claims are supported by the data shown in Figure 4, which shows that haploblocks are enriched for WRAs. I want to clarify a point about the wording here, as my understanding of the analysis is that the authors test if haploblocks are enriched with WRAs, not whether WRAs are enriched for haploblocks. The wording of the abstract is claiming the latter, but I think what they tested was the former. Let me know if I'm missing something here.

We are actually not interested in whether WRAs are enriched for haploblocks; we want to know if WRAs tend to occur more commonly within haploblocks than outside of them. We have tried to clarify that this is our aim in various places in the manuscript. Our analysis for Figure 5 is the one supporting these claims, and it uses the Chi-square test statistic to assess the number of WRAs and non-WRAs that fall within vs. outside of inversions, and a permutation test to assess the significance of this observation, for each environmental variable and phenotype. We don’t think that this test has any direction to it – it’s simply testing if there is non-random association between the levels of the two factors. Thus, we think the wording we have used is consistent with the test result and our aims. Perhaps the confusion arose from the two methods that we present in the Methods (one is used for Figure 5, the other for Figure S6C & D), so we have added clarifications there.

Notwithstanding the concerns about highly correlated environments potentially inflating some of the patterns in the manuscript, to my knowledge this is the first attempt in the literature to try this kind of comparison, and the results does generally suggest that inversions are more likely capturing, rather than accumulating adaptive variation. However, I don't think the authors can claim that repeated signatures are enriched with haploblock regions, and the authors should take care to refrain from stating the relative importance of different regions of the genome to adaptation without an analysis.

Actually, we don’t have a strong feeling about whether inversions are capturing vs. accumulating adaptive variation, as these results could be consistent with either. As described above, we do not understand why we can’t claim that repeated signatures are enriched within haploblocks. We thought the reviewer is perhaps referring to the fact that the points are pseudo-replicated in the figures due to environment? We note that a very large number of points are significantly different from random in terms of the distribution of WRAs within vs. outside of haploblocks (light- vs. dark-shaded symbols), and that almost all of them fall above the 1:1 line. While there may be pseudo-replication preventing a test of the bigger multi-environment/multi-species hypothesis across all phenotypes and environments, there is almost a complete lack of significant results in the other direction. This seems like quite strong evidence about enrichment of WRAs within haploblocks, across many environments/species contrasts. We have added some text to the description of patterns in figure 5 to try to clarify this.

"While a large number of genomic regions show evidence of repeated adaptation, most of the strongest signatures of association still tend to be species-specific, indicating substantial genotypic redundancy for local adaptation in these species." (Abstract)<br /> Figure 3B certainly makes it look like there is very little similarity among species in the genetic basis of adaptation, which leaves the question as to how important the repeated signatures really are for adaptation if there are very few of them. (Is 3B for the whole genome or only that region?). This result seems to be at odds with the large number of CRAs and the claims about the importance of haploblock regions to adaptation, which extend from my previous point.

Figure 3B is for the whole genome, we have added text to the figure caption to clarify this. We think that both interpretations are possible: that most of the regions of the genome that are driving adaptation are non-repeated, but that a small but significant proportion of regions driving adaptation are repeated above what would be expected at random. Thus, it seems that there is high redundancy, coupled with adaptation via some genes that seem particularly functionally important and non-redundant, and therefore repeated. We added clarifying text on lines 541-548.

"we have shown evidence of significant repeatability in the basis of local adaptation (Figure 4, 5), but also an abundance of species-specific, non-repeated signatures (Figure 3)"<br /> While the claim is a solid one, I am left wondering how much of these genomes show repeated vs. non-repeated signatures, how much of these genomes have haploblocks, and how much overlap there really is. Finding a way to intuitively represent these unknowns would greatly strengthen the manuscript.

We agree, and really struggled to find the best way to communicate both the repeated patterns and the large amount of non-repeated signatures. Unfortunately, we have more confidence in the validity of repeated patterns because for the non-repeated patterns, a strong signature of association to environment in only one species could just be the product of structureenvironment correlation, as we didn’t control for population structure. Thus, trying to quantify the proportion of non-repeated signatures is difficult to do with any accuracy and we preferred to avoid putting too much emphasis on the simple calculation of the proportion of top candidate windows that were also WRAs.

Overall, I think the main claims from the study, the statistical framework, and the results could be revised to better support each other.

Although the current version of the manuscript has some potential shortcomings with regards to the statistical approaches, and the impact of this paper in its present form could be stifled because the biology tended to get lost in the statistics, these shortcomings may be addressed by the authors.

With some revisions, the framework and data could have a high impact and be of high utility to the community.

Thank you for your very helpful comments and suggestions on our paper, we really appreciate it.

Recommendations for the authors: please note that you control which revisions to undertake from the public reviews and recommendations for the authors

Editor's comments:

The reviewers make a series of reasonable suggestions that I echo. I found the paper quite hard to follow, and got fairly lost in the various layers of analyses done. Partially, this represents the complexity of empirical genomic data, which rarely deliver simple stories of convergence at a few genes. However, the properties of the various statistics used to detail local adaptation and convergence are not particularly clear and the figures presented were not intuitive representations of the data. This leaves the reader with an incomplete view of how much weight to put in the various lines of evidence marshaled. I would suggest simplifying the presentation of the results considerably. I add a few additional comments below.

Great suggestion, we’ve added a schematic overview of the methods and main research questions to Figure S1 in the supplementary materials.

A figure would help showing some of the signals of SNPs with putative signals of convergent environmental correlations across species, e.g. frequencies plotted against climate variables. This would help readers get a sense of how strong these signals were. These could be accompanied by the statistics calculated for these SNPs, that would allow the reader to start to get some intuitive sense of what the numbers mean.

Great suggestion, we have added a schematic overview of the methods to Figure S1 that shows some of the values and illustrates how the methods work using visual examples from our data.

In general, the introduction and some of the discussion of the inversion results feel oddly framed:<br /> Abstract line 36: "This shows that while inversions may facilitate local adaptation, at least some of the loci involved can still make substantial contributions without the benefit of recombination suppression."

We have changed “some of the loci involved can still make substantial contributions without the benefit of recombination suppression” here to “some of the loci involved can still harbour mutations that make substantial contributions without the benefit of recombination suppression in species lacking a segregating inversion” as it hopefully clarifies that we’re not talking about individual alleles that are present in both species.

Models of the role of local adaptation in the establishment of inversions (Kirkpatrick & Barton) assume that there are multiple locally adapted alleles already present. It is the load created by these alleles being constantly maintained in the face of migration and subsequent recombination that allow an inversion to be selected for because it keeps together locally adapted alleles. Thus these models predict that there could well be standing local adaptation at these loci in the absence of the inversion in other species, and that these locally adapted alleles while not fixed may be at high frequency. (After establishment, inversions housing locally adapted alleles, can shield more weakly, locally beneficial alleles from migration allow other alleles to build up.) Empirically it's interesting to find signals of local adaptation in other species that don't contain putative inversions. But the logic of the different predictions is not particularly clear from the introduction, and only becomes somewhat clearer in the discussion.

Thank you for pointing out this murkiness, we have re-written portions of both the Introduction and Discussion to clarify this aspect.

From the introduction: Inversions have been implicated in local adaptation in many species (Wellenreuther and Bernatchez 2018), likely due to their effect to suppress recombination among inverted and noninverted haplotypes, and thereby maintain LD among beneficial combinations of locally adapted alleles (Rieseberg 2001; Noor et al. 2001; Kirkpatrick and Barton 2006). This has been approached by models studying the establishment of inversions that capture combinations of locally adapted alleles present as standing variation (e.g., Kirkpatrick and Barton 2006), as well as models examining the accumulation of locally adapted mutations within inversions (e.g., Schaal et al. 2022). If there is variation in the density of loci that can potentially contribute to local adaptation, inversions would be expected to preferentially establish and be retained in regions harbouring a high density of such loci (and this expectation would hold for both the capture and accumulation models). We would also expect to see stronger signatures of repeated local adaptation in such high density regions. Despite mounting evidence of their importance in adaptation, it is unclear how inversions may covary with repeatability of adaptation among species. A fundamental parameter of importance in these models is the relationship between migration rate and strength of selection on individual alleles, which may not make persistent contributions to local adaptation without the suppressing effects of recombination if selection is too weak (Yeaman and Whitlock 2011; Bürger and Akerman 2011). If most alleles have small effects relative to migration rate and can only contribute to local adaptation via the benefit of the recombination-suppressing effect of an inversion, then we would expect little repeatability at the site of an inversion – other species lacking the inversion would not tend to use that same region for adaptation because selection would be too weak for alleles to persist. On the other hand, if some loci are particularly important for local adaptation and regularly yield mutations of large effect, with these patterns being conserved among species, repeatability within regions harbouring inversions may be substantial. Thus, studying whether adaptation at the same genomic region harbouring an inversion is observed in other species lacking the inversion can give insights about the underlying architecture of adaptation, and the evolution and maintenance of inversions.

From the Discussion: The observed repeatability associated with inversions further supports the local adaptation model as an explanation for the long-term persistence of segregating inversions (at least in sunflowers, rather than mechanisms based on dominance or meiotic drive (Rieseberg 2001). If there is variation across the genome in the density of loci with the potential to be involved in local adaptation, then the establishment and maintenance of inversions would be biased towards regions harbouring a high density such loci under this model. If the genomic basis for local adaptation is conserved amongst species, then these same regions are more likely to have high repeatability. Thus, our observation of genomic regions harbouring inversions also being enriched for WRAs is consistent with this general model for inversion evolution. Unfortunately, our observations do not provide much insight into whether inversions evolve through the capture (e.g. Kirkpatrick and Barton 2006) or accumulation (e.g. Schaal et al. 2022) type of model, as either model would be consistent with our results. Most of the sunflower inversions are >1 My old, and therefore predate any current local adaptation patterns, but likely do not predate the genes underlying local adaptation (which appear to be shared among the species we studied). As for the alleles underlying local adaptation, they may be younger than the inversions, but as our work suggests, these regions are prone to harbouring locally adaptive alleles so it is possible that they also harboured other ancestral locally adaptive alleles.

As a minor comment, there's a fair number of places where a more nuanced view of the field is needed, e.g.:<br /> "Models in evolutionary genetics tend to focus on extremes: population genetic approaches explore cases where strong selection deterministically drives a change in allele frequency" --This seems like a strange strawman. Population genetic models span a huge parameter range. The empirical approaches of looking for sweeps by detecting genome-wide statistical outliers is predicated on strong selection, but there are numerous papers that have looked for signals of weak selection genome-wide.

Good point, we have changed our wording here.

Reviewer #1 (Recommendations For The Authors):

Comments

My main comment on the manuscript is that the different levels and diversity of analyses are slightly hard to follow on the first, and even second, read. As there are several layers of correlations and comparisons, as well as some independent analyses, I wonder if it might be helpful to have a summary schematic figure of how all analyses fit together.

Great idea, we have added Figure S1 that summarizes the main flow of the methods and research questions.

• L169-171: Would it be more accurate to say that SIPEC is maximized when both species have strong correlations for an environmental variable across the same phenotypes? But maybe I misunderstood the index.

Good point, we have now simplified SIPEC, reporting the max instead of the mean, which we think better reflects when similar patterns are happening in both species for some phenotype.

• L191: Given the discussion in the introduction and elsewhere about the correction for population structure, which version is used here? Same for Figure 3.

We have added clarification there.

• L348: One [environmental] variable?

Added

• L353: Maybe add a percentage indication for 387 so that it is comparable to the following 23.3%.

Good point, added

-> L388 and paragraph: You mention "significant repeatability" but it is hard from the results at this point to have a broad idea of the amount of signal that is repeatable. Would it be possible to add here some quantitative measure of the proportion of signal repeatable or not, even if approximated?

I wish we could, but I think the precision implied by such an approximation would involve a huge amount of uncertainty and likely inaccuracy. Because it is so hard to conclusively identify how many loci are significant but non-repeated, we really don’t have a good handle on the denominator here. We are pretty confident that the repeated loci are strongly enriched for true positives, but the non-repeated loci are also almost certainly strongly enriched for false positives. While we really want to be able to quantify this explicitly, we don’t think it’s possible given our data.

-L415-418: "If there is variation [...] involved in local adaptation", I do not follow this argument, could you rephrase?

Changed

-L447-450: As you say in the supplementary methods, your analyses exclude 3/4 of the genome. Do you think this choice has a large impact on the number of outliers observed here as the genome-wide baseline would change?

This is a very good question, but one that is quite complex and without a clear answer – we chose not to delve into it in the paper to keep the discussion streamlined. My (SY) feeling is that it is unlikely that regions harbouring transposable elements would contribute much to adaptation, but I think we really don’t know if that is true. Even excluding ¾ of the genome harbouring TEs, ¼ of the genome still constitutes a huge amount of sequence and a very large number of genes and it seems plausible that most genes and genic regions would not contribute to adaptation for a given trait, so I don’t think this would change the results too much in a qualitative way – but would almost certainly change the number of windows that are significant, etc.

• L455-457: "As we are unable [...] potentially important drivers" Could you provide the logical link here between loci of small effect and them being important drivers. I presume you mean that the large effect loci found here only account for a small proportion of the heritability?

Yes that’s what we meant here, so we’ve added some clarification.

• L482: "enriched within inversions" should that be 'in genomic regions where there exist inversions in at least one species'? Thanks for catching that, yes. Changed.

• Methods/SIPEC L512: Compared to the Results section it is unclear here what is referred to as an "environment" Is it a variable or a set of environment variables?

This is done per environmental variable.

I find the presence of the PCA for environment variables in Figure 2 misleading as my first interpretation was that PCs for environment were also used.

Good point, we have clarified this on line 190-193.

Maybe one potential addition to the formula would be to add an environment variable $j$ notation such that it reads "$SIPEC_j = \sum_i (|r_{ij,1}| + ...) ...$ where ... between environment variable $j$". I had initial difficulties to understand how this SIPEC was computed relating to environmental variables and this might help.

Given the other changes we made to SIPEC, we felt it was simpler to just present it as a single calculation on a given combination of phenotype and environment for a pair of species, and then discuss taking the mean and maximum of this later.

Finally, PCA axes explaining 95% of the variance are used, I would find it interesting to see how many PCs are used in comparison to the number of traits being measured.

We have added the following sentence to the methods describing this:

"For comparisons including H. argophyllus, 95% of the variance was typically explained by 8-10 PC axes (out of 28 or 29 phenotypes), whereas for comparisons among other taxa this included 21 or 22 PC axes (out of 65 or 66 phenotypes."

Typos

L52: --

Changed

L254: portions [of] their

Changed

L399: additional closing parenthesis

Changed

L458: signatures [of] repeated association

Changed

L554: performed [on]

Changed

L578: 5 ~~kp~~/kb windows

Changed

L601: ~~casual~~/causal SNPs

Changed

L615: ~~widow~~/window

Changed

L732: ~~Banding~~/Banting Postdoctoral Fellowship

Changed

L1002 & L960: [Supplementary] Figure

Changed

Supplementary: Some figure titles are in bold and others are not.

Changed

Reviewer #2 (Recommendations For The Authors):

Overall I found the writing to be very clear and easy to follow. Despite my comments, it was clear that a lot of thought went into how to conduct the tests and visualize the results. I recommend ending the Discussion on a positive note, rather than an impossible test.

Thanks for the positive suggestion, we have done this.

In Figure 5, is the temperature variable missing in the legend and in the plot?

No, for this plot we just combined the temperature/precipitation variables into one variable called “climate”.

I would remove this section because you already broke it down further up on the page.

Perhaps an alternative way is to have a tx log on each devices - which will only be accreted with newly observed ops. Then it's order never changes and we can use idx as identifiers that stay evergreen.

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Reply to the reviewers

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

*In their study, Yamano et al. dissect the mechanism of TBK1 activation and downstream effects, especially in its relation to mitophagy adaptor OPTN. The authors find that OPTN's interaction with ubiquitin and the autophagy machinery, forming contact sites between mitochondria and autophagic membranes, results in TBK1 accumulation and subsequent autophosphorylation. Based on these findings, the authors propose a self-propagating feedback loop wherein OPTN phosphorylation by TBK1 promotes recruitment and accumulation of OPTN to damaged mitochondria and specifically the autophagosome formation site. This formation site is then involved in TBK1 autophosphorylation, and the activated TBK1 can then further phosphorylate other pairs of OPTN and TBK1. A OPTN monobody investigation strengthens their findings. *

*Critique: *

• It would be helpful if the authors could more clearly highlight the previous findings in OPTN-TBK1 relationship and which gaps in the understanding their study addresses.* We thank the reviewer for this comment. As suggested, we have highlighted previous findings and detailed in the Discussion how the study advances our understanding of TBK1 activation.

• It is not always clear whether experiments have been replicated sufficiently; this should be indicated in the figure descriptions.* In the original manuscript, most of the data shown was derived from duplicated experiments. For the revised version, we repeated experiments as needed to generate the replication necessary (i.e, N = 3) for determining statistical significance. Error bars and statistical significance have been added to the graphs and figure legends accordingly.

• During the discussion, references to the figures that indicate conclusions should be added where appropriate.* We thank the reviewer for the suggestion. References to figures have been added were appropriate to the Discussion.

*Figure 1 / Result "OPTN is required for TBK1 phosphorylation and subsequent autophagic Degradation": *

*o In a) the TBK1 and TOMM20 blots feature an image artefact that makes it appear like the blots are stitched together or there was a problem with the digital imager. The quantification in b) seems to be missing replications. *

We found that the artifact came from an automatic pixel interpolation process in Adobe Photoshop when the image was rotated by a small angle. We have provided the original immunoblotting data below as evidence that the data were not stitched from separate images. More accurate representations of the images without the artifact are now shown in Fig1 A of the revised manuscript.

For Fig 1b, the experiment was independently replicated three times with error bars added to each plot on the graph.

*o g) should feature the wt cell line on the same blot for better comparability as well as quantification and replication like done in f) *

As suggested, we have included the WT cell line in the immunoblot (See Fig 1g). In addition, Reviewer 2 asked that we provide data for Penta KO cells without exogenous expression of the autophagy adaptors and expressed concern regarding the lower expression of NDP52 relative to OPTN. To address these issues, we repeated the mitophagy experiments and detected phosphorylated TBK1 in six different cell lines: WT, Penta KO, Penta KO stably expressing OPTN at both low and high expression levels, and Penta KO stably expressing NDP52 at low and high expression levels. Immunoblots of phos-TBK1(pS172), TBK1, OPTN, NDP52, TOMM20, and actin were generated under four different conditions (DMSO, valinomycin for 1 hr, valinomycin for 3 hrs, and valinomycin in the presence of bafilomycin for 3 hrs). In addition, phos-TBK1 abundance in the six cell lines was determined in response to val and baf for 3 hrs and the expression levels of NDP52 and OPTN were similarly determined in response to DMSO. Error bars based on three independent experiments have been incorporated into the data, which are shown in Figure 1g and 1h of the revised manuscript.

*o h) is missing the blots for controls actin and TOMM20 *

Immunoblots for actin and TOMM20 have been added, please see Fig 1i in the revised manuscript.

*o In the text to e/f), the authors write that NDP52 KO effect on pS172 are comparable to controls, though the quantitation in f) indicates that pS172 signal is indeed significantly reduced compared to wt *

The reviewer is correct, the phos-TBK1 (pS172) signal in NDP52 KO cells is reduced compared to that in WT cells, but is only moderately lower in NDP52 KO cells relative to OPTN KO. We regret the error, which has been corrected in the revised manuscript.

*o In the text to h/i), the authors write "there was a significant increase in the TBK1 pS172 signal in cells overexpressing OPTN", though the quantification in i) does not indicate significance levels *

We performed statistical analyses on the phos-TBK1 (pS172) levels between cells with or without OPTN overexpression and have added the degree of significance to Fig 1j. As indicated in the original manuscript, there was a significant increase in phos-TBK1 (pS172) levels when OPTN was overexpressed.

*Figure 2 / Result "OPTN association with the autophagy machinery is required for TBK1 activation": ** o In b), pTBK1 at val 1 hr only features one dot/experiment per cell line *

Three independent replicates of the experiment (val 1 hr) were performed. The levels of phos-TBK1 (pS172), total TBK1, and actin were quantified, and the graph was remade with error bars and statistical significance incorporated. Please see Fig 2b in the revised manuscript.

*o In the text to c), the authors claim that the mutants reduce/abolish the recruitment of OPTN to the autophagosome site. A costain for LC3, as done for SupFig 1b, would be necessary to support that specific claim. *

To address the reviewer’s concern regarding the recruitment of OPTN mutants to the autophagosomal formation site, we performed two different experiments. First, when OPTN WT is recruited to the contact site between the autophagosomal formation site and damaged mitochondria, it should be heterogeneously distributed across mitochondria. In contrast, OPTN mutants that are unable to associate with the autophagosome formation sites should be largely localized to damaged mitochondria since the mutants are still capable of binding ubiquitin. When we examined the mitochondrial distribution of OPTN WT following valinomycin treatment for 1 hr, more than 80% of the Penta KO cells exhibited a heterogeneous distribution, whereas only 10% of the cells showed a similar distribution for OPTN 4LA or OPTN 4LA/F178A (please see Fig 2g in the revised manuscript). Although the OPTN F178A mutant exhibited 50% heterogeneous distribution (Fig 2g), this may be because OPTN F178A retains the ability to interact with ATG9A vesicles. In fact, our previous mitophagy analyses (Keima-based FACS analysis, Yamano et al 2020 JCB), which are strongly correlated with OPTN mitochondrial distribution, showed that the OPTN F178A mutant moderately (~ 60%) induced mitochondrial degradation. This degradation effect was slightly higher (80%) with OPTN WT but significantly lower (9%) with the 4LA/F178A mutant. In the second experiment, Penta KO cells expressing either OPTN WT or the OPTN mutants were immunostained for exogenous FLAG-tagged OPTN, endogenous WIPI2, and HAP60 (a mitochondrial marker) after valinomycin treatment for 1 hr (see Fig 2e and 2f in the revised manuscript). Because LC3B is assembled on the autophagosomal formation site as well as completed autophagosomes, we detected endogenous WIPI2 because WIPI2 is only recruited to autophagosomal formation sites (Dooley et al. 2014 Mol Cell). Confocal microscopy images and their associated quantification data indicate that WIPI2 foci formation during mitophagy was reduced in Penta KO cells expressing the OPTN mutants (4LA, F178A and 4LA/F178A) as compared to Penta KO cells expressing OPTN WT.

*o d) and g) as simple confirmations of KO/KD efficiency might be better suited for the supplemental part, or blots for FIP/ATG be included with the blots in e) and h) *

Based on the reviewer comments, we performed additional experiments related to Figure 2 and have incorporated the new data into the revised figure. The original Figure 2d, e, f, g, h, and I have been moved to supplemental Figure 5.

*o In the text to e), the authors claim that the levels of pS172 in the KO cell lines did not increase during mitophagy, though the blot and quantification in f) seem to indicate an increase. The results therefore don't seem to align completely with the claims that pS172 generation in response to mitophagy requires the autophagy machinery, or that FIP200 and ATG9A rather than ATG5 are critical for TBK1 phosphorylation. *

Although newly generated pS172 TBK1 was reduced in FIP200 KO and ATG9A KO cells relative to WT cells, the signals gradually increased. In the autophagy KO cell lines (FIP200 KO and ATG9A KO), phos-TBK1 accumulates prior to mitophagy stimulation. Although suggesting it is mitophagy-independent, phos-TBK1 accumulation prior to mitophagy stimulation in autophagy KO cell lines complicated interpretation of the results. To avoid this issue, we used siRNA to transiently knock down FIP200 and ATG9A. As shown in the original manuscript (Fig 2g, h, I in the original manuscript, supplementary Fig 5d, e, f in the revised manuscript), knockdown of FIP200 and ATG9A prior to mitophagy induction allowed us to observe mitophagy-dependent phosphorylation of TBK1. This result strongly suggests that the autophagy machinery does induce TBK1 phosphorylation in response to Parkin-mediated mitophagy. However, TBK1 phosphorylation still increases, albeit very slightly, in the FIP200 and ATG9A knock down cells. Thus, it may be reasonable to assume that OPTN-dependent phosphorylation of TBK1 can occur to a certain degree even in the absence of autophagy components. We have noted this in the Discussion.

While conducting experiments for the revised manuscript, we determined that TAX1BP1 is responsible for the accumulation of phos-TBK1 in the autophagy KO cell lines under basal conditions. When TAX1BP1 is knocked down in FIP200 KO or ATG9A KO cells, the basal accumulation of phos-TBK1 was eliminated and then we could observe mitophagy-specific TBK1 phosphorylation (please see Fig 2h, i, j, k in the revised manuscript). These results showed that mitophagy-dependent phos-TBK1 is largely attenuated in FIP200KO and was almost completely eliminated in ATG9A KO cells (Fig 2k in the revised manuscript).

*o f) is missing significance indications. Its description has a typo: "bad" instead of "baf" *

Newly synthesized pTBK1 (pS172) during mitophagy was quantified and statistical significance incorporated into the figure (please see supplementary Fig 5c). The identified typo has been corrected.

*Figure 3 / Result "TBK1 activation does not require OPTN under basal autophagy conditions": *

*o In the text to SupFig2, the authors claim that pS172 levels are significantly elevated, but no significance levels are indicated *

Statistical significance was determined for all proteins shown in original supplementary Fig 2 and the results have been incorporated into the relevant figure. The original supplementary Fig 2 is now supplementary Fig 6.

*o In the text to a), NBR1 is claimed to colocalize with Ub, but no costaining with Ub is shown. The claimed lacking colocalization of OPTN with Ub is not obvious from the images; a quantification might be appropriate. *

Since the anti-NBR1 antibody used in the original manuscript is derived from mouse, we were unable to use it in conjunction with the mouse ubiquitin antibody. Because ubiquitin-positive foci and NBR1-positive foci contain p62 (original Fig 3a) and NBR1 and p62 are known to tightly interact each other (Kirkin et al. 2009 Mol Cell and Sanchez-Martin et al. 2020 EMBO Rep), we stated that "NBR1 colocalizes with Ub". However, the reviewer is correct. To remedy this confusion, we obtained a rabbit anti-NBR1 antibody (a gift from the Masaaki Komatsu group) and used it to co-immunostain with anti-Ub antibodies (please see supplementary Fig 7a of the revised manuscript). NBR1 foci colocalize with both ubiquitin and p62 in FIP200 KO and ATG9A KO cells. Further, based on comments from Reviewer 2, we purchased several anti-TBK1 antibodies and identified one that was able to detect endogenous TBK1 by immunostaining (see Figure 1 for reviewers in our response to Reviewer 2 below). Using this anti-TBK1 antibody, we showed that a part of TBK1 also associates with ubiquitin and p62-positive aggregates.

*o In the text to b), the authors make reference to significant changes, but replication/ quantification/ significance testing is missing. *

We independently performed the same experiments three times. The levels of TBK1, phos-TBK1 (pS172), all five autophagy adaptors, and TOMM20 in both the supernatants and pellets have been quantified with error bars and statistical significance indicated. These results have been incorporated into Figure 3c in the revised manuscript.

*Figure 4b) is missing the pTBK1 data that is referenced in the text. In the text to figure 5 c/d), the authors claim that certain mutants have no significant effect on mitophagy, though d) is missing significance testing *

*Figure 6 c/d/i) appear to be missing replication. *

For Figure 4b, phos-TBK1 was immunoblotted (See Fig 4b of the revised manuscript). For Figure 5b and d, statistical significance was determined for the effect of TBK1 mutations on autophosphorylation and OPTN phosphorylation and the effect of the TBK1 mutants on Parkin-mediated mitophagy. For Figure 6 c/d/I, the experiment was repeated; error bars and statistical significance have been added to the associated graphs.

*Reviewer #1 (Significance (Required)): Removal of damaged mitochondria by the mitophagy pathway provides an important safeguarding mechanism for cells. The Pink1/Parkin mechanism linked to numerous modulators and adaptor proteins ensures an efficient targeting of damaged mitochondria to the phagophore. The Ser/Thr kinase TBK1, in addition of multiple roles in innate immunity, is a major mitophagy regulator as has been revealed by the Dikic and Youle groups in 2016 (Richter et al., PNAS). The mechanistic insights provided by this manuscript add to a growing body of studies of how the autophagy machinery interconnects with cellular signalling networks. Although parts of the results need to be further validated, the data shown is of high quality, revealing an important conceptual advance. The paper is interesting and of general relevance beyond the signalling and autophagy community. *

We would like to thank Reviewer 1 for the comments and suggestions, many of which improved our manuscript. We hope that the reviewer’s comments have been adequately addressed in the revised manuscript.

*Reviewer #2 (Evidence, reproducibility and clarity (Required)): Summary In this manuscript, Yamano and colleagues show that as for Sting-mediated TBK1 activation, Optn provides a platform for TBK1 activation by autophosphorylation and that TBK1 is activated after the interaction of Optn with the autophagy machinery and ubiquitin and not before. They show that TBK1 phosphorylation is blocked by bafilomycine A1, an inhibitor of vacuolar ATPases that blocks the late phase of autophagy. Furthermore, they demonstrate that Optn is require for TBK1 phosphorylation since variation of Optn expression regulates TBK1 phosphorylation in response to PINK/Parkin-mediated autophagy. Interestingly, using immunofluorescence microscopy, they show that Optn forms sphere like structures at the surface of damage mitochondria which are more dispersed in the absence of TBK1. In addition, TBK1 is also recruited at the surface of damage mitochondria and as Optn and NDP52 (but not p62) colocalize with LC3B in response to PINK/Parkin-mediated mitophagy. Next, it is demonstrated that the Leucin zipper and LIR domains of Optn (which modulate Optn interaction with autophagosome) play an important role for TBK1 activation. Additionally, the autophagy core is shown to be required for TBK1 activation. Under basal conditions, depletion of the autophagosome machinery leads to an increase in autophagy receptors (except Optn) and TBK1 phosphorylation which colocalize with ubiquitin in insoluble moieties. In contrast, Optn remains cytosolic and is dispensable for TBK1 activation in these conditions. Then, using the fluoppi technic, the authors demonstrate that the generation of Optn-Ubiquitin condensates recruits and activates TBK1. They express in HCT116 TBK1-deficient cells engineered or pathological ALS mutations of TBK1 that affect ubiquitin interaction, structure, dimerization and kinase activity of TBK1. The expression level of TBK1 was only affected by the dimerization-deficient mutations. None of the mutations impaired Optn and TBK1 ubiquitination. Interestingly, some ALS-associated mutations affect TBK1 activity and it is said in the text that the dimerization-deficient mutations of TBK1 affect its activity proportionally to their level of expression, which is not really correct (the expression level of the mutants is very heterogenous and not always correlate to their activity). Regarding their effect on mitophagy, the authors claim that the phosphorylation of TBK1 correlate with mitophagy which is not really the case. By using TBK1 inhibitor or TBK1-depleted cells, the authors conclude that TBK1 is the only kinase phosphorylating Optn. However, BX-795 is not completely specific to TBK1. Finally, the authors use monobodies against Optn effective in inhibiting mitophagy in NDP52 KO cells. Some of the monobodies have been shown to form a ternary complex with Optn and TBK1, while others compete for the interaction between Optn and TBK1 which involves the amino-terminal region of Optn and the C-terminal region of TBK1. Monobodies that compete for the interaction of Optn with TBK1 could alter the cellular distribution of Optn and inactivate TBK1, but they do not alter the ubiquitination of Optn. Finally, these monobodies inhibit 50% of mitophagy. *

*Major and minor points: Introduction The first paragraph of the Introduction section is confused and difficult to read. First and second paragraphs (page 3 and top of page 4) are dedicated to macroautophagy processes but ended with one sentence on Parkin-mediated autophagy without further introduction, while all processes regarding mitophagy are detailed in the next paragraph. Links between ideas developed are also somewhat missing. For example, in page 6, the three last sequences detailed the phosphorylation of autophagosome component, the fact that Optn and TBK1 genes are involved in neurodegenerative diseases and autophosphorylation of TBK1 as a pre-requirement for TBK1 activation without evident links between them, except "interestingly". *

In response to the reviewer’s suggestion, we have rewritten the Introduction. The first paragraph focused on introducing the molecular mechanism underlying macroautophagy and the second paragraph focused on Parkin-mediated mitophagy. As the reviewer indicated, the ALS mutations and TBK1 phosphorylation during Parkin-mediated mitophagy are not well related, so we moved the background material on the relationship between OPTN and TBK1 in neurodegenerative diseases to the beginning of the section describing Figure 5. We believe these changes have made the Introduction easier to read and understand.

*Results *

*Major points: *

*1- Results are often over-interpreted regarding data obtained leading to inadequate conclusions (see below for details); *

We regret the reviewer’s concerns regarding over-interpretation. To address this issue, we have carefully considered the data, performed additional experiments where necessary, and rewritten the results accordingly. Please see our point-by-point responses below.

*2- Quantification of protein levels detected by western blot are provided as "relative intensities" without referring to specific loading control or to total protein when -phosphorylated forms are quantified (Fig. 1b, 1d, 1f, 1i, 2b, 2f, 2i, 5b, 7b, supplemental figures 2b). *

For the immunoblots, we loaded the same amount of total cell lysate and the phosphorylated forms were quantified relative to the total protein input. This has been mentioned in the Materials and Methods.

*3- In western blotting experiments, authors described slower migrating bands as "ubiquitinated" forms of detected proteins, but never provided experimental evidences that it could be the case. Use of non-specific deubiquitinase incubation of extracts prior to western blot could help to correctly identified ubiquitination versus other post-translational modifications such as phosphorylation, glycosylation, acetylation etc... *

We appreciate the reviewer’s suggestion. The cell lysates after mitophagy induction were incubated in vitro with a recombinant USP2 core domain (non-specific DUB), and then immunoblotted. As shown in supplemental Fig 1 of the revised manuscript, the slower migrating OPTN bands disappeared in a USP2-dependent manner. The slower migrating NDP52 and TOMM20 bands likewise disappeared. These results confirm that the slower migrating OPTN, NDP52, and TOMM20 bands are ubiquitinated.

*4- Conclusions from data obtained by immunofluorescent imaging are often drawn from only one image presented without further statistical analysis. *

Statistical significance was determined for the immunofluorescent data (original figures 1j, 2c and 3a). Please see Fig 1l, 2f, 2g, and 3a in the revised manuscript.

*Page 7: - authors referred to TBK1 phosphorylation induced by mitophagy induction as "TBK1 phosphorylation induced by Parkin-mediated ubiquitination" while mitophagy can be induced independently of Parkin (ex: via mitochondrial receptors) and without any evidence (according to referee's knowledge) of a link between ubiquitination by Parkin and TBK1 phosphorylation. *

As the reviewer indicated, Parkin-independent and ubiquitination-independent mitophagy pathways are also known (i.e. receptor-mediated mitophagy driven by NIX, BNIP3, BCL2L13, FKBP8, FUNDC1, or Atg32). Therefore, references to "mitophagy" in our manuscript were reworded as "Parkin-mediated mitophagy". Since TBK1 phosphorylation is observed before mitochondria are degraded and is dependent on Parkin-mediated ubiquitin (for example, see Fig 1c), we use the phrase "TBK1 phosphorylation triggered by Parkin-mediated OMM ubiquitination".

*Fig 1g: Western blots performed in Penta KO cells without exogene expression of any autophagy receptors should be provided as control. Furthermore, lower expression of NDP52 relative to that of Optn (using flag antibodies) should be discussed as it can explained the differential levels in TBK1 phosphorylation observed. *

As suggested, we repeated the experiment using Penta KO cells in the absence of exogeneous autophagy adaptor expression. Furthermore, we expressed different amounts of NDP52 and OPTN (indicated as low and high in the figure) in Penta KO cells to rule out the possibility that higher TBK1 phosphorylation is induced by simple overexpression of autophagy adaptor (please see Fig 1g and h in the revised manuscript). At high NDP52 expression (2.5-3.0-fold higher than endogenous NDP52), phosphorylated TBK1 was reduced to ~30% the level of that observed in WT cells after 3 hrs with val and baf. In contrast, Penta KO cells with higher OPTN expression (3.0-fold higher than endogenous OPTN) had phosphorylated TBK1 signals that were 2-fold higher than those in WT cells. Based on these results, we concluded that OPTN is an important adaptor for TBK1 activation during Parkin-mediated mitophagy.

*Page 8: Supplemental Fig 1a: - The inability of authors to observe TBK1 endogenous signal in HeLa cells using commercially available antibodies is surprising as many publications reported successful staining (see Figure 1 of Suzuki et al. 2013 Cell type-specific subcellular localization of phospho-TBK1 in response to cytoplasmic viral DNA. PLoS One. 8:e83639 among others) as well as commercial promotion (see Anti-NAK/TBK1 antibody from Abcam reference: ab235253). *

For the original manuscript, anti-TBK1 antibodies purchased from abcam (ab235253), CST (#3013S), Proteintech (28397-1-AP), and GeneTex (GTX12116) for immunostaining were unable to yield TBK1-positive signals (please see Fig 1 for reviewers below). WT and TBK1-/- HCT116 cells stably expressing Parkin were treated with valinomycin for 1 hr and immunostained with the indicated antibodies. Anti-phos-TBK1 antibody (CST, #5483) was used as a positive control. Based on these results, we stated in the original manuscript that the "endogenous TBK1 signal could not be observed using commercially available antibodies". At the reviewer’s suggestion, we purchased anti-TBK1 antibodies from abcam (ab40676) and CST (#38066). As shown in the figure below, the immunofluorescent signals generated by these antibodies were detected in WT, but not in TBK1-/- cells. The CST (#38066) antibody yielded a stronger signal, most of which was on damaged mitochondria. Thanks to this suggestion, we repeated the experiment using the new anti-TBK1 antibody. Furthermore, based on a suggestion from Reviewer 3, we detected mitochondrial recruitment of TBK1 during mitophagy stimulation (valinomycin for 30 min or 2 hrs in the presence and absence of bafilomycin; supplemental Fig 2 in the revised manuscript). We also detected association of endogenous TBK1 with ubiquitin-positive condensates in WT, FIP200KO, and ATG9A KO cells (Fig 3a and supplementary Fig 7a in the revised manuscript).

*- Conclusions of the localization of signal on mitochondria (dispersed, in the periphery or at contact sites) are clearly over-interpreted in the absence of other membrane or autophagosome specific labeling and statistical colocalization analyses of multiple images. It is particularly difficult to assess any difference between Tax1BP1, p62 and NBR1 localization on mitochondria subdomains. *

We previously expressed each FLAG-tagged autophagy adaptor in Penta KO cells and observed their localization during Parkin-mediated mitophagy and found that exogenous FLAG-tagged OPTN and NDP52, but not p62, colocalized with LC3B (Yamano et al 2020 JCB). No one has assessed and compared the localization of all five endogenous autophagy adaptors. Although we still believe that the results (supplemental Fig1 in the original manuscript) are informative for researchers in the autophagy field, we decided to remove that data from the revised manuscript since they are not the main focus of the study. We will consider publishing those data elsewhere in the future after co-staining with autophagosome markers and assessing the statistical significance of colocalization as the reviewer suggested.

*Page 9: *

*- First part of results ended without any conclusions. *

As detailed in the previous response, we have removed results for mitophagic recruitment of autophagy adaptors (supplementary Figure 1 in the original manuscript).

*- The observation that "TBK1 phosphorylation was not apparent in the Optn mutant cell lines, even after 3 hrs of valinomycin, ..." is inconsistent with detection of bands with anti-pS172-TBK1 antibodies in Fig 2a detected at 1hr (with F178A) and 3 hrs (4LA, F178A, and 4LA/F178A mutants) of treatment. *

We apologize for the confusion. This statement was clearly our mistake. We had intended to state when "all autophagy adaptors are deleted" no phosphorylated TBK1 was observed. We have rewritten this part as "TBK1 phosphorylation was not apparent in the Penta KO cells even after 3 hrs with valinomycin".

*- Similarly, decreased levels of phosphorylated TBK1 stated for F178A mutant was only observed at 1 but not 3hrs or at 3hrs in the presence of bafilomycin. *

Based on the mitophagy assay previously reported (Yamano et al 2020 JCB), the F178A mutant only moderately inhibited mitophagy (60% mitophagy with the F178A mutant vs 80% mitophagy with OPTN WT). Conversely, the 4LA mutant and 4LA/F178A double mutant had stronger inhibitory effects on mitophagy (35% for 4LA and 9% mitophagy for 4LA/F178A). Therefore, the levels of phos-TBK1 after 1 hr with valinomycin or 3 hrs with valinomycin in the presence of bafilomycin are consistent with mitophagy progression. When mitophagy proceeds efficiently, the amount of phos-TBK1 in the 1 hr val samples is reduced relative to the 3 hr val samples due to autophagic degradation.

To more clearly observe and compare the levels of mitophagy-dependent phos-TBK1 among Penta KO cells expressing OPTN WT and the mutants, we treated cells with valinomycin in the presence of bafilomycin for 0, 0.5, 1, and 2 hrs and quantified phos-TBK1. The results are shown in Fig 2c and d in the revised manuscript. The phos-TBK1 signal increased over time with val and baf treatment in all OPTN expressing cells. Cells with OPTN WT generated the most phos-TBK1, whereas the signal generated by the F178A mutant was 75% that of the OPTN WT-expressing cells and the 4LA and 4LA/F178A mutants were about 40%. The experiments were independently replicated three times and error bars and statistical significance were incorporated into the associated graph. These results indicate that OPTN association with the autophagy machinery, in particular ATG9A vesicles, is important for TBK1 activation.

*Page 10: *

*The results and their repartition between figure 2 d, e, f, g, h, I and figure 3 is a bit confusing. In these experiments, it is shown Figure 2 that the absence or depletion of the autophagy machinery increase the phosphorylation of TBK1 and in Figure 3 it is shown that not only the phosphorylation of TBK1 accumulate but also the expression of NDP52, Tax1BP1 and p62. Is it because their degradation by autophagy is blocked (like for phosphoTBK1)? *

The reviewer is correct that autophagy adaptors other than OPTN (especially TAX1BP1, p62 and NBR1) are constantly degraded by macro/micro autophagy (Mejlvang et al. 2018 J Cell Biol and Yamano et al. 2021 BBA Gen Subj). Therefore, these adaptors accumulate in autophagy deficient cell lines (original Fig 3). In this study, we found that in the absence of mitophagy stimulation phos-TBK1 accumulates in autophagy deficient cell lines. This suggests that the accumulated autophagy adaptors induce TBK1 phosphorylation under basal conditions. In the original manuscript, we claimed that TBK1 phosphorylation under basal conditions does not require OPTN since in FIP200 KO and ATG9A KO cells it did not accumulate and did not primarily colocalize with ubiquitin- and TBK1-positive foci (original Fig 3). To gain more direct evidence for the revised manuscript, we performed additional experiments and discovered that TAX1BP1 is the adaptor responsible for TBK1 autophosphorylation under basal autophagy. We treated FIP200KO and ATG9A KO cells with siRNAs against OPTN, NDP52, TAX1BP, p62, and NBR1, and immunoblotted total cell lysates with an anti-phos-TBK antibody. As shown in Fig 3f in the revised manuscript, TAX1BP1 siRNA treatment decreased phos-TBK1 levels without affecting total TBK1. This result indicates that the accumulation of TAX1BP1 in the FIP200 KO and ATG9A KO cells induced TBK1 autophosphorylation under basal conditions. Considering this result, we treated WT, FIP200 KO, and ATG9A KO cells with TAX1BP1 siRNA, and then induced Parkin-mediated mitophagy with valinomycin in the presence of bafilomycin. This strategy eliminated the basal accumulation of phos-TBK1 and allowed us to focus on mitophagy-dependent TBK1 phosphorylation. Please see revised Fig 2h, I, j, and k. The results showed that mitophagy-dependent phos-TBK1 is predominantly attenuated in FIP200 KO and ATG9A KO cells. In Figs 2 and 3, we would like to emphasize that OPTN is required for TBK1 phosphorylation in response to Parkin-mediated mitophagy, whereas TAX1BP1 is required for TBK1 phosphorylation in basal autophagy. Since Reviewer 3 commented that interpretation of the data in original Figs 2d, e, and f was challenging, we elected to move those results to the supplemental figures. We have incorporated the newly acquired data (mitophagy using FIP200 KO or ATG9A KO with TAX1BP1 siRNA cells) into the main figure. We believe that this makes the text easier for readers to understand.

*- Fig 2c: conclusions on *

*the reduction of recruitment of Optn mutants on autophagosome formation seem over-interpreted as: *

*1- no labeling with LC3 has been used to identified autophagsome, *

*2- immunofluorescent signals observed with mutants are dispersed throughout the entire mitochondria network (see the merged images) rendering impossible to distinguish between autophagosome-associated mitochondria and others. *

*The following conclusive sentence stating that association of Optn to damaged mitochondria is not sufficient for TBK1 activation based solely on IF of figure 2c seems therefore unrelated to the obtained data. *

To address concerns about the recruitment of OPTN mutants to the autophagosome formation site, we performed additional experiments. Penta KO cells and those expressing OPTN WT and mutants were treated with valinomycin for 1 hr, and FLAG-tagged OPTN, endogenous WIPI2, and HAP60 (mitochondrial marker) were detected by immunostaining. We detected endogenous WIPI2 because WIPI2 is recruited only to autophagosome formation sites (Dooley et al. 2014 Mol Cell), whereas LC3B assembles on autophagosome formation sites and is also associated with completed autophagosomes. Confocal microscopy images showed that cup-shaped OPTN WT that had been recruited to damaged mitochondria colocalized with WIPI2. Quantification further showed that during mitophagy the number of WIPI2 foci seen in cells expressing OPTN WT decreased in Penta KO cells and cells expressing OPTN mutants (4LA, F178A and 4LA/F178A). These data are shown in Fig 2e and f in the revised manuscript. In addition, we quantified the number of cells that either exhibited heterogeneous or homogeneous recruitment of OPTN to damaged mitochondria after treatment with valinomycin for 1 hr. More than 80% of Penta KO cells with OPTN WT had heterogeneous OPTN recruitment, whereas this distribution was only present in 10% of cells expressing either OPTN 4LA or OPTN 4LA/F178A. Although cells expressing the OPTN F178A mutant exhibited 50% heterogeneous recruitment, this may be because the mutant can interact with ATG9A. As mentioned above, our previous mitophagy analyses (Keima-based FACS analysis, Yamano et al 2020 JCB) showed that the OPTN F178A mutant induced ~60% mitochondrial degradation (which is correlated strongly with OPTN distribution), whereas it was 80% with OPTN WT and 9% with 4LA/F178A.

*- Fig 2d: authors should explain why ATG KO cells displayed lipidated LC3B in the absence of efficient autophagy processes. *

We thank the reviewer for the suggestion. We added the following sentence to explain the function of ATG5 in LC3B lipidation. "Since LC3B lipidation is catalyzed by ATG5, but not FIP200 and ATG9A, the lipidated form disappears only in ATG5 KO cells (Hanada et al 2007 J Biol Chem). "

*- Fig 2e: despite authors statement that TBK1 phosphorylation did not increase during mitophagy in ATG KO cells, increased pS172-TBK1 is visible in FIP200 and ATG5 KO cells especially between 1 and 3 hrs of stimulation, leading to inaccurate conclusions that TBK1 phosphorylation requires the autophagy machinery. Therefore, overall assumption that both ubiquitination and autophagy subunits are required for TBK1 autophosphorylation appears erroneous. *

As the reviewer indicated, phos-TBK1 levels gradually increased in ATG KO cells. The main text was rewritten to more accurately reflect this increase. Based on experiments using the monobodies and those conducted during the revision process, it is apparent that although the autophagy machinery may not be completely essential for TBK1 phosphorylation, it clearly facilitates TBK1 phosphorylation in response to Parkin-mediated mitophagy.

*Page 12: *

*- Fig 3a: conclusion that Optn signal is more cytosolic and did not localize with Ub condensates seems speculative as based on: *

*1- only one immunofluorescence image without statistical analysis *

*2- Optn and Ub signals are lower in images with Optn is analyzed compared to other images in which NDP52, TAX1BP1 and NBR1 are detected. *

To address these concerns, we compared and quantified the signal intensities of all endogenous autophagy adaptors in FIP200 KO and ATG9A KO cells. The quantification data are shown in Fig 3a and the immunofluorescence images are shown in supplementary Fig 6a of the revised manuscript.

*- Fig 3b: interpretation of western blot data is uncertain due to lack of appropriate loading control, especially with pellets (P) extracts. In addition, it is not clear how to conclude from the experiments in Fig 3b that autophagy adaptors other than Optn mediate TBK1 phosphorylation. *

When autophagy is inhibited, p62 accumulates in the cytosol as aggregates (Komatsu et al. 2007 Cell). Therefore, p62 should be a positive control. Indeed, Fig 3b in the original manuscript (Fig 3b and c in the revised manuscript) showed that the amount of p62 in the pellet fraction was elevated in FIP200 KO and ATG9A KO cells. Furthermore, these aggregates were also observed in the imaging data (Fig 3a and supplementary Fig 7 in the revised manuscript). As the reviewer indicated, the original manuscript did not clarify whether autophagy adaptors other than OPTN mediated TBK1 phosphorylation; however, our revised results clearly demonstrate that TAX1BP1 is the adaptor responsible inducing TBK1 autophosphorylation when basal autophagy is impaired (please see Fig 3f in the revised manuscript).

*Minor point: reference is missing in the last sentence of the paragraph stating that K48-linked chains dominate when autophagy pathways are impaired. *

While several autophagy adaptors preferentially interact with K48-linked ubiquitin chains (Donaldson et al. 2003 PNAS etc), TRAF6 is recruited to ubiquitin-condensates via p62-mediated K63-linked ubiquitination (Linares et al. 2013 Mol Cell). Furthermore, K33-linked ubiquitin chains are also present in p62-positive condensates (Nibe et al. 2018 Autophagy). Because it’s not clear which ubiquitin-linkage is dominant in the condensates, we decided to delete the sentence. We regret the confusion.

*Page 13: *

*Conversely to Optn, they find that the other autophagic receptors localize in insoluble fractions (what does it mean?) independently of TBK1 expression (experiments with DKO cells) and also independently of Optn (where is this shown?). Altogether, these experiments are far from the message of the manuscript. The title of the paragraph "TBK1 activation does not require Optn under basal autophagy conditions" is not correct because even if the level of expression of autophagic receptors and TBK1 phosphorylation are increase in response to the depletion of the autophagy machinery, it does not increase autophagy. *

According to the suggestion, we changed the title of the paragraph to "TAX1BP1, but not OPTN, mediates TBK1 phosphorylation when basal autophagy is impaired." In addition, we rewrote this section.

*- Fig 3d: authors should mention the nature of the upper band observed in Optn western blot and show the same experiment in since solely TBK1 depleted cells since they stated that "electrophoretic migration of Optn was not affected by TBK1 deletion". In addition, suggesting from these sole experiments that "NP52, TAX1BP1, p62, NBR1 and AZI2 form Ub-positive condensates where TBK1 is activated" seems over-interpretated. *

Reviewer 3 suggested we characterize the upper band in the OPTN blot (Fig 3d in the original manuscript). To determine if the band is genuine OPTN, we used phostag-PAGE to analyze cell lysates from cells treated with control siRNA or OPTN siRNA and found that both the lower and upper bands were OPTN species (please see "Figure 2 for reviewers" in our response to Reviewer 3). The same pattern was reported by the Wade Harper group (Heo et al. 2015 Mol Cell). They showed that the OPTN double band pattern on phos-tag PAGE was not affected by TBK1 deletion. We have cited this literature where appropriate in the revised manuscript. In WT cells, it is difficult to detect phosphorylation of autophagy adaptors by TBK1 because basal autophagy constantly degrades them. That’s why we used autophagy KO cell lines.

*Page 14: *

*- Fig 4: TBK1 phosphorylation was analyzed in Fig4d and not in Fig4b as stated. In addition, it is rather difficult to conclude from artificial multimerization experiments, as the authors have done, that interaction between Optn and autophagy components contributes to Optn multimerization in genuine conditions. *

Detection of phos-TBK1 has been corrected to Fig 4b. Although artificial, the fluoppi assay provides insights into how OPTN activates TBK1 and how the autophagy machinery contributes to TBK1 activation via OPTN. To determine if artificial OPTN multimerization could bypass the autophagy machinery requirement, we used the fluoppi assay. This assay was important for us to conclude that the autophagy machinery and Parkin-mediated ubiquitination allow OPTN to be assembled in close proximity to where TBK1 is activated. The main text was rewritten to better convey the benefits of the fluoppi assay.

*Page 15: *

*This work could have therapeutic consequences but the pathological mutants of TBK1 used affect ALS (Figure 5) while in the discussion it is proposed that monobodies could have a therapeutic interest in familial forms of glaucoma due to the E50K mutation of Optn. It should be better to target only one pathology. *

Both TBK1 and OPTN are causative genes for ALS and many pathogenic mutations are known to impact their function. In this study, we focused on ALS mutations in TBK1 that affect self-dimerization and investigated their impact in response to Parkin-mediated mitophagy. We created the monobodies as a tool to physically inhibit OPTN assembly at the contact site. Although our monobodies inhibit Parkin-mediated mitophagy, they would not be a useful therapeutic strategy for ALS due to the loss of function with the TBK1 mutations. However, because TBK1 E50K is a glaucomatous mutation that causes OPTN-TBK1 to bind more tightly, our monobodies might be applicable to glaucomatous pathology since they could disrupt this interaction. We thus feel that it is appropriate to mention the potential of the monobodies and their future utility in the Discussion.

*- Fig 5c, d: Authors stated that degree of TBK1 autophosphorylation correlated with OPTN phosphorylation at S177 whereas phosphorylated TBK1 is unaffected by L693Q and V700Q mutants that display decreased phosphorylated Optn In addition, authors interpretation of Figure 5 data is clearly problematic as they stated that: *

*1- neither 693Q and V700Q mutants had "significant effect on mitophagy", while decreasing efficiency from 78% to 37-51% *

*2- but conclude that 49.7% mitophagy levels of R357Q mutant is significant mitochondrial degradation. *

*Overall conclusion that mitophagy efficiency is correlated with phosphorylated TBK1 levels is therefore inaccurate. *

We regret that this section did not sufficiently describe the data. Reviewer 3 also noted that the text referencing Fig 5 was difficult to interpret. One of the reasons for the complicated data interpretation is the number of TBK1 mutants used. The L693Q and V700Q mutations used by Li et al. (2016 Nat Commun) were expected to inhibit Parkin-mediated mitophagy since those authors reported that the mutations prevented interactions with OPTN. However, our in-cell assay showed that the two mutations only moderately affected Parkin-mediated mitophagy. Furthermore, both the L693Q and V700Q mutations were engineered based on the X-ray structure, rather than being authentic pathogenic ALS mutations. To avoid any potential confusion, we decided to remove the L693Q and V700A data. We have re-evaluated the other data and have rewritten this section accordingly. Please see the revised main text.

*Discussion *

*Minor points: *

*page 20: - reference is missing in the sentence "Optn cannot oligomerize on its own on ubiquitin-decorated mitochondria". *

We have provided the appropriate reference.

*Major points: *

*Authors stated that they showed that Optn recruitment to damaged mitochondria, itself, is insufficient for TBK1 autophosphorylation, but did not show experiment of Optn recruitment to mitochondria and its consequences on TBK1 phosphorylation in the absence of mitophagy induction signal. Authors could for example target HA-Ash-6Ub to mitochondria in order to artificially recruit hAG-Optn to "ubiquitinated" mitochondria in the absence of mitophagy signal. *

We showed that the efficiency of TBK1 autophosphorylation was reduced in cells expressing the OPTN 4LA/F178A mutant, which cannot interact with the autophagy machinery (Fig 2c and d in the revised manuscript). Cells with FIP200 or ATG9A knockdown also have reduced phos-TBK1 (pS172) as shown in supplementary Fig 5e and f. The rate of phos-TBK1 (pS172) generation in ATG9AKO cells during Parkin-mediated mitophagy is reduced relative to that in WT cells (Fig 2j and k). Since a small amount of phos-TBK1 was generated in both ATG9A knockdown and KO cells (supplementary Fig 5e, f, Fig 2j and k), we concur that it would be premature to conclude that phosphorylation of TBK1 does not occur at all when autophagy core components are absent. A small amount of phos-TBK1 may be generated by OPTN that is freely distributed on the outer mitochondrial membrane. In the revised manuscript, we mention the possibility that TBK1 might be phosphorylated by OPTN independent of the autophagy machinery and were careful to avoid over-interpretation.

As shown in Fig 4, fusing OPTN with an Azami-Green tag can induce artificial multimerization and trigger the generation of phos-TBK1 (pS172). Therefore, we expect that mitochondria-targeted HA-Ash-6Ub would induce TBK1 phosphorylation in a hAG-OPTN-dependent manner as was observed with cytosolic HA-Ash-6Ub (Fig 4). The accumulation of OPTN at the contact site in Parkin-mediated mitophagy is important for TBK1 phosphorylation. Even if OPTN is forced to anchor to the mitochondria, this would induce isolation membrane formation and subsequent autophosphorylation of TBK1. Therefore, the utility of forcing OPTN to anchor to mitochondria is questionable.

*Similarly, experimental approaches used by authors lack dynamics parameters to conclude on formation and elongation of isolation membranes and contacts sites that could be probably obtained through video microscopy. *

Based on the reviewer’s comment, we performed time-lapse microscopy to observe OPTN recruitment to the contact site and followed its movement along with the elongation of isolation membranes during Parkin-mediated mitophagy. HeLa cells stably expressing GFP-OPTN and pSu9-mCherry (a mitochondrial marker) were treated with valinomycin (please see Fig 2l in the revised manuscript). Initial recruitment of GFP-OPTN near mitochondria was evident as small dot-like structures that then elongated over time to become cup-shaped structures and culminated in the formation of spherical structures. Considering the colocalization of OPTN with WIPI1/WIPI2 (markers of autophagosome formation site) in Fig 2e and supplementary Fig 2a, the time-lapse images strongly suggest that OPTN assembles at contact sites followed by elongation in tandem with isolation membranes during Parkin-mediated mitophagy.

*Finally, the model proposed by the authors does not take into account data showing that Optn basally interacts with ubiquitinated mitochondria and LC3 family members (see Wild et al., Phosphorylation of the autophagy receptor optineurin restricts Salmonella growth. Science. 2011 333:228-33), although at lower levels compared to induced conditions, relativizing the impact of the proposed model. *

According to the Reviewer 2 comment, we again read the Science paper (Wild et al. 2011) but could not find data showing that OPTN basally interacts with ubiquitinated mitochondria. At least, we think that under steady state conditions without mitophagy induction, mitochondrial ubiquitination and mitochondrial localization of OPTN are undetectable as shown in supplementary Figure 2 in our revised manuscript.

*In conclusion, this manuscript represents a lot of work but the experiments often lack controls and are over-interpretated. *

***Referees cross-commenting** *

*In my opinion, what emerges from these 3 reviews is that the results lack controls or have not been repeated enough to support the message that the interaction of Optn with ubiquitin and the ubiquitination machinery is sufficient to activate TBK1. In particular, as reviewer 1 says, the phosphorylation kinetics shown in Figure 1a are not consistent with TBK1 phosphorylation following the interaction of Optn with the ubiquitination machinery and ubiquitin. In Figure 1e, there is a decrease in TBK1 phosphorylation in contrast to WTcells as mentioned by Reviewer 1. In agreement with Reviewer 1, we believe that the WT cells are missing in Figure 1g. *

*With regard to Figure 2c, we agree with reviewer 1 that an LC3 label is missing in order to be able to interpret the data. In Figure 2e and f, we agree with reviewer 1 that it is difficult to understand why TBK1 phosphorylation increases in the absence of the autophagy machinery (FIP200 KO and ATG5KO). In Figure 3, loading controls are missing for 3b and c. The TBK1 KO cells alone are missing in Fig 2d. In Figure 2b, pTBK1 is missing. In agreement with reviewer 3, we believe that the data with fluoppi contradict the message of the manuscript since they show that TBK1 can be phosphorylated by ubiquitin in the absence of the ubiquitination machinery. In agreement with reviewer 3, we believe that the experiments in Figure 5 are very difficult to interpret. The first reviewer is right to ask the question of the replicates for figures 6c and d. *

We appreciate the summary of the reviewers’ comments. To address their concerns, we have included the appropriate controls and included the results of three independent experiments in the graphs, which now include appropriate error bars and statistical significance. Thus, we believe we have answered the most critical comments concerning the lack of controls.

In Fig 1a, phos-TBK1 was maximal following 30 min of valinomycin treatment. We confirmed using microscopy-based observations that recruitment of endogenous TBK1 and OPTN and the generation of phos-TBK1 and phos-OPTN at contact sites (marked by WIPI1) near damaged mitochondria was also maximal after 30 min of valinomycin treatment (supplementary Fig 2 and 3). Therefore, the kinetics of phos-TBK1 and phos-OPTN generation are consistent with the recruitment of OPTN-TBK1 to the contact site.

The data presented in Fig 2 clearly indicate that the autophagy components are involved in phos-TBK1 generation during Parkin-mediated mitophagy. Therefore, the claim that ubiquitination machinery is sufficient for TBK1 activation is incorrect. Although we agree that the autophagy gene deletions cannot completely inhibit TBK1 autophosphorylation, mitophagy-dependent generation of phos-TBK1 is largely impaired by ATG9A KO (Fig 2j and k). Thus, there is no doubt that isolation membrane formation is important for TBK1 activation following Parkin-mediated mitophagy.

Fig 1e - The reviewer is correct that phos-TBK1 is reduced in the NDP52 knockout. We have rewritten the main text. It is also true that NDP52 has a smaller effect on TBK1 autophosphorylation as compared to OPTN.

Fig 1g - Immunoblots using total cell lysates prepared from six different cell lines (WT, Penta KO alone, Penta KO stably expressing low or high OPTN or NDP52) under four different conditions (DMSO, valinomycin 1 hr, valinomycin 3 hrs, valinomycin + bafilomycin 3 hrs) showed that OPTN is a rate-limiting factor for TBK1 phosphorylation. Please see Fig 1g and h in the revised manuscript

Fig 2c - The recruitment of OPTN WT and associated mutants to the contact site was re-examined by immunostaining with WIPI2 labeling. We found that OPTN WT was both efficiently recruited to and formed the contact site. In contrast, the OPTN 4LA/F178A mutant was unable to interact with FIP200/LC3/ATG9A and was uniformly (i.e. homogenously) distributed on damaged mitochondria with the rate of autophagosome site formation reduced. Please see Fig 2e, f, g in the revised manuscript.

Fig 2e and f - KO of the autophagy core components FIP200 and ATG9A increased phos-TBK1 under basal, non-mitophagy-associated conditions (see Fig 3). The levels of autophagy adaptors other than OPTN also increased in FIP200 KO and ATG9A KO cells. Furthermore, as shown in Fig 3a and supplementary Fig 7, both phos-TBK1 and the autophagy adaptors accumulated in Ub-positive condensates. Based on previous reports (Mejlvang 2018 J Cell Biol), TAX1BP1, p62, and NBR1 have short half-lives and are quicky degraded by macro/micro autophagy. The accumulation of phos-TBK1 in the absence of autophagy occurs because autophagy-dependent degradation of TAX1BP1 (and other adaptors) is inhibited. This allows for the formation of Ub-positive condensates, which brings TBK1 into sufficient proximity for activation. This has been noted in the revised manuscript.

Fig 3b and 3c - We wonder if the "loading controls are missing for Fig 3b and 3c" statement might be a misinterpretation by the reviewer as TOMM20 was used as the loading control in the original Fig 3b. It was recovered in the supernatant fractions of WT, FIP200 KO, and ATG9A KO cells, indicating that the accumulation of autophagy adaptors in the pellet fractions depends on autophagy gene deletion. Similarly, actin and TOMM20 were used as loading controls in the original manuscript Fig 3c.

Fig 2d (perhaps meant to be Fig 3d) – A previous study reported that phos-tag PAGE blot of OPTN in TBK1 KO cells alone revealed no differences between WT and TBK1 KO cells (Heo et al 2015 Mol Cell). We cited this reference in the revised manuscript.

Fig 2b (perhaps meant to be Fig 4b) - Immunoblots of phos-TBK1 have been incorporated into the results of Fig 4b in the revised manuscript.

Fig 4 - We show in Fig 2 that induction of Parkin-mediated mitophagy promotes OPTN accumulation at contact sites formed by isolation membranes and ubiquitinated mitochondria, and that autophagy core subunits are required for efficient generation of phos-TBK1. Fig 3 shows that phos-TBK1 accumulates in Ub-positive condensates with TAX1BP1, rather than OPTN, and that it is responsible for phos-TBK1 accumulation. Together, these results suggest a model in which TBK1 is activated when OPTN and TBK1 are positioned near each other. We hypothesized that if we could force OPTNs into close proximity the autophagy machinery requirement for TBK1 activation might be bypassed. To assess this model, we designed the fluoppi assay shown in Fig 4. This assay was critical in that it provided an important clue for the molecular mechanism that OPTN and the autophagy machinery use to cooperatively induce TBK1 trans-autophosphorylation. Because the original manuscript may not have sufficiently conveyed our reasoning for the fluoppi analysis, we have rewritten this section. The main point of the fluoppi assay is that engineered OPTN multimerization was able to bypass the autophagy requirement for TBK1 activation.

Fig 5 - For easier interpretation, the L693Q and V700Q data, which are not related to ALS pathology, have been removed.

Fig 5d – Statistical significance has been determined for the mitophagy results and the main text has been rewritten for better clarity.

Fig 6c, d, and I – The experiments were independently replicated more than three times with statistical support and error bars incorporated into the associated graphs.

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

*this manuscript represents a lot of work but the experiments often lack controls and are over-interpretated. The manuscript is for a broad audience. *

For the revised manuscript, additional experiments were carefully performed with appropriate controls and the manuscript was rewritten to address concerns regarding over-interpretation. We hope that we have adequately addressed the reviewer’s comments.

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

*The authors investigated the mechanisms by which TBK1 is phosphorylated and thus activated in PINK1/Parkin-mediated mitophagy. They show data indicating that OPTN, by interacting both with ubiquitin-coated mitochondria and with the autophagy machinery, provides a platform where OPTN-bound TBK1 can be hetero-autophosphorylated by adjacent TBK1. *

*According to the prevailing model (prior to this manuscript), TBK1 activation via autophosphorylation leads to TBK1-mediated phosphorylation of OPTN S177 and subsequent pOPTN-mediated recruitment of autophagic isolation membranes to the mitochondria. However, based on the model provided in this manuscript, OPTN needs to interact first with both autophagic membranes and ubiquitin before TBK1 can become activated. *

*This is an important topic. Overall, the experimental data are of high scientific quality. For the most part, the manuscript is clearly written. The figures have been made with great care. The novel insights are relevant. However, a number of issues need to be addressed or clarified. *

*Major comments: *

• Fig. 1a-b shows that pTBK1 (pS172) formation already peaks after 30 min of valinomycin. Even when bafilomycin is added, pTBK1 level already reaches a near maximum after 30 min of valinomycin. If the model proposed by the authors is correct and pTBK1 (pS172) formation requires extensive interaction of OPTN with both ubiquitin and autophagic isolation membranes, they should be able to show (by immunostaining) that OPTN already extensively forms peri-mitochondrial cup/sphere-shaped structures that colocalize with isolation membrane markers after only 30 min of valinomycin. In the present manuscript, they only show formation of such structures after 1-3 h of valinomycin.* We thank the reviewer for the critical comments. Based on the suggestion, we performed immunostaining to observe the recruitment of TBK1 and OPTN to damaged mitochondria as well as the generation of phos-TBK1 (pS172) and phos-OPTN (pS177). HeLa cells stably expressing Parkin and 3HA-WIPI1 were treated with valinomycin for 30 min, and then TBK1, OPTN, phos-TBK1, and phos-OPTN were immunostained along with 3HA-WIPI1 (a marker of the autophagosome formation site) and TOMM20 (a mitochondria marker). Please see supplementary Fig 2a and 3a in the revised manuscript. The TBK1, OPTN, phos-TBK1, and phos-OPTN signals formed dot-like, cup-shaped, and/or spherical structures, most of which were peri-mitochondrial and colocalized with 3HA-WIPI1. In separate experiments, HeLa cells stably expressing Parkin were treated with valinomycin in the presence or absence of bafilomycin for 30 min or 2 hrs and then immunostained. Please see supplementary Fig 2b in the revised manuscript. After 30 min valinomycin in the absence of bafilomycin, many TBK1 and OPTN signals were observed on damaged mitochondria. These signals were quantified from more than 160 cells for each of the four conditions. Each microscopic image generated contained 18-36 cells and corresponds to one dot in supplementary Fig 2c. Based on these results, the abundance of TBK1 and OPTN on mitochondria after 30 min of valinomycin was much higher than that after 2 hrs with valinomycin (supplementary Fig 2c). Similar results were obtained for phos-TBK1 and phos-OPTN (supplementary Fig 3b and c). These results are consistent with the immunoblot data (Fig1a and b).

Furthermore, we show that Parkin expression levels affect the amount of phos-TBK1 generated during mitophagy. Please see supplementary Fig 4 in the revised manuscript. When PARKIN was integrated into HeLa cells under a CMV promoter via an AAVS1 (Adeno-associated virus integration site 1)-locus, the resultant cell line (referred to as high-Parkin) had higher Parkin levels than HeLa cells in which PARKIN was introduced by retrovirus infection (referred to as low-Parkin). In high-Parkin HeLa cells, phos-TBK1 levels reached a maximum after 30 min with valinomycin, while in low-Parkin HeLa cells, phos-TBK1 levels were comparable after 30 min and 1 hr. High-Parkin HeLa was used for Fig 1a, b, c, and d as well as supplementary Fig 1, 2, 3 and 4. For all other Figs, PARKIN genes were introduced by retrovirus infection. This is one of the reasons why val was used for 30 min in Fig1, but 1-3 hrs for the other Figs. Because 3 hrs valinomycin treatment may be unsuitable for evaluating OPTN recruitment to mitochondria/isolation membrane contact sites, we deleted the original Fig 2c and replaced it with the val 1 hr data (Please see Fig 2e in the revised manuscript).

• The authors propose that OPTN needs to interact both with ubiquitin on mitochondria and with isolation membrane proteins such as ATG9A to allow TBK1 phosphorylation. However, their fluoppi experiments in Fig. 4 seem to contradict this. In the fluoppi experiments, the authors generate multimeric OPTN-Ub foci and this is apparently sufficient to induced TBK1 phosphorylation at S172 (shown in 4d,f). In this experiment, there is no induction of autophagy or formation of isolation membranes, and TBK1 nevertheless gets activated.*

Figure 2 demonstrates that both ubiquitin on mitochondria and formation of the isolation membranes are needed to provide a platform for OPTN to assemble in close proximity to each other and subsequently induce TBK1 autophosphorylation. To determine if OPTN proximity is sufficient for TBK1 autophosphorylation (i.e., if engineered OPTN multimerization can bypass the autophagy machinery requirement for TBK1 autophosphorylation), we used the fluoppi assay. The results clearly showed that engineered OPTN multimerization induced TBK1 autophosphorylation without the need for the autophagy machinery. Although this is not a mitophagy experiment, the fluoppi assay provided crucial insights into the molecular mechanism underlying OPTN-mediated TBK1 autophosphorylation. The main text was rewritten to provide more clarity regarding the purpose of the fluoppi experiments.

• Can the authors be more concrete/specific in the discussion about the molecular mechanisms that explain why this 'platform' that is created by OPTN-autophagy machinery interactions is so crucial for TBK1 activation? If I understand the model in Fig. 7D correctly, the OPTN-autophagy machinery interactions are mainly important because they reduce the distance between OPTN-bound TBK1 molecules so that they can trans-phosphorylate each other. But if TBK1 autophosphorylation was just a matter of proximity between OPTN-bound TBK1 molecules, interaction of OPTN with densely ubiquitinated mitochondria should already be sufficient for TBK1 phosphorylation. When multiple OPTN molecule bind to one ubiquitin chain or to closely adjacent ubiquitin chains (similar to the fluoppi experiments), TBK1 molecules binding to OPTN would be expected to be already closely enough to one another for trans-autophosphorylation.*

The amount of phos-TBK1 during Parkin-mediated mitophagy was reduced in cells with the OPTN 4LA/F178A mutant, which cannot interact with the autophagy machinery (e.g. FIP200, ATG9A, and LC3) but can be targeted to mitochondria (see Fig 2c, d). ATG9AKO cells also had reduced amounts of phos-TBK1 relative to WT cells (See Fig 2j, k). Therefore, rather than OPTN-ubiquitin freely diffusing laterally on the outer membrane, we suggest that the contact site OPTN forms with ubiquitin and the autophagy machinery provides a more suitable platform for TBK1 autophosphorylation because it maintains TBK1 in a proximal position for a longer period of time.

The OPTN UBAN domain binds a ubiquitin-chain composed of two ubiquitin molecules (Oikawa et al. 2016 Nat Comm), and during Parkin-mediated mitophagy only shorter length poly-ubiquitin chains are generated on the mitochondrial surface (Swatek et al. 2019 Nature). Based on those findings, it is unlikely that multiple OPTN bind to one ubiquitin chain. Of course, we cannot rule out the possibility that TBK1 autophosphorylation does not occur on mitochondria in the absence of autophagy components. While full activation of TBK1 requires OPTN to associate with the isolation membrane, initial TBK autophosphorylation at the onset of mitophagy may occur based only on the OPTN-ubiquitin interaction. These explanations have been added to the Discussion in the revised manuscript.

Furthermore, based on comments from Reviewer 2, we performed time-lapse microscopy to observe OPTN dynamics during Parkin-mediated mitophagy (please see Fig 2l). HeLa cells stably expressing GFP-OPTN and pSu9-mCherry (a mitochondrial marker) were treated with valinomycin. GFP-OPTN was initially a peri- mitochondrial dot-like structure that elongated over time to a cup-shaped structure and which eventually ended up forming a spherical structure. The time-laps imaging showed that, at least in WT cells, OPTN is directly recruited to the contact sites and elongates along with the isolation membranes. We thus concluded that TBK1 is activated (autophosphorylated) at the contact site rather than on the outer membrane where OPTN-TBK can move freely.

• Fig. 5c,d and P. 16: the mitophagy experiments in TBK1-/- cells expressing the different mutant forms of TBK1 are hard to interpret because it is not clear which mitophagy differences are statistically significant. The main text about this part (p. 16) is also confusing.*

We regret the confusion. Reviewer 2 also noted that the main text for Fig 5 was difficult to interpret. One of the reasons that complicated interpretation of the data is the number of TBK1 mutants used. The L693Q and V700Q mutations used by Li et al. (2016 Nat Commun) were expected to inhibit mitophagy since those authors reported that the mutations prevented interactions with OPTN. However, our in-cell assay showed that the two mutants only moderately affected Parkin-mediated mitophagy. Furthermore, both L693Q and V700Q were engineered based on the X-ray structure and are not ALS pathogenic mutations. To simplify the data and to make data interpretation easier, we decided to delete the L693Q and V700A data. We also determined statistical significance and rewrote this section.

• Many graphs lack statistics: Fig. 2b (pTBK1), Fig. 2f, Fig. 5b, Fig. 5d, Fig. 6c.*

We apologize for the lack of statistical analyses. We repeated experiments (if the experiments had not been independently performed more than three times) with statistical significance and error bars incorporated into the relevant figures.

*Other comments: *

• Fig. 1a: how do they know that the upper OPTN band is ubiquitinated OPTN? Reviewer 2 raised the same question. To demonstrate that the upper OPTN band is ubiquitinated, cell lysates after mitophagy induction were incubated in vitro* with a recombinant USP2 core domain, and the samples immunoblotted. As shown in supplementary Fig 1 in the revised manuscript, the upper OPTN band disappeared in a USP2-dependent manner. The upper NDP52 and TOMM20 bands similarly disappeared. Therefore, the upper OPTN, NDP52 and TOMM20 bands observed after mitophagy induction are ubiquitinated.

• Fig. 1a,b: the bafilomycin stabilization of pTBK1, OPTN and pOPTN indicates that these proteins are substantially degraded by autophagy within 30-60 minutes. This seems extremely fast for mitophagy completion. Please discuss.*

According to Kulak et al. (2014 Nat Methods), autophagy adaptor abundance (OPTN: 2.32E+4 and NDP52: 3.34E+4 in HeLa cell line) is low compared to that of mitochondria (TOMM20: 1.45E+6 in HeLa cell line). This is one of the reasons why autophagic degradation of adaptors is easier to see. Degradation of phos-TBK1 was likewise easy to detect, whereas total TBK1 was not. This discrepancy is likely based on differences in the abundance of phos-TBK1 and total TBK1. In addition, because autophagy adaptors are localized outside of the mitochondrial membrane they may be easier targets for lysosomal degradation than matrix proteins, which are localized inside the outer and inner membranes.

• Fig. 1a and rest of the manuscript: is there a reason why the authors only looked at S177 phosphorylation of OPTN and not also at OPTN S473, which is also phosphorylated by TBK1?*

Both mass spectrometry and mutational analyses indicated that OPTN S473 is phosphorylated during Parkin-mediated mitophagy and that OPTN phosphorylated at S473 strongly binds ubiquitin chains (Richter et al. 2016 PNAS and Heo et al. 2015 Mol Cell). However, because a phos-S473 OPTN antibody is, to the best of our knowledge, currently not commercially available, we did not focus on S473 phosphorylation.

• Fig. 1e-f: the main text states that "NDP52 KO effects on the pS172 signal were comparable to controls", but the blot in 1e and the graph in 1f indicate a difference between NDP52KO and WT (significant difference shown in 1f). This is confusing.*

We regret the over-interpretation. As the reviewer indicated, the amount of phos-TBK generated in response to mitophagy was reduced in NDP52 KO cells relative to that in WT cells. This has been corrected. We would like to emphasize that, unlike OPTNdeletion, NDP52 deletion has relatively minor effects on TBK1 phosphorylation.

• P. 9: "TBK1 phosphorylation however was not apparent in the OPTN mutant lines, even after 3 hrs with valinomycin, indicating that autophagy adaptors are essential for TBK1 activation (Fig. 2a)". However, the pTBK1 blot in Fig. 1a does show pTBK1 formation in the OPTN mutant (4LA etc.) lines. This is confusing.*

We apologize for this error. We intended to state “TBK1 phosphorylation was not apparent in the Penta KO cells without OPTN expression even after 3 hrs with valinomycin, indicating that autophagy adaptors are essential for TBK1 activation”. This sentence has been corrected in the revised manuscript.

• P. 10: "we subtracted the basal phosphorylation signal from that generated post-valinomycin (1 hr) and bafilomycin (3 hr)". Do they mean "from that generated post-valinomycin (3 hr) and bafilomycin (3 hr)?*

The reviewer is correct, we have corrected the error.

• P. 10, same paragraph: "the phosphorylation signal was ~90 but was less than 30 in ATG9A KO cells." Unclear what they mean by 90 and 30. 90% and 30%? 90-fold and 30-fold?*

The newly generated pTBK1 levels following Parkin-mediated mitophagy were calculated as pTBK1 [val & baf 3 hrs] minus pTBK1 [DMSO]. Since pTBK1 [val & baf 3 hrs] in WT cells is set to 100%, the newly generated pTBK1 in WT cells was 100% - 5% = 95%. The calculated values for pTBK1 [DMSO] and pTBK1 [val & baf 3 hrs] in ATG9A KO cells were ~55% and ~85%, respectively. Consequently, newly generated pTBK1 in the ATG9A KO cells is ~85% - ~55% = 30%. For clarity, we modified the figure to make the meaning of the numbers more apparent.

• Fig. 3a: Do they have an idea what kind of ubiquitinated substrates are contained in the ubiquitin-positive condensates that accumulate in FIP200 KO and ATG9A KO cells (i.e. without valinomycin treatment)?*

According to Kishi-Itakura et al. (2014 J Cell Sci), ferritin accumulates in the p62 condensates in FIP200 KO and ATG9A KO cells. However, it is unknown if the ferritin in the condensates is ubiquitinated. In the original manuscript, we confirmed by immunostaining that the p62-NBR1 condensates contain ferritin (Fig 3a in the original manuscript and supplementary Fig 7b in the revised manuscript).

• P. 12 and Fig. 3a: please explain why they look at ferritin, to improve readability.*

We thank the reviewer for the suggestion. As mentioned, ferritin is a known substrate that accumulates in p62 condensates, we thus sought to confirm its presence. We have included this explanation in the revised manuscript.

• Fig. 3a: please also include Ub stain for NBR1.*

We thank the reviewer for the suggestion. We obtained a rabbit anti-NBR1 antibody that allowed us to co-immunostain with the mouse anti-ubiquitin antibody (please see supplementary Fig 7b in the revised manuscript).

• Fig. 3d: the OPTN blot shows 2 OPTN bands. What does the upper OPTN band represent here?*

To determine if the two bands are genuine OPTN, total cell lysates prepared from HeLa cells treated with control siRNA or OPTN siRNA were subjected to phos-tag PAGE followed by immunoblotting with an anti-OPTN antibody. As shown below (Figure 2 for reviewers), the two bands (indicated as blue arrowheads) were absent in the OPTN knock down cells, indicating that both are derived from OPTN. Since phosphorylated species migrate slower in phos-tag PAGE, the upper band might be a phosphorylated form. The specific Ser/Thr phosphorylated in OPTN, however, remains to be determined. Heo et al. (2015 Mol Cell) also reported the two OPTN bands on phos-tag PAGE and that both were unchanged in TBK1 KO cells, suggesting that at least the upper band is not affected by TBK1.

• P. 14 and Fig. 4b: "Here, we found that phosphorylation of ... TBK1 (S172) was induced by the OPTN-ub fluoppi (Fig. 4b)." However, Fig 4b does not show a pTBK1 blot.*

We immunoblotted phos-TBK1. Please see Fig 4b in the revised manuscript.

*Reviewer #3 (Significance (Required)): *

*The novel insights are relevant. *

*According to the prevailing model (prior to this manuscript), TBK1 activation via autophosphorylation leads to TBK1-mediated phosphorylation of OPTN S177 and subsequent pOPTN-mediated recruitment of autophagic isolation membranes to the mitochondria. However, based on the model provided in this manuscript, OPTN needs to interact first with both autophagic membranes and ubiquitin before TBK1 can become activated. *

Based on our time-lapse microscopy observations (Fig 2l), OPTN recruited to the vicinity of mitochondria was visible as a small dot-like structures that likely correspond to contact sites between mitochondria and the isolation membrane since OPTN colocalizes with WIPI1 (please see supplementary Fig 2). These results support our proposed model that OPTN interacts with both isolation membranes and ubiquitin at the onset of mitophagy. Without TBK1 activation, OPTN can interact with ATG9A vesicles, a seed for isolation membrane formation (Yamano et al 2020 JCB), and TBK1 can interact with the PI3K complex (Nguyen et al 2023 Mol Cell). Therefore, OPTN-TBK1 can be recruited to the contact site from the very beginning of mitophagy induction prior to TBK1 being fully activated. Furthermore, the proposed model also includes an OPTN-TBK1 positive feedback loop; however, the earliest reactions in the positive feedback loop are too difficult to observe. For example, it’s widely known that PINK1 and Parkin form a positive feedback loop to generate ubiquitin-chains on damaged mitochondria, but the initial reaction has yet to be observed. It remains unclear if PINK1 is the first to phosphorylate mitochondrial ubiquitin (if this is the case, it remains unknown how ubiquitin comes to mitochondria) or if cytosolic Parkin first adds ubiquitin to the outer membrane albeit with very weak activity. Similarly, in our proposed model, we cannot determine the earliest OPTN-TBK1 reaction. As described in the Discussion in the revised manuscript, it remains possible that in the absence of autophagy machinery OPTN distributed freely on the outer membrane can induce trans-autophosphorylation, albeit weakly, as the earliest reaction.

We would like to thank Reviewer 3 for the critical comments and suggestions. We have performed several of the suggested experiments, added new data, and rewritten the text. We hope that these changes have sufficiently addressed the reviewer’s concerns.

I didn't know that chain letters (mail) were a thing before the internet. I thought that it was just a thing to troll people on the internet for fun in order to increase shares to their posts, I didn't know that it originated from physical mail... it's crazy to think that people would do this to scheme others back then as if there wasn't anything better to do.

Of course, one ought to be cognizant of the fact that he knew (or should have known) he was patently wrong then too.

His statements as quoted here allow him to gloss over the fact that a lot of the blame rests at his own feet.

Where is everyone turning to for their news, and are the ramifications in the hands of influencers? Sad news for democracy

It's unfortunate that he has a lot of self hate. Even though he is black, he's angry as if his brother told him the worst thing in life. If I was the brother I would be taken back by anger. Especially because physically the brother takes their dad's black features, so I'd feel as if he hates me just as much as he hates his own blackness.

I have dread in my head every time I use cloud service that there is an issue about to happen. There will be a data breach, data loss, service becoming unavailable (e.g., they don't like you anymor, e.g., Github can block you from your private repos at their will), it's just a matter of time.

So I need to get my data out, back it up, if service been kind to allow for that. But that's a burden. It's a hidden cost you learn to recognize over time. May not be apparent from the start to everybody.

Yes, socio-economic status. I think being seen to afford it is the coping strategy here, not so much doing the thing as the thing itself is highly unlikely to be something else than a placebo. It's all curtain, no wizard. The French youth fishing urban waters may be much more real in effect than the high end stuff. One is escapist, the other is pretending to be. Also the assumption that sanity is a mask to maintain above a roiling sea of insanity is an odd comparison here wrt urban life. It makes the individual insane (or probably driven there) vs the insanity of systems.

Author Response

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

We thank the reviewers and editor for their thoughful and careful evaluation of our manuscript. We appreciate your time and effort and have incorporated many of these suggestions to improve our revised manuscript.

Reviewer #1 (Public Review):

Summary: Cullinan et al. explore the hypothesis that the cytoplasmic N- and C-termini of ASIC1a, not resolved in x-ray or cryo-EM structures, form a dynamic complex that breaks apart at low pH, exposing a C-terminal binding site for RIPK1, a regulator of necrotic cell death. They expressed channels tagged at their N- and C-termini with the fluorescent, non-canonical amino acid ANAP in CHO cells using amber stop-codon suppression. Interaction between the termini was assessed by FRET between ANAP and colored transition metal ions bound either to a cysteine reactive chelator attached to the channel (TETAC) or metal-chelating lipids (C18-NTA). A key advantage to using metal ions is that they are very poor FRET acceptors, i.e. they must be very close to the donor for FRET to occur. This is ideal for measuring small distances/changes in distance on the scales expected from the initial hypothesis. In order to apply chelated metal ions, CHO cells were mechanically unroofed, providing access to the inner leaflet of the plasma membrane. At high pH, the N- and C- termini are close enough for FRET to be measured, but apparently too far apart to be explained by a direct binding interaction. At low pH, there was an apparent increase in FRET between the termini. FRET between ANAP on the N-and Ctermini and metal ions bound to the plasma membrane suggests that both termini move away from the plasma membrane at low pH. The authors propose an alternative hypothesis whereby close association with the plasma membrane precludes RIPK1 binding to the C-terminus of ASIC1a.

Strengths: The findings presented here are certainly valuable for the ion channel/signaling field and the technical approach only increases the significance of the work. The choice of techniques is appropriate for this study and the results are clear and high quality. Sufficient evidence is presented against the starting hypothesis.

Weaknesses: I have a few questions about certain controls and assumptions that I would like to see discussed more explicitly in the manuscript.

My biggest concern is with the C-terminal citrine tag. Might this prevent the hypothesized interaction between the N- and C-termini? What about the serine to cysteine mutations? The authors might consider a control experiment in channels lacking the C-terminal FP tag.

While it is certainly possible that the C-terminal citrine tag is preventing the hypothesized interaction between the intracellular termini, there are a few things that mitigate (but not eliminate) this concern. First, previous work looking at the interaction between the intracellular termini used FPs on both the N- and C-termini and concluded that in fact there is an interaction (PMID:31980622). Our channels have only a single FP, and we use a higher resolution FRET approach. Second, we aVach our citrine tag with a 11-residue linker, allowing for enhanced flexibility of the region and hopefully allowing for more space for an interaction that was posited to be between the very proximal part of the C-terminus (near the membrane and away from the tag) and the untagged N-terminus. Third, we previously showed that Stomatin, a much larger protein than the NTD, could bind the distal C-terminus of rASIC3 with a large fluorescent protein connected by the same linker on the C-terminus. In the case of Stomatin, the interaction involved the residues at the distal portion of the C-terminus close to the bulky FP. Interestingly, while we did not publish this, without this flexible linker, Stomatin could not regulate the channel and likely did not bind.

Despite this, we agree that this is possible and have added a statement in our limitations section explicitly saying this.

Figure 2 supplement 1 shows apparent read-through of the N-terminal stop codons. Given that most of the paper uses N-terminal ANAP tags, this figure should be moved out of the supplement. Do Nterminally truncated subunits form functional channels? Do the authors expect N-terminally truncated subunits to co-assemble in trimers with full-length subunits? The authors should include a more explicit discussion regarding the effect of truncated channels on their FRET signal in the case of such co-assembly.

The positions that show readthrough (E6, L18, H515) were not used in the study. We eliminated them largely on the basis of these westerns. We elected to put the bulk of the blots in the supplement simply because of how many there were. We believe this is the best compromise. It allows us to show representative blots for all our positions without making an illegible figure with 7 blots.

The N-terminally truncated subunits would create very short peptides that are not able to create functional channels. A premature stop at say E8 would create a 7-mer. Our longest N-terminal truncation would only create a protein of 32 amino acids. These don’t contain the transmembrane segments and thus cannot make functional channels.

As the epitope used for the western blots in Figure 2 and supplements is part of the C-terminal tag, these blots do not provide an estimate of the fraction of C-terminally truncated channels (those that failed to incorporate ANAP at the stop codon). What effect would C-terminally truncated channels have on the FRET signal if incorporated into trimers with full-length subunits?

Alternatively, C-terminally truncated subunits would be able to form functional channels because they contain the full N-terminus, the transmembrane domains, the extracellular domain and a portion of the C-terminus. We don’t think this is a major contaminant to our experiments. The only two C-terminal ANAP positions we use are 464 and 505. In each of these cases, they are only used for memFRET. The ones that do not contain ANAP are essentially “invisible” to the experiment. Since we are measuring their proximity to the membrane, having some missing should not maVer. However, there is some chance that truncations in some subunits could allosterically affect the position of the CT in other subunits. We have added a discussion of this in the manuscript.

Some general discussion of these results in the context of trimeric channels would be helpful. Is the putative interaction of the termini within or between subunits? Are the distances between subunits large enough to preclude FRET between donors on one subunit and acceptor ions bound on multiple subunits?

Thank you for this comment. We did not directly test whether the distances are within or between subunits. We considered using a concatemer to do this, however, the concatemeric channels do not express particularly well. Then, UAA incorporation hurts the expression as well. It was unlikely we would be able to get sufficient expression for tmFRET.

However, the Maclean group has previously tested this using FRET between concatenated subunits and determined that FRET is stronger within than between subunits. We have updated the manuscript to reflect a more thorough discussion of our results in the context of their trimeric assembly.

The authors conclude that the relatively small amount of FRET between the cytoplasmic termini suggests that the interaction previously modeled in Rosetta is unlikely. Is it possible that the proposed structure is correct, but labile? For example, could it be that the FRET signal is the time average of a state in which the termini directly interact (as in the Rosetta model) and one in which they do not?

The proposed RoseVa model does not include the reentrant loop of the channel, so it is probable that this model would change if it were redone to include this new feature of the channel.

However, we do discuss the limitation of FRET as a method that measures a time average that is weighted towards closest approach in our discussion section. The termini are most certainly dynamic and it is possible that spend some time in close proximity. Given that FRET is biased towards closest approach, we actually think this strengthens our argument that the termini don’t spend a great deal of time in complex. In addition, our MST data suggests that the termini do not bind. We have added some commentary on this to the discussion section for clarity.

Reviewer #2 (Public Review):

Summary:

The authors use previously characterised FRET methods to measure distances between intracellular segments of ASIC and with the membrane. The distances are measured across different conditions and at multiple positions in a very complete study. The picture that emerges is that the N- and C-termini do not associate.

Strengths:

Good controls, good range of measurements, advanced, well-chosen and carefully performed FRET measurements. The paper is a technical triumph. Particularly, given the weak fluorescence of ANAP, the extent of measurements and the combination with TETAC is noteworthy.

The distance measurements are largely coherent and favour the interpretation that the N and C terminus are not close together as previously claimed.

Weaknesses:

One difficulty is that we do not have a positive control for what binding of something to either N- or Cterminus would look like (either in FRET or otherwise).

We acknowledge that this is a challenge for the approach. Having a positive control for binding would be great but we are not sure such a thing exists. You could certainly imagine a complex between two domains where each label (ANAP and TETAC) are pointed away from one other (giving comparatively modest quenching) or one where they are very close (giving comparatively large quenching), both of which could still be bound. This is essentially a less significant version of the problem with using FPs to measure proximity…they are not very good proxies for the position of the termini. These small labels are certainly beVer proxies but still not perfect. Our conclusion here is based more on the totality of the data. We tried many combinations and saw no sign of distances closer than ~ 20A at resting pH. We think the simplest explanation is that they are not close to one another but we tried to lay out the limitations in the discussion.

One limitation that is not mentioned is the unroofing. The concept of interaction with intracellular domains is being examined. But the authors use unroofing to measure the positions, fully disrupting the cytoplasm. Thus it is not excluded that the unroofing disrupts that interaction. This should be mentioned as a possible (if unlikely) limitation.

Thank you for your comment. We discuss unroofing as a potential limitation because it exposes both sides of the plasma membrane to changes in pH. We have updated this section to include acknowledgement of the possibility that unroofing disrupts the interaction via washout of other critical proteins.

Reviewer #3 (Public Review):

Summary: The manuscript by Cullinan et al., uses ANAP-tmFRET to test the hypothesis that the NTD and CTD form a complex at rest and to probe these domains for acid-induced conformational changes. They find convincing evidence that the NTD and CTD do not have a propensity to form a complex. They also report these domains are parallel to the membrane and that the NTD moves towards, and the CTD away, from the membrane upon acidification.

Strengths:

The major strength of the paper is the use of tmFRET, which excels at measuring short distances and is insensitive to orientation effects. The donor-acceptor pairs here are also great choices as they are minimally disruptive to the structure being studied.

Furthermore, they conduct these measurements over several positions with the N and C tails, both between the tails and to the membrane. Finally, to support their main point, MST is conducted to measure the association of recombinant N and C peptides, finding no evidence of association or complex formation.

Weaknesses:

While tmFRET is a strength, using ANAP as a donor requires the cells to be unroofed to eliminate background signal. This causes two problems. First, it removes any possible low affinity interacting proteins such as actinin (PMID 19028690). Second, the pH changes now occur to both 'extracellular' and 'intracellular' lipid planes. Thus, it is unclear if any conformational changes in the N and CTDs arise from desensitization of the receptor or protonation of specific amino acids in the N or CTDs or even protonation of certain phospholipid groups such as in phosphatidylserine. The authors do comment that prolonged extracellular acidification leads to intracellular acidification as well. But the concerns over disruption by unroofing/washing and relevance of the changes remain.

We acknowledge that unroofing is a limitation of our approach and noted it in the discussion. However, we have updated the section to include the possibility that the act of unroofing and washing could also disrupt the potential interaction between the intracellular domains as well as between these domains and other intracellular proteins. This was the best approach we could use to address our questions and it required that we unroof the cells. However, we look forward to future studies or new techniques that do not require the unroofing of the cells.

The distances calculated depend on the R0 between donor and acceptor. In turn, this depends on the donor's emission spectrum and quantum yield. The spectrum and yield of ANAP is very sensitive to local environment. It is a useful fluorophore for patch fluorometry for precisely this reason, and gating-induced conformational changes in the CTD have been reported just from changes in ANAP emission alone (PMID 29425514). Therefore, using a single R0 value for all positions (and both pHs at a single position) is inappropriate. The authors should either include this caveat and give some estimate of how big an impact changes spectrum and yield might have, or actually measure the emission spectra at all positions tested.

This is a reasonable concern and one we considered. Measuring the quantum yield would be quite difficult. However, we have measured spectra at a number of positions and see a relatively minimal shik in the peak. Most positions peak between 481 and 484nm. If you calculate the difference in R0 using theoretical spectra with a blue shik of 20nm, the difference in R0 is only ~1.5A. A shik of 20nm is on the higher side of anything we have seen in the literature (PMID 30038260) and since even with that large a shik, the difference is minimal we do not think measuring spectra for each position would impact the overall conclusions presented. As you noted, though, the quantum yield also changes. Assuming a change in yield from 0.22 to 0.47, the largest we found reported in the literature (PMID:29923827) , the R0 would increase by 2A. This same paper showed that the blue shiked position was the one with the higher extinction coefficient so these changes would be working in opposition to one another making the difference in R0 even smaller. It is important to note, that while tmFRET is a much more powerful measure of distance than standard FRET, these distances, as you point out, are quite challenging to measure precisely. Our conclusions are based less on the absolute distances and more on the observation that no positions show large quenching and that if there is any change upon acidification, it is in the wrong direction.

Overall, the writing and presentation of figures could be much improved with specific points mentioned in the recommendations for authors section.

See below.

The authors argue that the CTD is largely parallel to the plasma membrane, yet appear to base this conclusion on ANAP to membrane FRET of positions S464 and M505. Two positions is insufficient evidence to support such a claim. Some intermediate positions are needed.

We do not see in the paper where we suggest that the CTD is parallel. However, your point that we could try and determine if this was the case is correct. However, we aVempted to create several other CTD TAG mutants but struggled with readthrough and poor expression of these mutants so we opted to just include S464 and M505. Our point from these data is only that the distal CTD (505) must spend significant time near the membrane to explain our FRET data.

Upon acidification, NTD position Q14 moves towards the plasma membrane (Figure 8B). Q14 also gets closer to C515 or doesn't change relative to 505 (Figures 7C and B) upon acidification. Yet position 505 moves away from the membrane (Figure 8D). How can the NTD move closer to the membrane, and to the CTD but yet the CTD move further from the membrane? Some comment or clarification is needed.

This is a reasonable question and one that is hard to definitively answer. Our goal here was to test the hypothesis that the termini are bound at rest. Mapping the precise positions of the termini is difficult for reasons we will enumerate in the question that asks why we didn’t make a model. There are potentially multiple explanations but the easiest one would be that the CTD could move away from the membrane but closer to Q14, for instance, if the distal termini, say, rotated towards the NTD. This would move 505 closer and have no impact on whether or not the NTD and CTD moved away or toward the membrane.

Reviewer #1 (Recommendations For The Authors):

Minor concerns

The authors show the spectrum of ANAP attached to beads and use this spectrum to calculate R0 for their FRET measurements. Peak ANAP fluorescence is dependent on local environment and many reports show ANAP in protein blue-shiked relative to the values reported here. How would this affect the distance measurements reported?

This is an important point. See above for the answer.

Could the lack of interaction between the N- and C-terminal peptides in Figure 7 arise from the cysteine to serine mutations or lack of structure in the synthetic peptides. How were peptide concentrations measured/verified for the experiment?

It is possible that cysteine to serine mutations could prevent the interaction. It is also possible that these peptides are not capable of adopting their native fold without the presence of the plasma membrane or due to being synthetically created. However, the termini are thought to be largely unstructured. We received these peptides in lyophilized form at >95% purity and resuspended to our desired stock concentration (3 mM C-terminus, 1 mM N-terminus). Even if our concentration was off, we see no signs of interaction up to quite a high concentration.

How was photobleaching measured for correcting the data?

We executed several mock experiments at various TAG positions using either pH 8 and pH 6, where we performed the experiments as usual but with a mock solution exchange when we would normally add the metal. We normalized the L-ANAP fluorescence to the first image and averaged together these values for pH 8 and pH 6. We then corrected using Equation 2 in the manuscript..

We have updated the methods to include how we adjusted for bleaching.

The authors may wish to make it more explicit that their Zn2+ controls also preclude the possibility that a changing FRET signal between ANAP and citrine may affect their data.

Thank you for this comment. We agree, it would strengthen the manuscript to include this statement. We have now included this.

It might be useful to the reader if the authors could include (as a supplement) plots of their data (like in Figure 6), in which FRET efficiency has been converted to distance.

We considered this idea as well but felt like showing the actual data in the figures and the distances in a table would be best.

Figure 5D is mentioned in the text before any other figures. This is unconventional. Could this panel be moved to Figure 1 or the mention moved to later?

Changed

western blot is not capitalized.

Changed.

Figure 1, the ANAP structure shown is the methyl ester, which is presumably cleaved before ANAP is conjugated to the tRNA. The authors may wish to replace this with the free acid structure.

This is a fair point. We originally used the methyl ester structure to indicate the version of ANAP we chose to use. However, you are correct that the methyl ester is cleaved before conjugation to the tRNA. We replaced the methyl ester with the free acid structure to clarify this.

Figures 1 and 4 should have scale bars for the images.

Scale bars have been added to figures 1, 4, and 5.

In Figure 3, the letters in the structures (particularly TETAC) are way too small. Please increase the font size.

Changed

In Figure 3 and Figure 3 supplement 1, the axes are labeled "Absorbance (M-1cm-1)." Absorbance is dimensionless. The authors are likely reporting the extinction coefficient.

Thank you for catching this. We adjusted the axes to extinction coefficient.

In Figures 5 B and C, it might be clearer if the headers read "Initial, +Cu2+/TETAC, DTT" rather than "Initial, FRET, Recovery."

Changed

The panel labels for Figure 8 seem to be out of order.

Changed

The L for L-ANAP should be rendered, by convention, in small caps.

This is a good example of learning something new from the review process. This is the first I have ever heard of small caps. We can find no other papers that use small caps for L-ANAP so I am not 100% sure what convention this is referring to and don’t want to change the wrong thing in the paper. We are happy to change if the editorial staff at eLife agree but have lek this for now.

Reviewer #2 (Recommendations For The Authors):

With so many distances measured, why was not even a basic structural model attempted?

We certainly considered it, but a number of things lead us to conclude that it might imply more certainty about the structure of these termini than we hope to give. 1) Given that the FRET is a time average of positions, these distance constraints would not do much constraining. 2) Given that the termini are likely unstructured and flexible this makes the problem in 1 worse. 3) There is no structural information to use as a starting point for a model. 4) The flexibility of the linkers for each FRET pair also introduces uncertainty. This can, in theory, be modeled as they do in EPR but all of this together made us decide not to do this. What we hope readers take home, is the overall picture of the data is not consistent with the original RIPK1 hypothesis.

Maybe it would be good to draw a band on the graphs in Figure 6 for the FRET signal expected for interaction (and thus, disfavoured by these data). This would at least give context.

We agree this could be helpful, but it is not so easy to do. What distance would we choose? We could put a line at ~5Å (the model predicted distance). As we noted above, a number of distances could be compatible with an interaction. However, we think it’s unlikely that if a complex was formed that none of our measurements would show a distance closer than 20Å at rest and that an unbinding event would then lead to a decrease in distance. This, to us, is the take home message.

Minor points:

"Aker unroofing the cells, only fluorescence associated with the "footprint", or dorsal surface, of the cell membrane is lek behind."

The authors use dorsal and ventral in this section to describe parts of an adherent cell. But in the first instance, they remove the dorsal part of the cell, and then in this phrase, the dorsal part is lek behind....I am a bit confused.

Thank you for pointing out this mistake, we have fixed this. It is indeed the ventral surface lek behind.

"bind at rest an" - and?

Changed

"One previous study used a different approach to try and map the topography of the intracellular termini of ASIC1a comparable to our memFRET experiments." I think a citation is due.

Citation added

"great deal of precedent" even if this result is from my own lab, I would prefer that the authors note that it's one study from one lab! I think best just to delete "great deal of".

“Great deal of” deleted

I think the column "Significance" in the tables is unnecessary when the P value is given.

Thank you for this suggestion. We agree and have made the change.

Figure 7a Q14TAG has a clearly bimodal distribution at pH 8. What could be the meaning of this result? The authors do not mention it that I could find. Perhaps there is no meaning. The authors should state what they think is (or is not) going on.

This is a good question and we don’t have a good answer. It appears to be experimental variability. The data from the “low fret” in this experimental condition all came from the same days. So something was different that day. We considered that they might be outliers to exclude but thought showing all of our data was the beVer path. We reperformed the ANOVA here separating out the “outlier” day and nothing of substance changed. Both populations were still different with P value less than 0.001.

Typo: Lumencore

Changed

Maybe just a matter of taste but the panel created with Biorender in Figure 8 is not attractive and depicts the channel differently to in Figure 5D, which is again different from Figure 1A. Surely one advantage of using computer-generated artwork could be to have consistency.

We agree and have used the same cartoon for all of our images with the one exception being the schematics that are just meant to show the positions that are present in each bar graph.

Figure 4A was squashed to fit (text aspect ratio is wrong).

Fixed

Reviewer #3 (Recommendations For The Authors):

Citrine is used to report incorporation. Yet citrine has a strong tendency to dimerize (PMID 27240257). Did the authors use mCitrine or just Citrine? This is quite important in interpreting their data.

Thank you for pointing out this important distinction. We use mCitirine which we have added to the methods.

The manuscript has numerous instances of imprecise language. For example, page 10, last para, first line, "previous studies have looked at..." or page 7, final paragraph "tell a similar story". Related, the figures could be much better. For example, in Figure 1, where the authors depict the anap chemical in red, as opposed to the blue one might expect of a blue emiqng fluorophore. In figure 6, ANAP is also in red with the quenching group in green. This is opposite to how one typically thinks of FRET with the warmer color being the acceptor not the donor. Moreover, the pH 6 condition is also colored the same shade of red as the ANAP. Labels of Cys positions would again be useful here. In Figure 3, the heteroatoms of TETAC and C18-NTA are very small and difficult to see. It would also be good to label these structures, and the spectra below, so the reader can tell at a glance without looking at the caption, what the structures and spectra arise from. Also, how are the absorption spectra normalized? This is not discussed in the methods. The lack of attention to presentation mars an otherwise nice study.

Thank you for these points. We have made modifications to the manuscript to address these comments.

Abstract, second last line "Aker prolonged acidification, ...", 'prolonged' could be interpreted as 'it takes a while for the domain to move' or 'the movement only happens aker a while'. This not what the authors intend to convey. Consider modifying to just 'Aker acidification,'

We updated the main text to indicate that prolonged acidification is intended to describe acidification that occurs over the minutes timescale.

Pdf page 6, bottom para on Anap incorporation not altering channel function: What is meant by 'steady state pH dependence of activation'? This implies the authors applied a pH stimulus, then waited until equilibrium was achieved ie. until desensitization was complete and measured the current at that point. It seems more likely they simply applied different pH stimuli and measured the peak response and that the use of 'steady state' here is a typo.

We removed the phrase steady state.

Same section, controls of electrophysiology allude to 485, 505 and 515 ANAP-containing channels. In fact, the authors have no way of determining what fraction (if any) of the pH evoked currents arise from channels containing Anap in those positions versus from simply having a translation stop but still functioning. This should be mentioned.

This is correct. We cannot be sure the CTD TAG positions are not a mixture of ANAP-containing channels and truncations. See above for why we do not think this a big concern for the FRET experiments. Functionally, though, you are correct that we cannot tell. We now mention this in the paper.

Methods, the abbreviation for SBT should be defined somewhere.

Added.

Methods, unroofing section, middle paragraph, the authors use nM not nm to list wavelengths of light.

Changed.

Figure 3C-D: There's an unexpected blip in the Anap emission spectra at ~500 nm. Are the grating efficiency of the spectrograph and quantum efficiency of the camera accounted for in these spectra?

This is a good question. The data are not corrected for either camera efficiency or grating efficiency. We don’t have easy access to the actual data (although we can see a pdf version of each). There is a liVle blip in the grating efficiency graph that could partly explain the blip in our spectra.

Figure 5C, were recovery experiments routinely done? If so, would be good to show more than n = 1 in the plot to get an idea of reproducibility.

Recovery experiments were done in every experiment but are not shown for simplicity. We have included all FRET and recovery data for position Q14TAG-C469 at pH 6 in figure 5C to show reproducibility of our FRET and recovery data.

Table 1, considering adding a Δ distance column (pH 8 versus 6) so the magnitude of changes are more easily seen.

This is a reasonable suggestion but we decided not to include a Δ distance column. The data are whole numbers and people can easily determine the Δ distance. We felt that including that column would bring too much focus on what we think are preVy small changes. Our hope is that readers take away that the data are not consistent with complex formation between the determine and focus less on absolute distances.

Figure 7A, Q14tag pH 8 condition has a quite a bit of spread and, likely, two populations. These data, as well as G11, are unlikely to be parametric and hence ANOVA is inappropriate. A normality test, and likely Kruskal-Wallis test is called for.

Aker testing for normality, the data for Q14TAG C485 pH8 are non-normally distributed. However, a Kruskal Wallis is a non-parametric test for a one-way ANOVA and not applicable here. We separated the data out into population 1 and 2 and repeated the two-way ANOVA statistical test. When Q14TAG pH 8 is split into 2 populations, the statistics hardly change. When the data is not separated, Q14TAG pH 8 relative to pH 6 has a p-value <0.0001. When the 2 populations are separated, both populations relative to Q14TAG pH 6 still have a p-value of <0.0001.

• for: social superorganism - vs human multicellular being, social superorganism, Homni, major evolutionary transition, MET, MET in Individuality, Indyweb, Indranet, Indyweb/Indranet, CCE cumulative cultural evolution, symmathesy, Gyuri Lajos, individual/collective gestalt, interwingled sensemaking, Deep Humanity, DH, meta crisis, meaning crisis, polycrisis

• comment

  • True, there is no physical cohesion that binds human beings together into a larger organism, but there is another dimension - informational cohesion.
  • This informational cohesion expresses itself in cumulative cultural evolution. Even this very discussion they are having is an example of that
  • The social superorganism is therefore composed of an informational body and not a physical one and one can think of its major mentations as collective, consensual ideas such as popular memes, movements, governmental or business actions and policies
  • I slept on this and this morning, realized how salient Adam's question was to my own work
    • The comments here build and expand upon what I thought yesterday (my original annotations)
    • The main connections to my own sense-making work are:
      • Within our specific human species, the deep entanglement between self and other (the terminology that our Deep Humanity praxis terms the "individual / collective gestalt")
      • The Deep Humanity / SRG claim that the concurrent meaning / meta / poly crisis may be an evolutionary test foreshadowing the next possible Major Evolutionary Transition in Individuality.<br /> - https://jonudell.info/h/facet/?max=100&expanded=true&user=stopresetgo&exactTagSearch=true&any=MET+in+Individuality
        • As Adam notes, collective consciousness may be more a metaphorical rather than a literal so a social superorganism, (one reference refers to it as Homni
          • https://www.bbc.com/future/article/20140701-the-superorganism-engulfing-earth
        • may be metaphorical only as this higher order individual lacks the physical signaling system to create a biological coherence that, for instance, an animal body possesses.
        • Nevertheless, the informational connections do exist that bind individual humans together and it is not trivial.
        • Indeed, this is exactly what has catapulted our species into modernity where our cumulative cultural evolution (CCE) has defined the concurrent successes and failures of our species. Modernity's meaning / meta / polycrisis and progress traps are a direct result of CCE.
        • Humanity's intentions and its consequences, both intended and unintended are what has come to shape the entire trajectory of the biosphere. So the impacts of human CCE are not trivial at all. Indeed, a paper has been written proposing that human information systems could be the next Major System Transition (MST) that could lead to another future MET that melds biotic and abiotic
          • https://jonudell.info/h/facet/?max=100&expanded=true&user=stopresetgo&exactTagSearch=true&any=MST+-+information
          • or https://hyp.is/go?url=https%3A%2F%2Fwww.frontiersin.org%2Farticles%2F10.3389%2Ffevo.2021.711556&group=world
        • This circles back to Adam's question and what has just emerged for me is this question:
          • Is it possible that we could evolve in some kind of hybrid direction where we are biologically still separate individuals BUT deeply intertwingled informationally through CCE and something like the theoretical Indyweb/Indranet which is an explicit articulation of our theoretical informational connectivity?
          • In other words, could "collective consciousness be explicitly defined in terms of an explicit, externalized information system reflecting intertwingled individual/collective learning?
      • The Indyweb / Indranet informational laminin protein / connective tissue that informationally binds individuals to others in an explicit, externalized means of connecting the individual informational nodes of the social superorganism, giving it "collective consciousness" (whereas prior to Indyweb / Indranet, this informational laminin/connective tissue was not systematically developed so all informational connection, for example of the existing internet, is incomplete and adhoc)
      • The major trajectory paths that global or localized cultural populations take can become an indication of the behavior of collective consciousness.
        • Voting, both formal and informal is an expression of consensus leading to consensual behavior and the consensual behavior could be a reflection of Homni's collective consciousness

• insight

  • While socially annotating this video, a few insights occurred after last night's sleep:
    • Hypothes.is lacks timebound sequence granularity. Indyweb / Indranet has this feature built in and we need it for social annotation. Why? All the information within this particular annotation cannot be machine sorted into a time series. As the social annotator, I actually have to point out which information came first, second, etc. This entire comment, for instance was written AFTER the original very short annotation. Extra tags were updated to reflect the large comment.
    • I gained a new realization of the relationship and intertwingularity of individual / collective learning while writing and reflecting on this social annotation. I think it's because of Adam's question that really revolves around MET of Individuality and the 3 conversant's questioning of the fluid and fuzzy boundary between "self" and "other"
      • Namely, within Indyweb / Indranet there are two learning pillars that make up the entirety of external sensemaking:
        • the first is social annotation of the work of others
        • the second is our own synthesis of what we learned from others (ie. our social annotations)
      • It is the integration of these two pillars that is the sum of our sensemaking parts. Social annotations allow us to sample the edge of the sensemaking work of others. After all, when we ingest one specific information source of others, it is only one of possibly many. Social annotations reflect how our whole interacts with their part. However, we may then integrate that peripheral information of the other more deeply into our own sensemaking work, and that's where we must have our own central synthesizing Indyweb / Indranet space to do that work.
      • It is this interplay between different poles that constitute CCE and symmathesy, mutual learning.
      • adjacency between
        • Indyweb / Indranet name space
        • Indranet
        • automatic vs manual references / citations
      • adjacency statement
        • Oh man, it's so painful to have to insert all these references and citations when Indranet is designed to do all this! A valuable new meme just emerged to express this:
          • Pain between the existing present situation and the imagined future of the same si the fuel that drives innovation.

• quote: Gien

  • Pain between an existing present situation and an imagined, improved future is the fuel that drives innovation.
• date: 2023, Nov 8

• for: adjacency - consciousness - multiscale context

• adjacency between

  • Michael's example
  • my idea of how consciousness fits into a multiscale system
• adjacency statement
  • from a Major Evolutionary Transition of Individuality perspective, consciousness might be seen as a high level governance system of a multicellular organism
  • this begs the question: consciousness is fundamentally related to individual cells that compose the body that the consciousness appears to be tethered to
  • question: Is there some way for consciousness to directly access the lower and more primitive MET levels of its own being?

• for: panpsychism, Spinoza, universal consciousness

It's a bit annoying to see people throwing out false claims without real or any evidence behind it just so people will buy the product they are advertising. You don't even know if she actually uses castor oil at all, maybe she's just doing this once to get people to buy the product so she can make a profit off peoples negligence. But you would hope the general population would see that she is making false claims.

This is interesting: which one is it more likely to be? - My first assumption was the latter, but the first one could also make sense if you. think about how the experience of being in the club was more important to these participants than the people who were in it themselves. - But also it's clear that neither the Mild- nor No- Initiation groups experienced cognitive dissonance, so is this even relevant? They probably just rated the discussion low cuz it was boring af

Because of confounding factors that can affect how much someone has a favorable opinion towards an outcome they worked hard towards - Namely two factors: - 1) You might just like the result because it's good quality (which could be due to the amount of effort you put in, but doesn't mean you like it just because you put more effort in) - 2) You might like the result because you placed a higher value on it before even putting in the effort (which might make you work harder to achieve it, which could lead to liking it more, but doesn't mean you like it more just because you put more effort in)

reply to u/Paddy48ob at https://www.reddit.com/r/antinet/comments/17jscyk/comment/k80z1nn/?utm_source=reddit&utm_medium=web2x&context=3

Thanks for this pointer. As a note, when I compare my 1954 version against the photo of the 1990 edition (which has fewer pages), it's obvious that the "1. The ends of education" section in the 1954 edition is significantly more thorough with more references (and supplementary data) which don't exist in the 1990 edition. The 1990 edition presumably removes the references for the books which they may have removed from that edition (though it may have actually been even more--I didn't check this carefully).

Just comparing the two pages that I can see, I don't see any references to the added texts of the 1990 edition appearing in that version of the Syntopicon at all.

I took a quick look at the Syntopicon V1 (1990) via the Internet Archive and of the added texts that year I sampled searches for Voltaire, Erasmus, and John Calvin and the only appearances of them to be found are in the Addition Bibliography sections which is also where they appeared in the 1952 editions. My small sampling/search found no added references of any of these three to the primary portions of the main References sections, so they obviously didn't do the additional editorial work to find and insert those.

As a result, it appears that the 1952 (and reprint editions following it) have a measurably better and more valuable version of the Syntopicon. The 1990 (2nd Edition) is a watered down and less useful version of the original. It is definitely not the dramatically improved version one might have hoped for given the intervening 38 years.

reply to u/marco89lcdm at https://www.reddit.com/r/antinet/comments/17m7ggz/comment/k839k22/?utm_source=reddit&utm_medium=web2x&context=3

The way you're currently thinking is a top down approach in which you already know everything and you're attempting to organize it to make it easier for others who know nothing about the ideas to find them. The Luhmann model supposes you know nothing about anything to begin with and you're attempting to create order from the bottom up, solely by putting related ideas you're building on close to each other and giving them numbers so that you might find them again when you need them.

If your only use is for those two topics and closely related subtopics and nothing else, then consider not using a Luhmann-artig model? Leave off the numbers and create two tabbed cards with those headings (and possibly related subheadings) and then sort your related cards behind them. (This is closer to the commonplace book tradition maintained on index cards and used by those like Mortimer J. Adler et al., Robert Greene, Ryan Holiday and Billy Oppenheimer. Example: https://billyoppenheimer.com/notecard-system/)

Otherwise the mistake you may be making is mentally associating the top level numbers with the topics. Break this habit! The numbers are only there so you can index ideas against them to be able to find them again! These numbers aren't like the Dewey Decimal system where 510.### will always mean something to do with math. You'll specifically want to intermingle disparate topics, so the only purpose the numbers provide is the ability to find what you're looking for by using the index which will give you a neighborhood in which you'll find the ideas you know are going to be hiding there or very near by.

Cards that are near to each other (using the numbers as an idea of ordering and re-finding) create a neighborhood of related ideas, even if they're disparate in topics. This might allow you to intermingle two related ideas, one which is in anthropology and another from mathematics for example, but which would otherwise potentially be thousands of cards away from each other if done in a Dewey-like system.

Or to take your example, what do you do with an idea that relates to both management AND personal growth? If it's closer to an idea on management you might place it near a related idea on that branch rather than in the personal growth section where it may be potentially less useful in the future. (You can always cross index them if need be, but place it where it creates the closest link and thus likely the greatest value for building on top of your previous ideas.)

For more on this, try: https://boffosocko.com/2022/10/27/thoughts-on-zettelkasten-numbering-systems/

I suspect that Scheper suggests using the Academic Outline of Disciplines as a numbering structure because it's an early choice he made for himself and it provides a perch to give people a concrete place to start. Sadly this does a disservice because it's closer to the older commonplace topical method rather than to the spirit of the ordering that Luhmann was doing. It's especially difficult for beginners who have a natural tendency to want to do this sort of top-down approach.

I think Burnham makes an important point about how social media can exacerbate feelings of loneliness and disconnection. By constantly curating our online personas and viewing other people's life, we get caught in a cycle of performative connection that can feel isolating. As someone who struggles with social anxiety, I relate to that sense of detachment Burnham describes. Social media promises community but delivers the opposite.

I think this is true, we as parents are the example. But our kids have friends that text them most of the time, snap chat, instagram and tiktok are always on. The best thing that we as parents can do it add limitations to our childrens phones.

ContraPoints' video explores in great depth the psychology and sociological considerations underlying such behaviors within incel communities, particularly. "Masochistic epistemology" offers insight into this pursuit for truth that harms oneself directly based on one's negative self-perceptions.

I think it's interesting to note that the only way to continue to use these forests, ones that have been subjected to massive human intervention that has drastically changed the landscape, is with more human intervention.

It's interesting how we sometimes fail to understand/look for these backgrounds. I wonder how these types of observations through "an ethnographic eye" can help us understand capitalism in a more cultural lens and how it change our understandings.

Oh my gosh! do I relate to this so bad. Raise your hand if you're a Mexican woman that was raised to be good and obedient 🙋‍♀️ Now I am a grown up, and my mom gets mad at me because I am too scared to speak up for myself... the irony.<br /> Why did our parents think it was disrespectful when we questioned them? Why would they force us to do things against our will? I just realized it's a trauma response to the way the Spanish converted America.

In my experience, this is an ongoing issue. My collaborative classroom has been marginalized, and even my co-teacher suggests that we "move along" with or without understanding. Its hard to come back from the margins when its been happening all their lives. It's frustrating, and I want to challenge them, its just deciding what challenges will be most meaningful for them and what I can do to not exhaust my co-teacher!

Pretentious how & in what ways? All we know about her is that she was more qualified than Lasalle admitted to being. I wish we got more scene work in this essay, because as a woman reader it's honestly just hard to believe; is she pretentious our surrounded by less qualified men in the same position as she is? Interestingly, she is nameless as well, and Lasalle gives this kind of dismissal of her to readers - mentions her and her PhD, but shrugs off her contributions as clerical/secretarial - which is a full assumption on his part rather than fact.

Zuckerman mentions the DSA as his single hope, the only surprisal in this piece. Although the DMA is important wrt the silos too, as is the GDPR, it is the DSA that has the transparency reqs, plus actually describes the outside research access Zuckerman sees frustrated as mandatory. Says enforcement is slow however. Yes, at the same time it's not just reactive enforcement. It's about EU market access, pro-active disclosures are mandatory.

It's less critical in this case since you see no appreciable effect, but just worth noting as you use this drug in the future that the concentration used is quite high, above the level known to inhibit myosin 2, not just formins (ref: PMC8121067)

It's eye-opening to observe how systemic biases can exist in supposedly objective institutions, such as sentencing guidelines, and how they can disproportionately harm particular communities without any intentional discrimination on their part. It emphasizes the significance of tackling these structural challenges in order to achieve a fair and just society.

I feel like many people misunderstand this. Recommendation algorithms can never be 100% accurate as the algorithm is just based off feeding the data given. If the data from a user is more lenient towards Soccer one day and the other day it's Suits, there's only so much of a profile it can build off you. It's a huge misconception hat they produce biased outcomes, but they're simply built upon the data they're provided, so I don't think they're actually that biased in my opinion.

I wish there was some type of way for these recommendation algorithms to build full profiles on each of your friends. From my perspective, not all of your friends may have the same interests, and the algorithm might be misconveyed that if this entire friend group is friends with each other, they all like soccer. It's just an example of how even though it's typical that friend groups are formed on the basis of a similar interest, they're media interest could easily skew these algorithms for the rest of the friends.

Motion energy model - Contrast energy: when we have a response to a particular structure in the environment, you get a contrast energy response, which takes into account the response to a center excitatory region AND an off-center excitatory region - Take those responses, square them, add them together and you get a contrast energy model

• Motion energy: get more complicated because the response changes over time
• not just spatial, neurons are now spatio-temporal
• inhibititory/excitatory regions change over time
• excitatory region shifts as time advances
• means it's excitatory response to something as it moves, kind of tracks it
• everything not moving (static) gets removed, not present in the extracted motion energy
• idea is that we should be able to map this onto cortex, in a retinotopic/meaningful way

• step 1 is to measure motion energy and every instance of the video)

• Problem: time scale

• Motion energy response is much quicker than BOLD signal Filtered output is motion over time

Connection: Both these women share so much of themselves with like minded groups and individuals eager to engage and learn. I have had conversations with both of them separately and I am in awe of their generosity of time with how busy they both are in their careers and lives. This whole paragraph made me reflect on importance of community and how differently that can be defined. I am thinking specifically about the readings that focussed on black women building in the art world here and the emotional labour alone to get it done without any support. BAND, The State of Blackness database, “Black Wimmin: When and Where We Enter” https://canadianart.ca/essays/why-have-there-been-no-great-black-%20canadian-women-artists/ https://www.artsy.net/article/artsy-editorial-overlooked-black-women-altered-course-feminist-art

Where do stories for products come from?

Cinema is actually a motion of a couple images together that move fast, which is considered "the illusion of movement". All we see is one continuous movement when in reality, it's photographs flashing quick that trick our eyes to just see one movement.

Author Response

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

We appreciate the critical review of our manuscript. We believe that we have addressed the questions and concerns raised by the reviewers to the best of our ability. As part of the revision, we conducted two new experiments to enhance the rigor of the conclusions and to provide more insights into the mechanism of STEAP proteins, and we reorganized the Results section, as suggested by the reviewers, following to a clearer logical thread. The new data are briefly summarized below.

1) Reduction of L230G STEAP1 by reduced FAD. We made Leu230Gly STEAP1 mutant and measured the rate of heme reduction by reduced FAD. We found that the rate of heme reduction in L230G STEAP1 is slower than that in the wild type STEAP1. Since Leu230 is solvent accessible only from the intracellular side, this result supports the conclusion that reduced FAD binds to STEAP1 on the intracellular side and reduces the heme. This result also indicates that leucine, which is found at the equivalent position in STEAP1, 2 and 3, and Phe359 in STEAP4, has a significant role in mediating electron transfer from FAD to the bound heme.

2) Reduction of STEAP2 by reduced FAD. We showed that STEAP2 can be reduced when supplied with reduced FAD, and that the rate of heme reduction is significantly slower than that of reduction of STEAP1 by reduced FAD. This result is consistent with presence of the oxidoreductase domain (OxRD)† in STEAP2, which hampers direct entrance of the isoalloxazine ring of FAD to its binding pocket in the transmembrane domain (TMD). On the other hand, the rate of heme reduction by reduced FAD is much faster than that of heme reduction in the presence of NADPH and FAD, indicating that reduction of FAD by NADPH is rate-limiting in the electron transfer chain in STEAP2.

†: To be consistent with literature, we adopted the nomenclature “oxidoreductase domain (OxRD)” for the N-terminal soluble domain in STEAP proteins. We used the term “reductase domain (RED)” in the previous version of our manuscript.

Reviewer #1 (Public Review):

This important study reveals the structure of human STEAP2 for the first time and suggests the electron transport pathway, but some questions remain regarding the interpretation of the in vitro electron transport experiments, the lack of available redox couples, and potential alternative hypotheses that would if addressed, strengthen the claims in the manuscript.

Strengths

One of the clear strengths of the manuscript that stands out is the determination of the structure of human STEAP2. The structures of some other homologs are known, but STEAP2's structure was not, and STEAP2 seems to have an unusually low activity towards certain metal chelates. The approach of producing the human STEAP2 in insect cells with the supplementation of cofactor biogenesis components nicely results in cofactor-replete protein. The structure of STEAP2 reveals a domain-swapped trimer, with the NADPH-binding domain of the neighboring protomer within electron-transport distance of the FAD-heme axis. The FAD has an interesting and somewhat unusual extended conformation and abuts a Leu residue that may regulate electron transport. Another strength of the manuscript is the demonstration that STEAP1, which does not have the internal NADPH binding domain, can interact modestly and shuttle electrons to the heme in STEAP1 through FAD. These experiments nicely expand information on the function of STEAP1 and provide a structural basis for electron transport in STEAP2.

Weaknesses

A major weakness in the manuscript lies with the kinetics data and how the data, as presented, are unclear to the reader regarding their impact and their connection to the purported electron transport scheme. While multiple sets of data are reported, the analysis in all cases is simply the reduction of a hexacoordinate heme and its related spectra and kinetic parameters. In most cases, it's unclear to the reader which part of the electron pathway is being tested in which experiment. Simple diagrams would be helpful in each case. However, it's also unclear if all of the potential order of addition experiments were actually performed; i.e., flavin but no NADPH; NADPH but no flavin; flavin before NADPH; flavin after NADPH, etc. As there are multiple permutations that should be tested to demonstrate the electron transport pathway, presenting the data in a way that makes it clear to the reader is challenging. Particularly missing are the determined redox potentials of the hemes in both STEAP1 and STEAP2. Could differences in these heme redox potentials be drivers of the difference in metal reduction rates?

We re-structured the manuscript to follow a clearer logical thread. We provided explanations for which electron transfer steps are being examined in each experiment.

We cannot reliably determine EM due to insufficient amount of purified proteins. We are inclined to think that the bound heme on STEAP1 and STEAP2 have similar EM, based on their similar coordination geometry and nearly identical UV-Vis and MCD spectra. Thus, different rates of Fe3+-NTA reduction by STEAP1 and STEAP2 are likely due to differences in substrate binding site rather than different EM.

Also, the text indicates that STEAP2 does not show a reduction rate dependence on the [Fe3+NTA], but Figure 1A shows a difference in rates dependent on [Fe3+-NTA], and the shape of the reduction curve also changes based on [Fe3+-NTA]. This discrepancy should be rectified.

We fixed this error. The reduction of Fe3+-NTA by ferrous STEAP2 shows multiple phases and the reaction rates within the initial 2 seconds are weakly dependent on [Fe3+-NTA].

A second major weakness is the lack of any verification of the relevance of the STEAP2 oligomerization to its in vivo function. Is the same domain-swapped trimer known to exist in vivo? If the protein were prepared in other detergents, is the oligomerization preserved? It is alluded to in the text that another STEAP protein is also a trimer. Was this oligomerization verified in vivo?

The domain-swapped assembly is an interesting phenomenon, and it seems to provide a solution for bringing the FAD closer to heme. The same domain swapped trimeric assembly is also observed in the structure of STEAP4, which was purified in a different detergent (Nat Commun (2018), 9, page 4337). It is likely that this feature is shared by STEAP2, 3, and 4, and preserved during the purification process.

Could this oligomerization be disrupted to impede or abrogate electron transport to underscore the oligomerization relevance? This point is germane, as it would further suggest that the domain-swapped trimer observed in the STEAP2 cryo-EM structure is physiologically relevant, especially given how far the distance between the NADPH and the FAD would otherwise be to support electron transport.

We agree with the reviewer’s reasoning that the oligomeric assembly is required for proper function of STEAPs and thus could potentially be utilized for functional regulation. However, we are not aware of any physiologically relevant stimuli or treatment that would allow regulation of STEAP functions by inducing or forming different oligomeric states. Our experience with STEAP proteins is that the trimeric assembly is stable and well-preserved during the purification process and can only be disrupted under denaturing conditions such as SDS-PAGE.

Beyond these two areas in which the manuscript could be improved there are a couple of minor considerations. First, the modest resolution of the STEAP2 structure prevents assigning the states of NADP+/NADPH and FAD/FADH2 with confidence. An orthogonal measure would be useful for modeling the accurate states in the structure.

We agree. We clarified the ambiguity and stated in the main text that the cryo-EM structure of STEAP2 was determined in the presence of NADP+ and FAD.

Finally, the BLI b5R/STEAP1 binding/unbinding fits seem somewhat poor, especially at high concentrations of b5R in the dissociation regime, which likely influences the derived value of Kd. A different fitting equilibrium might yield better agreement between the experimental and theoretical results. Moreover, whether this binding strength is influenced by the reduction state of the NADPH would be helpful in understanding and contextualizing the weak binding affinity.

We think that non-specific binding likely causes deviations from the simple binding model at higher b5R concentrations. We made a comment on this in the main text. We agree with the reviewer that the interactions between b5R and STEAP1 could be redox dependent, for example, a reduced FAD on b5R may enhance the affinity. We could implement this by performing the binding experiments in an anaerobic chamber, but this is beyond the scope of the current study.

Reviewer #2 (Public Review):

The manuscript provides new insight into a family of human enzymes. It demonstrates that STEAP2 can reduce iron and STEAP1 can be promiscuous regarding the source of electron donors that it can use. The quality of the kinetics experiment and the structural analysis is excellent. I am less enthusiastic about the interpretation of data and the take-home message that the manuscript intends to deliver. Above all, the work combines data on STEAP2 and STEAP1 and it remains unclear which questions are ultimately addressed. A second critical point is about the interpretation of the experiment demonstrating that STEAP1 can be reduced by cytochrome b5 reductase. The abstract states that "We show that STEAP1 can form an electron transfer chain with cytochrome b5 reductase" whereas the main text discusses the data by suggesting that "we speculate that FAD on b5R may partially dissociate to straddle between the two proteins". The two statements seem to be partly contradictory. Cytochrome b5 reductases do not easily release FAD but rather directly donate electrons to heme-protein acceptors (PMID: 36441026). According to the methods section, no FAD was added to the reaction mix used for the cytochrome b5 reductase experiment. Overall, the data seem to indicate that the reductase might reduce the heme of STEAP1 directly. Would it be possible to check whether FAD addition affects the kinetics of the process?

We agree with the reviewer on this point. We do not have evidence indicating that FAD fully or partially dissociates from b5R to interact with STEAP1. We removed the statement in the revision.

We have not tried to add free reduced FAD to the mixture of STEAP1/b5R/NADH, because we feel that this would increase the complexity of the system and complicate data interpretation. We are working on determining the structure of b5R in complex with STEAP1 to visualize the electron transfer pathway, and we hope that such a structure would provide a framework for understanding electron transfer between the two proteins.

And to perform a control experiment to check that NAD(P)H does not directly reduce the heme of STEAP1 (though unlikely)?

We did the control experiment and included data in Fig. S3A. No reduction of heme by NADH alone.

A final point concerns the "slow Fe3+-NTA reduction by STEAP2". The reaction is not that slow as the initial phase is 2 per second. The reaction does not show dependence on the substrate concentration suggesting "poor substrate binding". I am not convinced by this interpretation. Poor substrate binding would give rise to substrate dependency as saturation would not be achieved. A possible interpretation could be that substrate binding is instead tight and the enzyme is saturated by the substrate. Can it be that the reaction is limited by non-productive substrate-binding and/or by interconversions between active and non-active conformations? We re-analyzed the data and revised the Results and Discussion.

We agree with the reviewer on this point. We re-analyzed the data and found that the reaction rates within the first 2 seconds are weakly dependent on [Fe3+-NTA] while the rates beyond 2 seconds do not show dependence on [Fe3+-NTA]. More studies are required to unravel the mechanism that leads to the complicated kinetic data.

Reviewer #3 (Public Review):

The six-transmembrane epithelial antigen of the prostate (STEAP) family comprises four members in metazoans. STEAP1 was identified as integral membrane protein highly upregulated on the plasma membrane of prostate cancer cells (PMID: 10588738), and it later became evident that other STEAP proteins are also over expressed in cancers, making STEAPs potential therapeutic targets (PMID: 22804687). Functionally, STEAP2-4 are ferric and cupric reductases that are important for maintaining cellular metal uptake (PMIDs: 16227996, 16609065). The cellular function of STEAP1 remains unknown, as it cannot function as an independent metalloreductase. In the last years, structural and functional data have revealed that STEAPs form trimeric assemblies and that they transport electrons from intracellular NADPH, through membrane bound FAD and heme cofactors, to extracellular metal ions (PMIDs: 23733181, 26205815, 30337524). In addition, numerous studies (including a previous study from the senior authors) have provided strong implications for a potential metalloreductase function of STEAP1 (PMIDs: 27792302, 32409586).

This new study by Chen et al. aims to further characterize the previously established electron transport chain in STEAPs in high molecular detail through a variety of assays. This is a wellperformed, highly specialized study that provides some useful extra insights into the established mechanism of electron transport in STEAP proteins. The authors first perform a detailed spectroscopic analysis of Fe3+-NTA reduction by STEAP2 and STEAP1, confirming that both purified proteins are capable of reducing metal ions. A cryo-EM structure of STEAP2 is also presented. It is then established that STEAP1 can receive electrons from cytochrome b5 reductase, and the authors provide experimental evidence that the flavin in STEAP proteins becomes diffusible.

The specific aims of the study are clear, but it is not always obvious why certain experiments are performed only on STEAP2, on STEAP1, or on both isoforms. A better justification of the performed experiments through connecting paragraphs and proper referencing of the literature would improve readability of the manuscript. Experimentally, the conclusions are appropriate and mostly consistent with the experimental data, although one important finding can benefit from further clarification. Namely, the observation that STEAP1 can form an electron transfer chain with cytochrome b5 reductase in vitro is an exciting finding, but its physiological relevance remains to be validated. The metalloreductase activity of STEAP1 in vitro has been described previously by the authors and by others (PMIDs: 27792302, 32409586). However, when over expressed in HEK cells, STEAP1 by itself does not display metal ion reductase activity (PMID: 16609065), and it was also found that STEAP1 over expression does not impact iron uptake and reduction in Ewing's sarcoma (cancer) cells (PMID: 22080479). Therefore, the physiological relevance of metal ion reduction by STEAP1 remains controversial. The current work establishes an electron transfer chain between STEAP1 and cytochrome b5 reductase in vitro with purified proteins. However, the conformation of this metalloreductase activity of the STEAP1-cytochrome b5 complex will be required in a cell line to prove that the two proteins indeed form a physiological relevant complex and that the results are not just an in vitro artefact from using high concentrations of purified proteins.

The work will be interesting for scientists working within the STEAP field. However, some of the presented data are redundant with previous findings, moderating the study's impact. For instance, the new structural insights into STEAP2 are limited because the structure is virtually identical to the published structures of STEAP4 and STEAP1 (PMIDs: 30337524, 32409586), which is not surprising because of the high sequence similarity between the STEAP isoforms. Moreover, the authors provide experimental evidence to prove the previous hypothesis (PMID: 30337524) that the flavin in STEAP proteins becomes diffusible, but the molecular arrangement of a STEAP protein, in which the flavin interacts with NADPH, remains unknown. Based on the manuscript title, I would also expect the in-depth characterization of STEAP1/STEAP2 hetero trimers (first identified by the authors), but this is only briefly mentioned. When taken together, this study by Chen et al. strengthens and supports previously published biochemical and structural data on STEAP proteins, without revealing many prominent conceptual advances.

We thank the reviewer for information and the broader context. We have revised the manuscript to have a clearer logical thread.

Reviewer #1 (Recommendations For The Authors):

Please see the "Public Review" for recommendations.

Reviewer #2 (Recommendations For The Authors):

Specific suggestions

-The introduction should more clearly state which questions are being addressed and why STEAP1 and STEAP2 are investigated.

We have revised the Introduction to make that clearer.

-The manuscript should discuss more extensively and provide possible explanations for the substrate-independent kinetics of iron-reduction by STEAP2.

We re-analyzed the data and found the rate constants of the reactions before 2 s are weakly [Fe3+NTA]-dependent. We ascribe the weak [Fe3+-NTA]-dependence to the partial rate-limiting by substrate binding. However, we do not have a good interpretation for the reaction kinetics after 2 s which does not show [Fe3+-NTA]-dependence.

-"The rate of STEAP1(Fe(II)) oxidation by Fe3+-NTA is similar to those by Fe3+-EDTA or Fe3+-citrate, but the rates are significantly faster than STEAP2(Fe(II)) re-oxidation by Fe3+NTA (Fig. 1B)." The rates for STEAP1 should be given to substantiate this statement.

We added Table S1 in the supplementary materials that includes the 2nd order association (kon) and the 1st order dissociation rate constants (koff) of iron substrates in STEAP1 and STEAP2. Data on Fe3+-EDTA or Fe3+-citrate by STEAP1 are from our previous study (Biochemistry, 2016). We also calculated the KDs of different iron substrates for STEAP1 and STEAP2.

• "Our results indicate that STEAP2 can supply reduce FAD to initiate electron transfer in STEAP1." As discussed above, this statement should be discussed and analyzed

We mixed 0.9 μM STEAP1, 1.1 μM STEAP2, and 2.2 μM FAD and added 60 μM NADPH to the system and found that the heme on both STEAP1 and STEAP2 are reduced. Since adding NADPH to STEAP1 plus FAD alone does not reduce the heme (Fig. S3B), we reasoned that reduction of the heme on STEAP1 is achieved by the reduced FAD produced on STEAP2. The reduced FAD likely dissociates from STEAP2 and then bind to STEAP1.

-Experiments on "STEAP1 reduction by STEAP2" The experiments show that "STEAP2 can supply reduce FAD to initiate electron transfer in STEAP1." Could these results be explained by heterotrimer formation in agreement with the previous data published by the authors?

In our experience, STEAP1 and STEAP2 homotrimers are stable and do not form heterotrimers when mixed. STEAP1/2 heterotrimers form only when the two proteins are co-expressed in cells (Biochemistry (2016) 55, 6673-6684).

Reviewer #3 (Recommendations For The Authors):

Major points:

1) As a very general point: the order in which the results are presented could be greatly improved to increase the readability for non-experts. To elaborate: The manuscript starts with the spectroscopic characterization of STEAP2, then suddenly the reductase activities of STEAP1 and STEAP2 are compared; subsequently, experiments are described involving STEAP1 and cytochrome b5 reductase; then the cryo-EM structure of STEAP2 is presented etc. As a non-expert reader, this presentation of the results is confusing, especially because the paragraphs are not always connected well, and there is a lot of switching between STEAP1 and STEAP2 data. A more logical order would be to first present the STEAP2 spectroscopy and structural data, then write a connecting paragraph on why it is important to also study the electron transfer chain in STEAP1, followed by the comparison of the STEAP isoforms and the data on STEAP1 alone. The authors should include sentences on why they performed each experiment. For example, why did they determine the structure of STEAP2. What were they after that they could not retrieve from the homologous STEAP4 and STEAP1 structures? Justification of the performed experiments will make it easier for the reader, and will establish a better connection between the paragraphs.

We reorganized the data presentation in Results per the reviewer’s suggestions.

2) The physiological relevance of metal ion reduction by STEAP1 remains controversial. Because the current work establishes an electron transfer chain between STEAP1 and cytochrome b5 reductase, could the authors perform an easy experiment where they over express both STEAP1 and cytochrome b5 reductase in a cell line? If such an experiment would reveal STEAP1-dependent metal-ion reduction, it would greatly improve this part of the manuscript. If no activity is observed, the established electron transfer chain could just represent an in vitro artifact from using high concentrations of purified proteins.

This is an excellent point. We are not set up to perform the proposed experiment but will do so in the future.

3) The manuscript states that metal ion reduction of purified STEAP2 is slow, and the authors explain this by the absence of density for the extracellular region between helices 3 and 4 that are present in the structures of STEAP4 and STEAP1, resulting in a less-well defined substratebinding site. Can the authors exclude that the less-well defined substrate-binding site is a result of the detergent extraction of STEAP2? Would it be possible to measure the reductase activity of STEAP2 in purified membranes?

Detergent mostly interacts with the transmembrane domains and since the TMD region of STEAP2 aligns well with those of STEAP1 and STEAP4, we do not think that the disordered substrate binding region in STEAP2 is a consequence of detergent solubilization. It is difficult to conduct pre-steady state kinetic experiments using STEAP2 in membrane fractions.

4) The manuscript would greatly benefit from citing the literature more comprehensively to acknowledge insightful findings from authors in the field; for example, the important work by the Lawrence lab from 2015 (PMID: 26205815), which biochemically proved that STEAPs bind a single heme and that FAD bridges the TMD and RED, is not cited. The authors also mention that STEAP proteins belong to the same family as NOX proteins and cite some NOX structure papers. However, they fail to cite the first NOX structure paper (PMID: 28607049), as well the manuscript that structurally compares NOXs and STEAPs (PMID: 32815713). Similarly, the authors use SerialEM for their cryo-EM data collection but cite an old paper instead of the more recent (and relevant) SerialEM publication (PMID: 31086343).

We agree and added the references.

5) Generally, the data presented in the manuscript appear of good technical quality. However, a 'Table 1' with all relevant cryo-EM data collection and refinement statistics is completely missing as far as I can see. The authors should definitely add this to allow for the judgement of structural data quality. Without it, the manuscript is not suitable for publication.

We added Table S2 that includes relevant cryo-EM statistics.

Minor points:

6) The authors write in the abstract: 'STEAP2 - 4, but not STEAP1, have an intracellular domain that binds to NADPH and FAD'. This is not correct, because it has clearly been established that FAD also majorly binds to the transmembrane domain (this is even shown by the authors in the current manuscript as well).

Agree, we corrected that in the revision.

7) Sentence from the abstract and introduction state: 'It is also unclear whether STEAP1 has metal ion reductase activity' and 'it is unclear whether STEAP1 can form a competent electron transfer chain from NADPH'. The authors should definitely add "physiologically relevant" to these sentences. Namely, the senior authors themselves concluded in their 2016 Biochemistry paper (PMID: 27792302) that STEAP1 is capable of reducing metal ion complexes. Further indications that the transmembrane domain of STEAP1 displays metalloreductase activity was published by the Gros lab (PMID: 32409586), and it was also shown that fusing the RED of STEAP4 to the TMD of STEAP1 yields a functional protein in cells that reduces metal ions.

Good point and we revised the text and included the references.

8) Why is scheme 1 not just a summarizing figure?

We could change Scheme 1 to a Figure if required by the copy editor.

9) What is the purpose of Fig. 6? A larger depiction of Fig. 5e would likely be more relevant and should be considered as a replacement. Alternatively, the structure of STEAP1 (pdb 6y9b) could be shown in combination with Fig. 7, as the mutation is performed in STEAP1.

We agree and made changes to the structural figures to enhance clarity.

10) The manuscript now contains many, single panel figures. Certain main figures could easily be combined, for example, Fig. 1 and 2 and/or Fig. 3 and 4.

We agree and made changes to reduce single panel figures.

11) In Fig. 2, 3 and 4, the spectra show changes in peak heights as a result of the ferric to ferrous heme transition. However, a time component is missing in the legend. How long do these transitions take?

We added the reaction times to the figure legends.

12) The last part of the discussion states: 'The assembly of an intracellular RED with a membrane-embedded TMD observed in NOX, DUOX, and STEAPs naturally led to the notion that NADPH, FAD, and heme form an uninterrupted rigid electron-transfer chain that shuttles electron from the intracellular cellular NADPH to the extracellular substrates. While this may be true for NOX and DUOX, in which rapid supply of electrons to their extracellular substrates are essential to their biological functions, it may not apply similarly to STEAPs since it has only one heme in the TMD, and their electron transfer relies on shuttling of FAD.' The authors should mention here that the activity of NOX and DUOX is tightly regulated by accessory proteins, Ca2+ etc. Similarly, do the authors expect that the large distance between NADPH and FAD in the structures could represent a way to regulate/dampen the metal ion reduction rates of STEAPs in vivo?

We agree. We mentioned the regulation of NOX and DUOX in Discussion. We remain puzzled by the large distance between NADPH and FAD in STEAPs and are in pursuit of a structure in which the two cofactors are “in touch” for electron transfer.

Author Response

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

eLife assessment

This important study provides a framework bearing on the role of Eph-Ephrin signaling mechanisms in the clinically condition of amyotrophic lateral sclerosis. It provides compelling evidence for the roles of glial cells in this condition. This novel astrocyte-mediated mechanism may help identify future therapeutic targets.

Drs. Huang and Zaidi: Thank you for considering this revision of our manuscript for potential publication in eLife. We have addressed the excellent comments of the two reviewers, including the addition of new data. We have included detailed response-to-reviewer comments below to address each specific point, and we have highlighted all the changes in the manuscript text (using a red font color) made in response to these comments. Based on the reviewers’ critiques, we feel our re-working of the manuscript has made for a greatly improved study.

Reviewer #1 (Public Review):

In the manuscript by Urban et al., the authors attempt to further delineate the role which non-neuronal CNS cells play in the development of ALS. Toward this goal, the transmembrane signaling molecule ephrinB2 was studied. It was found that there is an increased expression of ephrinB2 in astrocytes within the cervical ventral horn of the spinal cord in a rodent model of ALS. Moreover, the reduction of ephrinB2 reduced motoneuron loss and prevented respiratory dysfunction at the NMJ. Further driving the importance of ephrinB2 is an increased expression in the spinal cords of human ALS individuals. Collectively, these findings present compelling evidence implicating ephrinB2 as a contributing factor towards the development of ALS.

We thank Reviewer #1 for the very helpful critique. We address each of the specific comments below (in the “Recommendations for the Authors” section of this Response to Reviewer Comments document), and have made changes to the manuscript based on these excellent points.

Reviewer #2 (Public Review):

The contribution of glial cells to the pathogenesis of amyotrophic lateral sclerosis (ALS) is of substantial interest and the investigators have contributed significantly to this emerging field via prior publications. In the present study, authors use a SOD1G93A mouse model to elucidate the role of astrocyte ephrinB2 signaling in ALS disease progression. Erythropoietin-producing human hepatocellular receptors (Ephs) and the Eph receptor-interacting proteins (ephrins) signaling is an important mediator of signaling between neurons and non-neuronal cells in the nervous system. Recent evidence suggests that dysregulated Eph-ephrin signaling in the mature CNS is a feature of neurodegenerative diseases. In the ALS model, upregulated Eph4A expression in motor neurons has been linked to disease pathogenesis. In the present study, authors extend previous findings to a new class of ephrinB2 ligands. Urban et al. hypothesize that upregulated ephrinB2 signaling contributes to disease pathogenesis in ALS mice. The authors successfully test this hypothesis and their results generally support their conclusion.

Major strengths of this work include a robust study design, a well-defined translational model, and complementary biochemical and experimental methods such that correlated findings are followed up by interventional studies. Authors show that ephrinB2 ligand expression is progressively upregulated in the ventral horn of the cervical and lumbar spinal cord through pre-symptomatic to end stages of the disease. This novel association was also observed in lumbar spinal cord samples from postmortem samples of human ALS donors with a SOD1 mutation. Further, they use a lentiviral approach to drive knock-down of ephrinB2 in the central cervical region of SOD1G93A mice at the presymptomatic stage. Interestingly, in spite of using a non-specific promoter, authors note that the lentiviral expression was preferentially driven in astrocytes.

Since respiratory compromise is a leading cause of morbidity in the ALS population, the authors proceed to characterize the impact of ephrinB2 knockdown on diaphragm muscle output. In mice approaching the end stage of the disease, electrophysiological recordings from the diaphragm muscle show that animals in the knock-down group exhibited a ~60% larger amplitude. This functional preservation of diaphragm function was also accompanied by the preservation of diaphragm neuromuscular innervation. However, it must be noted that this cervical ephrinB2 knockdown approach had no impact on disease onset, disease duration, or animal survival. Furthermore, there was no impact of ephrinB2 knockdown on forelimb or hindlimb function.

We thank Reviewer #2 for the very helpful critique. We address each of the specific comments below, and have made changes to the manuscript based on all of these excellent points.

The major limitation of the manuscript as currently written is the conclusion that the preservation of diaphragm output following ephrinB2 knockdown in SOD1 mice is mediated primarily (if not entirely) by astrocytes. The authors present convincing evidence that a reduction in ephrinB2 is observed in local astrocytes (~56% transduction) following the intraspinal injection of the lentivirus. However, the proportion of cell types assessed for transduction with the lentivirus in the spinal cord was limited to neurons, astrocytes, and oligodendrocyte lineage cells. Microglia comprise a large proportion of the glial population in the spinal grey matter and have been shown to associate closely with respiratory motor pools. This cell type, amongst the many others that comprise the ventral gray matter, have not been investigated in this study. Thus, the primary conclusion that astrocytes drive ephrinB2-mediated pathogenesis in ALS mice is largely correlative.

This is an excellent point. While the majority of transduced cells were astrocytes, we did not identify the lineage of a portion of the transduced cells, which could consist of cell types such as microglia, endothelial cells and others, some of which have been linked to ALS pathogenesis. Nevertheless, we find that the cells expressing high levels of ephrinB2 in ventral horn of SOD1G93A mice are all astrocytes (as seen in Figure 1O-Q), strongly suggesting – though not definitively demonstrating – that astrocyte ephrinB2 is the pathogenic source in this model (even if our viral transduction did not solely target astrocytes).

In the revised version of the manuscript, we now include an extensive paragraph in the Discussion section dedicated to this point.

Importantly, we have toned down our conclusion by modifying the title by removing “…in spinal cord astrocytes…”. We changed the title from “EphrinB2 knockdown in spinal cord astrocytes preserves diaphragm innervation in a mutant SOD1 mouse model of ALS" to “EphrinB2 knockdown in cervical spinal cord preserves diaphragm innervation in a mutant SOD1 mouse model of ALS”.

Further, it is interesting to note that no other functional outcomes were improved in this study. The C3-C5 region of the spinal cord consists of many motor pools that innervate forelimb muscles. CMAP recordings conducted at the diaphragm are a reflection of intact motor pools. This type of assessment of neuromuscular health is hard to re-capitulate in the kind of forelimb task that is being employed to test motor function (grip strength). Thus, it would be interesting to see if CMAP recordings of forelimb muscles would capture the kind of motor function preservation observed in the diaphragm muscle.

We did perform forelimb grip strength analysis on these animals and found no effect of focal ephrinB2 knockdown. However, this functional assay is impacted more by distal forelimb muscle groups controlled by motor neuron pools located at more caudal locations of the spinal cord (i.e. low cervical and high thoracic), likely explaining the lack of effect on grip strength.

Unfortunately, we did not perform this CMAP recording on forelimb muscle, and these mice have all already been sacrificed. We have added discussion of this point to the revised manuscript.

On a similar note, the functional impact of increased CMAP amplitude has not been presented. An increase in CMAP amplitude does not necessarily translate to improved breathing function or overall ventilation. Thus, the impact of this improvement in motor output should be clearly presented to the reader.

This is a very important point. While CMAP recording is a powerful assay of functional innervation of diaphragm muscle by phrenic motor neurons, it does not directly measure respiratory function. There are assays to test outcomes such as ventilatory behavior and gas exchange (e.g. whole-body plethysmography; blood gas measurements, etc.). We did not however perform these analyses. Respiratory function involves contribution of a number of other muscle groups, and these muscles are innervated by various motor neuron pools located across a relatively-large expanse of the CNS neuraxis. As we focally targeted ephrinB2 knockdown to only a small area, we would not expect effects on these other functional assays, which is why we restricted our testing to CMAP recording since this can be used to specifically study the phrenic motor neuron pool (and can be combined with detailed histological analyses in the cervical enlargement and at the diaphragm NMJ).

Importantly, this is why we chose to use “preserves diaphragm innervation” in the manuscript title, as opposed to wording such as “preserves diaphragm function” in the title. In addition, have added this point to the Discussion section in the revised manuscript.

Further, to the best of my knowledge, expression of Eph (or EphB) receptors has not been explicitly shown at the phrenic motor pool. It is thus speculative at best that the mechanism that the authors suggest in preserving diaphragm function is in fact mediated through Eph-EphrinB2 signaling at the phrenic motor pool. This aspect of the study would warrant a deeper discussion.

We address this important comment with multiple pieces of data showing that Eph receptors are expressed in the phrenic motor neuron pool. EphrinB2 binds and activates EphBs, as well as EphAs such as EphA4. Importantly, previous work has linked expression of EphA4 in motor neurons to the rate of ALS progression (Van Hoecke, et al. Nature Medicine. 2012). Consistent with these studies, single-nucleus RNAseq on mouse cervical spinal cord shows that alpha motor neurons of cervical spinal cord express various EphA and EphB receptors (http://spinalcordatlas.org/; Blum et al., Nature Neuroscience, 2021; Alkaslasi et al., Nature Communications, 2021). In addition, this dataset identifies a phrenic motor neuron-specific marker (ErbB4); when we specifically look at the expression profile of only the ErbB4-expressing alpha motor neurons, the data reveal that phrenic motor neurons express a number of EphA and EphB receptors, including EphA4.

To validate expression specifically of EphA4, we performed IHC for phosphorylated EphA4 (a marker of activated EphA4) on C3-C5 spinal cord sections from SOD1G93A mice injected with shRNAephrinB2 or control vector. We find that large ventral horn neurons are positive for phosphorylated EphA4. The ventral horn at these cervical spinal cord levels includes motor neuron pools in addition to just phrenic motor neurons; therefore, this result by itself does not conclusively show that phrenic motor neurons express EphA4, though they likely do since we find EphA4 expression in most ventral horn neuron cell bodies in C3-C5. A representative image is included in Supplemental Figure 1.

In the revised manuscript, we added a paragraph to the Discussion section to address this important comment from the reviewer, including describing these data on Eph receptor expression.

Lastly, although authors include both male and female animals in this investigation, they do not have sufficient power to evaluate sex differences. Thus, this presents another exciting future of investigation, given that ALS has a slightly higher preponderance in males as compared to females.

As the reviewer notes, our studies are under-powered with respect to examining possible sex-specific effects. We now include a brief discussion of this issue in the revised manuscript.

In summary, this study by Urban et al. provides a valuable framework for Eph-Ephrin signaling mechanisms imposing pathological changes in an ALS mouse model. The role of glial cells in ALS pathology is a very exciting and upcoming field of investigation. The current study proposes a novel astrocyte-mediated mechanism for the propagation of disease that may eventually help to identify potential therapeutic targets.

Recommendations for the authors: please note that you control which revisions to undertake from the public reviews and recommendations for the authors.

Both reviewers were enthusiastic about your paper. Reviewer (1) had some technical queries (see his/her items 2 and 4). Reviewer (2) had some questions about principles (items 1 and 2) with the remaining points being technical queries.

We have addressed all comments of both reviewers. We detail our responses in this Response to Reviewer Comments document and have made the associated modifications to the revised manuscript.

Reviewer #1 (Recommendations For The Authors):

Questions and/or Recommendations:

There is convincing evidence that there is increased expression of ephrinB2 over time in the mouse model of ALS. Is there a corresponding increase in astrocytes in this animal model?

We previously published data showing quantification of astrocyte numbers within the spinal cord of this same SOD1G93A mouse model. Specifically, we performed this quantification in the ventral horn of the lumbar spinal cord following disease onset. We found that there was a modest increase in the number of GFAP+ astrocytes at this location and disease time point.

[ Lepore et al. Selective ablation of proliferating astrocytes does not affect disease outcome in either acute or chronic models of motor neuron degeneration. Experimental Neurology. 211 (2): 423-32, 2008. ]

One could speculate that the increase in ephrinB2 expression we observe across the ventral horn in the mutant SOD1 mice was solely due to this modest increase in astrocyte number. However, this is highly unlikely to be the case, as in wild-type mice and in mutant SOD1 mice prior to disease onset astrocytes (and all other cell types) express very low levels of ephrinB2. Throughout disease course in these mutant SOD1 mice, the ephrinB2 expression level in individual astrocytes dramatically increases (including across most or all astrocytes), suggesting that the total increase in ephrinB2 expression across the ventral horn was not due to just this modest increase in astrocyte numbers but was instead due to the dramatically elevated eprhinB2 expression in most/all astrocytes. We have added this point to the Discussion section in the revised manuscript.

It would help the reviewer and readers to show a lower magnification image of Figure 2, panels O and P to demonstrate the reduction of ephrin B2 in the ventral horns.

We have added the lower magnification images to Figure 2.

It is commended that not all data was "positive". Figure 4 especially shows some of the limitations of eprhinB2 knockdown. This provides a realistic image - strengths and limitations - of this approach. Very well done!

Thank you! In future work, we could employ alternative vector-based strategies to restrict transduction/knockdown to only astrocytes. With such experiments, it’s possible that the impact of ephrinB2 knockdown would not be the same, if ephrinB2 actions in non-astrocytes also plays a role in disease pathogenesis. We have added discussion of this same point to the revised manuscript in response to a similar comment above from Reviewer #2.

Reviewer comment 4: Fig 6 - if possible can you please add demographic (age/sex) with each band?

We have added this information to the Legend. For aesthetic reasons, we chose not to add this information directly to the figure itself and instead included all of this information for each sample/band in the Legend.

Reviewer #2 (Recommendations For The Authors):

Overall, the manuscript addresses a novel aspect of the role of astrocytes in mediating ALS pathogenesis. I commend the authors for a well thought-out and clearly presented study. However, a few concerns limit the enthusiasm and deserve attention to improve the clarity of the report.

The biggest limitation of this study is that microglia or other cell types (endothelial cells) have not been explored in this study. They constitute a big proportion of cell types in the spinal cord and to conclude that only astrocytes mediate ephrinB2 signaling in the ALS model would be a stretch without the proper stains.

Please see our comments above to address this same point from Reviewer #2.

A clear premise for the investigation of EphrinB2 ligands has not been presented in the introduction. The authors provide a good background on the emerging role of EphEphrin interactions in neurodegenerative diseases. But it is unclear how the authors landed on this sub-class of ephrins.

We have added this premise to the Introduction section of the revised manuscript. In published work, ephrinB2 has been shown to be upregulated in reactive astrocytes and to be involved in disease pathogenesis in other neurological disease models (e.g. traumatic spinal cord injury).

There are several acronyms that have not been defined in the manuscript, e.g. GPI.

We now define “GPI” and all other abbreviations in the revised manuscript.

While the authors state that males and females had been included in the study, their individual n's for various outcomes have not been presented in the results section. Further, n's are missing from the figure legends, which will aid the clarity of the presentation. Further, please clarify the ages of the mice in the methods section.

(1) We now provide the n’s for males versus females for all analyses in the figure legends. (2) We also now include the total n for each experimental condition in all of the figure legends. (3) We also now state the ages of the mice for the various analyses in the Methods section.

It appears that several statistical interactions have been summarized in the results section but inconsistently reported on figures.

We now provide the exact n’s for each analysis in all figure legends. We include all of the details of the statistical analysis in the text of the Results section and do not include this text in the Legends; we do this for all figures to maintain consistency.

I presume that when the authors write "the number of neurons with somal diameter greater than 200 μm and with an identifiable nucleolus was determined", the 200 was a typo. Mouse motor neurons do not have a diameter of 200 μm and perhaps the authors meant an area of 200μm2?

We have corrected this: 200 μm2

Authors should consider adding a quantification for the human tissue immunoblots.

We have added the quantification of these human tissue data for ephrinB2.

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

To,

The editors,

Review Commons.

                                                                                                        31st Oct, 2023

We thank the editor and all the reviewers for the detailed critical feedback on our manuscript. We have substantially revised the manuscript to address all the queries, and have incorporated changes that address most of the suggestions made by the reviewers. The revised manuscript includes new experimental data, as well as text changes that address and clarify comments raised by the reviewers. The manuscript has been significantly strengthened by these revisions. A detailed response to reviewer comments is included below.

In the response letter below as well as the revised manuscript, we have addressed all the concerns raised by reviewers 1, 3, and 4, and most comments of reviewer 2. Some of the experiments suggested by reviewer #2 (related to an in-depth phosphoproteomics analysis) are unfortunately well beyond the scope of this manuscript, and infeasible at this stage (with the explanations provided below). We have divided our response document into three sections. In the first paragraph of the response, we briefly summarize the new data that we have included in the revision in response to reviewer queries. In the second section of this document, we have addressed the common comments raised by all the reviewers, which mostly address comments regarding the identification of Far11 phosphosites and mechanistic details about Far complex assembly. In the third part of this response, we include a detailed, point-by-point response to each of the reviewer's concerns, pointing to new data and specific changes made in the main manuscript. We also include a marked-up (in blue) version of the manuscript text, for easier follow up.

Our responses to queries are provided in blue text.

__Section I: A Brief summary of all new experimental data included in this manuscript __

• In this revised version of our study, we assessed the impact of the combined deletion of Ppg1 and components of Far complex. Our analysis revealed that double deletion of Ppg1 and Far complex does not result in any additive effect on metabolic regulation, indicating Ppg1 and Far function sequentially within the same pathway. Given these observations, the expectation will be that cells lacking Far complex will also have similar effects as the Ppg1 knock-out to adapt to glucose depletion. To assess this hypothesis, we conducted a competition experiment and indeed found that Far complex is required for proper adaptation to glucose depletion, very similar to Ppg1.
• To understand if Ppg1-Far regulate gene expression changes to modulate gluconeogenic outputs, we assessed transcript levels of gluconeogenic enzymes from ppg1D cells. Interestingly we observed that the transcript levels of these enzymes remain unchanged in ppg1D cells, suggesting mechanisms involving other modes of regulation.
• Additionally, our investigations revealed that the increased carbon allocation to cell wall precursors in ppg1D cells also makes them more susceptible towards the cell wall stress agent, Calcofluor white, similar to results obtained with Congo red.
• We have also carried out growth and competitive growth experiments with Far complex mutants, similar to those carried out with ppg1D cells, and find that these phenocopy ppg1D cells with respect to adapting to growth in glucose-depleted conditions. Section II: Regarding the identification of Far11 phosphosites dephosphorylated by Ppg1

Our response to this concern:

We agree with the reviewers that in order to obtain the full mechanism of Far complex assembly, it will be important to identify the Far11 phosphosites regulated by Ppg1. However, current evidence already suggests that it will be very challenging to identify specific phospho-site targets of Ppg1. Currently available data from extensive phosphoproteomics studies in Saccharomyces cerevisiae have identified a very large number of phosphorylation sites on Far11 (Bodenmiller et al, 2010; Swaney et al, 2013; Soulard et al, 2010). The identified Ser/Thr residues on Far11 that are known to be phosphorylated are shown in the figure below, and include at least 19 Ser/Thr residues. Considering that so many Ser/Thr residues of Far11 are phosphorylated, it is hard to pinpoint specific phosphosites that are recognized and uniquely dephosphorylated by Ppg1. Additionally, it is widely established that Ser/Thr phosphatases, especially the PP2A family, have little selectivity. At least in vitro, as well as often in vivo, multiple PP2A family members non-selectively target the same residue. Therefore, most of the function is only revealed by a combination of genetic and biochemical data that identify contextual phenotypes (as we have done so, and established in this manuscript). At this stage of this study, these phosphoproteomics experiments are well beyond the scope of the current manuscript.

Identified Ser/Thr residues known to be phosphorylated in only Far11

__However, we acknowledge this important point raised, and now include it in the discussion in Line 507. __

The revised text now reads:

“Additionally, large-scale studies suggest over 19 putatively phosphorylated Ser/Thr residues in Far11 alone, indicating multiple kinase-phosphatase interactions on this protein (Bodenmiller et al, 2010; Swaney et al, 2013; Soulard et al, 2010). Phosphoproteomics experiments with Ppg1 mutants are therefore a good starting point, but in themselves may be insufficient to specifically identify Ppg1 specific phosphosites on Far11.”

We also now have an extensive discussion section included, on the challenges of identifying specific phosphatase sites on proteins (and the contrast with kinase dependent phosphorylation sites). This section reads as (Line 500):

“Separately, phosphoproteomics-based studies could provide avenues for identifying as yet unidentified substrates of Ppg1. However, phosphoproteomics approaches have been far more suited for elucidating kinase-mediated regulation, due to high substrate specificity of kinases (Li et al, 2019). Identifying the specific substrates of phosphatases has posed significant challenges because of the nature of phosphatases like PP2A, which exhibit low substrate specificity and often have overlapping and compensatory outputs (Virshup & Shenolikar, 2009; Millward et al, 1999). Hence, determining the specific outputs or substrates of phosphatases through these methods presents a formidable challenge. Additionally, large-scale studies suggest over 19 putatively phosphorylated Ser/Thr residues in Far11 alone, indicating multiple kinase-phosphatase interactions on this protein (Bodenmiller et al, 2010; Swaney et al, 2013; Soulard et al, 2010). Phosphoproteomics experiments with Ppg1 mutants are therefore a good starting point, but in themselves may be insufficient to specifically identify Ppg1 specific phosphosites on Far11.”

Section III: Point-by-point response to all individual reviewer comments:

Reviewer #1:

The authors study metabolic adaptation to glucose depletion in budding yeast. A non-essential protein phosphatase mutant screen reveals adaptation to glucose depletion (growth in post-diauxic phase) requires Ppg1. The authors show i) that, in post-diauxic phase, cells lacking Ppg1 accumulate more trehalose, glycogen, UDP-glucose, UDP-GlcNAc (i.e., gluconeogenic outputs) than wild-type cells, ii) that, in post-diauxic phase, cells expressing a catalytically inactive version of Ppg1 accumulate more trehalose and iii) that Ppg1 is required for adaptation and growth post-glucose depletion. The authors find that Ppg1 interacts with Far11 (a member of the Far complex) in cells growing in the post-diauxic phase and that Ppg1 promotes Far complex stability. Finally, the authors conclude that the Ppg1 promotes Far complex stability to maintain gluconeogenic outputs after glucose depletion.

We thank the reviewer for a careful reading of this manuscript, and many constructive comments.

Major comments:

• Figures 1 to 4. The authors show that loss of Far components phenocopies loss of Ppg1 and conclude that Ppg1 is upstream of Far. However, the authors do not determine the combined effect of the two mutations. The authors should assess the phenotype (e.g., gluconeogenic output levels) of cells lacking both Ppg1 and Far (or in far9_deltaTA far10_deltaTA cells lacking Ppg1). The authors' conclusion would be strengthened if there was no additive effect between the mutations. Thank you for raising this point. We have now investigated the combined effect of deletion of Far11 and Ppg1 on post-diauxic carbon metabolism by measuring trehalose amounts. From these measurements, we did not observe any additional increase in trehalose amounts in the cells lacking both Ppg1 and Far11. We also assessed the growth of these cells in the presence of the cell wall stress agent, Congo red. There was no additional growth defect in the double deletion strain in the presence of Congo red. This data strongly indicates that the double deletion of Ppg1 and Far11 does not have an additive effect, indicating that both proteins function in the same pathway. This data is now included in Fig. S3 D and E. Text changes are made accordingly in the results section line 300:

“To determine if Ppg1 and Far complex independently regulate carbon metabolism or function within the same pathway, we generated double deletion mutants of Ppg1 and Far11 (ppg1Dfar11D). We assessed trehalose accumulation in ppg1Dfar11D cells and found no additional increase in trehalose levels (Figure 3H). Furthermore, we studied the growth of these ppg1Dfar11D cells in the presence of Congo red and observed no additional growth defects (Figure 3I). Overall, these findings strongly indicate that Ppg1 and Far complex function sequentially within the same pathway, with Ppg1 upstream of Far.”

Data showing trehalose levels from WT, far11D and ppg1Dfar11D cells after 24hrs of growth in YPD medium (now new Figure 3H):

Data showing the growth of WT, far11D and ppg1Dfar11D cells in the presence of Congo red (now new Figure 3I):

• Related to Figure 6A-C. One would expect that cells lacking Far components (or far9_delta TA far10_deltaTA cells) show a similar phenotype (fail to adapt to growth in changing glucose compared with wild-type cells) as cells lacking Ppg1. Is this the case? We agree with this expectation. The cells with loss of components of Far complex fully phenocopy ppg1D cells, and have an imbalanced carbon metabolism. Therefore, the expectation is that these cells will exhibit similar defects as ppg1D to properly adapt to glucose depletion. To address this question, we carried out a competition experiment with wild-type and Far9DTAFar10DTA cells (where the Far complex can now no longer be anchored, and therefore assemble properly, as shown in Fig. 4F, G), specifically assessing adaptation to environments as glucose depletes (and done identically to those with ppg1D). Note: the choice of this strain was to enable quantitative estimation, since we needed strains that had a ‘fluorescence’ mark (mNeonGreen or mCherry) to quantitatively assess changes in each genotype. Similar to ppg1D cells, the relative proportion of Far9DTAFar10DTA cells decreased during the competition experiment (Fig S6A). Independently, we estimated changes in post-diauxic growth of far11D cells, starting in glucose-rich conditions. In batch culture, the far11D cells showed reduced growth specifically in the post-diauxic phase (Fig S6B). Effectively, the loss of the Far complex nearly perfectly phenocopies the loss of Ppg1 in enabling effective adaptation to glucose-depleting environments. This data reiterates the importance of the Far complex in adaptation to glucose depletion, as the mechanistic target of Ppg1 function. This data is now included in the new Fig. S6 A and B. The text changes are made accordingly (Line 423):

“To concurrently address the role of Far complex in enabling cells to adapt to glucose depletion, we carried out a similar competition experiment (as with ppg1D) with WT and Far9DTA10DTA cells. Note: the Far9DTA10DTA cells will not allow the Far complex to anchor and assemble within cells, as shown earlier, and therefore phenocopies far9D, and was utilized in this experiment for easier quantitative estimations based on fluorescence. Expectedly, the relative proportion of Far9DTA10DTA cells decreased during the course of the competition experiment (Figure S6A). We next examined the effect of loss of Ppg1 on steady-state batch culture growth, starting from a glucose-replete medium. The loss of Ppg1 did not affect growth in the glucose-replete log phase, but after cells entered the post-diauxic (glucose-depleted) phase, ppg1D cells showed reduced growth and a reduction in biomass accumulation (Figure 6D). Independently, we assessed the growth of far11D cells starting in glucose-replete conditions, and observed reduced growth of these cells specifically in the post-diauxic phase (Figure S6B), similar to ppg1D cells. Effectively, the loss of Ppg1 or the Far complex phenocopied each other, and collectively, these data reveal that Ppg1-Far mediated regulation enables adaptation and competitive growth fitness after glucose depletion.”

Data showing growth competition between WT and Far9DTA10DTA cells in changing glucose conditions (now new Fig. S6A):

Data showing growth dynamics of WT and far11D cells in the YPD medium (now new Fig. S6B):

• The manuscript would be considerably strengthened if the authors provided more information on the mechanism by which Ppg1 controls Far complex stability, e.g., can the authors about the phosphosite(s) in Far11 regulated by Ppg1? As the authors mention, it has been already suggested that Ppg1 is required for Far complex assembly (PMID: 33317697). This comment has been commonly addressed in the section 2 of this document. Briefly, while we are equally excited about this direction, given the complexity of phosphorylation of the Far11 protein (and the challenges specifically in the context of PP2A family phosphatase action), this component is likely to take years to address and is beyond the scope of this manuscript. We do include a more speculative section in the discussion in this regard.

Minor comments:

• The authors may consider to include data from Figures 6A, 6B and 6C (failure to adapt to glucose-changing conditions) after Figure 2 to show a complete characterization of the phenotype of cells lacking Ppg1. Figure 6 could show only the "proposed model". We appreciate the reviewer’s suggestion and had indeed considered this possibility in an early version of the writing of the manuscript. However, we prefer the current flow of this manuscript, where we identify Ppg1 as a putative regulator of gluconeogenic flux, and end with the mechanistic confirmation of function that links Ppg1 and Far complex to the same adaptation function. We hope the reviewer appreciates this viewpoint.

• Related to Figure 1. The authors mention that Ppg1 is a "notable hit" and that the "increase in post-diauxic trehalose levels are considerable". However, there is no reference to use as a comparison. Is there any other mutant strain known to accumulate trehalose at the post-diauxic shift? If yes, it would be informative if the authors compared the effect of such mutant strain to a ppg1-delta mutant. For this experiment, we did not employ another mutant as a reference for increased trehalose accumulation. However, the point raised by the reviewer in itself was interesting in itself. Looking into the literature, we find that there are no reliable, quantitative estimates of how much trehalose increases in yeast in the post-diauxic phase (compared to the log phase), although numerous manuscripts (including some of our own earlier work) allude to this point. Therefore, we did this experiment, to obtain absolute quantitative information on the trehalose amounts in YPD in cells after 4 hrs of growth in 2% glucose, vs. in cells the post-diauxic phase (24 hrs after starting growth in 2% glucose). The amounts of trehalose increase >10-fold in the post-diauxic phase compared to the log phase. We now mention this in the text, and include absolute quantitation of trehalose amounts in Fig S1B. __Hence, a ~1.5-fold further increase in trehalose amounts in the post-diauxic ppg1D cells compared to post-diauxic wild-type cells is considerable. The revised text now reads (Line 121):__

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